World Meteorological Organization
Global Ozone Research and Monitoring Project—Report No. 58

SCIENTIFIC ASSESSMENT OFOZONE DEPLETION: 2018

World Meteorological Organization
United Nations Environment Programme
National Oceanic and Atmospheric Administration
National Aeronautics and Space Administration
European Commission

World Meteorological Organization
7bis avenue de la Paix
Case postale 2300
CH-1211 Geneva 2
Switzerland
United Nations Environment Programme
Ozone Secretariat
P.O. Box 30552
Nairobi, 00100
Kenya
US Department of Commerce
National Oceanic and Atmospheric Administration
14th Street and Constitution Avenue NWHerbert C. Hoover Building, Room 5128
Washington, D. C. 20230
National Aeronautics and Space Administration
Earth Science Division
NASA Headquarters
300 E. Street SW
Washington, D.C. 20546-0001
European Commission
Directorate-General for Research
B-1049 Bruxelles
Belgium

Published online January 2019
ISBN: 978-1-7329317-1-8This report is available on the internet at the following locations:
http://ozone.unep.org/science/assessment/sap
https://www.esrl.noaa.gov/csd/assessments/ozone/2018/
This document should be cited as:
WMO (World Meteorological Organization), Scientific Assessmentof Ozone Depletion: 2018, Global Ozone Research and Monitoring
Project–Report No. 58, 588 pp., Geneva, Switzerland, 2018.
Example Chapter Citation:
Engel, A. and M. Rigby (Lead Authors), J.B. Burkholder, R.P. Fernandez,
L. Froidevaux, B.D. Hall, R. Hossaini, T. Saito, M.K. Vollmer, and B. Yao,
Update on Ozone-Depleting Substances (ODSs) and Other Gases ofInterest to the Montreal Protocol, Chapter 1 in Scientific Assessment of
Ozone Depletion: 2018, Global Ozone Research and Monitoring
Project–Report No. 58, World Meteorological Organization, Geneva, Switzerland, 2018.
Inside cover image: Adapted from Adobe Stock photoWorld Meteorological Organization
Global Ozone Research and Monitoring Project–Report No. 58

Scientific Assessment of
Ozone Depletion: 2018

World Meteorological Organization
United Nations Environment Programme
National Oceanic and Atmospheric Administration
National Aeronautics and Space Administration
European Commission

This page was intentionally left blank.

Scientific Assessment of Ozone Depletion: 2018
Assessment Co-chairs
David W. Fahey
Paul A. Newman
John A. Pyle
Bonfils Safari

Scientific Steering Committee
David W. Fahey
Paul A. Newman
John A. Pyle
Bonfils Safari
Martyn P. Chipperfield
David J. Karoly
Doug Kinnison
Malcolm K. Ko
Michelle Santee

Assessment Coordinator
Sarah J. Doherty

Chapter Lead Authors
Chapter 1: Update on Ozone-Depleting Substances (ODSs) and
Other Gases of Interest to the Montreal Protocol
Andreas Engel and Matthew Rigby
Chapter 2: Hydrofluorocarbons (HFCs)
Stephen A. Montzka and Guus J.M. Velders
Chapter 3: Update on Global Ozone: Past, Present, and FuturePeter Braesicke and Jessica Neu
Chapter 4: Polar Stratospheric Ozone: Past, Present, and Future
Ulrike Langematz and Matthew B. Tully
Chapter 5: Stratospheric Ozone Changes and Climate
Alexey Yu. Karpechko and Amanda C. Maycock
Chapter 6: Scenarios and Information for Policymakers
Lucy J. Carpenter and John S. Daniel
Twenty Questions and Answers About the Ozone Layer: 2018 Update
Ross J. Salawitch
Appendix A: Summary of Abundances, Lifetimes, ODPs, REs, GWPs, and GTPs
James B. Burkholder

Report Design and Production
Debra A. Dailey-Fisher and Ann M. Reiser
Full list of contributors given in Appendix Diii

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Remembrances
It is with sadness that we note the passing of the following scientists who have played leading roles in
the international scientific assessments of the ozone layer.
Ralph J. Cicerone (1943–2016) (Retired President of the National Academyof Sciences) passed away on 5 November 2016 in Milburn, New Jersey. Ralph
was born in New Castle, Pennsylvania on 2 May 1943. Ralph received his
undergraduate degree from the Massachusetts Institute of Technology in 1965,
and his doctoral degree from the University of Illinois at Urbana–Champaign in1970. While at the University of Michigan in 1974, Ralph and his colleague Rich
Stolarski showed that chlorine catalysis of ozone destruction could add a crucial
piece of the puzzle to the questions about the stratospheric ozone photochemicalbalance. Subsequently, Ralph moved to the Scripps Institution of Oceanography
at UC San Diego, and in 1980 became a senior scientist and director of the
Atmospheric Chemistry Division at the National Center for AtmosphericResearch in Boulder, Colorado. Ralph migrated west to the University of California, Irvine (UCI), in 1989
where he founded the Department of Earth System Science and eventually became the University Chancellor.
In 2005, Ralph became the 21st President of the US National Academy of Science. Amongst Ralph’s awards are
the 1999 Bower Award and Prize for Achievement in Science; the American Geophysical Union’s (AGU) 1979
James B. Macelwane Award; AGU’s 2002 Roger Revelle Medal; and the World Cultural Council’s 2004 Albert
Einstein World Award of Science. Ralph was also recognized in the 1995 Nobel Prize in chemistry awarded to
F. Sherwood Rowland, Mario Molina, and Paul Crutzen for his work on chlorine chemistry in the stratosphere.
Ralph was a co-author of the very first international assessment “The Stratosphere 1981”. Ralph was also a coauthor of “Atmospheric Ozone: 1985” that formed the scientific foundation for the Montreal Protocol. He also
contributed to the “Report of the International Ozone Trends panel – 1988”, and to the “Scientific Assessmentof Ozone Depletion: 1994”.
Photo credit: Courtesy of the National Academy of Sciences / Photographer: Mark Finkenstaedt
Ivar Sigmund Angell Isaksen (1937–2017) (Professor in the Departmentof Geosciences at the University of Oslo) passed away on 16 May 2017. Ivar
grew up in Djupvik in Lyngen in North Troms. He joined the University ofOslo’s Department of Geophysics in 1967 and received his doctoral degree
at the University of Oslo in 1973. He then became professor of theoreticalmeteorology in 1981 at the University of Oslo. Ivar’s awards included the
National Oceanographic and Administration (NOAA) Award for outstanding
scientific achievement in 1975; the 1990 Norwegian Ministry of Environment
award for Environmental Research; the University of Thessaloniki Award, 2000,
for Scientific Achievement; the European Physical Society and The Balkan
Physical Union Award for outstanding contribution in Environmental Physics,
2002; and an Honorary doctorate at the University of Athens, 2009. He was elected to the International Ozone
Commission in 1984, and was its President from 2004-2008. Ivar was a major contributor to the Scientific
Assessments of Ozone Depletion as an author, co-author, coordinator, and reviewer. He was an author of the very
first international assessment “The Stratosphere 1981”, and then contributed to every report up to “The Scientific
Assessment of Ozone Depletion: 2010”. Ivar’s final comments were from May 2013, when he recommended thatthe 2014 Assessment should tackle overlapping topics with the climate community.
Photo credit: CICERO (the Norwegian Center for International Climate Research)v

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Scientific Assessment of Ozone Depletion: 2018
CONTENTSHIGHLIGHTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.3
PREFACE  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.7
INTRODUCTION  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.11
EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.15
Chapter 1: UPDATE ON OZONE-DEPLETING SUBSTANCES (ODSs) AND
OTHER GASES OF INTEREST TO THE MONTREAL PROTOCOL
Lead Authors: Andreas Engel and Matthew Rigby
Scientific Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1.11.1 Summary of Findings from the Previous Ozone Assessment  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1.71.2 Updated Abundances, Trends, Lifetimes, and Emissions of  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
Longer-lived Halogenated Source Gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1.71.3 Very Short-lived Halogenated Substances (VSLSs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.30
1.4 Changes in Atmospheric Halogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.45
1.5 Changes in Other Trace Gases that Influence Ozone and Climate  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.61
References  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.66
Chapter 2: HYDROFLUOROCARBONS (HFCs)
Lead Authors: Stephen A. Montzka and Guus J.M. Velders
Scientific Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2.12.1 Summary of Findings from the Previous Ozone Assessment  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2.52.2 Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2.52.3 Atmospheric Observations and Derived Emission Estimates  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2.72.4 Atmospheric Chemistry of HFCs  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.32
2.5 Potential Future Changes  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.34
References  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.47
Chapter 3: UPDATE ON GLOBAL OZONE: PAST, PRESENT, AND FUTURE
Lead Authors: Peter Braesicke and Jessica Neu
Scientific Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  3.13.1 Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  3.53.2 Natural Ozone Variations and Trend Detection  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  3.93.3 Past Ozone in Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.22
3.4 Projected Ozone Changes  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.40
References  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.53
Appendix 3A: Data Sources  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.69
Chapter 4: POLAR STRATOSPHERIC OZONE: PAST, PRESENT, AND FUTURE
Lead Authors: Ulrike Langematz and Matthew B. Tully
Scientific Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  4.14.1 Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  4.54.2 Recent Polar Ozone Changes  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  4.6vii

4.3 Understanding of Polar Ozone Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.15
4.4 Recovery of Polar Ozone  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.28
4.5 Future Changes in Polar Ozone  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.36
References  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.49
Chapter 5: STRATOSPHERIC OZONE CHANGES AND CLIMATE
Lead Authors: Alexey Yu. Karpechko and Amanda C. Maycock
Scientific Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  5.15.1 Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  5.55.2 Observed Changes in Atmospheric Constituents and External Forcings that Relate to Climate  . .  5.65.3 Observed and Simulated Changes in Stratospheric Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.12
5.4 Effects of Changes in Stratospheric Ozone on the Troposphere and Surface  . . . . . . . . . . . . . . . . . . . 5.29
5.5 Climate Impacts of the Montreal Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.48
References  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.50
Chapter 6: SCENARIOS AND INFORMATION FOR POLICYMAKERS
Lead Authors: Lucy J. Carpenter and John S. Daniel
Scientific Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6.16.1 Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6.76.2 Issues of Potential Importance to Stratospheric Ozone and Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6.96.3 Metrics for Changes in Ozone and Climate  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.21
6.4 Scenarios and Sensitivity Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.25
References  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.46
Appendix 6A: Current State of Knowledge on Stratospheric Sulfate Geoengineering . . . . . . . . . . . . . . 6.57
Appendix 6B: Comparison of Past and Future Ozone Projections of the  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  		
GSFC 2-D Model with GEOSCCM 3-D Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.61
Appendix 6C: Evaluation of Alternative Scenarios Using New EESC Formalism . . . . . . . . . . . . . . . . . . . . . 6.69
Appendix A: SUMMARY OF ABUNDANCES, LIFETIMES, ODPs, REs, GWPs, GTPs . . . . . . . . . . . . . . . . . . . . . . . . . . A.1
Appendix B: CHEMICAL FORMULAE AND NOMENCLATURE  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  B.1Appendix C: ACRONYM DICTIONARY  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C.1
Appendix D: LIST OF AUTHORS, CONTRIBUTORS, AND REVIEWERS  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D.1

viii

World Meteorological Organization
Global Ozone Research and Monitoring Project – Report No. 58

E xecutive S ummary
Scientific Assessment of
Ozone Depletion: 2018

National Oceanic and Atmospheric Administration
National Aeronautics and Space Administration
United Nations Environment Programme
World Meteorological Organization
European Commission

World Meteorological Organization
United Nations Environment Programme
National Oceanic and Atmospheric Administration
National Aeronautics and Space Administration
European Commission

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Highlights
Scientific Assessment of Ozone Depletion: 2018
The Assessment documents the advances in scientific understanding of ozone depletion reflecting the thinking of the many
international scientific experts who have contributed to its preparation and review. These advances add to the scientificbasis for decisions made by the Parties to the Montreal Protocol. It is based on longer observational records, new chemistry-climate model simulations, and new analyses. Highlights since the 2014 Assessment are:Actions taken under the Montreal Protocol have led to decreases in the atmospheric abundance of controlled
ozone-depleting substances (ODSs) and the start of the recovery of stratospheric ozone. The atmospheric abundances of both total tropospheric chlorine and total tropospheric bromine from long-lived ODSs controlled under
the Montreal Protocol have continued to decline since the 2014 Assessment. The weight of evidence suggests that the
decline in ODSs made a substantial contribution to the following observed ozone trends:
The Antarctic ozone hole is recovering, while continuing to occur every year. As a result of the Montreal Protocol much
more severe ozone depletion in the polar regions has been avoided.
Outside the polar regions, upper stratospheric ozone has increased by 1–3% per decade since 2000.
No significant trend has been detected in global (60°S–60°N) total column ozone over the 1997–2016 period withaverage values in the years since the last Assessment remaining roughly 2% below the 1964–1980 average.
Ozone layer changes in the latter half of this century will be complex, with projected increases and decreases in different
regions. Northern Hemisphere mid-latitude total column ozone is expected to return to 1980 abundances in the 2030s,
and Southern Hemisphere mid-latitude ozone to return around mid-century. The Antarctic ozone hole is expected togradually close, with springtime total column ozone returning to 1980 values in the 2060s. [ES Sections 1 and 3]
The Kigali Amendment is projected to reduce future global average warming in 2100 due to hydrofluorocarbons(HFCs) from a baseline of 0.3–0.5 oC to less than 0.1 oC. The magnitude of the avoided temperature increase due tothe provisions of the Kigali Amendment (0.2 to 0.4 oC) is substantial in the context of the 2015 Paris Agreement, whichaims to keep global temperature rise this century to well below 2 oC above pre-industrial levels. [ES Section 2]
There has been an unexpected increase in global total emissions of CFC-11. Global CFC-11 emissions derived frommeasurements by two independent networks increased after 2012, thereby slowing the steady decrease in atmosphericconcentrations reported in previous Assessments. The global concentration decline over 2014 to 2016 was only twothirds as fast as it was from 2002 to 2012. While the emissions of CFC-11 from eastern Asia have increased since 2012,
the contribution of this region to the global emission rise is not well known. The country or countries in which emissions have increased have not been identified. [ES Section 1]
Sources of significant carbon tetrachloride emissions, some previously unrecognised, have been quantified. Thesesources include inadvertent by-product emissions from the production of chloromethanes and perchloroethylene,
and fugitive emissions from the chlor-alkali process. The global budget of carbon tetrachloride is now much better
understood than was the case in previous Assessments, and the previously identified gap between observation-based
and industry-based emission estimates has been substantially reduced. [ES Sections 1 and 5]
Continued success of the Montreal Protocol in protecting stratospheric ozone depends on continued compliancewith the Protocol. Options available to hasten the recovery of the ozone layer are limited, mostly because actions that
could help significantly have already been taken. Remaining options such as complete elimination of controlled anduncontrolled emissions of substances such as carbon tetrachloride and dichloromethane; bank recapture and destruction of CFCs, halons, and HCFCs; and elimination of HCFC and methyl bromide production would individually leadto small-to-modest ozone benefits. Future emissions of carbon dioxide, methane, and nitrous oxide will be extremely
important to the future of the ozone layer through their effects on climate and on atmospheric chemistry. Mitigation of
nitrous oxide emissions would also have a small-to-modest ozone benefit. [Figure ES-9, ES Section 5]
ES.3

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Contents

E xecutive S ummary
Scientific Assessment of Ozone Depletion: 2018
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.7
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.11
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.15[1] Concentrations and trends in ozone-depleting substances (ODSs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.15[2] Hydrofluorocarbons (HFCs)  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.20[3] Stratospheric ozone  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.22[4] Ozone change and its influence on climate  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.27[5] Policy considerations for stratospheric ozone and climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.29
Executive Summary Appendix: Scientific Summaries of the Chapters  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.33
Chapter 1: Update on Ozone-Depleting Substances (ODSs) and . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
other Gases of Interest to the Montreal Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.33
Chapter 2: Hydrofluorocarbons (HFCs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.37
Chapter 3: Update on Global Ozone: Past, Present, and Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.40
Chapter 4: Update on Polar Ozone: Past, Present, and Future  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.42
Chapter 5: Stratospheric Ozone Changes and Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.44
Chapter 6: Scenarios and Information for Policymakers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ES.46

ES.5

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Preface
This document contains information upon which the Parties to the Montreal Protocol on Substances that Deplete
the Ozone Layer (“The Parties”) will base their future decisions regarding protection of the stratospheric ozone layer
and climate from the production and consumption of ozone-depleting substances (ODSs) and their replacements.

The Charge to the Assessment Panels
Specifically, Article 6 of the Montreal Protocol on Substances that Deplete the Ozone Layer states:
Beginning in 1990, and at least every four years thereafter, the Parties shall assess the control measuresprovided for in Article 2 and Articles 2A to 2I on the basis of available scientific, environmental, technical and economic information.
To provide the mechanisms whereby these assessments are conducted, the Montreal Protocol further states:“. . . the Parties shall convene appropriate panels of experts” and “the panels will report their conclusions . . . to the Parties.”
To meet this request, the Scientific Assessment Panel (SAP), the Environmental Effects Assessment Panel, and theTechnology and Economic Assessment Panel each prepare, about every 3–4 years, major assessments that update
the state of understanding in their purviews. These assessments are made available to the Parties in advance of theirannual meetings at which they consider amendments and adjustments to the provisions of the Montreal Protocol.

Sequence of Scientific Assessments
The 2018 Assessment is the latest in a series of assessments prepared by the world’s leading experts in the atmosphericsciences and under the auspices of the Montreal Protocol in coordination with the World Meteorological Organization(WMO) and/or the United Nations Environment Programme (UN Environment). The 2018 Assessment is the ninth
in the series of major assessments that have been prepared by the Scientific Assessment Panel as direct input to theMontreal Protocol process. The chronology of the nine scientific assessments of ozone depletion, along with otherrelevant reports and international policy decisions, are summarized in Table ES-1.

2018 Assessment Terms of Reference
The terms of reference of the 2018 Assessment were decided at the 27th Meeting of the Parties to the Montreal
Protocol in Dubai, United Arab Emirates (1–5 November 2015) in their Decision XXVII/61:
5. To request the assessment panels to bring to the notice of the Parties any significant developments
which, in their opinion, deserve such notice, in accordance with decision IV/13;
7. To request the Scientific Assessment Panel to undertake, in its 2018 report, a review of the scientific knowledge as dictated by the needs of the Parties to the Montreal Protocol, as called for in
the terms of reference for the panels, taking into account those factors stipulated in Article 3 of theVienna Convention, including estimates of the levels of ozone-layer depletion attributed to the remaining potential emissions of ozone-depleting substances and an assessment of the level of global
emissions of ozone depleting substances below which the depletion of the ozone layer could becomparable to various other factors such as the natural variability of global ozone, its secular trend
over a decadal timescale and the 1980 benchmark level;1

Decision XXVII/6: Potential areas of focus for the 2018 quadrennial reports of the Scientific Assessment Panel, the Environmental Effects
Assessment Panel and the Technology and Economic Assessment Panel.
ES.7

Executive Summary | Executive Summaryand in their Decision XXVII/72:
7. To request the Technology and Economic Assessment Panel and the Scientific Assessment Panelto continue their analysis of the discrepancies between observed atmospheric concentrations andreported data on carbon tetrachloride and to report and provide an update on their findings to theTwenty-Eighth Meeting of the Parties.
Significant developments since the 2014 Assessment that are included in the 2018 Assessment are (i) the adoptionof the Kigali Amendment in 2016 to phase down global hydrofluorocarbon (HFC) production and consumption;(ii) the recognition of increased global emissions of CFC-11; and (iii) an improved understanding of the budget ofcarbon tetrachloride (CCl4).

The Assessment Process
The process of writing the current Assessment started early in 2016. The SAP co-chairs considered suggestions fromthe Parties regarding experts from their countries who could participate in the process. Further, an ad hoc international scientific advisory group was formed to suggest authors and reviewers from the world scientific community
and to help craft the Assessment outline. As in previous Assessments, the participants represented experts fromthe developed and developing world who bring a special perspective to the process and whose involvement in theAssessment contributes to capacity building. The Appendix provides a listing of the approximately 280 scientists
from 31 countries who contributed to the preparation and review of the Assessment.
An initial letter was sent to a large number of scientists and policy makers in November 2016 soliciting comments
and inputs on a draft outline along with suggestions for authors for the 2018 Assessment. This was followed byrevisions to the outline and recruitment of lead authors and co-authors. The steering committee and lead authors
met in London, UK, in May 2017 to review the revised chapter outlines. The chapter writing process produced fivedrafts between mid-September 2017 and August 2018 aided by two author team meetings (Boulder, Colorado, USA
and Les Diablerets, Switzerland). The first and third drafts were formally peer-reviewed by a large number of expert
reviewers. The chapters were revised by the author teams based on the extensive review comments (numbering over5000) and the review editors for each chapter provided oversight of the revision process to ensure that all comments
were addressed appropriately.
At a meeting in Les Diablerets, Switzerland, held on 16–20 July 2018, the Executive Summary contained herein
was prepared and completed by the 68 attendees of the meeting. These attendees included the steering committee,
chapter lead authors, review editors, some chapter co-authors (selected by the chapter leads), reviewers (selected by
the review editors), and some leading experts invited by the steering committee. The Executive Summary, initiallydrafted by the Assessment steering committee, was reviewed, revised, and approved line-by-line. The Highlights
section was drafted during the meeting to provide a concise summary of the Executive Summary.
The success of the 2018 Assessment depended on the combined efforts and commitment of a large internationalteam of scientific researchers who volunteered their time as lead authors, contributors, reviewers, and review editorsand on the skills and dedication of the assessment coordinator and the editorial and production staff, who are listed
at the end of this report.

2

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Decision XXVII/7: Investigation of carbon tetrachloride discrepancies.

Executive Summary | Executive Summary

Table ES-1. Chronology of scientific reports and policy decisions related to ozone depletion.
Year

Policy Decisions

1981

Scientific Reports
The Stratosphere 1981: Theory and Measurements. WMO No. 11.

1985

Vienna Convention

1987

Montreal Protocol

Atmospheric Ozone 1985. Three volumes. WMO No. 16.

1988

International Ozone Trends Panel Report 1988.
Two volumes. WMO No. 18.

1989

Scientific Assessment of Stratospheric Ozone: 1989.
Two volumes. WMO No. 20.

1990

London Adjustment
and Amendment

1991

Scientific Assessment of Ozone Depletion: 1991. WMO No. 25.

1992

Methyl Bromide: Its Atmospheric Science, Technology, and Economics(Montreal Protocol Assessment Supplement). UNEP (1992)

1992

Copenhagen Adjustment
and Amendment

1994

Scientific Assessment of Ozone Depletion: 1994. WMO No. 37.

1995

Vienna Adjustment

1997

Montreal Adjustment
and Amendment

1998
1999

Scientific Assessment of Ozone Depletion: 1998. WMO No. 44.
Beijing Adjustment
and Amendment

2002

Scientific Assessment of Ozone Depletion: 2002. WMO No. 47.

2006

Scientific Assessment of Ozone Depletion: 2006. WMO No. 50.

2007

Montreal Adjustment

2010

Scientific Assessment of Ozone Depletion: 2010. WMO No. 52.

2014

Scientific Assessment of Ozone Depletion: 2014. WMO No. 55.

2016
2018

Kigali Amendment
Scientific Assessment of Ozone Depletion: 2018. WMO No. 58.

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Introduction
The 1985 Vienna Convention for the Protection of the Ozone Layer is an international agreement in which United
Nations States recognized the fundamental importance of preventing damage to the stratospheric ozone layer. The
1987 Montreal Protocol on Substances that Deplete the Ozone Layer and its succeeding amendments, adjustments,
and decisions were subsequently negotiated to control the consumption and production of anthropogenic ozone-depleting substances (ODSs) and some hydrofluorocarbons (HFCs). The Montreal Protocol Parties base their decisions on scientific, environmental, technical, and economic information that is provided by their technical panels.
The Protocol requests quadrennial reports from its Scientific Assessment Panel that update the science of the ozone
layer. This Executive Summary (ES) highlights the key findings of the Scientific Assessment of Ozone Depletion: 2018,as put together by an international team of scientists. The key findings of each of the six chapters of the Scientific
Assessment have been condensed and formulated to make the ES suitable for a broad audience.
Ozone depletion is caused by human-related emissions of ODSs and the subsequent release of reactive halogen
gases, especially chlorine and bromine, in the stratosphere. ODSs include chlorofluorocarbons (CFCs), bromine-c­
ontaining halons and methyl bromide, hydrochlorofluorocarbons (HCFCs), carbon tetrachloride (CCl4), and methyl
chloroform. The substances controlled under the Montreal Protocol are listed in the various annexes to the agreement (CFCs and halons under Annex A and B, HCFCs under Annex C, and methyl bromide under Annex E)3.
These ODSs are long-lived (e.g., CFC-12 has a lifetime greater than 100 years) and are also powerful greenhousegases (GHGs). As a consequence of Montreal Protocol controls, the stratospheric concentrations of anthropogenic
chlorine and bromine are declining.
In addition to the longer-lived ODSs, there is a broad class of chlorine- and bromine-containing substances known
as very short-lived substances (VSLSs) that are not controlled under the Montreal Protocol and have lifetimes shorter than about 6 months. For example, bromoform (CHBr3) has a lifetime of 24 days, while chloroform (CHCl3) has
a lifetime of 149 days. These substances are generally destroyed in the lower atmosphere in chemical reactions. In
general, only small fractions of VSLS emissions reach the stratosphere where they contribute to chlorine and bromine levels and lead to increased ozone depletion.
The Montreal Protocol’s control of ODSs stimulated the development of replacement substances, firstly HCFCs andthen HFCs, in a number of industrial sectors. While HFCs have only a minor effect on stratospheric ozone, some
HFCs are powerful GHGs. Previous Assessments have shown that HFCs have been increasing rapidly in the atmosphere over the last decade and were projected to increase further as global development continued in the coming
decades. The adoption of the 2016 Kigali Amendment to the Montreal Protocol (see Annex F) will phase down the
production and consumption of some HFCs and avoid much of the projected global increase and associated climatechange.
Observations of atmospheric ozone are made by instruments on the ground and on board balloons, aircraft, andsatellites. This network of observations documented the decline of ozone around the globe, with extreme depletions
occurring over Antarctica in each spring and occasional large depletions in the Arctic, and they allowed us to report
some indications of recovery in stratospheric ozone in the 2014 Assessment. The chemical and dynamical processes
controlling stratospheric ozone are well understood, with ozone depletion being fundamentally driven by the atmospheric abundances of chlorine and bromine.
3

Montreal Protocol Handbook, 2018.

ES.11

Executive Summary | Executive Summary

Previous Assessments have shown projections of decreasing ODSs, and models show that global ozone should increase as a result. Models have also demonstrated that increasing concentrations of the GHGs carbon dioxide (CO2)and methane (CH4) during this century will cause global ozone levels to increase beyond the natural level of ozone
observed in the 1960s, primarily because of the cooling of the upper stratosphere and a change of the stratosphericcirculation. On the other hand, the chemical effect of increasing concentrations of nitrous oxide (N2O), another
GHG, will be to deplete stratospheric ozone.
This 2018 Assessment is the ninth in a series that is provided to the Montreal Protocol by its Scientific Assessment
Panel. In this Assessment, many of our previous Assessment findings are strengthened and new results are presented. A clear message of the 2018 Assessment is that the Montreal Protocol continues to be effective at reducing theatmospheric abundance of ODSs.

Terminology used in the Executive Summary
Equivalent effective stratospheric chlorine (EESC)
EESC is a metric for representing ODS levels in the stratosphere. It is calculated based upon three factors: surface
atmospheric concentrations of individual ODSs and their number of chlorine and bromine atoms, the relative
efficiency of chlorine and bromine for ozone depletion, and the time required for the substances to reach different stratospheric regions and break down to release their chlorine and bromine atoms. As EESC continues
to decrease in response to Montreal Protocol provisions, stratospheric ozone is expected to increase. In thisAssessment, EESC does not include chlorine and bromine from very short-lived substances (VSLSs).

Representative concentration pathways (RCPs)
Representative concentration pathways (RCPs) were developed by the Intergovernmental Panel on Climate
Change (IPCC) to help describe how climate change may evolve out to the year 2100. The RCPs define a timelineof atmospheric concentrations of greenhouse gases (GHGs) expressed in units of GtCO2-eq. The four pathways,
RCP-2.6, RCP-4.5, RCP-6.0, and RCP-8.5, are labeled by the radiative forcing assumed for 2100 (i.e., RCP-2.6
has a global-mean radiative forcing from GHGs in 2100 of 2.6 W m-2). RCP-2.6 assumes that GHG emissions
will peak before 2020; RCP-4.5 assumes a peak around 2040; RCP-6.0 assumes a peak around 2080; and RCP-8.5
assumes no peak before 2100. Each scenario includes certain socio-economic assumptions about fossil-fuel use
and other aspects related to GHG emissions.

Ozone-depleting substance (ODS)
An ODS is a substance that leads to stratospheric ozone depletion. Under the Montreal Protocol, most of thewidely used ODSs are controlled under Annexes A, B, C, and E. These include, among others, chlorofluorocarbons (CFCs), carbon tetrachloride (CCl4), methyl chloroform (CH3CCl3), halons, methyl bromide (CH3Br) andhydrochlorofluorocarbons (HCFCs). These ODSs typically have sufficiently long atmospheric lifetimes to reachthe stratosphere after being emitted at the surface. Methyl bromide is the shortest-lived of the controlled substances and has natural and anthropogenic sources. Other ODSs are not controlled under the Montreal Protocol.

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Executive Summary | Executive Summary

Ozone depletion potential (ODP)The Ozone Depletion Potential (ODP) is a metric for determining the relative strength of a chemical to destroyozone. The ODP of a substance is defined as the ratio of the change in global ozone for a given mass emissionof the substance to the change in global ozone for the same mass emission of CFC-11 (CFCl3). ODPs provide a
direct method for comparing the impacts of specific substances on the ozone layer.

Halogenated very short-lived substances (VSLSs)
Halogenated very short-lived substances (VSLSs) have atmospheric lifetimes less than 0.5 year and yet make acontribution to stratospheric chlorine or bromine levels. As short-lived ODSs, a large fraction of VSLS emissions
are destroyed in the troposphere, limiting the fraction of emissions that reaches the stratosphere and causes
ozone depletion. VSLS emissions that occur in regions with rapid transport to the stratosphere will make anenhanced contribution to stratospheric halogen levels. Hence, the ODP of a VSLS is generally dependent on
assumptions about the emission source region and time of the year of the emissions. VSLSs are not controlled
under the Montreal Protocol.

Global warming potentials (GWPs)
The global warming potential (GWP) is a metric for determining the relative contribution of a substance toclimate warming. GWP is defined as the ratio of the radiative forcing for a given mass emission of a substance
relative to the same mass emission of CO2 summed over a given time period (typically 20 or 100 years). In thisAssessment, a 100-yr time window is implied unless otherwise stated. For a given mass emissions of a substance,
the CO2-equivalent (CO2-eq) value is defined as the emissions multiplied by the respective GWP value, notingthat the GWP of CO2 is defined to be unity.

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Executive Summary

National Oceanic and Atmospheric Administration
National Aeronautics and Space Administration
United Nations Environment Programme
World Meteorological Organization
European Commission[1] Concentrations and trends in ozone-depleting substances (ODSs)
Total chlorine and total bromine
Our confidence that the Montreal Protocol is continuing to work is based on a sustained network of measurements of
the long-lived source gas concentrations over several decades. These measurements allow the determination of global
concentrations, their interhemispheric differences, and their trends. Combined with lifetime information, the data allow
us to derive historical emissions which can be compared with emissions derived from data reported to UN Environment.

• The atmospheric abundances of both total tropospheric chlorine and total tropospheric bromine fromlong-lived ODSs controlled under the Montreal Protocol have continued to decline since the 2014
Assessment (Figure ES-1, panels a, b; Table ES-2).

• During the period 2012–2016, the observed rate of decline in tropospheric chlorine due to controlled
substances was 12.7 ± 0.9 ppt Cl yr-1, which is very close to the baseline projection from the 2014
Assessment. The net rate of change was the result of a slower than projected decrease in CFC concentrations
and a slower than projected increase in HCFCs relative to the 2014 scenario. That scenario was based on the
maximum allowed production of HCFCs from Article 5 countries under the Montreal Protocol.

• The decrease of chlorine from controlled substances has partly been offset by increases in the mainly
natural CH3Cl and mainly anthropogenic very short-lived gases, which are not controlled under theMontreal Protocol.

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Executive Summary | Executive Summary

CFC-11 eq (Mt yr -1)

Timeline of ODSs and Ozone
1.4(a) CFC-11 equivalent emissions

1.0

CFC-12HCFC
CFC-11halons
CFC-113, -114, -115, CH3CCl3 CH3Cl
CCl4
CH3Br

0.6
0.2
1960

1980

2000

2020

2060

2080

2100

(b) Equivalent effective chlorine

4

EECl (ppb)

2040

3
2
1

Ozone (Dobson Units)

0
1960

1980

2000

2020

2060

2080

2100

295

(c) Annual global
total ozone

290
285
280
1960

1980

2000

2020

2040

2060

2080

2100

(d) October Antarctic total ozone

350

Ozone (Dobson Units)

2040

300

250

200
1960

ES.16

1980

2000

2020

Year

2040

2060

2080

2100

Executive Summary | Executive Summary

Table ES-2. Contributions of various long-lived ozone-depleting substances controlled under the Montreal Protocol to tropospheric organic chlorine and bromine in 2016, and annual average trends between 2012 and 2016.
Contribution to
tropospheric chlorine
and bromine in 20161(ppt Cl/Br)

Changes in tropospheric chlorine
and bromine (in parts per trillion(ppt) (Cl/Br) yr -1) from early-2012 tolate-2016

Controlled chlorine substances by groupchlorofluorocarbons (CFCs)
methyl chloroform (CH3CCl3)

1979
7.8

−12.0 ± 0.4−2.0 ± 0.5

carbon tetrachloride (CCl4)322−4.5 ± 0.2

hydrochlorofluorocarbons (HCFCs)

309+5.9 ± 1.3

halon-1211
Total Chlorine from controlled substances

3.6
2621

−0.1 ± 0.01
−12.7 ± 0.9

Controlled bromine substances by grouphalons

7.8−0.1 ± 0.01

2

methyl bromide (CH3Br)

6.8−0.04 ± 0.05

Total bromine from controlled substances

14.6−0.15 ± 0.04

1

Values are annual averages.

2

Not all CH3Br emissions are controlled. Some anthropogenic uses of CH3Br are exempted from Montreal
Protocol controls, and CH3Br has natural sources, which results in a natural background concentration.

Figure ES-1. Timeline of: a) CFC-11-equivalent emissions, b) equivalent effective chlorine, c) global total ozone,
and d) October Antarctic total ozone. Annual CFC-11-equivalent emissions are computed for the ODSs shownin the legend by multiplying mass emissions of a substance by its ODP (panel a). Historical emissions are derived
from the measured atmospheric concentrations of individual ODSs from measurement networks. The future projections of emissions assume full compliance with the Montreal Protocol and use standard methodologies basedon reported production, inventory-estimates of the banks and release rates. The annual abundances of equivalenteffective chlorine (EECl), shown for the global surface, are based on surface abundances (measured or derived
from projected emissions and lifetimes) of the chlorine- and bromine-containing substances (panel b). The bromine abundances are weighted by a factor of 65 to account for the greater efficiency of bromine in ozone destruction reactions in the atmosphere. Global total column ozone represents an average over 60°N to 60°S latitudes(panel c) and Antarctic total column ozone represents an average over 60°S to 80°S latitudes (panel d). Panels (c)and (d) include a comparison of chemistry-climate model results (black lines with gray shadings indicating uncertainty ranges) and available observations (data points). The model projections into the future assume compliancewith the Montreal Protocol and an increase in GHGs following the RCP-6.0 scenario. The lines with arrows markwhen CFC-11-equivalent emissions (a), EECl (b) and ozone abundances (c and d) return to their 1980 values. [Datasources are: panel (a) mixing ratios in Figure 6-2 and ODPs and lifetimes in Table A-1; panel (b) following Figure 6-2
with an alpha factor of 65; panel (c) Figure 3-28; and panel (d) Figure 4-18.]
ES.17

Executive Summary | Executive Summary

Unexpected increase in global total emissions of CFC-11
Observations of the persistent slowdown in the decline of CFC-11 concentrations have only recently allowed the robust
conclusion that emissions of CFC-11 have increased in recent years, as opposed to other possible causes for the slowdown
such as changing atmospheric circulation.

• Global CFC-11 emissions, derived from measurements by two independent networks, increased after

2012 contrary to projections from previous Assessments, which showed decreasing emissions (FigureES-2). This conclusion is supported by the observed rise in the CFC-11 hemispheric concentration difference. Global CFC-11 emissions for 2014 to 2016 were approximately 10 Gg yr-1 (about 15%) higher than thefairly constant emissions derived for 2002 to 2012; the excess emissions relative to projected emissions forrecent years is even larger. The increase in global emissions above the 2002–2012 average resulted in a global
concentration decline in CFC-11 over 2014 to 2016 that was only two-thirds as fast as from 2002 to 2012.

• The CFC-11 emission increase suggests new production not reported to UN Environment because

the increase is inconsistent with likely changes in the release of CFC-11 from banks associated withpre-phaseout production. Depending on how this newly produced CFC-11 is being used, substantial increases in the bank and future emissions are possible.

• Emissions of CFC-11 from eastern Asia have increased since 2012; the contribution of this region to theglobal emission rise is not well known. The country or countries in which emissions have increased have
not yet been identified.

Annual emission or production (Gg yr-1)

Figure ES-2. CFC-11 global emissions and
CFC-11 Annual Emissions and Production
reported production. Emissions of CFCReported production
11 derived from AGAGE (Advanced Global
140
Derived emissions
Atmospheric Gases Experiment; black) and
AGAGE observations
NOAA (National Oceanic and Atmospheric
NOAA observations120
2002–2012 mean
Administration; red) global network meaEmission projections
surements of CFC-11 concentrations (see also
100
Starting in 2006
Figure 1-4 of the Assessment) and a modelStarting in 2012using a CFC-11 lifetime of 52 years. Also shown80
are the production history reported to UN
Environment for all uses (green), the average of60
annual emissions over the 2002–2012 period
40(grey line), and scenario projections basedon observations through 2006 or through20
2012 (dotted and dashed lines). These emission projections are calculated using standard
0
methodologies based on reported production,
1995
2000
2005
2010
2015
inventory estimates of the bank, and an empirYear
ically determined release fraction from thebank over the seven years before 2006 or 2012,
which is then applied to subsequent years (see Chapter 1 and Chapter 6). Uncertainties in emissions, shown asvertical lines on the data points, include the influence of measurement and model representation uncertainties.
The uncertainties are smaller than those presented in Figure 1-4, because uncertainties related to factors constant across the whole time period, such as lifetimes and calibration scale, have been omitted.

ES.18

Executive Summary | Executive Summary

Persistent emissions of low abundance CFCs

• Observation-based analyses show unexpected stable or even increasing emissions of some of the low
abundance (less than 20 ppt) CFCs (CFC-13, CFC-113a, CFC-114, CFC-115) between 2012 and 2016.
For CFC-114 and CFC-115, atmospheric observations imply that a substantial fraction of global emissions
originated from China.

Ongoing substantial emissions of carbon tetrachloride (CCl4)• Sources of significant CCl emissions, some previously unrecognized, have been quantified. At least
4

25 Gg yr-1 of emissions have been estimated, mainly originating from the industrial production of chloromethanes, perchloroethylene and chlorine. This value can be compared with total global emissions of approximately 35 Gg yr-1, derived from atmospheric observations. The global CCl4 budget is now much better
understood and the previously identified gap between observation-based and industry-based emission estimates has been substantially reduced compared to the 2014 Assessment.

Hydrochlorofluorocarbons (HCFCs)• Total chlorine from HCFCs has continued to increase in the atmosphere since the last Assessment andreached 309 ppt in 2016. The annual average growth rate of chlorine from HCFCs decreased from 9.2 ± 0.3
ppt yr-1 for the 2008 to 2012 period to 5.9 ± 1.3 ppt yr-1 for the 2012 to 2016 period.

• Combined emissions of the major HCFCs have declined since the last Assessment which suggests an ef-

fective response to the 2007 Adjustment to the Montreal Protocol that limited HCFC emissions. Annual
emissions of HCFC-22 have remained relatively unchanged since 2012, whilst emissions of HCFC-141b
and-142b declined by around 10% and 18%, respectively, between 2012 and 2016. These findings are consistent with a decrease in reported HCFC consumption after 2012, particularly from Article 5 countries.

Tropospheric bromine• Total tropospheric bromine from controlled ODSs (halons and methyl bromide) continued to decrease

and by 2016 was 14.6 ppt, 2.3 ppt below the peak levels observed in 1998. In the 4-year period prior to thelast Assessment, this decrease was primarily driven by a decline in methyl bromide (CH3Br) abundance, witha smaller contribution from a decrease in halons. These relative contributions have now reversed, with halonsbeing the main driver of the decrease of 0.15 ± 0.04 ppt yr-1 tropospheric bromine between 2012 and 2016.

• Total bromine from halons has decreased from a peak of 8.5 ppt in 2005 to 7.7 ppt in 2016. Emissions of

halons derived from atmospheric observations declined or remained stable between 2012 and 2016 and arethought to originate primarily from banks.

• The atmospheric abundance of CH Br declined from a peak of 9.2 ppt in 1996–1998 to 6.8 ppt in 20163

as a consequence of controls under the Montreal Protocol. By 2016, controlled CH3Br consumption had
declined by more than 98% from its peak value. Reported consumption in quarantine and pre-shipment(QPS) uses of CH3Br, which are not controlled under the Montreal Protocol, has not changed substantiallyover the last two decades. Total reported anthropogenic emissions (controlled and not-controlled) havedeclined by about 85% from the peak value, and atmospheric CH3Br abundance is now near the expectednatural background.

Halogenated very short-lived substances (VSLSs)
Halogenated VSLS substances contribute to stratospheric chlorine and bromine loading and are not controlled by theMontreal Protocol. Chlorinated VSLSs are predominantly of anthropogenic origin, while brominated VSLSs have ­mainly
natural sources.

ES.19

Executive Summary | Executive Summary• Dichloromethane (CH Cl ) is the main component of VSLS chlorine and accounts for the majority of22

the rise in total chlorine from VSLSs between 2012 and 2016. A substantial fraction of the global CH2Cl2
emissions has been attributed to southern and eastern Asia. The current estimate is that total chlorine fromVSLS source gases increased by about 20 ppt between 2012 and 2016 to reach 110 ppt (Figure ES-3). The
growth rate shows large interannual variability.

• Several field campaigns conducted since the last Assessment have confirmed that brominated VSLSs
contribute 5±2 ppt to stratospheric bromine (Figure ES-3). There is no indication in measurements of along-term trend in the contribution of VSLSs to stratospheric bromine.

Total stratospheric chlorine and bromine
Total stratospheric chlorine and bromine both continue to decline. Even though the abundance of bromine is muchsmaller than that of chlorine, bromine has a significant impact because it is around 60–65 times more efficient thanchlorine in destroying ozone.

• Total chlorine entering the stratosphere from well-mixed ODSs declined by 405 ppt (12%) between the1993 peak (3582 ppt) and 2016 (3177 ppt). (Figure ES-3) This decline was driven by decreasing atmospheric abundances of methyl chloroform (CH3CCl3), CFC-11 and CCl4 (in order of importance). The VSLS
contribution (primarily anthropogenic) has increased over this period but remains below 4% of the total in
2016. About 80% of the chlorine entering the stratosphere annually is of anthropogenic origin.

• Total bromine entering the stratosphere from well-mixed ODSs declined by 2.4 ppt (15%) between the1998 peak (16.9 ppt) and 2016 (14.5 ppt). (Figure ES-3) This decline was driven by decreasing atmospheric abundances of methyl bromide (CH3Br), halon-1211, and halon 2402 (in order of importance). The VSLS
(primarily biogenic) contribution has no measurable change over this period, contributing about 25% to thetotal in 2016. The natural components of CH3Br and VSLSs now contribute more than half of the bromine
entering the stratosphere annually.

• HCl is the major chlorine component in the upper stratosphere. Its concentration in this region de-creased by about 6% between 2005 and 2016. This decrease is consistent with the decline in total chlorine
entering the stratosphere.

• Total stratospheric bromine derived from bromine monoxide (BrO) observations decreased by about

8% from 2004 to 2014. This decrease is consistent with the decline in total bromine entering the stratosphere.

[2] Hydrofluorocarbons (HFCs)
The Montreal Protocol phaseout of ODSs has led to the development of alternative substances for use in many sector
applications. Hydrofluorocarbons (HFCs) are a widely used category of ODS alternatives that do not contain ozone-depleting chlorine or bromine. Long-lived CFCs, HCFCs, and HFCs are all potent greenhouse gases. Ultimately the Kigali
Amendment to the Montreal Protocol, which was adopted in 2016 and will come into force in 2019, sets schedules
for the phasedown of global production and consumption of specific HFCs. Although the radiative forcing suppliedby atmospheric HFC abundances is currently small, the Kigali Amendment is designed to avoid unchecked growth inemissions and associated warming in response to projected increasing demand in coming decades. Discussed here are theanticipated overall effects of Kigali Amendment controls and existing national and regional HFC regulations on future
HFC abundances and associated climate warming. HFCs were included as one group within the basket of gases of the1997 Kyoto Protocol and, as a result, developed (Annex-I) countries supply annual emission estimates of HFCs to theUnited Nations Framework Convention on Climate Change (UNFCCC). HFC-23 is considered separately in the Kigali
Amendment and in this Assessment, primarily because it is emitted to the atmosphere as a by-product of HCFC-22
production. HFC-23 has one of the longest atmospheric lifetimes and highest global warming potentials (GWP) among
HFCs. HFC-23 is not included in the projections discussed here.
ES.20

Executive Summary | Executive Summary

Observed HFC abundances and associated emissions• Atmospheric abundances of most currently measured HFCs are increasing in the global atmosphere.

These increases are similar to those projected in the baseline scenario of the 2014 Assessment. HFC emissions derived from observations increased by 23% from 2012 to 2016 and currently amount to about 1.5%of total emissions from all long-lived greenhouse gases as carbon dioxide-equivalent emissions (GtCO2-eq).

Chlorine and Bromine Containing Gases Entering the Stratosphere3660

Totals3290−10%

VSLS: +44%

Other ODS: +9.0%
CFC-113: −13%

Human
Sources

2000

HCFCs: +175%
CCl4: −23%
CFC-11: −14%1000

CFC-12: +0.2%
Natural
Sources

1993

Totals

19.6
−11%

20

CH3 CCl 3 : −98%

3000

0

22.0

% change

Total Bromine (ppt)

Total Chlorine (ppt)

4000

Other Halons: −21%
Halon-1301: +23%

Human
Sources

15

Halon-1211: −8.2%
CH3Br Human: −68%

10

CH3Br Natural: 0%
Natural
Sources5

VSLS: 0%

CH3Cl: +1.1%

2016% change

0

1998

2016

Figure ES-3. Total chlorine and bromine entering the stratosphere from well-mixed ODSs and VSLSs.
These partitioned columns show the contributions of various chlorine-containing (left) and bromine-containing(right) substances from natural and human-related sources as derived from surface observations. The displayedamounts are representative of air entering the stratosphere in 2016 and in 1993 or 1998, when chlorine andbromine global abundances were near their respective peak values. Note the large difference between the totalabundances of chlorine and bromine. The percentage decline or increase of the abundance of each substance in2016 relative to the peak year and the total change are shown. Horizontal lines divide natural and human-related
contributions. Both total chlorine and bromine have declined substantially from respective peaks in the 1990s(10% and 11%, respectively, when considering the sum of well-mixed ODSs and VSLSs). Human activities are thelargest source of chlorine entering the stratosphere and CFCs are the largest fraction of the total; the human contribution to total chlorine is 14% lower in 2016 compared to 1993. Methyl chloride (CH3Cl) is the largest natural
source of chlorine. VSLS chlorine species from human activities are a small fraction of total chlorine (less thana few percent). Methyl bromide (CH3Br) and halons are the primary sources of stratospheric bromine. Methyl
bromide has both natural and human sources whereas halons are entirely due to human activity. The humancontribution to total bromine is 22% lower in 2016 compared to 1998. Bromine from VSLSs is entirely of natural
origin and is assumed constant at 5 ppt since 1998. In 2016, the sum of bromine from natural methyl bromide and
VSLSs supplied more than half of the total entering the stratosphere. Chlorine compounds labeled ‘Other ODSs’
include minor CFCs and halon-1211. Other chemical terms are carbon tetrachloride (CCl4) and methyl chloroform(CH3CCl3). Bromine compounds labeled ‘Other Halons’ include halon-1202 and halon-2402. In the vertical axes
label, ‘ppt’ denotes atmospheric abundance in units of parts per trillion. [Derived from Figure 1-17 and Table 6-4]

ES.21

Executive Summary | Executive Summary• HFC emissions estimated from the combination of inventory reporting and atmospheric observations
indicate that the HFC emissions originate from both developed and developing countries. Only developed (Annex I) countries are required to report HFC emissions to the UNFCCC, and these reported totals
account for less than half of global emissions (as CO2-eq) derived from observations.

• Radiative forcing from measured HFCs continues to increase; it currently amounts to 1% (0.03 W m )-2of the 3 W m-2 supplied by all long-lived greenhouse gases (GHGs) including CO2, CH4, N2O, and halocarbons. Total HFC radiative forcing in 2016 was about 10% of the 0.33 W m-2 supplied by all halocarbons.

• Global annual emissions of HFC-23, a potent greenhouse gas and a byproduct of HCFC-22 production,

have varied substantially in recent years. This variability in observationally derived global emissions isbroadly consistent with the sum of reported HFC-23 emissions associated with HCFC-22 production fromdeveloped countries and inventory-based estimates of HFC-23 emissions from developing countries. Future
HFC-23 emission trends will largely depend on the magnitude of HCFC-22 production and the effectiveness of HFC-23 destruction associated with that production.

• Some short-lived, low-GWP replacement substances for long-lived HCFCs and HFCs have been detect-

ed in the atmosphere (at concentrations typically below 1 ppt), consistent with the transition to thesesubstances being underway. Some of these substances are unsaturated HCFCs and unsaturated HFCs, also
known as hydrofluoroolefins or HFOs.

Projections of HFC emissions and temperature contributions• The HFC phasedown schedule of the 2016 Kigali Amendment to the Montreal Protocol substantiallyreduces future projected global HFC emissions (Figure ES-4). Emissions are projected to peak before
2040 and decline to less than 1 GtCO2-eq yr-1 by 2100 (Figure ES-4). Only marginal increases are projected
for CO2-eq emissions of the low-GWP alternatives (Figure ES-5) despite substantial projected increases intheir emission mass.

• The Kigali Amendment, assuming global compliance, is projected to reduce future radiative forcing
due to HFCs by about 50% in 2050 compared to a scenario without any HFC controls. The estimatedbenefit of the amendment is the avoidance of 2.8 - 4.1 GtCO2-eq yr-1 emissions by 2050 and 5.6 - 8.7 GtCO2eq yr-1 by 2100. For comparison, total CH4 emissions are projected to be 7 - 25 GtCO2-eq yr-1 by 2100 in the
RCP-6.0 and RCP-8.5 scenarios and total N2O emissions 5-7 GtCO2-eq yr-1 by 2100.

• The Kigali Amendment is projected to reduce future global average warming in 2100 due to HFCs
from a baseline of 0.3-0.5 oC to less than 0.1 oC (Figure ES-4). If the global production of HFCs were tocease in 2020, the surface temperature contribution of the HFC emissions would stay below 0.02 oC for thewhole 21st century. The magnitude of the avoided temperature increase, due to the provisions of the Kigali
Amendment (0.2 to 0.4 oC) is substantial in the context of the 2015 UNFCCC Paris Agreement, which aims
to limit global temperature rise to well below 2.0 oC above pre-industrial levels and to pursue efforts to limit
the temperature increase even further to 1.5 oC.

[3] Stratospheric ozone
The Montreal Protocol and its Amendments and Adjustments have been effective in limiting the abundance of ODSs in
the atmosphere. Detecting and attributing ozone trends during this period of slow ODS decline is challenging becauseof large natural variability in ozone, as well as confounding factors such as climate change and changes in troposphericozone. While most natural variability is quasi-periodic, episodic volcanic eruptions can drive large changes in ozone
in the presence of elevated halogen abundances. The Antarctic and the upper stratosphere, where the ozone depletion
signal has been clearest against the backdrop of natural variability, are now showing evidence of recovery. Although the
ES.22

Executive Summary | Executive Summary

10

HFC Emissions

Surface Temperature Change Due to HFCs
0.5

Without controls

Without controls

Based on Velders (2015)

0.4

Kigali Amendment (2016)
Phaseout in 2020

Temperature change ( C)

Emissions (GtCO2-eq yr-1)

8 With controls

6

4

2

0
2000

2020

2040

2060

Year

2080

2100

Xu et al. (2013)
Based on Velders (2015)

With controls

Kigali Amendment (2016)
Phaseout in 2020

0.3

0.2

0.1

0.0
2000

2020

2060
Year

2040

2080

2100

Figure ES-4. Scenarios of HFC emissions and global average surface-temperature response. Shown
are global HFC scenarios without global HFC controls and with full compliance with the Kigali Amendment.
Also shown is a scenario in which global production of HFCs is phased out in 2020. For comparison, theglobal surface temperature from all greenhouse gases is projected to increase, relative to the 1986–2005
average, by between 1.4 °C and 4.8 °C by the end of the 21st century in the RCP-6.0 and RCP-8.5 scenarios.
The contribution from HFC-23 emissions is not included here. [Figure adapted from Figure 2-20]

Arctic stratosphere is warmer and experiences much more meteorological variability, severe chemical ozone loss canoccur when cold conditions persist into March/April (Figure ES-6). Ozone in the tropical lower stratosphere shows little
response to changes in ODSs, because halogen-driven ozone depletion is small in this region.

Antarctic and Arctic ozone• For the first time, there are emerging indications that the Antarctic ozone hole has diminished in sizeand depth since the year 2000, with the clearest changes occurring during early spring. Although accounting for natural variability is challenging, the weight of evidence suggests that the decline in ODSs
made a substantial contribution to the observed trends.

• Even with these early signs of recovery, an Antarctic ozone hole continues to occur every year, withthe severity of the chemical loss strongly modulated by meteorological conditions (temperatures andwinds) (Figure ES-1). In 2015, the ozone hole was particularly large and long-lasting, as a result of a cold
and undisturbed polar stratospheric vortex. Aerosols from the Calbuco volcanic eruption are also believed
to have contributed to the large ozone hole area in 2015. Conversely, in 2017, the Antarctic ozone hole wasvery small due to a warm and unusually disturbed polar vortex.

ES.23

Executive Summary | Executive Summary

Mt yr-1

Climate Change: ODSs, HFCs, Alternatives

6(a) Emissions (mass)
5
ODSs
High-GWP HFCs
4

Low-GWP alternatives: Lower range
Low-GWP alternatives: Upper range

3
2

GtCO2-eq yr-1

1
0
1950

1975

2000

2050

2075

2100

2025

2050

2075

2100

2025

2050

2075

2100

2025

2050

2075

2100

12
(b) CO2-eq emissions
10
8
6
4
2
0
1950
1975
2000
0.5
(c) Radiative forcing
0.4

W m-2

2025

0.3
0.2
0.1
0.0
1950

Degrees Celsius

0.3

1975

2000

(d) Surface temperature

0.2
0.1
0.0
1950

1975

2000

Year

Figure ES-5. Historical and projected emissions, radiative forcing and surface temperature from ODSs,
HFCs, and HFC alternatives. Historical and projected contributions to climate change are shown for ODSs andhigh-GWP HFCs (excluding HFC-23) assuming full compliance with the provisions of the Montreal Protocol,
including the Kigali Amendment. Also shown are projections for low-GWP alternatives. Quantities shown are
the (a) mass emissions, (b) CO2-eq emissions, (c) radiative forcing and (d) contributions to surface temperaturechange, with a dashed vertical line indicating 2016. The surface temperature response is calculated with a parameterized climate model. Note that the values for low-GWP alternatives in the bottom three panels (yellow lines)
are sufficiently small that they require enlargement to be visible to the reader. [Figure adapted from Figures 2-20
and 6-8]
ES.24

Executive Summary | Executive Summary• In the Arctic, year-to-year variability in column ozone is much larger than in the Antarctic, preclud-ing identification of a statistically significant4 increase in Arctic ozone over the 2000–2016 period.
Exceptionally low ozone abundances, similar to those experienced in Arctic spring 2011, have not been
observed in the last four years. Extremely cold conditions in the 2015/2016 winter resulted in rapid chemicalozone loss, but a sudden warming of the polar stratosphere in early March curtailed further losses.

The term ‘statistically significant’ indicates that there is a very high likelihood (above some confidence limit that is typically 95%) that aquantity has undergone a change over a given time period and has not simply exhibited variability around an unchanged state. For a moredetailed discussion of trend uncertainties, see Chapter 3.

Observed and Modeled Arctic Ozone
Modeled ozone (with Montreal
Protocol) on March 26, 2011

Observed column ozone
on March 26, 2011
450

(a)

(b)

425

Column ozone (DU)

400
375
350
325
275
250
225
200
175
150

Modeled ozone (without Montreal
Protocol) on March 26, 2011

Modeled and observed Arctic ozone(c)

Minimum column ozone (DU)

4

300

(d)

250

200

150

100
July

– Observations
Model:– with Montreal Protocol– without Montreal Protocol

Sept

Nov

2010

Jan

Mar

May

July

2011

Figure ES-6. Observed and modeled column ozone in the Arctic. Without the success of the Montreal Protocol,
a deep ozone hole could have formed in the Arctic in 2011, and smaller Arctic ozone holes would have becomea regular occurrence. The 2010/2011 Arctic winter had unusually persistent low temperatures in the stratospherethat led to strong chemical ozone destruction. Satellite observations from the Ozone Monitoring Instrument(OMI) in March 2011 show a region of low column ozone surrounded by regions of higher ozone (panel (a)). The
March observations are well simulated by a chemistry-transport model run with observed abundances of ODSs,
as seen by comparing the maps of panels (a) and (b) as well as the black and blue curves in panel (d), which show
the measured and modeled timelines for the mid-2010 to mid-2011 period of daily minimum column-ozone values in the Arctic region (latitudes greater than 45°N). If the same model is run with projected ODS abundances in
the absence of Montreal Protocol controls, a much more severe and prolonged Arctic ozone hole is seen (panel(c) and the red curve in panel (d)). [See also Figure 4-17]
ES.25

Executive Summary | Executive Summary

Northern Mid-Latitude Ozone Trends

Altitude (km)Pressure (hPa)

Figure ES-7. Ozone trends inthe stratosphere. The largest
1
50
Average ozone from
relative depletion of ozone out35°N to 60°N
Satellite observations
side the polar regions occurredwith uncertainty
Model results
prior to 1997 in the northern40
with variancemid-latitude, upper stratosphere(left panel). The largest recovery
1030
has occurred in the same region,
with an upward trend of about
3% per decade since 2000 above
20
40-km altitude (right panel).
Ozone trends derived from sat100
ellite observations are shown in
–15
–10
–5
0
5
–5
0
5
Ozone trend 1985–1996
Ozone trend 2000–2016
brown, with uncertainty ranges(% per decade)(% per decade)given by horizontal lines. Ozone
trends derived from a set ofchemistry-climate models are shown in orange, with the model variance given by the yellow envelope. Ozone
trends from chemistry-climate models agree very well with the measured trends. [See also Figure 3-23]• Model simulations show that implementation of the Montreal Protocol has prevented much more severe ozone depletion than has been observed in the polar regions of both hemispheres (Figure ES-6).

Global ozone• No statistically significant trend has been detected in global (60°S–60°N) total column ozone over the
1997–2016 period (Figure ES-1). Average global total column ozone in the years since the last Assessment
remain roughly 2.2% below the 1964–1980 average. These findings are expected given our understanding of
the processes that control ozone.
○○ In the mid-latitudes, the increase in ozone expected to arise from the 15% decline in EESC since 1997 issmall (1% per decade) relative to the year-to-year variability (about 5%).

○○ In the tropics, where halogen-driven ozone loss is small in the lower stratosphere, total column ozone

has not varied significantly with ODS concentrations, except under conditions of high volcanic aerosol
loading (e.g., Mt. Pinatubo).

• Upper stratospheric ozone, which represents only a small fraction of the total column, has increasedby 1–3% per decade since 2000 outside of polar regions (Figure ES-7). Additional and improved data
sets and focused studies evaluating trend uncertainties have strengthened our ability to assess ozone profile changes since the last Assessment. The upward trend is largest and statistically significant in northern
mid-latitudes and maximizes above 40-km altitude.

○○ Model simulations attribute about half of the observed upper stratospheric ozone increase after 2000 to

the decline of ODSs since the late 1990s.

○○ The other half of the ozone increase is attributed to the slowing of gas-phase ozone destruction, which

results from cooling of the upper stratosphere caused by increasing GHGs.

• There is some evidence for a decrease in global (60°S–60°N) lower stratospheric ozone from 2000 to

2016, but it is not statistically significant in most analyses. Much of the apparent decline in the tropics

ES.26

Executive Summary | Executive Summary

and mid-latitudes was reversed by an abrupt increase in ozone in 2017, showing that longer records areneeded to identify robust trends. Model simulations indicate that, on multiannual timescales, variations inozone in this region are primarily controlled by transport rather than chemistry.

Ozone recovery and ozone-climate interactions
A refined ODS scenario and new GHG emissions scenarios were used in chemistry-climate models for this Assessment,
leading to a delay of about 5 to 15 years in the return dates relative to the last Assessment, depending on the latitude
region.

• Updated chemistry climate model projections based on full compliance with the Montreal Protocol andassuming the baseline estimate of the future evolution of GHGs (RCP-6.0) show that:○○ The Antarctic ozone hole is expected to gradually close, with springtime total column ozone return-ing to 1980 values shortly after mid-century (about 2060) (Figure ES-1);○○ Arctic springtime total ozone is expected to return to 1980 values before mid-century (about 2030s).

Substantial Arctic ozone loss will remain possible in cold winters as long as ODS concentrations are well
above natural levels. In contrast to the Antarctic, the timing of the recovery of Arctic total ozone inspring will be strongly affected by anthropogenic climate change;○○ Northern-Hemisphere, mid-latitude column ozone is expected to return to 1980 abundances before

mid-century (2030s), and Southern Hemisphere, mid-latitude ozone is expected to return around
mid-century.

• Outside the Antarctic, CO , CH , and N O will be the main drivers of stratospheric ozone changes in2

42

the second half of the 21st century, assuming full compliance with the Montreal Protocol. These gases
impact both chemical cycles and the stratospheric overturning circulation, with a larger response in stratospheric ozone associated with stronger climate forcing. By 2100 the stratospheric column is expected to:○○ decrease in the tropics by about 5 DU for RCP-4.5 and about 10 DU for RCP-8.5, with the net total

column change projected to be smaller (about 5 DU) because of offsetting increases in troposphericozone; and○○ not only to recover but to exceed 1960–1980 average values in mid-latitudes and the Arctic, with spring-time Arctic ozone being higher by about 35 DU for RCP-4.5 and about 50 DU for RCP-8.5.

[4] Ozone change and its influence on climate
Ozone is important in the climate system and its changes can influence both the troposphere and the stratosphere. Past
Assessments have discussed evidence for how stratospheric ozone depletion has affected Southern Hemisphere climate.
The climate impacts of ozone depletion are expected to reverse over coming decades as stratospheric ozone recovers.
However, projected increases in atmospheric GHG concentrations will continue to be a key driver of future Southern
Hemisphere climate. The relative importance of ozone recovery for future Southern Hemisphere climate will depend on
the evolution of atmospheric GHG concentrations.

Influence on stratospheric climate• Discrepancies between estimates of stratospheric cooling rates from different satellite temperature re-trievals have been substantially reduced since the last Assessment. This result has led to greater confidence in the attribution of observed stratospheric temperature changes since the late 1970s.

ES.27

Executive Summary | Executive Summary

Antarctic Ozone Depletion Impacts on
Summer Southern Hemisphere Climate
Stratosphere
Cooling due tothe ozone hole

Accelerated stratos

phericovert
urDelayedbreakupof polarvortexni n g

circu
latio
n

Troposphere
Southward
expansion of
the Hadley cell

Southward
shift ofwesterly
winds

Increased
subtropicalrainfallSouthward shift of
mid-latitude rain
Antarctica

Figure ES-8. Schematic illustration ofSouthern Hemisphere climate impacts in
austral summer associated with Antarctic
ozone depletion. Ozone depletion has
cooled the Antarctic stratosphere, leading toa delayed breakup of the stratospheric polar
vortex and an accelerated stratospheric
overturning circulation. Impacts haveextended into the troposphere with theregion of strong westerly winds and associated rainfall shifted southward, affecting
the ocean circulation. The subtropical edgeof the tropical circulation has also expanded
poleward, leading to reduced precipitationin mid-latitudes and enhanced precipitationin the subtropics. [Also appears as Figure
5-12]

Changes in ocean circulationand temperature

Polar Regions

Middle Latitudes

Subtropics• Decreases in stratospheric ozone caused by ODS increases have been an important contributor to ob-served stratospheric cooling. New studies find that ODSs thereby contributed approximately one third of
the observed cooling in the upper stratosphere from 1979 to 2005, with two thirds caused by increases inother GHGs.

• Satellite temperature records show weaker global-average cooling throughout the depth of the strato-

sphere between 1998 and 2016 relative to between 1979 and 1997. The difference in the rate of stratospheric cooling between the two periods is consistent with differences in the observed ozone trends for eachperiod.

Influence on surface climate and oceans• New studies strengthen the conclusion from the last Assessment that lower stratospheric cool-ing due to ozone depletion has very likely been the dominant cause of late 20th century changes inSouthern Hemisphere climate in summer. These changes include the observed poleward shift in Southern
Hemisphere tropospheric circulation, with associated impacts on surface temperature and precipitation(Figure ­ES-8). No robust link between stratospheric ozone depletion and long-term surface climate changes
in the Northern Hemisphere has been established.

• Changes in tropospheric circulation driven by ozone depletion have contributed to recent trends inSouthern Ocean temperature and circulation; the impact on Antarctic sea ice remains unclear.

• New studies since the last Assessment have not found a causal link between ozone depletion and thenet strength of the Southern Ocean carbon sink over the last few decades. This result updates the 2010
Assessment where such a link was suggested.

ES.28

Executive Summary | Executive Summary[5] Policy considerations for stratospheric ozone and climate
Policy-relevant alternative scenarios related to future ozone changes
Changes in total column ozone and GWP-weighted emissions in response to various control measures and alternative
scenarios are shown in Figure ES-9. The baseline scenario used here and those used in previous Assessments assume
compliance with the Montreal Protocol. The alternative scenarios assessed here include the elimination of banks, production, and emissions of gases that are both controlled and uncontrolled by the Montreal Protocol. Key conclusions arefound in the bullet points below.

• The recently derived increase in global emissions of CFC-11 (Figure ES-2, Section 1) indicates that thereis global production that is not reported to UN Environment. Assuming total emissions of CFC-11 continue at their average level from 2002–2016 (67 Gg yr-1), the return of mid-latitude and polar EESC values
to their 1980 values would be delayed by about 7 and 20 years, respectively, compared to the recovery dateexpected from the continued declining bank emissions expected from the baseline scenario in which no unreported production is considered. Avoiding this scenario (blue bar in top panel of Figure ES-9) would havea larger positive impact on future ozone than any of the other mitigation options considered in Figure ES-9.

• Future emissions from ODS banks continue to be a slightly larger contributor than future ODS produc-tion to ozone layer depletion over the next four decades in the baseline scenario. The baseline scenarios
assume compliance with the Montreal Protocol. Future emissions from the banks of halons, CFCs, and
HCFCs are projected to contribute roughly comparable amounts to EESC over the next few decades.

• CCl emissions inferred from atmospheric observations continue to be much greater than those as4

sumed from feedstock uses as reported to UN Environment. A significant part of these additional emissions has been identified as inadvertent by-product emissions from chloromethanes and perchloroethylene
plants and fugitive emissions from the chlor-alkali process. Elimination of all CCl4 emissions in 2020 would
accelerate the return of mid-latitude EESC to 1980 levels by almost three years.

• Elimination of future quarantine and pre-shipment (QPS) production of methyl bromide (CH Br)
3would accelerate the return of mid-latitude EESC to 1980 levels by about a year. Production for QPS
applications is not controlled by the Montreal Protocol. QPS has remained nearly unchanged over the lasttwo decades, and now constitutes almost 90% of the reported production of CH3Br because of the phaseoutof other uses.

A number of gases of anthropogenic origin that are not controlled by the Montreal Protocol can have direct chemical
effects on stratospheric ozone, for example dichloromethane (CH2Cl2) and N2O.

• Emissions of anthropogenic VSLS chlorine contribute to ozone depletion. Observed growth in the con-centrations of CH2Cl2, which accounted for the majority of the recent rise in total chlorine from VSLSs,
continues to be highly variable and there is insufficient information to confidently predict the future concentrations of CH2Cl2 (see Section 1). Response to any action taken to reduce emissions would be rapid andeffective in reducing atmospheric concentrations since CH2Cl2 is a short-lived substance (Figure ES-9 upper
panel).

• Reducing N O emissions from those in RCP-6.0 to achieve the Concerted Mitigation scenario would
2

have a similar positive impact on stratospheric ozone as eliminating future production of HCFCs from2020 (Figure ES-9 upper panel). The Concerted Mitigation scenario5 is an average of four scenarios that leadto lower N2O emissions in 2050 than were experienced in 2005. This N2O mitigation scenario has a larger
benefit to climate (2020 to 2060) than do the ODS alternative scenarios considered (Figure ES-9 lower panel).

5

UNEP 2013. Drawing Down N2O to Protect Climate and the Ozone Layer. A UNEP Synthesis Report. United Nations Environment
Programme (UNEP), Nairobi, Kenya.
ES.29

Executive Summary | Executive Summary

Change in Ozone in Response to Alternative Scenarios
Gases controlled byMontreal Protocol

CFCs
halons
HCFCs

Bank recapture anddestruction in 2020

HCFCs
CH3Br

Elimination of production
from 2020 onwards

CCl4

Elimination of total emissions
from 2020 onwards
Continued constant emissions
at 67 Gg/yr

CFC-11
Gases not controlled byMontreal Protocol–0.2% ozonedecrease

–0.1

0

+0.1

Percent change in total column ozone relative tothe baseline scenario averaged over 2020–2060

N2O

Concerted Mitigation scenario
from UNEP (2013)

CH2Cl2

No future emissions+0.2% ozoneincrease

Change in GWP-Weighted Emissions in Response to Alternative Scenarios
Gases controlled byMontreal Protocol

Elimination of high-GWP
production from 2020 onwards(excl. HFC-23)

HFCs
CFCs
halons(0.21)

Bank recapture anddestruction in 2020

HCFCs
HCFCs
CCl4
CFC-11
Gases not controlled
by Montreal Protocol+20

Increasedemissions

+10

0

N2O
–10

–20

–30

–40

–50

–60

Changes in cumulative GWP-weighted emissions over
2020–2060 relative to the baseline scenario (GtCO2-eq)

Elimination of production
from 2020 onwards
Elimination of total emissions
from 2020 onwards
Continued emissions
at 67 Gg/yr
Concerted Mitigation scenario
from UNEP (2013)

ReducedemissionsFigure ES-9. Changes in ozone and GWP-weighted emissions for a selection of alternative
scenarios. Changes in total column ozone (upper panel) and GWP-weighted emissions (lower
panel) that occur in the alternative scenarios as described in the figure. The values are differences in average ozone (top panel) over the period 2020–2060 and cumulative GWP-weighted
emission (lower panel) over the same period between the alternative scenarios and the baseline
scenario. A decrease in total ozone and an increase in GWP-weighted emissions occur when
the alternative scenario emissions are higher than in the baseline scenario. Numerical valuesof these changes are shown in Table 6-5. Note that the change resulting from the eliminationof HFC production shown in the lower panel is not shown in the upper panel because HFCs playsuch a small role in the chemical destruction of ozone. Chemical terms used in the figure are
methyl bromide (CH3Br), carbon tetrachloride (CCl4), nitrous oxide (N2O), and dichloromethane(CH2Cl2). [Also Figure 6-1]
ES.30

Executive Summary | Executive Summary

Climate impact of gases controlled by the Montreal Protocol
Historical and projected contributions to climate change from emissions of ODSs, high-GWP HFCs, and low-GWP alternatives have been calculated, assuming full compliance with the Montreal Protocol, including the Kigali Amendment(Figure ES-5).

• Future CO -eq emissions of HFCs from 2020 to 2060 under the Kigali Amendment are about half of2

those in a scenario without any HFC controls. Assuming compliance, projected cumulative emissions of
HFCs from 2020 to 2060 decrease to approximately 60 GtCO2-eq. CFCs from known banks and HCFCs
cumulatively contribute approximately 3 Gt and 9 GtCO2-eq emissions, respectively, over the same time
period. For comparison, cumulative CO2 emissions from fossil fuel usage are projected over this time period
to be 760 GtCO2 in the RCP-2.6 scenario and 1700 GtCO2 in the RCP-6.0 scenario. The peak in annual CO2eq emissions of all HFCs together is expected to be much smaller than the peak in ODS emissions (FigureES-5).

• A faster phasedown of HFCs than required by the Kigali Amendment would further limit climate change

from HFCs. One way to achieve this phasedown would be more extensive replacement of high-GWP
HFCs with commercially available low-GWP alternatives in refrigeration and air-conditioning equipment. Figure ES-9 shows the impact of a complete elimination of production of HFCs starting in 2020, andtheir substitution with low-GWP HFCs, which would avoid an estimated cumulative 53 GtCO2‑eq emission
during 2020–2060.

• Improvements in energy efficiency in refrigeration and air-conditioner equipment during the transi-tion to low-GWP alternative refrigerants can potentially double the climate benefits of the HFC phasedown of the Kigali Amendment.

• Total radiative forcing from the controlled ODSs and their replacements continues to be strongly limit-

ed by the Montreal Protocol, including the Kigali Amendment. The radiative forcing from CFCs has been
declining since the early 2000s. The sum of CFC and HCFC radiative forcing has been stable for about two
decades and is just starting to decline. The total forcing from CFC and HCFCs and their HFC replacements
is projected to continue to increase gradually for the next decade or two. After that, the ODS and HFC
restrictions of the Montreal Protocol, if adhered to, ensure a continued decline in total RF from ODSs andtheir replacements through the rest of the century.

• Global warming potentials, global temperature potentials, and ozone depletion potentials of hundredsof HCFCs have been calculated and are presented, most for the first time in an assessment. These data
include all the HCFCs listed under Annex C, Group I of the Montreal Protocol, many of which did not haveestimated GWPs at the time of the adoption of the Kigali Amendment. This information is significant since
the Kigali Amendment uses CO2-eq production and consumption of HFCs and HCFCs as a metric for thebaseline determination of the HFC phasedown.

Impacts of climate change and other processes on future stratospheric ozone
Anthropogenic activity associated with climate change could have potentially important impacts on the future of theozone layer.

• The wide range of possible future levels of CO , CH , and N O represents an important limitation to2

42

making accurate projections of the ozone layer [see Ozone recovery and ozone-climate interactions section]. Because future ODS atmospheric concentrations are highly constrained by the Montreal Protocol, therange in projected ozone levels across the ODS scenarios is much smaller than that associated with GHG
changes.

ES.31

Executive Summary | Executive Summary
32• Intentional long-term geoengineering applications
that substantially increase stratospheric aerosols to

mitigate global warming by reflecting sunlight would alter the stratospheric ozone layer. The estimatedmagnitude and even the sign of ozone changes in some regions are uncertain because of the high sensitivity
to variables such as the amount, altitude, geographic location, type of injection and the halogen loading. An
increase of the stratospheric sulfate aerosol burden in amounts sufficient to substantially reduce global radiative forcing would delay the recovery of the Antarctic ozone hole. Much less is known about the effects onozone from geoengineering solutions using non-sulfate aerosols.

ES.32

Executive Summary Appendix
Scientific Summaries of the Chapters
Chapter 1: Update on Ozone-Depleting Substances (ODSs) andother Gases of Interest to the Montreal Protocol
This chapter concerns atmospheric changes in ozone-depleting substances (ODSs), such as chlorofluorocarbons (CFCs),
halons, chlorinated solvents (e.g., CCl4 and CH3CCl3) and hydrochlorofluorocarbons (HCFCs), which are controlled
under the Montreal Protocol. Furthermore, the chapter updates information about ODSs not controlled under theProtocol, such as methyl chloride (CH3Cl) and very short-lived substances (VSLSs). In addition to depleting stratospheric ozone, many ODSs are potent greenhouse gases.
Mole fractions of ODSs and other species are primarily measured close to the surface by global or regional monitoring
networks. The surface data can be used to approximate a mole fraction representative of the global or hemispherictropospheric abundance. Changes in the tropospheric abundance of an ODS result from a difference between the rate ofemissions into the atmosphere and the rate of removal from it. For gases that are primarily anthropogenic in origin, thedifference between northern and southern hemispheric mole fractions is related to the global emission rate because thesesources are concentrated in the northern hemisphere.

• The abundances of the majority of ODSs that were originally controlled under the Montreal Protocol

are now declining, as their emissions are smaller than the rate at which they are destroyed. In contrast,
the abundances of most of the replacement compounds, HCFCs and hydrofluorocarbons (HFCs, which
are discussed in Chapter 2), are increasing.

Tropospheric chlorine
Total tropospheric chlorine is a metric used to quantify the combined globally averaged abundance of chlorine in the
troposphere due to the major chlorine-containing ODSs. The contribution of each ODS to total tropospheric chlorine is
the product of its global tropospheric mean mole fraction and the number of chlorine atoms it contains.

• Total tropospheric chlorine (Cl) from ODSs continued to decrease between 2012 and 2016. Total troposphericchlorine in 20161 was 3,287 ppt (where ppt refers to parts per trillion as a dry air mole fraction), 11% lower
than its peak value in 1993, and about 0.5% lower than reported for 2012 in the previous Assessment. Of the2016 total, CFCs accounted for about 60%, CH3Cl accounted for about 17%, CCl4 accounted for about 10%,and HCFCs accounted for about 9.5%. The contribution from CH3CCl3 has now decreased to 0.2%. Very
short-lived source gases (VSL SGs), as measured in the lower troposphere, contributed approximately 3%.
○○ During the period 2012–2016, the observed rate of decline in tropospheric Cl due to controlled sub-stances was 12.7 ± 0.92 ppt Cl yr−1, similar to the 2008–2012 period (12.6 ± 0.3 ppt Cl yr−1). This rateof decrease was close to the projections from the A1 scenario3 in the previous Assessment. However,the net rate of change was the result of a slower than projected decrease in CFCs and a slower HCFC
increase than in the A1 scenario, which assumed that HCFC production from Article 5 countries would
follow the maximum amount allowed under the Montreal Protocol.

123Here and throughout this chapter, values that are given for a specific year represent annual averages, unless mentioned otherwise.
The ranges given here represent the interannual variability in observed growth rate or rate of decrease.
A1 Scenario is given in Table 5A-2 of Harris and Wuebbles et al. [2014].
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Executive Summary | Executive Summary○○ When substances not controlled under the Montreal Protocol are also included, the overall decrease intropospheric chlorine was 4.4 ± 4.1 ppt Cl yr−1 during 2012–2016. This is smaller than the rate of decline
during the 2008–2012 period (11.8 ± 6.9 ppt Cl yr−1) and smaller than the rate of decline in controlled
substances because VSLS, predominantly anthropogenic dichloromethane (CH2Cl2), and methyl chloride (CH3Cl), which is mostly from natural sources, increased during this period.

• Starting around 2013, the rate at which the CFC-11 mole fraction was declining in the atmosphere slowed
unexpectedly, and the interhemispheric difference in its mole fraction increased. These changes are very likely
due to an increase in emissions, at least part of which originate from eastern Asia. Assuming no change inatmospheric circulation, an increase in global emissions of approximately 10 Gg yr−1 (~15%) is required for
2014–2016, compared to 2002–2012, to account for the observed trend and interhemispheric difference. The
rate of change and magnitude of this increase is unlikely to be explained by increasing emissions from banks.
Therefore, these findings may indicate new production not reported to the United Nations Environment
Programme (UN Environment). If the new emissions are associated with uses that substantially increase thesize of the CFC-11 bank, further emissions resulting from this new production would be expected in future.

• Compared to 2008–2012, for the period 2012–2016, mole fractions of CFC-114 declined more slowly, CFC-13
4

continued to rise, and CFC-115 exhibited positive growth after previously showing near-zero change. Thesefindings likely indicate an increase or stabilization of the emissions of these relatively low abundance compounds, which is not expected given their phaseout for emissive uses under the Montreal Protocol. For CFC114 and -115, regional analyses show that some of these emissions originate from China. There is evidence
that a small fraction of the global emissions of CFC-114 and -115 are due to their presence as impurities insome HFCs. However, the primary processes responsible are unknown.

• The rate at which carbon tetrachloride (CCl ) has declined in the atmosphere remains slower than expected
4from its reported use as a feedstock. This indicates ongoing emissions of around 35 Gg yr−1. Since the previous
Assessment, the best estimate of the global atmospheric lifetime of CCl4 has increased from 26 to 32 years, due
to an upward revision of its lifetime with respect to loss to the ocean and soils. New sources have been proposed
including significant by-product emissions from the production of chloromethanes and perchloroethylene andfrom chlor-alkali plants. With these changes in understanding, the gap between top-down and bottom-up
emissions estimates has reduced to around 10 Gg yr−1, compared to 50 Gg yr−1 previously.

• Combined emissions of the major HCFCs have declined since the previous Assessment. Emissions of HCFC-22

have remained relatively stable since 2012, while emissions of HCFC-141b and -142b declined between 2012
and 2016, by around 10% and 18%, respectively. These findings are consistent with a sharp drop in reported
HCFC consumption after 2012, particularly from Article 5 countries.

• Emissions of the compounds HCFC-133a and HCFC-31, for which no current intentional use is known, havebeen detected from atmospheric measurements. Research to date suggests that these gases are unintentional
by-products of HFC-32, HFC-134a, and HFC-125 production.

Tropospheric bromine
Total tropospheric bromine is defined in analogy to total tropospheric chlorine. Even though the abundance of bromine
is much smaller than that of chlorine, it has a significant impact on stratospheric ozone because it is around 60–65 times
more efficient than chlorine as an ozone-destroying catalyst.

• Total tropospheric bromine from controlled ODSs (halons and methyl bromide) continued to decrease andby 2016 was 14.6 ppt, 2.3 ppt below the peak levels observed in 1998. In the 4-year period prior to the last
Assessment, this decrease was primarily driven by a decline in methyl bromide (CH3Br) abundance, with a

4

ES.34

Here, CFC-114 refers to the combination of CFC-114 and CFC-114a isomers.

Executive Summary | Executive Summary

smaller contribution from a decrease in halons. These relative contributions to the overall trend have now
reversed, with halons being the main driver of the decrease of 0.15 ± 0.04 ppt Br yr−1 between 2012 and 2016.

• The mole fractions of halon-1211, halon-2402, and halon-1202 continued to decline between 2012 and 2016.

Mole fractions of halon-1301 increased during this period, although its growth rate dropped to a level indistinguishable from zero in 2016. Emissions of halon-2402, halon-1301, and halon-1211, as derived from atmospheric observations, declined or remained stable between 2012 and 2016.

• Methyl bromide (CH Br) mole fractions continued to decline between 2012 and 2015 but showed a small in3

crease (2–3%) between 2015 and 2016. This overall reduction is qualitatively consistent with the controls under
the Montreal Protocol. The 2016 level was 6.8 ppt, a reduction of 2.4 ppt from peak levels measured between
1996 and 1998. The increase between 2015 and 2016 was the first observation of a positive global change
for around a decade or more. The cause of this increase is yet to be explained. However, as it was not accompanied by an increased interhemispheric difference, it is unlikely that this is related to anthropogenic
emissions in the Northern Hemisphere. By 2016, controlled CH3Br consumption dropped to less than 2%of the peak value, and total reported fumigation emissions have declined by more than 85% since their peakin 1997. Reported consumption in quarantine and pre-shipment (QPS) uses of CH3Br, which are not controlled under the Montreal Protocol, have not changed substantially over the last two decades.

Halogenated very short-lived substances (VSLSs)
VSLSs are defined as trace gases whose local lifetimes are shorter than 0.5 years and have nonuniform troposphericabundances. These local lifetimes typically vary substantially over time and space. Of the very short-lived source gases(VSL SGs) identified in the atmosphere, brominated and iodinated species are predominantly of oceanic origin, whilechlorinated species have significant additional anthropogenic sources. VSLSs will release the halogen they contain almost
immediately once they enter the stratosphere and will thus play an important role in the lower stratosphere in particular. Due to their short lifetimes and their atmospheric variability the quantification of their contribution is much moredifficult and has much larger uncertainties than for long-lived compounds.

• Total tropospheric chlorine from VSL SGs in the background lower atmosphere is dominated by anthropogenic
sources. It continued to increase between 2012 and 2016, but its contribution to total chlorine remains small.
Global mean chlorine from VSLSs in the troposphere has increased from about 90 ppt in 2012 to about 110
ppt in 2016. The relative VSLS contribution to stratospheric chlorine input derived from observations in thetropical tropopause layer has increased slightly from 3% in 2012 to 3.5% in 2016.

• Dichloromethane (CH Cl ), a VSL SG that has predominantly anthropogenic sources, accounted for the major2

2

ity of the change in total chlorine from VSLSs between 2012 and 2016 and is the main source of VSLS chlorine.
The global mean abundance reached approximately 35–40 ppt in 2016, which is about a doubling comparedto the early part of the century. The increase slowed substantially between 2014 and 2016. Emissions fromsouthern and eastern Asia have been detected for CH2Cl2.

• There is further evidence that VSLSs contribute ~5 (3–7) ppt to stratospheric bromine, which was about 25%of total stratospheric bromine in 2016. The main sources for brominated VSLSs are natural, and no long-termchange is observed. While the best estimate of 5 ppt has remained unchanged from the last Assessment, theassessed uncertainty range has been reduced. Due to the decline in the abundance of regulated bromine
compounds, the relative contribution of VSLSs to total stratospheric bromine continues to increase.

Stratospheric chlorine and bromine
In the stratosphere, chlorine and bromine can be released from organic source gases to form inorganic species, which
participate in ozone depletion. In addition to estimates of the stratospheric input derived from the tropospheric observations, measurements of inorganic halogen loading in the stratosphere are used to determine trends of stratosphericchlorine and bromine.
ES.35

Executive Summary | Executive Summary• Hydrogen chloride (HCl) is the major reservoir of inorganic chlorine (Cl ) in the mid to upper stratosphere.
ySatellite-derived measurements of HCl (60°N–60°S) in the middle stratosphere show a long-term decrease of HCl
at a rate of around 0.5% yr−1, in good agreement with expectations from the decline in tropospheric chlorine. In
the lower stratosphere, a decrease was observed over the period from 1997 to 2016, while significant differences in the trends are seen over the period 2005 to 2016 between various datasets and altitude/geographicalregions. A similar behavior is observed for total column measurements, likely reflecting variability in stratospheric dynamics and chemistry. Total chlorine input to the stratosphere of 3,290 ppt is derived for 2016
from measurements of long-lived ODSs at the surface and VSLSs in the upper troposphere. About 80% of
this input is from substances controlled under the Montreal Protocol.

• Total stratospheric bromine, derived from observations of bromine monoxide (BrO), continued to decrease at

a rate of about 0.75% yr−1 from 2004 to 2014. This decline is consistent with the decrease in total troposphericorganic bromine, based on measurements of CH3Br and the halons. A total bromine input to the stratosphere of 19.6 ppt is derived for 2016, combined from 14.6 ppt of long-lived gases and 5 ppt from VSLSs notcontrolled under the Montreal Protocol. Anthropogenic emissions of all brominated long-lived gases arecontrolled, but as CH3Br also has natural sources, more than 50% of the bromine reaching the stratosphereis now estimated to be from sources not controlled under the Montreal Protocol. There is no indication of along-term change in natural sources to stratospheric bromine.

Equivalent effective stratospheric chlorine (EESC)
EESC is the chlorine-equivalent sum of chlorine and bromine derived from ODS tropospheric abundances, weighted toreflect their expected depletion of stratospheric ozone. The growth and decline in EESC depends on a given troposphericabundance propagating to the stratosphere with varying time lags (on the order of years) associated with transport.
Therefore, the EESC abundance, its peak timing, and its rate of decline are different in different regions of the stratosphere. Recent suggestions of a refinement in the calculation method for EESC result in somewhat lower estimates on
how far the stratospheric reactive halogen loading has recovered.

• By 2016, EESC had declined from peak values by about 9% for polar winter conditions and by about 13–17% for

mid-latitude conditions. This drop is 31–43% of the decrease required for EESC in mid-latitudes to return to the1980 benchmark level, and about 18–19% of the decrease required for EESC in polar regions to return to the1980 benchmark level5. The rate at which EESC is decreasing has slowed, in accordance with a slowdown of
the decrease in tropospheric chlorine. The ranges given reflect the different methods for calculating EESC.
Differences in halogen recovery levels from previous Assessments are also due to differences in assumed
fractional release factors.

Tropospheric and stratospheric fluorine
While fluorine has no direct impact on stratospheric ozone, many fluorinated gases are strong greenhouse gases, andtheir emission is often related to the replacement of chlorinated substances regulated under the Montreal Protocol. For
this reason, trends in fluorine are also assessed in this report.

• The main sources of fluorine in the troposphere and in the stratosphere are CFCs, HCFCs, and HFCs. In contrast

to total chlorine, total fluorine in the troposphere continued to increase between 2012 and 2016, at a rate of 1.7%
yr−1. This increase shows the decoupling of the temporal trends in fluorine and chlorine due to the increasing­emissions of HFCs (see Chapter 2). The total atmospheric-column abundance of inorganic fluorine, which ism­ainly stratospheric, has continued to increase at a rate of about 1% yr−1 over the period 2007–2016.

5

ES.36

As in previous Assessments, 1980 levels of EESC are used as a benchmark for recovery, although this value is somewhat arbitrary andsome ozone loss has occurred prior to 1980. Also, recovery of EESC to 1980 values does not necessarily imply a recovery of ozone to 1980
levels, as other parameters, e.g. stratospheric circulation, may change.

Executive Summary | Executive Summary

Effect of ozone-depleting substances (ODSs) on climate• The total direct radiative forcing of CFCs continues to be much higher than that of HCFCs. However, radiative
6

forcing from CFCs has dropped by about 7% since its peak in 2000 to about 250 mW m−2 in 2016 (approximately 13% that of CO2), while radiative forcing from HCFCs increased to 58 mW m−2 in 2016 (approximately 3% that of CO2). The total direct radiative forcing due to CFCs, HCFCs, halons, CCl4 and CH3CCl3
was 327 mW m–2 in 2016 (approximately 16% that of CO2).

• CO -equivalent emissions of CFCs and HCFCs were approximately equal in 2016. The CO -equivalent emission
2

7

2

from the sum of all CFCs or the sum of all HCFCs was approximately 0.8 Gt in 2016. The CO2-equivalentemission from the sum of CFCs, HCFCs, halons, CCl4 and CH3CCl3 was approximately 1.7 Gt in 2016.

Other gases that affect ozone and climate• Mole fractions of many other gases that affect both ozone and climate have changed since the previous

Assessment. The atmospheric abundance of methane has continued to increase following a period of stagnation in the early 2000s. The drivers of the changing trend are disputed. Nitrous oxide continues to growrelatively steadily in the atmosphere. The global mole fractions of the fluorinated species sulfur hexafluoride(SF6), nitrogen trifluoride (NF3), sulfuryl fluoride (SO2F2), and the perfluorocarbons (PFCs such as CF4 andC2F6) have continued to grow. In contrast, the abundance of the sulfur-containing compounds sulfur dioxide(SO2) and carbonyl sulfide (COS) has not changed substantially.

Chapter 2: Hydrofluorocarbons (HFCs)
The Montreal Protocol is an international agreement designed to heal the ozone layer. It outlines schedules for the phaseout of ozone-depleting substances (ODSs) such as chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs),
chlorinated solvents, halons, and methyl bromide. As a result of this phase-out, alternative chemicals and procedures
were developed by industry for use in many applications including refrigeration, air-conditioning, foam-blowing, electronics, medicine, agriculture, and fire protection. Hydrofluorocarbons (HFCs) were used as ODS alternatives in manyof these applications because they were suitable substitutes and they do not contain ozone-depleting chlorine or bromine;in addition, most HFCs have smaller climate impacts per molecule than the most widely used ODSs they replaced. Longlived HFCs, CFCs, and HCFCs, however, are all potent greenhouse gases, and concerns were raised that uncontrolled
future use of HFCs would lead to substantial climate warming.
As a result of these concerns, HFCs were included as one group of greenhouse gases for which emissions controls wereadopted by the 1997 Kyoto Protocol under the 1992 United Nations Framework Convention on Climate Change(UNFCCC). Consequently, developed countries (those listed in Annex I to this Convention, or “Annex I” Parties) supply
annual emission estimates of HFCs to the UNFCCC.
Since the Kyoto Protocol only specified limits on the sum of all controlled greenhouse gases, emissions of HFCs were
not explicitly controlled. However, following the Kyoto Protocol, some countries enacted additional controls specificallylimiting HFC use based on their global warming potentials (GWPs). Ultimately the Kigali Amendment to the Montreal
Protocol was agreed upon in 2016, and this Amendment supplies schedules for limiting the production and consumptionof specific HFCs. Although the radiative forcing supplied by HFCs is currently small, this Amendment was designed
to ensure that the radiative forcing from HFCs will not grow uncontrollably in the future. The Kigali Amendment will
come into force at the start of 2019. HFC concentrations are currently monitored through atmospheric measurements.
All HFCs with large abundances are monitored, as are most with small abundances.

6
7

A measure of the change in net irradiance (incoming minus outgoing) at the tropopause.
CO2 equivalents are determined here by weighting emissions estimates by the global warming potential (GWP) of each gas, integrated
over a 100-year time horizon.
ES.37

Executive Summary | Executive Summary

Most HFCs that are emitted to the atmosphere are intentionally produced for use in a variety of applications that wereonce dependent on ODSs. An exception is HFC-23, which is emitted to the atmosphere primarily as a by-product of
HCFC-22 production. HFC-23 is also unique in that it has a substantially longer atmospheric lifetime and higher
GWP than nearly all other HFCs. As a result, the Kigali Amendment includes different control schedules for HFC-23
production than for other HFCs. To date, HFC-23 emissions have been partially abated in developed countries throughregulations or voluntary measures and in developing countries with assistance from the UNFCCC’s Clean Development
Mechanism (CDM).

• Atmospheric mole fractions of most currently measured HFCs are increasing in the global atmosphereat accelerating rates, consistent with expectations based on the ongoing transition away from use ofozone-depleting substances.
○○ HFC-134a remained the most abundant HFC in the atmosphere, reaching a global mean surface mole

fraction of nearly 90 ppt in 2016. Its rate of increase averaged 5.6 ± 0.2 ppt yr−1 (7.3 ± 0.2 % yr−1) during
2012–2016, which is about 0.6 ppt yr−1 faster than the mean increase for 2008–2012.

○○ The next four most abundant HFCs in 2016 were HFC-23, HFC-125, HFC-143a, and HFC-32. Their global

mean surface mole fractions in 2016 were 28.9 ppt, 20.4 ppt, 19.2 ppt, and 11.9 ppt, respectively. Mole
fractions of these HFCs increased during 2012–2016 by an average of 1.0 ppt yr−1 for HFC-23, 2.1 ppt
yr−1 for HFC-125, 1.5 ppt yr−1 for HFC-143a, and 1.6 ppt yr−1 for HFC-32; for all of these gases, these
rates are faster than the average growth rates reported for 2008–2012 in the last Assessment.

○○ Global mole fractions of most HFCs increased through 2016 at rates similar to those projected in the base-line scenario of the last Assessment, despite the fact that this scenario was created nearly a decade ago.
The HFCs for which mole fractions are increasing substantially less rapidly than originally projected
include HFC-152a, HFC-365mfc, and HFC-245fa.

• Radiative forcing from measured HFCs continues to increase; it currently amounts to 1% of the total forcing
from all long-lived greenhouse gases. The radiative forcing arising from measured atmospheric mole fractions of HFCs totaled 0.030 W m−2 in 2016, up by 36% from 0.022 W m−2 in 2012; HFC-134a accounted for
47% of this forcing in 2016, while the next largest contributors were HFC-23 (17%), HFC-125 (15%) andHFC-143a (10%). Total HFC radiative forcing in 2016 accounted for ~10% of the 0.33 W m−2 supplied by
ODSs (see Chapter 1), and 1.0% of the 3 W m−2 supplied by all long-lived GHGs combined, including CO2,
CH4, N2O, ODSs and HFCs.

• Global emissions of nearly all measured HFCs continue to increase; they currently amount to ~1.5% of total

emissions from all long-lived greenhouse gases (CO2, CH4, N2O, and long-lived halocarbons) in CO2-equivalentemissions. As derived from atmospheric observations, total emissions of HFCs increased by 23% from 2012
to 2016 and summed to 0.88 (± 0.07) GtCO2-eq yr−1 in 2016; this increase outpaced decreases in CO2-eq
emissions from CFCs and HCFCs. These CO2-eq HFC emissions stem primarily from four gases: HFC-134a(34% of total), HFC-125 (24% of total), HFC-23 (18% of total), and HFC-143a (16% of total). HFC CO2-eq
emissions were comparable to those of CFCs (0.8 ± 0.3 GtCO2-eq yr−1) and HCFCs (0.76 ± 0.11 GtCO2-eqyr−1) in 2016.

• HFC emissions estimated from the combination of inventory reporting and atmospheric observations indi-cate that the HFC emissions originate from both developed and developing countries. Large differences areobserved between global total emissions derived from atmospheric observations and the totals reported to
the UNFCCC. These differences arise primarily because only developed (Annex I) countries are obligatedto report HFC emissions to the UNFCCC. When summed, these reported HFC emissions account for lessthan half of the global total inferred from observations (as CO2-eq emissions).

ES.38

Executive Summary | Executive Summary• Annual global emissions of HFC-23 derived from atmospheric measurements have varied substantially in re-cent years. This variability is mostly consistent with expectations based on reported HCFC-22 production andreported and estimated HFC-23 emissions. This long-lived HFC is emitted to the atmosphere primarily as aby-product of HCFC-22 production. HFC-23 emissions, after reaching a low of ~10 Gg yr−1 (0.13 GtCO2‑eqyr−1) 2009–2010, owing in part to destruction in developing countries facilitated under the UNFCCC’s
Clean Development Mechanism (CDM), increased and subsequently peaked at ~14 Gg yr−1 (0.18 GtCO2-eqyr−1) in 2013–2014. The mean global emission rate over 2013–2014 is slightly higher than that derived for
2005–2006, when CDM-facilitated destruction had yet to be fully implemented. Global emissions estimatedfrom observations for 2015 and 2016 dropped below the 2013–2014 peak; emissions in 2016 were 12.3 ±0.7 Gg yr−1 (0.16 GtCO2-eq yr−1), or approximately 2 Gg yr−1 below those in 2014. New controls put in place
under the Kigali Amendment mandate HFC-23 by-product destruction, to the extent practicable, beginningin 2020. These controls are expected to limit future emissions and thus slow or reverse atmospheric concentration increases of this potent greenhouse gas.

• Some next-generation substitute chemicals with very low GWPs (unsaturated HCFCs and unsaturated HFCs,

also known as hydrofluoroolefins, or HFOs) have now been detected in ambient air, consistent with the transition to these compounds being underway. Unsaturated HFCs and HCFCs are replacement compounds forsome long-lived HCFCs and HFCs. Because unsaturated HFCs have short atmospheric lifetimes (days) and
GWPs typically less than 1 they are not included as controlled substances in the Kigali Amendment to theMontreal Protocol. Very low mole fractions (typically below 1 ppt) of two unsaturated HFCs (HFC-1234yf
and HFC-1234ze(E)) have been measured at a continental background European site.

• Global adherence to the HFC phasedown schedule of the 2016 Kigali Amendment to the Montreal Protocol
would substantially reduce future projected global HFC emissions. Emissions are projected to peak before
2040 and decline to less than 1 GtCO2-eq yr−1 by 2100. Only marginal increases are projected for CO2-eq
emissions of the low-GWP alternatives despite substantial projected increases in their emission mass. The
estimated avoided HFC emissions as a result of this Amendment is 2.8–4.1 GtCO2-eq yr−1 emissions by 2050
and 5.6–8.7 GtCO2-eq yr−1 by 2100. For comparison, total CH4 emissions in 2100 are projected to be 7.0 and
25 GtCO2-eq yr−1 in the RCP-6.0 and RCP-8.5 scenarios, respectively, and total N2O emissions in 2100 are
projected to be 5.0 and 7.0 GtCO2-eq yr−1 in these same scenarios.

• The 2016 Kigali Amendment to the Montreal Protocol, assuming global compliance, is expected to reduce fu-

ture radiative forcing due to HFCs by about 50% in 2050 compared to the forcing from HFCs in the baseline
scenario. Currently (in 2016), HFCs account for a forcing of 0.025 W m−2 not including 0.005 from HFC-23;
forcing from these HFCs was projected to increase up to 0.25 W m−2 by 2050 (excluding a contribution from
HFC-23) with projected increased use and emissions in the absence of controls. With the adoption of theKigali Amendment, a phasedown schedule has been agreed for HFC production and consumption in developed and developing countries under the Montreal Protocol. With global adherence to this Amendment in
combination with national and regional regulations that were already in place in, e.g., Europe, the USA, andJapan, along with additional recent controls in other countries, future radiative forcing from HFCs is projected to reach 0.13 W m−2 by 2050 (excluding HFC-23), or about half the forcing projected in the absenceof these controls.

• The Kigali Amendment and national and regional regulations are projected to reduce global average warmingin 2100 due to HFCs from 0.3–0.5°C in a baseline scenario to less than 0.1°C. If the global production of HFCs
were to cease in 2020, the surface temperature contribution of HFC emissions would stay below 0.02°C for
the whole 21st century. The magnitude of the avoided temperature increase due to the provisions of theKigali Amendment is substantial in the context of the 2015 UNFCCC Paris Agreement, which aims to limit
global temperature rise to well below 2.0°C above pre-industrial levels and to pursue further efforts to limit
the temperature increase to 1.5°C.
ES.39

Executive Summary | Executive Summary• Improvements in energy efficiency in refrigeration and air-conditioning equipment during the transi-tion to low-GWP alternative refrigerants can potentially double the climate benefits of the HFC phasedown of the Kigali Amendment. The conversion from equipment using HFC refrigerants with high GWPs
to refrigerants with lower GWPs, which will most likely result from the Kigali Amendment, provides anopportunity to consider other technological improvements that offer additional climate benefits. The total
climate impact related to refrigerant use and associated emissions is not only associated with the radiative
properties and lifetime of the refrigerant, but also with CO2 emissions resulting from the energy used by theequipment over its entire life cycle. The use of a refrigerant with a lower GWP than the currently-used HFCs
(i.e., following the Kigali Amendment) offers the opportunity to redesign equipment and improve its energy
efficiency. For example, a 30% improvement in the energy efficiency of the global stock of mini-split air
conditioners (the most widely used air conditioning systems today) in 2030 would provide a climate benefit
comparable to replacing the mix of current HFC refrigerants commonly used in this application (whichhave GWPs averaging about 2,000) with a mix of alternatives that have GWPs of less than about 5 to about
700. An energy efficiency improvement of 30% is estimated to be technically and economically feasible andcost-effective in many economies.

• Some HFCs degrade in the environment to produce trifluoroacetic acid (TFA), a persistent toxic chem-

ical. The environmental effects of this source of TFA are currently small and are expected to remain
negligible over the next decades. Atmospheric degradation of HFC-1234yf, a replacement compound forsome long-lived HCFCs and HFCs, produces TFA. Potential impacts beyond a few decades of this TFA
source could require future evaluation due to the environmental persistence of TFA and uncertainty infuture emissions of HFC-1234yf and other HFCs that produce TFA upon degradation.

• Improvements in the understanding of reaction rates have been incorporated into revised lifetimee­ stimates for saturated and unsaturated HFCs. Most of these changes are small, although lifetimes of HFC245cb (CF3CF2CH3), octafluorocyclopentene (cyclo-CF=C4F7-), (E)-HFO-1214yc ((E)-CF3CH=CHCF3),
and (E)-HFO-1438mzz ((E)-CF3CH=CHC2F5) were noticeably changed because the relevant reaction rate
information has become available for the first time. Lifetimes for a few HFCs considered here remain estimates based on either analogy with similar compounds or structure–activity relationships.

Chapter 3: Update on Global Ozone: Past, Present, and FutureThis chapter deals with the evolution of global ozone outside of the polar regions. The increase of ozone-depleting substance (ODS) concentrations caused the large ozone decline observed from the early satellite era (circa 1980) to themid-1990s. Since the late 1990s, concentrations of ODSs have been declining due to the successful implementation of
the Montreal Protocol. Ozone concentrations show latitudinally dependent increases in the upper stratosphere for the
2000–2016 period; changes in other parts of the stratosphere are not yet statistically significant. A new suite of model
simulations confirms previous results for the upper stratosphere that about half of the observed increase is associatedwith declining ODSs. Ozone column trends are likewise positive but not generally statistically significant. Their overall
evolution is, however, compatible with the decline in equivalent effective stratospheric chlorine (EESC).
Over the next decades, we expect increasing global mean stratospheric ozone columns, as ODSs continue to decline.
Emissions of greenhouse gases (GHGs), especially carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), will
also affect the evolution of global stratospheric ozone, particularly in the second half of the 21st century, when ODS
concentrations are expected to be low.

Past changes in total column ozone• Ground- and space-based observations indicate that there is no statistically significant trend in near-global(60°S–60°N) column ozone over the 1997–2016 period. These datasets show an increase of between 0.3% andES.40

Executive Summary | Executive Summary

1.2% decade−1 since 1997, with uncertainties of about 1% decade−1. These findings are consistent with ourunderstanding of the processes that control ozone.
○○ In middle and high latitudes, the increase in total column ozone expected to arise from the 15% decline

in EESC since 1997 is small (~1% decade−1) relative to the large, dynamically forced year-to-year variations of ~5%.

○○ In the tropics, where halogen-driven ozone loss is small in the lower stratosphere, total column ozone

has not varied significantly with ODS concentrations, except under conditions of high volcanic aerosol
loading (e.g., from the eruption of Mt. Pinatubo in 1991).

• Outside the tropics, present-day (2014–2017) total ozone columns from ground-based and space-based
observations remain lower than 1964–1980 column ozone by○○ about 2.2% for the near-global average (60°S–60°N)○○ about 3.0% in the Northern Hemisphere mid-latitudes (35°N–60°N)○○ about 5.5% in the Southern Hemisphere mid-latitudes (35°S–60°S)

These values are essentially the same as in the last Assessment, given uncertainties associated with naturalvariability and instrumental accuracy. The larger depletion in the Southern Hemisphere is linked to the
Antarctic ozone hole.

Past changes in ozone profiles
Additional and improved datasets and focused studies evaluating trend uncertainties have strengthened our ability
to assess ozone profile changes. Analysis of data from the upper stratosphere shows increases that are consistent withthose suggested in the last Assessment. There is some evidence for a dynamically driven decrease in ozone in the lower
stratosphere from 2000 to 2016, but robust trends have not been identified for this region. New chemistry–climate model(CCM) simulations that include realistic time variations of GHG and ODS concentrations are analyzed using the same
trend model as for the observations; this allows attribution of changes in ozone to different processes.

• Measurements show increases of ozone in the upper stratosphere over the period 2000-2016. Following a large

decline of 5 to 7% decade−1 through the 1980s and middle 1990s, upper stratospheric ozone has increasedby 1 to 3% decade−1 since 2000. The largest confidence is in northern mid-latitudes, where the positive trend
is statistically significant between 35- and 45-km altitude. Confidence in trends in the tropics and southern mid-latitudes is not as high due to larger discrepancies between trends from individual measurement
records.

• Model simulations attribute about half of the observed upper stratospheric ozone increase after 2000 to thedecline of ODSs since the late 1990s. The other half of the ozone increase is attributed to the slowing of gasphase ozone destruction cycles, which results from cooling of the upper stratosphere caused by increasing
GHGs.

• There is some evidence for a decrease in lower stratospheric ozone from 2000 to 2016. This decrease is mostconsistent across datasets in the tropics, but is not statistically significant in most analyses. Much of theapparent decline was reversed by an abrupt increase in ozone in 2017, indicating that longer records areneeded to robustly identify trends in this region. Model simulations attribute the variations in lower stratospheric ozone over this period primarily to dynamical variability.

• Assessing the consistency between stratospheric profile trends and total column ozone trends requires changes in tropospheric ozone to be well quantified. A recent assessment of tropospheric column ozone trends,

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however, shows large disagreements in the sign and magnitude of the observed trends over the past decadeand a half.

Future ozone changes
The baseline climate change scenario used in the new model simulations differs from the previous Assessment, because
new emissions scenarios were used. The key drivers of future ozone levels continue to be declining ODS concentrations,
upper stratospheric cooling because of increased GHGs, and the possible strengthening of the Brewer-Dobson circulationfrom climate change. The new emissions scenarios lead to slight differences in the relative contributions of these processes
in various latitude and altitude regions and a delay in return dates for ozone compared to the previous Assessment.

• Estimated dates of return of total column ozone to 1980 values are generally a few years later than givenin the previous Assessment and vary considerably between scenarios. For the baseline scenario (RCP6.0), they are:○○ around mid-century for near-global mean annually averaged ozone;○○ most likely before the middle of the century (~2035) for annually averaged Northern Hemisphere

mid-latitude ozone;○○ around mid-century for annually averaged Southern Hemisphere mid-latitude ozone.

• CO , CH , and N O will be the main drivers of 60°S–60°N stratospheric ozone changes in the second half of the
2

4

2

21st century. These gases impact both chemical cycles and the stratospheric overturning circulation, with alarger response in stratospheric ozone associated with stronger climate forcing. By 2100, the stratosphericcolumn is expected to decrease in the tropics by about 5 DU for RCP-4.5 and about 10 DU for RCP-8.5
relative to 1980 values, with the net total column change projected to be smaller (about 5 DU) because ofoffsetting increases in tropospheric ozone.

• Given that ODS levels are expected to decline slowly in coming years, a large enhancement of stratospheric sul-fate aerosol in the next decades would result in additional chemical ozone losses. Possible sources of additional
stratospheric sulfate aerosol include volcanic eruptions (like Mt. Pinatubo in 1991) and geoengineering.
Even when ODS levels have declined substantially, a large injection of volcanic halogens into the stratosphere could drive substantial ozone losses in the presence of aerosol surfaces.

• Future ozone recovery and the projected strengthening of the Brewer-Dobson circulation (BDC) are likely tolead to increases in the stratosphere-to-troposphere (STT) flux of ozone via increases in mid-latitude lower
stratospheric ozone and mass flux. The net impact of increased STT flux on the tropospheric ozone burden
is highly model and scenario dependent. Most studies suggest it will be small relative to other factors, suchas concurrent changes in precursor emissions, temperature, and water vapor.

Chapter 4: Update on Polar Ozone: Past, Present, and Future
The chemical and dynamical processes controlling polar ozone are well understood. Polar ozone depletion is fundamentally driven by anthropogenic chlorine and bromine, with the severity of the chemical loss each year in both hemispheres
strongly modulated by meteorological conditions (temperatures and winds), and, to a lesser extent, by the stratosphericaerosol loading and the solar cycle. As noted in prior Assessments, the stratospheric halogen concentration resulting
from the emission of ozone-depleting substances (ODSs) reached its peak in the polar regions around the turn of the
century and has been gradually declining since then in response to actions taken under the Montreal Protocol andits Amendments and adjustments. Early signs of ozone recovery are now beginning to appear in the Antarctic; as the

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observational record lengthens, clearer ozone hole recovery trends are expected to emerge against the background ofnatural variability. Nevertheless, the Antarctic ozone hole will continue to be a recurring phenomenon until the middleof the century. The Arctic is more dynamically variable, precluding identification of a significant increase in Arctic ozone,
and cold conditions conducive to substantial ozone loss may still occur in a particular year in the coming decades. New
chemistry–climate model (CCM) projections largely confirm previous studies that in both hemispheres, spring polar
total column ozone will return to 1980 historical levels in the coming decades, albeit with a delay of a few years due toupdated future ODS and greenhouse gas (GHG) emissions scenarios.

Observed changes in polar ozone• The characteristics of the October Antarctic ozone hole in the years since 2014 have generally been within therange observed since the early 1990s. In 2015, however, the ozone hole was particularly large and long-lasting,as a result of a cold and undisturbed polar stratospheric vortex. Aerosols from the Calbuco volcanic eruptionin April 2015 are also believed to have contributed to the large ozone hole observed that year. Conversely, in
2017, the Antarctic ozone hole was very small due to a warm and unusually disturbed polar vortex.

• Several lines of evidence have started to emerge indicating an increase in Antarctic stratospheric ozone during
September. Statistically significant trends since the year 2000 have now been identified showing an increasein observed ozone and a decrease in ozone hole size and depth. Although accounting for the large degreeof natural variability is challenging, the weight of evidence from statistical analyses and modeling studies
suggests that the decline in ODSs made a substantial contribution to these trends.

• In the Arctic, the exceptionally low ozone abundances of spring 2011 have not been observed again in the last

four years. Arctic stratospheric springtime ozone is dominated by large year-to-year dynamically induced
variability of the polar vortex, with severe ozone loss occurring in very cold years, such as 2011. Extreme
meteorological conditions in the early 2015/2016 winter led to rapid ozone loss, but a sudden stratosphericwarming (SSW) at the beginning of March 2016 curtailed the chemical processes which lead to ozone destruction about a month earlier than in 2011, keeping ozone above record low levels. Arctic ozone trends
are small compared to the dynamical variability, and thus a recovery trend remains undetectable in observations over the 2000–2016 period.

Understanding of factors controlling polar ozone• Observations in the Arctic winter have demonstrated that large nitric acid trihydrate (NAT) particles are aregularly occurring phenomenon in the lower stratosphere. This knowledge improves our understanding ofpolar stratospheric cloud (PSC) formation and denitrification, which is important for catalytic ozone loss
cycles.

• Bromine-containing very short-lived substances (VSLSs) of natural origin have an important impact on thestratospheric halogen loading and consequently on stratospheric ozone loss in the polar regions. The inclusionof additional stratospheric bromine from VSLSs is necessary for models to produce a realistic simulation ofpolar ozone loss.

• The effects of tropospheric dynamical forcing in winter on Arctic polar ozone are now better quantified. Ozone

depletion in northern winters with SSWs is on average two-thirds less than in winters without SSWs, with
depletion ending about one month earlier in the year. Such an SSW was a major influence on ozone levels
observed in the Arctic winter of 2015/16.

• Polar ozone in the middle and upper stratosphere varies by 10–15% from year to year due to energetic particle

precipitation (EPP) related to solar variability. Satellite observations and model results show that NOy produced in the aurora is transported from the thermosphere down into the stratosphere in each winter, leading
to stratospheric ozone decreases modulated by geomagnetic activity. The resulting variation in total column
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Executive Summary | Executive Summaryozone is small (a few percent) but can persist for 2–3 years. Full EPP-effects were not included in current
assessment models.

• Model simulations show that the Montreal Protocol and its Amendments and adjustments have already brought
about substantial ozone benefits. In the polar regions of both hemispheres, much larger ozone depletion thancurrently observed has been avoided through implementation of the Protocol.

Future evolution of polar ozone• Updated CCM projections based on full compliance with the Montreal Protocol and assuming the baseline

estimate of the future evolution of GHGs (RCP-6.0) have confirmed that the Antarctic ozone hole is expected togradually close, with springtime total column ozone returning to 1980 values shortly after mid-century (about
2060). The timing of the recovery of the ozone hole will not be significantly affected by increases in GHG
concentrations. There are no substantial differences between Antarctic total ozone columns at the end of thiscentury for the various GHG scenarios (Representative Concentration Pathways [RCPs]).

• The timing of the recovery of Arctic total ozone in spring will be affected by anthropogenic climate change.
Based on full compliance with the Montreal Protocol and assuming the baseline estimate of the future evolution of GHGs (RCP-6.0), Arctic springtime total ozone is expected to return to 1980 values before mid-century(2030s). New model simulations confirm that in the Arctic, enhanced GHG concentrations cause an earlier
return of total column ozone to historical values than a reduction of ODSs alone.

• In the second half of the 21st century CO , CH , and N O will be the dominant drivers of Arctic ozone chang242

es, assuming full compliance with the Montreal Protocol. These gases impact both chemical cycles and thestratospheric overturning circulation, with a larger response in stratospheric ozone associated with stronger
climate forcing. By 2100, the stratospheric ozone column is expected to not only recover but to exceed
1960–1980 average values in the Arctic, with springtime Arctic ozone being higher by about 35 DU for RCP4.5 and about 50 DU for RCP-8.5.

• In the coming decades, substantial Arctic ozone loss will remain possible in cold winters as long as ODS con-centrations are well above natural levels. Increasing GHG concentrations may cool the lower stratosphereand lead to enhanced formation of polar stratospheric clouds (PSCs) early in the Arctic winter. However,
one recent study indicates that no corresponding cooling is expected in March, which is the month when
persistent low temperatures lead to large chemical ozone losses.

Chapter 5: Stratospheric Ozone Changes and Climate
Since the 2014 Ozone Assessment, new research has better quantified the impact of stratospheric ozone changes on
climate. Additional model and observational analyses are assessed, which examine the influence of stratospheric ozone
changes on stratospheric temperatures and circulation, tropospheric circulation and composition, surface climate, theoceans, and sea ice. The new results support the main conclusions of the previous Assessment; the primary advances are
summarized below.

Stratospheric temperatures• New estimates of satellite-observed stratospheric temperature changes show net global stratosphericcooling of around 1.5 K (at 25–35 km), 1.5 K (at 35–45 km), and 2.3 K (at 40–50 km) between 1979 and2005, with differences between datasets of up to 0.6 K.
○○ There are now better estimates of observed stratospheric temperature trends than were available during

the last Assessment. Two datasets from satellite measurements have been re-processed and now show
greater consistency in long-term temperature trends in the middle and upper stratosphere.

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Executive Summary | Executive Summary○○ Satellite temperature records show smaller stratospheric cooling rates over 1998–2015 compared to

1979–1997, consistent with the observed differences in stratospheric ozone trends during these periods.

○○ Global average temperature in the lower stratosphere (13–22 km) cooled by about 1 K between 1979 andthe late 1990s but has not changed significantly since then.

• In the lower stratosphere (13–22 km), ozone trends were the major cause of the observed cooling be-

tween the late 1970s and the mid-1990s. In the middle and upper stratosphere, however, increases inlong-lived greenhouse gases played a slightly larger role than ozone changes in cooling trends over thisperiod. Ozone recovery will continue to play an important role in future stratospheric temperature trends.
○○ There is now improved understanding of the causes of stratospheric temperature trends and variability.

For the upper stratosphere (40–50 km), new studies suggest that one-third of the observed cooling over
1979–2005 was due to ozone-depleting substances (ODSs) and associated ozone changes, while twothirds was due to other well-mixed greenhouse gases.

○○ Chemistry–climate models show that the magnitude of future stratospheric temperature trends is de-pendent on future greenhouse gas concentrations, with most greenhouse gas scenarios showing coolingin the middle and upper stratosphere over the 21st century. The projected increase in global stratospheric ozone during this period would offset part of the stratospheric cooling due to increasing greenhousegases.

Stratospheric overturning circulation• There are indications that the overturning circulation in the lower stratosphere has accelerated over the
past few decades.

○○ Observations of the latitudinal profile of lower stratospheric temperature trends and changes in constit-

uents show that tropical upwelling in the lower stratosphere has strengthened over the last ~30 years, in
qualitative agreement with model simulations and reanalysis datasets.

○○ New studies using measurements provide evidence for structural changes in the stratospheric overturn-ing circulation which is comprised of a strengthening in the lower stratosphere and a weakening in the
middle and upper stratosphere.

○○ According to models, in addition to well-mixed greenhouse gases, changes in ODSs (and associatedchanges in ozone) are an important driver of past and future changes in the strength of the stratospheric overturning circulation, notably the increase in downwelling over the Antarctic over the late 20th
century.

○○ Estimates of externally forced long-term changes in the stratospheric overturning circulation from ob-

servations remain uncertain, partially due to internal variability.

○○ Models project future increases in stratosphere–troposphere exchange of ozone as a consequence of astrengthening of the stratospheric overturning circulation and stratospheric ozone recovery.

Impacts on the troposphere, ocean, and sea ice• New research supports the findings of the 2014 Ozone Assessment that Antarctic ozone depletion was

the dominant driver of the changes in Southern Hemisphere tropospheric circulation in austral summer during the late 20th century, with associated weather impacts.

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Executive Summary | Executive Summary○○ Over the period 1970 to 2000, tropospheric jets in the Southern Hemisphere shifted poleward andstrengthened, the Southern Annular Mode (SAM) index increased, and the southern edge of the Hadley
Cell expanded poleward. Since 2000, the SAM has remained in a positive phase.

○○ For austral summer, most model simulations show a larger contribution to these trends from Antarctic

ozone depletion compared to increases in well-mixed greenhouse gases during the last decades of the20th century. During other seasons, the contribution of ozone depletion to circulation changes is comparable to that from well-mixed greenhouse gases.

○○ Paleoclimate reconstructions of the SAM index suggest that the current period of prolonged positivesummer SAM conditions is unprecedented in at least the past 600 years.

○○ No robust link between stratospheric ozone depletion and long-term Northern Hemisphere surface

climate has been established; there are indications that occurrences of extremely low springtime ozone
amounts in the Arctic may have short-term effects on Northern Hemisphere regional surface climate.

• Changes in tropospheric weather patterns driven by ozone depletion have played a role in recent tem-

perature, salinity, and circulation trends in the Southern Ocean, but the impact on Antarctic sea ice
remains unclear.
○○ Progress has been made since the last Assessment in understanding the physical processes involved in

the Southern Ocean response to ozone depletion, which is now believed to entail a fast surface coolingfollowed by a slow long-term warming.

○○ Modeling studies indicate that ozone depletion contributes to a decrease in Antarctic sea ice extent andhence cannot explain the observed sea ice increase between 1979 and 2015. This is in agreement withthe conclusions of the previous Assessment. However, in general, climate models still cannot reproduce
the observed Antarctic sea ice trends since 1979, which limits the confidence in the modeled sea ice
response to ozone depletion.

• New observation-based analyses indicate that a causal link between the strength of the Southern Ocean
carbon sink and ozone depletion cannot be established, in contrast to earlier suggestions.

○○ New observation-based analyses confirmed the previously reported slowdown of the carbon sink be-

tween the 1980s and early 2000s but also revealed a remarkable reinvigoration of the carbon sink sincethen. The new results indicate that atmospheric circulation changes (whether driven by ozone depletionor not) have not had a considerable impact on the net strength of the Southern Ocean carbon sink.

Montreal Protocol climate impacts• New studies since the 2014 Ozone Assessment have identified that future global sea level rise of at least

several centimeters has been avoided as a result of the Montreal Protocol. This would have arisen fromthermal expansion of the oceans associated with additional global warming from unregulated ozone depleted substances emissions.

Chapter 6: Scenarios and Information for Policymakers
In the sections below, we note the significance of various improvements in our understanding concerning actions related
to the Montreal Protocol and its Amendments that could alter the recovery of the ozone layer and/or impact Earth’s
climate. As in previous Assessments, we use equivalent effective stratospheric chlorine (EESC) as a proxy for the amountof stratospheric ozone depletion caused by ozone-depleting substances (ODSs) that contain chlorine and/or bromine andreside in the atmosphere for more than a few months. The return of EESC to 1980 values is used as a metric to compare
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the effects of different future scenarios of production and emission of ozone-depleting gases on ozone layer recovery.
In this chapter, we also use 2-D model simulations to estimate changes in future ozone depletion for these different
scenarios. (Note that 3-D model projections of global and polar ozone and analyses of expected recovery dates are presented in Chapters 3 and 4. These calculations include changes in greenhouse gas levels and in atmospheric transport
and are not expected to be equivalent to the EESC recovery dates). Our ability to predict future changes in the ozone
layer is limited more by uncertainties in future levels of CO2, CH4, and N2O than by uncertainties in the levels of ODSs,
especially as we approach the 1980 values of EESC. Indeed, ozone levels in some regions of the atmosphere could exceed
natural levels, due to climate change, with possible consequences to humans and natural ecosystems, assuming naturallevels represent a harmonious balance. The influence of CO2 occurs through its role in the climate system as a driver ofchange in temperature and atmospheric circulation. The influences of CH4 and N2O occur primarily through their roles
as chemical reagents in the atmosphere. ODSs themselves are greenhouse gases, and their influence on climate and ozone
layer depletion are intricately intertwined, even though we note them separately for clarity of presentation. Lastly, notethat the various additional actions discussed below impact future ozone to a much smaller degree than what has alreadybeen accomplished by the Montreal Protocol.

Post-Kigali information of interest and concern• The Kigali Amendment to the Montreal Protocol, along with regional and national regulatory and voluntaryactions taken before Kigali entered into force, is expected to substantially limit future climate forcing by HFCs.
Projections of HFC emissions that include compliance with Kigali Amendment control measures suggestthat the radiative forcing (a metric for global warming) from HFCs, currently 0.025 W m−2 (not including
HFC-23), will reach 0.13 W m−2 by 2050, about half as high as that projected without the Kigali Amendment
and prior national and regional regulation. The estimated benefit of these actions is 2.8–4.1 Gt CO2-eq. yr−1
of avoided Global Warming Potential (GWP)-weighted emissions by 2050. The projected surface temperature contribution from HFCs (excluding HFC-23) reduces from 0.3–0.5 oC to less than 0.1 oC in 2100 due toentry into force of the Kigali Amendment.

• Options are available to further decrease the climate impact of HFCs. Use of commercially available low-GWP
alternatives in place of high-GWP HFCs in refrigeration and air-conditioning equipment, thermal insulating
foam, metered-dose inhalers, fire protection, and miscellaneous HFC applications during the phasedown
would further reduce climate change. Additional benefits would be gained by such actions via developmentof more energy-efficient equipment and thermal insulating foam that use these low-GWP replacements.

• Sustained increases in anthropogenic chlorinated very short-lived substances (VSLSs Cl) emissions, as seen

for CH2Cl2 in the 2000s, would decrease stratospheric ozone levels in the coming decades. However, observed
growth rates of CH2Cl2 continue to be highly variable, and there is insufficient information to confidently predict future concentrations. If the growth in emission rates seen during the first decade of this century continues, CH2Cl2 is projected to deplete as much column ozone between 2020 and 2060 as that by the controlled
ODSs emitted during that period. However, such large growth projections do not account for a more recent
reduction in the CH2Cl2 growth rate, nor have they been shown to be consistent with expectations for global
demand over the coming decades. Any control of CH2Cl2 production and consumption under the Montreal
Protocol would be rapidly effective, since this VSLS will be cleansed out of the stratosphere within a few
years.

Ozone-depleting substances (ODSs) and equivalent effective
stratospheric chlorine (EESC)
Below, we discuss potential changes in the projected trajectory of ozone depletion and EESC that result from improvements in our understanding of the emissions or other characteristics of individual gases or groups of gases. We reference
these potential changes to the so-called baseline scenario—which should be considered a plausible future pathway forthese gases. The baseline scenario for ODSs is developed from atmospheric concentration observations, combined with
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Executive Summary | Executive Summary

estimates of the amounts of ODSs in existing equipment or other products containing ODSs, referred to as banks. The
2018 baseline scenario for HFCs takes into account global control measures introduced by the Kigali Amendment andother regional and national actions. For all baseline scenarios, we assume that the long-lived greenhouse gases N2O,
CH4, and CO2 follow the Representative Concentration Pathway (RCP) 6.0 scenario. Note that for some of the metrics
the combined consequence of these gases is generally not simply the addition of each of the changes. It is also importantto recognize that the return date of EESC to 1980 levels is quite sensitive to any change in EESC concentration becauseof the relatively small rate at which EESC is projected to decline in the middle of this century.

• Global emissions of CFC-11 derived from atmospheric observations show an increase in recent years that is

not consistent with our understanding of release from its banks and suggests new global production that is notreported to the United Nations Environment Programme (UN Environment). If total emissions of CFC-11 were
to continue at levels experienced from 2002–2016 (67 Gg yr-1), the return of mid-latitude and polar EESC to
the 1980 value would be delayed by about seven years and 20 years, respectively. Such an assumption of continuing emissions implicitly assumes that the unidentified emissions will grow to counteract the expected
decline in bank emissions.

• Emissions from current ODS banks continue to be a slightly larger future contribution than ODS production

to ozone layer depletion over the next four decades, assuming maximum production levels allowed by theMontreal Protocol. Future business-as-usual emissions from HCFCs and from banks of CFCs and banks ofhalons are each projected to contribute roughly comparable amounts to EESC in the next few decades.

• Elimination of future production of methyl bromide (CH Br) for quarantine and pre-shipment (QPS) applica3

tions, not controlled by the Montreal Protocol, would accelerate the return of mid-latitude EESC to 1980 levels
by about a year. Production for QPS use has remained relatively stable over the last two decades and now
constitutes almost 90% of the reported production of CH3Br since emissions from other uses have declined
dramatically. Non-QPS applications of CH3Br were completely phased out in 2015, except for approved
critical use exemptions, which have declined by a factor of ~30 since 2005.

• If CCl emissions continue to decline at the rate observed over the last two decades of 2.5% yr , future concen4−1

trations will be about 14 ppt higher in 2050 than projected in the previous Assessment. CCl4 emissions inferredfrom atmospheric observations continue to be much greater than those assumed from feedstock uses asreported to UN Environment; by-product emissions from chloromethane and perchloroethylene plants andfugitive emissions from the chlor-alkali process have been quantified as significant contributors to these
additional emissions. Elimination of all CCl4 emissions in 2020 would accelerate the return of mid-latitude
EESC to 1980 levels by almost three years compared to the baseline scenario of a continued emissions decrease of 2.5% yr−1. Alternatively, if future emissions do not decline but remain at the current level, the returnof mid-latitude EESC to 1980 levels would be delayed almost two years.

• The return of mid-latitude EESC to 1980 levels is estimated to be delayed by almost two years compared to theprevious Assessment, due primarily to the higher projected future concentrations of CCl4. The mid-latitude
EESC change from CCl4 alone leads to a delay larger than two years, but future CH3Br baseline projections
are now lower than in the previous Assessment and offset some of the effect from CCl4. The delay in polar
EESC returning to 1980 levels is slightly more than two years when compared with the previous Assessment.
A new EESC formalism alters the time evolution of EESC and dates when EESC returns to 1980 levels, but it
has little effect on the relative impacts of the various alternative future scenarios. When compared with theprevious Assessment’s EESC formalism, the new EESC formalism leads to a projected EESC return to the1980 level 11 years later at mid-latitudes and by less than two years later at polar latitudes.

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Executive Summary | Executive Summary• Reducing anthropogenic emissions of N O from those in RCP-6.0 to the Concerted Mitigation scenario would
8

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have a similar positive impact on stratospheric ozone over the next four decades as eliminating production of
HCFCs from 2020. This N2O emissions reduction would have a larger benefit to climate over 2020–2060 than
the sum of all the options for controlled ODSs considered (based on GWP-weighted emissions).

Updates on the climate impact of gases controlled by the Montreal Protocol• Future emissions of HFC-23, a potent greenhouse gas and a by-product of HCFC-22 production, are expected

to be limited by the Kigali Amendment, which mandates the destruction of HFC-23 to the extent practicable.
Globally, HCFC-22 is currently produced in roughly equal quantities for controlled emissive uses, which are
declining, and for the uncontrolled feedstock uses, which grew rapidly over the last few decades but haverecently stabilized. Future emission trends will largely depend on the extent to which HFC-23 is destroyed
by HCFC-22 production facilities and the amount of HCFC-22 produced.

• Future emissions of HFCs, HCFCs, and CFCs contribute approximately 60, 9, and 3 cumulative Gt CO 2

equivalent emissions, respectively, from 2020 to 2060 in the baseline scenario. Of the 60 Gt CO2-eq emissions from HFCs, 53 arise from future production. For reference, cumulative CO2 emissions from fossil
fuel usage are projected over this time period to be 1,700 Gt CO2 in the RCP-6.0 scenario and 760 Gt CO2
in the ­RCP-2.6 scenario. The total radiative forcing from CFC and HCFCs and their HFC replacements is
projected to continue to increase gradually for the next decade or two. After that point, the ODS and HFC
restrictions of the Montreal Protocol, if adhered to, ensure a continued decline in total RF from ODSs andtheir replacements through the rest of the century.

• Global warming potentials, global temperature change potentials, and ozone depletion potentials of hundredsof HCFCs are presented, most for the first time in an assessment. This comprehensive assessment includes all
the HCFCs listed under Annex C, Group I of the Montreal Protocol, many of which did not have estimated
GWPs at the time of the signing of the Kigali Amendment.

Updates on impacts of climate gases and other processes on future
stratospheric ozone
In this section, we summarize potentially important impacts on the future of the ozone layer that could result from anthropogenic activity not associated with ODS production or consumption and not controlled by the Montreal Protocol.
As noted above, a major issue is that uncertainties in future changes in the ozone layer will be influenced more byuncertainties in CO2, CH4, and N2O levels than by uncertainties in the levels of ODSs, especially as we approach the1980 values of EESC. Increases in greenhouse gas concentrations are predicted to lead to increases in upper-stratosphericozone at all latitudes, with a more complex pattern of ozone changes in the lower stratosphere, including a decrease in
low latitudes due to changes in dynamics and transport. These processes are discussed in detail in Chapters 3 and 4. Notethat natural forces such as large explosive volcanic eruptions could also adversely affect ozone recovery over the next
decade, while ODS levels remain high.

• The wide range of possible future levels of CO , CH , and N O represents an important limitation to making
242

accurate projections of the ozone layer. Global mean warming as well as stratospheric cooling will driveozone changes through both atmospheric circulation and chemistry. Future ozone levels depend on thepath of greenhouse gas and aerosol emissions as well as the sensitivity of the climate system to these emissions. Future ODS atmospheric concentrations are more certain than atmospheric concentrations of climate
forcing emissions, as long as there is adherence to the Montreal Protocol. This chapter considers various climate scenarios, using the Representative Concentration Pathways (RCPs) adopted by the IPCC for its Fifth
Assessment Report (AR5). The Paris Agreement, with a stated objective to limit globally averaged warming
to less than 2°C, requires emissions closest to RCP-2.6, the lowest emission scenario of all the RCP scenarios.

8

UNEP 2013. Drawing Down N2O to Protect Climate and the Ozone Layer. A UNEP Synthesis Report. United Nations Environment
Programme (UNEP), Nairobi, Kenya.
ES.49

Executive Summary | Executive Summary• Intentional long-term geoengineering applications that substantially increase stratospheric aerosols to miti-

gate global warming by reflecting sunlight would alter stratospheric ozone. The estimated magnitudes andeven the sign of ozone changes in some regions are uncertain because of the high sensitivity to variables suchas the amount, altitude, geographic location and type of injection, and the halogen loading. An increase of
stratospheric sulfate aerosol burden in amounts sufficient to substantially reduce global radiative forcing
would delay the recovery of the Antarctic ozone hole. Much less is known about the effects on ozone fromgeoengineering solutions using non-sulfate aerosols.

• Rocket launches presently have a small effect on total stratospheric ozone (much less than 0.1%). Space industry
developments indicate that rocket emissions may increase more significantly than reported in the previous
Assessment. Their impacts will depend on rocket design (particularly the altitude of emissions), launch vehicle sizes, launch rates, spaceport locations, and fuel types. Important gaps remain in understanding rocket
emissions and their combined chemical, radiative, and dynamical impacts on the global stratosphere and inprojections of launch rates. These gaps limit the confidence level of predictions of present and future impactsof rocket emissions on stratospheric ozone and suggest periodic assessments are warranted. The lifetime of
the most important rocket emissions is limited, and the stratospheric accumulation of rocket-emitted black
carbon and alumina particles varies in correspondence with global launch rates and altitude of emissions.

Update on other environmental impacts of Montreal Protocol gases
Here, we refer to all gases controlled under the Montreal Protocol and its various Amendments, including the Kigali
Amendment, as Montreal Protocol Gases.

• There is increased confidence that trifluoroacetic acid (TFA) produced from degradation of HFCs, HCFCs, and
HFOs will not harm the environment over the next few decades. This assessment is based on the current
estimates of future use of hydrocarbons, HCFCs, and HFOs. It is noteworthy that HFCs and HCFCs haveatmospheric lifetimes long enough to globally distribute any TFA emissions, while HFOs have atmospheric lifetimes so short that TFA emissions are deposited near the point of emission. Periodic re-evaluation
is prudent, given the uncertainties in the sources and sinks of TFA and because of its persistence in theenvironment.

Summary of the impacts of mitigation options and particular scenarios
Figure 6-1 (also Figure ES-9) shows what ozone and climate-relevant changes could be avoided if various actions
were taken. These changes are shown as the differences in global total column ozone averaged over 2020–2060 andin cumulative CO2-equivalent emissions over 2020–2060 relative to the baseline (A1) scenario (which includes theKigali Amendment for HFCs). The options available to hasten the recovery of the ozone layer are limited, mostly
because actions that could help significantly have already been taken.
○○ For CFCs, halons, and HCFCs, the most effective mitigation option, not considering technical feasibili-ty, is expanded bank recapture and destruction; elimination of HCFC production starting in 2020 would
be somewhat less effective.

○○ For CH3Br, elimination of production for currently uncontrolled quarantine and pre-shipment (QPS)

applications is shown.

○○ For CCl4, the impacts of total emissions elimination starting in 2020 are shown.
○○ For CH2Cl2, an uncontrolled ozone-depleting gas whose exact sources are unknown, we show that im-

mediate emissions elimination would have a greater positive impact on total column ozone than total
emissions elimination of CCl4.

ES.50

Executive Summary | Executive Summary○○ For N2O, the impacts of the Concerted Mitigation average scenario from UNEP [2013] are shown,compared to the RCP-6.0 scenario. The Concerted Mitigation scenario was developed by averaging thefour published mitigation scenarios (RCP-2.6, SRES B2, and scenarios 4 and 5 from Davidson [2012])
that lead to lower N2O emissions in 2050 than were experienced in 2005.

○○ For HFCs, the impact of a hypothetical complete global phaseout of production (excluding HFC-23)

starting in 2020 is shown. As discussed in Chapter 2, for this scenario the surface temperature contribution of the HFC emissions would stay below 0.02 oC for the entire 21st century and beyond.

Further detail on these options and scenarios is given in Section 6.4 and Table 6-5.
All the scenarios discussed above hasten the ozone layer recovery (CFCs, halons, HCFCs, CH3Br, CCl4, CH2Cl2 andN2O) and reduce warming (HFCs, CFCs, halons, HCFCs, CCl4, and N2O). An additional scenario for emissions that
may result from a violation of the Montreal Protocol is shown, namely continuing unexplained emissions of CFC-11
at 67 Gg yr −1, which is the average calculated annual emission from atmospheric concentration observations over
2002–2016. This scenario leads to more ozone depletion and climate warming. Avoiding this scenario would have alarger positive impact on future ozone than any of the other mitigation options considered here.

Errata (December 2018)
Executive Summary content:• In Figure ES-1 (panel (a)), the units label was corrected to (Mt yr –1).
• In the caption of Figure ES-8 the cross-reference was changed to Figure 5-12.
• In Figure ES-1 (panel (b)), the label and caption were changed
to EECl (equivalent effective chlorine).
Appendix:• Addition was made to the Contributors list to include Sydnee Masias, Science andTechnology Corporation, NOAA Office of the Chief Administrative Officer, USA.
• Chapter 1 Scientific Summary (pg. ES.34) footnote was changed to “Here, CFC114 refers to the combination of the CFC-114 and CFC-114a isomers.”• The spelling of Helen Walter-Terrinoni’s name was corrected.

ES.51

Chapter 1
Update on Ozone-Depleting Substances (ODSs)
and Other Gases of Interest to the Montreal Protocol
Lead Authors
A. Engel
M. Rigby
Coauthors
J.B. Burkholder
R.P. Fernandez
L. Froidevaux
B.D. Hall
R. Hossaini
T. Saito
M.K. Vollmer
B. YaoContributors
E. Altas
P. Bernath
D.R. Blake
G. Dutton
P. Krummel
J.C. Laube
E. Mahieu
S.A. Montzka
J. Mühle
G. Nedoluha
S.J. O’Doherty
D.E. OramK. Pfeilsticker
R.G. Prinn
B. Quack
I.J. Simpson
R.F. Weiss
Review Editors
Q. Liang
S. Reimann

Cover photo: View of the high-altitude Jungfraujoch research station, perched at ca. 3,500 m in the Swiss Alps.
Both in-situ atmospheric background measurements of halocarbons and related s­ pecies, as well as remote sensing measurements of halogen species taken at the Jungfraujoch, have contributed to this report. Photo: Courtesyof jungfraujoch.ch.

Chapter 1
Update on Ozone-Depleting Substances (ODSs)
and Other Gases of Interest to the Montreal Protocol
CONTENTSSCIENTIFIC SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1
1.1

SUMMARY OF FINDINGS FROM THE PREVIOUS OZONE ASSESSMENT . . . . . . . . . . . . . . . . . . . . .  7

1.2 UPDATED ­ABUNDANCES, TRENDS, ­LIFETIMES, AND ­EMISSIONS OF ­LONGER-LIVED  . . . . . . . . .
	­HALOGENATED SOURCE GASES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  7
1.2.1 Chlorofluorocarbons (CFCs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Box 1-1. Inferring Emissions Using Atmospheric Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.2.2 Halons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.2.3 Carbon Tetrachloride (CCl4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.2.4 Methyl Chloroform (CH3CCl3)  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.2.5

Hydrochlorofluorocarbons (HCFCs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.2.6

Methyl Chloride (CH3Cl) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

1.2.7 Methyl Bromide (CH3Br)  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1.3

VERY SHORT-LIVED ­HALOGENATED SUBSTANCES (VSLSS)  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
1.3.1 Tropospheric Abundance, Trends, and Emissions of Very ­Short-Lived
Source Gases (VSL SGs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
1.3.1.1

Chlorine-Containing Very Short-Lived Source Gases . . . . . . . . . . . . . . . . . . . . . . 30

1.3.1.2

Bromine-Containing Very Short-Lived Source Gases . . . . . . . . . . . . . . . . . . . . . . 35

1.3.1.3

Iodine-Containing Very Short-Lived Source Gases . . . . . . . . . . . . . . . . . . . . . . . . 35

Box 1-2. Regional Variability and Modeling of VSLS Transport to the Stratosphere . . . . . . . . . . 36
1.3.2 Input of VSLS Halogen to the ­Stratosphere  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.3.2.1

Source Gas Injection (SGI)  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

1.3.2.2

Product Gas Injection (PGI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Box 1-3. Heterogeneous Chemistry of Very Short-Lived Product Gases  . . . . . . . . . . . . . . . . . . . . 42
1.3.2.3
1.4

Total Halogen Input into the Stratosphere from VSLSs . . . . . . . . . . . . . . . . . . . . 43

CHANGES IN ­ATMOSPHERIC HALOGENS  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
1.4.1 Tropospheric and ­Stratospher­ic C­ hlorine Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
1.4.1.1

Tropospheric Chlorine Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

1.4.1.2

Stratospheric Chlorine Changes  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

1.4.2 Tropospheric and ­Stratospheric ­Bromine Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
1.4.2.1

Tropospheric Bromine Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

1.4.2.2

Stratospheric Bromine Changes  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

1.4.3 Tropospheric and Stratospheric Iodine Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
1.4.4 Changes in Ozone-Depleting ­Halogen Abundance in the Stratosphere . . . . . . . . . . . . . 53
Box 1-4. Equivalent Effective Stratospheric Chlorine (EESC) and Fractional Release Factors . 54
1.4.5 Tropospheric and Stratospheric Fluorine Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
1.5

CHANGES IN OTHER TRACE GASES THAT INFLUENCE OZONE AND CLIMATE . . . . . . . . . . . . . 61
1.5.1 Nitrous Oxide (N2O)  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
1.5.2 Methane (CH4)  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
1.5.3 Aerosol Precursors: Carbonyl ­Sulfide (COS) and Sulfur Dioxide (SO2) . . . . . . . . . . . . . . . . 63
1.5.4 Other Fluorine-Containing Gases (SF6, PFCs, NF3, SO2F2, SF5CF3, HFEs) . . . . . . . . . . . . . . 64

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Chapter 1
Update on Ozone-Depleting Substances (ODSs)
and Other Gases of Interest to the Montreal Protocol
SCIENTIFIC SUMMARY
This chapter concerns atmospheric changes in ozone-depleting substances (ODSs), such as chlorofluorocarbons(CFCs), halons, chlorinated solvents (e.g., CCl4 and CH3CCl3) and hydrochlorofluorocarbons (HCFCs), which arecontrolled under the Montreal Protocol. Furthermore, the chapter updates information about ODSs not controlled
under the Protocol, such as methyl chloride (CH3Cl) and very short-lived substances (VSLSs). In addition to depleting stratospheric ozone, many ODSs are potent greenhouse gases.
Mole fractions of ODSs and other species are primarily measured close to the surface by global or regional monitoring networks. The surface data can be used to approximate a mole fraction representative of the global or hemispheric tropospheric abundance. Changes in the tropospheric abundance of an ODS result from a difference between
the rate of emissions into the atmosphere and the rate of removal from it. For gases that are primarily anthropogenic
in origin, the difference between northern and southern hemispheric mole fractions is related to the global emission
rate because these sources are concentrated in the northern hemisphere.

• The abundances of the majority of ODSs that were originally controlled under the Montreal

Protocol are now declining, as their emissions are smaller than the rate at which they are destroyed.
In contrast, the abundances of most of the replacement compounds, HCFCs and hydrofluorocarbons (HFCs, which are discussed in Chapter 2), are increasing.

TROPOSPHERIC CHLORINE
Total tropospheric chlorine is a metric used to quantify the combined globally averaged abundance of chlorine in the
troposphere due to the major chlorine-containing ODSs. The contribution of each ODS to total tropospheric chlorineis the product of its global tropospheric mean mole fraction and the number of chlorine atoms it contains.

• Total tropospheric chlorine (Cl) from ODSs continued to decrease between 2012 and 2016. Total tropo-

spheric chlorine in 20161 was 3,287 ppt (where ppt refers to parts per trillion as a dry air mole fraction),
11% lower than its peak value in 1993, and about 0.5% lower than reported for 2012 in the previous
Assessment. Of the 2016 total, CFCs accounted for about 60%, CH3Cl accounted for about 17%, CCl4
accounted for about 10%, and HCFCs accounted for about 9.5%. The contribution from CH3CCl3 has
now decreased to 0.2%. Very short-lived source gases (VSL SGs), as measured in the lower troposphere,
contributed approximately 3%.
○○ During the period 2012–2016, the observed rate of decline in tropospheric Cl due to controlled sub-stances was 12.7 ± 0.92 ppt Cl yr−1, similar to the 2008–2012 period (12.6 ± 0.3 ppt Cl yr−1). This rateof decrease was close to the projections from the A1 scenario3 in the previous Assessment. However,the net rate of change was the result of a slower than projected decrease in CFCs and a slower HCFC
increase than in the A1 scenario, which assumed that HCFC production from Article 5 countries
would follow the maximum amount allowed under the Montreal Protocol.

1Here and throughout this chapter, values that are given for a specific year represent annual averages, unless mentioned otherwise.

2

The ranges given here represent the interannual variability in observed growth rate or rate of decrease.

3

A1 Scenario is given in Table 5A-2 of Harris and Wuebbles et al. (2014).
1.1

Chapter 1 | ODSs and Other Gases○○ When substances not controlled under the Montreal Protocol are also included, the overall decreasein tropospheric chlorine was 4.4 ± 4.1 ppt Cl yr−1 during 2012–2016. This is smaller than the rateof decline during the 2008–2012 period (11.8 ± 6.9 ppt Cl yr−1) and smaller than the rate of decline
in controlled substances because VSLSs, predominantly anthropogenic dichloromethane (CH2Cl2),
and methyl chloride (CH3Cl), which is mostly from natural sources, increased during this period.

• Starting around 2013, the rate at which the CFC-11 mole fraction was declining in theatmosphere slowed unexpectedly, and the interhemispheric difference in its mole fraction increased. Thesechanges are very likely due to an increase in emissions, at least part of which originate from eastern Asia.
Assuming no change in atmospheric circulation, an increase in global emissions of approximately 10
Gg yr−1 (~15%) is required for 2014–2016, compared to 2002–2012, to account for the observed trend
and interhemispheric difference. The rate of change and magnitude of this increase is unlikely to be explained by increasing emissions from banks. Therefore, these findings may indicate new production notreported to the United Nations Environment Programme (UN Environment). If the new emissions areassociated with uses that substantially increase the size of the CFC-11 bank, further emissions resulting
from this new production would be expected in future.

• Compared to 2008–2012, for the period 2012–2016, mole fractions of CFC-1144 declined more slowly, CFC-

13 continued to rise, and CFC-115 exhibited positive growth after previously showing near-zero change.
These findings likely indicate an increase or stabilization of the emissions of these relatively low abundancecompounds, which is not expected given their phaseout for emissive uses under the Montreal Protocol. For
CFC-114 and -115, regional analyses show that some of these emissions originate from China. There isevidence that a small fraction of the global emissions of CFC-114 and -115 are due to their presence as
impurities in some HFCs. However, the primary processes responsible are unknown.

• The rate at which carbon tetrachloride (CCl4) has declined in the atmosphere remains slower than expectedfrom its reported use as a feedstock. This indicates ongoing emissions of around 35 Gg yr−1. Since the previous Assessment, the best estimate of the global atmospheric lifetime of CCl4 has increased from 26 to 32
years, due to an upward revision of its lifetime with respect to loss to the ocean and soils. New sources havebeen proposed including significant by-product emissions from the production of chloromethanes andperchloroethylene and from chlor-alkali plants. With these changes in understanding, the gap between
top-down and bottom-up emissions estimates has reduced to around 10 Gg yr−1, compared to 50 Gg
yr−1 previously.

• Combined emissions of the major HCFCs have declined since the previous Assessment. Emissions of

HCFC-22 have remained relatively stable since 2012, while emissions of HCFC-141b and -142b declined between 2012 and 2016, by around 10% and 18%, respectively. These findings are consistent witha sharp drop in reported HCFC consumption after 2012, particularly from Article 5 countries.

• Emissions of the compounds HCFC-133a and HCFC-31, for which no current intentional use is known,

have been detected from atmospheric measurements. Research to date suggests that these gases are unintentional by-products of HFC-32, HFC-134a, and HFC-125 production.

TROPOSPHERIC BROMINE
Total tropospheric bromine is defined in analogy to total tropospheric chlorine. Even though the abundance of bromine is much smaller than that of chlorine, it has a significant impact on stratospheric ozone because it is around60–65 times more efficient than chlorine as an ozone-destroying catalyst

41.2Here, CFC-114 refers to the combination of the CFC-114 and CFC-114a isomers.

ODSs and Other Gases | Chapter 1• Total tropospheric bromine from controlled ODSs (halons and methyl bromide) continued to decrease andby 2016 was 14.6 ppt, 2.3 ppt below the peak levels observed in 1998. In the 4-year period prior to the last
Assessment, this decrease was primarily driven by a decline in methyl bromide (CH3Br) abundance, witha smaller contribution from a decrease in halons. These relative contributions to the overall trend have
now reversed, with halons being the main driver of the decrease of 0.15 ± 0.04 ppt Br yr−1 between 2012
and 2016.

• The mole fractions of halon-1211, halon-2402, and halon-1202 continued to decline between 2012 and 2016.

Mole fractions of halon-1301 increased during this period, although its growth rate dropped to a level indistinguishable from zero in 2016. Emissions of halon-2402, halon-1301, and halon-1211, as derived fromatmospheric observations, declined or remained stable between 2012 and 2016.

• Methyl bromide (CH3Br) mole fractions continued to decline between 2012 and 2015 but showed a small

increase (2–3%) between 2015 and 2016. This overall reduction is qualitatively consistent with the controls under the Montreal Protocol. The 2016 level was 6.8 ppt, a reduction of 2.4 ppt from peak levels
measured between 1996 and 1998. The increase between 2015 and 2016 was the first observation of apositive global change for around a decade or more. The cause of this increase is yet to be explained.
However, as it was not accompanied by an increased interhemispheric difference, it is unlikely that
this is related to anthropogenic emissions in the Northern Hemisphere. By 2016, controlled CH3Br
consumption dropped to less than 2% of the peak value, and total reported fumigation emissions havedeclined by more than 85% since their peak in 1997. Reported consumption in quarantine and pre-shipment (QPS) uses of CH3Br, which are not controlled under the Montreal Protocol, have not changed
substantially over the last two decades.

HALOGENATED VERY SHORT-LIVED SUBSTANCES (VSLSS)VSLSs are defined as trace gases whose local lifetimes are shorter than 0.5 years and have nonuniform troposphericabundances. These local lifetimes typically vary substantially over time and space. Of the very short-lived sourcegases (VSL SGs) identified in the atmosphere, brominated and iodinated species are predominantly of oceanic origin, while chlorinated species have significant additional anthropogenic sources. VSLSs will release the halogen
they contain almost immediately once they enter the stratosphere and will thus play an important role in the lower
stratosphere in particular. Due to their short lifetimes and their atmospheric variability the quantification of theircontribution is much more difficult and has much larger uncertainties than for long-lived compounds.

• Total tropospheric chlorine from VSL SGs in the background lower atmosphere is dominated by anthropo-

genic sources. It continued to increase between 2012 and 2016, but its contribution to total chlorine remains
small. Global mean chlorine from VSLSs in the troposphere has increased from about 90 ppt in 2012
to about 110 ppt in 2016. The relative VSLS contribution to stratospheric chlorine input derived fromobservations in the tropical tropopause layer has increased slightly from 3% in 2012 to 3.5% in 2016.

• Dichloromethane (CH2Cl2), a VSL SG that has predominantly anthropogenic sources, accounted for the

majority of the change in total chlorine from VSLSs between 2012 and 2016 and is the main source of
VSLS chlorine. The global mean abundance reached approximately 35–40 ppt in 2016, which is about adoubling compared to the early part of the century. The increase slowed substantially between 2014 and2016. Emissions from southern and eastern Asia have been detected for CH2Cl2.

• There is further evidence that VSLSs contribute ~5 (3–7) ppt to stratospheric bromine, which was about

25% of total stratospheric bromine in 2016. The main sources for brominated VSLSs are natural, and nolong-term change is observed. While the best estimate of 5 ppt has remained unchanged from the last

1.3

Chapter 1 | ODSs and Other Gases

Assessment, the assessed uncertainty range has been reduced. Due to the decline in the abundance ofregulated bromine compounds, the relative contribution of VSLSs to total stratospheric bromine continues to increase.

STRATOSPHERIC CHLORINE AND BROMINE
In the stratosphere, chlorine and bromine can be released from organic source gases to form inorganic species, which
participate in ozone depletion. In addition to estimates of the stratospheric input derived from the troposphericobservations, measurements of inorganic halogen loading in the stratosphere are used to determine trends of stratospheric chlorine and bromine.

• Hydrogen chloride (HCl) is the major reservoir of inorganic chlorine (Cly) in the mid to upper stratosphere.

Satellite-derived measurements of HCl (60°N–60°S) in the middle stratosphere show a long-term decreaseof HCl at a rate of around 0.5% yr−1, in good agreement with expectations from the decline in troposphericchlorine. In the lower stratosphere, a decrease was observed over the period from 1997 to 2016, while
significant differences in the trends are seen over the period 2005 to 2016 between various datasets andaltitude/geographical regions. A similar behavior is observed for total column measurements, likely reflecting variability in stratospheric dynamics and chemistry. Total chlorine input to the stratosphere of
3,290 ppt is derived for 2016 from measurements of long-lived ODSs at the surface and VSLSs in theupper troposphere. About 80% of this input is from substances controlled under the Montreal Protocol.

• Total stratospheric bromine, derived from observations of bromine monoxide (BrO), continued to decreaseat a rate of about 0.75% yr−1 from 2004 to 2014. This decline is consistent with the decrease in total tropospheric organic bromine, based on measurements of CH3Br and the halons. A total bromine input to thestratosphere of 19.6 ppt is derived for 2016, combined from 14.6 ppt of long-lived gases and 5 ppt from
VSLSs not controlled under the Montreal Protocol. Anthropogenic emissions of all brominated longlived gases are controlled, but as CH3Br also has natural sources, more than 50% of the bromine reaching the stratosphere is now estimated to be from sources not controlled under the Montreal Protocol.
There is no indication of a long-term change in natural sources to stratospheric bromine.

EQUIVALENT EFFECTIVE STRATOSPHERIC CHLORINE (EESC)
EESC is the chlorine-equivalent sum of chlorine and bromine derived from ODS tropospheric abundances, weighted
to reflect their expected depletion of stratospheric ozone. The growth and decline in EESC depends on a given tropospheric abundance propagating to the stratosphere with varying time lags (on the order of years) associated withtransport. Therefore, the EESC abundance, its peak timing, and its rate of decline are different in different regionsof the stratosphere. Recent suggestions of a refinement in the calculation method for EESC result in somewhat lower
estimates on how far the stratospheric reactive halogen loading has recovered.

• By 2016, EESC had declined from peak values by about 9% for polar winter conditions and by about 13–17% for

mid-latitude conditions. This drop is 31–43% of the decrease required for EESC in mid-latitudes to return to
the 1980 benchmark level, and about 18–19% of the decrease required for EESC in polar regions to returnto the 1980 benchmark level5. The rate at which EESC is decreasing has slowed, in accordance with aslowdown of the decrease in tropospheric chlorine. The ranges given reflect the different methods forcalculating EESC. Differences in halogen recovery levels from previous Assessments are also due todifferences in assumed fractional release factors.

5

1.4

As in previous Assessments, 1980 levels of EESC are used as a benchmark for recovery, although this value is somewhat arbitrary andsome ozone loss had occurred prior to 1980. Also, recovery of EESC to 1980 values does not necessarily imply a recovery of ozone to1980 levels, as other parameters, e.g. stratospheric circulation, may change.

ODSs and Other Gases | Chapter 1

TROPOSPHERIC AND STRATOSPHERIC FLUORINE
While fluorine has no direct impact on stratospheric ozone, many fluorinated gases are strong greenhouse gases, andtheir emission is often related to the replacement of chlorinated substances regulated under the Montreal Protocol.
For this reason, trends in fluorine are also assessed in this report.

• The main sources of fluorine in the troposphere and in the stratosphere are CFCs, HCFCs, and HFCs. In con-trast to total chlorine, total fluorine in the troposphere continued to increase between 2012 and 2016, at a rateof 1.7% yr−1. This increase shows the decoupling of the temporal trends in fluorine and chlorine due tothe increasing e­ missions of HFCs (see Chapter 2). The total atmospheric-column abundance of inorganic
fluorine, which is m­ainly stratospheric, has continued to increase at a rate of about 1% yr−1 over the period
2007–2016.

EFFECT OF OZONE-DEPLETING SUBSTANCES (ODSs) ON CLIMATE• The total direct radiative forcing6 of CFCs continues to be much higher than that of HCFCs. However,radiative forcing from CFCs has dropped by about 7% since its peak in 2000 to about 250 mW m−2 in2016 (approximately 13% that of CO2), while radiative forcing from HCFCs increased to 58 mW m−2
in 2016 (approximately 3% that of CO2). The total direct radiative forcing due to CFCs, HCFCs, halons,CCl4 and CH3CCl3 was 327 mW m­–2 in 2016 (approximately 16% that of CO2).

• CO2-equivalent emissions7 of CFCs and HCFCs were approximately equal in 2016. The CO2-equivalentemission from the sum of all CFCs or the sum of all HCFCs was approximately 0.8 Gt in 2016. The CO2equivalent emission from the sum of CFCs, HCFCs, Halons, CCl4 and CH3CCl3 was approximately 1.7
Gt in 2016.

OTHER GASES THAT AFFECT OZONE AND CLIMATE• Mole fractions of many other gases that affect both ozone and climate have changed since the previous
Assessment. The atmospheric abundance of methane has continued to increase following a period ofstagnation in the early 2000s. The drivers of the changing trend are disputed. Nitrous oxide continues
to grow relatively steadily in the atmosphere. The global mole fractions of the fluorinated species sulfur hexafluoride (SF6), nitrogen trifluoride (NF3), sulfuryl fluoride (SO2F2), and the perfluorocarbons(PFCs such as CF4 and C2F6) have continued to grow. In contrast, the abundance of the sulfur-containing compounds sulfur dioxide (SO2) and carbonyl sulfide (COS) has not changed substantially.

6

A measure of the change in net irradiance (incoming minus outgoing) at the tropopause.

7

CO2 equivalents are determined here by weighting emissions estimates by the global warming potential (GWP) of each gas, integrated
over a 100-year time horizon.
1.5Chapter 1 | ODSs and Other Gases

1.6

Chapter 1
Update on Ozone-Depleting Substances (ODSs)
and Other Gases of Interest to the Montreal Protocol
1.1 SUMMARY OF FINDINGS FROM THE
PREVIOUS OZONE ASSESSMENT
Chapter 1 of the 2014 Assessment report (Carpenter
and Reimann et al., 2014) provided updates on
ozone-depleting substances (ODSs) and other gases
of interest to the Montreal Protocol. These includedhydrofluorocarbons (HFCs), which have been used toreplace ODSs; they are not ozone-depleting substances, but they do add to climate warming.
Chapter 1 from the 2014 Assessment showed that, inthe 5-year period 2008–2012, total tropospheric chlorine from substances regulated under the Montreal
Protocol had declined at an average rate of 13.4 ± 0.9
ppt yr−1 (ppt defined as dry air mole fraction in parts
per trillion), while bromine from regulated substances was declining at a rate of 0.14 ± 0.02 ppt yr−1. All
major CFCs (chlorofluorocarbons) showed decreasing mole fractions and continued to be the main carriers of chlorine, with a contribution of 61% to total
tropospheric chlorine. The only class of compounds
that were regulated yet still showed increasing mole
fractions were hydrochlorofluorocarbons (HCFCs).
A continuing discrepancy in the emissions of CCl4
inferred from observations versus those derived
from reports to the United Nations Environment
Programme (UN Environment) was documented.
An increase in lower tropospheric abundances ofchlorinated very short-lived substances (VSLSs)
was observed. Dichloromethane (CH2Cl2) increased
particularly strongly; the global mean mole fraction
had increased by about 60% between 2001 and 2012.
However, the total contribution of VSLSs to stratospheric chlorine remained small at approximately 3%
in 2012, including the contribution of inorganic product gases entering the stratosphere.
Total tropospheric bromine showed an overall decline,
consistent with the projections from the scenarios inthe 2010 Assessment. The decline was driven by thecontinued decrease of CH3Br and, for the first-time,

an observed decrease in total tropospheric bromine
from halons, with all halons except for halon-1301 decreasing in the atmosphere. The relative contributionof brominated VSLSs to total bromine was much larger than the contribution of chlorinated VSLSs to total
chlorine, with about 5 ppt of the total 20 ppt of stratospheric bromine attributed to short-lived substances.
Input of bromine from VSLSs to the stratosphere inboth organic and inorganic forms was included.
Equivalent effective stratospheric chlorine (EECS),
which is the chlorine-equivalent sum of chlorine andbromine derived from ODS tropospheric abundances
weighted to reflect their expected depletion of stratospheric ozone, was assessed to have declined from its
maximum value in polar regions by about 10% andin mid-latitudes by about 15%; this is equivalent toabout 20% and 40% of the decline required to returnto 1980 benchmark levels, respectively.
The influence of ODS and HFC emissions on climate
was assessed in terms of their equivalent in gigatonnesof carbon dioxide (CO2-equivalent emissions) using
100-year Global Warming Potential (GWP). The
CO2-equivalent emissions of CFCs, HCFCs, and
HFCs were roughly equal to each other in 2012 withrespect to their climate influence. However, the emissions of HFCs were increasing, while the emissions of
CFCs were declining and those of HCFCs remained
relatively constant.

1.2 UPDATED ­ABUNDANCES,TRENDS, ­LIFETIMES, AND­EMISSIONS OF ­LONGER-LIVED­HALOGENATED SOURCE GASES
Observations of ODSs have been carried out overmultiple decades by several groups with different sampling strategies, who have, in most cases, developed
independent, but regularly compared, calibration
scales (Figure 1-1, Table 1-1). Global and hemisphericmean mole fractions are derived using data from networks with air sampling stations that are distributed
1.7

Chapter 1 | ODSs and Other Gases
280

Global surface mole fraction (MF, ppt); interhemispheric difference (IHD, ppt); or growth rate (GR, ppt yr –1)

270

Mole

260
250

fract
ion

CFC-11
(MF
)

230

500

220
8

480
20

4

10

15

6
2

IHD

0

-2

5
0

GR

-4
1990

1995

2000

84

2005

2015

CFC-113

80
76

IHD

1995

2000

GR

-10

GR
2005

2010

2015

CCl4

MF

-15
1990
250

1995

2000

2005

2010

2015

2010

2015

HCFC-22

200

MF

150

80

100

4

20

3
2

IHD

15

1

IHD

0

10
5

GR
1995

2000

2005

2010

2015

CFC-114 + CFC-114a

MF

10

GR
1995

2000

2005

Year

2010

2015

2000

2005

CFC-115

8
7

NOAA
AGAGE
UEA (S.H.)
A1 (2014)
A1 (2010)

MF

6

CFC-114a (x10) (UEA S.H.)
1995

0
1990

9

CFC-114 (UEA S.H.)

10
8
1990

2015

F

-5

IHD

90

12

2010

M

0

100

16

2005

CH3CCl3

120

5

1990
110

18

2000

10

2

-2
1990

1995

0
15

4

-1

GR

40

Adcock et al. (2018) (S.H.)

6

0

-5
1990

IHD

80

14

1.8

2010

MF

72

CFC-12

MF

520

NOAA
AGAGE
A1 (2014)
A1 (2010)

240

540

5

4
1990

1995

2000

2005

Year

2010

2015

Global surface mole fraction (MF, ppt); interhemispheric difference (IHD, ppt); or growth rate (GR, ppt yr –1)

ODSs and Other Gases | Chapter 1

25

25

HCFC-141b

20

20
15

15

MF

10

0
2.5

3

2

IHD

1

GR

1

0

1995

2000

2005

2010

2015

halon-1211

NOAA
AGAGE
UEA (S.H.)
A1 (2014)

3.0
2.5

2.0
0.4
0.3

GR

1995

2000

2005

2010

2015

MF

2.0
1.5
0.2

1990

0.7
0.6
0.5
0.4

MF

0.3

0.2

IHD

GR

2000

2005

2010

2015

CH3Br

0.0
1990
600

2005

2010

2015

CFC-113a
CFC-112
CFC-216ba (x10)CFC-13 (x0.1)
CFC-112a
1995

2000

2005

CFC-216ca (x10)
2010

2015

CH3Cl

580

MF

560

MF

2000

MF

0.1

9
8

1995

0.2

halon-1202 (x10)
1995

2015

2.5

0

halon-2402

0.3

2010

halon-1301

-0.05

0.4

540

7
1990

2005

0.1

0.5

0.1
1990
10

2000

0.05

-0.1

-0.2
1990

1995

0.15

IHD

0.2

1990

3.5
3.0

MF

3.5

0

GR

0.5

0

0.1

IHD

1.5

2

4.0

HCFC-124

5

0
4

1990

HCFC-31 (x100)
HCFC-133a (x10)

10

5

4.5

HCFC-142b

MF

520
1995

2000

2005

Year

2010

2015

500
1990

1995

2000

2005

Year

2010

2015

Figure 1-1. Annual mean global surface mole fractions (MF; expressed as dry air mole fractions in parts per trillion or ppt) ofozone-depleting substances from independent sampling networks and from scenario A1 of the previous Ozone Assessments
over the past 26 years (1990–2016) (Daniel and Velders, 2011; Harris and Wuebbles et al., 2014). The baseline scenarios fromprevious Assessments (A1-2010, A1-2014) are projections from 2009 and 2013, respectively. Only A1-2014 data are shown forsome species. Shown are measured global surface annual means from the NOAA network (red) and AGAGE network (black).
Southern Hemispheric data obtained by the University of East Anglia (UEA) (blue) are shown for some species. NOAA and
AGAGE CFC-113 data likely represent some combination of CFC-113 and CFC-113a (although the influence of CFC-113a on
NOAA and AGAGE measurements of CFC-113 is likely small), whereas UEA measures CFC-113 and CFC-113a separately (Adcock
et al., 2018). UEA CFC-113 data (annual Southern Hemispheric means from Adcock et al., 2018) were adjusted downward by
2% to be consistent with the NOAA scale determined by gas chromatography-mass spectrometry (GC-MS) as opposed to gas
chromatography-electron capture detection (GC-ECD). HCFC-124 data were taken from Simmonds et al. (2017). HCFC-31 data
were taken from Schoenenberger et al. (2015). For some gases, we also show growth rates (GR) and interhemispheric differences (IHD; NH mean minus SH mean) in a second panel, using the same color scheme as in the corresponding upper panel.
1.9Chapter 1 | ODSs and Other Gases

Table 1-1. Measured mole fractions and changes of ozone-depleting gases from ground-based sampling
networks (expressed in dry air mole fractions as parts per trillion (ppt), or relative units).
Chemical
FormulaCommon orIndustrial
Name

Annual Mean
Mole Fraction (ppt)
2012
2015
2016

Change(2015–2016)(ppt yr–1) (% yr–1)

Network, Method

CFCs
CCl3F

CCl2F2

CFC-11

CFC-12

235.5

230.9

229.6

-1.3

-0.6

AGAGE, in situ1

235.2

231.1

229.8

-1.3

-0.6

NOAA2, flask & in situ

235.3

229.2

227.4

-1.8

-0.8

UCI, flask

527.8

519.7

516.1

-3.6

-0.7

AGAGE, in situ

524.7

515.3

512.2

-3.1

-0.6

NOAA, flask & in situ

522.5

519.5

515.6

-3.9

-0.8

UCI, flask

CClF3

CFC-13

2.94

3.01

3.04

0.03

1.0

AGAGE, in situ

CCl2FCCl2F

CFC-112

0.44

0.42

0.42

0.00

0.0

UEA, flask (Cape Grim)

CCl3CClF2

CFC-112a

0.066

0.066

0.067

0.001

1.5

UEA, flask (Cape Grim)

74.0

72.1

71.4

-0.7

-0.9

AGAGE, in situ3

74.0

72.1

71.5

-0.6

-0.8

NOAA, flask3

74.2

71.8

71.1

-0.7

-1.0

UCI, flask3

0.43

0.62

0.66

0.04

6.5

UEA, flask (Cape Grim)

16.3

16.3

16.3

0.0

-0.1

AGAGE, in situ4

15.2

14.8

14.6

-0.2

-1.4

UEA, flask (Cape Grim)5

1.05

1.05

1.04

-0.01

-1.0

UEA, flask (Cape Grim)5

8.40

8.46

8.49

0.03

0.4

AGAGE, in situ

8.48

8.63

8.67

0.04

0.5

NIES, in situ (Japan)

219.3

233.6

237.4

3.8

1.6

AGAGE, in situ

218.0

233.0

237.5

4.5

1.9

NOAA, flask

214.5

238.0

242.3

4.3

1.8

UCI, flask

0.31

0.37

0.39

0.02

5.4

UEA, flask (Cape Grim)

22.45

24.22

24.47

0.25

1.0

AGAGE, in situ

22.27

24.22

24.53

0.31

1.3

NOAA, flask

21.80

24.49

24.59

0.10

0.4

UCI, flask

21.92

22.51

22.56

0.05

0.2

AGAGE, in situ

21.36

21.84

22.01

0.17

0.8

NOAA, flask

21.80

23.26

23.16

-0.10

-0.4

UCI, flask

0.018

0.015

0.014

-0.001-6.7

UEA, flask (Cape Grim)5

4.01

3.71

3.59

-0.12

-3.2

AGAGE, in situ

3.92

3.61

3.52

-0.09

-2.5

NOAA, flask

CCl2FCClF2

CFC-113

CCl3CF3

CFC-113a

CClF2CClF2

CFC-114

CCl2FCF3

CFC-114a

CClF2CF3

CFC-115

HCFCs6
CHClF2CH2ClCF3
CH3CCl2F

CH3CClF2

HCFC-22
HCFC-133a
HCFC-141b

HCFC-142b

Halons1.10

CBr2F2halon-1202

CBrClF2

halon-1211

ODSs and Other Gases | Chapter 1

Chemical
FormulaCBrClF2(continued)

CBrF3

CBrF2CBrF2

Common orIndustrial
Name
halon-1211(continued)

halon-1301

halon-2402

Annual Mean
Mole Fraction (ppt)
2012
2015
2016

Change(2015–2016)(ppt yr–1) (% yr–1)

3.96

3.66

3.54

-0.12

-3.3

NOAA, in situ

3.97

3.61

3.51

-0.10

-2.8

UEA, flask (Cape Grim)

4.14

3.80

3.70

-0.10

-2.6

UCI, flask

3.30

3.36

3.36

0.00

0.0

AGAGE, in situ

3.19

3.25

3.25

0.00

-0.1

NOAA, in situ

3.10

3.17

3.17

0.00

0.0

UEA, flask (Cape Grim)

0.44

0.42

0.41

-0.01

-2.4

AGAGE, in situ7

0.44

0.42

0.42

-0.01

-1.2

NOAA, flask

0.39

0.37

0.36

-0.01

-2.7

UEA, flask (Cape Grim)5

539.9

544.7

552.7

8.0

1.5

AGAGE, in situ

541.4

550.0

559.1

9.1

1.7

NOAA, flask

84.2

81.1

79.9

-1.2

-1.5

AGAGE, in situ

85.7

82.2

81.2

-1.0

-1.2

NOAA, flask & in situ

86.7

82.2

81.9

-0.3

-0.3

UCI, flask

5.21

3.09

2.61

-0.48

-16

AGAGE, in situ

5.25

3.07

2.60

-0.47

-15

NOAA, flask

5.7

3.48

3.05

-0.43

-12

UCI, flask

7.06

6.66

6.80

0.14

2.1

AGAGE, in situ

6.95

6.64

6.86

0.22

3.3

NOAA, flask

Network, Method

Chlorocarbons
CH3Cl

methyl chloride

CCl4

carbontetrachloride

CH3CCl3

methylchloroform

Bromocarbons
CH3Br

methyl bromide

Mole fractions in this table represent independent estimates measured by different groups for the years indicated. Results inbold text are estimates of global surface mean mole fractions. Regional data from relatively unpolluted sites are shown (in italics)
where global estimates are not available, where global estimates are available from only one network, or where regional data
provide additional long-term records. Absolute changes (ppt yr –1) are calculated as the difference between 2015 and 2016 annual
means; relative changes (% yr -1) are the same difference relative to the 2015 value. Small differences between values reported
in previous Assessments are due to changes in calibration scale and methods for estimating global mean mole fractions from alimited number of sampling sites.
These observations are published in or are updated from the following sources: (Adcock et al., 2018; Butler et al., 1998; Laubeet al., 2016; Laube et al., 2014; Montzka et al., 2003; Montzka et al., 2018; Montzka et al., 2015; Newland et al., 2013; Prinn et al.,2018; Rigby et al., 2014; Simmonds et al., 2017; Simpson et al., 2007; Vollmer et al., 2016, 2018; Yokouchi et al., 2006); AGAGE, Advanced Global Atmospheric Gases Experiment (http://agage.mit.edu/; Prinn et al., 2018); NOAA, National Oceanic and Atmospheric Administration, USA (http://www.esrl.noaa.gov/gmd/dv/site/); UEA, University of East Anglia, United Kingdom (http://www.uea.
ac.uk/environmental-sciences/research/marine-and-atmospheric-sciences-group); UCI, University of California, Irvine, USA (http://
ps.uci.edu/~rowlandblake/research_atmos.html); NIES, National Institute for Environmental Studies, Japan (http://db.cger.nies.
go.jp/gem/moni-e/warm/Ground/st01.html); Cape Grim: Cape Grim Baseline Air Pollution Station, Australia.
Notes:
1

Global mean estimates from AGAGE are calculated using atmospheric data and a 12-box model (Cunnold et al., 1983; Rigby et al.,2014; Rigby et al., 2013). 2The NOAA CFC-11 data have been updated following a calibration scale change in 2016 (Montzka etal., 2018). 3Measurements of CFC-113 likely represent a combination of CFC-113 and CFC-113a due to co-elution, with the effectof CFC-113a on CFC-113 dependent on the analytical method. 4AGAGE measurements of CFC-114 are a combination of the
CFC-114 and CFC-114a isomers, with a relative contribution of ~7% CFC-114a (Laube et al., 2016). At UEA, CFC-114 and CFC-114a
are quantified separately. 5Mole fractions for 2016 represent averages from January to July for UEA data for these compounds.
6
Updates to HCFC-124 mole fractions are not provided as the AGAGE calibration scale has not been finalized. 7Compared to theprevious Assessment, AGAGE halon-2402 data are now on an independent calibration scale.

1.11

Chapter 1 | ODSs and Other Gases

around the world: the Advanced Global AtmosphericGases Experiment (AGAGE) network, the National
Oceanic and Atmospheric Administration (NOAA)
network, and the University of California, Irvine(UCI) network. Further data representative of regional or hemispheric scales are available for some species
from the National Institute for Environmental Studies(NIES) and the University of East Anglia (UEA).
Because these networks maintain independent calibration scales, and because they have different sampling locations and frequencies, small differences areobserved (typically on the order of a few percent or
less; see Table 1-1) in the burden and trend estimatedfrom each dataset. Therefore, for much of this section, global trends and inferred emissions are given
separately for each network. Data from regionally
representative (e.g., Southern Hemisphere) sites areused when global network data are not available. In
some circumstances, these data can be extrapolated toderive global-scale mole fractions or emissions using
an atmospheric transport model (e.g., Box 1-1). Thisis the case where AGAGE mole fraction records havebeen extended back before Northern Hemispheric
air samples were available, through the assimilationof Cape Grim Air Archive (CGAA; Langenfelds et al.,1996) data into an AGAGE 12-box model inversion(e.g. Rigby et al., 2014). Column observations are alsoavailable for some species based on ground-based orsatellite-based remote sensing methods (Figure 1-2,
Table 1-2).

For the long-lived ODSs that are primarily of anthropogenic origin, we derive radiative forcing from global mean near-surface mole fractions using the methods outlined in Ramaswamy et al. (2001) (Figure1-3). Emissions, along with global and hemisphericmean mole fractions, are estimated using a box modelof atmospheric transport and chemistry, constrainedusing baseline atmospheric data, following Rigby etal. (2014) (Figure 1-4, Box 1-1). The model includes
estimates of the major loss processes, the magnitudesof which are mostly based on the SPARC Lifetimes
assessment (SPARC, 2013) (Table A-1, Figure 1-4).
Emissions estimates were combined with estimatesof 100-year time horizon Global Warming Potentials(GWPs) and Ozone Depletion Potentials (ODPs), assummarized in Table A-1, to calculate CO2-equivalent
and CFC-11-equivalent emissions of ODSs and related substances (Figure 1-5).

1.2.1 Chlorofluorocarbons (CFCs)
Observations of Atmospheric AbundanceMole fractions of the three most abundant CFCs—
CFC-12 (CCl2F2), CFC-11 (CCl3F), and CFC-113(CCl2FCClF2)—continued to decline since 2012,reaching approximately 514 ppt, 230 ppt, and 71 ppt,
respectively in 2016 (Figure 1-1). The atmosphericabundance of CFC-12 has fallen increasingly rapidly throughout this period, with the rate of decline
increasing from 2.9 ppt yr−1 in 2011–2012 to around
3.6 ppt yr−1 in 2015–2016 (Figure 1-1, Table 1-1). The

Total column abundance (1015 molecules cm-2)

9
8

Pressure normalized monthly means

7
6

5
3

2

CFC-12
CFC-11
HCFC-22
CCl4

1

0

1.12

Figure 1-2. Monthly mean total verticalcolumn abundances (in molecules per
square centimeter) for CFC-12, CFC-11,
CCl4, and HCFC-22 above Jungfraujoch
station, Switzerland, from 1986 to 2016(updated from Zander et al., 2008 andRinsland et al., 2012). The bootstrap
resampling tool described by Gardineret al. (2008) and Rinsland et al. (2012)
was used for the trend evaluations (seeTable 1-2). Note the discontinuity in thevertical scale.

CFC-12, -11, HCFC-22, and CCI4 above Jungfraujoch

1990

1995

2000

Year

2005

2010

2015

ODSs and Other Gases | Chapter 1

rate of decline in CFC-113 has remained relatively
constant at around 0.7 ppt yr−1. In contrast, there wasa slowdown in the rate at which the global abundanceof CFC-11 was falling, starting around 2013 (Montzka
et al., 2018): Rates of decline remained relatively close
to 2.0 ppt yr−1 (0.8% yr−1) between around 2002 and2012 (±0.2 ppt yr−1 interannual variability, 1-sigma),
but that rate has since dropped to approximately 1.3 ppt yr−1 (0.6% yr−1) between 2014 and 2016.
Coincident with this feature, the interhemisphericdifference (IHD; the difference between Northern
Hemisphere and Southern Hemisphere mean mole
fractions) of CFC-11 increased from 1.8 ppt in 2012
to 2.7 ppt in 2016, suggesting that the increase is driven by Northern Hemispheric sources.
Measurements of the 2010–2016 trends in Northern
Hemispheric CFC-11 and CFC-12 abundances madeusing ground-based Fourier transform infrared(FTIR) spectroscopy at Jungfraujoch, Switzerland,

agree within uncertainties with those derived using
surface-based in situ observations (Table 1-2, Figure1-2). However, in contrast to the surface data, column
CFC-11 trends were not found to be statistically different between the periods 2008–2012 (–1.24 ± 0.23%yr−1) and 2013–2016 (−1.28 ± 0.42% yr−1). This discrepancy is likely due to the larger interannual variability found in the column data, which complicatesthe comparison between column and surface trends
over short timescales.
A full atmospheric history of CFC-13 (CClF3) has
recently been published based on samples from firn,archived air, and AGAGE in situ measurements(Vollmer et al., 2018). This compound increased relatively rapidly in the atmosphere until the mid-1990s,
after which growth slowed but remained positive
until the most recent measurements in 2016, when
the global mole fraction reached 3.04 ppt (meangrowth rate since 1996 of 0.02 ppt yr–1). This new

Table 1-2. Comparison of annual trends of ODSs, CF4, and SF6 from in situ and remote sensing measurements.
Relative trends in ODSs and halogenated greenhouse gases for the 2010–2016 time period derived from surface
measurements and remote sensing observations. This time period was selected because interannual variability
in remote sensing data makes robust quantification of trends challenging over shorter periods. Surface in situ
trends were derived from monthly mean mole fractions, weighted by the surface area in the region 30°N to 90°N.
Shown are the averages of trends derived independently from NOAA and AGAGE data (% yr -1 relative to 2013
annual mean). Uncertainties were estimated from uncertainties in the linear trends and differences between
trends derived from independent networks. For CF4, only AGAGE in situ data were used, and the uncertainty wasderived from the uncertainty in the slope. For remote sensing observations, relative annual rates of change werecomputed over the 2010–2016 time period from FTIR observations at Jungfraujoch station, Switzerland, with thebootstrap resampling tool described in Gardiner et al. (2008), using the year 2013 as reference. All uncertainties
are estimated at 2-sigma.
Annual Trend 2010–2016 (% yr -1 relative to 2013)
Substance

In situ

Remote sensing

References

CFC-11-0.64 ± 0.05

-0.70 ± 0.17

Updated from Zander et al. (2008)
and references in Table 1-1.

CFC-12-0.55 ± 0.05

-0.47 ± 0.08

Updated from Zander et al. (2008)
and references in Table 1-1.

CCl4

-1.32 ± 0.09

-1.03 ± 0.23

Updated from Rinsland et al. (2012)
and references in Table 1-1.

HCFC-22

2.21 ± 0.10

2.54 ± 0.14

Updated from Zander et al. (2008)
and references in Table 1-1.

HCFC-142b

0.97 ± 0.17

-0.6 ± 1.1

Updated from Mahieu et al. (2017)
and references in Table 1-1.

SF6

3.90 ± 0.06

4.34 ± 0.19

Updated from Zander et al. (2008)
and Hall et al. (2011).

CF4

0.94 ± 0.01

1.11 ± 0.09

Updated from Mahieu et al. (2014b)
and references in Table 1-1.
1.13

Chapter 1 | ODSs and Other Gases

60

500

CH4

400
ODSs (CFCs, HCFCs, halons, solvents)

200
100

20

Kyoto protocol synthetics(HFCs, PFCs, SF6, NF3)

10

Radiative forcing (mW m 2)

ODSs (CFCs, HCFCs, halons, solvents)

250

CFCs

Kyoto protocol synthetics(HFCs, PFCs, SF6, NF3)

40
35
30

HFCs

25

200
150
100
50

CFC-1220

CFC-11

10

15
PFCs

5
CFC-11300
525
Solvents

SF6

4
3

CCl4

2

10

0

HCFC-142b
HCFC-141b

0

300

5

HCFC-22

30

N2 O

0

15

50
40

300

20

HCFCs

CH3CCl3
Halons
1985

1990

1995

2000
Year

2005

2010

2015

1

SO2F2

0

NF3
2015

1985

1990

1995

2000
Year

2005

2010

Figure 1-3. Direct radiative forcing due to ODSs, HFCs, CH4, N2O, and other greenhouse gases. Selectedgroupings of gases are shown in bold and selected compounds or collections of compounds that fallwithin these groupings are shown as dashed lines. The ODS group here refers to combined CFCs, HCFCs,
halons, and solvents (CCl4 and CH3CCl3). Kyoto protocol synthetics are defined as HFCs (see Chapter 2), perfluorocarbons (PFCs, which include CF4 and C2F6), SF6, and NF3 (Section 1.5). Lower tropospheric annual
mean mole fractions were taken from AGAGE data (Table 1-1, Figure 1-1). Radiative forcing was calculatedusing the expressions in Ramaswamy et al. (2001), with radiative efficiencies as summarized in Table A-1
and preindustrial global surface mean mole fractions of 722 ppb, 270 ppb, and 36 ppt for CH4, N2O, andCF4, respectively. For comparison, the radiative forcing due to CO2 was approximately 2 W m−2 in 2016.

1.14

ODSs and Other Gases | Chapter 1

measurement time series is around 25% lower thanprevious records, due primarily to differences in calibration scales (Culbertson et al., 2004; Oram, 1999).
The previous Assessment reported a slowly declining
combined mole fraction of the CFC-114 (CClF2CClF2)
and CFC-114a (CCl2FCF3) isomers. This downward
trend has continued but at a slower rate than wasreported between 2011 and 2012 (Figure 1-1, Table
1-1; Vollmer et al., 2018). In 2016, the global meanmole fraction of combined CFC-114 and CFC-114a
was approximately 16 ppt, and the mole fraction of
CFC-114a measured in the CGAA was around 1 ppt.
The 2014 Assessment estimated a 10% contributionof CFC-114a to total CFC-114 in the atmosphere. A
new study using CGAA samples shows the CFC-114a
contribution to total CFC-114 increasing from 4.1% in
the late 1970s to 6.5% in the mid-2010s (Laube et al.,2016). The sum of the abundances of the two CFC-114
isomers in the CGAA agree between this and anotherrecently published record of combined-isomer measurements (Laube et al., 2016; Vollmer et al., 2018).
The 2010 and 2014 Assessments found that mole
fractions of CFC-115 (CClF2CF3) had stabilized at approximately 8.4 ppt since around 2000. However, mole
fractions have grown since 2012, reaching 8.5 ppt in2016 (Vollmer et al., 2018). While the magnitude ofthis change is comparable with the uncertainties on theobservations (around 0.1 ppt in 2016), the fact that it isobserved at all remote AGAGE stations strongly suggests a renewed global increase (Vollmer et al., 2018).
Since the last Assessment, CFC-112 (CCl2FCCl2F),
which had a Southern Hemispheric mole fraction of
0.42 ppt in 2016, has continued to decline in the atmosphere, and CFC-112a (CClF2CCl3) has remained
relatively stable at close to 0.07 ppt in the Southern
Hemisphere (update to Laube et al., 2014; see Table
1-1). In contrast, CFC-113a has continued to increasein the Southern Hemisphere at an accelerated rate since2012, reaching 0.68 ppt in 2016 (Adcock et al., 2018).
CFC-216ba (CClF2CClFCF3) and CFC-216ca(CClF2CF2CClF2) were measured for the first timein the CGAA (Kloss et al., 2014). The Southern
Hemispheric mole fraction of CFC-216ba was foundto be relatively constant over the last 20 years at 0.04
ppt. CFC-216ca exhibited a small positive trend, witha mole fraction in the CGAA of 0.02 ppt in 2012.

With respect to their influence on climate, in 2016,
CFCs contributed 77% of the total direct radiative
forcing due to ODSs regulated under the Montreal
Protocol, with a combined radiative forcing of 250
mW m−2 (Figure 1-3). The radiative forcing due to
CFCs has declined by 7% since its peak in 2000, driven
primarily by the reduction in abundance of CFC-11
and CFC-12; by 2016 the radiative forcing due to eachgas had declined by 9 mW m−2 from their respective
peaks in 1994 and 2002.
Emissions and Lifetimes
Since the previous Assessment, there has been little
new work on CFC lifetimes. Therefore, our lifetimes
estimates for these compounds are still based on
SPARC (2013), as summarized in Table A-1.
Given the global phaseout of the production of CFCs
for dispersive uses under the Montreal Protocol,
emissions to the atmosphere are now expected to be
due only to leakage from banks. These emissions are
generally expected to decline with time as the size ofthe banks decrease, as is reflected in the monotonically decreasing emissions in previous baseline (A1) scenarios of CFC emissions (Harris and Wuebbles et al.,2014). One potential exception was identified in the
IPCC/TEAP Special Report: Safeguarding the Ozone
Layer and the Global Climate System (Ashford et al.,2005), where a global increase in emissions could coincide with the decommissioning of buildings withfoams containing CFCs (primarily CFC-11).
Broadly in line with the expectation of declining
emissions from banks, inferred emissions of CFC-12
have continued to fall since the previous Assessment,with 2016 emissions being approximately 35 Gg yr−1,
around 20% lower than in 2012, and 93% lower thantheir peak value in 1988 (Figure 1-4). Emissions of CFC113 have remained at very low levels (<10 Gg yr −1, compared to a maximum of around 243 Gg yr−1 in 1988).
The findings, since the previous Assessment, of a slowdown in the rate of decline of CFC-11 and an increasein the IHD suggest an increase in emissions, althoughchanges in atmospheric transport could also play arole (Montzka et al., 2018; Prinn et al., 2018). Figure
1-4 shows CFC-11 emissions inferred from AGAGE
and NOAA data, assuming interannually repeatingtransport and a global lifetime of 52 years (also shown
1.15

Chapter 1 | ODSs and Other Gases

400

CFC-11

700

120

600

100
80

300

40

100
0

1980

250

1985

1990

0

1995

2000

2005

2015

10

0

1980

1995

2000

2005

2010

2015

2016: 7.2 (5.4)

2000

2005

1985

1990

200

2010

2015

0.0

1980

1985

1990

CH3CCl3

1980

1995

2000

1995

CCl4

2000

2005

2010

2015

2015

0

1980

2005

2010

2015

10
0

2005

2010

2015

2016: 2.1 (1.8)
1985

1990

1995

2000

UNEP (2014) bottom-up

2005

2010

2015

halon-1211

20

20

15

Liang et al. (2016)
bottom-up

0
2000

2005

2010

2015

2016: 3.4 (2.1)

10
5

2016: 38 (15)
1990

2010

20

200

60
40

1985

2005

30

25

100

0

2000

CFC-13
CFC-113a
CFC-112
CFC-112a (x10)600

2016: 2.1 (0.9)
2016: 1.2 (0.5)

150

50

1995

400

10

1990

2015

0.5

CFC-114
CFC-115

1985

2010

2016: 35 (22)

15

1980

2005

1.0

800

20

0

2000

1.5

0

1995

1995

2.0

20

1990

0

2.5

5

1.16

2010

50

200

30

1985

100

300

3.0

50

25

2015

40

100

1980

2010

50

CFC-113

150

0

2005

100

200

Global annual emissions (Gg yr -1)

2000

2016: 72 (11)

1995

150

400

20

NOAA
AGAGE
UEA
projection 2006
projection 2012
reported production

200

500

60

200

250

CFC-12

1995

2000

Year

2005

2010

2015

0

1980

1985

1990

1995

2000

Year

2005

2010

2015

ODSs and Other Gases | Chapter 1

7halon-1301

6

2016: 1.1 (0.4)

4

1.0

2

NOAA
AGAGE
Newland et al. (2013)
UNEP (2014) bottom-up

Global annual emissions (Gg yr -1)

1

450
400
350
300

1985

1990

1995

2000

0.5

2005

2010

2015

HCFC-22
2016: 372 (52)

3

1985

1990

1995

2000

2005

2010

2015

0

1980

50

HCFC-141b

1995

2000

2005

2010

2015

2010

2015

HCFC-133a
Laube et al. (2014)
updated from Vollmer et al. (2015b)

1985

1990

1995

2000

2005

HCFC-142b

40
30

40

2016: 60 (8)

20

1980

1990

1

Simmonds et. al. (2017)(consumption-based)

60

0

1985

2

150

80

1980

4

200

1980

0.0

5

250

100

2016: 0.4 (0.2)

1.5

3

1980

halon-2402

2.0

5

0

2.5

1985

1990

1995

2000

Year

2005

2010

2015

20

2016: 24 (5)

10
0

1980

1985

1990

1995

2000

Year

2005

2010

2015

Figure 1-4. Top-down and bottom-up global emission rate estimates (Gg yr –1) for ozone-depleting substances. Top-down emissions rates from AGAGE (black) and NOAA (red) atmospheric data were calculatedusing a global 12-box model (Box 1-1; Cunnold et al., 1983; Rigby et al., 2013). For the CFCs, stratospheric lifetimes were assumed to be equal to the total lifetimes from Table A-1 (no other losses were assumed). For thehalons, lifetimes are summarized in Vollmer et al. (2016). A lifetime of 32 years was used, derived from stratospheric, ocean, and soil lifetimes of CCl4 (Butler et al., 2016; Rhew and Happell, 2016; SPARC, 2013). For theother species, stratospheric lifetimes from Table A-1 were imposed, with OH rate constants from Burkholder
et al. (2015). Global steady-state lifetimes for each species were: CFC-11 (52 years), CFC-12 (101 years), CFC-13(640 years), CFC-113 (93 years), combined CFC-114/CFC-114a (189 years), CFC-115 (540 years), halon-1211 (16years), halon-1301 (72 years), halon-2402 (28 years), HCFC-22 (11.6 years), HCFC-141b (9.2 years), HCFC-142b(17.6 years), and HCFC-133a (4.6 years). For some of these species, small differences can be seen between
these global steady-state lifetimes calculated using the 12-box model and those in Appendix Table A-1,
due to differences in assumed OH and model transport. Emissions were estimated using a Bayesian inverse
method, in which the emissions growth rates from bottom-up inventories were used as a priori constraints(Rigby et al., 2011; Rigby et al., 2014) with minor update in Vollmer et al. (2018). Descriptions of bottom-up
datasets are given in Rigby et al. (2014); Rigby et al. (2013); Simmonds et al. (2017); Vollmer et al. (2016); and1.17

Chapter 1 | ODSs and Other Gases

Vollmer et al. (2018). As described in Vollmer et al. (2018), the uncertainty in the a priori emissions growthrate was assumed to be 20% of maximum prior emissions. Posterior uncertainties (gray shading for AGAGE
and red dashed lines for NOAA) include contributions from the observations, the model, the prior constraint,
and the lifetime uncertainties from SPARC (2013), using the method in Rigby et al. (2014). For CFC-11, uncertainties are larger here than presented in the Executive Summary, as the systematic components of theuncertainty (i.e. due to lifetime and calibration scale) are omitted from Figure ES.2. For CFC-112, CFC-112a,
CFC-113a, and the halons, emissions were calculated using UEA data from the Southern Hemisphere (blue;
Laube et al. 2014). Emissions were calculated from 2013 to 2015 for CFC-112, CFC-112a, and CFC-113a using
a 1-box model and scaled to match those reported in Laube et al. (2014) for previous years. HCFC-133a emissions were taken from Vollmer et al. (2015b) and Laube et al. (2014). Uncertainties for CFC-112, CFC-112a,and CFC-113a are not shown for clarity (see Laube et al., 2014). Numerical values of emissions estimates for
2016, shown in some panels, were calculated as the mean of estimates based on AGAGE and NOAA network
data, with 1-sigma uncertainties (in parentheses) taken from the AGAGE estimates. For CCl4, the bottom-up
industrial estimate from Liang et al. (2016) is shown as a green diamond in the CCl4 inset, with the potential
magnitude of legacy emissions shown as a green bar extending upwards. Bottom-up estimates for halons(violet triangles) were updated from (UNEP, 2014a). For the major HCFCs, consumption-based estimates from
Simmonds et al. (2017) are shown (solid green triangles). These estimates are calculated from reported consumption and estimates of immediate and ongoing release rates that were chosen to be consistent with the
top-down emissions estimates.

in Executive Summary Figure ES.2). Following an
initial decline after the late 1980s, emissions did not
drop substantially after about 2002–2005, with the
2002–2012 average being 66 Gg yr–1 or 64 Gg yr–1
(using AGAGE or NOAA observations, respectively),
which is about 82% lower than the peak in 1987. The
inversions then show an increase in emissions beginning around 2013 and reaching an average of 72 or
75 Gg yr–1 between 2014 and 2016 (for AGAGE or
NOAA data, respectively). This represents a 7 Gg yr−1
(or 10%) to 11 Gg yr−1 (or 17%) increase in emissions
over the 2002 to 2012 average. These emissions arehigher overall than the NOAA-data-based estimate ofMontzka et al. (2018), who assumed a longer lifetime
than the SPARC (2013) estimate used here. However,
due to the use of a different inverse modeling approach, they found a slightly larger magnitude of the
post-2013 increase, of 13 ± 5 Gg yr−1 (25 ± 13%) for
2014–2016 compared to 2002–2012. Considered together, these estimates using AGAGE and NOAA data
show an increase in emissions of around 10 Gg yr−1
between these two periods. Following the methodology used in previous Assessments and Montzka et al.
(2018), two projections were created to examine theexpected decline in emissions after 2006 (near the beginning of the period during which emissions did not
decline) and 2012 (after which emissions increased)
1.18(Figure 1-4 and Executive Summary Figure ES-2).
These projections are based on reported CFC-11 production history, an estimate of the magnitude of thebank for the year 2002 (IPCC/TEAP, 2005), and theassumption of a constant release fraction from thebank following 2006 or 2012. The release fractions
used in the projections were estimated as the mean
release fractions during the 7-year periods prior to2006 or 2012 and were based on the yearly inferredbank size and top-down emissions over these periods. The projections indicate that emissions may havebeen higher than expected since the mid-2000s, although this has only recently become clear given therelatively large uncertainties considered in the past on
top-down emissions and on projections. The projections also highlight that the recent emissions increase
may be significantly larger than 10 Gg yr−1, when
considered relative to the expected emissions decline
during this period. Montzka et al. (2018) argue thatthe recent increase is too large and too rapid to be
explained by the release of CFC-11 from its bank, including from the decommissioning of old buildings,
given our understanding of the bank size and past release rates. Therefore, they propose that new production is taking place that has not been reported to the
UN Environment Ozone Secretariat. This would beinconsistent with the CFC-11 phaseout agreed underODSs and Other Gases | Chapter 1

2010

2015

Mt

2005

2005

2010

2015

Year

Figure 1-5. (a) 100-year GWP-weighted
emissions and (b) ODP-weighted emissions. Emissions are the average of
those derived from AGAGE and NOAA
data, converted to CO2-equivalents and
CFC-11-equivalents using 100-year time
horizon GWPs and ODPs from Table A-1.
Species are grouped into CFCs, HCFCs,
HFCs (see Chapter 2), halons, solvents(CCl4 and CH3CCl3), and other F-gases (SF6,
CF4, C2F6, C3F8, NF3, SO2F2, see Section
1.5). Totals are shown as dashed blacklines and shading indicates the 1-sigma
uncertainty.

the Montreal Protocol. If the new emissions are associated with uses that substantially increase the sizeof the CFC-11 bank, further emissions resulting fromthis new production would be expected in future. The
recent increase in emissions and any associated future
emissions will delay the expected rate of recovery ofstratospheric ozone relative to previous projections.

Inferred emissions of the lower-abundance compound
CFC-13 show a strong decline in the first decade following a maximum in emissions in the late 1980s of
2.5 Gg yr−1 (Vollmer et al., 2018). However, for the last
decade, emissions have plateaued at around 0.5 Gg
yr–1 (approximately 85% lower than their peak value).
CFC-13 was used primarily in refrigeration; the sizeof the CFC-13 bank and rate of release from it were
expected to continue to decline with time.
Emissions of the combined CFC-114/CFC-114a isomers have plateaued for at least the last decade, at 1.9
Gg yr–1, which is about 10% of the maximum value,
reached in the late 1980s (Vollmer et al., 2018). Basedon a study that can separate the two isomers (Laubeet al., 2016), stagnant emissions were found for CFC114 (1.8 Gg yr –1; data through 2014), while those of
the minor CFC-114a isomer have slightly declined.
This indicates that the sources of the two isomers are,at least in part, decoupled. Laube et al. (2016) speculated that emissions of CFC-114a could be linked tothe production of HFC-125 and HFC-143a.
Global emissions of CFC-113a increased strongly between 2009 and 2012 and since then have remained at
approximately 1.7 Gg yr–1 (Adcock et al., 2018). Thisis opposite to the trend exhibited by the major isomer
CFC-113 and, similar to the relative changes in CFC114 and CFC-114a, indicates that the two isomers
may have some different sources.
Emissions of CFC-115 appear to have increased since
the previous Assessment, with Vollmer et al. (2018)
reporting mean emissions for 2015–2016 of 1.14 ± 0.5
Gg yr−1, which is approximately double that of the period 2007–2010, when emissions were at a minimum(Figure 1-4). Recent emissions are around 5–10% of
the maximum, found in the late 1980s. While some
CFC-115 was found as an impurity in samples of therefrigerant HFC-125 (CHF2CF3), this was not thought
to be significant enough of a source to explain global
emissions. Therefore, the cause of this emissions increase is unknown.
Several regional studies have examined CFC emissions using atmospheric observations. Between 2008
and 2014, emissions within the USA of the threemajor CFCs (CFC-11, CFC-12, and CFC-113) were
estimated to have declined (L. Hu et al., 2017). These
results suggest that the USA is unlikely to be the
1.19

Chapter 1 | ODSs and Other Gases

Box 1-1. Inferring Emissions Using Atmospheric Data
In this Assessment, as in previous reports, emissions of ODSs (and of HFCs in Chapter 2) are inferredusing atmospheric observations and a model of atmospheric transport and chemistry. Here, we describe
the principle considerations behind these “top-down,” or “inverse,” calculations. An overview of the various
methods for estimating ODS emissions can be found in Montzka and Reimann et al. (2010).
If we assume that the atmosphere consists of a single box into which trace gases are emitted, and within
which some loss takes place, mass balance considerations allow the rate of change in the burden (B, the total
mass of the gas in the atmosphere) to be written as:
dB
B
=Q−
dt
τ

Here, Q is the globally integrated emission rate (in mass per unit time) and τ is the overall lifetime of the gas
in the atmosphere. The latter is determined by a variety of sinks such as photolysis (e.g., in the stratosphere),reaction with oxidants (e.g., the hydroxyl radical), and loss at the surface (e.g., to soils or the ocean). The
previous Assessment discussed how the lifetimes from these different processes can be combined to calculate overall lifetimes.
For long-lived gases (τ≥0.5yr) that are relatively well mixed throughout the atmosphere, surface mole fraction data from global networks such AGAGE and NOAA provide estimates of the trace gas global burden
and its rate of change.
For the majority of gases in this chapter, the magnitudes of the global lifetimes are relatively well known,
compared to uncertainties in bottom-up emissions estimates. These lifetimes estimates, primarily taken fromthe SPARC Lifetimes assessment (SPARC, 2013), are based on a combination of satellite observations, in situ
measurements of tracer-tracer correlations, photochemical model simulations, and estimates of oceanic andterrestrial fluxes, which are independent of the observations used to infer emissions in this chapter. However,it should be noted that SPARC (2013) also included lifetimes estimates inferred using AGAGE and NOAA
observations for some species, which leads to some circularity if used to infer emissions.
We can rearrange Equation 1 to infer global emissions rates (Q) from the information on the global burden(B) and its trend (dB/dt) and estimates of the global lifetime (τ). Such emissions estimates are sensitive
to uncertainties in the observed burden (e.g., random and representation errors in the observations andsystematic calibration scale errors) and uncertainties in the lifetime, both of which should be propagatedthrough to the uncertainties in the inferred emissions (e.g. Rigby et al., 2014).
While this discussion illustrates the broad principles behind the inference of emissions at the global scale,some additional factors are introduced in the calculations presented in this chapter. Firstly, a model of atmospheric transport and chemistry is used to simulate the nonuniform distribution of gases in the atmosphere,
improving our estimates of the global burden compared to the single-box approach above. The model primarily used in this chapter is the AGAGE 12-box model, which separates the atmosphere into boxes withlatitudinal boundaries at 90°N, 30°N, 0°N, 30°S, and 90°S, and vertical boundaries at 1000 hPa, 500 hPa, 200hPa and 0 hPa (Cunnold et al., 1994; Cunnold et al., 1983; Rigby et al., 2013). Transport of each gas occurs
via parameterized mixing and advection between boxes. Removal from the atmosphere takes place via reaction with the hydroxyl radical, via first-order processes parameterizing non-OH photochemical losses andvia loss to the ocean or land. This model was designed to simulate baseline mole fractions (i.e.,
1.20

ODSs and Other Gases | Chapter 1

Box 1-1, continued.
observations that have not been strongly influenced by nearby sources and can be considered representativeof zonal averages) for long-lived gases that have small spatial gradients in the atmosphere. However, forshorter-lived substances, which exhibit strong spatial and temporal variability, atmospheric distributions
may be more poorly represented. Secondly, a Bayesian statistical approach is employed that allows priorbeliefs about emissions to be incorporated into the inversion and provides a framework for propagating
prior and observational uncertainty through to the derived emissions estimates (e.g., see the supplementarymaterials in Rigby et al., 2014).
Regional emissions estimates are possible where spatially and/or temporally dense measurements are madewithin or downwind of certain areas (e.g., Graziosi et al., 2015; L. Hu et al., 2017). The regional approachrequires a model that can simulate the three-dimensional atmospheric transport of a gas from the source
to the measurement points. Such simulations can then be compared to the data and fluxes at regional andnational scales inferred through examination of the difference between the two. In contrast to global estimates, for long-lived compounds, regional flux inversions are insensitive to uncertainties in the atmospheric
lifetime. However, significant uncertainties can arise through the need to accurately simulate trace gas transport at high resolution.
source of the increase in global CFC-11 emissions thatstarted in 2013. In aggregate, the emissions of thesegases agreed well with bottom-up estimates by the US
Environmental Protection Agency (EPA). However,species-specific differences were found, particularly
for CFC-113. Where the emissions inventory had predicted negligible emissions since 1996, emissions inferred from atmospheric concentrations were statistically higher than zero (by around 0.5–3 Gg yr−1) until
2013. While regional inverse modeling of CFC-11
emissions from eastern Asia has not yet been carriedout, Montzka et al. (2018) note increased variability
in CFC-11 measured at Mauna Loa, Hawai'i, beginning after 2012, along with emerging correlations
with other anthropogenic species during the autumn
months, when this site is strongly influenced by flows
from eastern Asia. These signals are consistent withan increase in CFC-11 emissions from eastern Asia.
Regional inverse modeling using data from the Gosan
Station, South Korea, showed evidence of emissionsof combined CFC-114/CFC-114a and CFC-115 from
China (Vollmer et al., 2018). The inferred emissions
for each of these gases were of a magnitude that wasa significant fraction of the respective global total.
Persistent sources of CFC-113a and CFC-114a fromeastern Asia were also identified (Adcock et al., 2018;
Laube et al., 2016).

In summary, while emissions of almost all CFCs havedeclined substantially since their peaks in the 1980s
or 1990s, and emissions of CFC-12 and -113 continue
to decline, there are strong indications that emissionsof several CFCs are no longer following the downward trajectory expected under a scenario of globally
depleting banks. Most important, CFC-11 emissions
have increased by around 10 Gg yr−1 for 2014–2016,
relative to 2002–2012. A study into these CFC-11
trends proposes that new production not reported to
the UN Environment Ozone Secretariat may be taking place and that at least some of the new emissions
originate from eastern Asia (Montzka et al., 2018).
Regional studies find evidence for continuing or increasing emissions of some of the more minor CFCs
from eastern Asia (Vollmer et al., 2018).
In terms of both CO2- and CFC-11-equivalents, inferred combined emissions of all CFCs have declined
markedly since the late 1980s (Figure 1-5). In 2016,
CO2-equivalent emissions of the CFCs were 0.8 ± 0.3
Gt yr−1, approximately 90% lower than the highest inferred value of 9.1 ± 0.4 Gt yr−1 in 1988. If the recentchange in the CFC-11 growth rate is due to emissions
alone, the increase since 2013 has added around 0.05
Gt yr−1 CO2-equivalent to this total. Total ODPweighted emissions for all CFCs dropped by around
90% since the peak (in 1987) and reached 110 ± 30 Gg
yr−1 CFC-11-equivalent in 2016.
1.21

Chapter 1 | ODSs and Other Gases

1.2.2 Halons
Observations of Atmospheric Abundance
Halon-1211 (CBrClF2), halon-2402 (CBrF2CBrF2),
and halon-1202 (CBr2F2) abundances continued to
decline from their peak values, observed in the earlyand mid-2000s. Global surface mean mole fractionsof approximately 3.5 ppt and 0.42 ppt were observedfor halon-1211 and -2402, respectively, in 2016, andSouthern Hemispheric mole fractions of approximately 0.014 ppt were recorded for halon-1202 (Table
1-1, Figure 1-1) (Newland et al., 2013; Vollmer et al.,2016). Halon-1301 (CF3Br) growth rates, which werereported as being positive in the previous Assessment,
declined to <0.01 ppt yr–1 in 2016, when a global meanmole fraction of 3.36 ppt or 3.25 ppt was reached for
AGAGE and NOAA, respectively.
New measurements of halon-2311 (CF3CHClBr, halothane, an anesthetic that is no longer widely used)show low abundances in the atmosphere with a mole
fraction that declined from 0.025 ppt in 2000 to <0.01
ppt in 2016 in the Northern Hemisphere (update ofVollmer et al., 2015c).
The direct contribution of halons to global radiative
forcing was small, 2.2 mW m−2 in 2016, equivalent to0.9% of the radiative forcing of CFCs (Figure 1-3).
When their influence on ozone depletion is also considered, radiative forcing due to halons is negative(Daniel et al., 1995).
Emissions and Lifetimes
Lifetimes of the three most abundant halons are takenfrom SPARC (2013), and are summarized in Table A-1.
For these three halons, emissions derived from observations generally agree within their uncertainties for theestimates made from NOAA, AGAGE, and UEA measurements (Figure 1-4; Vollmer et al., 2016; Newland
et al., 2013). For each gas, these emissions have continued to decline since the previous Assessment. Bottomup emissions were revised in 2014 by the Halon
Technical Options Committee (HTOC), and updates
are provided here (UNEP, 2014a).
Top-down estimates of emissions of halon-1211
show a decline to 3.4 ± 2.1 Gg yr−1 in 2016 (averageof emissions inferred from AGAGE and NOAA data),
70% lower than the peak value in 1998. Compared
1.22

to previous bottom-up estimates (UNEP, 2011), themost recent HTOC emissions for this species havebeen revised downward for the last decade, creating alarger gap (~50%) with the observation-based values.
In contrast, for halon-1301, bottom-up and observation-based emissions now show closer agreement
than in the previous Assessment, with top-down values for 2016 of 1.1 ± 0.4 Gg yr−1 and HTOC estimatesof 1.1 Gg yr−1. The 2016 top-down values are 80%
lower than their peak of 5.4 ± 0.6 in 1989. Halon-2402
bottom-up emissions are now available for a longer
time period than in the previous Assessment and are
significantly larger than previously estimated (UNEP,
2014a). They show a similar trend to emissions inferred from observations, which grew until 1988 andthen declined. However, the HTOC estimates werelarger throughout, at 0.56 Gg yr–1 in 2016, compared
to top-down estimates of 0.37 ± 0.2 Gg yr–1; these are
80% lower than their peak value.
Global emissions of the lower-abundance halon-2311
inferred from atmospheric observations declined
from 0.49 Gg yr−1 in 2000 to 0.25 Gg yr−1 in 2014,
likely reflecting a continuing reduction of its use as ananesthetic (Vollmer et al., 2015c).
Total CO2-equivalent halon emissions were small
in 2016, 2% that of CFCs, as shown in Figure 1-5.
However, due to their high ODPs (Table A-1), theircontribution to ozone depletion remains significant,with ODP-weighted emissions of 50 ± 20 Gg yr−1
CFC-11-equivalent in 2016, just under half that ofglobal CFC emissions.

1.2.3 Carbon Tetrachloride (CCl4)
Observations of Atmospheric Abundance
Carbon tetrachloride (CCl4) has continued to decline at a rate similar to that reported in the previous
Assessment. AGAGE observations showed a declineof 1.5% between 2015 and 2016, with a mole fractionof 79.9 ppt in 2016, and NOAA reported a decline of
1.2% during the same period and a 2016 mole fraction of 81.2 ppt (Table 1-1, Figure 1-1). These differences are broadly consistent with known calibration
scale differences, although the level of agreement haschanged over time, suggesting some drift in one orboth scales or time-dependent analytical issues. Data
from UCI show a smaller decline, of 0.3%, between

ODSs and Other Gases | Chapter 1

these years and a 2016 mole fraction of 81.9 ppt. The
IHD, estimated from AGAGE and NOAA networks,
has exhibited a gradual decline since 2000, with a rateof 0.04 and 0.03 ppt yr−1, respectively, for each network.
Ground-based remote sensing observations of CCl4from Jungfraujoch show a slightly lower rate of decline
between 2010 and 2016 than the AGAGE and NOAA
networks, although the uncertainties overlap at the2-sigma level (Table 1-2). New observations with
global coverage in the upper troposphere and lower
stratosphere have become available from the MIPAS
instrument, onboard the Envisat satellite (Eckert etal., 2017; Valeri et al., 2017). The upper-tropospherictrends derived from these observations between 2002
and 2012 confirm that atmospheric mole fractions
declined during this period with a magnitude thatwas broadly consistent with the ground-based measurements (Valeri et al., 2017). However, stratospherictrends derived from these observations were found to
be nonuniform, with some (generally non-statistically significant) positive trends even being found in
the middle stratosphere in the Southern Hemisphere(Eckert et al., 2017). This high variability, comparedto surface data, reflects the additional impact of variability in transport on the temporal evolution of tracegases in the stratosphere.
Radiative forcing due to CCl4 declined to 14 mW m−2
in 2016, equivalent to 6% of the radiative forcing due
to CFCs (Figure 1-3).
Emissions and Lifetime
Previous Assessments highlighted a significant discrepancy between CCl4 trends observed in atmospheric data and those calculated from known sources andour understanding of atmospheric sinks (Carpenter
and Reimann et al., 2014; Montzka and Reimann etal., 2010). In the previous Assessment, the best estimate of the CCl4 lifetime was 26 years, which led toa top-down global emissions estimate of 57 (40–74)
Gg yr–1. In contrast, bottom-up estimates of emissions
due to feedstock use, based on the difference between
reported production and destruction (Montzka andReimann et al., 2010), were less than 4 Gg yr–1 in 2012
(Carpenter and Reimann et al., 2014). In light of these
discrepancies, the CCl4 budget was re-examined in
the 2016 SPARC Report on the Mystery of Carbon
Tetrachloride (Liang et al., 2016). Here we summarize

the primary findings of this report, along with additional studies that have been carried out in the interim
period.
The global lifetime of CCl4 has been revised upwardfrom 26 years, initially to 33 (28–41) years in Lianget al. (2016), primarily due to an increase in the estimated lifetime due to ocean loss and uptake fromsoils. This has subsequently been reduced slightly to
32 (26–43) years, following a revision to the lifetime
with respect to ocean loss, the best estimate for which
is now 183 (147–241) years (Butler et al., 2016). The
current best estimate for the lifetime due to soil uptake is 375 (228–536) years, which has increased
from the previously estimated 195 years (Rhew andHappell, 2016). Estimates of the lifetime due to stratospheric loss remained unchanged at 44 (36–58) years(SPARC, 2013). Of these sinks, the remaining uncertainties in the ocean uptake were found to have thepotential to most significantly alter model estimatesof the atmospheric trend (Chipperfield et al., 2016).
Global emissions, derived from atmospheric trends
and a model parameterized with a 33-year lifetime,
were reduced to 40 ± 15 Gg yr–1 (2007–2014 average)
in Liang et al. (2016), compared to 57 ± 17 Gg yr–1
in the previous Assessment (2011–2012 average).
Estimates based on the observed atmospheric IHD
were found to be similar to the trend-based estimate,
at 30 ± 5 Gg yr–1 (2010–2014 average; update of Lianget al., 2014b). Updated estimates (Figure 1-4), using
the new lifetime of 32 years and AGAGE and NOAA
observations, show a relatively rapid drop in emissions
in the late-1980s and early-1990s, then a relatively
slow decline since the late-1990s. Since 2000, emissions have been declining at a rate of ~1.2 Gg yr−1 per
year (Figure 1-4). Emissions inferred here for 2016
from the AGAGE and NOAA data, respectively, were
35 ± 16 Gg yr–1 and 40 ± 15 Gg yr–1 (around 30% of thepeak value, which occurred in the mid-to-late 1980s).
Since the previous Assessment, global bottom-up estimates have been made of emissions from a range ofindustrial sources. Liang et al. (2016) and Sherry et al.
(2017) proposed that 13 Gg yr–1 may be due to unreported, non-feedstock emissions from chloromethaneand perchloroethylene production plants. In addition,
unreported, inadvertent emissions during chlorine
production (e.g., from chlor-alkali plants) and usage(e.g., in industrial and domestic bleaching) and legacy
1.23

Chapter 1 | ODSs and Other Gases

emissions from landfills and contaminated soil were
estimated to contribute up to 10 Gg yr–1 (which is towards the lower end of estimates previously made by
Fraser et al. (2014)). Similar to previous Assessments,
2 Gg yr−1 fugitive emissions from feedstock usage
were estimated (e.g., in the production of HFCs andother compounds). Together, these sources could
total around 25 Gg yr−1.
Regional studies of CCl4 emissions have been carriedout for the USA, Europe, and East Asia. NOAA observations across the USA were used to infer fluxes of
4.0 (2.0–6.5) Gg yr−1 from 2008 to 2012 (L. Hu et al.,2016a), a value two orders of magnitude larger thanreported by the US Environmental Protection Agency(EPA) and around 10% of the global top-down value
estimated here during the same period. The spatial
distribution of emissions derived for the USA wasfound to be more consistent with the location of industrial sources in the EPA reports (e.g., chlor-alkali
plants) than other potential sources that would be
more widely distributed (e.g., uncapped landfill). Thissuggests that emissions may be underreported forthese industries. Similarly, using European AGAGE
data, Graziosi et al. (2016) found that the spatial
distribution of emissions in Europe was similar tothat of industrial sources in the European Pollutant
Release and Transfer Register and the location ofchlor-alkali plants. Also in common with the USA,these top-down emissions estimates are significantly larger than the bottom-up reports. The top-down
mean European emission rate was estimated to be 2.2± 0.8 Gg yr−1 from 2006 to 2014 (around 5% of the
global mean estimated here during the same period),
declining at an average rate of 6.9% yr−1. Bottom-up
estimates for China showed an increase in emissions
during the 1990s, which reached a peak in 2002 of
14.0 (9.1–19.5) Gg yr−1 (Bie et al., 2017). This was followed by a decline to 5.2 (2.4–8.8) Gg yr−1 in 2014,
with the sharpest drop occurring between 2009 and2011. It was proposed that this decrease was primarily due to a reduction in CCl4 use as a process agent.
Top-down estimates for China from late 2006 to early2008 by Vollmer et al. (2009) are consistent with thesebottom-up values, within uncertainties (15 (10–22)
Gg yr−1). However, the subsequent decline estimatedby Bie et al. (2017) has not yet been confirmed by atmospheric observations at the national scale. The sumof the available regional top-down studies suggests
1.24

emissions of around 20 Gg yr−1, although it shouldbe emphasized that these studies cover different time
periods and that this cannot be considered a global
total, as several potentially important regions are notobserved by the current monitoring network (e.g.,
India, Russia, Africa, and South America).
In summary, the upward revision of the atmospheric lifetime and the proposal of significant emissions
from sources such as chloromethane, perchloroethylene, and chlor-alkali plants have substantially reduced the gap in the CCl4 budget since the previous
Assessment. Regional inverse modeling studies in
the USA and Europe support the idea that reported
emissions are significantly underestimated and thatindustrial sources could be much larger than previously thought. However, there remains a difference ofaround 10 Gg yr−1 between the global top-down estimate, based on our updated knowledge of the sinks,
and recent global bottom-up estimates.
CO2-equivalent CCl4 emissions were relatively small
in 2016, 8% that of the CFCs (Figure 1-5). However,ODP-weighted emissions were significant, at 27 ± 10
Gg yr−1 in 2016, 23% as large as the CFCs.

1.2.4 Methyl Chloroform (CH3CCl3)
Observations of Atmospheric Abundance
The global mean mole fraction of methyl chloroform(1,1,1-trichloroethane, CH3CCl3) continued to decline between 2012 and 2016, decreasing to 2.6 ± 0.7
ppt in 2016, 2% of its maximum value of 133 ± 4 ppt,
which was reached in 1992 (Figure 1-1, Table 1-1).
The IHD reached a maximum in 1990 (28 ppt) andhas since declined to 0.078 ppt in 2016. The radiative
forcing due to methyl chloroform is now negligible(Figure 1-3).
Emissions and Lifetime
Assuming a constant CH3CCl3 global lifetime of 5.0
years (SPARC, 2013), we infer emissions that havecontinued to decline since 2012 (Figure 1-1, Table
1-1), with mean values of 1.7 and 2.5 Gg yr−1 in 2016
(AGAGE and NOAA, respectively); the emissions
are not statistically different from zero at the 1-sigma
level. Using global box models, non-zero emissions
have been inferred for up to at least 2014 (Rigby etODSs and Other Gases | Chapter 1

al., 2017; Turner et al., 2017), with estimated global
emissions in the range of 0.5 to 2 Gg yr−1 in 2014.
Observations of above-baseline mole fractions at one
AGAGE station (Scripps Institution of Oceanography,
La Jolla, California, USA) confirm the continued release of CH3CCl3 near this location until at least 2014
(Rigby et al., 2017). Above-baseline events were also
found up until 2012 at Monte Cimone, Italy, andJungfraujoch, Switzerland, with inferred emissions in
Europe declining from around 1.1 Gg yr−1 in 2002 to
0.2 Gg yr−1 in 2012 (Maione et al., 2014).
Rigby et al. (2017) and Turner et al. (2017) usedmethyl chloroform observations from AGAGE andNOAA to infer changes in tropospheric hydroxyl
radical (OH) concentrations, the primary sink for
CH3CCl3, between the 1980s and the mid-2000s.
They both found maximum likelihood troposphericconcentrations of OH that increased during the late
1990s and early 2000s and fell afterwards. However,both studies noted that the uncertainty in these inferred changes was large, such that a solution with noOH variability (and therefore no change in CH3CCl3
lifetime) was also possible. Rigby et al. (2017) inferreda global mean OH concentration that was 5–10%
higher than was estimated in Rigby et al. (2013) andSPARC (2013), suggesting that the CH3CCl3 lifetime(and that of many other compounds whose primary
sink is OH) may be shorter than the SPARC estimate,although their uncertainties suggest that this difference is not statistically significant.
The influence of CH3CCl3 emissions on climate andozone depletion is now very small (Figure 1-5). In 2016,
100-year-GWP-weighted emissions were 0.3 ± 0.3 Mt
yr−1 (0.04% as large as the CFCs), and ODP-weighted
emissions were 0.3 ± 0.3 Gg yr−1 (0.3% as large as theCFCs).

1.2.5

Hydrochlorofluorocarbons (HCFCs)

Observations of Atmospheric Abundance
The global surface mean mole fraction of the mostabundant HCFC, HCFC-22 (CHClF2), has continued to increase since the previous Assessment andwas around 237 ppt in 2016 (Figure 1-1, Table 1-1).
However, its growth rate has declined relative to previous years (Figure 1-1) and is now comparable to
the growth rate observed in the early 2000s. Growth

rates of the less abundant HCFCs—HCFC-141b(CH3CCl2F) and HCFC-142b (CH3CClF2)—have declined substantially since 2012 (Figure 1-1). Global
mean mole fractions of these two gases were about
24.5 ppt and 22 ppt in 2016, respectively. Abundancesof all three HCFCs have grown more slowly than projected in the previous Assessment, with 2016 mole
fractions being about 7.5%, 4.5%, and 8% lower thanthe A1-2014 scenario for HCFC-22, HCFC-141b, and
HCFC-142b, respectively. This scenario assumed thatafter 2012, all Article 5 countries would continue producing HCFCs at the maximum level allowed under
the Montreal Protocol.
Recent trends of HCFC-22 and HCFC-142b are compared between surface in situ (including grab samples) and ground-based total column remote sensing
methods (Table 1-2). Trends calculated during theperiod 2010–2016 are similar for the two methodsfor HCFC-22 but do not agree for HCFC-142b. For
HCFC-142b, the remote sensing observations show atrend that is not statistically different from zero, whilein situ observations show a small positive (1% yr−1)
trend.
Upper tropospheric trends based on global satellite
observations of HCFC-22 from MIPAS agree withsurface trends measured by AGAGE and NOAA networks for the period from 2005 to 2012 (Chirkov etal., 2016). Stratospheric trends determined from satellite data largely reflect tropospheric trends but withadditional variability, possibly caused by variability instratospheric circulation.
HCFC-133a (CH2ClCF3), which is an intermediate
in HFC-125, HFC-134a, and HFC-143a production,
has increased only slightly since 2012, and its abundance remains less than 1 ppt. HCFC-31 (CH2ClF),
a relatively short-lived compound with an atmospheric lifetime of 1.2 years, is an intermediate in thesynthesis of HFC-32 (CH2F2) and was first reported
by Schoenenberger et al. (2015). It was found to bepresent in the Northern Hemisphere at 0.17 ppt in
2011, and updates to these measurements have showna decline to 0.11 ppt in 2016. The compound HCFC225ca (CF3CF2CHCl2), which was used as a drop-in
replacement for CFC-113, has been measured in the
CGAA since the previous Assessment (Kloss et al.,2014). It appeared in the observational record in theearly 1990s, and its abundance peaked at 0.05 ppt in
1.25

Chapter 1 | ODSs and Other Gases

2001, after which it declined to 0.02 ppt in 2012.
The radiative forcing due to HCFCs reached 58 mW
m−2 in 2016 (Figure 1-3), which is comparable to thatof CFC-11 (60 mW m−2) and 23% as large as total
CFC radiative forcing. The major contributor to total
HCFC radiative forcing was HCFC-22, which was responsible for 50 mW m−2 in 2016.
Emissions and Lifetimes
Lifetimes of the major of HCFCs have not been significantly updated since (SPARC, 2013), the values
from which are used here (Table A-1).
Emissions of HCFC-22 inferred from atmospheric observations have remained relatively constant at~370 Gg yr−1 since 2012 (Figure 1-4), while emissionsof HCFC-141b and HCFC-142b have declined by approximately 10 Gg yr−1 (~10%) and 6 Gg yr−1 (~18%),
respectively, between 2012 and 2016, reaching valuesof around 60 Gg yr−1 and 24 Gg yr−1 in 2016. For all
three of these gases, the top-down emissions trends
generally agree with consumption-based estimates(Simmonds et al., 2017) (Figure 1-4).
Emissions of HCFCs as a whole, expressed as
CO2-equivalent, have declined since the previous
Assessment (Figure 1-5). This is contrary to theprojected increase in emissions in the A1 scenarios,
which were based on the assumption that Article 5
countries would produce the maximum amount of
HCFCs allowed under the Montreal Protocol (Harrisand Wuebbles et al., 2014; Simmonds et al., 2017).
This emissions decrease is consistent with a sharp
drop in reported HCFC consumption after 2012,
particularly in Article 5 countries. By 2016, reported
HCFC consumption i​n Article 5 countries​declined
by 30% ​compared to the 2008–2012 average (UNEP,
2017). These recent changes suggest that the 2007 adjustment to the Montreal Protocol has been highly effective in limiting emissions of these gases (Montzka
et al., 2015; Simmonds et al., 2017). ​​Emissions trends have been inferred for some of
the more minor HCFCs. Emissions of HCFC-124(CHClFCF3) have declined from ~7 Gg yr−1 in 2003 to~3.5 Gg yr−1 in 2015 (Simmonds et al., 2017). Emissionsof HCFC-133a (Figure 1-4), which had been reported
as increasing prior to the previous Assessment (Laubeet al., 2014), were found to suddenly decline after
1.26

reaching a peak of 3 Gg yr−1 in 2011 and were at 1.5
Gg in 2014 (Vollmer et al., 2015b). As this compoundis thought to be an intermediate during the manufacture of HFC-134a, HFC-143a, and HFC-125, this reduction may be related to better containment during
production of these HFCs (Vollmer et al., 2015b).
However, the trend appears to have reversed again
after 2014. (Figure 1-4). Emissions of the compound
HCFC-31 were recently inferred for the first timefrom atmospheric observations (Schoenenberger etal., 2015). Emissions were found to increase from2000 to 2011, reaching 0.9 (0.7–1.0) Mg yr-1, before
declining until the last available measurements in2014. The reasons for the decline are unknown but
may be related to changes in HFC-32 production
methods. Emissions of HCFC-225ca were inferred toincrease between 1992 and 1999, reaching 1.5 Gg yr−1
before declining to 0.5 Gg yr−1 in 2011 (Kloss et al.,2014). This trend was thought to be consistent withan increase due to its use as a CFC replacement, thensubsequent phasedown due to controls on HCFCs.
Regional emissions estimates using atmospheric observations indicate substantial declines in HCFC-22
and HCFC-142b emissions in the USA and HCFC22 emissions in Europe, as would be expected fromthe phasedown schedule for non–Article 5 countries(Figure 1-6) (Graziosi et al., 2015; Hu et al., 2017).
However, in both regions, significant differenceswith the bottom-up estimates were found, perhaps
indicating incomplete or inaccurate reporting and assumptions relating to release rates and/or atmospheric modeling uncertainties. Because the phaseout of
HCFCs in Article 5 countries is delayed compared to
non–Article 5 countries (e.g., the USA and European
countries), recent emissions estimates using bottom-up methods suggest a continued increase of
HCFC-22, HCFC-141b, and HCFC-142b emissions
from China since the 1990s (Figure 1-6) (Han et al.,2014; Li et al., 2016; Wang et al., 2015b). A number ofobservation-based estimates are generally consistent
with these bottom-up estimates (Fang et al., 2012; Liet al., 2011; Liu et al., 2015; Stohl et al., 2010; Stohl
et al., 2009; Vollmer et al., 2009; Wang et al., 2014;
Yokouchi et al., 2006).
In terms of CO2-equivalent emissions, HCFCs werecomparable to the CFCs (and HFCs; Chapter 2) in
2016, at 0.8 ± 0.1 Gt CO2 yr−1 (Figure 1-5). The major

ODSs and Other Gases | Chapter 1

contributor to this total was HCFC-22, with emissions of 0.7 ± 0.1 Gt yr−1 CO2-equivalent in 2016.
CO2-equivalent emissions due to all HCFCs peaked in2010 and then declined 6% by 2016. When weighted

by their ODPs, emissions of HCFCs were relatively
small in 2016, at 17 ± 2 Gg yr−1, 16% as large as the
CFCs. ODP-weighted emissions reached a maximumin 2011 and have declined by 6% since.

Year

b)

Figure 1-6. Regional emissions estimates of (left to right) HCFC-22, HCFC-141b, and HCFC-142b from (top tobottom) China, USA, and Europe. Bottom-up estimates are shown as solid blue lines with shading indicating
uncertainty (where available). Top-down estimates from a variety of studies and 1-sigma uncertainties areshown in green.
1.27

Chapter 1 | ODSs and Other Gases

Mole fraction (ppt)

12

SH
NH
Global

11
10
9
8
7

Consumption and emission (Gg yr –1) Interhemispheric difference (ppt)

6

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2016

2018

3.5
NH-SH
Running average

3.0
2.5
2.0
1.5
1.0
0.5
0.0
80 1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

Consumption for QPS uses

70

Consumption for non-QPS uses

60

Total consumption

50

Emission from QPS uses

40

Emission from non-QPS uses

30

Total emission

20
10
0
1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

Year
Figure 1-7. Upper panel: Trends in methyl bromide monthly mean mole fractions for the NH (blue), SH (red),and globe (black) from NOAA data (Montzka et al., 2003, updated). Middle panel: Interhemispheric difference(NH–SH) as monthly means (black) and as a 12-month running average (red). Lower panel: Consumption(dashed lines) as reported in the UNEP database (UNEP, 2017), for non-QPS uses (blue) and QPS uses (red),and emissions (solid lines) from non-QPS uses (blue) and QPS uses (red). Total consumption and emissions
are shown as black dashed and solid lines, respectively. Soil fumigation emission rates are estimated as 65%
of reported consumption (UNEP, 2006) and QPS emission rates are estimated as 84% of reported consumption (UNEP, 2006).
1.28

ODSs and Other Gases | Chapter 1

1.2.6

Methyl Chloride (CH3Cl)

Observations of Atmospheric Abundance
Methyl chloride (CH3Cl) is largely natural in origin
and is not controlled under the Montreal Protocol.
The 2016 global mean mole fraction determined fromthe AGAGE and NOAA global networks was 553 and559 ppt, respectively (Table 1-1). These values arearound 2–3% higher than the 2012 values reported in
the previous Assessment, although such changes are
consistent with historical variability (Figure 1-1).
Emissions and Lifetime
The estimate of the total global lifetime of CH3Cl(0.9 years) remains unchanged from the previous
Assessment. Major sinks include oxidation by the hydroxyl radical, uptake by soils, degradation in oceans,and photolysis in the stratosphere. Among the sinks,
only the partial lifetime due to loss in the stratosphere
has been addressed since the last Assessment. The estimate of a 35 ± 7-year stratospheric lifetime (Umezawa
et al., 2015) is consistent with the 30.4-year estimate
in the last Assessment.
The major sources of methyl chloride are tropical andsubtropical plants, biomass burning, the ocean, salt
marshes, and fungi. The major anthropogenic sourceis thought to be coal combustion (McCulloch et al.,1999). The previous Assessment summarized known
CH3Cl sources, highlighting that the global source
strength is about 20% lower than the magnitude of
known sinks. A recent study based on atmospheric observations from Gosan, South Korea, foundthat CH3Cl emissions from industrialized regions ofChina may have been underestimated (Li et al., 2017)
and suggested that the chemical industry may be asource that has not been accounted for in previous
budgets. If confirmed, these findings could substantially reduce the gap in the CH3Cl budget. Emissions
from bread-baking have also been proposed, although
the magnitude was thought to be small compared toother sources (Thornton et al., 2016).
Some process-level studies have investigated emissions of CH3Cl from a range of natural and anthropogenic sources, including coastal salt marshes andan invasive plant (perennial pepperweed) in North
America (Khan et al., 2013; Rhew et al., 2014), a fern
species (Yokouchi et al., 2015), and coastal heathland

fires in Australia (Lawson et al., 2015). However, theimplications of these studies for the global budget
have not yet been established.

1.2.7 Methyl Bromide (CH3Br)
Observations of Atmospheric Abundance
The 2016 global mean surface mole fractions of methyl bromide from the AGAGE and NOAA networks,
respectively, were 6.80 ppt and 6.86 ppt (Figure 1-1,
Figure 1-7, and Table 1-1), ~25% lower than thepeak of about 9.2 ppt observed between 1996-1998
and around 1.3 ppt (~25%) higher than the preindustrial Southern Hemisphere mole fraction of 5.5 ±0.2 ppt from ice core measurements (Carpenter andReimann et al., 2014). The global mean mole fraction
declined until 2015, when it reached 6.6 ppt. However,between 2015 and 2016, NOAA and AGAGE observations showed positive growth rates of 0.22 ppt yr−1
(3.3%) and 0.14 ppt yr−1 (2.1%), respectively. This is
the highest growth rate observed in the last decade ormore. The annual mean IHD has continued to decline
since the previous Assessment, with the NOAA value
reaching 0.68 ppt in 2016, 70% lower than the peak in
1996–1998. The increase in growth between 2015 and2016 does not appear to coincide with an increase in
IHD.
Emissions and Lifetime
The global total lifetime of CH3Br is estimated to be
0.8 years, unchanged from the previous Assessment.
Atmospheric CH3Br has both natural and anthropogenic sources. Its use is controlled under the Montreal
Protocol for the fumigation of soils, post-harvest
storage of commodities, and the fumigation of structures, although some “critical use” exemptions fromthese controls have been awarded (e.g.UNEP, 2014b).
Quarantine and pre-shipment (QPS) use of CH3Br,
mainly for pest control for the transport of agricultural products, is exempt from the phaseout. Naturalor partly anthropogenic sources include biomassburning and emissions from oceanic and terrestrial
ecosystems (for further details, see Table 1-4 in theprevious Assessment; Carpenter and Reimann et al.,2014). Reported non-QPS consumption dropped to
0.94 Gg in 2016, around 1% of its peak value (UNEP,
2017; Figure 1-7). The reported consumption for QPS
1.29

Chapter 1 | ODSs and Other Gases

was 8.4 Gg yr−1 in 2016. Compared to the non-QPS
consumption, this value has been relatively stable forthe previous two decades (UNEP, 2017). Combined
reported consumption from these uses has decreased
by approximately 87% since its peak, which occurredin the late-1990s. This decrease is qualitatively consistent with the observed decline in atmospheric mole
fraction and IHD. The cause of the anomalous growthobserved between 2015 and 2016 is not yet known.
However, the fact that the growth increase did notcoincide with an increase in IHD suggests that theassociated changes in sources and/or sink must be
distributed across hemispheres.
The previous Assessments noted a discrepancy between total known sources of CH3Br and its total loss
rate, with the sinks being around 39 Gg yr−1 larger
than emissions. Some recent studies have investigatedpotentially new or poorly studied sources that could
reduce this gap. Thornton et al. (2016) found a relatively minor contribution to the global budget fromemissions occurring during bread-baking (<1%).
Similarly, seagrass meadows are not thought to contribute significantly to the global budget (Weinberg
et al., 2015). Several regional or process-level studies
have been carried out focusing on subtropical salt
marshes, peatland pastures, heathland fires, or thephotochemical halogenation of terrestrial dissolved
organic matter in estuarine outflow (Khan et al., 2013;
Lawson et al., 2015; Mendez-Diaz et al., 2014; Rhew etal., 2014). However, the implications of these studies
for the global budget have not yet been established.
Therefore, the cause of the discrepancy identified in
the previous Assessment remains unknown.

1.3 VERY SHORT-LIVED ­HALOGENATED
SUBSTANCES (VSLSs)
As in the last Assessment, VSLSs are considered toinclude source gases (SGs; i.e., very short-lived halogenated substances present in the atmosphere in
the form they were emitted from natural and anthropogenic sources), halogenated product gases (PGs)
arising from SG degradation, and other sources of
tropospheric inorganic halogens. VSLSs have tropospheric lifetimes of around 0.5 years or less. While longer-lived ODSs account for the majority of the present-day stratospheric halogen loading, there is strong
evidence that VSLSs make a significant contribution
1.30

to stratospheric bromine and that they contribute tostratospheric chlorine (Carpenter and Reimann etal., 2014; Montzka and Reimann et al., 2010), andpossibly iodine. These gases thus contribute to stratospheric ozone destruction, but their radiative forcing
is small due to their short lifetimes. Also, due to theirshort lifetimes, VSLSs show much higher variability inthe troposphere than long-lived ODSs and are partly
chemically broken down during the transport to thestratosphere. In order to assess the amount of halogen
delivered to the stratosphere, the sources, transport
pathways, and the chemical transformation of VSLSs
during transit need to be understood. In this section,
we use data from global networks to assess the mean
tropospheric mixing ratios, whereas observations
close to the tropical tropopause are used to infer the
input of VSL SGs to the stratosphere.

1.3.1 Tropospheric Abundance, Trends, andEmissions of Very ­Short-Lived Source
Gases (VSL SGs)
For the principal VSL SGs, a detailed compilation of
local lifetimes was given in Table 1-5 of Carpenter andReimann et al. (2014), and is updated in Table A-1of this Assessment. Box 1-1 in the 2014 Assessment
provides a discussion of different VSL SGs’ lifetimes.

1.3.1.1

Chlorine-Containing Very
Short-Lived Source Gases

This section focuses on the chlorinated VSLSs mostwidely reported in the background atmosphere:
dichloromethane (CH2Cl2), chloroform (trichloromethane, CHCl3), tetrachloroethene (perchloroethylene, CCl2CCl2, shortened to C2Cl4), trichloroethene(C2HCl3) and 1,2-dichloroethane (CH2ClCH2Cl).
Long-term measurements are available from boththe NOAA and AGAGE surface networks for CH2Cl2
and C2Cl4, while CHCl3 is available from AGAGE
only. Hemispheric mean mole fractions and annual emissions derived from these data using a global
12-box model (see Box 1-1) are shown in Figure 1-8
and Table 1-3. It should be noted that because these
relatively short-lived compounds exhibit spatial gradients that will not be well represented at the coarse
resolution of a box model, these estimates are likely
to be subject to significant, but poorly quantified, representation uncertainties. Industrial emissions dominate over natural sources for these gases except forODSs and Other Gases | Chapter 1

Global emissions (Gg yr -1) Global emissions (Gg yr -1)

Mole fraction (ppt)

Mole fraction (ppt)

Mole fraction (ppt)8060

CH2Cl2

40

AGAGE
NOAA
(solid = N.H.; dashed = S.H.)

20
0
15

CHCl3

10
5
0
8

C2Cl4

6
4
2
0
1200

CH2Cl2

800
400
500

CHCl3

400
300
200
100
1995

C2Cl4
2000

2005

Year

2010

2015

Figure 1-8. Upper three panels: Monthly hemispheric mean mole fractions of CH2Cl2, CHCl3, andC2Cl4 derived from AGAGE (black) and NOAA (red) observations in the NH (solid lines) and SH(dashed lines). Lower two panels: Global emissions estimates (Gg yr–1) calculated using a global
12-box model (using methods described in Box 1-1 and Figure 1-4), with 1-sigma uncertainties
indicated by shading (AGAGE) or dotted lines (NOAA).
1.31

Chapter 1 | ODSs and Other Gases

Table 1-3. Annual global mean mole fractions of chlorinated VSL source gases and estimated emissions
from the global networks. Emissions based on AGAGE and NOAA surface data were calculated using a global
12-box model (Cunnold et al., 1983; Rigby et al., 2013), identical to the global emissions shown in Figure 1-4 forlonger-lived ODSs. The calculations assume parameterized global total steady-state lifetimes of 0.54, 0.58, and0.38 years for CH2Cl2, CHCl3, and C2Cl4, respectively.

FormulaAnnual Mean Mole
Fraction (ppt)2012 2015 2016

Growth(2015–2016)
ppt yr –1% yr –1

Annual Global Emissions (Gg yr –1)
2012

2015

2016

Network

26.0

32.0

32.7

0.7

2.2

780 (±135)885 (±164)937 (±172)

AGAGE

30.4

37.8

39.2

1.4

3.7

881 (±169)957 (±204)

1037 (±213)

NOAA

7.6

8.7

8.9

0.2

2.3

290 (±60)324 (±69)

331 (±70)

AGAGE

1.17

1.10

1.07

-0.03

-2.7

87 (±18)84 (±17)83 (±17)

AGAGE

1.16

1.22

1.20

-0.02

-1.6

97 (±20)103 (±22)

103 (±21)

NOAA

CH2Cl2

CHCl3

C2Cl4

CHCl3 (Montzka and Reimann et al., 2010). Detailed
information on industrial uses is available on the U.S.
Environmental Protection Agency’s (https://www.
epa.gov) and the EU European Chemicals Agency’s(https://echa.europa.eu/) websites.

1.32

Dichloromethane (CH2Cl2) exhibits a strong IHD,
with NH (Northern Hemisphere) mole fractions afactor of ~3 larger than those in the SH (Southern
Hemisphere), reflecting NH industrial sources (e.g.,
Figure 1-8; Hossaini et al., 2017). Measurementsfrom the NOAA network suggest CH2Cl2 emissions
increased by a factor of ~2 between 2000 (~508 ± 109
Gg yr−1) and 2016 (1037 ± 213 Gg yr−1). The annual
mean mole fraction in 2016 was 39.2 ppt based on the
NOAA network, which is about a doubling comparedto the beginning of the century. Global mean mole
fractions increased by 12.3 ppt between 2007 and2016 (a relative increase of 60%), reaching 32.7 ppt in2016 based on the AGAGE record. Particularly large
CH2Cl2 growth occurred between 2012 and 2013 (~6ppt yr−1 from NOAA and ~4 ppt yr−1 from AGAGE),
though more recent growth rates (2015 to 2016) arecomparatively small (1.4 ppt yr−1 for NOAA and 0.7
ppt yr−1 for AGAGE) (Table 1-3). At present, it cannot be assessed if this recent decrease in growth rate
reflects a stabilization of emissions or is a transient

effect reflecting the large atmospheric variability.
The discrepancy between the two CH2Cl2 datasets ofabout 13 ppt of tropospheric chlorine in 2016 reflects
differences in calibration scales, which are on theorder of 10%, and also differences in sampling locations between the networks. The latter is particularly
evident in the 0–30° northern latitude band, where
NOAA observations (from the Pacific: Mauna Loa
and Cape Kumukahi, Hawai'i) are around 30% higher
than the AGAGE measurements (from the Atlantic:
Ragged Point, Barbados). It is beyond the capabilityof the 12-box model used here to resolve such differences, which may be due to longitudinal gradients inthe atmosphere.
The upward CH2Cl2 trend during the last decade iscorroborated by upper tropospheric aircraft datafrom the CARIBIC (Civil Aircraft for the Regular
Investigation of the atmosphere Based on an
Instrument Container) mission (Leedham Elvidge
et al., 2015a; Oram et al., 2017). The contribution ofregional CH2Cl2 sources to global emission trends
are not well quantified, though emissions from theIndian subcontinent may have increased by a factor of
2–4 between 1998–2000 and 2008 (Leedham Elvidge
et al., 2015a) and substantial emissions from eastern
Asia have been proposed (Oram et al., 2017).

ODSs and Other Gases | Chapter 1

Table 1-4. Summary of observed mole fractions (in ppt) of VSL source gases from the marine boundary layer(MBL) to the tropical tropopause layer (TTL) and above. Note, many of the upper tropospheric measurements
were made at least one decade ago in the case of brominated and iodinated SGs. As chlorinated SGs have significant anthropogenic sources and some show trends, data are based on measurements from 2013/2014 only.
Marine Boundary
Layer (MBL)
Height Range
Potential Temperature Range

Lower TTLLZRH (z0)1

Upper TTL

TropicalTropopause

12–14 km

14.5–15.5 km

15.5–16.5 km

16.5–17.5 km

340–355 K

355–365 K

365–375 K

375–385 k

Median2Range3

Mean2

Range3

Mean2

Range3

Mean2

Range3

Mean2

Range3

CH2Cl2

57.5

46.6–68.1

42.0

36.4–47.6

36.4

29.6–44.3

33.9

28.4–41.6

32.5

26.3–38.5

CHCl3

10.3

8.5–12.3

7.9

7.1–8.7

7.1

6.4–8.0

6.8

6.2–7.8

6.5

5.7–7.2

CH2ClCH2Cl

12.8

10.4–18.3

9.0

6.8–11.3

7.4

5.2–9.5

6.9

5.4–8.3

6.6

5.7–7.5

C2HCl30.2

0.1–0.9

0.14

0.02–0.25

0.08

0.00–0.16

0.06

0.0–0.13

0.04

0.00–0.08

C2Cl4

1.3

1.0–2.2

0.87

0.68–1.05

0.73

0.49–0.95

0.66

0.49–0.83

0.52

0.38–0.71

CH2Br2

0.9

0.6–1.7

0.96

0.72–1.15

0.81

0.59–0.98

0.73

0.43–0.94

0.64

0.32–0.89

CHBr3

1.2

0.4–4.0

0.57

0.30–1.11

0.36

0.05–0.72

0.28

0.02–0.64

0.19

0.01–0.54

CH2BrCl

0.10

0.07–0.12

0.12

0.07–0.16

0.13

0.08–0.20

0.14

0.10–0.20

0.12

0.07–0.20

CHBr2Cl

0.3

0.1–0.8

0.12

0.06–0.23

0.09

0.04–0.19

0.08

0.02–0.16

0.05

0.02–0.14

CHBrCl2

0.3

0.1–0.9

0.26

0.18–0.55

0.18

0.08–0.49

0.15

0.07–0.31

0.12

0.05–0.32

CH3I

0.8

0.3–2.1

0.16

0.00–0.49

0.08

0.0–0.32

0.04

0.0–0.25

0.03

0.00–0.14

Total Cl177

144–221130

111–149 112

91–136105

88–127100(92)483–117(75–110)4

Anthrop. Cl5

150

122–189109

93–12675–12793

73–11888

69–10898

Total Br

6.5

2.8–18.0

4.3

2.7–6.8

3.2

1.6–5.2

2.8

1.1–4.6

2.2

0.8–4.2

Total I

0.8

0.3–2.1

0.16

0.00–0.49

0.08

0.0–0.32

0.04

0.0–0.25

0.03

0.00–0.14

Notes:
1

LZRH (z0) corresponds to the level of zero clear-sky radiative heating (see Box 1-3 of Carpenter and Reimann et al. (2014)). As in theprevious Assessment, this level is at about 15 km or 360 K, where there is a transition from clear-sky radiative cooling to clear-sky
radiative heating. In general, air masses above this level are expected to enter the stratosphere.

2

Abundances in the MBL are median values. MBL CH2Cl2, CHCl3, CH2ClCH2Cl, C2HCl3, and C2Cl4 data are from the CAST and CONTRAST missions (Andrews et al., 2016; Pan et al., 2017). MBL CHBr3, CH2Br2, and CH3I data are from the compilation of Ziska et al.
(2013). MBL CH2BrCl, CHBr2Cl, and CHBrCl2 data are from the previous (Carpenter and Reimann et al., 2014) Assessment. In andabove the TTL, abundances are mean values. For brominated VSLSs and CH3I, data have been compiled from observations obtained during the Pre-AVE, CR-AVE, TC4, HIPPO, SHIVA, CONTRAST, and ATTREX aircraft campaigns (Navarro et al., 2015; Pan et al.,2017; Sala et al., 2014; Wofsy et al., 2011), and from balloon observations (Brinckmann et al., 2012). ATTREX values used here differ
from those used in Wales et al. (2018), as they have been filtered by altitude instead of applying any tracer-tracer correlation. For
chlorinated VSLSs, data are from the CONTRAST (2014) and ATTREX (2013/2014) missions in 2013/2014 only (Navarro et al., 2015),
with the exception of CH2ClCH2Cl, which does not include data from ATTREX 2013. See below for definitions of field mission acronyms. Note that calibration scales for VSLSs may differ among different research groups (e.g. Hall et al., 2014; Jones et al., 2011).

3

In the MBL the stated observed range is 10th to 90th percentile. Above the MBL, the stated observed range represents the smallest
mean minus 1 standard deviation and the largest mean plus 1 standard deviation.

4

Values for 2016 based on the model by (Hossaini et al., 2017), which are used to derive total stratospheric VSL SGI for chlorine, as
explained in section 1.3.2.1, in order to reduce variability from individual campaigns in assessing total Cl input to the stratosphere.

5

The anthropogenic fraction of VSLS (Anthrop. Cl) is approximate and has been calculated from the sum of 90% of CH2Cl2, 50% of
CHCl3, and 100% of other compounds.

Pre-AVE = Pre-Aura Validation Experiment (2004); CR-AVE = Costa Rica-Aura Validation Experiment (2006); TC4 = Tropical Composition, Cloud and Climate Coupling missions (2007); HIPPO = HIAPER (High-Performance Instrumented Airborne Platform for Environmental Research) Pole-to-Pole Observations (2009–2011); SHIVA = Stratospheric Ozone: Halogen Impacts in a Varying Atmosphere;
ATTREX = Airborne Tropical Tropopause Experiment (2011, 2013, and 2014); CAST = Co-ordinated Airborne Studies in the Tropics(2014); CONTRAST = Convective Transport of Active Species in the Tropics (2014).
1.33

Chapter 1 | ODSs and Other Gases

In contrast to CH2Cl2, global perchloroethylene(C2Cl4) emissions and mole fractions in backgroundair have largely been in decline at least since the mid1990s (Figure 1-8). C2Cl4 observations from AGAGE
are only available from 2004, though relative (−44%)
and absolute (−0.8 ppt) changes since then are comparable to NOAA trends over the same period (−32%,−0.6 ppt). The global mean C2Cl4 mole fraction was~1 ppt in 2016 (Table 1-3).
For chloroform (CHCl3), AGAGE measurements
show stable global mean mole fractions in the range of
7.3–7.7 ppt over the 1997–2010 period, followed by asubsequent increase to 8.9 ppt in 2016. Emissions between 2011 and 2016 are estimated to have increasedby ~20% (Figure 1-8). During this period, the IHD of
surface CHCl3 mole fractions increased from 4.3 ppt
to 6.3 ppt, suggesting an increase in NH anthropogenic emissions.
No 1,2-dichloroethane (CH2ClCH2Cl) measurements are available from either AGAGE or NOAA,
thus its budget and emissions are poorly constrained.
Based on recent aircraft observations, boundary layer
CH2ClCH2Cl mole fractions are of the order ~10–20
ppt in the NH (Table 1-4), with SH mole fractionsa factor of ~6 lower (Hossaini et al., 2016a), indicative of dominant anthropogenic sources. Given theseabundances, CH2ClCH2Cl is potentially an importantsource of chlorine to the troposphere (smaller than
CH2Cl2 but comparable to CHCl3). However, owing to
its relatively short lifetime compared to other VSLSs(Table A-1), its importance as a source of stratospheric chlorine is estimated to be lower.
Trichloroethene (C2HCl3) is a relatively minor tropospheric chlorine source. A limited set of AGAGE
measurements show NH surface mole fractions of~1.1 ppt in the early 2000s (Simmonds et al., 2006),
with recent aircraft data confirming its low abundance(Table 1-4).
Short-lived halogenated unsaturated hydrocarbons(halogenated olefins) have recently been used to replacehigh-GWP HCFCs and HFCs. In this Assessment,
hydrofluoroolefins (HFOs) are discussed in Chapter
2. Hydrochlorofluoroolefins (HCFOs) are also in use,
and the only atmospheric record currently available
is that for HCFO-1233zd(E) (trans-CF3CH=CHCl)from central Europe (Vollmer et al., 2015a). While this
1.34

compound was detectable in only 30% of the samples
measured at Jungfraujoch in 2013, this increased to100% by 2016, with a mean mole fraction of 0.03 ppt
in that year (update from Vollmer et al., 2015a).
While measurements of VSLSs from NOAA and
AGAGE largely reflect background concentrations(e.g. see Simmonds et al., 2006), regional emissions
may lead to higher and more variable abundances.
Elevated levels in urban air and at sites likely influenced by regional industrial/commercial processes
are reported over the USA (Logue et al., 2010) andChina, with the latter including up to several tensof ppt of C2Cl4 and several hundred ppt or more ofCH2Cl2, CHCl3 and CH2ClCH2Cl (Mao et al., 2009;
M. Yang et al., 2016; Zhang et al., 2014). The regional sum of chlorine from CH2Cl2, CHCl3, C2Cl4, andCH2ClCH2Cl can therefore exceed the backgroundglobal mean (Table 1-3) by up to an order of magnitude, as observed, e.g., over Malaysia and the islandof Taiwan and (see e.g. Oram et al., 2017)). Oramet al. (2017) estimated anthropogenic CH2Cl2 emissions from China to be 455 ± 46 Gg yr−1 in 2015 (i.e.,~40‒50% of the top-down derived global emissions
in Table 1-3. Similarly, CH2ClCH2Cl emissions from
China of 203 (±20) Gg yr−1 were derived for 2015.
Natural CHCl3 sources from marine (phytoplankton)
and terrestrial (e.g., soils, peatlands, and plants) environments have been identified (e.g. Albers et al.,2017; Forczek et al., 2015; Khalil et al., 1999; Khan etal., 2011) and account for 50–90% of global emissions(McCulloch, 2003; Worton et al., 2006). For CH2Cl2,
biomass burning may be a small (Lobert et al., 1999)
or negligible global source (Leedham Elvidge et al.,2015a; Lawson et al., 2015; Simpson et al., 2011),
though oceanic emissions may be more significant.
Based on limited observational data, estimates ofocean CH2Cl2 emissions range from 124 ± 38 Gg
yr−1 (Xiao, 2008) to 192 Gg yr−1 (Khalil et al., 1999).
Natural CH2Cl2 sources are therefore uncertain but
are likely small relative to industrial emissions, which
are estimated to account for 90% of total CH2Cl2 emissions (Carpenter and Reimann et al., 2014; Montzka
and Reimann et al., 2010). An increase in industrial
emissions is the most probable cause of recent CH2Cl2
growth.
Global combined tropospheric chlorine from thethree VSLSs measured by the NOAA and AGAGE

ODSs and Other Gases | Chapter 1

networks (CH2Cl2, CHCl3 and C2Cl4) was 92–110
ppt Cl in 2016 (Table 1-3), with a 4.0 ± 1.5 ppt Cl
yr−1 increase over the 5-year period 2012 to 2016. The
given uncertainty range includes the differences in
the NOAA and AGAGE calibration scales. In the NH
tropical boundary layer, total tropospheric chlorine
from VSLSs in 2014, including additional contributions from CH2ClCH2Cl and C2HCl3, is estimatedfrom aircraft observations at 177 (144–221) ppt Cl(Table 1-4).

1.3.1.2	Bromine-Containing Very
Short-Lived Source Gases
Bromoform (CHBr3) and dibromomethane (methylene dibromide, CH2Br2) are the principal brominated VSL SGs. Along with bromochloromethane(CH2BrCl), dibromochloromethane (CHBr2Cl), andbromodichloromethane (CHBrCl2), these VSL SGs
are predominantly of natural marine origin, withocean phytoplankton and macroalgae being the dominant sources (e.g. Carpenter et al., 2007; Leedham
Elvidge et al., 2015b; Hamed et al., 2017; Quack etal., 2007)). Typical tropospheric mole fractions of theabove brominated compounds are summarized inTable 1-4. Though poorly characterized, minor terrestrial sources include peatland and organic-rich soils(Albers et al., 2017; Carpenter et al., 2005) and production due to water chlorination processes (Worton
et al., 2006). In coastal zones, industrial discharge ofchlorinated effluents to seawater is also identified as asource of several brominated VSL SGs, in particular
CHBr3 (Boudjellaba et al., 2016; Hamed et al., 2017;
Yang, 2001). The importance of this source on a global
scale is not clear (Liu et al., 2011).
Since the last Assessment, brominated VSL SG emissions in several oceanic regions have been reported
from ship cruises, including the Yellow, Sulu, andSouth China Seas (Nadzir et al., 2014; Yang et al.,2014), the tropical Atlantic (Hepach et al., 2015)
Pacific Peruvian coastal waters (Fuhlbrugge et al.,2016a), and the west Indian Ocean (Fiehn et al.,2017). CH2Br2 emissions derived in these studies werein some cases significantly larger than those estimated from previous studies (Butler et al., 2007; 2015;
Hepach et al., 2014; Tegtmeier et al., 2012) and confirm large spatiotemporal variability in sea–air VSL
SG fluxes (e.g. Montzka and Reimann et al., 2010),
varying by an order of magnitude or more during agiven cruise. Many factors contribute to such variability, including subsurface biogeochemical processes,
proximity to coastal sources, wind speed, and sea
surface temperature, among others (Stemmler et al.,2015). The correlation between CHBr3 and CH2Br2
concentrations in background air suggests the twocompounds are largely derived from a common process at the global scale (Carpenter and Liss, 2000;
Yokouchi et al., 2017).
While there is evidence for seasonal variations in oceanic VSL SG emissions from observations (Hughes etal., 2009; Yang et al., 2014; Yokouchi et al., 2017) andocean biogeochemical models (Stemmler et al., 2015),
much of the observed seasonality of background surface CHBr3 and CH2Br2 is well explained by seasonalchanges in OH and other chemical sinks (Hossaini etal., 2016b).
Recent bottom-up estimates of global CHBr3 emissions are a factor of two lower than top-down estimates, most likely due to poor temporal and spatial
data coverage, resulting in missing sources andemission hot spots (Stemmler et al., 2015; Ziska etal., 2013). There is no clear evidence for long-termchanges in the atmospheric abundance of brominated VSLSs, although small increases in global emissions (~6−8% between 1979 and 2013 for CHBr3 andCH2Br2) are suggested to have occurred due predominantly to increases in surface wind speed (Ziska etal., 2017). For CHBr3, the global source estimate of
120–820 Gg Br yr−1 given in the last Assessment isunchanged (Carpenter and Reimann et al., 2014). For
CH2Br2, the range from the last Assessment (57–280
Gg Br yr−1) is reduced to 57–100 Gg Br yr−1, reflecting the most up-to-date emission climatologies (e.g.
Lennartz et al., 2015; Ziska et al., 2013) and supportedby the fact that global models reproduce tropospheric CH2Br2 observations well using emissions at thelower end (Hossaini et al., 2016b). Global CH2BrCl,
CHBr2Cl, and CHBrCl2 emissions are estimated at
4–6 Gg Br yr−1, 15–43 Gg Br yr−1, and 8–11 Gg Br yr−1,
respectively (Brinckmann et al., 2012; Ordonez et al.,2012; Warwick et al., 2006; Yokouchi et al., 2005).

1.3.1.3 	Iodine-Containing Very
Short-Lived Source Gases
Methyl iodide (CH3I), with mainly oceanic sources, is the main iodine-containing VSL SG. Since thelast Assessment, further evidence for oceanic CH3I
1.35

Chapter 1 | ODSs and Other Gases

Box 1-2. Regional Variability and Modeling of VSLS Transport to the Stratosphere
Eleven global models participated in the first concerted VSLS multi-model intercomparison (Hossaini et al.,2016b), testing a range of prescribed emissions and using a common chemistry scheme. Despite differences intransport schemes, most of the models were capable of reproducing observed CHBr3 and CH2Br2 mole fractions in the TTL, providing confidence in a proper representation of the average transport processes affecting
VSLS stratospheric injection.
While the average transport is thus rather well represented, global and trajectory models have intrinsic limitations in properly representing very deep convection of air parcels from the boundary layer to the stratosphere(Feng et al., 2011; Hosking et al., 2010; Hoyle et al., 2011; Orbe et al., 2017), although increasing the model
resolution has been found to improve the representation of the strength and location of convective events(Russo et al., 2015).
Modeled SG concentrations in the TTL do not depend only on total emissions, but in particular also on thegeographical distribution of the sources. These are mainly natural oceanic sources for bromine; for the input of
VSLS chlorine to the stratosphere on a regional scale, the transport of anthropogenic pollution plumes to theupper atmosphere plays a crucial role (Anderson et al., 2016; Ashfold et al., 2015; Chen et al., 2016; Oram etal., 2017). For brominated and iodinated species, which have mostly natural sources, the collocation of natural,
mainly oceanic, sources and effective vertical transport is important. The Maritime Continent (Indonesia,
Philippines, New Guinea, and other Southeast Asia islands) (Tegtmeier et al., 2012, 2015), the tropical Indian
Ocean (Liang et al., 2014a), and the Tropical Western Pacific (Fernandez et al., 2014) have been suggested as
particularly important source regions in this respect. The collocation of elevated emissions with the Asianmonsoon circulation during boreal summer and with the tropical pacific warm pool during boreal winter also
likely provides oceanic VSL SGs an efficient transport route to the stratosphere (Fiehn et al., 2017; Hossaini etal., 2016b; Liang et al., 2014a).

production via biotic (e.g., phytoplankton and cyanobacteria) and abiotic (e.g., photochemical breakdown of dissolved organic matter) processes has been
provided by laboratory and field studies (e.g. Allard
and Gallard, 2013; Hepach et al., 2015; Mendez-Diazet al., 2014; Ooki et al., 2015; Shi et al., 2014; Yuan etal., 2016). Terrestrial CH3I sources are poorly quantified, though include soils/vegetation, rice paddies,
wetlands, salt marshes, and biomass burning (e.g.
Akagi et al., 2011; Lawson et al., 2015; Lee-Taylor andRedeker, 2005; Manley et al., 2006; Sive et al., 2007),
accounting for up to 30% of total CH3I emissions insome inventories (Bell et al., 2002).
In the marine boundary layer (MBL), recently observed mean CH3I mole fractions of 0.59 ± 0.30 ppt
over the Tropical Western Pacific (Fuhlbrugge et al.,2016b) and 0.84 ± 0.12 ppt over the Indian Ocean(Fiehn et al., 2017) are generally consistent with previous observations (Table 1-4). Generally, larger mole
fractions are observed at low latitudes than toward
1.36

the poles (Hu et al., 2016b). No new information on
long-term atmospheric CH3I trends has been reported since the last Assessment (Carpenter and Reimann
et al., 2014), when trends were shown to have varied
over the past decades (Yokouchi et al., 2012). The
best estimate of coastal plus open-oceans emissions
is unchanged and is in the range of 157–550 Gg I yr−1
(Carpenter and Reimann et al., 2014).
Several other iodinated VSLSs have been detectedin the MBL, including CH2ICl, CH2IBr, CH2I2 andC2H5I (Allan et al., 2015; Jones et al., 2010; Shimizu
et al., 2017). Owing to their short lifetimes (minutes
to hours at the surface) these VSL SGs are not expected to reach the upper troposphere. However, theirphotolysis is a significant source of tropospheric inorganic iodine (Carpenter et al., 2013; Saiz-Lopez etal., 2014; Sherwen et al., 2016) and may contribute toparticulate iodine (Sherwen et al., 2016) observed in
the upper troposphere and stratosphere (Murphy etal., 2014; Murphy and Thomson, 2000).

ODSs and Other Gases | Chapter 1

1.3.2 Input of VSLS Halogen to the­Stratosphere

Chlorine (ppt)

Chlorine (ppt)

VSL SGs can be chemically broken down duringtransport to the stratosphere. We differentiate between stratospheric halogen input arising from halogen atoms crossing the tropopause in the form of
the emitted source gases (source gas injection, SGI)
and in the form of product gases released from sourcegas photodecomposition (product gas injection,
PGI). Since the last Assessment, new observationsof brominated, chlorinated, and iodinated SGs havebeen obtained in the tropical troposphere, including
the near-tropopause region, allowing constraints on
SGI especially from chlorinated and brominated SGs.
New measurements of brominated PGs have also
been reported from several aircraft campaigns, andglobal models with a more explicit treatment of PG
chemistry have reported PGI estimates. In situ measurements of chlorinated and iodinated PGs remain
sparse. Transport processes relevant to the input of
VSLSs to the stratosphere show a large regional andseasonal variability, which is discussed in Box 1-2. In
this subsection, we assess the magnitude of both SGI
and PGI for the different halogens.

(a) CH2Cl2

80

Dynamical processes relevant to the transport of
VSLSs from the marine boundary layer (MBL) to thetropical tropopause layer (TTL) and from the TTL to
the stratosphere have been discussed in detail in previous Assessments (see Section 1.3.2 in Montzka andReimann et al., 2010; see also Carpenter and Reimann
et al., 2014). Briefly, the TTL is the region between
the lapse rate minimum (~12 km / θ = 345 K) andthe cold point tropopause (CPT; ~17 km, θ = 380 K)(see Fueglistaler et al., 2009). The level of zero radiative heating (LZRH; ≈ 15 km / θ = 360 K) marks thetransition from clear-sky radiative cooling (below)
to clear-sky radiative heating (above). Once a tropospheric air parcel crosses the LZRH, it is expected toreach the lower tropical stratosphere.

1.3.2.1

SGI describes the stratospheric input of halogenated SGs in the same form as they were emitted at thesurface. The efficiency of SGI is different for eachcompound and depends on its tropospheric loss rate
(mainly through reaction with OH or photolysis) andthe timescales for troposphere-to-stratosphere transport (Aschmann and Sinnhuber, 2013; Schofield et al.,2011). SGI—the global average halogen mixing ratio

18

40

14

5
4
3
2
1
0

2000

2005

2010

2015

(c) C2Cl4

2000

2005

2010

(b) CHCl3

22

60
20

2015

Source Gas Injection (SGI)

10

25
20
15
10
5
0

2000

2005

2010

2015

(d) CH2ClCH2Cl

2000

2005

2010

2015

Chlorine (ppt)(e) Total Cl from VSL SGs
120
100
80
60
40

Model mean +/- 1 σ
Observed mean +/- 1 σ
2000

2005

2010
Year

2015

Figure 1-9. Modeled and observed stratospheric chlorine SGI (ppt Cl) evaluated at the tropical tropopause from (a) CH2Cl2, (b) CHCl3, (c) C2Cl4, (d)CH2ClCH2Cl, and (e) total. Update of Hossaini et al. (2015).
1.37

Chapter 1 | ODSs and Other Gases

Since the last Assessment, new aircraft measurementsof chlorinated, brominated, and iodinated SGs in thetropical upper troposphere have been reported. These
are from the 2013 and 2014 NASA Airborne TropicalTRopopause EXperiment (ATTREX) in the Easternand Western Pacific (Jensen et al., 2017; Jensen et al.,2013), the 2014 CONTRAST (Convective Transportof Active Species in the Tropics) mission, located in
the Western Pacific (Pan et al., 2017), and the 2014
CAST (Coordinated Airborne Studies in the Tropics)
mission, also in the Western Pacific (Harris et al.,2017). These campaigns provide new information on
the abundance of VSL SGs from the MBL to the TTL
and around the tropical tropopause (Table 1-4).

20
ATTREX
Convective
outflow

Altitude ( km )15

10

CONTRAST

5
CAST

0
0

2
4
6
Short-Lived Organic Bromine (ppt)

820

SGI from Chlorinated VSLSs
Tr opopause
WMO18 range~Altitude ( km )15

TTL base
10

Multi-model mean
and model spread

5

0
0

6
4
2
Organic Bromine from CHBr3 and CH2BR2 (ppt)2

8

Figure 1-10. Top panel: Vertical profile of totalorganic bromine (ppt Br) from brominated
VSL SGs (CHBr3, CH2Br2, CHBr2Cl, CH2BrCl, and
CHBrCl2) observed during 2014 CAST, CONTRAST, and ATTREX missions over the TropicalWestern Pacific (modified from Pan et al., 2017).
Bottom panel: Organic bromine from the twomain source gases CHBr3 and CH2Br2 (ppt Br)
from the TransCom-VSLS model intercomparison. The multi-model mean (black line) andmodel spread (gray shading) are shown, alongwith the best estimate (red circle) and range (redline) from this Assessment (Table 1-4). Modified
from Hossaini et al. (2016b).

transported into the stratosphere—can be quantified
from SG measurements around the tropical tropopause (~17 km) or from global models.
1.38

Observation-based chlorine SGI from VSLSs is 100(83–117) ppt Cl based on recent ATTREX measurements only (Table 1-4), due to the recent increases ofmainly anthropogenic chlorinated VSLSs (see Section
1.3.1.1). CH2Cl2, CHCl3, and CH2ClCH2Cl accountfor ~65%, ~20%, and ~13% of this total, respectively; the remaining ~2% is from C2Cl4 and C2HCl3.
This chlorine SGI estimate is ~40% larger than the
72 (50–95) ppt Cl derived in the last Assessment andpredominantly reflects larger observed mole fractionsof CH2Cl2 and CH2ClCH2Cl around the tropical tropopause during ATTREX compared to the previous
measurement compilation. Local upper tropospheric(10–12 km) enhancements of CH2Cl2, CHCl3, C2Cl4,
and CH2ClCH2Cl over Southeast Asia have been observed (Oram et al., 2017), with a sum of chlorine inthese VSLSs of up to 330 ppt Cl (compared to the average 111–149 ppt Cl from Table 1-4 in the lower TTL).
An estimate of chlorine SGI (2000 to 2016) from the
TOMCAT chemical transport model is shown in
Figure 1-9 (update of Hossaini et al., 2015). The model
is constrained by time-dependent, latitudinal-varying
boundary conditions, based on NOAA and AGAGE
surface measurements (except CH2ClCH2Cl, whose
emissions were assumed to remain constant). The
model reproduces observed CH2Cl2 mole fractions
around the tropical tropopause reasonably well andshows that chlorine SGI from CH2Cl2 increased from28 (23–33) ppt Cl to 64 (52–76) ppt Cl between 2000
and 2014. Total modeled chlorine SGI is 94 (77–110)
ppt Cl in 2014 and is thus in reasonable agreementwith the measurement-derived 100 (83–117) ppt Cl

ODSs and Other Gases | Chapter 1

assessed here (Table 1-4). Due to the high variability of VSLS observations in the TTL and near thetropical tropopause (see Figure 1-9e), the use of SG
observations from individual campaigns will lead tolarge year-to-year variability, which may not be representative of the global mean input to the stratosphereand may not be a good basis for trend estimates. For
this Assessment, we therefore use the model data
constrained by surface boundary conditions, as themodel is able to reproduce the observations from various campaigns reasonably well (see Figure 1-9) but
at the same time eliminates the variability from theindividual campaigns. Based on this model, the total
VSL SG injection to the stratosphere is thus assessed
to be 92 (75–110) ppt for the year 2016.
SGI from Brominated VSLSs
Total organic bromine from CHBr3, CH2Br2, CHBr2Cl,
CH2BrCl, and CHBrCl2 observed during the CAST,CONTRAST, and ATTREX campaigns is shown in
Figure 1-10. These campaigns were conducted aroundthe Tropical Western Pacific warm pool, a region characterized by strong convective activity where the troposphere-to-stratosphere transport of brominated VSLSs
is particularly rapid (e.g. Fernandez et al., 2014; Hosking
et al., 2010; Tegtmeier et al., 2015). Intercalibration ofstandards during CAST, CONTRAST, and ATTREX
revealed generally good agreement between VSLS
measurements performed by different instruments(relative standard deviation of <10%) (Andrews etal., 2016). CONTRAST measurements show a totalof ~4.3 (2.1–7.7) ppt Br in the lower TTL for the five
VSL SGs noted above, similar to previous estimatesobtained over Southeast Asia (Sala et al., 2014; Wisher
et al., 2014). At the LZRH, the CONTRAST mean is~3.6 (2.0–5.9) ppt Br, ~30% larger than that reported
by Carpenter and Reimann et al., (2014). Similarly,both 2013 and 2014 ATTREX data (Navarro et al.,2015) showed roughly equal SGI for bromine over theWestern (~3.3 ± 0.5 ppt Br) and Eastern Pacific (~3.0 ±0.4 ppt Br), which lie around the upper limit of the previously assessed range. These higher values most likely
reflect spatiotemporal variability of VSLS sources andtransport and should not be taken as an indicator of
long-term growth in abundance.
Incorporating the new ATTREX measurements(Navarro et al., 2015) with previously compiled
data (Carpenter and Reimann et al., 2014), our best

estimate of SGI from brominated VSLSs has increased
from 1.4 (0.7–3.4) ppt Br to 2.2 (0.8–4.2) ppt Br. CHBr3
and CH2Br2 dominate this supply, accounting for~84% of the total (Table 1-4). The factor of ~5 uncertainty (of similar magnitude to the last Assessment)
likely reflects spatiotemporal variability in VSL SG
surface emissions (Fiehn et al., 2017; Tegtmeier et al.,2012), tropospheric VSL SG sinks (Aschmann andSinnhuber, 2013; Rex et al., 2014), and transport processes (Ashfold et al., 2012; Kruger et al., 2008), allof which influence the amount of VSLSs reaching thetropical tropopause.
The revised bromine SGI estimate of 2.2 (0.8–4.2) ppt
Br is in broad agreement with a ~2.0 ppt Br estimate
from recent global modeling (Fernandez et al., 2014)
and the 2.9 ± 0.6 ppt Br derived from recent tracer-tracer correlation of stratospheric VSLS and CFC11 observations (Wales et al., 2018). For CHBr3 andCH2Br2, it is also in good agreement with the climatological multi-model mean of 2.0 (1.2–2.5) ppt Br fromthe TransCom-VSLS model intercomparison (Figure
1-10b; Hossaini et al., 2016b).
SGI from Iodinated VSLSs
With the exception of CH3I, there is no evidence thatsignificant levels of iodinated VSLSs are present in
the TTL (Carpenter and Reimann et al., 2014). From
Table 1-4, mean CH3I mole fractions are below 0.1
ppt at 15 km, decreasing to below 0.05 ppt around thetropical tropopause. Iodine SGI is assessed to be in
the range of 0 to 0.1 ppt, in agreement with modeling(Saiz-Lopez et al., 2015; Tegtmeier et al., 2013).

1.3.2.2	Product Gas Injection (PGI)
A variable fraction of the halogenated PGs, whicharise from the degradation of source gases (SGs),
experience efficient dry and wet scavenging duringtransit to the stratosphere. In principle, PGs can beproduced from any halocarbon, but VSL SGs are themain source of PGs. Recent aircraft campaigns haveprovided new information regarding the vertical distribution of halogenated PGs; these are complemented by box- and global-modeling studies to provide
new constraints on stratospheric PGI.
PGI from Chlorinated VSLSs
Product gases arising from chlorinated SGs include
phosgene (COCl2) and hydrogen chloride (HCl) and1.39

Bry (ppt)

Potential temperature

Chapter 1 | ODSs and Other Gases

Bry (ppt)

CFC-11 (ppt)

Figure 1-11. Left panel: Vertical variation of inferred inorganic bromine (Bry) from VSLSs within the TTL
during the CONTRAST and ATTREX missions (adapted from Koenig et al., 2017). Right panel: SGI and PGIinferred from the correlation between stratospheric VSLSs and CFC-11 measured in the Tropical Western
Pacific. Inferred Bry from DOAS (green) and CIMS (blue) instruments are shown, together with the tracer-tracer correlation for VSL SGs (black line) and the SGI + PGI best estimation (purple line). Modified from
Wales et al. (2018).

may contribute to PGI. In the last two Assessments,
PGI was estimated from COCl2 (up to 32 ppt Cl) andHCl (up to 20 ppt Cl) observations around the LZRH
(Brown et al., 2011; Marcy et al., 2007; Mébarki et al.,2010). Since both COCl2 and HCl are also produced
from degradation of long-lived ODSs, an unknown
amount of these products could be recirculated fromthe stratosphere into the troposphere. VSLS-derived
products were estimated to contribute 0–100%, or ~25(0–50) ppt, to the observed chlorine PGI (Carpenter
and Reimann et al., 2014).

VSLSs but maintained the upper limit with respect
to previous Assessments, giving a best estimate of 25(8–50) ppt Cl PGI from VSLSs. The lower limit reflects the lower limit from the above modeling work
(Hossaini et al., 2015), considering COCl2 and HCl
only. This estimate does not include a possible additional chlorine input from ClO or ClONO2, both ofwhich can be strongly influenced by heterogeneous
chlorine activation in the upper troposphere andlower stratosphere (Solomon et al., 2016; von Hobeet al., 2011).

Using a chemical transport model, Hossaini et al.
(2015) derived a total VSLS PGI of ~18 (±8) ppt Cl in
2013, with equivalent contributions from COCl2 andHCl and a small contribution from other chlorinatedorganic PGs that have yet to be observed (~2 ± 0.8 ppt
Cl). Simulated chlorinated PGI increased by ~50%
between 2005 and 2013, owing to surface trends inSGs, notably CH2Cl2, during this period (see Section
1.3.1.1, Figure 1-8).

PGI from Brominated VSLSs

All recent studies estimate a non-zero PGI contribution from chlorinated VSLSs. We have therefore
increased the lower limit of total chlorine PGI from
1.40

Several aircraft campaigns targeting brominatedPGs in the tropical free troposphere and TTL havebeen performed since the last Assessment (Chen etal., 2016; Koenig et al., 2017; Le Breton et al., 2017;
Volkamer et al., 2015; Wang et al., 2015a; Werner et al.,2017). Bromine monoxide (BrO) and total inorganic
bromine (Bry) mole fractions rapidly increased withaltitude, doubling from ~2.5 ppt at the bottom of the
TTL to ~5 ppt above the CPT. PGI inferred from BrO(Figure 1-11) ranged from 3.4 ± 1.2 ppt within the
Tropical Eastern Pacific during ATTREX (Werner etODSs and Other Gases | Chapter 1

al. (2017) to 2.7 (2.4–3.0) ppt in the Tropical Western
Pacific during CONTRAST (Koenig et al., 2017).
Based on the relationship between inferred stratospheric Bry and CFC-11, measurements obtained
during CONTRAST and ATTREX (Wales et al., 2018)
estimated a global PGI from brominated VSLSs of 2.1± 2.1 ppt (Figure 1-11). This is well reproduced bya wide range of Chemistry Climate Models (CCMs),
mostly from those considering an explicit treatmentof tropospheric chemistry and sources of VSLSs(Figure 1-11).

In summary, there is now consistent observational evidence confirming the prevalence of gas-phase
inorganic bromine throughout the free troposphereand TTL. This finding is consistent with previous PGImodel estimates and with the amount of bromine
required to reconcile Bry inferred from stratospheric
BrO retrievals with the input from long-lived brominated source gases to the stratosphere (see Section
1.4). Based on both modeling and measurement studies, our current best estimate of total PGI is thus ~2.7
(1.7–4.2) ppt Br.

The previous Assessment (Carpenter and Reimann etal., 2014) estimated a global PGI contribution between
1.1 and 4.3 ppt Br, based mainly on global modelingstudies that considered only the two major VSL SGs(CHBr3 and CH2Br2) and a simplified treatment ofgas-phase Bry speciation and washout (Aschmann
and Sinnhuber, 2013; Hossaini et al., 2012; Liang et al.,2014a). New model developments, including a comprehensive heterogeneous recycling scheme on upper-tropospheric ice crystals (Box 1-3), suggest a Bry
injection of 3.0 (±1.9) ppt Br in the Eastern Pacific and
2.0 (±0.2) ppt Br in the Western Pacific (Navarro et al.,2015). The contribution from minor VSLSs (CHBr2Cl,
CHBrCl2, CH2BrCl, and CH2BrI) to PGI was modeled
to be ~0.3 ppt Br (Fernandez et al., 2014), which matches the upper limit of previous estimates (Carpenter andReimann et. al., 2014; Montzka and Reimann et al.
(2010).

PGI from Iodinated VSLSs
The possible injection of significant amounts of inorganic iodine to the stratosphere is under debate. New
daytime iodine monoxide (IO) aircraft observations
at low solar zenith angles (SZA <45º) within the tropical upper troposphere and lower TTL suggest thatsignificant levels of total reactive iodine (between 0.25
and 0.7 ppt Iy) could be injected to the stratosphere(Saiz-Lopez et al., 2015). This value is 2 to 5 times
larger than the recommended PGI upper limit givenin the previous Assessment (PGI <0.1 ppt Iy), which
was based on balloon-borne solar occultation (SZA
≈ 90º) measurements performed in the tropical lower
stratosphere (Bösch et al., 2003; Butz et al., 2009). The
discrepancy between the new and previous studies
lies in the different chemical scheme used to derive
Iy (particularly the treatment of higher-order iodine

Table 1-5. Summary of estimated VSL source gas injection (SGI) and product gas injection (PGI) contributions to stratospheric halogens (based on observations and model results).
SGI1

PGI2

Total (SGI + PGI)3Chlorine

92 (75–110)

25 (8–50)

115 (75–160)

Bromine2.2 (0.8–4.2)

2.7 (1.7–4.2)

5 (3–7)

Iodine

0–0.1

0–0.7

0–0.8

VSLS Best Estimate (ppt)

Notes:
1

Due mainly to the increasing trend in chlorinated compounds, the SGI estimate for chlorinated VSL SGs is representative ofyear 2016 based on model data (see Table 1.4). SGI for bromine and iodine represent the global mean from 2004 onwards as
there are no reports of long-term trends.

2

PGI for chlorine has been estimated based on HCl and COCl2 only (additional input from ClO and ClONO2 has not been considered). PGI for bromine and iodine has been estimated by box- and global-modeling studies based on BrO and IO measurements, respectively.

3

The SGI and PGI lower/upper limits are not strictly additive because whenever SGI increases (for example due to rapid lifting),
the correspondent PGI arising from SG photodecomposition decreases (and vice-versa).
1.41

Chapter 1 | ODSs and Other Gases

Box 1-3. Heterogeneous Chemistry of Very Short-Lived Product Gases
Atmospheric particles (such as cloud droplets, ice crystals, and aerosols) can affect the tropospheric burden of inorganic product gases (PGs) arising from the degradation of halogenated very short-lived sourcegases (SGs). The competition, fate, and efficiency of heterogeneous reactions occurring on these particles
are still some of the largest uncertainties affecting the stratospheric halogen burden from PGI (Aschmann
and Sinnhuber, 2013; Fernandez et al., 2014; Koenig et al., 2017; Liang et al., 2014a; Saiz-Lopez et al., 2015;
Schmidt et al., 2016; Wang et al., 2015a).

ice crystals

sea salt
dehalogenation

Box 1-3 Figure 1. Schematic representation of tropospheric heterogeneous recycling processes
affecting PGI. The upward arrows on the right represent the vertical ascent of halogenated inorganic PGs (yellow) arising from VSL SGs (orange) decomposition. The different transport regimes
within each region (e.g., convective lifting and large-scale ascent) are shown by different arrowlengths, while the color gradient indicates the relative SG/PG partitioning. Green arrows indicate
the occurrence of sea salt dehalogenation, which constitute an additional source of inorganic PGs(α and β denote the variability in the aerosol enrichment for each halogen family, specified by X
and Y). Red downward arrows represent the net sinks of soluble PGs occurring through uptake on
liquid/ice clouds followed by precipitation/sedimentation. Blue arrows represent the ice/aerosol
mediated repartitioning of inorganic reservoirs, which do not necessarily represent either a net sinkor a source of PGs. Note that the distinction of the height at which each heterogeneous process
occurs is qualitative, and all processes can occur during the day and night.

1.42

ODSs and Other Gases | Chapter 1

Box 1-3, continued.
Oceanic halides comprising the bulk of sea-salt aerosols can be released to the atmosphere as an additional inorganic halogen source through heterogeneous oxidation (Box 1-3, Figure 1) (Vogt et al., 1996).
The efficiency of this process, usually referred to as sea-salt aerosol dehalogenation, depends on the total
halogen fraction prevailing in the aerosol (the so-called enrichment factor), the net rate of gas-phase halogen adsorption and reactive uptake, and the concurrence/collocation of significant sea-salt production andconvective transport throughout the troposphere (Yang et al., 2005). Sea-salt recycling has been estimatedto provide an additional tropospheric chlorine source of ~5–6 Tg Cl yr −1 (Hossaini et al., 2016a; Schmidt
et al., 2016) and between 1.4 to 3.5 Tg Br yr −1 for the case of bromine (Chen et al., 2017; Fernandez et al.,2014; Parrella et al., 2012; Schmidt et al., 2016; Yang et al., 2005). In the case of bromine, this source has been
estimated to be between two and four times larger than the global tropospheric emissions from brominated
VSL SGs (Schmidt et al., 2016; Yang et al., 2005). Inclusion of this source in models has helped to reduce
differences between models and observations of tropospheric BrO columns densities (Koenig et al., 2017;
Schmidt et al., 2016; Wang et al., 2015a).
The processes involved in heterogeneous processing of halogen species are complex. The adsorption
followed by effective washout of hydrophilic reservoir species depends on the aqueous/gas-phase partial pressure equilibrium of each individual halogen species (Sander, 2015). However, if the adsorptionof reservoirs occurs on top of ice crystals or sulfate/nitrate aerosols, many heterogeneous reactions canproceed (Box 1-3 Figure 1), reducing the washout efficiency by transforming the soluble reservoir species into more volatile and photolabile compounds. Inclusion of heterogeneous recycling on ice crystals
and upper-tropospheric aerosols has been reported as a necessary process to be considered in box andchemistry-climate models in order to reconcile satellite tropospheric columns with model troposphericabundances (Parrella et al., 2012; Schmidt et al., 2016), as well as to reproduce the inorganic bromine(Fernandez et al., 2014; Koenig et al., 2017) and iodine (Dix et al., 2013; Saiz-Lopez et al., 2015) verticalprofiles throughout the tropical upper troposphere and TTL. Concurrent estimation of gas-phase inorganic PGs and aerosol halide content at high altitudes is required to close the halogen budget in the TTL
and lower stratosphere and thus to improve the assessment of PGI. However, as many of the heterogeneous
processes are poorly constrained and the uncertainty involved in their parameterization is very large, theevaluation of the overall effect of heterogeneous chemistry on VSLS halogen input to the stratosphere iscurrently difficult to be quantified.

oxides) as well as on the different temporal field ofview (i.e., SZA range) of the measurement techniquedeployed on each field campaign.
In light of the new results, we cannot provide a central value for iodinated PGI but only a wide interval
ranging from zero to an upper limit (0–0.7 ppt Iy). If
the maximum PGI value is considered, the impact of
iodine on the lower tropical stratosphere could be aslarge as that of brominated VSLSs.

1.3.2.3	Total Halogen Input into the 		
Stratosphere from VSLSs

Table 1-5 presents the current best estimate of thetotal injection to the stratosphere from halogenated
VSLSs, discriminating SGI and PGI contributions.
The relative importance of PGI to SGI depends on thechemical transformation processes during transport
from the sources to the stratosphere. The fraction of
PGI to total VSLS halogen derived here is higher forbromine than it is for chlorine. While the uncertainty associated with iodine PGI has increased since theprevious Assessment, the uncertainty range for the
stratospheric flux of organic and inorganic bromine
and chlorine has in general been reduced with respect
to the previous Assessment.
1.43

a)

Tropospheric chlorine from CFCs (ppt)

Total tropospheric chlorine (ppt)

Chapter 1 | ODSs and Other Gasesb)

Tropospheric chlorine from HCFCs (ppt)

Figure 1-12. (a) Total tropospheric organic chlorine
from the combined global measurement networks,
based mainly on NOAA and AGAGE data (black line)
in comparison to the A1 scenarios from the 2010
(green line) and 2014 (blue line) Assessments. Quantities are based on global mean mole fractions of CFC11, CFC-12, CFC-113, CH3CCl3, CCl4, CH3Cl, HCFC-22,
HCFC-141b, HCFC-142b, and halon-1211 determinedby the respective networks. CFC-114, CFC-115, CFC112, CFC-13, and CFC-113a are estimated from measurements of Cape Grim archive air samples (Laubeet al., 2014; Newland et al., 2013). Organic chlorine
also includes contributions from VSL SG, in particular
CHCl3, CH2Cl2, and C2Cl4 (see also Table 1-6). Observations of these gases are only available back to themid-1990s. Before that, constant mixing ratios are
assumed. For species which are not included in the scenarios (some minor CFCs and VSLSs), the observed
values were included here in order to provide a comparison based on the same set of compounds. (b) and (c)Show the same as panel a for the sum of the tropospheric chlorine contents of the CFCs and the troposphericchlorine content of all HCFCs, respectively.
c)

Total Input from Chlorinated VSLSs
A “best estimate” of total chlorine (SGI + PGI) from
VSLSs reaching the stratosphere is obtained by summing the contribution from individual SGs aroundthe tropopause (Table 1-4 and Section 1.3.2.1) andadding the estimated PG contribution from COCl2
and HCl (see Section 1.3.2.2). As explained in Section
1.3.2.1, we use model data of SGI, constrained by surface observations (update of Hossaini et al., 2015) here
1.44

rather than individual campaign observations, since
campaign-based estimates are subject to seasonal andregional variability. This yields total Cl from VSLSs entering the stratosphere of 115 (75–160) ppt Cl for 2016
(Table 1-5). This value is very similar to the global average of chlorine VSLSs of 110 ppt derived in Section
1.4.1. Around the tropopause, the anthropogenic
contribution to the total stratospheric chlorine injection is estimated to be ~85% (Table 1-4). The overallODSs and Other Gases | Chapter 1

contribution of chlorine from VSLSs has increased toabout 3.5% (see discussion in Section 1.4).
Total Input from Brominated VSLSs
Various lines of evidence show that brominated VSLSs
may provide ~2.2 (0.4–4.2) ppt Br to the stratosphere
via SGI and ~2.7 (1.7–4.2) ppt Br via PGI. The best
estimate of global stratospheric bromine from VSLSs
is ~5 (3–7) ppt Br (see Table 1.5), with approximately
half due to SGI and half due to PGI. While the central
value is unchanged with respect to previous reports,
the uncertainty range is reduced based on recent observations and modeling studies. The overall SGI andPGI partitioning is well reproduced by a wide rangeof Chemistry Climate Models (CCMs), mostly fromthose considering an explicit treatment of VSL tropospheric chemistry and sources (Wales et al., 2018).
Although many of the new aircraft campaigns yieldeda total VSLS bromine injection lying in the upper halfof the assessed range (Navarro et al., 2015; Werner etal., 2017), this should not be taken as an indicationof a positive trend in the contribution from VSLSs tostratospheric Bry in recent years: Most measurements
were performed within regions where the source
strength is larger and the vertical transportation of
VSLSs is faster. The 5 (3–7) ppt Br global mean also
matches the indirect estimate derived from total
stratospheric bromine abundances as described in
Section 1.4. Due to the decline in the abundance ofregulated long-lived bromine compounds, the relative
contribution of VSLSs to total stratospheric bromine
has continued to increase, reaching ~26% by year
2016 (see Figure 1-17).
Total Input from Iodinated VSLSs
While the SGI of iodine in the form of CH3I is expected to be very low (<0.1 ppt), there is a new debate
regarding the possible injection of iodine from VSLSs
to the stratosphere through PGI. New measurementsof IO confirm rather low IO levels, but model calculations suggest an additional impact of higher iodine oxides to the total Iy fraction (Saiz-Lopez et al.,2015). The revised upper limit of total iodine input
from VSLSs is therefore extended to 0.8 ppt (0.1 ppt
SGI + 0.7 ppt PGI, see Table 1.5), which is considerably larger than the upper limit given in previous
Assessments (total iodine injection < 0.15 ppt, PGI< 0.1 ppt) (Carpenter and Reimann et al., 2014;
Montzka and Reimann et al., 2010).

1.4 CHANGES IN ­ATMOSPHERIC
HALOGENS
In this section we discuss the total halogen loadingin the atmosphere and its changes. These are discussed separately for chlorine, bromine, and iodine
in Sections 1.4.1–1.4.3 for the troposphere basedon organic sources (ODSs and VSL SGs) and for the
stratosphere based on inorganic halogen observations. The total chlorine and bromine input to thestratosphere, including contributions from VSLSs, isthen discussed in Section 1.4.4. This input should bereflected in inorganic halogen levels, especially in theupper stratosphere, where virtually all halogen has
been transferred to the inorganic form. Section 1.4.4
also discusses equivalent effective stratospheric chlorine (EESC), which is used as a proxy to describe thehalogen impact on stratospheric ozone (see Box 1-4).
EESC does not include contributions from VSLSs.
Finally, fluorine, while not contributing to ozone depletion, is discussed in Section 1.4.5.
Estimates of tropospheric halogen loading primarily
originate from the global surface networks, which areaveraged together when mole fractions from multiple
networks are available. In general, a simple average isused; however, where data from one network are available for some years but not others, existing network
data are scaled to remain consistent with the two-network average (consistent with previous Assessments).

1.4.1 Tropospheric and ­Stratospher­ic­Chlorine Changes
1.4.1.1	Tropospheric Chlorine Changes
As stated in previous Assessments (e.g., Carpenter
and Reimann et. al., 2014; Montzka and Reimann etal., 2010), the total tropospheric chlorine has been
decreasing continuously since its peak abundance
observed during the years 1993–1994 (Figure 1-12a).
The maximum annual average total chlorine observed
was about 3,660 ppt in 1993. The maximum rate of decrease in total tropospheric organic chlorine was close
to 40 ppt yr−1 in the years 1995 and 1996, mainly driven by the decrease of the rather short-lived CH3CCl3(lifetime of about 5 years). As expected, the rate ofdecrease has since slowed and has continued to slow
down since the last Assessment. Total chlorine fromcontrolled ODSs declined at an average rate of 12.7
ppt yr−1 between early 2012 and late 2016 (Table 1-6).
1.45

Chapter 1 | ODSs and Other Gases

Table 1-6. Contributions of long-lived ODSs and VSL SGs to total chlorine in the troposphere.
Total Cl (ppt)1
2008

2016

2012

Contribution
to Total (%)
2008
2012
2016

Average Rate of Change of Total Cl2 (ppt yr–1)
2004–2008
2008–20122012–2016

All CFCs

2079

2027

1979

62.1

61.4

60.2

−11.6 (1.5)–12.9 (0.7)–12.0 (0.4)

CCl4

359

340

322

10.7

10.3

9.8

–4.4 (0.4)–4.7 (0.3)–4.5 (0.2)

HCFCs

248

285

309

7.4

8.6

9.4

9.1 (1.6)

9.2 (0.7)

5.9 (1.3)

CH3CCl3

32

16

7.8

1.0

0.5

0.2

–8.3 (2.0)–4.1 (0.8)–2.0 (0.5)

halon-1211

4.25

3.96

3.55

0.13

0.12

0.11

–0.02 (0.03)–0.07 (0.01)–0.1 (0.01)

Total
Controlled 2722
Chlorine

2672

2621

81.3

80.9

79.7

–15.2 (1.9)–12.6 (0.3)–12.7 (0.9)

CH3Cl

545

539

556

16.3

16.3

16.9

2.7 (5.2)–1.8 (4.7)

4.0 (3.6)

VSLSs

82

91

110

2.4

2.8

3.3

2.5 (1.1)

2.6 (3.1)

4.3 (4.9)

Total
Chlorine

3349

3302

3287

–11.8 (6.9)–10.0 (6.7)(–0.29% yr-1) (–0.35% yr-1)–4.4 (4.1)(–0.13% yr-1)

1

Chlorine mid-year mole fractions were derived using AGAGE, NOAA, and archive data.

2

Total and relative Cl changes over 5-year periods, as indicated. The values in parentheses represent the standard deviation of theannual growth rates during the respective period, which reflects the observed variability. Relative changes in total chlorine (inpercent per year) were calculated relative to values at the beginning of each period (3,390 ppt total chlorine in 2004). We referto these periods as 5-year periods, as they are based on annual average values, e.g., from the beginning of 2012 to the end of
2016.

Values for past years differ slightly from previous Assessments because of updated calibration information, different methods fordetermining global mean mole fractions, rounding errors, and the inclusion of CFC-112 (Kloss et al., 2014) and CFC-113a (Adcock
et al., 2018). Total Cl also includes 82, 91, and 110 ppt as VSLSs in 2008, 2012, and 2016, respectively.

This recent decrease of chlorine from controlled substances has been partly offset by an increase in substances not controlled under the Montreal Protocol(e.g., CH3Cl and VSLSs). In addition to the mainly
natural CH3Cl (which increased at 4.0 ppt yr−1), chlorine from chlorine-containing VSLSs also increased
at an average rate of 4.3 ppt yr−1 between 2012 and2016. While chlorine from controlled substances has
decreased by 12.7 ppt yr−1, these increases in chlorine
from compounds not controlled under the Montreal
Protocol have partly offset this decrease, leading to adecrease of total chlorine of only 4.4 ppt yr−1 (0.13%yr−1) during this 5-year period. Overall, the fractional
contribution of substances not controlled under theMontreal Protocol to total tropospheric chlorine has
increased and is now above 20%, mainly due to CH3Cl(16.9%).
Figure 1-12a compares the observed decrease in total
chlorine with the projected trend based on the A1
1.46

scenarios from the 2010 and 2014 Assessments. Total
chlorine from controlled substances is decreasing at
close to the expected overall rate. However, this is partially due to the offsetting effects of a slower than projected increase in HCFCs (Figure 1-12c; note that theA1 scenarios for the HCFCs in previous Assessments
assumed that Article 5 countries would produce
the maximum amount allowed under the Montreal
Protocol) and a slower than projected decrease in
CFCs (Figure 1-12b). This slower than expected decrease in chlorine from CFCs is dominated by CFC11 but CFC-12 and CFC-113 have also recently contributed to this difference.

1.4.1.2

Stratospheric Chlorine Changes

As mentioned above, total organic chlorine in the troposphere peaked in the early 1990s and has since been
declining, although the rate of decline is not constantin time. One would expect a similar global long-term
response in stratospheric inorganic chlorine, shifted

ODSs and Other Gases | Chapter 1

Total column abundance (x1015 molecules cm–2)

June-to-November
monthly means

Year-roundmonthly means

Year
Figure 1-13. Multi-decadal monthly mean total column time series of the two main chlorine reservoirs,
HCl and ClONO2, and their summation, as monitored at the Jungfraujoch station (Swiss Alps, 46.5°N,
3,580 m altitude) and the Lauder station (New Zealand, 45.0°S, 370 m altitude), in the framework of
the NDACC network. For the Jungfraujoch, in contrast to the data shown in Figure 1-2, the datasets are
restricted to the June to November months in an effort to limit the variability caused by atmospherictransport and subsidence during winter and spring. The continuous lines come from non-parametricleast-squares fits involving an integration time of about 3 years and help to visualize the non-monotonic and non-linear changes in stratospheric chlorine after the peak from 1996 to 1997.

by a timescale relating to transport into that region(age of air) and photochemical conversion. Significant
short-term temporal and latitudinal variability in inorganic chlorine was noted in the lower stratosphere,
based on HCl measured from ground-based column

data (mostly sensitive to the lower stratosphere) andsatellites (Mahieu et al., 2014a). Based on the latter
work, increases were observed in total column andlower-stratospheric northern mid-latitude HCl fromabout 2005 to 2011. These increases were attributed
1.47

Chapter 1 | ODSs and Other Gases

HCI Trend: 60°S–60°N
1

50

GOZCARDS: 2005–2016
GOZCARDS: 1997–2016

46
42
38
34

10

30
26

Altitude (km)Pressure (hPa)ACE-FTS: 2004–2017

22
18

100
-1.5

-1.0

-0.5
Trend (% yr –1)

0.0

14
0.5

Figure 1-14. Trends (% yr –1) in near-global (60°S to 60°N) HCl abundances, based on different satellite-based
datasets and time periods. The longer-term (1997–2016) average linear trend profile (orange dots) is
obtained from GOZCARDS merged HCl lower-stratospheric data (updated from Froidevaux et al. (2015),
using additional Aura MLS data through 2016). The same dataset is also analyzed for 2005–2016 (black dots).
The GOZCARDS trends are derived using a multi-linear regression model that includes seasonal and shorter
period cycles, as well as QBO-type variations. The blue dots arise from a linear trend calculation (Bernathand Fernando, 2018) for a seasonally-averaged (and de-seasonalized) ACE-FTS time series (60°S to 60°N,
2005 through 2017) based on version 3.5/3.6 data; these trends do not change appreciably if the 2005–2016
period is used instead. The ACE-FTS trend errors are 2-sigma estimates, taking into account the auto-correlation of the residuals. The GOZCARDS error bars provided here are 2-sigma estimates, based on a bootstrap
resampling method (using 20,000 resampling cases from blocks of data).

to variability in the circulation affecting the Northern
Hemisphere. Figure 1-13 shows an update to thelong-term total column data for HCl and ClONO2
and their sum, which represents most of the inorganic
chlorine in the stratosphere, at Jungfraujoch (46.5°N)
and at Lauder (45°S), through the end of 2016. At
the Jungfraujoch station, statistically significant decreases are observed for both species for the period
from 1997 through 2016: –0.42±0.23% yr−1 for HCl
and –0.60±0.39% yr−1 for ClONO2 (based on June toNovember data; updated from Mahieu et al., 2014a).
The trend over the past decade is, however, not significant. Similar trends have been reported for the
1.48

mid-latitudes of the Northern Hemisphere from other
NDACC (Network for the Detection of AtmosphericComposition Change) stations (Kohlhepp et al.
(2012). For the same period (1997–2016), slightly larger negative trends are derived from the Lauder data(including all months) for the Southern Hemisphere,with a decrease of –0.51 ± 0.12% yr−1 for HCl and–0.74 ± 0.59% yr−1 for ClONO2. HCl from the Global
Ozone Chemistry And Related trace gas Data records
for the Stratosphere (GOZCARDS), based on the
HALOE, ACE-FTS, and Aura MLS satellite instruments (Froidevaux et al., 2015), shows similar trends(1997–2016) for Northern Hemisphere mid-latitudes.

ODSs and Other Gases | Chapter 1

20

1200

10

halons + CH3Br

600
400

5

0.4
0.2
0.0
−0.2

Br trend (ppt yr -1)

Total tropospheric bromine (ppt)800

halonseq. Cl (ppt)

1000

15

Figure 1-15. Tropospheric bromine
time series and annual changes
from long-lived gases, separated
by contributions due to halonsand CH3Br. No contribution fromVSLS species is included here. The
values are also shown expressedin equivalent chlorine (right-handaxis of upper panel), adapting avalue of α=65 to account for thehigher efficiency of bromine incatalyzing ozone destruction. All
values are derived from a merged
dataset based on NOAA, AGAGE,
and UEA data.

−0.4
1990

1995

2000

2005
Year

2010

GOZCARDS also shows latitude-dependent, shortterm lower-stratospheric HCl changes (for different
6- to 8-year periods), with steadier/larger decreases
at southern latitudes contrasting with increases atnorthern mid-latitudes.
Near-global (60°S–60°N) trends of HCl fromGOZCARDS and ACE-FTS are compared for the
12-year period 2005–2016 in Figure 1-14. While
GOZCARDS is dominated by MLS results, as amerged satellite product it also contains some information from ACE-FTS, so the two estimates are notcompletely independent. ACE-FTS shows a decreasein the uppermost stratosphere at a rate of about –0.48± 0.02% yr−1 (Bernath and Fernando, 2018) and good
agreement with the GOZCARDS result near 10 hPa.
Taking into account the time shift between the troposphere and the upper stratosphere of about 5 years,
this rate of decrease is in good agreement with theaverage rate of decrease in tropospheric chlorine over
the time period from 2000–2010. There is increasing
divergence between these two satellite-based datasets at the lower altitudes, although not significant at
the 2-sigma level. In the ACE-FTS trend calculation,

2015

dynamical variability has been removed based ona regression model using N2O time series (Stolarski
et al., 2018). Such a procedure was not applied to the
GOZCARDS results in Figure 1-14, but it would likely reduce the differences in lower-stratospheric trends
versus the ACE-FTS result. Sampling differences
between MLS and ACE-FTS, and instrument or retrieval issues, could also play a role in explaining thesedifferences. Long-term changes in HCl could also be
influenced by changes in stratospheric dynamics andchlorine partitioning, especially in the ratio between
ClONO2 and HCl. There is also evidence for significant latitudinal differences in the trends obtained
from various FTIR column ClONO2 time series(Kohlhepp et al., 2012), although the ratio between
HCl and ClONO2 at Jungfraujoch does not show any
significant trend within the 95% confidence interval.
Variability in atmospheric circulation has been suggested as a reason for the lack of a significant trend at
Northern Hemisphere mid-latitude HCl over the past
decade (Mahieu et al., 2014a). This is corroborated byobserved changes in the mean age of air in Northern
Hemisphere mid-latitudes over the same time period(Haenel et al., 2015; Stiller et al., 2012) and smaller
1.49

Chapter 1 | ODSs and Other Gases

than expected trends in source gases (Chirkov et al.,2016; Nedoluha et al., 2015).
We conclude that, despite some complications in thelower-stratospheric HCl measurement comparisons,
there is continuing evidence for locally non-monotonic latitude-dependent HCl changes in this region,
with a slowdown in the decrease of lower-stratospheric HCl since the initial period (about 1997–2005) after
its peak concentrations. Upper-stratospheric HCl(close to the abundance of inorganic chlorine, Cly) is
continuing to decrease steadily, based on ACE-FTS
HCl data.
Other evidence for continuing decreases in stratospheric inorganic chlorine comes from ground-based
and satellite ClO measurements over Antarctica(Nedoluha et al., 2016). ClO trend detection in this region is complicated by large, meteorologically driven,interannual variability. Temperature-adjusted ClO (asa rough proxy for Cly) over that region shows a trend
from the Scott Base data of –0.6 ± 0.8 (2 sigma) % yr−1
for 1996–2015 and –0.5 ± 0.4% yr−1 for 2004–2015
as measured zonally by Aura MLS at the Scott Baselatitudes. Based on millimeter-wave emission measurements at Mauna Kea, Hawai'i, ClO near 33–37
km continued to show decreases (Connor et al., 2013).
Updated ClO trends (from day-minus-night data) are
as follows: –1.08 ± 0.40% yr−1 for the early period from1995 to 2004 (unchanged from the above reference),–0.49 ± 0.12% yr−1 for 1995–2015, and –0.32 ± 0.26%
yr−1 for the 2005–2015 period, 2-sigma uncertainties.
To first order at least (since upper-stratospheric ClO is
not an exact proxy for Cly), these results suggest broad
agreement, with other evidence pointing to a continuing decrease with a gradual slowdown in the rate ofdecrease in stratospheric (and tropospheric) chlorine.

1.4.2 Tropospheric and S­ tratospheric­Bromine Changes
1.4.2.1	Tropospheric Bromine Changes
As stated in previous Assessments, total ­troposphericbromine from CH3Br and halons, the brominatedsubstances controlled under the Montreal Protocol,
reached a maximum in 1998 with an annual averagevalue of 16.9 ppt. Since 1998, its abundance has been
decreasing at an average rate of 0.15 ± 0.1 ppt yr−1
(1% yr−1) over the period from 2000 to 2016 (Figure1-15), reaching a value of 14.6 ppt by mid-2016. This
1.50

value does not include bromine from VSLSs, such as
CH2Br2 and CHBr3, which are not regulated under theMontreal Protocol. While bromine from CH3Br has
been decreasing since the late 1990s, bromine fromhalons did not start to decrease until 2006. For theperiod from 2008 to 2012, total bromine decreased at
a rate of 0.16 ppt yr−1. Halons contributed ~38% tothis decline, while CH3Br contributed ~62%. In the
more recent period from 2012 to 2016, total bromine
declined at a rate of 0.15 ± 0.04 ppt yr−1 (1% yr−1).
However, the contributions of halons and CH3Br were
nearly reversed compared to the previous period: halons contributed ~70% (–0.10 ppt yr−1), while CH3Br
contributed ~30% (–0.04 ppt yr−1). For the first time,the decrease in total bromine over the past 5-year period was thus not dominated by a decrease in CH3Br
but rather by a decrease in halons. The reduced contribution of CH3Br to the decline in total bromine wascaused by an as-yet-unexplained increase in CH3Br
in 2016 (see Section 1.2.7). The observed decreasein halons is in overall good agreement with the decrease projected by the A1 scenarios from the 2014
Assessment (Harris and Wuebbles et al., 2014).

1.4.2.2

Stratospheric Bromine Changes

Bromine is transported to the stratosphere in the formof long-lived ODSs, mainly the halons and CH3Br
as well as from VSLSs, both in organic and inorganic forms. In general, the amount of bromine in thestratosphere can either be determined by summingup the long-lived ODSs and VSLSs (Brinckmann etal., 2012; Navarro et al., 2015; Pfeilsticker et al., 2000;
Sala et al., 2014; Wamsley et al., 1998) or by combining
measurements of BrO with a modeled ratio of BrO/
Bry (Dorf et al., 2006; Dorf et al., 2008; Höpfner et al.,2009; Kreycy et al., 2013; Millán et al., 2012; Stachnik
et al., 2013; Werner et al., 2017). Bry is the sum of all inorganic bromine species. The BrO-based method will
also yield an estimate of total bromine if the measurements are performed in the upper stratosphere, where
all organic long-lived ODSs and VSL SGs are broken
down and all bromine is thus in the inorganic form.
If this method is applied in the lower stratosphere,
additional measurements of the remaining long-lived
ODSs and VSL SGs are needed to determine total bromine (Wales et al., 2018; Werner et al., 2017).
In the case of the source gas-based technique, the determination of the contribution from the long-lived

ODSs and Other Gases | Chapter 1

ODSs is straightforward, as they are sufficiently longlived to be transported into the stratosphere and theirstratospheric entry mole fraction can thus be estimated using the global mean from the observational networks (see Sections 1.2 and 1.4.2.1). For thecontribution from the VSLS gases, both organic (SGI)and inorganic (PGI) forms need to be considered. The
sum of these two was assessed to be 5 (3–7) ppt in
Section 1.3 (see Table 1-5). As there is no indicationof a long-term change in this value, total organic
bromine input to the stratosphere is thus derived byadding 5 ppt to the respective sum of the long-lived
source gases.
Figure 1-16 compares studies that have used the BrObased technique, based on total column and vertically resolved measurements, with source gas observations of CH3Br and halons. Figure 1-16 is an update28
26

Total stratospheric bromine (ppt)

24
22
20
18
16
14
12
10
8
6
4
2
0

1980

1985

1990

1995

2000

2005

2010

2015

2020

Year of stratospheric entry
Figure 1-16. Changes in total stratospheric Bry (ppt) derived from balloon-borne (black squares) (updateof Dorf et al., 2006) and airborne (purple open squares) (Werner et al., 2017) BrO observations and annual
mean mole fractions calculated from ground-based UV-visible measurements of stratospheric BrO madeat Harestua (60°N) and Lauder (45°S) stations (filled and open orange triangles, respectively (adapted from
Hendrick et al., 2007 and Hendrick et al., 2008). All UV-visible measurements of stratospheric BrO were
evaluated using a common BrO absorption cross section (based on Wahner et al., 1988), frequency-shiftedto match the wavelength scale (Wilmouth et al., 1999). For the balloon-borne observations, bold/faint error
bars correspond to the precision/accuracy of the estimates, respectively. For the ground-based measurements (triangles), the error bars correspond to the total uncertainties in the Bry estimates. For stratosphericdata, the date corresponds to the time when the air was last in the troposphere, i.e., sampling date minus
estimated mean age of the stratospheric air parcel. Time series of CH3Br and halons have been updated(see Carpenter and Reimann et al. (2014) for details). The blue lines show the expected stratospheric Bry,
assuming an additional input of 3, 5, and 7 ppt of brominated VSLSs, respectively. For tropospheric data,
the date corresponds to the sampling time. This figure updates Figure 1-20 from the previous Assessment(Carpenter and Reimann et al., 2014).
1.51

Chapter 1 | ODSs and Other Gases
3600 ppt

3000

7.6%

Methyl Chloroform 0.5%, 0.2%

3.9%

Other: 1.4%, 1.5%, 1.5%
VSLSs: 2.2%, 3.0%, 3.5%

8.6%

9.4%

10.3%

9.8%

Carbon Tetrachloride

6.7%

6.5%

CFC-113

21.3%

20.9%

CFC-11

29.5%

31.8%

31.2%

CFC-12

15.0%

16.3%

16.9%

Methyl Chloride

1996

2012

2016

21.3 ppt
4.7%

20.1 ppt

19.6 ppt

11.4%
Human activity

Chlorine (ppt)

7.0%
2000

0

Naturalsources

1000

4.5%

4.4%

Other Halons

17.0%

Halon-1301

19.7%

18.1%

Halon-1211

7.4%

6.8%

25.8%

27.3%

28.1%

23.5%

24.8%

25.6%

1996

2012

2016

12.0%
Human activity

15

22.1%

16.2%
16.9%
17.1%

10

5

0

HCFCs

Methyl Bromide

Naturalsources

Bromine (ppt)203310 ppt 3290 ppt

Very short-lived species

Figure 1-17. Chlorine and bromine input to the stratosphere for 1996, 2012, and 2016 for different species
and classes of compounds. The year 1996 represents a reference which is close to the maximum of bothchlorine and bromine loading of the troposphere. Mole fractions of long-lived gases are derived from surface
observations (global networks). VSLS contributions for bromine are included as a constant 5 ppt, as discussed
in Section 1.3 and summarized in Table 1-5. The VSLS chlorine contribution is based on the VSL SG input
from a model constrained by observed surface boundary conditions (update of Hossaini et al., 2015), seediscussion in Section 1.3.2.1). Total VSLS Cl input derived in this way is 80 ppt, 100 ppt, and 115 ppt for years
1996, 2012, and 2016, respectively. For chlorine, “other” includes minor CFCs and halon-1211. For bromine,“other halons” is the sum of bromine contained in halon-1202 and halon-2402. Methyl chloride is counted ashaving purely natural sources, despite some indications of anthropogenic contributions (see Section 1.2.6).
1.52

ODSs and Other Gases | Chapter 1of data presented in the last Assessment (Carpenter
and Reimann et. al, 2014; Dorf et al., 2006; Hendrick
et al., 2008; Hendrick et al., 2007) and includes new
data from the ATTREX campaign (Werner et al.,2017). The stratospheric data are plotted against the“year of stratospheric entry” (respective air mass age)in order to take into account the time to transport
air upward in the stratosphere, as characterized by
the mean age of air. From these data, a long-term decrease of stratospheric bromine of –0.16 ± 0.07 ppt
yr−1 is derived for the period from 2004 to 2014, in
excellent agreement with the trend in troposphericbromine derived for the same time period. A valueof 19–20 ppt for total bromine in the stratosphere is
deduced for the year 2016, in good agreement withthe bottom-up method if an input of about 5 ppt
from VSLSs (sum of PGI and SGI) is added to thelong-lived ODSs. This good agreement enhances theconfidence in our overall understanding of bromine
input to the stratosphere due to VSLSs. It should also
be noted that due to the decline in the compounds
regulated under the Montreal Protocol, less than 50%
of bromine entering the stratosphere is now due toanthropogenic emissions.

1.4.3 Tropospheric and Stratospheric
Iodine Changes
The main organic source gas for iodine is CH3I.
There are no updated data on tropospheric trends
since those published by Yokouchi et al. (2012), andthere is consensus that the direct input of CH3I to thestratosphere is small, i.e., below 0.1 ppt (Carpenter
and Reimann et al., 2014; Tegtmeier et al., 2013).
Observations also agree on the amount of IO in the
TTL, which is on the order of 0.15 ppt or less (Böschet al., 2003; Butz et al., 2009; Dix et al., 2013; SaizLopez et al., 2015). The recent debate about the possibility of a higher PGI to the stratosphere of up to
0.7 ppt (Saiz-Lopez et al., 2015) (see Section 1.3) does
not concern a long-term change but rather a different
partitioning of total iodine to IO. If the mechanism
suggested by Saiz-Lopez et al. (2015), involving higher oxides of iodine, is effective, this would imply ahigher iodine content of the stratosphere but not necessarily a long-term change.

1.4.4 Changes in Ozone-Depleting ­Halogen
Abundance in the Stratosphere
Chlorine and Bromine Input to the StratosphereDue to the regulations of the Montreal Protocol, many
long-lived ODSs that contribute chlorine and bromine
to the stratosphere are now decreasing in the atmosphere. Aside from some minor CFCs (Table 1-1), theexception to this are HCFCs, which are still increasing, though the rates of increase are slowing down(see Section 1.2). On the other hand, chlorinated
VSL SGs have shown significant increases during thepast decade. This has resulted in changes in the total
burden and the relative contributions of different species to total chlorine and bromine in the troposphereand thus to the input of halogens to the stratosphere.
Figure 1-17 shows the changes in total bromine andtotal chlorine input to the stratosphere. This input isderived by assuming that the global average values derived for long-lived compounds are representative of
the amount transported to the stratosphere. For chlorine and bromine from short-lived substances, VSLS
input is included from measurements and modelingat the tropical tropopause, as discussed in Section 1.3
and shown in Table 1-5, considering both source gas
injection (SGI) and product gas injection (PGI). For
bromine, the absolute contribution from VSL SGs isconstant, but the relative contribution is increasing,reaching about 25% in 2016. For chlorine, both theabsolute and relative contribution of VSL SGs is increasing, although the relative contribution remains
small compared to bromine, at 3.9% in 2016. For bothbromine and chlorine, there have been no signs ofsignificant long-term changes in the natural sources,
so the absolute contributions of natural sources tostratospheric chlorine and bromine remain constant,
but their relative contribution is increasing. In thecase of bromine, it is now estimated that more thanhalf of the stratospheric bromine input is due to natural sources.
Equivalent Effective Stratospheric Chlorine (EESC)
While the total amount of chlorine and brominetransported to the stratosphere is important, themost relevant metric for the impact on stratosphericozone is the inorganic halogen loading. The inorganic
halogen loading depends not only on the amount of
1.53

Chapter 1 | ODSs and Other Gases

Box 1-4. Equivalent Effective Stratospheric Chlorine (EESC)
and Fractional Release Factors
EESC is a metric used to describe the combined impact of chlorine and bromine on stratospheric ozone
and the temporal development of this effect due to tropospheric trends. The basic concept of EESC, as wellas that of fractional release factors, which are needed for the calculation of EESC, has been presented anddiscussed in previous Assessments (Daniel and Velders et al., 2011; Harris and Wuebbles et al., 2014). EESC
is expressed as an equivalent chlorine and is commonly defined as “the sum of the time-dependent chlorine
and bromine derived from ODSs tropospheric abundances, weighted to reflect their potential influence onozone” (Harris and Wuebbles et al., 2014). EESC is derived from observed or projected tropospheric mole
fractions of ODSs. The calculation of EESC does not take into account changes in stratospheric transportor photochemistry. Therefore, it is important to emphasize that any discussion of EESC changes and

0.20

0.4
(1−f(t’))*G(t’)

G

0.2

0.1

0.0

G(t’)

0.15

Fraction (yr -1)

Fraction (yr-1)

0.3

2

4

6

Transit time t’ (years)

0.10(1−f(t’))*G(t’)0.05

f(t’)*G(t’)0f(t’)*G(t’)

8

10

0.00

0

2

4

6

8

10

Transit time t’ (years)

Box 1-4 Figure 1. Age spectra G (black line) for an inert tracer compared to the transit time distributions
weighted with chemical loss (red and blue line) for a mean age of 3 years (a), and 5.5 years (b). The red line
represents the transit time distribution for the remaining organic fraction of a source gas; the blue line represents the fraction which has been released and is thus in the inorganic form. The loss function has been
approximated as a function of transit time in order to represent a tracer similar to CFC-11 (Engel et al., 2018).
The first moments of the three functions differ substantially: the first moment of the red curve (representing
the remaining organic fraction) is younger, and that of the blue curve (describing the inorganic halogen
released from the source gas) is older than the mean age value. Note that this figure is purely for illustrative
purposes, as the loss function has been approximated.

percentage reductions of EESC does not imply a similar response of ozone. Such a relationship between
EESC and ozone would only be expected in the absence of any other factors influencing ozone. The formulation used in the previous Assessments to calculate EESC was based on Newman et al. (2007), whichtakes into account the time delay and mixing during transport in the stratosphere by adapting an age spectrum. For polar winter conditions, a mean age value of 5.5 years is used, and a value of 3 years is adapted
for mid-latitudes, in line with previous Assessments. The efficiency with which each chlorine or bromine
compound releases their halogen content is taken into account using age-of-air-dependent fractional releasefactors in the calculation of EESC. These fractional release factors describe the fraction of a halogen-carrying
source gas that is photochemically lost as a function of mean age. Fractional release factors are higher for 5.5
years of mean age, leading to overall higher values of EESC under polar winter conditions in comparison to
mid-latitude conditions.
1.54

ODSs and Other Gases | Chapter 1

Box 1-4, continued.
Fractional release factors are mostly derived from observations, again taking into account the age spectrum
to compensate for the time-lag between the troposphere and the stratosphere and the tropospheric trends.
Fractional release factors should be constant in time as long as stratospheric transport and chemistry do
not change, thus representing a molecular property for a given atmospheric state. In particular, they should
be independent of the tropospheric trend of a trace gas; i.e., fractional release factors derived during different periods should be very similar, as atmospheric transport is expected to change much less rapidly thantropospheric trends of the source gases. It has recently been shown by Ostermöller et al. (2017) that this is
not the case for the current formulation used to calculate fractional release factors (Newman et al., 2007),
because the age spectrum used to calculate fractional release is that of an inert tracer and does not take into
account chemical loss. Based on the work of Plumb et al. (1999), Ostermöller et al. (2017) suggested that adifferent transit time distribution should be used to better take into account the interaction of tropospherictrends, chemical breakdown, and stratospheric transport. They presented a new method to calculate fractional release based on a different age spectrum, which is weighted by the chemical loss. As discussed byEngel et al. (2018), the actual loss function will depend on both transit time and the transport pathway of
the individual fluid elements of an air parcel, with transport pathways that reached higher altitudes generally
showing larger fractional loss. Nevertheless, it has been shown (Engel et al., 2018) that when describing theloss only as a function of transit time, a much better agreement between EESC calculated from model-derived fractional release factors and inorganic halogen loading from model calculations can be achieved. Box
1-4 Figure 1 shows age spectra for mean ages of 3 and 5.5 years for a compound with a loss function f(t')
approximated as function of transit time t' which is approximately representative of CFC-11. As chemicalloss is more pronounced in the fraction having long transit times, the transit time distribution describing
the remaining organic fraction is weighted more strongly at shorter transit times and thus has a lower meanvalue. The mean value of this distribution has been termed the “mean arrival time” (Plumb et al., 1999); andthe distribution itself, the “arrival time distribution.” Using this formulation, fractional release factors can bederived that are largely independent of the tropospheric trend (Ostermöller et al., 2017).
Based on the same concept that the age spectrum is weighted with chemical loss, Engel et al. (2018) showedthat the calculation method for EESC could also be improved. While the arrival time distribution used todescribe the remaining organic fraction is weighted with the remaining fraction (1–f(t')) (red curve in Box
1-4 Figure 1), the fraction describing the released inorganic halogen fraction is weighted with the loss function f(t') (blue curve in Box 1-4 Figure 1). The transit time distribution describing the released inorganic
halogen has been termed the “release time distribution,” in analogy to the arrival time distribution, and its
first moment has been termed the “mean release time.” The mean release time will always be longer than themean age.
In this Assessment we present results for EESC from the method presented by Newman et al. (2007) and themethod suggested by Engel et al. (2018).

source gases transported into the stratosphere but also
on the efficacy with which halogens are released fromthe source gases. Furthermore, both transport fromthe troposphere to the stratosphere and transport andmixing in the stratosphere need to be considered, withtimescales for the latter being on the order of several
years. The different efficiencies with which ODSs and
VSLSs release their halogen content are describedby fractional release factors. Stratospheric transportand mixing are described by an age-of-air spectrum.
While mean age is the average time it takes for an
air parcel to be transported from the troposphere toa certain location in the stratosphere, this age spectrum describes the probability distribution for different transit times. Furthermore, it has to be taken
into account that bromine is a much more effective
1.55

Chapter 1 | ODSs and Other Gases

Polar Winter Conditions
a)

4000

20

EESC (ppt)

40

3000

60

2500

80

1500

100

Engel et al., 2018

Newman et. al. 2007
1980

1990

2000

Year

120

1800

40
60
80
100

1600

40
60

1400

80

1200

100

Engel et al., 2018

1000

Newman et. al. 2007

120

120

800

2010

20

20

1980

1990

2000

Year

40
60
80
100
120

EESC Newman (% recovery)

20

0

0

b)

EESC Engel (% recovery)

0

EESC Newman (% recovery)

0

EESC Engel (% recovery)

3500

2000

Mid-Latitude Conditions

2000

EESC (ppt)

4500

140

2010

Figure 1-18. Level of EESC recovery towards 1980 benchmark values comparing the EESC calculation as usedin the 2014 Assessment (Carpenter and Reimann et al., 2014; Newman et al., 2007) to the new method suggested by Engel et al. (2018). Plot (a) is for 3 years of mean age, representative of mid-latitude conditions,while plot (b) is for 5.5 years of mean age, representative of polar winter conditions. In all cases, the different
age spectra (see Box 1-2) were parameterized as suggested by Newman et al. (2007) using half of the mean
age as width of the age spectrum and an inverse Gaussian function as the shape of the age spectrum. The
age spectrum has been integrated over a time period of 20 years. Fractional release factors were used as in
the last Assessment report and in Velders and Daniel (2014) for the formulation according to Newman et al.
(2007). In the calculation using the formulation suggested by Engel et al. (2018), the same fractional releasefactors were used, but these were modified to be consistent with the formulation of fractional release suggested by Ostermöller et al. (2017), as explained in Engel et al. (2018). The same tropospheric data were used
as in Section 1.4.1.1 and Section 1.4.2.1. VSLS contributions to EESC are not included in this calculation. In
the calculation of EESC, the higher efficiency of bromine to destroy stratospheric ozone is taken into account.
As in previous Assessments, we adopt a factor of 60 for mid-latitude conditions and a factor of 65 for polar
winter conditions.
Table 1-7. EESC values for early 1980 and early 2017 as well as the value when EESC was at its maximum.
Values are given for 3 and 5.5 years of mean age and for the two calculation methods discussed (see text in
Section 1.4 for details). Also shown are decreases achieved by early 2017 with respect to the maximum andthe percent recovery with respect to the 1980 values. The calculated decreases and recovery rates for 3 years ofmean age are significantly smaller using the new method of Engel et al. (2018) than when using the Newman etal. (2007) method.
3 Years of Mean Age; Mid-Latitude Conditions
EESC

EESC

EESC

% decrease% Recovery

1980

maximum

early 2017from maximum

to 1980

Newman et al. (2007) 1161

1928

1601

–17%

43%

Engel et al. (2018)

1902

1649

–13%

31%

1080

5.5 Years of Mean Age; Polar Winter Conditions

1.56

EESC

EESC

EESC

% decrease% Recovery

1980

maximum

early 2017from maximum

to 1980

Newman et al. (2007) 2161

4148

3774

–9%

19%

Engel et al. (2018)

4154

3794

–9%

18%

2151

ODSs and Other Gases | Chapter 1

ozone destruction catalyst than chlorine. As in previous Assessments, we adopt a factor of 60 for mid-latitudes and 65 for high-latitude polar winter conditions
for the relative efficiency of bromine versus chlorinewith respect to ozone destruction. These factors are
combined in the metric of equivalent effective stratospheric chlorine (EESC). New formulations to derive
fractional release factors (Ostermöller et al., 2017) and
EESC (Engel et al., 2018) have recently been proposed(see Box 1-4), with a refined treatment of the interaction between chemistry and transport. In the past,different fractional release factors have also been used,mainly based on work by Newman et al. (2007) and
Laube et al. (2013). The work by Laube et al. (2013)
has recently been re-evaluated using a new method(Ostermöller et al., 2017) and taking into account possible offsets in mean age of air in this work due to theuse of SF6-derived mean age (Leedham Elvidge et al.,2018). With this there is much better agreement withthe fractional release values used in (Engel et al., 2018;
Newman et al., 2007; Velders and Daniel, 2014) andrecent WMO reports.

Total tropospheric fluorine (ppt)

A refinement in the method to calculate EESC has
recently been suggested by Engel et al. (2018). The
concept of EESC, the methods of calculation, andthe changes in the concept suggested by Engel et al.

(2018) are explained in Box 1-4. Here, we will present
results from both methods. Significant differences inthese methods are derived for mid-latitude conditions
only, while for polar winter conditions both methods
yield very similar results. We have retained the same
parameterization of the age spectrum as in previous
Assessments; i.e., the width of the distribution is taken
as half the value of the mean age. Here, we integrate
the age spectrum over 20 years instead of 10 years,
as was done in the previous Assessment. As can beseen in Box 1-4 Figure 1, a significant fraction of air
has transit times greater than 10 years for a mean ageof 5.5 years. We use a 20-year integration period tobetter account for all contributing air parcels.
The different methods in how EESC is calculated, andthe integration periods considered, result in slightly
different maximum EESC values as well as different
benchmark values calculated for the year 1980. EESC
in 1980 has been used as a benchmark in many previous Assessments, although this is certainly somewhat
arbitrary, as ozone loss occurred prior to 1980. We also
note that a return to 1980 EESC levels does not imply arecovery of the ozone layer to the 1980 state, as ozone
is influenced by additional parameters like changes instratospheric dynamics and chemistry. Independent of
the formulation used to calculate EESC and the time

4000
3000
2000
1000
120

0

100

tot. F others

80

tot. F ODS
tot. F

60
40
20
0

1980

1990

2000
Year

2010

F trend (ppt yr -1)

tot. F HFCs

Figure 1-19. Tropospheric total
fluorine time series and annualchanges from long-lived gases, separated by contributions due to ODS,
HFCs (see Chapter 2), and otherfluorinated gases (such as SF6,
NF3, and CF4). ODS mole fractions
were derived from a merged dataset based on NOAA, AGAGE, andUEA data. HFC mole fractions were
derived from AGAGE and NOAA
data. HFC records prior to regularglobal measurements were supplemented with estimates from Miller
et al. (2010); Mühle et al. (2010);
O’Doherty et al. (2009); Oram etal. (1996); Simmonds et al. (1998);­Vollmer et al. (2011); and Weiss et al.
(2008).
1.57

Total column abundance (1015 molecules cm-2)

Chapter 1 | ODSs and Other Gases

Year

Figure 1-20. Multi-decadal monthly mean total column time series of the two main fluorine reservoirs, HF
and COF2, and their summation (Fy), as monitored at the Jungfraujoch station (Swiss Alps, 46.5°N, 3,580 m
altitude), in the framework of the NDACC network. The datasets are restricted to the June to November
months, so as to reduce the variability caused by atmospheric transport and subsidence during winter andspring. The continuous lines come from non-parametric least-squares fits involving an integration time ofabout 3 years and help to visualize the non-monotonic and non-linear changes in stratospheric chlorine
after the peak in 1996–1997.

over which the spectrum is integrated, a continuous
decline of EESC is derived. For a mean age of 5.5 years,
as used for typical polar winter conditions, fractional
release is nearly complete, and both formulations of
EESC converge. However, in the case of a 3-year meanage, which is typically used for mid-latitude conditions, there are significant differences. Applying the
new formulation, inorganic chlorine is expected tolag the tropospheric source gases more than expectedbased on a mean age representation of an inert tracer(see discussion in Box 1-4). One consequence of thisis that a lower EESC is derived for the 1980 benchmark. Using the formulation for EESC from Newman
et al. (2007) as in the last Assessment, but with a 20year integration time, an early-1980 benchmark valueof 1,160 ppt is calculated for a 3-year mean age, while
1,080 ppt is calculated using the new formulation of
EESC suggested by Engel et al. (2018) (Figure 1-18,
Table 1-7). This lower EESC value implies that it willtake longer for EESC to decline to 1980 benchmark
values. Accordingly, the percentage rate of recovery
1.58

already achieved is also lower. For a mean age of 5.5
years, very similar values are derived with both methods (see Table 1-7). Using the new method, by 2017
we derive recovery of 31% towards 1980 values of
EESC for mid-latitudes and of 18% for polar winterconditions; using the formulation by Newman et al.
(2007) as in the previous Assessment, but with a 20year integration time, by early 2017 we derive recovery of 43% for mid-latitude conditions and 19% forpolar winter conditions.
Changes in EESC over the past 5 years (early 2012 toearly 2017) are very similar for both methods, withmaximum differences of 0.1%. The average change
derived from both methods is given here. EESC, excluding contributions from short-lived substances,declined 4.3% from early 2012 to late 2016 in mid-latitudes and by 3.6% in high latitudes. The main driverof the decrease in EESC between early 2012 and early2017 were the CFCs, which contributed 1.3% and 1.4%
to this decline for mid-latitude and polar conditions,

ODSs and Other Gases | Chapter 1

respectively. CH3Br was the second-most important
contributor to changes in EESC, reducing EESC by
1.2% and 1% for mid-latitude and polar conditions,
respectively. The impact of decreases in CH3CCl3 has
now decreased to 0.8% (it was an important contributor during the period 2008–2012, with a decreaseof about 1.5% for both polar winter conditions andmid-latitudes). EESC from halons decreased by 0.9%
and 0.3% for mid-latitude and polar conditions, respectively. This is the first time that a decrease in
EESC from halons is now also calculated for polar
winter conditions. The decrease in tropospheric CCl4
has resulted in a decrease of 0.8% in EESC at mid-latitudes and 0.6% in polar latitudes. The continuing increase in HCFCs has offset the total decrease by 0.4%
at mid-latitudes and 0.6% for polar winter conditions.

1.4.5 Tropospheric and StratosphericFluorine Changes
Fluorine is not an efficient catalyst for stratosphericozone depletion, due to the stability of the inorganic reservoir gases HF and COF2, which are breakdown products of fluorine-containing source gases.
However, many fluorinated source gases have a highradiative efficiency and are thus strong greenhousegases, and most important fluorine gases are regulated under the Montreal Protocol. This regulation
under the Montreal Protocol for chlorine- and fluorine-containing CFCs and HCFCs was motivated by
the chlorine content. The regulation of HFCs, which
are also included in the Kyoto Protocol, has only
recently been added to the Montreal Protocol (seedetails in Chapter 2) in the framework of the Kigali
Amendment, as these gases are replacement compounds for substances already regulated under theMontreal Protocol.
Figure 1-19 shows the time series of tropospheric fluorine in organic and some inorganic (SF6 and NF3, see
Section 1.5) gases, separated by compound classes. In
contrast to chlorine, a continuing increase in tropospheric fluorine is observed. A trend in total fluorineof 51.6 ± 2.3 ppt yr−1, or 1.7 ± 0.07% yr−1, is derived forthe time period 2012–2016, which is comparable tothe trend of 50.1 (±1.9) ppt yr−1, or 1.7 (±0.07) % yr−1,
for the period 2008–2012. However, the drivers of thistrend have changed. From 2008 to 2012, this increase
was due to increases in ODSs (CFCs and HCFCs; 7.8
ppt yr−1), HFCs (36.4 ppt yr−1), and other fluorinatedgases (6.6 ppt yr−1). For the most recent 5-year period(2012–2016), it is more strongly dominated by HFCs
(45 ppt yr−1). Total fluorine input from ODSs has decreased to an average of 0.9 ± 1.9 ppt yr−1, and thecontribution from other fluorinated gases has been
relatively stable at 5 ppt yr−1. Total tropospheric fluorine and total tropospheric chlorine do not follow thesame trajectory, largely because the largest contribution to a decrease in total Cl since ~1988 results fromchanges in CH3CCl3, which does not contain fluorine.
However, the contribution to total tropospheric fluorine from all ODSs has been declining since the late1980s, and the trend went from positive to negative inthe last year (Figure 1-19, lower panel).
As can be seen from Figure 1-19, the contributions ofdifferent compounds and classes of compounds to thetotal tropospheric fluorine have changed significantly
over time. As different gases release their fluorine withdifferent efficiency, this implies that a direct reflectionof tropospheric trends of fluorinated compounds inthe inorganic fluorine content of the stratosphere is
not necessarily expected.
A good proxy for the total inorganic fluorine (Fy) inthe stratosphere is obtained by the weighted combination of the two most abundant fluorinated reservoirs,i.e., hydrogen fluoride (HF) and two times carbonyl
fluoride (COF2); this can thus be used as an independent check of the fluorine budget. Figure 1-20 shows
the multi-decadal monthly mean total column timeseries of inorganic fluorine above the Jungfraujoch
station (Swiss Alps, 46.5°N, 3,580 m altitude), restricted to the June-to-November months, when atmospheric variability is at a minimum. A non-parametric least-squares fit to the time series helps to identify
fluctuations in the rise of the fluorine loading in the
Northern Hemisphere, fluctuations which are related
to short-term variability in the atmospheric circulation and dynamics. The total column of HF increased
at an average rate of 0.89 ± 0.17% yr−1 and that of
COF2 at 1.07 ± 0.14% yr−1 (update from Duchatelet etal., 2010; Duchatelet et al., 2009), as calculated using
a bootstrap method (Gardiner et al., 2008). Total inorganic fluorine (Fy) has increased at a rate of 0.98 ±
0.15% yr−1 between 2007 and 2016.
Based on datasets from HALOE and ACE-FTS and amerged dataset from GOZCARDS, (Harrison et al.,2016) found a substantial slowdown in the rate of
1.59

Chapter 1 | ODSs and Other Gases

Table 1-8. Measured mole fractions of selected fluorinated compounds (PFCs, SF6, NF3, SO2F2, SF5CF3) and othergases of interest.
Chemical
FormulaCommon orIndustrial
Name

2012

2015

2016

Change(2015–2016)
ppt yr–1% yr–1

Annual Mean Mole Fraction (ppt)

Network, Method

Perfluorocarbons (PFCs)
CF4

PFC-14

79.7

81.9

82.7

0.8

1.0

AGAGE in situ (Global)

C2F6

PFC-116

4.2
3.7

4.5
3.9

4.6
4.0

0.1
0.1

2.1
1.9

AGAGE in situ (Global)
UEA, Cape Grim1

C3F8

PFC-218

0.57
0.54

0.62
0.60

0.63
0.60

0.02
0.00

2.6
0.7

AGAGE in situ (Global)
UEA, Cape Grim

c-C4F8PFC-c318

1.26

1.39

1.44

0.05

3.6

UEA, Cape Grim

n-C5F12

PFC-41-12

0.142

0.149

0.148

–0.001–0.7

UEA, Cape Grim1

7.6

8.6

8.9

0.3

3.8

7.6

8.6

8.9

0.3

3.9

Other fluorinated compounds
AGAGE, flask and in situ
(Global)NOAA, flask and in situ
(Global)SF6sulfurhexafluoride

NF3

nitrogentrifluoride

0.9

1.3

1.5

0.1

11.5

AGAGE in situ (Global)

SO2F2

sulfuryl
fluoride

1.8

2.1

2.3

0.1

6.2

AGAGE in situ (Global)

0.153

0.154

0.153

-0.001-0.65

UEA, Cape Grim
AGAGE in situ (Global)
NOAA, flask and in situ
(Global)UCI, flask (Global)
CSIRO, flask (Global)
WMO/GAW (Global)

SF5CF3
Other compounds

CH4 (ppb)

methaneN2O (ppb)

nitrous oxide

COS (ppt)

carbonylsulfide

1809
1808

1834
1834

1842
1843

8
9

0.4
0.5

1808
1806
1819

1830
1833
1844

1840
1841
1853

10
8
9

0.5
0.4
0.5

325.6
325.0

328.5
328.1

329.3
328.9

0.8
0.8

0.2
0.2

324.9
325.1

327.8
328.1

328.6
328.9

0.8
0.8

0.2
0.2

501

499

505

6

1.2

AGAGE in situ (Global)
NOAA, flask and in situ
(Global)CSIRO, flask (Global)
WMO/GAW (Global)
NOAA, flask and in situ
(Global)Mole fractions in this table are from various monitoring networks that measure long-term trends in these gases. Results in bold text
are estimates of global surface mean mole fractions. Values in italics represent observations from only one site and therefore do
not represent a global mean. AGAGE (Advanced Global Atmospheric Gases Experiment, https://agage.mit.edu/) data are describedin Arnold et al. (2013), Cunnold et al. (2002), Mühle et al. (2010), Mühle et al. (2009), Prinn et al. (2018) and Rigby et al. (2010), andglobal averages are calculated using data from five baseline AGAGE stations, assimilated into an atmospheric box model (extensionof Rigby et al., 2014). NOAA (National Oceanic and Atmospheric Administration, USA, http://www.esrl.noaa.gov/gmd/dv/site/) data
are described in Dlugokencky et al. (2011), Hall et al. (2011), and Montzka et al. (2007), global means are calculated as area-weighted means from observations at 12 sites for SF6, and 45 sites for CH4 (including shipboard sampling). UCI (University of California,
Irvine, USA, http://ps.uci.edu/~rowlandblake/research_atmos.html) data are described in Simpson et al. (2012). UEA (University of
East Anglia, United Kingdom, http://www.uea.ac.uk/environmental-sciences/research/marine-and-atmospheric-sciences-group)
data are described in Laube et al. (2012); Leedham Elvidge et al. (2017), Oram et al. (2012), and Sturges et al. (2012). CSIRO data
(Commonwealth Scientific and Industrial Research Organisation) are described in Francey et al. (2003). Cape Grim refers to the Cape
Grim Baseline Air Pollution Station, Australia; WMO/GAW, World Meteorological Organization, Global Atmosphere Watch, World Data
Centre for Greenhouse Gases, (http://ds.data.jma.go.jp/gmd/wdcgg and https://public.wmo.int/en/resources/library/wmo-greenhouse-gas-bulletin). Differences between each network are due to calibration scales, differences in spatial and temporal samplingstrategies, and the different methods for estimating global means.
Note:
1.60

1

Mole fractions for 2016 represent averages from January to July for UEA data for these compounds.

ODSs and Other Gases | Chapter 1

increase of global stratospheric HF. Trends of 0.52 ±
0.03% yr−1 were obtained from 2004–2012, in comparison to 1.12 ± 0.08% yr−1 for 1998–2005 and 4.97
± 0.12% yr−1 for 1991–1997. The observed trends
and the slowdown are in good overall agreementwith results from modeled HF time series, although
the model calculated a slightly lower increase for the
1991–1997 period. Significant short-term and latitudinal variability was observed in the trends of HF,
which is attributed to dynamical variability in thestratosphere.
Overall, these data show a continuing increase in total
fluorine. The magnitudes of the total troposphericfluorine and stratospheric inorganic fluorine trends
are somewhat different. As explained above, this may
be due to the changing relative importance of different fluorinated gases, with different efficiencies offluorine release in the stratosphere. The troposphericincrease is now mainly driven by increases in HFCs
(Chapter 2).

1.5 CHANGES IN OTHER TRACEGASES THAT INFLUENCEOZONE AND CLIMATE
This section describes recent trends in gases that are
not covered by the Montreal Protocol but that indirectly affect ozone. The gases nitrous oxide (N2O)
and methane (CH4) play a role in stratospheric ozone
chemistry and contribute to climate change and are
discussed in Sections 1.5.1 and 1.5.2. Section 1.5.3
addresses the sulfur-containing gases carbonyl sulfide(COS) and sulfur dioxide (SO2), which are transported to the stratosphere where they contribute to stratospheric sulfuric acid aerosol. Finally, changes in somefluorine-containing greenhouse gases (GHGs), whichindirectly influence ozone through their contribution to global warming (Chapter 5), are discussed in
Section 1.5.4. Carbon dioxide (CO2) is not discussed
here, as it is described in detail elsewhere (e.g., Ciais
and Sabine et al., 2013; Le Quéré et al., 2018). In contrast to previous Assessments, HFCs are no longer
covered in this section, as they are now the subject ofChapter 2.

1.5.1 Nitrous Oxide (N2O)
N2O is the dominant source of reactive nitrogento the stratosphere, which can lead to depletion ofstratospheric ozone. Currently, natural and anthropogenic emissions of N2O make a larger contribution
to stratospheric ozone depletion than emissions of
any of the individual ODSs discussed in Section 1.2(Ravishankara et al., 2009). While it is likely to remaina major contributor to ozone depletion throughout
the 21st century, due to changes in stratosphericchemistry and dynamics brought about by increasing
GHG concentrations, there remains some uncertainty about its long-term impact on ozone (Revell et al.,2015). Owing to its relatively high GWP, N2O is thethird-most important long-lived GHG after CO2 andCH4 (Myhre and Shindell et al., 2013; Ravishankara
et al., 2009). The previous Assessment noted thatN2O had been growing relatively steadily, at a rate ofaround 0.8 ppb yr−1. This trend has continued through2016, for which an annual surface global mean mole
fraction of around 329 ppb was reached (Table 1-8).
As a result of this growth, the contribution of N2O toradiative forcing has continued to rise, reaching 0.19
W m−2 in 2016 (Figure 1-3), approximately 10% thatof CO2.
Recent studies have attempted to better constrain
N2O sources and sinks. The previous Assessment
summarized the findings of the SPARC Lifetimes
Assessment, which estimated the N2O lifetime to be
123 (104–152) years (2-sigma “most likely” range,
SPARC (2013)). Based on observations from theMicrowave Limb Sounder (MLS) and a radiativetransfer model, Prather et al. (2015) recommend
a lifetime of 116 ± 9 years, which is lower than the
maximum likelihood SPARC estimate but within
their uncertainties. Emissions of N2O originate primarily from natural and agricultural soils and theocean, with approximately one-third of emissions
from anthropogenic sources (Ciais and Sabine et al.,2013). Thompson et al. (2014) produced top-down
estimates of N2O emissions using four different
chemical transport models. Their estimate of global
annual emissions of between 16.1 and 18.7 TgN yr−1
was broadly in agreement with IPCC AR5 (Ciais andSabine et al., 2013) and therefore does not substantially alter our previous understanding of the global
budget. More recently, Wagner-Riddle et al. (2017)
proposed a 1.07 ± 0.59 TgN yr−1 source of N2O induced by freeze-thaw cycles over croplands, which
has not been included in previous budgets.

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Chapter 1 | ODSs and Other Gases

1.5.2 Methane (CH4)
Methane, the second-most important anthropogenic
GHG, is a source of water vapor and HOx (OH andHO2) radicals to the stratosphere and thereby also
influences stratospheric ozone. Methane has continued to grow between 2012 and 2016, with the global
mean mole fraction increasing between 32 and 35 ppb
during this period (Table 1-8). The radiative forcing
due to CH4 was 0.5 W m−2 in 2016 (Figure 1-3), 25%
the value of CO2.

Emissions (Gg yr -1)

Mole fraction (ppt)

A recent study re-evaluated the global CH4 budget
from 2000–2012 and concluded that around 60% of

global emissions are anthropogenic, although there
remains a mismatch between emissions estimatedusing bottom-up and top-down methods (Saunois
et al., 2016). Major sources of uncertainty in the CH4budget were found to be due to wetland CH4 emissions and the magnitude and variability of the globalhydroxyl radical (OH) concentration. Several recent
studies have focused on the causes of the pause in
CH4 growth that occurred between 2000 and 2007,
and the subsequent renewed rise. Nisbet et al. (2016)
and Schaefer et al. (2016) used data from CH4 isotopologues to conclude that the renewed growth
was likely being driven by an increase in tropicalFigure 1-21. Global mean mole fractions (left panels) and emissions (right panels) for fluorinated greenhousegases (excluding CFCs, HCFCs, and HFCs). Mole fractions are output from the AGAGE 12-box model (Cunnold
et al., 1983; Rigby et al., 2013) and constrained using AGAGE data described in Arnold et al. (2013); Mühle etal. (2010); Mühle et al. (2009); Prinn et al. (2018); and Rigby et al. (2010). For SF6, pluses represent data fromNOAA (Hall et al., 2011, extended). Emissions were estimated using the Bayesian method described above(Figure 1-4), with atmospheric lifetimes as summarized in Table A-1.
1.62

ODSs and Other Gases | Chapter 1

wetland or agricultural (primarily ruminant animal)
emissions, respectively. By analyzing global livestock
populations and revising emissions factors, Wolf etal. (2017) also proposed that growing emissions fromanimals could be a significant contributor to the rise.
Hausmann et al. (2016) pointed to the coincident
growth in atmospheric ethane (C2H6) to infer anincrease in CH4 emissions related to oil and gas extraction since 2008, while Dalsoren et al. (2016) proposed an increase in anthropogenic emissions from
East Asia. Schwietzke et al. (2016) used new estimatesof isotopic source signatures to revise upwards thecontribution of fossil fuel to the global CH4 budget,compared to bottom-up inventories. They inferred a
gradual decline in fossil fuel emission since the early
2000s, coincident with a gradual rise in emissions
from microbial sources. In contrast, Worden et al.
(2017) proposed that recent trends are consistent witha decline in biomass burning emissions and increasein fossil fuel emissions. McNorton et al. (2016), Rigby
et al. (2017), and Turner et al. (2017) inferred global
OH concentrations using AGAGE and NOAA methyl
chloroform (CH3CCl3) data. They found an increase
in OH in the 1990s and early 2000s, followed by adecline in OH that could explain much of the pause
and renewed growth, although the uncertainties were
found to be large compared to the magnitude of theinferred change. Overall, the uncertainty in the global CH4 budget remains considerable, and there is noconsensus on the drivers of recent trends.

1.5.3 Aerosol Precursors: Carbonyl ­Sulfide(COS) and Sulfur Dioxide (SO2)The sulfur-containing gases COS and SO2, which aretransported to the stratosphere, can be oxidized toyield sulfuric acid, which can condense on preexisting
particles or nucleate to form new particles. Injection of
sulfur gases occurs sporadically during volcanic eruptions, as well as more continuously through the atmospheric transport of tropospheric ­sulfur-containing
gases to the stratosphere. As particles can interact withsolar and terrestrial radiation, this has an influence on
both tropospheric and stratospheric temperatures. In
addition, heterogeneous reactions on aerosol surfaces
in liquid aerosol particles can influence stratospheric chemistry. A review of stratospheric aerosols andtheir precursor gases has recently been published byKremser et al. (2016).

Carbonyl sulfide (COS) is the most important substance responsible for transporting sulfur into thestratosphere. Recent modeling estimates of the sulfur
fluxes from COS suggest that this accounts for 56%(Sheng et al., 2015) to 70% (Brühl et al., 2012) of thestratospheric aerosol burden. The magnitude of thesulfur fluxes calculated in the models is in agreementwith sulfur fluxes from COS derived from observations (Barkley et al., 2008; Krysztofiak et al., 2015).
There are large uncertainties in the global sources andsinks of carbonyl sulfide, with current sink estimatessurpassing the source assessments, due to a recent
factor-of-two upward revision in the global surface
sink estimate (Kremser et al., 2016). While Berry et al.
(2013) and Launois et al. (2015) suggested that missing sources may be due to oceanic emissions, this was
not confirmed by Lennartz et al. (2017). Therefore,
there are currently remaining uncertainties concerning the sinks and sources of COS. The main anthropogenic source of COS is thought to be CS2 emissions
from rayon production, which are rapidly oxidized toform COS in the atmosphere (Campbell et al., 2015).
The main source region for these emissions is thought
to have shifted to China during the past decades(Campbell et al., 2015).
Recent atmospheric observations of COS confirm
that there is currently no or only a very small longterm trend in tropospheric COS (Lejeune et al., 2017).
Between 2015 and 2016, tropospheric background values of COS increased by about 6 ppt (Table 1-8), but
this is more likely a short-term variability than a signof a long-term increase. Preindustrial levels of COS areconfirmed to have been significantly lower than thisbased on ice core and firn air measurements (Aydin
et al., 2014; Campbell et al., 2017). Comprehensive
observations of COS in the stratosphere have recently become available from the MIPAS instrument
onboard Envisat for June 2002 to April 2012. Theseobservations also do not show a significant trend in
the stratosphere (Glatthor et al., 2017). Balloon measurements reveal less than 5% change in stratospheric
COS over the past 25 years (Toon et al., 2017).
Following volcanic eruptions, it has been shown thatstratospheric sulfur loading from the upper troposphere to the upper stratosphere is enhanced (Höpfner
et al., 2015; Höpfner et al., 2013). A large fraction of
the SO2 observed in the stratosphere in the absence ofrecent volcanic emission is from oxidation of COS in
1.63

Chapter 1 | ODSs and Other Gases

the stratosphere. The relevance of the direct transportof SO2 to the stratosphere remains unclear, with large
variations between different models (Kremser et al.,2016; Sheng et al., 2015); however, an analysis of model
results combining in situ measurements with MIPAS
data indicates that it is near negligible (Rollins et al.,2017). Consequently, the impact of changes in tropospheric emissions—for example, the recent reduction in
Chinese emissions of SO2 (van der A et al., 2017)—remains unclear.

1.5.4 Other Fluorine-Containing Gases(SF6, PFCs, NF3, SO2F2, SF5CF3, HFEs)
Sulfur Hexafluoride (SF6)
Sulfur hexafluoride, which was regulated under the
Kyoto Protocol, is used primarily for electrical insulation (e.g. Ko et al., 1993). Its atmospheric global surface mean mole fraction increased from 7.6 to 8.9 ppt
between 2012 and 2016, contributing 5.1 mW m−2 toglobal radiative forcing in 2016 (Table 1-8, Figure 1-3,
Figure 1-21). During the period from 2010 to 2016,
FTIR measurements above Jungfraujoch showed anincrease in the atmospheric column mean mole fraction of 4.34 ± 0.19% yr−1, which is slightly higher thanthe corresponding increase in the lower troposphereof 3.90 ± 0.06% yr−1 based on ground-based measurements (see Table 1-2). Emissions inferred from
AGAGE and NOAA observations show that SF6 global
emissions have increased during this period, reaching
8.7 ± 0.4 Gg yr−1, equivalent to 205 ± 9 Mt CO2 yr−1,
in 2016 (Figure 1-21). These emissions are now 72%
higher than a minimum inferred around the year 2000
(Levin et al., 2010; Rigby et al., 2010).
Using atmospheric SF6 observations, Fang et al.
(2014) estimated increasing emissions from East Asia,
from 2.4 Gg in 2006 to 4.1 Gg in 2012, which is on
average ~50% of the global emissions for this period.
Recent studies have suggested a significant downward revision of the SF6 lifetime, for which a value of
3,200 years has been widely used (Ravishankara et al.,1993). Using a 3-D model with updated atmospheric electron density, Kovacs et al. (2017) estimated anaverage lifetime of 1,278 years, with a range 1,120 to
1,475 years. From observations of SF6 in the Arctic
polar vortex, Ray et al. (2017) estimated a lifetime of
850 (580–1,400) years. Since the lifetime remains very
1.64

long, these estimates will not significantly influence
SF6 emissions derived using observed atmospheric trends, or GWPs over a 100-year time horizon or
shorter. However, climate impacts over longer timescales will be influenced.
Perfluorocarbons (PFCs)
Perfluorocarbons are compounds that consist of only
carbon and fluorine and typically have very long lifetimes and high radiative efficiencies. The major PFCs
are primarily emitted during aluminum and semiconductor production, and they were regulated under
the Kyoto Protocol (e.g. Mühle et al., 2010). Since
the previous Assessment, atmospheric abundancesof all major PFCs have continued to increase (Table
1-8, Figure 1-21). CF4 (PFC-14; lifetime greater than
50,000 years) increased by 0.8 ppt yr−1 between 2015
and 2016, reaching 82.7 ppt in 2016; C2F6 (PFC-116;
lifetime greater than 10,000 years), at 0.1 ppt yr−1 to
4.6 ppt; and C3F8 (PFC-218; lifetime 2,600 years), at~0.02 ppt yr−1 to 0.63 ppt. c-C4F8 (PFC-c318; lifetime 3,200 years) has reached 1.44 ppt at Cape Grim
(update to Oram et al., 2012). Collectively, the PFCs
contributed 6.3 mW m−2 to global radiative forcing in2016 (Figure 1-3). FTIR-based remote sensing observations at Jungfraujoch showed an increase of 1.11 ±
0.09% yr−1 for CF4, which is slightly higher than therate of 0.94 ± 0.01% yr−1 over the period from 2010
to 2016 derived from ground-based measurements(see Table 1-2). Emissions of the major PFCs haveremained relatively stable since 2012 (Figure 1-21).
In 2016, emissions of CF4, C2F6, and C3F8 were 12.6
± 0.7, 2.0 ± 0.2, and 0.52 ± 0.05 Gg yr−1, respectively,
equivalent to CO2 emissions of 84 ± 5, 22 ± 2, 4.6 ±0.5 Mt yr−1.
Trudinger et al. (2016) used measurements of CF4,
C2F6, and C3F8 from ice cores, firn, archived air samples, and field stations to reconstruct atmosphericabundances since 1800. They inferred an increasein emissions during the 20th century for these compounds until the early 1980s (CF4) or early 2000s(C2F6 and C3F8), after which emissions declined. The
growth in emissions of CF4 and C2F6 was found not tohave kept pace with global aluminum production (themajor source of these gases), suggesting a decrease in
the emissions factor from this industry. Apart froman apparent drop in emissions in 2009, attributed toODSs and Other Gases | Chapter 1

the global financial crisis, aggregate emissions of thesecompounds were found to be relatively stable fromthe late 2000s onwards (also shown in Figure 1-21).
Nitrogen Trifluoride (NF3)
Since the previous Assessment, NF3 (lifetime 569
years, GWP100 15,750), which is primarily used insemiconductor manufacture and was included in the
Kyoto Protocol (Arnold et al., 2013), has grown by
more than 10% yr−1, to 1.5 ppt in 2016 (Table 1-8,
Figure 1-21). Its contribution to radiative forcing
remained relatively small in 2016 at 0.3 mW m−2(Figure 1-3). Emissions of NF3 inferred for 2016reached 1.9 ± 0.1 Gg yr−1, equivalent to 30 ± 2 Mt CO2
yr−1 (Figure 1-21). The compound is primarily usedin the electronics industry as a source of reactive fluorine, in place of C2F6. Arnold et al. (2013) estimated aconsiderable climate benefit of this transition, due tothe higher efficiency at which reactive fluorine can beextracted from NF3, compared to C2F6. However, theyalso estimated global emissions that were significantly
larger than expected under industrial “best practices.”
Both the atmospheric abundance and inferred emissions reported here for 2016 are more than 60% higher than the values for 2011 in Arnold et al. (2013).
Sulfuryl Fluoride (SO2F2)
SO2F2 is used as a fumigant for structural andpost-harvest agricultural fumigation. It is increasingly being used in place of the ozone-depleting methyl
bromide. The first atmospheric observations of SO2F2
were reported by Mühle et al. (2009), who estimateda lifetime of 36 ± 11 years, primarily due to loss tothe oceans. The rate of growth of SO2F2 has increased
since the previous Assessment. The 2016 atmospheric abundance was 2.25 ppt, 25% higher than in 2012
(Table 1-8, Figure 1-21). In 2016, sulfur fluoride contributed 0.45 mW m−2 to radiative forcing of climate(Figure 1-3). Inferred emissions increased by 35%
between 2012 and 2016, from 2.7 ± 0.3 Gg yr−1 to 3.6± 0.4 Gg yr−1, which is equivalent to 14 ± 2 Mt CO2
yr−1 (Figure 1-21).
(Trifluoromethyl) Sulfur Pentafluoride (SF5CF3)This compound has a long lifetime (650–950 years), avery high radiative efficiency (0.59 W m−2 ppb−1), andmay have been emitted to the atmosphere during the
production of perfluorooctanyl sulfonate (Sturges etal., 2012). Data from the Southern Hemisphere showthat SF5CF3 has remained at 0.153 ppt for the last four
years (Table 1-8), contributing around 0.09 mW m−2
to radiative forcing. This observed abundance is similar to that given by Sturges et al. (2012), who showedthat growth, and therefore emissions, ceased aroundthe late 1990s.
Halogenated Ethers (HFEs)
Information on atmospheric halogenated ethers(HFEs) is sparse. The first atmospheric observationsof three inhalation anesthetics—desflurane (HFE236ea2, CHF2OCHFCF3), isoflurane (HCFE-235da2,
CHF2OCHClCF3), and sevoflurane (HFE-347 isomer,(CF3)2CHOCH2F)—were published by Vollmer etal. (2015c). Using flask samples from the Northern
Hemisphere and Antarctica, combined with in situ
measurements from Jungfraujoch, 15-year records
showed an increase in global mean abundances to levels in 2014 of 0.097 ppt, 0.30 ppt, and 0.13 ppt for isoflurane, desflurane, and sevoflurane. Using radiative
efficiencies in Table A-1, these mole fractions equate
to radiative forcings of 0.04, 0.14, and 0.04 mW m−2,
respectively. Using a box model and updated lifetimes
from Sulbaek Andersen et al. (2010) and Sulbaek
Andersen et al. (2012), global emissions for 2014 were
estimated at 0.88 Gg yr−1, 0.96 Gg yr−1, and 1.2 Gg
yr−1 for the three anesthetics, respectively, equivalent
to approximately 3 Mt CO2 yr−1 in total.

1.65

Chapter 1 | ODSs and Other Gases

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1.87

Chapter 2Hydrofluorocarbons (HFCs)
Lead Authors
S.A. Montzka
G.J.M. Velders
Coauthors
P.B. Krummel
J. Mühle
V.L. Orkin
S. Park
N. Shah
H. Walter-Terrinoni
Contributors
P. Bernath
C. BooneL. Hu
M.J. Kurylo
E. Leedham Elvidge
M. Maione
B.R. Miller
S. O’Doherty
M. Rigby
I.J. Simpson
M.K. Vollmer
R.F. Weiss
Review Editors
L.J.M. Kuijpers
W.T. Sturges

Cover photo: Plenary discussion during the 28th Meeting of the Parties to the Montreal Protocol in ­Kigali, Rwanda. The historic Kigali Amendment was adopted on 15 October 2016 limiting future production and consumption of hydrofluorocarbons. Photo: Courtesy of IISD/ENB | Kiara Worth at http://enb.iisd.org/ozone/resumedoewg38-mop28/11oct.html

Chapter 2Hydrofluorocarbons (HFCs)
CONTENTSSCIENTIFIC SUMMARY .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 12.1SUMMARY OF FINDINGS FROM ­PREVIOUS ASSESSMENTS .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 5

2.2INTRODUCTION .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 5

2.3ATMOSPHERIC OBSERVATIONS AND DERIVED EMISSION ESTIMATES .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 7

2.3.1 Global HFC Concentration Changes and  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .
Estimated Emissions on Regional to Global Scales .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 72.3.1.1

HFC-134a (CH2FCF3) .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 14

2.3.1.2

HFC-23 (CHF3)  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 18

2.3.1.3

HFC-32 (CH2F2), HFC-125 (CHF2CF3), HFC-143a (CH3CF3) .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 22

2.3.1.4

HFC-152a (CH3CHF2)  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 24

2.3.1.5

HFC-245fa (CHF2CH2CF3), HFC-365mfc (CH3CF2CH2CF3), HFC-227ea  .  .  .  .  .  .  .  .  .  .
(CF3CHFCF3), HFC-236fa (CF3CH2CF3) .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 26

2.3.1.6

HFC-43-10mee (CF3CHFCHFCF2CF3) .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 27

2.3.2 Summed Radiative Forcing and CO2-eq Emissions Attributable to HFCs  .  .  .  .  .  .  .  .  . 27
2.3.3 Comparison of Recent Observed Changes Versus ­Projections Made in the Past . . 28
2.3.4 Aggregate Sums of HFC Emissions Reported to the ­UNFCCC and  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .
Contributions from ­Non-Reporting Countries  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 29
2.3.5 Next Generation Substitutes  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 30
2.3.5.1
2.4

HFC-1234yf (CF3CF=CH2) and HFC-1234ze(E) ((E)-CF3CH=CHF) .  .  .  .  .  .  .  .  .  .  .  . 30

ATMOSPHERIC C­ HEMISTRY OF HFCs  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 32

2.4.1 New Developments on Loss Rates and Lifetimes  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 32
2.4.2 Updates on TFA Formation and ­Tropospheric Ozone Formation .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 33
2.5

POTENTIAL FUTURE CHANGES .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 34

2.5.1 Scenarios .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 34
2.5.1.1

HFC Scenarios Without Consideration of Controls: “Baseline” Scenarios .  .  .  . 34

2.5.1.2

Effect of National and Regional HFC Control .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .
Measures on Future Projections .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 38

2.5.1.3

Projected Impact of the Kigali Amendment  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 38

2.5.1.4

Climate Impacts of HFCs .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 40

2.5.1.5

HFC-23 Projection  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 43

2.5.2 HFC Alternatives .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 43
2.5.2.1

Alternatives: HFOs, Hydrocarbons, CO2, NH3, Not-in-Kind Alternatives .  .  .  .  . 43

Chapter 2 | HFCs

2.5.2.2

Safety Issues .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 44

2.5.2.3

Energy Efficiency vis-à-vis GWP .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 44

REFERENCES  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 47

Chapter 2Hydrofluorocarbons (HFCs)
SCIENTIFIC SUMMARY
The Montreal Protocol is an international agreement designed to heal the ozone layer. It outlines schedules forthe phase-out of ozone-depleting substances (ODSs) such as chlorofluorocarbons (CFCs), hydrochlorofluorocarbons(HCFCs), chlorinated solvents, halons, and methyl bromide. As a result of this phase-out, alternative chemicals
and procedures were developed by industry for use in many applications including refrigeration, air-conditioning, foam-blowing, electronics, medicine, agriculture, and fire protection. Hydrofluorocarbons (HFCs) were used
as ODS alternatives in many of these applications because they were suitable substitutes and they do not containozone-depleting chlorine or bromine; in addition, most HFCs have smaller climate impacts per molecule than themost widely used ODSs they replaced. Long-lived HFCs, CFCs, and HCFCs, however, are all potent greenhousegases, and concerns were raised that uncontrolled future use of HFCs would lead to substantial climate warming.
As a result of these concerns, HFCs were included as one group of greenhouse gases for which emissions controls wereadopted by the 1997 Kyoto Protocol under the 1992 United Nations Framework Convention on Climate Change(UNFCCC). Consequently, developed countries (those listed in Annex I to this Convention, or “Annex I” Parties)
supply annual emission estimates of HFCs to the UNFCCC.
Since the Kyoto Protocol only specified limits on the sum of all controlled greenhouse gases, emissions of HFCs were
not explicitly controlled. However, following the Kyoto Protocol, some countries enacted additional controls specifically limiting HFC use based on their global warming potentials (GWPs). Ultimately the Kigali Amendment to theMontreal Protocol was agreed upon in 2016, and this Amendment supplies schedules for limiting the production andconsumption of specific HFCs. Although the radiative forcing supplied by HFCs is currently small, this Amendment
was designed to ensure that the radiative forcing from HFCs will not grow uncontrollably in the future. The Kigali
Amendment will come into force at the start of 2019. HFC concentrations are currently monitored through atmospheric measurements. All HFCs with large abundances are monitored, as are most with small abundances.
Most HFCs that are emitted to the atmosphere are intentionally produced for use in a variety of applications thatwere once dependent on ODSs. An exception is HFC-23, which is emitted to the atmosphere primarily as a by-product of HCFC-22 production. HFC-23 is also unique in that it has a substantially longer atmospheric lifetime andhigher GWP than nearly all other HFCs. As a result, the Kigali Amendment includes different control schedules for
HFC-23 production than for other HFCs. To date, HFC-23 emissions have been partially abated in developed countries through regulations or voluntary measures and in developing countries with assistance from the UNFCCC’s
Clean Development Mechanism (CDM).

• Atmospheric mole fractions of most currently measured HFCs are increasing in the global atmo-

sphere at accelerating rates, consistent with expectations based on the ongoing transition away fromuse of ozone-depleting substances.
○○ HFC-134a remained the most abundant HFC in the atmosphere, reaching a global mean surface mole

fraction of nearly 90 ppt in 2016. Its rate of increase averaged 5.6 ± 0.2 ppt yr−1 (7.3 ± 0.2 % yr−1)
during 2012–2016, which is about 0.6 ppt yr−1 faster than the mean increase for 2008–2012.

○○ The next four most abundant HFCs in 2016 were HFC-23, HFC-125, HFC-143a, and HFC-32. Their

global mean surface mole fractions in 2016 were 28.9 ppt, 20.4 ppt, 19.2 ppt, and 11.9 ppt, respectively. Mole fractions of these HFCs increased during 2012–2016 by an average of 1.0 ppt yr−1 for
2.1

Chapter 2 | HFCs

HFC-23, 2.1 ppt yr−1 for HFC-125, 1.5 ppt yr−1 for HFC-143a, and 1.6 ppt yr−1 for HFC-32; for allof these gases, these rates are faster than the average growth rates reported for 2008–2012 in the last
Assessment.
○○ Global mole fractions of most HFCs increased through 2016 at rates similar to those projected in thebaseline scenario of the last Assessment, despite the fact that this scenario was created nearly a decade ago. The HFCs for which mole fractions are increasing substantially less rapidly than originallyprojected include HFC-152a, HFC-365mfc, and HFC-245fa.

• Radiative forcing from measured HFCs continues to increase; it currently amounts to 1% of the total forc-

ing from all long-lived greenhouse gases. The radiative forcing arising from measured atmospheric mole
fractions of HFCs totaled 0.030 W m−2 in 2016, up by 36% from 0.022 W m−2 in 2012; HFC-134a
accounted for 47% of this forcing in 2016, while the next largest contributors were HFC-23 (17%),
HFC-125 (15%) and HFC-143a (10%). Total HFC radiative forcing in 2016 accounted for ~10% of the
0.33 W m−2 supplied by ODSs (see Chapter 1), and 1.0% of the 3 W m−2 supplied by all long-lived GHGs
combined, including CO2, CH4, N2O, ODSs and HFCs.

• Global emissions of nearly all measured HFCs continue to increase; they currently amount to ~1.5% of

total emissions from all long-lived greenhouse gases (CO2, CH4, N2O, and long-lived halocarbons) in CO2equivalent emissions. As derived from atmospheric observations, total emissions of HFCs increased by
23% from 2012 to 2016 and summed to 0.88 (± 0.07) GtCO2-eq yr−1 in 2016; this increase outpaced
decreases in CO2-eq emissions from CFCs and HCFCs. These CO2-eq HFC emissions stem primarily
from four gases: HFC-134a (34% of total), HFC-125 (24% of total), HFC-23 (18% of total), and HFC143a (16% of total). HFC CO2-eq emissions were comparable to those of CFCs (0.8 ± 0.3 GtCO2-eq yr−1)
and HCFCs (0.76 ± 0.11 GtCO2-eq yr−1) in 2016.

• HFC emissions estimated from the combination of inventory reporting and atmospheric observations in-dicate that the HFC emissions originate from both developed and developing countries. Large differences
are observed between global total emissions derived from atmospheric observations and the totals reported to the UNFCCC. These differences arise primarily because only developed (Annex I) countries
are obligated to report HFC emissions to the UNFCCC. When summed, these reported HFC emissions
account for less than half of the global total inferred from observations (as CO2-eq emissions).

• Annual global emissions of HFC-23 derived from atmospheric measurements have varied substantially inrecent years. This variability is mostly consistent with expectations based on reported HCFC-22 production
and reported and estimated HFC-23 emissions. This long-lived HFC is emitted to the atmosphere primarily as a by-product of HCFC-22 production. HFC-23 emissions, after reaching a low of ~10 Gg yr−1
(0.13 GtCO2‑eq yr−1) 2009–2010, owing in part to destruction in developing countries facilitated under
the UNFCCC’s Clean Development Mechanism (CDM), increased and subsequently peaked at ~14 Gg
yr−1 (0.18 GtCO2-eq yr−1) in 2013–2014. The mean global emission rate over 2013–2014 is slightly higher than that derived for 2005–2006, when CDM-facilitated destruction had yet to be fully implemented.
Global emissions estimated from observations for 2015 and 2016 dropped below the 2013–2014 peak;emissions in 2016 were 12.3 ± 0.7 Gg yr−1 (0.16 GtCO2-eq yr−1), or approximately 2 Gg yr−1 below thosein 2014. New controls put in place under the Kigali Amendment mandate HFC-23 by-product destruction, to the extent practicable, beginning in 2020. These controls are expected to limit future emissions
and thus slow or reverse atmospheric concentration increases of this potent greenhouse gas.

• Some next-generation substitute chemicals with very low GWPs (unsaturated HCFCs and unsaturated

HFCs, also known as hydrofluoroolefins, or HFOs) have now been detected in ambient air, consistent withthe transition to these compounds being underway. Unsaturated HFCs and HCFCs are replacement
compounds for some long-lived HCFCs and HFCs. Because unsaturated HFCs have short atmospheric

2.2

HFCs | Chapter 2

lifetimes (days) and GWPs typically less than 1 they are not included as controlled substances in the
Kigali Amendment to the Montreal Protocol. Very low mole fractions (typically below 1 ppt) of two
unsaturated HFCs (HFC-1234yf and HFC-1234ze(E)) have been measured at a continental background
European site.

• Global adherence to the HFC phasedown schedule of the 2016 Kigali Amendment to the Montreal Protocol

would substantially reduce future projected global HFC emissions. Emissions are projected to peak before 2040 and decline to less than 1 GtCO2-eq yr−1 by 2100. Only marginal increases are projected forCO2-eq emissions of the low-GWP alternatives despite substantial projected increases in their emission
mass. The estimated avoided HFC emissions as a result of this Amendment is 2.8–4.1 GtCO2-eq yr−1
emissions by 2050 and 5.6–8.7 GtCO2-eq yr−1 by 2100. For comparison, total CH4 emissions in 2100 are
projected to be 7.0 and 25 GtCO2-eq yr−1 in the RCP-6.0 and RCP-8.5 scenarios, respectively, and total
N2O emissions in 2100 are projected to be 5.0 and 7.0 GtCO2-eq yr−1 in these same scenarios.

• The 2016 Kigali Amendment to the Montreal Protocol, assuming global compliance, is expected to reduce

future radiative forcing due to HFCs by about 50% in 2050 compared to the forcing from HFCs in thebaseline scenario. Currently (in 2016), HFCs account for a forcing of 0.025 W m−2 not including 0.005
from HFC-23; forcing from these HFCs was projected to increase up to 0.25 W m−2 by 2050 (excluding acontribution from HFC-23) with projected increased use and emissions in the absence of controls. With
the adoption of the Kigali Amendment, a phasedown schedule has been agreed for HFC production
and consumption in developed and developing countries under the Montreal Protocol. With global
adherence to this Amendment in combination with national and regional regulations that were alreadyin place in, e.g., Europe, the USA, and Japan, along with additional recent controls in other countries,
future radiative forcing from HFCs is projected to reach 0.13 W m−2 by 2050 (excluding HFC-23), or
about half the forcing projected in the absence of these controls.

• The Kigali Amendment and national and regional regulations are projected to reduce global average warm-

ing in 2100 due to HFCs from 0.3–0.5°C in a baseline scenario to less than 0.1°C. If the global production of
HFCs were to cease in 2020, the surface temperature contribution of HFC emissions would stay below
0.02°C for the whole 21st century. The magnitude of the avoided temperature increase due to the provisions of the Kigali Amendment is substantial in the context of the 2015 UNFCCC Paris Agreement,
which aims to limit global temperature rise to well below 2.0°C above pre-industrial levels and to pursue
further efforts to limit the temperature increase to 1.5°C.

• Improvements in energy efficiency in refrigeration and air-conditioning equipment during the tran-

sition to low-GWP alternative refrigerants can potentially double the climate benefits of the HFC
phasedown of the Kigali Amendment. The conversion from equipment using HFC refrigerants withhigh GWPs to refrigerants with lower GWPs, which will most likely result from the Kigali Amendment,
provides an opportunity to consider other technological improvements that offer additional climate
benefits. The total climate impact related to refrigerant use and associated emissions is not only associated with the radiative properties and lifetime of the refrigerant, but also with CO2 emissions resulting
from the energy used by the equipment over its entire life cycle. The use of a refrigerant with a lower
GWP than the currently-used HFCs (i.e., following the Kigali Amendment) offers the opportunity toredesign equipment and improve its energy efficiency. For example, a 30% improvement in the energy
efficiency of the global stock of mini-split air conditioners (the most widely used air conditioning systems today) in 2030 would provide a climate benefit comparable to replacing the mix of current HFC
refrigerants commonly used in this application (which have GWPs averaging about 2,000) with a mix ofalternatives that have GWPs of less than about 5 to about 700. An energy efficiency improvement of 30%
is estimated to be technically and economically feasible and cost-effective in many economies.

2.3

Chapter 2 | HFCs

• Some HFCs degrade in the environment to produce trifluoroacetic acid (TFA), a persistent toxic chem-

ical. The environmental effects of this source of TFA are currently small and are expected to remain
negligible over the next decades. Atmospheric degradation of HFC-1234yf, a replacement compound forsome long-lived HCFCs and HFCs, produces TFA. Potential impacts beyond a few decades of this TFA
source could require future evaluation due to the environmental persistence of TFA and uncertainty infuture emissions of HFC-1234yf and other HFCs that produce TFA upon degradation.

• Improvements in the understanding of reaction rates have been incorporated into revised lifetimee­ stimates for saturated and unsaturated HFCs. Most of these changes are small, although lifetimes of HFC245cb (CF3CF2CH3), octafluorocyclopentene (cyclo-CF=C4F7-), (E)-HFC-1214yc ((E)-CF3CH=CHCF3),
and (E)-HFC-1438mzz ((E)-CF3CH=CHC2F5) were noticeably changed because the relevant reaction rate
information has become available for the first time. Lifetimes for a few HFCs considered here remain estimates based on either analogy with similar compounds or structure–activity relationships.

2.4

Chapter 2Hydrofluorocarbons (HFCs)
2.1

SUMMARY OF FINDINGS FROM­PREVIOUS ASSESSMENTS

Information pertaining to HFCs was included in
Chapter 1 and Chapter 5 of the 2014 Assessment report. That Assessment reported that troposphericmole fractions of HFCs continued to increase, adding
to the warming of Earth’s climate. The radiative forcing supplied by HFCs in 2012, the last year assessedin that report, was small compared to that from CFCs
and HCFCs, but projections suggested the potential
for significant increases in HFC-associated warmingin the future. Furthermore, the documented climate
benefits achieved by the Montreal Protocol through reductions in the production and emission of CFCs and
HCFCs might be substantially offset if emissions of
the substitute HFCs were allowed to continue unabated. The sum of HFC emissions in 2012 had reached0.72 ± 0.05 GtCO2-equivalent yr−1 (for CO2-eq considering a 100-yr time horizon) after having increasedby nearly 7% yr−1 from 2008 to 2012. This total was
similar to the magnitude of emissions from CFCs (0.9
± 0.3 GtCO2 yr−1) and HCFCs (0.78 ± 0.10 GtCO2-eqyr−1) for 2012. This total included a global emissionof approximately 0.16 ± 0.01 GtCO2-eq yr−1 from thepotent greenhouse gas HFC-23 (CHF3), which is emitted primarily as a by-product during the productionof HCFC-22 and not as a result of use in industrial
applications and products. Although global emissionsof this HFC had decreased from 2005 to 2009, they
increased after 2009 and by 2012 were ~40% above theminimum recorded for 2009.

2.2

INTRODUCTION

Hydrofluorocarbons have been used in refrigeration,
air conditioning, thermal insulating foam, and miscellaneous applications since the 1990s, replacing the
CFCs and HCFCs that were traditionally used in theseapplications. The first widespread HFC use was of
HFC-134a beginning in the early 1990s, as a substitutefor CFC-12 in mobile air conditioning (Montzka et al.,
1996; Oram et al., 1996; Andersen et al., 2013). Withina decade most mobile air conditioners used this HFC(Papasavva et al., 2009), and this remains true today.
With the global CFC phase-out in 2010 and the ongoing HCFC phase-out, the use of HFCs has increased
substantially, not only for various refrigeration and air
conditioning applications, but also as foam blowing
agents, as medical aerosols, and to a lesser extent ascleaning, etching, and fire-fighting agents. As was true
for ODSs, emissions of HFCs follow production andconsumption with a delay of months to decades, depending on the type of application in which the HFCs
are used.
HFCs do not contain ozone-depleting chlorine or
bromine, but are potent greenhouse gases (Harris andWuebbles et al., 2014). To ensure that radiative forcing
from the substitute HFCs does not offset climate gains
provided by the Montreal Protocol phase-out of CFCs
and HCFCs, Parties to the Montreal Protocol agreed
to an Amendment in Kigali, Rwanda in October 2016,
to include some HFCs as controlled substances and tophase down their production and consumption (GWPweighted) in coming decades (UNEP, 2016a; for a listof controlled HFCs, see footnote to Table 2-1 [or HFCs
with asterisks in Table 2-2]). The Kigali Amendment
will enter into force on January 1, 2019, since morethan 20 Parties have ratified, accepted, or approved
this Amendment. Limiting climate change is not the
primary goal of the 1985 Vienna Convention for theProtection of the Ozone layer, but climate change considerations are addressed in this Convention. Limiting
climate change was also a contributing factor to the2007 Adjustment of the Montreal Protocol for an accelerated HCFC phase-out (UNEP, 2007).
Since 1997, HFCs have been included in the Kyoto
Protocol to the United Nations Framework Convention
on Climate Change as one group among many greenhouse gases for which emissions overall should be reduced by up to 8% in the period 2008–2012 by somedeveloped countries relative to baseline levels (mostly
the year 1990). HFC emissions were not directly controlled by the Kyoto Protocol, however, since controls
applied to the sum of all greenhouse gases.
2.5

Chapter 2 | HFCs

Table 2-1. Base level and phasedown schedule for production and consumption of controlled HFCs1, expressed
as CO2-eq, under the Kigali Amendment to the Montreal Protocol (UNEP, 2016a).
Developing Countries*

Developed Countries*

A5 Group 1 Countries2

A5 Group 2 Countries3

Non-A5 Countries4

Base level:

Average HFCs 2020–2022
plus 65% of HCFC base level

Average HFCs 2024–2026
plus 65% of HCFC base level

Average HFCs 2011–2013
plus 15% of HCFC base level5

Freeze:

2024

2028

-

1st step:

2029: 10% reduction

2032: 10% reduction

2019: 10% reduction

2nd step:2035: 30% reduction

2037: 20% reduction

2024: 40% reduction

3rd step:

2040: 50% reduction

2042: 30% reduction

2029: 70% reduction

4th step:
Plateau:

2034: 80% reduction
2045: 80% reduction

2047: 85% reduction

2036: 85% reduction* In the UNFCCC, developing countries are referred to as “non-Annex I” countries, and developed countries are referred to as“Annex I countries.”
1

HFCs controlled by the Kigali Amendment include: HFC-23, HFC-134, HFC-134a, HFC-143, HFC-245fa, HFC-365mfc, HFC-227ea,
HFC-236cb, HFC-236ea, HFC-236fa, HFC-245ca, HFC-43-10mee, HFC-32, HFC-125, HFC-143a, HFC-41, HFC-152, HFC-152a. This
Amendment also specifies that emissions of HFC-23 generated during production of HCFCs or HFCs be destroyed to the extent
practicable beginning January 2020.

2

Group 1: Article 5 (developing) countries not part of Group 2. Article 5 of the Montreal Protocol.

3Group 2: Article 5 (developing) countries: Bahrain, India, the Islamic Republic of Iran, Iraq, Kuwait, Oman, Pakistan, Qatar, Saudi
Arabia and the United Arab Emirates.

4

Non-A5 countries, also referred to as A2 countries (developed). Article 2J of the Montreal Protocol.

5

For Belarus, Russian Federation, Kazakhstan, Tajikistan, Uzbekistan 25% HCFC component of base level and different initial two
steps (1) 5% reduction in 2020 and (2) 35% reduction in 2025. Article 2J of the Montreal Protocol.

Note: Non-Article 5 (developed) and UNFCCC Annex I (developed) countries include all EU-28 countries, Australia, Belarus, Canada,
Iceland, Japan, Liechtenstein, Monaco, New Zealand, Norway, the Russian Federation, Switzerland, Ukraine, and the USA. Further,
non-Article 5 countries are Andorra, Azerbaijan, the Holy See, Israel, Kazakhstan, San Marino, Tajikistan, and Uzbekistan. Turkey issolely an Annex I country.

In previous Assessments, observed concentrations,derived emissions, and atmospheric effects of HFCs
were discussed together with those of ozone-depleting
substances. In this Assessment, issues related to HFCs
are covered in this separate chapter, with the mainfoci being updating observations of HFC atmospheric mole fractions and understanding what they imply
for emissions on global to regional scales; determining
if the observed mole fraction changes are consistent
with expectations and emission magnitudes reported
to UNFCCC and elsewhere; quantifying the associated climate- and environmental-related effects arising from HFC emissions and associated atmosphericchanges; and considering how these influences mightchange in the future, especially in light of controls on
HFC production and consumption specified in the
Kigali Amendment to the Montreal Protocol.
2.6With respect to climate influences, increases in radiative forcing resulting from recent increases in HFC
atmospheric mole fractions are documented in thischapter. For HFCs with lifetimes longer than 1 year
(e.g., those that are well mixed in the lower atmosphere), radiative forcing is linearly proportional to
global mean surface mole fractions and the efficiencyof the particular HFC in trapping outgoing terrestrial radiation (i.e., its radiative efficiency; see Table
A-1). Total emission rates are also derived from these
global-scale observations with consideration of lifetime-determined loss rates (Table 2-2) in an inverse
budget analysis performed with box models (see Box1-1). Emission magnitudes are considered here on amass basis and with mass emissions weighted by 100yr GWPs to enable an assessment of integrated radiative forcing supplied by an HFC emission relative to an
equivalent CO2 emission over a 100-year time horizon

HFCs | Chapter 2(CO2-eq emissions, Figure 2-1; Table 2-2). GWPs associated with a 20-year time horizon are also tabulated, but they are not considered further in this chapter.
Determining if atmospheric concentrations of ODSs
or HFCs are changing as expected has always been an
important remit for authors of this Assessment. For
HFCs, this task is facilitated by the reporting by developed countries (Annex I Parties) to the UNFCCC
of national emission magnitudes derived from country-specific analyses of production, imports, sales, exports, and use. For HFC-23, emission estimates for developing countries derived from information collectedby the Montreal Protocol’s Multilateral Fund1 are also
considered. In this chapter, these inventory-based HFCemission totals are compared to national and global
totals derived from atmospheric data on both a compound-specific and aggregate basis. Factors contributing to differences between UNFCCC-reported and observation-derived emissions for individual HFCs are
discussed; they include inaccuracies in methods forderiving emissions from atmospheric mole fraction
measurements, inaccuracies in emissions reporting by
Annex I (developed) countries, reporting emissions
as aggregated mixes of HFCs or HFCs and perfluorocarbons (PFCs), and the potential for significant
emissions from non-Annex I (developing) countries
not obligated to report their HFC emissions to the
UNFCCC2. Recent inverse analyses of atmosphericmeasurements made in the USA and Europe provide
a means to assess UNFCCC inventory reporting fromthese regions. Similar observations from other regions
are also considered and add to our understanding ofemission magnitudes from countries not required toreport emissions to the UNFCCC.
In Chapter 6 of this Assessment, as in related chaptersin previous Assessments, scenarios are constructed
for ODS concentrations in the future as part of the1

The Multilateral Fund (MLF) for the implementation of theMontreal Protocol was established in 1991 to assist developing
countries (Article 5 countries) to meet their Montreal Protocolcommitments. Financial contributions to the MLF come fromdeveloped countries (non-Article 5 countries).

2

UNFCCC Parties listed in Annex I (as amended in 1998)
include all the developed countries in the Organization for
Economic Cooperation and Development, it also includes
Economies in Transition, which are characterized as national
economies in the process of changing from a planned economic system to a market economy. By default, other countries arereferred to as non-Annex I countries.

Assessment itself, since such scenarios are usually notavailable in the literature. Also consistent with previous Assessments, new HFC scenarios have not been
constructed here or in Chapter 6. Instead, projectionsof HFC use and emission magnitudes are taken fromthe literature and are discussed in this chapter. These
scenarios were created based on data available at thetime they were created, and they have not been updated to consider the most recent observational data. As aresult, discrepancies between projected and observed
HFC concentrations and emissions are apparent inyears after the scenarios were created.
Discussions in this chapter extend those presented in
specific chapters of the most recent Intergovernmental
Panel on Climate Change (IPCC) assessment report(Hartmann, Tank, and Rusticucci et al., 2013; Myhreand Shindell et al., 2013) by updating observed mixing
ratios and associated radiative forcings through 2016.
Laboratory kinetic data published since that time areconsidered in providing updated lifetime estimates.
Furthermore, the scenarios discussed here are basedon more recent analyses, whereas those in the IPCC
report are from the Representative Concentration
Pathways (RCPs) scenarios (Meinshausen et al., 2011).

2.3

ATMOSPHERIC OBSERVATIONS ANDDERIVED EMISSION ESTIMATES

2.3.1

Global HFC Concentration
Changes and Estimated Emissions
on Regional to Global Scales

Atmospheric abundances of HFCs are regularly measured throughout the global atmosphere by a few surface-based measurement networks at remote sites, byaircraft, and by satellite-borne instruments (Figure 2-2
and Table 2-3). These results imply substantial emissions of HFCs on a global scale, when independently-determined atmospheric removal rates (or lifetimes)
are considered (Figure 2-1; Table 2-2). Emission
rates are also derived on regional spatial scales using
trace-gas measurements in non-remote regions (seeBox 1-1). These results can provide estimates of region-specific HFC emissions that are independentof inventory-based approaches used in reporting to
UNFCCC. Regional emission rates are typically derived from data analysis methods such as inter-species
correlation and inverse modeling. Regional emission
estimates typically have larger relative uncertainties
2.7

Chapter 2 | HFCs

Table 2-2. Trace gas lifetimes for selected halocarbons: partially fluorinated alkanes, partially fluorinated olefins,and perfluorinated olefins.

Industrial
Designationof Chemical
Name

Chemical
FormulaTotal
Lifetime,aWMO-2014(years, unlessotherwise
indicated)

Total
Lifetime,b,c
this
Assessment(years, unlessotherwise
indicated)

Radiative
Efficiency d(W m−2
ppb−1)

228

228

0.18

11,085 12,690

1

GWP at Given
Time Horizon,
thisAssessment
20-yr

Notes

100-yrHydrofluorocarbons

2.8

HFC-23 **CHF3

HFC-32 **

CH2F2

5.4

5.4

0.11

2,530

705

1

HFC-41 **

CH3F

2.8

2.8

0.02

430

116

1

HFC-125 **CHF2CF3

31

30

0.23

6,280

3,450

1

HFC-134 **CHF2CHF2

9.7

10

0.19

3,625

1,135

1

HFC-134a **CH2FCF3

14

14

0.16

3,810

1,360

1

HFC-143 **CH2FCHF2

3.5

3.6

0.13

1,250

340

1

HFC-143a **CH3CF3

51

51

0.16

7,050

5,080

1

HFC-152 **CH2FCH2F

146 days(114–335days)

172 days

0.04

64

17

1, b

HFC-152a **CH3CHF2

1.6

1.6

0.10

545

148

1

HFC-161

CH3CH2F

66 days(51–154 days)

80 days

0.02

20

6

1, b

HFC-227ca

CF3CF2CHF2

28.2

30

0.27

5,260

2,865

2

HFC-227ea **CF3CHFCF3

36

36

0.26

5,250

3,140

1

HFC-236cb **CH2FCF2CF3

~13

13.4

0.23

3,540

1,235

1

HFC-236ea **CHF2CHFCF3

11.0

11.4

0.30

4,190

1,370

1

HFC-236fa **CF3CH2CF3

222

0.24

6,785

7,680

1

HFC-245ca **CH2FCF2CHF2

6.5

6.6

0.24

2,530

720

1

HFC-245cb

CF3CF2CH3

47.1

39.9

0.24

6,340

4,000

1

HFC-245ea

CHF2CHFCHF2

3.2

3.3

0.16

880

240

1

HFC-245eb

CH2FCHFCF3

3.2

3.2

0.20

1,070

290

1

HFC-245fa **CHF2CH2CF3

7.9

7.9

0.24

2,980

880

1

HFC-263fb

CH3CH2CF3

1.1

1.1

0.10

250

68

1

HFC-272ca

CH3CF2CH3

2.6

~9

0.07

1,580

480

3

HFC-281ea

CH3CHFCH3

23 days(19-46 days)

27 days

–

–

–

1, b

HFC-329p

CHF2CF2CF2CF3

~30

32

0.31

4,720

2,630

4

HFC-338pcc

CHF2CF2CF2CHF2

12.9

13.5

–

–

–

1

HFC-356mcf

CH2FCH2CF2CF3

1.2

1.2

–

–

–

1

213

HFCs | Chapter 2

HFC-356mff

CF3CH2CH2CF3

8.3

8.5

–

–

–

1

HFC-365mfc **CH3CF2CH2CF3

8.7

8.9

0.22

2,660

810

1

HFC-43-10mee **CF3CHFCHFCF2CF3

16.1

17.0

0.359

3,770

1,470

1, 19

HFC-458mfcf

CF3CH2CF2CH2CF3

22.9

23.8

–

–

–

1

HFC-55-10mcff

CF3CF2CH2CH2CF2CF3

7.5

7.7

–

–

–

1

HFC-52-13p

CHF2CF2CF2CF2CF2CF3

32.7

35.2

–

–

–

5

HFC-72-17p

CHF2(CF2)6CF3

23.8

–

–

–

6

Fluorinated Olefinic HFCs – indicated here as HFOs
HFO-1123

CHF=CF2–

1.5 days0.0019<1<1

1, 7, b

HFO-1132a

CH2=CF2

4.0 days(3.0–5.7 days)

4.6 days

0.004

<1

<1

1, b

HFO-1141

CH2=CHF

2.1 days(1.4–3.1 days)

2.5 days

0.002

<1

<1

1, b

HFO-1234ye(E)(E)-CHF=CFCHF2<5 days<5 days

–

–

–

8, b

HFO-1234ye(Z)(Z)-CHF=CFCHF2<5 days<5 days

–

–

–

8, b

HFO-1225ye(E)(E)-CF3CF=CHF

4.9 days(3.7–6.9 days)

5.7 days

0.01

<1

<1

1, b

HFO-1225ye(Z)(Z)-CF3CF=CHF

8.5 days(6.2–12 days)

10 days

0.02

<1

<1

1, b

HFO-1234ze(E)(E)-CF3CH=CHF

16.4 days(12.8-24 days)

19 days

0.04

4

<1

1, b

HFO-1234ze(Z)(Z)-CF3CH=CHF

10.0 days

10 days

0.02

1

<1

9, b

HFO-1234yf

CF3CF=CH2

10.5 days(8.4–16 days)

12 days

0.02

1

<1

1

HFO-1261zf

CH2FCH=CH2

0.7 days(0.5–1.0 days)

0.8 days

–

–

–

1, b

HFO-1234yc

CF2=CFCH2F~2 days~2 days

–

–

–

8, b

HFO-1225zc

CF2=CHCF3~2 days~2 days

–

–

–

8, b

HFO-1234zc

CF2=CHCHF2<5 days<5 days

–

–

–

8, b

HFO-1336mzz(E)(E)-CF3CH=CHCF3(16–30 days)

122 days

0.13

60

16

10, b

HFO-1336mzz(Z)(Z)-CF3CH=CHCF3(16–32 days)

27 days

0.07

6

2

1, b

HFO-1243zf

CHCF3=CH2

9 days

0.01

<1

<1

1, b

HFO-1345fz

CHC2F5=CH2

7.9 days(5.8–11.4
days)

9 days

0.01

<1

<1

1, b

HFO-1438mzz(E)(E)-CF3CH=
CHCF2CF3(16–30 days)~122 days

–

–

–

11, b

HFO-1447fz

CH2=
CHCF2CF2CF3(6–10 days)

9 days

–

–

–

12, b

7.6 days(5.5–11 days)

2.9

Chapter 2 | HFCs

HFO-1549fz
1H,1H,2H-­
Perfluorohexene

CHC4F9=CH2

7.6 days

9 days

0.03

<1

<1

13, b

HFO-174-13fz
1H,1H,2H-­
Perfluoro-1octene

CHC6F13=CH2

7.6 days

9 days

0.03

<1

<1

13, b

HFO-194-17fz
1H,1H,2HCHC8F17=CH2
Heptadecafluoro-­
1-decene

7.6 days

9 days

0.03

<1

<1

13, b

43 days

0.34

42

11

14, b

HFO-1438ezy(E)(E)-(CF3)2CFCH=
CHF

Perfluorinated Olefins
PFC-1114

CF2=CF2

1.1 days(0.7–1.6 days)

1.2 days

0.002

<1

<1

1, b

PFC-1216

CF3CF=CF2

4.9 days(3.3–7.1 days)

5.5 days

0.01

<1

<1

1, b

Perfluoro
buta-1,3-diene

CF2=CFCF=CF2

1.1 days(0.8–1.6 days)

1.1 days

0.003

<1

<1

1, b

Perfluoro
but-1-ene

CF3CF2CF=CF2

6 days6 days

0.02

<1

<1

15, b

Perfluorobut2-ene (isomerblend: 71% (E)and 29% (Z))

CF3CF=CFCF3

4.8

1.3

16

(E)-Perfluro-2-butene(E)-CF3CF=CFCF3–

22 days

0.05

3.6

1.0

1, b

(Z)-Perfluro-2-butene(Z)-CF3CF=CFCF3–

35 days

0.07

7.8

2.1

1, b

Perfluoro(2-methyl-2-­
pentene)(CF3)2C=CFCF2CF3–

192 days

–

–

1, b

3,3,4,4-Tetraflurocyclo-CH=
cyclobut-1-ene
1H,2H-Tetrafluro CHCF2CF2cyclobutene

84 days

0.09

37

10

17, b

2,3,3,4,4Pentafluoro
cyclobut-1-ene
1H-Pentafluorocyclobutene

cyclo-CH=
CFCF2CF2-

270 days

0.19

214

58

17

Hexafluorocyclobutene

cyclo-CF=
CFCF2CF2-

1.2

0.25

420

110

17

–

Fluorinated Cycloolefins

2.10

HFCs | Chapter 2Octafluorocyclopentene

cyclo-CF=
CFCF2CF2CF2-

31 days

1.05

0.27

300

82

18

Table Heading Footnotes:
Although the designation HFC is applicable to both saturated and unsaturated chemicals, it is used to refer only to partially fluorinated alkanes in this table. Partially fluorinated unsaturated compounds, hydrofluoroolefins, are designated as HFO in this table foreasier identification.
**

Those HFCs listed as controlled substances in Annex F of the Kigali Amendment to the Montreal Protocol. Additional reporting
requirements were adopted at the 29th Meeting of the Parties of the Montreal Protocol (Decision XXIX/12) on consideration of
HFCs not listed as controlled substances. This decision requested that production and consumption of HFCs with GWPs higher
than the smallest GWP listed in Annex F also be reported to the Ozone Secretariat for informational purposes.

aTotal lifetime, τHFC, reported in the 2014 Assessment (indicated as “WMO-2014”; Carpenter and Reimann et al., 2014).

b

Lifetimes for VSLSs (values given in days) are calculated relative to the methyl chloroform (MCF) partial lifetime due to reaction
with OH with the same procedure that is used for long-lived gases. Local lifetimes for VSLSs will depend on the season and location of the emission. Nevertheless, the characteristic values are within the range of likely lifetimes for an emission between theequator and midlatitudes. More detailed modeling is required to derive VSLS lifetimes associated with emission from a specificregion and season. A representative range of local lifetimes taken from the 2014 Assessment (Carpenter and Reimann et al., 2014)(Tables 1-5 and 1-11) is given in parentheses where available.

c

Italicized values indicate estimated lifetimes when no experimental data on OH reactivity is available.

dRadiative efficiency values are taken from recommendations given in Hodnebrog et al. (2013) based on a literature review ofexperimental data and a reanalysis of this information, unless another source is specified in notes to the table.

Lifetime Footnotes:
1

OH reaction rate constant was taken from JPL Publication 15-10 (Burkholder et al., 2015b).

2

OH reactivity assumed the same as CHF2CF3 (HFC-125).

3

OH reactivity calculated using the structure activity relationships of DeMore (DeMore, 1996) assuming an E/R of 1700 K.

4

OH reactivity calculated using the room-temperature rate constant reported by Young et al. (2009a) assuming an E/R of 1700 K,
which is similar to that of CHF2CF3.

5

OH reactivity taken from the IUPAC (Ammann et al., 2017) recommendation.

6

OH reaction rate constant was taken from Chen et al. (2011).

7

Radiative metrics calculated using the infrared spectrum from Baasandorj and Burkholder (2016).

8

No experimental data were available for OH reaction rate constants, so lifetimes were estimated based on reactivity trends offluorinated ethenes and propenes.

9

OH reaction rate constant was taken from Zhang et al. (2015).

10

The atmospheric lifetime and radiative efficiency were calculated using the kinetic and infrared data reported by Baasandorj etal. (2018).

11

OH reactivity assumed the same as (E)-CF3CH=CHCF3.

12

		 OH reactivity assumed the same as CH2=CHCF3.

13

		 OH reactivity was recommended by JPL Publication 15-10 based on the room temperature rate constant reported by Sulbaek
and Andersen et al. (2005) assuming E/R of -170, which is similar to that of CH2=CHCF3.

14

		 OH reaction rate constant and radiative efficiency values were taken from Papadimitriou and Burkholder (2016).

15

		 OH reactivity calculated using the room temperature OH rate constant reported by Young et al. (2009b) assuming E/R of -415
K, which is similar to that of CF3CF=CF2.

16

		 Industrial Perfluorobut-2-ene is a mixture of two stereo-isomers, (E)-Perfluoro-2-butene and (Z)-Perfluoro-2-butene, with theratio of ~71% and ~29%, respectively. Atmospheric lifetimes of individual isomers are given in Table 2-2.

		 GWP of the mixture was calculated based on estimated GWPs of individual isomers for each time horizon. The radiative efficiency recommended by Hodnebrog et al. (2013) for CF3CF=CFCF3 was used to estimate this parameter for each stereo-isomer,
after corresponding corrections to account for its dependence upon lifetimes of the individual stereo-isomers.
17		 OH reaction rate constant was taken from Jia et al. (2013). Radiative efficiency was taken from Jia et al. (2013), and lifetime given
here includes the correction suggested by Hodnebrog et al. (2013).

18

		 OH reaction rate constant was taken from Zhang et al. (2017). Radiative efficiency was taken from Zhang et al. (2017) and lifetime given here includes the correction suggested by Hodnebrog et al. (2013).

19

		 Radiative efficiency was taken from Le Bris et al. (2017).

2.11

Chapter 2 | HFCs

8

100

4

50

0
40

0

30

AGAGE
NOAA

150

UNFCCC 2017

100

20
15

20

100

0
40

60

250

HFC-125(CHF2CF3)

200

5

60

0

HFC-152a(CH3CHF2)

6

40

4

20

2

0
6

4

HFC-227ea(CF3CHFCF3)

16
12

3
2
1
0

1980

1990

2000

2010

150
100

0
16
12

10

50

0

0
6

0

12

5

50

HFC-245fa(CHF2CH2CF3)

4
8

8
4

4

0
2.0

0

1.6

HFC-365mfc(CH3CF2CH2CF3)

4
3

3

2

2

1

1

HFC-43-10mee(C5H2F10)

2.5
2.0

1.2

1.5

8

0.8

4

0.4

0.5

0

0.0

0.0

1.0

1980

1990

0
0.4

2000

2010

0.3

0
3.0

HFC-236fa(CF3CH2CF3)

2.5
2.0

0.2

1.5
1.0

0.1
0.0

0.5
1980

1990

2000

2010

0.0

Emissions (MtCO2-eq yr –1)

50

HFC-143a(CH3CF3)

100
20

10
8

30

0
200

150 20

40

10
10

200

50

25

HFC-32(CH2F2)300

HFC-134a(CH2FCF3)

Emissions (MtCO2-eq yr –1)

Emissions (Gg yr –1)

200

80

0
80

Emissions (Gg yr –1)

Air archive

150

Emissions (MtCO2-eq yr –1)

Emissions (Gg yr –1)

12

250

200

HFC-23(CHF3)

Emissions (MtCO2-eq yr –1)

Emissions (Gg yr –1)

16

Year

Figure 2-1. Total global emissions estimated from a budget analysis of atmosphere measurements at remotesites (those in Figure 2-2) compared to total emissions reported to UNFCCC (2017). Atmospheric measurement-based emissions are derived with a 12-box model and the lifetimes in Table 2-2; the methods and model
used here are discussed by Rigby et al. (2014). Emissions are presented in units of Gg compound y r−1 on theleft-hand axis, and the right-hand axis has been scaled by chemical-specific 100-yr GWPs (Table 2-2) to indicate emission magnitudes in MtCO2-eq yr −1 (1 Mt = 1012 g). Shaded regions represent 1 standard deviation in
global emissions derived from measured mole fractions and a 12-box model (Rigby et al., 2014). Uncertainties
in mole fractions and their model representation are propagated through to the posterior emissions estimatesusing a Bayesian framework. These uncertainties are augmented by lifetime and calibration scale uncertainties
following Rigby et al. (2014). The model uses interannually repeating meteorology and, therefore, errors may
be underestimated for periods with large circulation changes, although this will likely affect short-term (e.g.,annual) variations more than long-term trends.

2.12

HFCs | Chapter 2

Mole fraction (ppt)

35
30
25

15

Mole fraction (ppt)20

HFC-32(CH2F2)10
0
10

Mole fraction (ppt)

M&K-2011
best practices
reference caseless mitigation

20

10
30

8

HFC-152a(CH3CHF2)

6
4
2
0
1.6

Mole fraction (ppt)

Global air archive (AGAGE)
Cape Grim air archive (UEA)
SCISAT
AGAGE
UCI
NOAA
V-2009 A2
V-2009 A1
V-2015 upper
V-2015 lower

HFC-23 (CHF3)

1.2

HFC-227ea(CF3CHFCF3)

40
30

100
80

HFC-125(CHF2CF3)

40
20

30

20

20

10

10

0
4

0
1.6

3

HFC-245fa(CHF2CH2CF3)

1.2

2

0.8

1

0.4

0
0.3

0.0
0.20

HFC-43-10mee(C5H2F10)

0.2

HFC-134a(CH2FCF3)

60

0
40

0.16

HFC-143a(CH3CF3)

HFC-365mfc(CH3CF2CH2CF3)

HFC-236fa(CF3CH2CF3)

0.12

0.8
0.4

120

0.08

0.1

0.04

0.0
0.0
0.00
1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 2020

Year

Figure 2-2. Annual mean mole fractions of HFCs and recent projections. Shown are global means estimatedfrom in-situ instrumentation at five remote sites (AGAGE, dark blue filled circles) and independent estimatesderived from weekly flasks filled at 8 remote sites (NOAA, open red circles). Global means for HFC-134a fromquarterly sampling in the Pacific Basin are also shown (pink filled circles; UCI = the University of California
at Irvine; Simpson et al., 2014). Global means derived from air archives in both hemispheres (AGAGE, purple
crosses) (Arnold et al., 2014; O’Doherty et al., 2014; Rigby et al., 2014; Simmonds et al., 2016; Simmonds et al.,2018; Vollmer et al., 2011) are calculated using a 12-box model (Cunnold et al., 1983; Rigby et al., 2013). Results
reported for the southern hemisphere from analyses of the Cape Grim Air Archive only are also shown (UEA
data, khaki triangles; Oram et al., 1998; Leedham Elvidge et al., 2018). Results from satellite absorption retrievals(SCISAT) represent upper troposphere-lower stratosphere means averaged from 60°N to 60°S (black diamonds;
Nassar et al., 2006; Harrison et al., 2012). Uncertainties on these satellite retrievals represent statistical variability in the data used in the averaging and do not include any systematic errors. Also shown are projections for
global means that were considered in the previous Ozone Assessment (V-2009 = Velders et al., 2009; Carpenter
and Reimann et al., 2014) and updated projections based on observations through 2012 (V-2015 = Velders
et al., 2015; M&K-2011 = Miller and Kuijpers, 2011). Data are updates to published measurement records (seeTable 2-1 for sources not mentioned here).
2.13

Chapter 2 | HFCs

Table 2-3. Global surface mean mole fractions of hydrofluorocarbons estimated from ground-based air sampling
networks.
Change(2015–2016)

Common orIndustrial Name

Annual Mean
Mole Fraction (ppt)
2012
2015
2016

CHF3

HFC-23

24.9

28.1

28.9

ppt yr−1
0.8

CH2F2

HFC-32

6.28

10.7

12.6

1.9

18%

AGAGE, in situ (Global)

4.97

9.18

11.2

2.0

22%

NOAA, flasks (Global)

CHF2CF3

HFC-125

12.1

18.4

20.8

2.4

13.0% AGAGE, in situ (Global)

11.7

17.7

20.1

2.4

13.4% NOAA, flasks (Global)

67.7

83.3

89.3

6.0

7.2% AGAGE, in situ (Global)

67.5

83.4

89.6

6.1

7.4% NOAA, flasks (Global)

68.9

84.9

92.1

7.2

8.5% UCI, flasks, (global)

13.4

17.7

19.3

1.6

9.2% AGAGE, in situ (Global)

13.2

17.4

19.0

1.6

9.0% NOAA, flasks (Global)

6.84

6.68

6.72

0.03

0.5% AGAGE, in situ (Global)

Chemical Formula

CH2FCF3

HFC-134a

CH3CF3

HFC-143a

CH3CHF2

HFC-152a

CHF2CH2CF3

HFC-245fa

CH3CF2CH2CF3

HFC-365mfc

CF3CHFCF3

HFC-227ea

CF3(CHF)2CF2CF3

HFC-43-10mee

CF3CH2CF3

HFC-236fa

0.11

Network, Method% yr−1
2.9% AGAGE, in situ (Global)

6.65

6.57

6.61

0.04

0.6% NOAA, flasks (Global)

1.71

2.23

2.43

0.20

8.9% AGAGE, in situ (Global)

0.72

0.92

1.00

0.08

8.4% AGAGE, in situ (Global)

0.61

0.79

0.87

0.08

9.9% NOAA, flasks (Global)

0.82

1.12

1.24

0.11

10.2% AGAGE, in situ (Global)

0.80

1.07

1.17

0.10

9.6% NOAA, flasks (Global)

0.23

0.25

0.27

0.01

4.6% AGAGE, in situ (Global)

0.14

0.15

0.01

5.7% AGAGE, in situ (Global)

Notes:
Stated mole fractions represent independent estimates of global surface means determined by different observational networks
at different sampling locations; annual values represent calendar year means. Absolute changes (ppt yr −1) are calculated as thedifference in annual means; relative changes (% yr −1) are that same difference relative to the 2015 value. Small differences between
values from previous Assessments are due to changes in calibration scale and methods for estimating global mean mole fractions
from a limited number of sampling sites. These observations are updated from the following sources: Montzka et al., 1996; Miller
et al., 2008; Miller et al., 2010; Vollmer et al., 2011; Arnold et al., 2014; Rigby et al., 2014; Simpson et al., 2014; Montzka et al., 2015;
Simmonds et al., 2016, 2017, 2018. They are available at http://agage.mit.edu/ (for AGAGE data); at http://www.esrl.noaa.gov/gmd/
dv/site/ (for NOAA data); and at http://ps.uci.edu/~rowlandblake/research_atmos.html (for UCI data). Global mean estimates from
AGAGE are calculated using atmospheric data and a 12-box model (Cunnold et al., 1983; Rigby et al., 2013). AGAGE calibrations areas specified in CDIAC (2016) and related primary publications. NOAA-determined values are directly estimated from measurements
made at 8 to 12 remote surface sites with cosine-of-latitude weighting.

than global-scale emission estimates. Uncertainties
associated with regional-scale inverse analyses, forexample, include the inversion setup, the assumed
priors, the uncertainty assumed for those priors andfor the observations, the geographic extent of the sampling network, and sampling frequency, among otherparameters. Furthermore, some emission estimates
are derived from observations that have limited spatial
coverage and limited sensitivity to emissions across anentire region of interest (countrywide, for example)
or incomplete coverage across all seasons. While uncertainties in regional-scale emission estimates would
ideally encompass all of these factors, this is often not
2.14

feasible as this entails estimating uncertainties in parameters with insufficient objective information.

2.3.1.1 HFC-134a (CH2FCF3)
HFC-134a remains the most abundant HFC in the
global atmosphere and has the largest annual growthrate and emission. The global annual mean mole fraction reached 89.5 ppt in 2016, up from 67.7 ppt in 2012
(AGAGE and NOAA data; UCI results over this period are ~3% higher; Table 2-3). The rate of increase
averaged 5.6 ± 0.2 ppt yr−1 (7.3 ± 0.2% yr−1) during
2012–2016, which is about 0.6 ppt yr−1 larger than theHFCs | Chapter 2

mean increase for 2008–2012, as reported in the last
Assessment. This observed increase in global mole
fraction is similar to that projected nearly a decadeago (Velders et al., 2009) and more recently (Velders
et al., 2015); it is also consistent with the largest increases projected in the Representative Concentration
Pathways (RCPs), namely RCP8.5 (Figure 2-2;
Carpenter and Reimann et al., 2014; Velders et al.,2009; Meinshausen et al., 2011).
Mixing ratios of HFC-134a have been estimated fromthe ACE-FTS instrument on board SCISAT for recent
years (updates to Nassar et al., 2006) using updated
absorption cross sections (Harrison, 2015). The upper
tropospheric annual means derived for HFC-134afrom SCISAT are very consistent with mean mole
fractions measured by the surface networks; differences between results from SCISAT and the surface
networks are much smaller than the uncertainties associated with the satellite retrievals. Rates of change
in HFC-134a mixing ratios determined for 2012–2016
from these independent measurements systems arealso consistent, with 7.0 ± 0.8 % yr−1 derived fromannual changes in SCISAT results, 7.3 ± 0.1 % yr−1 derived from NOAA data, and 7.2 ± 0.2 % yr−1 derived
from AGAGE data (Figure 2-2).
Globally, HFC-134a contributed a radiative forcing(RF) of 14.3 mW m−2 in 2016. This is the largest radiative forcing contributed by any other HFC or otherfluorinated gas (i.e., PFCs, SF6, SO2F2, and NF3; see
Chapter 1). The mean rate of increase has been slightly larger over the past 4 years (5.4–5.8 ppt yr−1) thanduring 2008–2012 (4.9–5.1 ppt yr−1). HFC-134a has
been used as a substitute for CFC-12 since the mid1990s in mobile air conditioning (MAC), in stationary
refrigeration and air conditioning, in metered-dose
inhalers, and in foam-blowing applications; it has also
been used as a fire suppressant and for dry etching.
Due to its high GWP (Table 2-2), controls on HFC134a use have been adopted in some sectors in the
E.U., USA, Japan, and other countries (see Section
2.5.1). Refrigerants with substantially lower GWPs
such as HFC-1234yf and HFC-1234ze(E), among others, are starting to replace HFC-134a in various applications (see below) (UNEP, 2017a).
Total global emissions of HFC-134a estimated froma budget analysis of measured mole fractions at remote sites increased nearly linearly from 177 ± 17 Gg
yr−1 in 2012 to 223 ± 22 Gg yr−1 in 2016 (Figure 2-1;
update from Rigby et al., 2014; Montzka et al., 2015;
Simmonds et al., 2017; AGAGE and NOAA data).
Global emissions have increased by an average of 10
Gg yr−1 since 2008, faster than emissions of any other
HFC. Global emissions derived with different modeling frameworks (but these same network data) areconsistent with the emissions derived here (FortemsCheiney et al., 2015; Lunt et al., 2015; Xiang et al.,2014).
The total global emissions derived for HFC-134a fromatmospheric observations are over two times larger than total emissions reported to UNFCCC fromAnnex I countries (see Figure 2-3). Furthermore,this emission gap has become larger over time; theHFC-134a emission (Gg yr –1)







 

  Series5     


















Figure 2-3. A comparison of HFC-134aemission totals reported to UNFCCC (UNFCCC, 2017; green lines) and derived fromatmospheric observations (blue lines;
from Figure 2-1). Totals are indicated assolid lines (total reported for UNFCCC
and global total for observation-based).
The sum of emissions from the U.S. andEurope are indicated with a dashed green
line for UNFCCC reporting and a dashedblue line for the atmospheric measurement-based estimate (from Figure 2-4
and Figure 2-5).


2.15

Chapter 2 | HFCs

HFC-134a totals reported to UNFCCC have decreasedslightly since 2010, in contrast to the increases derived
for global total emissions from the atmospheric data
(Figure 2-1 and Figure 2-3).
The difference between UNFCCC reporting and atmosphere-based global total emission is not likely
from underreporting of emissions from the USA andEurope, the two regions that account for most (~80%)
emissions reported to the UNFCCC (Figure 2-3).
Regional emissions derived from inverse modeling
analysis of measurements within the USA and Europe
suggest that the inventory-based reporting from theseregions is accurate or slightly high (Graziosi et al., 2017;
Hu et al., 2017; Say et al., 2016) (Figure 2-4, Figure2-5). A similar conclusion was derived for HFC-134a
a 100
(a)

20

HFC-134a

80

15

60

HFC-125

10 EDGAR x 0.5

40

USA emissions (Gg yr -1)

from all Annex I countries from an inverse modeling
analysis of measurements at globally-distributed sites(Lunt et al., 2015). For 2014, emissions of HFC-134afrom the USA and Europe summed together were 71
Gg yr−1 in UNFCCC reporting, and they were 61 ± 8
Gg yr−1 from inversion-based modeling analyses of atmospheric observations. The observation-based emissions derived for the USA totaled 43 ± 6 Gg yr−1 (Hu etal., 2017) and for Europe totaled 18 ± 6 yr−1 (Graziosi
et al., 2017) in 2014. Other inversion-based analyses ofatmospheric data in Europe suggest European HFC134a emissions that are consistent with those derived in
Graziosi et al. for 2009 (Keller et al., 2012); they are between 9 and 16 Gg yr−1 higher in other years (Brunner
et al., 2017 for 2011; and Fortems-Cheiney et al., 2015
for multiple years). Slightly higher European emissions

20

5

0
15

0
10

10 EDGAR x 0.5

HFC-143a

HFC-32

5
5
1

0
0.6

0
HFC-227ea

0.8
0.6
0.4

U.S. EPAEDGAR
Hu et al.

HFC-365mfc

0.4

EDGAR x 0.5

EDGAR x 0.1
0.2

0.2
0

2006 2008 2010 2012 2014

0

Year

2006 2008 2010 2012 2014(b) 0.2
USA emissions (Gt CO2-eq yr -1)

0.18

U.S. EPA
Hu et al.

0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0

2.16

2008

2008

Year

2008

2008

Figure 2-4. USA national HFC emissions estimated from an atmospheric sampling network andfrom two inventory-based analyses: EDGAR (Emissions Databasefor Global Atmospheric Research)and U.S. EPA, which is subsequently
reposted by UNFCCC (adapted from
Hu et al., 2017). a) These six panels show results by compound. b)
Aggregated results as Gt CO2-eq
emissions yr −1, with contributions
from these six different gases indicated by the different colors. The
EPA inventory result for these six
gases (solid red line and red-filled
symbols in both panels) is shownfor comparison, as is the contribution to the inventory totals of additional HFCs not included in the Hu
et al., 2017 study (dashed red line).
Uncertainties in both panels represent ranges in derived emissions
based on multiple inversions, given
uncertainties associated with thesampling network, assumed prioremissions, upwind mole fractions(background conditions), transportmodels, and meteorology.

Emissions (Gg yr-1)
Emissions (Gg yr-1)
Emissions (Gg yr-1)
Emissions (Gg yr-1)

Emissions (Gg yr -1)
Emissions (Gg yr -1)

Emissions (TgCO2-eq yr -1)

Year

HFC-245fa

Emissions (TgCO2-eq yr -1)

Emissions (TgCO2-eq yr -1)

HFC-365mfc

HFC-227ea

Emissions (TgCO2-eq yr -1)

HFC-236fa

HFC-143a

Emissions (TgCO2-eq yr -1)

HFC-152a

Emissions (TgCO2-eq yr -1)

Emissions (Gg yr -1)

HFC-134a

HFC-125

Emissions (TgCO2-eq yr -1)

Emissions (TgCO2-eq yr -1)

Emissions (Gg yr -1)

HFC-32Emissions (TgCO2-eq yr -1)

5

Emissions (TgCO2-eq yr -1)

Emissions (Gg yr -1)

HFCs | Chapter 2

Total

Year

Figure 2-5. Emissions of nine HFCs from the European geographic domain, and related uncertainties, fromJanuary 2003 to December 2014. Emissions are given in Gg yr −1 (left axis) and in TgCO2-eq yr −1 (right axis). The
lower right panel (labeled “total”) shows aggregated CO2-eq emissions yr −1 from the nine HFCs. Emissions
derived from atmospheric measurements are given as red filled circles: Graziosi et al. (2017); green filledsquares: Lunt et al. (2015); and dark blue filled diamonds: Brunner et al. (2017). Emissions derived from inventories are shown as light blue unfilled circles (UNFCCC) and purple unfilled triangles (EDGAR v4.2 FT2010).
Note that Lunt et al. (2015) and Brunner et al. (2017) data are shifted slightly in time for clarity; the Lunt et al.
data are a 3-yr average and the Brunner et al. (2017) data refer to a smaller domain. Note that 100 Tg = 0.1 Gt.
2.17

Chapter 2 | HFCs

of HFC-134a would make the sum of European and
USA emissions more consistent with reporting to the
UNFCCC (Figure 2-3). The only other recent analysis of USA emissions based on USA observations(Fortems-Cheiney et al., 2015) suggests between 10 and
20 Gg yr−1 larger emissions than derived in Hu et al. forthe overlapping years of 2008–2010, but these higher
values may be less representative of the USA total as
they are derived from observations at a smaller numberof USA sites.
Most of the remaining HFC-134a emissions reported
to the UNFCCC (20% of reported total) come from
Canada, Japan, Australia, and Russia and have ranged
from 1.5 to 4 Gg yr−1 per country in recent years. Of
these countries, atmosphere measurement-based estimates are available for Japan and Australia. Japaneseemissions during 2008 have been estimated at 3.1 ±0.2 Gg yr−1 in one study (Stohl et al., 2010) and 4.7(4.5–5) Gg yr−1 in another (Li et al., 2011); emissions
during 2010-2012 were estimated at 3.7 (0.6–7.4) Gg
yr−1 in one study (Lunt et al., 2015) and between 2.1
and 4.3 Gg yr−1 in another (Saito et al., 2015). Theseestimates are not significantly different from the 2.5
to 2.9 Gg yr−1 reported to UNFCCC by Japan in theseyears, although higher amounts were inferred for 2010
in one study (12 ± 2 Gg yr−1; Fortems-Cheiney et al.,2015). Atmosphere-based estimates for Australia for2006 (4.5 Gg yr−1; Stohl et al., 2009) indicate comparable emissions to Japan’s, but they are higher than the
1.9 Gg reported by Australia to UNFCCC for that year.
The absence of substantial underestimates in nationalemissions reported to UNFCCC, as is implied fromthe above discussion, indicates that the factor of approximately two difference (> 100 Gg−1) between totals
reported to UNFCCC and atmosphere-based global
totals likely stems from significant emissions of HFC134a from non-reporting countries (i.e., non-Annex I,
developing countries). Regional atmospheric studies in
Asia support this conclusion. Emissions for 2008 from
East Asia were estimated to be 15–20 Gg yr−1 (basedon values from Li et al., 2011 of 15.2 [12.5–18.6] Gg
yr−1 and from Stohl et al., 2010 of 19.2 ± 2.5 Gg yr−1).
Atmosphere-based estimates suggest that emissions
from China accounted for 8–13 Gg of these East Asian
HFC-134a emissions during 2008 (based on estimatesof 8.3 [6.2–11] Gg yr−1 from Li et al., 2011, 8.7 [6.5–12]
Gg yr−1 from Kim et al., 2010, and 12.9 ± 1.7 Gg yr−1
from Stohl et al., 2010). Emissions at the higher end of
2.18

this range were derived for 2010-2012 (12 [5–21] Gg
yr−1 in Lunt et al., 2015). Based on emissions derived
from atmospheric observations, South Korea has accounted for approximately 1–3 Gg yr−1 of HFC-134a
in recent years (1.5–2.2 Gg yr−1 for 2008 [Stohl et al.,2010 and Li et al., 2011] and (1.83 [0.58-3.13] Gg yr−1
for 2010-2012 [Lunt et al., 2015]).
An analysis of HFC production and consumption in
China also indicates significant Chinese emissionsof HFC-134a and suggests increasing emissions overtime with 9.2–11 Gg in 2008 and 33 Gg in 2013 (Su etal., 2015 and Fang et al., 2016). These results and theatmosphere measurement-based estimates, however,suggest that emissions from China account for lessthan half of the difference between global emissions
and totals reported to UNFCCC from Annex I countries (Figure 2-6). As such, they imply that significant
emissions of HFC-134a, perhaps as much of 30% of
the global total, are currently arising from non-Annex
I countries other than China that are not required toreport HFC emissions to the UNFCCC.

2.3.1.2	HFC-23 (CHF3)
HFC-23 is emitted into the atmosphere primarily as
a by-product from over-fluorination during HCFC-22
production; much smaller emissions are associatedwith HFC-23 use as feedstock for halon-1301 production, in semiconductor fabrication (plasma etching
and chamber cleaning), in very low-temperature refrigeration, and in specialty fire suppression systems(Miller et al., 2010; Montzka et al., 2010; Oram et al.,
1998; Simmonds et al., 2018; US EPA, 2017).
Atmospheric mole fractions of HFC-23 continue to increase in the global atmosphere and reached 28.9 ppt
in 2016 (up from 25 ppt in 2012; AGAGE data only;
Table 2-3). This global abundance accounted for 5.2
mW m−2 in 2016, the second largest radiative forcing of all individual HFCs and other F-gases (PFCs,
SF6, NF3, SO2F2, SF5CF3; see Chapter 1). The HFC-23
global mole fraction increased by 0.83 ppt yr−1 (2.9%yr−1) in 2015–2016, similar to the 0.9 ppt yr−1 increasemeasured for 2011–2012 (Carpenter and Reimann etal., 2014), but less than the peak rate of 1.1 ppt yr −1
observed during 2014. The average rate of increase for
2012–2016 was 1.0 ppt yr −1, which is slightly faster thanthe 0.8 ppt yr −1 reported in the previous Assessment
for 2008–2012. In recent years, observed global mole

HFCs | Chapter 2

160

140

120

HFC−134a

x 105

Difference, Global minus UNFCCC (I)Difference, Global minus UNFCCC (I,a)
East Asia (a)China (a−1)
China (a−2)
China (I−1)

2
1.8
1.6
1.4

100

1.2
80
1
60

0.8
0.6

40

0.4

Emissions (Gg CO2-eq yr -1)

Emissions (Gg yr –1)

China (I−2)

20
0.2
0
1990

1995

2000

Year

2005

2010

2015

0

Figure 2-6. Differences between the global emissions derived from atmospheric measurements and the total
emissions reported to UNFCCC (UNFCCC, 2017) for HFC-134a. Annual differences between atmosphere-based
global total HFC-134a emissions (from Figure 2-1) and inventory-reported totals (UNFCCC, 2017) are plotted as the green line and shading (Global minus UNFCCC(I); shading reflects global emission uncertainties);
the dark blue line with shading (Global minus UNFCCC(I,a)) represents a similar quantity, but with UNFCCC
reporting for the USA and Europe replaced by the regional emission estimates supplied by atmosphericobservations in these regions (Hu et al., 2017; Graziosi et al., 2017). Also shown are emissions derived fromatmospheric measurements (or other inventory-based estimates) for China and East Asia, which are not obligated to report emissions to the UNFCCC. (These estimates do not represent emissions from all regions not
obligated to report to the UNFCCC). East Asian (Stohl et al., 2010, a, black) and Chinese emissions are frominventory-based and atmosphere-based approaches (Lunt et al., 2015, a-1, red; Fortems-Cheiney et al., 2015,
a-2, pink and shading; Su et al., 2015, I-2, light blue upside down triangles; Fang et al., 2016, I-1, dark blue
triangles).

fractions of HFC-23 are also consistent with the largest
emissions projected in the RCPs, which are in RCP8.5(Meinshausen et al., 2011).
Mixing ratios of HFC-23 have been estimated overtime from the ACE-FTS instrument on board the
SCISAT satellite (Harrison et al., 2012) using updated

absorption cross sections (Harrison, 2013). The upper
tropospheric means are approximately 2 ppt below
those estimated from surface-based data (AGAGE), although the relative rate of change averaged over 2012–2016 from the two independent measurements is not
significantly different (3.1 ± 0.8% yr−1 from SCISAT
versus 3.8 ± 0.6% yr−1 from AGAGE) (Figure 2-2).
2.19

Chapter 2 | HFCs

Total global HFC-23 emissions derived from a budget analysis of measured mole fractions at remotesites show gradual increases before 1999 and thensubstantial variations, with minima in 2001–2002
and in 2009–2010 and maxima in 2005–2006 and2013–2014. Derived peak emissions were 14.5 ±0.7 Gg yr−1 in 2014, and have since decreased, with
12.3 ± 0.7 Gg yr−1 estimated for 2016 (Figure 2-1,
Simmonds et al., 2018).
A comparison between atmospheric measurement-based global emission estimates and Annex
I country totals reported to UNFCCC shows fairly
good consistency during 1990–1995 (Figure 2-1).
After 2000, however, UNFCCC totals decrease andatmospheric measurement-derived global emissions
increase. UNFCCC totals were between 1 and 1.5 Gg
yr−1 for 2009–2015 while measurement-based global
emission totals ranged between 9.5 and 15 Gg yr−1. Thisdifference increased concurrently with the substantial increases in HCFC-22 production and associatedHFC-23 emission from developing countries not required to report HFC emissions to the UNFCCC (i.e.,
non-Annex I countries) (Miller et al., 2010; Montzka
et al., 2010; Simmonds et al., 2018).
Inventory-based emissions of HFC-23 from countries
not obligated to report emissions to the UNFCCC arederivable from information provided to the Montreal
Protocol’s Multilateral Fund (MLF) by these countries(UNEP, 2017b; Simmonds et al., 2018). These inventory emissions of HFC-23 are estimated in some countries and, in others, are derived from a combinationof country-based reporting of HCFC-22 production
for all uses, HFC-23 production rates, and quantities
of HFC-23 destroyed or otherwise transformed as
feedstock.
The sum of inventory-based emissions derived from
reporting to the UNFCCC and estimated using theinformation collected by the Montreal Protocol’s MLF
fairly closely tracks the wide swings in global emissions
derived from atmospheric mole fraction measurements(Figure 2-7), although emissions in a few years (e.g.,
2008, 2013, and 2015) are significantly underestimated by these inventories (Simmonds et al., 2018). Thisoverall consistency suggests that the inventory-derived
HFC-23 emissions associated with HCFC-22 production are fairly accurately estimated (within ± 2 Gg in
total) in both developed and developing countries.
2.20

This consistency also provides an understanding of
HFC-23 emission changes over the past decade. The
minimum in emissions centered around 2009–2010
stems from a significant decrease in the ratio of HFC23 emission relative to HCFC-22 production (E23/P22)(Figure 2-7), which was primarily the result of HFC23 destruction facilitated by the UNFCCC’s CDM
projects (Miller et al., 2010; Montzka et al., 2010). The
subsequent increase in HFC-23 emissions after 2010
appears to be the result of increases in total HCFC22 production and slight increases in the E23/P22 ratio
as the CDM projects were terminated. The ~2 Gg decrease in annual emissions after 2014 is associated withslightly reduced total HCFC-22 production as dispersive-use production was capped in 2013, although amuch larger emission decline was expected in 2015
from inventory reporting than is apparent in the measurement-derived emission estimate for that year. The
decline in reported emissions in 2015 is due primarily
to a drop in the reported E23/P22 ratio (Figure 2-7).
The inventory-based reporting totals also suggest asignificant shift in regions emitting HFC-23 in thepast. Emissions were primarily from the USA, Russia,
the UK, and Japan during the early 1990s. Since 2009,
however, between 84% and 89% of global HFC-23
emissions have come from China, which is consistent
with China being the largest producer of HCFC-22
in recent years in reporting to the UN Environment
Ozone Secretariat (UNEP, 2017b; Simmonds et al.,2018).
Independent evidence for substantial HFC-23 emissions from non-Annex I countries in recent years
comes from a number of observational studies, although measurements have not been conducted near
all developing countries reporting HFC-23 emission(i.e., Argentina, China, India, Mexico, North Korea,
and Venezuela). Emissions from China, for example, were estimated for 2008 from observations ineastern Asian countries and different data analysis
methods and range between 6 and 12 Gg yr−1 (basedon estimates from Stohl et al. [2010] of 6.2 ± 0.7 Gg
yr−1, Fang et al., [2015] of 6.2 ± 0.6 Gg yr−1, Li et al.
[2011] of 10 (7.2–13) Gg yr−1, and Kim et al. [2010]of 12 (8.6–15) Gg yr−1). The lower end of this range
is in good agreement with the inventory-based estimates provided to the MLF from China of 5.7 Gg
yr−1 for 2008 (UNEP, 2017b; Simmonds et al., 2018).
For comparison, total global emissions derived from

HFCs | Chapter 218

HFC-23 Emission (Gg yr –1)

16
14

Atmosphere - derived
Reported: UNFCCC + MLF
Reported: UNFCCC

12
10
8
6
4
2

2.0

900
800
700
600
500

1.5

400

3.0

E23/P22 (%)

E23 (Atmos. derived) / HCF-22 prod.
E23 (UNFCCC + MLF) / HCFC-22 prod.
Total HCFC-22 prod.
Feedstock HCFC-22 prod.

2.5

300
200
100
0

1.0
0.5
0.0
1990

1995

2000

2005

2010

HCFC-22 production (Gg yr –1)

0
3.5

2015

Year
Figure 2-7. Top panel: Emissions of HFC-23 derived from atmospheric measurements (blue lines) and inventories (red and green lines). Measurement-based emissions are derived from analyses of southern-hemispheric firn air (light-blue line; Montzka et al., 2010), a southern-hemispheric air archive before 2007 coupledwith ongoing results from multiple sites during 2007–2009 (light blue circles; Miller et al., 2010), and froma combination of air-archive, firn air, and ongoing measurements (dark blue diamonds; Simmonds et al.,2018). Uncertainties are one standard deviation of estimates. Inventory results are from Annex I reporting to
UNFCCC (green line with filled circles; UNFCCC, 2017) and from the sum of reporting to UNFCCC and to theMontreal Protocol’s Multilateral Fund (MLF; red line, Simmonds et al., 2018). Bottom panel: the ratio of HFC-23
emissions relative to HCFC-22 production for all uses (E23/P22 by mass, left-hand axis; with emissions being
the total from inventories (red) or global magnitudes estimated from atmospheric measurements and theiruncertainty (blue line) from the upper panel, and total global HCFC-22 production reported for all uses (blackline; right-hand axis) and for feedstock only (black dashed line; right-hand axis).

measured atmospheric changes at remote sites was
11.2 ± 0.6 Gg yr−1 in 2008.
By 2012, atmospheric measurement-based analyses indicated that HFC-23 emissions from China
accounted for at least two-thirds of global HFC-23

emissions. Atmospheric observations in that year suggest Chinese emissions of 8.8 ± 0.8 Gg yr−1 (Fang et al.,2015) compared to the 10.8 Gg yr−1 estimate provided
to the MLF (UNEP, 2017b; Simmonds et al., 2018),
while the atmosphere measurement-based global estimate in 2012 was 12.9 ± 0.7 Gg yr−1 and UNFCCC
2.21

Chapter 2 | HFCs

Annex I reporting totaled 1.2 Gg yr−1. A small fractionof the global emission arises from other non-Annex
I countries such as South Korea and Taiwan (Stohl etal., 2010; Li et al., 2011; Fang et al., 2015), ~0.1–0.2 Gg
yr−1 in 2008 (compared to the estimates derived frominformation provided to the MLF of less than 0.1 Gg
yr−1 for South Korea). Analyses of atmospheric observations also indicate that emissions from Japan were~0.2–0.3 Gg yr−1 in 2008 (Stohl et al., 2010; Li et al.,2011; Fang et al., 2015), somewhat larger than the 0.04
Gg yr−1 reported to UNFCCC, while European emissions (Keller et al., 2011; Simmonds et al., 2018) aresimilar or slightly larger than the UNFCCC submission values.

2.3.1.3 HFC-32 (CH2F2), HFC-125 (CHF2CF3),
HFC-143a (CH3CF3)
Global mean mole fractions of HFC-32, HFC-125, andHFC-143a continue to rise in the atmosphere primarily because these chemicals are used as HCFC substitutes in major refrigeration blends (HFC-125 also hasa minor application for fire protection) (O’Doherty etal., 2014; Montzka et al., 2015; Simmonds et al., 2015,2017; US EPA, 2017). In 2016, average global meanmole fractions were 11.9 (11.2–12.6) ppt for HFC-32,
20.4 (20.1–20.8 ppt) for HFC-125, and 19.2 (19.0–
19.3) ppt for HFC-143a (NOAA and AGAGE data;
Table 2-3). These mole fractions are approximately
twice the 2012 values for HFC-32 and HFC-125, andare 50% higher for HFC-143a. Mole fractions of these
HFCs increased during 2012–2016 by an average of 1.6
ppt yr−1 for HFC-32, 1.5 ppt yr−1 for HFC-143a, and
2.1 ppt yr−1 for HFC-125. These rates are considerablylarger than measured during 2008–2012 as reported in
the previous Assessment, by a factor of 1.7 for HFC-32
and HFC-125, and by a factor of 1.2 for HFC-143a.
The mole fraction increases observed for these gases
since 2007 are similar to the scenario projections
discussed in the previous Assessment (Figure 2-2;
Carpenter and Reimann et al., 2014; Velders et al.,2009) and to those in newer scenarios (Velders et al.,2015), although projected HFC-32 mole fractions
were slightly higher than observed.
The radiative forcings associated with these 2016 global mole fractions were 1.31 (1.23–1.39) mW m−2 from
HFC-32, 4.70 (4.62–4.79) mW m−2 from HFC-125,
and 3.06 (3.04–3.09) mW m−2 from HFC-143a.
2.22

Total global emissions of these three HFCs derived
from a budget analysis of measured mole fractions atremote sites continue to increase, with estimates for2016 of 35 ± 4 Gg yr−1 for HFC-32, 62 ± 5 Gg yr−1
for HFC-125 and 28 ± 2 Gg yr−1 for HFC-143a. These
were higher than their 2012 emissions of 20 Gg yr−1,
44 Gg yr−1, and 22 Gg yr−1, respectively (Figure 2-1;
update from O’Doherty et al., 2009, 2014; Rigby etal., 2014; Lunt et al., 2015; Montzka et al., 2015; andSimmonds et al., 2017). In 2014, the global emission
estimates for each of these three gases were about two
times higher than the corresponding UNFCCC totals
from reporting countries.
As was true for HFC-134a, the difference between
UNFCCC reporting and atmosphere-based global total
emission for these three gases is not likely from underreporting of emissions from the USA and Europe,
the two regions that account for most emissions reported to UNFCCC (~83–90%). Regional emission
magnitudes derived from measurements within the
USA and Europe suggest that the inventory-based
reporting from these regions is accurate or even overestimated. In Europe, atmosphere-based emissions
estimated for these gases have been consistent withvalues reported to UNFCCC in most recent years;
an exception is that UNFCCC reporting for HFC-32
has increased above the atmosphere-based estimatesin the most recent years (Figure 2-5) (Graziosi et al,
2017; Brunner et al., 2017). A similar divergence isobserved in the USA for HFC-32 and HFC-125, with
the UNFCCC inventory increasing faster than atmosphere-based estimates (Figure 2-4); this divergenceis less pronounced for HFC-143a. For Japan, both atmosphere-based estimates (Saito et al., 2015; Lunt etal., 2015) and UNFCCC reporting suggest Japaneseemissions are < 2 Gg yr−1 for each of these gases.
The absence of substantial underestimates in national emissions reported to UNFCCC for these three
HFCs indicates that the factor of approximately two
difference in reporting totals versus atmosphere-based
global totals likely stems from significant emissions
from developing countries not required to report theiremissions to UNFCCC (non-Annex I). This conclusion has also been reached in an analysis of production
estimates and market demand in developing countries,
based on the Montreal Protocol phase-out schedules
for ODSs (Fang et al., 2016; Zhang and Wang, 2014;
Velders et al., 2015).

HFCs | Chapter 2

Emissions estimates based on atmospheric measurements in East Asia confirm substantial HFC-32
emissions from non-Annex I countries. Averaged
over 2010–2012, Chinese emissions of HFC-32 were
estimated to be 7.0 (4.9–9.2) Gg yr−1 and those fromSouth Korea were estimated at 0.43 (0.1–0.52) Gg yr−1
(Lunt et al., 2015). These Chinese emissions are larger
than the mean emissions estimated for the USA of 3.3± 0.5 Gg yr−1 (Hu et al., 2017) or for Europe of 2.5 ± 0.9
Gg yr−1 (Graziosi et al., 2017) during these same years.
They also are significantly larger than those from Japan(0.5–0.7 Gg yr−1) (Lunt et al., 2015; Saito et al., 2015).
Consideration of HFC-32 production and consumption magnitudes along with use patterns has allowed
inventory-based emission estimates in China. This
analysis suggests emissions of 12 Gg yr−1 of HFC-32
in 2013 and 7.7 Gg yr−1 for 2010–2012 (Fang et al.,2016), consistent with the atmosphere-based regional
estimate from Lunt et al. quoted above (7.0 [4.9–9.2]
Gg yr−1 for 2010–2012). These results suggest that
substantial emissions of HFC-32 have arisen from
China as a result of the phase-out of ODSs and that

25

these Chinese emissions explain most if not all of thedifference between global estimates and totals being
reported to UNFCCC for this chemical (Figure 2-8).
Atmosphere-based studies also suggest substantial
emissions of HFC-125 from non-Annex I countries(Figure 2-9). Emissions from China averaged over
2010–2012 were 5.7 (4.0–7.7) Gg yr−1, and those fromSouth Korea were substantially smaller (0.5 [0.3–0.7]
Gg yr−1; Lunt et al., 2015). By comparison, average
emission during this period was estimated to be 7.9 ±1.2 Gg yr−1 from the USA (Hu et al., 2017) and 8.6 ±2.7 Gg yr−1 from Europe (Graziosi et al., 2017). Smaller
emissions of 0.8 (0.3–1.5) Gg yr−1 were estimated fromJapan for 2010–2012 (Lunt et al., 2015).
Consideration of HFC-125 production and consumption magnitudes along with use patterns has allowed
inventory-based emission estimates in China. This
analysis suggests emissions of 6.3 Gg yr−1 averaged over
2010–2012 and 12 Gg yr−1 of HFC-125 in 2013 (Fanget al., 2016), consistent with the atmosphere-based regional estimate from Lunt et al. (2015). While theseHFC−32Difference, Global minus UNFCCC(I)
Difference, Global minus UNFCCC(I,a)
China(a−1)

16000

China(I−1)
20

14000

Emissions (Gg yr –1)

15

10000

8000
10

6000

Emissions (Gg CO2-eq yr -1)

12000

4000

5

2000

0
1990

1995

2000

2005

2010

2015

0

Year

Figure 2-8. As in Figure 2-6, but for HFC-32. Here Chinese emissions are from inventory-based (Fang et al.,2016; I-1, blue triangles) and atmosphere-based (Lunt et al., 2015, a-1, yellow) estimates.
2.23

Chapter 2 | HFCs

45

40

HFC−125

x 104

Difference, Global minus UNFCCC(I)
Difference, Global minus UNFCCC(I,a)
China(a−1)
China(I−1)

15

30

10

25

20

15

5

Emissions (Gg CO2-eq yr -1)

Emissions (Gg yr –1)

35

10

5

0
1990

1995

2000

2005

2010

2015

0

Year
Figure 2-9. As in Figure 2-8, but for HFC-125.

results indicate increasing emissions of HFC-125 from
China during the phase-out of ODSs, they also demonstrate that Chinese emissions explain only approximately half of the difference between global emissionsof HFC-125 and totals reported to UNFCCC (Figure2-9). This difference suggests substantial emissions of
HFC-125 from other non-Annex I countries not required to report emissions to the UNFCCC.
Atmosphere-based studies also suggest substantial
emissions of HFC-143a from non-Annex I countries(Figure 2-10). Emissions from China averaged over
2010–2012 were 2.1 (1.3–3.1) Gg yr−1, and those fromSouth Korea were 0.13 (0.06–0.21) Gg yr−1 (Lunt et al.,2015). By comparison, during this period emissions of
4.2 ± 0.7 Gg yr−1 were estimated from the USA (Hu etal., 2017) and 6.2 ± 2.1 Gg yr−1 from Europe (Graziosi
et al., 2017). Smaller emissions were estimated fromJapan (0.3 [0.1–0.6] Gg yr−1) and S.E. Australia (0.2
[0.1–0.5] Gg yr−1) for 2010–2012 (Lunt et al., 2015).
Consideration of HFC-143a production and consumption magnitudes along with use patterns has allowed
2.24

inventory-based emission estimates in China. This
analysis suggests Chinese emissions of 3 Gg yr−1 ofHFC-143a in 2012 (Fang et al., 2016), consistent withthe atmosphere-based regional estimate from Lunt etal. (2015), likely as a result of the phase-out of ODSs.
As was true for HFC-125, Chinese emissions accountfor less than half of the difference between global estimates and totals being reported to UNFCCC for HFC143a (Figure 2-10).

2.3.1.4 HFC-152a (CH3CHF2)
The global annual average HFC-152a mole fraction hasvaried relatively little over the past 4 years, between 6.5
and 6.8 ppt (AGAGE and NOAA data; 6.8 ppt was measured in 2012; Carpenter and Reimann et al., 2014).
This is notably different from projections, which havesuggested HFC-152a mole fractions would increaseover time (Figure 2-2; Carpenter and Reimann et al.,2014; Velders et al., 2009, 2015). The global abundancein 2014 contributed a radiative forcing of 0.67 mW
m−2, relatively little compared to the HFCs discussed
above. HFC-152a is used as a foam-blowing agent,HFCs | Chapter 2

18

x 104
9

Difference, Global minus UNFCCC(I)
Difference, Global minus UNFCCC(I,a)
China(a−1)
China(I−1)

8

14

7

12

6

10

5

8

4

6

3

4

2

2

1

0
1990

0
1995

2000

Year

2005

2010

Emissions (Gg CO2-eq yr -1)

Emissions (Gg yr –1)

16

HFC−143a2015

Figure 2-10. As in Figure 2-8, but for HFC-143a.

aerosol propellant, and in some refrigeration blends
to replace CFCs, HCFCs, and recently HFC-134a in
automobile air conditioners (Simmonds et al., 2016;
UNEP, 2017a; US EPA, 2017).
Total global HFC-152a emission derived from a budget analysis of measured mole fractions at remote sites
was 53 Gg yr−1 in 2016, not appreciably different fromthe 51 ± 8 Gg yr−1 estimated for all years since 2010
(Figure 2-1, update from Rigby et al., 2014, Lunt et al.,2015, Montzka et al., 2015 and Simmonds et al., 2016).
While HFC-152a emissions are larger than most other
HFCs, its impact on atmospheric concentrations andclimate is relatively small because its lifetime (1.6 yr)
and associated GWP (100-yr GWP = 148) are small
compared to most other HFCs whose production
and consumption is also controlled by the Kigali
Amendment to the Montreal Protocol.
HFC-152a emissions reported to UNFCCC accountfor only 10 to 15% (6 to 7 Gg yr−1 for 2009–2015) of
the global totals derived from measured atmosphericchanges in the remote atmosphere. A likely reason forthis large difference is that emissions of HFC-152a fromthe USA are not included in UNFCCC totals considered here and displayed in Figure 2-1. This is because
emissions totals of HFC-152a are only reported by the
U.S. Environmental Protection Agency (EPA) to the
UNFCCC together with other chemicals (HFC-227ea,
HFC-245fa, HFC-43-10mee, some hydrofluoroolefins(HFOs), and some minor PFCs) owing to confidentiality issues. If half of the 10.7 MtCO2-eq total reportedby the USA for this mix in 2015 was due to emissionsof HFC-152a, it would account for approximately 36
Gg yr−1 of HFC-152a emission, or most of the ~45 Gg
difference shown in Figure 2-1 between reported andatmosphere measurement-derived estimates in recent
years.
Atmospheric observations from limited regions using
different modeling/inversion techniques provide evidence for substantial USA HFC-152a emissions inrecent years: 10–15 Gg yr−1 in 2005–2006 (Stohl et al.,2009), 25 (11–50) Gg yr−1 in 2004–2009 (Miller et al.,2012), 32 ± 4 Gg yr−1 in 2008 (Barletta et al., 2011), 28
2.25

Chapter 2 | HFCs

(23–33) Gg yr−1 in 2007–2009 and 32 (25–39) Gg yr−1
in 2010–2012 (Lunt et al., 2015; Simmonds et al., 2016).
HFC-152a emissions from Europe have been estimatedat 2.9 Gg yr−1 in 2009 (Keller et al., 2012), 4.1–7.5 Gg
yr−1 in 2007–2012 (Lunt et al., 2015; Simmonds et al.,2016), and an average of 4.1 ± 1.0 Gg yr−1 over 2003–
2014, with a slight decreasing trend over this period(Graziosi et al., 2017; uncertainty represents 1 standard
deviation of annual estimates). These magnitudes andthe decreasing trend are consistent with values reported to UNFCCC over this period (Graziosi et al., 2017).
Evidence exists for substantial HFC-152a emissions
from countries not required to report emissions to the
UNFCCC. Atmospheric measurements in eastern Asia
suggest emissions of ~5 Gg yr−1 (Stohl et al., 2010) and
6.8 Gg yr−1 (Li et al., 2011) in 2008, 6.2 (5–9) Gg yr−1 in2007–2009 and 7.0 (5–10) Gg yr−1 in 2010–2012 (Luntet al., 2015; Simmonds et al., 2016). A sizable fraction ofthese eastern Asian emissions arises from China, withestimates of 2.0–2.9 Gg yr−1 in 2010–2011 (Yao et al.,2012) and 3.4–5.7 Gg yr−1 in 2004–2005 (Yokouchi etal., 2006) and 2008 (Kim et al., 2010; Stohl et al., 2010).

2.3.1.5 HFC-245fa (CHF2CH2CF3), HFC-365mfc(CH3CF2CH2CF3), HFC-227ea(CF3CHFCF3), HFC-236fa (CF3CH2CF3)
HFC-245fa and HFC-365mfc (normally blended with
HFC-227ea to reduce flammability) replace HCFC141b in polyurethane structural foam blowing, andthey have potential uses in solvent applications andmedical aerosols; HFC-245fa is also used in small
quantities in centrifugal chillers (Vollmer et al., 2006;
Stemmler et al., 2007; Laube et al., 2010; UNEP, 2010;
Vollmer et al., 2011; UNEP, 2016b). The global meanmole fraction of both gases continues to steadily increase. The global mean estimated for HFC-245fa(AGAGE data) for 2016 was 2.4 ppt, up from 1.7 ppt
in 2012, with an annual increase of +0.2 ppt yr−1 (8.9%yr−1) from 2015 to 2016 (Table 2-3). For HFC-365mfc,
networks report a global average of 0.94 (0.87–1.00)
ppt, up from 0.67 ppt in 2012, with a growth rate of+0.08 ppt yr−1 (9.2% yr−1) (NOAA and AGAGE data;
Table 2-3).
HFC-227ea and HFC-236fa are used as fire retardants
to replace halon-1211 and halon-1301; HFC-227ea isalso used in mixtures with other HFCs as a propellant
2.26

in metered-dose inhalers and in blends with HFC365mfc in polyurethane foam blowing; HFC-236fa
is also used as a coolant in specialized applications(UNEP, 2010; Vollmer et al., 2011; UNEP, 2014b,
2016b; US EPA, 2017). Global mean mole fractions ofboth compounds are relatively small but continue toincrease steadily in the atmosphere. For 2016, a global
average of 1.21 (1.17–1.24) ppt is estimated for HFC227ea, up from 0.81 ppt in 2012 (NOAA and AGAGE
data). The global mean estimated for HFC-236fa in2016 was 0.15 ppt (AGAGE data), up from 0.11 ppt
in 2012. Growth rates were 0.11 ppt yr−1 (9.9% yr−1)
for HFC-227ea and 0.01 ppt yr−1 (8.9% yr−1) for HFC236fa in 2015–2016 (Table 2-3). In 2016, the radiative
forcings from these four HFCs were still very small
with 0.58 mW m−2 from HFC-245fa, 0.21 mW m−2
from HFC-365mfc, 0.31 mW m−2 from HFC-227ea,
and 0.04 mW m−2 from HFC-236fa.
Increases projected for global mole fractions of HFC227ea and HFC-236fa have been fairly accurate overtime, whereas those for HFC-245fa and HFC-365mfc
were initially overestimated (Figure 2-2; Carpenter
and Reimann et al., 2014; Velders et al., 2009, 2015)
because of the lack of observations and production
statistics for these HFCs being available when the initial projections were developed.
Total global emissions derived from a budget analysisof measured mole fractions at remote sites have increased nearly linearly in recent years for HFC-245fa(from 9.5 ± 1.5 Gg yr−1 in 2012 to 11.7 ± 1.9 Gg yr−1 in2016), HFC-365mfc (from 3.4 ± 0.7 Gg yr−1 in 2012 to
4.6 ± 0.9 Gg yr−1 in 2016), and HFC-227ea (from 3.6± 0.4 Gg yr−1 in 2012 to 4.3 ± 0.5 Gg yr−1 in 2016). By
contrast, HFC-236fa emissions have remained at 0.29
± 0.07 Gg yr−1 since 2012 (Figure 2-1, update from
Vollmer et al., 2011, Rigby et al., 2014 and Montzka etal., 2015).
Emissions reported to UNFCCC for these HFCs account for only a small fraction of global totals inferredfrom measurements at remote sites (Figure 2-1).
Some of these discrepancies may arise because several
countries report significant CO2-eq emissions of “unspecified mix of HFCs” and “unspecified mix of HFCs
and PFCs”, likely due to confidentiality concerns. For
example, the U.S. National Inventory Reports specifies
that the latter category includes HFC-227ea and HFC245fa emissions.

HFCs | Chapter 2

Few regional emission estimates exist for these four
HFCs, and they only explain a small fraction of global
emissions.
HFC-245fa emissions in 2014 from Europe were estimated to be 0.7 ± 0.5 Gg yr−1 from observations at
European sites (Graziosi et al., 2017) compared to ~10
Gg yr−1 globally in that year. An inventory-based analysis suggests Chinese emissions of 0.07 Gg yr−1 in 2009
(Fang et al., 2016) compared to ~7 Gg yr−1 globally inthat year.
HFC-365mfc emissions in 2014 were estimated to be
1.1 ± 0.4 Gg yr−1 from Europe (Graziosi et al., 2017)
and 0.25 ± 0.1 Gg yr−1 from the USA (Hu et al., 2017),
based on observations in these regions, compared
to ~4 Gg yr−1 globally in that year. For 2008, Li et al.
(2011) estimated East Asian emissions of 0.2–0.3 Gg
yr−1 based on atmospheric measurements in that region, mostly from Japan, compared to global estimatesof ~3 Gg yr−1 in that year.
HFC-227ea emissions in 2014 were estimated at 0.6 ±0.1 Gg yr−1 from the USA (Hu et al., 2017) and 0.4 ±0.2 Gg yr−1 from Europe (Graziosi et al., 2017) basedon atmospheric measurements in those regions, compared to ~3.9 Gg yr−1 globally for that year. For 2009,
Fang et al. (2016) estimated 0.5 Gg yr−1 from China
based on an analysis of inventories and markets, compared to the global estimate of ~2.8 Gg yr−1 for thatyear.
HFC-236fa emissions in 2014 were 0.025 ± 0.019 Gg
yr−1 from Europe compared to ~0.29 Gg yr-1 globallyfor that year (Graziosi et al., 2017).

2.3.1.6 HFC-43-10mee (CF3CHFCHFCF2CF3)
HFC-43-10mee is used for cleaning applications in
the electronics industry where it replaces CFC-113,
methyl chloroform, and HCFC-141b; it is also used inaerosol sprays and is an alternative to PFCs in certain
applications (Arnold et al., 2014; UNEP, 2016b; Le Briset al., 2017). It continues to slowly accumulate in theatmosphere. In 2016, a global mean mole fraction of
0.27 ppt was measured, slightly up from 0.23 ppt in
2012, with a very small growth rate of 0.01 ppt yr−1
(4.6% yr−1) in 2015–2016 (AGAGE data only; Table
2-3). In 2016, the resulting radiative forcing was still
small at 0.10 mW m−2 relative to the other HFCs discussed here. Total global emissions derived from abudget analysis of measured mole fractions at remotesites were 1.1 ± 0.3 Gg yr−1 in 2016 and have not increased appreciably since 2007 (Figure 2-1, updatefrom Arnold et al., 2014).

2.3.2

Summed Radiative Forcing andCO2-eq Emissions Attributable to HFCs

The contribution to climate change from the atmospheric concentration of a long-lived trace gas ata particular point in time is expressed as a radiative
forcing (Myhre and Shindell et al., 2013). Radiative
forcing from all HFCs in the atmosphere approximately doubled over the past decade, reaching 30 mW m−2
in 2016 (Figure 2-11); this climate warming influenceis 36% larger than the 22 mW m−2 reported for 2012
in the last Assessment (Carpenter and Reimann etal., 2014). Nearly half (47%) of the radiative forcing
from HFCs in 2016 is attributable to HFC-134a; thenext three largest contributors are HFC-23, HFC-125,
and HFC-143a, which together account for 42% of
the total. Total radiative forcing from HFCs in 2016accounted for ~10% of the 0.33 W m−2 from ODSs (seeChapter 1), and it was approximately 1.0% of the 3 W
m−2 supplied in recent years by all long-lived GHGs
combined, including CO2, CH4, N2O, ozone-depleting substances, and HFCs (Myhre and Shindell et al.,2013; more recent values are posted at: https://www.
esrl.noaa.gov/gmd/aggi/aggi.html).
The time-integrated radiative forcing supplied fromthe emission of a given HFC includes consideration of
the persistence or lifetime of HFCs. The GWP is themetric commonly used to express the time-integrated
forcing from the emission of a trace gas relative to theforcing arising from the equivalent emission of CO2
over a given time interval. One-hundred years is often
the time interval considered, although GWPs considered over shorter time intervals (e.g., 20-yr GWPs) aresubstantially larger for all HFCs except one, HFC-23(see Table 2-2). CO2-eq emissions of HFCs have increased over time (Figure 2-12) and totaled 0.88 ± 0.07
GtCO2-eq yr−1 emissions in 2016, up 23% from the 0.72
± 0.05 GtCO2-eq yr−1 estimated for 2012 (Carpenter
and Reimann et al., 2014). HFC-134a accounted for0.30 ± 0.03 GtCO2-eq yr−1 in 2016, or 34% of the CO2eq emissions from all HFCs considered here. The next
three largest contributors were HFC-125 (0.21 ± 0.02
GtCO2-eq yr−1), HFC-143a (0.14 ± 0.01 GtCO2-eqyr−1), and HFC-23 (0.16 ± 0.01 GtCO2-eq yr−1); these
2.27

Chapter 2 | HFCs
35

Radiative forcing (mW m-2)

30

HFC-236fa
HFC-43-10mee25

HFC-365mfc
HFC-227ea20

HFC-245fa
HFC-152a15

HFC-32
HFC-143a10

HFC-125
HFC-23

5

HFC-134a02007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Radiative forcing (mW m-2)

35
30
25

Projected with observations:through 2006, High-A1
through 2006, Low-A2
through 2013
Series5
Observed:

20
15
102007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Year

Figure 2-11. Upper panel: Radiative forcing supplied by individual HFCs and their sum during the past decade
based on observed global mole fractions and their change over time (as given in Figure 2-2). These forcings are derived by multiplying measured mole fractions by radiative efficiencies (Tables 2-1 and 2-2). Lower
panel: the overall observed increase in summed radiative forcing from HFCs in the upper panel (black line)
was slightly less rapid than was projected nearly a decade ago based on observations through the end of
2006 and a market analysis done at that time (high and low projections from baseline scenarios in Velders
et al., 2009; see Section 2.5). This projection analysis was updated in 2015 based on observations through2013 (Velders et al., 2015; high and low projections overlay one another on this time scale). Radiative forcingsfor HFCs are derived assuming pre-industrial concentrations of zero ppt, which is consistent with the atmospheric measurement records and no known natural sources of HFCs.

top four contributors accounted for 93% of total CO2eq yr−1 emissions from HFCs in 2016.
In 2016, HFC CO2-eq emissions were comparable tothose of CFCs (0.8 ± 0.3 GtCO2-eq yr−1) and HCFCs(0.76 ± 0.11 GtCO2-eq yr−1; see Chapter 1), and theemissions of these HFCs represent ~1.5% of the sum
all emissions from long-lived greenhouse gases in recent years (CO2, CH4, N2O, and halocarbons; Montzka
et al., 2011, updated).
2.28

2.3.3

Comparison of Recent Observed
Changes Versus ­Projections
Made in the Past

Large increases in emissions, mole fractions, and radiative forcing from HFCs were projected for the future before an amendment to the Montreal Protocol
was agreed in Kigali in 2016 (Velders et al., 2009;
Gschrey et al., 2011; Velders et al., 2015; UNEP, 2014c;
see Section 2.3). Atmospheric measurements throughHFCs | Chapter 2
900

Global emissions HFCs (MtCO2-eq)

800
700

HFC-4310mee
43-10mee
HFC-236fa
HFC-365mfc
HFC-152a
HFC-245fa
HFC-227ea
HFC-32
HFC-23
HFC-143a
HFC-125
HFC-134a

600
500
400
300
200
100
0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

Year

Figure 2-12. Global emissions of HFCs (MtCO2-eq) derived from a budget analysis of global observations(see Figure 2-1; mean of AGAGE & NOAA network results when both are available) using the AGAGE 12-box
model (Rigby et al., 2016). Emissions are weighted by 100-yr GWP to consider the heating supplied by thesechemicals integrated over a 100-yr period relative to an equivalent emission of CO2.

2016 show that mole fractions of most HFCs increased
over the previous decade at rates similar to those projected in a baseline scenario created nearly a decade
earlier based on an analysis of atmospheric data andmarket trends through 2006 (Velders et al., 2009). Thisbaseline scenario has been updated to include data
through 2012 (Velders et al., 2015). This consistency is
noted for those HFCs currently contributing the mostto radiative forcing and CO2-eq emissions (Figures
2-1 and 2-2). Some less abundant HFCs (e.g., HFC152a, HFC-245fa, and HFC-365mfc) have increased
substantially more slowly than was projected. As a result, radiative forcing from the sum of HFCs used as
ODS substitutes (i.e., not including HFC-23) increasedslightly more slowly than in the baseline projections
created nearly a decade ago (Figure 2-11).
These results indicate that mole fractions and emissions for most HFCs have continued to change in a
fairly predictable manner during the global phaseout of CFC and HCFC production and consumption.
These projections included a slowdown in the HFC
emission increases from non-Article 5 countries (alsoreferred to as Article 2 countries or Parties to theMontreal Protocol) and, beginning in the mid-2000s,
significant contributions from Article 5 countries thatwere projected to increase substantially after the 2013

cap on global HCFC production. These projections did
not include country- and region-specific controls thatwere introduced or were being considered during that
decade (see Section 2.5.1.2), except the 2006 EU MAC
directive (EU, 2006), and this likely explains in part theslightly slower increases in summed radiative forcing
from HFCs in observations compared to projectionsin Figure 2-11. While production and consumption ofsome HFCs are capped in the future by controls outlined in the Kigali Amendment (see Section 2.5.1.3
and Table 2-1), they may temporarily increase in somecountries in the future, particularly those countries
for which caps on production and consumption limit
these quantities beginning in 2024 or 2028 based on
baseline magnitudes determined for future years (e.g.,
2020–2022 or 2024–2026, depending on developing
country Group; Table 2-1).

2.3.4

Aggregate Sums of HFC Emissions
Reported to the ­UNFCCC
and ­Contributions from
­Non-Reporting Countries

Throughout Section 2.3, substantial differences werenoted for all HFCs between total emissions reported
to the UNFCCC and global total emissions estimated from atmospheric data. Those differences have
2.29

Chapter 2 | HFCs

900

Emissions (MtCO2-eq yr -1)

800
700
600
500

Atmosphere-derived:
GlobalGlobal minus HFC-23
UNFCCC Annex I reported:
Total
Total without- HFC-23
U.S. + Europe sum:
Atmosphere-derived
UNFCCC-reported

400
300
200
100
0
1990

1995

2000

2005

2010

2015

Year

continued to grow over time for most gases (Figure2-1) and in the aggregate of total HFC emission (Figure2-13). On a CO2-eq basis, aggregate HFC emissions
reported to the UNFCCC in 2015, the latest year forwhich UNFCCC reporting is available, accounted for
only 39% of the total global HFC emissions derived
from global atmospheric mole fraction changes measured in the remote atmosphere; this value increases
to 46% if emissions of only those HFCs used as ODS
substitutes are considered (i.e., not HFC-23) (Figure2-13).
Inverse analyses of atmospheric measurements madein the USA and in Europe suggest aggregate HFC emissions from these regions may actually be somewhat
lower (by 10 to 20% in recent years, excluding HFC23 emissions) than the totals reported to UNFCCC.
Given that HFC emissions from the USA and Europe
accounted for over 80% of total HFC emission reported
to the UNFCCC for 2015 (excluding HFC-23; Figure2-13), it is unlikely that inaccurate emission reportingfrom Annex I countries explains the increasing gap between global emissions derived from atmospheric data
and totals reported to UNFCCC.
The dramatic increase in this emission gap over time
is consistent with substantial increases in HFC use indeveloping countries not obligated to report emissions
2.30

Figure 2-13. Summed HFC emissions,expressed as CO2-eq, reported to UNFCCC (UNFCCC 2017) and based on atmospheric measurements, for the global
and for the sum of the U.S. and European regions. (Global emission values
from Figure 2-1; measurement-based
regional estimates for the U.S. andEurope are taken from Figure 2-4 andFigure 2-5). Sums including emissionsof HFC-23 (solid lines) and without HFC23 (dashed lines) are shown. Uncertainties on global estimates represent one
standard deviation of estimates (seeFigure 2-1); Uncertainties on regional
estimates are approximate, basedon the range and standard deviation
quoted in the original sources (Graziosi
et al., 2017 and Hu et al., 2017).

to the UNFCCC, as had been projected (Velders et al.,2009, 2015; Gschrey et al., 2011). Inverse analyses ofatmospheric measurements of HFCs in East Asia support this conclusion (see Section 2.3.1). Furthermore,
for some HFCs, such as HFC-23 and HFC-32, emissions from China or East Asia explain most, if not all,of the unreported emissions (see Figures 2-7 and 2-8).
For other HFCs (HFC-134a, HFC-125, and HFC143a), emissions from China or East Asia account for
only a fraction of unreported emissions (see Figures
2-9 and 2-10).

2.3.5

Next Generation Substitutes

2.3.5.1 HFC-1234yf (CF3CF=CH2) andHFC-1234ze(E) ((E)-CF3CH=CHF)
Unsaturated HFCs, also known as hydrofluoroolefins (HFOs), are being used as substitutes for higher
GWP-HFCs in a number of applications. Given theirshort lifetimes and small GWPs, they are not included as controlled substances in the Kigali Amendment
to the Montreal Protocol. HFC-1234yf and HFC1234ze(E) are hydrofluoroolefins with estimated tropospheric OH-lifetimes of 12 days and 19 days, respectively, (Table 2-2) and 100-yr GWPs of less than 1(Hodnebrog et al., 2013). HFC-1234yf has been identified as the main replacement for HFC-134a in MAC

HFCs | Chapter 2

Table 2-4. Composition of HFC blends referenced in this chapter
HFC BlendComposition

100-yr GWP *R-404A

44% HFC-125, 52% HFC-143a, 4% HFC-134a

4,210

R-407A

20% HFC-32, 40% HFC-125, 40% HFC-134a

2,070

R-407C

23% HFC-32, 25% HFC-125, 52% HFC-134a

1,730

R-407F

30% HFC-32, 30% HFC-125, 40% HFC-134a

1,790

R-410A

50% HFC-32, 50% HFC-125

2,080

R-446A of L-41 or L-41-1

68% HFC-32, 29% HFC=1234ze(E), 3% isobutane

480

R-447A or L-41-2

68% HFC-32, 3.5% HFC-125, 28.5 HFC-1234ze(e)600R-448A

26% HFC-32, 26% HFC-125, 20% HFC-1234yf, 21% HFC-134a,
7% HFC-1234ze(E)1,370

R-449A

24.3% HFC-32, 24.7% HFC-125, 25.3% HFC-1234yf, 25.7% HFC-134a

1,370

R-449B

25.2% HFC-32, 24.3% HFC-125, 23.2% HFC-1234yf, 27.3% HFC-134a

1,390

R-450A

58% HFC-1234ze(E), 42% HFC-134a

570

R-452B or DR55

67% HFC-32, 7% HFC-125, 26% HFC-1234yf

715

R-459A or ARM71a

68% HFC-32, 26% HFC-1234yf, 6% HFC-1234ze(E)480R-513A

56% HFC-1234yf, 44% HFC-134a

600

DR5

72.5% HFC-32, 27.5% HFC-1234yf

510

L-41a

73% HFC-32, 15% HFC-1234yf, 12% HFC-1234ze(E)

515

L-41b

73% HFC-32, 27% HFC-1234ze(E)515

ARM70a

50% HFC-32, 10% HFC-134a, 40% HFC-1234yf

490

D2Y60

40% HFC-32, 60% HFC-1234yf

282

HPR2A

76% HFC-32, 6% HFC-134a, 18% 1234ze(E)

615

HPR1D

60% HFC-32, 6% CO2, 34% HFC-1234ze(E)425* 100-yr GWPs (Global Warming Potentials from this assessment) are calculated as the global warming influence of this gas relative
to that by an equivalent mass emission of CO2. See additional text in the Introduction.

systems in Europe, the USA (US EPA, 2017), and othercountries, although use in developing countries may
be limited because of current high production costs(UNEP, 2017a). HFC-1234ze(E) could replace HFC134a for extruded polystyrene foam blowing (possibly in blends) and perhaps in metered-dose inhalers
if toxicology, flammability, and cost concerns can beaddressed (US EPA, 2017; UNEP, 2017a). Both compounds are also being investigated for use in refrigeration blends with saturated HFCs to replace fluidswith high GWPs (UNEP, 2017a); various blends havebeen found to be non-flammable and acceptable foruse (R-448A, R-449A, R-449B, R-450A, R-513A) evenin domestic applications (US EPA, 2015b; 2016). See
Table 2-4 for the composition of HFC blends.
The first ambient-air measurements of these new
olefinic HFCs were reported by Vollmer et al. (2015)
from two sites in Switzerland (see Figure 2-14). In
2011, HFC-1234yf was undetectable at the high-altitude Jungfraujoch observatory, but by mid-2016
observed mole fractions were often in the 0.02–0.20
ppt range; higher values were observed at the urban
Dübendorf site where they were often in the 1–4 ppt
2.31

Chapter 2 | HFCs

10

Jungfraujoch

8

Dubendorf (urban)

0.3

6

0.2

4

0.1

2

0

0

5

25

4

20

3

15

2

10

1

5

0

0
2011

2012

2013

Year

2014

2015

HFC-1234ze(E) (ppt)
Dübendorf (urban)

HFC-1234ze(E) (ppt)
Jungfraujoch

0.4

HFC-1234yf (ppt)
Dübendorf (urban)

HFC-1234yf (ppt)
Jungfraujoch

0.5

2016

Figure 2-14. The emergence of HFC-1234yf and HFC-1234ze(e) in the atmosphere at two European sites, themountaintop site Jungfraujoch (3,580 meters above sea level) and the urban Dübendorf site (updated from
Vollmer et al., 2015). Different colors indicate results from different sites. Results at or below the detection
limit (~0.003 ppt) are plotted at 0.003 ppt. By mid-2016, 1 to 4 ppt of HFC-1234yf and ~1 ppt of HFC-1234ze(E)were regularly observed at Dübendorf, with pollution events containing up to 20 ppt of HFC-1234ze(E), suggesting that it is more prevalently used in this region.

range. HFC-1234ze(E) has been observed in most
samples since the beginning of dedicated ongoing observations. By mid-2016, ~0.025 ppt was commonly
observed at Jungfraujoch and ~1 ppt at Dübendorf,
where pollution events containing up to 20 ppt of this
HFC have been seen, suggesting more prevalent use of
HFC-1234ze(E) in this region (Vollmer et al., 2015).

2.4

ATMOSPHERIC C­ HEMISTRY OF HFCs

2.4.1

New Developments on Loss
Rates and Lifetimes

HFCs are removed from the atmosphere mainly by
their reactions with hydroxyl radicals (OH) in the troposphere. The residence times of HFCs in the atmosphere determined by reaction with tropospheric OH,
τ OH
HFC , are derived from the corresponding lifetime of
2.32

methyl chloroform, CH3CCl3, (Spivakovsky et al.,2000) as described in previous Assessments (e.g., Box
1-1 in Carpenter and Reimann et al., 2014).
Although HFCs do not absorb stratospheric UV
radiation, stratospheric loss processes, such as reactions with OH and O(1D), can contribute to the loss
rate of long-lived HFCs and, therefore, slightly affecttheir lifetimes. Photolysis at the Lyman-a wavelength(121.6 nm) can only affect lifetimes of very long-lived
species (such as perfluorinated compounds) that areinert enough to reach the mesosphere (≥ 70 km) inappreciable quantities; it has a negligible effect on the
lifetimes of HFCs. Recent intensive modeling efforts(SPARC, 2013) provide detailed information on model-derived total lifetimes for a number of compounds,
partial lifetimes due to specific photochemicalHFCs | Chapter 2

removal processes (reactions with OH, reactions with
O(1D), and photolysis), and partial lifetimes associated with the atmospheric removal regions (troposphere and stratosphere). These results have been used
to derive empirical correlations for estimating partial
lifetimes due to stratospheric reactions of other HFCs,τ str
HFC . The total atmospheric lifetimes, τHFC, reported in)-1 +Table 2-2 have been calculated as (τHFC)-1 = ( τ OH
HFC
str
-1
(τ HFC ) to account for the stratospheric loss of HFCs.
Table 2-2 presents updated estimates of the lifetimes
for partially fluorinated alkanes (HFCs), partiallyfluorinated olefins (HFCs that are also called HFOs)
and perfluorinated olefins. The abundances of HFOs
and perfluorinated olefins are also primarily controlled by their reactions with the hydroxyl radical.
Compounds with atmospheric lifetimes shorter than
~0.5 years have been designated as very short-lived
substances (VSLSs) as in previous Assessments. These
compounds generally have non-uniform troposphericdistributions because their lifetimes are comparable toor shorter than the characteristic time of mixing processes in the troposphere.
The lifetime of VSLSs released into the atmosphere depends on local atmospheric conditions at the emission
location and, therefore, the concept of a single global
lifetime is inappropriate (e.g., Hodnebrog et al., 2013and previous Ozone Assessment reports). Hence, theVSLS lifetimes presented in Table 2-2 (with units ofdays) should not be considered as the global average atmospheric lifetime of a VSLS once emitted.
Nevertheless, these estimates provide a useful scaling
among such compounds and distinguish them fromlonger-lived HFCs that are well mixed in the troposphere. It should be noted that the local lifetimes of
VSLSs tabulated in this report are ~10–20% longer
than in previous Assessments primarily because they
have now been calculated with the same approach thatis used for longer-lived HFCs (i.e., relative to the global mean lifetime of methyl chloroform against OH oxidation), thereby avoiding arbitrary differences arisingfrom the use of two different approaches.
For a few compounds in Table 2-2, experimental dataon OH reactivity are not available. Lifetimes for thesegases have been estimated based on either structure
activity relationships or reactivity trends among similar compounds and appear in italics in Table 2-2.

OH reactivity for several compounds has been revised
since the last Assessment based on new experimental
data and/or analyses. However, these revisions do notsubstantially change the recommended atmospheric
lifetimes. Other changes since the previous Assessment
are listed below. (References related to these updates
can be found in the notes to Table 2-2):• HFC-72-17p, CHF2(CF2)6CF3, has been added.
• The lifetime of HFC-245cb is 15% shorter than es-

timated previously based on an analogy to HFC143a. This revision reflects a new recommendation(Burkholder et al., 2015b), which is now based onlaboratory-measured OH reactivity data.

• The

estimated
lifetimeofHFC-272ca(CH3CF2CH3) is based on OH reactivity calculated using the structure activity relationships. There
are no experimental data.

• Lifetimes of shorter-lived HFC-152 (CH2FCH2F),and
HFC-281ea
HFC-161(CH3CH2F),(CH3CHFCH3) are estimated to be ~18% longer
than previous estimates. This revision is the resultof the estimates now being made in a manner consistent with other HFCs.

• The list of fluorinated olefins has been expanded

to include 7 new chemicals including stereo-isomers with different lifetimes and cyclic unsaturated fluorocarbons.

• The lifetimes of (E)-CF3CH=CHCF3 and (E)-

CF3CH=CHCF2CF3 are a factor of ~6 longer because experimental data on the OH reactivity of(E)-CF3CH=CHCF3 became available for the firsttime since the last Assessment.

• The lifetime of perfluorocyclopentene is more thanan order of magnitude longer because experimental data on its reactivity toward OH became available for the first time since the last Assessment.

2.4.2

Updates on TFA Formation and­Tropospheric Ozone Formation

Trifluoroacetic acid (TFA; CF3COOH) is produced as
the result of the breakdown in the atmosphere of several HCFCs and HFCs, such as HCFC-123 and HCFC124, HFC-134a, HFC-143a, HFC-1234yf, and possibly
HFC-1234ze (Burkholder et al., 2015a; Wallington et2.33

Chapter 2 | HFCs

al., 2015; Javadi et al., 2008). TFA is also produced
as a breakdown product of a large number of otherchemicals and is produced synthetically by the chemical industry, resulting, in many cases, in direct releaseto the atmosphere (Solomon et al., 2016). TFA also
arises from natural processes. TFA is a stable compound that can accumulate in lakes and the ocean. As
an acid or as a salt, TFA is of low to moderate toxicity
to a range of organisms. The salts of TFA are inert andnot of toxicological or environmental concern in thesmall concentrations (~200 ng L−1) that are present in
the ocean (UNEP, 2014a). The contribution of mostsources to the total TFA budget is uncertain, although
the source strength from atmospheric oxidation of
HCFCs and HFCs is now better quantified.
Solomon et al. (2016) estimated TFA added to theoceans as a result of unregulated use of HCFCs and
HFCs (including HFOs) up to 2050. Under an upper
range scenario of global HFC use (Velders et al.,2015), it was estimated that by 2050 the total additional contribution of TFA to the oceans would be lessthan 7.5% of the approximately 200 ng acid equivalents L−1 estimated to be present at the start of themillennium (Solomon et al., 2016). With the 2016
Kigali Amendment to the Montreal Protocol, the projected use of HFCs is expected to be much lower (seeSection 3.1) with consequently lower estimated TFA
production from HFCs. Increased use of HFOs could
augment TFA production and partially offset reductions in TFA production from saturated HFCs. The
environmental effects of TFA, from the breakdown of
HCFCs and HFCs, are therefore considered too small
to be a risk to the environment over the next few decades (Solomon et al., 2016). However, potential longer-term impacts could require future evaluation due
to the environmental persistence of TFA, uncertainty
in future uses of HFOs, and because of uncertainties
in the global budget of TFA. See Section 6.2.6 formore information on TFA.
The atmospheric degradation of HFC-1234yf (anHFO) can also contribute tropospheric ozone andthereby contribute to reduced air quality. Luecken
et al. (2010) showed that the additional tropospheric ozone from the conversion of HFC-134a to HFC1234yf in mobile air conditioners in the USA is small
compared with ambient ozone levels in cities in the
USA.
2.34

2.5

POTENTIAL FUTURE CHANGES

Projections have suggested that the use and emissions of HFCs could increase substantially with thephase-out of HCFCs in developed countries by 2030
and in developing countries by 2040 (Velders et al.,2009; Gschrey et al. 2011; Rigby et al., 2014; Velders
et al., 2015; UNEP, 2014c; Fang et al., 2016; Purohitand Höglund-Isaksson, 2017). Because many HFCs in
use currently have high GWPs and these HFCs leakfrom appliances and other applications, they contribute to the radiative forcing (RF) of climate. The 2016
Kigali Amendment requires large reductions (up to
85% by 2035 or 2045 relative to a base level) for GWPweighted HFC production and consumption for all
developed and developing countries.

2.5.1

Scenarios

In this section, HFC scenarios developed elsewhere—with and without the implementation of the Kigali
Amendment—are discussed, including their implications for radiative forcing of climate. Also discussed
are alternatives to HFCs and factors that might berelevant for future HFC use.

2.5.1.1	HFC Scenarios Without Considerationof Controls: “Baseline” Scenarios
The Kigali Amendment of October 2016, assuming
global implementation, will significantly affect the future demand for HFCs in developed and developing
countries and consequently their emissions, mixing
ratios, and radiative forcing of climate. The effectsof the Amendment can be viewed relative to HFCbaseline scenarios that were constructed in the past
without including specific global control measures on
HFC production or consumption. Here the results ofseveral of these HFC baseline scenarios are compared
before discussing the effects of the Kigali Amendment
on future HFC emissions (Section 2.5.1.3). HFC-23
is not considered in these scenarios since it is not used
as a replacement compound in applications that traditionally used ODSs, and it is also in a separate groupin the Kigali Amendment (see Section 2.5.1.5).
In Figure 2-15 the GWP-weighted emissions andcorresponding radiative forcings are shown for HFCs
in several of these baseline scenarios. The projected
emissions in Velders et al. (2015) are an update ofHFCs | Chapter 2

HFC Emissions

6
5

0.30

HFC range
Velders (2015)
Velders (2009)
RCP scenarios
Derived fromTEAP (2014)

4

0.25

Radiative forcing (W m-2)

Emissions (Gt CO2-eq yr -1)

7

HFC Radiative Forcing

Gschrey (2011)
Purohit (2017)

3
2

0.15
0.10
0.05

1
0
2000

0.20

HFC range
Velders (2015)
Velders (2009)
RCP scenarios
Derived fromTEAP (2014)
Derived from
Gschrey (2011)

2010

2020

2030

2040

2050

Year

0.00
2000

2010

2020

2030

Year

2040

2050

Figure 2-15. HFC emissions and radiative forcing scenarios from baseline scenarios (excluding HFC-23) from
Velders et al. (2009), Gschrey et al. (2011), RCPs (Meinshausen et al., 2011), UNEP (2014c), Velders et al. (2015),
and Purohit and Höglund-Isaksson (2017). The Velders et al. (2009) scenarios are shown, because they were
the reference scenarios in the previous (2014) Assessment. The emissions and radiative forcing shown here
are based on the GWPs and radiative efficiencies in the corresponding publications, which may differ fromthose in Table 2-2; the difference in CO2-eq emissions is less than 1%.

those of Velders et al. (2009) based on more detailed
and more current information on HFC use by sector
and region. The scenarios of Velders et al. (2009) are
shown here because they were the reference scenarios in the previous Assessment (Harris and Wuebbles
et al., 2014). The HFC emissions in Velders et al.
(2015) are similar to those in UNEP (2014c); they
are slightly higher than projected in other sector-specific scenarios (Gschrey et al., 2011; Purohit and
Höglund-Isaksson, 2017; Höglund-Isaksson et al.,2017); and they are significantly higher than in the
Representative Concentration Pathways (RCPs) scenarios (Meinshausen et al., 2011). The latter two scenarios included different assumptions for the HCFC
replacement pattern and/or different growth rate projections for HFC use in applications. In these scenarios, the lifetimes of the HFCs are kept constant. Modelcalculations show that HFC lifetimes might change
towards 2100 due to changes in temperatures and OH
abundances. Most models show a decrease in lifetimeby 5–10% in 2100 relative to 2000 (SPARC, 2013).
These changes depend on the future greenhouse gas
abundances and are highly uncertain.
The recent sector- and region-specific HFC baseline
scenarios of Velders et al. (2015) (Figure 2-16) assume that current uses (substances and technologies)of HFCs for specific sectors would continue without
control measures and that developing countries would
follow the same transitions from HCFCs to HFCs andnot-in-kind alternatives as has occurred in developed
countries, but at a later time. So these scenarios do nottake into account the 2016 Kigali Amendment or the
recent regulations of HFC use in the EU (revised F-gas
regulation of the European Union; EU, 2014), USA(US EPA, 2015a) and Japan (METI, 2015). The scenarios are based on (1) robust historical HFC consumption data by sector for developed countries derived
from their UNFCCC National Inventory Submissions(UNFCCC, 2014), (2) historical HFC consumption
2.35

Chapter 2 | HFCs

7

GWP-weighted: Upper range

6
Emissions (Gt CO2-eq yr -1)

Emissions per sector

Emissions per region

5
4
3

2

6

11% M. East + N. Africa
8% Latin America
9% Africa Middle + South
16% Other Asian
7% Indi a
31% China
2% Russia etc.
1% Other OECD
2% Japan
10% USA
3% EU

Emissions (Gt CO2-eq yr -1)

7

1
0
2000

5
4

GWP-weighted: Upper range
3% Other
5% Foams
1% Domestic refrigeratio n
7% Mobile AC
32% Stationary AC
52% Ind/Com refr.

3
2
1

2010

2020 2030
Year

2040

2050

0
2000

2010

2020

2030

Year

2040

2050

Figure 2-16. Regional and sectoral contributions to HFC GWP-weighted emissions for the upper-range baseline scenario of Velders et al. (2015), excluding HFC-23. The percentages refer to the global average relative
contributions of each sector to the total GWP-weighted emissions in 2050. In the scenario, 10 HFCs are considered. The 11 regions are Europe, USA, Japan, other OECD countries, States of the former Soviet Republics
and Yugoslavia (Russia), China, India, other Asian countries, Middle and Southern Africa, Latin America, andthe Middle East plus Northern Africa. The six use sectors are (1) industrial, commercial (open compressor),commercial (hermetically sealed compressor), and transport refrigeration, (2) stationary AC, (3) mobile AC,(4) domestic refrigeration, (5) foams—extruded polystyrene, polyurethane, and open cell foams, and (6)other—aerosol products, fire extinguishing systems, and solvents. The emissions shown here are based on
the GWPs used in Velders et al. (2015), which differ somewhat from those in Table 2-2; the difference inCO2-eq emissions is less than 1%.

data for China (Zhang and Wang, 2014) and someother developing countries, (3) historical HCFC consumption data from UNEP (2015), (4) replacements
pattern of HCFCs by HFCs and not-in-kind technologies (Velders et al., 2009; 2015), (5) scenarios of grossdomestic product (GDP) and population from Shared
Socioeconomic Pathway (SSP) projections (O’Neill etal., 2012) as drivers for the demand for HFCs, (6) observed atmospheric abundances of HFCs from 1990
to the beginning of 2013 as constraints on the historical consumption data, and (7) leakage rates (i.e., emission factors) derived from the UNFCCC National
Inventory Submissions; these are kept constant overtime in the scenarios.

2.36

The largest historical HFC use and emissions are in
the developed countries, primarily the USA, EU, andJapan (Figure 2-16). In the baseline scenario, China
is projected to become the largest emitter of HFCs by
2020, and Chinese emissions are projected to reach
31% of total CO2-eq emissions (100-yr time horizon)of all HFCs by 2050 in the upper range HFC scenario(Velders et al., 2015). In all countries or regions, thelargest contributions in CO2-eq emissions come fromindustrial and commercial refrigeration (range of
40–58% for the different regions by 2050), because of
the large use of HFCs and relatively large leakage rates
from these applications, while the second largest HFC
source comes from stationary air conditioning (AC)(21–40% by 2050) (Figure 2-16). Historically, mobile

HFCs | Chapter 2

AC has been responsible for the largest fraction ofCO2-eq HFC emissions, but this sector is projectedto account for only 10–18% of developed country
emissions by 2050. In developing countries in thesescenarios, mobile AC contributes only 3–12% of total
CO2-eq HFC emissions in 2050. It should be notedthat this baseline scenario does not include the effects
in the EU and potentially around the globe of the EU
MAC directive which has recently banned the use of
HFC-134a in new cars (see Section 2.5.1.2).
In 2050, in the scenarios of Velders et al. (2015), CO2eq HFC emissions (excluding HFC-23) sum to 0.8–1.0
GtCO2-eq yr−1 for the developed countries and 3.2–4.4
GtCO2-eq yr−1 for the developing countries, resulting
in a global total of 4.0–5.3 GtCO2-eq yr−1. In comparison, these values are equivalent to 5–11% of the global CO2 emissions in 2050 in the RCP6.0 and RCP8.5
scenarios. The HFC radiative forcing (RF) (excluding
HFC-23) in 2050 in these scenarios is 0.22–0.25 W
m−2. In comparisons of projected growth, the 2015–
2050 increase in HFC CO2-eq emissions is 9–29% ofthat for CO2 over the same time period in the RCP
scenarios. These CO2-eq emissions represent only the

direct emissions of the HFCs. Indirect CO2 emissions
associated with the production of fossil fuel-derived
energy used by the appliances or in other applications
need to also be considered for the overall impact ofHFCs on radiative forcing (see Section 2.5.2.3).
In the refrigeration and air conditioning sectors, pure
HFCs or blends of HFCs are used as refrigerants.
The most-used HFC refrigerants in the scenarios are
HFC-134a and the blends R-404A (mix of HFC-125,
HFC-134a, HFC-143a) and R-410A (mix of HFC-32
and HFC-125). Consequently, these HFCs are projected to have the largest future emissions by mass or
CO2-eq (Figure 2-17). See Table 2-4 for more detailabout the composition of HFC blends.
The assumptions about market saturation are important aspects for the projections of HFCs. In the scenarios of Velders et al. (2015) the demand for HFCs per
capita in developing countries is limited to the demand
per capita in the developed countries. These scenarios
do not take into account the potentially higher future
demand for stationary AC as a result of increased ambient temperatures due to climate change. They also do

CO2-eq emissions

Emissions (mass)

7

3.5

Emissions (Mt yr-1)

2.5
2.0
1.5

6
Emissions (GtCO2-eq yr -1)

3.0<1% HFC-43-10mee
3% HFC-365mfc
4% HFC-245fa<1% HFC-236fa<1% HFC-227ea
4% HFC-152a
12% HFC-143a
23% HFC-134a
26% HFC-125
16% HFC-32

1.0

4
3
2
1

0.5

0

0.0
2000

5

<1% HFC-43-10mee
1% HFC-365mfc
2% HFC-245fa<1% HFC-236fa
1% HFC-227ea<1% HFC-152a
30% HFC-143a
15% HFC-134a
45% HFC-125
6% HFC-32

2010

2020

2030

Year

2040

2050

2000

2010

2020

2030

Year

2040

2050

Figure 2-17. Contributions of the different HFCs to global emissions (in Mt yr −1 and GtCO2-eq yr−1) in the upper
range baseline scenario of Velders et al. (2015). The percentages refer to the relative contributions in 2050.
2.37

Chapter 2 | HFCs

not consider the fact that many developing countries
have higher ambient temperatures than the developed
countries and could, therefore, have a higher demand
for stationary AC and higher emissions per capita.

2.5.1.2	Effect of National and Regional HFC
Control Measures on Future Projections
In the EU, the 2006 MAC Directive, which addresses the use of mobile air conditioning (EU, 2006), andthe 2014 revised F-gas Regulation (EU, 2014) ban theuse of high-GWP HFCs in certain sectors. Although
there is no common definition of high- or low-GWP,
in the European Union Fluorinated Greenhouse Gas
Regulations, a value of 150 or higher is often referred
to as high. In addition to these bans on specific HFCuse, there is an HFC phasedown schedule reducing
the allowable amount (GWP-weighted) of HFCs
placed on the EU market starting from a cap at the
2009–2012 average in 2015 and reaching a 79% reduction by 2030 relative to that average. The USA
has already implemented incentive credits for use of
low-GWP refrigerants (US EPA, 2012) in support ofgreenhouse gas emission standards for light duty vehicles; it also removed certain high-GWP HFCs fromthe Significant New Alternatives Policy (SNAP) list ofallowable technologies for specific sectors as of 2015
(US EPA, 2015a)3. Japan also adopted a regulation in2015 to limit the use of high-GWP HFCs for specificsectors (METI, 2015).
The regulations in the EU, USA, Japan, and elsewhere
likely will drive changes in sector technologies (i.e.,
technologies and HFC-blends that currently use
high-GWP HFCs) such as refrigeration, stationary
and mobile AC, and foams. Consequently, these newtechnologies will likely also be available for othercountries, thereby increasing the climate benefits ofthese national regulations. With global adoption ofthese technologies in a revised scenario, the 2050
emissions (excluding HFC-23) in the baseline scenario of 4.0–5.3 GtCO2-eq yr−1 are reduced to 1.5–1.9
GtCO2-eq yr−1 following the regulations in the EU, to
1.9–2.5 GtCO2-eq yr−1 following the regulations in
3

2.38

In the USA, a process in the U.S. Court of Appeals is underway
for SNAP rule 20, related to enabling a transition away from
HFCs under Section 612 of the Clean Air Act. Implementationof the Court’s direction is unclear under the EPA SNAP structure, so EPA has provided guidance stating that they will not
enforce the HFC aspects of the rule while it is rewritten in compliance with the decision. (US EPA, 2018).

the USA, and to 2.0–2.6 GtCO2-eq yr−1 following theregulations in Japan (Figure 2-18). These regulations
will also lead to slower increases in radiative forcing
from HFCs. The GWP-weighted emissions following
the regulations are anticipated to more or less level
off after 2030, at slightly more than half (reductionof 51–65%) the emissions of the baseline scenario;
however, the radiative forcing continues to increase
and is only reduced by 28–41% in 2050 compared tothe baseline scenario because of the long atmospheric
lifetimes (5–50 years) of the major high-GWP HFCs
(Figure 2-18).

2.5.1.3	Projected Impact of the Kigali Amendment
Under the Kigali Amendment there are different baselevel years and phasedown schedules for developed
countries (non-A5 Parties) and two groups of developing countries (A5 Parties) (Table 2-1). Following
the Amendment, the allowable GWP-weighted HFC
production and consumption will have to be reduced
to 15–20% of the base level by 2045–2047 for developing countries and to 15% of the base level by 2036 fordeveloped countries.
The Kigali Amendment requires global implementation to significantly limit future radiative forcing from
HFCs. The national (e.g., USA and Japan) and regional (EU) regulations (see Section 2.5.1.3) that are already in place will aid and accelerate developed anddeveloping countries’ efforts to meet the provisionsof the Amendment. The largest effect from the Kigali
Amendment, though, is expected from the reductionsin HFC production and consumption in developing
countries. Some reductions in HFC use might haveoccurred in developing countries as a result of regulations in developed countries through technology
transfer, but quantifying such reductions is difficult.
Instead, the Kigali Amendment ensures legally binding reductions in HFC production for the first time inboth developed and developing countries. Therefore,
we discuss here the reductions in emissions and radiative forcing that result from applying the phasedownof the Kigali Amendment to the global baseline scenario, acknowledging that national and regional regulations also play an important role in limiting futureclimate forcing from HFCs.
With compliance to the Kigali Amendment controls(Table 2-1) and national and regional regulations, theHFCs | Chapter 2

Emissions (GtCO2-eq yr -1)

6
5
4

EmissionsRadiative forcing
0.30

Global effect regulations
Baseline Velders (2015)
Japan: Regulation
USA: SNAP change
EU: MAC + F-gas
Kigali Amendment

3
2

0.20

Baseline Velders (2015)
Japan: Regulation
USA: SNAP change
EU: MAC + F-gas
Kigali Amendment

0.15

0.10

0.05

1
0
2000

Global effect regulations

0.25

Radiative forcing (W m-2)

7

2010

2020

2030

Year

2040

2050

0.00
2000

2010

2020

Year

2030

2040

2050

Figure 2-18. Response of global HFC emissions (GtCO2-eq yr −1) and global radiative forcing (W m−2) in thebaseline scenario of Velders et al. (2015) to global implementation of three national regulations and of theKigali Amendment (HFC-23 not included). In each case, the national regulation is applied to all countries,
with a 5-year delay of the regulations for developing countries. The emissions and radiative forcing shown
here are based on the GWPs and radiative efficiencies used in Velders et al. (2015), which differ somewhat
from those in Table 2-2. (Note: see Section 2.5.1.2 for the status of the HFC regulations under SNAP).

peak in global production and consumption of HFCs
is expected to occur around 2025. HFC emissions areprojected to peak about a decade later (Figures 2-18
and 2-19; note these figures do not include HFC-23)
because HFCs used as refrigerants in refrigeration
and air conditioning systems are emitted gradually
over a period of about a decade from the equipment
during and after use (so-called banks). The total HFCbank, therefore, represents a potentially substantial
source of emissions and radiative forcing even after
production ceases (Velders et al., 2014). Despite thisbank, adherence to controls in the Kigali Amendment
results in HFC emissions from developed countries
that are reduced from the baseline scenario level of
0.8–1.0 GtCO2-eq yr−1 to 0.16 GtCO2-eq yr−1 by 2050,
and in developing countries, emissions are reduced
from 3.2–4.4 GtCO2-eq yr−1 to 1.1 GtCO2-eq yr-1, fora total reduction from 4.0–5.3 GtCO2-eq yr−1 to 1.3
GtCO2-eq yr−1. The total reduction by 2100 is from
6.1–9.3 GtCO2-eq yr−1 to 0.6 GtCO2-eq yr−1. So, theestimated benefit of the Amendment is the avoidanceof 2.8–4.1 GtCO2-eq yr−1 emissions by 2050 and 5.6–
8.7 GtCO2-eq yr−1 by 2100. For comparison, total CH4emissions are projected to be 7.0–25 GtCO2-eq yr−1 by
2100 in the RCP6.0 and RCP8.5 scenarios and total
N2O emissions 5.0–7.0 GtCO2-eq yr−1 by 2100 (RCPemissions from Meinshausen et al., 2011; GWPs of
N2O and CH4 from Myhre and Shindell et al., 2013).
Radiative forcing from HFCs is projected to peakaround the middle of the century, or a decade after the
peak in emissions, due to continued emissions fromthe banks and the slow breakdown of the HFCs in
the atmosphere (i.e., lifetimes up to about 50 years for
HFCs used as ODS substitutes). The response of the
global HFC radiative forcing to the Kigali Amendment
and national controls is a reduction from 0.22–0.25 W
m−2 in the baseline scenario to 0.13 W m−2 by 2050,
and from 0.48–0.77 W m−2 to 0.08 W m−2 by 2100.
The effects of the Kigali Amendment on global emissions and RF through 2050 are similar to the effectsof the different national regulations (discussed above)
when implemented globally (Figure 2-18).
2.39

Chapter 2 | HFCs

HFC Radiative forcing

HFC Emissions

Emissions (GtCO2-eq yr-1)

6
5
4

0.30

GWP-weighted (100-yr)
Developing countries

0.25

Baseline Velders (2015)
Kigali Amendment

Radiative forcing (W m-2)

7

Developed countries
Baseline Velders (2015)
Kigali Amendment

3
2

Kigali Amendment
0.20
Developed countries
Baseline Velders (2015)
0.15

Kigali Amendment

0.10

0.05

1
0
2000

Developing countries
Baseline Velders (2015)

2010

2020

2030

Year

2040

2050

0.00
2000

2010

2020

Year

2030

2040

2050

Figure 2-19. Response of HFC emissions (GtCO2-eq yr−1) and radiative forcing in the baseline scenario(Velders et al., 2015) for developed and developing countries to implementation of the Kigali Amendment to the Montreal Protocol (excluding HFC-23). Extending the scenarios beyond 2050 under thesame assumptions gives a radiative forcing for the baseline scenario of 0.07–0.12 W m−2 and 0.40–0.65
W m−2 in 2100 for the developed and developing countries, respectively. The 2100 radiative forcing under the Kigali Amendment is 0.01 W m−2 and 0.06–0.07 W m−2 for the developed and developing countries, respectively. The emissions and radiative forcing shown here are based on the GWPs
and radiative efficiencies used in Velders et al. (2015), which differ somewhat from those in Table 2-2.

2.5.1.4 Climate Impacts of HFCs
The radiative forcing from HFCs contributes tochanges in atmospheric circulation, temperature,and sea level (see Chapter 5). Atmospheric changes in temperature and circulation arising from HFCemissions lead to a weak, indirect depletion of stratospheric ozone (Hurwitz et al. 2015). Ozone Depletion
Potentials (ODPs) of the most relevant HFCs range
from 0.00039 for HFC-32 to 0.03 for HFC-23 (seeChapter 1).
Xu et al. (2013) calculate, using a parameterized integrated carbon and radiant energy balance model,
a global average surface warming by HFCs of about
0.1°C by 2050 and 0.35–0.50°C by 2100, based on the
scenarios of Velders et al. (2009) (Figure 2-20). Witha different parameterized climate model, they calculated a surface temperature change of 0.10–0.12°C for
2.40

2050 and 0.28–0.44°C for 2100 based on the scenariosof Velders et al. (2015). These scenarios differ in theirassumption for the projections of the demand for
HFCs past 2050 and the way potential market saturation is taken into account. To calculate the temperature response of HFCs in 2100, the emissions over
the whole period from 2000 to 2100 are important. In
Figure 2-20 HFC emissions and temperature changes
are shown based on the Velders et al. (2015) scenario
in which the same assumptions and model are used
for the period past 2050 as for 2000–2050.
With the Kigali Amendment and national and regional regulations, the future production and consumption of HFCs is strongly limited (Table 2-1). Under
the provisions of the Amendment, the contribution of
HFCs to the global average surface temperature is projected to reach a maximum around 2060, after which
it slowly decreases to about 0.06°C by 2100 (Figure

HFCs | Chapter 2

Emissions (GtCO2-eq yr-1)

80.5

HFC emissions
Without measures
Based on Velders (2015)

0.4

Temperature change (oC)10

Kigali amendment(2016)
6

Phaseout 2020

4

2

0
2000

2020

2040
2060
Year

2080

2100

Surface temperature HFCs
Without measures
Xu et al. (2013)
Based on Velders (2015)

0.3

Kigali amendment(2016)
Phaseout 2020

0.2

0.1

0.0
2000

2020

2040

2060

Year

2080

2100

Figure 2-20. HFC emissions and the contribution of HFCs to the global average surface warming of Earthwith and without the Kigali Amendment. The scenarios without the measures are based on Xu et al. (2013)
and Velders et al. (2015) which differ in their assumptions for the projections of the demand for HFCs past
2050. Also shown is a hypothetical scenario assuming that the global production of HFCs would cease in
2020. The surface temperature change based on Velders et al. (2015) is calculated using the MAGICC6 model.
For comparison, the total warming from all greenhouse gases is projected to be 1.4–4.8°C by the end of the
21st century following the RCP6.0 and RCP8.5 scenarios (Collins and Knutti et al., 2013). The contribution
from HFC-23 is not included here. The emissions shown here are based on the GWPs used in Velders et al.
(2015), which differ somewhat from those in Table 2-2; the difference in CO2-eq emissions is less than 1%.

2-20). In contrast, the surface temperature contribution from HFCs in the baseline scenario is 0.3–0.5°C
in 2100 (based on Xu et al., 2013 and Velders et al.,2015). The difference in projected temperatures isrelevant in the context of the 2015 UNFCCC Paris
Agreement, which aims to limit the global temperature increase to well below 2°C relative to pre-industrial levels.

rise through thermal expansion is even slower thanchanges in radiative forcing from the controls on HFC
production. After HFCs, or any other greenhouse gas,stop being emitted, the climate system is not initiallyin equilibrium with radiative forcing, and the ocean
will continue to take up heat until equilibrium isreached (see, e.g., Zickfeld et al., 2017 and Hu et al.,2013).

In Figure 2-20, the effects are also shown of a hypothetical scenario in which the global production of
HFCs ceases in 2020. In this case, the emissions start
decreasing immediately and the surface temperature
contribution of the accumulated HFC emissions
is projected to stay below 0.02°C for the whole 21st
century. These calculated surface warmings do notinclude emissions from HFC-23.

Historical and projected concentrations, emissions,and contributions to climate change have been calculated for ODSs, high-GWP HFCs, and low-GWP
alternatives (Figure 2-21), assuming full compliancewith the Montreal Protocol, including the Kigali
Amendment. The phase-down schedule for HFC production and consumption substantially reduces future
projected global HFC emissions. Summed HFC emissions are expected to peak before 2040 and decline toless than 1 GtCO2-eq yr−1 by 2100. This peak emission
is well below summed ODS emissions at their peakDue to the thermal inertia of the ocean, the responseof surface temperature and even more so sea level

2.41Chapter 2 | HFCs

Mt yr -1

6
5

Climate change: ODSs, HFCs, alternatives
a. Emissions (mass)

Low-GWP alternatives: Upper range
Low-GWP alternatives: Lower range
High-GWP HFCs
ODSs

4
3
2

GtCO2-eq yr-1

1
0
1950
12
10

2000

2025

2050

2075

2100

2025

2050

2075

2100

2025

2050

2075

2100

2050

2075

2100

b. CO2-eq. emissions

8
6
4
2
0
1950
0.5
0.4

W m-2

1975

1975

2000

c. Radiative forcing

0.3
0.2
0.1
0.0
1950

Degrees Celsius

0.3

1975

2000

d. Surface temperature

0.2
0.1
0.0
1950

1975

2000

2025

Year

Figure 2-21. Projected contributions to climate change from ODSs, high-GWP HFCs, and low-GWP alternatives assuming full compliance to the provisions of the Montreal Protocol, including the Kigali Amendment.
Shown are (a) emissions by mass, (b) CO2-eq emissions, (c) radiative forcing, and (d) average surface temperature contributions. Only the direct GWP-weighted emissions, radiative forcing, and temperature effects of
the ODSs and HFCs are shown. The ODS data are from the baseline (A1) scenario of Chapter 6 of this Assessment. The ODS emissions from around 1980 through 2016 are derived from atmospheric observations andafter 2016 are from the scenario (distinction indicated by dashed vertical lines). The projected HFC quantities
follow the controls of the Kigali Amendment assuming full compliance (Figure 2-20). The projections of the
low-GWP alternatives result from imposing the controls of the Kigali Amendment to the HFC baseline scenario (curve in Figure 2-20 labeled ‘without measures’; Velders et al., 2015). The difference between the HFC
baseline scenario and the Kigali Amendment scenario are assigned here to the low-GWP alternatives. These
could be chemicals with a GWP of less than 20 or alternative technologies. The low-GWP alternatives scenario
assumes that the alternatives use the same amount (by mass) per application as the HFCs they replace. The
curve with the low-GWP alternatives is based on the upper and lower range scenarios from Velders et al.
(2015) and Figure 2-20. The contributions of the low-GWP alternatives in panels b, c, and d are smaller than
the thickness of the green curves. Not included here are contributions from HFC-23, indirect radiative effects
from ozone depletion, and indirect effects associated with the energy used by equipment and the associatedCO2 emissions.
2.42

HFCs | Chapter 2

in the 1980s. Only marginal increases are projected
for CO2-eq emissions of the low-GWP alternatives
despite substantial projected increases in the mass of
their emissions.

2.5.2 HFC Alternatives

A more complete understanding of the climate impactof refrigerant-using equipment can be accomplishedthrough more detailed analysis of, for example, theLife Cycle Climate Performance (LCCP), which includes direct and indirect emissions (i.e., those associated with energy use) as well as associated emissions
from production and disposal (see e.g., Papasavva etal., 2010; Section 2.5.2.3).

Commercially-available alternatives with low- to­medium-GWPs are available for high-GWP HFCs formany refrigeration and AC sector applications; however, more supporting work (e.g., standards development and code adoption) is needed for use of someof the lowest GWP options and fastest growing sectors, such as certain air conditioning sectors. Efforts
to optimize cooling capacity and energy efficiency
performance of refrigeration and air conditioning
equipment containing low-GWP and zero-ODP refrigerants continue to make progress, as do standards
development in all sectors.

2.5.1.5	HFC-23 Projection
Emissions of HFC-23 originate predominantly as aby-product of HCFC-22 production, and they havecontinued despite mitigation efforts. HFC-23 is astrong infrared absorber and has the longest lifetime(228 years) and highest GWP (12,690 for a 100-yeartime horizon; Table 2-2; see Section 2.3) of the HFCs
considered in this Assessment. The amount of HFC23 emitted depends on the amount of HCFC-22
produced, the yield of HFC-23 from the production
process, and the degree to which produced HFC-23
is incinerated. Although HFC-23 is included under
the phasedown schedule with other HFCs (Table2-1), a separate provision is additionally included for
HFC-23 in the Amendment that states: “Each country
manufacturing HCFC-22 or HFCs shall ensure thatstarting in 2020 the emissions of HFC-23 generated in
production facilities are destroyed to the extent practicable using technology approved by the Montreal
Protocol” (UNEP, 2016a). Without abatement, HFC23 emissions were projected to increase to ~20 Gg yr−1
by 2016 and ~24 Gg yr−1 by 2035 (Miller and Kuijpers,
2011). Emissions for 2016, derived from atmosphericobservations, are 12.3 Gg yr−1, well below the worstcase scenario, but above the best-practice scenario of~11 Gg yr−1. With implementation of the provisionsof the Kigali Amendment, future HFC-23 emissions
are expected to be limited significantly.
Recently, developments in chemical synthesis may
have opened up the use of HFC-23 as feedstock forthe production of a wide range of -CF3 containing
fluorochemicals (Grushin, 2014), which may affectfuture HFC-23 emissions.

2.5.2.1	Alternatives: HFOs, Hydrocarbons,
CO2, NH3, Not-in-Kind Alternatives

In recent years, there has been a focus on natural refrigerants (CO2, hydrocarbons, and ammonia), lowGWP HFCs, and HFOs alone or in blends with saturated HFCs to replace fluids with high-GWP. The useof hydrocarbons (e.g., iso-butane [R-600a] and propane [R-290]), ammonia (R-717), and carbon dioxide(R-744) continues.
European domestic refrigerator and freezer manufacturers have been required to use refrigerants with a
GWP less than 150 since 2015 (EU, 2014). In the USA,domestic appliance manufacturers have created a voluntary commitment to convert away from HFC-134a
use as a refrigerant (AHAM, 2016). A charge size ofup to 57 g of iso-butane (R-600a) has been allowed inrefrigeration and has been in commercial use for sometime in self-contained refrigerated food cases andsmaller domestic refrigerators (US EPA, 2011). Other
options to replace HFC-134a include HFC-1234yf,
HFC-152a, and non-flammable blends of HFOs and
HFCs. European commercial refrigeration GWP limits are phased in gradually; limits are initially placed
on HFCs with GWPs greater than 2,500, and in 2020,
on HFCs with GWPs larger than 150 in applications
where alternatives are available and affordable. In addition, there is a service ban on HFCs having GWPs
greater than 2,500 starting in 2020 (EU, 2014). In theUSA, the HFC-blend R-404A is not allowed in new
equipment as of 1 January 2017 for supermarkets,as of 1 January 2018 for condensing units, and as of
1 January 2019 for self-contained systems (2020 forlarger systems). High-GWP blends such as R-404A
2.43

Chapter 2 | HFCs

are not allowed in new refrigerated food processing
and dispensing equipment from 1 January 2021 and in
new warehouse applications from 1 January 2023 (USEPA, 2015a). Canada has published draft recommendations that limit the use of fluids having GWPs above
650 starting in 2020 for stand-alone, medium temperature commercial refrigeration systems and they
limit the use of fluids having GWPs above 1,500 instand-alone low temperature commercial refrigeration(Environment and Climate Change Canada, 2017).
Low-GWP refrigerant blends of HFC/HFO/hydrochlorofluoroolefins (such as R-448A, R-449A, R-449B,
R-450A, and R-513A; Table 2-4) have GWPs that are
50–70% lower than refrigerants they typically replace,and they are in commercial use in refrigeration equipment. The use of R-407A and R-407F (at approximately half the GWP of R-404A) continues to grow inmany parts of the world. Non-halocarbon refrigerants
such as CO2 (R-744) and propane (R-290) are increasingly being used in supermarkets.

2.5.2.2	Safety Issues
Some alternatives to high-GWP HFCs (e.g., waterand inert gases) pose no safety risk to handle but others do entail some risk. According to safety ratings,
hydrocarbons have higher flammability than mostHFCs, while ammonia and some HFOs have lower
flammability, and CO2 is non-flammable. Safety ratings also indicate higher toxicity for ammonia thanfor most HFCs and lower toxicity for hydrocarbons,
HFC-1234yf, and CO2 (ASHRAE, 2016). Safety issues
can be resolved by changes in design of equipment,
limiting the refrigerant charge sizes, and limiting potential emissions (UNEP, 2011).

2.5.2.3	Energy Efficiency vis-à-vis GWP
The overall life-cycle climate impact of refrigeration
and air conditioning applications that use halocarbons as refrigerants depends on many factors including, but not limited to• the GWP of the refrigerant used,• the loss (or recovery) of the refrigerant at the endof life of the equipment,• the leakage rate of the refrigerant and recharge
rates during service,

2.44• the energy required to operate the coolant device,• the number of hours that the equipment is used,and• the carbon intensity of the electricity used to
power the equipment.

Of these, for most applications, emissions due to theenergy use of the equipment tend to dominate thelife-cycle emissions (unless the grid is exceptionally
free of carbon-emitting energy sources or the hours ofuse are very low). For example, Goetzler et al. (2016)
estimated 73–76% of global CO2–eq emissions fromAC systems in 2010 to be indirect emissions fromthe energy use. Hence the energy efficiency resulting
from the use of specific refrigerants is a very important consideration.
In the context of the Montreal Protocol, energy efficiency4 has had a range of commonly acceptedmeanings, which include, but are not limited to thefollowing:
1. The performance of a refrigerant relative to an
HFC, HCFC, or CFC refrigerant being replacedsuch that it consumes less energy to perform thesame service in the same (or similar) refrigeration or air-conditioning equipment, henceforthreferred to as “refrigerant energy efficiency”.
2. The performance of refrigeration or air-conditioning equipment by replacing one or morecomponents other than the refrigerant such thatit consumes less energy to perform the same
service, henceforth referred to as “equipment energy efficiency”.
There are other methods to reduce energy consumption in refrigeration, air conditioning, or other applications in which halocarbons are used, which fallunder a broader consideration of “energy efficiency”,
such as using building insulation or maintenance toensure optimal operation. Such methods (sometimes
called “not-in-kind”) are less germane to a discussionof energy efficiency in the context of the Montreal
4

Sometimes “energy efficiency” is also used to denote a metricor quantitative measure designed to measure the amount ofenergy used to perform a particular service, e.g., reduce thetemperature of one ton of chilled water by 1 degree C. The
less energy used to perform the same service, the more energy
efficient the technology being used.

HFCs | Chapter 2

Protocol, since the influence of these approaches
is generally independent of the chemical chosen as
coolant.
Many studies have been conducted to evaluate bothtypes of energy efficiency improvement mentioned
above (e.g., Schultz, 2016; Abdelaziz et al., 2015). For
example, in a study of refrigerant energy efficiency in
air conditioners, Schultz (2016) found that some alternate low-GWP refrigerants, e.g., HFC-32 and R-452B(blend of 67% HFC-32, 7% HFC-125, and 26% HFC1234yf, also referred to as DR-55), perform as wellas or better than the HFC refrigerant R-410A (blendof 50% HFC-32 and 50% HFC-125). Meanwhile,
Abdelaziz et al. (2015) found that the low-GWP refrigerant R-290 (propane) achieved a slightly higher
efficiency than HCFC-22, but with a slightly lower
cooling capacity under test conditions.
Equipment energy efficiency has been evaluatedmainly under the aegis of market transformation
programs for energy efficiency such as those run by
the EU Ecodesign program (EuP, 2008) and the U.S.
Department of Energy (DOE, 2017). Shah et al. (2013)
found that a ~30% efficiency improvement, relative tocurrent technologies, for air conditioners was cost-effective in many economies.
The conversion from using HFC refrigerants withhigh GWPs to refrigerants with lower GWPs, which
will most likely result from the Kigali Amendment,
provides an opportunity to consider other technological improvements that offer additional climate
benefits through improvements in equipment energy
efficiency.
Shah et al. (2015) found that if, in 2030, the world
mini-split AC stock (i.e., the total number of installed

and operational mini-split ACs5) transitioned fromhigh-GWP, low-efficiency equipment to low-GWP
refrigerants such as HFC-32 or propane (R-290), coupled with improved equipment that was 30% more
efficient, the climate benefit over the ~10 year lifetimeof the ACs would be over 25 GtCO2-eq emissions.
This would roughly double the CO2-eq emission reductions in comparison with either policy (refrigeranttransition or equipment energy efficiency improvement) implemented in isolation. The combined direct
and indirect emissions abatement from both policies
implemented together for mini-split ACs would beroughly 98 GtCO2-eq emissions by 2050, as a resultof the large projected growth in ACs in developing
countries (Figure 2-22). Regions with higher hours ofuse or more carbon-intensive electricity grids would
benefit more from energy efficiency improvement.
Based on the various alternate refrigerant testing programs listed above and the various energy efficiency
market transformation studies, similar results (in %terms) may also be found for other air-conditioning
and refrigeration equipment. Results are dependent
on hours of use, emissions during operation or at endof life, grid carbon intensity, growth rates, and otherrelevant factors.
In conclusion, improvements in energy efficiency inrefrigeration and air conditioning equipment during
the transition to low-GWP alternative refrigerants
can potentially double the climate benefits of the
HFC phasedown of the Kigali Amendment. The potential magnitude of these combined benefits would
contribute to achieving the targets of the UNFCCC
2015 Paris Agreement, which aims to keep global
temperature rise this century well below 2°C above
pre-industrial levels.

5

Mini-split ACs are the most widely used type of AC system,
representing roughly ~70% of sales by unit worldwide.
2.45

Chapter 2 | HFCs
30
25

Direct emissions
abatement fromrefrigerant transition

20
15

Indirect emissions
abatement fromrefrigerant transition

Lifetime emissions
abatement potential
10
in 2030(Gt CO2 -eq)5

Indirect emissions
abatement fromefficiency
improvement

0

HPR1D

HPR2A

R-32/R-134a

D2Y60

ARM71a

ARM70a

L-41b

L-41a

L-41-2

L-41-1

DR-55

R-32

R-32/R-152a

DR-5

-10

R-290

-5

Figure 2-22. Estimated emissions abatement potential of air conditioning (AC) Stock in 2030 over 10-year
AC lifetime. Positive numbers indicate CO2 emission reductions while negative numbers indicate increases in
CO2 emissions1. Source: Shah et al. (2015). The purple bar indicates the “indirect” emissions abatement fromthe ~30% equipment efficiency improvement (compared with current technologies) while the yellow bar
indicates the “indirect” emissions abatement refrigerant energy efficiency improvement (or reduction), andthe blue bar indicates the direct emissions abatement due to the lower GWP of the refrigerant compared tothe commonly used AC refrigerant R-410A2. See Table 2-4 for the acronyms of the different refrigerants. R32/
R152a is a blend of 95% HFC-32 and 5% HFC-152a. R32/R134a is a blend of 95% HFC-32 and 5% HFC-134a.

2.46

1

Shah et al. (2015) estimated the world AC stock (# of installed units) will grow from ~900 million units in 2015 to roughly 1.6
billion units in 2030.

2

Indirect emissions are emissions from the electricity grid due to the energy consumption of the equipment, while directemissions are emissions of the refrigerant from the equipment due to leakage or at the end of life.

HFCs | Chapter 2

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Chapter 3
Update on Global Ozone: Past, Present, and Future
Lead Authors
P. Braesicke
J. NeuCoauthors
V. Fioletov
S. Godin-Beekmann
D. Hubert
I. Petropavlovskikh
M. Shiotani
B.-M. Sinnhuber
Contributors
W. Ball
K.-L. Chang
R. Damadeo
S. Dhomse
S. Frith
A. Gaudel
B. Hassler
R. Hossaini
S. Kremser
S. Misios
O. Morgenstern
R. Salawitch
V. Sofieva
K. Tourpali
O. Tweedy
D. Zawada
Review Editors
W. Steinbrecht
M. WeberCover photo: Image of Earth from space. The ozone layer is a small part of the atmosphere which is a very thinlayer surrounding the planet. Photo: Adapted from an Adobe Stock photo.

Chapter 3
Update on Global Ozone: Past, Present, and Future
CONTENTSSCIENTIFIC SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1
3.1.

INTRODUCTION  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  5
3.1.1 Summary of Findings from the ­Previous Ozone Assessment  . . . . . . . . . . . . . . . . . . . . . . . .  5
3.1.2 Major New Developments Since 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  5
3.1.3 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6
3.1.4 Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6

3.2

NATURAL OZONE VARIATIONS AND TREND DETECTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  9
3.2.1 Natural Variability  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  9
3.2.1.1

Solar Variability  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  9

3.2.1.2

Quasi-Biennial Oscillation (QBO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2.1.3

El Niño–Southern Oscillation (ENSO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2.1.4

Effects of Stratospheric Aerosol Loading  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Box 3-1. Origin of Stratospheric Aerosols at Mid-latitudes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2.1.5

Other Dynamical Variations  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2.1.6

Attributing Variability in Regression ­Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2.2 Trend Models  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3

PAST OZONE IN OBSERVATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.1 Changes in Total Column Ozone  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.1.1

Interannual Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.3.1.2

Total Ozone Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.3.2 Trends in Ozone Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.2.1

Time Series  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.3.2.2

Ozone Trends 2000–2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.3.2.3

Trend Profiles  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3.2.4

Consistency of Total Column Trends and Integrated Profile Trends  . . . . . . . . 32

3.3.3 Impacts of Changes in Ozone-­Depleting Substances and  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
	­Greenhouse Gases on Ozone Trends  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.3.1

Effects of Very Short-Lived Substances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.3.3.2

Tropical Ozone Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Box 3-2. Modelling past and future changes in ozone: Model heritage and application . . . . .40
3.4

PROJECTED OZONE CHANGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4.1 Expected Return to 1980 ­Levels and Ozone Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.4.2 Effects of Future Stratospheric ­Temperature and Circulation Changes  . . . . . . . . . . . . . . 43
Box 3-3. Ozone Return Dates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.4.3 Sensitivity to the Specification of ­Different Future Scenarios  . . . . . . . . . . . . . . . . . . . . . . . 46
3.4.3.1

Effects of Different Representative ­Concentration Pathways  . . . . . . . . . . . . . . 46

3.4.3.2

Influence of Nitrous Oxide and Methane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.4.3.3

Sensitivity to Geoengineering/Solar ­Radiation Management . . . . . . . . . . . . . . 49

3.4.4 Impacts on Tropospheric Ozone  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
APPENDIX 3A: DATA SOURCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3A.1

Ground-based Measurements  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3A.2

Space-Based Ozone Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .69

3A.3

Space-Based Total Ozone Columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Chapter 3
Update on Global Ozone: Past, Present, and Future
SCIENTIFIC SUMMARY
This chapter deals with the evolution of global ozone outside of the polar regions. The increase of ozone-depleting
substance (ODS) concentrations caused the large ozone decline observed from the early satellite era (circa 1980) to
the mid-1990s. Since the late 1990s, concentrations of ODSs have been declining due to the successful implementation of the Montreal Protocol. Ozone concentrations show latitudinally dependent increases in the upper stratosphere
for the 2000–2016 period; changes in other parts of the stratosphere are not yet statistically significant. A new suiteof model simulations confirms previous results for the upper stratosphere that about half of the observed increase isassociated with declining ODSs. Ozone column trends are likewise positive but not generally statistically significant.
Their overall evolution is, however, compatible with the decline in equivalent effective stratospheric chlorine (EESC).
Over the next decades, we expect increasing global mean stratospheric ozone columns, as ODSs continue to decline.
Emissions of greenhouse gases (GHGs), especially carbon dioxide (CO2 ), methane (CH4   ), and nitrous oxide (N2O),
will also affect the evolution of global stratospheric ozone, particularly in the second half of the 21st century, when
ODS concentrations are expected to be low.

PAST CHANGES IN TOTAL COLUMN OZONE• Ground- and space-based observations indicate that there is no statistically significant trend in near-global(60°S–60°N) column ozone over the 1997–2016 period. These datasets show an increase of between 0.3%
and 1.2% decade−1 since 1997, with uncertainties of about 1% decade−1. These findings are consistent
with our understanding of the processes that control ozone:○○ In middle and high latitudes, the increase in total column ozone expected to arise from the 15%

decline in EESC since 1997 is small (~1% decade−1) relative to the large, dynamically forced yearto-year variations of ~5%;○○ In the tropics, where halogen-driven ozone loss is small in the lower stratosphere, total column

ozone has not varied significantly with ODS concentrations, except under conditions of high volcanic aerosol loading (e.g., from the eruption of Mt. Pinatubo in 1991).

• Outside the tropics, present-day (2014–2017) total ozone columns from ground-based and space-based
observations remain lower than 1964–1980 column ozone by:○○ about 2.2% for the near-global average (60°S–60°N);○○ about 3.0% in the Northern Hemisphere mid-latitudes (35°N–60°N);○○ about 5.5% in the Southern Hemisphere mid-latitudes (35°S–60°S).

These values are essentially the same as in the last Assessment, given uncertainties associated with natural variability and instrumental accuracy. The larger depletion in the Southern Hemisphere is linked
to the Antarctic ozone hole.

PAST CHANGES IN OZONE PROFILES
Additional and improved datasets and focused studies evaluating trend uncertainties have strengthened our ability
to assess ozone profile changes. Analysis of data from the upper stratosphere shows increases that are consistent with
3.1

Chapter 3 | Global Ozone

those suggested in the last Assessment. There is some evidence for a dynamically driven decrease in ozone in the lower
stratosphere from 2000 to 2016, but robust trends have not been identified for this region. New chemistry–climate
model (CCM) simulations that include realistic time variations of GHG and ODS concentrations are analyzed using
the same trend model as for the observations; this allows attribution of changes in ozone to different processes.

• Measurements show increases of ozone in the upper stratosphere over the period 2000-2016. Following alarge decline of 5 to 7% decade−1 through the 1980s and middle 1990s, upper stratospheric ozone has
increased by 1 to 3% decade−1 since 2000. The largest confidence is in northern mid-latitudes, where thepositive trend is statistically significant between 35- and 45-km altitude. Confidence in trends in the tropics and southern mid-latitudes is not as high due to larger discrepancies between trends from individual
measurement records.

• Model simulations attribute about half of the observed upper stratospheric ozone increase after 2000 to

the decline of ODSs since the late 1990s. The other half of the ozone increase is attributed to the slowingof gas-phase ozone destruction cycles, which results from cooling of the upper stratosphere caused byincreasing GHGs.

• There is some evidence for a decrease in lower stratospheric ozone from 2000 to 2016. This decrease is mostconsistent across datasets in the tropics, but is not statistically significant in most analyses. Much of theapparent decline was reversed by an abrupt increase in ozone in 2017, indicating that longer records areneeded to robustly identify trends in this region. Model simulations attribute the variations in lower
stratospheric ozone over this period primarily to dynamical variability.

• Assessing the consistency between stratospheric profile trends and total column ozone trends requires

changes in tropospheric ozone to be well quantified. A recent assessment of tropospheric column ozone
trends, however, shows large disagreements in the sign and magnitude of the observed trends over the
past decade and a half.

FUTURE OZONE CHANGESThe baseline climate change scenario used in the new model simulations differs from the previous Assessment, because new emissions scenarios were used. The key drivers of future ozone levels continue to be declining ODS concentrations, upper stratospheric cooling because of increased GHGs, and the possible strengthening of the Brewer-Dobson
circulation from climate change. The new emissions scenarios lead to slight differences in the relative contributions ofthese processes in various latitude and altitude regions and a delay in return dates for ozone compared to the previous
Assessment.

• Estimated dates of return of total column ozone to 1980 values are generally a few years later thangiven in the previous Assessment and vary considerably between scenarios. For the baseline scenario(RCP-6.0), they are:○○ around mid-century for near-global mean annually averaged ozone;○○ most likely before the middle of the century (~2035) for annually averaged Northern Hemisphere

mid-latitude ozone;○○ around mid-century for annually averaged Southern Hemisphere mid-latitude ozone.

• CO2, CH4, and N2O will be the main drivers of 60°S–60°N stratospheric ozone changes in the second half ofthe 21st century. These gases impact both chemical cycles and the stratospheric overturning circulation,with a larger response in stratospheric ozone associated with stronger climate forcing. By 2100, the stratospheric column is expected to decrease in the tropics by about 5 DU for RCP-4.5 and about 10 DU for

3.2

Global Ozone | Chapter 3

RCP-8.5 relative to 1980 values, with the net total column change projected to be smaller (about 5 DU)
because of offsetting increases in tropospheric ozone.

• Given that ODS levels are expected to decline slowly in coming years, a large enhancement of stratosphericsulfate aerosol in the next decades would result in additional chemical ozone losses. Possible sources ofadditional stratospheric sulfate aerosol include volcanic eruptions (like Mt. Pinatubo in 1991) and geoengineering. Even when ODS levels have declined substantially, a large injection of volcanic halogens
into the stratosphere could drive substantial ozone losses in the presence of aerosol surfaces.

• Future ozone recovery and the projected strengthening of the Brewer-Dobson circulation (BDC) are likely

to lead to increases in the stratosphere-to-troposphere (STT) flux of ozone via increases in mid-latitude
lower stratospheric ozone and mass flux. The net impact of increased STT flux on the tropospheric ozone
burden is highly model and scenario dependent. Most studies suggest it will be small relative to otherfactors, such as concurrent changes in precursor emissions, temperature, and water vapor.

3.3

Chapter 3 | Global Ozone

3.4

Chapter 3
Update on Global Ozone: Past, Present, and Future
3.1. INTRODUCTION
This chapter updates the corresponding chapter fromthe previous Assessment (Chapter 2, WMO, 2014); it
describes our current understanding of past changes
in global (60°S–60°N) ozone and its expected future
development. The chapter focuses on detection andattribution of ozone changes and the robustness ofozone trends and their associated uncertainties. The
chapter also describes how ozone is expected to change
in the future. This includes the modeled response to
the continuing decline in stratospheric chlorine- andbromine-containing compounds and the response ofozone to climate change. A key benchmark, as always,
is the date of return of ozone to its 1980 value.

3.1.1 Summary of Findings from the­Previous Ozone Assessment
The 2014 Assessment (WMO, 2014) for the first time
provided evidence that stratospheric ozone concentrations have increased in response to reductions in
the emissions of ozone-depleting substances (ODSs)
imposed by the Montreal Protocol. In particular,
measurements of ozone in the upper stratosphereshowed a statistically significant positive trend, which­chemistry–climate models (CCMs) suggested is attributable equally to decreased ODS concentrations
and to colder temperatures resulting from increased
greenhouse gases (GHGs). Total column ozone had
not increased significantly (1%±1.7%). Large dynamic variability and differences between datasets wereshown to make trend detection difficult given the~1% expected increase in column ozone associatedwith ODS decline.
CCM results indicated that a large enhancement of
sulfate aerosol from either a volcanic eruption or
geoengineering would result in significant ozone loss
while ODS levels remain high. CCM simulations werealso used to examine how assumptions about future
GHG emissions affect ozone in the late 21st century,
when chemical ozone destruction by halogens will
be negligible. The effects of increasing nitrous oxide(N2O), which chemically depletes global ozone, compete against the effects of increasing carbon dioxide(CO2) and methane (CH4), which increase ozone in
the extratropics via both changes in chemistry andstrengthening of the circulation. CCMs showed differences of 7% in global average total column ozone
for the year 2100 between maximum and minimum
radiative forcing Representative Concentration
Pathways (RCPs). Significant decreases in tropicalcolumn ozone were projected under all scenarios
despite increases in the upper stratosphere associated with GHG-induced cooling. The column reductions occurred primarily because strengthening of
the circulation decreases tropical ozone in the lower
stratosphere.

3.1.2 Major New Developments Since 2014
With four additional years of data and the advent ofboth new and consolidated merged datasets, this chapter revisits evidence for the detectability of positive
ozone trends that might be attributable to decreases
in stratospheric chlorine- and ­bromine-containing
compounds.
The Long-term Ozone Trends and Uncertainties in
the Stratosphere (LOTUS) initiative has undertakena systematic assessment of the significance of observed ozone profile trends. LOTUS (2018) robustly
quantified the degree to which ozone variability canbe attributed to the various proxies used to represent
natural process that drive ozone changes. It also examined available trend models and formulated a bestpractice, applying a common methodology to updated satellite and ground-based datasets (including
merged and homogenized data). The resulting trend
profiles include a traceable error characterization for
the assessment of significant (recovery) trends. The
new analysis confirms the general trends derived forthe 2014 Ozone Assessment, but with larger estimateduncertainties, in particular in the upper stratosphere.
In addition, the LOTUS trend model used for the profile observations is applied to model integrations ofozone under various scenarios, allowing a consistent
3.5

Chapter 3 | Global Ozone

comparison of observed and modeled trends up to thepresent day.
The Chemistry-Climate Model Initiative Phase 1(CCMI-1; Morgenstern et al., 2017) provides newmodel integrations that simulate past, present, and future ozone. For the past, f­ree-running and “specified
dynamics” model integrations are available and capture many important features of the observed ozone
variability and trends. The baseline future projections
use the RCP-6.0 scenario to represent climate change.
In this respect, this chapter deviates from the 2014 and2011 Ozone Assessments (WMO, 2014; WMO, 2011),
where CCMVal-2 integrations were used, which are
based on the SRES A1B scenario. However, the response of ozone to different climate change scenarios is evaluated using additional RCP scenarios andidealized sensitivity studies (e.g., fixed ODSs or fixedGHGs). Return dates are derived in a comprehensive
way by calculating filtered multi-model mean time series and analyzing if and when 1980 ozone values arereached (Dhomse et al., 2018).

3.1.3 Data Sources
This Assessment relies on essentially the same groundbased, in situ and satellite ozone datasets as were used
for the 2014 Assessment. Since then, all records havebeen extended to the present, and some have been revised and reprocessed, in part or in full. In addition,a few new data records have emerged. Appendix 3A
summarizes the data records used in this chapter.
Because single-instrument records do not provide
sufficient temporal and/or spatial coverage to assess
global long-term trends, merging is required; quantification of uncertatinties associated with merging
is discussed in Section 3.1.4. Each approach has its
merits and weaknesses, and the availability of a number of complementary, independent global ozone
datasets is essential to comprehensively quantifying
uncertainties in trend assessments.

3.1.4 Data Quality
Data quality is one of the key drivers of trend uncertainty, with other important contributions coming from natural variability, methodological choicesin the regression analyses, and assumptions on how
trend results are combined (see Section 3.2). Merged
datasets provide comprehensive multi-instrument
3.6

records, with improved temporal and spatial coverage and reduced uncertainties compared to a­single-instrument data record (Tummon et al., 2015).
The challenges of merged records highlighted in thelast Assessment, however, are still relevant: Interinstrument biases and drift, differences or changes inspatiotemporal sampling patterns, different (vertical)
coordinate systems, and different spatiotemporal resolutions can all impact the accuracy of trends derived
from merged records.
Instrument biases lead to time-dependent artifacts(“jumps”) when continuous or partially overlapping
records are merged without prior adjustment to a common absolute reference (Ball et al., 2017; Weatherhead
et al., 2017). The accuracy of such bias corrections increases with the amount of data available and depends
on the length of the overlap periods for different records. Many single-instrument records were revised
in recent years (Appendix 3A), and a series of intercomparisons revisited and refined the estimated biases between satellite data records (Kramarova et al.,2013; Frith et al., 2014, 2017; Tegtmeier et al., 2013;
Coldewey-Egbers et al., 2015; Rahpoe et al., 2015;
Froidevaux et al., 2015; Davis et al., 2016; Sofieva etal., 2017), ground-based datasets (Van Malderen etal., 2016; Deshler et al., 2017), and ground-based andsatellite data records (Koukouli et al., 2015; Hubert etal., 2016; Thompson et al., 2017; Garane et al., 2018;
Sterling et al., 2018). Single-sensor ozone profile datasets agree to within about 5% in the height range of
20–45 km. Once adjustments are made by the merging algorithms, the residual inter-instrument biases
are reduced considerably. However, it is likely that
uncertainties associated with bias corrections are, insome cases, not negligible; e.g., for the merged SBUV
satellite data records (due to short overlap periods;
Ball et al., 2017; Frith et al., 2017) and for the SAGEMIPAS-OMPS satellites (due to a sparse sampler thatacts as transfer standard between MIPAS and OMPS;LOTUS, 2018).
Removing inter-instrument drift is a challenge that
requires considerable temporal overlap of data records and a reliable statistical analysis (Stolarski andFrith, 2006). Drift correction schemes have been developed for combined data from dense nadir-viewing
samplers (Coldewey-Egbers et al., 2015), but thus far
such corrections have only rarely been tested for limb
merging algorithms (Eckert et al., 2014; Damadeo

Global Ozone | Chapter 3

et al., 2018). Intercomparisons between single records generally show inter-instrument drifts below
1% decade−1 for total column data (Frith et al., 2014;
Koukouli et al., 2015; Garane et al., 2018) and less than
3–5% decade−1 for profilers (Kramarova et al., 2013;
Rahpoe et al., 2015; Hubert et al., 2016; Frith et al.,2017). Large drifts (i.e., 5% decade−1 or more) foundin previous versions of the OSIRIS and SCIAMACHY
satellite data records (Hubert et al., 2016) have beencorrected, improving agreement with other datasets(Sofieva et al., 2017; Bourassa et al., 2018; LOTUS,
2018). Instabilities in the NCEP temperature data in
the 1980s (McLinden et al., 2009; Maycock et al., 2016)
have been shown to have introduced a ~6% decade−1systematic error on the trend in SAGE II v6.2 volume
mixing ratio data in the tropical upper stratosphere(Froidevaux et al., 2015; Ball et al., 2017). The current

SAGE II v7.00 release, used by all merged limb records considered here, utilizes MERRA temperature
profiles that substantially reduce this systematic error.
Time-dependent biases can appear in datasets thatare based on a collection of observations with nonhomogeneous sampling (e.g., SAGE, HALOE, and
ACE-FTS). This can also be true for an instrument
such as SBUV that drifts in local overpass time.
Ignoring SBUV data close to the terminator avoids
most, but not all, of this issue. A study comparing
trends regressed from monthly zonal mean (MZM)
solar occultation data to those from data close to thenative resolution of the measurements (Figure 3-1)inferred that diurnal sampling biases that change overtime affect the MZM-derived trends by about 1% decade−1 in the mid-latitude upper stratosphere, which

Long-term Ozone Trend 2000–2012
Raw MZM

Sampling-corrected MZM
(b)

0

4

2

0

(a) 50
45

2

0

−2

2

0

2

2

35

0

2

2

2

2

40

0

30

−4

2

% decade−1

Altitude (km)

0

2

0

−6
25

−25

0

Latitude

25

0

2

−50

-2

-2

0

20

0

25

50

2

0

0

−8

50

−50

−25

Latitude−10

Figure 3-1. Long-term ozone trends in % decade−1 for the period 2000–2012 derived from both (a) a regression of monthly zonal mean data (MZM) and (b) monthly zonal mean data corrected for sampling biases
due to the diurnal and seasonal cycle. The diurnal correction has the greatest influence on the upper stratosphere, while the seasonal correction has the greatest influence at higher latitudes. Stippling denotes areas
where the trend results are not significant at the 2σ level. Contour lines are plotted at 2% decade−1 intervals.
Adapted from Damadeo et al. (2018).
3.7

Chapter 3 | Global Ozone

constitutes about half of the trend in past two decades(Damadeo et al., 2018). Seasonal sampling biases wereshown to be more prevalent at higher latitudes andin the tropical middle stratosphere. The analysis led
to a sampling bias correction scheme for the SAGE II
dataset, which was used for the SAGE-OSIRIS-OMPS
record, but not for other limb records. Sampling biases in total column or limb profile data records are
generally considered random in nature but are notfully quantified (Coldewey-Egbers et al., 2015; Millán
et al., 2016).
The coherent propagation of uncertainties throughmerging algorithms is a complex challenge.
Addressing this challenge by applying a Monte Carlotechnique to simulate the SBUV error time series fortwo different merging algorithms results in a trend
uncertainty of 1–2.4% decade−1 (1σ), which explains
the differences in profile trends for the merged SBUV
MOD and SBUV COH records (Frith et al., 2014,2017; see also Appendix 3A and Figure 3-2). So far,a comprehensive error propagation analysis has notbeen done for the merged limb profile records. The
most advanced attempt, based on singular value decomposition of the differences between four merged
limb and nadir profile data records, estimated uncertainties of up to 5% for earlier versions of MZM datafrom the GOZCARDS and SWOOSH data records(Ball et al., 2017). However, the impact of measurement uncertainties on trends was not investigated.
Differences in merged data records are dominated by
the selection of instruments rather than the choiceof the merging technique (Tummon et al., 2015).
Differences are smallest in the mid-latitude lower
and middle stratosphere (5%) and increase in theupper stratosphere (8%) and tropical lower stratosphere (10%), consistent with the biases between
single-instrument data records. Recent modifications
to profile records have addressed, at least partially,some of the identified issues, and current versions ofmerged ozone profile records are in better agreement
than the versions used in the previous Assessment(LOTUS, 2018). Differences between merged spaceand ground-based total column records (comparedas monthly zoone mean total column datasets) are on
average less than 1–2% and they drift apart less than
0.5–1% decade−1 (Chiou et al., 2014; Bai et al., 2017).
Recent reanalysis datasets (Dee et al., 2011; Dragani
2011; and Wargan et al., 2017) have been shown to
produce a “realistic representation of total ozone”(Davis et al., 2017), but they are not included in thisAssessment.

ILT Trend 2001–2015 (40°–50°S)
1.6–1.0
2.5–1.6
4–2.5

40

6–4
35

10–6
16–10

30

SBUV MOD v8.6
SBUV COH v8.6

-4

3.8

-2

0

2

4

Trend (% decade−1)

6

8

25–16
10

Pressure level (hPa)Approximate altitude (km)45

Figure 3-2. Independent linear trends(ILTs) fit from 2001 to 2015 using SBUV
MOD (dark yellow) and SBUV COH(green) merged ozone records. Trends
are in % decade−1 and plotted as a function of pressure layers for 40–50°S. Horizontal lines indicate trend uncertainty(2σ): Solid lines represent the statistical uncertainty from the unexplained
variability in the multiple linear regression (MLR) analysis (Section 3.2.1), anddotted lines show the total uncertainty
obtained by adding the statistical andmerging uncertainty in quadrature. The
latter is estimated from Monte Carlo simulations that model how uncertainties inindividual SBUV data records propagate
through the merging chain. Adaptedfrom Frith et al. (2017).

Global Ozone | Chapter 3

3.2 NATURAL OZONE VARIATIONSAND TREND DETECTION
3.2.1 Natural Variability
The natural variation and long-term trends of stratospheric ozone are generally quantified using multiple
linear regression (MLR) models. Such models havebeen discussed in previous ozone assessments (e.g.,
WMO, 2014). They use explanatory variables (i.e.,
predictors) to describe natural and anthropogenic
variability in long-term ozone time series. The typicalmulti-linear regression can be written in the following form (e.g., Chehade et al., 2014, Steinbrecht et al.,2017, Weber et al., 2018):

Z(t) = Z0 + Trend · (t–t0) +n
i 0 аi Pi (t) + ε(t) (1)where Z(t) represents a monthly or yearly averagedozone time series and Z0 is the value at t0. The time series
usually describes deviations from a climatology rather
than the absolute amount of ozone. The Trend term is
discussed in Section 3.2.2. The predictors, or proxies,
Pi (t) are the variables used to explain ozone interannual and long-term variability. The predictors mostcommonly used in ozone trend studies are listed inTable 3-1 and are discussed in detail below. The lastterm, ε(t), is the residual variability not explainedby the MLR, which most analyses assume to be firstorder autoregressive noise. The terms in the model
must be linearly independent and are assumed to be
sufficiently orthogonal to provide independent pieces of information such that the regression can attribute, with confidence, ozone variability in the observed or modeled time record (see Section 3.2.2).
When terms exhibit significant covariations, as is
the case, for example, for the quasi-biennial oscillation (QBO) and the El Niño–Southern Oscillation(ENSO) over particular time periods, the ability of
MLR to determine attribution is detrimentally impacted and confidence intervals, which take into
account the covariance matrix of the regression coefficients, are correspondingly larger.
While MLR models are often applied to zonally averaged satellite data, there can be large longitudinal
asymmetries in the influence of some of the processes represented by the various proxies on ozone. In
particular, ENSO and the North Atlantic Oscillation(NAO) have large regional impacts that can be seen
in non-zonally averaged data, as discussed in moredetail in the sections below.

3.2.1.1	Solar Variability
The solar cycle influences ozone through photochemical and dynamical processes in the stratosphere(Haigh, 1994; Hood and Soukharev, 2003). Ozone in
the upper-middle atmosphere is produced at wavelengths shorter than 242 nm, and it is primarily destroyed at longer wavelengths through photochemical
processes. Understanding changes in UV irradianceis therefore important for the ozone and radiationbudget. The solar ozone response (SOR) to changes
in solar irradiance further plays a potentially important role in climate variability through modulation ofstratospheric temperatures and wind. These changes
in the stratosphere can influence tropospheric climate
through both direct radiative effects and dynamical
coupling, with impacts on extratropical modes of variability (e.g., Gray et al., 2010). Thus, understandingof the coupling between solar cycle variability, ozone
changes, and circulation is of great importance forassessing the climate response to solar cycle change.
The 2014 Assessment reported a 2–4% variation of
SOR in the upper stratosphere (3% in total ozone) inphase with the 11-year solar cycle. However, it wasstated that the “exact shape of the solar response profile depends on the type of data and/or analysis, thelength of data records, and the time periods underinvestigation.” In the 2014 Assessment, the uncertainties regarding solar-induced variability in observedozone fields were related to the brevity of ozone records (spanning only a few solar cycles) as well as
incomplete understanding of the accuracy of modern
solar spectral irradiance (SSI) observed records (i.e.,
data from the SORCE satellite; McClintock et al.,2005). The lack of sufficient spectral resolution in theradiation schemes of global climate models was also
noted as the reason for the models not being able toreproduce the solar–ozone relationship detected inobservations.
Since the last Assessment, several papers have­re-evaluated SOR estimates using both updated satellite observations and models. Uncertainties in themagnitude and structure of SOR estimates remain andcontinue to complicate the validation of atmosphericchemistry models (Dhomse et al., 2016). The primary
3.9

Chapter 3 | Global Ozone

Table 3-1. Table of proxies used in Equation (1), including representative data sources. The proxies and data
sources used in analyses presented in Section 3.3 are shaded in dark orange.
Proxy

Parameter

Data Sources
NOAA National Centers for Environmental Information:
https://www.ngdc.noaa.gov/stp/solar/flux.html

10.7 cm solar radio flux
Solar cycle

QBO1 and QBO2
(orthogonal­components of
the quasi- biennial­oscillation, QBO)

30 cm solar radio flux

CNES Collecte Localisation Satellites Space Weather
Services: https://spaceweather.cls.fr/services/radioflux/

Core-to-wing ratio of Mg II
doublet (280 nm)

University of Bremen: http://www.iup.uni-bremen.de/
UVSAT/Datasets/mgii

EOF1 and EOF2Free University of Berlin: www.geo.fu-berlin.de/en/
met/ag/strat/produkte/qbo/

Tropical zonal winds at 2 pres- NOAA National Weather Service
sure levels (e.g., 30 hPa and 50 Climate Prediction Center: http://www.cpc.ncep.noaa.
hPa or 10 hPa and 30 hPa)
gov/data/indices/
Multivariate ENSO index
Niño 3.4 index

ENSO
Southern Oscillation index

Aerosols

Other Dynamical
ProxiesNational Research Council Canada Dominion Radio Astrophysical Observatory: ftp://ftp.geolab.nrcan.gc.ca/
data/solar_flux/

Mean aerosol optical depth
at 550 nm

NOAA Earth System Research Laboratory:
https://www.esrl.noaa.gov/psd/enso/mei/
NOAA National Weather Service
Climate Prediction Center: http://www.cpc.noaa.gov/
data/indices/
http://www.cpc.ncep.noaa.gov/products/precip/
CWlink/MJO/enso.shtml
NASA Goddard Institute for Space Studies:
https://data.giss.nasa.gov/modelforce/strataer/tau.
line_2012.12.txt
Khaykin et al. (2017)https://www.atmos-chem-phys.net/17/1829/2017/acp17-1829-2017.pdf

Brewer–Dobson circulation(BDC): eddy heat flux (EHF) at
100 hPaNOAA National Weather Service
Climate Prediction Center: http://www.cpc.ncep.noaa.
gov/products/stratosphere/polar/polar_body.html

North Atlantic Oscillation(NAO) index(daily or monthly)

NOAA National Weather Service
Climate Prediction Center: http://www.cpc.ncep.noaa.
gov/products/precip/CWlink/pna/nao.shtml

Arctic Oscillation (AO) index(daily or monthly)

NOAA National Weather Service
Climate Prediction Center: http://www.cpc.ncep.noaa.
gov/products/precip/CWlink/daily_ao_index/ao.shtml

Antarctic Oscillation (AAO)
index (daily or monthly)

NOAA National Weather Service
Climate Prediction Center: http://www.cpc.ncep.noaa.
gov/products/precip/CWlink/daily_ao_index/aao/aao.
shtml
NOAA Earth System Research Laboratory https://www.
esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.
tropopause.html

Tropopause pressure (TP)NASA Global Modeling and Assimilation Office:https://gmao.gsfc.nasa.gov/reanalysis/MERRA/data_access/https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/
data_access/

3.10

Global Ozone | Chapter 3

new result, shown by two studies, is that updated SAGE
II and SBUV mixing ratio datasets suggest a decreasein the magnitude of the SOR in the tropical upper
stratosphere relative to earlier assessments (from~4% in the 2014 Assessment to ~1% here) (Maycock
et al., 2016; Dhomse et al., 2016). The SAGE II v7.0
number density dataset is consistent with v6.2, but
the mixing ratio dataset exhibits a smaller signal,largely due to the use of a different temperature reanalysis product to convert ozone number densities
to mixing ratios. SBUV MOD VN8.6 also shows asmaller and less significant SOR in the tropical upper
stratosphere than the SBUV Merged Cohesive VN8.5
and closely resembles the SAGE II v7.0 mixing ratio
data. However, given known issues with reanalysis
temperatures, the authors concluded that the use of
number density is more robust for SOR analyses thanconverting to mixing ratio for data records for which
number density is the native coordinate, in agreementwith previous findings (Remsberg et al., 2014). Oneof the studies also showed that the SAGE–GOMOS
merged number density datasets are consistent with
the SOR in SAGE II alone while SAGE-OSIRIS is not(Maycock et al., 2016). It further notes that limb sampling is too sparse to extract sub-annual variationsin the SOR but that the SBUV MOD VN8.6 dataset
suggests substantial month-to-month variations, particularly in the winter extratropics.
The investigations of SSI data and their reproducibility by solar models is important for the simulation ofsolar cycle effects on both stratospheric ozone andsurface climate (e.g. Ermolli et al., 2013, and Matthes
et al., 2017). Two new studies find that at pressures <5hPa, the ozone response to solar variability simulatedusing output from solar models, such as SATIRE-S and
NRLSSI, as forcings in climate models is consistent
with observations, while simulations using SORCE
data are not (Figure 3-3; Dhomse et al., 2016; Ball etal., 2016). These studies support earlier evidence that
SORCE measurements strongly overestimate solar
cycle variability in the UV range. Large differences in
the amplitude and spectral features of the most recent
solar cycle (C24, which began in December 2008)
from earlier periods, including a reduction in total
solar irradiance amplitude of 35% from the previous
cycle, are an area of active investigation.
Previous studies have reported a secondary maximum
in the ozone response to the solar cycle in the tropicallower stratosphere (e.g., Soukharev and Hood, 2006;
Gray et al., 2010). This lower stratospheric signal isgenerally attributed to a dynamical response to increased heating in the upper stratosphere during solar
maxima, but it could be a result of aliasing in MLR
analyses due to the presence of volcanic eruptions at
solar maxima (Chiodo et al., 2014). However, further
evidence for the dynamical response comes from thefact that the secondary peak has also been seen in
IASI satellite data (2008–2013) using daily solar flux
measurements in the regression analysis (Wespes etal., 2016).

3.2.1.2

Quasi-Biennial Oscillation (QBO)

The quasi-biennial oscillation (QBO) influences
stratospheric ozone through its impact on dynamical
and chemical processes. The QBO signal in tropicalozone consists of a primary maximum in amplitude
at a pressure of ~7 hPa, a secondary maximum near
20–30 hPa, and a minimum near 15 hPa (e.g., Naoeet al., 2017). However, other modes of variability suchas ENSO can also influence tropical stratosphericozone (e.g., Oman et al., 2013; Section 3.2.1.3), andanomalies do not always show a direct correlation
with the QBO phase (Nedoluha et al., 2015a). The
QBO proxy in MLR analyses (Table 3-1) is often represented by the wind speeds measured at two differentpressure levels by radiosonde soundings in Singapore(Baldwin, 2001) or, alternatively, by two orthogonal
QBO time series derived from principal component
analyses (Wallace et al., 1993; Randel and Wu, 1996).
The period since the last Assessment was marked
by an unprecedented disruption of the QBO during
the NH winter of 2015–2016 (Newman et al., 2016;
Osprey et al., 2016; Dunkerton et al., 2016). Usually,
alternating westerly and easterly zonal wind regimes
propagate downward with time with a ~28-month period. In 2016, an anomalous upward displacement of
the westerly phase occurred from ~30 hPa to 15 hPa,and easterly winds appeared at 40 hPa (see Figure3-4). Such a disruption of the QBO has never before
been observed in tropical wind measurements, whichbegan in 1953. The first two empirical orthogonal
functions (EOFs) of the QBO, which describe theprimary modes of variability in tropical zonal winds,
typically account for ~95% of the variance in thesewinds; in 2016, they explain only 71% of the variance(Tweedy et al., 2017).
3.11

Chapter 3 | Global Ozone

1984–2005 Solar Cycle Signal in Tropical Ozone
60

Altitude (km)

50

A_NRL
B_SAT
C_SOR
SAGE7.0
SAGE6.2

40

30

20
−6

−4

−2

0

2

4

6

Figure 3-3. Estimated solar
cycle signal in tropical ozone
volume mixing ratio (VMR)
from SAGE II (1984–2005) v7.0
(black) and v6.2 (light blue).
The signal was dervied using a
recently developed regression
model (Damadeo et al., 2014).
All error bars are 2-σ; none areshown for SAGE data. The modeled ozone solar cycle signals
for climate model simulations
using output from the NRLSSI(green) and SATIRE-S (orange)solar models and from SORCE
data (red) for the 1984–2005
period are also shown. Adapted
8 from Dhomse et al. (2016).

Ozone solar cycle signal (%)

The anomalous zonal wind pattern drove a decreasein tropical upwelling from 50 to 30 hPa, which was
associated with a positive ozone anomaly, and increased upwelling at pressures >50 hPa, which was
associated with a negative ozone anomaly (Figure3-5) (Tweedy et al., 2017). In the extratropics, reduced downwelling balanced the decrease in tropical ascent from 50 to 30 hPa, resulting in a negativeozone anomaly. In fact, SBUV observations show
near-record low levels of total ozone in the subtropics in August 2016 of both hemispheres (Tweedy etal., 2017). At nearly the same time as the QBO disruption, there was a very strong El Niño event anda very strong stratospheric polar vortex in early to
mid-winter (Nedoluha et al, 2015; Cheung et al.,2016; Hu et al., 2016; Scaife et al., 2017), which may
have also contributed to ozone variability. In fact,
while ENSO and QBO are assumed to be orthogonalterms in MLR analyses, they are sometimes in phasefor long periods of time, complicating attribution ofozone changes (e.g., Neu et al., 2014).
The occurrence of the 2016 QBO cycle, as well asother less pronounced anomalies in the magnitude
and phase of the QBO in the past decade (Nedoluha
3.12

et al., 2015), are possible indications of changes in thenormal behavior of the processes that impact the global stratospheric ozone distribution and inter-annual
variability. The causes of this anomalous behavior andits potential implications for the future evolution ofozone are still under investigation.

3.2.1.3	El Niño–Southern Oscillation (ENSO)
El Niño–Southern Oscillation (ENSO) affects tropical upwelling, which in turn leads to fluctuations intemperature and ozone in the tropical lower stratosphere (Bodeker et al., 1998; Randel et al., 2009; andreferences therein). ENSO is generally represented
in MLR analyses either by the Niño 3.4 index or by
the multivariate ENSO index (MEI) (Wolter, 2013),
which is based on the first principle component of six
atmospheric parameters (Table 3-1). In the tropicalupper troposphere and lower stratosphere (UTLS),
the ENSO coefficient is negative, with low ozone
during El Niño years and high ozone during La Niña
years; the opposite signal is seen in mid-latitudes (e.g.,
Neu et al., 2014; Oman et al., 2013; Olsen et al., 2016;
Wespes et al., 2016). Regression of MLS satellite measurements suggests up to a ~20 ppb K−1 response ofozone in the tropical lower stratosphere to changes

Global Ozone | Chapter 3

70

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

30
28
26
24
22
20

30
28
26
24
22
20

Altitude (km)40

Altitude (km)

20

30
28
26
24
22
20

Altitude (km)Pressure (hPa)

10

1992

Pressure (hPa)

10
20
40
70

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Pressure (hPa)

10
20
40
70

2005

2006

2007

2008

2009

2010

−40

−20

2011
0

U (m s-1)

20

2012

2013

2014

2015

2016

40

Figure 3-4. Monthly mean zonal wind U(m s−1) derived from Singapore radiosondes (1°N, 104°E) between
70 and 10 hPa for 1981 through July 2016. Easterlies are shown in cyan/blue, while westerlies are shown ingreen/brown. Contours are every 20 m s−1, with easterlies dashed and westerlies solid, and a thick black line
for zero wind. The red squares show the dates of the 40 hPa easterly-to-westerly transition, while the red stars
show the dates of the 10 hPa westerly-to-easterly transition. From Newman et al. (2016).

in sea surface temperatures in the Niño 3.4 region(Oman et al., 2013). This likely represents a maximum
value, as ENSO and the QBO were in phase throughout much of the analyzed period, making it impossible to separate their contributions to ozone variability
through linear regression (Neu et al., 2014). The period since the last Assessment has seen not only the
disruption of the QBO described in Section 3.2.1.2
but also the 2015 El Niño, which was the strongest on
record since 1997 and the third strongest since 1950.
The impact of this event on stratospheric ozone has
not yet been assessed.
The lag between the ENSO signal in atmosphericcomposition and the ENSO index increases withheight, and optimizing the lag has been shown to
reduce trend uncertainty in the lower stratosphere(Sioris et al, 2014; Sofieva et al., 2017). One study, however, did not find that inclusion of the lag in an MLR
model improved the fit to the 8-year long IASI ozone
time series, perhaps due either to the brevity of the record or the broad vertical smoothing of IASI (Wespes
et al. 2016). This second possibility is consistent withanother analysis that found the ENSO contribution
to ozone variability to be statistically insignificant inmany geographical regions in low-vertical-resolution
NDACC FTIR ground-based data (Vigouroux et al.
2015).
3.13

Chapter 3 | Global Ozone

2015–2016 QBO

Climatology
6

12

18

08-201408-2015

08-201650

Altitude (km)

2.4
1.8
1.2
0.6
0
−0.6
−1.2
−1.8
−2.4

24
18
12
6
0
−6
−12
−18
−24

Ozone (%)

50

20

70

Altitude (km)

10
30
20
25

30
50

20
−12

70
−6

0

6

12

18

−12

Months

−6

0

6

12

−18

T (%)

30

−9

Pressure (hPa)25

0Pressure (hPa)

10
20

9

−27

70

30

18

Zonal winds (m s−1)

30

27

Pressure (hPa)

20

10
20

r
te

25

0

r
ea
sh
rly
te
r
es
ea
W
sh
ly

30

-6

s
Ea

Altitude (km)

-12

18

MonthsFigure 3-5. Impact of the 2015–2016 QBO disruption on stratospheric variables. The rows show (top) the
MERRA-2 zonal-mean zonal wind component (in m s−1), (middle) deseasonalized MLS temperature anomalies (in %), and (bottom) ozone anomalies (in %) as a function of time and pressure (­ relative to the long-termmonthly averages), averaged over 5°S–5°N. The left column shows the composite of the easterly-to-westerly
shear transitions based on 4 shear transitions at 40 hPa. The right column shows the 2015–2016 QBO cycle,
which includes the data from April 2014 to September 2016, with month 0 (vertical dashed lines) being May
2016. The thick black contours denote the zero wind shear. The horizontal dashed lines indicate the 40 hPa
level. Adapted from Tweedy et al. (2017).

Even in vertically resolved datasets, however, the
ENSO impact on stratospheric ozone is regional. Itchanges sign between the eastern and western regionsof the Pacific Ocean (Oman et al., 2013), and even in
the extratropics there are large regions of both positive and negative coefficient estimates in total column
ozone (Rieder et al., 2013; Frossard et al., 2013). Thus,
the ENSO signal, while important for regional ozone
3.14

variability, is typically small in zonal averaged ozone
time series that are analyzed in this chapter (e.g, Sioris
et al., 2014). LOTUS (2018) shows that inclusion ofan unlagged ENSO proxy in MLR trend analyses ofvertically resolved datasets changes trends by 1–2%
decade−1 and reduces trend uncertainties by 1%
decade−1.

Global Ozone | Chapter 3

3.2.1.4	Effects of Stratospheric Aerosol Loading
Volcanic eruptions are a major source of sulfate aerosol in the stratosphere. In the absence of volcanic
eruptions, the background stratospheric aerosol layer
is attributed to sulfuric gas precursors such as carbonyl sulfide (OCS) and sulfur dioxide (SO2) that are
emitted at the surface and lofted into the stratosphereby deep convection. See Box 3-1 for a general description of the origin of stratospheric aerosols and theirimpacts on ozone through radiative processes and heterogeneous chemistry (e.g., Kremser et al., 2016 andreferences therein). Aerosol surface area has tended toundergo significant variations on decadal timescales,with major eruptions in the 1970s (Fuego), 1980s (ElChichón), and 1990s (Mount Pinatubo). There is thus
potential for significant aliasing between the solar
cycle and aerosol terms in MLR analysis (Solomon,
1996).
Long-term observational records of stratosphericaerosol are very important for the interpretation of
global temperature changes and ozone layer variability. Ground-based lidar observations provide stable,
high-quality measurements of stratospheric aerosol.
Satellite data are also a very important source of information because they provide the global distribution of aerosols, although the derived aerosol surface
area from satellite extinction measurements is rather
uncertain (Kremser et al., 2016). In situ stratosphericaerosol measurements from optical particle counters(OPCs) have been extensively used to validate satellite measurements from SAGE II and HALOE (e.g.,
SPARC, 2006). The discrepancies between aerosol
properties inferred from in situ and SAGE II measurements during volcanically quiescent periods havebeen reduced recently due to improvements in bothdata records (Thomason et al., 2008; Kovilakam and
Deshler, 2015).
One study presented a new combined data record
from continuous stratospheric aerosol lidar observations spanning 1994–2015 at the French HauteProvence Observatory (OHP; 44°N, 6°E) comparedwith satellite data from SAGE II, GOMOS, OSIRIS,CALIOP, and OMPS (Khaykin et al., 2017). Figure
3-6, modified from this study, shows the time seriesof monthly averaged stratospheric aerosol opticaldepth between 17- and 30-km altitude derived from
OHP lidars and satellite datasets. Remarkable agreement is found between all datasets despite the large

variety of measurement techniques. Merged datasets
such as the Global Space-based Stratospheric Aerosol
Climatology (described by Thomason et al., 2018),
provide input to the construction of stratosphericaerosol forcing datasets for chemistry–climate model
simulations. Gap-filling of the record after the 1991
Mount Pinatubo eruption, when the stratosphere was
too optically opaque for SAGE II measurements, hastypically been done with ground-based lidar data.
A new study finds that using CLAES measurementsfrom the UARS satellite instead of these ground-based
lidar measurements leads to less aerosol loading in
the tropical lower stratosphere and less ozone loss
following the eruption, in better agreement with observations (Revell et al., 2017).
As discussed in Box 3-1, enhanced aerosol levels following major volcanic eruptions cause ozone changes
via heterogeneous chemical processes on the particle
surfaces and dynamical effects related to the radiative
heating of the lower stratosphere (e.g., SPARC, 2006).
Ensemble sensitivity simulations using a coupledatmosphere–ocean chemistry–climate model have­been used to assess how these dynamical and chemical processes affect stratospheric ozone and NH polar
vortex dynamics (Muthers et al., 2015). The study
found that ozone is affected globally by a volcanic
eruption for several years. At current ODS levels, thedominant ozone response is depletion linked to heterogeneous chemistry involving halogen compounds,
with radiative and dynamical perturbations playing aless important role. However, a major volcanic eruption could directly inject volcanic HCl into the stratosphere, triggering substantial ozone loss even when
ODS levels are significantly lower than today (Klobas
et al., 2017).
As seen in Figure 3-6, there has been no volcanic
eruption with Volcanic Explosivity Index (VEI) >5
since Mount Pinatubo (VEI 6), but some small eruptions occurred in the last decade. Studies have shownthat smaller volcanic eruptions can inject aerosol into
the stratosphere (e.g.,Vernier et al., 2011). Therefore,
it is thought that these eruptions may have impacted the ozone column at mid-latitudes over the past
decade, as atmospheric chlorine levels have slowly
decreased. The Calbuco volcano, which erupted insouthern Chile on 22 April 2015, increased the stratospheric aerosol optical depth by a factor of 2, with an
e-folding time of 90 days (Bègue et al., 2017).
3.15

Chapter 3 | Global Ozone

Box 3-1. Origin of Stratospheric Aerosols at Mid-latitudes
C. Junge (Junge et al., 1961) discovered the presence of a layer of aqueous sulfuric acid aerosols in the stratosphere in the early 1960s. The composition of these aerosols is dominated by droplets of sulfuric acid/water(H2SO4–H2O) solution, with smaller amounts of meteoritic and non-sulfate materials. The main precursors
of sulfate aerosols are sulfur dioxide (SO2) and carbonyl sulfide (OCS), which are transported to the stratosphere through dynamical transport mechanisms occurring mainly in the tropics. Volcanic eruptions canalso directly inject SO2 into the stratosphere. SO2 and OCS are then oxidized to form H2SO4, which rapidly
nucleates to form condensation nuclei. These nuclei grow into larger aerosol particles through condensationand coagulation mechanisms.
The key processes relating to the origin of stratospheric aerosols (adapted from Kremser et al., 2016) are
given in Box 3-1 Figure 1:

Impact of Stratospheric Aerosols on Ozone
Stratospheric aerosols play a role on the stratospheric ozone budget through chemical, radiative, and dynamical processes:• Chemical processes○○ Nitrogen oxides (e.g., N2O5) are converted to HNO3 through heterogeneous chemical reaction

at the surface of the particles. This slows down NOX catalytic cycles and enhances ozone in the
middle stratosphere.

3.16

Global Ozone | Chapter 3

Box 3-1, continued.
○○ In the lower stratosphere, the removal of nitrogen oxides leads to increased production anddecreased loss of reactive chlorine via HOX and ClX cycles. This results in ozone loss in thepresence of ODSs.

• Radiative and dynamical processes○○ Enhancement of the stratospheric aerosol layer by volcanic eruptions increases atmospheric

optical depth in the solar shortwave radiation domain, inducing a cooling at Earth’s surface. At
the same time, volcanic aerosols increase the absorption of solar longwave radiation, inducing
a heating of the lower stratosphere.

○○ For volcanic eruptions occurring in the tropics, the warming of the tropical stratosphere en-hances the meridional temperature gradient, which perturbs the stratospheric circulation. The
enhanced upwelling linked to heating of the lower tropical stratosphere results in lower ozone
levels in the tropics and higher ozone at mid-latitudes. At polar latitudes, the strengtheningof the vortex due to the larger meridional gradient enhances polar ozone destruction underpresent-day ODS levels.

The Asian monsoon circulation has been highlighted recently as an important pathway for transportof aerosols into the stratosphere (e.g., Vernier et al.,2015, 2011). Both the Asian and North American
summer monsoon circulations are accompanied by
low temperatures in the lowermost stratosphere subtropics. Using a nudged chemistry–climate model,
one study showed that significant heterogeneous
chlorine activation on volcanic and non-volcanic particles could occur along the southeastern flank of themonsoon anticyclones (Solomon et al., 2016a). This
conversion of HCl into reactive chlorine led to small
modeled ozone decreases of 1.5–2.5% in the 16- to
18-km altitude range when averaged over 2009–2012
and 0°–30°N.

3.2.1.5	Other Dynamical Variations
The Brewer–Dobson circulation (BDC) is a residual
meridional circulation driven largely by the deposition of momentum by planetary-scale waves. Changes
in the BDC drive variations in ozone both throughtransport and chemistry. For example, variations intropical upwelling have been shown to have a statistically significant impact on ozone in the m­ id-latitude
lower stratosphere (Neu et al., 2014); Nedoluha etal. (2015) hypothesized that a significant decreasein tropical ozone from 2004 to 2013 seen near 10
hPa in measurements from MLS and other satellites(Kyrola et al., 2013; Gebhardt et al., 2014; Eckert etal., 2014), for which an increase in tropical upwelling
was deemed an insufficient explanation (Eckert etal., 2014), could have instead resulted from chemicalchanges associated with a decrease in upwelling overthe period (Aschmann et al., 2014). Using a 2-dimensional model, this study showed that such a decrease
in upwelling would increase the residence time of N2O
and therefore its conversion into NOy, which would inturn deplete ozone near the ozone maximum, where it
is very sensitive to NOy.
Because year-to-year variations in the BDC can havesuch an important influence on ozone variability (e.g.,
Fusco and Salby, 1999; Newman et al., 2001; Dhomse
et al., 2006), they are often taken into account in MLR
analyses. However, variability in ­middle-stratospherictropical upwelling associated with the QBO andENSO can be as large as 40% (Flury et al., 2013; Neu etal., 2014; Minschwaner et al., 2016), making it unclear
to what degree BDC proxies provide information independent of these terms in MLR analyses, particularly in the tropics. As discussed in Section 3.2.1, the
LOTUS study found that inclusion of an eddy heat flux
(EHF) proxy, which is a measure of the vertical groupvelocity of planetary-scale waves and is proportional
to the vertical component of the BDC, has a negligible impact on ozone profile trends computed fromzonal average ozone fields (LOTUS, 2018). However,
3.17

Chapter 3 | Global Ozone

sAOD1730

Aerosol Optical Depth @ 532 nm, 17–30 km, OHP 44°N and Satellites Zonal Mean 40°–50°N Latitude

SAGE IIGOMOSOSIRISCALIOP

OMPS

Figure 3-6. Time series of monthly mean stratospheric aerosol optical depth between 17- and 30-km altitude(sAOD1730 ) from OHP lidars and monthly and zonal mean sAOD1730 within 40–50°N from satellite sounders.
From Khaykin et al. (2017).

another study found that while the QBO dominates
total column ozone (TCO) variability in the tropics,
using a winter-mean EHF at 100 hPa as a proxy for the
BDC accounts for most of the variability in TCO from50 to 60° in both hemispheres, with an explained variance of up to 7 DU in the SBUV MOD v8.6 and GSG
datasets and a 15–35% larger signal in SBUV MOD
8.0 (Chehade et al. 2014; see Appendix 3A for a description of these datasets). While studies that utilize
a BDC proxy tend to focus on interannual changes
in ozone and on the lower-stratospheric circulation,
Ball et al. (2016) developed a new upper-branch
Brewer–Dobson circulation (UBDC) index based on
mid-latitude temperature variations near 5 hPa thatreflect rapid changes in the upper branch of the BDC
that occur on timescales of a month or less. They
found that this index explains more of the variability
in ozone at 2 hPa (up to 60%) than the QBO index
and reduces uncertainties on the estimated trend inupper-stratospheric equatorial ozone by up to 20%.
Other dynamical terms in MLR analyses include
tropopause pressure, which has been shown to be a
3.18

strong predictor of short-term variability in Fouriertransform infrared (FTIR) ground-based ozone
records (Vigouroux et al., 2015), and the Arctic
Oscillation (AO), North Atlantic Oscillation (NAO),
and Antarctic Oscillation (AAO) indices. The AO andNAO are essentially different ways of describing NH
high-latitude pressure gradients, which influence thezonality of the jet stream. The AAO is the SH counterpart of the AO (e.g., Weiss et al., 2001; Frossard et al.,2013; Rieder et al., 2013; and references therein). The
NAO/AO and AAO contributions to zonally averagedozone variations are generally small (Chehade et al.,2014; Wespes et al., 2016; LOTUS, 2018), but theseoscillations explain much of the variability in ozone
at individual ground stations (Petropavlovskikh et al.,2015; Vigouroux et al., 2015). This is likely due to thefact that there are large regions of both positive andnegative coefficients for the NAO north of 40°N andfor the AAO south of 50°S that are associated with theshift in the jet stream between positive and negative
phases (Rieder et al., 2013; Frossard et al., 2013).

Global Ozone | Chapter 3

3.2.1.6	Attributing Variability in	Regression ­Analysis
In addition to there being covariances between thevarious proxies describing natural variability, theseproxies are not fully orthogonal to the trend term andthereby influence trend estimates and their sensitivity. This long-recognized issue has been the subjectof continued efforts in recent years to quantify trend
sensitivity to the combination and description ofnatural proxies (de Laat et al., 2015; LOTUS, 2018).
Figure 3-7 shows an example of an ozone time series,
the proxies for natural variability used in the LOTUS
analysis (scaled by their regression coefficients), andthe ozone fit residuals resulting from subtraction of
those proxy terms.
As discussed in Section 3.2.1.5, inclusion of the AO,
AAO, NAO, or EHF proxies has a negligible impact on
trends for most zonally averaged satellite profile datasets (LOTUS, 2018). Trend uncertainties are slightly
affected by these terms but not to the extent that insignificant trends become significant, or vice versa. The
use of solar, QBO, and ENSO proxies is well established. Their omission results in 1–2% decade−1 changes in piecewise linear profile trends and a decrease in
overall significance levels (LOTUS, 2018). The impactof the solar cycle term on upper-stratospheric trends
diminishes to 0.5% decade−1 for regression analyses ofdata extending past 2014. Furthermore, the choice of
solar proxy is found to be not particularly important
for time series of this length (LOTUS, 2018). Usinga lag for the ENSO term (see Section 3.2.1.3) mainly affects trend uncertainties, not the trends, but noconsistent picture emerges regarding the magnitudeof the impact or the parts of the atmosphere for whicha lag is important. Adding a third QBO EOF into theregression has negligible impacts on the trend anduncertainty results (LOTUS, 2018).
Including an aerosol proxy primarily affects trend
results in the lower stratosphere. Some aerosol dependence is seen across datasets in the middle toupper stratosphere, and coherence of this dependence
across datasets adds confidence that it is likely real
(LOTUS, 2018). The proxy terms for El Chichón andMount Pinatubo scale differently, and it is often necessary to separate them and use different time lagsfor each term. It is quite important to accurately represent the Pinatubo event, because it tends to have alarge impact on the trend term, especially when using

piecewise linear trends. In recent years, there havebeen numerous small volcanic eruptions (Solomon
et al., 2016b; Section 3.2.1.4), and the aerosol proxytime series have not yet been extended to cover these
events. A pragmatic approach used in LOTUS is torepeat the last month of the aerosol proxy time series (September 2012) to extend the record (LOTUS,
2018). This choice has a negligible effect on trend
results since the aerosol regression term is primarily constrained by the period immediately following
the Pinatubo eruption rather than by aerosol loading
during the last five years (Figure 3-7).

3.2.2 Trend Models
The proxies discussed in Section 3.2.1 describe periodic or transient variations in ozone in Equation (1),with the longer-term evolution characterized by the
Trend term in Equation (1). Trends are often modeled
as two linear terms that are either connected (piecewise linear trend, PLT) or disconnected (independent
linear trend, ILT). Alernatively, an additional proxyfunction (e.g., equivalent effective stratospheric chlorine, EESC) can be used to attribute long-term changes in ozone to a particular process, such as changes in
ODSs.
Both PLT and ILT trend estimates are sensitive tothe start- and endpoints of the time series, but thissensitivity decreases as the length of the time series
increases. For stratospheric ozone trends, the advantages of ILT over PLT are that outliers in the mid
1990s affect only one trend, not both, and that nolinear model is forced during the turnaround period,
when the time series behaves nonlinearly. The inflection time in the PLT model is generally fixed to ~1997(Harris et al., 2008; Kyrölä et al., 2013; Chehade etal., 2014; Damadeo et al., 2014), coinciding with theturnaround in ODS concentrations (see Chapter 1).
Changing this inflection point impacts PLT trends
in the upper stratosphere; for datasets that end in
2016, PLT trends systematically increase by up to
0.3% ­decade−1 (at mid-latitudes) for every forward
shift in inflection time of one year. Changing the startyear for recovery in the ILT analyses from 1997 to2000 leads to a change in the trends of up to 1–1.5%­decade−1 (Figure 3-8; LOTUS, 2018). Hence, trends
calculated over different time periods are not directlycomparable, and the level of disagreement depends
on the trend model and the dataset (LOTUS, 2018).
3.19

Chapter 3 | Global Ozone

SAGE-CCI-OMPS v1 (35°–60°N, 42 km)
+10
+6
+2
0
-2
-6
-10

Satellite observations
Regressed natural variations + ILT trends
Regressed ILT trends
Regressed natural variations
1979

1982

1985

1988

1991

1994

2000

1997

2003

2006

2009

2012

2015

2018

Ozone anomaly (%)

+10
+6
+2
0
-2
-6
-10

Residuals: Observations –(regressed natural variations + ILT trends)
1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

2012

2015

2018

1991

1994

1997

2000

2003

2006

2009

2012

2015

2018

+2
0
-2
+2
0
-2

Solar cycle
QBO

-2
0
-2

ENSO

+2
0
-2

Stratospheric aerosol
1979

1982

1985

1988

Year
Figure 3-7. Terms in the regression of monthly deseasonalized ozone anomalies from SAGE-CCI-OMPS at
42 km in the 35°–60°N zonal band. The top panel shows observed monthly mean anomalies (in %; gray line)
relative to the annual cycle of ozone. The black line is the result of the regression model including the independent linear trends (ILTs; thick blue lines). The light blue line shows the sum of the terms of the regression
model without the ILTs included. The middle panel shows the residual in the observed ozone when the longterm trend and regressed natural variations are subtracted. The bottom panel shows the relative contributions of (from top to bottom) the solar flux, QBO, ENSO, and aerosols to the reconstructed time series (light
blue line) in the top panel. The same vertical scale is used for all time series. Dashed lines fall outside theperiod used by the MLR. Observations and regression model are those used by LOTUS (2018).

This issue should not be overlooked when comparing
trends and their significance from different analyses.

3.20

Long-term changes in ozone can also be represented
as a nonlinear process; e.g., proportional to a measureof total stratospheric halogen loading such as an EESC
proxy. EESC is calculated from emission rates of chlorofluorocarbons and related halogenated compounds,
given their individual Ozone Depletion Potentials(ODPs) and certain assumptions regarding transportGlobal Ozone | Chapter 3

times into the stratosphere. EESC-derived trends areprimarily used for attribution studies. They are notwell suited to detection of recent trends because themodel uses a single fit coefficient, which is primarily
constrained by the early period of large EESC increases and ozone depletion rates. The small ozone changes
observed in recent years are poorly described by themodest post-turnaround decline in EESC (Damadeo
et al., and 2014; Frith et al., 2017). In fact, the strong­anti-correlation between EESC and ozone in the early
period can force an erroneous, statistically significant

positive trend in the latter period, even through synthetic time series in which EESC does not decrease(Kuttippurath et al., 2015). Models with two orthogonal EESC terms avoid such trend bias by effectively
leaving the turnaround time and ­ozone depletion/recovery rate as free parameters (Damadeo et al., 2014,2018), but this renders attribution of trends to changes in ODSs less straightforward. Adaptive techniques,
such as dynamic linear regression models (DLMs;
Laine et al., 2014, and Ball et al., 2017) and ensemble empirical mode decomposition (EEMD) methods

GOZCARDS v2.20 (40°N–50°N)

SBUV MOD v8.6 (40°N–45°N)

SBUV MOD v8.6 (40°N–45°N)

Trend (% decade-1)

Trend (% decade-1)

Pressure (hPa)

Pressure (hPa)GOZCARDS v2.20 (40°N–50°N)

Figure 3-8. Sensitivity of ozone profile trend estimates to the modeling of the trend in the regression­(independent, piecewise, or EESC) and its starting point. Results are shown for GOZCARDS v2.20 (top row)
and SBUV MOD v8.6 (bottom row) at mid-northern latitudes for the past two decades. Trends in the upper
stratosphere vary by 1–2 % decade−1 depending on the trend model and regression period. Adapted from
LOTUS (2018).
3.21

Chapter 3 | Global Ozone(Bai et al., 2017), do not rely on a priori information
for the long-term behavior of ozone. Trends are allowed to evolve over time, more accurately reflecting
observed changes, especially when compared to single
EESC models (Bai et al., 2017). The diagnosed difference of several percent per decade between PLT andDLM trends is not significant. However, uncertainties
of DLM trends are generally larger, such that they are
more likely to be insignificant than PLT trends. Tests
on synthetic data hint that the DLM method provides
more accurate errors (Ball et al., 2017).

3.3.1.1	Interannual Variations

Artifacts in the time series—such as sudden changes in the mean value or noise level, or more gradualchanges due to drifting satellite orbits or instrument
performance (e.g., Weatherhead et al., 2017)—areusually not modeled in a regression. Some artifacts
are large and contribute considerably to the random
component of the trend uncertainty budget, whileothers contribute to the systematic error. Accurate
estimates of the latter remain challenging for a number of reasons, despite considerable progress in recent
years, particularly in analysis of data from the middle
and upper stratosphere (Section 3.1.4).

Polar ozone depletion contributes to these negative
anomalies as ozone-depleted polar air moves into
lower latitudes. Strong negative anomalies at SH
high latitudes in late 2015 to early 2016 reflect a large
Antarctic ozone hole, whose extent was close to the
all-time record. Record low temperatures likewise
occurred in the Arctic stratosphere in late 2015 toearly 2016 (see Chapter 4). In the Arctic, however,
one study estimates that polar ozone depletion is responsible for only about one-third of the variability in
NH mid-latitude ozone in spring, with dynamicallydriven differences in ozone transport between warmand cold winters responsible for the other two-thirds(Strahan et al., 2016).

Random errors derived from regression residuals
are typically in the range of 0.5–1% decade−1 (1σ).
Discrepancies in trends for individual merged data
records are estimated to range from 1.1 to 3.2%
­decade−1 (1σ). Uncertainties in the regression analysis
contribute 0.5–1% decade−1, and uncertainty in thelong-term stability of satellite profile data records iscurrently thought to be about 1–3% decade−1 (LOTUS,
2018). Averaging different trend results reduces someof the biases, assuming they are uncorrelated between
the data analyses, but the assumptions going into the
combination of trends derived from individual data
records can play a considerable role in quantifying
uncertainties in long-term changes (Harris et al.,2015; Steinbrecht et al., 2017; and LOTUS, 2018).
This is further discussed in Section 3.3.2.

3.3 PAST OZONE IN OBSERVATIONS
3.3.1 Changes in Total Column Ozone
The time series for (near) global mean, mid-latitude,
and tropical total column ozone are shown in Figure
3-9 (Weber et al., 2018).
3.22

As discussed in Section 3.2.1, a large fraction of thetotal ozone variability is related to natural processes.
The impact of these processes on the ozone layer depends on season and latitude. To illustrate this, Figure
3-10 shows a time–latitude cross section of total ozone
deviations from the 1964–1980 annual cycle, clearly showing a large degree of interannual variability.
However, the ozone deviations in the extratropics aremostly negative (relative to pre-1980 levels) after the
mid-1980s, indicating an overall decrease in ozone.

The 2015–2016 boreal winter was also characterized
by an unprecedented disruption in the downward
propagation of the QBO westerly phase (Newman etal., 2016; see Section 3.2.1.2). The associated decreasein tropical upwelling led to a positive perturbation intropical total ozone, while weaker extratropical downwelling decreased extratropical total ozone from April
to September 2016 (Tweedy et al., 2017; and Weber etal., 2017).

3.3.1.2	Total Ozone Trends
The total ozone trend estimates presented below are
based on the ILT approach (Weber et al., 2018). The
regression model (see Section 3.2.1) also includes
seasonal, QBO, solar, and ENSO terms using theproxy data sources described in Table 3-1.
The five bias-corrected merged time series (Appendix
3A) for the tropical (20°S–20°N) and mid-latitude(35–60°S and N) zonal bands, along with ILT trend fits
to the time series, are shown in Figure 3-11. Prior to
1997, total ozone trends over the mid-latitudes of bothGlobal Ozone | Chapter 3

Near Global (60oS–60oN)

295
290
285

Annual mean total column ozone (DU)280

NH (35o N–60o N)

350

Figure 3-9. Time series of annual mean
total ozone in four zonal bands. Data
are from WOUDC ground-based measurements combining Brewer, Dobson,
SAOZ, and filter spectrometer data (redlines; ­Fioletov et al. 2002); the BUV-SBUVSBUV/2 v8.6 merged products fromNASA (MOD v8.6; dark blue lines; Chiou
et al., 2014, Frith et al., 2014), and NOAA
(light blue lines; Wild et al., 2016); theGOME-SCIAMACHY-GOME-2 (GSG) product from the University of Bremen (darkgreen lines; Weber et al., 2011) and GTO
product from ESA/DLR (light green lines;
Coldewey-­
Egbers et al., 2015). WOUDC
values for 2016 are preliminary because
not all ground station data were available.
From Weber et al. (2018).

340
330
320

Tropics (20o S–20o N)

270
265
260
255
330

SH (35o–60° S)

320
310
300

WOUDC
SBUV v8.6 NASASBUV v8.6 NOAAGOME/SCIA GSG
GOME/SCIA/OMI GTO

1970

1980

1990

2000

2010

Year

hemispheres were about −3% ± 1.5% (2σ) decade−1.
The 35°–60°S and 35°–60°N trends for 1997–2016 areabout +0.6% decade−1 and +0.2% decade−1, respectively, based on the average of trend estimates for fivedatasets. The total column ozone trends are not statistically significant except for the GSG and GTO datasets (Appendix 3A) in the SH, which show a +0.7%± 0.7% decade−1 increase that just reaches the 2σ
uncertainty level. Given the large (~5%) year-to-year
variability in mid-latitude total column ozone, the observed trends for 1997–2016 are consistent with the
expected trends from EESC changes, which are about+1% decade−1. Note that the two independent linear
fits are almost joined together in the SH but not in the
NH. The tropical belt trends are nearly zero.
Total ozone trends for the same five datasets areshown as a function of latitude for 5-degree latitude
bins in Figure 3-12. The latitudinal dependence ofthe pre-1997 decrease was discussed in previous
Assessments. The trend is nearly zero at the equatorand becomes negative and statistically significant
toward the poles. The trends over middle and highlatitudes are about −3 to −6% decade−1, or about−5.5 to −11% over the entire 1979–1996 period. The
trends over the 1997–2016 period, on the other hand,
do not show any clear latitudinal dependence. They
are close to zero over the equator and NH middle
and high latitudes (35°–60°N). Positive values of up
to 0.8% decade−1 are seen over the 30°–60°S latitude
band, though they are not statistically significant. The
trend at 20°N is positive and statistically significant
in some datasets. An increase at that latitude can also
been seen in Figure 3-10, where ozone values in the
mid-1990s are noticeably lower than in recent years.
As discussed in Section 3.2.2, a common approach toestimating total ozone trends is to fit the ozone time
3.23

Chapter 3 | Global Ozone

Total Ozone Deviations (%), All Data
-20

-7.0

-5.0

-3.0

-1.5

0

1.5

1989

1994

3.0

5.0

7.0

2004

2009

20

60°

Latitude

30°

0°

−30°

−60°
1964

1969

1974

1979

1984

1999

2014

2019

Year
Figure 3-10. Latitude–time cross section of deviations (%) of total ozone from the long-term (1964–1980) mean,
estimated from the five datasets shown in Figure 3-9. Measurements from 1964–1970 are from the WOUDC
ground-based dataset. Zonal averages were calculated for each dataset for each month of the year using data
averaged over the years 1997-2016, when data from all five data sets are available. These monthly zonal averages for each dataset were subtracted from the original data, yielding monthly deviations (in percent) for theentirety of each data record. The deviations then were averaged over all of the datasets for each zonal band
to form a single set of monthly zonal deviations. Finally, baseline monthly mean, zonal mean deviations were
calculated from the 1964-1980 average, and differences from these baseline deviations were plotted.

series using EESC. This approach works particularly
well for ozone data integrated over the globe (with orwithout the polar regions), because dynamical fluctuations are largely reduced by the global integration.
Figure 3-13 shows global ozone with seasonal, QBO,
solar, and volcanic effects removed and the best fit for
the EESC curve overlaid, providing a picture of theextent of “unexplained” ozone variability (updated
from WMO, 2011). While this method does not estimate the rates of ozone change prior to and after theturning point separately, it is useful as an illustration
because it indicates the overall agreement of ozone
and ODS changes.
One study used a different technique, called a fourstep adaptive ozone trend estimation scheme, to isolate the long-term zonal ozone variability related toanthropogenic forcing (Bai et al, 2017). The technique
3.24

does not require any a priori assumptions about theshape of the ozone trend. With this technique, theturning point of the ozone change was determined tooccur in the year 2000; i.e., later than the maximumof stratospheric ODSs and also later than the turning point typically used in ILT analyses. Otherwise,
the study’s conclusions were similar to those fromconventional methods. It finds that the rate of ozone
change is positive after 2000 but that the record is still
too short to identify a significant trend.
The datasets discussed above are based on measurements in the UV or visible parts of the spectrum.
Fourier transform infrared (FTIR) measurements ofthe solar spectrum can also provide total ozone columns. The records of ground-based FTIR measurements are now long enough to examine the trends.
Data from eight FTIR sites with records starting from

Global Ozone | Chapter 3

NH (35o–60°N)

350

Annual mean total column ozone (DU)

340
330
320
Tropics (20°S–20°N)

270
265
260
255
330

SH (35o–60oS)

320
310
300
1970

WOUDC
SBUV v8.6 NASASBUV v8.6 NOAAGOME/SCIA GSG
GOME/SCIA/OMI GTO

1900

1990

Year

2000

2010

Figure 3-11. Annual mean total ozone timeseries of five bias-corrected, merged datasets in the 35°–60°S, 35°–60°N and 20°S–20°N­l­­atitude bands. The thick orange lines are theresult from applying a multiple linear regression (MLR) model. The solid black lines indicate the linear trends before and after the ODS
peak in 1997, respectively. The shaded areas
show the 2σ uncertainty of the MLR trend­­estimates. Adapted from Weber et al. (2018).

Trend (% decade−1)

Figure 3-12. Total ozone linear
Total Ozone Trend
trend in % decade−1 as a function of latitude for 1979–1996
1997–2016(bluish colors) and 1997–2016
2(reddish colors) estimated forNASA MOD, NOAA SBUV, GTO,
0
GSC, and WOUDC datasets.
The thick blue line with blue
-2
2σ error bars and the thick red
1979–1996
line with red 2σ error bars rep-4
resent a weighted mean trend
from the five (or three before
WOUDC, NOAA, NASA
-6WOUDC, NOAA, NASA, GTO, GSG
1996) datasets for 1979–1996
ensemble average 1979–1996
and 1997–2016, respectively.
-8
ensemble average 1997–2016
The weights are the inversesof the trend uncertainties-10
from the individual datasets.
-60
-40
-20
0
20
40
60
Trends estimated using theLatitudeindividual datasets are shownby the thin lines as indicated. The 1979–1996 trends are not shown, as the GSG and GTO are available only
after 1995. The uncertainty of the mean trend is the weighted standard deviation resulting from the averaging. However, the uncertainties are sometimes very small, as all datasets agree at some latitudes very well.
Therefore, the larger value of the weighted standard deviation and the mean of the individual uncertainties
is plotted (in most cases, it is the latter). Adapted from Weber et al. (2018).
3.25

Chapter 3 | Global Ozone

Ozone Change (DU)

5
0
-5
-10
-15
1964

1972

1980

1988

1996

2004

2012

2020

Figure 3-13. Deseasonalized, areaweighted total ozone deviations for
60°S–60°N from the WOUDC dataset,adjusted for seasonal, solar, volcanic,
and QBO effects (black line). The orange
line represents the component of ozone­variability due to changes in the equivalent effective stratospheric chlorine(EESC) based on a fit to data from 1964–
2016. The EESC curve for the m­ id-latitude
lower stratosphere is used for this plot.

Year

3.26

around 1995 have been found to be consistent with
UV/VIS measurements (Vigouroux et al., 2015). The
total ozone trends over middle and low latitudes are
small and not significant for all stations except for
Wollongong, located at 34°S, where the 1996–2012
trend was 1.9% ± 1.1% decade−1.

(Section 3.2.1.3) may have contributed to these large
deviations. However, it is difficult to quantify theseeffects. Unlike in the Northern Hemisphere, summertime ozone values in the Southern Hemisphere areaffected by the ozone hole and dilution of polar ozone
to mid-latitudes (e.g., Fioletov and Shepherd, 2003).

Total ozone changes during the solstice seasons for
35°–60°S and 35°–60°N for the period 1970–2016
are shown in Figure 3-14. The ozone variability over
mid-latitudes is highest during winter and is much
lower during summer, though the winter and summer deviations are highly correlated. The long-termozone decline in the Northern Hemisphere shows aseasonal dependence, with the average winter andspring 1997–2016 deviations from pre-1980 levels of 3–4% and summer and autumn deviations of~2–2.5%. In contrast, there is no seasonal differencein the ozone decline in the Southern Hemisphere. The
average 1997–2016 values are about 6% lower thanthe pre-1980 level for all seasons. In the Northern
Hemisphere, large winter–spring ozone deviations
such as those seen in 1993 and 1995 (points a andb in Figure 3-14) which were related to the Mount
Pinatubo eruption, have not occurred recently.
Summer deviations in 2011 and 2016 (points c and din Figure 3-14) however, are among the largest in therecord and comparable to those that occurred in 1993
and 1995. The large Arctic ozone depletion eventsin 2011 and 2016, as well as the QBO disruption in2016 (Section 3.2.1.2) and the large 2015 ENSO event

Long-term total ozone variations and trends are generally determined from zonal or global averages, butzonal asymmetries do exist. One study investigated the
ENSO impact on the detectability of regional trends
in total ozone (Coldewey-Egbers et al., 2014). Both it
and another study (Knibbe et al., 2014) that used a
different dataset (van der A et al., 2010, 2015) foundthat the effect of ENSO on total ozone is primarily
seen over the Pacific. The contributions from mostother factors showed little longitudinal dependence.
In summary, the main results of this Assessment
related to total column ozone are similar to thosefrom the 2014 Assessment (WMO, 2014): column
ozone remains below pre-1980 levels by 2–3% over
NH mid-latitudes (35°–60°N) and by 5–6% over SH
mid-latitudes (35°–60°S), with no major changes over
the tropical region (20°S–20°N). Despite the fact thatit has been nearly 20 years since the ODS turning pointin 1997, we still do not see a statistically significant
positive total ozone trend over the NH mid-latitudes.
The trend is only +0.2% decade−1, which is consistent
with expectations given the slow rate of ODS decline
and large interannual variability. The trend over theGlobal Ozone | Chapter 3

35°–60°N
Dec–Jan–Feb

Jun–Jul–Augc dDeviations (%)

ab1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Year

Year

35°–60°S

Dec–Jan–Feb

Deviations (%)

Jun–Jul–Aug

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Year

WOUDC

NASA SBUV

GSG

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Year

GTO

NOAA SBUV

Figure 3-14. Seasonal, area-weighted total ozone deviations from the 1964–1980 means, calculated for four
seasonal averages for the latitude bands 35°–60°N (top row) and 35°–60°S (bottom row). The years on thehorizontal scale correspond to the first month of the season. The arrows indicate large NH ozone deviations
that occurred in the winter–spring of 1993 (a) and 1995 (b), as well as in the summer of 2011 (c) and 2016 (d)that are discussed in the text. Updated from WMO (2007).

SH mid-latitudes is about +0.7% decade−1, which isjust above the 2σ uncertainty level.

3.3.2 Trends in Ozone Profiles
3.3.2.1	Time Series
Figures 3-15 and 3-16 show time series of annual
mean ozone concentration anomalies at four pressurelevels from 70 to 2 hPa from various ground-based
and satellite-based measurements records (Appendix
3A), averaged over four latitude bands (LOTUS,

2018). Chemistry–climate model simulations from
CCMI-1 are also shown. Ozone anomalies are relative to the 1998 to 2008 climatology of each individual
dataset. Figures 3-15 and 3-16 show overall consistency among observations and between observations
and models at the various pressure levels and latitude
bands. The relatively narrow model ranges, as compared to the interannual variability of the observations, can be explained to some extent by the lack ofvolcanic perturbations in some of the CCMI-1 simulations, as well as by a 3-year smoothing applied to themodel output. The larger variability of ground-based
3.27

Chapter 3 | Global Ozone

observations relative to the satellite measurements is
expected, given the small number of measurement
stations providing long-term ozone measurements.
As in the last Assessment, all datasets show an ozone
decline up to the late 1990s, with a leveling off sincethen. At 2 hPa, most records indicate a slight increaseof ozone over the 2000–2016 period that is most pronounced in the Northern Hemisphere. In the CCMI-1

simulations, the ozone decline before 2000 is linked toincreasing ODS levels. After 2000, the ozone increase
due to ODS decline is enhanced by upper-stratospheric cooling associated with increases in GHGs (e.g.,
Randel et al., 2016) (Section 3.3.3). At pressures greater than 10 hPa, no increase in ozone is seen in the observational records, in broad agreement with the rangeof model simulations at these pressure levels.

Figure 3-15. Evolution of annual mean deseasonalized ozone anomalies at the 2-hPa/42-km (left column)
and 10-hPa/31-km (right column) levels. Satellite data and measurements from ground-based stations areaveraged over four different latitude bands. Gray shadings correspond to the 10th–90th percentiles of the
CCMI-1 model results. The model mean and median are also plotted, together with the ±2σ range of themodels (gray lines). All anomalies are calculated relative to the base period 1998–2008; CCMI-1 model data
are shown as 3-year weighted running means with a double weight for the central year (i.e., 1-2-1 weighting).
Adapted from LOTUS (2018).
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Ozone Anomalies 2 hPa/42 km

20
15
5

Ozone anomaly (%)

60°S–60°N

-5

20

5

10
5

0

0

35°–60°N

-5

20

5

10
5

0

0

20°S–20°N

-5

20

5

10
5

0

60°–35°S-560°–35°S

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Year

Year

SBUV MOD v8.6
SBUV COH v8.6
SWOOSH v2.6
GOZCARDS v2.20

3.28

20°S–20°N
20°S–20°N

10

15

-5

35°–60°N

10

15

0

60°S–60°N

10

15

-5

Ozone Anomalies 10 hPa/31 km

0

0

-5

10
5

10

-5

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

SAGE-CCI-OMPS
SAGE-OSIRIS-OMPS
SAGE-MIPAS-OMPS

Ozonesonde
Lidar
Microwave
UmkehrFTIRCCMI mean
CCMI median
CCMI mean ±2σ
CCMI perc10/perc90

Global Ozone | Chapter 3

3.3.2.2	Ozone Trends 2000–2016
The last Assessment examined ozone profile trends
for both the period of ODS increases, up to about
1997, and after the turnaround in ODS concentrations. For the first part of the record, it confirmed thelarge negative trends of about −6 to −8% decade−1 inthe upper stratosphere (around 2 hPa or 35–45 km altitude) that had been found in previous Assessments.
For the period after the ODS peak, it reported a significant increase in ozone of 2.5–5% at around the same
altitudes in the mid-latitudes and in the tropics. Thesefindings were in agreement with CCMVal-2 model

simulations, which attributed about half of this upper-stratospheric ozone increase to declining ODSs.
The other half was attributed to increasing GHGs,
which cool the upper stratosphere and reduce catalytic ozone loss rates.
Shortly after the release of the 2014 Assessment,
several papers that reassessed ozone trends were
published as part of the SPARC/IO3C/IGACO-O3/
NDACC (SI2N) initiative. The most prominent study
re-evaluated long-term ozone profile trends fromground-based, single-satellite and merged satellite
ozone records over the period 1979–2012 (Harris
et al., 2015). Trends obtained before the ODS peakFigure 3-16. Same as Figure 3-15 but for levels 20 hPa/26 km (left) and 70 hPa/19 km (right). Adapted from
LOTUS (2018).
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Ozone Anomalies 20 hPa/26 km

10

-10

60°S–60°N

10

20

5

10

-10

35°–60°N

10

20

5

10

-10

20°S–20°N

10

20

5

10

20°S–20°N

00-5

35°–60°N

00-5

60°S–60°N

00-5

Ozone Anomalies 70 hPa/19 km

00

Ozone anomaly (%)

20
10

5

-5

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

-10

60°–35°S

60°–35°S

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Year

Year

SBUV MOD v8.6
SBUV COH v8.6
SWOOSH v2.6
GOZCARDS v2.20

SAGE-CCI-OMPS
SAGE-OSIRIS-OMPS
SAGE-MIPAS-OMPS

Ozonesonde
Lidar
Microwave
UmkehrFTIRCCMI mean
CCMI median
CCMI mean ± 2σ
CCMI perc10/perc90

3.29

Chapter 3 | Global Ozone

were found to be similar to those reported previously, including in the 2014 Assessment. For the period
after the peak in ODS concentrations, positive trendsof ~2% decade−1 in mid-latitudes and ~3% decade−1
in the tropics were found in the upper stratosphere.
However, several methods were used to investigate thesignificance of the increasing trends and in all cases
they were found to be insignificant throughout thestratosphere. One method, similar to that used in theprevious Assessment, estimated the uncertainty of average trends from the weighted mean of the individual variances but added an extra term for measurement
drift, which was found to be ±3–5% decade−1 in the
20–40 km altitude range and larger elsewhere (Hubert
et al., 2016). A second method used the joint distribution of the individual variances around the arithmetic
mean of the estimators e.g., (SPARC 2013). The main
conclusion of the Harris et al. (2015) study was thatthe analyses included in the 2014 Assessment had underestimated the uncertainties in the combined ozone
records and that, given these uncertainties, it was too
early to detect a significant trend given the length of
the “recovery” period (1997–2012).
The differences in the conclusions regarding the significance of increasing ozone trends between the 2014
Assessment and Harris et al. (2015) were revisitedunder the LOTUS initiative (LOTUS, 2018). In parallel, one study using both newly released merged satellite records and ground-based datasets (Steinbrecht
et al., 2017) and another using just satellite records(Sofieva et al., 2017) re-evaluated ozone profile trends
using multi-linear regression analyses. Both studies
found significant ozone increases in the upper stratosphere of about 2–2.5% decade−1 in the mid-latitudes
of both hemispheres. Other recent analyses using
ground-based datasets have also reported significant
positive trends of ozone in the upper stratosphere(e.g., Moreira et al., 2015; Nair et al., 2015; Vigouroux
et al., 2015).
However, at least in the case of SBUV long-term observations, inclusion of uncertainties in the calibrationand drift of individual instruments in ozone profile
merged datasets can result in relatively large errors inozone trend results. When Monte Carlo simulations
are used to estimate uncertainties in ozone trends
based on the SBUV MOD merged dataset over the period 2000–2015, the significant ozone trends derived
over the 1.6–1.0 hPa range from standard multi-linear
3.30

regression analyses become insignificant at the 2σ
level (Frith et al., 2017).
Altitude–latitude cross sections of ozone trends derived from the merged satellite ozone datsets considered in the LOTUS initiative are shown in Figures
3-17 and 3-18 for the periods 1985–1997 and 2000–
2016, respectively. Trends were determined using the
ILT regression model (Section 3.2.2). In Figure 3-17,
the negative trends obtained in the upper stratosphere(e.g., above 35 km) are consistent in magnitude withprevious studies. These trends range from −4 to −9%
decade−1, depending on the satellite record, with larger
negative trends in the Southern Hemisphere in somecases. For the 2000–2016 period (Figure 3-18), mostrecords show significant positive ozone trends in theupper stratosphere, consistent with the recent studies discussed above. These positive trends are largest
in the mid-latitudes of both hemispheres and range
from 2 to 4% decade−1. Both SBUV merged records
also show significant positive trends in the tropicalupper stratosphere, although the significance is likely
overestimated due to omission of the merging uncertainies of the SBUV datasets (Section 3.1.4; Frith etal., 2017). Some significant negative trends are foundin the tropical middle to lower stratosphere; these areseen primarily in the SBUV and SWOOSH records(LOTUS, 2018). While merging uncertainties were
not available for all datasets and thus not explicitly
considered within LOTUS, they are to some extent
taken into account in that the final trend uncertainty(Section 3.3.2.3) was derived from the trend ensemble using the noise in the regression residuals and the
spread in the derived trends.

3.3.2.3	Trend Profiles
Figure 3-19 shows individual trend profiles derived
from the various data records used in LOTUS (2018)
in four broad latitude bands. The pre-1997 trends arevery similar across the datasets, within the uncertainties of the measurements; the exceptions are both
SBUV merged records, which show large differencesfrom the other datasets in some regions due to larger merging uncertainty in the mid 1990s (LOTUS,
2018). In the post-2000 period, all individual merged
records show significant trends, ranging from 2 to 5%
decade−1, in the 5–2 hPa pressure range for 35°–60°N.
Smaller positive trends are found in the other latitude
bands, and some trend values are not significant,Global Ozone | Chapter 3

Pre-1997 ILT Trends
SBUV MOD v8.6

1

10

20

50

1

20°
20 40°
40 60°
60

SWOOSH v2.6

5

5

20

20
50
20°
20 40°
40 60°
60

Latitude20°
20 40°
40 60°
60

SAGE-OSIRIS-OMPS

0

40
35

-5

30
25

50

-60°
-60 -40°-40 -20°-20 0°
015-60°-60 -40°-40 -20°-20 0°
0

45

10

10

5

30

50

2

Pressure (hPa)

2

35

20

-60°
-60 -40°-40 -20°-20 0°
0

SBUV COH v8.6

40

% decade−1

20°
20 40°
40 60°
60

10

25

50

-60°
-60 -40°-40 -20°-20 0°
0

Pressure (hPa)

5

10

20

SAGE-CCI-OMPS

45

Altitude (km)

5

1

50

2

Pressure (hPa)

Pressure (hPa)2

GOZCARDS v2.20

Altitude (km)

1

20

-60°
-60 -40°-40 -20°-20 0°
020°
20 40°
40 60°
60

Latitude15-60°-60 -40°-40 -20°-20 0°
0-10
20°
20 40°
40 60°
60

LatitudeFigure 3-17. Ozone trends for the period 1985–1997 estimated from six satellite data records using the independent linear trend (ILT) model. Gray stippling denotes results that are not significant at the 2σ level. Data
are presented on the latitudinal grid and vertical coordinates associated with each dataset. Adapted from
LOTUS (2018).

especially in the tropics. In the middle and lower
stratosphere, trends are generally not significantly different from zero. This includes the NH mid-latitudes,
where most records show negative but not significant
trends below 70 hPa.
To facilitate comparison with model simulations, it isuseful to calculate an average ozone trend profile fromthe individual trends derived from each observational
record. However, the use of different averaging techniques and different approaches to the calculation of
average trend uncertainties can result in contradicting statements regarding the significance of ozone
recovery in the stratosphere (Figure 3-20). Various

techniques have been used to merge the uncertainties
in the past (e.g., Harris et al., 2015; Steinbrecht et al.,2017); LOTUS (2018) introduced a new, more statistically robust method. This method includes both
simple error propagation, which captures uncertainties introduced by the data and the analyses, and thestandard error of the mean, which captures systematic uncertainties such as those introduced by drifts
between datasets. Another important parameter that
impacts the uncertainty of combined trends is the estimated number of independent datasets. In LOTUS(2018), this number is determined from the correlation of the fit residuals from the trend simulations; it
3.31

Chapter 3 | Global Ozone

Post-2000 ILT Trends
SBUV MOD v8.6

1

10
20
50

SBUV COH v8.6

10
20Pressure (hPa)

10
20
50

20° 40° 60°

SWOOSH v2.6

20° 40° 60°

Latitude530

15-60° -40° -20° 0°50

20° 40° 60°

SAGE-OSIRIS-OMPS

0

45
40

5
10
20
50

-60° -40° -20° 0°

35

20

2

5

40

25

-60° -40° -20° 0°
1

10% decade−1

20° 40° 60°

2

Pressure (hPa)550-60° -40° -20° 0°

SAGE-CCI-OMPS

45

Altitude (km)

5

1

50

2

Pressure (hPa)

Pressure (hPa)2

GOZCARDS v2.20

Altitude (km)

1

35

-5

30
25
20

-60° -40° -20° 0°

20° 40° 60°

Latitude15-60° -40° -20° 0°-10
20° 40° 60°

LatitudeFigure 3-18. Ozone trends for the period 2000–2016 estimated from six satellite data records using the independent linear trend (ILT) model. Gray stippling denotes results that are not significant at the 2σ level. Data
are presented on the latitudinal grid and vertical coordinates associated with each dataset. Adapted from
LOTUS (2018).

has been more subjectively determined in previous
studies (Steinbrecht et al., 2017). The combined trends
from the merged satellite datasets used in the LOTUS(2018) study are shown in Figure 3-21 and comparedto the results from the last Ozone Assessment, as well
as from the two major studies on ozone profile trends
published since then (Harris et al., 2015; Steinbrecht
et al., 2017). A significant ozone increase is found in
the NH mid-latitude upper stratosphere regardless ofthe method used. In the tropics and SH mid-latitude
upper stratosphere, however, trends are smaller, andtheir degree of significance depends on the method
used to evaluate combined uncertainties.

3.32

3.3.2.4

Consistency of Total Column Trends andIntegrated Profile Trends

As shown in Figure 3-12, total ozone trends for the
1997–2016 period are close to zero over all latitudes,with small positive but not statistically significant
values at middle and high latitudes. Despite the small
magnitude of these trends, the time series of global
mean total ozone is, in fact, consistent with changes
in EESC resulting from ODS changes (Figure 3-13).
However, ozone profile trends (Section 3.3.2) show
a significant increase in ozone mixing ratios in theupper stratosphere; this is also thought to be due

Global Ozone | Chapter 3

SBUV MODSBUV COH

GOZCARDSSWOOSH60°-35°S
1

1985–1997

Pressure (hPa)

1

-8

-6

-4

-2

0

2

4

6

8

2000–2016

10

100

-8

-6

-4

-2

0

2

SAGE-CCI-OMPS

20°S-20°N

10100

SAGE-OSIRIS-OMPS

4

6

8

35°-60°N

1

1

10

10

100

-8

-6

-4

-2

0

2

4

6

8

100

1

1

10

10

100

-8

-6

-4

-2

0

2

4

6

8

100

-8

-6

-4

-2

0

2

4

6

8

-8

-6

-4

-2

0

2

4

6

8

Ozone trend (% decade−1)

Figure 3-19. Ozone profile trends with 2σ uncertainties for the 1985–1997 (top row) and 2000–2016 time
periods (bottom row) from the ILT regression for latitude bands 35°–60°S (left column), 20°S–20°N (center­column), and 35°–60°N (right column). Colored lines are the trend estimates from six individual merged datasets on their original vertical grid. Adapted from LOTUS (2018).

to the decline in ODSs, with enhancement of upper-stratospheric cooling associated with increases
in GHGs (Section 3.3.3). Though the contributionof the upper stratosphere to column ozone changes issmall, it is necessary to understand the consistency orlack thereof among ozone trends in the upper, middle,
and lower stratosphere and their relationship to total
column changes.
There is recent evidence for a continuous decline
in lower-stratospheric ozone, based on merged
SWOOSH-GOZCARDS-SAGE II-CCI-OMPS and
SAGE II-OSIRIS-OMPS data (Ball et al., 2018).
Partial column ozone anomalies (60°S–60°N) were
calculated for three layers: the upper stratosphere(10–1 hPa), the middle stratosphere (32–10 hPa), andthe lower stratosphere (100–32 hPa). The study foundthat for the 1998–2016 period, there was a highly
probable recovery in the upper stratosphere, a relatively flat trend in the middle stratosphere, and a continuous decrease in ozone in the lower stratosphere.

Similar observational results showing a decline inlower-stratospheric ozone have also been noted byothers (e.g., Bourassa et al., 2014, 2018; Steinbrecht
et al., 2017; Sofieva et al., 2017) and can be seen in
Figures 3-18 and 3-19 of this report. These figures
show that the decline is largest and most consistentacross datasets in the tropics. Several studies haveaddressed attribution of ozone trends in the tropicallower stratosphere (see Section 3.3.3.2).
Ball et al. (2017) also found that stratospheric column
ozone, which is dominated by lower-stratosphericozone, decreased from 1998 to 2016. Using the OMI/
MLS tropospheric ozone residual column (Ziemke
et al., 2006), the study suggests that the total column
change is near zero because of increases in tropospheric ozone that compensate for the decline in thestratosphere.
Though there does appear to be evidence from merged
satellite datasets for a decrease in lower-stratospheric
3.33

Chapter 3 | Global Ozone
60°–35°S
1

20°S–20°N
1985–1997

Pressure (hPa)

10

100
1

-8

-6

-4

-2

0

2

4

6

8

1

10

10

100

-8

-6

-4

-2

0

2

4

6

8

1

2000–2016

-6

-4

-8

-6

-4

-2

0

2

4

6

8

2

4

6

8

1

10

-8

100

Harris et al. (2015)
Steinbrecht et al. (2017)
LOTUS (2018)

10

100

35°–60°N

1

-2

0

2

4

6

8

100

10

-8

-6

-4

-2

0

2

4

6

8

100

-8

-6

-4

-2

0

Ozone trend (% per decade )−1

Figure 3-20. Combined trends of six satellite data records with 2σ uncertainties for the pre-1997 period(top row) and the post-2000 period (bottom row) from the LOTUS analysis for three latitude bands 35°–
60°S (left column), 20°S–20°N (center column) and 35°–60°N (right column). Central trend estimates are
identical for all methods (black lines). Green lines represent uncertainties calculated using the LOTUS
method (Equation 5.1 in LOTUS, 2018); Blue lines represent the method of Steinbrecht et al. (2017)with the number of independent datasets estimated as 2 for pre-1997 and 3 for post-2000 trends; andred lines represent the J-distribution method used by Harris et al. (2015). Results shown here for thelatter two methods were obtained using formulae that were modified to use an unbiased estimatorof sample variance. Adapted from LOTUS (2018).

ozone over the 1998–2016 period, it is not easy toassert the statistical significance of the trends because the uncertainties are very large there, as seenin Figure 3-19. In particular, the lower stratosphereis one of the most difficult regions for obtaining accurate satellite observations because of steep verticalgradients in various atmospheric parameters aroundthe tropopause. Natural variability is also large in thelower stratosphere. In fact, in a 9-member ensembleof a free-running CCM, 1998–2016 trends in thelower stratosphere varied from −6% decade−1 to +6%
decade−1 among the ensemble members (Stone et al.,2018). Furthermore, a recent study showed that lower-stratospheric ozone and the total ozone column bothincreased sharply from 2016 to 2017, reversing much
3.34

of the apparent decline in recent years (Chipperfield
et al., 2018). Using a c­ hemistry-transport model, thatstudy concluded that the observed changes in lower-stratospheric ozone have been dominated by dynamically driven variability and that there is no needto invoke the VSLS-driven ozone loss suggested byBall et al. (2018) to explain them. Enhanced isentropic transport between the tropical and extratropicallower stratosphere from 1998 to 2016, particularly in
the NH mid-latitudes, has been proposed as a mechanism for this dynamically driven decrease in ozone in
the lower stratosphere (Wargan et al., 2018).
The tropospheric ozone trend and its role in total column changes require further investigation. OMI/MLSGlobal Ozone | Chapter 3

is the only record of tropospheric ozone to show continuous, near-global, monotonic increases in troposphericozone since 2004 (Gaudel et al., 2018). Other OMI records based on profile retrievals show much smaller increases, and IASI measurements actually show a decline
in tropospheric ozone since 2008. There is currently no
consistent picture of changes in ozone throughout thetroposphere over the past decade (Figure 3-22).

A comprehensive interpretation of observational
records must be supported by chemistry–climate orchemistry-transport modeling. CCMI-1 modeledozone profile trends are in excellent agreement withthe observed trends (Section 3.3.2.3), except in the

Harris et al. (2015)

WMO (2014)60-35°S1

3.3.3 Impacts of Changes in Ozone-­
Depleting Substances and­Greenhouse Gases on Ozone Trends

pre-turnaround

Steinbrecht et al. (2017)

20°S–20°N

1

2
3

5
7
10

5
7
10

5
7
10

20
30

20
30

20
30

50
70
100

50
70
100

50
70
100

Pressure (hPa)

2
3

1

-8

-6

-4 -2

0

2

4

6

8

post-turnaround

1

-8

-6

-4 -2

0

2

4

6

8

1
2
3

5
7
10

5
7
10

5
7
10

20
30

20
30

20
30

50
70
100

50
70
100

50
70
100

Pressure (hPa)

2
3

2
3

-8

-6

-4 -2

0

2

4

6

8

-8

-6

-4 -2

0

2

35–60°N

1

2
3

4

6

8

LOTUS (2018)

-8

-6

-4 -2

0

2

4

6

8

-8

-6

-4 -2

0

2

4

6

8

Combined O3 Trend (% decade−1)
Figure 3-21. Ozone profile trends from WMO (2014), Harris et al. (2015), Steinbrecht et al. (2017), and LOTUS(2018) are shown in red, orange, blue, and black, respectively1. The top row shows trends before the turnaround of ODSs; the bottom row shows trends since the turnaround. Shaded areas and error bars represent
the 95% confidence interval for the combined trend. Colored profiles are slightly offset on the vertical axis fordisplay purposes. Steinbrecht et al. (2017) did not report or discuss pre-1997 trends but did calculate themand made them available for comparison here. Adapted from LOTUS (2018).

1

Data included in the trends from each study are from the following periods:
Pre-turnaroundPost-turnaround

WMO (2014)

May 1970 / Feb 1979 / Oct 1984­–1997

Jan 2000–Dec 2013

Harris et al. (2015) (Sections 2.1 & 2.2)

Feb 1979 / Oct 1984–Dec 1997

Jan 1998–Dec 2012

Steinbrecht et al. (2017) (Table 3)

May 1970 / Feb 1979 / Oct 1984–Dec 1996

Jan 2000–Dec 2016

LOTUS (2018) (Table ES.1)

Jan 1985–Dec 1996

Jan 2000–Dec 2016

3.35

Chapter 3 | Global Ozone

SH lowermost stratosphere during the period ofozone depletion (Figure 3-23). Idealized scenarios
run by a subset of these models allow the attributionof ODS and GHG effects on ozone changes.
The evolution of stratospheric ozone in a changing
climate depends on (in addition to ODS change):cooling of the stratosphere due to increases in
GHGs; changes in transport (Brewer–Dobson circulation); and changes in ozone chemistry (N2O/NOx
and CH4/HOx).
While past changes in stratospheric ozone wereprimarily due to anthropogenic emissions of­ozone-depleting substances (ODSs), the evolution ofstratospheric ozone in the 21st century will be controlled not only by the decline in ODSs but also, toa large extent, by the increase in greenhouse gases(GHGs) such as CO2, CH4, and N2O. This section
updates the attribution of past changes in ozone tochanges in ODSs and GHGs, separated for the periods 1979–1996 and 2000–2016. This attribution isbased on a multi-model analysis of simulations from
CCMI-1 (Figure 3-24).
Figure 3-24 provides an update of the attribution ofozone trends due to ODS and GHG changes, based375

on output from the seven CCMI-1 models that performed all of the required simulations. In addition to
the standard (REF-C2) model simulations including
all forcings (ODSs and GHGs), a set of sensitivity
simulations was run with either changes in ODSs only
(i.e., with fixed GHGs) or with changes in GHGs only
(i.e., with fixed ODSs). In the REF-C2 simulations,
ODS and GHG concentrations follow the RCP-6.0
scenario, consistent with observed concentrations
before 2005. Sea surface temperatures were eitherprescribed from an offline model simulation, which
was performed using either the same model or a different model, or they were internally calculated via
a coupled ocean model (Morgenstern et al., 2017).
Trends were calculated as independent linear trends(ILTs) consistent with observed trends, using proxies
derived from the variability in each model for QBO,
ENSO, solar cycle, and aerosol optical depth.
In line with previous results, this analysis shows thatnegative ozone trends over the period 1979–1996 areprimarily due to the increase in ODS concentrations.
Model simulations with fixed ODSs exhibit small,statistically insignificant trends, except in the tropicallower stratosphere. In this region, the simulations indicate that the 1979–1996 ozone decrease may mostly

60°S–60°N

change, Tg y r−1

p-value

OMI/MLS

1.79 ± 0.66

0.00

IASI-FORLI-2.15 ± 1.03

0.00

IASI-SOFRID

1.34 ± 0.92

0.00

GOME/OMI

1.63 ± 0.45

0.00

OMI-RAL

2.85 ± 1.16

0.00

SCIAMACHY

1.50 ±1.39

0.03

Ozone burden (Tg)

350
325
300
275
250
225
96 97 98 99 0

1

2

3

4

5

6

7

Year

8

9 10 11 12 13 14 15 16

TES

Figure 3-22. Time series of the tropospheric ozone burden, calculated from measured tropospheric ozone
columns for seven satellite records. All instruments are nadir-viewing and have differing vertical sensitivities. The black line is the OMI/MLS tropospheric ozone residual product; the dark red and orange lines are
IASI retrievals using the Fast Optimal Retrievals on Layers (FORLI) and SOftware for a Fast Retrieval (SOFRID) algorithms; respectively; the blue line is the OMI optimal estimation retrieval from Rutherford Appleton­Laboratories (RAL); and the gold line is a combined GOME and OMI time series from Smithsonian A­ strophysical­Observatory. The green and purple lines are standard products from SCIAMACHY and TES, respectively. See
Gaudel et al. (2018) and references therein. Adapted from Gaudel et al. (2018).
3.36

Global Ozone | Chapter 3

CCMI-1 Mean ± 2σ1

35°–60°N

50

40
10

30

1

Altitude (km)Pressure (hPa)

Combined Satellite ± 2σ

35°N–60°N

Pressure (hPa)

CCMI-1 Median

CCMI-1 (7 Models)
Fixed GHGs (ODSs Only)
REF-C2 Mean ± 2σ
Fixed ODSs (GHGs Only)
REF-C2 Median

10

20

100

100
–10

–5

0

5

–5

0

5

Ozone Trend 1985–1996
Ozone Trend 2000–2016(% decade-1)(% decade-1)
20°S–20°N

-10

-5

0

Ozone Trend 1979–1996(% decade−1)

5

-5

0

5

10

0

5

10

0

5

10

Ozone Trend 2000–2016(% decade−1)

20°S–20°N

Lo

50

40
10

30

1

Altitude (km)Pressure (hPa)

1

-15

10

Pressure (hPa)

–15

10

20

100

100
–15

–10

–5

0

5

–5

0

5

Ozone Trend 1985–1996
Ozone Trend 2000–2016(% decade-1)(% decade-1)
60°S–35°S

-10

-5

0

Ozone Trend 1979–1996(% decade−1)5-5

60°–35°S

1

50

10

30

Pressure (hPa)40

Ozone Trend 2000–2016(% decade−1)

1

Altitude (km)Pressure (hPa)

-15

10

10

20

100

100
–15

–10

–5

0

Ozone Trend 1985–1996(% decade-1)

5

–5

0

5

Ozone Trend 2000–2016(% decade-1)

10

Figure 3-23. Measured and modeled ozone
trends for the NH mid-latitudes (top row), tropics(­­­middle row), and SH mid-latitudes (bottom row)for the pre-1997 (left column) and post-2000 (right­column) periods. The black line is the combined­satellite-based ozone trend from LOTUS, with 2σuncertainties, the blue line is the median model,
and the gray line is the multi-model mean. The
gray envelope shows the 2σ variance of the models. CCMI-1 REF-C2 simulations are shown.

-15

-10

-5

0

Ozone Trend 1979–1996(% decade−1)

5-5Ozone Trend 2000–2016(% decade−1)

Figure 3-24. Attribution of stratospheric ozone
profile trends for 1979–1996 (left column) and
2000–2016 (right column) due to ODSs and GHGs
based on CCMI-1 model simulations. The top row
shows the trends for NH mid-latitudes (35°–60°N);
the middle row, for the tropics (20°N–20°S); andthe bottom row, for SH mid-latitudes (60°–35°S).
The gray lines show the multi-model mean ofseven REF-C2 simulations including all forcings,
with the shading indicating the 2σ range. The light
blue lines show the median for all forcings. The
red and blue lines represent the median trends
due to ODSs only (i.e., with fixed GHGs) and due to
GHGs only (i.e., with fixed ODSs), respectively. The
same subset of seven models are used in all cases.
Adapted from LOTUS (2018).

3.37

Chapter 3 | Global Ozone

be due to an increase in GHGs. However, uncertainties
in the lower stratosphere are large, as expressed by the
relatively large spread in model results. Positive ozone
trends in the lower stratosphere over the 2000–2016
period in the mid-latitudes of both hemispheres are
consistent with the decline in ODSs, as the all-forcing
REF-C2 simulations show positive ozone trends inthis region similar to the ODS-only simulations.
In the upper stratosphere, ODS and GHG changes
both contribute more or less equally to the positive
ozone trends over the 2000–2016 period, again largely in agreement with previous results. The GHGinduced ozone increase in the upper stratosphere is
primarily a result of cooling associated with GHGs,
which reduces catalytic ozone loss rates. Furthermore,these simulations confirm previous findings that in
the upper stratosphere, the effects of declining ODSs
and increasing GHGs add approximately linearly.

3.3.3.1	Effects of Very Short-Lived Substances
Halogens from very short-lived substances (VSLSs;i.e., from halogenated ozone-depleting substanceswith lifetimes shorter than a few months) may contribute to ozone trends, in particular in the lowermost
stratosphere, even when the concentrations of VSLSs
themselves exhibit no trend (e.g., Dvortsov et al., 1999;
Salawitch et al., 2005; Sinnhuber et al., 2009). They doso by providing additional chemical reaction partners
for bromine and chlorine from anthropogenic longlived source gases. One study described simulations
with the chemistry–climate model EMAC over thetimeframe 1960–2005 with and without the inclusionof brominated VSLSs (Sinnhuber and Meul, 2015).
It found that a constant contribution of brominated
VSLSs, producing about 6 ppt of additional bromine,
led to column ozone changes in better agreement withobservations and a stronger negative ozone trend in
the mid-latitude lowermost stratosphere for the 1979–
1995 period. The study also found a stronger positive
trend for the 1996–2005 period, but the consideredtime period was too short for a robust estimate of theeffect on ozone trends. The largest effect of brominated VSLSs on ozone was found for polar winter–springozone loss in the lower stratosphere and, in particular,
the Antarctic ozone hole (Chapter 4, Sections 4.3.3
and 4.5.3.4).
Model calculations show that column ozone is reduced by about 1% at low latitudes and about 5% at
3.38high latitudes relative to a model simulation without
VSLSs when best estimates for the present-day stratospheric loading of brominated and chlorinated VSLSs
are used (Figure 3-25; Hossaini et al., 2015). The
largest ozone reductions due to VSLSs occur in thelowermost stratosphere below 20-km altitude, where
present-day ozone reductions due to VSLSs could
be almost half as large as the ozone depletion due tolong-lived ODSs, depending on the assumed VSLS
scenario (Figure 3-25). Although the effectivenessof VSLSs to deplete ozone depends on the anthropogenic halogen loading, VSLSs have contributed to thelower-stratospheric ozone decrease since preindustrial times. Lower-stratospheric ozone changes due tothe presence of brominated VSLSs have resulted in an
estimated radiative forcing of about 0.02 W m-2 since
the preindustrial period (Hossaini et al., 2015).
Brominated VSLSs are emitted primarily from biogenic oceanic sources (such as phytoplankton andseaweed) and contribute significantly to the current
stratospheric bromine loading (see Chapter 1); in
contrast, chlorinated VSLSs are emitted primarily
from anthropogenic sources (although there are alsosignificant natural sources of methyl chloride) andcurrently contribute little to stratospheric chlorine
loading. However, some of the chlorinated VSLSs, andin particular CH2Cl2, have shown relatively large increases in recent years (Hossaini et al., 2015; Hossaini
et al., 2017; Oram et al., 2017). The impact of anthropogenic chlorinated VSLSs on past ozone trends isstill small (Hossaini et al., 2015), but may become
important in the future if the observed increase inemissions of CH2Cl2 continues (Hossaini et al., 2017).
All CCMI-1 models now include additional bromine
from VSLSs (Morgenstern et al., 2017). Two studies
have investigated the impact of VSLSs on ozone recovery in chemistry–climate model simulations, witha focus on polar ozone, where the effects are largest(Oman et al., 2016; Fernandez et al., 2017; see Chapter
4, Section 4.5.3.4).

3.3.3.2	Tropical Ozone Changes
Past Assessments (WMO, 2011; WMO, 2014) have reported observed negative ozone trends in the tropicallower stratosphere, in overall agreement with available chemistry–climate model simulations. Modeledchanges in tropical stratospheric ozone columns
using the CMAM chemistry–climate model over theGlobal Ozone | Chapter 3

60
50

−2

Altitude (km)Column O3 change (%)

0−4Upperstratospherestratosphere
Upper

40

30

Best estimate−6

Uncertainty due
to VSLS loading

20

0

10
−1400

Lowerstratospherestratosphere
Lower

−8
−90

−60

−30

Latitude (°)

30

60

90

−1200

−1000

−800

−600

ΔO 3 (ppb)

−400

−200

0

Figure 3-25. Effect of VSLSs on present-day (2011) ozone columns (calculated as the difference between
simulations with and without VSLSs) as a function of latitude (left), and the impact of ODSs (purple) and
VSLSs (black) on global mean ozone profiles (right). Shading indicates the range of current estimates of thecontribution of VSLSs to stratospheric bromine. Adapted from Hossaini et al. (2015).

1960–2010 period are broadly consistent with satellite observations based on LIMS, SAGE I and II, andMIPAS, although the modeled decrease in tropicalozone is smaller than that deduced from SAGE II(Shepherd et al., 2014). The model shows a decreaseof tropical (25°S–25°N) stratospheric ozone of 5.2± 1.7 DU between the 1964–1978 and 1996–2002
periods, with the largest decrease in tropical stratospheric ozone before 1990 and little change since themid-1990s. This is consistent with results presented
in this Assessment and with the analysis in the last
Assessment (WMO, 2014); while both model simulations and observations show negative trends in thetropical lower stratosphere since 2000 (Figure 3-23),
these trends are not significant except at 30–50 hPa
in the SBUV MOD dataset. In CMAM, the 1990s decrease in stratospheric ozone is partly compensatedby a modeled increase in tropical tropospheric ozone
of 2.9 ± 0.7 DU over the same period (Shepard et al.,2014). The modeled decrease in stratospheric ozone
can be largely attributed to the increase in ODSs; amodel simulation with constant ODSs shows only asmall and insignificant decrease in the tropical stratospheric ozone column of 0.3 ± 1.8 DU between the
1964–1978 average and the 1996–2002 average.

Another study similarly demonstrated that observed
variations in tropical lower-stratospheric ozone arecaptured by a chemistry-transport model driven withmeteorological reanalysis fields (Aschmann et al.,2014). The lack of ozone decline after the mid-1990s
was explained by changes in not only the strength but
also the location of the tropical upwelling region. This
argument, that structural changes in upwelling are responsible for changes in tropical ozone trends, is supported by another analysis using satellite observations(Stiller et al., 2017). MIPAS measurements indicate
a southward shift of up to 5 degrees in the lower- to
middle-stratospheric upwelling region during theperiod 2002–2012. This shift also appears to explain
observed hemispheric asymmetries in trends in otherlong-lived trace gases (Stiller et al., 2017).
Separating chemical- and transport-related effects(in particular ozone reductions due to an increase intropical upwelling; Chapter 5, Section 5.3.2) is nottrivial, as recent papers (Oberländer-Hayn et al., 2015;
Polvani et al., 2017) have emphasized that ODS changes may have had a significant or even dominant impact on changes in tropical upwelling, thus leading tothe observed decreases in tropical ­lower-stratosphericozone. One study used a set of simulations from the
3.39

Chapter 3 | Global Ozone

GEOSCCM with small ensembles of single forcingsand concluded that dynamical responses to ODSs are
the main driver for past changes in tropical upwelling (Polvani et al., 2017). It suggests that the mechanism of such a response may relate to changes in thethermal structure of the lower stratosphere caused bypolar ozone depletion, which could alter the propagation and dissipation of planetary-scale waves. Anotherstudy, based on simulations with the EMAC chemistry–climate model, finds that ODSs contributed about

equally with other GHGs to past changes in tropicallower-stratospheric upwelling (Oberländer-Hayn etal., 2015).

3.4 PROJECTED OZONE CHANGES
Models have always been tools to test process understanding and to project possible future developments.
Box 3-2 summarizes the evolution of models used in
Ozone Assessments since 2002 and introduces themodel runs used in this Assessment.

Box 3-2. Modelling past and future changes in ozone: Model heritage and application
Modelling in support of the Ozone Assessments has a long tradition. For example, the2002 Assessment used a selection of early 3-dimensional (3-D) Chemistry-Climate Models(CCMs) to assess polar ozone changes. However, a large amount of information, in particular in the global ozone chapter, was derived from 2-dimensional (2-D) models. In the 2006
Assessment, 3-D Chemistry Transport Models (CTMs) were extensively used to discuss pastozone changes and were complemented with 3-D CCMs. The projected changes for the 21st
century were extensively discussed in a separate chapter that used 2-D models and (veryextensively) 3-D CCM results, largely emerging from the CCMVal (CCM Validation) activity. The model results were used to characterize future changes in ozone, including thereturn dates. The 3-D CCM modelling results for the 2010 Assessment were produced in thecontext of a comprehensive model evaluation in the framework CCMVal-2 (SPARC, 2010).
A summary of the participating models can be found in Morgenstern et al. (2010). In the2014 Assessment, results from this exercise were also used and complemented with resultsfrom newer publication. Prior to the 2014 report the CCMVal activity was superseded by the
Chemistry-Climate Modelling Initiative (CCMI), which has a wider remit than CCMVal.
Earlier versions of most models contributing to CCMI in the context of stratospheric ozone
variability and trends were evaluated in CCMVal-2. As can be expected, some models havebeen changed more than others; however their fidelity is always assessed by modelling the recent past. A
summary of relevant models and their configurations can be found in Morgenstern et al. (2017). Generally,
there is a trend to higher spatial resolution, more processes and the option to run more models with interactive ocean and sea-ice coupling. In previous CCM projections there was a clearer disjoint between model
configurations used for CCMVal and model configurations used for CMIP (Coupled Model Intercomparison
Project). Often, when projecting ozone changes, CCMs prescribed the future sea-surface temperatures andsea-ice coverage from their nearest CMIP relative. The CMIP model would be run without chemistry (prescribing ozone – perhaps from CCM integrations – in the radiation), but with an interactive ocean andsea-ice. This circularity can now be overcome by running atmosphere-ocean CCMs consistently. However,
it is important to remember that the ozone projection will rely in both cases on the chosen climate change
scenario (see table below and Meinshausen et al., 2011 for the definition of the RCPs).
Other model changes relate to the complexity of the chemistry (more emitted species, more reactions in the
troposphere) and to a better understanding of the halogen budget, including the role of VSLSs. The impact
of concentration and flux boundary conditions on lifetimes of halogen-containing species was evaluated in
the SPARC Report No. 6 using a subset of CCMs from CCMVal-2/CCMI.
3.40

Global Ozone | Chapter 3

Box 3-2, continued.
Name of
Simulation
REF-C1REF-C1SD

REF-C2

SEN-C2RCP2.6
SEN-C2RCP4.5
SEN-C2RCP8.5

Type of Simulation
Hindcast simulationof the period
1960–2010
Hindcast simulationof the period
1980–2010
Consistent
simulation fromthe past into the
future for the period
1960–2100
Same as in REF-C2
but for the period
2000–2100
Same as in REF-C2
but for the period
2000–2100
Same as in REF-C2
but for the period
2000–2100

Same as in REF-C2
SEN-C2-fODS for the period
1960-2100
SEN-C2fGHG

Same as in REF-C2
for the period
1960–2100

PurposeFeatures

GHG*, ODS*, background and volcanic aerosol,
To produce realistic simulations ofsolar variability, ozone and aerosol precursors,
the past atmospheric state
SST*, SIC*, VSLSs* are prescribed from observations
Same as REF-C1, but with theSame as in REF-C1; SSTs and SIC are consistent with
CCM dynamics nudged tometeorological reanalyses
observed meteorology
Observations until 2005; then prescribed future
To produce best estimates of the
scenarios for GHG and ozone/aerosol precursors
future ozone and climate changes(medium RCP6.0), ODS (WMO, 2011), backgroundunder specific assumptions ofaerosol, projected solar variability, VSLSs; SST and
GHG and ODS evolution
SIC modeled
To assess the future evolution ofSame as in REF-C2, but GHG and ozone/aerosol
ozone and climate change under precursors according to RCP2.6 scenario; SST and
GHG scenarios other than RCP6.0 SIC consistent with RCP2.6 scenario
To assess the future evolution ofSame as in REF-C2, but GHG and ozone/aerosol
ozone and climate change under precursors according to RCP4.5 scenario; SST and
GHG scenarios other than RCP6.0 SIC consistent with RCP4.5 scenario
To assess the future evolution ofSame as in REF-C2, but GHG and ozone/aerosol
ozone and climate change under precursors according to RCP8.5 scenario; SST and
GHG scenarios other than RCP6.0 SIC consistent with RCP8.5 scenario
To assess the effect of halogens
on stratospheric ozone andSame as in REF-C2, but with halogens (ODS) fixed
climate change in the presence of at 1960 levels
climate change
To assess the impact of halogens Same as in REF-C2, but with GHG fixed at 1960
on the atmosphere in the absence levels and 1955–1964 average values from REF-C2
of climate change
for SST and SIC repeating each year

*GHG: Greenhouse gases; ODS: Ozone depleting substances; SST: Sea surface temperature; SIC: Sea-ice concentration; VSLSs:
Very short- lived halogenated substances

3.4.1 Expected Return to 1980 ­Levels
and Ozone Recovery
One of the most critical roles that models play in theassessment process is to provide projections of the future evolution of ozone, with the return to 1980 values
being a convenient definition of an ozone recovery
milestone. Previous Assessments have discussed tropical and global annual mean total column ozone timeseries from individual models or multi-model means(see Box 3-2 for a timeline of modeling and current
experimental setups). In the last two Assessments, theresults of the CCMVal-2 initiative have been utilized.
Here, we provide an update using recent integrations
from CCMI-1. The increased availability of simulations from models and ensemble integrations within
CCMI-1 provides a new resource for assessing pastand future ozone changes. However, the ability to discern the robustness of ozone return dates is limitedby the newness of the results and the resulting lack ofcomprehensive validation across models.
Figure 3-26 presents the modeled total column ozone
time series from 1960 to 2100 from bias-adjusted
CCMI-1 multi-model means for the tropical belt andfor global annual means. To provide a measure of
the spread that is realistic but not overly influenced
by outliers, the envelope is calculated from models
that fall within 1σ of the multi-model mean (denoted MMM1S; see Dhomse et al., 2018 for details).
The MMM1S return dates are generally found to beconsistent with those derived from the median model(Dhomse et al., 2018).
3.41

Chapter 3 | Global Ozone

The most appropriate (based on the chosen boundary conditions) free-running model representation of
past total ozone is REF-C1 (blue lines in Figure 3-26).
REF-C1SD (green lines) should be an even more accurate representation, as the individual models’ circulation and thermal structure are relaxed toward
analyzed values, thus providing extra information
about “realistic” temporal changes to the models.
Theoretically, REF-C1SD should provide the best representation of observed ozone anomalies (in particular the timing of low/high ozone events). However,there are large differences among models in how thespecified dynamics were implemented. REF-C1 is still
able to capture the long-term variability as imposedby the boundary conditions, but it does not accurately
capture extremes or particular events. By construction, REF-C2 (red lines in Figure 3-26) should be
similar to REF-C1 for the past. However, the boundary conditions are extended into the future following
the RCP-6.0 scenario in REF-C2, allowing the identification of possible return thresholds in total ozone.
A common point of reference is the occurrence ofozone values at 1980 levels (indicated by the horizontal dashed lines in Figure 3-26). For a more detailed
discussion of the use of return dates, see Box 3-3 on“Ozone return dates”.

300

Tropics (annual)

270

295

265

290

TCO (DU)

TCO (DU)

275

Figure 3-27 provides a comparison of the 1980 total
column ozone return dates from the last Assessment(WMO, 2014) with the corresponding MMM1S results from CCMI-1 (Dhomse et al., 2018) (see alsodetails in Box 3-3). Many of the 20 CCMs analyzed
provided ensemble integrations of the REF-C2 scenario; if only a single model integration was available,
then a 3-year boxcar smoothing algorithm was applied
before ensemble mean model output and individual
integrations were brought together (Dhomse et al.,2018). Due to the large number of model realizations
that are combined to derive the multi-model mean,internal variability contributes only a small amountto the uncertainty in return dates (indicated by range
bars in Fig. 3-27). However, when determining whether the 1980s total ozone threshold has been met witheither measurements or output from an individualmodel, the year-to-year variability of ozone due tointernal atmospheric variability must be considered(e.g., Keeble et al., 2018). The multi-model median return dates are in good agreement with the MMM1S results, demonstrating the robustness of the analysis, butthey have larger uncertainties (Dhomse et al., 2018).
The MMM1S return year is used in this Assessment
because it is a more comparable metric to the analysis
in the last Assessment than the median return year.

260
255

Global (annual)

285
REF-C1REF-C1SD
REF-C2SBUV MOD280

1960 1980 2000 2020 2040 2060 2080 2100

Year

1960 1980 2000 2020 2040 2060 2080 2100

Year

Figure 3-26. Adjusted and smoothed multi-model mean time series of annual mean tropical (left) and global(right) total column ozone (TCO) from CCMI models (colored lines) and from observations (solid black lines
and symbols). Note that the multi-model mean uses a 1σ threshold (MMM1S) for filtering (for method see
Dhomse et al., 2018, Figure 2, bottom). The dashed black lines represent 1980 values of TCO.
3.42

Global Ozone | Chapter 3

TCO Return Dates Relative to 1980
2080
2070
2060
2050

Year

2040
2030
2020
2010
2000
1990
1980
CCMVal-2 CCMIGLOBAL

CCMVal-2 CCMISH MID- LAT

CCMVal-2 CCMITROPICS

CCMVal-2 CCMINH MID-LAT

Figure 3-27. Bar chart comparing WMO (2014) projected 1980 return dates (labeled CCMVal-2) to this Assessment’s CCMI-1 1980 return dates as analyzed from the multi-model mean (MMM1S) time series. The global
mean refers to 90°S–90°N for WMO (2014) and to a near-global mean (60°S–60°N), consistent with the observations presented in this chapter) in the case of MMM1S. The true and near-global mean should be very
similar (Dhomse et al., 2018). A comprehensive table with more details is provided by Dhomse et al. (2018).

The delay in return dates seen in this Assessment relative to the 2014 Assessment results primarily fromdifferent assumptions made in the scenarios (seeBox 3-3). The current best estimates for the range ofyears in which total ozone will return to 1980 values
are 2042–2051 for the near-global mean, 2039–2050
for the SH mid-latitudes, and 2020–2044 for the NH
mid-latitudes. The systematic delay of return years
relative to the last Assessment (WMO, 2014) does not
imply deficits in understanding, but rather reflects
updates to prescribed scenarios describing the future
evolution of GHGs (chosen to be in line with CMIP5)
and ODSs. The extended range of return dates for thetropical belt reflects the fact that some models will not
return to their 1980 ozone values in the projected time
window given the competing effects of climate change(declining lower-stratospheric ozone) and declining
ODSs (increasing ozone) in the tropics.

3.4.2 Effects of Future Stratospheric­Temperature and Circulation Changes
As mentioned throughout this chapter, the evolutionof stratospheric ozone in the 21st century will be controlled not only by the decline of ODSs but also, toa large extent, by changes in GHG concentrations,most importantly CO2, CH4, and N2O. These GHGs
affect stratospheric ozone through temperature andsubsequent changes in dynamics and transport; CH4
and N2O also affect ozone chemistry (e.g., Butler
et al., 2016), as discussed in more detail in Section
3.4.3.2. The impact of possible future GHG scenarios
described by different Representative Concentration
Pathways (RCPs) is discussed in Section 3.4.3.1.
The effects of GHG-induced temperature and circulation changes on ozone are compared to the effects
of declining ODSs in Figure 3-28, which shows results from time-slice simulations with different forcings from the UM-UKCA chemistry–climate model
3.43

Chapter 3 | Global Ozone(Banerjee et al., 2016). The decrease in ODSs between
the years 2000 and 2100 will lead to an ozone increase
essentially everywhere in the atmosphere, with thelargest percentage changes in the upper stratosphereat around 40 km and in the lower stratosphere at highlatitudes, particularly in the Southern Hemisphere,
where recovery of the Antarctic ozone hole is mostnotable. In contrast, changes in GHG concentrations will lead to a more complex pattern of ozone
changes, with increases in the upper stratosphere at

Box 3-3. Ozone Return Dates
A critical role of Ozone Assessments is to provide years at which (column) ozone is expected to return to aparticular, historic level – so-called return dates. This Assessment tabulates ozone return to 1980 values only.
Here, we explain how return dates are computed and clarify why our current estimate for when global, totalozone will return to the 1980 level is systematically delayed relative to the 1980 return given in the prior two
Assessments.
Comprehensive Chemistry-Climate Models (CCMs) project climate, and interactively calculate ozone, forprescribed future abundances of greenhouse gases (GHGs) and ozone depleting substances (ODSs). Threetypes of uncertainties must be considered for return date estimates: internal variability, structural uncertainty, and scenario uncertainty.
Internal Variability. CCMs exhibit internally-generated variability that impacts ozone (e.g., polar stratospheric warmings). If multiple runs from one model exist for the same climate scenario, these so-called
ensembles are averaged together. If only one run is available, the model output is smoothed with respect totime, prior to being combined with results from other models. Due to the large number of runs and models
that are combined to derive the multi-model mean, internal variability contributes only a small amount tothe uncertainty of return dates.
Structural Uncertainty. CCMs represent processes, and their interactions, differently. This may lead to different return dates, for the same prescribed future levels of GHGs and ODSs. Several of the latest CCMs haveinteractive oceans and/or sea-ice modules, enlarging the degrees of freedom. In this and prior Assessments,
structural uncertainty is quantified by examining output from many different models. While structural uncertainty is certainly important for quantifying the range of expected return years, the introduction of three
additional CCMs for this Assessment is not responsible for the delay of the return dates relative to prior
Assessments. Structural uncertainty is, however, the driving factor of lower and upper limits for the rangeof years given in Table 3-4.
Scenario Uncertainty. CCM projections use prescribed scenarios for future ODSs and GHGs. The returndates reported in WMO (2011) and WMO (2014) were based on ODSs from the baseline scenario of WMO(2007) with an adjustment for HCFCs and GHGs from the Special Report on Emissions Scenarios (SRES)
A1b scenario developed in IPCC (2000). Here, we use ODSs from the baseline scenario of WMO (2011)
and GHGs from the Representation Concentration Pathway (RCP) 6.0 scenario developed by Masui et al.
(2011) for IPCC (2013). As illustrated below, differences in future specifications of CO2, CH4, and ODSs are
the likely cause of the obvious delay in return of ozone to the 1980 value relative to the projections of theprevious Assessments.
Most CCMs prescribe future abundances of GHGs and ODSs at the surface and compute the concentrations in the atmosphere as a function of time. Panels a) and b) on the next page show significant differencesin the ­assumed evolution of CO2 and CH4 as used in model runs for the previous assessments (SRES A1b)
and this assessment (RCP 6.0). Note that these differences in CO2 and CH4 have both direct and indirect
3.44

Global Ozone | Chapter 3

Box 3-3, continued.
­consequences for the climate change impact on ozone. Panel c) shows effective equivalent stratosphericchlorine (EESC) that can be used as a proxy of how the halogen loading evolves within the CCMs. The
approximately 5-year delay in EESC drawdown in the WMO (2011) baseline scenario used in this assessment relative to the previously-used scenario reflects new knowledge of lifetimes and emissions ofvarious ODSs. This is another factor contributing to the overall delay in return dates.
(b)
RCP 6.0
SRES A1b

RCP 6.0
SRES A1b

CH4 (ppm)

CO2 (ppm)(c)

EESC (ppt)

(a)

Year

Year

WMO (2011)
Prior Assessments

YearTime series of a) CO2, b) CH4, and c) equivalent effective stratospheric chlorine (EESC) used as boundary conditions for the CCM simulations analyzed for ozone return dates of this assessment (black solid)
and of the prior two assessments (black dashed). For the baseline return dates of this assessment, futureabundances of CO2, CH4, and all other GHGs are from the RCP 6.0 scenario developed for IPCC (2013)
and ODS mixing ratios that drive EESC originate from WMO (2011). The prior two Assessments utilized time series of GHGs from the SRES A1b scenario and ODSs from WMO (2007) with an adjustment for HCFCs. The EESC curves in this figure were calculated for mid-latitude air with a lifetime of 3
years, using the Newman et al. (2007) release factors.

all latitudes, primarily due to GHG-induced coolingthat slows down gas-phase ozone loss reactions. In
the low-latitude lower stratosphere, however, ozone
is projected to decrease due to changes in dynamics
and transport, and, in particular, a strengthening of
the BDC. This increase in the strength of the BDC is arobust finding in the CCMI-1 models, although there
are still uncertainties in the magnitude (Morgenstern
et al., 2018) and attribution of the strengthening(Chapter 5, Section 5.3.2). Lower-stratosphericozone in middle to high latitudes may either increaseor decrease, depending on the hemisphere and on
the GHG scenario. In the more extreme RCP-8.5 scenario, the upper-stratospheric increase in ozone due
to GHGs is larger than that due to ODS decline. In
the tropical lower stratosphere, ozone changes due to

BDC alteration that are potentially driven by GHG
increases will dominate (see also Section 3.4.3).
A study diagnosing ozone sensitivity to varying
GHGs and ODSs in CCMI-1 simulations finds varying degrees of consistency in the models’ responses inozone to individual forcings, including some considerable disagreement. It is suggested that some of thesedifferences could be linked to circulation differences
between the models (Morgenstern et al., 2018). The
results in Figure 3-28 (Banerjee et al., 2016) are therefore an illustration of principle and not a universally
valid result.

3.45

Chapter 3 | Global Ozone

3.4.3 Sensitivity to the Specification of­Different Future Scenarios

ΔCC4.5-Base

Altitude (km)3.4.3.1	Effects of Different Representative­Concentration Pathways

0

90°S

60°S

30°S

EQ

30°N

0

10

Latitude−40 −30 −20 −10

20

60°N

90°N

30

40

60°N

90°N

30

40

Altitude (km)ΔCC8.5-Base

0

90°S

60°S

30°S

EQ

30°N

0

10

Latitude−40 −30 −20 −10

20

Altitude (km)ΔODS-Base

0

90°S

60°S

30°S

EQ

30°N

0

20

Latitude−80 −60 −40 −20

40

60°N
60

90°N
80

Figure 3-28. Modeled percent changes in annual
mean ozone between 2000 and 2100 due to GHG
changes according to RCP-4.5 (top) and RCP-8.5(middle) and due to ODS changes (bottom). Shownare differences between a year-2000 baseline simulation with single forcing changes for year-2100
conditions: RCP-4.5 and RCP-8.5 assume year2100 SST, sea-ice, and GHG concentrations with
year-2000 ODS and ozone precursor emissions.
From Banerjee et al. (2016).
3.46

A number of CCMI-1 models provide estimates of thesensitivity of ozone to changes in other traces gases
using simulations with different scenarios. The different RCPs are GHG concentration trajectories first chosen for the Coupled Model Intercomparison Project
Phase 5 (CMIP5) to span a range of corresponding radiative forcings by the year 2100 (see Box 3-2). While
the standard reference simulations of CCMI-1 use the
RCP-6.0 scenario, sensitivity simulations for RCP-2.6,
RCP-4.0, and RCP-8.5 have also been carried out.
Figure 3-29 summarizes the evolution of total ozone
columns for the different RCPs (Dhomse et al., 2018).
For global mean total ozone columns, the return to1980 values is faster and the possibility of super-recovery (i.e., the increase of ozone above historical levels) is higher for the RCPs with larger GHG increases.
This effect is largest in mid-latitudes, where RCP-8.5
results in ozone increases of more than 20 DU above
average 1980 levels at the end of the 21st century.
RCP-4.5, in contrast, results in a return to 1980 global mean ozone columns in NH ­mid-latitudes by the
middle of the 21st century and shows little change afterwards. Tropical ozone columns for the multi-model mean do not fully return to pre-1980 levels in anyof the scenarios.
One study shows changes in tropical total column
ozone for three altitude ranges (upper stratosphere,
lower stratosphere, and troposphere) for three RCPs
(4.5, 6.0, 8.5) from calculations with the chemistry–
climate model EMAC (Meul et al., 2016; Figure 3-30).
Upper-stratospheric ozone columns (p<10 hPa) recover to 1980 levels as early as ~2025 in all scenariosand show a super-recovery afterwards, with the largest
super-recovery for RCP-8.5. In contrast, ozone columns in the lower stratosphere (10 hPa < p < 100 hPa)
do not return to pre-1980 levels at all but continue todecrease, with the largest decrease for RCP-8.5. (Thesesimulations show a small increase in tropical lower-stratospheric ozone columns between the late 1990s
and around 2025, but it is not clear if this is significant.)
Tropical total ozone columns also depend critically on
the evolution of tropospheric partial column ozone,
which is either decreasing or increasing in the future,depending on the RCP scenario (Igelsias-Suarez et al.,Global Ozone | Chapter 3

305

275

Global (annual)TCO (DU)

TCO (DU)

300
295
300
285

265
260
255
250

280
1960 1980 2000 2020 2040 2060 2080 2100

370

Year
360

NH mid-lat

350

SH mid-lat

340

TCO (DU)

360

TCO (DU)

SEN-C2-RCP85
SBUV MOD

REF-C2
SEN-C2-RCP45

1960 1980 2000 2020 2040 2060 2080 2100

Year
380

Tropics (annual)

270

350
340

330
320

330

310

320

300

1960 1980 2000 2020 2040 2060 2080 2100

Year

1960 1980 2000 2020 2040 2060 2080 2100

Year

Figure 3-29. Adjusted and smoothed multi-model mean time series of annual mean TCO from observations(solid black lines and symbols) and for RCP scenarios from CCMI models: REF-C2=RCP-6.0 (red lines), SENC2-RCP45=RCP-4.5 (blue lines), and SEN-C2-RCP85=RCP-8.5 (orange lines). The black dashed lines represent
1980 values of TCO. From Dhomse et al. (2018).

2016; Meul et al., 2016). In most scenarios, tropical
tropospheric ozone columns decrease during the second half of the 21st century, though there are large differences among models (Section 3.4.4). The exception
is RCP-8.5, which shows a strong increase in tropical
tropospheric ozone in most models due to a strong increase in methane. There is some cancelation between
the projected upper-stratospheric increase in tropicalozone (e.g., due to GHG-induced cooling) and the
projected decrease in lower-stratospheric ozone, but
the overall effect on stratospheric ozone columns depends on the RCP scenario (Banerjee et al., 2016).

3.4.3.2	Influence of Nitrous Oxide and Methane
Methane (CH4) and nitrous oxide (N2O) affect stratospheric ozone not only through the temperaturechanges associated with their radiative forcing, butalso through chemistry: N2O is the main source of
odd nitrogen (NOy) in the stratosphere, while CH4
is an important source for hydroxyl radicals (HOx)but is also involved in chlorine deactivation into HCl.
Currently, N2O is the most important ODS emitted
in terms of its Ozone Depletion Potential (ODP).
However, the ODP metric was traditionally developed
for long-lived halogen-containing ODSs. It uses CFC11 as a reference gas, and it is not an optimal metric3.47

Chapter 3 | Global Ozone

Past and Projected Tropical Ozone Anomalies(a) 15

Total Column Ozone (20°S–20°N)

Anomalies (DU)

10
5
0
−5
−10
−15

Anomalies (DU)(b) 105Partial Column Ozone (< 10 hPa)
RCP-4.5
RCP-6.0
RCP-8.5

0
−5
−10

(c)

5

Partial Column Ozone (100–10 hPa)

Anomalies (DU)

0
−5
−10
−15
−20
−25

Anomalies (DU)(d) 15

Partial Column Ozone (1000–100 hPa)

10
5
0
−5
1960

1980

2000

2020

2040

2060

2080

2100

Year

Figure 3-30. Past and projected tropical ozone
for different RCPs: (a) total-column ozone, (b)upper-stratospheric partial column ozone, (c)lower-stratospheric partial column ozone, and (d)tropospheric partial column ozone. From Meul etal. (2016).

3.48

for assessing the impact of future N2O emissions.
Specifically, the ODP value for N2O depends criticallyon the level of other GHGs and the degree of halogen loading. This sensitivity is due to the chemicalinteractions of NOx with HOx and ClOx, as well as thetemperature dependence of those interactions (Revell
et al., 2015). The ODP of N2O was calculated using thechemistry–climate model SOCOL (Revell et al., 2015)
for year-2100 conditions under a range of different
CH4 and CO2 concentrations (Figure 3-31). Figure
3-31a shows the calculated global mean total ozone
column as a function of CO2 and CH4 global meansurface mixing ratios, with all other parameters held
constant according to RCP-6.0. Global mean totalozone increases for increasing CO2 and increasing
CH4 concentrations. Reductions in global mean totalozone relative to the modeled value for the year 2000
of 314 DU (white contour) are primarily due to reductions in tropospheric ozone columns resulting fromreductions in ozone precursor emissions in the model
scenarios (Revell et al., 2015). Figure 3-31b shows thecalculated ODP of N2O in the year 2100 as a function of CO2 and CH4. The ODP of N2O for 2100 will,
under essentially all conditions, be larger than the calculated value of 0.015 for the year 2000 because of interactions of NOx chemistry with chlorine chemistry.
Under high chlorine loading, increasing NOx reducesozone depletion by deactivating reactive chlorine.
Higher CO2 levels induce cooling of the stratosphere,
which increases the chemical destruction of NOx andreduces the efficiency of ozone destruction by N2O(Stolarski et al., 2015). Higher levels of CH4 also slow
NOx-driven ozone loss, but they lead to an increased
ODP of N2O. This is because increased CH4 reduces the efficiency of CFC-11 at destroying ozone, and
CFC-11 is used as a reference gas in the ODP concept(Revell et al., 2017).
One study analyzing the effect of N2O and CH4changes on ozone from a range of CCMI-1 models
shows that the global-average impact of N2O increases on total column ozone is highly model-dependent(Morgenstern et al., 2017). Another analysis of simulations from the WACCM model with different N2O
and CH4 scenarios concludes that extratropical totalozone could either remain weakly depleted or evenGlobal Ozone | Chapter 3

3.4.3.3	Sensitivity to Geoengineering/Solar­Radiation Managementchanges in solar radiation and corresponding photolysis rates, both of which impact ozone chemistry, and3) through resulting changes in stratospheric dynamics and transport. The most discussed and studied
SRM schemes involve enhancement of stratosphericsulfate aerosols. Chapter 6, Section 6.2.5 provides adetailed overview of the impact of deliberate climate
interventions. A number of recent chemistry–climate
modeling studies, including coordinated multi-modelstudies (GeoMIP, Kravitz et al., 2013), have been performed to study the impact of deliberate stratosphericsulfate aerosol enhancements on the ozone layer (e.g.,
Pitari et al., 2014). The effect on stratospheric ozone
depends critically on the assumed stratospheric halogen loading; i.e., it will be different under a scenario
where halogen loading is enhanced (e.g., for the middle of the 21st century) than under a scenario withlittle halogen loading (e.g., corresponding to the endof the 21st century). While much can be learned fromthe effects of enhanced sulfate aerosol loading following volcanic eruptions (see also Chapter 5, Section
5.2.3), the long-term effects of deliberate sulfate aerosol increases will be different from volcanic effects
due to the different timescales involved (Pitari et al.,2014). In addition to their use in studying stratospheric chemistry perturbations taking place throughthe enhanced surface area density of the sulfuric acidaerosols, chemistry–climate models have been used
to examine the impact on long-lived species transport
due to the aerosol-driven surface cooling coupled
to the stratospheric warming (Visioni et al., 2017).
Perturbed concentrations of CH4, N2O, and otherlong-lived tracers would feed back on short-lived species that regulate stratospheric ozone depletion. Any
significant reduction in stratospheric ozone associated with sulfate aerosol enhancement would further
lead to decreases in tropospheric ozone through decreased stratosphere-to-troposphere transport (STT)of ozone and increased UV radiation (Xia et al., 2017).

Deliberate increases in stratospheric aerosol loading
to counter the effects of GHG-induced global warming have been discussed in recent years. This is often
termed geoengineering or, more specifically, solar
radiation management (SRM; see also Chapter 6).
These schemes would impact stratospheric ozone
in a number of ways: 1) through chemical effectsof the increased aerosol loading, 2) through resulting changes in stratospheric temperature as well as

More recent studies have explored strategic injections of sulfate aerosols into the stratosphere at locations other than the equator. One examination of a
scenario in which injection at 15°N, 15°S and 30°N
was steadily increased over the 21st century foundthat mean surface temperature and the equator-topole temperature gradients were kept near 2020 levels despite the strong climate forcing of the RCP-8.5
scenario (Richter et al., 2018). Polar column ozone(a)RCP-8.5

3500

330

CH 4(ppb)

320
2500
315
2000
RCP-6.0
1500

RCP-2.6
400

310

Total Column Ozone (DU)

325
3000

RCP-4.5
500

600

700

800

900

CO2 (ppm)(b)

RCP-8.5

0.03

3500

0.025

2500
0.02

2000
RCP-6.0

RCP-4.5
1500

RCP-2.6400

ODP of N2O

CH4 (ppb)

3000

0.015
500

600

700

800

900

CO2 (ppm)

Figure 3-31. Effect of different abundances of CO2
and CH4 on (a) global mean total column ozone
and on the (b) ODP of N2O in 2100. Black squares
indicate the CO2 and CH4 surface concentrationsin 2100 for the various RCP scenarios. White contour lines show values of ozone (314 DU) and the
ODP of N2O (0.015) for year-2000 conditions. From
Revell et al. (2015)

increase well above historical levels, depending on theCH4 and N2O scenario (Butler et al., 2016).

3.49

Chapter 3 | Global Ozone

recovered to pre-ozone hole conditions by the end of
the century but, in contrast to RCP-8.5, did not exceed pre-1980 levels. In the middle and high latitudes,
ozone recovered and even exceeded RCP-8.5 values insome latitude bands and some months. Another study
looked at differences between high- and low-altitude
injections, assuming injection latitudes of 15°N and
15°S (Tilmes et al., 2018). Ozone destruction was lesssevere when sulfate aerosols were injected at 70 hPa
rather than 30 hPa, and for middle and high latitudes,
the low-altitude injections resulted in more column
ozone than without geoengineering in winter.

in stratospheric ozone, with a calculated global total
column increase of about 8% (Nowack et al., 2016).
Because SRM schemes will not substantially reduce
the upper-stratospheric cooling due to GHG increases, stratospheric ozone will strongly increase. In addition, reduced solar radiation under SRM would leadto further chemical ozone enhancement, according
to this study. Increases in tropical upwelling (BDC)would be reduced with SRM, mediating the climatechange-induced reduction in tropical lower-stratospheric ozone.

A number of studies have also investigated the effectof solid particles for SRM schemes (e.g., Tang et al.,2014; Tang et al., 2016; Keith et al., 2016; Weisenstein
et al., 2015). These highly refractive particles willlead to much less warming of the tropical lower
stratosphere compared to sulfate aerosols (Keith etal., 2016). In particular salts of alkaline metals suchas calcite (CaCO3) have been proposed for SRM;
these would have the side effect of chemically removing acids such as HCl or HNO3 that are involved inozone depletion. Model simulations have been used
to investigate and compare the impact of proposed
geoengineering schemes using sulfate, titania, andblack carbon particles on the stratosphere (Jones etal., 2016). However, so far not enough information isavailable to assess how these proposed SRM schemeswith solid aerosol particles will affect the evolution ofstratospheric ozone under different GHG and ODS
scenarios.

3.4.4 Impacts on Tropospheric Ozone

A recent chemistry–climate modeling study investigated the effect of solar radiation management
schemes on stratospheric and surface ozone. It focused on the generic impact of SRM schemes; i.e.,ozone changes due to the reduction in solar radiation,
stratospheric temperature changes, and resulting
changes in dynamics and transport (Nowack et al.,2016). In this study, the radiative forcing due to increased GHGs was balanced by a reduction in solar
radiation at the top of the atmosphere without explicit
treatment of stratospheric aerosol enhancements (animplementation colloquially termed space mirrors).
This generic scheme thus shows some general aspectsof SRM schemes without the particular chemical andradiative aspects that are specific to sulfate or otheraerosol enhancements. The study shows that this generic SRM scheme would lead to strong enhancements
3.50

Both ozone recovery and the projected strengtheningof the stratospheric circulation associated with GHG
increases act to increase the downward transport ofozone from the stratosphere to the troposphere in
the extratropics (e.g., Zeng and Pyle, 2003; Hegglin
and Shepherd, 2009; Banerjee et al., 2016; see also
Chapter 5, Section 5.3.3). The impact on tropospheric ozone is greatest in the upper troposphere(UT), where ozone’s radiative effect is largest. Climatechange has the largest impact in the subtropics, andozone recovery primarily affects UT ozone in theextratropics (Banerjee et al., 2016). The net impactsof these processes, however, given concurrent changes in precursor emissions, temperature, and water
vapor, are highly model- and scenario-dependent,
as described below. Figure 3-32 shows the sensitivity of ozone to ODSs and long-lived GHGs for seven
CCMI-1 models (Morgenstern et al., 2018). There aresizable discrepancies among the models throughout
the atmosphere, but these discrepancies are particularly large in the troposphere. A separate set of simulations showed a somewhat surprising sensitivity of
tropospheric ozone to changes in N2O; this sensitivity
was dominated by chemical depletion of stratosphericozone by N2O (Morgenstern et al., 2018).
Using the UM-UKCA model under both RCP-4.5 and
RCP-8.5 emissions scenarios in which the methaneboundary condition was held constant, a recent study
found that the projected increase in STT associatedwith climate change and ozone recovery offsets decreases in net chemical production associated with
reductions in ozone precursor emissions (Banerjee
et al., 2016), in agreement with earlier work (Sekiya
and Sudo, 2014). Enhanced STT increases the global tropospheric lifetime of ozone (τO3) because itGlobal Ozone | Chapter 3

CCSRNIES-MIROC3.2 Jul
10

100

100

1000

1000

CESM1-WACCM Jul

10

10

100

100

1000
10

10

100

100

10

10

100

100

10

10

100

100

10

10

100

100

10

100

100

-50

0

.2

.5

1

3

ppbv (O3)/ppbv(Clyeq)

UMSLIMCAT Jul

1000

50

Latitude (deg)−500 −100−10 −3 −1 −.5 −.2 0

ULAQ-CCM Jul

1000

UMSLIMCAT Jul

10

1000

NIWA-UKCA Jul

1000

ULAQ-CCM Jul

1000

CMAM Jul

1000

NIWA-UKCA Jul

1000

CHASER-MIROC-ESM Jul

1000

CMAM Jul1000

CESM1-WACCM Jul

1000

CHASER-MIROC-ESM Jul

1000

Pressure ( hPa)CCSRNIES-MIROC3.2 Jul10

10 100 300

−10

-50

−1

0

50

Latitude (deg)−.1 −.01 −.005 0.002 .005

.01

.1

1

10

ppbv (O3)/ppmv(CO2eq)

Figure 3-32. Ratio of zonal mean ozone volume mixing ratio (VMR) changes to VMR in (left column) equivalent Cl (Newman et al., 2007) and (right column) equivalent CO2 (sum of CO2, HFCs, PFCs, and SF6) for seven
CCMI-1 models for July. The solid black lines indicate 150 ppbv of ozone. Adapted from Morgenstern et al.
(2018).

3.51

Chapter 3 | Global Ozone

increases the ozone burden in the UT, where ozone
is long lived. For climate change, this enhancement
is offset by greater water vapor-induced loss of tropospheric ozone (reduction in lifetime of 0.4–6.7%,
depending on scenario); for ozone recovery, the increase in τO3 is enhanced by decreases in OH associated with decreased photolysis rates (increase of~0.5%) (Bannerjee et al., 2016; Zhang et al., 2014).
This decrease in OH may enhance the intercontinental transport of ozone and other pollutants, increasingozone attributable to Asian emissions by up to 15%, or
0.3 ppbv, in the Northern Hemisphere (Zhang et al.,2014). It should be noted, however, that global models
differ by nearly a factor of 2 in their predictions of OHchanges associated with stratospheric ozone recovery(Madronich et al., 2015).
While ozone recovery and projected increases in theozone flux associated with STT have a clear influence
on τO3, the net change in lifetime depends strongly
on changes in precursor emissions, temperature,and humidity and is highly scenario-dependent.
Uncertainties in the future evolution of CH4 dominate differences in tropospheric ozone among RCP
scenarios (e.g., Revell et al., 2012, 2015; Young et al.,2013; Naik et al., 2013). Within any given scenario,changes in tropospheric ozone depend primarilyon changes in ozone precursors. The SOCOL CCM,
using RCP-6.0, simulates a 23% decrease in global
mean tropospheric ozone between 1990 and 2060.
This decrease is dominated by reductions in NOx

3.52

emissions. The increased STT associated with climatechange contributes <1 ppb of additional ozone at 500
hPa between 1960 and 2090. This additional ozone,
as well as that attributable to increased production ofNOx by lightning, was largely offset by increases inozone chemical loss driven by higher temperaturesin the model. Under a “solar mirror” geoengineering
scenario in the HadGEM3 CCM, higher stratosphericozone levels combined with lower atmospheric-specific humidity resulted in an overall increase in surface ozone of ~5% (Nowack et al., 2016), assuming nochange in precursor emissions.
Observations cannot yet be used to evaluate projections of the tropospheric ozone response to strengthening of the stratospheric circulation and ozone
recovery. However, satellite observations have beenused to quantify the link between year-to-year variations in circulation strength and lower-stratosphericozone abundances, with a 40% increase in circulation
associated with a ~25% increase in NH mid-latitude
lower-stratospheric ozone (Neu et al., 2014). Thesechanges are similar to the long-term increases in circulation and ozone predicted by CCMs from 1960 to2100 (Hegglin and Shepherd, 2009) and are associatedwith a ~2% increase in tropospheric ozone (Neu et al.,2014). Taken together with the modeling studies described above, this study suggests that future changes
in tropospheric ozone are likely to be dominated bychanges in precursor emissions, with the stratosphereplaying a relatively minor role in increasing tropospheric ozone abundances and lifetime.

Global Ozone | Chapter 3

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Young, P.J., A.T. Archibald, K.W. Bowman, J.-F. Lamarque, V. Naik, D.S. Stevenson, S. Tilmes, A.
Voulgarakis, O. Wild, D. Bergmann, P. Cameron-Smith, I. Cionni, W.J. Collins, S.B. Dalsøren,R.M. Doherty, V. Eyring, G. Faluvegi, L.W.
Horowitz, B. Josse, Y.H. Lee, I.A. MacKenzie, T.
Nagashima, D.A. Plummer, M. Righi, S.T. Rumbold, R.B. Skeie, D.T. Shindell, S.A. Strode, K.
Sudo, S. Szopa, and G. Zeng, Pre-industrial toend 21st century projections of troposphericozone from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP),
Atmos. Chem. Phys., 13, 2063–2090, doi:10.5194/
acp-13-2063-2013, 2013.
Zawada, D.J., L.A. Rieger, A.E. Bourassa, and D.A.
Degenstein, Tomographic retrievals of ozone
with the OMPS Limb Profiler: Algorithm description and preliminary results, Atmos. Meas.
Tech., 11, 2375–2393, doi:10.5194/amt-11-23752018, 2018.
Zeng, G., and J.A. Pyle, Changes in tropospheric ozone between 2000 and 2100 modeled in achemistry‐climate model, Geophys. Res. Lett., 30(7), doi:10.1029/2002GL016708, 2003.
Zhang, L., D.J. Jacob, X. Yue, N.V. Downey, D.A.
Wood, and D. Blewitt, Sources contributing tobackground surface ozone in the US Intermountain West, Atmos. Chem. Phys., 14, 5295–5309,
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Chapter 3 | Global Ozone

doi:10.5194/acp-14-5295-2014, 2014.
Ziemke, J.R., S. Chandra, B.N. Duncan, L. Froidevaux,
P.K. Bhartia, P.F. Levelt, and J.W. Waters, Tropospheric ozone determined from Aura OMI and
MLS: Evaluation of measurements and comparison with the Global Modeling Initiative’s Chemical Transport Model, J. Geophys. Res., 111 (D19),
D19303, doi:10.1029/2006JD007089, 2006.

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Global Ozone | Chapter 3

Appendix 3A
Data Sources
3A.1

Ground-based Measurements

Ground-based instruments at numerous stations around the globe monitor changes in total column and profileozone amounts at local to regional scales (WMO, 2014; Hassler et al., 2014). These instruments include:• remote-sensing instruments such as Dobson and Brewer spectrophotometers (Fioletov et al., 2002,

2008; Petropavlovskikh et al., 2005, 2009, 2011), SAOZ spectrometers (Hendrick et al., 2011), filterozonometers (Bojkov et al., 1994), FTIR spectrometers (Hase et al., 1999; Vigouroux et al., 2015), lidars(Claude et al., 1988; Godin et al., 1989; McDermid et al., 1990), and microwave radiometers (MWR;
Parrish et al., 1992; McDermid et al., 1998a,b; McPeters et al., 1999; Calisesi et al, 2003; Studer et al.,2013; Nedoluha et al., 2015); and• in situ instruments such as balloon-borne ozonesondes (Komhyr, 1969; Smit et al., 2007; Deshler et al.,2008, 2017) and aircraft-mounted sensors (Thouret et al., 1998; Nédélec et al., 2015).

Since the last Assessment, most ground-based data records were merely extended in time. However, several station records were revised to address inhomogeneities in time (changes in measurement process at the site) or inspace (differences in measurement process between sites in the network). Such revisions were done for 2 MWR
stations and for about 30 ozonesonde sites. The Bern microwave instrument was upgraded and the entire timeseries was referenced to the current spectrometer (Moreira et al., 2015). The Payerne microwave data changed asa result of improvements in the retrieval method and in the integration of the measurements (private communications with PI Maillard-Barras). Both revised MWR records were used for this Assessment. Over the past few
years, the ozonesonde community has put considerable effort into reducing uncertainties in the measurements
to 5–10%. Biases between different types of ozonesonde instrumentation have been characterized, correction
schemes developed, and the ozone profile records of 30 stations in the NDACC, GAW, and SHADOZ networks
reprocessed accordingly (Tarasick et al., 2016; Van Malderen et al., 2016; Deshler et al., 2017; Witte et al., 2017;
Sterling et al., 2018). Further efforts to assess the outcome of this homogenization activity are ongoing. At thetime of this Assessment, not all reprocessed sonde data were available.
The profile trends assessed here are based on observations at the sites listed in Table 3A-1, most of which haveoperated continuously for at least 20 years. Zonally averaged, ground-based data records, one for each measurement technique, were computed from deseasonalized anomaly time series at each site in a given latitude band
(Fioletov et al., 2002, 2008; WMO, 2014; Steinbrecht et al., 2017; LOTUS, 2018). Such an approach reduces theimpact of station-dependent instrument biases, temporal coverage, and sampling.
The WOUDC ground-based total column dataset is based on available Dobson, Brewer, SAOZ, and filter ozonometer data that have been averaged monthly and zonally (using a TOMS v7 climatology to translate deviations in ozone at a single point into zonal mean deviations) and then binned in 5-degree intervals (Fioletov etal., 2002). Time series based on these relatively sparse ground-based measurements may not always reproduce
monthly zonal fluctuations well, particularly in the tropics and Southern Hemisphere. However, seasonal (andlonger) averages can be estimated with a precision comparable with satellite-based datasets (~1%) (Chiou et al.,2014).

3A.2

Space-Based Ozone Profiles

Space-based observations of stratospheric ozone are performed in nadir-, limb-, or ­­­occultation-viewing geometry
in different wavelength ranges using different measurement techniques (Chiou et al., 2014; Hassler et al., 2014;
3.69

Chapter 3 | Global Ozone

Table 3A-1. Overview of the sources of ozone profile observations by ground-based techniques used for themonthly zonal mean data considered in this Assessment. Stations are sorted by instrument type and chronologically by the starting year of the record. Those with an asterisk are located outside the attributed latitude
zones.
Instruments andData Archives

35°–60°S

20°S–20°N

35°–60°N
Goose Bay (1963),
Uccle (1965),
Hohenpeißenberg (1966),
Payerne (1968),
Edmonton (1970),
Wallops Island (1970),
Lindenberg (1975),
Legionowo (1979),
Praha (1979),
Boulder (1991),
De Bilt (1992),
Valentia (1994),
Huntsville* (1999)

Ozonesonde (0– 30 km)
http://www.ndsc.ncep.
noaa.gov/data,
http://www.woudc.org/
data/explore.php?lang=en,
https://tropo.gsfc.nasa.
gov/shadoz/Archive.html

Lauder (1986),
Macquarie Island(1994),
Broadmeadows(1999)

Hilo (1982),
Ascension Island(1998),
Kuala Lumpur (1998),
Nairobi (1998),
Natal (1998),
Pago Pago (1998),
Suva (1998),
Hong Kong
Observatory* (2000)

Lidar (15–50 km)
http://www.ndsc.ncep.
noaa.gov/data

Lauder (1994)

Mauna Loa (1993)

OHP (1986),
Hohenpeißenberg (1987),
Table Mountain (1988)

Microwave (MWR)(20–70 km)
http://www.ndsc.ncep.
noaa.gov/data

Lauder (1992)

Mauna Loa (1995)

Bern (1994),
Payerne (2000)

FTIR (0–50 km)
http://www.ndsc.ncep.
noaa.gov/data

Wollongong(1996),
Lauder (2001)

Izaña* (1999)

Jungfraujoch (1995)

Mauna Loa (1984)

Arosa (1956),
Boulder (1984),
OHP (1984),
Fairbanks (1994)

Dobson/Brewer Umkehr(0–50 km)ftp://aftp.cmdl.noaa.gov/
data/ozwv/DobsonUmkehr/Stray%20light%20
corrected/monthlymean

3.70

Stations (Start of Data Record)

Perth (1984),
Lauder (1987)

Global Ozone | Chapter 3

Table 3A-2. Merged satellite vertical ozone profile datasets used in this Assessment (monthly zonal meandata).
Instruments and Data
Version

SBUV-NASA
MOD v8.6(release 6)

Nimbus 4 BUV v8.6
Nimbus 7 SBUV v8.6
NOAA 11 SBUV/2 v 8.6
NOAA 14 SBUV/2 v8.6
NOAA 16 SBUV/2 v8.6
NOAA 17 SBUV/2 v8.6
NOAA 18 SBUV/2 v8.6
NOAA 19 SBUV/2 v8.6

Partial columns overpressure layers

80°N–80°S
5 deg

50–0.5 hPa,
9 layers
1970–2016(~6–15 km thick)

SBUV-NOAA
COH v8.6

Nimbus 4 BUV v8.6
Nimbus 7 SBUV v8.6
NOAA 11 SBUV/2 v 8.6
NOAA 14 SBUV/2 v8.6
NOAA 16 SBUV/2 v8.6
NOAA 17 SBUV/2 v8.6
NOAA 18 SBUV/2 v8.6
NOAA 19 SBUV/2 v8.6

Partial columns overpressure layers

80°N–80°S
5 deg

50–0.5 hPa,
13 layers
1978–2016(~6–15 km thick)

GOZCARDS
v2.20

SAGE I v5.9_rev,
SAGE II v7.0,
HALOE v19,
Aura MLS v4.2

Mixing ratio atpressure levels

90°S–90°N,
10 deg

215–0.2 hPa

1979–2016

SAGE II v7.0,
HALOE v19,
SWOOSH v2.60 UARS MLS v5,
SAGE III v4,
Aura MLS v4.2

Mixing ratio atpressure levels

90°S–90°N,
10 deg (also 5
and 2.5 deg)

316–1 hPa,
~3 km

1984–2016

SAGE II v7.0,
SAGE IIOSIRIS v5.10,
OSIRIS- OMPSOMPS USask-2D v1.0.2

Number density(anomaly) ataltitude levels

60°S–60°N,
10 deg0–50 km,
1 km

1984–2016

SAGE II v7.0,
OSIRIS v5.10,
GOMOS ALGOM2s v1,
MIPAS IMK/IAA v7, ­SCIAMACHYUBr v3.5,
ACE-FTS v3.5/3.6,
OMPS USask-2D v1.0.2

Number density(­anomaly) ataltitude levels

90°S–90°N,
10 deg

10–50 km,
1 km

1984–2016

Deseasonalizedozone anomalies ataltitude levels

60°S–60°N,
10 deg

10–50 km,
1 km

1984–2017

SAGE IICCIOMPSSAGE II v7.0,
SAGE IIMIPAS IMK/IAA v7,
MIPAS- OMPSOMPS NASA v2.0

Ozone
­Representation

Latitude
Altitude
Coverage and C­ overage andSampling
Sampling

Merged Data
Set

TemporalCoverage

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Chapter 3 | Global Ozone

Table 3A-3. Merged total ozone column datasets used in this Assessment (annual zonal mean data).
Instruments

RecordlengthReference

URL

WOUDC

Dobson,Brewer,SAOZ,
Filter ozonometer

1964–2016

Fioletov et al. (2002,2008)

http://woudc.org/archive/Projects-Cam
paigns/ZonalMeans

SBUV NASA MOD v8.6
(release 6)

Nimbus 4 BUV
Nimbus 7 SBUVNOAA 11 SBUV/2
NOAA 14 SBUV/2
NOAA 16 SBUV/2
NOAA 17 SBUV/2
NOAA 18 SBUV/2
NOAA 19 SBUV/2

1970–2016

Frith et al. (2014)

http://acdb-ext.gsfc.nasa.gov/Data_services/merged

SBUV NOAA
COH v8.6

Nimbus 4 BUV
Nimbus 7 SBUVNOAA 11 SBUV/2
NOAA 14 SBUV/2
NOAA 16 SBUV/2
NOAA 17 SBUV/2
NOAA 18 SBUV/2
NOAA 19 SBUV/2

1978–2016

Wild et al. (2016)

ftp://ftp.cpc.ncep.noaa.gov/SBUV_CDR

GTO

GOME,
SCIAMACHY,
GOME-2A,
OMI

1995–2016

Coldewey-Egbers etal. (2015)

http://www.esa-ozone-cci.
org/?q=node/163

GSG

GOME,
SCIAMACHY,
GOME-2A

1995–2016

Weber et al. (2011,2016)

http://www.iup.uni-bremen.de/gome/
wfdoasMerged dataset

Weber et al., 2017; Garane et al., 2018). Revised datasets have been released for most instruments in recent years:(nadir) GOME, SCIAMACHY, GOME-2, OMI (Wever et al., 2017; Garane et al., 2018); (limb) OSIRIS v5.10(Bourassa et al., 2018), SCIAMACHY IUP v3.5 (Jia et al., 2015), Aura MLS v4.2 (Livesey et al., 2018); (occultation) SAGE II v7.0 (Damadeo et al., 2013, 2014), GOMOS ALGOM2s v1 (Sofieva et al., 2017), and ACE-FTS
v3.6 (Boone et al., 2013; Sheese et al.; 2015). Revisions include modification of calibration and altitude-registration data as well as updates to radiative transfer models, retrieval and screening algorithms, and meteorologicaldatasets used to convert retrieved ozone values to different units. The largest improvements in stability of thedata record were achieved for OSIRIS (altitude registration), MIPAS (calibration data), SCIAMACHY (retrieval
algorithm), and SAGE II (meteorological data).
The records of two additional instruments have now reached sufficient length and maturity to be used in trend
assessments: The Ozone Mapping and Profiler Suite (OMPS), onboard the Suomi-NPP platform launched in2011, provides total column data (nadir) in addition to profile data in nadir and limb geometry (Jaross et al.,2014; Kramarova et al., 2014, 2018; Arosio et al., 2018; Zawada et al., 2018). The Infrared Atmospheric Sounding
Interferometer (IASI), on the MetOp-A platform launched in 2006, provides nadir ozone profiles (Clerbaux etal., 2009; Dufour et al., 2012; Boynard et al., 2016; Wespes et al., 2016, 2018).
Merged, space- and time-gridded profile records can be categorized in a number of ways, depending on the typeof instruments used (nadir versus limb), the profile representation (altitude/pressure, partial columns/volume
mixing ratio/number density), the adjustment procedure (single versus multiple references) and the averaging
3.72

Global Ozone | Chapter 3

method (absolute versus anomaly-based, weighted versus unweighted). The following families of merged profile
records are used in Chapter 3 (see Table 3A-2):
SBUV MOD and SBUV COH. These records are based on the series of SBUV/2 v8.6 data. Since the last
Assessment, NOAA-19 data were added, but only minor changes were made to the merging algorithms of the
Merged Ozone Data (MOD) release 6 (Frith et al., 2017) and Cohesive data record (COH; Wild et al., 2016).
The approach used for the MOD dataset is to average data from different records during overlap periods; theapproach used for COH is to adjust and chain contiguous records sequentially. SBUV COH also incorporates
some corrections to individual satellite profiles and excludes measurements from some SBUV instruments (e.g.,
the NOAA-9 ascending node data).
GOZCARDS and SWOOSH. A second family of merged datasets is built around SAGE II and Aura MLS:GOZCARDS v2.20 and SWOOSH v2.6 (Froidevaux et al., 2015; Davis et al., 2016). Both records use SAGE II
as an absolute reference, but differences are expected from their use of different instruments, adjustment procedures, and averaging methods. For GOZCARDS, each record is weighted equally in the average after adjusting
derived monthly zonal mean data during the overlap period. For SWOOSH, space–time collocated profile pairs
are used for the adjustments, and weighting is done according to the number of observations. Current versionsof GOZCARDS and SWOOSH differ considerably from those used in the previous Assessment; these differences are largely a result of revisions in the input data records and/or a different selection of instruments.
SOO, SCO, and SMO. The last family comprises records constructed from SAGE II and two (or more) other
instruments: SAGE-OSIRIS-OMPS (SOO), SAGE-CCI-OMPS (SCO), SAGE-MIPAS-OMPS (SMO) (Bourassa
et al., 2014, 2018; Sofieva et al., 2017). All of these were constructed by 1) adjusting deseasonalized anomalies ofindividual records to those of SAGE II and then 2) either computing an unweighted (SOO) or weighted (SMO)
average or the median (SCO). The latter exploits the larger ensemble of up to five instruments in the 2002–2012
period. The MIPAS-based record requires using ACE-FTS as a transfer standard between MIPAS and OMPS,
which leads to larger uncertainties in the adjustments. This 3-member family can be considered new to the
WMO Assessment. Besides the addition of OMPS data, the previously used SAGE-GOMOS and SAGE-OSIRIS
records were based on older and less stable versions of GOMOS and OSIRIS data.

3A.3

Space-Based Total Ozone Columns

Zonal and global total ozone time series are regularly updated and reported; e.g., in the annual State of the
Climate reports published in the Bulletin of the American Meteorological Society (BAMS) (Weber et al., 2016,2017). They are based on ground-based measurements, as well as merged data from multiple satellite instruments. The following total ozone datasets are used in this report (Table 3A-3; see Weber et al., 2018 for details).
SBUV MOD and SBUV COH. Both datasets are based on integrated vertical ozone profiles from the SBUV
MOD and SBUV COH datasets described in Section 3A.2 (Bhartia et al., 2013).
GTO. The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record (Coldewey-Egbers etal., 2015) is based on GOME, SCIAMACHY, OMI, and GOME-2A measurements. The total ozone columns wereretrieved using the GOME-type Direct FITting (GODFIT) version 3 algorithm (Lerot et al., 2014). Adjustments
to OMI measurements were used to merge data from different instruments into one record in order to correct
for small remaining inter-sensor biases and temporal drifts. The record was validated using ground-based measurements (Coldewey-Egbers et al., 2015; Koukouli et al., 2015; Garane et al., 2018).
GSG. The merged GOME-SCIAMACHY-GOME-2A (GSG) total ozone time series (Kiesewetter et al., 2010;
Weber et al., 2011, 2016) consists of total ozone data that were retrieved using the University of Bremen
Weighting Function DOAS algorithm (Coldewey-Egbers et al., 2005; Weber et al., 2005). The SCIAMACHY
and GOME-2A observations were successively adjusted for apparent offsets to be continuous with the original
3.73

Chapter 3 | Global Ozone

GOME data. These offsets were determined as a function of latitude in steps of 1 degree using monthly zonalmeans and then smoothed over 10-degree latitude bands (Weber et al., 2018).
Thus, similar to the SBUV MOD and COH datasets, GTO and GSG are not independent and are in fact based on
almost the same measurements by GOME, SCIAMACHY, and GOME-2A, although GTO also uses OMI data.
The main difference is in the processing algorithms and/or how the data from different satellites were merged
together.

3.74

Chapter 4
Polar Stratospheric Ozone: Past, Present, and Future
Lead Authors
U. Langematz
M. Tully
Coauthors
N. Calvo
M. Dameris
A.T.J. de Laat
A. Klekociuk
R. Müller
P. Young
Contributors
S. Alexander
S. Dhomse
B. Funke
J.-U. Grooß
S. Kremser
G.L. Manney
S. Molleker
E. NashM.C. Pitts
A. Saiz-Lopez
F. Schmidt
M. Sinnhuber
R. Spang
I. Tritscher
P. von der Gathen
M. Weber
Review Editors
W. Randel
S. Solomon

Cover photo: An ozonesonde launch on a small balloon from Davis Station in Antarctica. Ozonesondes provide
high-resolution measurements of the vertical profile of ozone concentrations in the atmosphere. Long-term monitoring, conducted at a small number of stations in the Arctic and Antarctic as part of the WMO Global Atmosphere
Watch Programme, provides essential data for understanding the processes that affect polar ozone and ozone
changes over time. Photo: Courtesy of V. Heinrich, Bureau of Meteorology, Australia

Chapter 4
Polar Stratospheric Ozone: Past, Present, and Future
CONTENTSSCIENTIFIC SUMMARY .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 14.1INTRODUCTION .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 5

4.1.1 Summary of Findings from the Previous Ozone Assessment  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 54.1.2 Scope of Chapter .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 54.2RECENT POLAR OZONE CHANGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

4.2.1 Measurements of Ozone and Related Constituents .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 64.2.2 Evolution of Polar Temperatures and Vortex Characteristics  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 64.2.2.1

Temperatures and PSC Volume  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 6

4.2.2.2

Polar Vortex Breakup Dates  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 8

4.2.3 Ozone Depletion in ­Antarctic Springs (2014–2017) .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 94.2.3.1

Antarctic Spring 2015: Volcanic and Dynamical Influence on Ozone .  .  .  .  .  .  . 12

4.2.3.2

Antarctic Spring 2017: Dynamical Influences on Ozone .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 12

4.2.4 Ozone Depletion in ­Arctic Springs (2014–2017)  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 13
4.2.4.1
4.3

Arctic Spring 2016: Record Ozone Depletion Halted by Major Warming  .  .  . 15

UNDERSTANDING OF POLAR OZONE PROCESSES .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 15

4.3.1 Polar Stratospheric Clouds .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 15
4.3.1.1

Observations of PSC Extent and Composition .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 15

4.3.1.2

Gravity Waves and Orographic Forcing .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 17

4.3.2 Polar Chemistry .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 18
4.3.2.1

Observations of Polar Chemistry .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 18

4.3.2.2

Laboratory Studies, Theoretical Basis, and Models .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 19

4.3.3 Very Short-Lived ­Halogenated Substances  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 21
4.3.4 Polar Dynamical Processes  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 21
4.3.4.1

Dynamical Control of Polar Ozone  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 21

4.3.4.2

Refined Understanding of Dynamical Variability  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 22

4.3.5 Other Factors Affecting Polar Ozone .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 25

4.4

4.3.5.1

Solar Variability by Energetic Particle Precipitation .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 25

4.3.5.2

Volcanic Eruptions  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 27

RECOVERY OF POLAR OZONE .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 28

4.4.1 Polar Ozone Recovery in ­Previous Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.4.2 Long-Term Antarctic Ozone Trend  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 29

Chapter 4 | Polar Stratospheric Ozone

4.4.2.1

Onset of Antarctic Ozone Depletion  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 29

4.4.2.2

Onset of Antarctic Ozone Recovery .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 29

Box 4-1. Methods Applied to Calculate Polar Ozone Trends .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 32
4.4.2.3

Summary .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 35

4.4.3 Long-Term Arctic Ozone Trend .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 35
4.4.4 Benefits Achieved by the Montreal Protocol .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 36
4.5

FUTURE CHANGES IN POLAR OZONE .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 36

4.5.1 New Ozone Projections from Chemistry–Climate Models .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 36
4.5.2 Long-Term P­ rojections of Polar Ozone  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 39
4.5.2.1

Future Antarctic Spring Total Column Ozone .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 39

4.5.2.2

Future Arctic Spring Total Column Ozone  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 39

4.5.3 Factors Controlling ­Future Polar Ozone  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 40
4.5.3.1

Changing Roles of ODSs and GHGs  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 40

4.5.3.2

Dynamic Variability in Arctic Spring .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 41

4.5.3.3

The Role of GHG Scenarios  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 42

4.5.3.4

The Role of VSLSs  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 44

4.5.4 Uncertainty in Polar Ozone Projections  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 44
4.5.4.1

Model Uncertainty .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 45

4.5.4.2

Uncertainty in Ozone Return Dates  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 46

REFERENCES .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 49

Chapter 4
Update on Polar Ozone: Past, Present, and Future
SCIENTIFIC SUMMARY
The chemical and dynamical processes controlling polar ozone are well understood. Polar ozone depletion is fundamentally driven by anthropogenic chlorine and bromine, with the severity of the chemical loss each year in bothhemispheres strongly modulated by meteorological conditions (temperatures and winds), and, to a lesser extent,
by the stratospheric aerosol loading and the solar cycle. As noted in prior Assessments, the stratospheric halogen
concentration resulting from the emission of ozone-depleting substances (ODSs) reached its peak in the polar regions
around the turn of the century and has been gradually declining since then in response to actions taken under theMontreal Protocol and its Amendments and adjustments. Early signs of ozone recovery are now beginning to appear
in the Antarctic; as the observational record lengthens, clearer ozone hole recovery trends are expected to emerge
against the background of natural variability. Nevertheless, the Antarctic ozone hole will continue to be a recurring
phenomenon until the middle of the century. The Arctic is more dynamically variable, precluding identification ofa significant increase in Arctic ozone, and cold conditions conducive to substantial ozone loss may still occur in aparticular year in the coming decades. New chemistry–climate model (CCM) projections largely confirm previous
studies that in both hemispheres, spring polar total column ozone will return to 1980 historical levels in the coming decades, albeit with a delay of a few years due to updated future ODS and greenhouse gas (GHG) emissions
scenarios.

OBSERVED CHANGES IN POLAR OZONE• The characteristics of the October Antarctic ozone hole in the years since 2014 have generally been

within the range observed since the early 1990s. In 2015, however, the ozone hole was particularly
large and long-lasting, as a result of a cold and undisturbed polar stratospheric vortex. Aerosols fromthe Calbuco volcanic eruption in April 2015 are also believed to have contributed to the large ozone
hole observed that year. Conversely, in 2017, the Antarctic ozone hole was very small due to a warm andunusually disturbed polar vortex.

• Several lines of evidence have started to emerge indicating an increase in Antarctic stratosphericozone during September. Statistically significant trends since the year 2000 have now been identified
showing an increase in observed ozone and a decrease in ozone hole size and depth. Although accounting for the large degree of natural variability is challenging, the weight of evidence from statisticalanalyses and modeling studies suggests that the decline in ODSs made a substantial contribution tothese trends.

• In the Arctic, the exceptionally low ozone abundances of spring 2011 have not been observed again

in the last four years. Arctic stratospheric springtime ozone is dominated by large year-to-year dynamically induced variability of the polar vortex, with severe ozone loss occurring in very cold years, suchas 2011. Extreme meteorological conditions in the early 2015/2016 winter led to rapid ozone loss, but asudden stratospheric warming (SSW) at the beginning of March 2016 curtailed the chemical processes
which lead to ozone destruction about a month earlier than in 2011, keeping ozone above record low
levels. Arctic ozone trends are small compared to the dynamical variability, and thus a recovery trend
remains undetectable in observations over the 2000–2016 period.

4.1

Chapter 4 | Polar Stratospheric Ozone

UNDERSTANDING OF FACTORS CONTROLLING POLAR OZONE• Observations in the Arctic winter have demonstrated that large nitric acid trihydrate (NAT) parti-

cles are a regularly occurring phenomenon in the lower stratosphere. This knowledge improves ourunderstanding of polar stratospheric cloud (PSC) formation and denitrification, which is important for
catalytic ozone loss cycles.

• Bromine-containing very short-lived substances (VSLSs) of natural origin have an important im-pact on the stratospheric halogen loading and consequently on stratospheric ozone loss in the polar
regions. The inclusion of additional stratospheric bromine from VSLSs is necessary for models to produce a realistic simulation of polar ozone loss.

• The effects of tropospheric dynamical forcing in winter on Arctic polar ozone are now better quantified. Ozone depletion in northern winters with SSWs is on average two-thirds less than in winters
without SSWs, with depletion ending about one month earlier in the year. Such an SSW was a major
influence on ozone levels observed in the Arctic winter of 2015/16.

• Polar ozone in the middle and upper stratosphere varies by 10–15% from year to year due to ener-

getic particle precipitation (EPP) related to solar variability. Satellite observations and model results
show that NOy produced in the aurora is transported from the thermosphere down into the stratospherein each winter, leading to stratospheric ozone decreases modulated by geomagnetic activity. The resulting variation in total column ozone is small (a few percent) but can persist for 2–3 years. Full EPP-effects
were not included in current assessment models.

• Model simulations show that the Montreal Protocol and its Amendments and adjustments have al-

ready brought about substantial ozone benefits. In the polar regions of both hemispheres, much larger
ozone depletion than currently observed has been avoided through implementation of the Protocol.

FUTURE EVOLUTION OF POLAR OZONE• Updated CCM projections based on full compliance with the Montreal Protocol and assuming thebaseline estimate of the future evolution of GHGs (RCP-6.0) have confirmed that the Antarctic
ozone hole is expected to gradually close, with springtime total column ozone returning to 1980
values shortly after mid-century (about 2060). The timing of the recovery of the ozone hole will not besignificantly affected by increases in GHG concentrations. There are no substantial differences between
Antarctic total ozone columns at the end of this century for the various GHG scenarios (Representative
Concentration Pathways [RCPs]).

• The timing of the recovery of Arctic total ozone in spring will be affected by anthropogenic climatechange. Based on full compliance with the Montreal Protocol and assuming the baseline estimateof the future evolution of GHGs (RCP-6.0), Arctic springtime total ozone is expected to return to1980 values before mid-century (2030s). New model simulations confirm that in the Arctic, enhanced
GHG concentrations cause an earlier return of total column ozone to historical values than a reductionof ODSs alone.

• In the second half of the 21st century CO2, CH4, and N2O will be the dominant drivers of Arctic

ozone changes, assuming full compliance with the Montreal Protocol. These gases impact bothchemical cycles and the stratospheric overturning circulation, with a larger response in stratosphericozone associated with stronger climate forcing. By 2100, the stratospheric ozone column is expected to
not only recover but to exceed 1960–1980 average values in the Arctic, with springtime Arctic ozone
being higher by about 35 DU for RCP-4.5 and about 50 DU for RCP-8.5.

4.2

Polar Stratospheric Ozone | Chapter 4• In the coming decades, substantial Arctic ozone loss will remain possible in cold winters as long as

ODS concentrations are well above natural levels. Increasing GHG concentrations may cool the lower
stratosphere and lead to enhanced formation of polar stratospheric clouds (PSCs) early in the

Arctic winter. However, one recent study indicates that no corresponding cooling is expectedin March, which is the month when persistent low temperatures lead to large chemical ozone
losses.

4.3

Chapter 4 | Polar Stratospheric Ozone

4.4

Chapter 4
Polar Stratospheric Ozone: Past, Present, and Future
4.1

INTRODUCTION

This chapter builds on the long sequence of chaptersin most of the previous Assessments that have specifically considered stratospheric ozone in the polar
regions of the earth. This history reflects the large
scientific effort that has been dedicated to observing
and understanding polar ozone changes, as well as
the great long-standing interest in polar ozone among
policymakers and the general public.

4.1.1

Summary of Findings from thePrevious Ozone Assessment

WMO (2014) reported that the Antarctic ozone hole
had continued to appear each spring, with year-toyear changes in the depth and area of the hole predominantly controlled by temperature variations,
given the slow rate of decrease of equivalent effective
stratospheric chlorine (EESC; see Chapter 1) expected in Antarctica since its peak value around theyear 2000. Somewhat reduced ozone depletion wasobserved in the years 2010, 2012, and 2013, but not
in 2011. These observations of less severe ozone depletion were broadly consistent with the anticipatedeffects of the declining levels of ozone-depleting substances (ODSs) due to the Montreal Protocol and its
Amendments and adjustments. However, a definitive
conclusion that the observed changes were due to
EESC decline could not yet be reached. Nonetheless,
WMO (2014) stated that an estimate of the increase ofspringtime Antarctic total ozone of 10 to 25 Dobson
Units (DU) could be made over the years 2000 to2010 by attempting to remove the effects of naturalvariability.
In the Arctic, exceptionally low ozone concentrations
were observed in the winter and spring of 2010/2011
between 15 and 23 km, but importantly, WMO (2014)
reported that chemistry transport models (CTMs)
were able to successfully reproduce the measured
depletion, given the observed meteorological conditions. This was seen as providing confidence that theunderstanding of Arctic ozone depletion processes

was largely correct.
Continued observations from satellites and aircraft
campaigns had further refined knowledge of PSC
formation and polar chemical processes. Althoughseveral important scientific questions had not been
resolved, these uncertainties did not hinder the
successful simulation of polar ozone destruction,
which is ultimately driven by temperatures falling
below threshold values for the activation of chlorine.
Previous issues surrounding conflicting values of the
ClOOCl absorption cross-section were consideredby WMO (2014) to be resolved, with more recent
observational studies and laboratory investigations
confirming the role of the catalytic ozone destruction
cycle initiated by the ClO+ClO reaction, with a significant contribution also from the cycle initiated by
BrO+ClO.
Additionally, WMO (2014) stated the apparent trend
towards Arctic cold winters becoming colder, as expressed through the VPSC diagnostic, had become lesscertain, as subsequent research found statisticallyweaker results.
While WMO (2011) made extensive use of model
results from the SPARC Chemistry-Climate Model
Validation-2 (CCMVal-2) initiative, no such large
suite of results was available for WMO (2014).
However, it was reported that no work subsequent to
CCMVal-2 had significantly challenged the previous
findings pointing to an earlier recovery of ozone to1980 levels in the Arctic than the Antarctic (2030 and
2050, respectively), with future levels of Arctic ozone
also showing a greater sensitivity to the greenhousegas (GHG) scenario used in model projections.

4.1.2

Scope of Chapter

This chapter updates the state of our knowledge about
stratospheric ozone in the polar regions of both hemispheres, taking advantage of systematic observational programs and special measurement campaigns,
laboratory studies, and a wide array of computer
4.5

Chapter 4 | Polar Stratospheric Ozone

modeling. The long-term record of meteorologicalconditions and ozone depletion in the polar vortex ofboth hemispheres is presented, updated for the years
following the last Assessment. The particular winter–spring seasons of 2015 and 2017 in the Antarctic and2015/2016 in the Arctic are considered in more detail.
Progress in the understanding of the many chemicaland physical processes underlying and influencing
polar ozone depletion is then reported. Recent studies
seeking to identify a statistically significant trend due
to declining anthropogenic halogen levels (known as
the second stage of Antarctic ozone recovery as set
out in WMO (2007)) are discussed, before the chapter
concludes with the presentation of results from the
SPARC/IGAC Chemistry-Climate Model Initiative(CCMI) and the latest projections of polar ozone overthe coming decades.

4.2

RECENT POLAR OZONE CHANGES

4.2.1

Measurements of Ozone andRelated Constituents

Scientific study of polar ozone is made possible by thecontinuation of long-term measurements of ozone
made by ground-based, balloon, aircraft, and satellite
instruments, supplemented by measurements of related chemical constituents and meteorological parameters. In general, these measurement programs havelargely been maintained since WMO (2014).
Long-standing ground-based and balloon measurements of both total column and vertically resolved
ozone have continued under the WMO Global
Atmosphere Watch and its contributing network
NDACC (Network for the Detection of AtmosphericComposition Change). Observational data from these
networks are freely available from the World Ozone
and Ultraviolet Radiation Data Centre (www.woudc.
org). A summary of available satellite measurements
relevant to polar ozone was provided in Table 3A-1
of WMO (2014). Many satellite missions launchedin the early 2000s have continued to collect data wellbeyond their expected lifetimes. A number of specificmeasurement campaigns have also been conducted inaddition to long-term monitoring programs. The understanding that these missions and campaigns havecontributed to will be discussed in Section 4.3.
It is also important to note the contribution of recent
4.6

work that has combined observational ozone datafrom different instruments into single homogeneouslong-term data sets (Sections 4.2.3, 4.2.4, and 4.4)
and the extra insight and usefulness obtained byassimilating meteorological observations and producing reanalysis products (discussed more fully in
Section 4.2.2).

4.2.2

Evolution of Polar Temperatures
and Vortex Characteristics

4.2.2.1	Temperatures and PSC Volume
Polar ozone depletion is highly dependent on stratospheric temperature, as this acts as a strong control on
the potential for polar stratospheric cloud (PSC) formation and thus the rate of heterogeneous reactions.
Figure 4-1 shows the annual climatological cycle(1979–2017) of 50 hPa polar minimum temperaturefor both the Arctic and Antarctic, with four recent
years highlighted.
As is well established, winter and spring temperatures
show a far higher degree of interannual variability over
the Arctic than over Antarctica, indicative of the large
year-to-year variability in dynamics and the more frequent disturbances to the Arctic vortex in the form ofsudden stratospheric warmings (SSWs) (Labitzke andvan Loon, 1999). Since the last Assessment (WMO,
2014), Arctic minimum temperatures have been in
the 10–90% envelope about the climatological mean,
except for winter/spring of 2015/2016. This period
was characterized by December and January minimum temperatures that were close to, or at, the lowest
minimum values for the whole period, with a strong
and undisturbed vortex (Manney and Lawrence, 2016;
Matthias et al., 2016). However, a major final SSW occurred on 5–6 March 2016 and the vortex broke up
in early April (Manney and Lawrence, 2016). Afterthe SSW, the polar minimum temperatures were thennear, or at, their highest for the 1979–2017 period.
The effects of these conditions on Arctic ozone levels
will be further discussed in Section 4.2.4.1.
The less dynamically disturbed Antarctic vortex
shows much lower interannual temperature variability. Recent winter/spring temperatures over Antarctica
have typically been within 30–70% of the long-termclimatological mean, with the exception of the 2015
spring where they were close to, or at, the lowest

Polar Stratospheric Ozone | Chapter 4
230

NH

Temperature (K)

220
210
200
Cl activation threshold

190
180
170

Ice PSC threshold2013−42014−5
2015−6
2016−7

J

A

S

O

N

D

J

F

M

A

M

J

Month
230

SH

Temperature (K)

220
210
200
Cl activation threshold

190
180
170

Ice PSC threshold

2014
2015
2016
2017

J

F

M

A

M

J

J

A

S

O

N

D

Figure 4-1. Annual cycle and variability at 50 hPa (~20 km) of minimum
temperature for the Northern Hemisphere (50°N–90°N, top) and theSouthern Hemisphere (50°S–90°S,
bottom) from ERA-Interim reanalysis data (Dee et al., 2011). The thickblack line shows the climatological mean annual cycle, with thedark and light gray shaded areas toindicate the 30–70% and 10–90%
probabilities, respectively; the thingray lines indicate the record maximum and minimum values for theperiod; and the colored lines show
the annual cycle of temperatures forrecent years. The data cover the periods 1978/1979 to 2016/2017 for the
Northern Hemisphere and 1979–2017 for the Southern Hemisphere.
Also indicated are the threshold
temperatures for chlorine activation and ice PSC formation. Updated
from Figure 3-1 in WMO (2014).

Monthminimum temperatures during October and earlyNovember, and 2017, when they were above the previous maximum during September. The effects onozone of the 2015 low temperature in combination
with the volcanic eruption of Calbuco in southern
Chile are discussed in Section 4.2.3.1.
A vortex-wide and season-long picture of the potential for ozone depletion in a winter/spring period isgiven by a measure of the time-integrated volume of
air within the vortex, between the 400 K and 700 K
isentropic surfaces, where heterogeneous ozone loss
can occur. This metric, hereafter integrated VPSC, isdefined using height-resolved temperature data fromradiosondes or reanalyses, together with a standard,
non-denitrified profile of nitric acid, in order to estimate the temperature threshold for existence ofnitric acid trihydrate (NAT) PSCs (Rex et al., 2002).
(Due to the use of a non-denitrified nitric acid profile, this does not strictly define threshold temperatures for NAT existence, but rather is a proxy for low
temperatures.)
Figure 4-2 shows the long-term evolution of Arctic integrated VPSC, updating the similar figure from WMO(2014), which used VPSC, rather than integrated VPSC.
The addition of the time integration emphasizes theimportance of duration of the low temperatures (see4.7

Chapter 4 | Polar Stratospheric Ozone

Integrated VPSC (109 km3 d)

Figure 4-2. Time-integrated Arctic VPSC,
integrated by day (d) from 1 November to
630 April for each winter/spring, on isentropic surfaces from 400 to 700 K. Results
4
are shown for different meteorologicalreanalyses: NCEP (Kalnay et al., 1996),
2
NCEP/CFSR (Saha et al., 2010), MERRA-2(Gelaro et al., 2017), and ERA-Interim
0
1970
1980
1990
2000
2010
2020 (Dee et al., 2011). The figure is an updateYearfrom WMO (2014) with the refinement oftime integrating VPSC and the addition of new reanalysis products. The VPSC metric is calculated based on themethod described by Rex et al. (2006) and estimates the volume within the vortex in which the temperature
is low enough for NAT PSCs to exist, based on a standardized non-denitrified nitric acid profile.
8

NCEP
NCEP/CFSR
ERA-Interim
MERRA-2also Strahan et al., 2016). The figure shows integrated
VPSC calculated from several reanalysis products toprovide an idea about structural uncertainty arisingfrom the different reanalysis systems. The three more
recent reanalyses (NCEP/CFSR, ERA-Interim, and
MERRA-2) are in closer agreement with each otherthan they are with the earlier NCEP reanalysis (seethe figure caption for references). NCEP is shownfor comparison with earlier reports but is no longer
recommended for stratospheric analyses due to theavailability of more recent reanalyses that have beenshown to be more suitable (Lawrence et al., 2015;
Long et al., 2017).
Apparent from Figure 4-2 is the large integrated
VPSC value for the 2015/2016 winter/spring, driven
by the particularly cold December and January in
2015/2016 (Manney and Lawrence, 2016). It exceeds
the corresponding value associated with the stronger
Arctic ozone depletion of 2011, even though March
total ozone values were not anomalously low (Figure4-4). This suggests an instance where the integrated
VPSC metric alone is insufficient as an ozone depletion proxy. Arctic ozone depletion in the winter andspring of 2015/2016 is discussed in Section 4.2.4.1.
In the last Assessment, the evidence for a significant
climate change-driven trend towards larger VPSC values occurring in the coldest winters (Rex et al., 2004;
2006) was challenged by studies using extreme value
statistics (Rieder and Polvani, 2013) or sunlit vortex
volumes (Pommereau et al., 2013). Since then, there
have been no in-depth analyses of the VPSC metric and despite the large (integrated) VPSC value for2015/2016, due to the large year-to-year variability no
4.8

significant trend can be detected. More recent studies
have explored the drivers of polar stratospheric trends
using observations and model simulations; these are
discussed in Section 4.5.3.

4.2.2.2	Polar Vortex Breakup Dates
Figure 4-3 shows the date at which the Arctic andAntarctic polar vortices breakup each spring, due tostrong planetary wave breaking and the returning sun
resulting in a warming of the polar stratosphere. As
in Figure 4-2, the dates are calculated from a rangeof different reanalysis products, using wind data
along the vortex edge (Nash et al., 1996). Similar topolar temperature variability, there is a higher degreeof interannual variability of the vortex breakup date
for the Arctic, compared to the Antarctic. As notedin the previous Assessment, this Arctic variability isgreater since 2000 (average standard deviation acrossthe reanalyses: 30.7 days) compared to the 1990s (average standard deviation: 18.8 days), although theamount depends on the reanalysis product. Breakup
dates over Antarctica also show a degree of interannual variability, which is more marked after the first
decade of the record shown, although again this varies between the reanalysis products. Overall, there isevidence of decadal variability in both hemispheres.
In general, there is better agreement between the
different reanalyses for the Antarctic vortex breakup
dates (average max–min range between the reanalysesof 5.6 days) than the Arctic (average max–min rangeof 16.7 days), with the Arctic having some years where
the range between different reanalyses exceeds one
month. Differences in the simulation of stratosphericPolar Stratospheric Ozone | Chapter 4

winds between the reanalysis products (e.g., Butler
et al., 2017; Long et al., 2017) explain the differences
between the lines in Figure 4-3. There are a range ofdefinitions for breakup date, but no systematic comparison exists in the literature. Other definitions,such as a temperature-based metric (e.g., Haigh andRoscoe, 2009) or computer image analysis techniques(Lawrence and Manney, 2017) may yield better agreement between reanalyses.

160

May

120

Apr

100
80

Mar

Day of year

140

60

NH
1980

400

2000

2010

2020

4.2.3

SH
Jan

NCEP/CFSRMERRA-2
ERA-Interim

380
360
340

Nov

320
300

1980

1990

2000

2010

Ozone Depletion in ­Antarctic
Springs (2014–2017)

Figures 4-4 and 4-5, updates to figures that have beenused in previous Assessments, represent the evolutionof polar ozone loss in both hemispheres. Figure 4-4
shows mean polar cap (63°–90° latitude) total column ozone averaged for the months of March (forthe Arctic) and October (for the Antarctic) compiled
from various satellite datasets since 1970. For the
Antarctic, generally reliable and well-sampled satellite
measurements of total ozone have been available for
October since 1979 from the TOMS/OMI/OMPS series of instruments, with some gaps in 1993 and 1995
where alternate instruments were used.

Dec

Day of year

1990

Feb

40

2020

Year

Figure 4-3. Breakup dates for the Arctic (top) andAntarctic (bottom) polar vortices, defined as when
the wind speed on the 500 K isentropic surface
falls below 15.2 m s−1, as per Nash et al. (1996).
Dates are determined using reanalysis data from
NCEP/CFSR (Saha et al., 2010), MERRA-2 (Gelaro etal., 2017), and ERA-Interim (Dee et al., 2011). Notethat the Nash et al. (1996) definition does not yielda breakup date in all reanalyses for certain years.
Updated from Figure 3-2 of WMO (2014).

In Figure 4-5, rather than mean ozone, the minimum of
the daily mean ozone is shown, and further, an attempt
is made to confine the averaging region to within thepolar vortex through use of a threshold for equivalent
latitude on the 475 K isentrope (Müller et al., 2008).
In the Antarctic, both metrics share the general
Total Ozone Average (63˚–90˚N)

500
450

Total ozone (DU)

Figure 4-4. Total ozone average (Dobson units) over 63°–90° latitude inMarch (Northern Hemisphere, NH)
and October (Southern Hemisphere,
SH). Symbols indicate the satellite
data that have been used in different
years. The horizontal gray lines represent the average total ozone for theyears prior to 1983 in March for the NH
and in October for the SH. Updated
from Figure 3-4, WMO (2014).

NH March

400
350
SH October

300
BUV
EPTOMS_V8C
METEOR3_V8
NIMBUS7_NRT
OMI_V8F
OMPS_V21
SBUV_MOD

250
200
1970

1980

1990

2000

2010

Year
4.9

Ozone Column (DU)

Figure 4-5. Time series of the minimum of
the daily average column ozone (Dobson
units) within the 63° contour of equivalent latitude (Φe) in (top) March in the Arctic and (bottom) October in the Antarctic.
Arctic winters in which the polar vortex
broke up before March (1987, 1999, 2001,
2006, 2009, and 2013) are shown by open
symbols; dotted lines connect surrounding years. Adapted from WMO (2014),
updated using the Bodeker Scientific
combined total column ozone data base(version 3.0; circles; Müller et al., 2008)
through March 2013 and Aura OMI measurements thereafter (diamonds).

Ozone column (DU)

Chapter 4 | Polar Stratospheric Ozone

March, Arctic

400
380
360
340
320
300

October, Antarctic

300
250
200
150

features of a clear decline over the 1980s and 1990s,
followed by a period without a clear trend but showing increased year-to-year variability, with the variability reduced in Figure 4-5 relative to Figure 4-4
due to the use of equivalent latitude. For 1988, 2002,
and 2012, relatively high temperatures in the winterstratosphere limited the severity of ozone loss in thefollowing spring (Klekociuk et al., 2015); in the case of
2002, an unprecedented major sudden stratosphericwarming in September disrupted the polar vortex andallowed unusually strong transport of ozone-rich air
into the polar cap (Newman and Nash, 2005; WMO,
2007).
Mean October ozone values for three of the last four
years since the last Assessment—2014, 2016, and2017—were all at the higher end of the range of values
observed since the year 2000, as were the two preceding years 2012 and 2013. In contrast, the Octobermean value for 2015 was the lowest on record. The
relative positions are similar for minimum daily vortex-averaged ozone (Figure 4-5), although 2014 and2016 are closer to the middle of the range, and the2015 minimum is not as low as 2006, which is the record low value for this metric.
The evolution of Antarctic ozone has also been represented in previous Assessments by the time seriesof three standard metrics: area of the ozone hole,minimum ozone within the ozone hole and ozone
hole mass deficit (Figure 4-6—refer to the caption
for the specific definitions and time periods of the
4.10

Minimum of Daily Mean Ozone Column (Φe>63°)

440
420

1980

1990

2000

2010

Year

metrics shown). For ozone hole area, the last four
years have varied within the general range observed
since the early 1990s, with 2015 being among thelargest recorded and 2017 the second smallest since
the late 1980s. Ozone hole minimum is similar in
Figure 4-6 to the minimum vortex average shown in
Figure 4-5 but shows a greater general increase since
2000. Several years from the period 2000–2013 hadlower ozone hole minimum values than all four years
2014–2017. Ozone hole mass deficit (OMD) shows
the most evident change since 2000, with the 2017
value being the smallest since 1988. To illustrate thedevelopment of the Antarctic ozone hole over winterand spring in the four years, Figure 4-7 shows the estimated daily ozone mass deficit for July to Decemberin years 2014 to 2017 and also the extreme years of
2002 and 2006 for comparison. This metric is the difference in column ozone from 220 DU expressed asa mass integrated over the area of the ozone hole. A
similar figure was last presented in WMO (2007). The
onset of significant ozone depletion in the Antarctic
normally occurs between the beginning and middleof August, when total column abundances usually
begin to drop below the 220 DU threshold that was
introduced by Stolarski et al. (1990) as a definitionof the Antarctic ozone hole (see Uchino et al. (1999),
Bodeker et al. (2005), and Huck et al. (2007) for additional definitions, and Pazmiño et al. (2018) for
different thresholds). Ozone loss typically maximizes
at the beginning of October after which ozone concentrations tend to increase through the remainder ofOzone hole area
(million km2)

Polar Stratospheric Ozone | Chapter 4
30
25

Area

20
15
10
5

Average daily
Ozone mass deficit
minimumozone (DU)
(million tons)

0

Minimum

200
150
100

40

Mass Deficit

30
20
10
1980

1990

2000

Year

2010

50

Ozone mass deficit (million tons)

0

Figure 4-6. (top) Area of the Antarctic ozone hole for
1979–2017, averaged from daily total ozone area values contained by the 220 DU contour for 21–30 September. (middle) An average of daily minimum ozone
values over Antarctica during the period from 21 September to 16 October. (bottom) Ozone mass deficit(OMD) averaged over the 21–30 September period.
For all three panels, the vertical gray bars indicate therange of values over the same time period. The darkgray curves show the fits to each quantity as was shownin Newman et al. (2004) using EESC, as derived in Newman et al. (2006), updated with the current baseline
A1 scenario. The EESC has a mean age of 5.5 years, anage spectrum width of 2.75 years, and a bromine-scaling factor of 65. The fit is quadratic in EESC. This figure was generated from TOMS (1979–2004), Aura OMI(2005–2015), and Suomi NPP OMPS (2016–2017) data.
Updated from Figure 4-8 from WMO (2007).

2017
2016
2015
2014
2006
2002

40

30

20

10

0

Jul

Aug

Sep

Oct

Nov

Dec

Month

Figure 4-7. Daily ozone mass deficit over Antarctica for various years compared with therange of values over the period 1990–2016 (gray shaded area). The thick dark gray line shows
daily values averaged over the 1990–2016 period. Values have been computed using datafrom TOMS, Aura OMI, and Suomi NPP OMPS. Updated from Figure 4-9 from WMO (2007).
4.11

Chapter 4 | Polar Stratospheric Ozone

the year, but do often show significant variability on
timescales of days to weeks due to disturbance of thepolar vortex by Rossby wave activity.
The growth and decline of daily ozone mass deficit in2014 and 2016 were generally similar to each other,and took place over a somewhat contracted period relative to the long-term mean, with a 1-2 weeks
slower development in early September and an earlier
zero-crossing in November (Figure 4-7). In terms of
the broad features of the daily time series of deficit
and area, these specific ozone holes exhibited behavior similar to some years in the early 1990s, although
their total ozone mass deficits were generally greaterthan pre-1990 levels (Figure 4-6; Newman et al., 2015;
Weber et al., 2015; Newman et. al., 2017; Weber et al.,2017). In 2016, the latest date for which total column
ozone values were below 220 DU was November 20(Figure 4-7), which was the earliest such date for theperiod 2014-2017. This early elevation of levels above
the 220 DU threshold was brought about by an episode of strong warming in the polar cap during lateNovember of that year (Figure 4-1). Some evidenceof a shift in the timing of the formation and growthof the ozone hole towards a later date since 2000 has
also been presented (Solomon et al., 2016). The evolution of the ozone mass deficit in years 2015 and 2017,
which were distinctly different to 2014 and 2016, arediscussed in Sections 4.2.3.1 and 4.2.3.2, respectively.

4.2.3.1	Antarctic Spring 2015: Volcanicand Dynamical Influence on Ozone
In terms of total annual ozone mass deficit, the 2015
Antarctic ozone hole was the largest of the period
2014–2017 (Figure 4-7). The 2015 ozone hole was notable in achieving a large maximum area, being comparable to the largest values observed for 2003 and2006 (Figure 4-6). Through much of the period fromearly October to mid-December, the Antarctic ozone
hole of 2015 set records in daily area and mass deficit
and had unusually low minimum column abundances(Nash et al., 2016; Weber et al., 2016). Additionally, its
onset was approximately two weeks later than typical(Nash et al., 2016).
The large area of the 2015 Antarctic ozone hole was
influenced in part by the chemical effects in the lowermost stratosphere (particularly around the 100–150
hPa pressure level) of aerosols that were entrained
4.12

in the polar vortex from the eruption of the Calbuco
volcano in southern Chile in April 2015 (Solomon etal., 2016; Ivy et al., 2017; Stone et al., 2017). Studies
comparing ozone hole metrics with and without theinclusion of prescribed aerosol loading derived fromobserved volcanic SO2 emissions found simulations
with prescribed aerosols provide an ozone hole areathat closely matches observations, and concluded thatchemical ozone depletion enhanced by heterogeneous
processes associated with the volcanic aerosols was
the primary factor behind the ozone hole achieving
record size. Dynamical and temperature feedbacks
from the ozone loss were less important (Solomon etal., 2016; Ivy et al., 2017). Measurements by ozonesondes and the Aura MLS satellite instrument areconsistent with the model results and indicate that theaerosol-influenced ozone loss was most significant in
the lowermost stratosphere (Stone et al., 2017).
It should be noted that the contribution of volcanic
aerosol particles took place in the setting of a notably
cold and stable Antarctic vortex in 2015 (Figures 4-1
and 4-12). The level of disturbance to the Antarctic
vortex was relatively low in the winter and spring.
The eddy heat flux at 100 hPa was generally below thelong-term average from July to October, and particularly in October, which favored the ozone hole having
greater persistence than on average (Nash et al., 2016).
Much of 2015 was marked by a positive value for theSouthern Annular Mode index, which did not favor
Rossby wave propagation to high (> 60°S) southernlatitudes (Fogt, 2016).

4.2.3.2	Antarctic Spring 2017: Dynamical
Influences on Ozone
Antarctic ozone loss in the spring of 2017 was unusually low and comparable in some metrics, particularly
mass deficit, with that for 2002 (when the stratospheric vortex exhibited an unprecedented major warming
as noted earlier) and most years prior to 1989. The
small size of the ozone hole in terms of its maximum
area and total mass deficit compared with other years
can be seen in Figure 4-6. The rate of increase ofozone mass deficit in 2017 was notably below average throughout September (Figure 4-7), during the
majority of which time stratospheric temperatures
were the warmest in the 1979–2017 record (Figure4-1). Unusually for the Southern Hemisphere, theratio of September to March ozone from 50° to 90°S

Polar Stratospheric Ozone | Chapter 4

was greater than one, consistent with the high valueof poleward eddy heat flux integrated over winter(Figure 4-12).

4.2.4

Ozone Depletion in ­Arctic
Springs (2014–2017)

The annual time series of polar cap mean and minimum ozone in March for the Arctic are shown in
the upper panels of Figures 4-4 and 4-5. Some issues around the interpretation of these figures in the
Arctic are discussed in Section 3.2.3 of WMO (2014).
These averages have been used to assess the long-term
behavior of Arctic stratospheric ozone as chemical
depletion normally peaks in March, unless the polar
vortex dissipates earlier. In the Arctic polar cap, ozone
concentrations in the lower stratosphere are morestrongly influenced by horizontal transport than in
the Antarctic, owing to the relatively weak and disturbed nature of the northern polar vortex. The Arctic
vortex is smaller and more spatially variable than the
Antarctic vortex, and variability in the amount ofozone transported to the polar cap can complicate
the interpretation of total column measurements. In
spite of these limitations, which can be minimizedby considering partial columns (e.g., Strahan et al.,2016), these metrics have been used to highlight years
where ozone concentrations in the Arctic are notably
low. However, as discussed in detail in WMO (2014),
years of particularly low Arctic ozone in March, suchas 1997 and 2011, can show distinctly different behavior in chemical ozone loss (which was strong in 2011
but only moderate in 1997) and dynamical influences(which reduced ozone levels in the latter part of the1997 winter). More recently, chemical depletion ofozone has been shown to account for one-third of thedifference from the pre-1983 mean (the gray shaded
area in Figure 4-4) in most years, with the remainder being due to variations in dynamical resupplyof ozone (Strahan et al., 2016; see also WMO, 2010
Figure 2-15).
As for the Antarctic, Figures 4-4 and 4-5 display anevident negative trend in ozone in the 1980s which
did not continue past the late 1990s. In terms of Marchmean ozone across the geographic polar cap (Figure4-4), the four years since the last Assessment have all
been well within the range of typical values observed
since 2000, with only small differences between eachof the years and significantly above the value for 2011

which experienced very large ozone depletion. The
differences between the four years are much larger inFigure 4-5 however, with minimum ozone in March
2015 now at the higher end of the range of values since1990 and March 2016 being one of the lowest values.
To consider the development of Arctic ozone in winter and spring over these four recent years, Figure
4-8 shows time series of the daily average concentration of specific species relevant to ozone chemistry
at a height of approximately 18 km—representative
for the lower stratosphere within the Arctic polar
vortex. The 2013/2014 Arctic vortex was large, cold(see also Figure 4-1), and relatively strong throughout the winter; temperatures near 18-km altitude
were below chlorine activation thresholds until approximately mid-March. The observed decrease inozone was slightly more than the average up until
April 2014 (Bernhard et al., 2015). Over the period
December 2014 to April 2015, ozone concentrationsin the Arctic lower stratosphere near 18 km altitude
were the highest in the Aura MLS record (whichstarted in August 2004; Figure 4-8) (Manney et al.,2015b; Bernhard et al., 2016). A minor sudden stratospheric warming (SSW) event in early January 2015
(Figure 4-1) raised temperatures and limited further
development of ozone-depleting chemistry within thepolar vortex. Additionally, Aura MLS data showed evidence for above average transport of ozone-rich air
into the polar cap through the upper branch of the
Brewer-Dobson circulation (Manney et al., 2015b),
which also elevated HNO3 concentrations to levels in
the lower stratosphere not previously observed in the
Aura MLS record. The enhanced temperatures andtransport were both the result of increased high-latitude wave activity (Manney et al., 2015b).
The early part of the 2016/2017 Arctic winter was unusually warm in the lower stratosphere with a weak
vortex, although with a cold and strong vortex in the
middle and upper stratosphere. Several minor SSWs
occurred, with two of them near the threshold to be
defined as “major” (according to definitions by Butler
et al., 2015). Temperatures below chlorine activationthresholds in the lower stratosphere did not appear
until late December. However, cold conditions wereconsistently present thereafter until early March, andsignificant chlorine activation and ozone loss wereobserved. As indicated by relatively high levels of
HNO3 in the lower stratosphere (top panel of Figure
4.13Chapter 4 | Polar Stratospheric Ozone

HNO3 (ppbv)

14

Arctic1062H2O (ppmv)

6

5

4

HCI (ppbv)

2

1

0

CIO (ppbv)

1.2
0.8
0.4

O3 (ppmv)

0.0

3

2

2013/14 2014/15 2015/16 2016/17
1
1 Dec
1 Jan
1 Mar
1 Feb

4.14

1 Apr

Figure 4-8. Time series of­vortex-averaged HNO3, H2O,
HCl, ClO, and O3 from Aura
Microwave Limb Sounder(MLS) on the 480 K potential
temperature surface (~18 km,~50 hPa) for winters and springin the Arctic. Gray shadingshows the envelope of behavior observed by Aura MLS overthe 2005–2013 period. A data
gap from 27 March to 18 April
2011 has produced a noticeable artefact in the envelope ofsome panels. The last four winters are highlighted by coloredlines as indicated in the legend,
for which the given year refers
to January to April. Updated
from Figure 3-6 from WMO(2014).

Polar Stratospheric Ozone | Chapter 44-8), denitrification was not particularly pronounced
within the vortex during 2016/2017.

4.2.4.1	Arctic Spring 2016: Record Ozone
Depletion Halted by Major Warming
The 2015/2016 Arctic winter had significant potential
for large ozone loss (Manney and Lawrence, 2016).
Exceptionally low temperatures occurred throughout
the period from December 2015 to February 2016,
which were the lowest in the 68-year observational record (Matthias et al., 2016). Strong denitrification anddehydration occurred (Manney and Lawrence, 2016;
Figure 4-8), which were associated with extensive
PSC formation (Khosrawi et al., 2017; Bernhard et al.,2017). The low stratospheric temperatures, particularly in November and December of 2015, were linked
with weak planetary wave-1 activity associated withthe prevailing enhanced tropospheric meridionaltemperature gradient that appears to have increased
the vertical wind shear at northern mid-latitudes andreduced the ability of the planetary waves to propagate upward (Matthias et al., 2016).
The cold conditions led to very strong chlorine activation within the polar vortex in 2015/2016 until theend of February. Levels of HCl at the 480 K surface inthe vortex in January were the lowest in the Aura MLS
record, below those of 2010/2011 when ozone loss was
exceptional and levels of ClO were correspondingly
high (Figure 4-8). The amount of ozone depletion
was enhanced by the strong level of denitrification(Manney and Lawrence, 2016). However, the overallozone loss in this season was halted by a major final
SSW in early March, which terminated stratospheric chlorine activation approximately a month earlier
than was the case for the 2010/2011 season (Bernhard
et al., 2017). From this point on, ClO levels dropped
rapidly (Figure 4-8). As a result of the warming, record Arctic ozone depletion that might have occurredwith such high levels of chlorine activation earlier inthe season, did not take place.

4.3

UNDERSTANDING OF POLAROZONE PROCESSES

4.3.1

Polar Stratospheric Clouds

Polar stratospheric clouds (PSCs) and cold sulfate
aerosols impact polar ozone and chlorine chemistry by converting chlorine from inactive reservoir

species (mainly hydrochloric acid (HCl) and chlorine nitrate (ClONO2)) to active ozone-destroying
species. Furthermore, PSCs can both temporarily
sequester HNO3 from the gas phase (substantiallyreducing gas-phase concentrations of HNO3 as longas the PSCs exist) and irreversibly redistribute HNO3
by gravitational sedimentation of large nitric acidtrihydrate (NAT) particles (referred to as “denitrification”; WMO (2014), Box 3-1). These impacts onpolar ozone and chlorine chemistry are now considered to be well understood. However, many aspectsof the microphysics of PSCs and their formation still
remain unclear, such as the nucleation mechanism for
NAT particles (in particular, large NAT particles), theimpact of rapid cooling rates (gravity waves), and theorigin and nature of refractory (that is, non-volatile)
particles in the polar vortex. As well, the substantial
uncertainties in the reactivity on NAT surfaces (particularly the heterogeneous reaction HCl + ClONO2)
identified many years ago (WMO, 1998; Carslaw
and Peter, 1997) remain unresolved. Most results,
such as comparisons of chemical transport models
to observations, are generally robust to the detailsof the assumptions employed regarding PSCs andheterogeneous chemistry (e.g., Kirner et al., 2015a;
Wohltmann et al., 2013). Nonetheless, there are conditions where the exact rates of heterogeneous chlorine activation reactions and thus also PSC composition will be significant; for example, when chlorine
activation occurs in the Arctic within a limited spatialor temporal region (Wegner et al., 2016) or in the
Antarctic under conditions of a direct competition of
the rate of gas-phase deactivation and heterogeneous
activation of chlorine (Solomon et al., 2015). Further,an accurate representation of PSC processes in models is required to ensure the reliability of projectionsof the future development of polar ozone under conditions of changed atmospheric concentrations of key
species. The contribution of the latest observational,
laboratory, and modeling studies to addressing thesequestions is described in the following sections.

4.3.1.1	Observations of PSC Extent
and Composition
Long-term data sets of the occurrence of different
types of PSC particles over the polar regions are now
available from both satellite and ground-based observations. The MIPAS instrument, which was carried by the ENVISAT satellite, was an infrared limb
4.15

Chapter 4 | Polar Stratospheric Ozone

sounder providing a pole-covering day and night time
climatology of PSC distributions and their composition from July 2002 until April 2012. The CALIOP
instrument onboard CALIPSO is a two-wavelengthpolarization-sensitive lidar that provides night-timehigh-resolution vertical profiles of PSCs with datacollection beginning in mid-June 2006 and lasting
until the present day. In addition, ground-based lidar
systems provide important long-term information
on PSC properties; measurements are available since1997 at Esrange, Sweden (Achtert and Tesche, 2014)
and for 1995–2001 and 2006–2010 at McMurdo,
Antarctica (Di Liberto et al., 2014). Data from theseground-based systems are compared with satellite-based measurements and are used to test and develop PSC classification schemes.
The most recent data products from MIPAS andCALIOP (Spang et al., 2016; Lambert et al., 2016)
show a very good agreement regarding PSC classification and occurrence as seen in the daily altitude-resolved time series of three PSC classes in 2009 for thetwo instruments (Figure 4-9) (Spang et al., 2018).
Given the very different measurement principles usedby CALIOP and MIPAS, the agreement between thetwo data sets is encouraging and will allow analyses
on the temporal and spatial development of PSCs overmultiple winters to be performed with the potential
for the validation and improvement of PSC schemesin CTMs and CCMs (e.g., Zhu et al., 2017a).
WMO (2014) reported on the detection of unusuallylarge particles (up to 35 μm), so-called “NAT-rocks”,
in synoptic-scale PSC fields during aircraft campaigns
in the Arctic in 2010 and 2011 (von Hobe et al., 2013).
It has been argued for a long time that the sequestering of major amounts of nitric acid in relatively large
particles leads to efficient denitrification (Salawitch
et al., 1989). Further analysis of these observations(Molleker et al., 2014) showed that the optically measured size distribution (Figure 4-10) could only be explained by either strong asphericity of the particles oran alternate composition (e.g., water ice coated with
NAT). While there has been previous evidence (Fahey
et al., 2001; Brooks et al., 2003) for NAT-rocks, theirobservational basis has been expanded by these measurements and the occurrence of large NAT particles
now appears to be a regular feature of synoptic-scale
PSCs in the Arctic.
4.16

Further, analysis of the spectral signature of the PSC
field over northern Scandinavia obtained by airborne passive infrared limb emission measurements
in December 2011 revealed a distinctive “shoulder-like” signature in the spectral region around 820
cm−1 (Woiwode et al., 2016). This observed signature
is best explained by the combination of the absorption, emission, and scattering characteristics of large(log-normal distribution with a mode radius of 4.8μm) highly aspherical (aspect ratios of 0.1 or 10)
NAT particles. The measurement of excess gas-phase
HNO3 observed in a nitrification layer directly below
the observed PSCs further supports the role of suchlarge aspherical particles in denitrification.
Comparison of CALIOP observations for the
Antarctic in winter 2010 with model simulations
suggests that two major NAT particle formation
mechanisms must exist. Homogeneous nucleationfrom supercooled ternary solution (STS; see WMO(2014), Box 3-1) droplets produces large NAT particles which are needed to reproduce the observed rates
of denitrification, while heterogeneous nucleation of
NAT on ice produces small particles which accountfor the large backscattering ratio from NAT observed
by CALIOP (Zhu et al., 2017a; 2017b).
It has also previously been proposed that refractory
particles of meteoric origin could serve as condensation nuclei of NAT-rocks (Curtius et al., 2005).
Laboratory experiments using analogues of meteoric
materials show that such surfaces have the capacity tonucleate nitric acid hydrates (James et al., 2018). As reported in WMO (2014), aircraft measurements madein the Arctic in late winter 2010 detected a large number of refractory particles with diameters above 500
nm (von Hobe et al., 2013). The abundance of refractory aerosols in the lower stratosphere during late winter in the Arctic vortex appears to be a regular feature
rather than an exception (Weigel et al., 2014). At thetime of these measurements, the air mass subsidence
inside the Arctic winter vortex from the upper stratosphere and mesosphere was well-advanced, leading to
the conclusion that the refractory particles had beentransported from higher altitudes into the lower stratosphere. In contrast, in the flight samples collected inan early winter situation (December 2011, and therefore a young vortex in which the air masses had seenmuch less descent), no large refractory particles wereobserved (Ebert et al., 2016). The observed generalPolar Stratospheric Ozone | Chapter 4

10
May 1

20
15

30

Jul 1

CALIOP STS Area: 2009

20
15
10
May 1

Jul 1

Sep 1

15
Jul 1

MIPAS NAT Area: 2009

20
15

30

Jul 1

20
15
10
May 1

Jul 1

Sep 1

13.0

20

10.0

15
Jul 1

Sep 1

MIPAS ICE Area: 2009

7.0
5.0
3.0

25
20

2.0

15

1.0

10
May 1

CALIOP NAT Area: 2009

16.0

25

30

Sep 1

25

MIPAS ICE Area: 2002−2011 [10 6 km2 ]

10
May 1

Sep 1

25

10
May 1

Sep 1

25

20

30

Altitude (km)Altitude (km)

25

25

10
May 1

Sep 1

MIPAS STSmix Area: 2009

10
May 1

Altitude (km)

Jul 1

30

Altitude (km)15

MIPAS NAT Area: 2002−2011

Altitude (km)Altitude (km)20

Altitude (km)Altitude (km)

25

30

30

30

Altitude (km)MIPAS STSmix Area: 2002−2011

30

Jul 1

Sep 1

CALIOP ICE Area: 2009

0.7
0.5

25

0.3

20

0.2

15
10
May 1

0.1
Jul 1

Sep 1

Figure 4-9. Time series of the daily Antarctic PSC areas (top row) from MIPAS climatology (10-year mean
[2002, 2003, and 2005–2011]; MIPAS data weighted by the area of the applied latitude bins south of 55°S
in the altitude range 12–30 km), (middle row) from MIPAS for the year 2009, and (bottom row) CALIOP V2 forthe year 2009, based on the identical computation of PSC area for each data set. Presented are all relevant
PSC classes for the MIPAS and CALIOP satellite retrievals (ice, NAT, and STSmix). For better comparability withCALIOP, MIPAS observations in the middle row are restricted to latitudes north of 82°S, the latitude coverageof CALIOP. Red triangles at 10.5 km altitude in the MIPAS and CALIOP time series (middle and bottom rows)
mark data gaps caused by the measurement mode. Adapted from Spang et al. (2018).

tendency of a lower abundance of refractory particles
during PSC events compared to non-PSC situations
supports the hypothesis that such particles can provide
a surface for heterogeneous nucleation during PSC
formation. Further, recent analysis of particles originally collected in the Arctic stratosphere from January
until March 2000 found a high abundance of carbonaceous refractory particles. Based on their chemistry
and nanostructure, many non-meteoric sources forthese carbonaceous particles can be excluded (Schütze
et al., 2017). However, the exact source of the large refractory particles in the stratosphere and their impact
on PSC formation remain to be accurately determined.

4.3.1.2	Gravity Waves and Orographic Forcing
Atmospheric gravity waves yield substantial smallscale temperature fluctuations that can trigger theformation of PSCs (e.g., Murphy and Gary, 1995). Orr
et al. (2015), based on case studies over the Antarctic
Peninsula, investigated the representation of stratospheric mountain-wave-induced temperature fluctuations by the UK Met Office Unified Model (UM)at climate scale and mesoscale, compared to observations. They found that, at high horizontal resolution (4km), the regional mesoscale configuration of the UM
is able to correctly simulate the magnitude, timing,
4.17

Chapter 4 | Polar Stratospheric Ozone

RECONCILE, 25 January 2010

10–1

avg. time: 28000–30000 UTC sec.

FSSP-100
ESSenCe, 11 December 2011
avg. time: 49150–51520 UTC sec.

FSSP-100dN/dlogDp (cm−3)

10–2

CDP

Fahey et al. (NOy)Brooks et al. (optical)
10–3

10–4

10–5

1

2

3

4

5

6

7

8

9

10

2

3

4

Particle Diameter Dp (µm)

Figure 4-10. Size distributions of large HNO3-containing PSC particles with high number densities fromin situ airborne measurements in 2010 and 2011. These measurements are compared to previous measurements by Fahey et al. (2001) (retrieved from total NOy) and Brooks et al. (2003) (optical measurements). Adapted from Molleker et al. (2014).

and location of the measured temperature fluctuations. While a climate configuration of the model withlower horizontal resolution was not able to resolve thefluctuations, in this case, the use of a mountain wave
parameterization scheme gave reasonable agreementwith observations. A 10-year satellite record of gravity
wave activity in the polar lower stratosphere based on
AIRS indicates that orography as well as jet and storm
sources are the main causes of the observed gravity
waves. There is a strong seasonal cycle in wave activity
with wintertime maxima at high latitudes, the cycle
lasting 2–6 months in the Northern Hemisphere and
5–9 months in the Southern Hemisphere (Hoffmannet al., 2017). A comparison of the satellite observations with temperature fluctuations in the ECMWF
operational analysis (16-km horizontal resolution)
showed that gravity wave patterns occur in the rightlocations, but that wave amplitudes were typicallyunderestimated by a factor of 2–3 (Hoffmann et al.,2017). Further, Lambert and Santee (2018) find thatthe potential to form ice PSCs in model studies driven by various reanalyses varies significantly becauseof the underlying differences in the representation of
4.18

mountain wave activity. Moreover, CALIOP data indicate that simulations are missing clouds containing
small NAT particles with large number densities; suchparticles are most likely to form from ice clouds or
STS in gravity waves (Zhu et al., 2017b).
These recent findings emphasize the importance ofhigh spatial resolution, state-of-the-art meteorological reanalyses, and high-quality schemes for the representation of gravity waves in model studies aimed at
PSC formation and existence.

4.3.2

Polar Chemistry

4.3.2.1	Observations of Polar Chemistry
Measurements taken with balloon-borne MIPAS-B
and TELIS instruments in northern Sweden on 31
March 2011 inside the polar vortex provided vertical profiles of inorganic and organic chlorine species
over the whole altitude range in which chlorine had
been undergoing activation and deactivation (Wetzel
et al., 2015). A total chlorine (Cly) concentration of
3.41 ± 0.30 ppbv is inferred above 24 km from thePolar Stratospheric Ozone | Chapter 4

measurements. This value is consistent with theslightly reduced chlorine loading of the stratospherein 2011 compared to its peak value a decade earlier(see Chapter 1).
Strahan et al. (2014) reported Aura MLS-inferred Cly
showed large variations from year to year due to thevariability of transport to the Antarctic vortex. The
mean expected annual Cly decline due to the Montreal
Protocol is estimated to be −20 ppt yr−1; however,fluctuations of the concentration in a given year havevaried up to 200 ppt below and 150 ppt above themean. (The concentration of Cly in 2013 at 450 K in
the vortex was estimated by this method to have been
2650 ppt.) Because of this large interannual variabilityof Cly, it requires at least 10 years of chlorine decline
after the chlorine maximum for an Antarctic ozone
recovery (in the sense of an ozone increase caused byhalocarbon reductions) to be attributable to a declineof stratospheric chlorine in a statistically significant
manner (Strahan et al., 2014).

phase have been simultaneously investigated forstratospheric temperatures in the range 175–200 K(Iannarelli and Rossi, 2016). In these experiments,
NAT was investigated in two phases; α-NAT, which
exists at temperatures below 185 K and is metastable and β-NAT, which exists above this temperatureand is the form predominantly found in PSCs (seealso the following section). In the laboratory experiments, initial spontaneous formation of α-NAT wasfound, followed by the gradual transformation of
α- to β-NAT at T > 185 K; further nitric acid dihydrate (NAD) was spontaneously formed at somewhat
larger partial pressures of HNO3 deposited on pureH2O ice (Iannarelli and Rossi, 2016). The improved
experimental instrumentation suggests, in contrast to
previous studies, the formation of α-NAT proceeds
without prior formation of an amorphous HNO3∕H2O
layer and always results in the formation of β-NAT.
Chlorine Chemistry and Heterogeneous Reactions

Laboratory, PSCs

Chlorine activation and subsequent ozone depletion only occur because of heterogeneous reactions(Solomon et al., 1986); chlorine activation rates aremainly controlled by temperature, with only a limited dependence on PSC type (e.g., Salawitch et al.,
1988; Kawa et al., 1997; WMO, 2014). Beyond heterogeneous chemistry, NAT particles have an impact
on gas-phase chemistry through removal of HNO3
(denitrification). Initial chlorine activation is notdirectly related to chemical ozone loss and chlorine
activation is often saturated because of the lack ofavailable ClONO2, so that gas-phase chemistry becomes important (Solomon et al., 2015; Müller et
al. 2018). However, strong polar ozone loss requires
both a nearly complete activation of chlorine and themaintenance of high levels of active chlorine for an
extended period (e.g., Solomon et al., 2015; Müller etal., 2018). Recent work has focused on the sensitivityof simulated ozone loss on PSC types, temperaturethresholds and the maintenance of high levels ofactivated chlorine, and the chemical processes responsible for chlorine activation (Solomon et al., 2015;
Kirner et al., 2015a; Wegner et al., 2016; Wohltmann
et al., 2017; Müller et al., 2018).

New experiments on the heterogeneous kinetics of
H2O, HNO3, and HCl on HNO3 hydrates have been
performed using a multidiagnostic stirred-flow reactor in which the gas phase as well as the condensed

Multi-year simulations of a CCM (nudged to ERAInterim reanalysis meteorological fields) show the impact that the various types of PSCs (Box 3.1 in WMO(2014)) have on Antarctic chlorine activation andIn the Arctic vortex of the 2009/2010 winter, satellite
observations showed the initial activation of chlorine
occurred in association with the formation of PSCs
over the eastern coast of Greenland at the beginning ofJanuary 2010 (Wegner et al., 2016). Although this areaof PSCs covered only a small fraction of the vortex,
it was responsible for almost the entire initial chlorine activation throughout the vortex. Observations
show that HCl mixing ratios decreased rapidly in anddownstream of this region. Simulations of heterogeneous reaction rates along trajectories intersectingwith the PSCs indicate that the initial phase of chlorine activation occurred in just a few hours. These
calculations further suggest that the very rapid chlorine activation in Arctic winter 2009/2010 can only be
explained by an increase in surface area density due
to PSC formation (Wegner et al., 2016), as reactions
on the background binary aerosol would have been
too slow.

4.3.2.2	Laboratory Studies, Theoretical
Basis, and Models

4.19

Chapter 4 | Polar Stratospheric Ozone

ozone loss (Kirner et al., 2015a). In these simulations,in high southern latitudes, heterogeneous chemistryon liquid particles accounts for more than 90% ofozone depletion, with reactions on ice particles adding less than 5% of further ozone depletion and NAT
particles less than 1%, although NAT particles play anessential role in denitrification. Simulations of HNO3,
ClO, and ozone agree closely with observations from
MLS (Kirner et al., 2015a).
Polar ozone depletion simulations based on the
WACCM model for the year 2011 indicate that totalozone depletion in both hemispheres is dependent on
low temperatures (below 192 K) and associated heterogeneous chemistry on polar stratospheric cloudparticles (Solomon et al., 2015). Reactions limited
to temperatures above 192 K, or on binary (sulfate/water) liquid aerosols, yield little simulated polarozone depletion in this model in either hemisphere.
The simulated ozone loss is sensitive to sulfate, which
provides additional surface area for heterogeneous
reactions (Tabazadeh et al., 2002); enhancing stratospheric sulfate by a factor of three increases ozone loss
by up to 20 Dobson Units (DU) in the Antarctic and
15 DU in the Arctic. These assumed enhanced sulfate
levels are similar to those observed following recent
relatively small volcanic eruptions since 2005. Ozone
losses in the model are strongly sensitive to temperature, with a test case cooler by 2 K producing as muchas 30 DU additional ozone loss in the Antarctic and
40 DU in the Arctic. The modeled result compareswith an earlier analysis of observations that calculated
the mean dependence on stratospheric temperature of
Arctic ozone loss as 15.6 DU K−1 (Rex et al., 2006).
Moreover, Solomon et al. (2015) corroborate earlier
findings (Jaeglé et al., 1997) that in the edge regionof the Antarctic vortex, transport of ClONO2 fromlower latitudes to higher latitudes as well as latitudinal
excursions of air parcels in and out of sunlight during
winter enhances ClONO2 and HOCl available for reaction with HCl and hence net chlorine activation.
The onset of chlorine activation by heterogeneous
processes is mostly limited by the amount of available ClONO2, as confirmed by a recent combinationof CALIOP PSC and MLS HCl and ClO observations
with model simulations (Nakajima et al., 2016).
Recent studies focus on a quantitative analysis of thechemical reactions involved in polar ozone depletion
4.20

in the stratosphere (for specific winters) and of therelevant reaction pathways and cycles (Wohltmann
et al., 2017; Müller et al., 2018). Wohltmann et al.
(2017), based on NASA’s Jet Propulsion Laboratory(JPL) 2011 recommendation (Sander et al., 2011), findthat the ClO dimer cycle contributes about 50% to thevortex-averaged ozone loss at 54 hPa in both hemispheres, while the BrO–ClO cycle contributes about
40%. Further, in the Southern Hemisphere, there isa clear shift from chlorine activation by the ClONO2+ HCl reaction in early winter to activation by the
HOCl + HCl reaction later in winter (Wohltmann etal., 2017; Müller et al., 2018). The HOCl + HCl reaction accounts for about 70% of the activation of Cl
in the Southern Hemisphere, while it accounts for
30% of the activation in the Northern Hemisphere(Wohltmann et al., 2017). In the core of the Antarctic
vortex, in the lowermost stratosphere, high levels ofactive chlorine are maintained by effective chemical
cycles (HCl null-cycles) where the formation of HCl
is balanced by immediate reactivation, which allows
active chlorine levels to be maintained and thus rapid
ozone destruction to occur. For the observed almost
complete activation of stratospheric chlorine in thelower stratosphere, the production of HOCl via HO2+ ClO, with the HO2 resulting from photolysis of
CH2O, is essential (Müller et al., 2018).
In the dark core of the polar vortex MLS observations
show a much faster depletion of HCl than simulated
by current state-of-the-art models (ATLAS, CLaMS,WACCM, and TOMCAT/SLIMCAT) (Wohltmann
et al., 2017; Grooß et al., 2018). This points to some
unknown process that is currently not fully represented. There is only a minor impact of about 2% on theoverall ozone column loss over the course of Antarctic
winter and spring, however, because the HCl discrepancy and the associated underestimation of chlorine
activation occur in early winter, when ozone loss rates
are slow.
Reaction Kinetics
Canty et al. (2016) showed that the most recent recommendations for the kinetics that govern the partitioning of ClO and ClOOCl (put forth by the JPL panel)(Burkholder et al., 2015) are in extremely good agreement with the atmospheric observations of ClO andClOOCl. The most important difference with respect
to calculations that rely on older recommendations is

Polar Stratospheric Ozone | Chapter 4

the temperature at which loss of ozone by the ClOOCl
catalytic cycle terminates. The current recommendation (Burkholder et al., 2015) suggests that ClOOCl
is less stable than previously assumed, resulting in an
approximate 2 K downward shift in the termination
temperature of polar ozone loss due to the ClOOCl
catalytic cycle (Canty et al., 2016).

4.3.3

Very Short-Lived­Halogenated Substances

Recent observations show that the atmosphericconcentration of dichloromethane (CH2Cl2)—anozone-depleting gas not controlled by the Montreal
Protocol—is increasing (Leedham Elvidge et al., 2015;
Hossaini et al., 2017; Chapter 1 of this Assessment).
The future evolution of atmospheric dichloromethane
is uncertain, but for the present day (2015) the simulated contribution of dichloromethane to total inorganic
chlorine in the polar lower stratosphere (100 hPa) is
about 3% (Hossaini et al., 2017). Using atmosphericmodel simulations, Hossaini et al. (2017) show thatthe largest ozone decreases attributable to dichloromethane are simulated in the Southern Hemisphere.
The impact of dichloromethane in these simulations
is modest at the present time, with springtime zonal
mean column ozone in the Southern Hemisphereup to 3%, or 6 DU, lower in simulations in which dichloromethane is considered (Hossaini et al., 2017).
Beyond dichloromethane, a number of very short-lived
substances (VSLSs; for example, 1,2-Dichloroethane,
C2H4Cl2) have also been detected in Earth’s atmosphere, although atmospheric measurements of thesecompounds are sparse (Hossaini et al., 2015a). While
the major contribution to stratospheric chlorine fromchlorine-containing VSLSs comes from dichloromethane, these other VSLSs also contribute. The additional chlorine loading from all chlorine-containing
VSLSs in 2013 amounted to about 100 ppt (Hossaini
et al., 2015a).
It is also important to account for the contribution ofbromine-containing VSLSs to the stratospheric halogen loading (e.g., Frieler et al., 2006). Recent CCM
simulations of the evolution of the Antarctic ozone
hole show a significant additional reduction of thetotal ozone column over the polar cap and better agreement with observations, when brominated VSLSs are
considered (Figure 4-11, Braesicke et al., 2013; Yanget al., 2014; Sinnhuber and Meul, 2015; Oman et al.,2016; Fernandez et al., 2017). For instance, Sinnhuber
and Meul (2015) found that due to the inclusion of
VSLSs, up to 25% more ozone was destroyed locallyin the southern polar lower stratosphere (60°–90°S)
in October in the period 1979 to 1995, leading to aregional reduction of total column ozone by about
10% (October) due to bromine-containing VSLSs.
Compared to OMI satellite measurements, Oman etal. (2016) obtained better agreement in the very low
ozone concentrations in the deep Antarctic lower
stratospheric polar vortex during late September toearly October from the late 1990s to the early 2000s
when bromine from natural VSLSs was considered intheir model. At the time of maximum chlorine loading
around the year 2000, the 5 ppt of very short-lived Bry
increased the ozone hole area by about 40% (5 million
km2) and enhanced the ozone mass deficit by about
75% (8 million tons) (Fernandez et al., 2017, Figure4-11). Although the strongest impact of bromine-containing VSLSs is in the Antarctic, there is also an impact on Arctic ozone levels in spring (e.g., Yang et al.,2014). However, it should be noted that the impact ofbromine on stratospheric ozone occurs through the
ClO/BrO–ClO chemical cycle and thus is only strongfor enhanced stratospheric chlorine levels.

4.3.4

Polar Dynamical Processes

4.3.4.1	Dynamical Control of Polar Ozone
Year-to-year variability of stratospheric polar ozone
is controlled by dynamical and chemical processes.
Both are coupled to temperature changes which, inturn, are strongly influenced by wave activity (WMO,
2014). Recent studies of the dynamical contribution
to temperature trends and ozone variability in Arctic
spring essentially confirmed the important role of dynamics (Bohlinger et al., 2014; Bednarz et al., 2016;
Ivy et al., 2016; Strahan et al., 2016). Ozonesondemeasurements over Belgrano (Antarctica) show, forexample, that the largest ozone depletion occurs in thecoldest years (55–60% decrease of total ozone columnin spring in the years 2000, 2003, and 2006), while theozone loss in warm winters is smaller (20% in 2002
due to the Southern Hemisphere (SH) major sudden
stratospheric warming) (Parrondo et al., 2014).
Planetary wave driving of the polar stratosphere is
generally stronger and more variable in Northern
4.21

Chapter 4 | Polar Stratospheric Ozone(a)

Ozone Hole Area

25

Reference run
Reference run + VSLS
Observations

Area (million km2)

20
15
10
5
0

1960

Ozone (million tons)

(b)

1980

2000

2020 2040
Year

2060

2080

Ozone Mass Deficit

30
25
20
15
10
5
0

Reference run
Reference run + VSLS
Observations

Difference

1960

1980

2000

2020 2040
Year

2060

2080

Hemisphere (NH) than in SH winter, leading to awarmer Arctic polar vortex and less chemical ozone
depletion (see Figures 4-1, 4-4, and 4-5). In contrast,
in austral winter and spring, Antarctic lower stratospheric temperatures are low enough for continuingheterogeneous ozone depletion, as the Antarctic vortex is much less disturbed by wave forcing (WMO,
2014; Solomon et al., 2014). The inter-hemisphericdifferences in the relationship between wave activity
and the spring-to-fall ozone ratio are illustrated in
Figure 4-12 (update of Weber et al., 2011 and Figure
3-13 in WMO, 2014). Figure 4-12 shows the compact
linear relationship between the mean winter eddy heat
flux at 100 hPa and the spring-to-fall high-latitude
ozone ratio, combining data from both hemispheres.
The winter eddy heat flux is consistently lower in theSH than in the NH. As a result, chemical ozone loss
dominates in the SH lower stratosphere, and ozone
values are lower in spring than in fall (except for 2002
and 2017). In contrast, the larger NH eddy heat flux
leads to enhanced transport of ozone throughout the
4.22

8
4
0
2100

10
5
0
2100

Figure 4-11. Temporal evolution of (a)the Antarctic ozone hole area and (b)ozone mass deficit for model experiments with the Community Atmosphere
Model with Chemistry (CAM) without
brominated VSLSs (black, reference)
and with brominated VSLSs (blue, reference + VSLS) on the left axis, as well as
the difference between runs (red) on
the right axis. Solid thick lines show theensemble mean for each experiment,
while the dashed, dotted, and dashed-­
dotted thin lines correspond to each of
the three independent simulations foreach run. Red dots in the upper panels
show observations. From Fernandez etal. (2017).

winter. As shown in Figure 4-12, this relationshipheld for all Antarctic and Arctic winters since thelast Assessment, including the year 2015 (with theeruption of the Chilean volcano Calbuco enhancing
Antarctic ozone depletion (see Section 4.2.3.1) andthe northern winter 2015/2016 (see Section 4.2.4.1).

4.3.4.2	Refined Understanding of
Dynamical Variability
The mechanisms involved in the generation, propagation, and dissipation of planetary waves are wellknown from theoretical, observational, and modelingapproaches. Nevertheless, our understanding of theprocesses that determine the degree and interannual
variability of wave driving of the polar stratosphereis still incomplete (WMO, 2014). Investigating thesources of interannual variability of wave activity inthe stratosphere is still an intense area of research. The
following sections report on advances since the last
Assessment.

100

Oct 2015

150

200

250

300

350

Oct 2002

400

450

DU
Polar
Stratospheric
Ozone | Chapter 4
500
550

Mar 2011

Mar 2010

Dobson
Unit
550
500
450

GOME(2)/SCIAMACHY TO3 Ratios (NH: Mar/Sep, SH: Sep/Mar)

Total Ozone Ratio 50°–90°

400

1.6
350

1.4

300

1.2

2017

1.0

2015/162016/17

250
200

2016

0.8

150

6

10
12
14
8
100hPa Winter Eddy Heat Flux (ECMWF Interim) (K m s−1)

1996 1997 1998 1999
2000 2001 2002 2003
2004 2005 2006 20072008 2009 2010 20112012 2013 2014 20152016 2017

1995/1996
1999/2000
2003/2004
2007/20082011/20122015/201616
100

1996/1997 1997/1998 1998/1999
2000/2001 2001/2002 2002/2003
2004/2005 2005/2006 2006/2007
2008/2009 2009/2010 2010/2011
2012/2013 2013/2014 2014/2015
2016/2017

Figure 4-12. Spring-to-autumn ratio of observed high latitude total ozone (>50º latitude) as a function of
the absolute extratropical winter mean eddy heat flux (September to March in the Northern Hemisphereand March to September in the Southern Hemisphere) derived from ECMWF ERA-Interim data. Data fromthe Southern Hemisphere are shown as triangles (September over March ozone ratios) and from the Northern Hemisphere as solid circles (March over September ratios). Polar total ozone distributions for selectyears are shown at the top. Updated from Weber et al. (2011).

Sources of Dynamical Variability: SSTs and ENSO
Since van Loon and Labitzke (1987), it is well known
that variations in sea surface temperatures (SSTs) leadto anomalous stratospheric polar vortices, affecting
stratospheric polar ozone. Recent studies have refinedprevious work by addressing in more detail the underlying mechanisms linking SST anomalies to stratospheric polar vortex variability, the role of the location of SST anomalies and differences in the impact of
SSTs on the Arctic and Antarctic polar vortices and on
stratospheric ozone.

SST anomalies generate anomalous upward wave flux
into the stratosphere. Hence, increasing global SSTs andtheir latitudinal gradients modulate the polar vortices
through an enhancement of the Brewer-Dobson circulation in both hemispheres. While globally uniform
SST changes have a stronger impact on the Southern
Hemisphere, changes in the SST gradients affect the
Arctic vortex more significantly. This asymmetry is
due to differences in the properties and transmissionof the waves in both hemispheres (Hu et al., 2014).
4.23

Chapter 4 | Polar Stratospheric Ozone

Moreover, depending on the longitudinal position of
the SST anomalies, they can either enhance or weaken upward wave propagation into the stratospherethrough positive or negative interference with theclimatological wave patterns. Hemispheric differences in the extratropical impact of regional tropical SST
anomalies have been linked to differences in transient
eddy forcing and stationary wave activity between
the hemispheres (Li et al., 2010). Thus, higher SSTs ineither the tropical Eastern Pacific Ocean (e.g., Calvo
et al., 2017) or the North Atlantic (Omrani et al.,2014), or lower SSTs in either the North Pacific (e.g.
Hurwitz et al., 2012) or the Indian Ocean (Fletcher
and Kushner, 2011) lead to a weaker Arctic polar vortex. Using satellite observations and reanalysis data,
Tian et al. (2017) reported a high correlation between
SSTs in the East Asian marginal seas and lower stratospheric ozone over Antarctica in austral spring, withhigh SSTs reducing planetary wave activity in the SH,
strengthening the stratospheric polar vortex, andthereby enhancing chemical ozone loss (with the opposite effects for low SSTs). According to their model
simulations, ~17% of the decline of Antarctic lower
stratospheric ozone between 1955 and 2005 may be
associated with increasing SSTs over the marginal seasof East Asia.
Recent studies of the El Niño–Southern Oscillation(ENSO) highlighted the role of the location of thestrongest SST anomalies (i.e., in the Eastern Pacific[EP] or the central Pacific [CP]) for the effects of
ENSO on polar ozone. ‘Canonical’ EP El Niño events
tend to weaken the polar vortex (e.g., Calvo et al., 2017
and references therein) and enhance stratosphericcolumn ozone at high latitudes (e.g., Cagnazzo et al.,2009), while during La Niña events (characterized bynegative SST anomalies in the central-eastern Pacificarea), a stronger and colder polar Arctic vortex is
observed (Iza et al., 2016), implying a reduced polar
total ozone column. Compared to canonical El Niño
events, CP El Niño events are more effective in the
Antarctic where they lead to higher stratospherictemperatures and ozone in the lower stratosphere
during austral summer and autumn (Zubiaurre andCalvo, 2012; Evtushevsky et al., 2015). However, theeffects of CP El Niño on the Arctic stratosphere arestill under debate (Hurwitz et al., 2014).

4.24

Dynamical Variability in NH Stratospheric Winters
Sudden stratospheric warmings (SSWs) in northern
winter are induced by anomalously strong upward
wave propagation from the troposphere and dissipation in the middle and high latitudes of the stratosphere. They weaken the polar vortex, warm the polar
stratosphere, and enhance the Brewer-Dobson circulation (BDC) (e.g., Andrews et al., 1987; Charlton
and Polvani, 2007). Since the previous Assessment,a growing number of studies investigated the impactof SSWs on polar processes and polar ozone. The enhanced temperature and BDC around the SSW datelead to a reduction of ozone loss and more ozone
transported towards the pole (e.g., Strahan et al.,2016; Manney et al., 2015b; Damiani et al., 2014; Taoet al., 2015). In addition, meridional mixing increases
during and after SSWs although its impact on ozone
is not clear due to large case-to-case variability (Tao etal., 2015; Damiani et al., 2014; Manney et al., 2015a;
Manney and Lawrence, 2016).
By comparing simulations with a CTM driven withobserved meteorological conditions with and without
heterogeneous chemistry, Strahan et al. (2016) quantified the chemical ozone depletion over the Arctic cap
for the recent past (2005–2015). They showed that thelinear relationship between the chemical ozone loss
rate and the number of days cold enough for PSC particle formation within the polar vortex, found in earlier studies (Rex et al., 2004, 2006; Tilmes et al., 2003),
still holds. While enhanced chemical ozone loss takes
place in an undisturbed, cold, and stable polar vortex,
the occurrence of a major SSW in mid-winter limits
the number of cold days. As a result, ozone depletion in winters with a stable and cold polar vortex
is roughly three times greater than in winters witha major SSW before mid-February. With five cold
Arctic winters (2005, 2007, 2008, 2011, and 2014)
and six winters with SSWs occurring in the period
2005–2015, a large part of the interannual variabilityof Arctic ozone over this period is explained (correlation of 0.97 between the maximum seasonal Arctic
cap column ozone depletion and the number of colddays) (Strahan et al., 2016). Ozone loss in cold Arctic
winters can additionally be amplified by high water
vapor mixing ratios in the lower stratosphere, further
increasing the probability of PSC formation and effective heterogeneous chlorine activation, as derived
by Khosrawi et al. (2016) from satellite observations.

Polar Stratospheric Ozone | Chapter 4

Particularly strong SSWs may perturb the mesosphere
for many weeks. These events are characterized by anelevated stratopause which forms at pressure levels as
high as 0.5 Pa (~70 km) about 10–14 days after the
peak of the SSW. During the recovery phase of such
elevated stratopause events, observations show astrong descent of polar mesospheric NOx-rich air into
the stratosphere (e.g., Pérot et al., 2014; Orsolini etal., 2017), inducing polar Arctic upper stratosphericozone loss.
Recently, Siskind et al. (2016) proposed a new mechanism by which dynamical variability in northern
spring may affect polar ozone in the following summer. In winters with strong mesospheric descent thatare followed by dynamically quiet spring seasons (as inboreal winter 2009), relatively low values of CH4 andhigh values of ClO may persist in the upper stratosphere throughout the summer. In 2009, these variations caused up to a 5% reduction in upper stratospheric ozone throughout the summer and early fall.
Downward Planetary Wave Reflection
As discussed in the previous section, the upward
propagation and dissipation of planetary waves is
the important driver of the BDC in the boreal winterstratosphere and largely determines the dynamical resupply of Arctic ozone in winter and spring. However,
the overall effect of the planetary wave forcing on
Arctic ozone levels in midwinter and spring not only
depends on the tropospheric planetary wave sources but also on the stratospheric conditions for wave
propagation, as highlighted in a recent CCM study
by Lubis et al. (2017). Downward planetary wave reflection may occur in the stratosphere when upward
pulses of wave activity decelerate the flow in the upper
stratosphere, forming a downward-reflecting surface
that redirects waves back to the troposphere (e.g.,
Harnik and Lindzen, 2001). These types of events
lead to a weaker BDC and a colder polar vortex. Thus,
there is a direct effect by planetary wave reflection onozone due to transport, such that less ozone is advected towards the polar region, and an indirect effect due
to the induced lower temperatures in the polar vortex,
which enhance heterogeneous chemical ozone loss
(Lubis et al., 2017).

4.3.5

Other Factors Affecting Polar Ozone

4.3.5.1 Solar Variability by Energetic
Particle Precipitation
In addition to the impact on global ozone by decadal
variations in solar ultraviolet irradiance (see Chapter
3.2.1.1), polar ozone can be destroyed by energetic
particle precipitation (EPP) resulting in total ozone
loss up to 10–20 DU after strong solar proton events(SPEs) (Vogel et al., 2008). EPP is strongly linked tosolar activity either directly by coronal mass ejections(CMEs) producing sporadically large fluxes of solar
energetic particles or indirectly by the quasi-continuous impact of the solar wind on Earth’s magnetosphere resulting in precipitation of energetic electrons(see e.g., the review by Mironova et al., 2015). The
presence of EPP affects the ionization levels in the
middle and upper polar atmosphere, leading to significant changes of the chemical composition including
ozone (see e.g., the review by Sinnhuber et al., 2012).
Solar proton events caused by CMEs are particularly frequent around the maximum of the solar cycle.
A recent intercomparison study demonstrated theoverall ability of specialized atmospheric models
to reproduce the direct EPP effect by solar protons
after the 2003 “Halloween” SPE in late October andearly November 2003 (Funke et al., 2011). This event
was characterized by short-term (days) mesosphericozone depletions up to 70%, followed by longer-lasting (weeks to months) depletions of up to 35% in
the upper stratosphere. After the “Halloween” event,which has been discussed in detail in WMO (2006),
SPE-related composition changes of smaller magnitude have also been observed and modeled in otheroccasions, namely in November 2004 (Hocke, 2017),
January 2005 (Jackman et al., 2011; Verkhoglyadova
et al., 2015), as well as in January and March 2012(von Clarmann et al., 2013; Jackman et al., 2014;
Päivärinta et al., 2016). A statistical investigation of
average changes in ozone from sonde measurements
following 191 SPEs from 1989 to 2016 was carriedout by Denton et al. (2018). Their results indicate thatSPEs are linked to a ~5–10% decrease in ozone at ~20km altitude during the polar winter. The greatest decrease occurs ~10–20 days following SPEs with ozone
depleted for ~30 days on average.
Energetic electron precipitation is associated withgeomagnetic storms and occurs mainly in the polar
4.25

Chapter 4 | Polar Stratospheric Ozone

auroral and sub-auroral regions with an intensity being
largest about two years after the maximum of the solar
cycle. Precipitation from mid-energy and from auroral electrons affects the mesosphere (50–100 km) andthe lower thermosphere (95–120 km), respectively.
The NOx produced by EPP at these altitudes is longlived during polar winter and transported down into
the stratosphere to altitudes well below 30 km. Satellite
observations have provided clear evidence of this EPP
indirect effect (IE) occurring in every polar winter

EPP-NOy 70°–90°S

70

Altitude (km)with a magnitude modulated by the solar cycle (e.g.,
Randall et al., 2007; Hendrickx et al., 2015; Funke etal., 2016). The EPP-generated NOy contributes to thepolar winter NOy column at 20–70 km by 10–40% in
the Southern Hemisphere (SH) (see Figure 4-13, top
panel) (Funke et al., 2014). Stronger wave activity in
Arctic winters is responsible for the generally smaller and more variable contributions in the Northern
Hemisphere (up to 30%). Recently, observational evidence of polar ozone losses due to the EPP IE has

1.000

60

ppmv

0.100

50
40

0.010

30

0.001

20

Pressure (hPa)Jan
112003
Jan
2003

Jan
112007
Jan
2007

Jan
112005
Jan
2005

Jan
112009
Jan
2009

Year

Jan
112011Jan
2011

EMAC Ozone (UBC-BASE), (%), 70°–90°S

0.01

1
0
−1
−2
−5
−10
−30
−50
−70
−90

0.10
1.00
10.00

100.00
2004

2006

2008

2010

2012

Columnchange (%)

Year

EMAC Ozone Column (UBC-BASE), (%), 70°–90°S

0
−2
−4
−6
−8
2004

2006

2008

2010

2012

Year

Figure 4-13. (top) Temporal evolution of the NOy contribution produced by energetic particle precipitation(EPP-NOy) (in ppmv) at 70°–90°S taken by the Michelson Interferometer for Passive Atmospheric Sounding(MIPAS) on board the Envisat satellite during 2002–2012. The contribution of EPP-NOy has been discriminated from that produced by N2O oxidation using a tracer correlation method based on MIPAS CH4 and CO
observations. (Adapted from Funke et al., 2014). (middle and bottom) Ozone loss due to EPP as a functionof pressure level (middle) and for the total ozone column (bottom) at southern high latitudes (70°–90°S).
Shown is the percentage difference between EMAC model simulations with and without EPP impact. The
EPP effect is prescribed as an upper boundary condition of NOy based on MIPAS observations; solar proton events (e.g., in October/November 2003 or January 2005) are prescribed by modeled ionization rates.
Adapted from Sinnhuber et al. (2018).
4.26

Polar Stratospheric Ozone | Chapter 4

been provided: SH polar stratospheric ozone loss dueto the EPP IE peaks around 30–40 km in late winterwith an average magnitude of about 10–15% (Fytterer
et al., 2015; Damiani et al., 2016). Chemistry–climate
models accounting for the EPP IE are able to reproduce the observed effects reasonably well. Forced by
EPP-induced NOy anomalies from satellite data between 2002 and 2012, Sinnhuber et al. (2018) show arecurring average decrease in Antarctic total column
ozone around 4% in each winter/spring (ranging between 2% and 3% in 2009 and 2010, and 8% in 2003;
see Figure 4-13, middle and bottom panel, adaptedfrom Sinnhuber et al., 2018). Although the decrease
in total column ozone by EPP-generated NOy is lessthan one-tenth of the halogen-induced ozone depletion in the Antarctic polar vortex (see Figure 2-29 in
WMO, 2010), the EPP effect needs to be consideredin models to simulate realistic total ozone columns
in polar winter. However, most CCMI models do not
yet incorporate the effects of EPP-generated NOy onpolar ozone loss.

4.3.5.2 Volcanic Eruptions
Sulfate aerosols increase in the stratosphere after volcanic eruptions, providing surfaces on which heterogeneous chemical reactions occur favoring ozone loss
(Hofmann and Solomon, 1989). In addition, volcanic
aerosols also reduce polar ozone by an indirect dynamical mechanism through radiative heating of thelower stratosphere which increases the equator-topole temperature gradient. This leads to a strengthening of the polar vortex—either by thermal windbalance (Kodera, 1995) or reduced planetary wave
forcing (Bittner et al., 2016)—and thus to more ozone
loss.
While some climate models are able to reproduce therobust response of the polar vortex to volcanic eruptions (Pitari et al., 2016; Muthers et al., 2015; Raible
et al., 2016), others are not (Driscoll et al., 2012;
Charlton-Perez et al., 2013; Toohey et al., 2014), or
their response depends on the size of the eruption(Bittner et al., 2016). The reasons for these discrepancies are still not well understood. Some models tend tooverestimate the warming of the tropical stratosphereand thus magnify the polar dynamical response(Muthers et al., 2015). Moreover, the polar vortex response seems to be very sensitive to the spatio-temporal distribution of the volcanic forcing (Toohey etal., 2014) and also to the choice of the ozone climatology in climate models without interactive chemistry(Muthers et al., 2014).
While no major volcanic eruptions comparable in size
to Mt. Pinatubo have occurred since 1991, satellite
measurements revealed further injections of volcanic
SO2 into the stratosphere by a number of moderate
eruptions at different latitudes during the past decade(WMO, 2011; Carn et al., 2016). Solomon et al. (2016)
showed that including the SO2 emissions from thesemoderate eruptions in specified-dynamics CCM simulations enlarged the size of the modeled Antarctic
ozone hole in September and led to better agreementwith the observed ozone hole. Moreover, they foundabout a 10% reduction of the modeled post-2000
healing of the Antarctic ozone hole in September as aresult of the chemical effects of increased volcanic activity in the latter part of 2000–2014. Likewise, observations from ozonesondes and the Aura MLS suggestthat stratospheric volcanic particles from the 2015
eruption of the Chilean volcano Calbuco enhanced
Antarctic ozone depletion and contributed to therecord-large Antarctic ozone hole in October 2015
(Stone et al., 2017) (see also Section 4.2.3.1). Thisozone loss after volcanic eruptions is driven by heterogeneous chemical processes associated with SO2emissions, while radiative and dynamical feedbacks
only play a minor role (Ivy et al., 2017).
A further potential impact of explosive volcanic eruptions on stratospheric ozone is the direct injection of
halogens into the stratosphere. Recent developments
in measurement technology allowed for improved
estimates of halogen ejections from large historicaleruptions (Kutterolf et al., 2015). As these substances
are diluted on their transport from the troposphere to
the stratosphere through scavenging by hydrometeors(Tabazadeh and Turco, 1993), estimates of the injection efficiency into the stratosphere vary widely for
individual eruptions. Nevertheless, direct injectionsof significant quantities of volcanic halogens haverecently been confirmed by remote sensing: MLS recorded stratospheric HCl:SO2 ratios of 0.01–0.03 for
14 eruptions spanning the years 2005 to 2014 (Carn etal., 2016). Based on petrological constraints, Cadoux
et al. (2015) found that the Late Bronze Age ‘Minoan’eruption of the Santorini volcano released far more
halogens than sulfur. Even if only 2% of these halogens had reached the stratosphere, they would have
4.27

Chapter 4 | Polar Stratospheric Ozone

resulted in reductions in ozone columns of 20% to>90% at northern high latitudes.
Recent Assessments have not considered the impact ofthe volcanic halogen loading on stratospheric ozone
partly because of the large uncertainties in its magnitude and also because this process was determined tobe small compared to anthropogenic halogen loading
after the eruptions of El Chichón (in 1982) and Mt.
Pinatubo (in 1991). However, volcanic halogens are
expected to become more relevant in the future as anthropogenic halogens decline. The response of the totalozone column to the injection of SO2 results from two
chemical regimes causing opposite ozone changes: (1)in the lower stratosphere (pressures >30 hPa), heterogeneous chemistry on sulfate aerosol surfaces leads
to chlorine activation and ozone depletion and (2) inthe upper stratosphere (pressures <30 hPa), catalyticozone depletion in the NOx cycle is suppressed (Tieand Brasseur, 1995). Hence, in an atmosphere withlow chlorine levels, such as in the era before industrial halogen production, upper stratospheric chemistry dominates and the total polar ozone column is
expected to increase after volcanic eruptions (WMO,
2014; Muthers et al, 2015). Similarly, in the future,when anthropogenic halogen is expected to decrease,major volcanic eruptions that inject SO2 into thestratosphere may cause an ozone increase (e.g., Naiket al., 2017). However, Klobas et al. (2017) found thatin a future medium Representative Concentration
Pathway RCP-6.0 GHG scenario there is still significant net loss of total column ozone after volcanic SO2
emissions after mid-century. With increasing GHG
concentrations, the post-volcanic chemical ozone
depletion weakens due to stratospheric cooling andincreased methane concentrations (Klobas et al.,2017; Naik et al., 2017). Klobas et al. (2017) further
show that in a future low-halogen environment, thepresence in the stratosphere of bromine from natural, very short-lived biogenic compounds is criticallyimportant for determining whether future eruptions
will lead to ozone depletion. The additional injectionof volcanic halogens would induce substantial ozone
reductions, particularly in polar regions. Projecting
how future volcanic eruptions might affect stratospheric polar ozone remains highly uncertain due tothe complex interactions between volcanic aerosols,
rising GHG concentrations and VSLSs.

4.28

4.4

RECOVERY OF POLAR OZONE

4.4.1

Polar Ozone Recovery in­Previous Assessments

WMO (2007) defined three stages of current and future stratospheric ozone recovery: (1) a slowing in therate of ozone decline, (2) the onset of ozone increases
above the previous minimum values (so-called “turnaround”) due to declining EESC, and (3) full recovery
from ODSs. WMO (2007) concluded that, while stabilization of Antarctic ozone levels had been observed
at a similar time as the expected peak of EESC, due
to the influence of both saturation of depletion andanomalously high temperatures, the attribution wasinconclusive and it was therefore not possible to statethat either the first or second stages of recovery had
yet occurred. WMO (2007) also included predictionsof a slow recovery of Antarctic total column ozone,
with an increase in springtime ozone of 5–10% between 2000 and 2020, or 0.25–0.5% yr−1 (approximately 0.5–1 DU yr−1) over that period.
WMO (2011) concluded that the leveling off ofAntarctic stratospheric ozone since the late 1990s
could be attributed to the slight decline in Antarctic
stratospheric ODSs, based on the analysis of Yang etal. (2008).
WMO (2014) discussed recovery of Antarctic springtime stratospheric ozone in more detail. Further studies of both vertically resolved and total column ozone
had been published since WMO (2011), generally
making use of multiple linear regression methods toaccount for non-chemical effects. It was concluded
that Antarctic total column ozone appeared to havestarted to increase since reaching a minimum at the
beginning of the 21st century and that the rate ofincrease appeared consistent with declining ODSs.
The definitive conclusion that Antarctic stratospheric ozone was increasing due to declining ODSs could
not yet be reached, however, due to uncertainties inmeasurements and statistical methods.
Compared to the Antarctic, the Arctic shows larger interannual variability in springtime ozone andsmaller ozone depletion, and detection of changes
in ozone due to decreases in EESC are therefore expected to take longer than in the Antarctic. WMO(2007) reported that no slowing of the decline in
Arctic stratospheric ozone had yet been found. WMO

Polar Stratospheric Ozone | Chapter 4(2011) and WMO (2014) both reported little progressin assessing Arctic stratospheric ozone recovery since
WMO (2007).

4.4.2

Long-Term Antarctic Ozone Trend

4.4.2.1	Onset of Antarctic Ozone Depletion
As noted in previous Assessments, the general choiceof 1980 as the reference year for ozone levels should
not be taken to mean that there was no anthropogenic ozone depletion in Antarctica prior to that year.
Comparing simulations from 17 CCMVal-2 models
with total column ozone measurements from four
long-term Dobson sites in the Antarctic (Faraday,
Halley, South Pole, and Syowa) combined with measurements from multiple space-based instruments(Bodeker et al., 2005), it was estimated that about
half of the ozone loss attributable to halogens between 1960 and 2000 actually took place before 1980
(Langematz et al., 2016). Updated results of this study
for more years of observations and new CCMI model
simulations are shown in Figure 4-14. The observed

halogen-induced, pre-1980 Antarctic spring totalozone depletion reaches about 54% of that in the
1960–2000 period, comparable in size to the previous
estimate. Similar to the CCMVal-2 models, the CCMI
models slightly underestimate the observed estimate,with a comparable spread in both model groups.

4.4.2.2	Onset of Antarctic Ozone Recovery
Since WMO (2014), several studies have taken advantage of the increasing length of record to identify parameters that quantify various aspects of ozone loss,
which show a positive trend in ozone (i.e. a decreasein ozone depletion) since the year 2000. Different
sources of data have been used to derive these parameters, such as ozonesondes or ground-based
data from the global Dobson and Brewer networks.
Merged multi-year ozone time series were generated
from different satellite instruments, which allowed
the quantification of long-term trends in total column
ozone and vertical ozone profiles before and after theturnaround of Antarctic EESC. More details of the applied ozone data sets are given in the respective studies

Percentage of Ozone Depletion 1960–1980 Relative to 1960–2000

Percentage

60

40

20

EM

io
ns
at

rv
se

S

UM

Ob

3

AC MA
−L
90
M
A
GE
OS
C
Ha
dG CM
EM
3−
M
ES
RI
−E
SM
NI
1r
W
1
A−
UK
CA
SO
CO
UL
L3
AQ
−C
CM

47

5−
EM

AC
−L

AM

−C
M
M

CM

CN
R

AC
CM

m

CE
S

M

1−

W

.2
M

4C
he

OC
3

IR
SM

1−

CA

M
S−

T

CA

LIM

CE

CC
S

RN

IE

AC
CE
S

S−

CC
M

0

Figure 4-14. Halogen-induced Antarctic ozone depletion in September to November for the period
1960–1980 in % of the depletion for the period 1960–2000 in CCMI simulations and observations. Uncertainties of 1σ were derived by applying error propagation rules. For the analysis, the extended observational data base until 2016 (Bodeker et al., 2005) has been used. Adapted from Langematz et al. (2016).
Lorem ipsum
4.29

Chapter 4 | Polar Stratospheric Ozone

listed in Table 4-1. From these ozone observations,long-term changes in Antarctic ozone were derived byapplying various mathematical methods that provide
estimates of trends in a number of ozone parameters(e.g., total column ozone, ozone within the particularheight range at which depletion has previously been
greatest, or metrics used to describe the severity of
the Antarctic ozone hole) for specific Antarctic locations (e.g., Antarctic ground-based or ozonesonde
stations or polar cap (60°–90°S) average) and season(e.g., September, October, or September to November
average) and trend periods (i.e., different starting andfinishing years). Box 4-1 describes in more detail themethods applied to calculate Antarctic ozone trends.
Although the recent studies have differed in their approaches, they have found broadly consistent results.
Table 4-1 gives an overview of some of the identified
trends discussed in the following subsections, including the ranges of uncertainty. It should be noted thatthe table only gives a subset of results, with most of
the studies reported on having also considered additional or differently defined parameters and variationsof method such as filtering criteria. There is consensus
among all studies that, particularly in the month of
September, several metrics of Antarctic ozone haveshown reductions of depletion in the years following
the peak in EESC. In contrast, in October, no significant trends in metrics of Antarctic ozone have beenfound to this time.
Individual studies find positive trends in Antarctic
total column ozone in September that are significant
at the 90% or 95% confidence levels (e.g., Solomon
et al. 2016; Kuttipurath and Nair, 2017), while othermetrics do not yield trends significant at the 2σ level.
Apart from the impact of dynamical variability, uncertainties in derived trends arise from the formulation of the regression model, the use of different
proxies, and the time period of the trend (Knibbe etal., 2014; de Laat et al., 2015; Chipperfield et al., 2017;
Weber at al., 2018). Other factors not incorporated in
the purely statistical uncertainty range include possible drifts in the observational data sets (Hubert et al.,2016), the procedures used to merge and homogenize
data records from different instruments (e.g., Hassler
et al., 2014; Frith et al., 2017), or the representativeness of sparse ground-based data particularly in lightof changes to the structure of the vortex over time
(Hassler et al., 2011a).
4.30

Total Column Ozone
Total column ozone over Antarctica in springtime has
increased since 2000 at a mean rate estimated to be
between 5 and 10% decade−1, approximately equivalent to 1–2 DU yr−1 (Knibbe et al., 2014; Solomon etal., 2016; Kuttipurath and Nair, 2017; Chipperfield etal., 2017; Weber et al., 2018; Pazmiño et al., 2018).
Solomon et al. (2016) found a positive trend in total
column ozone in the month of September over the period 2000–2014 as measured by ozonesondes at South
Pole of 2.5 ± 1.5 DU yr−1, and SBUV measurements
over the polar cap (as available) of 2.5 ± 1.6 DU yr−1,
with both ranges at the 90% confidence level. These
results are in good agreement with Pazmiño et al.
(2018) who derived total column ozone trends inside
the Antarctic polar vortex ranging between 1.85 and2.67 DU yr−1 depending on the methods and data
sets over the 2001–2016 period. Their trends are statistically significant at the 2σ level. Kuttippurath andNair (2017) found a positive trend in September toNovember total column ozone of 1.72 to 1.80 ± 0.80%
yr−1 (95% confidence level), depending on how thevortex is defined, over the period 2001–2013. The
value of the calculated trend was not greatly sensitive
to the choice of proxies.
Knibbe et al. (2014) derived a range of positive total
column ozone trends depending on the inflection year
in their piecewise linear trend (PWLT) analysis. Using
2001 (i.e., the year with maximum EESC over the SH
polar cap), they found an increase of 3.1 ± 5.8 DU yr−1
for the period 2001–2010. The trend was, therefore,
not statistically significant at the 95% confidence level.
More recently, Chipperfield et al. (2017) and Weberet al. (2018) both derived independent linear trends(ILTs) for September in Antarctica. Chipperfield et al.
(2017) calculated the September Antarctic (60°–90°S)
trend from 2000 to 2015 to be 4.7 ± 9.1% decade−1
using NASA SBUV data, while Weber et al. (2018),
using five merged data sets from satellite and groundbased observations, found the 2000–2016 trend in
September across the five data sets ranged between
8 to 10% decade−1 with a 2σ uncertainty of 7%. By
contrast, the trend in October was only 3% decade−1
and not statistically significant (Figure 4-15). The factthat these two studies used almost the same data sets
and similar methods but determined different results

Polar Stratospheric Ozone | Chapter 4

Table 4-1. Trends in a selection of metrics of Antarctic ozone in spring since 2000 (the year with maximum halogen loading), derived from various data sets and using a variety of analysis methods. Box 4-1 gives descriptionsof the methods used. Note that only a subset of the results for the polar regions of each study are shown, eachof which also considered additional parameters not shown here.
Parameter

Data Source

Trend

Confidence
Level

Time
PeriodMethodReference±2σ

2001–2010

MLR (PWLT)

Knibbeet al. (2014)90%

2000–2014

Linear trend
excluding 2002

Solomonet al. (2016)

TOTAL COLUMN OZONE
Total column ozone
September–November
Antarctic

MSR +­SCIAMACHY

3.1 ± 5.8 DU year–1

Total column ozone
SeptemberSouth Pole

Ozonesondes

2.5 ± 1.5 DU year–1

Total column ozone
September,south of 63°S

SBUV

2.5 ± 1.6 DU year–1

Total column ozone
locations of Antarctic
stations
September–November

TOMS/OMI

1.72-1.80 ± 0.8% year–1

95%

2001–2013

MLR (PWLT) withvortex filtering

Kuttipurathand Nair(2017)

Total column ozone
September, 60°–90°S

NASA SBUV

4.7 ± 9.1 ­­%­ decade–1±2σ

2000–2015

MLR (ILT)

Chipperfield
et al. (2017)

Total column ozone
September, 60°–90°S

Mergedsatellite data
and WOUDC

8.1–10.1 ± 7% decade–1±2σ

2000–2016

MLR (ILT)

Weber
et al. (2018)

Total column ozone
15 September–
15 October
Antarctic vortex

MSR-2

1.42 ± 0.92 DU
year–1±2σ

2001–2017

MLR (PWT) withvortex filtering

Pazmiño
et al. (2018)

VERTICALLY RESOLVED OZONE
Ozone mixing ratio
September–November
Antarctic vortex
Ozone partial column
SeptemberSouth Pole, Syowa

Ozonesondes

Up to 8 % year–1,
significant ­between
325 and 550 K
levels95%

2001–2013

MLR (PWLT) withvortex filtering

Kuttipurathand Nair(2017)

Ozonesondes“Clear increase”
from 100 to 50 hPa

90%

2000–2015

Linear trend
excluding 2002

Solomonet al. (2016)

OZONE HOLE METRICS
Ozone hole arealate September
in cold years

AURA-MLS

Smaller in 2008,2011 due todecreased Cly—

2004–2012

Linearrelationship

Strahanet al. (2014)

Ozone hole area
September

TOMS/OMI–4.5 ± 4.1
million km2

90%

2000–2015

Linear trend
excluding 2002

Solomonet al. (2016)

Ozone hole mass­deficit between
days 220 and 280

MSR-2+GOME-2–0.77 ± 0.17
Mt year–1±2σ

2000–2015

Linear trend 6
warmest years
filteredde Laat
et al. (2017)

Ozone hole mass
deficit
15 September–
15 October

MSR-2–0.68 ± 0.37
Mt year–1±2σ

2001–2017

MLR (PWT) withvortex filtering

Pazmiño
et al. (2018)

4.31

Chapter 4 | Polar Stratospheric Ozone

Box 4-1. Methods Applied to Calculate Polar Ozone Trends• Linear Trend

Linear trends since the year 2000 were used by Solomon et al. (2016), with the extreme year of 2002
filtered out. Similarly, de Laat et al. (2017) calculated a linear trend since 2000, after removing the six
warmest years of the 1979–2015 record (1986, 1988, 2002, 2004, 2010, and 2012).

• MLR (Multiple Linear Regression)Multiple linear regression is the most commonly used method to calculate the Antarctic ozone trend
remaining after removing the influence of known sources of variability, such as eddy heat flux, solar
variability, the Quasi-Biennial Oscillation (QBO), El Niño–Southern Oscillation (ENSO), and stratospheric aerosol loading. The significance of the resulting trend is sensitive to the choice of proxies used
to represent physical processes, and the choices of spatial and temporal averaging and time period (deLaat et al., 2015). In previous Assessments, EESC was often used as a regressor variable.

• MLR with PWLT (Piecewise Linear Trends)
Following Kuttippurath et al. (2015), who warned that the fit of EESC to ozone time series was determined mostly by the years prior to turnaround, and therefore could result in an inaccurate recovery
trend, fits to EESC are not used in this Assessment. Regression using PWLT instead fits two linear
trends to the ozone time series before and after an appropriate “turnaround” year, usually either 2000
or 2001. The trends are constrained to give a common value at the turnaround year. PWLT was usedby Knibbe et al. (2014).

• MLR with ILT (Independent Linear Trends)
Independent linear trends differ from PWLT in that the two linear trends are not constrained to meetat a common value at the turnaround year. This introduces an additional degree of freedom to theregression. Both Chipperfield et al. (2017) and Weber at al. (2018) used ILT.

• MLR with PWT (Piecewise Trends)
The “modified PWLT” model used by Pazmiño et al. (2018) is similar to ILT, but instead joins the twolinear trends with a parabolic curve. This allows an overall improved regression result, as EESC andozone show a non-linear growth rate around the period of the EESC peak.

reflects the uncertainty of the multiple linear regression method—in this case, the results show somesensitivity to the length of the time period and thetreatment of the proxies.
Vertically Resolved Ozone
WMO (2014) reported the results of Hassler et al.
(2011b) showing that ozone loss rates in springtime(measured by ozonesondes at South Pole between 100
and 40 hPa) had reached a maximum in the period
1991–1995 and had subsequently stabilized but had
4.32

not shown any significant reduction between 2001
and 2010. The additional years of observational datafrom ozonesondes (Solomon et al., 2016; Kuttipurathand Nair, 2017) and Aura MLS (Strahan and Douglass,2018) do now show evidence that Antarctic ozone
within this height range has significantly increased
since the year 2000.
Solomon et al. (2016) studied ozonesonde measurements from Syowa and South Pole stations in themonth of September. They found a statistically significant increase in ozone over the period 2000–2015

Polar Stratospheric Ozone | Chapter 4
Total Ozone Trend 1979–2016 (60°–90°N)

March total ozone (DU)

500

1.2 (3.7) % decade−3.4 (6.4) % decade

−1

−1

450

400

350

300

WOUDC
SBUV V8.6 NASASBUV V8.6 NOAAGOME/SCIA GSG
GOME/SCIA/OMI GTO
MLR (NOAA)

1980

r2 =0.81
m =11
n =37
χ =14.5 DU

1990

2000

2010

September total ozone (DU)

Year
Total Ozone Trend 1979–2016 (90°–60°S)

350

250

150
400

October total ozone (DU)

8.2(6.2)% decade−1−14.8(9.7)% decade−1

300

200

WOUDC
SBUV V8.6 NASASBUV V8.6 NOAAGOME/SCIA GSG
GOME/SCIA/OMI GTO
MLR (GSG)

r2 =0.90
m =11
n =37
χ =12.8 DU

Ozone Hole Metrics

300
250

150

according to their location as inside or outside of the
Antarctic vortex at each altitude. For 2001–2013, theyfound the largest trends of up to 8% yr−1 around 15km, with trends being significant at the 95% confidence level between approximately 12 and 22 km.
Using simple linear trends without any dynamical oraerosol proxies gave similar results but increased theuncertainty of the trend, such that the trend was nolonger significant over some of the height range.

2.7(7.9)% decade−1−17.0(12.5)% decade−1

350

200

Figure 4-15. Total ozone time series for (top panel)
the Arctic (60°–90°N) in the month of March and(bottom two panels) for Antarctica (60°–90°S) in themonths of September and October, derived from fivelong-term observational data sets: WOUDC (basedon the GAW network of ground-based Dobson andBrewer instruments), SBUV 8.6 processed by NASA,SBUV 8.6 processed by NOAA, GOME-SCIAMACHYGOME-2(GSG) and GOME-type Total Ozone (GTO). In
each panel, one data set has been chosen as labelled
to show the results of applying a multiple linear
regression with independent linear trends analysis (orange). Regressor terms include the solar cycle,
QBO, ENSO, volcanic aerosol, and the strength of the
Brewer-Dobson circulation. From Weber et al. (2018).

WOUDC
SBUV V8.6 NASASBUV V8.6 NOAAGOME/SCIA GSG
GOME/SCIA/OMI GTO
MLR (GTO)

1980

1990

r2 =0.81
m =11
n =38
χ =17.2 DU
2000

2010

Year

between 100 and 50 hPa, approximately half the sizeof the decrease measured in Syowa at this height between 1980 and 2000 (Figure 4-16). Kuttipurath andNair (2017) also found positive trends since 2001 reversing the pre-2000 vertically resolved trend usingozone profiles from ozonesonde stations distributed
across Antarctica during the period 1979–2013 filteredThree metrics that have been widely used for many
years to report on the state of the ozone hole fromyear to year are shown in Figure 4-6—the dailyozone hole area averaged from 21 to 30 September,the average of daily minimum ozone values from 21
September to 16 October, and the daily ozone mass
deficit (OMD) averaged from 21 to 30 September. A
fit of Antarctic EESC to each metric is also shown.
It is apparent that all three metrics show a clear stabilization after the year 2000 when Antarctic EESC
is calculated to have peaked, with the OMD showing both the greatest apparent turnaround since thatdate but also the largest variability. Note that in thisfigure, the fit has not taken into account dynamicalvariability, most evident for all three metrics in theyear 2002 when the Antarctic vortex experienced an
unprecedented major sudden stratospheric warming. Negative trends (that is, towards a smaller ozonehole) can now be seen in ozone hole area (Strahan et4.33

Chapter 4 | Polar Stratospheric Ozone
SH September Ozone Trends

Lorem ipsum

63°–90°S

Pressure (hPa)

10
20

20

30

30

50
70
100

50
70
100
Chem-Dyn-Vol (2000–2015)
Vol-Clean (2000–2015)
Chem-Only (2000–2015)
Syowa (1980–2000)
Syowa (2000–2015)
South Pole (2000–2015)

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−1.0

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Ozone (DU km -1 yr -1)

Figure 4-16. Trends in ozonesonde data from Syowa (69°S, 39.58°E) and South Pole stations with years asmarked, with shading indicating the 90% confidence interval. Overlaid are model results from the WACCM
model for regions 60°–90°S (left) and 85°–90°S (right) forced with different combinations of halogen changes(“chem”), specified dynamics (“dyn”), and injection of aerosols from volcanic eruptions (“vol”). (From Solomon, S., D.J. Ivy, D. Kinnison, M.J. Mills, R.R. Neely III, and A. Schmidt (2016), Emergence of healing in the Antarctice ozone layer, Science, 353 (6296), 269-274. Reprinted with permission from AAAS.)

al., 2014; Solomon et al., 2016) and ozone hole mass
deficit (de Laat et al., 2017) once temperature fluctuations have been taken into account. The closenesswith which particular ozone hole metrics follow theevolution of EESC varies with different choices ofthreshold and period of the year, due to saturation
effects and, as already noted, the increased influenceof dynamics in October compared to September(Solomon et al., 2016; Pazmiño et al., 2018).
Strahan et al. (2014) found that, comparing the ozone
hole area in the very cold years of 2006, 2008, and2011, the progressive reduction in size was proportional to declining EESC inferred from Aura MLS observations. Solomon et al. (2016) considered the sizeof the Antarctic ozone hole in September of each year,
derived from TOMS/OMI data. From 2000 to 2015,
the area decreased by 4.5 ± 4.1 million km2.
The OMD between days 220 and 280 (from earlyAugust to early October) of each year was considered
by de Laat et al. (2017) as derived from total column
ozone measurements from multiple satellite instruments. Including all years in the trend, the decrease
from 2000 to 2015 was 0.52 ± 0.50 Mt yr−1. When excluding the six warmest years (1986, 1988, 2002, 2004,
2010, and 2012) from the record in order to remove
4.34

the largest fluctuations caused by meteorological variability, the OMD decreased by 0.77 ± 0.17 Mt yr−1(2σ). From 2000 to 2015, OMD defined in this way
was estimated to have decreased about 30% from its
peak value. Similar values were calculated using MLR
with PWT (Box 4-1) by Pazmiño et al. (2018), whofound a negative trend of OMD in September of 0.86
± 0.36 Mt yr−1 since 2001, or 0.65 ± 0.33 Mt yr−1 ifaveraged over the period of maximum depletion, 15
September to 15 October.
Attribution to Decline in EESC
Attributing the trends in Antarctic ozone discussed
in the previous sections to a decline in stratospherichalogen abundances is challenging, as the trend in
EESC is small compared to the large variability in Cly
due to transport, as derived from Aura MLS measurements between 2004 and 2012 (Strahan et al., 2014).
Nevertheless, Strahan and Douglass (2018) using
a longer Aura MLS data set until 2016, showed thatvortex-averaged ozone loss (defined as the observedchanges in partial column ozone between 261 and 12
hPa from July to mid-September) decreased over the
12-year data record because of the decline in lower
stratospheric Cly levels. By analyzing ozone changes

Polar Stratospheric Ozone | Chapter 4

over this seasonal average, dynamical contributions toozone change are minimized.
Further evidence of a response of Antarctic ozone to
declining ODSs was given by Solomon et al. (2016) bycomparing measurements with CCM sensitivity simulations with different specified forcings. They showed(using model runs that held the dynamical conditions, temperatures, and volcanic aerosols constantat 1999 levels) about half of the lower stratosphericozone increase in September observed between 2000
and 2014 was due to halogen decrease, with the remainder attributed to changes in dynamics and temperature (Figure 4-16). Likewise, the model results
suggest that about half of the total column increase in
September observed over that period is due to declining ODS levels (~ +1.3 DU yr−1). The ozone hole wasestimated to have decreased by 3.5 ± 0.3 million km2
as the result of decreasing chlorine and bromine.
Further, Solomon et al. (2017) analyzed the seasonality of modeled and observed trends in Antarctic
stratospheric ozone and stratospheric temperatures.
The changes observed prior to 2000 were followedby oppositely signed changes after 2000 very similarly patterned in terms of season and altitude. These“mirrored” changes were largely able to be replicated by CCM runs only when forced with measured
ODS levels, with a relatively small role being playedby unforced dynamical changes. This finding is supported by de Laat et al. (2017) who found in theiranalysis of OMD in multi-sensor reanalysis (MSR)
data that the ratio of pre- and post-2000 trends in
OMD matched those in Antarctic EESC, seconded
by regression results suggesting long-term changes in PSC volume and pre-winter ozone levels had
played only minimal roles.
Modeling results (Solomon et al., 2017; Randel etal., 2017) suggest that observed changes in Antarctic
ozone have significantly contributed to the observed
cooling in Antarctic stratospheric springtime temperature from 1979 to the late 1990s and the subsequent warming trend to the present time. A component of the temperature variability discussed in thecontext of detecting ozone recovery should therefore
be considered a feedback from EESC changes rather
than being purely unforced.

4.4.2.3 Summary
While in the prior Assessment only two to three studies claimed to have found early signs of Antarctic
ozone recovery, a number of studies has been presented since then. These used various new merged data
sets and observations, including four more years since
WMO (2014), as well as chemistry–climate model
simulations to attribute the observed changes.
It is noteworthy that independent of the data set, time
period, and analysis method, all studies derive trends
in different metrics of Antarctic ozone of the same
overall sign; i.e., they all show increasing total column
ozone and ozone in the lower stratosphere, decreasing ozone hole area, and decreasing ozone mass deficit since about 2000 in Antarctic springtime. Sometrends are statistically significant at the 2σ level, whileothers are either barely significant or not significant
for various reasons, with differences arising for example from the applied regression model or the time
period. However, it was found that the significance ofthe derived trends rises for the month of September
when dynamical activity of the Antarctic polar vortex
is small and chemical ozone depletion not saturated
as in October. Particularly in the month of September,several metrics of Antarctic ozone have shown significant reductions of depletion in the years following thepeak in EESC. By employing model simulations orother means, it has been shown that a portion of thepositive trend in ozone can now be attributed to declining ODS levels. Therefore, it can now be concluded that the early signs of the second stage of Antarctic
ozone recovery are becoming apparent.

4.4.3

Long-Term Arctic Ozone Trend

As stated in previous Assessments, detection ofozone recovery in the Arctic is much more difficult
than the Antarctic, chiefly because of the much larger
dynamical variability. Knibbe et al. (2014) analyzed
spatial variations in monthly total column ozone forthe period 1979–2012 in the Arctic but did not findany trend. Solomon et al. (2016) did not find statistically significant trends in springtime Arctic SBUV
data (63°–90°N) for the period 2000–2014, with theirmodel results suggesting the small positive trend expected from EESC decline is currently overwhelmedby dynamical effects. The study of Weber et al. (2018)
described above found trends in the Arctic in March
4.35

Chapter 4 | Polar Stratospheric Ozone

were less than 1% decade−1 and not significant(Figure 4-15).

4.4.4

Benefits Achieved by theMontreal Protocol

By comparing model simulations using an uncontrolled growth of ODSs (unaffected by the Montreal
Protocol) with simulations using ODS mixing ratios
controlled by the Montreal Protocol, it is possible toidentify how polar ozone would have developed ina “world avoided” with continuously growing ODS
abundances. Moreover, the extent to which the polarozone layer has already (in the recent past) benefited
from the Montreal Protocol can be derived. CCM simulations assuming either a total chlorine loading of 9
ppbv for ~2025 or a growth rate of 3% yr−1 (leading to
9 ppbv in 2019) showed that uncontrolled growth in
the emissions of ODSs would lead to ozone depletionin the coming decades much larger than projected
for a controlled chlorine loading (Morgenstern et al.,2008; Newman et al., 2009; WMO, 2011; Garcia et al.,2012).
Chipperfield et al. (2015) used a state-of-the-art 3D
chemistry transport model to investigate a “world
avoided” scenario, comparing a simulation based on
observed atmospheric ODS loading to one in which
continued growth in ODS production of 3% yr−1
after 1987 is assumed (Figure 4-17). In Arctic winter2010/2011, when the OMI satellite instrument showsa local ozone column of around 230 DU, the integration without Montreal Protocol regulation indicates agreatly reduced ozone column below 120 DU (Figure
4-17e). On 26 March 2011, a region of relatively low
column ozone (250–275 DU) emerges in the observations (Figure 4-17a) and the model run with observed ODSs (Figure 4-17b). With the “world avoided” scenario, however, a further dramatic decrease incolumn ozone by up to 130 DU over a wide regionof the Arctic occurs. Without the Montreal Protocol,
a deep Arctic ozone hole would have developed in2011 (Figure 4-17c). The Antarctic ozone hole would
have been 40% larger by 2013 (with enhanced loss
at subpolar latitudes) and longer-lived each year.
Smaller Arctic ozone holes would have become a regular occurrence as chemical ozone depletion would
have a stronger effect on Arctic ozone in spring than
dynamic variability.
4.36

4.5

FUTURE CHANGES IN POLAR OZONEThis section discusses the future evolution of polarozone as projected by new chemistry–climate model(CCM) simulations coordinated within the IGAC/
SPARC Chemistry-Climate Model Initiative CCMI
(Eyring et al., 2013a). The CCMI model data set
provides an update of previous ozone projections
obtained from the second phase of the SPARC
Chemistry-Climate Model Validation (CCMVal, referred to as CCMVal-2; SPARC CCMVal, 2010) activity that formed the basis of projections of future ozone
for the prior two Assessments (WMO, 2011; 2014).

4.5.1

New Ozone Projections from
Chemistry–Climate Models

As a result of continuing global CCM activities, newmodel simulations have been produced. The majority of CCMs that participated in CCMI had alreadybeen part of CCMVal-2; many of these models
have been further refined since the earlier activity.
Improvements of some of the CCMI models compared to their CCMVal-2 versions include• more detailed chemistry schemes, with enhanced

tropospheric chemistry, a more consistent representation of sulfate surface area densities, and theconsideration of the effects of naturally produced
very short-lived (bromine) substances (VSLSs) on
ozone depletion, and• interactive coupling of the atmosphere-only

CCMs to deep-ocean models, hence improving the representation of climate feedbacks
in particular (as in ‘classical’ Coupled Model
Intercomparison Project Phase 5 (CMIP5)-typeclimate models).

A detailed overview of the applied CCMs including model expansions and improvements since
CCMVal-2 is given in Morgenstern et al. (2017).
The performance of the previous CCM generationthat provided the ozone projections for the prior two
Assessments has been evaluated in detail within the
SPARC CCMVal activity. SPARC CCMVal (2010)
compared the quality of the dynamics and transport,
as well as of the radiation and chemistry schemes,
and offered a useful baseline for evaluating the resultsof later model studies. So far, a similar coordinatedPolar Stratospheric Ozone | Chapter 4
Column Ozone OMI March 26, 2011

Model Run MP March 26, 2011
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Column Ozone Difference Runs NoMP - MP
450

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450

(b)

OMI

250
200
150
100

07/2010 09/2010 11/2010 01/2011 03/2011 05/2011 07/2011

Date

Figure 4-17. Evolution of column ozone in the Arctic from satellite observations and model simulations for
winter 2010/2011. Column ozone (DU) on 26 March 2011 (a) observed by OMI, (b) from model run with ODSs
controlled by the Montreal Protocol (MP), and (c) from model run with uncontrolled ODSs (NoMP) (with the
220 DU contour indicated in white). (d) Difference in column ozone between runs NoMP and MP. (e) The daily
minimum ozone column in the Arctic region (latitude >45°N) from mid-2010 to mid-2011 as observed by the
Ozone Monitoring Instrument (OMI) (black points), along with equivalent model results from run MP (blue)
and run NoMP (red). From Chipperfield et al. (2015).
4.37

Chapter 4 | Polar Stratospheric Ozone

evaluation has not been completed for the current
CCM versions, though a large number of analysesof the CCMI models focusing on different topics isunderway (e.g., Wales et al., 2018; Dietmüller et al.,2018) A comprehensive comparison of ozone returndates in the CCMI, CMIP5, and CCMVal-2 simulations is presented in Dhomse et al. (2018).
A suite of CCMI simulations has been performedby the modeling groups using a standard set of specific forcings according to the recommendations in
Eyring et al. (2013a); the relevant simulations andforcings for this Assessment are summarized in Box
3-2, “Modeling past and future changes in ozone:
Model heritage and application”. The most probable400

projections of the future evolution of polar ozone relyon the reference simulations (REF-C2) that are driven
by an assumed decline of ODSs (WMO, 2011), the effects of brominated VSLSs, and a concurrent increasein GHG concentrations according to the RCP-6.0
scenario (Meinshausen et al., 2011). Sensitivity (SEN)
simulations address the uncertainty of polar ozone
recovery induced by different GHG scenarios andthe attribution of ozone recovery to future changes in
ODSs and GHGs.
As a novel aspect of CCMI, CCM simulations withspecified dynamics (SD) have been performed forthe historical period (1960–2010). The setup of these
REF-C1SD simulations is specified in Box 3-2. The

550

Antarctic (Oct)500

TCO (DU)

TCO (DU)

350

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250

450

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350
1975

400

2000

2025

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2025

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300

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350
1975

2000

2025

Year

2050

2075

2100

1975

2000

2025

2050

Year

Figure 4-18. Multi-model mean (MMM1S) total column ozone time series (in Dobson Units, DU) fromCCMI REF-C1 (light blue), REF-C1SD (green) and REF-C2 (red) simulations for the (left) SH polar (October)and (right) NH polar (March) regions. The dashed black lines show the 1980 reference value for each latitude band. The top row shows the unadjusted modeled values and the bottom row shows the time series
adjusted with respect to mean 1980–1984 observations. Also shown are the merged SBUV observations.
From Dhomse et al. (2018).
4.38

Polar Stratospheric Ozone | Chapter 4

dynamics in the REF-C1SD simulations are adjusted(“nudged”) towards observations or reanalysis datathat represent the observed dynamical behavior of theatmosphere. Through the nudging, the model dynamics in the REF-C1SD simulations are forced to closely
follow observed dynamical variability from year toyear. These simulations thus provide realistic variability in the transport of chemical compounds andtemperatures, in contrast to free-running CCMs thatdevelop their own internal dynamical variability. The
REF-C1SD runs therefore allow for a more detailed
evaluation of the chemical processes (e.g., Solomon
et al., 2015). A comparison of the REF-C1SD resultswith those from free-running REF-C1 simulations
also helps to identify inaccuracies in the representation of dynamical processes in CCMs.
Figure 4-18 presents the multi-model mean
(MMM1S) total column ozone time series from the
REF-C1 (light blue) and REF-C1SD (green) simulations. MMM1S results represent the mean of the models that lie within one standard deviation (1σ) of themulti-model mean (MMM). Time series are shownfor the unadjusted multi-model means (MMM1S) of
the REF-C1, REF-C1SD, and REF-C2 simulations (toprow) and the same MMM1S adjusted with respect
to the mean 1980–1984 observations (bottom row)in Antarctic October (left) and Arctic March (right)(for more details see Dhomse et al., 2018). The top
panels demonstrate that, as expected, the REF-C1SD
simulations (i.e., in which the models are nudged
towards analyzed meteorology) better reproduce theobserved evolution of total column ozone than the
REF-C1 simulations (i.e., the free-running CCMs).
Both sets of simulations show the decline of the totalozone column until about the year 2000 in spring of
the Northern and Southern Hemispheres. The REFC1SD runs, however, better capture the observed
year-to-year variability. Due to the averaging of individual model results, enhanced total ozone variability
is suppressed in the MMM1S of the REF-C1SD simulations, as for example in the Arctic around the turn ofthe century and in the Antarctic during the following
decade. Nevertheless, with constrained dynamics, theCCMs perform well in simulating the ozone evolution
in the Antarctic and Arctic, giving confidence that thebasic chemical processes and the ozone response tolong-term ODS changes are understood.

4.5.2

Long-Term ­Projectionsof Polar Ozone

This section focuses on the future evolution of
Antarctic and Arctic polar ozone projected by stateof-the-art CCMs following the best estimates of future
decline in ODSs and increase in GHG concentrations.
For this purpose, the CCMI REF-C2 simulations areanalyzed for which the medium RCP-6.0 GHG scenario has been prescribed. Apart from refinements ofthe models, the experimental setup differs from the
projections shown in WMO (2011) and WMO (2014)
due to updates in the ODS and GHG scenarios (seealso Box-3-3, “Ozone Return Dates”) and the consideration of brominated VSLSs. The potential implications of these changes on the ozone return to historical values will be discussed in Section 4.5.4.

4.5.2.1 Future Antarctic Spring
Total Column Ozone
Figure 4-18 (left) shows the future evolution of total
column ozone (TCO) in the Antarctic (60°–90°S) in
October for the multi-model mean of the REF-C2
simulations (red lines in Figure 4-18) from 20 CCMs
(Dhomse et al., 2018). In the past (1960–2010), themulti-model mean (MMM1S) TCO shows good
agreement with observations including the strong
decrease of Antarctic ozone in the 1980s and early
1990s. A broad TCO minimum occurs around theyear 2000. It is about 80 DU lower than the 1980 value,
confirming the results presented in WMO (2011). In
the future, the ozone hole will recover and a return of
TCO to values of the year 1980 is expected to occur
shortly after mid-century (between 2055 and 2066).
Compared to the last Assessments (WMO, 2011,2014), the current estimate is postponed by about 10
years. The previous earliest and latest projected returndates of Antarctic TCO (referring to the 1980 level)in October are also delayed by about 5 years (see also
Figure 4-22). Possible reasons for these discrepancies
are discussed in Section 4.5.4.

4.5.2.2 Future Arctic Spring Total
Column Ozone
The temporal evolution of Arctic spring TCO derived
from the CCMI simulations is presented for March
in Figure 4-18 (right). It shows the TCO MMM1S(60°–90°N) until 2100 derived from the REF-C2
4.39

Chapter 4 | Polar Stratospheric Ozone

simulations (red lines in Figure 4-18) of 20 CCMs
(Dhomse et al., 2018). Again, model results representing the past are indicating qualitative agreementwith observations concerning the long-term behavior, although the strong decrease of TCO in the
1990s, particularly in the years after the eruption of
Pinatubo, is underestimated by the MMM1S. In the
future, a return to TCO values of 1980 is expected
before mid-century (between 2025 and 2043). Hence,
the new projections suggest a return date for Arctic
spring TCO that is delayed by 4 years compared to
the CCMVal-2 estimate (WMO, 2011; 2014) (see also
Figure 4-22). The range of potential TCO return dates
is broader in the CCMI models, extending from 2025
(as for the CCMVal-2 models) to 2043, i.e., 8 years
later than the CCMVal-2 models.

4.5.3

Whereas ODSs are expected to continue to decrease
due to the controls of the Montreal Protocol, GHG
concentrations are expected to continue to rise.
Hence, the relative effects of ODSs and GHGs onpolar ozone will change with time. For the most likely
scenario of future ODS and GHG changes (i.e., REFC2) the models project a return of TCO to historicalvalues in the coming decades (see Figure 4-18). In
this section, CCMI sensitivity simulations with separated forcings are discussed in order to disentangle
the individual impacts of the ODS and GHG changes
on the evolution of ozone.
In simulations with constant ODSs between 1960
and 2100 and growing GHG abundances (i.e., SENC2-fODS simulations), Antarctic total column ozone(TCO) in October shows only a small, non-significant positive long-term trend with weak year-to-year
variations (Figure 4-19, top panel). A slight ozone
increase after the middle of the 21st century resultsfrom stratospheric cooling forced by rising GHG

Factors Controlling­Future Polar Ozone

4.5.3.1 Changing Roles of ODSs and GHGs

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Antarctic (Oct)375

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4.40

2060

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2100

Figure 4-19. Temporal evolution of multi-modelmeans (MMM1S) of total column ozone (in Dobson Units, DU) derived from REF-C2 (red), SENC2-fODS (brown), and SEN-C2-fGHG (green)
CCMI scenario calculations for Antarctic spring(October, top panel) and Arctic spring (March,bottom panel). SEN-C2-fODS simulations use
fixed ODS levels of the year 1960, while SEN-C2fGHG simulations use fixed GHG levels of theyear 1960. Black triangles indicate observations
derived from SBUV MOD data. The black dashedlines denote the 1960 reference values for eachregion. Adapted from Dhomse et al. (2018).

Polar Stratospheric Ozone | Chapter 4

concentrations. When GHGs are held constant attheir 1960 values but ODSs vary according to the prescribed reference scenario (i.e., SEN-C2-fGHG simulations), the evolution of TCO closely follows thatof the REF-C2 reference simulation. After the middleof the century, the GHG effect in the REF-C2 simulations accelerates Antarctic ozone recovery and leads
to about 10 DU higher TCO around 2100 than withconstant 1960 GHG concentrations. Hence, the evolution of the ODSs exerts the dominant influence on
Antarctic TCO change modulated by a minor effectof GHGs in the second half of the century. Although
the GHG effect strengthens Antarctic ozone recovery from ODSs, the 1960 baseline value will not bereached by the year 2100.
In Arctic spring, TCO gradually increases with timein the SEN-C2-fODS simulation with constant 1960
ODS levels (Figure 4-19, bottom panel). This ozone
increase is caused by rising GHG concentrations
which (a) cool the stratosphere, thereby reducing
chemical gas-phase ozone depletion and (b) strengthen the Brewer-Dobson circulation (BDC) leading to a
growing poleward and downward transport of ozone
in Arctic spring (e.g., Oman et al., 2010; Oberländer
et al., 2013). When prescribing GHG values for the
1960s (i.e., in SEN-C2-fGHG) throughout the wholeprojection, TCO closely follows the values of the REFC2 reference simulation until about 2020. Afterwards,
TCO in the simulation with constant 1960 GHG
concentrations gradually approaches its 1960 values
until the end of the 21st century, in contrast to thereference simulation where the rising GHG abundances induce an additional ozone increase, as described above. Hence, as in the Antarctic, the ODSs
have been the primary driver of observed Arctic TCO
trends in the past. However, in contrast to Antarctica,changes in GHGs will exert the dominant control overArctic ozone distributions by the late 21st century. As
demonstrated in CCM sensitivity studies with specified single or combined forcings (Rieder et al., 2014;
Douglass et al., 2014; Kirner et al., 2015b; see also
Section 4.5.3.2), past ODS changes mainly affected
Arctic lower stratospheric ozone, while changes of
GHG concentrations mainly affect the upper stratosphere. In addition, the growing GHG abundancesin the REF-C2 scenario drive a stronger transport ofozone into the Arctic stratosphere. In total, whereas

the decline in ODSs allows TCO to recover towards
its 1960 baseline in the Arctic, the concurrent increasein GHGs induces not only an earlier return of Arctic
TCO to its 1960 baseline value (in the 2040s for the
REF-C2 MMM1S) but also a further TCO increase by
about 20 DU by the end of the century.
These results reinforce the major findings of Eyring
et al. (2010a) which were based on similar sensitivity simulations from a limited number of CCMVal-2
models. The additional CCMI simulations thus enhance the confidence in the models’ responses tochanging ODS and GHG concentrations.

4.5.3.2	Dynamic Variability in Arctic Spring
Particularly in the Arctic, CCM results show that therole of dynamical processes for determining springtime ozone will increase in the future. Individual
CCM simulations indicate that even after 2040, when
Arctic ozone is expected to have increased due tothe effects of declining ODSs and rising GHGs, early
springtime Arctic total column ozone can episodically drop by about 50 to 100 DU below the long-termmean for that period, reaching stratospheric ozone
values characteristic of the near-present-day averageozone level (e.g., Langematz et al., 2014; Bednarz etal., 2016). This is due to the large year-to-year variability of the Arctic polar vortex. In the presence of avery cold, strong, and persistent polar vortex in late
winter and early spring (as observed during March of
1997, 2011, and 2015), enhanced formation of polar
stratospheric clouds (PSCs), and elevated halogen-induced ozone losses well above the long-term meancontinue to occur. Together with reduced poleward
transport of ozone, these factors contribute to the low
total column ozone values as for instance measured inspring 1997.
For the future, CCM studies project a significant cooling trend in the Arctic winter mid- and upper stratosphere due to enhanced GHG concentrations (e.g.,
Oberländer et al., 2013; Rieder et al., 2014). There is,however, less confidence in the projected temperature trends in the Arctic lower stratosphere (e.g.,Langematz et al., 2014; Rieder et al., 2014; Bednarz etal., 2016). Langematz et al. (2014) found in their CCM
study that rising GHG concentrations lead to a cooling of the Arctic lower stratosphere in early winter.

4.41

Chapter 4 | Polar Stratospheric Ozone

However, their model did not show any significant
temperature changes in late winter or spring. Neither
was a statistically significant increase in the volumeof PSCs (VPSC), a temperature-dependent metric thatis linearly correlated with wintertime chemical ozone
loss (Rex et al., 2004), found throughout the 21st century. Nevertheless, CCM projections suggest the possibility that in the presence of a cold and strong polar
vortex higher VPSC and halogen-induced ozone losses
may occur in individual Arctic winter/spring seasons
until the middle of the 21st century (Langematz etal., 2014) or even into the second part of the century(Bednarz et al., 2016).

4.5.3.3	The Role of GHG Scenarios
As discussed in the previous sections, rising GHG
concentrations influence the future recovery of stratospheric polar ozone. Ozone is not only affected bycarbon dioxide (CO2) through cooling of the upper
stratosphere and modifying ozone transport but also
by the GHGs methane (CH4) and nitrous oxide (N2O)
through both radiative and chemical effects (Chapter
2 in WMO, 2014). Therefore, a projection of the returnof ozone to a historical baseline will depend on thespecific GHG scenario. Three different climate change
scenarios (representative concentration pathways,
RCPs, Meinshausen et al., 2011), i.e. RCP-4.5, RCP6.0 (= REF-C2), and RCP-8.5 have been discussed in
Dhomse et al. (2018). Note that the modest RCP-2.6
scenario was not included in the analysis because thenumber of model realizations for this scenario was
too low. In Figure 4-20 the total column ozone (TCO)(top row) as well as the partial column ozone (PCO)
for the upper (second row) and lower stratosphere(third row) and the troposphere (bottom row) arepresented for the subset of CCMI models that carriedout simulations with different RCPs.
In Antarctic spring (October) (left column in Figure4-20) slight differences between the RCPs begin to
emerge in the upper stratosphere (US) and the troposphere in the middle of the century. In the US, ozone
directly responds to the RCP scenario with the smallest ozone increase in the RCP-4.5 scenario and thelargest ozone increase in RCP-8.5. In the RCP-8.5 scenario, the GHG-induced cooling is most pronounced,
and subsequently, the temperature-dependent reduction in chemical ozone depletion strongest. All RCP
scenarios project higher US partial ozone columns
4.42

by the end of the century than existed in the US before the start of ODS-induced ozone depletion. In the
troposphere, the assumed strong increase in CH4 of
the RCP-8.5 scenario will enhance ozone production
more than in the other scenarios. No significant differences between the RCPs appear, however, in thelower stratosphere (LS). The projected warming of the
Antarctic LS in spring, particularly in the first half ofthe 21st century when the ozone hole starts to diminish (see Figure 5-8 in Chapter 5 of this Assessment),
more than offsets a potential GHG-induced cooling(leading to heterogeneous ozone depletion). When integrated over the layers, TCO in Antarctic spring does
not reveal significant differences between the GHG
scenarios throughout the century. In the US, whereozone differences by the RCPs are most pronounced,
PCO returns earlier to its 1980 values than TCO.
However, differences in the US PCO return dates between the RCPs are not significant. As discussed in
the previous section, ODSs are the dominant driver ofozone recovery in the South Polar lower stratosphere,
even with more extreme future GHG abundance.
In Arctic spring, the effects of the extreme RCP-8.5
scenario become progressively more important for
TCO and the stratospheric and tropospheric PCOs
in the second half of the century (Figure 4-20, rightcolumn). While the stratospheric cooling in the
RCP-8.5 scenario drives the stronger increase of US
PCOs, the LS ozone growth is related to the projected
strengthening of the Brewer-Dobson circulation withenhanced ozone transport into the LS. Troposphericozone increase is strongest in the RCP-8.5 scenario
due to steadily rising levels of atmospheric CH4 thatreach 3.7 ppm by the end of the century. Adding theozone changes in the different atmospheric layers
yields higher TCO for the RCP-8.5 scenario comparedto the more moderate scenarios. In the second half ofthe 21st century, the impact of GHGs is the dominant
driver of stratospheric ozone changes. For all GHG
scenarios, a “super-recovery” of Arctic spring TCO(i.e., an ozone increase above its concentrations in the
1960s and 1970s, when anthropogenic ozone depletion started) is projected. By 2100, the Arctic springtime stratospheric ozone column is expected to exceed
1960–1980 average values by about 35 DU for RCP-4.5
and about 50 DU for RCP-8.5 (Dhomse et al., 2018).
The different hemispheric sensitivity of future polar
TCO to the climate change scenario was also found

200
2020

2040

2060

2080

2100

US column (DU)

2000

45
40
1980

2000

2020

2040

2060

2080

250
200
150
100
1960

1980

2000

2020

2040

2060

2080

2100

30
20
10
1960

1980

2000

REF-C2

2020

2040

2060

Year
SEN-C2-RCP45

2080

2100

550

Arctic (Mar)

500
450
400
350
300
1960
55

SBUV MOD

1980

2000

2020

2040

2060

2080

2100

1980

2000

2020

2040

2060

2080

2100

1980

2000

2020

2040

2060

2080

2100

1980

2000

2020

2040

2060

2080

2100

50
45
40
35
1960

2100

LS column (DU)

US column (DU)

1980

50

35
1960
300

LS column (DU)

SBUV MOD

Total column (DU)

300

1960
55

Trop. column (DU)

Antarctic (Oct)400

Trop. column (DU)

Total column (DU)

Polar Stratospheric Ozone | Chapter 4

400
350
300
250
1960
70
60
50
40
1960

SEN-C2-RCP85

Year

BSVertOzone

Figure 4-20. (top) Evolution of total column ozone (in Dobson Units, DU) MMM1S for the CCMI REF-C2
simulation (red lines) and the CCMI RCP scenario simulations SEN-C2-RCP45 (blue-green lines) and SEN-C2RCP85 (orange lines). The REF-C2 simulation uses the RCP-6.0 scenario. Also shown are the partial ozone
columns for the upper stratosphere (second row), the lower stratosphere (third row) and the troposphere(bottom). For comparison the respective total and partial ozone columns are shown. The black dashed lines
denote the 1980 reference values for each region and layer. Adapted from Dhomse et al. (2018).

in analyses of the CMIP5 model subset with interactive chemistry (e.g., Eyring et al., 2013b), as well as in
the ACCMIP model ensemble (Iglesias-Suarez et al.,2016). In the CCMI projections, however, the influence of the RCPs on TCO starts later in the century,and the TCO spread between RCPs is smaller than for
the CMIP5 models in both hemispheres.
The RCP scenarios consist of projected concentration
changes for a number of GHGs (most importantly
CO2, CH4, and N2O), which all have similar radiative

effects, but differ in their chemical effects on ozone.
As discussed previously, during the latter half of the
21st century, as ODS concentrations are expected
to decline, GHGs become more relevant for ozone.
Model studies indicate that in particular the future
CO2, N2O, and CH4 will have significant impacts on
global total ozone (e.g., Fleming et al., 2011). The
quantification of the net impact of these gases on future polar ozone is complicated by competing effects:
Increasing N2O concentrations will produce more
NOy and enhance ozone depletion, thereby reducing
4.43

Chapter 4 | Polar Stratospheric Ozone

the ozone increase due to CO2-induced cooling andincreases in CH4 (Oman et al., 2010; Revell et al.,2012). However, ozone depletion by NOy is less effective with decreasing temperature in the middle andupper stratosphere (Rosenfield and Douglass, 1998).
In addition, chemical feedback processes reduce theefficiency of increasing N2O to deplete ozone, as withincreasing CO2, less NOy is produced (e.g., Portmann
et al., 2012; Stolarski et al., 2015). Nevertheless, Revell
et al. (2015) show that the ozone depletion potential(ODP) of N2O for the year 2100 varies under different
scenarios and is mostly larger than for the year 2000.
Butler et al. (2016) describe how mitigation strategies
may be adapted to the evolution of the individual
gases: if reductions of CO2 and CH4 concentrations
to RCP-2.6 levels (a low emissions scenario) could beachieved by the end of the 21st century, no super-recovery of stratospheric ozone is expected and N2O
mitigation would become important to avoid further
ozone depletion. On the other hand, if CO2 and N2O
were reduced to RCP-2.6 levels but CH4 concentrations increased, stratospheric ozone would increase
toward historical levels and large increases in global
tropospheric ozone would be expected.

4.5.3.4	The Role of VSLSs
Since the last Assessment, considerable progresshas been made in implementing the effects of bromine-containing very short-lived substances (VSLSs)
on ozone (see also Section 4.3.3) into CCMs. Model
simulations show clear signatures of brominated
VSLSs in stratospheric ozone (e.g., Hossaini et al.,2015a; Sinnhuber and Meul, 2015; Oman et al., 2016;
Fernandez et al., 2017; Falk et al., 2017). Brominated
VSLSs are projected to affect the Antarctic ozone holearea and depth until about the middle of the century(Fernandez et al., 2017). As the abundance of longlived halogens declines, the relative importance of brominated VSLSs regarding ozone reduction is predicted
to increase in the coming decades and surpass ozone
destruction by chlorine by about 2070 (Fernandez etal., 2017). However, as the depletion of stratospheric ozone due to brominated VSLSs depends also on
the availability of chlorine, the impact of bromine on
stratospheric ozone will continuously fade. Therefore,
Fernandez et al. (2017) do not find a significant change
in the return date of Antarctic ozone to 1980 values.
This result is in contrast to model projections by Yang
4.44

et al. (2014) and Oman et al. (2016) which show that
active bromine from VSLSs is expected to delay the
return of TCO to historical values by about 6–8 years(Yang et al., 2014) to a decade (Oman et al., 2016). The
differences between these projections may be due tothe applied models, the number of model realizations,or the specifications of future VSLSs. Hence moremodel studies are needed for a robust projection of
the effect of brominated VSLSs on the Antarctic ozone
return date.
As discussed in WMO (2014), increasing emissionsof synthetic, chlorine-containing VSLSs, in particulardichloromethane (CH2Cl2), represent another, more
recently detected component of VSLSs that affects
polar ozone. Recent observations indicate that the atmospheric concentration of dichloromethane (which
is not controlled by the Montreal Protocol) is growing (see Chapter 1 for further details). Using CTM
and CCM simulations, Hossaini et al. (2017) showedthat the impact of dichloromethane on stratospheric ozone has clearly increased in recent years from a
relative ozone decrease by dichloromethane of 3 DU
in 2010 to 6 DU in 2016. Assuming a continuous increase of dichloromethane in coming decades at themean rate observed over the 2004–2014 period, theyfound a delay of the return of Antarctic ozone to the1980 baseline by nearly 20 years compared to a simulation without CH2Cl2 abundance (Figure 4-21).
A sustained future increase in atmospheric concentrations of dichloromethane would therefore further
slow the recovery of the Antarctic ozone hole. The future evolution of dichloromethane is however uncertain, given its recent decline in growth rate between
2014 and 2016 (see Chapter 1).
While the above cited studies have clearly shown thatbrominated and chlorinated VSLSs have a significant
impact on stratospheric polar ozone, their consequences for the return dates of polar ozone to historical baseline values will depend on the future abundances of VSLSs in the atmosphere and are therefore
uncertain, as addressed in more detail in Section
4.5.4.2.

4.5.4

Uncertainty in Polar
Ozone Projections

Future ozone projections are affected by uncertainty
due to internal variability of the atmosphere, structural

Polar Stratospheric Ozone | Chapter 4

Ozone (DU)

100
501980 baseline

0
-5 0

-100
1980

2000

2020

2040

2060

2080

2100

Year
Figure 4-21. Future impact of growth in dichloromethane (CH2Cl2) on Antarctic column ozone and ozone
trend derived from a CCM study. Results are shown for two UMSLIMCAT simulations: one run without
CH2Cl2 and another where surface CH2Cl2 concentrations continue to increase at the mean rate observed
over the 2004–2014 period. The figure shows the temporal evolution of October mean Antarctic stratospheric ozone column (in Dobson Units, DU) relative to 1980. While interannual variability is large, thetwo ozone time series are statistically different at the 95% significance level according to a Student’s
t-test (Pvalue = 0.02). Ozone returns to the 1980 baseline in the year 2064 (without CH2Cl2; black line) andin 2081 (with CH2Cl2; gold line). Adapted from Hossaini et al. (2017).

uncertainty due to differences between models anduncertainty in the future scenarios for ODSs and
GHGs (see also Box 3-3). The role of internal dynamical variability in particular for Arctic polar ozone has
been addressed in Section 4.3.4. In the following, theaspect of model uncertainty will be revisited for the
new CCMI model simulations (Section 4.5.4.1) andthe new ozone return dates will be discussed in thecontext of the ODS and GHG scenarios used (Section
4.5.4.2).

4.5.4.1	Model Uncertainty
As discussed in Section 4.5.1, CCMs differ in theirtreatment of the relevant physical, dynamical, andchemical processes. The broad range of total column
ozone projections in the new CCMI simulations
could be a result of the enhanced complexity of theapplied CCMs. Most of the models participating in
CCMI have been improved since CCMVal-2, being
now more physically based, with enhanced resolution and more frequently coupled to ocean models,
which leads to enhanced diversity in model results(Morgenstern et al., 2017).

In particular, the responses in total column ozone of
the CCMI models to anthropogenic forcings, such aschanges in ODSs and GHGs, were found to be lessconsistent across the different CCMs than those of theozone profiles (Morgenstern et al., 2018). The likely
cause of this is lower-stratospheric transport anddynamical responses, such as in the Brewer-Dobson
circulation, exhibiting substantial inter-model differences. Good agreement of CCM results was found in
the middle and upper stratosphere. Obvious differences were identified particularly in the troposphere,
possibly caused by differences in the formulation andcomplexity of the tropospheric chemistry modules
used in the CCMs.
These conclusions are confirmed by Dhomse et al.
(2018) who investigate ozone recovery of partial
columns in the lower stratosphere (LS, from the tropopause up to 10 hPa) and in the upper stratosphere(US, at 10 hPa and lower pressures) from 14 individual CCMI models. In the LS, where ozone has a longphotochemical lifetime, the adjusted results fromthe CCMs show clear differences among the models
in the polar regions. This suggests issues with thedescriptions of dynamical (transport) and chemical
4.45

Chapter 4 | Polar Stratospheric Ozone(heterogeneous chemistry) processes due to temperature biases in the CCMs. For the upper stratosphere,ozone in the polar regions behaves more similarlyin the different models, as dynamical processes areless important for ozone, while the feedback of temperature changes on ozone becomes more important(Haigh and Pyle, 1982).
A further source of uncertainty in the CCMI ozone
projections lies in existing uncertainties of rate constants for the N2O and CH4 reactions, which havebeen reassessed recently (SPARC, 2013). The CCMI
models generally used the kinetic rate constants recommended at the time of the simulations (Sander
et al., 2011). However, using updated estimates ofkinetic and photochemical parameters, Fleming etal. (2015) report that uncertainties in the commonlyused recommendations for the rates of chemical loss
processes of N2O and CH4 lead to a substantial range
in model ozone, both for present day and long-term
projections of future ozone recovery. For October SH
polar ozone, the largest uncertainty is due to the Cl +CH4 reaction, which impacts the amount of chlorine
in reservoir versus radical forms, resulting in a totalozone range of ±6% for present day chlorine loading.
However, this range will diminish to less than ±1% by
2100 as atmospheric chlorine decreases.

4.5.4.2	Uncertainty in Ozone Return Dates
The return date of total or stratospheric ozone to a historical baseline is an important indicator to assess the
success of the Montreal Protocol and its Amendments
and adjustments. Ozone return dates have been presented previously based on CCMVal-2 projections(Eyring et al., 2010a, b; WMO, 2011) and CMIP5
simulations using different GHG scenarios (Eyring
et al., 2013b; WMO, 2014). Estimates of ozone returndates from the new CCMI simulations have been derived in Dhomse et al. (2018). This section comparesthe different estimates of ozone return dates and discusses reasons generating uncertainty in the derived
dates. It is important to note that all model projections use prescribed scenarios for future abundancesof ODSs assuming a future decline of ODSs according
to the regulations of the Montreal Protocol. This section does not assess the uncertainty in ozone returndates arising from potential non-compliance with
the Montreal Protocol. (For more information on
this topic see Chapter 6). The return dates for polar
4.46

total column ozone from the CCMVal-2 projections
in WMO (2011) and from the CCMI REF-C2 simulations as well as their estimated ranges are shown in
Figure 4-22.
To account for model uncertainty, it is a common
approach to give ozone return dates in terms of theirmean value plus uncertainty range. Three different
measures of ozone return dates were provided by
Dhomse et al. (2018) for the CCMI simulations: (1)the multi-model mean (MMM) with the 1σ standard deviation defining the model range of recovery
dates, (2) the median with the 10th and 90th percentiles defining the range of recovery dates, and (3) the
multi-model mean including only models within one
standard deviation of the MMM (MMM1S) with the
1σ standard deviation of the MMM1S defining themodel range of recovery dates. For both polar regions,the median and MMM1S approaches provide (near)
identical return dates giving confidence in the derived
results.
Sensitivity to ODS and GHG Scenarios
As shown in Sections 4.5.2.1 and 4.5.2.2, the returndates of polar total column ozone (TCO) to 1980
baseline values are delayed in the CCMI models(MMM1S) by 4 years in the Arctic and 10 years in
the Antarctic compared to the CCMVal-2 models(SPARC CCMVal, 2010; WMO, 2011). This delay inpolar ozone recovery can partially be ascribed to updates in the ODS and GHG scenarios prescribed to
the CCMI reference simulations (for more details see
Box 3-3 and Dhomse et al., 2018). The change in the
ODS scenario from the adjusted A1 scenario of WMO(2007) used in CCMVal-2 to the A1 scenario of WMO(2011) in CCMI caused a delayed decline in stratospheric halogen levels. In addition, the transition
from the SRES-A1b GHG scenario used in CCMVal-2
to the RCP-6.0 scenario in CCMI reduces the chemically driven increase of ozone due to a slower rise of
CO2 and CH4 in the RCP-6.0 scenario, explaining afurther delay in TCO recovery in the CCMI models.
The expected effect of rising N2O concentrations in
the REF-C2 simulations (about 40% between 1960
and 2100) is to delay the ozone return date due to an
enhanced production of NOx and catalytic ozone depletion. However, while the return date of global andnorthern mid-latitude stratospheric column ozone isPolar Stratospheric Ozone | Chapter 4

TCO RETURNDATESRELATIVE
TO 1980
TCO Return
Dates
Relative to 1980
2070
2060
2050

Year

2040
2030
2020

2010
2000
1990

WMO
MMM1S
SH POLAR (October)

WMO
MMM1S
NH POLAR (March)significantly earlier in a scenario with fixed 1960 N2O
surface mixing ratios than in the REF-C2 simulations, it remains nearly unchanged in both polar areas(see Table 4 in Dhomse et al., 2018). Morgenstern
et al. (2018) suggest that the negative effect of N2O
increases on ozone in the upper stratosphere might
be compensated by a self-healing effect in the lower
stratosphere initiated by the upper stratosphere ozone
decline. However, the detailed processes leading to azero response of the polar ozone return date to N2O
increases still need to be understood.
CH4 concentrations increase in the RCP-6.0 scenarioof the REF-C2 simulations by 57% between 1960 andtheir maximum in 2070, and by 128% between 1960
and 2100 in the RCP-8.5 scenario. Consistent withprevious work (e.g., Revell et al., 2012), stratosphericozone returns earlier in the CCMI simulations withrising CH4 concentrations than in the constant-1960
scenario. This effect is stronger for the more extreme
CH4 increase in the RCP-8.5 scenario and stronger
in Arctic spring (about 10 years) than in Antarctic
spring (6 years). Note, however, that for the N2O- andCH4-sensitivity simulations only one realization by alimited number of models was available, reducing therobustness of the stated return dates.
Sensitivity to VSLSs
Section 4.5.3.4 revealed that both bromine- and­chlorine-containing VSLSs have the potential to delay
the return of the Antarctic ozone hole to historicalvalues by somewhere between several years and up

Figure 4-22. Estimated returnyears of total column ozone(TCO) to 1980 historical levels
for the SH polar cap in October
and the NH polar cap in Marchbased on CCMVal-2 simulations(WMO) and on CCMI REF-C2
simulations (MMM1S). The estimated 1-σ uncertainties areshown with black vertical lines.
Adapted from Dhomse et al.
(2018).

to three decades. Hence, the future evolution of the
VSLS emissions represents a further source of uncertainty for the projection of the ozone return date.
While the CCMVal-2 models generally did not include the effects of brominated VSLSs on ozone, most
CCMI models took their effects into account. The additional ozone depletion by the VSLSs could therefore
be a further contribution to the delay in CCMI TCO
return dates (e.g., Oman et al., 2016).
The concentrations of the oceanic brominated VSLSs
seem to have remained relatively stable over the
past decades (see Figure 1-16 in Chapter 1). Basedon this fact, most model studies assume a constantmixing ratio of brominated VSLSs of 5 ppt in simulations of past and future ozone. Based on presentday, observed global oceanic and atmospheric concentrations (ERA-Interim) and historical and futuredata from three CMIP5 models, Ziska et al. (2017)derived, however, an increase of brominated VSLSs
for the period 1979–2005. For the RCP-8.5 scenario,
oceanic brominated VSLS emissions could increaseby 30% from 2010 to 2100 (Tegtmeier et al., 2015;
Ziska et al., 2017). In a transient CCM study using
the RCP-6.0 GHG scenario and interactive VSLS
emissions, an increase of the ocean–atmosphere fluxof brominated VSLSs of about 8–10% by the end of
the 21st century compared to present day was found(Falk et al., 2017). However, under the low chlorine
loading at the end of the century, brominated VSLSs
are projected to have less impact on total Antarctic
stratospheric ozone depletion than in the presentday (in agreement with studies using constant VSLS
4.47

Chapter 4 | Polar Stratospheric Ozone

mixing ratios) and may not act as a major sourceof future stratospheric ozone depletion (Falk et al.,2017). More CCM studies including the effects of climate change on VSLS emissions from the ocean need
to be carried out to provide a robust estimate of the
impact of future changes in brominated VSLSs on the
return of polar ozone to historical values.
Chlorine-containing VSLSs are predominantly produced by industry. Among those, dichloromethane (CH2Cl2), has increased rapidly in recent years.
Between 2000 and 2012, surface concentrations of

4.48

CH2Cl2 increased at a global mean rate of almost 8%
yr−1, with the largest growth in South and Southeast
Asia (Hossaini et al., 2015b; Oram et al., 2017). As discussed in Section 4.5.3.4, Hossaini et al. (2017) obtained a substantial delay of Antarctic ozone return,if the recently observed global mean CH2Cl2 growthrate of 2 ppt yr−1 continues. Whether such a growthrate would be likely to continue until the end of the
century is arguable given the current global production capacity for CH2Cl2.

Polar Stratospheric Ozone | Chapter 4

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Volk, E. Hösen, F. Ravegnani, A. Ulanovsky, andV. Yushkov, Uncertainties in modelling heterogeneous chemistry and Arctic ozone depletionin the winter 2009/2010, Atmos. Chem. Phys., 13,
3909–3929, doi:10.5194/acp-13-3909-2013, 2013.
Wohltmann, I., R. Lehmann, and M. Rex, A quantitative analysis of the reactions involved instratospheric ozone depletion in the polar vortex core, Atmos. Chem. Phys., 17, 10,535–10,563,
doi:10.5194/acp-17-10535-2017, 2017.
Woiwode, W., M. Höpfner, L. Bi, M.C. Pitts, L.R. Poole,
H. Oelhaf, S. Molleker, S. Borrmann, M. Klingebiel, G. Belyaev, A. Ebersoldt, S. Griessbach,J.-U. Grooß, T. Gulde, M. Krämer, G. Maucher,
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Polar Stratospheric Ozone | Chapter 4

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4.63

Chapter 5
Stratospheric Ozone Changes and Climate
Lead Authors
A.Yu. Karpechko
A.C. Maycock
Coauthors
M. Abalos
H. Akiyoshi
J.M. ArblasterC.I. Garfinkel
K.H. Rosenlof
M. SigmondContributors
V. Aquila
A. Banerjee
A. Chrysanthou
D. Ferreira
H. Garny
N. Gillett
P. Landschützer
E.-P. Lim
M.J. Mills
W.J. Randel
E. RayW. Seviour
S.-W. Son
N. Swart
Review Editors
C. Cagnazzo
L. Polvani

Cover photo: An image of a cyclone off the coast of Antarctica from the MODIS satellite instrument.
Stratospheric ozone depletion has led to a poleward shift in summertime cyclone frequency over the Southern
Ocean. Photo: NASA

Chapter 5
Stratospheric Ozone Changes and Climate
CONTENTSSCIENTIFIC SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1
5.1

INTRODUCTION AND SCOPE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  5
5.1.1 Summary of Findings from the ­Previous Ozone Assessment . . . . . . . . . . . . . . . . . . . . . . . . .  5
5.1.2 Scope of the Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6

5.2

OBSERVED CHANGES IN ­ATMOSPHERIC ­CONSTITUENTS AND EXTERNAL ­FORCINGSTHAT RELATE TO CLIMATE  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6
5.2.1 Long-Lived Greenhouse Gases and Ozone-Depleting Substances  . . . . . . . . . . . . . . . . . .  6
5.2.2 Stratospheric Water Vapor  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  7
Box 5-1. Why Does Increasing CO2 Cool the Stratosphere?  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  7
5.2.3 Stratospheric Aerosols  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5.2.4 Ozone  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5.2.5 Solar Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

5.3

OBSERVED AND SIMULATED CHANGES IN STRATOSPHERIC CLIMATE . . . . . . . . . . . . . . . . . . . . 12
5.3.1 Stratospheric Temperatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.3.1.1

Observed Temperature Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

5.3.1.2

Simulation and Attribution of Past Global Stratospheric
Temperature Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5.3.1.3

Simulation and Attribution of Past Polar Stratospheric
Temperature Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5.3.1.4

Simulated Future Stratospheric Temperature Changes . . . . . . . . . . . . . . . . . . . . 19

5.3.2 Brewer–Dobson Circulation  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5.3.2.1

Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Box 5-2. What Is the Age of Stratospheric Air?  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.3.2.2

Simulated Past and Future Changes of the BDC . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5.3.3 Stratosphere–Troposphere Exchange  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5.3.4 Stratospheric Winds  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.4

5.3.4.1

Polar Vortices  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.3.4.2

Quasi-Biennial Oscillation Disruption and Implications  . . . . . . . . . . . . . . . . . . . 29

EFFECTS OF CHANGES IN STRATOSPHERIC OZONE ON THE TROPOSPHEREAND SURFACE  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5.4.1 Tropospheric Circulation Effects  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.4.1.1

The Southern Hemisphere: Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.4.1.2

The Southern Hemisphere: Model Simulations of the Past  . . . . . . . . . . . . . . . . 33

5.4.1.3

The Southern Hemisphere: Magnitude of Past Changes in Models  . . . . . . . . 37

5.4.1.4

The Southern Hemisphere: Model ­Simulations of the Future . . . . . . . . . . . . . . 38

5.4.1.5

The Northern Hemisphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.4.2 Mechanisms for Stratosphere–Troposphere Dynamical Coupling  . . . . . . . . . . . . . . . . . . 39
5.4.3 Surface Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.4.3.1

Interannual Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.4.4 Ocean and Ice Impacts  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.4.4.1

Ocean Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.4.4.2

Sea Ice Impacts  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.4.4.3

Ocean Carbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.4.5 Changes in Radiative Forcing and Feedbacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.5

CLIMATE IMPACTS OF THE MONTREAL PROTOCOL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.5.1 World Avoided by the Montreal ­Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.5.2 Projected Climate Impacts of the ­Kigali Amendment  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Chapter 5
Stratospheric Ozone Changes and Climate
SCIENTIFIC SUMMARY
Since the 2014 Ozone Assessment, new research has better quantified the impact of stratospheric ozone changes
on climate. Additional model and observational analyses are assessed, which examine the influence of stratospheric ozone changes on stratospheric temperatures and circulation, tropospheric circulation and composition, surface climate, the oceans, and sea ice. The new results support the main conclusions of the previous
Assessment; the primary advances are summarized below.

STRATOSPHERIC TEMPERATURES• New estimates of satellite-observed stratospheric temperature changes show net global stratospheric cooling of around 1.5 K (at 25–35 km), 1.5 K (at 35–45 km), and 2.3 K (at 40–50 km) between 1979
and 2005, with differences between datasets of up to 0.6 K.
○○ There are now better estimates of observed stratospheric temperature trends than were available

during the last Assessment. Two datasets from satellite measurements have been re-processed andnow show greater consistency in long-term temperature trends in the middle and upper stratosphere.

○○ Satellite temperature records show smaller stratospheric cooling rates over 1998–2015 compared

to 1979–1997, consistent with the observed differences in stratospheric ozone trends during these
periods.

○○ Global average temperature in the lower stratosphere (13–22 km) cooled by about 1 K between 1979

and the late 1990s but has not changed significantly since then.

• In the lower stratosphere (13–22 km), ozone trends were the major cause of the observed coolingbetween the late 1970s and the mid-1990s. In the middle and upper stratosphere, however, increases
in long-lived greenhouse gases played a slightly larger role than ozone changes in cooling trends
over this period. Ozone recovery will continue to play an important role in future stratospheric temperature trends.
○○ There is now improved understanding of the causes of stratospheric temperature trends and vari-ability. For the upper stratosphere (40–50 km), new studies suggest that one-third of the observed
cooling over 1979–2005 was due to ozone-depleting substances (ODSs) and associated ozone
changes, while two-thirds was due to other well-mixed greenhouse gases.

○○ Chemistry–climate models show that the magnitude of future stratospheric temperature trends isdependent on future greenhouse gas concentrations, with most greenhouse gas scenarios showing
cooling in the middle and upper stratosphere over the 21st century. The projected increase in global
stratospheric ozone during this period would offset part of the stratospheric cooling due to increasing greenhouse gases.

STRATOSPHERIC OVERTURNING CIRCULATION• There are indications that the overturning circulation in the lower stratosphere has accelerated overthe past few decades.

5.1

Chapter 5 | Ozone and Climate○○ Observations of the latitudinal profile of lower stratospheric temperature trends and changes inconstituents show that tropical upwelling in the lower stratosphere has strengthened over the last
~30 years, in qualitative agreement with model simulations and reanalysis datasets.

○○ New studies using measurements provide evidence for structural changes in the stratospheric over-turning circulation which is comprised of a strengthening in the lower stratosphere and a weakening in the middle and upper stratosphere.

○○ According to models, in addition to well-mixed greenhouse gases, changes in ODSs (and associatedchanges in ozone) are an important driver of past and future changes in the strength of the stratospheric overturning circulation, notably the increase in downwelling over the Antarctic over the late
20th century.

○○ Estimates of externally forced long-term changes in the stratospheric overturning circulation fromobservations remain uncertain, partially due to internal variability.

○○ Models project future increases in stratosphere–troposphere exchange of ozone as a consequenceof a strengthening of the stratospheric overturning circulation and stratospheric ozone recovery.

IMPACTS ON THE TROPOSPHERE, OCEAN, AND SEA ICE• New research supports the findings of the 2014 Ozone Assessment that Antarctic ozone depletion

was the dominant driver of the changes in Southern Hemisphere tropospheric circulation in australsummer during the late 20th century, with associated weather impacts.
○○ Over the period 1970 to 2000, tropospheric jets in the Southern Hemisphere shifted poleward andstrengthened, the Southern Annular Mode (SAM) index increased, and the southern edge of the
Hadley Cell expanded poleward. Since 2000, the SAM has remained in a positive phase.

○○ For austral summer, most model simulations show a larger contribution to these trends from

Antarctic ozone depletion compared to increases in well-mixed greenhouse gases during the last
decades of the 20th century. During other seasons, the contribution of ozone depletion to circulation changes is comparable to that from well-mixed greenhouse gases.

○○ Paleoclimate reconstructions of the SAM index suggest that the current period of prolonged posi-tive summer SAM conditions is unprecedented in at least the past 600 years.

○○ No robust link between stratospheric ozone depletion and long-term Northern Hemisphere sur-face climate has been established; there are indications that occurrences of extremely low springtime ozone amounts in the Arctic may have short-term effects on Northern Hemisphere regionalsurface climate.

• Changes in tropospheric weather patterns driven by ozone depletion have played a role in recent

temperature, salinity, and circulation trends in the Southern Ocean, but the impact on Antarctic sea
ice remains unclear.
○○ Progress has been made since the last Assessment in understanding the physical processes involvedin the Southern Ocean response to ozone depletion, which is now believed to entail a fast surface
cooling followed by a slow long-term warming.

○○ Modeling studies indicate that ozone depletion contributes to a decrease in Antarctic sea ice extent

and hence cannot explain the observed sea ice increase between 1979 and 2015. This is in agreementwith the conclusions of the previous Assessment. However, in general, climate models still cannot

5.2

Ozone and Climate | Chapter 5

reproduce the observed Antarctic sea ice trends since 1979, which limits the confidence in the modeled sea ice response to ozone depletion.

• New observation-based analyses indicate that a causal link between the strength of the Southern
Ocean carbon sink and ozone depletion cannot be established, in contrast to earlier suggestions.

○○ New observation-based analyses confirmed the previously reported slowdown of the carbon sinkbetween the 1980s and early 2000s but also revealed a remarkable reinvigoration of the carbon sinksince then. The new results indicate that atmospheric circulation changes (whether driven by ozone
depletion or not) have not had a considerable impact on the net strength of the Southern Ocean
carbon sink.

MONTREAL PROTOCOL CLIMATE IMPACTS• New studies since the 2014 Ozone Assessment have identified that future global sea level rise of atleast several centimeters has been avoided as a result of the Montreal Protocol. This would have arisen from thermal expansion of the oceans associated with additional global warming from unregulated
ozone depleted substances emissions.

5.3

Chapter 5 | Ozone and Climate

5.4

Ozone and Climate | Chapter 5

Chapter 5
Stratospheric Ozone Changes and Climate
5.1 INTRODUCTION AND SCOPE
The 2006 Ozone Assessment was the first to include
a dedicated chapter on ozone–climate interactions(Baldwin and Dameris et al., 2007). The focus of thatchapter was mostly on how anthropogenic climatechange affects stratospheric ozone. Ozone–climate
interactions were considered in a broader perspective in both the 2010 and 2014 Ozone Assessments.
Chapter 4 of the 2014 Assessment (Arblaster andGillett et al., 2014) addressed changes in stratospheric climate, their coupling with stratospheric ozone
changes, and the impacts of stratospheric changes on
tropospheric climate. This chapter is similar in scopeand provides an assessment of the advances in scientific understanding of ozone–climate coupling since
the last Assessment. To provide a basis for discussingthese advances in the subsequent sections, the main
conclusions from Chapter 4 of the 2014 Assessment
are summarized here.

5.1.1 Summary of Findings from the­Previous Ozone Assessment
The last Assessment concluded that the lower stratosphere (near 20 km) cooled in the global mean byapproximately 1 K over the period 1979–1995, after
which temperatures remained approximately constant. It also concluded that the middle (25–35 km)
and upper (35–50 km) stratosphere have cooled in the
global mean over this period; however, the available
satellite datasets showed substantial differences in theestimated magnitude of cooling. In agreement withprevious Ozone Assessments, it was concluded thatstratospheric ozone changes were the primary causeof the observed lower-stratospheric cooling, whileboth ozone decreases and greenhouse gas (GHG) increases (primarily carbon dioxide, CO2) made morecomparable contributions to cooling in the middle
and upper stratosphere.
The 2014 Ozone Assessment concluded from observations of composition and temperature that overthe past several decades there has been an increasein tropical lower-stratospheric upwelling, consistent
with a strengthening of the shallow branch of thestratospheric overturning circulation. At the sametime, long-term changes in the deep branch of theoverturning circulation were concluded to be uncertain. No long-term changes were found in stratospheric water vapor concentrations since 2000.
In agreement with the findings of earlier Assessments,
the 2014 Assessment concluded that the ozone-induced springtime cooling of the Antarctic lower stratosphere has strongly affected the Southern Hemisphere(SH) tropospheric climate in austral summer. It wasassessed that stratospheric temperature changes due
to ozone depletion were likely the dominant driverof the observed summertime poleward shift of the
mid-latitude tropospheric jet and have contributed tothe poleward expansion of the SH Hadley Cell. It wasnoted that, in response to ozone depletion, some climate models simulate a poleward shift of subtropicalprecipitation patterns and that consistent changes areseen in observations. Changes in the Southern Ocean
were discussed, and it was concluded that changes insurface wind stress, associated with the troposphericcirculation response to ozone depletion, likely caused
the intensification of the subtropical ocean gyres,the meridional overturning circulation, and subsurface warming. Contrary to the findings of the 2010
Assessment, the 2014 Assessment concluded, thoughwith low confidence, that stratospheric ozone depletion induces a decrease in Antarctic sea ice extent andtherefore cannot explain the small observed increasein Antarctic sea ice extent over the past several decades. Possible impacts of ozone depletion-induced
surface wind stress changes on carbon uptake in theSouthern Ocean were also assessed to be uncertain.
The 2014 Assessment did not find a robust link between ozone changes and tropospheric climate in the
Northern Hemisphere.
The 2014 Assessment concluded that stratosphericozone depletion contributed to a decrease in the flux ofozone into the troposphere but that coincident increases in emissions of ozone precursor species led to an
5.5

Chapter 5 | Ozone and Climate

overall increase in the tropospheric ozone burden. The
overall global radiative forcing (RF) between 1850 and2011 due to the effect of long-lived ozone-depleting
substances (ODSs) on both stratospheric and tropospheric ozone was estimated to be about −0.15 W m−2.
For the future, the 2014 Assessment concluded thatthe impacts of ozone depletion on tropospheric climate will reverse as a result of ozone recovery andthat this will offset part of the GHG-induced changes
in SH tropospheric circulation in summer during thefirst half of this century. The projected strengthening of the stratospheric overturning circulation wasassessed to have important implications for future
stratospheric and tropospheric ozone abundances.
The RF due to future stratospheric ozone changes wasassessed to be uncertain even regarding its sign, due
to uncertainty in model projections of tropical lower-stratospheric ozone trends.

5.1.2 Scope of the Chapter
Following Chapter 4 of the 2014 Ozone Assessment,
this chapter begins with an assessment of changes
in stratospheric constituents and external forcings(Section 5.2). Only a brief discussion of changes in
ODSs and stratospheric ozone is given here, sincethese are assessed in detail in Chapter 1 (ODS changes) and Chapters 3 and 4 (ozone changes). Section
5.3 assesses changes in stratospheric temperaturesand circulation. That section includes an attributionof observed temperature and circulation changes todifferent drivers and also an analysis of projectedchanges. Stratosphere–troposphere exchange of ozone
is also discussed, but since changes in tropospheric ozone have been recently assessed in detail in the
Tropospheric Ozone Assessment Report (TOAR), led
by the International Global Atmospheric Chemistry(IGAC) project (Young et al., 2018), the discussionof tropospheric chemistry is limited to assessing theeffects of stratospheric ozone on the troposphericozone budget. Section 5.4 discusses the effects ofstratospheric ozone on tropospheric circulation, surface climate, the ocean, and sea ice, as well as the current scientific understanding of physical mechanisms
for the downward dynamical coupling between thestratosphere and troposphere. Lastly, Section 5.5 assesses the climate impacts that have been avoided asa result of the successful regulation of ODS emissions
under the Montreal Protocol, as well as the future
5.6

climate impacts that will be avoided if nations adhere
to the phasedown of hydrofluorocarbons under the2016 Kigali Amendment (see also Chapter 2).

5.2 OBSERVED CHANGES IN­ATMOSPHERIC ­CONSTITUENTS
AND EXTERNAL ­FORCINGSTHAT RELATE TO CLIMATEThe species detailed in this section play a role in climate through their effects on radiative and chemical
processes. Changes in these species can alter stratospheric ozone concentrations either through direct
effects on ozone chemistry and/or via their effect on
stratospheric temperatures and transport.

5.2.1 Long-Lived Greenhouse Gases andOzone-Depleting Substances
Carbon dioxide (CO2), methane (CH4), and nitrous
oxide (N2O) are the three most important anthropogenically emitted GHGs in the atmosphere in terms ofhistorical RF (Myhre and Shindell et al., 2013); however, it should be noted that ODSs and their replacement compounds are also significant GHGs. Suchgases are more transparent to incoming (shortwave)
radiation from the sun compared to outgoing infrared(longwave) radiation. Increases in the atmosphericconcentrations of these gases lead to warming at thesurface, producing a direct global climate response.
These gases may also cause changes in stratospheric temperatures through effects on the local radiative balance; for example, increasing CO2 cools thestratosphere (see Box 5-1). Ozone photochemistry
responds to stratospheric temperature changes, aswell as to changes in abundances of ODSs. Similarly,changes in ozone affect the stratospheric radiativebalance; decreases in ozone will result in stratospheric cooling due to less absorption of solar ultraviolet
radiation. Changes in the stratospheric overturning (Brewer–Dobson) circulation may be forced bychanges in well-mixed GHGs and ozone concentrations (see Section 5.3.2); this may also impact the
distributions of stratospheric and tropospheric ozone
through transport changes (see Section 5.3.3).
Recent concentrations and growth rates for ODSs,
including N2O, are described in Chapter 1 and for
ODS replacement compounds in Chapter 2. Globally
averaged annual average mole fraction values for 2017

Ozone and Climate | Chapter 5

were 405 ppm (parts per million) for CO2 and 1,850
ppb (parts per billion) for CH4. Global concentrations and growth rates for CO2 and CH4 are shownin Figure 5-1. These show that 2015 and 2016 hadhigh CO2 growth rates relative to the 1980–2016 average. This is likely related, at least in part, to the El
Niño conditions that persisted from late 2014 throughearly 2016 (Le Quéré et al., 2018); the CO2 growthrate is known to increase during El Niño conditions
and decrease during La Niña conditions (Kim et al.,2016; Betts et al., 2016; Le Quéré et al., 2018). The
CH4 growth rate peaked in 2014 but remained positive and greater than 5 ppb yr−1 in 2015 and 2016.
Multiple drivers have been suggested for the higher
CH4 growth rates since the 2000s and are discussed
in Section 1.5.2 (see also Saunois et al., 2016). These

include changes in the atmospheric concentrations of
the hydroxyl (OH) radical, increased emissions fromoil and gas extraction, increased emissions from wetlands, and increased emissions from anthropogenic
sources in East Asia.

5.2.2 Stratospheric Water Vapor
Stratospheric water vapor (SWV) modulates Earth’s
climate directly, mainly through longwave radiative
processes, and indirectly through its influence on
stratospheric ozone abundances. It impacts stratospheric ozone chemistry through its role as the major
source of reactive hydrogen oxide molecules (HOx) in
the stratosphere and through the formation of polar
stratospheric clouds (see Chapter 4). Changes toBox 5-1. Why Does Increasing CO2 Cool the Stratosphere?
Although carbon dioxide (CO2) in the stratosphere plays only a small direct role in chemical processes, it isvery important for the atmosphere’s radiative balance. CO2 emits and absorbs radiation mainly in the infrared part of the electromagnetic spectrum, with the strongest emission and absorption at wavelengths close
to 15 μm. At these wavelengths, the absorption of infrared radiation by CO2 is so efficient that most radiation
emitted by Earth’s surface is absorbed in the troposphere and does not reach the stratosphere. Radiationentering the stratosphere from below therefore comes from the relatively cold upper troposphere. At thesame time, CO2 in the stratosphere emits radiation to space, cooling the stratosphere. The largest coolingrates are in the upper stratosphere, where temperatures are highest due to absorption of incoming ultraviolet radiation by ozone. Since this local emission is not balanced by the absorption of upwelling radiationfrom below, CO2 contributes a net cooling in the stratosphere. When the atmospheric CO2 concentration
increases, there is only a small increase in the absorption of radiation from the troposphere, which does notcompensate for a relatively larger increase in local emission, leading to a greater loss of radiation to space.
An increase in CO2 therefore radiatively cools the stratosphere at all altitudes, with the largest cooling in theupper stratosphere.
Increases in other greenhouse gases (GHGs) that absorb and emit infrared radiation, such as methane, nitrous oxide, and chlorofluorocarbons (CFCs), also contribute to cooling in the upper stratosphere, because
at stratospheric altitudes they emit more radiation to space than they absorb from below. However, whenin the lower stratosphere, these gases, whose absorption bands lie at wavelengths between 7 and 12 μm, canabsorb radiation from the warm lower troposphere and from Earth’s surface. Therefore, an increase in thesegases can contribute to warming of the lower stratosphere, although their contribution is usually small.
Some non-CO2 GHGs are also chemically reactive (such as methane, nitrous oxide, and CFCs) and thus
have an indirect effect on stratospheric temperatures through changing ozone abundances (see Chapters 3
and 4). In some cases, this indirect effect on stratospheric temperatures via changes to ozone may be larger
than the direct radiative effect of the gas itself.
In summary, increases in stratospheric CO2 and other GHGs lead to stratospheric cooling, with the largest
changes occurring in the upper stratosphere.

5.7

Chapter 5 | Ozone and Climate

SWV also impact ozone indirectly by changing stratospheric temperatures, which in turn alter the rates ofphotochemical reactions.
Air enters the stratosphere with a water vapor concentration largely controlled by tropical tropopause
temperatures. The primary source of SWV internal to
the stratosphere is in situ oxidation of CH4, yielding
two water molecules for each CH4 molecule oxidized.
Convective overshooting of ice particles and transport across the extratropical tropopause are minor
sources of SWV. The primary loss process internal to
the stratosphere is dehydration through ice particle
formation and sedimentation in polar regions (mostly
in the Antarctic) during winter.
SWV has been measured by multiple in situ and remote sensing techniques, starting with World War
II measurements aimed at understanding contrails
using a manually operated aircraft-borne frost point
hygrometer (FPH) (Brewer, 1946). The Boulder FPH
record extends from 1980 to present day. A detailed
analysis of time variations in the Boulder record
using breakpoints revealed periods of both increases
and decreases in SWV and variations in trends withaltitude (Hurst et al., 2011). While the net source of
SWV from CH4 oxidation was found to vary withtime, it is estimated to have caused about 25% of theincrease in SWV in the altitude range 16–26 km between 1980 and 2010 (Hurst et al., 2011). Although
the lack of continuous long-term measurements complicates SWV trend determination, several studies
have shown an overall long-term increasing trend(Oltmans et al., 2000; Rosenlof et al., 2001; Hurst
et al., 2014). In terms of the consistency between insitu and satellite measurements of SWV, a comparison of balloon FPH measurements with retrievalsfrom the Aura Microwave Limb Sounder (MLS) satellite instrument for the period 2004–2012 revealed
there was no statistically significant drift (Hurst etal., 2014). Subsequent analysis including more recent
data (Hurst et al., 2016) shows a trend in the difference between the balloon FPH measurements and theAura MLS measurements, the reasons for which arestill under investigation.
Although it is known that tropical cold point temperatures and in situ production from CH4 oxidation
largely control SWV concentrations, there are still issues reproducing the absolute value of measured SWV
5.8using global temperature analyses. To produce accurate simulations (to within 0.5–1 ppmv [parts per million by volume]) of tropical stratospheric water vapor
entry concentrations using trajectory models driven
by global temperature analyses, proper representationof waves, convective influences, and microphysicalprocesses are needed (Ueyama et al., 2014). There isalso evidence (Avery et al., 2017) that injections of ice
can at times impact SWV during extreme events.
Tropical Pacific sea surface temperature (SST) variability affects SWV entry through impacts on tropical
tropopause temperatures; it has been suggested that
SST changes contributed to the observed decrease in
SWV in the lower stratosphere around the year 2000
(Rosenlof and Reid, 2008; Brinkop et al., 2016; Ding
and Fu, 2017; Garfinkel et al., 2018). Variations of
SWV are detailed in the State of the Climate reports
published annually in the Bulletin of the American
Meteorological Society; the most recent update(Blunden and Arndt, 2017) shows recent extreme
variability of SWV in the tropical lower stratosphere,
where water vapor enters the stratosphere, ranging
from very high values to very low values between
December 2015 and December 2016 (Figure 5-2);
part of this change may be related to the transition
from extreme El Niño conditions to weak La Niña
conditions at the end of the period (Konopka et al.,2016; Garfinkel et al., 2018).
Trends in tropical tropopause temperature and atmospheric CH4 concentrations are expected to be
the major drivers of future SWV trends, but there arealso suggestions from model simulations that trendsin overshooting ice particles could contribute totrends in SWV (Dessler et al., 2016). Climate models predict that tropical lower-stratospheric humidity
will increase in the future due to increased transport
through the tropical tropopause layer (Smalley et al.,2017), though it should be noted that many climate
models do not properly capture the processes that
affect tropical tropopause temperatures (Kim et al.,2013). The magnitude of modeled increases in SWV
over the 21st century, particularly in the middle andupper stratosphere, is strongly affected by future atmospheric CH4 concentrations (Revell et al., 2016).
It has been suggested that convective injection ofwater vapor into the lower stratosphere could lead toenhanced heterogeneous destruction of ozone andOzone and Climate | Chapter 5

Global Monthly Mean CO2

ppm

400
380
360
340

ppm yr -1

1980

3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1980

1900
1850

1990

Year

2000

2010

CO2 Global Growth Rate

1990

Year

2000

2010

Global Monthly Mean CH4

ppb

1800
1750
1700
1650
1600
1980

ppb yr -1

20
15

1990

Year

2000

2010

CH4 Global Growth Rate

10
5
0
–5
–10
1980

1990

Year

2000

2010

Figure 5-1. Time series of concentrations and growth rates for globally averaged CO2 (top two panels) and CH4(bottom two panels). These time series were constructed with data provided by Ed Dlugokencky and Pieter
Tans, NOAA/ESRL, and are available at www.esrl.noaa.gov/gmd/ccgg/trends/. See Masarie and Tans (1995)
and Dlugokencky et al. (1994) for details of measurements.
5.9

Chapter 5 | Ozone and Climate

reduced Northern Hemisphere (NH) mid-latitude
column ozone amounts (Anderson et al., 2012, 2017;
Anderson and Clapp, 2018). However, observational
evidence that synoptic convective systems lead to enhanced catalytic ozone destruction in mid-latitudes iscurrently inconsistent (Schwartz et al., 2013; Solomon
et al., 1997; Anderson et al., 2012, 2017). Though it has

been posited that this mechanism may be enhanced
in a warmer climate (Anderson and Clapp, 2018), thelack of evidence for any role for this mechanism in thecurrent climate and the fact that in the future there
will be lower atmospheric chlorine levels and hence
reduced catalytic ozone destruction mean that there
is low confidence in this proposed feedback.

1.5

Mixing ratio (ppmv)

1.0
Figure 5-2. Top panel:
Lower-stratospheric wa­tervapor anomalies (black,
red, and blue circles, plotted as difference from themonthly mean over theperiod 2004–2018) fromballoonmeasurements
taken between 1980 and2018 at Boulder, Colorado(USA); Hilo, Hawaii (USA);and Lauder (New Zealand). Bottom panel: Thisgraph is the same as theone above but shows only
Boulder
measurements(black circles) between
2004 and 2018, as well as
zonally averaged water
vapor anomalies (35–45°N)
from the Aura Microwave
Limb Sounder (MLS; green
dashes). The frost point(FP) data extend the information plotted in Figure
2.55 of Blunden and Arndt(2017).

0.5
0.0

−0.5
−1.0
−1.5
1980

1990

2000

2010

Year

1.5

Mixing ratio (ppmv)

1.0
0.5
0.0
−0.5
−1.0
−1.5
2004

5.10

2006

2008

2010

2012

Year
Year

2014

2016

2018

Ozone and Climate | Chapter 5

5.2.3 Stratospheric Aerosols
Stratospheric aerosols influence climate by scatteringsunlight back to space, by modifying cloud microphysical processes, and by altering ozone chemistry. Trends and variability in stratospheric aerosols
and their impact on ozone are discussed in detail in
Chapters 3 and 4 (see Sections 3.2.1.4, 4.2.3.1, and4.3.5.2). Because they reflect sunlight, artificial enhancement of stratospheric aerosols has been proposed as a possible method for solar radiation management to cool the planet (see Chapter 6, Section
6.2.5.2). Stratospheric aerosols also warm the stratosphere by absorbing infrared radiation, and as such,they are important drivers of the observed stratospheric temperature variability (see Section 5.3.1.2).
Major increases in stratospheric aerosols result fromvolcanic eruptions. The last major volcanic eruptionthat significantly perturbed stratospheric aerosols was
Mount Pinatubo (in the Philippines) in 1991. What
are believed to be background levels of stratosphericaerosols were reached in the late 1990s (Kremser etal., 2016), and since then, there have been moderate
eruptions that have increased stratospheric aerosol
loading (Neely et al., 2013). Figure 5-3 (from Mills
et al., 2016) shows the progression of the global aerosol burden from 1980 to 2015. Peak aerosol loading
follows the Pinatubo eruption in 1991, with several
shorter-lived increases following moderate eruptions
during the early 21st century, the largest of which
occurred in 2008. Sulfur-rich particles dominate
stratospheric aerosols, but recent work has also highlighted the importance of organic aerosols (Murphy
et al., 2014; Vernier et al., 2015; Yu et al., 2016) andhas shown that they have likely increased significantly
since the preindustrial period (Yu et al., 2016).
Increases in stratospheric aerosols in the presence ofelevated stratospheric chlorine produce ozone loss.
For example, the large October 2015 Antarctic ozone
hole has been attributed to the presence of volcanic
aerosols from the Calbuco eruption (in southern
Chile) (Solomon et al., 2016). The potential for aerosols to enhance ozone loss is expected to decrease as
chlorine loadings continue to decrease in the future(Klobas et al., 2017), but uncertainty in future levels of
volcanic aerosol introduces uncertainty to determining when ozone recovery to 1980 levels is expected tooccur (Naik et al., 2017).

Since the 2014 Ozone Assessment, there have beensignificant improvements in understanding of theexistence of the Asian Tropopause Aerosol Layer(ATAL) (Vernier et al., 2011), which became evident
only after aerosol concentrations returned to pre-Pinatubo concentrations. The ATAL is hypothesized tohave a significant anthropogenic origin (Vernier et al.,2015) and, according to one study, likely contributes
as much to the background aerosol in the Northern
Hemisphere as small to moderate volcanic eruptions(Yu et al., 2017).

5.2.4 Ozone
Stratospheric ozone changes can impact climate bychanging the large-scale atmospheric state, including
impacts on the tropospheric circulation and ultimately surface weather (see Section 5.4), or by changing
the amount of UV radiation that reaches the surface,
both impacting surface temperatures and biogenic
processes.
Since the late 1990s, concentrations of ODSs havedeclined in response to the implementation of theMontreal Protocol (see Chapter 1). Chapter 3 reportsthat global (60°S–60°N) column ozone has been increasing by between 0.3% and 1.2% per decade since
the late 1990s, but this increase is not statistically significant, owing to the comparatively large uncertainty
of 1% per decade arising from dynamically forced interannual variability. Global column ozone is expected to increase with further reductions in the abundance of ODSs in the stratosphere. Current tropicalcolumn ozone is found to be unchanged compared tothe period 1964–1980, consistent with the findings ofthe 2014 Assessment. Upper-stratospheric (35–45 km
altitude) ozone in the tropics and mid-latitudes has
increased by 1–3% per decade over the 2000–2016
period; these increases are statistically significant andare thought to be caused by a combination of reductions in ODSs and GHG-induced cooling. Climate
models predict a decrease in tropical lower-stratospheric ozone due to a modeled increase in strengthof the stratospheric overturning circulation. However,
due to large internal variability, which is also seen inmodels, this decrease has not been detected in a statistically significant manner since 2000. As noted in
Chapter 4, the characteristics of the Antarctic ozone
hole in October during recent years are similar to
those in the early 1990s; its size and duration are still
5.11

Stratospheric burden (Gg S)

Chapter 5 | Ozone and Climate

SO4
103
102

OCS
SO2

101
100

H2SO4

1980

2000

1990

2010

Year
Figure 5-3. Calculated global mass burdens of major sulfur-bearing species from a specified dynamics (SD-)
WACCM VOLC simulation above the tropopause, shown as a function of time from 1 January 1980 to 31
December 2015 (updated from Mills et al., 2016). The black line shows SO4 (sulfate); the green line, OCS (carbonyl sulfide); the yellow line, H2SO4 (sulfuric acid); and the red line, SO2 (sulfur dioxide). Mass burdens areshown in units of Gg (=109 g) of S. Note that the burden of dimethylsulfide in the stratosphere (10−3–10−2 Gg S)
is too small to be shown. The spikes in the SO2 trace (red line) indicate where volcanic eruptions reached thestratosphere. The actual eruptions used are detailed in Mills et al. (2016). Note: there was an error in the originally published version due to an incorrect adjustment for molecular weights, so the burdens of the gases
have shifted.

impacted in cases of high volcanic aerosol loading,such as from the Calbuco eruption in 2015 (Solomon
et al., 2016). However, statistically significant positive
trends in ozone have been observed in the Antarctic in
September since 2000 (Solomon et al., 2016). Overall,there have been minimal long-term ozone changes
found in the Arctic, where dynamically forced interannual variability in ozone in winter and spring islarge compared to the long-term changes.

5.2.5 Solar Activity
Total solar irradiance (TSI), which measures theamount of solar radiation at the top of Earth’s atmosphere, has been directly monitored by satellites
since 1978. TSI varies on a wide range of timescales,
the most relevant of which for understanding recent
stratospheric climate and ozone changes is the approximately 11-year cycle during which TSI varies by
about 1 W m−2 (<0.1%) between solar cycle maximum
and minimum (Haigh, 2007). When solar activity
is high, incoming solar UV radiation is enhanced,
5.12

impacting ozone production in the stratosphere andmesosphere (Haigh, 2007). Changes in the absorptionof UV radiation by ozone then impacts stratospheric temperature distributions and, consequently, circulation and climate (Gray et al., 2010). The impact
of solar cycle variations on ozone depends on solar
spectral irradiance (SSI) and, in particular, the fraction of variance in the UV spectral region. The peakof the current 24th 11-year solar cycle, which started
in December 2008, was weaker than previous cycles(Hathaway, 2015). At present, the sun is approachinga minimum phase of the solar cycle.

5.3 OBSERVED AND SIMULATEDCHANGES IN STRATOSPHERIC CLIMATE
Section 5.2 reviewed observed changes in some of
the major constituents and external drivers of stratospheric climate. This section describes the current
understanding from observations and model simulations of recent and future changes in stratosphericclimate and their drivers.

Ozone and Climate | Chapter 5

5.3.1 Stratospheric Temperatures
Stratospheric temperature trends are a key marker foranthropogenic effects on the climate system (IPCC,
2013; USGCRP, 2017). Moreover, stratospheric temperature trends affect stratospheric ozone abundances
through effects on the rates of photochemical reactions (see Chapters 3 and 4). The 2014 Assessment
concluded that over the past several decades, increases in atmospheric GHGs and decreases in stratospheric ozone abundances have been the major radiative drivers of global mean cooling in the middle
and upper stratosphere. In the lower stratosphere,
observed global mean cooling was largely attributed
to stratospheric ozone changes over the past few decades. The latitudinal structure of stratospheric temperature trends is strongly influenced by changes inthe stratospheric overturning circulation (see Section
5.3.2), which may be externally forced and/or associated with internal variability. This section focuses onwhat has been learned about stratospheric temperature trends since the 2014 Ozone Assessment, notably,
improved constraints on satellite-observed temperature trends and new efforts to attribute observed andmodel-simulated temperature variability and trends
to natural and anthropogenic drivers.

km; and SSU Channel 1 [SSU1], 25–35 km), the middle stratosphere (SSU Channel 2 [SSU2], 35–45 km),and the upper stratosphere (SSU Channel 3 [SSU3],
40–50 km). These instruments were replaced by the
Advanced Microwave Sounding Unit (AMSU), whichstarted flying in 1998. Continuing the record has required merging the MSU/SSU data with AMSU dataor with measurements from other recent satellite records (see below).

5.3.1.1	Observed Temperature Changes

The 2014 Ozone Assessment highlighted a significant
discrepancy in global long-term temperature trends inthe middle stratosphere between the two independent
analyses of the SSU record from the UK Met Officeand the National Oceanographic and Atmospheric
Administration (NOAA). The NOAA Center for
Satellite Applications and Research (STAR) SSU v1.0
dataset (Wang et al., 2012) showed temperature trends
over 1979–2006 of −1.24 ± 0.13, −0.93 ± 0.14, and −1.01± 0.19 K decade−1 in SSU1, SSU2, and SSU3, respectively (Wang et al., 2012). These could be comparedwith trends in the UK Met Office SSU dataset available
at that time of −0.52 ± 0.23, −0.40 ± 0.23, and −1.27 ±0.33 K decade−1 (Wang et al., 2012). The NOAA STARdataset therefore showed substantially larger cooling
trends in SSU1 and SSU2 and a weaker cooling trend
in SSU3 compared to the UK Met Office dataset.

Observations of stratospheric temperatures comefrom operational radiosondes, operational polar
orbiting satellites, GPS Radio Occultation satellite
networks, and from research satellites and rocketsondes. Radiosonde observations span the longest
time period (starting in the late 1950s), but there are
discontinuities due to changes in instrumentationand location of stations, and they cover only the lower
part of the stratosphere. Consequently, homogenizeddatasets based on radiosondes have been constructed to improve the accuracy of radiosonde temperature time series, e.g., IUK (Sherwood and Nishant,2015); RATPAC (Lanzante et al., 2003); RAOBCORE
and RICH (Haimberger et al., 2012); and HADAT2(Thorne et al., 2005). Global temperature data forthe stratosphere are available from the Microwave
Sounding Unit (MSU) and Stratospheric Sounding
Unit (SSU) satellite instruments that flew on operational polar orbiters and provided coverage from late1978 to 2005. MSU and SSU measure stratospherictemperatures over four broad layers covering thelower stratosphere (MSU Channel 4 [MSU4], 13–22

Since the 2014 Ozone Assessment, both groups havepublished revised versions of their SSU datasets (Nashand Saunders, 2015; Zou et al., 2014). The reprocessed
SSU records show much greater consistency in the estimated global and annual mean temperature trends
throughout the stratosphere than was reported in the2014 Assessment (Seidel et al., 2016) (Figure 5-4).
This reflects substantial progress in understanding thesources of differences in temperature trends between
the two SSU datasets, but differences remain that arelarger than the uncertainty estimates provided byeach research team (Seidel et al., 2016). The satellite
observations in Figure 5-4 show global stratospheric cooling of about 1.5 K (25–35 km), 1.5 K (35–45km), and 2.3 K (40–50 km) between 1979 and 2005.
The largest outstanding discrepancies are in SSU2
and SSU3, where the NOAA dataset shows stronger
cooling in SSU2 by about 0.6 K and weaker coolingin SSU3 by about 0.3 K than in the UK Met Officedataset. However, the reprocessed NOAA SSU dataset
shows a vertical coherency in stratospheric temperatures that is more consistent with model simulations
5.13

Chapter 5 | Ozone and Climate

than the UK Met Office dataset (Seidel et al., 2016),
suggesting that the NOAA SSU dataset provides amore physically consistent representation of stratospheric temperatures.
Since the 2014 Assessment, there have been efforts
to extend the SSU record, which ended in 2006, tonear-present day using more recent satellite measurements, including AMSU-A (Zou et al., 2016;
McLandress et al., 2015), SABER, and MLS (Randel
et al., 2016). The signal weightings as a function ofaltitude of the more recent satellite instruments aredifferent from those of the SSU instruments. Recent
studies have focused on developing methods to map
the current satellite retrievals onto the SSU weighting
functions in order to produce a consistent merged record. Analysis of stratospheric temperature trends insatellite records covering the recent past has revealed
weaker trends after around 1997 (Zou et al., 2016;
McLandress et al., 2015; Randel et al., 2016; Khaykin
et al., 2017) (Figure 5-5), which is consistent with current understanding of the timing of peak atmospheric chlorine loading (see Chapter 1), the coincident
changes in stratospheric ozone trends (see Chapters
3 and 4), and associated effects on stratospheric temperatures (Ferraro et al., 2015; Randel et al., 2017).
In the lower stratosphere (13–22 km), the three
MSU4 records, NOAA/STAR v4.0 (Zou et al., 2006),
the Remote Sensing Systems (RSS) v3.3 (Mears et al.,2011), and the University of Alabama in Huntsville(UAH) v6.0 data sets (Christy et al., 2003), show a net
cooling in the global mean between 1979 and 2016 of
about 1 K. The majority of the observed global lower
stratospheric cooling in the MSU4 record occurred
before the mid-1990s (Figure 5-4). Since then there
has been little overall global temperature change in
the MSU4 record (Seidel et al., 2016; Polvani et al.,2017). The long-term cooling is interspersed by shortterm global stratospheric warming for a few years following the two major tropical volcanic eruptions in
the epoch (El Chichón in 1982 and Mt. Pinatubo in1991). The stratospheric heating from volcanic aerosols peaks in the lower stratosphere (Figure 5-4d) but
is also evident in the middle and upper stratosphere(Figure 5.4a–5.4c).
The University of Alabama in Huntsville (UAH)
MSU4 dataset shows slightly stronger cooling over
the record, by about 0.2 K, compared to the NOAA
5.14

STAR and Remote Sensing Systems (RSS) MSU4
datasets (Figure 5-4d) (Seidel et al., 2016). The majority of the differences in temperature trends between the three MSU4 datasets are associated withtemperature changes at high latitudes (Seidel et al.,2016). The three MSU4 records agree reasonably
well, in the global mean, with the radiosonde datasets
RAOBCORE and RICH (Figure 5-4d), but the comparison with the radiosonde data is problematic because the disagreement between the two radiosonde
datasets is as large as the difference between either ofthem and the MSU4 datasets.
As reported in the last Assessment, long-term MSU4
temperature trends show considerable structure inlatitude and by season. Figure 5-6a shows MSU4
temperature trends over 1979–1997. The trends show
significant cooling throughout most of the year in thetropics and also in mid-latitudes, with enhanced cooling in the Antarctic in austral spring and summer. An
enhanced cooling in the Arctic in mid-winter as well
as a warming in SH high latitudes in August are also
observed, but these are not reproduced by the chemistry–climate models (Figure 5-6b), suggesting this is
likely a manifestation of the large internal variability
in the polar stratosphere during winter affecting the
calculated trends over a relatively short 19-year period (see Section 5.3.1.3). Over the period 1998–2016(Figure 5-6c), the tropics is the only region where
significant cooling has been observed in the MSU4
record in boreal late spring and early summer.
In addition to satellite and in situ stratospheric temperature measurements, there are numerous meteorological reanalysis datasets produced by the world’s
meteorological services. Reanalysis products arewidely used in the literature for atmospheric process studies, but developers have cautioned against
their use for climate trend studies, owing to potential
discontinuities in the records that can be introducedby the integration of different satellite records into
the model data assimilation system (Simmons et al.,2014). The WCRP SPARC Reanalysis Intercomparison
Project (S-RIP) has recently assessed the representation of long-term stratospheric temperature changes
in a number of current reanalysis systems (Long et al.,2017). These reanalysis products have been comparedwith the NOAA STAR MSU/AMSU v3.0 and SSU v2.0
SSU1 and SSU2 records by sampling the pressure level
output fields with the satellite weighting functions

Ozone and Climate | Chapter 5(a) SSU3 (40–50 km)

Temp anom (K)

1
0
−1
-2

(b) SSU2 (35–45 km)

Temp anom (K)

1
0
−1
−2

(c) SSU1 (25–35 km)

Temp anom (K)Temp anom (K)

1
0
−1
−2

NOAA/STAR v3.0
UK Met Office
CCMI models(d) MSU4 (13–22 km)

2

NOAA/STAR v4.0
RSS v3.3
UAH v6.0
RICH v1.5
RAOBCORE v1.5
CCMI models

1
0
−1
1979

1984

1989

1994

Year

1999

2004

2009

2014

Figure5-4.
Timev1.5
series of global mean stratospheric temperature anomalies from 1979 to 2016. Panels show
UK Met-2
RAOBCORE
2
1
0
-1
RICH
UAH
RSS
NOAA/STAR
CCMI
v3.3
v6.0
v1.5
models
Officev3.0
v4.0
SSU Channels 3, 2, 1 (SSU3, SSU2, SSU1; a, b, c) and MSU channel 4 (MSU4; d) for the altitude ranges, datasets,
and model outputs indicated in the legends. Gray lines indicate results from a total of 23 ensemble members
across 14 Chemistry-Climate Model Initiative (CCMI) models for the REF-C2 experiment, weighted by theappropriate satellite weighting function for comparison with observations. All data in panel d are shown asmonthly averages except the UK Met Office dataset, which uses 6-month averages, and the two radiosonde
datasets, which are annual means. The radiosonde data are as in Figure 2.8 of Blunden and Arndt (2017).
Anomalies are shown relative to 1979–1981. Adapted from Maycock et al. (2018).
5.15

Chapter 5 | Ozone and Climate(a)

1979–1997 Trend(b)

1998–2016 Trend

SSU2
MSU/AMSU
SSU1

K decade-1

K decade-1

MSU/AMSU
SSU2

SSU1

SSU3

SSU3

LatitudeLatitudeFigure 5-5. Observed annual mean stratospheric temperature trends in a merged satellite (SSU and MLS)
record for the periods (a) 1979–1997 and (b) 1998–2016. Thick solid lines show MSU/AMSU, thin solid lines
show SSU1, dashed lines show SSU2, and dotted lines show SSU3. Updated from Randel et al. (2016).

(Long et al., 2017). In the lower stratosphere, the reanalyses generally show weaker long-term coolingcompared to MSU4, by up to ~0.5 K (~50%) overthe period 1979–2015. There are larger differencesin the temperature trends among the reanalyses in
the middle and upper stratosphere, with the NCEP
Climate Forecast System Reanalysis (CFSR) showing particularly large and unrealistic interannual anddecadal variations, owing to its construction frommultiple streams (Long et al., 2017). In other current
reanalysis datasets, the differences in the long-term
global mean temperature change in SSU1 and SSU2
compared to observations are typically <1 K over
1979–2015. In conclusion, current reanalyses show
deficiencies in capturing both short- and long-term
variations in stratospheric temperatures found in satellite measurements.

5.3.1.2

Simulation and Attribution of Past Global
Stratospheric Temperature Changes

New studies published since the 2014 Assessment
have quantified the contribution of different external
factors, such as changing GHG concentrations andozone (or ozone-depleting substance; ODS) concentrations, to observed stratospheric temperaturechanges over the satellite era.
According to one study, which applied a standard detection and attribution analysis to global stratospheric
5.16

temperature records from the NOAA/STAR SSU v1.0
dataset, the effects of GHGs and ozone were not distinguishable separately in the middle to upper stratosphere (Mitchell, 2016), consistent with the conclusion of the 2014 Assessment. Another study, whichanalyzed chemistry–climate model experiments withincrementally added forcing agents and prescribed
observed SSTs, found that ODSs contributed about 0.4
K (one-quarter) of the global mean cooling in SSU1
and about 0.7 K (one-third) of the cooling in SSU2
between 1979 and 1997, with virtually all cooling after
2000 being attributed to GHGs (Aquila et al., 2016)(see Figure 5-7 for SSU2). In the upper stratospherein SSU3, both a standard detection and attribution
approach (Mitchell, 2016) and a chemistry–climate
model study (Aquila et al., 2016) attribute about
two-thirds of the long-term global average coolingbetween 1979 and 2005 to GHGs and one-third to
ODSs. Chemistry–climate model experiments withincrementally added forcings further demonstrate thatthe relatively rapid decreases in global upper-stratospheric temperatures that occurred in the early 1980s
and early 1990s were likely the result of a coincidence
between a relative decrease in temperature following
warming from major tropical volcanic eruptions andthe declining phase of the 11-year solar cycle (Aquila etal., 2016). Stratospheric water vapor changes may havealso contributed to cooling in the lower stratosphere
over the last 30 years (Maycock et al., 2014); however,Ozone and Climate | Chapter 5

0

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(b) CCMI-1 trend 1979-1997 [K decade-1]-0.5(a) MSU4 trend 1979-1997 [K decade-1]

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75
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Figure 5-6. Lower-stratospheric temperature trends over the periods (a, b) 1979–1997 and (c, d) 1998–2016 in
K per decade as a function of latitude and month from (a, c) satellite MSU/AMSU NOAA/STAR v4.0 (MSU4) and(b, d) multi-model mean MSU4 temperatures from 12 CCMI models for the REF-C2 simulation. The following
years, which were influenced by volcanic eruptions, are treated as missing data in panels a and b: 1982, 1983,
1991, 1992, 1993. The hatching in panels a and c shows where the magnitude of the observed trend is within the
5–95% range of simulated internal variability, estimated from the spread in modeled trends, and is therefore
estimated to be not statistically significant (at the 95% confidence level). Hatching in panels b and d shows where
the observed MSU4 trends in panels a and c lie outside the 5–95% range of model trends, thus showing areas
where the simulated and observed trends are inconsistent. Based on datasets described in Maycock et al. (2018).

the magnitude of this effect is not well constrained
due to uncertainties in global long-term stratosphericwater vapor trends (see Section 5.2.2).
In the last Assessment, simulations from climate models and chemistry–climate models were found to show
weaker global average cooling than estimated fromobservations in the lower and upper stratosphere.
In the middle stratosphere (35–45 km), the modeled
trends were within the range of the observational uncertainty (Thompson et al., 2012). Figure 5-4 shows
simulated global average stratospheric temperature
anomalies in the CCMI REF-C2 experiment (seeChapter 3 and Morgenstern et al. (2017) for model
details) alongside the satellite observation datasets
described above (Maycock et al., 2018). The model

pressure level output has been sampled according to
the satellite weighting functions to facilitate the comparison with observations. The main new findings
are that the model-simulated temperature changes
are now in good agreement with the revised NOAASTAR SSU dataset in the upper stratosphere (40–50km), whereas the revised UK Met Office record stillshows stronger cooling than simulated in the chemistry–climate models, as was reported in the 2014
Assessment. In the lower stratosphere in the MSU4
(13–22 km) and SSU1 (25–35 km), the models show
on average slightly weaker long-term cooling than observed, similar to the findings of the 2014 Assessment,
though the observed trends lie within the range ofindividual model realizations (Maycock et al., 2018).
5.17

2.5
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1990

2000

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Year
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T (K)

T (K)

T (K)

Chapter 5 | Ozone and Climate

1990

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1990

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1990

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T (K)

Figure 5-7. SSU2 (35–45 km) global mean monthly
temperature anomalies in satellite observations(black line) (McLandress et al., 2015) and the Goddard
Earth Observing System Chemistry-Climate Model(GEOSCCM; red line). The five panels show experiments with forcings added incrementally: (a) SSTs (seasurface temperatures), (b) SSTs + GHGs (greenhouse
1990
2000
2010
gases), (c) SSTs + GHGs + ODSs (ozone-depleting subYear
stances), (d) SSTs + GHGs + ODSs + Volc (volcanoes),(e) SSTs + GHGs + ODSs + Volc + Sun (solar). Time series are plotted as anomalies relative to the 1995–2011
average. The solid red lines show the model ensemble means, and the shaded areas show the spread acrossthe three ensemble members. Adapted from Aquila et al. (2016).

2.5
2.0
1.5
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1980

The difference in global mean lower-stratospherictemperature trends is at least partly associated with
the CCMI multi-model mean showing weaker cooling in the tropics than found in observations (Figure5-6b). Note that many of the CCMI models did notinclude the radiative effects of volcanic aerosols in
the REF-C2 experiment, following the interpretationof the experimental design by modeling groups, andhence most of the models do not capture stratosphericwarming following the two major volcanic eruptions
since 1979 (Figure 5-4). In conclusion, there is now
greater consistency between observed and modeled
global stratospheric temperature trends in all the SSU
channels, and this is largely the result of the updates to
5.18

the satellite records since the 2014 Assessment rather
than any major changes in the modeled temperature
trends (McLandress et al., 2015; Maycock et al., 2018).

5.3.1.3

Simulation and Attribution of Past Polar
Stratospheric Temperature Trends

In addition to the attribution of global mean stratospheric temperature changes to different external factors, studies have separately analyzed the contributionof radiative and dynamical processes to seasonal polar
stratospheric temperature trends. Dynamical contributions to temperature changes occur through adiabatic heating (cooling) associated with downwelling(upwelling) motion. In the Arctic, studies indicate anOzone and Climate | Chapter 5

important role for dynamical processes in determining the observed long-term lower-stratospheric cooling in boreal spring, though the precise magnitude ofthe dynamical contribution depends on the approach
used to separate the radiative and dynamical contributions (Bohlinger et al., 2014; Ivy et al., 2016). In the
Arctic in boreal summer, the observed stratosphericcooling at 50 hPa is smaller in magnitude and is, as
expected, dominated by the radiative effects of increasing GHGs and ozone. In the Antarctic, changes
in dynamics have acted to slightly enhance the radiative cooling from ozone loss in austral spring but haveoffset part of the radiative cooling in austral summer,
resulting in weaker lower-stratospheric temperature trends than would arise from radiative effects
alone (Keeley et al., 2007; Orr et al., 2013; Ivy et al.,2016). Thus, the observed long-term cooling trend in
the Antarctic lower stratosphere in austral spring isslightly enhanced by the effect of dynamical feedbacks
to the observed ozone trends (see Chapter 4). Since
2000, during the period when emergence of healing of
the Antarctic ozone hole has been observed (Solomon
et al., 2016), Antarctic lower-stratospheric temperature trends show a warming in austral spring, which
can be partly attributed to radiative effects of ozone
trends as well as to dynamical changes that may be
associated with internal variability (Solomon et al.,2017).
Chemistry–climate model experiments show substantial differences in polar temperature trends, particularly in the lower stratosphere, between different
ensemble members forced with identical observed
SSTs, sea ice, and external forcing agents and initialized using a range of atmospheric initial conditions(Randel et al., 2017; Maycock et al., 2018) or withslight differences in the model parameterizations(Garfinkel et al., 2015a). In fact, the spread of simulated trends suggests that recent observed polar temperature trends (Figure 5-6b) are not inconsistent
with internal variability, assuming that these models offer a realistic representation of the forced andunforced components of stratospheric temperaturechange. For example, although the CCMI REF-C2
multi-model mean does not capture the recent observed Arctic warming in boreal winter and cooling in boreal spring in the MSU4 (Figure 5-6b), theobserved trends in the Arctic lie within the range ofmodel simulations (Maycock et al., 2018). Observed

SST changes have been estimated to account for about
half of the recent Arctic stratospheric cooling trend
in boreal spring (Garfinkel et al., 2015a), which isbroadly in agreement with the estimated dynamical
contribution to Arctic temperature trends discussed
in Section 5.3.1.2 (Bohlinger et al., 2014; Ivy et al.,2016). The models in Figure 5-6b either included acoupled ocean or used SST and sea ice boundary conditions from another model simulation, and thus theevolution of SSTs will likely differ from observations,though any forced component of SST change and its
effect on polar temperature trends should be at least
partly captured.

5.3.1.4 Simulated Future Stratospheric	Temperature Changes
As described in the 2014 Ozone Assessment, future
global stratospheric temperature trends will be determined by the relative rates of change in the major
drivers of temperature in the stratosphere: CO2, ozone,
and, to a lesser extent, stratospheric water vapor. For alow GHG scenario, projected increases in ozone may
result in a weak or even a small positive global temperature trend in the upper stratosphere (Maycock,2016). For higher GHG scenarios, global cooling in
the upper stratosphere due to projected CO2 increases
dominates over the warming effect from increasingozone, and therefore temperatures are projected todecrease over the 21st century (Stolarski et al., 2010;
Douglass et al., 2012; Maycock, 2016). One possible
source of uncertainty in future temperature trends,
particularly in the lower stratosphere, is the large
spread in projected stratospheric water vapor concentrations (Smalley et al., 2017), though this effect has
not yet been quantified.
The latitudinal and seasonal patterns of future temperature trends in the lower stratosphere also dependon the GHG scenario. Figure 5-8 shows projected temperature trends at the altitude of the MSU4
channel from the CCMI REF-C2 experiment. The
projected warming in the Antarctic in austral springand summer is very prominent over the first half of
this century as the ozone hole reduces in depth andextent (see Chapter 4). This warming is about a factor of two smaller for the medium-to-high GHG scenario (RCP-6.0; Figure 5-8a) than for the low GHG
scenario (RCP-2.6; Figure 5-8c). Polar stratospherictemperature trends are also affected by changes in
5.19

Chapter 5 | Ozone and Climate

the deep branch of the stratospheric overturning circulation (see Section 4.3.4). In the Arctic, the CCMI
models simulate a mid-winter warming in the lower
stratosphere over the first half of the century. Future
Arctic lower-stratospheric temperature trends will be
determined by a balance between changes in high-latitude wave driving and associated changes in downwelling over the pole as well as radiative effects fromchanges in ozone and GHGs (Oberländer et al., 2013;
Rieder et al., 2014; Langematz et al., 2014; Bednarz
et al., 2016). These findings are generally consistent
with the 2014 Ozone Assessment. Over the second
half of the 21st century, there is projected warming in
the lower stratosphere across most of the tropics andsubtropics in the low GHG scenario (Figure 5-8d),
whereas the models project cooling in this region forthe medium-to-high GHG scenario (Figure 5-8b).
Thus, the sign of projected tropical lower-stratospheric temperature trends over the second half of the 21st
century is dependent on the GHG scenario.

(a) T Ttrend 2000–2049 [K decade-1] - RCP6.0
0.0

The BDC is not directly measured and thus has to be
derived indirectly from temperature observations,dynamical reanalysis fields, or tracer measurements.
While a strengthening of the BDC is simulated in
response to climate change, it has remained elusive
in the observations. The 2014 Ozone Assessment(b) T Ttrend 2050–2099 [K decade-1] - RCP6.0
50

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Changes in the strength of the stratospheric overturning circulation, or the Brewer–Dobson circulation(BDC), are key drivers of changes in stratospherictemperature (see Section 5.3.1), tracer concentrations (see Chapter 3, Section 3.4.2, and Chapter 4,
Section 4.3.4), and stratosphere–troposphere exchange (Section 5.3.3). This section is dedicated toassessing the main advances in understanding of the
BDC since the last Assessment, with special emphasison the long-term trends.

0.2

latitude[deg]
Latitude°75

5.3.2 Brewer–Dobson Circulation

N

D

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T trend [K decade-1]

Figure 5-8. CCMI multi-model mean zonal-mean MSU4 channel (~13–22 km) temperature trends (K per
decade) over the periods (a, c) 2000–2049 and (b, d) 2050–2099 under (a, b) the medium-to-high greenhousegas (GHG) scenario (RCP-6.0; 12 models) and (c, d) the low GHG scenario (RCP-2.6; 4 models). Regions where
less than two-thirds of the models agree on the sign of the trend are hatched. Based on model simulations
described in Morgenstern et al. (2017) and Maycock et al. (2018).

5.20

Ozone and Climate | Chapter 5

examined a few studies that provided evidence of anacceleration in lower-stratospheric tropical upwellingin recent decades (Fu et al., 2010; Young et al., 2012),
while no consistent trends in the BDC were found in
the upper stratosphere. Since then, additional studies
have inferred BDC trends from satellite and radiosonde temperature observations in the lower stratosphere (Ossó et al., 2015; Fu et al., 2015), obtaining an
estimated acceleration of annual mean upwelling in
the lower stratosphere of about 2% per decade, whichis in quantitative agreement with climate model trends(e.g., Butchart, 2014). The 2014 Assessment highlighted inconclusive results on BDC trends inferred fromreanalysis data; however, new studies have obtained
estimates of an acceleration in lower-stratospherictropical upwelling of 2–5% per decade in reanalyses(Fueglistaler et al., 2014; Abalos et al., 2015; Miyazaki
et al., 2016), consistent with climate model trends.
This advance is due to the combination of several
reanalysis datasets and estimates to extract common
signals among them. Nevertheless, these studies reveal a large spread in both the baseline magnitude andthe long-term trends of the BDC among different reanalysis datasets and different methods for estimating
the circulation. Moreover, in contrast with the broad
agreement on acceleration in the lower-stratospheric
BDC, the sign of the trends in the middle and upper
stratosphere remains uncertain. This is because reanalyses are affected by major discontinuities above~10 hPa, which hampers deriving trends at these levels (Simmons et al., 2014; Abalos et al., 2015). Notethat in general, reanalyses are deemed to be unreliable
for studying long-term stratospheric changes (seeSection 5.3.1.1). While this undermines confidencein estimated BDC trends from reanalyses, reanalyses
remain the only available observational-based sourcewith global coverage; therefore, these estimates currently cannot be verified against independent data.
Long-lived tracer measurements in the stratospherecan be used to derive the age of air (AoA), a measure of the net tracer transport circulation strengththat integrates effects of both the advection by theoverturning circulation and mixing (see Box 5-2).
Reconciling the observed and modeled AoA trends
has been a major issue since an analysis of balloon
measurements revealed a small aging of stratospheric
air over the last decades (Engel et al., 2009), which wasinconsistent with the negative AoA trends produced

by climate models (Waugh, 2009). While the observed trends in AoA reported in one study (Engel etal., 2009) were not highly statistically significant, they
have been recently supported with extended observations for 2015–2016 (Engel et al., 2017). In the 2014
Assessment, it was mentioned that spatiotemporal
sparseness of the measurements could be a key issue
for interpreting the disagreement between models
and data (Garcia et al., 2011). To address this issue,
long-term (>30 years) AoA trends have been obtainedby combining observations with models of varying
complexity (Ray et al., 2014; Hegglin et al., 2014).
These combined data–model-derived AoA estimatesshow negative trends in the lower stratosphere andpositive trends in the middle stratosphere (consistent
with Engel et al., 2017). One such example is illustrated in Figure 5-9, which shows the AoA trends
for the NH mid-latitudes as a function of altitude derived from tracer observations (Ray et al., 2014). AoA
trends derived from the ERA-Interim reanalysis show
a qualitatively similar structure (Diallo et al., 2012;
Ploeger et al., 2015), although this result is likely todepend on the reanalysis dataset. The decrease in AoA
in the NH mid-latitude lower stratosphere shown in
Figure 5-9 and in another study (Hegglin et al., 2014)
is consistent with the estimated increase in the overturning circulation described above; however, as outlined in Box 5-2, AoA is strongly affected by mixing,
and hence its local trends do not necessarily indicate
changes in overturning circulation. Importantly, these
new studies also demonstrate that the observed decrease in AoA in the lower stratosphere can be reconciled within uncertainties with the trends derived
from chemistry–climate models, while the disagreement between observations and models remains at
higher altitudes.
Global estimates of AoA from satellite tracer measurements are available only for about a decade after
2002. Over this recent period, the observations show
trends of opposite sign in the two hemispheres, with
AoA decreasing in the Southern Hemisphere and increasing in the Northern Hemisphere (Stiller et al.,2012; Haenel et al., 2015). This behavior is consistent
with AoA trends over the same period independently derived from HCl measurements (Mahieu et al.,2014) and from the ERA-Interim reanalysis (Ploeger
et al., 2015). The main difference between the decadal
and the long-term trends in the reanalysis is that the
5.21

Chapter 5 | Ozone and Climate

Box 5-2. What Is the Age of Stratospheric Air?
The Brewer–Dobson circulation (BDC) controls, to a large extent, the global distributions of tracers in thestratosphere, including ozone (see Chapters 3 and 4). Despite its relevance, there remain large uncertainties
in the mean magnitude and the long-term trends of the BDC, as this planetary-scale circulation cannot
be directly measured (Butchart, 2014). The mean age of stratospheric air (AoA) is an estimate of the timeof residence of an air parcel in the stratosphere since it entered through the tropical tropopause (Hall andPlumb, 1994; Waugh and Hall, 2002). Mean AoA can be inferred from observations of long-lived tracers
with near-linear tropospheric sources, such as SF6 or CO2, and thus constitutes a useful benchmark for the
representation of stratospheric transport in models (Waugh and Hall, 2002). The annual mean climatologyof AoA derived from the MIPAS satellite data is shown in Box 5-2 Figure 1a. There is broad agreement
among independent observations showing ages of 4–6 years in the lower stratosphere mid-latitudes (~40–60°N, ~20–25 km) and less agreement at high and low latitudes and at higher levels (Haenel et al., 2015).

LatitudeYears

Box 5-2 Figure 1. (a) Climatology of annual mean age of air 2002–2012 from MIPAS SF6 measurements and (b) schematic of the net stratospheric tracer transport circulation, including overturning
circulation and mixing components. (a) Note that MIPAS mean age is overestimated at high latitudes
due to an SF6 mesospheric sink (Stiller et al., 2012). (a) Adapted from Haenel et al. (2015). (b) Adaptedfrom Garny et al. (2014).

The AoA structure in Box 5-2 Figure 1a results from the combined effect of two components, as illustrated
in Box 5-2 Figure 1b: slow mean advection by the overturning circulation (including shallow and deep
branches; e.g., Birner and Boenisch, 2011) and rapid two-way quasi-isentropic irreversible mixing (see also
Plumb, 2002; Shepherd, 2007). Accordingly, the AoA can be separated into the residual circulation transit
timescale (RCTT) (Birner and Boenisch, 2011) and the time due to mixing processes (aging by mixing)(Garny et al., 2014). While the overturning circulation tends to steepen the meridional AoA gradients, mixing between the tropics and the extratropics causes recirculation of air parcels, increasing AoA throughout
most of the stratosphere (see example trajectory in panel b).

5.22

Ozone and Climate | Chapter 5

Box 5-2, continued.
Furthermore, due to irreversible mixing, an air parcel traveling in the stratosphere does not maintain its
identity, and the AoA at any location is fully described by a transit time distribution referred to as the age
spectrum, which reflects a diversity of individual pathways followed by the elements forming the air mass
(Hall and Plumb, 1994; Waugh and Hall, 2002). The mean age constitutes the first moment of the statistical
distribution. Typical stratospheric age spectra feature long tails of old transit times due to aging by mixing,
such that the mean age is usually longer than the modal age (peak of the age spectrum). Multiple peaks inthe age spectrum reflect seasonal and interannual variability in the circulation (Ploeger and Birner, 2016).
Understanding the separate contributions of the distinct transport mechanisms to the mean age and theage spectrum is key to constraining transport processes in models and their long-term trends, which are
currently subject to large uncertainties.
AoA reduction in the NH lower stratosphere disappears for the most recent period. This could be related to an interruption in the acceleration of tropical
upwelling in the lower stratosphere at the beginningof the 21st century (Aschmann et al., 2014), which isconsistent with the observed reduced cooling of thetropical lower stratosphere over the first decade of the
21st century as compared to the previous two decades(see Section 5.3.1.1) (Aquila et al., 2016; Polvani etal., 2017; Randel et al., 2016; Khaykin et al., 2017).
Different mechanisms have been proposed to explain the changes in tropical upwelling since around2000, including ocean multi-decadal variability(Aschmann et al., 2014), the change in trend of atmospheric ODS concentrations since the late 1990s (seeSection 5.3.2.2), and internal atmospheric variability(Garfinkel et al., 2017a). Moreover, the opposite signof mean AoA trends in each hemisphere over the period 2002–2012 can be understood as a southward shiftof the tropical upwelling region (Stiller et al., 2017);
while the cause of this shift remains unclear, when
its effects on AoA are removed, the remaining mean
AoA trends are consistent with model predictions in
the lower stratosphere (Stiller et al., 2017).
In addition to estimating long-term trends in the
BDC, there are substantial uncertainties in estimatesof the absolute strength of the overturning circulation
among reanalyses (e.g., Abalos et al. (2015) estimate a
40% uncertainty). Using the meridional age gradient

Mean Age Trends NH Mid-latitudes 1975–2012
35

Altitude (km)

30

25

20

15

−6

−4

−2

0

% Decade-1

6

4

6

Figure 5-9. Profiles of trends in the Northern Hemisphere (NH) mid-latitude mean
age of air (in percent per decade) from1975 to 2012. Green symbols are trends
derived from in situ tracer observations
combined with a simple mechanistic
model. Red symbols represent averagetrends from four ocean-coupled CCMVal2 models. Orange symbols represent
average trends from nine ocean-coupled CCMI models. Error bars represent
the standard deviation of the mean of
all model trends at each level and, forobservations, the 1σ uncertainty on thetrend estimate at each level. Update to
Ray et al. (2014).

5.23

Chapter 5 | Ozone and Climate

as a metric of the strength of the stratospheric overturning circulation, good agreement is found between
two different observational estimates at an altitude of
20 km (Neu and Plumb, 1999; Linz et al., 2016; Linz
et al., 2017). The inferred value for the strength of thecirculation is also shown to fall within the reanalysis
uncertainty range and to agree with estimates from a
climate model (Linz et al., 2017). In contrast, at higher
levels, there is a 100% uncertainty in the circulation
strength, with reanalyses and models showing significantly faster circulation than the observational estimate. This difference could be due to the fact that the
AoA derived from SF6 observations is overestimated,because this tracer has mesospheric sinks that lead tosmaller concentrations of the tracer at high latitudes
than if it were passively transported (e.g., Waugh andHall, 2002; Haenel et al., 2015; Kovács et al., 2017).
Nevertheless, the absolute strength of the overturning
circulation remains highly uncertain in the middle
and upper stratosphere (Linz et al., 2017).

5.3.2.2

Simulated Past and Future Changesof the BDC

The 2014 Ozone Assessment highlighted that chemistry–climate models robustly predict a long-term
acceleration of the BDC in response to anthropogenic
climate change (Hardiman et al., 2014; Palmeiro etal., 2014). This result stands for the new CCMI simulations (Morgenstern et al., 2018), while updated
observational estimates still feature a near-zero trend
or decelerating net transport circulation at mid-latitudes in the NH middle stratosphere (see Figure 5-9).
The main recent advances in the analysis of trends inmodels have been through taking into account thepotential contribution of large internal atmospheric variability on trends by assessing the role of thelength of the period considered and through showing
the importance of having several ensemble members
for each simulation type. It was found that, while
the BDC robustly accelerates over the last 55 years
in chemistry–climate model simulations, when the
period is limited to the last 25 years, some ensemble
members show a slowing of tropical upwelling and anincrease in NH mid-latitude mean AoA in the middle to upper stratosphere (Garfinkel et al., 2017a). It
has also been found that the minimum record length
needed to statistically distinguish a forced signal fromthe internal variability is about 30 years for a BDC
trend of 2% per decade (Hardiman et al., 2017). This
5.24

implies that shorter observational records do not necessarily reflect forced long-term trends. These results
could potentially reconcile the modeled and observed
AoA trends over shorter periods when most data havebeen collected, though discrepancies still remain withthe longest observational records that began morethan 40 years ago (Figure 5-9). On a wider perspective, one model study (Muthers et al., 2016) suggeststhat changes in mean AoA in the 20th and 21st centuries are unprecedented since the 1600s. However, thissingle-model result remains to be confirmed by otherstudies. Regarding future BDC trends, several CCMI
models show the entire BDC being lifted as the tropopause rises (Oberländer-Hayn et al., 2016). Such close
connection between the tropopause rise and the BDC
acceleration may have implications for stratosphere–
troposphere exchange (see Section 5.3.3).
A number of recent studies have highlighted theimportance of assessing changes in isentropic eddy
mixing in addition to the overturning circulation forinterpreting long-term trends in the net stratospheric
tracer transport and for comparing models to observations (Garny et al., 2014; Ray et al., 2014; Ploeger
et al., 2015; Abalos et al., 2016; Miyazaki et al., 2016;
Ray et al., 2016; Ploeger and Birner, 2016; Dietmüller
et al., 2017). These studies build on a few previous
studies addressing this issue (Ray et al., 2010; Li etal., 2012). They show that AoA trends derived from
ERA-Interim reanalysis can be largely attributed tolong-term trends in the isentropic mixing (Ploeger etal., 2015). Because isentropic mixing is a fundamental component of stratospheric tracer transport andits effect is integrated in AoA (see Box 5-2), it has tobe adequately represented in models. Although twoway mixing and the mean overturning circulation
are intrinsically coupled (both are driven by Rossby
wave breaking), their combined effects on AoA, anintegrated Lagrangian measure of transport, are notstraightforward (Garny et al., 2014; Ploeger et al.,2015; Ploeger and Birner, 2016).
Previous model studies have examined the impact ofODS-driven ozone depletion and recovery on pastand future BDC and AoA trends (e.g., Li et al., 2008;
Oman et al., 2009; McLandress et al., 2010; Oberländer
et al., 2013). Since the last Assessment, additional evidence from chemistry–climate model simulations
has shown that Antarctic ozone depletion is the main
driver of the observed BDC acceleration in the SH

Ozone and Climate | Chapter 5

summer over the past several decades (Keeble et al.,2014; Oberländer-Hayn et al., 2015; Garfinkel et al.,2017a; Polvani et al., 2018; Morgenstern et al., 2018;
Li et al., 2018). Figure 5-10a shows the dominant roleof ODSs in driving the SH polar downwelling acceleration until the year 2000. The changes in annual mean
downwelling seen in Figure 5-10a predominantly reflect changes in austral summer (Polvani et al., 2018).
Also shown in Figure 5-10a is a trend in polar downwelling in the 21st century of opposite sign, due tothe ozone recovery (Oberländer et al., 2013; Polvani etal., 2018). The ODS influence on the BDC is not limited to the Antarctic region, and a number of recent
modeling studies have quantified the global influence(Oberländer-Hayn et al., 2015; Polvani et al., 2017;
Garfinkel et al., 2017a; Polvani et al., 2018). Figure
5-10b shows that, in a chemistry–climate model, a significant fraction (about 50%) of the past accelerationof the annual mean upwelling in the tropical lower
stratosphere is attributed to ODSs. In the future, thedecrease of ODSs and ozone recovery are expected
to significantly reduce the GHG-driven BDC annual
mean acceleration trends (Figure 5-10b). Finally, theODS impact on the NH polar downwelling trends is
negligible (Figure 5-10c). These results from a single

chemistry–climate model remain to be tested in othermodels. Interestingly, recent modeling works haveshown that the ozone hole, despite driving a strong
acceleration of the downwelling over the boreal summer Antarctic lower stratosphere, leads to an increasein AoA in that region and season (Morgenstern et al.,2018; Li et al., 2018). This is attributed to the delayin the polar vortex breakup date, which implies that
relatively old air remains isolated over this region.
This result highlights the importance of consideringchanges in mixing for interpreting AoA trends.
The influence of ODS-induced ozone changes on the
net overturning circulation occurs through changes inwave forcing associated with thermally driven changes in the background zonal wind. The wave forcingof the BDC cannot be viewed as predefined or fixed,
as it may be affected by changes in radiation (Minget al., 2016). Furthermore, there are important compensating processes between waves of different spatial
scales, some of which cannot be directly represented
in models and so are parameterized (e.g., Cohen et al.,2014; Sigmond and Shepherd, 2014).

Residual circulation (mm s-1)

Annual Mean Residual Circulation at 70 hPa-0.2(a) Downwelling 60°S–90°S(b) Upwelling 30°S–30°N
0.3

−0.25

All forcings
Fixed ODSs
Fixed GHGs

0.28

−0.48
−0.5
−0.52

0.26

−0.3

−0.54

0.24

−0.35

(c) Downwelling 60°N–90°N
−0.46

−0.56

0.22

−0.58

−0.4

-0.6
1975

2000

2025

Year

2050

2075

1975

2000

2025

Year

2050

2075

1975

2000

2025

Year

2050

2075

Figure 5-10. Time series of annual mean residual (overturning) circulation at 70 hPa over the (a) SH polar cap(60°S–90°S), (b) tropics (30°S–30°N), and (c) NH polar cap (60°N–90°N) for different runs from the Community
Earth System Model (CESM) with Whole Atmosphere Community Climate Model (WACCM) as its atmosphericcomponent. The thin colored lines represent all forcing runs (blue), runs with ODSs fixed to 1960 levels (red),and runs with GHGs fixed to 1960 levels (green). Three members are shown for each simulation type. Thicklines show the linear fit for each ensemble mean for the periods 1965–2000 and 2000–2080. Adapted from
Polvani et al. (2018).

5.25

Chapter 5 | Ozone and Climate

5.3.3 Stratosphere–Troposphere Exchange
Since ~90% of the total ozone column resides in thestratosphere, changes in stratosphere–troposphereexchange (STE) are critical to the evolution of thetropospheric ozone burden and thus air quality (e.g.,
Collins et al., 2003; Zeng and Pyle, 2005; Hegglin andShepherd, 2009). Some of the factors affecting future changes in tropospheric ozone are addressed in
Section 3.4.4 of Chapter 3 and in the recent IGAC
Tropospheric Ozone Assessment Report (TOAR;
see Young et al., 2018), which reports on the current
understanding of tropospheric ozone in detail. Here
we briefly assess the main advances since the 2014
Assessment regarding STE.
The 2014 Assessment discussed improvements tochemistry–climate models through the merging of
tropospheric and stratospheric chemistry schemes,
and it discussed the contribution of STE to the tropospheric ozone budget. The merged schemes resulted in improvements in modeled tropospheric ozone
concentrations due to the inclusion of stratosphericozone changes by ODSs and changes in the strengthof the BDC (see Section 5.3.2). It was assessed thatmodels consistently showed reduced ozone STE in
the present compared to preindustrial times due tostratospheric ozone depletion, although the magnitude of the estimated change is model-dependent. On
the other hand, model studies showed that concurrent
ODS decreases and GHG increases in the future would
lead to increased STE of ozone, with the magnitudeof the change depending on the RCP scenario (seeSection 4.5.3 in WMO (2014) and Section 11.3.5.1.2
in IPCC (2013)). Since the last Assessment, our qualitative understanding of the expected future changes
in STE has not been modified. Stratospheric ozone influx into the troposphere is still expected to increasein the future, with the magnitude of the increase stillmodel- and scenario-dependent, as discussed below.
STE typically occurs due to isentropic mixing, often
during the formation of tropopause folds and cutoff
lows associated with mid-latitude cyclonic disturbances, for example in the Atlantic and Pacific storm
tracks (Stohl et al., 2003). New studies on STE havebeen conducted, quantifying its spatial and seasonalvariability (e.g., Yang et al., 2016), investigating themechanisms of ozone transport from the lowermost
stratosphere to the surface (Škerlak et al., 2014; Lin
5.26

et al., 2015; Albers et al., 2018), and giving quantitative observational constraints of the magnitude of
tropospheric ozone changes due to STE (e.g., Neu
et al., 2014). Stratosphere-to-troposphere ozone
transport peaks in late spring and early summer in
the Northern Hemisphere and shows little seasonality in the Southern Hemisphere (Yang et al., 2016).
However, because it migrates seasonally in altitude
following the subtropical jets, STE is strongest in winter in the Northern Hemisphere at the lower isentropes. This winter maximum is consistent with the peakof stratospheric ozone influence observed near thesurface (Škerlak et al., 2014).
Nonetheless, difficulties remain in estimating themagnitude of STE. Large uncertainties have been reported in the magnitude, geographic distributions,seasonality, and long-term changes of STE, dependingon the definition of the tropopause and the reanalysis
dataset used (Boothe and Homeyer, 2017). In addition, it has been shown that the use of monthly mean
residual circulation vertical velocities yields large errors in the estimated magnitude of ozone STE, resulting in different magnitude estimates in comparison toother methodologies (Hsu and Prather, 2014).
Ozone STE is controlled by the amount of ozone available in the lowermost stratosphere and also by the frequency and location of stratospheric intrusion events.
Because these are governed by different mechanisms
and their relative importance remains unclear, thevariability of ozone STE, and in particular the relationship with El Niño–Southern Oscillation (ENSO),
is still under discussion (Neu et al., 2014; Hess et al.,2015; Lin et al., 2015; Olsen et al., 2016; Albers et al.,2018). New research since the last Assessment provides more evidence that both the acceleration of the
BDC (see Section 5.3.2.2) and stratospheric ozone recovery (see Chapter 3) will tend to increase the future
global tropospheric ozone burden through enhanced
STE. Two new studies find a substantial correlation
between the strength of the BDC, STE, and tropospheric ozone during the observed period using satellite observations (Neu et al., 2014) and chemistry–
climate model simulations constrained by observed
SSTs and validated against observed ozone variability(Hess et al., 2015). The covariability between STE andtropospheric ozone from observations was used todeduce that the projected strengthening of the BDC
alone (that is, without accounting for ozone recovery)Ozone and Climate | Chapter 5

could lead to an increase in zonal mean troposphericozone of 2% by the end of the 21st century (Neu et al.,2014). A larger increase in mid-tropospheric ozone of
6% by 2100 due to BDC strengthening was obtainedin one model study (Hess et al., 2015). The threefold
difference between the two estimates highlights considerable quantitative uncertainty in the future evolution of ozone STE.
Several studies have estimated the role of STE for
future tropospheric ozone using chemistry–climate
model simulations that include the effects of climatechange, in general agreeing that STE increases contribute importantly to future tropospheric ozone
abundances. These studies examine the influence ofthe stratosphere on the tropospheric ozone throughSTE by including a stratospheric ozone tracer (nochemical ozone production in the troposphere) in
the simulations (Banerjee et al., 2016; Meul et al.,2018). Figure 5-11 shows the changes in the stratospheric ozone tracer due to climate change and ODS
reduction, as a diagnostic of the impact of changes in
STE on tropospheric ozone between 2000 and 2100
(Banerjee et al., 2016). A strengthened BDC underclimate change following the RCP-8.5 scenario has its
strongest effect on tropospheric ozone in the tropics
and subtropics (Figure 5-11a), while stratosphericozone recovery from declining long-lived ODSs has
a larger role in the mid-latitudes and extratropics(Figure 5-11b). These results are consistent with recent estimates that the stratosphere-to-tropospheretransport of ozone will increase more than 50% by
2100 under an RCP-8.5 scenario (Meul et al., 2018).
Such increases in stratospheric ozone influx into thetroposphere are consistent with those inferred in a
multi-model study (Young et al., 2013) and model
sensitivity studies (Kawase et al., 2011; Abalos et al.,2017). These results highlight STE as an importantfactor for determining future changes in troposphericozone, although its quantitative role remains uncertain due to the limited number of studies and the variations across current model results.
Future changes in tropospheric ozone will be determined by a complex interplay between chemical
and transport processes. While all studies agree thatSTE changes will tend to increase future tropospheric ozone, the relative importance of STE versus tropospheric chemistry for future tropospheric ozone
trends remains an open question. A strong sensitivity

to GHG scenario, as reported in the last Assessment,is supported by new model results, with studies
finding a net decrease in the global burden of tropospheric ozone in 2100 compared to that in 2000 in
the RCP-6.0 scenario (Sekiya and Sudo, 2014; Revell
et al., 2015) and an increase in the RCP8.5 scenario (Banerjee et al., 2016; Meul et al., 2018). A major
source of uncertainty in projections of troposphericozone is the future evolution of methane concentrations, which are much larger in the RCP8.5 scenario
than in the others. However, scenario dependence is
not the only source of uncertainty in future tropospheric ozone. A study using new simulations frommultiple ­chemistry–climate models finds considerable disagreement among models in troposphericozone even when the same scenario is considered,with much of the model spread being likely due to theuncertainty in stratospheric transport and STE trends(Morgenstern et al., 2018). A more detailed discussion of future tropospheric ozone changes is given in
Chapter 3, Section 3.4.4.

5.3.4 Stratospheric Winds
5.3.4.1	Polar Vortices
The characteristics of stratospheric wintertime polar
vortices, such as their strength and duration, have alarge impact on polar stratospheric ozone variability
and can also affect tropospheric climate. Observed
and projected variability and long-term changes inpolar vortex characteristics are discussed in Chapter
4. Here, we briefly review polar vortex changes with afocus on their implications for the troposphere.
Previous assessments reported an observed strengthening of the Antarctic polar vortex during austral
spring and a delay in the vortex breakup date resulting from diabatic cooling associated with ozone
loss. Figure 4-3 in Chapter 4 shows that the trend
toward later breakup dates did not continue during
recent years, which were instead characterized by alarge variability in breakup dates ranging between
mid-November and mid-December. This year-toyear variability is linked to variability in planetary
wave activity, which is mostly driven by internal
climate dynamics. No recent studies have analyzed
projected changes in the Antarctic vortex and, in linewith previous Assessments, it is expected that ozone
recovery will lead to a weakening of the polar vortex
5.27

ppbv

Altitude (km)

Chapter 5 | Ozone and Climate

0
90°S

60°S

30°S

30°N

60°N

90°N

30°N

60°N

90°N

ppbv

Altitude (km)Latitude

0
90°S

60°S

30°S

Figure 5-11. Modeled changes inozone mixing ratio [ppbv] between
2000 and 2100 due to (a) changes
in physical climate (GHGs, sea surface temperatures, and sea ice)following the RCP-8.5 scenarioand (b) changes in ODS concentrations. Shown are changes for a
stratospheric ozone tracer that isconstrained to stratospheric values above the tropopause but thatevolves freely in the troposphereand undergoes only troposphericloss processes (i.e., there is no
tropospheric ozone production).
The inclusion of a separate stratospheric ozone tracer in the model
simulations allows separation of
the STE contribution from thechanges due to tropospheric processes. Adapted from Banerjee etal. (2016).

Latitudeand a return to earlier breakup dates. A trend toward
later breakup dates of the Antarctic vortex may reappear in the late 21st century as a result of tropical upper-tropospheric warming driven by increased GHG
concentrations and associated strengthening of themeridional temperature gradient near the tropopause(Wilcox and Charlton-Perez, 2013).
The large interannual variability in the Arctic polar
vortex effectively masks any trends driven by changes in external forcings including ODSs and ozone,
though there has been a shift toward weaker vortices in mid-winter since 1990 (consistent with thetemperature changes discussed in Section 5.3.1)(Garfinkel et al., 2017b). It has been suggested in a
number of studies that a loss of Arctic sea ice can lead
5.28

to a weakening of the stratospheric vortex (e.g., Kim
et al., 2014), and one study argued that the observed
ice loss has shifted the vortex toward Eurasia (Zhang
et al., 2016). However, another study demonstrated
that the observed shift is also consistent with unforced decadal variability (Seviour, 2017). Two studies (Garfinkel et al., 2017b; Kretschmer et al., 2018)presented evidence that the weakening of the Arctic
polar vortex since 1990 contributed to the hiatus in
GHG-induced near-surface warming over Eurasia inboreal winter; however, the vortex weakening itself
was not attributed to external forcing, implying that it
is likely a result of internal variability.
Several new studies have analyzed future dynamicalchanges in the Arctic vortex and their implications

Ozone and Climate | Chapter 5

for surface climate. Coupled climate models disagree about the sign of the projected vortex changes(Simpson et al., 2018), although weakening of thepolar night jet has been reported to be a preferred
response across the CMIP5 multi-model ensemble(Manzini et al., 2014) and single-model large ensemble (Peings et al., 2017) by the end of the 21st century
under a high GHG scenario. Nevertheless, episodes ofa cold and strong polar vortex are projected to occur
throughout the 21st century (Bednarz et al., 2016).
While simulated future vortex changes are small incomparison to the observed large interannual variability, the intermodel spread in vortex changes is asignificant component of uncertainty in future tropospheric climate change (Manzini et al., 2014; Zappa
and Shepherd, 2017; Simpson et al., 2018).
The mechanisms of the projected changes in the Arctic
polar vortex remain unclear. While projected Arctic
amplification and sea ice loss can contribute to vortex weakening in coupled model simulations, as discussed above (Kim et al., 2014; Manzini et al., 2018),
the weakening is also found in atmosphere-onlymodel simulations omitting sea ice loss as a response
to global SST warming (Karpechko and Manzini,2017). The lack of understanding of the mechanisms,
together with large intermodel spread in projections,indicates that our confidence in projected Arctic vortex changes is low.

5.3.4.2 Quasi-Biennial Oscillation
	Disruption and Implications
The influence of the Quasi-Biennial Oscillation(QBO) on stratospheric ozone is relatively well understood (Chapter 3, Section 3.2.1.2). However, thedisruption of the QBO that took place in 2016 (seeChapter 3 for a discussion of its effects on ozone)
raised questions about how well we understand the
QBO’s generating mechanisms, the response of the
QBO to climate change, and how the QBO will affectfuture ozone evolution.
During early 2016, a downward propagation of theeastward QBO phase was unexpectedly interrupted
by the appearance of a westward jet at 40 hPa (Osprey
et al., 2016; Newman et al., 2016). Several papers haveconcluded that the immediate cause of the interruption was a flux of easterly momentum associated witha pulse of planetary waves propagating from the NH
extratropics (Osprey et al., 2016; Coy et al., 2017;

Barton and McCormack, 2017; Watanabe et al., 2017).
There is evidence that the strong 2015–2016 El Niño
was implicated in forcing the wave pulse (Barton and
McCormack, 2017; Hirota et al., 2018), and one study
also suggests a role for the very low Arctic sea ice concentrations in that year (Hirota et al., 2018). The effectof the QBO interruption on ozone was consistent with
our existing understanding of QBO–ozone linkages(see Chapter 3, Section 3.2.1.2). The interruption
was not predicted by operational seasonal prediction
models (Osprey et al., 2016), but it was reproduced
retrospectively by an atmospheric model driven byobserved SSTs (Watanabe et al., 2017). Although suchan event is unprecedented in the more than 60 years
of QBO observations (Newman et al., 2016), analogous events are found in model simulations (Osprey
et al., 2016), and the effect of the anomalous wave flux
on the QBO is consistent with our understanding of
QBO generating mechanisms. Since the disruption in2016, the QBO has recovered to its expected eastward
phase. Analysis of simulations of future QBO, corroborated by observational evidence, suggests that QBO
amplitude in the lower stratosphere will decrease(Kawatani and Hamilton, 2013; Schirber et al., 2015;
Naoe et al., 2017) as a result of a projected increase in
the mean tropical upwelling (see Section 5.3.2.2) andthat the amplitude of QBO-induced ozone variability
will consistently decrease in the lower stratosphere; inthe upper stratosphere, it will increase due to ozone
recovery (Naoe et al., 2017). However, this result isbased on only a few available studies. Furthermore,
our current understanding of changes in wave forcing contributing to the QBO is incomplete, which
prevents firm conclusion about future QBO changes
or whether QBO interruptions can occur more frequently in future climate.

5.4 EFFECTS OF CHANGES INSTRATOSPHERIC OZONE ON THE
TROPOSPHERE AND SURFACE
The influence of stratospheric ozone change on SH
tropospheric and surface climate has been analyzed
and investigated in an increasingly mature body ofresearch. A key result is that ozone depletion is assessed to be the dominant driver of austral summer(December–January–February; DJF) atmosphericcirculation changes ranging from subpolar to tropical latitudes over the period in which stratospheric
5.29

Chapter 5 | Ozone and Climateozone was rapidly decreasing. We focus here on what
has been learned since the 2014 Ozone Assessment(Arblaster and Gillett et al., 2014). This includes improved quantification of the forced response to ozone
in the context of natural internal variability and improved understanding of the role of recent changes
in SSTs in driving observed SH circulation changes.
We also highlight a growing body of evidence thatsuggests that the Southern Ocean response to ozone

depletion is timescale-dependent. The effects of ozone
depletion on the climate of the Southern Hemisphere,
which span from the stratosphere to the oceans, aresummarized in the schematic shown in Figure 5-12.
We begin by assessing the effects of stratosphericozone changes on the tropospheric circulation, followed by an assessment of the resultant impacts on
surface climate, the ocean, and sea ice.

Antarctic Ozone Depletion Impacts on
Summer Southern Hemisphere Climate
Stratosphere
Cooling due tothe ozone hole

Accelerated stratos

phericovert
urDelayedbreakupof polarvortexni n g

circu
latio
n

Troposphere
Southward
expansion of
the Hadley cell

Southward
shift ofwesterly
winds

Increased
subtropicalrainfallSouthward shift of
mid-latitude rain
Antarctica

Changes in ocean circulationand temperature

Polar Regions

Middle Latitudes

Subtropics

Figure 5-12. Schematic illustration of Southern Hemisphere climate impacts in austral summer associatedwith Antarctic ozone depletion. Ozone depletion has cooled the Antarctic stratosphere, leading to a delayed
breakup of the stratospheric polar vortex and an accelerated Brewer–Dobson circulation. Impacts extended
into the troposphere: A region of strong westerly winds and associated rainfall shifted southward, affecting
the ocean circulation. The subtropical edge of the tropical circulation also expanded poleward, leading to
reduced precipitation in mid-latitudes and enhanced precipitation in the subtropics. Update to Figure 4-22
in Arblaster and Gillett et al. (2014).

5.30

Ozone and Climate | Chapter 5

5.4.1 Tropospheric Circulation Effects
As described in Section 5.3.1, the primary effect ofAntarctic stratospheric ozone depletion is to produce
a strong cooling in the lower stratosphere over the
Antarctic in austral spring. Over the period in whichozone depletion was increasing from 1960 to around2000, large changes were observed in the SH mid-­latitude and tropical circulations in austral summer:
The SH tropospheric mid-latitude maximum in zonal
winds (that is, the jet) shifted poleward; the Southern
Annular Mode (SAM) index, which corresponds toopposite-signed changes in sea level pressure overhigh latitudes and mid-latitudes, shifted more into its
positive phase (i.e., decreased sea level pressure overhigh latitudes); and the subtropical edge of the Hadley
Cell shifted poleward. We first review the observational evidence for these changes and then discussmodel simulations that causally link these changes toozone depletion. An assessment of the current understanding of the mechanisms through which this shift
occurs appears in Section 5.4.2.

5.4.1.1	The Southern Hemisphere: Observations
The SH general circulation includes a band of strong
westerly winds, which are associated with the storm
track (that is, synoptic-scale eddies and rainfall inthe mid-latitudes). The latitude of the maximum
zonal mean westerly winds in the lower troposphereis referred to as the mid-latitude near-surface jet(or for brevity, jet) and is climatologically centeredaround 52°S. Global monitoring of the location of
the near-surface jet is available only since 1987 fromthe launch of the Special Sensor Microwave Imager(Goodberlet et al., 1989). Since then, a series of satellite microwave radiometers and scatterometers
have continually observed near-surface wind speed.
Measurements from the various satellite missions
can then be combined into a gridded dataset, eitherwith a reanalysis product or in a stand-alone productsuch as the Cross-Calibrated Multi-Platform (CCMP)
ocean surface wind vector analyses (Atlas et al., 2011).
Before 1987, winds were observed by available radiosondes and (after 1979) estimated from satellite
measurements. Modern reanalysis products such as
ERA-Interim and MERRA more accurately capture
variability and trends in near-surface winds, windstress, and the SAM after 1979 than earlier reanalyses(Swart et al., 2015) when compared to station data (forthe SAM) and satellite data (for near-surface winds).
The recent evolution of the latitude of the near-surface
jet in CCMP is shown in Figure 5-13. Trends in the
jet are strongest in DJF and are statistically significant
at the 95% confidence level in that season (Swart et al.,2015), consistent with the findings of previous assessments. Since 2000, the jet in DJF has shifted equatorward, though trends are not statistically significant.
Trends are weaker and not statistically significant inother seasons, with the exception of a significantly
stronger jet in May (Ivy et al., 2017a).
The SAM is the leading mode of variability in the SH
extratropical circulation and, as mentioned above,
corresponds to opposite-signed changes in sea levelpressure between subpolar latitudes and mid-latitudes. The SAM index generally tracks changes in thecharacteristics of the mid-latitude jet (as evidenced
by the large correlation on interannual timescales in
Figure 5-13), with the positive phase corresponding
to a poleward jet shift, though the variations in the
SAM can also be associated with variations in thestrength of the mid-latitude jet (Monahan and Fyfe
2006; Swart et al., 2015; Solomon and Polvani, 2016).
The SAM index can be calculated from station pressure observations, which are available for a longer period than Southern Ocean surface wind observations
as they do not rely on satellite retrievals. Hence theSAM has historically been used to quantify changes in the large-scale mid-latitude circulation. After
1979, there is generally good agreement between
the SAM index calculated from station observations
and that calculated from reanalyses, whereas prior to
1979, some reanalyses are known to have deficiencies(Marshall, 2003) and tend to simulate trends that are
too strong (Swart et al., 2015).
Figure 5-13 shows the historical evolution of the SAM
index from station observations of sea level pressure(based on an update of Marshall, 2003). The largest
seasonal trends over the period in which ozone depletion was increasing (through 2000) are found in DJF
and MAM (March–April–May), and these changes
dominate the response in the annual mean. Since
2000, the SAM has stayed mostly in its positive phase(with respect to the 1971–2000 period). Evidence
from paleoclimate reconstructions of the SAM index
derived from networks of surface temperature proxies
and from multiple studies suggests that the current
5.31

Chapter 5 | Ozone and Climate

DJF

− 54S
− 52S

0

− 50S

−1
−2
1960

1980
2000
Year
2

SAM trend pre-2000:
0.3 ±0.23 std/dec

1980

2000

2 JJA

− 56S

1

− 54S

0

− 52S

−1

− 50S

−2

− 48S

1960

SAM index (std dev)

1960

1980
2000
Year

1

MAM

− 54S
− 52S

0
−1
−2
1960

− 50S
SAM trend pre-2000:
0.3±0.24 std/dec

1980

2000

2 SON

− 56S

1

− 54S

0

− 52S

−1

− 50S

−2

− 48S

1960

Jet latitude (°)

SAM trend pre-2000:− 50S
0.33 ± 0.23 std/dec−2

Jet latitude (°)−1

Jet latitude (°)

− 52S

0

SAM index (std dev)

1

1

Jet latitude (°)

2

− 54S

2 ANN

Jet latitude (°)

SAM index (std dev)

SAM index (std dev)

SAM index (std dev)

Observed Changes in the Southern Hemisphere Circulation

1980
2000
Year

Figure 5-13. SAM index (black) and mid-latitude jet positions (blue) time series from 1958–2017 for the four
seasons and annual mean. The SAM index is normalized by its standard deviation and is defined as in Marshall (2003). The jet position is based on the maximum of CCMP satellite-based surface wind speed (Atlas etal., 2011) (available for download at http://www.remss.com/measurements/ccmp), which starts in 1987. A
linear trend line of the SAM changes before 2000 is shown when statistically significant, and the slope of thebest fit line and its corresponding 95% uncertainty bounds are shown.

period of prolonged positive summer SAM conditions is unprecedented over at least the past 600 years(Villalba et al., 2012; Abram et al., 2014; Dätwyler etal., 2017). Reconstructions for the summer season(Dätwyler et al., 2017) are very robust across multiple
methods and datasets, but some discrepancies exist in
the magnitude of reconstructed low frequency SAM
changes during the preindustrial period for the annually averaged SAM.
On interannual timescales, the position/strength of the
mid-latitude jet and the Hadley Cell edge are correlated
5.32(Kang and Polvani, 2011; Ceppi and Hartmann, 2013;
Staten and Reichler, 2014), raising the question ofwhether the subtropical Hadley Cell edge would also
have shifted poleward. Recent studies have confirmedthat the subtropical edge of the Hadley Cell has expanded poleward, confirming the results of the previous Assessment (Garfinkel et al., 2015a; Lucas andNguyen, 2015; Davis and Birner, 2017; Solomon et al.,2017; Kim et al., 2017). This poleward shift is most pronounced in austral summer in the Atlantic and Indian
Ocean sectors (Choi et al., 2014; Kim et al., 2017), thesame region in which the upper-­tropospheric polar

Ozone and Climate | Chapter 5

jet has shifted poleward most sharply (Manney and
Hegglin, 2018). Quantifying the rate of the observed
Hadley Cell expansion has been challenging, however.
Different studies have used a wide variety of metrics
to track Hadley Cell width, and two recent studies(Davis and Birner, 2017; Solomon et al., 2017) suggest that metrics based on upper-tropospheric quantities are only weakly correlated with metrics basedon ­mid-tropospheric and surface processes, thoughanother study (Mantsis et al., 2017) suggests that in
CMIP5 models, a metric based on outgoing longwave
radiation is well correlated with mid-tropospheric
metrics. Furthermore, different reanalysis products
do not agree as to the rate of expansion even when acommon definition of a single metric is applied. For
example, one study (Garfinkel et al., 2015a) comparedfive different reanalysis products (including MERRA
output from two different stages in the assimilation
cycle) and found that the trends in different reanalysis products (or even from two different stages in theassimilation cycle of the same reanalysis system) canbe significantly different at the 90% confidence levelin the Southern Hemisphere over the period 1980 to
1999, with rates of expansion ranging from 1 degree
per decade to 0.3 degree per decade (Figure 5-14).
Differences are even larger over the period from 1980
to 2009. Thus, while the Hadley Cell expansion isrobust, its magnitude has large uncertainty, which ispartly related to disagreement among applied metrics
for the Hadley Cell edge. The development of a robust
observational metric (or a set of metrics) of Hadley
Cell width is still an area of active research.

5.4.1.2	The Southern Hemisphere: Model
Simulations of the Past
We now assess modeling studies that have attempted to pin down the cause of the observed changes in
the SH circulation, and we begin with the period inwhich ozone depletion was increasing, between the
1960s and 2000. The cleanest way to establish theimportance of ozone for past changes in the SH circulation is to perform model simulations of the historical period both with and without ozone depletion.
The 2014 Assessment described several such studies
and concluded that ozone depletion is very likely thedominant driver of the changes in the SAM in summer. Since the previous Assessment, one modelingstudy has supported the conclusion that ozone depletion has led to a change in the summer SAM bycomparing integrations with and without ozone depletion (Keeble et al., 2014). An additional study has
also concluded that summertime Hadley Cell trends
are strong in CMIP5 models only when ozone depletion is included (Tao et al., 2016) (Figure 5-15). A
third study compared the summertime tropospheric
response to ozone depletion from 1960 to 2000 in asuite of climate model simulations of varying configurations (for example, prescribed SSTs versus theinclusion of a coupled ocean, as well as prescribedozone concentrations versus the inclusion of interactive chemistry) and found a consistent wideningof the Hadley Cell and poleward shift of the jet in
austral summer (Seviour et al., 2017a). Figure 5-15
summarizes the trends in both the SAM and the subtropical Hadley Cell edge as simulated in the CMIP5
multi-model mean. Both the positive SAM trend andthe poleward expansion of the Hadley Cell maximize
in austral summer during the period from the early
1970s to around 2005, when the models are forced
with all external climate drivers, including anthropogenic (ozone depletion, increasing GHG concentrations, and aerosols) and natural (solar cycle andvolcanoes) factors. The separate contribution of ozone
depletion and GHGs can be seen in both variables,with ozone playing a dominant role in austral summer and GHGs playing a major role during the otherseasons. Overall, the majority of studies that havecompared simulations forced with ozone depletion tosimulations forced with no ozone depletion have concluded that ozone is the dominant forcing of changes
in the SH circulation over the period in which ozone
depletion was increasing.
Two studies that compared simulations with andwithout ozone disagreed with this consensus and concluded that ozone depletion was not the dominantcause of recent changes in the Southern Hemispherein austral summer (Staten et al., 2012; Quan et al.,2014). However, there are methodological issues withboth studies (Waugh et al., 2015): They both usedprescribed ozone fields that underrepresent the magnitude of observed Antarctic ozone depletion, thus
leading to a weakened response to ozone. Specifically,
one of the studies (Quan et al., 2014) used ozone forcing (Lamarque et al., 2012) which underestimates observed ozone depletion by a factor of two (Figure 2f ofEyring et al., 2013). The other study (Staten et al., 2012)
implicitly assumes that there was negligible ozone
5.33

Chapter 5 | Ozone and Climate

SH Hadley Cell Expansion (deg decade-1), 1980−1999

Poleward

(a)

DJF

1.8
1.35
0.9

−1.8

(b)

Annual Average

1.35
0.9
0.45

−1.8

SST−only

SST−only EM

All forcings

all−forcing EM

rean.

NCEP1

−1.35

NCEP2

−0.9

ERA−I

−0.45

MERRA−ASM

0
MERRA−ANA

Poleward

1.8

Equatorward

SST−only

SST−only EM

All forcings

all−forcing EM

rean.

NCEP1

−1.35

NCEP2

−0.9

ERA−I

−0.45

MERRA−ASM

0
MERRA−ANA

Equatorward

0.45

Figure 5-14. Poleward expansion of the SH Hadley Cell as determined by the zero crossing of the 500
hPa stream function in five reanalysis products and in each member of an ensemble of Goddard Earth
Observing System Chemistry-Climate Model (GEOSCCM) simulations and in the ensemble mean (EM)in (a) DJF and (b) the annual average from 1980 to 1999. Integrations forced with time-­varying ODS
and GHG concentrations in addition to observed SSTs are in blue, while integrations with fixed ODS
and GHG concentrations are in orange. Vertical lines or bars represent the 95% confidence interval on
the trend as deduced by a Student’s t test, and the center horizontal line indicates the trend. The uncertainty for the ensemble-mean trends are indicated by a rectangle, while that of individual ensemble members/reanalysis are indicated by a vertical line. The ensemble members for each ensemble areordered by their expansion trend before they are plotted for clarity. Adapted from Garfinkel et al. (2015).

depletion between 1870 and 1979, as they use 1979
ozone values for their “preindustrial/1870” simulations, yet significant ozone loss occurred before this(Eyring et al., 2013) (see Chapter 4); hence the difference between their “preindustrial” simulation and2000 simulation has too weak an ozone change. The
5.34

net effect is that SST- and GHG-induced changes are
considered over the period 1870 to 2000 as comparedto ozone induced-changes from 1979 to 2000, which
necessarily underestimates the relative impact ofozone on surface climate compared to other drivers.

Ozone and Climate | Chapter 5(a) Southern Annular Mode Trend

SAM Trend (hPa decade-1)

2.0
1.5
0.1
0.5
0.0
−0.5

Latitude trend (deg decade-1)

DJF

0.4

MAM

SeasonJJASON(b) Southern Hemisphere Hadley Cell Edge Poleward Trend

0.3
0.2
0.1
0.0
−0.1

Natural
Aerosols

−0.2
−0.3

DJF

MAM

GHG
Ozone

All

JJA

SON

Season

The weight of the evidence from studies that havecompared the impact of ozone depletion to otherforcings in a methodologically appropriate manner
clearly points to stratospheric ozone depletion as thedominant driver of the changes in the summer SAM
over the period in which an ozone hole was formed(prior to 2000).
The trends in tropospheric zonal wind in CCMI models from 1960 to 2000, when forced with natural andanthropogenic forcings including ozone depletion,
are similar to those in reanalysis data (­ Figure ­5-16a–c;
Son et al., 2018). Trends are somewhat weaker in
CMIP5 models (Figure 5-16d; Rea et al., 2018); however, the weaker trends are most pronounced in those

Figure 5-15. Seasonal-mean
trends in the (a) SAM index forthe period 1971–2006 and (b)the latitude of the poleward
edge of the SH Hadley circulation at 500 hPa for the period
1970–2005 (1970–2000 forozone only), derived from CMIP5
historical simulations. Positive
trends indicate poleward expansion and negative values indicate equatorward retreat. Error
bars are 90% confidence level.
Units are (a) hPa per decadeand (b) degree per decade. The
colors denote model experiments including the following
forcings: all anthropogenic andnatural (red); greenhouse gases
only (orange); troposphericand stratospheric ozone only(light blue); natural (solar andvolcanic) only (dark blue); andanthropogenic aerosols only
(gold). Adapted from Gillett etal. (2013) and Tao et al. (2016).

CMIP5 models that did not use interactive chemistry(Figure 5-16f; as noted by Eyring et al., 2013); trendsin CMIP5 models that used interactive chemistry arequantitatively similar to those simulated by the CCMI
models and observations (Figure 5-16e). While theobserved zonal wind trend is generally consistent
with the forced response to ozone depletion, the windfield by itself does not provide a unique fingerprint ofozone depletion due to the large internal variability inthe climate system (Schneider et al., 2015).
Although there is uncertainty in the magnitude of the
Hadley Cell widening (see Section 5.4.1.3), studies
agree that the widening has continued (e.g., Mantsis
et al., 2017). At the same time, the SAM has mostly
5.35

Chapter 5 | Ozone and Climate

stayed in its positive phase, though the jet latitude has
shifted somewhat equatorward from its 2000 position in austral summer (Figure 5-13). As discussed
in Chapter 4, SH polar ozone depletion peakedaround 2000 and has slowly begun its recovery, andhence ozone cannot be the sole driver of changes in
the Southern Hemisphere since 2000. Rather, several
studies have concluded that recent changes in tropicaland subtropical SSTs (due to both internal variability and GHG-induced warming), and in particular
decadal variability associated with the Pacific Decadal
Oscillation (also known as the Interdecadal PacificOscillation), drove recent changes in SH circulation(Allen et al., 2014; Waugh et al., 2015; Garfinkel etal., 2015b; Franzke et al., 2015; Nguyen et al., 2015;
Clem et al., 2016; Schneider et al., 2015; Allen and

DJF [U] Trend
Pressure (hPa)50(a) JRA-55

200

200

500

500

1000
80°S

60°S

40°S

20°S

EQ

(c) CCMI-C2(b) CCMI-C1

2.0

1000
80°S
50

1.6
60°S

40°S

20°S

EQ
1.2

(d) CMIP5

0.8
0.4
200

200

0.0
500

500
1000
80°S
50

60°S

40°S

20°S

EQ

(e) CMIP-CHEM

−0.4

1000
80°S
50

60°S

40°S

20°S

EQ

(f ) CMIP5-NoCHEM(m s-1 dec -1)

Pressure (hPa)50

Pressure (hPa)

50

−0.8
−1.2
−1.6

200

200

500

500

1000
80°S

60°S

40°S

Latitude

20°S

EQ

1000
80°S

−2.0

60°S

40°S

20°S

EQ

LatitudeFigure 5-16. Long-term mean (thin black contour) and linear trend (color) of zonal-mean DJF zonal winds over
1960–2000 (the period of ozone loss) for (a) Japanese 55-year Reanalysis (JRA-55); (b, c) CCMI models in REF-C1
and REF-C2 simulations, respectively; and (d, e, f) CMIP5 models. The positive (negative) trends on the poleward (equatorward) flanks of the mean jet characterize a poleward shift of the jet. A comparison between
CMIP5 models with and without chemistry is shown in panels e and f. The ­JRA-55 is the most recent reanalysis
product to cover the full period of ozone loss; trends in other reanalysis products analyzed over a shorter period
are quantitatively similar (e.g., Son et al., 2010). Contour interval of climatological wind is 10 m s-1 ­starting from
10 m s-1 at the outer-most contour. Adapted from Son et al. (2018).
5.36

Ozone and Climate | Chapter 5

Kovilakam 2017; Mantsis et al., 2017; Fogt et al., 2017;
Amaya et al., 2018).
As discussed in Section 5.4.1.1, significant trends inthe strength of the tropospheric mid-latitude jet, arealso evident in May, in addition to summer; however,unlike in summer, a role of ozone depletion in the May
trends is unclear. While ozone trends and stratospheric cooling have been observed in late fall, implyingthat they might have contributed to the tropospherictrends in May, similar stratospheric changes have also
been observed earlier in fall and in summer but without concurrent significant trends in the troposphericcirculation. Furthermore, observational and modeling studies suggest a stronger role of observed changes in tropical SSTs in forcing SH tropospheric circulation changes in autumn, especially after 2000, when
the ozone forcing has begun to recover (Schneider etal., 2015; Clem et al., 2016). Finally, SAM trends inautumn are not robust in modeling studies, with somestudies finding positive trends (Stone et al., 2016) (seeFigure 5-15) and others finding no trend (Swart et al.,2015; Tao et al., 2016), implying the trends may not be
attributed to external forcing.
Finally, there is no consensus on the importance ofchanges in aerosol concentrations for changes in the
SH tropospheric circulation: Some studies find that
historical changes in aerosols largely canceled the effect of GHGs on the SAM in the last half of the 20th
century in CMIP5 models (e.g., Fyfe et al., 2012; Gillett
et al., 2013), while others find a relatively weak role
for aerosols in CMIP5 models for both the SAM andthe Hadley Cell changes (Steptoe et al., 2016; Tao etal., 2016). This discrepancy could be due to differences in the number of models considered by each study.

5.4.1.3	The Southern Hemisphere: Magnitudeof Past Changes in Models
Modeled ensemble-mean trends in the jet position
and SAM are somewhat weaker than those observedin several atmosphere–ocean and chemistry–climate
model ensembles (see Figure 5-16) (Swart et al., 2015;
Rea et al., 2018; Iglesias-Suarez et al., 2016; Purich etal., 2016a; Son et al., 2018). Climate models, on average, have been shown to underestimate trends in thestrength of the SH mid-latitude jet (Swart and Fyfe,
2012; Swart et al., 2015; Purich et al., 2016a). These
underestimations have important implications forunderstanding changes to SH surface wind stress andhence attribution of the trends in ocean circulationand sea ice (Section 5.4.4). Similarly, the ensemble,
or multi-model, mean response of the Hadley Cell tohistorical forcings in climate models is weaker thanobserved, as noted in the previous Assessment (Quanet al., 2014; Garfinkel et al., 2015a). Recent research
has suggested that this difference might not reflect
any model biases but rather could be due to internalvariability contributing to the observed trend, while
averaging over an ensemble damps any such contribution from internal variability (Garfinkel et al.,2015a; Davis and Birner, 2017; Mantsis et al., 2017).
Namely, the multi-model-mean trends illustrate theforced component in these circulation changes, while
the reanalysis trends indicate one particular realization impacted by natural variability. For Hadley Cell
expansion, individual climate simulations that include observed time-varying SSTs, ozone, and GHG
concentrations can simulate trends as large as thoseevident in reanalysis and observational products(Garfinkel et al., 2015a; Allen and Kovilakam, 2017;
Davis and Birner, 2017) (see Figure 5-14). According
to these results, the magnitude of the forced response
is comparable to the magnitude of the internal variability (Figure 5-14). In fact, CMIP5 models withneither time-varying GHGs nor ozone can simulate
20-year expansion trends larger than those inferredfrom satellite data (Mantsis et al., 2017). Similarly, individual simulations of the past climate covering theperiod of ozone depletion can reproduce the magnitude of the trends in the SH mid-latitude jet evident
in reanalyses, implying that any discrepancy between
multi-model mean and observed trends is within theuncertainty due to internal variability (Schneider etal., 2015; Swart et al., 2015; Rea et al., 2018).
There are some known model deficiencies, outlined
below, that can affect the magnitude of simulated SH
tropospheric circulation trends. On the one hand, inthe case of models that prescribe ozone as a boundary condition rather than internally simulate it, themagnitude of simulated tropospheric circulation response to ozone depletion can be affected by unrealistic ozone forcing. For example, ozone depletion in
the CMIP5 ozone forcing is weaker than observed,
and it has been suggested that this may lead to an underestimation of the response in the extratropical SH
circulation (Figure 5-16d; Young et al., 2014). In addition, a monthly-mean and zonal-mean ozone dataset misses the maximum amplitude of the ozone hole,
5.37

Chapter 5 | Ozone and Climate

which may lead to an underestimated tropospheric
response (Crook et al., 2008; Waugh et al., 2009; Neelyet al., 2014). On the other hand, the response of the­mid-latitude jet to ozone forcing does not depend onwhether the atmosphere is interactively coupled withthe ocean in a model or is forced with prescribed SSTs
(Sigmond et al., 2010; Seviour et al., 2017a; Son et al.,2018) (see Figure 5-16).
Other model deficiencies that can affect simulation of
tropospheric response to ozone forcing include a lackof fully resolved stratospheric dynamics (Rea et al.,2018) and an orientation of the simulated polar vortex
that is too zonal (Dennison et al., 2017), both of which
may lead to underestimation of the tropospheric response. By contrast, a delay in springtime breakdownof the polar vortex present in many climate models(SPARC, 2010) may lead to an overly strong response(Sheshadri and Plumb, 2016; Lin et al., 2017) (seeSection 5.4.2).
In addition to the stratospheric biases, most current climate models exhibit an equatorward bias in
the position of the SH mid-latitude jet in summer
as compared to reanalysis data (Wilcox et al., 2012;
Swart and Fyfe, 2012), due in part to biases in thecloud distribution (Ceppi et al., 2012), though thisbias is reduced in CCMI models (Son et al., 2018).
The 2014 Assessment noted that the magnitude of thesimulated tropospheric response to the ozone hole
may depend on the severity of this bias, with models that exhibit a more equatorward climatological jet
bias also showing a larger poleward shift of the jet in
response to ozone depletion (e.g., Sigmond and Fyfe,
2014). New studies, however, do not find similar relationships in the CCMI simulations (Son et al., 2018),
nor in CMIP5 models when limited to the australsummer season (Simpson and Polvani, 2016), nor inozone-only forced simulations (Seviour et al., 2017a),suggesting that the relationship between a larger climatological jet bias and a larger response to ozone
depletion is not robust.
Considering the contribution of internal variability
to uncertainty in simulated tropospheric circulation
response to ozone depletion, new modeling studies
using a large single-model ensemble (Solomon andPolvani, 2016) and long control simulations (Thomas
et al., 2015) suggest that the internal variability of the
SH mid-latitude jet is smaller than its forced response
5.38

to combined anthropogenic forcing during the 20th
century as well as its projected response during the
21st century (Solomon and Polvani, 2016), in agreement with the previous Assessment. One study reported that during the period 1980–2004, the forced
component exceeds internal variability in the caseof a poleward jet shift but not for the SAM trend(Thomas et al., 2015). For the SAM, a stronger forcing
over a longer period is needed for a robust positive
trend to emerge over internal variability (Gillett et al.,2013; see Figure 5-15), consistent with the previous
Assessment.
Overall, we assess with high confidence that stratospheric ozone depletion is the dominant external
driver of changes in the SH summer troposphericcirculation before the year 2000; however, existingmodel deficiencies preclude a quantitative separationof the magnitudes of the forced (mostly due to ozone)
and unforced components of the observed trends.

5.4.1.4	The Southern Hemisphere: Model­Simulations of the Future
The role of ozone in future changes in the SH large-scaleatmospheric circulation has received comparatively
little attention since the 2014 Assessment, with studies
generally finding results consistent with those reported in 2014. Ozone recovery will have impacts opposite
to those associated with ozone depletion (Solomon
and Polvani, 2016; Dennison et al., 2016) and hence
will mitigate some of the poleward shift of the jet due
to projected increases in concentrations of GHGs.
The degree of mitigation is dependent on the rate ofincrease in GHGs (Iglesias-Suarez et al., 2016; Tao etal., 2016; Rea et al., 2018). Some of the GHG-induced
poleward shift will be compensated by the effect of
GHGs on ozone (i.e., super-recovery; Morgenstern
et al., 2014; see also Chapter 3). Uncertainty in themagnitude of the mitigation for a given scenario canbe reduced if careful attention is paid to jet biases andto the magnitude of lower-stratospheric temperature
trends over Antarctica (Wenzel et al., 2016), as well as
to biases in present-day sea ice concentration for eachmodel (Bracegirdle et al., 2018).
5.4.1.5	The Northern Hemisphere
The last Ozone Assessment found no robust l­inkages
between stratospheric ozone depletion and tropospheric circulation in the Northern Hemisphere,
though a weak positive Northern Annular Mode

Ozone and Climate | Chapter 5

trend is evident in CMIP5 models forced with ozone
concentrations. While there has been little work since2014 focusing on changes on decadal timescales, several studies have explored whether interannual variability in late spring ozone concentrations may modulate surface climate (Section 5.4.3).

5.4.2 Mechanisms for Stratosphere–
Troposphere Dynamical Coupling
As described in Section 5.4.1, stratospheric ozone loss
in the Southern Hemisphere has led to a poleward shift
in the tropospheric mid-latitude jet. We now discuss
progress toward understanding the dynamical mechanisms for this observed downward coupling. As stated in the previous Assessment, it is well establishedthat the impact of stratospheric ozone depletion on
the troposphere occurs through a cooling of the lower
polar stratosphere, which is associated with anomalously strong westerly winds and a positive anomaly
in stratospheric potential vorticity. It is well acceptedthat the balanced response in the troposphere to thispositive potential vorticity anomaly is an accelerationof the zonal flow on the poleward flank of the jet (e.g.,
Hartley et al., 1998; Thompson et al., 2006), consistent
with the sign of the observed change. However, thisbalanced response is too weak to explain the magnitude of the observed circulation shift.
Studies with idealized atmospheric models in particular suggest that eddy feedbacks amplify the impact ofstratospheric cooling and so play a critical role in themechanism, as discussed in the last Assessment. The
relative roles of synoptic and planetary waves, withzonal wavenumbers greater than 3 and less than 3,
respectively, have been the subject of two recent idealized modeling studies (Yang et al., 2015; Smith andScott, 2016). These point to both categories of waves
being important for the amplification of the tropospheric circulation response, as are substantial nonlinear eddy–eddy interactions (consistent with Orr etal., 2012). Specifically, these studies indicate that thetropospheric response cannot be due solely to the imposed radiative cooling modifying tropospheric synoptic waves alone and that planetary scale waves are
crucial in the downward influence. It is very difficult
to tease out the nature of these interactions, however,as it is likely impossible to clarify how the changes inwaves have modified the zonal-mean flow after thezonal-mean flow has already changed (Garfinkel etal., 2013; Garfinkel and Waugh, 2014).
While the SH tropospheric response to ozone depletion in December is simulated by the climate models,
it is unclear based on the current literature whether
the response can be quantitatively explained by thestrengthened stratospheric westerly winds and thedelay in the breakdown of the stratospheric polar
vortex only. Two studies (Sun et al., 2014; Byrne etal., 2017) conclude that the delay in the breakdown
can account for the tropospheric impacts, but a third
study (Sheshadri et al., 2014) argues that it cannot account for the full impact (though it does contribute).
Finally, the onset date for the vortex breakdown isgenerally too late in the current climate models (e.g.,
Wilcox and Charlton-Perez, 2013), in part due totoo-weak gravity wave drag in the polar stratospherenear 60°S (McLandress et al., 2012; Geller et al., 2013;
Garcia et al., 2017; Garfinkel and Oman, 2018), andthis bias impacts the magnitude and seasonality of thetropospheric response to ozone depletion (Sheshadri
and Plumb, 2016; Lin et al., 2017).

5.4.3 Surface Impacts
The last Assessment noted that extratropical rainfall
in the Southern Hemisphere is tied to the position of
the mid-latitude jet and, for the first time, suggested
that recent changes in both extratropical and subtropical austral summer rainfall may be related to ozone
depletion. However, only a few studies were available,
and most either did not isolate the effect of ozone depletion from other anthropogenic forcings and/or theyused simplified models or experiments. Subsequentstudies have sought to understand the dynamicalmechanisms for the subtropical rainfall increase insummer. One study (Hendon et al., 2014), attributeit to a poleward shift of the subtropical dry zone.
Understanding the extratropical rainfall response ishampered by the quality of observational products,
with little in situ data and changes in satellite products leading to substantial differences across reanalysis results. Nonetheless, a weighted average across fivereanalyses suggests the changes in summer from 1979
to 2010 are dynamically consistent with increases insynoptic eddy activity (Solman and Orlanski, 2016),
which is primarily associated with ozone depletion.
Also, one study (Bai et al., 2016), using maximum covariance analysis and principal component analysis toattribute increases in SH extratropical rainfall, found
5.39

Chapter 5 | Ozone and Climate

a predominant role of ozone depletion over GHGs.
However, a firm conclusion about the role of ozone
depletion cannot be reached based on these statisticalstudies only, because statistical relationships are nottypically able to determine causality.
Since the last Assessment, a small number of studies have investigated links between regional rainfallchanges and ozone depletion. In particular, there
has been a focus on the significant increase of austral summer rainfall in southeastern South America(SESA) over the past 50 to 100 years. This region,
which includes northern Argentina, Uruguay, southern Brazil and Paraguay, has experienced one of thelargest increases in rainfall worldwide (Gonzalez et al.,2014). Most studies using ensembles of climate models or single-model ensembles attribute this increase
to anthropogenic forcing (Vera and Díaz, 2015; Díaz
and Vera, 2017; Zhang et al., 2016; Wu and Polvani,2017); however, they disagree on whether the increaseis driven by GHG increases (Zhang et al., 2016) or byozone depletion (Yu and Polvani, 2017). One study(Zhang et al., 2016) notes that its model may underestimate the rainfall response to ozone depletion andaerosols, which somewhat undermines its attributionof the rainfall increase to GHGs. Internal decadal
variability due to changes in SSTs was also likely an
important driver of the rainfall changes (Zhang etal., 2016), which could explain why the ­multi-model
rainfall increase due to anthropogenic forcing is
weaker than that observed (Vera and Díaz, 2015; Díaz
and Vera, 2017). Hence, there is a wide range of conclusions for the attribution of rainfall increases in the
SESA region, with a large sensitivity to the model andtime period analyzed.
Other regional rainfall changes have received relatively little attention since the last Assessment. An imprintof ozone depletion has been identified in changes ofthe position of the South Pacific Convergence Zone
(SPCZ) over the 1961–1996 period, with increases in rainfall on the northern edge and decreases tothe south (Brönnimann et al., 2017). Projections bychemistry–climate models suggest that these changes
will reverse as a result of ozone recovery. One study
suggested the role of ozone in recent winter rainfall
declines in southwestern Australia (Delworth et al.,2014), although another study argued that ozone depletion is unlikely to be an important factor in thisseason (Karoly, 2014). In East Africa, the SAM has
5.40

been identified as the leading cause of changes insummer rainfall, surface temperature, and the diurnal
temperature range, implying the role of ozone during
the period of depletion (Manatsa et al., 2013, 2015,2016). A small anthropogenic component was also
found in long-term drying trends in Chile since thelate 1970s, but the contribution of ozone depletion
has not been isolated (Boisier et al., 2016). Given thatthese are single studies on each region, it is difficult tomake an overall assessment of their significance.
Research since the last Assessment to tease out the
impact of ozone recovery on future rainfall trends has
been limited by the lack of ozone-only simulations
for the 21st century under the CMIP5 framework.
One study (Lim et al., 2016) discusses future rainfallchanges related to the SAM, finding a robust impactof SAM changes on SH summer rainfall, with a positive SAM opposing the thermodynamically driven
projected changes in the subtropics to mid-latitudes
while enhancing the increases in the high latitudes.
Ozone recovery would drive a more negative SAM
and the reverse of these impacts on rainfall.
In terms of surface temperature changes, the previous
Assessment found that the largest surface temperature
response was over the high-latitude Southern Ocean(see Section 5.4.4) rather than Antarctica. While acontribution of ozone depletion to Antarctic surface
temperature trends has been shown in a number ofprevious studies (e.g., McLandress et al., 2011), recent studies have emphasized an important role fornatural variability in explaining some of the observed
temperature changes over the Antarctic Peninsula
and West Antarctica in recent decades (Jones et al.,2016; Turner et al., 2016; Smith and Polvani, 2017).
One study (Smith and Polvani, 2017) analyzes theAMIP5 and CMIP5 models, as well as the observed
relationship between the SAM and surface warming
over Antarctica, concluding that the pattern of warming matches neither anthropogenically forced trends
nor trends congruent with the SAM and that internalvariability likely played a key role. Similarly, another study (Turner et al., 2016) notes both the regionaland seasonal sensitivity of the temperature changes
and the dominant processes involved, concluding thatwhile ozone depletion likely contributed to warmingof the eastern Antarctic Peninsula during summer,
the warming across the peninsula is not inconsistent
with natural variability, particularly when placed inOzone and Climate | Chapter 5

the context of paleoclimate records, which show previous multidecadal periods of strong warming (Joneset al., 2016). Another study (Chiodo et al., 2017) also
finds a negligible radiative impact of ozone depletionon Antarctic surface temperatures, suggesting thatthe high albedo of the snow-covered surface simply
reflects any increases in shortwave radiation. Hence,
while these studies do not rule out that the ozone
depletion has likely contributed to Antarctic surface
temperature trends in some regions and seasons, as
was previously suggested, they do show that the large
natural variability of the region and the sparsity of data
and large model biases in the Antarctic and Southern
Ocean regions (e.g., Eyring et al., 2013; Marshall andBracegirdle, 2014; Purich et al., 2016a; see also Figure5-16) impact our confidence in attribution and projection studies there.
Several additional impacts of stratospheric ozone
depletion have now been documented. For example, one study (Dennison et al., 2015), using a single
chemistry–climate model, finds that ozone depletion
leads to an increased frequency of extreme anomalies
and increased persistence of the SAM in the stratosphere and stronger, more persistent s­tratosphere–
troposphere coupling. Additionally, another study(Dennison et al., 2016) finds that ozone depletion
leads to an increase in blocking frequency—as defined by persistent positive anomalies in 500 hPa
geopotential heights—in the South Atlantic region
and little change in the South Pacific in their model,
consistent with ERA-Interim reanalysis trends overthe satellite era. Though this indicates a potential impact of stratospheric ozone on blocking-induced heatwaves and rainfall patterns, this result would need tobe substantiated with additional models, particularly
given well-known model biases in underestimating
blocking frequency (Ummenhofer et al., 2013).

5.4.3.1	Interannual Variability
The last Assessment noted two studies (Son et al.,2013; Bandoro et al., 2014) linking interannual variability of Antarctic ozone anomalies in spring to SH
summer surface temperature and rainfall changes.
Recent modeling studies, using a range of approaches, have examined the possible connection between
Arctic spring ozone and surface climate and have obtained mixed results. One study (Cheung et al., 2014)
probed whether the extreme Arctic ozone depletionof 2011 had an effect on tropospheric climate with the
UK Met Office operational weather forecasting model.
It found no improvement in spring tropospheric forecast skill when forcing the model with more realistic
ozone concentrations as compared to climatologicalozone. Another study (Karpechko et al., 2014) founda relationship between the 2011 low-Arctic stratospheric ozone anomalies and tropospheric climate inatmospheric general circulation model simulations,but it noted that specifying the ozone anomalies inisolation of SST anomalies did not result in a significant surface impact. A third study (Smith and Polvani,2014) found that the prescribed ozone forcing needed
for a robust tropospheric response in its simulations
appeared to be larger than that historically observed.
In contrast, a coupled chemistry–climate simulation
study (Calvo et al., 2015) found a robust ­stratospheric–tropospheric response in low versus high ozone years:
a positive phase of the North Atlantic Oscillation(NAO), a poleward shift of the North Atlantic tropospheric jet, and corresponding regional surface
temperature anomalies. This study used an ensembleof simulations driven by historically observed ODSs,
and the link between stratospheric ozone and tropospheric circulation was found only during the recent
period of high ODSs, suggesting the importance ofchemistry feedback on the dynamics. The fully coupled approach of this study (Calvo et al., 2015) allows
consistency between the evolving ozone distributions
and dynamical conditions, which may explain the
differences between its conclusions and those of studies prescribing ozone concentrations. Future work isneeded to evaluate whether differences in the ozone
forcings, as well as other inter-model differences,among the various studies have contributed to therange of conclusions.
Two recent observational studies have also suggested
that interannual variability in ozone can modify surface climate. The first study (Ivy et al., 2017b) findsthat extreme Arctic stratospheric ozone anomalies inMarch are associated with NH tropospheric climate
in spring (March–April) in specific regions of theNorthern Hemisphere; the effects are generally consistent with those found in a chemistry-climate model(Calvo et al., 2015). Finally, another study (Xie et al.,2016) suggests that Arctic stratospheric ozone anomalies influence the North Pacific Oscillation (NPO)5.41

Chapter 5 | Ozone and Climate

and that an anomalous NPO modulates subtropical
SSTs. This subtropical SST anomaly might then leadto improved predictability of ENSO, though future
work is needed to confirm many aspects of this chainof associations (Garfinkel, 2017). However, it is wellknown that a delayed or advanced final warming of
the Arctic stratospheric vortex can lead to surface
impacts (Black and McDaniel, 2007; Ayarzagüena etal., 2009; Hardiman et al., 2011), and distinguishingthe dynamical impact of the final warming from theradiative impact of the ozone anomaly that typicallyaccompanies a final warming also requires additional
work.
Thus, our assessment is that interannual variabilityin springtime Antarctic and Arctic ozone may be important for surface climate, but work remains to better
quantify this connection.

5.4.4 Ocean and Ice Impacts
5.4.4.1	Ocean Impacts
As discussed above, observations show trends in the
SAM and low-level tropospheric winds that are largest
during austral summer (Figures 5-13 and 5-15), andthese summertime trends have been mainly attributed to stratospheric ozone depletion. A positive SAM
trend implies a poleward shift and/or strengtheningof the surface wind stress, which plays a fundamental role in the ocean circulation. Westerly wind stressacts to drive northward transport in the underlying
ocean (via Ekman transport), creating a region of divergence and upwelling on the poleward side of thesurface wind maximum and a region of convergenceand downwelling equatorward of the surface windmaximum (e.g., Arblaster and Gillett et al., 2014).
The previous Assessment reported that observations
indicate a strengthening of the horizontal and vertical circulations in the Southern Ocean, of which asubstantial part was likely caused by ozone-induced
westerly wind trends. These wind trends were also
linked to subsurface warming below and north of the
Antarctic Circumpolar Current (ACC), but the relative importance of the wind trends and other forcings(notably warming due to increased GHGs) had notbeen quantified.
Since the last Assessment, more evidence has appeared indicating a substantial role for ozone depletion in recent trends of the Southern Ocean
5.42

circulation. For austral summer, one study (Solomon
et al., 2015) showed that modeled trends in the vertical ocean circulation are mainly attributable to ozone
depletion, while a different study (Wang et al., 2014)
showed that ozone recovery acts to mitigate future
GHG-induced changes in the horizontal ocean circulation. Observed changes in the thermal structureof the Southern Ocean (Figure 5-17) are dominatedby a warming that maximizes along the northernflank of the ACC, around 40–50°S, with the largest
warming in the upper 1,000 m (Armour et al., 2016;
Schneider and Deser, 2018). Two noticeable exceptions are the regions with subsurface cooling northof 35°S (Armour et al., 2016) and widespread SST
cooling that occurred since the late 1970s in the region south of 50°S (Fan et al., 2014; Jones et al., 2016;
Armour et al., 2016; Schneider and Deser, 2018). The
high-latitude surface cooling is intimately linked tothe observed increase in Antarctic sea ice over that
period (Parkinson and DiGirolamo, 2016) and will be
discussed in Section 5.4.4.2.
Focusing first on the region between 30–60°S, a modeling study identified increasing GHGs as the main
driver of the warming in this region, with ozone
depletion playing a secondary role (Solomon et al.,2015). This appears consistent with a recent study(Armour et al., 2016) that suggests that the structureof upper ocean warming, with delayed warming southof the ACC and enhanced warming to the north, isfundamentally shaped by the mean (climatological)
meridional circulation in the Southern Ocean. This
study finds that wind-driven upwelling of water thathas not been warmed by GHGs slows the warming
south of the ACC, while the GHG-induced heat istaken up and transported northward and then stored
just north of the ACC. This mechanism does not rely
on changes in the meridional ocean circulation, possibly explaining why ozone-induced atmospheric circulation changes play a secondary role in accounting
for recent Southern Ocean warming.
In conjunction with these warming trends, theSouthern Ocean has also experienced freshening (i.e.,a decline in salinity), with the exception of a strong
salinification trend north of 45°S in the upper 500
m (Figure 5-17). A recent modeling study suggeststhat 30% of the modeled Southern Ocean freshening can be attributed to ozone depletion (Solomon
et al., 2015). Sources of the freshening are believed

Ozone and Climate | Chapter 5

Salinity Trend

Potential Temperature Trend

Depth (m)1,200(PSI decade-1)(°C per decade-1)

1,000
1,400
70°S

50°S

30°S

Latitude

70°S

50°S

30°S

LatitudeFigure 5-17. Observed 1979–2013 trend in annual-mean, zonal-mean ocean potential temperature andsalinity from the Met Office Hadley Centre’s EN4 dataset (Good et al., 2013).

to be located in the high latitudes and may include
GHG- and ozone-induced changes in high-latitude
precipitation minus evaporation (Fyfe et al., 2012), a­wind-driven increase in northward freshwater transport by sea ice (Haumann et al., 2016), and basal
melting of Antarctic ice shelves (Bintanja et al., 2013).
Note that the melting of ice shelves was not considered by this study (Solomon et al., 2015).
Finally, we note that unforced internal variability of
the Southern Ocean may have played a role in observed trends. While its magnitude is highly uncertain, some studies suggest that it is potentially large(Latif et al., 2013; Zhang et al., 2017).

5.4.4.2

Sea Ice Impacts

We now turn to the observed high-latitude (southof 50°S) surface cooling and the associated increasein Antarctic sea ice since 1979. New studies suggestthat these trends reflect multi-decadal variability, with
opposite trends in SSTs observed over the 1950–1978
period (Fan et al., 2014) and a recently recovered­satellite-based estimate of Antarctic sea ice extent suggesting a decreasing sea ice trend from the mid-1960s
to 1979 (Meier et al., 2013; Armour and Bitz, 2015;
Gagné et al., 2015). The magnitude and sign of theozone hole contribution to Southern Ocean temperature and Antarctic sea ice trends since 1979 have been
topics of much discussion. In the last Assessment, it
was reported that all climate model simulations thatisolated the impact of stratospheric ozone depletion(including time-slice simulations in models with andwithout resolved ocean eddies, a fully coupled chemistry–climate model, and all six CMIP5 models withozone-only simulations available) simulated decreased
sea ice extent associated with ozone-induced changes
in the Southern Ocean circulation, suggesting thatozone depletion had not contributed to the observedhigh-latitude ocean cooling and increase in Antarctic
sea ice. The 2014 Assessment also cautioned that due
to inconsistencies between the observed and modeledsea ice trends, confidence in the simulated response to
the formation of the ozone hole was limited.
New modeling studies have corroborated the findingsof the last Assessment that a realistic, time-evolving
ozone hole leads to Antarctic sea ice decline and thus
cannot explain the observed increase in sea ice since
1979 (Solomon et al., 2015) nor the regional patternsof Antarctic sea ice trends (Landrum et al., 2017).
However, other recent studies have highlighted discrepancies between modeled and observed sea ice
trends since 1979 (Gagné et al., 2015; Hobbs et al., 2015;
Rosenblum and Eisenman, 2017), which may be related to model biases in the ocean mean state (Kostov etal., 2016, 2018; Schneider and Deser, 2018), Southern
Ocean deep convection (Behrens et al., 2016), theweaker simulation of recent wind trends (Purich etal., 2016a), the underestimation of the zonal asymmetries in the atmospheric circulation (Haumann etal., 2014), the underestimation of historical surface
freshening (Purich et al., 2018), and the lack of aninteractive ice shelf component (Pauling et al., 2017).
These studies 1) confirm that the confidence in the
5.43

Chapter 5 | Ozone and Climate

Despite these persistent model uncertainties, significant advances have been made in the understanding of processes responsible for the modeled oceanand sea ice response to ozone depletion. Idealized
model simulations where the stratospheric ozone
concentrations were instantaneously changed frompre-ozone hole to ozone hole conditions (Ferreira etal., 2015) demonstrated that the high-latitude ocean
response entails two timescales (Figures 5-18 and
Figure 5-19). On the shorter timescales (months toyears), the response is characterized by ocean coolingat the surface associated with increased northward
Ekman transport of colder high-latitude waters. Thisis consistent with well-known correlations between(a)Fast Response(≈ Year)

Pole

60°S

Antarctica

Depth!"#$%&'&$(

40°S

(

(

month-to-month variations in the SAM, SST and sea
ice extent. On longer timescales (years to decades),this surface cooling is then replaced by warming associated with enhanced upwelling of relatively warm
waters beneath the mixed layer (see also Marshall etal., 2014). This two-timescale behavior was also seen
in a different coupled climate model with significantly
different characteristics of ocean convection (Seviour
et al., 2016, 2017b) (Figure 5-19c). It should be notedthat part of the initial cooling response in Figure
5-19c may be a reflection of the initial conditions:
As a corresponding ensemble of control simulations
was not available, the unforced time evolution of the
SSTs was estimated indirectly. Furthermore, in spiteof a relatively long cooling phase, the sea ice did not
expand in those simulations. Nonetheless, based on
the initial cooling of SSTs in response to instantaneous ozone forcing, some have argued that ozone(b)

Slow Response(≈ Decade)

Pole

60°S

40°S

Antarctica

simulated response to ozone depletion is still limited
and 2) argue that the conclusion that the ozone hole
has not contributed to recent high-latitude ocean andsea ice trends may be preliminary.

T
Figure 5-18. Schematic of the two-timescale response of the ocean and sea
ice to a stepwise ozone hole as simulated by climate models. The dot at 60°S
and × at 40°S indicate the dipole in surface wind stress associated with thestrengthened SAM in response to ozone depletion. (a) The fast response isdominated by surface cooling associated with increased northward Ekman
transport, whereas (b) in the slow response this cooling is replaced by warming, which is the result of enhanced upwelling of relatively warm waters frombeneath the mixed layer. Blue patches represent the sea ice cover (expanding
in the fast response and contracting in the slow response). Adapted from Ferreira et al. (2015).
5.44

Latitude

LatitudeLatitudeOzone and Climate | Chapter 5

Figure 5-19. Zonal-mean and annual-mean SST response to a stepwise ozone depletion in three coupledmodels: (a) MITgcm, with an idealized Double-Drake configuration, and two comprehensive models, (b)CCSM3.5 and (c) GFDL ESM2Mc. All three models show a two-timescale response, consisting of a coolingfollowed by a warming in the Southern Ocean region (50–70°S). However, their magnitude and timescales
vary greatly. The net cooling lasts approximately 20 years in the MITgcm, 3 years in CCSM3.5, and 30 years in
GFDL ESM2Mc. Adapted from Ferreira et al. (2015) and Seviour et al. (2016).

depletion could drive a transient expansion of sea
ice (Marshall et al., 2014; Ferreira et al., 2015). The
impact of a realistically prescribed (i.e., time-varying)ozone hole on Antarctic sea ice trends critically depends on the timescale of transition from the initialcooling to subsequent warming. Unfortunately, thistimescale is not well constrained, varying greatly between the three models with the prescribed stepwise
ozone perturbation (Figure 5-19). Other attempts toquantify the impact of the ozone hole on Antarctic sea
ice are more indirect and rely on statistical techniques(such as convolution theorem) to extract the SST or
sea ice response to a hypothetical step increase in
the SAM index. Consistent with the idealized ozone
experiments, these studies find a large intermodel
spread between CMIP5 models in the SST response:
In response to a SAM increase, some models simulate a short transition time from initial cooling to
5.45

Chapter 5 | Ozone and Climate

subsequent warming, while other models continue tocool for at least 20 years (Kostov et al., 2016, 2018).
This intermodel spread has been related to biases in
the models’ mean ocean stratification and to dynamical processes, including oceanic (parameterized) eddy
fluxes, mixed layer dynamics, and air–sea interactions(Ferreira et al., 2015; Kostov et al., 2016). This technique has also been applied to Antarctic sea ice, revealing a large range in modeled transition timescalefrom initial sea ice expansion to subsequent sea ice
contraction in response to a step function in the SAM(Holland et al., 2016). Applying the modeled sea ice
response function to SAM variability but driven by
the observed SAM time series, one study (Holland etal., 2016) suggests that for the multi-model mean, theobserved variations in the SAM have driven a modest sea ice decline. While these studies based on the
convolution theorem are generally consistent with theidealized ozone forcing perturbations, it remains to
be demonstrated that the convolution theorem canaccurately predict the sea ice response to the SAM ineach model.
The new studies that have quantified the responseto an instantaneous ozone perturbation or SAM increase have provided important, novel insights into
the physical processes involved in the sea ice response
to ozone depletion. It should be emphasized, however, that these idealized experiments are tools toprobe the physics of the climate system and that they
were not meant to represent the real ozone hole, as
its formation and the corresponding SAM increase
have occurred over several decades. Nonetheless,these studies suggest that when forced with the real,time-varying ozone hole, models with a long coolingtimescale would simulate increased sea ice. However,this does not seem to be consistent with the fact thatall studies that have analyzed climate model experiments forced with realistic, time-varying ozone depletion consistently find decreasing sea ice.
It also needs to be emphasized that the modest increase in Antarctic sea ice extent is the result ofnear-canceling regional trends, with the strongest sea
ice increase in the Ross Sea and strongest decrease in
the Bellingshausen and Amundsen Seas (Turner etal., 2015; Hobbs et al., 2016). This pattern in the sea
ice trends is qualitatively consistent with a deepeningof the Amundsen Sea Low (ASL). There is some evidence that the ozone hole has contributed to these
5.46

atmospheric circulation trends (England et al., 2016)
in the summer months, but it has also been noted thatthe observed ASL trends are within the bounds ofmodeled internal variability (Turner et al., 2015). An
increasing body of evidence suggests that the ozone
hole is not the main driver of ASL trends and points
to decadal variations in the tropical Pacific (Meehl etal., 2016; Purich et al., 2016b; Schneider and Deser,2018), or possibly the Atlantic (Li et al., 2014), as thelikely drivers.
We further note that during the austral spring(September, October, November; SON) of 2016, unprecedented retreat of Antarctic sea ice was observedthat was 46% faster than the mean rate of loss in springover the satellite era (Turner et al., 2017). This led torecord-low sea ice extent anomalies, well exceeding 3
standard deviations of the observed 1979–2016 ice extent (Stuecker et al., 2017). This observation contrasts
sharply with the long-term increasing sea ice trend
discussed above. Studies have linked the unprecedented retreat to record negative values of the SAM(Turner et al., 2017; Doddridge and Marshall, 2017;
Stuecker et al., 2017) and extratropical SST anomalies
forced by the extreme 2015–2016 El Niño (Stuecker
et al., 2017). These studies suggest that the unprecedented retreat was the result of tropically forced andinternal SH atmospheric variability (and hence that it
was unrelated to the slow, long-term warming simulated by the models), but more studies are needed tocome to robust conclusions.
In conclusion, since the last Assessment, significant
progress has been made regarding the understandingof processes involved in the response of Antarctic sea
ice to ozone depletion, which is now believed to entail
a fast surface cooling followed by a slow, long-term
surface warming. However, the role that the ozone
hole has played in observed Antarctic sea ice changes remains unclear. While the conclusion of the last
Assessment that the ozone hole cannot explain recent
trends in Antarctic sea ice is supported by new modeling studies, confidence in this result remains low. Thisis because climate models generally do not reproduce
observed Antarctic sea ice trends since 1979 and because new studies have identified several systematic
biases. Future progress could be made by model improvements and repetition of realistic ozone-only simulations performed with more coupled climate models.

Ozone and Climate | Chapter 5

Atmospheric inversion
Expected based on atm. increase

Air–sea flux anomaly (PgC yr-1)

Neural network
Mixed layer scheme
saturation period

0.4
0.2
0
−0.2

rein

vig

−0.4
1980

1985

1990

1995
Year

2000

ora

tion

2005

per

iod

2010

Figure 5-20. Evolution of the Southern Ocean carbon sink based on (red and blue lines and shading) two
methods to interpolate surface ocean observations and (black solid line and gray shading) an atmospheric
inversion method. The expected uptake based on the growth of atmospheric CO2 alone is shown by thedashed black line. Negative values indicate anomalous uptake by the ocean. Adapted from Landschützer etal. (2015).

5.4.4.3	Ocean Carbon
The Southern Ocean plays a crucial role in the global
carbon cycle, as it accounts for about 40% of the global oceanic uptake of anthropogenic CO2 (Khatiwala etal., 2009; Frölicher et al., 2015). The strength of the carbon sink is mainly dependent on the air–sea gradientsof CO2. In the absence of other changes, the increase inanthropogenic CO2 in the atmosphere would lead toincreased air–sea gradients and hence to an increased
carbon sink (dashed black line in Figure 5-20). It waspreviously suggested that the carbon sink had slowed
down (Le Quéré et al., 2007) and that this slowdown
was partly related to ozone-induced changes in thesurface winds, which bring up carbon-rich water andhence reduce the air–sea gradient of CO2. These results relied on atmospheric inversion methods (which
estimate carbon fluxes from atmospheric CO2 measurements) and forward ocean models. The realism of
this slowdown has been debated in the literature, as
inversion models have been shown to depend on data
selection (Law et al., 2008) and the coarse resolution

ocean models employed in most studies do not represent critical processes such as mesoscale eddies(Swart et al., 2014). Furthermore, the last Assessment
reported that for the 1990–2009 period, estimates of
the trends in the carbon sink depended on the analysis, with atmospheric inversions generally showing
a slowdown in the uptake and ocean biogeochemical
models indicating no slowdown (Lenton et al., 2013).
Since the last Assessment, further evidence has appeared suggesting that the apparent slowdown of the
carbon sink is not robust (Landschützer et al., 2015,
Munro et al., 2015) (Figure 5-20). These studies employed observations of the surface ocean CO2 measurements and hence did not rely on imperfect ocean
biogeochemical models. While they confirmed earlier
studies that the carbon sink slowed down between the
1980s and early 2000s, they also found a remarkable
reversal with a reinvigoration of the sink between 2002
and 2012 (Figure 5-20). Averaged over the Southern
Ocean, these decadal variations were found to bereasonably robust to the method used to interpolate
5.47Chapter 5 | Ozone and Climate

the sparse measurements in space and time, thoughsignificant uncertainties remain on smaller spatial
scales (Ritter et al., 2017). As shown in Figure 5-20,
the strength of the carbon sink is now thought to becomparable to that expected based on atmospheric
CO2 increases alone. These results suggest that atmospheric circulation changes (whether driven by ozone
or not) have not had a considerable impact on the net
strength of the Southern Ocean carbon sink.

5.4.5 Changes in Radiative
Forcing and Feedbacks

halogenated ODSs. It found a total (stratosphere andtroposphere) ozone RF between 2000 and 2100 of 0.07
W m-2. This is in quantitative agreement with anotherstudy (Iglesias-Suarez et al., 2018) that used a different
chemistry–climate model and estimated the ozone
RF due to future ODSs to be 0.129 ± 0.081 W m-2.
Thus, the future decline in ODSs over the 21st century
will induce a small indirect positive RF from ozone.
However, this positive RF is substantially smaller than
the negative direct RF due to declining atmospheric ODSs as a result of the Montreal Protocol and its
Amendments (see Chapter 6).

Since the last Assessment, there have been no major
published updates to the estimated preindustrial
to present-day ozone radiative forcing (RF) due tothe effects of long-lived ODSs on stratospheric and­tropospheric ozone abundances. However, one study(Hossaini et al., 2015) highlighted 1) that halogenatedvery short-lived substances (VSLSs) of both anthropogenic and natural origin tend to destroy ozone in
the lower stratosphere more efficiently than long-lived
ODSs and 2) that since the ozone radiative effect permolecule is stronger in the lower stratosphere than in
the upper stratosphere, this may be important for the
global radiative balance. This study estimated a global
radiative effect from the stratospheric ozone changes
in response to observed VSLSs (bromine [Br], chlorine[Cl], and iodine species) of −0.08 W m-2. Although
the study found no trend in the influence of VSLSs on
global ozone between 1979 and 2013, the influence ofbrominated VSLSs on global ozone in the preindustrial (i.e., with low anthropogenic Cl) was ~30% smaller
than present day owing to the coupling between Br
and Cl chemical cycles. The study therefore estimatedan indirect ozone RF from VSLSs of −0.014 W m-2,
which is about one-tenth of the study’s estimated totalozone RF due to long-lived ODSs.

A number of modeling studies published since the last
Assessment have examined the impact of changes inozone in response to an abrupt increase in CO2 on estimates of the equilibrium climate sensitivity (ECS)—
the equilibrium change in global mean near-surface
temperature in response to a doubling in CO2. As discussed in Chapters 3 and 4, CO2 affects stratosphericozone abundances through effects on transport andchemistry (via changes to stratospheric temperatures). New estimates of the effect of ozone changes
on ECS in different chemistry–climate models range
from no change (Marsh et al., 2016) to a reduction in
ECS of about 20% (Nowack et al., 2014), with somestudies finding a decrease in ECS of a smaller magnitude (Dietmüller et al., 2014; Muthers et al., 2014).
The differences in the relative importance of ozone
changes for inferred ECS are likely to be related to the
distribution and magnitude of ozone and stratospheric water vapor changes simulated in the individualmodels. We therefore conclude that ozone changes
and their associated effect on climate feedbacks inresponse to increased CO2 are more likely to reducethan to increase ECS; however, there is currently large
quantitative uncertainty in the magnitude of thisfeedback.

New studies since the last Assessment have quantified the stratospheric and tropospheric ozone RF due
to the projected major drivers of atmospheric ozone
concentrations over the 21st century: declining halogenated ODS concentrations, climate change due toanthropogenic forcing, changes to tropospheric ozone
precursor species including methane, and changes tochemically active greenhouse gases (nitrous oxide[N2O]). One study (Banerjee et al., 2018) used achemistry–climate model to estimate the 21st-century ozone RF due to the projected decline in long-lived
5.48

5.5 CLIMATE IMPACTS OF THE
MONTREAL PROTOCOL
5.5.1 World Avoided by the Montreal­Protocol
World-avoided simulations evaluate the environmental and climate impacts that have been avoided as aresult of the successful regulation of ODS emissions
under the Montreal Protocol. This is generally done
by a comparison of climate model simulations with

Ozone and Climate | Chapter 5

and without ODS emissions regulations. In the scenarios with unregulated ODSs, the ODSs are generally assumed to increase at a constant rate of 3–3.5%
per year (e.g., Prather et al., 1996; Velders et al., 2007;
Newman et al., 2009; Garcia et al., 2012). As reported
in the 2014 Assessment, chemistry–climate models
suggest that continued accumulation of ODSs in theatmosphere in the absence of the Montreal Protocol
would have led to a collapse of the global ozone layer
by the mid-21st century, with devastating environmental implications (Newman et al., 2009; Garcia etal., 2012). The last Assessment also reported on the
additional (mostly unanticipated) benefits of ODS
regulations for mitigation of global climate change.
Specifically, it was reported that by later this century,
unregulated ODS increases could have led to global
surface temperature increases comparable to temperature increases caused by other greenhouse gases(Velders et al., 2007) and could have almost doubledchanges in the hydrological cycle (precipitation minus
evaporation) over the next few decades.
While new literature on this topic since the last
Assessment is limited, such studies have highlighted
other aspects of the climate benefits of the Montreal
Protocol. One modeling study found that in theworld-avoided scenario, the projected increase in thepotential intensity of tropical cyclones is nearly threetimes larger in 2065 than in a scenario accounting forwarming due to other greenhouse gases only (Polvani
et al., 2016). Two other studies have attempted toquantify the implications of the avoided temperature and precipitation changes from restricting ODS
emissions for global sea level rise (SLR). One study
found that under an idealized scenario in which theemissions of gases regulated under the Montreal
Protocol had instead been eliminated in 2050, additional thermal SLR of between 4–14 cm would be expected in the 21st century (Zickfeld et al., 2017), withthe large range coming from the uncertainty in ocean

heat uptake efficiencies in climate models. The otherstudy found that by 2065, thermal SLR avoided by theMontreal Protocol is about 5 cm, but note that part ofthat SLR is balanced by changes in the hydrologicalcycle over Antarctica (Previdi and Polvani, 2017).

5.5.2 Projected Climate Impacts of the­Kigali Amendment
Other new studies have simulated future climate
impacts that will be avoided if nations adhere to thephasedown of hydrofluorocarbons (HFCs) under the
Kigali Amendment. In the simulations without HFC
regulations, a wide range of HFC scenarios is typicallyconsidered (Chapter 2, Section 2.3.1). As discussed
in Chapter 2, these new studies indicate that the anticipated phasedown of HFCs is expected to avoid up
to 0.4 K of global mean surface warming by 2100. The
atmospheric impacts of HFC regulations have beenfurther quantified using a 2-D (latitude–pressure)interactive chemistry, radiation, and dynamics model(Hurwitz et al., 2015, 2016). These studies foundthat unregulated increases in HFCs would result ina warming of the troposphere and stratosphere. In
a business-as-usual HFC emissions scenario, thetropical lower stratosphere in 2050 was up to 0.41 K
warmer than in a scenario with zero HFC emissions(Hurwitz et al., 2015). In that same business-as-usual
scenario, the 10–16 km global mean temperature wasprojected to increase by 0.11–0.13 K between 2015
and 2050, while HFC mitigation scenarios similar to
those proposed under the Kigali Amendment were
found to avoid most of that warming (Hurwitz et al.,2016). In a business-as-usual scenario, HFCs were also
found to impact the atmospheric circulation, slightly
enhancing the Brewer–Dobson circulation above 18
km (resulting in a small decrease of the stratosphericmean age of air by −0.3%) and weakening the Hadley
Cell circulation below 18 km (Hurwitz et al., 2015).
More studies are needed to confirm this result withmore comprehensive models.

5.49

Chapter 5 | Ozone and Climate

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Chapter 6
Scenarios and Information for Policymakers
Lead Authors
L . J. Carpenter
J.S. Daniel
Coauthors
E.L. Fleming
T. Hanaoka
J. Hu
A.R. Ravishankara
M.N. Ross
S. Tilmes
T. J. Wallington
D. J. Wuebbles
Contributors
J.B. Burkholder
A. EngelØ. Hodnebrog
R. Hossaini
L. J.M. Kuijpers
S.A. Montzka
R. J. Salawitch
W.R. Tribett
G. Velders
H. Walter-Terrinoni
Review Editors
N.R.P Harris
M. J. Prather

Cover photo: The development of future scenarios of ODSs and HFCs are key products of the Montreal Protocol
Scientific Assessment Panel. These products are developed using computer models that incorporate observed concentrations, production and consumption estimates, and the amounts of manufactured chemicals in existing equipment and application banks. These scenarios are an important part of the Executive Summary, which conveys policy
relevant information to the Parties of the Montreal Protocol. Photo: Lynn Daniel and Konner Syed.

Chapter 6
Scenarios and Information for Policymakers
CONTENTSSCIENTIFIC SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1
6.1

INTRODUCTION  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  7

6.1.1 Summary of Findings from the Previous Ozone Assessment . . . . . . . . . . . . . . . . . . 7
6.1.2 Key Issues to Be Addressed in This Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
6.2

ISSUES OF POTENTIAL ­IMPORTANCE TO ­STRATOSPHERIC OZONE AND CLIMATE . . . . . . . . . .  9

6.2.1 Halocarbons Controlled Under the Montreal Protocol  . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
6.2.1.1

Replacement Compounds  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

6.2.1.2

Kigali Amendment  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

6.2.1.3

HFC-23  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

6.2.2 Breakdown Products  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
6.2.3 Very Short-Lived Substances (VSLSs; Biogenic and Anthropogenic) . . . . . . . . . . . . . . . . . 14
6.2.4 The Key Climate Gases: Carbon Dioxide, Methane, and Nitrous Oxide  . . . . . . . . . . . . . . 15

6.2.5 Deliberate Climate Intervention  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
6.2.5.1

Geoengineering via Stratospheric Sulfate Aerosol Modifications . . . . . . . . . . 16

6.2.5.2

Geoengineering via Solar Irradiance Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . .17

Box 6-1. Stratospheric Aerosol Geoengineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6.2.6 Other Potential ­Influences on Stratospheric Ozone  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.3

METRICS FOR CHANGES IN OZONE AND CLIMATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

6.3.1 Metrics for Changes in Ozone  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
6.3.2 Metrics for Changes in Climate  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.4

SCENARIOS AND ­SENSITIVITY ANALYSES  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

6.4.1 Tools Used in Analyses of Ozone and Climate Effects  . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.4.2 Baseline Scenario for Ozone and Climate  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6.4.3 Alternative Future Scenarios  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
6.4.3.1

Stratospheric Ozone Implications  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.4.3.2

Climate Implications  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
APPENDIX 6A: CURRENT STATE OF KNOWLEDGE ON ­ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
STRATOSPHERIC SULFATE GEOENGINEERING  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

6A.1 Impact of Stratospheric ­Sulfate Geoengineering on  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
Net ­Chemical Ozone Production  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Chapter 6 | Information for Policymakers

6A.2 Impact of Stratospheric Sulfate Geoengineering on Dynamics  . . . . . . . . . . . . . . . . . 59
6A.3 Impact of Sulfate Aerosol ­Geoengineering on Column Ozone  . . . . . . . . . . . . . . . . . . 59
APPENDIX 6B: COMPARISON OF PAST AND FUTURE OZONE PROJECTIONS OF THE  . . . . . . . . . . . . . .
GSFC 2-D MODEL WITH GEOSCCM 3-D SIMULATIONS  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
APPENDIX 6C: EVALUATION OF ALTERNATIVE SCENARIOS USING NEW EESC FORMALISM  . . . . . 69

Chapter 6

Scenarios and Information for Policymakers
SCIENTIFIC SUMMARY
In the sections below, we note the significance of various improvements in our understanding concerning actions
related to the Montreal Protocol and its Amendments that could alter the recovery of the ozone layer and/or impact
Earth’s climate. As in previous Assessments, we use equivalent effective stratospheric chlorine (EESC) as a proxy
for the amount of stratospheric ozone depletion caused by ozone-depleting substances (ODSs) that contain chlorine
and/or bromine and reside in the atmosphere for more than a few months. The return of EESC to 1980 values isused as a metric to compare the effects of different future scenarios of production and emission of ozone-depletinggases on ozone layer recovery. In this chapter, we also use 2-D model simulations to estimate changes in future ozone
depletion for these different scenarios. (Note that 3-D model projections of global and polar ozone and analyses of
expected recovery dates are presented in Chapters 3 and 4. These calculations include changes in greenhouse gaslevels and in atmospheric transport and are not expected to be equivalent to the EESC recovery dates). Our ability
to predict future changes in the ozone layer is limited more by uncertainties in future levels of CO2, CH4, and N2O
than by uncertainties in the levels of ODSs, especially as we approach the 1980 values of EESC. Indeed, ozone levels
in some regions of the atmosphere could exceed natural levels, due to climate change, with possible consequences
to humans and natural ecosystems, assuming natural levels represent a harmonious balance. The influence of CO2
occurs through its role in the climate system as a driver of change in temperature and atmospheric circulation. The
influences of CH4 and N2O occur primarily through their roles as chemical reagents in the atmosphere. ODSs themselves are greenhouse gases, and their influence on climate and ozone layer depletion are intricately intertwined,even though we note them separately for clarity of presentation. Lastly, note that the various additional actions
discussed below impact future ozone to a much smaller degree than what has already been accomplished by theMontreal Protocol.

Post-Kigali information of interest and concern• The Kigali Amendment to the Montreal Protocol, along with regional and national regulatory and volun-tary actions taken before Kigali entered into force, is expected to substantially limit future climate forcing
by HFCs. Projections of HFC emissions that include compliance with Kigali Amendment control measures suggest that the radiative forcing (a metric for global warming) from HFCs, currently 0.025 W m−2(not including HFC-23), will reach 0.13 W m−2 by 2050, about half as high as that projected without theKigali Amendment and prior national and regional regulation. The estimated benefit of these actions is
2.8–4.1 Gt CO2-eq. yr−1 of avoided Global Warming Potential (GWP)-weighted emissions by 2050. The
projected surface temperature contribution from HFCs (excluding HFC-23) reduces from 0.3–0.5 °C toless than 0.1 °C in 2100 due to entry into force of the Kigali Amendment.

• Options are available to further decrease the climate impact of HFCs. Use of commercially available

low-GWP alternatives in place of high-GWP HFCs in refrigeration and air-conditioning equipment,
thermal insulating foam, metered-dose inhalers, fire protection, and miscellaneous HFC applications
during the phasedown would further reduce climate change. Additional benefits would be gained bysuch actions via development of more energy-efficient equipment and thermal insulating foam that use
these low-GWP replacements.

• Sustained increases in anthropogenic chlorinated very short-lived substances (VSLSs Cl) emissions, asseen for CH2Cl2 in the 2000s, would decrease stratospheric ozone levels in the coming decades. However,observed growth rates of CH2Cl2 continue to be highly variable, and there is insufficient information to

6.1

Chapter 6 | Information for Policymakers

confidently predict future concentrations. If the growth in emission rates seen during the first decade ofthis century continues, CH2Cl2 is projected to deplete as much column ozone between 2020 and 2060
as that by the controlled ODSs emitted during that period. However, such large growth projections do
not account for a more recent reduction in the CH2Cl2 growth rate, nor have they been shown to beconsistent with expectations for global demand over the coming decades. Any control of CH2Cl2 production and consumption under the Montreal Protocol would be rapidly effective, since this VSLS will
be cleansed out of the stratosphere within a few years.

Ozone-depleting substances (ODSs) and equivalent effective stratospheric chlorine (EESC)
Below, we discuss potential changes in the projected trajectory of ozone depletion and EESC that result from improvements in our understanding of the emissions or other characteristics of individual gases or groups of gases. We
reference these potential changes to the so-called baseline scenario—which should be considered a plausible future
pathway for these gases. The baseline scenario for ODSs is developed from atmospheric concentration observations,combined with estimates of the amounts of ODSs in existing equipment or other products containing ODSs, referred
to as banks. The 2018 baseline scenario for HFCs takes into account global control measures introduced by the
Kigali Amendment and other regional and national actions. For all baseline scenarios, we assume that the longlived greenhouse gases N2O, CH4, and CO2 follow the Representative Concentration Pathway (RCP) 6.0 scenario.
Note that for some of the metrics the combined consequence of these gases is generally not simply the addition ofeach of the changes. It is also important to recognize that the return date of EESC to 1980 levels is quite sensitive to
any change in EESC concentration because of the relatively small rate at which EESC is projected to decline in the
middle of this century.

• Global emissions of CFC-11 derived from atmospheric observations show an increase in recent years thatis not consistent with our understanding of release from its banks and suggests new global productionthat is not reported to the United Nations Environment Programme (UN Environment). If total emissionsof CFC-11 were to continue at levels experienced from 2002–2016 (67 Gg yr-1), the return of mid-latitude
and polar EESC to the 1980 value would be delayed by about seven years and 20 years, respectively. Suchan assumption of continuing emissions implicitly assumes that the unidentified emissions will grow tocounteract the expected decline in bank emissions.

• Emissions from current ODS banks continue to be a slightly larger future contribution than ODS produc-tion to ozone layer depletion over the next four decades, assuming maximum production levels allowed by
the Montreal Protocol. Future business-as-usual emissions from HCFCs and from banks of CFCs andbanks of halons are each projected to contribute roughly comparable amounts to EESC in the next few
decades.

• Elimination of future production of methyl bromide (CH3Br) for quarantine and pre-shipment (QPS) applications, not controlled by the Montreal Protocol, would accelerate the return of mid-latitude EESC to1980 levels by about a year. Production for QPS use has remained relatively stable over the last two decades and now constitutes almost 90% of the reported production of CH3Br since emissions from otheruses have declined dramatically. Non-QPS applications of CH3Br were completely phased out in 2015,
except for approved critical use exemptions, which have declined by a factor of ~30 since 2005.

• If CCl4 emissions continue to decline at the rate observed over the last two decades of 2.5% yr−1, future con-centrations will be about 14 ppt higher in 2050 than projected in the previous Assessment. CCl4 emissions
inferred from atmospheric observations continue to be much greater than those assumed from feedstock u­ ses as reported to UN Environment; by-product emissions from chloromethane and perchloroethylene plants and fugitive emissions from the chlor-alkali process have been quantified as significant
contributors to these additional emissions. Elimination of all CCl4 emissions in 2020 would accelerate
the return of mid-latitude EESC to 1980 levels by almost three years compared to the baseline scenario

6.2

Information for Policymakers | Chapter 6

of a continued emissions decrease of 2.5% yr−1. Alternatively, if future emissions do not decline but
remain at the current level, the return of mid-latitude EESC to 1980 levels would be delayed almost two
years.

• The return of mid-latitude EESC to 1980 levels is estimated to be delayed by almost two years compared tothe previous Assessment, due primarily to the higher projected future concentrations of CCl4. The mid-­latitude EESC change from CCl4 alone leads to a delay larger than two years, but future CH3Br baseline
projections are now lower than in the previous Assessment and offset some of the effect from CCl4. The
delay in polar EESC returning to 1980 levels is slightly more than two years when compared with theprevious Assessment. A new EESC formalism alters the time evolution of EESC and dates when EESC
returns to 1980 levels, but it has little effect on the relative impacts of the various alternative futurescenarios. When compared with the previous Assessment’s EESC formalism, the new EESC formalism
leads to a projected EESC return to the 1980 level 11 years later at mid-latitudes and by less than two
years later at polar latitudes.

• Reducing anthropogenic emissions of N2O from those in RCP-6.0 to the Concerted Mitigation scenario1
would have a similar positive impact on stratospheric ozone over the next four decades as eliminatingproduction of HCFCs from 2020. This N2O emissions reduction would have a larger benefit to climate
over 2020–2060 than the sum of all the options for controlled ODSs considered (based on GWP-weighted
emissions).

Updates on the climate impact of gases controlled by the Montreal Protocol• Future emissions of HFC-23, a potent greenhouse gas and a by-product of HCFC-22 production, are ex-pected to be limited by the Kigali Amendment, which mandates the destruction of HFC-23 to the extent
practicable. Globally, HCFC-22 is currently produced in roughly equal quantities for controlled emissive uses, which are declining, and for the uncontrolled feedstock uses, which grew rapidly over the lastfew decades but have recently stabilized. Future emission trends will largely depend on the extent towhich HFC-23 is destroyed by HCFC-22 production facilities and the amount of HCFC-22 produced.

• Future emissions of HFCs, HCFCs, and CFCs contribute approximately 60, 9, and 3 cumulative Gt CO2-

equivalent emissions, respectively, from 2020 to 2060 in the baseline scenario. Of the 60 Gt CO2-eq emissions from HFCs, 53 arise from future production. For reference, cumulative CO2 emissions from fossil
fuel usage are projected over this time period to be 1,700 Gt CO2 in the RCP-6.0 scenario and 760 Gt
CO2 in the ­RCP-2.6 scenario. The total radiative forcing from CFC and HCFCs and their HFC replacements is projected to continue to increase gradually for the next decade or two. After that point, theODS and HFC restrictions of the Montreal Protocol, if adhered to, ensure a continued decline in total
RF from ODSs and their replacements through the rest of the century.

• Global warming potentials, global temperature change potentials, and ozone depletion potentials of hundreds of HCFCs are presented, most for the first time in an assessment. This comprehensive assessment
includes all the HCFCs listed under Annex C, Group I of the Montreal Protocol, many of which did nothave estimated GWPs at the time of the signing of the Kigali Amendment.

Updates on impacts of climate gases and other processes on future stratospheric ozone
In this section, we summarize potentially important impacts on the future of the ozone layer that could result fromanthropogenic activity not associated with ODS production or consumption and not controlled by the Montreal
Protocol. As noted above, a major issue is that uncertainties in future changes in the ozone layer will be influenced
more by uncertainties in CO2, CH4, and N2O levels than by uncertainties in the levels of ODSs, especially as we
1

UNEP 2013. Drawing Down N2O to Protect Climate and the Ozone Layer. A UNEP Synthesis Report. United Nations Environment
Programme (UNEP), Nairobi, Kenya.
6.3

Chapter 6 | Information for Policymakers

approach the 1980 values of EESC. Increases in greenhouse gas concentrations are predicted to lead to increases inupper-stratospheric ozone at all latitudes, with a more complex pattern of ozone changes in the lower stratosphere,
including a decrease in low latitudes due to changes in dynamics and transport. These processes are discussed indetail in Chapters 3 and 4. Note that natural forces such as large explosive volcanic eruptions could also adversely
affect ozone recovery over the next decade, while ODS levels remain high.

• The wide range of possible future levels of CO2, CH4, and N2O represents an important limitation to making

accurate projections of the ozone layer. Global mean warming as well as stratospheric cooling will driveozone changes through both atmospheric circulation and chemistry. Future ozone levels depend on thepath of greenhouse gas and aerosol emissions as well as the sensitivity of the climate system to theseemissions. Future ODS atmospheric concentrations are more certain than atmospheric concentrationsof climate forcing emissions, as long as there is adherence to the Montreal Protocol. This chapter considers various climate scenarios, using the Representative Concentration Pathways (RCPs) adopted by
the IPCC for its Fifth Assessment Report (AR5). The Paris Agreement, with a stated objective to limit
globally averaged warming to less than 2°C, requires emissions closest to RCP-2.6, the lowest emission
scenario of all the RCP scenarios.

• Intentional long-term geoengineering applications that substantially increase stratospheric aerosols to

mitigate global warming by reflecting sunlight would alter stratospheric ozone. The estimated magnitudes
and even the sign of ozone changes in some regions are uncertain because of the high sensitivity to variables such as the amount, altitude, geographic location and type of injection, and the halogen loading.
An increase of stratospheric sulfate aerosol burden in amounts sufficient to substantially reduce global
radiative forcing would delay the recovery of the Antarctic ozone hole. Much less is known about theeffects on ozone from geoengineering solutions using non-sulfate aerosols.

• Rocket launches presently have a small effect on total stratospheric ozone (much less than 0.1%). Space

industry developments indicate that rocket emissions may increase more significantly than reported in
the previous Assessment. Their impacts will depend on rocket design (particularly the altitude of emissions), launch vehicle sizes, launch rates, spaceport locations, and fuel types. Important gaps remain inunderstanding rocket emissions and their combined chemical, radiative, and dynamical impacts on the
global stratosphere and in projections of launch rates. These gaps limit the confidence level of predictions of present and future impacts of rocket emissions on stratospheric ozone and suggest periodic assessments are warranted. The lifetime of the most important rocket emissions is limited, and the stratospheric accumulation of rocket-emitted black carbon and alumina particles varies in correspondencewith global launch rates and altitude of emissions.

Update on other environmental impacts of Montreal Protocol gases
Here, we refer to all gases controlled under the Montreal Protocol and its various Amendments, including the Kigali
Amendment, as Montreal Protocol Gases.

• There is increased confidence that trifluoroacetic acid (TFA) produced from degradation of HFCs, HCFCs,
and HFOs will not harm the environment over the next few decades. This assessment is based on the current estimates of future use of hydrocarbons, HCFCs, and HFOs. It is noteworthy that HFCs and HCFCs
have atmospheric lifetimes long enough to globally distribute any TFA emissions, while HFOs haveatmospheric lifetimes so short that TFA emissions are deposited near the point of emission. Periodic
re-evaluation is prudent, given the uncertainties in the sources and sinks of TFA and because of its
persistence in the environment.

6.4

Information for Policymakers | Chapter 6

Summary of the impacts of mitigation options and particular scenarios
Figure 6-1 (also Figure ES-9) shows what ozone and climate-relevant changes could be avoided if various actions were taken. These changes are shown as the differences in global total column ozone averaged over 2020–
2060 and in cumulative CO2-equivalent emissions over 2020–2060 relative to the baseline (A1) scenario (which
includes the Kigali Amendment for HFCs). The options available to hasten the recovery of the ozone layer are
limited, mostly because actions that could help significantly have already been taken.
○○ For CFCs, halons, and HCFCs, the most effective mitigation option, not considering technical fea-

sibility, is expanded bank recapture and destruction; elimination of HCFC production starting in2020 would be somewhat less effective.

○○ For CH3Br, elimination of production for currently uncontrolled quarantine and pre-shipment(QPS) applications is shown.

○○ For CCl4, the impacts of total emissions elimination starting in 2020 are shown.
○○ For CH2Cl2, an uncontrolled ozone-depleting gas whose exact sources are unknown, we show that

immediate emissions elimination would have a greater positive impact on total column ozone than
total emissions elimination of CCl4.

○○ For N2O, the impacts of the Concerted Mitigation average scenario from UNEP (2013) are shown,compared to the RCP-6.0 scenario. The Concerted Mitigation scenario was developed by averaging
the four published mitigation scenarios (RCP-2.6, SRES B2, and scenarios 4 and 5 from Davidson,2012) that lead to lower N2O emissions in 2050 than were experienced in 2005.

○○ For HFCs, the impact of a hypothetical complete global phaseout of production (excluding HFC-23) starting in 2020 is shown. As discussed in Chapter 2, for this scenario the surface temperature
contribution of the HFC emissions would stay below 0.02 °C for the entire 21st century and beyond.

Further detail on these options and scenarios is given in Section 6.4 and Table 6-5.
All the scenarios discussed above hasten the ozone layer recovery (CFCs, halons, HCFCs, CH3Br, CCl4, CH2Cl2
and N2O) and reduce warming (HFCs, CFCs, halons, HCFCs, CCl4, and N2O). An additional scenario for emissions that may result from a violation of the Montreal Protocol is shown, namely continuing unexplained emissions of CFC-11 at 67 Gg yr −1, which is the average calculated annual emission from atmospheric concentration
observations over 2002–2016. This scenario leads to more ozone depletion and climate warming. Avoiding this
scenario would have a larger positive impact on future ozone than any of the other mitigation options considered here.

6.5

Chapter 6 | Information for Policymakers

Change in Ozone in Response to Alternative Scenarios
Gases controlled byMontreal Protocol

CFCs
halons
HCFCs

Bank recapture anddestruction in 2020

HCFCs
CH3Br

Elimination of production
from 2020 onwards

CCl4

Elimination of total emissions
from 2020 onwards
Continued constant emissions
at 67 Gg/yr

CFC-11
Gases not controlled byMontreal Protocol–0.2% ozonedecrease

–0.1

0

+0.1

Percent change in total column ozone relative tothe baseline scenario averaged over 2020–2060

N2O

Concerted Mitigation scenario
from UNEP (2013)

CH2Cl2

No future emissions+0.2% ozoneincrease

Change in GWP-Weighted Emissions in Response to Alternative Scenarios
Gases controlled byMontreal Protocol

Elimination of high-GWP
production from 2020 onwards(excl. HFC-23)

HFCs
CFCs
halons(0.21)

Bank recapture anddestruction in 2020

HCFCs
HCFCs
CCl4
CFC-11
Gases not controlled
by Montreal Protocol+20

Increasedemissions

+10

0

N2O
–10

–20

–30

–40

–50

–60

Elimination of production
from 2020 onwards
Elimination of total emissions
from 2020 onwards
Continued emissions
at 67 Gg/yr
Concerted Mitigation scenario
from UNEP (2013)

Reducedemissions

Changes in cumulative GWP-weighted emissions over
2020–2060 relative to the baseline scenario (Gt CO2-eq yr-1)

Figure 6-1. Ozone and climate-relevant impacts of alternative future scenarios compared with the baseline scenario. The climate-relevant metric is chosen to be the integrated GWP-weighted emission from2020 to 2060, and the ozone-relevant metric is the percentage change in total column ozone averaged
over 2020 to 2060. A decrease in total ozone and an increase in GWP-weighted emissions occur when
future emissions are higher than in the baseline scenario for the compounds considered. Numerical values of these changes are shown in Table 6-5.

6.6

Chapter 6
Scenarios and Information for Policymakers
6.1

INTRODUCTION

The World Meteorological Organization (WMO)
Ozone Assessments have reported on the ozone layer
and related processes since the 1980s. Since 1989,
the Assessments have focused on reviewing progressof the control measures introduced under the 1987
Montreal Protocol and its subsequent Amendments
and adjustments. The Protocol is widely acknowledged to have been highly successful, resulting in
striking reductions in the total amount of ODSs in theatmosphere (see Chapter 1) and more recently in an
upturn in upper-stratospheric ozone levels (Chapter3), giving confidence in the projections that ozone
will recover sometime around mid-century at mid-­latitudes and the Arctic, and somewhat later for the
Antarctic. In addition, it was shown around a decadeago that the Montreal Protocol and its Amendments
and adjustments had contributed more to climatechange mitigation than any other existing international agreement at that time (Velders et al., 2007).
In this chapter, we focus on possible options andscenarios to aid policymakers in decisions related
to protecting stratospheric ozone and minimizing
effects on climate from ODS halocarbons and theirreplacements. As production and consumption ofcontrolled ODSs have continued to decline, options
for reducing their future emissions have become
somewhat more limited but still exist and with significant potential for ozone and climate protection.
One potentially important new result is a slower
decline in CFC-11 atmospheric concentrations thanprojected in the last Assessment; the implications of
this result are discussed in Section 6.4. Also, growthin very short-lived substances, such as CH2Cl2, andother compounds not covered by the Protocol (e.g.,
N2O) could have important effects on the future evolution of stratospheric ozone.
A major new development relating to policy is theKigali Amendment to the Montreal Protocol, which
introduces controls on HFCs. HFCs are replacement
compounds that have only small effects on ozone
depletion. However, many of these compounds incurrent commercial use are strong greenhouse gases
and therefore affect climate. The Kigali Amendment
comes into force on January 1, 2019, as it has now
been ratified by the threshold 20 parties to theMontreal Protocol. This chapter summarizes the projected impacts of the Kigali Amendment on climate(which are discussed in detail in Chapter 2) and also
examines some other processes and policies unrelated
to Montreal Protocol gases, including impacts of proposed stratospheric aerosol geoengineering interventions that might alter stratospheric ozone.

6.1.1

Summary of Findings from thePrevious Ozone Assessment

Chapter 5 of the previous Assessment evaluated theimpacts of various hypothetical policy options for
reducing future emissions of ODSs, including elimination of production and bank recapture and destruction (Harris and Wuebbles et al., 2014). Updates to
Ozone Depletion Potentials (ODPs), Global Warming
Potentials (GWPs) and, for the first time in an Ozone
Assessment, Global Temperature change Potentials(GTPs), were also presented. The main findings were•Emissions from the 2015 ODS banks throughto 2050 were projected to lead to greater futureozone depletion and climate forcing than thosecaused by future ODS production. Halon banks
were projected to contribute most to ozone depletion, while CFC and HCFC banks were expected
to contribute most in terms of GWP-weighted
emissions.

•

The impact on the recovery of stratospheric ozone
of further policy actions on ODSs that had alreadybeen controlled was becoming smaller, but if all
ODS emissions ceased (including from banks),
then the return to 1980 mid-latitude equivalenteffective stratospheric chlorine (EESC) values
would be brought forward by about 11 years fromthe baseline scenario of 2047.

•

Global ozone was expected to increase to above
pre-1980 levels due to future projected increases
6.7

Chapter 6 | Information for Policymakers

in carbon dioxide (CO2) and methane (CH4),
which act to increase globally averaged ozone.
•

Future CCl4 emissions remained more uncertain
than for other ODSs due to incomplete understanding of the current budget—with likely missing source(s)—but were expected to remain an
important factor in the evolution of EESC.

•

There was still insufficient research available toconfidently compare the options of mitigating
emissions of anthropogenic very short-lived substances (VSLSs) with those of the longer-lived
ODSs, but VSLSs were expected to play a relatively larger role in future ozone depletion if
long-lived controlled halocarbons followed theirprojected decline.

•

Quarantine and pre-shipment (QPS) uses of
CH3Br—which are exempted uses not controlled
by the Montreal Protocol—constituted an annual consumption that was larger than that fromuses controlled by the Protocol. The eliminationof future emissions from QPS uses would havebrought forward the date of EESC return to 1980
levels by 1.1 years relative to the baseline scenario.

•

The direct radiative forcing (RF) from ODS halocarbons (CFCs, halons, and HCFCs) in 2014 wasabout 0.33 W m−2 and near its expected peak. The
RF was projected to decrease to about 0.20 W
m−2 by 2050 and to near 0.10 W m−2 by 2100, assuming continued compliance with the Montreal
Protocol.

•While HFCs constituted less than 1% of the RF on
climate (0.02 W m−2), if the mix of HFCs remained
unchanged, increasing demand would have implied a radiative forcing for HFCs as high as 0.4
W m−2 by 2050. Replacement of the current mixof high-GWP HFCs with low-GWP compounds
was projected to have the potential to lead to a decrease in the projected RF possibly by as much as
0.07 W m−2 by 2030 relative to the baseline scenario. HFC banks were also stated to be an importantconsideration when estimating the impact of HFC
mitigation on future climate change.

•

6.8

If (hypothetical) geoengineering of the climate
system via anthropogenic increases of stratospheric sulfate aerosols were to occur within thenext few decades, it could deplete stratosphericozone, with the largest effects in the polar regions,although quantitative studies were limited.
Since the last Assessment, in addition to peer-reviewed publications, several reports have addressed
topics of direct interest for this chapter:•

The SPARC report, Solving the Mystery of Carbon
Tetrachloride (SPARC, 2016). This report identifies four specific emission pathways for CCl4,
which have not been well quantified by previous
Ozone Assessments, and shows that these pathways, combined with revised lifetime estimatesfor CCl4, result in a reduced discrepancy between
known atmospheric sources and sinks. These pathways are (1) fugitive emissions from incineration,
feedstock usage, and process agents; (2) unreported non-feedstock emissions during productionof chlorinated methanes and perchloroethylene;(3) unreported inadvertent emissions during the
production and use of chlorine gas; and (4) legacy
emissions from contaminated land areas.

•

Reports produced by the UNEP Technology
and Economic Assessment Panel (TEAP). These
reports continue to assess the technologicaland economic possibilities for phasing in commercially available replacements for ODSs (e.g.,
UNEP, 2016). This provides key information on
the technical feasibility of scenarios considered inthis chapter that assume reductions in future production or the capture and destruction of banks.

6.1.2

Key Issues to Be Addressedin This Assessment

The majority of this chapter is dedicated to assessing
the potential future impacts of a number of ozone-relevant processes and activities on ozone depletionand climate forcing, in order to aid policy decisions
regarding stratospheric ozone protection and related climate issues. Simple, well-established metrics
are used to provide information about the effectof emissions from human activity on stratospheric ozone and climate. Ozone metrics include Ozone
Depletion Potentials (ODPs) and equivalent effective stratospheric chlorine (EESC). Climate metrics
used are Global Warming Potentials (GWPs), Global
Temperature change Potentials (GTPs), and radiative

Information for Policymakers | Chapter 6

forcing (RF). GWPs and GTPs are presented for an
extensive set of less common HCFCs for the first timeand are updated for other HCFCs and HFCs in GroupI of Annexes A, C, and F of the Kigali Amendment.
New ozone and climate metrics for short-lived haloolefins, which are characterized by very small ODPs,
GWPs, and GTPs, are also reported.
New scenarios that incorporate previously reported
bottom-up bank estimates, the latest ODS mixing
ratio observations, and reported production are generated in this chapter, and the potential impacts on future ozone depletion and climate forcing are calculated. These scenarios investigate effects of hypotheticalchanges in emissions and are illustrative of potential
mitigation actions. For the first time, projections ofan anthropogenic VSLS (CH2Cl2) are incorporated
into these scenarios. The impact of future HFC abundances on climate forcing, with regard to the Kigali
Amendment (Section 6.2.1.2), is also assessed, using
the scenarios presented in Chapter 2.
Consistent with Chapters 3 and 4 in this Assessmentin their analyses of future projections of ozone, we use
the Representative Concentration Pathway (RCP) 6.0
scenario (IPCC, 2013) as the baseline emission scenario for CO2, CH4, and N2O. The sensitivity of the
projected impacts of these greenhouse gases on stratospheric ozone is investigated by additionally using the
RCP2.6, RCP4.5, and RCP8.5 scenarios. Our primaryreason for using a variety of future greenhouse gas(GHG) scenarios is to assess the range of ozone impacts of the compounds relative to those controlled
under the Montreal Protocol. The RCP2.6 scenario is
the one that most closely complies with the stated goalof the Paris Agreement to hold “the increase in the
global average temperature to well below 2°C above
pre-industrial levels and pursuing efforts to limit thetemperature increase to 1.5°C above preindustrial
levels” (IPCC, 2013a). It should be noted that a newgroup of scenarios (Shared Socioeconomic Pathways,
or SSPs) has recently been developed. The SSPs are
based on alternative socioeconomic projections that
could arise from plausible major global developments(O’Neill et al., 2017; Riahi et al., 2017). This new scenario framework, established by the climate change
research community, will be used for the new generation of earth system models as part of the Coupled
Model Intercomparison Project Phase 6 (CMIP6).
Here, in common with the rest of this Assessment, we

use the RCP scenarios that were adopted by the IPCC
for its Fifth Assessment Report (IPCC, 2013b).
In common with previous Assessments, this chapter also assesses some other potential influences on
stratospheric ozone that do not involve the emissionof chlorine- and bromine-containing source gases,
including deliberate climate intervention and rockets.
Here, and elsewhere in the chapter, key gaps in our
understanding that prevent a firm assessment of future ozone levels are identified.

6.2

ISSUES OF POTENTIAL­IMPORTANCE TO ­STRATOSPHERICOZONE AND CLIMATE

6.2.1

Halocarbons Controlled Under
the Montreal Protocol

Implementation of the Montreal Protocol controls
has resulted in significantly lower EESC than would
otherwise have occurred (WMO, 2014, and preceding reports) as well as significant reductions in radiative forcing of climate change. The majority of ODSs
that were originally controlled under the Montreal
Protocol are now declining in the atmosphere. The atmospheric abundance of CFCs has declined substantially (Chapter 1) mainly due to their substitution by
HCFCs, HFCs, and not-in-kind (NIK) solutions including non-fluorinated compounds; although some
CFCs, notably CFC-11, have not dropped as quickly
as expected over the last few years, and the reasonsfor this are not well understood. The observed rate of
decline of atmospheric CCl4 also remains slower than
predicted, and there is new understanding of potential
additional sources that include by-product emissions
from chloromethane and perchloroethylene plants,
although a discrepancy between sources and sinks
still exists. Section 6.4 discusses the implications ofthese uncertainties on future EESC.
Atmospheric CH3Br results from both natural and anthropogenic emissions, the latter mainly from its use
as a fumigant. The atmospheric abundance of CH3Br
has continued to decline, most likely due to the phaseout (completed in 2015) of controlled industrial production and consumption (Chapter 1). Critical use
exemptions (CUEs) for the controlled uses, applied forby developed countries since 2005 and by developing
countries since 2015, have declined dramatically from
6.9

Chapter 6 | Information for Policymakers

their peak in 2005 of 21.8 Gg yr−1 and now represent
only a small fraction (<10% or 0.7 Gg) of annual useof CH3Br. Quarantine and pre-shipment (QPS) usesof CH3Br, which are not controlled by the Montreal
Protocol, have remained relatively stable over the last
decade and in 2016 constituted an annual consumption of 8.4 Gg.
HCFCs, which are transitional replacement compounds, are still increasing in the atmosphere, although growth rates have slowed in response to theirphaseout schedules, which were established first in
1992 with an accelerated phaseout established in 2007
(Harris and Wuebbles et al., 2014). In non–Article
5 countries, HCFC production started decreasingaround 2000, well before the phaseout schedule
started in 2004, whereas the production of HCFCs
in Article 5 parties continued to rise until 2012 andstarted decreasing just when the phaseout schedule
went into effect in 2013 (UNEP, 2017). The consumption of HCFCs in Asia in Article 5 parties in 2015
accounts for the majority of global consumption ataround 70%. Thus, for achieving further global HCFC
emissions reductions, complying with or even accelerating the earlier phaseout schedule (by promoting
the replacement of HCFCs by low-GWP fluorinatedcompounds or non-fluorinated compounds and other
NIK solutions) in Asia plays an important role in reducing future HCFC emissions. An important sourceof emissions not controlled by the Montreal Protocol
is emissions from banked ODSs, mainly from uses
such as refrigerants and foams. If no further policy
actions for banked ODSs are considered in Article
5 parties, especially in Asia, emissions from banked
HCFCs will become larger than those of banked CFCs
(Daniel et al., 2011). Thus, banked HCFCs will be amajor source of both ODSs and climate emissions in
the coming decades. Effective measures for reducing
emissions from banked HCFCs are recovery and destruction from banked ODSs when equipment thatuses refrigerants and foams is disposed, together withappropriate management for reducing leakage during
the operation of such equipment (Box 5-1 of Harrisand Wuebbles et al., 2014). In Section 6.4 we discuss
the effect on future EESC and ozone of policy options
that include reducing HCFC leakage by bank recapture and destruction and eliminating production of
HCFCs.

6.10

Halons are particularly important to ozone depletion
because they contain bromine, which is roughly 60
times more effective at depleting ozone than chlorine.
However, because of their smaller atmospheric concentrations, halons influence climate to a much smaller degree than other ODSs such as CFCs, HCFCs, andCCl4. In Harris and Wuebbles et al. (2014), futureleakage of halons from their banks was found to be
the largest contributor to ozone depletion throughto 2050. While, for the Assessments, we assume thatbank capture is 100% effective, in reality the accessibility and profitability of the various banks are important factors in destroying emissions; for example,
halon bank capture from fire-fighting equipment for
use in new aircraft where alternatives are not available
is generally much more cost effective than CFC and
HCFC bank capture from foams used in home insulation where alternatives are widely available.

6.2.1.1	Replacement Compounds
The reduction in ODS emissions has occurred as aresult of NIK technology; containment, recovery, andrecycling actions; and replacement by compounds
that do not have significant ODPs. Examples of NIK
technology include mechanical pumps or hydrocarbons to replace ODS propellants in consumer andcommercial applications, CO2 or hydrocarbons toreplace ODSs in foam-blowing applications, and ammonia in industrial refrigeration. Ammonia and thehydrocarbons used (ethane, propane, and butane)
do not have any significant impact on stratosphericozone or radiative forcing of climate change. Of thein-kind replacement compounds, HFCs are by far
the most important (for climate; they are non-ozone
depleting), with HFC-134a accounting for the majority of HFC production, emissions, atmosphericconcentration, and radiative forcing (see Chapter2). Oxygenated compounds (e.g., hydrofluoroethers)
have found use in niche applications. The decreased
use of CFCs, and now HCFCs, has resulted in increased HFC use, particularly in the refrigeration
and air conditioning sectors and, to a lesser degree,in the foam and fire protection sectors. Annual global
production of CFCs peaked in the late 1980s (UNEP,
2017), HCFC production peaked in the late 2000s(UNEP, 2017), and HFC production and emissions
continue to increase (McCulloch and Midgley, 2001;
Velders et al., 2015a) (see also Chapters 1 and 2). As

Information for Policymakers | Chapter 6

will be discussed in Section 6.2.6, the atmosphericdegradation of HFCs neither contributes significantly
to tropospheric ozone formation nor yields productsthat pose a significant known risk to human or ecosystem health.
HFCs typically have lower GWPs than the CFCs and
HCFCs they have replaced. Yet, some HFCs such as
HFC-23 (GWP =12,400), HFC-143a (GWP = 3,170),
and HFC-125 (GWP = 4,800), and to a lesser extent
HFC-134a (GWP = 1,300), have high GWPs. Whilethe current contribution of HFCs to radiative forcingof climate is small at approximately 0.02 W m− 2 (seeChapter 2), the potential future growth in the emissions of high-GWP HFCs has given rise to concerns
about their possible future climate impact (UNEP,
2011; Velders et al., 2009; Velders et al., 2015b; Velders
et al., 2012; WMO, 2011; 2014; Wuebbles et al.,2013) and to the Kigali Amendment of the Montreal
Protocol discussed in the next section.
To minimize ozone layer depletion and have minimal
impact on climate, ODS replacements need to have
low ODPs and low GWPs. Meeting such conditions
requires replacement compounds to not contain chlorine or bromine, have short lifetimes, and/or haveweak infrared (IR) absorption cross-sections.
Halogenated alkenes (halogenated olefins, HFOs) suchas HFO-1234yf, HFO-1234ze(E), HCFO-1233zd(Z),and HCFO-1233zd(E) are commercially important
short-lived replacement compounds (Brown, 2009;
Burkholder et al., 2015; Wallington et al., 2010; 2017).
Members of this class of compounds have low ODPs
and GWPs (Appendix A) and have been developedfor use as refrigerants, aerosol propellants, degreasing
agents, and foam-blowing agents (Burkholder et al.,2015; Wallington et al., 2017). HFOs can be used on
their own or as blends with HFCs. A number of othershort-lived compounds have also been proposed as
replacements for long-lived ODSs and HFCs (seeChapter 1).
The energy efficiency of the equipment used can also
be very important to the indirect impact of replacement compounds on climate forcing. In fact, for some
applications, the indirect impact on climate throughenergy efficiency can be far more important than the
direct impact through emissions of the gas itself, as
discussed below and in Chapter 2.

6.2.1.2	Kigali Amendment
The Kigali Amendment comes into force on January
1, 2019, as it has now been ratified by the threshold
20 parties to the Montreal Protocol. Under Annex F,
it includes 18 controlled HFC substances and forms aframework of regulations in 4 country groupings; themain non–Article 5 (non-A5) parties, other non-A5
parties, Group I A5 parties, and Group II A5 parties.
It subsumes all current policies for HFCs. Unlike thecurrent restriction of ODSs listed in the Montreal
Protocol, rather than aim for a complete phaseout,
the amendment aims to achieve 80–85% phaseout of
production and consumption of HFCs compared tobaseline levels. The amendment aims to implement
the phasedown of HFC production and consumption
starting in 2019 for most developed countries, in 2024
for most developing countries, and in 2028 for somedeveloping countries. The Amendment also mandates
that HFC-23 emissions should be destroyed to the extent practicable by all countries.
The Kigali Amendment will significantly limit the future production and consumption of HFCs. Under thecurrent control measures, emissions of HFCs are projected to peak around 2035, about a decade after the
peak in global production and consumption, due togradual emissions from refrigeration and air-conditioning equipment (so-called banks). The HFC bank,therefore, represents a substantial source of emissions
and radiative forcing after production is phased down
(Velders et al., 2014). Emissions of HFC-23 (formed asa by-product of HCFC-22 manufacture, see Section
6.2.1.3) should also be significantly limited. The contribution of HFCs (excluding HFC-23) to the global
average surface temperature change is projected toreach a maximum—assuming compliance with theKigali Amendment—of around 0.07°C by 2060, after
which it decreases slowly to about 0.06°C by 2100
(Chapter 2). Without Kigali, surface temperature
warming from HFCs might have been as high as 0.3°–
0.5°C by 2100 (Chapter 2). Of course, adjustments
to the HFC control schedules analogous to historicaladjustments to the ODS control schedules could substantially reduce the climate impact.
The Kigali Amendment regulations mandate not only
different phasedown schedules of production andconsumption (for the different country groupings)
but also different settings of base year and baseline
6.11

Chapter 6 | Information for Policymakers

expressed in CO2 equivalents. The baselines in nonA5 parties are set based on historical data of HFCs
(2011–2013) plus 15% of the baseline consumption of
HCFCs, whereas the baselines in A5 parties are set atthe average amounts of HFCs from 2020 to 2022 inGroup I countries, and the average amounts of HFCs
from 2024 to 2026 in Group II countries, plus 15% of
the baseline consumption of HCFCs for both groups(Chapter 2, Table 2-3). Thus, the effects of emissions
reductions due to the Kigali Amendment will dependon the levels of baseline consumption in A5 parties
even before the phasedown schedule starts, which
in turn depends on incentives to reduce the consumption of HFCs and promote the replacement to
non-fluorinated compounds.

non–Annex 1 countries (mainly China) was increasing, including from plants that did not have abatement
technology (Fang et al., 2014; Simmonds et al., 2018).
The Montreal Protocol regulates for dispersive uses of
HCFC-22 (such as refrigerants and foams), which arebeing reduced. However, around half of the current
production of HCFC-22 is for feedstock uses (Miller
et al., 2010; WMO, 2014) (such as fluorine-contained
resin and components of sophisticated technologies),
which are uncontrolled. Total HCFC-22 production
increased rapidly in the past few decades but has recently stabilized (Miller et al., 2010), showing that
expansion of feedstock use has not matched the decrease in emissive uses and indeed also appears tohave stabilized over the last few years.

Options are available to accelerate the phasedown
schedule and provide additional avoided GWPweighted emissions of HFCs, including use of technologically feasible low-GWP alternatives in place ofhigh-GWP HFCs in refrigeration and air-­conditioning
equipment during the phasedown (Xu et al., 2013).
Additional benefits of the Kigali Amendment could be
gained via the development of more energy-efficientequipment that uses these low-GWP replacements,
since the CO2 emissions resulting from the energy
used by the equipment are important contributors to
the total climate impact related to refrigerant use (e.g.,
Shah et al., 2015).

The Kigali Amendment mandates all HCFC-22 producing facilities to collect and destroy the emitted
HFC-23 by-product “to the extent practicable,” although reduction schedules and frameworks are still
under discussion. The emission reductions can begained for most developing countries because many
companies set up destruction facilities under the
CDM. However, although the incremental cost of
HFC-23 destruction is far less than the price paid by
the CDM, the cost for renewal of the destruction facilities and operating the incineration may be an issue
for some developing countries. In summary, emissions of HFC-23 are expected to be reduced under
the full implementation of the Kigali Amendment,
assuming declines mandated by the Protocol outpace
any increases in HCFC-22 production. However, thefuture trajectory of HFC-23 emissions is uncertainand depends on the amount of HCFC-22 produced,
the efficiency of avoiding unwanted HFC-23 by-­products, and whether the amount of HFC-23 incinerated increases or decreases.

6.2.1.3	HFC-23
HFC-23 is formed as a by-product at the reactor stageof the manufacture of HCFC-22. Atmospheric emissions can be avoided if HCFC-22 production is managed for better containment and if the HFC-23 is incinerated. Incineration projects in developing countries
have been supported through the Clean Development
Mechanism (CDM) of the Kyoto Protocol (http://
cdm.unfccc.int), allowing a significant fraction of
the HFC-23 produced in HCFC-22 facilities to beincinerated during the period 2006–2013. Despite
these mitigation efforts, there has been a resurgencein emissions since 2009 (Rigby et al., 2014), withemissions in 2013–2015 similar to or slightly higher
than in 2006, when CDM-facilitated destruction had
yet to be fully implemented (Simmonds et al., 2018).
This increase in emissions is attributed mainly to thefact that no new CDM projects were awarded after
2009, while HCFC-22 production for feedstock use in
6.12

6.2.2

Breakdown Products

The atmospheric degradation of HCFCs, HFCs, and
HFOs is initiated by reaction with OH radicals leading
to the formation of halogenated carbonyl compounds,
which undergo further oxidation to yield HF, HCl,
CO2, and, in some cases, trifluoroacetic acid (TFA)(Burkholder et al., 2015; Calvert et al., 2008; IPCC/
TEAP, 2005; Wallington et al., 1994; WMO, 2011,2014). The photochemical ozone creation potentials
of HCFCs, HFCs, and HFOs are very small, andtropospheric ozone formation resulting from their

Information for Policymakers | Chapter 6

degradation is of negligible importance (Hayman andDerwent, 1997; Luecken et al., 2010; Wallington etal., 2015; WMO, 2011, 2014). The additional burdenof HF, HCl, and CO2 at the concentrations expectedfrom atmospheric degradation of HCFCs, HFCs, and
HFOs is of no consequence.
TFA is a product of the atmospheric degradation of
HCFC-123 and several commercially important ODS
replacement compounds such as HFC-134a, HFO1234yf, and HFC-227ea. HFO-1234yf has five times
the TFA breakdown products than the HFC-134a it replaces in mobile air conditioners. TFA is a ubiquitous
natural component of the hydrosphere, with manysources beyond the halocarbons controlled under
the Montreal Protocol (e.g., Scheurer et al., 2017). It
is present in ocean water, even at great depths and inremote locations, at a concentration of approximately
200 ng /l (Solomon et al., 2016). In surface freshwater,
TFA levels are typically 10–300 ng l−1. TFA is stable in
the environment and accumulates in terminal water
bodies such as salt lakes. TFA levels in the Dead Sea
have been reported to be 6,400 ng l−1 (Boutonnet et al.,1999). Currently, the oxidation of HFC-134a makes
the largest contribution from ODS replacements to
TFA formation. The global background atmosphericconcentration of HFC-134a is approximately 100 ppt.
Assuming a 7–20% molar yield of TFA (Wallington
et al., 1996), rainout as the sole atmospheric fate of
TFA, and annual global precipitation of 5 x 1017 liters
Warneck and Williams (2012) give an estimate of
20–50 ng l−1 for the current average TFA concentration in global precipitation resulting from HFC-134adegradation. Local concentrations will be higher orlower than the global average depending on local precipitation volumes and photochemical activity. The
concentrations of TFA observed in rainwater typically
substantially exceed those that can be accounted forby HFC-134a degradation, indicating the presenceof significant sources other than HFC degradation(Frank et al., 1996; McCulloch and Lindley, 2003;
Wu et al., 2014a, b). HFC-134a is currently being replaced by HFO-1234yf in applications such as mobile
air conditioners. HFO-1234yf has a higher, 100%,
molar TFA yield and degrades more rapidly and closer to its emission sources than HFC-134a with 20%
molar TFA yield. Luecken et al. (2010), Russell et al.
(2012), Henne et al. (2012), Kazil et al. (2014), andWang et al. (2018) have estimated TFA concentrations

in rainwater resulting from future use of HFO-1234yf
and report similar findings. Average concentrations inrainwater are projected to be of the order of 1,000 ng
l−1. Regions with lower rainfall have higher concentrations but lower total deposition. Increases in TFA
levels in terminal water bodies in North America of
1,000–15,000 ng l−1 with a maximum of 200,000 ng l−1
in the Sonoran Desert were projected in a modelingstudy of 50 years of future HFO-1234yf use (Russell
et al., 2012).
It has been shown recently that the reaction of Criegee
intermediates with TFA in the gas phase is extremely
rapid (Chhantyal-Pun et al., 2017). Criegee intermediates are present in the atmosphere as a result of thereaction of ozone with alkenes and play an important role in atmospheric chemistry over landmasses
with vegetation, where biogenic emissions of alkenes(e.g., isoprene and terpenes) are significant and ozone
is available. Reactive loss of TFA via reaction with
Criegee intermediates could be an important loss
mechanism for TFA, and it has not been accounted forin atmospheric models of TFA deposition. Inclusionof this new gas-phase chemistry in atmosphericmodels could decrease the projected deposition of
TFA over landmasses (by as much as a factor of two)
(Chhantyal-Pun et al., 2017).
The effects of TFA on human and ecosystem healthresulting from the use of compounds regulated
under the Montreal Protocol have been assessed by
Solomon et al. (2016). Mammals are insensitive to
TFA (Boutonnet et al., 1999), and plants and otheranimals have a high tolerance to TFA. Solomon et al.
(2016) tested a worst-case scenario with upper-limit
TFA levels estimated for the future use of HFC-134a,
HFO-1234yf, HFC-143a, and HFC-227ea through theyear 2050 and selecting the most sensitive biological
endpoints for different species. The no-observed-effect-concentrations for aquatic organisms consideredby Solomon et al. (2016) were in the range 3 x 107–2.4
x 109 ng l−1, with an outlier at 1.2 x 105 ng l−1. Risks
for humans, terrestrial vertebrates, plants exposed via
soil, and aquatic plants and animals were assessed to
be de minimis (Solomon et al., 2016). Risks for organisms in salt lakes and playas were not assessed
because there are no data on the toxicity of TFA forsuch organisms. It was noted that future increases
in TFA levels resulting from ODS replacement degradation in salt lakes will be small compared to the
6.13

Chapter 6 | Information for Policymakers

existing burden of other natural salts in such locations(Solomon et al., 2016). The large body of publishedfield measurements, toxicological studies, modelingstudies, and environmental assessments point to aclear conclusion: The current and estimated future
concentrations of TFA and its salts resulting fromdegradation of HCFCs, HFCs, and HFOs do not pose
any known significant risk to human or ecosystem
health (Solomon et al., 2016).

6.2.3

Very Short-Lived Substances (VSLSs;
Biogenic and Anthropogenic)

VSLSs have not been controlled by the Montreal
Protocol since it has been mistakenly assumed they
have a negligible impact on stratospheric ozone dueto their short atmospheric lifetimes and/or because
they are dominated by biogenic sources. Previous
Assessments had considered n-propyl bromide as an
ODS with a latitude-dependent ODP. Since the last
Assessment, a number of new studies predict a significant impact of VSLSs on ozone as discussed below.
Chlorinated VSLSs are predominantly anthropogenic
in origin (Chapter 1). They currently contribute onlya small fraction (<10%) to total stratospheric chlorine but are becoming more relevant for stratosphericozone due to increased emissions of CH2Cl2, which
is used as an industrial solvent, as a blowing agent in
the production of foam plastics, and as a feedstock
or by-product in the production of other chemicals(Campbell and Shende, 2005; Simmonds et al., 2006).
Surface concentrations have increased by around 8%(2.85 ppt) per year between 2004 and 2014 (Hossaini
et al., 2017). Assuming this mean growth rate continues linearly, Hossaini et al. (2017) predict that CH2Cl2
could delay the return of total lower stratospheric Cly
to pre-1980 levels by 15–17 years, and by 2050, reduce annual mean ozone concentrations in the lower
stratosphere by 6%—effects that are much larger than
the influence of potentially eliminating future small
levels of production or emission of CFCs and HCFCs.
In Section 6.4 we examine the effects of eliminating
emissions of CH2Cl2 on future ozone and the influence of different RCPs and varying OH levels on projections of the impact of CH2Cl2 on future ozone.
Biogenic VSLSs—mainly CHBr3 and CH2Br2—account for an appreciable fraction (~30%) of total
stratospheric bromine. The majority of CHBr3 and
6.14

CH2Br2 emissions come from oceanic marine algae,
mainly seaweeds (Carpenter and Liss, 2000). Whilemost models currently assume fixed emissions or
atmospheric mixing ratios of VSLS Br (Hossaini etal., 2016), oceanic emissions may undergo futureclimate-induced or other anthropogenically inducedchanges. Changes in surface winds and sea surface
temperature and removal of sea ice would likely increase the oceanic sea–air fluxes of VSLS Br (Tegtmeier
et al., 2015; Falk et al., 2017); however, possible changes in biological oceanic production are not sufficiently
well understood. While currently believed to be small(Leedham et al., 2013), future anthropogenic emissions of VSLS Br in the form of seaweed aquaculture
have also been projected to substantially increaseover the next years, particularly from Southeast Asia
(Radulovich et al., 2015; WMO, 2014). The ODPs of
VSLSs are highly sensitive to growing emissions inthis region (e.g., Tegtmeier et al., 2015).
The effectiveness of both brominated and chlorinated
VSLSs as ODSs depends not only on their emissions
but also on chemical processing and the strengthand location of convective transport (Chapter 1). In
addition, the phaseout of the long-lived chlorinatedsource gases under the Montreal Protocol and theresulting decline in stratospheric chlorine mean thatbromine-mediated O3 destruction via the BrO + ClO
catalytic cycle will decrease over the 21st century.
However, there is not currently a consensus on whether the combination of these factors causes a delay inthe return of Antarctic ozone to pre-1980 levels due tobromine VSLS. Falk et al. (2017) find that changes inatmospheric chemistry and transport and a decreaseof anthropogenic chlorine may result in a decreasein the total amount of stratospheric Br y VSLS and its
impact on ozone during the 21st century, despite increasing VSLS Br emissions. In contrast, Tegtmeier etal. (2015) project a 31% increase of the ODP-weighted
emissions of CHBr3 by 2100, compared to present values under the RCP8.5 scenario, due to a larger convective updraft mass flux in the upper troposphere andincreasing emissions, offset by less effective catalyticozone destruction. Fernandez et al. (2017) find that
VSLS Br chemistry affects the depth and duration of
the Antarctic ozone hole and will dominate Antarctic
ozone seasonality by 2100, but that its inclusion ina global model does not result in a significant delay
to the modelled ozone return date to 1980 levels.

Information for Policymakers | Chapter 6

Conversely, Yang et al. (2014) and Oman et al. (2016)derived an increase of between 7 and 10 years in the
return date when VSLS Br are included.
In summary, emissions of VSLS Br from seaweedfarming and from physical sea–air exchange may increase in the future, but overall changes in VSLS Br
emissions from these combined effects, along withpotential climate-induced changes in natural oceanic
production, are not known to any degree of certainty.
Whether increased future VSLS Br emissions would
actually lead to a delay in O3 recovery is also highly
uncertain, with contrasting results due to structural differences between models and internal model
variability.
Previous Assessments have concluded that iodine
chemistry likely has a negligible role in determining levels of stratospheric ozone, based on available­remote-sensing measurements of iodine in the lowermost stratosphere. Saiz-Lopez et al. (2015) however
calculate that significant levels of total reactive iodine,
between two and five times larger than the currently
assumed upper limits, can be injected into the stratosphere and exert an ODP similar to, or even larger
than, that of VSLS Br. There are currently no projections of how iodinated VSLSs might affect the future
evolution of the stratospheric ozone layer, althoughoceanic iodine emissions are predicted to have increased over the 20th century due to increases in surface ozone concentrations (Sherwen et al., 2017).

6.2.4

The Key Climate Gases: Carbon­Dioxide, Methane, and Nitrous Oxide

The most important drivers of climate change overthe last century are the well-mixed greenhouse gases(GHGs) carbon dioxide (CO2), methane (CH4), andnitrous oxide (N2O), with HFCs as a growing new
threat to climate as a consequence of its widespread
use as a transitional substitute for ODSs. With atmospheric lifetimes of a decade or more, these gases
are circulated and mixed around the globe to yieldsmall inter-hemispheric gradients. The atmosphericabundances and associated radiative forcings on climate from these gases have increased substantially in
the industrial era (see Chapters 1, 3, and 5). Futurechanges in halogen concentrations will take placeagainst the backdrop of the changes occurring in
the climate and in the chemical and radiative effectsof these GHGs. The Paris Agreement within the
United Nations Framework Convention on Climate
Change (UNFCCC) enhances previous UNFCCC
targets for reducing overall climate forcing, startingin the year 2020, and aims to limit the global average temperature increase this century to well below
2°C above preindustrial levels and to pursue efforts
to limit the temperature increase to 1.5°C. Within theAgreement, each country determines its own contribution towards the global goals, called Nationally
Determined Contributions (NDCs). One key aspect
for the Montreal Protocol is that the Paris Agreement
does not give stringent guidance on the levels of themost important climate gases for stratospheric ozone,
namely CH4 and N2O. Changing concentrations of
CH4 and N2O can significantly affect the amounts ofhydrogen oxides (HOx) and nitrogen oxides (NOx)concentrations in the stratosphere, which also affectthe concentration and distribution of ozone. The NOx
produced from dissociation of N2O decreases stratospheric ozone, while CH4 can lead to regions of net
ozone production, particularly in the troposphere,
and to regions of depletion, but in the global averageleads to additional ozone. The continuing increase in
the global CO2 concentration and, to a lesser degree,
the increase in global CH4 concentration also haveimportant effects on stratospheric ozone throughcooling of the stratosphere, which slows the ozone
chemical loss rates. The resulting climate change fromincreasing GHGs also strengthens the stratospheric
Brewer–Dobson circulation (BDC), which will redistribute ozone (see Chapter 5).
Future ozone levels will be strongly dependent on theactual future emissions and concentrations of theseclimate gases. Once the NDCs of the major countries
are available beyond the current estimates of 2030,
going out to mid-century or beyond, it will be possible
to better estimate the impact of the Paris Agreementon the future state of the ozone layer and also better
determine the dates for the return of the ozone layer
to its 1980 values. As shown in Rogelj et al. (2016), theinitial NDCs do not hold globally averaged temperature increases below 2°C, and so further reductionsin projected GHG emissions, with associated different
impacts on stratospheric ozone, are expected if climate actions are taken to meet the Paris Agreement.
Section 6.4 examines how the changing concentrations of these gases according to selected RCPs
6.15

Chapter 6 | Information for Policymakers

adopted by the IPCC for its Fifth Assessment Report(IPCC, 2013b) could affect future changes in ozone
relative to the changing emissions and concentrationsof halogens. Of the RCPs, the RCP2.6 scenario is theclosest one holding global temperature increase below
2°C (IPCC, 2013a).

6.2.5

Deliberate Climate Intervention

6.2.5.1	Geoengineering via StratosphericSulfate Aerosol Modifications
Increasing the burden of stratospheric aerosols, also
called stratospheric aerosol geoengineering, has been
proposed as a method to increase the reflectivity ofEarth’s atmosphere in order to counteract some effects
of climate change. The most discussed application isusing the continuous injection of SO2 (or H2SO4) into
the tropical stratosphere. Despite similarities to shortterm impacts of volcanic eruptions, the impact of geoengineering strategies on the climate system would be
different, since they would have to be applied over an
extended period of time to continuously cool Earth’s
surface. The increase in stratospheric aerosol surface
area density (SAD) as a result of increasing sulfur injections over a continuous time period would increase
surface cooling, but it would also cause an increasein heterogeneous ozone loss cycles involving reactive
chlorine (ClOx), bromine (BrOx), and hydrogen (HOx)(see Appendix 6A). On the other hand, the reductionof nitrogen oxides through increasing heterogeneous
reactions (mostly important in the tropical mid-­stratosphere) could actually increase ozone and counteract a potential decrease in tropical column ozone
as the result of projected increasing GHGs. Thesechemical effects of geoengineering on ozone would
be reduced by the end of the 21st century because of
the projected future decrease in ODSs and consequent
halogen activation through heterogeneous reactions.
In addition to chemical changes, an increased stratospheric aerosol burden could also cause larger dynamical changes, including heating of the lower tropical stratosphere and a speedup of the BDC, includingchanges in tracer transport towards high latitudes,
which in part would counteract the change in ozone
due to chemistry (Appendix 6A). For large injection
amounts, this may even cause an increase in column
ozone in the Northern Hemisphere (NH) wintermid-latitudes compared to a non-geoengineering
6.16

scenario (Tilmes et al., 2018). Changes in tracer transport and UV as a result of changes in column ozone
and aerosol scatter may also increase troposphericmethane lifetime (Visioni et al., 2017b), may alter
stratosphere-to-troposphere exchange of ozone andother tracers (Xia et al., 2017), and would weaken the
tropospheric jet streams (Richter et al., 2018). The potential heating of the tropopause may further cause asignificant increase in stratospheric water vapor, impacting radiation and chemistry.
Column ozone changes as the result of stratospheric aerosol geoengineering therefore depends on theinjection amount, timing (ODS loading), and injection strategy (influencing aerosol size and location;
Appendix 6A). Relatively small and constant injections of 2.5–4 Tg S yr−1 between 2020 and 2070, which
would result in 0.5°C of surface cooling, are calculated
to lead to an approximately 4% reduction in the global
stratospheric column ozone for 2020 and only 1% reduction by 2070 (Pitatry et al., 2014; Xia et al., 2017).
Much larger injection amounts that would lead to asurface temperature cooling of around 2°C in 2040–
2050, based on a single model study, would result in
reductions in column ozone of 28–40% in Octoberover Southern Hemisphere (SH) high latitudes and
8–18% for NH high latitudes in March, with varying
values depending on the injection altitude (Tilmes etal., 2018). Injections closer to the tropopause cause astronger dynamical response and could result in up
to an 8% increase in column ozone in NH wintermid- and high latitudes. A single modeling transient
simulation based on RCP8.5 greenhouse gas forcings
with continuously increasing SO2 injections between
2020 and 2099 and decreasing ODSs would result inapproximately constant change in column ozone inhigh polar latitudes (20–23% in October over the SH
and 10–12% in March over the NH polar latitudes)
and slightly larger (3–5%) column ozone values compared to non-geoengineering conditions for tropics
and winter northern mid-latitudes by the end of the
21st century (Richter et al., 2018).
Use of Other Aerosols
The use of high refractive index solid particles suchas Al2O3, TiO2, and CaCO3 have been suggested as
stratospheric aerosol geoengineering options withlower stratospheric heating and lower surface areas

Information for Policymakers | Chapter 6

for heterogeneous reactions than sulfate aerosols(Dykema et al., 2014; Keith et al., 2016). Limited heating in the stratosphere and reduced reactivity of theparticles may change the dynamical response compared to sulfate aerosols. Based on simple 2-D model
simulations, Keith et al. (2016) estimate that a radiative forcing of −1 W m− 2 achieved using stratosphericinjection of CaCO3 particles could result in a 3.8%
increase in global column ozone. Estimated aerosol
properties and uptake rates still need to be confirmedby lab studies. Impacts on chemistry and dynamics
in comprehensive earth system models have not been
investigated.

6.2.5.2	Geoengineering via Solar
Irradiance Reduction
Other geoengineering activities proposed to help
counteract climate change via solar radiation management involve modifying Earth’s energy balance by

Box 6-1. Stratospheric Aerosol Geoengineering
Climate geoengineering via reduction of incoming solar radiation is a strategy to deliberately mitigate someof the effects of anthropogenic global warming (Crutzen, 2006). Since it does not address the cause of climatechange (the increase in greenhouse gases) it could be only a temporary solution to help reduce the worst
impacts, including heat waves, floods, sea level rise, etc., until decarbonization has effectively stabilized theclimate (Tilmes et al., 2016; Wigley, 2006). Stratospheric aerosol geoengineering is a proposed method to reflect incoming shortwave solar radiation to cool Earth’s surface, also called solar radiation management. The
idea of this approach is to inject aerosols or gases that form aerosols (most studies have performed calculations for sulfur dioxide [SO2]) into the tropical stratosphere. These are distributed around the globe withinapproximately 1–2 months, similar to what has been observed after large volcanic eruptions. The continuous
injection of SO2 or aerosols is assumed to form a persistent aerosol layer that achieves a certain amountof global cooling, with a cooling efficiency reaching up to 1°C per 10 TgSO2 yr−1 injections (Pierce et al.,2011; Kravitz et al., 2018), although with potentially reduced efficiency with increasing injection amounts(Niemeier and Schmidt, 2017). Earth system models have a range of approximately a factor of three in theforcing efficiency per injection amount, which depends on the aerosol microphysical descriptions and radiation scheme, as well as feedbacks including changes in ice clouds and assumptions regarding levels of othergreenhouse gases (Visioni et al., 2017). There is large uncertainty in the regional impacts of stratosphericaerosol geoengineering. The following is a brief overview of currently known potential benefits, side effects,
and risks (Robock, 2016).
Benefits: Earth system models have shown that globally averaged temperatures can be balanced (Kravitz etal., 2013), extreme temperatures and large precipitation events can be reduced (Curry et al., 2014), aridity
can be reduced (Tilmes et al., 2016), and the melting of the Arctic sea ice can be significantly slowed oreven reversed (Kravitz et al., 2017). The cooling of Earth’s surface via stratospheric aerosol geoengineering
has potential positive impacts on air quality (Xia et al., 2017) and agriculture and crop yields (Pongratz etal., 2012). Limited investigation also suggests surface ozone levels could decline (Eastham et al., 2018; Xiaet al., 2017), which would lead to health benefits. A potential decrease in column ozone may be beneficial
if it counteracts the increasing column ozone above preindustrial levels from a projected super recovery.
Strategically placed injections may reverse the shortening of the Quasi-Biennial Oscillation (QBO) period
from increasing greenhouse gases in the future (Richter et al., 2018). Changes in direct to diffuse radiation
ratio have been shown to be beneficial for plant growth and may have other benefits for the biosphere (Xia etal., 2016). The strong cooling potential of stratospheric aerosols would allow for a quick response to sudden
climate changes, and the relatively short lifetime of stratospheric sulfate aerosols of about 2 years would
allow for a phaseout of geoengineering in a short time if required. The approach would not largely change
ocean acidification but may reduce the bleaching of coral reefs (Zhang et al., 2017).

6.17

Chapter 6 | Information for Policymakers

Box 6-1, continued.
Side Effects: Models show that past or present-day climate conditions cannot be perfectly restored with geoengineering and that, depending on how it is implemented, it may lead to unintended side effects. Models
agree that cooling Earth’s surface via shortwave radiation (as opposed to mitigating the heating caused
by increased trapping of longwave radiation) slows the hydrological cycle, which leads to reductions in
global precipitation (Tilmes et al., 2013). While the largest reductions occur over the oceans, this method
may lead to a disruption of the monsoonal precipitation. Other side effects include significant heating in
the tropical stratosphere, a substantial increase in stratospheric water vapor, a strengthening of the polarnight jets, and a weakening of subtropical jets and tropospheric storm tracks (Tilmes et al., 2018; Richteret al., 2018). Changes in stratosphere–troposphere exchange may impact tropospheric ozone and methane
lifetimes (Visioni et al., 2017b). Enhanced sulfur deposition resulting from injections was shown to be unimportant (Kravitz et al., 2009). Changes in stratospheric ozone, including the delay of the recovery of ozone
at high altitudes and the potential increase of ozone for some regions and seasons, and changes in aerosols
impact surface ultraviolet (UV) radiation. Substantial changes in UV, either increased or decreased frompre-ozone hole values, may be harmful for life on Earth. Further, potential side effects include changes inocean currents, carbon budget, effects on land and ocean biosphere, energy production for solar generators,
and visible astronomy. New strategies are being currently developed that aim to reduce some of these side
effects (Kravitz et al., 2017).
Risks: Attempting to offset elevated global temperatures requires consistent injections until greenhousegases are sufficiently reduced. Depending on the pathway, this approach may require hundreds of years
of application (Tilmes et al., 2016). A sudden termination of such an application would lead to significant
climate change within 10 years after the termination (Jones et al., 2013). Uncertainties regarding futureclimate change mean that the injection amounts may be higher than anticipated. There are other possible
risks that are not included in the models, for example, impacts on the biosphere and continued sea level rise.
Additionally, feedback processes might be larger than model predictions, and the current model parameterizations might not be correct in a “geo-engineered” world. Technical injection strategies have not beendeveloped to date, and the required costs would depend on many factors (McClellan et al., 2012).

reflecting sunlight before it enters Earth’s atmosphere(e.g., Early, 1989; Seifritz, 1989; Angel, 2006). Recent
model studies investigating the stratospheric responsein the Geoengineering Model Intercomparison
Project (GeoMIP) G1 experiment computed a global ozone increase of 2–8% throughout most of thestratosphere due to a 4% reduction in the total solar
irradiance (TSI), with a global total column ozone increase of 1.6% (Jackman and Fleming, 2014; Nowack
et al., 2016). This resulted in up to a 20% reduction
in local UV radiation, with potential adverse effects
on life on Earth, including vitamin-D deficiency andan increase in tropospheric ozone. The main driversof the ozone increase were reductions in atomic oxygen and temperature caused by the 4% TSI decrease,which subsequently slowed the ozone photochemicalloss rates. Reductions in stratospheric water vapor andatomic oxygen excited state, O(1D), also contributed
6.18

to the ozone enhancement by decreasing odd hydrogen concentrations and therefore the HOx-ozone loss
rates.

6.2.6

Other Potential ­Influenceson Stratospheric Ozone

Emissions from Rockets
Since WMO (2014), the orbital launch rate has increased by about a factor of two (Doncaster et al., 2016;
FAA, 2016). Recent developments suggest that rocket launches and emissions will continue to increase
and possibly accelerate. New space systems, such asreusable and heavy-lift launch vehicles and communication satellite constellations using thousands ofsatellites in low earth orbit, have emerged (Klinkrad,
2017; Pelton and Jacque, 2016). Maturation of these

Information for Policymakers | Chapter 6

systems ensures that launch emissions will increasein coming years. Detailed scenarios of future launchemissions based on known, likely, or speculative future space transportation requirements have not beendeveloped.
Studies of the atmospheric impacts of rockets haveprimarily focused on stratospheric chemical perturbations associated with the various componentsof rocket engine emissions. In particular, ozone loss
caused by solid rocket motors (ammonium perchlorate oxidizer) occupies the greatest portion of theliterature, as summarized in Harris and Wuebbles etal. (2014). Existing model predictions are necessarily
incomplete, however, because they do not account forthe several types of fuels used by the space industry or
the rapid evolution in the global space launch industry and because of the sparsity of new research using
modern models that couple atmospheric radiationand chemistry.
For several decades (1981–2011), the Space Shuttle
was the largest single rocket emission source, andresearch focused on its solid rocket motor emissions.
After the Space Shuttle ended service in 2011, solid
rocket motor emissions from other launch vehicles(Ariane V, Vega, and others) have increased such thatsolid rocket motor emissions into the stratosphere (~4Gg yr−1) have remained nearly constant over the past
decade. Global models (Voigt et al., 2013), using prescribed HCl and alumina aerosol emissions, generally
agree that as recently as a few years ago, solid rocket
motor emissions produce a global total column ozone
loss of about 0.03%, approximately equally partitioned between HCl gas-phase reactions and alumina
surface heterogeneous chlorine activation reactions.
The alumina surface heterogeneous contribution is
not well understood, however. Two microphysicalparameters, acknowledged as poorly understood in
Daniel et al. (2011), determine the magnitude of thealumina impact. These are (1) the size distribution ofemitted alumina (specifically, the submicron mode
mass fraction, which determines steady-state stratospheric alumina surface area density) and (2) the
chlorine activation rate constant. Models have tended to adopt values representative of lower bounds forthese parameters. Extrapolations of model results toparameter upper bounds suggest that alumina-related
global ozone loss could be a factor of 10 larger than
the widely assumed value of 0.03% (Voigt et al., 2013).

No research has been done since WMO (2014) toconclusively eliminate the possibility of upper-boundsubmicron mass fraction or chlorine activation rate.
Ross and Sheaffer (2014) considered the radiative
effects of the black carbon (BC; i.e., “soot”) and alumina aerosol components of rocket emissions andnoted that coupling between radiative and chemical impacts presents a potentially important pathfor ozone loss from rocket emissions. BC emissions
from kerosene-fueled rockets have a relatively longstratospheric lifetime (~3 years) and accumulate in
the upper stratosphere (Ross et al., 2010). This BC
scatters and absorbs incoming solar radiation, possibly increasing stratospheric temperatures and thereby
accelerating the rate of ozone-destroying chemical reactions. Models of BC-based geoengineering (Kravitz
et al., 2012) and limited nuclear exchanges (Mills etal., 2014) can be viewed as analogues to rocket BC
emissions, though scaled up by orders of magnitude.
Downward extrapolations using these models suggestthat stratospheric heating in the present-day rocket
BC accumulation (Ross et al., 2010) could produce
global ozone loss comparable to that purely fromchemical loss from solid rocket motors. The fractionof global launches using propellants that have a relatively large BC emission index (mainly kerosene) has
trended upward in recent years, increasing the steadystate BC accumulation. Indirect ozone loss caused bystratospheric heating associated with rocket BC emissions has yet to be studied using the required coupledchemistry and climate models.
Larson et al. (2017) modeled the impact of­hydrogen-fueled rockets emitting only water vapor(typically, propellants emit ~400 g [H2O] per kg offuel). They found global ozone loss from rocket H2Oemissions to be three orders of magnitude less thanfrom an equivalent emission from solid rocket motors. The present water vapor component of rocket
emissions produces ozone loss less than 0.0001%.
Even under the most expansive plausible launch
growth scenario, ozone loss from hydrogen-fueled
rockets does not become significant.
Larson et al. (2017) also examined ozone loss causedby spacecraft descent from orbit. Intense atmosphericheating in the mesospheric portion of the reentry corridor produces NOx, which while not directly emitted
is a source arising from rocket activity. Larsen et al.
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Chapter 6 | Information for Policymakers

Table 6-1. Atmospheric lifetimes, fractional halogen release factors, and Ozone Depletion Potentials (ODPs) for
long-lived halocarbons. In this Assessment, lifetimes are based on SPARC (2013) and SPARC (2016). Fractional
release factors (mid-latitude conditions) used in this Assessment are from Newman et al. (2007), with ODPs calculated using the fractional release values from Laube et al. (2013), shown in parentheses. Lifetime uncertainties
are based on SPARC (2013) lifetimes as evaluated by Velders and Daniel (2014); the uncertainty associated with
the CCl4 lifetime has not been updated for the revised lifetime and so is left blank. See Chapter 1 for further discussion on atmospheric lifetimes.
Halocarbon

Atmospheric Lifetime(years)Fractional
ReleaseFactors

ODPs
This
Assessment
Recommendation

WMO(2014)This
Assessment

Lifetime
Uncertainty(1σ)

WMO (2014)
and this
Assessment

In
Montreal
ProtocolCFC-11

52

52

±22%

0.47

1.0

1.0

CFC-12

102

102

±15%

0.23

0.73 (0.81)

1.0

CFC-113

93

93

±17%

0.29

0.81 (0.82)

0.8

CFC-114

189

189

±12%

0.12

0.50

1.0

CFC-115

540

540

±17%

0.04

0.26

0.6

halon-1301

72

72

±13%

0.28

15.2 (19.0)

10.0

halon-1211

16

16

±29%

0.62

6.9 (7.7)

3.0

halon-2402

28

28

±19%

0.65

15.7

6.0

26 a

32

0.56

0.87 (0.87)

1.1

5.0 b

5.0 b

±3%

0.67

0.14 (0.17)

0.1

12

12

±16%

0.13

0.034 (0.024)

0.055

Annex A-I

Annex A-II

Annex B-II
CCl4
Annex B-III
CH3CCl3
Annex C-I
HCFC-22c

0.02

HCFC-123

1.3

1.3

0.02

HCFC-124

5.9

5.9

0.022 c0.022

HCFC-141b

9.4

9.4

±15%

0.34

0.102 (0.069)

0.11

HCFC-142b

18

18

±14%

0.17

0.057 (0.023)

0.065

c

0.025

HCFC-225ca

1.9

1.9

0.025

HCFC-225cb

5.9

5.9

0.033 c

0.033

0.8 d

0.8 d

±17%

0.60

0.57

0.6

2.5

2.5

±33%

0.62

1.7

±18%

0.44

0.015

Annex E
CH3Br
Othershalon-1202
CH3Cl
6.20

0.9

e

0.9

e

Information for Policymakers | Chapter 6
Notes:a

The partial lifetime for CCl4 is 44 years for atmospheric loss (from SPARC, 2013) and is assumed to be 183 years for ­oceanic
loss (Butler et al., 2016) and 375 years for soil loss for a total lifetime of 32 years (see Chapter 1).

b

The partial lifetime for CH3CCl3 is 5 years for atmospheric loss (from SPARC, 2013).

c

ODPs taken from Papanastasiou et al. (2018).

d

The total lifetime for CH3Br is 1.5 years for atmospheric loss (from SPARC, 2013), 3.1 years for oceanic loss, and 3.3–3.4 years
for soil loss.

e

The partial lifetime for CH3Cl is 1.3 years for atmospheric loss (from SPARC, 2013) and 3 years for oceanic and soil losses.

(2017) modeled the NOx emission for spacecraft returning from orbit, finding ozone column loss would
not exceed 0.1% at a rate of 105 reentries per year. For
comparison, the present reentry rate (including large
space debris) is less than 102 per year so that current
ozone loss from reentry NOx emissions is inferred tobe less than 0.0001%.

6.3

METRICS FOR CHANGES INOZONE AND CLIMATE

6.3.1

Metrics for Changes in Ozone

The two primary metrics for studies of stratosphericozone are equivalent effective stratospheric chlorine(EESC) and Ozone Depletion Potentials (ODPs).
An updated analysis of ODPs was given in the last
Assessment (see Section 5.3 and especially Box 5-2
in Harris and Wuebbles et al. (2014) for the basic
description of the EESC and ODP concepts; see also
Tables 5-2 and 5-3 in that Assessment for the derived
values of ODPs). Uncertainty estimates of the ODPs
were also included in Table 5-2, and to our knowledge, have not been updated. A discussion of theuncertainties associated with ODPs and EESC from avariety of sources can be found in Velders and Daniel(2014). Semi-empirical ODPs (see Box 5-2 in Harrisand Wuebbles et al., 2014) and EESC take advantageof observations to determine fractional release factors(FRFs), which quantifies how much of a trace gas isbroken down by the time it reaches a particular region of the stratosphere. Section 6.4 and Chapter 1
describe a recent update to the FRF formalism forchlorine- and bromine-containing compounds. This
leads to relatively minor changes in FRFs, and thus
to semi-empirical ODPs, and to more significant
changes in calculated EESC. This update does not,however, affect model-calculated ODPs. Relative tothe last Assessment, the only lifetime change for themost important long-lived halocarbons is for CCl4.
As discussed in Chapter 1, the CCl4 lifetime has
been updated from 26 to 32 years, leading to a 23%
increase in its ODP (Table 6-1 and Appendix A). It isalso important to recognize that ODPs can depend on
the background atmosphere. Revell et al. (2015) haveconfirmed that this is especially the case for nitrous
oxide (N2O), where ODP values are likely to be larger(by as much as a factor of two depending on levelsof chlorine and methane in the stratosphere) for 2100
than in the present day.
In addition to the updates above, there are only a few
new studies of ODPs since WMO (2014), primarily
for compounds considered to play a relatively minor
role in ozone depletion. The discussion of these follows below in two parts, for long-lived gases (atmospheric lifetimes greater than 1 year) and for shortlived gases. Overall, the findings for ODPs are similar
to prior Assessments, but this Assessment includes
ODP estimates for a few additional compounds.
Long-Lived Gases
Davis et al. (2016), using the NASA Goddard two-dimensional chemistry-climate model, evaluated theatmospheric lifetimes, ODPs, and GWPs for several
CFCs not previously examined, namely CFC-112,
CFC-112a, CFC-113a, and CFC-114a. The first observations of the small atmospheric concentrationsof CFC-112, CFC-112a, and CFC-113a were reported by Laube et al. (2014), along with budget analyses with emission sources dating back to the 1960s(see Chapter 1). The first long-term measurementsof CFC-114 and CFC-114a, separately (Laube et al.,
6.21

Chapter 6 | Information for Policymakers2016), have also been reported since WMO (2014).
As expected, the ODPs for these CFCs are quite large:
0.98, 0.86, 0.73, and 0.72 for CFC-112, CFC-112a,
CFC-113a, and CFC-114a, respectively.
Short-Lived Gases
Wallington et al. (2015) analyzed the atmospheric chemistry for a number of different short chain
haloolefins; however, only HCFO-1233zd(E) (26day lifetime using a simple scaling relative to global-­
averaged OH (hydroxyl) concentrations) and HCFO1233zd(Z) (12-day lifetime) had non-zero ODPs, andthese were already included in Harris and Wuebbles
et al. (2014) based on the earlier study by Patten
and Wuebbles (2010). Patten and Wuebbles (2010),
using a three-dimensional atmospheric chemistry-­climate model, found an atmospheric lifetime of 40days (for emissions assumed to be on all landmasses from 30°N to 60°N) and an ODP of 0.00034 for
HCFO-1233zd(E) and an atmospheric lifetime of 13days and an ODP of <0.00034 for HCFO-1233zd(Z).
In a new study updating the reaction rates, Sulbaek
Andersen et al. (2017) found a slightly smaller lifetime for HCFO-1233zd(E) of 36 days and reduced the
ODP to 0.00030 for the same emissions assumptions
as made in Patten and Wuebbles (2010). As emphasized in prior assessments, ODPs for very short-lived
substances (VSLSs) that contain bromine or chlorine
are strongly dependent on the geographic locationand season of emission. Therefore, it is important toprovide the emissions assumptions when reporting
VSLS ODP derivations. Although ODP-weighted
emissions have been used for some time in analysesof long-lived gases, Tegtmeier et al. (2015) extend thisapproach to short-lived compounds through analysesof CHBr3 emissions from the ocean by accounting
for the area-based variations in ODPs and emissions.
They found that ODP-weighted emissions of CHBr3
were about 9% of the total ODP-weighted emissions
by the long-lived halogenated ODPs and that they are
expected to grow over the rest of the century due toclimate change.
Indirect ODPs
As strong radiative forcers, HFCs increase tropospheric and lower-stratospheric temperatures, thereby enhancing ozone-destroying catalytic cycles and
6.22

modifying the atmospheric circulation. These changes lead to a weak indirect depletion of stratosphericozone. Incorporating the interactions between chemistry, radiation, and dynamics, model-calculated
ODPs for HFC-32, HFC-134a, HFC-125, HFC-143a,and HFC-23 range from 0.39 × 10−3 to 30.0 × 10−3
(Hurwitz et al., 2015). These values are approximately
100 times larger than previous ODP estimates, whichwere based solely on the direct chemical potential
to deplete ozone via catalytic loss cycles that involve
fluorine (Ravishankara et al., 1994). Nevertheless,their total projected impact on globally averaged totalozone from HFCs remains less than 0.1 DU (Dobsonunit) by 2050 (Hurwitz et al., 2016), even for the highgrowth HFC scenario from Velders et al. (2015).

6.3.2

Metrics for Changes in Climate

Radiative forcing (RF), Global Warming Potentials(GWPs), and Global Temperature change Potentials(GTPs) are the primary metrics used to consider
the climate effects of halocarbons and other gases.
An updated analysis of GWPs and GTPs was givenin the last Assessment (see section 5.3 and especially Box 5-3 in Harris and Wuebbles et al. (2014) forthe basic description of the GWP and GTP concepts;
Table 5A-1 in that Assessment gives the derived values of GWPs and GTPs from IPCC, 2013b). Table
5-5 in Harris and Wuebbles et al. (2014) provides
an update for a number of halocarbons based on the
updated SPARC (2013) lifetimes, while Section 5.3.2(and Tables 5-6 and 5-7) discuss uncertainties in the
GWP and GTP derivations. Shortcomings of using
RF, GWPs, and GTPs as proxies for climate response
have been studied extensively and are summarizedin Chapter 8 of IPCC (2013b). More recent work has
examined how the GWP concept can be appropriately used to compare different climate-forcing agents(Allen et al., 2016).
In this Assessment, updates for many GWPs and GTPs
are provided. Table 6-2 presents values for selectedlong-lived ODSs and HFCs, following the approach
used in IPCC AR5 (2013b) that is currently being
used by policymakers. This Assessment also includes
GWPs and GTPs for the 274 HCFCs in Annex C of
the Montreal Protocol (Appendix A). These values
are potentially useful as the Parties continue the process of phasing out HCFC production and consumption; many of them are provided for the first time inInformation for Policymakers | Chapter 6

an assessment. The new values are based on estimatesof lifetimes and calculations of infrared absorption
characteristics (Papanastasiou et al., 2018). Because
these quantities are not experimentally measured,
the metrics calculated from this information typicallyhave larger uncertainties associated with them thanthose based on laboratory measurements.
As in the last Assessment, these metrics were calculated based on the evaluation and assessment of IPCC
(Myhre et al., 2013), with updates based on Etminan etal. (2016). The new analyses by Etminan et al. (2016)
include shortwave effects not adequately consideredpreviously; these affect the radiative forcing efficiency and GWP for CH4 but have no significant effects
on the GWPs for other compounds. Also shown are
the atmospheric lifetimes and radiative efficiencies
used in these analyses. As in WMO (2014) and IPCC
(2013b), the CO2 radiative efficiencies (and hence the
GWPs and GTPs) of non-CO2 greenhouse gases presented in Table 6-2 and Appendix A are calculatedwith a CO2 level corresponding to 391 ppm.
The following discusses the few new studies of GWPsand GTPs (based on updates to atmospheric lifetimes or new radiative efficiencies for the compounds
in question) that have been published since WMO(2014) in two parts: long-lived gases (atmospheric
lifetimes greater than 1 year) and short-lived gases.
Overall, with the exception of the values for CH4,
there have been minor changes in the derived GWPsand GTPs for the compounds evaluated.
Long-Lived Gases
As mentioned above (Section 6.3.1), Davis et al.
(2016) have provided 100-year GWP values for CFCs
that have not previously received much attention:
CFC-112, CFC-112a, CFC-113a, and CFC-114a. Lu
et al. (2017) updated analyses of the radiative forcing for NF3 and derived GWPs and GTPs for 20- and100-year integrations. Their GWP and GTP values
are smaller than those derived previously by IPCC
(2013b), primarily due to their derivation of a smaller
radiative efficiency. In contrast, a study by Totterdill
et al. (2016) found a 25% larger radiative efficiency
for NF3 than IPCC (2013b) and therefore larger GWP
and GTP values.

Short-Lived Gases
Most of the new analyses since WMO (2014) of shortlived halocarbons have been associated with shortlived haloolefins, which generally have extremely
small GWP values. A short description is providedof some of the key studies. Wallington et al. (2015),
along with their analyses of ODPs, provide estimated
GWPs for a number of HCFOs, all having 100-year
GWPs of 1 or less. Sulbaek Andersen et al. (2017) derive GWP values for HCFO-1233zd(E) of 19, 5, and 1
for 20-, 100- and 500-year time horizons, respectively,
using the radiative forcing three-dimensional modeling studies from Wuebbles et al. (2013) for emissions
assumed to be distributed across all landmasses from
30°N to 60°N; these GWPs are larger than prior values
but reasonable for the assumed landmass emissions.
Orkin et al. (2014) examined the photochemical
properties of HCFO-1233zd(Z) and estimated an atmospheric lifetime of 46 days assuming a well-mixed
distribution (which would be very unlikely for such ashort-lived gas) and a relatively small 100-year GWP
of 14 (but this again reflects an even distribution of
the concentration of the gas).
Climate Carbon-Cycle Feedbacks
New studies of the Climate–Carbon cycle Feedbacks(CCFs) on GWPs and GTPs show the potential importance of accounting for these feedbacks with an
explicit CCF model rather than with a linear feedback
approach, especially for long-time horizons (Sternerand Johansson, 2017). While values of GWPs and
GTPs change less than 10% for all well-mixed greenhouse gases when the time horizon is limited to 100
years or less, the values for long time horizons, such as
500 years, can be substantially lower (by up to 30% for
the GWP and up to 90% for the GTP) with the explicit
CCF model than with the linear feedback approach.
This Assessment does not account for the CCF effects
in the values of GWPs and GTPs presented here.
Indirect GWPs
There are multiple types of indirect (100-yr) GWPs
that have been discussed in the literature. Usually,
these relate to the chemical impact a source gas has onother gases and their subsequent climate forcing. One
indirect effect that has been shown to be important
6.23

Chapter 6 | Information for Policymakers

Table 6-2. Lifetimes, radiative efficiencies, direct global warming potentials (GWPs), and Global Temperaturechange Potentials (GTPs) for selected gases (based on a radiative efficiency for CO2 based on [CO2] = 391 ppm).
The CO2 AGWPs a for the 20- and 100-yr time horizons are 2.495 x 10-14 and 9.171 x 10-14 W yr (m2 kg)-1; the CO2
AGTPs a for the 20-, 50-, and 100-yr time horizons are 6.841 x 10-16, 6.167 x 10-16, and 5.469 x 10-16 K kg-1. GTPs forthe 50-yr time horizon are not included in this table but may be found in Appendix A.
Industrial Designation or
Common Name

Lifetime(years)

GWP
20-yr

GWP
100-yr

GTP
20-yr

GTP
100-yr

CFC-11

52

7,090

5,160

7,160

2,920

CFC-12102

10,80010,30011,3008,590

CFC-113

93

6,560

6,080

6,830

4,860

CFC-114

189

7,710

8,580

8,180

8,530

CFC-115

540

5,780

7,310

6,210

8,290

halon-1301

72

7,930

6,670

8,160

4,700

halon-1211

16

4,590

1,750

3,950

300

halon-2402

28

3,920

2,030

3,730

615

32

3,790

2,110

3,670

750

5

555

153

300

21

HCFC-22

12

5,310

1,780

4,230

265

HCFC-141b

9.4

2,590

800

1,900

114

HCFC-142b

18

5,140

2,070

4,530

390

Annex A-I

Annex A-II

Annex B-II
CCl4
Annex B-III
CH3CCl3
Annex C-I

Annex E
CH3Br

0.8

7.6

2

2.4

<1

Othershalon-1202

2.5

720

CH3Cl

0.9

16

HFC-23

228

196
4.3

285
5.1

27
<1

11,085

12,69011,825

13,150

HFC-32

5.4

2,530

705

1,440

90

HFC-125

30

6,280

3,450

6,040

1,180

HFC-134a

14

3,810

1,360

3,170

215

HFC-143a

51

7,050

5,080

7,110

2,830

HFC-152a

1.6

545

148

190

21

HFC-227ea

36

5,250

3,140

5,140

1,260

HFC-245fa

7.9

2,980

880

2,040

124

Notes:
a

6.24

From the mass of the atmosphere (5.135 x 1018 kg; Trenberth and Smith, 2005), average molecular mass of dry air (28.964 g ­mol-1;
Warneck and Williams, 2012), and molecular mass of CO2 (44.01 g mol-1) the conversion factor 1 ppm CO2 = 7.803 x 1012 kg isderived. This conversion factor can be used to convert the CO2 AGWPs and AGTPs given above to units of per ppm rather than
per kg.

Information for Policymakers | Chapter 6

results from the destruction of stratospheric ozone by
the ODSs. Because ozone is a greenhouse gas itself,
destruction of ozone will lead to a cooling influenceon climate. For gases like the halons, this indirect effect is actually larger than, and opposite in sign to, thedirect forcing caused by the presence of the halons
themselves. There have been no new studies updating
this indirect effect for the ODSs, so we update themhere (Table 6-3) only for the revised CO2 AGWP (absolute GWP, which is the radiative forcing integrated over a given time horizon, resulting from a pulse
emission of the gas) and the updated lifetime of CCl4.
As interest in shorter-lived compounds replacing longer-lived greenhouse gases has grown, the importanceof identifying the degradation products of these compounds and understanding the physical propertiesof the products has been pointed out. This is anothersituation in which the indirect GWP can actually belarger than the direct GWP of an emitted compound(Bravo et al., 2011; Jubb et al., 2015).

6.4

SCENARIOS AND­SENSITIVITY ANALYSES

6.4.1

Tools Used in Analyses ofOzone and Climate Effects

In this chapter, as in the past two Ozone Assessments(WMO, 2011, 2014), we use two primary tools to compare the climate and ozone impacts of various futurescenarios. The first is a simple box model (Harris andWuebbles et al., 2014), which allows for the calculationof the ozone metrics EESC and ODP-weighted emissions and of the climate metrics RF (radiative forcing)and GWP-weighted emissions (100-yr time horizon).
EESC has been shown to be a reasonable proxy forthe amount of stratospheric ozone depletion caused
by a given abundance of a long-lived ODS (Daniel
et al., 2010), and RF is a quantity that describes theenergy imbalance often due to the presence of somecompound in the atmosphere, and is roughly proportional to the global average surface temperaturechange it will produce (Myhre et al., 2013). The second evaluation tool used in this chapter is the NASA/
Goddard Space Flight Center (GSFC) 2-D coupledchemistry–radiation–dynamics model (Fleming etal., 2011) driven with mixing ratio boundary conditions calculated from the box model. Earlier versionsof this model were also used in Daniel and Velders,
et al. (2011) and Harris and Wuebbles et al. (2014).
The inclusion of the 2-D model allows us to compare
impacts of the long-lived ODSs that are controlled by
the Montreal Protocol with the impacts of CO2, CH4,
and N2O as well as with very short-lived (VSLS) compounds like CH2Cl2. The GSFC 2-D model compares
well with observations and with the 3-D Goddard
Earth Observing System Chemistry Climate Model(GEOSCCM) in simulating temperature and varioustransport-sensitive features in the meridional plane,such as the horizontal and vertical gradients of longlived stratospheric tracers and age of air (Fleming etal., 2011; SPARC, 2013). Projections of future ozone
using this 2-D model are also in very good agreement with those of more complex 3-D models used in
Chapters 3, 4, and 5 of this Assessment to examine theimpacts of various processes and emissions on ozone
and climate (see Appendix 6A). 3-D models provide
our best understanding of chemical, dynamical, andradiative processes and how they interact to explain
the past and future state of the atmosphere. However,these models take large amounts of computer time,
which makes evaluation of many dozens of alternative
scenarios impractical. Thus, in this chapter, we do not
use any 3-D model calculations.
RF is calculated throughout this chapter as it was inHarris and Wuebbles et al. (2014), using the radiative efficiencies found in the Appendix Table A-1.
EESC is also calculated as in WMO (2014) and isused as the basis for comparison of different scenarios (Section 6.4.3.1). However, we also discusscalculations of EESC using the updated approachof Ostermoller et al. (2017) and Engel et al. (2017).
The fundamental advance in these papers is the recognition that the difference between the average ageof ODSs that have dissociated and the average ageof inert tracers can be important to the estimatedamount of Cly and Br y in the stratosphere and thus
for EESC. They demonstrate that the use of mean
age of air in the calculation of both fractional releaseand in EESC (Newman et al., 2007) leads to a bias inthose quantities. The impact of the updated theoretical approach on calculated fractional release factors(FRFs) is not large overall, but it does alter the FRF
of a few compounds (Engel et al., 2017). The effecton the change to polar EESC is also not particularly
large since the average age of the dissociated ODSs
6.25

Chapter 6 | Information for Policymakers

Table 6-3. Indirect GWPs from ozone depletion (direct forcing from ODSs, themselves, is not included). Approach is taken from Daniel et al. (1995), assuming a radiative forcing due to ozone depletion in 2011 of –0.15 W
m–2 (IPCC, 2013b). Uncertainty in this radiative forcing leads to an uncertainty in these GWPs of ±100%. Direct
GWPs are shown for comparison.
GAS

Indirect GWP 100-yr

Direct GWP 100-yr

CFC-11–2,8605,160

CFC-122,050–2,050

10,300

CFC-113–2,1806,080

CFC-114

–880

8,580

CFC-115

–210

7,310

HCFC-22

–98

1,780

HCFC-123–35

80

HCFC-124–45

530

HCFC-141b

–250

800

HCFC-142b

–160

2,070

CH3CCl3

–310

153

CCl4

–2,610

2,110

CH3Br

–1,2102

halon-1211–18,500

1,750

halon-1301–46,100

6,670

halon-2402–44,800

2,030

HCFC-225ca

–39

127

HCFC-225cb

–58

525

are generally not too different from the mean age ofan inert tracer. At mid-latitudes, on the other hand,the new approach leads to significantly older effective
ages for dissociated ODSs than for an inert tracer inmany cases, assumed to be three years here and inprevious Assessments (WMO, 2007, 2011, 2014).
The return of EESC to 1980 levels continues to be auseful metric to compare future scenarios. It is important to recognize, however, that relatively smallchanges in stratospheric chlorine and bromine loading (i.e., EESC) can lead to large changes in this return
6.26

time because of how gradually EESC is declining in
the baseline scenario. It is also important to recognizethat while numerous sources of uncertainty, particularly in atmospheric lifetimes, limit our ability to
pinpoint the return of EESC to 1980 levels to within
25–40 years (95% confidence interval) (Velders andDaniel, 2014), this metric can be used meaningfully tocompare differences in return dates of various scenarios, assuming that the relative atmospheric lifetimes,
production, and bank estimates of different substances or groups of substances are well defined.

Information for Policymakers | Chapter 6

6.4.2

Baseline Scenario for
Ozone and Climate

Future atmospheric concentrations of ODSs dependon the amount emitted to the atmosphere and therate at which destruction occurs. The destruction rate
can change over time due to changes in atmosphericcirculation, changes in solar irradiances at the relevant wavelengths, or changes in reactive chemicals
like OH, O(1D), and atomic chlorine. Release to theatmosphere depends on multiple factors, which can
include the amount released during and after production, whether production is ultimately for use asa feedstock or for dispersive uses, and on the rate at
which the ODS is released from existing applications,also called banks.
Because of inherent uncertainties in these sources andsinks, it is not possible to perfectly predict future ODS
concentrations. Therefore, the baseline (A1) scenario
should be considered a plausible future pathway, andnot the most likely future pathway in some statisticalsense. In fact, here and in Chapter 1 we show the extent
to which historical observations have differed fromthe baseline scenario projections of past Assessments.
It is also important to keep in mind that the purposeof the projections in this chapter is not for them toserve as predictions, but instead to be used to evaluate
the impact of potential policy options regarding thefuture production and consumption of various ODSs
as well as emissions from banks.
The baseline scenario in this Assessment has beendeveloped using the same methodology as has beenused in the past several Assessments (WMO, 2007,2011, 2014). Observations from Chapter 1 are used toconstrain the mixing ratios over the time period when
they are available, which is generally from around1980 through 2016. Before this time period, mixing
ratios are taken from the previous Assessment (Harrisand Wuebbles et al., 2014), except for CFC-114 (seediscussion below). The recent mixing ratios are usedin conjunction with the bottom-up bank estimates for2008 (UNEP, 2009) and the annual production reported to the Ozone Secretariat to estimate bank values
through the beginning of 2016 using the relationship
Bi+1 = Bi + Pi – Ei
where Bi is the bank at the beginning of year i, and Pi
and Ei are the production and emission, respectively,

throughout year i. Knowing the annual emissions
and bank values allow for a calculation of the bank
fraction that is annually released over the past several years. The annual release fractions, averaged overthe last 7 years, are then assumed to remain constantin the future; by assuming that future production isequal to what is permitted by the fully adjusted andamended Montreal Protocol and that this is added tothe banks, these release fractions can be used to estimate future annual emissions.
Table 6-4 contains the long-lived ODS mixing ratios
for the baseline scenario, and Figure 6-2 includes acomparison of the current baseline scenario with thebaseline scenario from WMO (2014). Many of the current projections remain very similar to the previous
ones (e.g., CFC-12, CFC-113, and CFC-114 and halons 1211 and 1301). Such agreement is to be expected
since our understanding of global lifetimes for mostof the compounds has not changed, there has been noreported production to UNEP for CFCs and halons,and we continue to assume the same 2008 bank levels as in Harris and Wuebbles et al. (2014). There has
been a slight downward revision in the CFC-114 mixing ratios from 1979 through the mid-1980s becauseof recent additions of firn data to the historical record(Chapter 1) (Laube et al., 2014). Thus, CFC-114 mixing ratios preceding the atmospheric measurement
record have been scaled by a constant factor (0.92) toavoid a discontinuity in 1979. Figure 6-2 also shows
how the individual mixing ratios change in the different alternative scenarios. For the scenarios shown,the HCFCs show much more dependence on future
emissions than do the CFCs in relation to their current atmospheric concentrations, but CFC emissions,in particular those from the CFC-11 banks, remain
significant.
The CCl4 projection in the current Assessment ishigher than in Harris and Wuebbles et al. (2014) because emissions are assumed to decline at a rate of
2.5% yr−1 rather than the 6% yr−1 assumed in Harrisand Wuebbles et al. (2014). There is substantial interannual variability in the emission trend, but the updated rate is more consistent with the long-term trend
over the last two decades.
HCFC projections are similar to those of the previous
Assessment (Harris and Wuebbles et al., 2014), although the three major HCFCs shown in Figure 6-2
6.27

Chapter 6 | Information for Policymakers

ppt

300

400

100

200

ppt
ppt
ppt

100
80
60
40
20
0
1960

120
100
80
60
40
20
0
1960

300
250
200
150
100
50
0
1960

40
30

1990

2020

2050

CFC-113ppt

4
3

CFC-12

1990

2020

2050

CFC-114

10

1990

2020

2050

CCl 4

5
0
1960

150

1990

2020

2050

CH 3CCl 3

100
50

1990

2020

2050

HCFC-22

0
1960

40
30

1990

2020

2050

HCFC-141b

20

1990

2020

2050

10
0
1960

2050

5
4
3
2
1
0
1960

2050

12
10
8
6
4
2
0
1960

HCFC-142b

1990

2020

halon-1301

2
1
0
1960

0
1960

20
15

20
10
0
1960

1990

2020

Year

6.28

600

200

0
1960

ppt

CFC-11

1990

2020

2050

halon-1211

1990

2020

2050

CH 3BrApproximate Natural Level
1990

Year

2020

2050

Information for Policymakers | Chapter 6

all have concentrations somewhat lower for the next
few years, owing to recent growth in concentrations
that was less than projected previously and to the factthat reported production over the past few years has
been less than was assumed in Harris and Wuebbles
et al. (2014) .
CH3Br projections are developed by calculating thetotal atmospheric loss of CH3Br (using the approach
used in Table 1-4 of Carpenter and Reimann et al.,2014) with a global atmospheric lifetime of 0.8 years,
and assuming equivalent total atmospheric emissions. (Note that the baseline scenario of the previous
Assessment assumed a global atmospheric CH3Br lifetime of 0.75 years as in WMO, 2011). All emissions,
including QPS and CUE emissions, are assumed tocontinue at the 2016 level indefinitely into the future.
This is a minor difference from the baseline scenarioof the last Assessment, where CUE emissions were assumed to be zero after 2012. This change makes little
difference to the calculations since production under
CUE has continued to decline and was less than atenth of production for QPS in 2016.
A lower assumed total atmospheric emission is theprimary reason for a slight lowering of future CH3Br
atmospheric concentrations in the baseline scenario
when compared with the previous Assessment (6.7ppt currently relative to the previous 7.0 ppt). Notethat the CH3Br budget continues to have a significant
imbalance between sources and sinks, and there is alarge uncertainty in both terms (WMO, 2014). The
key issue, however, is the level of anthropogenic production and consumption that could be controlled,
if desired, which is well defined. We also note that(continued) reduction in the global atmosphericmole fraction of CH3Br leads to an increase in the net
sea–air flux of CH3Br, which can somewhat dampen

policy actions taken to reduce anthropogenic emissions. While the ocean response to recent and predicted future changes in atmospheric CH3Br is now verysmall (resulting in a calculated increase in net ocean
emissions according to the budget terms of Carpenter
and Reimann et al. (2014) of ~0.4 Gg yr−1 from 2012
to 2016), we note that since the mid-1990s, the net
ocean source has likely increased by ~10 Gg yr−1 (Huet al., 2012; WMO, 2014).
The baseline scenario for HFCs is taken directly from
Chapter 2 and includes global control measures
introduced by the Kigali Amendment and other regional and national actions. In order to estimate theimpact of the Kigali actions, we use the reference scenarios for HFCs developed in Velders et al. (2015a),which are projections without consideration of specific global control measures. These HFC scenarios are
discussed in detail in Chapter 2, Section 2.5.1.
Potentially, one of the more important differences from projections of previous Assessments is that
CFC-11 has declined more slowly than projected fora number of years, and the discrepancy has increased
since 2012. While this is not apparent from Figure
6-2 due to the large scale on the y-axis, it has the potential to be important and will be discussed later in
Section 6.4.3. We continue to treat CFC-11 emission
in the baseline scenario as has been done in previous
Assessments, i.e., as arising solely from its banks. This
does lead to a higher implied annual bank releasefraction, since the ratio of emissions to bank size hasgone up substantially since 2013.
In addition to evaluating the impact of future ODS
emissions and concentrations, we use the 2-D model
to examine the impact of future concentrations of
N2O, CH4, and CO2, as well as of short-lived halocarbon (i.e., CH2Cl2) emissions, on stratospheric ozone.

Figure 6-2. Comparison of long-lived halocarbon mixing ratios in the current baseline scenario (shown as
solid black curve) with those from the baseline scenario of WMO (2014) (dot-dashed black line); future mixing ratio projections for the “no emission from 2020 onward” (dark blue), “bank capture and destruction in2020” (cyan), “bank capture and destruction in 2025” (yellow), and “no production from 2020 onward” (red)scenarios. Shaded regions represent the time periods when mixing ratios are constrained to observational
estimates (see Chapter 1). The approximate natural concentration of CH3Br is noted by the dotted blue linein the lower right-hand panel (see Chapter 1). The green curves for CFC-11 and CCl4, respectively, show concentrations for scenarios in which annual emissions remain at 67 Gg yr−1 of CFC-11 and 33 Gg yr−1 of CCl4.
6.29

Chapter 6 | Information for Policymakers

Table 6-4. Mixing ratios (ppt) of the ODSs considered in the baseline scenario. Values are for the beginning of
the corresponding year. Shaded areas indicate when the mixing ratio values are forced to equal global average
estimates inferred from observations (see Chapter 1).
Year

6.30

CFC-11

CFC-12

CFC-113

CFC-114

CFC-115CCl4

CH3CCl3

1955

3.3

14.3

1.3

2.4

0.0

42.3

0.1

1960

9.5

29.5

1.9

3.5

0.0

52.1

1.5

1965

23.5

58.8

3.1

4.6

0.0

64.4

4.7

1970

52.8

114.3

5.5

5.9

0.2

75.9

16.3

1975

106.1

203.1

10.4

7.6

0.6

85.5

40.0

1980

162.4

296.6

19.0

10.1

1.6

93.0

82.3

1981

170.7

311.3

21.5

10.6

1.9

94.6

89.0

1982

179.4

329.6

25.3

11.0

2.2

96.0

93.9

1983

187.6

345.4

28.5

11.6

2.4

97.2

97.8

1984

196.4

362.8

32.0

12.2

2.8

98.5

102.1

1985

205.6

378.1

36.8

12.7

3.1

99.8

106.6

1986

215.4

397.2

41.9

13.3

3.5

101.1

110.2

1987

226.5

415.9

47.4

14.2

3.9

102.7

113.3

1988

237.6

437.9

54.2

14.5

4.3

103.7

118.3

1989

247.5

458.8

61.1

15.0

4.8

104.9

122.9

1990

255.2

476.2

67.7

15.5

5.2

106.0

127.2

1991

260.6

489.6

73.3

15.8

5.7

106.2

130.7

1992

264.0

500.6

78.4

16.0

6.1

105.8

133.3

1993

266.3

510.1

81.3

16.1

6.5

105.3

130.4

1994

266.9

516.3

83.1

16.2

6.9

104.4

122.1

1995

266.3

522.4

83.7

16.3

7.2

103.8

110.6

1996

265.2

528.5

83.8

16.3

7.5

102.8

98.2

1997

264.2

533.0

83.6

16.4

7.7

101.8

84.1

1998

262.8

536.3

83.2

16.4

7.9

100.8

71.1

1999

261.5

539.1

82.7

16.4

8.0

99.8

59.5

2000

259.9

541.2

82.1

16.4

8.1

98.6

49.7

2001

258.4

542.9

81.8

16.4

8.2

97.6

41.5

2002

256.7

543.6

81.2

16.4

8.3

96.6

34.5

2003

254.5

543.6

80.4

16.4

8.3

95.6

28.8

2004

252.5

543.5

79.6

16.4

8.3

94.6

24.0

2005

250.5

542.7

78.9

16.3

8.3

93.7

20.0

2006

248.4

541.8

78.4

16.2

8.4

92.6

16.7

Information for Policymakers | Chapter 6

HCFC-22

HCFC-141b HCFC-142b halon-1211 halon-1202 halon-1301 halon-2402 CH3Br

CH3Cl

1.0

0.0

0.0

0.00

0.00

0.00

0.00

6.3

491.3

2.1

0.0

0.0

0.00

0.00

0.00

0.00

6.5

510.3

4.9

0.0

0.0

0.00

0.00

0.00

0.00

6.7

528.1

12.1

0.0

0.0

0.02

0.00

0.00

0.02

7.0

539.9

23.8

0.0

0.2

0.12

0.01

0.04

0.06

7.4

545.8

39.6

0.2

0.7

0.70

0.01

0.35

0.15

7.8

548.4

43.8

0.2

0.7

0.82

0.01

0.41

0.17

7.9

548.6

48.0

0.2

0.8

0.94

0.01

0.50

0.19

8.0

548.9

52.0

0.2

0.8

1.09

0.01

0.59

0.21

8.0

549.1

55.6

0.2

0.8

1.25

0.01

0.71

0.23

8.1

549.3

59.7

0.2

0.9

1.40

0.01

0.84

0.25

8.2

549.4

65.6

0.2

0.9

1.56

0.02

1.01

0.27

8.3

549.5

71.1

0.2

1.0

1.75

0.02

1.21

0.30

8.4

549.6

75.1

0.2

1.0

1.94

0.02

1.41

0.32

8.5

549.7

80.2

0.2

1.1

2.13

0.02

1.60

0.35

8.6

549.8

86.3

0.3

1.3

2.33

0.02

1.77

0.38

8.7

549.8

92.5

0.3

1.9

2.55

0.02

1.94

0.40

8.8

549.9

98.8

0.3

2.8

2.74

0.03

2.10

0.42

8.9

549.9

103.3

0.5

3.9

2.92

0.03

2.23

0.44

9.0

549.9

108.4

1.3

5.0

3.11

0.03

2.35

0.46

9.2

550.0

113.2

2.6

6.2

3.32

0.04

2.44

0.47

9.2

560.9

119.0

4.5

7.2

3.48

0.04

2.53

0.48

9.2

544.9

123.7

6.4

8.4

3.63

0.04

2.60

0.48

9.1

535.0

128.4

8.2

9.3

3.81

0.04

2.66

0.49

9.3

555.4

134.3

10.1

10.4

3.95

0.05

2.72

0.49

9.4

563.3

139.1

11.8

11.4

4.07

0.05

2.78

0.49

9.0

552.6

144.7

13.5

12.4

4.17

0.04

2.84

0.49

8.6

540.2

150.5

14.8

13.3

4.23

0.04

2.91

0.49

8.3

536.3

155.4

16.1

13.9

4.27

0.04

2.97

0.49

8.3

541.5

160.5

17.0

14.6

4.31

0.04

3.02

0.48

8.1

536.4

165.7

17.5

15.2

4.34

0.03

3.05

0.48

8.0

538.7

171.9

17.9

15.9

4.34

0.03

3.08

0.48

7.8

537.1

6.31

Chapter 6 | Information for Policymakers

Year

6.32

CFC-11

CFC-12

CFC-113

CFC-114

CFC-115CCl4

CH3CCl3

2007

246.2

539.8

77.7

16.3

8.4

91.5

14.0

2008

244.1

537.6

76.9

16.3

8.4

90.3

11.7

2009

242.2

535.5

76.1

16.4

8.4

89.1

9.9

2010

240.4

532.8

75.7

16.3

8.4

87.9

8.3

2011

238.4

530.2

75.0

16.3

8.4

86.7

6.9

2012

236.4

527.7

74.4

16.1

8.4

85.5

5.8

2013

234.4

524.8

73.7

16.1

8.4

84.5

4.8

2014

232.9

521.9

73.0

16.1

8.4

83.3

4.0

2015

231.7

519.1

72.4

16.0

8.5

82.3

3.4

2016

230.3

515.9

71.7

16.0

8.5

81.1

2.7

2017

229.2

512.6

71.2

16.0

8.5

79.9

2.3

2018

227.0

507.6

70.4

15.9

8.5

78.8

1.8

2019

224.8

502.6

69.7

15.8

8.5

77.8

1.5

2020

222.5

497.7

68.9

15.8

8.5

76.7

1.2

2021

220.1

492.9

68.2

15.7

8.5

75.6

1.0

2022

217.6

488.1

67.4

15.6

8.5

74.5

0.8

2023

215.1

483.3

66.7

15.5

8.5

73.4

0.7

2024

212.5

478.6

66.0

15.5

8.5

72.3

0.6

2025

209.9

473.9

65.3

15.4

8.5

71.2

0.5

2030

196.4

451.2

61.9

15.0

8.5

65.9

0.2

2035

182.6

429.7

58.6

14.6

8.5

60.7

0.1

2040

168.9

409.1

55.6

14.2

8.4

55.8

0.0

2045

155.7

389.5

52.7

13.9

8.4

51.1

0.0

2050

143.1

370.9

49.9

13.5

8.3

46.7

0.0

2055

131.2

353.2

47.3

13.2

8.2

42.6

0.0

2060

120.0

336.3

44.8

12.8

8.2

38.8

0.0

2065

109.7

320.2

42.5

12.5

8.1

35.2

0.0

2070

100.1

304.9

40.3

12.2

8.0

31.9

0.0

2075

91.3

290.3

38.1

11.8

8.0

28.9

0.0

2080

83.2

276.4

36.1

11.5

7.9

26.1

0.0

2085

75.7

263.2

34.3

11.2

7.8

23.6

0.0

2090

68.9

250.6

32.5

10.9

7.7

21.3

0.0

2095

62.7

238.6

30.8

10.6

7.7

19.2

0.0

2100

57.0

227.2

29.2

10.4

7.6

17.2

0.0

Information for Policymakers | Chapter 6

HCFC-22

HCFC-141b HCFC-142b halon-1211 halon-1202 halon-1301 halon-2402 CH3Br

CH3Cl

179.1

18.5

16.9

4.32

0.03

3.11

0.47

7.7

542.0

187.3

19.1

18.1

4.28

0.03

3.15

0.47

7.5

544.7

195.2

19.6

19.3

4.22

0.02

3.17

0.46

7.3

543.0

202.5

20.1

20.0

4.16

0.02

3.19

0.46

7.1

539.1

210.0

20.9

20.8

4.08

0.02

3.21

0.45

7.1

534.7

216.0

21.9

21.5

4.01

0.02

3.24

0.44

7.1

535.8

221.4

22.8

21.8

3.91

0.02

3.27

0.44

6.9

542.3

226.5

23.5

22.1

3.81

0.02

3.30

0.43

6.7

538.7

231.5

24.1

22.2

3.71

0.01

3.32

0.42

6.7

546.0

235.3

24.4

22.2

3.61

0.01

3.32

0.42

6.8

555.3

239.3

24.5

22.3

3.51

0.01

3.32

0.41

6.8

550.6

244.0

25.1

22.5

3.40

0.01

3.33

0.40

6.7

539.5

247.9

25.6

22.7

3.29

0.01

3.33

0.39

6.7

539.5

251.1

26.1

22.8

3.17

0.00

3.34

0.39

6.7

539.5

253.7

26.6

22.9

3.06

0.00

3.34

0.38

6.7

539.5

255.0

27.0

22.9

2.94

0.00

3.34

0.37

6.7

539.5

255.5

27.4

22.9

2.83

0.00

3.34

0.37

6.7

539.5

255.2

27.7

22.9

2.72

0.00

3.33

0.36

6.7

539.5

254.3

28.0

22.9

2.61

0.00

3.33

0.35

6.7

539.5

235.0

28.5

22.2

2.10

0.00

3.28

0.31

6.7

539.5

193.4

26.9

20.5

1.66

0.00

3.20

0.28

6.7

539.5

144.6

23.9

17.8

1.30

0.00

3.10

0.25

6.7

539.5

103.1

20.4

15.0

1.01

0.00

2.98

0.22

6.7

539.5

71.1

16.9

12.3

0.77

0.00

2.86

0.19

6.7

539.5

48.1

13.8

9.9

0.59

0.00

2.73

0.17

6.7

539.5

32.2

11.2

7.9

0.44

0.00

2.60

0.15

6.7

539.5

21.5

8.9

6.2

0.34

0.00

2.46

0.13

6.7

539.5

14.2

7.1

4.9

0.25

0.00

2.33

0.11

6.7

539.5

9.4

5.6

3.8

0.19

0.00

2.20

0.10

6.7

539.5

6.2

4.5

2.9

0.14

0.00

2.08

0.08

6.7

539.5

4.1

3.5

2.2

0.10

0.00

1.96

0.07

6.7

539.5

2.7

2.8

1.7

0.08

0.00

1.84

0.06

6.7

539.5

1.8

2.2

1.3

0.06

0.00

1.73

0.05

6.7

539.5

1.2

1.7

1.0

0.04

0.00

1.62

0.05

6.7

539.5

6.33

Chapter 6 | Information for Policymakers

RCP6.0 is used in the baseline scenario, with sensitivity calculations performed using RCP2.6, RCP4.5,
and RCP8.5 scenarios. While the specific RCP does
not affect conclusions regarding the importance ofthe various ODS emission sources to future ozone
depletion, the wide range of concentrations of N2O,
CH4, and CO2 across the RCPs can lead to a rather
large difference in the date when global column ozone
returns to 1980 levels.
Given the multiple sources of CH2Cl2 (Leedham
Elvidge et al., 2015) and continued variability ingrowth rates, we assume that it is reasonable to project constant emissions forward; thus, the baseline forCH2Cl2 maintains current atmospheric mixing ratios
into the future. We note, however, that there are major
uncertainties in future emissions of CH2Cl2 due to alack of bottom-up information on its industrial sources. A constant stratospheric VSLS Br of 5 ppt is usedin all the 2-D model runs.

6.4.3

Alternative Future Scenarios

As in past Assessments, we consider multiple alternative future sensitivity cases to assess which emission
sources are responsible for the projected concentrations of the various ODSs and of EESC. This information can inform policy discussions by quantifying theeffects of various potential policy controls.
Zero-emission scenarios are run for all ODSs, both individually and collectively, and assume no future anthropogenic emission into the atmosphere from any
source; thus, the future concentrations are governed
exclusively by the current concentrations and theglobal lifetimes. These scenarios represent minimum
concentrations that can be achieved through directcontrols, assuming the lifetimes used in the model are
accurate and unchanging. There are also scenarios inwhich there is continued production into the future asallowed by the Montreal Protocol but current banks
are eliminated, and other scenarios in which current
banks continue to emit into the future but future
production is eliminated. None of these alternative
scenarios is presented as a likely, or even necessarily a possible, future path. Instead, they are meant assensitivity studies, which can aid in determining theimpact of some lesser reduction. For example, if 10%of the CFC bank were captured and destroyed in
2020, the magnitude of the impact is expected to be
6.34

about 10% of the effect of the case considered here, inwhich the entire CFC bank in 2020 was captured anddestroyed. There is a slight nonlinearity introduced
tied to the return of EESC to 1980 levels, because alarger mitigation will cause EESC to cross below the1980 threshold sooner, thus changing the ending timeof the integration. The time when emissions occur
can also lead to a response that does not scale linearly.
Thus, for example, some metrics for the combined impact of a zero-production scenario with a zero-bankscenario are not expected to be exactly the same as themetrics for a zero-emission scenario.
Designing the alternative scenarios for most ODSs isrelatively straightforward since they are entirely, or
almost entirely, emitted from human activity. CH3Br
is an exception. As discussed previously, the key aspect to evaluating the controllable contribution of
CH3Br to stratospheric bromine and ozone depletionis the amount that is emitted from human activity
in comparison to natural emissions. As in previous
Assessments, we consider emissions from QPS (7.3
Gg yr−1 in 2016) and CUE (0.7 Gg yr−1 in 2016) tobe the controllable emissions. We do not consider
emissions from indoor or outdoor biomass burning as
being controllable, nor do we consider any potential
emissions reduction from leaded gasoline, due to its
small estimated contribution to total emissions.
As stated above, the concentration of CFC-11 has not
dropped as quickly as expected over the last few years
or as quickly as it had been dropping over the precedingten years. This observation is particularly unexpected
because reported global production of the CFCs, intotal, has been below zero (i.e., more destruction than
production) since 2010 (UNEP, 2017). This discrepancy could be attributed to several potential causes:(1) a circulation change that resulted in lower naturalloss rates; (2) increased emission from existing equipment; or (3) emissions from production that have notbeen reported to the Ozone Secretariat for allowed
uses as feedstock or process agents or from illegal uses
for new equipment or to service existing equipment.
Identifying the underlying cause(s) is key to quantifying the potential implication for ozone depletion. If,
for example, a temporary circulation change is entirely
responsible for the slower decline, there is little longterm impact. While atmospheric circulation changes
have likely played a role, 3-D models cannot explain
the observed atmospheric concentrations without

Information for Policymakers | Chapter 6

emission increases after 2012 (Montzka et al., 2018).
An abrupt and substantial increase in emissions fromexisting banks, required to solely explain the observations, is considered highly unlikely: While building
demolition may lead to increasing CFC-11 emissions
as the insulating foams in the buildings are destroyed,such emissions are expected to ramp up slowly andto primarily occur in developed countries, which is
not shown by the observations (Montzka et al., 2018).
These lines of evidence suggest the possibility that theemissions could be related to unreported production(Montzka et al., 2018). Thus, we have included an additional sensitivity case in which we assume the future
emissions of CFC-11 do not decline but remain at 67
Gg yr−1, the average calculated top-down emissions
over 2002–2016.
As discussed previously and in Chapter 1, new potential sources of CCl4 emissions have been identified
from industry and from legacy uses that are currently not captured in reporting to UNEP. There are also
likely additional missing source(s). Given that future
CCl4 emissions remain uncertain, we have included
an additional alternative scenario for CCl4 in whichcurrent estimated top-down emissions (33 Gg yr−1)
remain constant indefinitely.
Projecting future CH2Cl2 emissions is one of themore uncertain aspects of the scenarios consideredhere. As discussed in Chapter 1, tropospheric mixing
ratios of CH2Cl2 demonstrated strong growth fromthe early 2000s to around 2014. Growth has slowed
since then, although growth rates continue to behighly variable. A potentially large source of CH2Cl2
is as a co-product of CHCl3 manufacture, which isused almost entirely for HCFC-22 production (Oramet al., 2017). Oram et al. (2017) calculate that around715 kt of CH2Cl2 (in 2015) could be produced inassociation with HCFC-22 production in China,of which ~455 Gg (nearly half of estimated global
CH2Cl2 annual emissions) could be used for emissive
applications. If indeed CH2Cl2 production is closely
linked to the demand for HCFC-22, then its emissions could decline in the future, as long as noncontrolled feedstock production of HCFC-22 does not
outweigh declines in controlled HCFC-22 emissions
mandated by the Montreal Protocol. However, thevariable growth rates of CH2Cl2 and the lack of a definitive understanding of its global budget mean that
reliable projections are currently not possible. Thus,

we develop two alternative scenarios for CH2Cl2, intended as sensitivity studies to examine the potential
influence on stratospheric O3: (1) continued strong
growth in emissions, assuming that surface mole
fractions grow consistently at 2.85 ppt per year (themean rate observed during 2004–2014 as in scenario
1 from Hossaini et al., 2017), and (2) immediate cessation of emissions.
It is important to recognize that N2O remains the mostsignificant ODP-weighted emission among all the
ODSs. Thus, even though N2O is not controlled under
the Montreal Protocol, we run two mitigation scenarios to compare with the ODS emission cases. These alternative scenarios will also have climate implications
because N2O is a long-lived greenhouse gas. The two
scenarios are unchanged from Harris and Wuebbles etal. (2014). In one, all future anthropogenic emissions
are eliminated, and in the other, the average of the“concerted mitigation” scenarios from UNEP (2013)is assumed. These scenarios are RCP2.6, SRES B2, andscenarios 4 and 5 from Davidson (2012). Future assumptions in these scenarios vary, but as an example,
scenario 4 in Davidson (2012) considered improved
agricultural efficiency and emissions reductions of
50% in the transportation/industrial sectors and frombiomass burning relative to a baseline scenario in
2050. Scenario 5 incorporates scenario 4 assumptions,as well as a reduction in meat consumption.
Policy options can be directly compared with thebaseline scenario of this Chapter (Table 6-5) using:(1) the return of EESC to 1980 levels, (2) integrated
EESC above 1980 levels, (3) integrated ODP- and (4)GWP-weighted ODS emissions from 2020 through2060, and (5) integrated ozone depletion. As in past
Assessments, 1980 is the reference year—identified
as a time when the return of EESC or global column
ozone to levels experienced then signifies an important milestone in moving towards recovery. It is important to recognize, however, that even when EESC
or global column ozone returns to 1980 levels, there
will almost certainly be differences in the ozone spatial distribution both for the total column as well as in
the vertical profile. These differences are unavoidable
as long as greenhouse gases like CO2, CH4, and N2O
remain perturbed from their 1980 levels. The actual
year of return to 1980 global column ozone also will
depend on natural variations (e.g., in meteorology
and atmospheric circulation) that can affect ozone,
6.35

Chapter 6 | Information for Policymakers

Table 6-5. Comparison of scenarios and cases a : the year when EESC b drops below the 1980 value for both
mid-latitude and Antarctic vortex, and integrated EESC differences (mid-latitude case) relative to the baseline(A1) scenario c. Also shown are changes in integrated ODP- and GWP-weighted emissions and, for selected cases,
integrated global ozone depletion for 2020−2060. Future changes in CH4 and CO2 may also significantly alterozone levels, potentially by amounts larger than any of the cases considered in this table.

ScenarioandCases

Change in­Cumulative
ODP-­
Weighted e
Emission:
2020−2060

Change in­Cumulative
GWP-­
Weighted f
Emission:
2020−2060(Million tons
CFC-11-eq)(Billion tons
CO2-eq)2075.7

0.00

0.0

0.00

Percent ­Difference in­Integrated EESC Relative
to ­Baseline ­Scenario fortheMid-­latitude CaseYear When EESC is
Expected to Drop
Below 1980 Value

Mid-latitude c, dAntarctic
Vortex d

χ

χ

1980

2020

∫ EESC dt ∫ EESC dt

Percent­Difference in
Integrated
O3­Depletion g :2020−2060

Scenarios
A1: Baseline
scenario

0.0

0.0

2049.4

Cases a of zero production from 2020 onward of:
P0: All ODS

–4.2

–19.1

2044.62070.3

–0.88

–5.8

–0.21

CFCs

–0.0

–0.0

2049.42075.7

–0.00

–0.0

–0.00

Halons–0.0–0.0

2049.42075.7

–0.00

–0.0

–0.00

HCFCs

–0.8

–3.9

2048.62075.3

–0.12

–4.0

–0.05

CH3Br for QPS
and CUE h

–1.5

–6.8

2048.22074.2

–0.18

–0.0

–0.06

CCl4

–2.2

–9.9

2046.62072.3

–0.59

–1.8

–0.17

Cases a of zero emissions from 2020 onward of:
E0: All ODS(does notinclude N2O)

–8.0

–36.8

2039.62064.2

–2.30

–13.9

–0.48

CFCs

–1.7

–7.9

2047.32073.2

–0.62

–3.3

–0.09

Halons–2.2–9.9

2047.12073.0

–0.61

–0.2

–0.10

HCFCs

–2.4

–11.0

2047.52074.7

–0.30

–8.7

–0.12

i

–2.2

–9.9

2046.62072.4

–0.59

–1.8

–0.12

CH3CCl3

–0.0

–0.0

2049.42075.7

–0.00

–0.0

–0.00

CH3Br for QPS
and CUE h

–1.5

–6.8

2048.22074.2

–0.18

–0.0

–0.06

Total anthropogenic N2O j

-

-

-

-

–5.25

–81.8

–0.45

N2O
mitigation

-

-

-

-

–1.23

–19.1

–0.04

CCl4

6.36

Information for Policymakers | Chapter 6

Cases a of full recovery of the 2020 banks of:
B0: All ODS

–5.1

–23.2

2043.92069.7

–1.42

–8.1

–0.27

CFCs

–1.7

–7.9

2047.32073.2

–0.62

–3.3

–0.09

Halons–2.2–9.9

2047.12073.0

–0.61

–0.2

–0.10

HCFCs

–1.6

–7.4

2048.32075.1

–0.19

–4.7

–0.08

Cases a of full recovery of the 2025 banks of:
B1: All ODS

–3.1

–14.1

2045.02070.6

–1.09

–6.9

–0.18

CFCs

–0.9

–4.3

2047.92073.8

–0.46

–2.4

–0.06

Halons–1.2–5.5

2047.72073.5

–0.46

–0.2

–0.06

HCFCs

–1.2

–5.7

2048.12074.9

–0.18

–4.3

–0.07

+18.3

2056.72096.0

+2.06

+10.6

+0.20

+4.0

2051.22080.6

+0.42

1.3

+0.05

-

-0.17

Continued emission of CFC-11:
Constant at
67 Gg yr−1

+4.0

Continued emission of CCl4:+0.9

Cases relating to the VSLS CH2Cl2:
No future anthropogenic
emission

-

-

-

-

-

Increasing
emission

-

-

-

-

-

+0.17

Notes:
aSignificance of ozone-depleting substances for future EESC were calculated in the hypothetical “cases” by setting production oremission to zero in 2020 and subsequent years or the bank of the ODS to zero in the year 2020 or 2025.

b

EESC is calculated as in WMO (2014).

c

EESC is integrated above the 1980 level and until it returns to this level, denoted as year “x”

d

For mid-latitude conditions, an average age of air of 3 years, corresponding fractional release values, and a bromine efficiency factor (alpha) of 60 are assumed. For Antarctic vortex conditions, an average age of air of 5.5 years, corresponding fractional releasevalues, and an alpha value of 65 are assumed. In all cases, age spectra are applied as in Newman et al. (2007).

eSemi-empirical ODPs from Table 6-1.

f

GWPs with 100-year time horizon (Table 6-2).

gIntegrated globally averaged total column ozone changes are taken from 2-D model runs described in this chapter.

h

It is assumed that 84% of production for QPS use is emitted to the atmosphere and that 65% of production under CUE is emitted(Harris and Wuebbles et al., 2014). The alternative scenario evaluated here includes elimination of emissions from both QPS use
and under CUE. Note that emissions under CUE are 15 times smaller than emissions from QPS use in future years of the baseline
scenario.

iBanks are assumed to be zero. Emissions include uncertain sources such as possible fugitive emissions and unintended otheremissions.

j

The integrated ODP- and GWP-weighted emissions correspond to the reduction of anthropogenic N2O emissions from RCP6.0 to
two mitigation cases (see text). The weaker “N2O mitigation” scenario is only projected through 2050, so ODP- and GWP-weighted
emissions are calculated for 2020–2050.

6.37

Chapter 6 | Information for Policymakers

The “No Future Emissions” scenario represents thefastest that EESC could recover, assuming no changes in lifetimes or fractional release values in the future. The close alignment of the “Zero 2020 Bank,”“Zero 2025 Bank,” and “No Future Production” cases
demonstrates the comparable importance of current
banks and future production when ODSs are examined together. If emissions were completely stopped
in 2020, it could result in an earlier return of mid-­latitude and polar EESC to 1980 levels by about adecade. However, for perspective, it is important torecognize the relatively small impacts that additional
controls could have on ODSs when compared withwhat the Montreal Protocol has already accomplished(e.g., Figures 5-6 and 5-8 of Harris and Wuebbles etal., 2014).

regardless of EESC levels and the amount of anthropogenic climate change.

6.4.3.1 Stratospheric Ozone Implications
We project that mid-latitude EESC will return to1980 levels around 2049 and polar EESC will returnaround 2076 for the baseline scenario. This is almost
2 years later for mid-latitude EESC and slightly morethan 2 years later for polar EESC when compared withthe baseline scenario of WMO (2014). Both of thesedifferences are primarily a result of higher concentrations of CCl4, which are caused primarily by slower
projected decreases in future emissions. The difference in total EESC between the WMO (2014) baseline
scenario and the current one is shown in Figure 6-3.
The differences appear very small at the scale shown
all the way to 2100.

As previously discussed, a new approach to calculate
EESC (Engel et al., 2017) has been proposed, which

2000

EESC

1500

1000

Current Baseline
No Future Emissions
Zero 2020 Bank
Zero 2025 BankNo Future Production
CFC-11 Continuing Emission
WMO (2014)

500

0
1960

1980

2000

2020

2040

2060

2080

2100

Year
Figure 6-3. EESC for the current baseline scenario (mid-latitude conditions) compared with EESC fromthe WMO (2014) baseline scenario; also shown are the four major alternative scenarios that represent
current mitigation examples considered in this Assessment, and a scenario that assumes a continuationof CFC-11 emissions through the end of the century at the level estimated over 2002–2016 (67 Gg yr−1)(Montzka et al., 2018). All of the EESC curves are calculated using the approach from Newman (2007).
The difference between the current baseline curve and the WMO (2014) curve is indistinguishable until
after 2020.
6.38

Information for Policymakers | Chapter 6

differs from the methodology used in this chapter andin previous Assessments in that it attempts to accountfor the fact that the average age of air for source gasesthat have been dissociated in the stratosphere is long­er than the average age of inert tracers in the same
stratospheric location. For the scenarios consideredin this chapter, the use of the new EESC approachleads to a delay in mid-latitude return to 1980 levelsof about another decade, with a much smaller effectfor polar EESC. Despite these quite large changes in
the return dates for mid-latitude EESC, the relative
importance of the various ODS emission sources toozone depletion metrics changes little between the
two approaches (cf. Tables 6-5 and 6C-1). For the restof this chapter we will use the older EESC approach.
The importance of future emissions from CFCs, halons, HCFCs, CCl4, and CH3Br (mainly from QPS)
are all comparable, even more so than in the previous
Assessment. As in WMO (2014), future emissions
from CFCs and halons in the baseline scenario continue to arise entirely from the existing banks, whilebanks of CCl4 and CH3Br are assumed to be negligibly
small, so future emissions for them arise exclusively
from future production. Future HCFC emissions arise
from both current banks as well as future projected
production, with current banks contributing morethan future production.
If the emissions indicated from the recent slowdown
in the decline of CFC-11 concentrations continue
into the future, the recovery of EESC and ozone will
be delayed. As stated above, we have included a scenario in which CFC-11 emissions continue at 67 Gg
yr−1 indefinitely. This is the level implied by atmospheric concentration trends over 2002–2016 if it isassumed that atmospheric dynamics played no role inthe changing trends (Montzka et al., 2018) (Chapter1). In this alternative scenario, the mid-latitude EESC
return to 1980 levels is delayed by about 7 years, andpolar EESC return is delayed by about 20 years. For
context, the ODP-weighted CFC-11 emissions in this
scenario exceed those of the baseline scenario by 2.1
million ODP-weighted tons over 2020–2060, thus
almost doubling ODP-weighted emissions from thelong-lived halocarbon ODSs over that period compared with the baseline scenario. Continuing emission of CCl4 at 33 Gg yr−1 also has implications forozone recovery: It delays the return of EESC to 1980
levels at mid-latitudes and in the Antarctic vortex

by ~2 years and ~5 years, respectively, relative to thebaseline scenario.
Figure 6-4a compares the impact of selected scenarios on the globally averaged total column ozone as calculated with the 2-D model. As expected, the ozone
response exhibits a roughly inverse relationship with
the EESC curves shown in Figure 6-3. Continued
CFC-11 emissions at 67 Gg yr−1 causes a change inozone that grows over time and eventually leads tomore ozone depletion than is caused by all future
halocarbon ODS emissions in the baseline scenario.
Shown in Figure 6-4b are the responses for the N2O
mitigation scenarios. N2O exerts a similar ozone response to that shown in Harris and Wuebbles et al.
(2014). After about 30 years, the impact of future anthropogenic N2O emissions on ozone is larger thanthe combined impact of all future long-lived halogenated ODS emissions, and the N2O influence continues to grow. The significance of N2O is also apparent
from its cumulative ODP- and GWP-weighted emissions shown in Table 6-5; the total N2O anthropogenic emissions over 2020–2060 are more than two times
that of the ODSs for ODP-weighted emissions, and six
times for GWP-weighted emissions.
Figure 6-4c shows the impacts on ozone of the rangeof RCP scenarios (RCP2.6, 4.5, 6.0, and 8.5). The influences of CO2, CH4, and N2O are shown, individually, by varying each one alone while holding the othertwo gases at 2015 levels. The baseline ODS scenario
is used in all runs. The processes responsible for theozone impacts of these greenhouse gases (GHGs) are
discussed in Chapter 3. When compared with Figure
6-4a, it is apparent that the variations of each of thesethree gases across the RCP scenarios lead to a substantially wider range of possible future ozone levels
than from the ODS scenarios alone. For example,
the difference in global ozone in 2100 between thebaseline ODS scenario and a scenario with no ODS
emissions from 2020 is less than 1 DU (Figure 6-4a).
This contrasts with differences of 11, 16, and 6 DU,
due to differences in CO2, CH4, and N2O, respectively, between the RCP2.6 and RCP8.5 scenarios. Thus,
policies that affect the future evolution of these three
GHGs in particular will be important for predicting
how ozone will change. Furthermore, the potential
increase of global ozone above preindustrial levels
means that in the future, policy decisions that lead toless climate forcing and less ozone depletion may no
6.39

Chapter 6 | Information for Policymakers

11% change

DU change

22

55

Change in total column ozone (%)

Change in column ozone (DU)

GlobalozoneChangechangefrom
1980
Globalaverage
Average total
Total Ozone
from
1980

00

00

–1
-1

–5
-5

–2
-2
–3
-3
-4
–4

–10
-10

1960 1980
1980 2000
2000 2020
2020 2040
2040 2060
2060 2080
2080 2100
2100
1960
Year

Baseline
Base
No emissions
emissions from
2020
No
from
2020
Zero banks
Zerobanksinin2020
2020
Zero banks
Zerobanksinin2025
2025
No Production
Production fromNo
from2020
2020−1
Constant
at 33atGg
CCl4 Emission
CCl
Emission
Constant
33yrGg/yr−1
CFC-11 Emission
Constant
at 67
CFC-11
Emission
Constant
atGg
67 yr
Gg/yr

Figure 6-4a. Influence of selected scenarios on globally averaged total ozone.
The scenarios include the baseline scenario; no long-lived ODS emissions
from 2020; full capture and destruction of the bank in 2020, but allowing
continued production; full capture anddestruction of the bank in 2025, butallowing continued production; no production of long-lived ODSs from 2020,
but allowing banks to continue to emit;
continued annual emission of CFC-11
at 67 Gg yr−1; and continued annual
emission of CCl4 at 33 Gg yr−1, with allother assumptions following the baseline scenario. Calculations are from the
GSFC 2-D model. In all scenarios shown,
the decline just before 2100 is caused
by a decrease in tropospheric column
ozone and is not due to additional
stratospheric ozone depletion. The zeroline represents 1980 levels.

Change in Global Average Total Ozone Relative to 1980
4
3
2

5

1
0

0
-1

-5

-2
-3

-10
1960

1980

2000

2020

2040

Year

2060

Baseline
No future anthropogenic N2O emissions
N2O mitigation scenario

6.40

2080

-4
2100

Change in total column ozone (%)

Change in column ozone (DU)

10

Figure 6-4b. Influence of N2O mitigation
on future ozone. Globally averaged total
column ozone for the baseline scenario(black) is compared with the scenarios
in which no future anthropogenic N2Oemissions occur and for a weaker N2O
mitigation scenario (see text). Calculations are from the GSFC 2-D model.

Information for Policymakers | Chapter 6

Change in Global Average Total Ozone Relative to 1980

4

CO 2

10

2

Baseline

ODS

0

0

N2O

-5

CH 4(2.6)
-10
1960

-2

Change in total column ozone (%)

Change in column ozone (DU)

15

5

6

CH 4
(8.5)

-4
1980

2000

2020

Year

2040

2060

2080

2100

Figure 6-4c. Separate influence of N2O, CH4, and CO2 on future global ozone in
the presence of decreasing ODSs. The baseline ODS scenario is used in all runs.
The shading depicts the range of impacts, among RCP2.6, 4.5, 6.0, and 8.5, due
to future increasing CO2 (purple) and N2O (yellow). The CH4 impacts for RCP2.6
and 8.5 are depicted by the thick blue lines. The thinner colored lines show the
separate impacts of N2O (yellow), CH4 (blue), and CO2 (purple) under the RCP6.0
scenario. The thick green line shows the response to only decreasing ODSs.
For CO2, RCP8.5 gives the largest ozone response and RCP2.6, the smallest. For
N2O, RCP8.5 gives the most negative ozone response relative to ODS only, and
RCP2.6 gives the least negative ozone response. For all simulations, the gases(of N2O, CH4, and CO2) that are not being varied are fixed at 2015 levels. Calculations are from the GSFC 2-D model, which compares well with 3-D models,
including for CH4 perturbations (see Appendix 6B).

6.41

Chapter 6 | Information for Policymakers

CH 2Cl 2 Impacts
2

5

1
0

0
–1

–5

–2
–3

–10

1960

200

2000

2020 2040
Year

2060

2080

–4

2100

Global Average Surface Mixing Ratio of CH2CI2

150

CH2Cl2 (ppt)

1980

Baseline
Constant linear increase after 2015
Decay to background after 2015

Change in Total Column Ozone (%)

Change in Column Ozone (DU)

Change in Global Average Total Ozone Relative to 1980

Figure 6-5. Influence of CH2Cl2 on globally
averaged total ozone. The change in totalozone from 1960 through 2100 is shownfor the three CH2Cl2 scenarios considered(upper panel); assumed constant emissions(our baseline scenario), linearly increasing
emissions (adapted from Hossaini et al.,2017), and an elimination of anthropogenic emissions beginning in 2015. Surface concentrations of CH2Cl2 for the three
scenarios are also shown (lower panel).
Calculations are from the GSFC 2-D model.

100

50
0
1960

1980

2000

2020

2040

2060

2080

2100

Year

longer be considered a win-win proposition (Butler
et al., 2016).
The two alternative scenarios for CH2Cl2, namely (1)continued strong growth in emissions and (2) immediate cessation of emissions, are shown in Figure 6-5
along with its mixing ratios in the baseline scenario.
Unlike the CFCs, CH2Cl2 has a short lifetime and thus
responds rapidly to changes in emissions. If emissions
quickly decrease in the future, the contribution ofCH2Cl2 to stratospheric chlorine will also fall rapidly. Under scenario (1), the 2-D model predicts that
integrated ozone depletion over 2020–2060 (shownin the final column of Table 6-5) would increase byeven more than it would decrease if all future controlled ODS production were eliminated beginningin 2020. However, the continuing large variability inits surface abundances causes us to be unable to confidently predict future concentrations or to evaluate
the plausibility of this scenario. If, on the other hand,
6.42

all anthropogenic emissions of CH2Cl2 had ceased in2015, the effect on integrated ozone depletion from2020–2060 would be about two-thirds of the effect ofeliminating production of all controlled ODSs beginning in 2020.

6.4.3.2 Climate Implications
From the projections for 2020 through 2060, HCFC
emissions contribute about two-thirds to the total
GWP-weighted emissions for all ODSs (not including N2O), with both future production and current
banks playing comparable roles. Projected CFC banks
in 2020 represent the next most important class of
GWP-weighted emissions, contributing just over
20% of the total in the baseline scenario. If, however, unreported CFC-11 production is and continues
to be an important factor, the GWP-weighted emissions of all the controlled ODSs from 2020 to 2060
would almost double compared with the baseline

Information for Policymakers | Chapter 6

Radiative Forcing (W m−2)

0.4

0.3
Figure 6-6. Historical andprojected future radiative
forcing from long-lived ODS
halocarbons. The forcing forthe mitigation scenarios andthe constant CFC-11 emission
scenario shown in Figure 6.2
and 6.3 are also shown.

0.2

Current Baseline
No Future Emissions
Zero 2020 Bank
Zero 2025 BankNo Future Productio
CFC-11 Continuing Emission

0.1

0.0

1960

1980

2000

2020

2040

Year

scenario (assuming CFC-11 emissions continue at 67
Gg yr−1), and CFCs would be the ODS group whose
future emissions contribute most to climate change.
Continuing CCl4 emissions at 2016 levels lead to amuch smaller additional climate impact. As a point ofcomparison, the amount of CO2 emitted in 2015 fromfuel combustion was 32 Gt CO2, and the sum from2020 to 2060 in the RCP6.0 scenario is 1,700 Gt CO2(IEA, 2017), while the total CO2-equivalent emissions
in the baseline scenario from ODSs controlled by theMontreal Protocol over 2020–2060 is projected to be
13.8 Gt CO2-eq yr-1.
As seen in Figure 6-6, the maximum difference inradiative forcing between the baseline scenario andthe zero-emission scenario is less than 0.05 W m−2,
and by 2100, no ODS policy action could reduce ODS
radiative forcing by as much as 0.01 W m−2 whencompared with the baseline scenario, which assumes
compliance with the Montreal Protocol. If there is notcomplete adherence to the Protocol, the RF would
be expected to rise above the current baseline curve,with the actual RF path depending on the extent of theMontreal Protocol violation. If, for example, CFC-11
emissions were to continue at 67 Gg yr−1 indefinitely,
RF would be 0.03 W m−2 higher in 2100 than in thebaseline scenario (Figure 6-6).

2060

2080

2100

Figure 6-7 shows the contribution of the various
ODSs and their replacements, specifically HFCs,
to future RF. The RF from CFCs has been declining
since the latter half of the 20th century. The subsequent increase in the HCFC replacement compounds
is projected to offset this decline through to about2020. Once the transition to HFCs advances, the projected HFC concentration increases more than offset
the decline in ODS RF for at least a decade. After thatpoint, the HFC restrictions of the Kigali Amendment,
if adhered to, ensure a continued decline in total RF
from ODSs and their replacements through the restof the century. This is one of the primary expectedsuccesses of the Kigali Amendment; in the absenceof Kigali, there would have been a possibility that
uncontrolled growth of HFCs could have led to increasing total RF through the end of the century. Ourcurrent projections suggest that the total RF fromODSs and their replacements will be below 0.2 W m−2
by the end of the century if there is global adherence
to the Kigali Amendment, meaning the RF from all
Montreal Protocol gases (ODSs and HFCs) would be
only slightly higher than the RF of CFC-12, by itself, inthe early 2000s, when it was at its peak concentration.
Cumulative GWP-weighted HFC emissions under
the Kigali Amendment (excluding HFC-23) are calculated to be 62–63 Gt CO2-eq yr-1 from 2020 to 2060
compared with potential emissions in the absence of
6.43

Chapter 6 | Information for Policymakers

Radiative Forcing (W m−2)

0.6

HFCs
(Uncontrolled)
0.4

HCFCs

HFCs
(Kigali)

0.2
CFCs

0.0

1960

1980

2000

2020

Year

2040

2060

2080

2100

Figure 6-7. Contribution to historical and projected future radiative forcing from long-lived
ODS halocarbons and HFCs (not including HFC-23). The lowest black line represents the forcing
from CH3Cl and the cyan line represents the additional contribution from CH3CCl3 and CCl4. The
shaded range for uncontrolled HFCs represents that range between the “low” and “high” projections in Velders et al. (2015b). HFC-23 is not included in the RF contributions from HFCs since it isgenerally not used as a replacement compound in applications that traditionally used ODSs, and
it is also in a separate group in the Kigali Amendment (see Chapter 3, Section 2.5.1.5).

Kigali (baseline scenario of Velders et al., 2015b) overthe same time period of 125–155 Gt CO2-eq yr-1. A
hypothetical immediate global phaseout of HFC
production in 2020 could reduce these cumulativeemissions to 9.5–9.6 Gt CO2-eq yr-1, which represent
continuing emissions from the banks. See Chapter 2
for further discussion.
The climatic influence of the ODSs and their replacements are shown in comparison with the three dominant GHGs, CO2, CH4, and N2O, in Figure 6-8. The
figure demonstrates the large range in CO2-equivalentemissions and radiative forcing for these three climatically important gases (using the RCP2.6 projection
6.44

as the minimum and the RCP8.5 projection as the
maximum emissions scenario). The substantial benefit of the Kigali Amendment is apparent from the figure, in comparing the HFC curves without the Kigali
Amendment (red dashed) with the Kigali curves (redsolid). The sizable reduction in the climate impact ofODSs, in response to actions taken as a consequenceof the Montreal Protocol, is also evident. In contrast,
the relative reductions that can be made in future
ODS and HFC emissions lead to a substantially smaller climate influence, assuming compliance with theProtocol.

Information for Policymakers | Chapter 6

CO 2-eq Emissions

150

Radiative Forcing

8

Total

6
CO 2

W m−2

Gt CO2-eq yr−1

100

4

50
2

CH 4
ODSs

0

N 2O
1960

1980

2000

2020

2040

Year

2060

2080

2100

0

1960

1980

2000

2020

2040

2060

2080

2100

Year

Figure 6.8. Emissions and radiative forcing of ODSs and HFCs compared to these quantities for the three
long-lived greenhouse gases: CO2, CH4, and N2O. Maximum (minimum) values for CO2, CH4, and N2O represent the RCP8.5 (RCP2.6) future projections. The total represents the sum of all curves, with the maximum
total including the baseline curves for ODSs and HFCs, and the RCP8.5 scenario for the other three greenhouse gases. The minimum total is the baseline ODS and HFC scenario and the RCP2.6 scenario for CO2,
CH4, and N2O. The HFC curves (red) are not labeled due to the lack of available space. The solid red curves
are the baseline HFC scenario consistent with the Kigali Amendment and the dashed HFC curves represent
the “low growth” future HFC scenario described in Chapter 2 and is from Velders et al. (2015a). The dashed
ODS curves represent the “no future ODS emission” scenario described in the text.

6.45

Chapter 6 | Information for Policymakers

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Appendix 6A
Current State of Knowledge on ­Stratospheric Sulfate Geoengineering
Since geoengineering may be considered in the future, chemical and dynamical changes and their impacts on
future column ozone have to be understood. Models incorporate stratospheric chemistry and dynamics withvarying degrees of sophistication, and very few single-model studies have investigated changes in ozone due togeoengineering with consideration of interactions between dynamics, chemistry, aerosols, and climate. In thisappendix, the current state of knowledge on stratospheric sulfate geoengineering is summarized in more detailthan covered in the main chapter.

6A.1

Impact of Stratospheric ­Sulfate Geoengineering on Net ­Chemical Ozone Production

An increasing sulfate aerosol burden from possible continuous injection of SO2 into the tropical stratospherewould result in an enhanced aerosol surface area density in the mid- and lower stratosphere (i.e., up to about
10 hPa in the tropics). This would increase the surface area available for heterogeneous reactions, similar to thatobserved after large volcanic eruptions (Heckendorn et al., 2009; Pitari et al., 2014; Tilmes et al., 2009; Tilmes etal., 2012; Visioni et al., 2017a). The magnitude and spatial pattern of the increased aerosol surface area density
and the associated mass and size distributions, which are strongly model dependent, vary with the amount andlocation of injections (e.g., Dai et al., 2018; English et al., 2012; Laakso et al., 2017; Niemeier and Timmreck,
2015; Pitari et al., 2014; Tilmes et al., 2017) and on the injection substance. The impact of increased aerosol
surface area is particularly significant for three heterogeneous reactions:
N2O5 + H2O → 2HNO3(1)   ClONO2 + H2O → HOCl + HNO3 for T < 200(2)

ClONO2 + HCl → Cl2 + HNO3 for T < 200K

(3)Reaction (1), the reduction of nitrogen oxides (NOy) via hydrolysis of N2O5 (e.g., Fahey et al., 1993) would
increase ozone abundance, which is in part counteracted by the increase in ozone loss cycles involving reactive chlorine (ClOx), bromine (BrOx), and hydrogen (HOx) families. This reaction is dominant in the tropical
mid-stratosphere (Figure 6A-1, top left).
Increasing/decreasing the surface area density and NOy would result in an increase/decrease of the importanceof reaction (1), although this effect would saturate at very high aerosol loadings (e.g., Berthet et al., 2017).
Reaction (2), the hydrolysis of ClONO2, results in production of HOCl, increased HOx and ClO concentrations, and increased ozone loss via the catalytic ClOx and HOx cycles. Heterogeneous reactions of ClONO2
with hydrogen chloride HCl result in additional reactive chlorine. Reactions (2) and (3) are most important incold regions, especially in the lower stratosphere in polar regions in winter and spring (Figure 6A-1, top left).
Additional reactions, including the hydrolysis of BrONO2, play an important role for warmer conditions (Tilmes
et al., 2012), as recent observations after small volcanic eruptions have demonstrated (Berthet et al., 2017).
The importance of these reactions will decline with the projected decreasing stratospheric halogen burden. A
potential increase in the cold point temperature, as the result of aerosol geoengineering and a resulting increasein stratospheric water vapor content (see Section 6.2.5), leads to an additional increase in the HOx ozone loss
cycle throughout the stratosphere. Resulting changes in net chemical ozone production are most important in
the lower and upper tropical stratosphere, especially in the summer Northern Hemisphere (Tilmes et al., 2018).
An increase in the odd oxygen cycle involving reactive oxygen (Ox), resulting from a temperature increase in
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Chapter 6 | Information for Policymakers

Change in Net Chemical Ozone Production

70

20

150
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60S

30S

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60N

40
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30
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90S

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–0.18 –0.12 –0.06 0.03 0.09 0.15

–12–10 –8 –6 –4 –2 2 4 6 8 10 12

10
40

30
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20

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60S

30S

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ppm

30N

–0.72 –0.48 –0.24 0.12 0.36

60N

90N

Difference (%)

30

90N

Change in Column Ozone0

Height (km)Pressure (hPa)30S

C (degree)

Change in Ozone

90S

60S

106molecules/cm3 s-1

Height (km)30
30

Pressure (hPa)40

Height (km)Pressure (hPa)Decrease
Increase

Change in Temperature-10

January
March
July
October

-20
-30
-40
-50

-60

-30

0

Latitude

30

60

0.6

Figure 6A-1. Processes impacting stratospheric ozone in 2042–2049 following RCP8.5 for 16 Tg S yr−1 injections at 15°N/15°S. Impact of enhanced sulfate aerosols on zonal and annual averaged net rate of chemical
production of ozone (top left), temperature (top right), and ozone concentration (bottom left). Changes in thedominant ozone loss cycles are shown in blue if decreasing (net chemical production is increasing) and in
red if increasing (net chemical production decreasing) (top left). The example is based on a RCP8.5 control
simulation between 2042 and 2049 and a geoengineering simulation with injection of 16 Tg S yr−1 evenly
distributed between two single injection locations at 15°N and 15°S latitude, 180° longitude, and at 70 hPa(about 19 km) to counteract surface warming of about 2°C, using a fully interactive model, CESM1 (WACCM)(Richter et al., 2018; Tilmes et al., 2018). The tropopauses in the control and geoengineering simulations areshown by the black and blue lines, respectively. Changes in ozone concentration with geoengineering are
driven mainly by dynamical changes, especially in the tropics and the Northern Hemisphere; changes inozone concentration in polar regions mainly reflect enhanced chemical loss. Differences in column ozone (%)
between the geoengineering and the control simulation in 2042–2049 are illustrated for different months(bottom right).

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Information for Policymakers | Chapter 6

the tropical stratosphere, would also contribute to the change in net chemical ozone. Finally, the decrease inphotolysis rates due to scatter by aerosols has been estimated to decrease ozone in the tropical lower stratosphere(Pitari et al., 2014).

6A.2

Impact of Stratospheric Sulfate Geoengineering on Dynamics

According to modeling studies, geoengineering via stratospheric aerosol injection would affect dynamics throughtwo main processes: (1) cooling of the troposphere as the result of reduced incoming shortwave radiation and(2) substantial warming of the tropical stratosphere as the result of diabatic heating caused by the increased
sulfate aerosol layer (Figure 6A-1, top right) (e.g., Pitari et al., 2014; Tilmes et al., 2018; Richter et al., 2018).
These processes result in a drop in tropopause altitude, a weakening of the subtropical jets, and an increase in
the tropical cold point temperature, which may increase stratospheric water vapor by up to 90% for very large
injections of 40 Tg SO2 yr−1 (Richter et al., 2018; Tilmes et al., 2018). The vertical component of the residual
circulation in the tropics is reduced below the injection location as the result of a decrease in the temperature
gradient between the tropics and mid-latitudes above the subtropical jets. On the other hand, the increased
tropical upwelling above the injection locations and increased downwelling in high latitudes is consistent with astrengthening of the gravity wave drag and Eliassen–Palm flux (EPF) divergence in mid- and high latitudes andis aligned with a strengthening of the polar night jet (Tilmes et al., 2018; Richter et al., 2018). The strengtheningof the polar night jet in high latitudes results in additional ozone depletion (Tilmes et al., 2009), while increases
in horizontal advection, especially in winter mid-latitudes in the Northern Hemisphere stratosphere, can resultin an increase of ozone above values of non-geoengineered conditions, for example, as shown for large injectionsof 16 Tg S yr−1 at about 1 km above the tropical tropopause (Figure A6-1, bottom left). Changes in advection ofozone, other gases, and sulfate aerosols interact with chemical changes as well as stratosphere-to-­troposphereexchange. Resulting changes in tropospheric chemistry, temperature, and UV are estimated to increase methane
lifetime by 16% for continuous 4 Tg S yr−1 injections (Visioni et al., 2017b).
Simulations with injections of sulfur at the equator identified a significant prolonging of the westerly phaseof the Quasi-Biennial Oscillation (QBO) with increasing injection amounts (Aquila et al., 2014). This would
lead to a stronger confinement of particles in the tropics (Niemeier and Schmidt, 2017). However, Richter et al.
(2017) have shown that geoengineering in a model with interactive stratospheric chemistry and coupled ocean
has a reduced impact on the QBO due to reductions in heating as the result of reductions in ozone around 30
hPa. Furthermore, different injection scenarios at 15°N and 15°S or in addition at 30°N and 30°S with injections
up to 25 Tg S yr−1 by the end of the 21st century (Richter et al., 2018) would instead lead to a QBO that is closer
to present-day conditions. Large differences and shortcomings in the representation of the QBO in different
models exist, and differences in the response to geoengineering have to be investigated in more detail.

6A.3

Impact of Sulfate Aerosol G­ eoengineering on Column Ozone

In addition to potential changes in column ozone as the result of increasing greenhouse gases (GHGs) (Butler
et al., 2016), chemical and dynamical changes due to geoengineering, as discussed above, would affect futurecolumn ozone and in part counteract the projected increase of column ozone (“super-recovery”) over most latitudes (Chapter 3). In one modeling study, a fixed injection of 4 Tg S yr−1 between 2020 and 2070, which resultsin 0.5°C of surface cooling, leads to approximately a 4% reduction in the global stratospheric column ozone for2020 and only a 1% reduction by 2070 (Xia et al., 2017). These results are similar to calculations based on fourmodels using fixed injections of 2.5 Tg S yr−1, which show an average decrease in global column ozone of 2.8%
over the same period (Visioni et al., 2017a). The impact of aerosols geoengineering on ozone is therefore small if
applied later in the century, when global column ozone absent geoengineering is projected to increase by about~4% (from 2020 to 2070) due to changes in ODS and GHG concentrations (Cionni et al., 2017).
Simulations using a fully interactive earth system model that includes an interactive aerosol microphysical
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Chapter 6 | Information for Policymakers

scheme coupled to interactive chemistry and radiation, and with an internally generated QBO (Mills et al.,2017), point to the importance of chemistry and dynamical changes on column ozone. Very large injections of
16 Tg S yr−1, to cool the surface by about 2°C in 2042–2049, reduce column ozone values towards present-day
conditions in winter and spring high altitudes for both hemispheres (Figure 6A-1, bottom right). Maximum
reductions of 8% in March over the Arctic, and 28% in October over Antarctica are reached in comparison to the
RCP8.5 control simulation (Figure 6A-1, bottom right). On the other hand, an increase of column ozone abovenon-­geoengineered levels up to 8% is simulated for the Northern Hemisphere mid-latitudes in winter. A different experiment that reached the same surface cooling but applied injections at higher altitudes (~5 km about thetropopause) indicated that advection is less important and resulted in a larger decrease in column ozone at highlatitudes, with up to 18% loss over the Arctic and up to 40% over Antarctica in spring. The impact on column
ozone is therefore dependent on the injection strategy (Richter et al., 2018; Tilmes et al., 2018).
A transient simulation based on RCP8.5 GHG forcings with continuously increasing SO2 injections at 15°N,
15°S, 30°N, and 30°S at ~5 km about the tropopause required injections up to 25 Tg S yr−1 by the end of the
century to maintain temperatures at 2020 levels (Kravitz et al., 2017). In this simulation, ozone recovery in theSouthern Hemisphere polar vortex was delayed until the end of the 21st century. Besides, column ozone reachedvalues close to pre-ozone hole conditions for the Southern Hemisphere and tropics and well above pre-ozone
hole conditions for northern mid-latitudes in winter and spring (Richter et al., 2018). By the end of the 21st
century, geoengineering resulted in higher column ozone values compared to non-geoengineering conditions
for tropics and winter northern mid-latitudes.

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Appendix 6B
Comparison of Past and Future Ozone Projections of the
GSFC 2-D Model with GEOSCCM 3-D Simulations
In this appendix, predictions of past and future ozone with the GSFC 2-D model (GSFC 2D), used in thischapter, are shown to be in very good agreement with the GEOSCCM 3-D simulations used in Chapters 3, 4,
and 5. The GEOSCCM has a comprehensive tropospheric and stratospheric chemical mechanism (e.g., Oman
et al., 2016) and performed well in both chemical- and transport-related process evaluations (SPARC CCMVal,
2010; Strahan et al., 2011; Douglass et al., 2012). The GSFC 2-D model has complete stratospheric chemistry
but contains a limited subset of tropospheric species (Fleming et al., 2011; Jackman et al., 2016). The GSFC 2-D
model compares well with observations and the GEOSCCM in simulating various transport-sensitive featuresin the meridional plane (e.g., long-lived tracers), as well as long-term changes in temperature and age of air overthe 1950–2100 period (Fleming et al., 2011; SPARC, 2013). In this appendix, we also show comparisons of thebaseline simulation with the CCMI multi-model mean (MMM, ±1σ) for the total and stratospheric column, andwith selected observations where available.
Figure 6B-1 shows comparisons of the GSFC 2-D model and GEOSCCM global/annually averaged ozone fromthe CCMI baseline REF-C2 simulations for 1960–2100. These simulations include past stratospheric aerosol
variations and solar ultraviolet flux variability associated with the 11-year solar cycle, with a repeating 11-year
cycle projected out to 2100. The 2-D model stratospheric column ozone agrees quite well with the GEOSCCM,
both in absolute amount and the pre-2000 decline and future ozone recovery out to 2100 (Figure 6B-1b).
While tropospheric column ozone is similar in the two models during the 1960s, GSFC 2-D underestimates thetime-dependent increases in tropospheric ozone in the GEOSCCM from ~1970 through the mid-21st century(Figure 6B-1c). This is likely due to the limited tropospheric chemical scheme used in the 2-D model, as mentioned above. This results in a low bias in tropospheric ozone throughout the 21st century, which is as large as
15% (5 DU) in 2050–2060. This low bias is also reflected in the future total column ozone comparison throughthe 21st century (Figure 6B-1a). For the total and stratospheric column, GSFC 2D and GEOSCCM show overall
agreement with the observations but show a stronger decline and stronger recovery compared with the CCMI
MMM (gray shading indicates ±1σ). For the tropospheric column, the limited available data fall between the twomodels (see the figure caption for details of the observations and MMM).
Figure 6B-2 shows stratospheric column ozone from the REF-C2 simulations at selected latitude zones. The
GSFC 2D low bias in tropospheric ozone, and therefore in the total column, is similar to that of the global
average (Figure 6B-1); therefore, the focus here is on stratospheric ozone. The 2-D model captures well the decline and recovery of stratospheric ozone simulated by the GEOSCCM and CCMI MMM during the Antarctic
spring and the tropical and Northern mid-latitude annual average. The models show general agreement withthe observations, which have significant year-to-year variability, although the CCMI MMM shows a somewhat
weaker ozone decline during the Antarctic spring. The models also show the GHG-induced “super-recovery”
at Northern Hemisphere mid-latitudes, where stratospheric ozone is 15–20 DU higher in 2100 than in 1960
(the CCMI MMM shows a somewhat smaller increase). The GEOSCCM, GSFC 2D, and CCMI MMM all show
a similar decrease in tropical stratospheric ozone during the late 21st century, again driven primarily by GHG
changes as discussed in Chapter 3 (see Sections 3.4.2 and 3.4.3.1 and Figures 3-26, 3-29, and 3-30).
GSFC 2D also compares well with the GEOSCCM in simulating the ozone response to the CH4 perturbations
discussed in Chapters 3 and 4. Figure 6B-3 shows the profile ozone mixing ratio difference (ppm) between
the RCP8.5 (high) CH4 simulation and REF-C2 (RCP6.0 CH4), averaged over 2070–2100. In the stratosphere,
increased CH4 loading leads to increased ozone due to the conversion of active chlorine to reservoir chlorine via
the reaction CH4 + Cl -> HCl + CH3, which reduces the chlorine-catalyzed ozone loss, although this process will
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Chapter 6 | Information for Policymakers

REF-C2 Global Annual Average
Dobson Units

310(a) Total column

300
290
280
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Dobson Units

280

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2020

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270

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1980

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26
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1980

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Year
Figure 6B-1. Comparison of past and future globally averaged ozone from the REF-C2 simulations and observations. This shows the ozone columns below (c) and above (b) the latitude- and seasonally-dependent tropopause, and the total column (a), from the GSFC 2-D model (red line) and GEOSCCM 3-D model (blue line)
for 1960–2100. Also shown are the CCMI multi-model mean (MMM, dark gray line) with ±1σ (gray shading)(see Sections 3.4 and 4.5 of this Assessment, and Dhomse et al., 2018 for details). The observations are (a)ground-based total ozone for 1964–2016 updated from Fioletov et al. (2002); (b) stratospheric column ozone
from: Aura/MLS version 4.2 for 2005–2017 (black +), and Global OZone Chemistry And Related trace gas Data
records for the Stratosphere (GOZCARDS) version 2.20 for 1985–2016, updated from Froidevaux et al. (2015)
and time-interpolated to fill in missing data (orange Δ); and (c) tropospheric column ozone derived fromOMI/MLS averaged over 60°S to 60°N for 2005–2016 (Ziemke and Cooper, 2017; 2018). To facilitate visualcomparison and minimize the model biases in the 1960s, the following offsets were applied: (a) MMM: +6.5
DU; (b) MMM: –3 DU, GOZCARDS: +3 DU.
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Information for Policymakers | Chapter 6

REF-C2 Stratospheric Column
Dobson Units

400
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245

Dobson UnitsGEOSCCMGSFC 2D
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1980

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320

300
280
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1980

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2040

Year
Figure 6B-2. Comparison of past and future stratospheric column ozone in select latitude bands fromthe baseline REF-C2 simulations and observations. This shows the ozone column above the tropopause(­seasonally- and latitude-dependent) from the GSFC 2-D model (red line) and GEOSCCM 3-D model (blue line)
for 1960–2100. Also shown are the CCMI multi-model mean (MMM, dark gray line) with ±1σ (gray shading)(see Sections 3.4 and 4.5 of this Assessment, and Dhomse et al., 2018 for details). The observations are stratospheric column ozone from: Aura/MLS version 4.2 for 2005–2017 (black +), and Global OZone Chemistry And
Related trace gas Data records for the Stratosphere (GOZCARDS) version 2.20 for 1985–2016, updated from
Froidevaux et al., 2015 and time-interpolated to fill in missing data (orange Δ). To facilitate visual comparisonand minimize the model biases in the 1960s, the following offsets were applied: (a) MMM: +15 DU; (b) MMM:
–16 DU, GOZCARDS: +2 DU; (c) GEOSCCM: –5 DU, GOZCARDS: +2 DU.

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Chapter 6 | Information for Policymakers

Ozone Difference: RCP8.5 CH4 - REFC2GEOSCCM60

Pressure (hPa)

40
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0

ppm
– .14

– .1

– .05

0

.05

.1

.15

Figure 6B-3. Profile ozone sensitivity to CH4. This shows the ozone mixing ratio difference (ppm) between
simulations using the RCP8.5 CH4 (high) scenario and the baseline REF-C2 (RCP6.0 CH4) from the GEOSCCM
and GSFC 2-D model, averaged over the period 2070–2099 to minimize interannual variability in the GEOSCCM. Negative differences are indicated by the blue colors, positive differences by the red colors.

6.64

Information for Policymakers | Chapter 6

become less important as chlorine diminishes through the late 21st century. Methane oxidation also increases
stratospheric HOx, which (1) increases the HOx-ozone loss and (2) sequesters NOx in the reservoir HNO3 via thereaction OH + NO2, thereby reducing ozone loss in the mid-stratosphere (Nevison et al., 1999; Randeniya et al.,2002). There is also a contribution due to the increased water vapor from methane oxidation, which enhances
stratospheric cooling and reduces the ozone chemical loss rates (e.g., WMO, 2014). In the troposphere andlowermost stratosphere, CH4 oxidation leads to enhanced NOx-induced ozone production, which is strongly
dependent on the amount of ambient NOx (e.g., see Jacob, 1999; Portmann and Solomon, 2007; Fiore et al., 2008;
Kawase et al., 2011; WMO, 2014). The net impact of these processes yields ozone increases throughout most of
the stratosphere below ~42 km and ozone decreases above ~42 km. The small area of negative ozone change inthe tropical mid-stratosphere is likely due to “reverse self-healing,” in which increased ozone concentrations at
higher altitudes allow less UV radiation to penetrate to lower altitudes, thereby reducing ozone production (e.g.,
Haigh and Pyle, 1982; Portmann and Solomon, 2007).
Figure 6B-4 shows time series of the global ozone difference from the REF-C2 simulation using (1) fixed (low)1960 CH4 throughout 1960–2100 and (2) RCP8.5 (high) CH4 for 2000–2100. Globally, tropospheric and stratospheric column ozone both increase with larger methane concentrations and decrease with smaller methane
concentrations. GEOSCCM and GSFC 2D give very similar global tropospheric and stratospheric ozone responses to both low and high methane concentrations throughout 1960–2100 (Figure 6B-4b and c). Although
GSFC 2D has limited tropospheric chemistry and underestimates the GEOSCCM baseline tropospheric ozone,
the large-scale NOx distribution is similar to the GEOSCCM. As a result, GSFC 2D simulates quite well themethane-induced global tropospheric ozone perturbations (difference from REF-C2) simulated by GEOSCCM(Figure 6B-4c). The total column ozone responses are therefore also quite similar between the two models(Figure 6B-4a), which gives confidence in the fidelity of the 2-D model total ozone responses to the CH4 sensitivity simulations discussed in Section 6.4.3.1 (Figure 6-4c). Figure 6B-4 also indicates that changes in stratospheric and tropospheric ozone each account for roughly 50% of the total ozone response to the CH4 sensitivity
simulations shown in Figure 6-4c.
Figure 6B-5 shows the model ozone mixing ratio sensitivity to N2O as discussed in Chapters 3 and 4. The
difference between simulations using fixed (low) 1960 N2O versus the baseline REF-C2 (RCP6.0 N2O) resultsin positive ozone changes in the mid-upper stratosphere, owing to the reduced NOx-ozone loss. Negative ozone
changes occur in the lower stratosphere and upper troposphere as the reduced NOx decreases the NOx-inducedozone production cycle. Some of the negative changes are also likely caused by “reverse self-healing” as discussed above. The zero ozone difference line occurs at ~28 km in the tropics and descends with latitude to ~18km at the poles (see also Revell et al., 2012; Figure 2-25 of Parwon, Steinbrecht, et al., 2014; and Chapter 3 ofthis Assessment). To emphasize these positive and negative ozone differences, time series of the global column
ozone below and above this zero difference line are shown in Figure 6B-6c and d. GSFC 2D is similar to the
GEOSCCM in simulating the positive ozone changes above the zero difference line through the 21st century(Figure 6B-6c). However, GSFC 2D underestimates the negative ozone differences below the zero difference line
at Northern Hemisphere mid-high latitudes at 10–18 km (Figure 6B-5) and in the global average (Figure 6B6d). As a result, the 2-D model has larger positive changes in the global total column in the later part of the 21st
century (Figure 6B-6a). However, GSFC 2D is very similar to the GEOSCCM in simulating the positive column
ozone differences above 28 km, the primary region of stratospheric NOx-ozone loss (Figure 6B-6b).

6.65

Chapter 6 | Information for Policymakers

CH4 Sensitivity: Difference from REF-C2

Dobson Units

10
5

RCP8.5 CH4GSFC 2D

fixed 1960 CH4-5

6

Dobson UnitsGEOSCCM

0

-10
1960

1980

2000

2020

2040

2060

2080

2100

2040

2060

2080

2100

2040

2060

2080

2100

(b) Stratospheric column

4
2
0
-2
-4
-6
1960

6

Dobson Units(a) Total column

1980

2000

2020

(c) Tropospheric column

4
2
0
-2
-4
1960

1980

2000

2020

Year
Figure 6B-4. Comparison of the past and future global ozone sensitivity to CH4 variations. This shows the
1960–2100 global ozone difference from the baseline REF-C2 (RCP6.0 CH4) of simulations using fixed (low)1960 CH4 (negative ozone anomalies) and the RCP8.5 CH4 (high) scenario (positive ozone anomalies). Shownare the global tropospheric (c) and stratospheric (b) columns (separated by the latitude- and seasonally-­
dependent tropopause), and the total column (a) from the GSFC 2-D model (red line) and GEOSCCM 3-D
model (blue line). Note that interannual variability in the 2-D model, due to tropospheric NOx and CO emissions and the 11-year solar cycle, is the same for all simulations, so that the 2-D model difference curves (red)show minimal variability.

6.66

Information for Policymakers | Chapter 6

Ozone Difference: Fixed 1960 N2O-REFC2GEOSCCM

50
40

10

30
20

100
-90

-60

-30

0

30

Altitude (km)Pressure (hPa)

1

10
90

60

GSFC 2D

50
40

10

30

100

20

-90

-60

-30

0

30

Altitude (km)Pressure (hPa)

1

10
90

60

ppm
-.1

0

.1

.2

.3

.4

.5

.6

.7

Figure 6B-5. Profile ozone sensitivity to N2O. This shows the ozone mixing ratio difference (ppm) between
simulations using fixed (low) 1960 N2O and the baseline REF-C2 (RCP6.0 N2O) from the GEOSCCM and GSFC
2-D model, both averaged over the period 2070–2099 to minimize interannual variability in the GEOSCCM.
Negative differences are indicated by the blue colors, positive differences by the red colors.

6.67

Chapter 6 | Information for Policymakers

Dobson Units

Fixed 1960 N20: Difference from REF-C2
8(a) Total column

40Dobson Units

1960
8

Dobson Units

2000

2020

2040

2060

2080

2100

2040

2060

2080

2100

2080

2100

2080

2100

(b) Column above 28 km
GEOSCCMGSFC 2D

4
0
1960
8

1980

2000

2020

(c) Column above zero difference line

4
0
1960
4

Dobson Units

1980

1980

2000

2020

2040

2060

(d) Column below zero difference line

0
-4
1960

1980

2000

2020

2040

Year

2060

Figure 6B-6. Comparison of the past and future global ozone sensitivity to N2O. This shows the 1960–2100
global ozone difference between simulations using fixed (low) 1960 N2O and the baseline REF-C2 (RCP6.0
N2O). As seen in Figure 6A-5, positive ozone differences occur in the mid-upper stratosphere, and negative
differences in the lower stratosphere: the zero difference line occurs at ~28 km in the tropics and descends
with latitude to ~18 km at the poles (see also Figure 2-25 of Pawson, Steinbrecht, et al., 2014, and Chapter 3of this Assessment). To emphasize these positive and negative differences, the column ozone below (d) andabove (c) the zero difference line is shown in the bottom two panels. Also shown is column ozone above 28
km (b), the primary region of NOx ozone loss, and the total column (a), from the GSFC 2-D model (red line)
and GEOSCCM 3-D model (blue line). Note that interannual variability in the 2-D model, due to tropospheric
NOx and CO emissions and the 11-year solar cycle, is the same for all simulations, so that the 2-D model difference curves (red) show minimal variability.

6.68

Appendix 6C
Evaluation of Alternative Scenarios Using New EESC Formalism
Table 6C-1. Same as Table 6-5, for the part shown, but using the approach to calculating EESC andthe fractional release values from Engel et al. (2017). Table 6-5 and its footnotes provide additional
information about the scenarios and the calculations used to populate the table.
Scenario
and
Cases

Percent Difference in ­Integrated
EESC Relative to Baseline Scenario
for the Mid-latitude CaseYear When EESC is Expected toDrop Below 1980 Value

Mid-latitudeχ∫ EESC dt

1980

Antarctic Vortex

χ∫ EESC dt

2020

Scenarios
A1: Baseline scenario

0.0

0.0

2060.42077.3

P0: All ODS

–5.0

–16.4

2054.72071.9

CFCs

0.0

0.0

2060.42077.3

Halons0.0

0.0

2060.42077.3

HCFCs

–0.9

–3.0

2059.82076.9CH3Br for QPS and CUE

–1.6

–5.4

2058.92075.8

CCl4

–2.7

–8.9

2056.92073.9

E0: All ODS(does not include N2O)

–9.9

–32.3

2048.92065.6

CFCs

–2.1

–6.7

2058.02074.9Halons–2.6–8.6

2057.82074.6

HCFCs

–2.5

–8.3

2058.82076.2

CCl4

–2.7

–8.9

2056.92073.9CH3CCl30.0

0.0

2060.42077.3

CH3Br for QPS and CUE

–1.6

–5.4

2058.92075.8

B0: All ODS

–6.0

–19.5

2054.42071.3

CFCs

–2.1

–6.7

2058.02074.9Halons–2.6–8.6

2057.82074.6

HCFCs

–1.6

–5.3

2059.52076.7

B1: All

–4.0

–13.1

2055.42072.4

CFCs

–1.3

–4.1

2058.62075.4

Halons

–1.6–5.3

2058.42075.1

HCFCs

–1.4

–4.5

2059.32076.5

+6.0

+19.7

2072.72098.4+4.92063.72080.8Continued emission of CFC-11:
Constant at 67 Gg yr−1

Continued emission of CCl4 at current levels:
+1.5

6.69

Appendix A
Summary of Abundances, Lifetimes,
Ozone Depletion Potentials (ODPs),
Radiative Efficiencies (REs),
Global Warming Potentials (GWPs), andGlobal Temperature change Potentials (GTPs)
Lead Author
J.B. Burkholder
Contributors
Ø. Hodnebrog
V.L. Orkin2

Cover photo: Experimental apparatus used in fundamental kinetic and photochemical laboratory studies. Laboratory measurements provide key input to the derivation of the parameters reported in this appendix. Photo:
W. von Dauster, NOAA.

Appendix A
Summary of Abundances, Lifetimes,
Ozone Depletion Potentials (ODPs),
Radiative Efficiencies (REs), Global Warming Potentials (GWPs), andGlobal Temperature change Potentials (GTPs)
CONTENTSAPPENDIX A: INTRODUCTION .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 1APPENDIX A: SUMMARY OF ABUNDANCES, LIFETIMES, ODPs, REs, GWPs, AND GTPs  .  .  .  .  .  .  .  .  .  .  .  . 2Hydrocarbons .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 2Oxygenated Hydrocarbons .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 2Chlorofluorocarbons .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 2Hydrochlorofluorocarbons  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 4Hydrofluorocarbons .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 18
Unsaturated Hydrofluorocarbons .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 20
Chlorocarbons and Hydrochlorocarbons .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 22
Unsaturated Hydrochlorocarbons and Chlorocarbons .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 24
Unsaturated Chlorofluorocarbons and Hydrochlorofluorocarbons  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 24
Bromocarbons, Hydrobromocarbons and Halons .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 24
Unsaturated Bromofluorocarbons  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 26
Unsaturated Bromochlorofluorocarbons .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 26
Fully Fluorinated Species .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 26
Halogenated Ethers .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 28
Fluoroesters .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 32
Halogenated Alcohols .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 34
Halogenated Ketones  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 36
Iodocarbons  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 36
Special Compounds .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 36
Table Heading Footnotes .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 38
Abundance Footnotes .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 39
Lifetime Footnotes  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 39
ODP Footnotes .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 42
RE, GWP, and GTP Footnotes .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 43
REFERENCES .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 44

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Appendix A
Introduction
Table A-1 in this appendix contains a compilation of atmospheric abundance, lifetime, ozone depletion potential (ODP), and radiative metrics for ozone depleting substances (ODSs), replacement compounds, and related
species covered under the umbrella of the present ozone assessment. The table builds upon the metrics reported
in various previous assessments from the Intergovernmental Panel on Climate Change (IPCC, 2013) and theWorld Meteorological Organization and United Nations – Environment (WMO, 2014).
The abundances and metrics reported in Table A-1 were evaluated based on the best available data and analysis
methods as described in the table heading footnotes. Table entries have associated abundance, lifetime, ODP,
and radiative metric footnotes that provide the literature source, parameters, or method used to derive thereported metric. Long- and short-lived (lifetimes <~0.5 years) source compounds are included in the table.
Metrics given for short-lived species are dependent on the time and location of their emission because they do
not become atmospherically well-mixed and, hence, the abundances and metrics reported are not valid for all
emission scenarios.
In the absence of experimental kinetic or photochemical data for some molecules, the OH radical reactivity andUV photolysis rates were estimated using structure activity relationships (SARs), trends in reactivity and photolysis for a class of compounds, or comparison with similar molecules where experimental data are available.
In the absence of experimental infrared absorption spectra, radiative efficiencies were calculated, in some cases,
based on theoretically calculated spectra (e.g. for many of the hydrochlorofluorocarbons (HCFCs) included in
the table).
The ODPs and global warming potentials (GWPs) given in the table may differ, in some cases, from the official
metrics for controlled substances reported in the Montreal Protocol Handbook (Handbook, 2018) due to consideration of recent experimental data, methods of analysis, and/or assessment recommendations (Ammann etal., 2017; Burkholder et al., 2015; IPCC, 2013; Ko et al., 2013; WMO, 2014).

A.1

Appendix A | Appendix Table A-1

Appendix A

2

Summary of Abundances, Lifetimes, Ozone Depletion Potentials (ODPs), Radiative Efficiencies (REs),
Global Warming Potentials (GWPs), and Global Temperature change Potentials (GTPs)
Table A-1. Atmospheric abundances, lifetimes, ozone depletion potential (ODPs), radiative efficiencies (REs),
Global Warming Potentials (GWPs) for 20 and 100-year time horizons, and Global Temperature change Potentials(GTPs) for 20, 50, and 100-year time horizons. Atmospheric abundances are taken from the present Assessment as
noted in the footnotes. Global, annually averaged, atmospheric lifetimes (total, arising from tropospheric OH reaction,
and arising from stratospheric loss) were derived using the methods and kinetic and photochemical data describedin the footnotes. The ODPs reported here are semi-empirical values or from atmospheric model calculations as citedin the compounds footnote. The radiative metrics reported here are based on a CO2 abundance of 391 ppm (the CO2

Industrial Designation or
Chemical Name

Chemical Formula

AtmosphericAbundance(2016) a

Carbon dioxide

CO2402.9 ppm

MethaneCH41842 ppb

Fossil methane #CH4

Nitrous oxide

N2O

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

–
12.4

12.4

10.4

12.4

12.4

10.4

329 ppb

121

123

–

Hydrocarbons
Propene

CH2=CHCH3–

0.35 days(0.27–0.50 days)

0.4 days(0.27–0.50 days)

0.4 days(0.27–0.50 days)

Isobutene(CH3 )2C=CH2–

0.20 days(0.15–0.29 days)

0.2 days(0.15–0.29 days)

0.2 days(0.15–0.29 days)

Propane, R-290

CH3CH2CH3–

12.5 days(9.9–27 days)

15 days(9.9–27 days)

15 days(9.9–27 days)

Isobutane, R-600a(CH3 )2CHCH3–

6.0 days(5.2–10.7 days)

7 days
(5.2–10.7 days)

7 days
(5.2–10.7 days)

n-pentane

CH3CH2CH2CH2CH3–

3.4 days(2.7–6.5 days)

3 days
(2.7–6.5 days)

3 days
(2.7–6.5 days)

Cyclopentane

c-CH2CH2CH2CH2CH2–

2.7 days(2.2–5.3 days)

3 days
(2.2–5.3 days)

3 days
(2.2–5.3 days)

Isopentane(CH3 )2CHCH2CH3–

3.4 days(2.9–6.0 days)

4 days
(2.9–6.0 days)

4 days
(2.9–6.0 days)

Methyl formate

CH3OCHO–66 days(60–143 days)

87 days(60–143 days)

87 days(60–143 days)

Isopropanol(CH3 )2CHOH–

2.0 days(1.5–2.9 days)

2 days
(1.5–2.9 days)

2 days
(1.5–2.9 days)

Methylal

CH3OCH2OCH3–

2.2 days(1.5–2.8 days)

2 days
(1.5–2.8 days)

2 days
(1.5–2.8 days)

52

52

–

Oxygenated Hydrocarbons

Chlorofluorocarbons

A.2

CFC-11

CCl3F

230.2 ppt

CFC-12

CCl2F2515.9 ppt

102

102

–

CFC-13CClF3

3.0 ppt

640

640

–

CFC-112CCl2FCCl2F

0.4 ppt

59

63.6

–

Appendix Table A-1 | Appendix A

absolute GWPs for the 20- and 100-yr time horizons are 2.495 × 10-14 and 9.171 × 10-14 W yr/(m2 kg); the CO2 absolute
GTPs for the 20-, 50-, and 100-yr time horizons are 6.841 × 10-16, 6.167 × 10-16, and 5.469 × 10-16 K/kg, see Chapter
6) and are consistent with the values reported in IPCC (2013) and the last ozone assessment (WMO, 2014). Radiative
efficiencies were calculated using the methods given in Hodnebrog et al. (2013) with lifetime and stratospherictemperature change adjustments applied. Climate-carbon feedbacks are included for CO2 (see IPCC (2013) for further
details). The derivation of GTP assumes a climate sensitivity of 1.06 K (W m-2)-1, equivalent to a 3.9 K equilibrium
temperature increase in response to a doubling of CO2, toward the higher end of the uncertainty in climate sensitivity.
For further details on the specific values used see Supplementary Material Section S8.12 and references therein in
IPCC (2013).
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

01.375e-5

1

1

1

1

1 A1

L1

R1

0

3.63e-4

84

28

67

14

4

A2

L1

R1

0

3.63e-4

85

30

68

15

6

A2

L1

123

–

3.00e-3

264

265

277

282

234

A3

L :2,3–0

1.5e-4

<<1

<<1

<<1

<<1

–

0

6.8e-5

<<1

<<1

<<1

–

0

3.6e-4

<1

<1

–

0

2.5e-4<<1–0

1.7e-4

–

0

–

R1
O1

R1

<<1

O2

R2

<<1

<<1

O2

R2

<1

<1

<1

O2

R2

<<1

<<1

<<1

<<1

L4

O2

R2

<<1

<<1

<<1

<<1

<<1

L4

O2

R2

1.3e-4

<<1

<<1

<<1

<<1

<<1

L4

O2

R2

0

2.4e-4

<<1

<<1

<<1

<<1

<<1

L4

O2

R2

–

0

0.045

40

11

12

1.8

1.5

L5

O2

R2

–

0

1.4e-3

<<1

<<1

<<1

<<1

<<1

O2

R2

–

0

4.0e-3

<<1

<<1

<<1

<<1

<<1

O2

R2

55

1.0

0.26

7,090

5,160

7,160

5,480

2,920

103

0.73–0.81

0.32

10,80010,30011,30011,0008,590

A4

L :2,3

O:3,4

R3

–

1.0

0.25

10,90013,90011,70014,20015,900

A4

L7

O5

R3

65.4

0.98

0.29

5,500

4,370

5,631

4,715

2,875

A4

L :2,8

O6

R4

L6

A4

L :2,3R3

A.3

Appendix A | Appendix Table A-1
4

Industrial Designation or
Chemical Name

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

CFC-112a

CClF2CCl30.07 ppt

51

52

–

CFC-113

CCl2FCClF2

71.7 ppt

93

93

–

CFC-113a

CCl3CF3

0.66 ppt

59

55

–

CFC-114

CClF2CClF2

15 ppt

189

189

–

CFC-114a

CCl2FCF3

1 ppt

~100

105

–

CFC-115

CClF2CF3

8.5 ppt

540

540

–

CFC-216ba

CClF2CClFCF3

38 ppq–135–

CFC-216ca

CClF2CF2CClF2

20 ppq–~135–(E)-R316c ((E)-1,2-dichlorohexafluoro-cyclobutane)(E)-1,2-c-C4F6Cl2

75

75

–

(Z)-R316c ((Z)-1,2-dichlorohexafluoro-cyclobutane)(Z)-1,2-c-C4F6Cl2

114

114

–

HCFC-21

CHFCl2

1.7

1.7

1.8

HCFC-22

CHF2Cl235.3 ppt

11.9

11.9

13.0

HCFC-31

CH2FCl

0.080 ppt

1.2

1.2

1.3

HCFC-121

CHCl2CCl2F

–

1.11

1.17

HCFC-121a

CHClFCCl3

–

2.67

2.96

HCFC-122

CHCl2CClF2

–

0.9

0.96

HCFC-122a

CHClFCCl2F

–

3.1

3.4

HCFC-122b

CHF2CCl3

–

9.31

12.6

HCFC-123

CHCl2CF3

1.3

1.3

1.38

HCFC-123a

CHClFCClF2

4.0

4.0

4.3

HCFC-123b

CHF2CCl2F

~6

11.8

15.1

HCFC-124

CHClFCF3

5.9

5.9

6.3

HCFC-124a

CHF2CClF2

~9.2

17

19

HCFC-131

CHCl2CHClF

–

0.76

0.752

HCFC-131a

CH2ClCCl2F

–

2.57

2.8

HCFC-131b

CH2FCCl3

–

2.33

2.55

HCFC-132

CHClFCHClF

–

1.73

1.81

HCFC-132a

CHCl2CHF2

–

1.12

1.18

HCFC-132b

CH2ClCClF2

–

3.5

3.7

HCFC-132c

CH2FCCl2F

4.3

4.1

4.5

HCFC-133

CHClFCHF2

–

3.1

3.21

HCFC-133a

CH2ClCF3

4.0

4.6

4.7

HCFC-133b

CH2FCClF2

–

7.2

7.71

HCFC-141

CH2ClCHClF

–

1.14

1.19

HCFC-141a

CH2FCHCl2

–

0.50

0.49

Hydrochlorofluorocarbons

A.4

1.1 ppt

0.38 ppt

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

53.8

0.86

0.26

4,770

3,455

4,823

3,690

1,970

A4

L :2,8

O6

R4

94.5

0.81–0.82

0.30

6,560

6,080

6,830

6,510

4,860

A4

L :2,3

O:3,4

R3

57.5

0.73

0.25

5,040

3,750

5,114

4,020

2,230

A4

L :2,8

O6

R4

191

0.50

0.31

7,710

8,580

8,180

9,010

8,530

A4

L :2,3

O:3,4

R3

106.7

0.72

0.29

6,960

6,670

7,287

7,175

5,650

A4

L :2,8

O6

R4

664

0.26

0.20

5,780

7,310

6,210

7,500

8,290

A4

L :3,9

O:3,4

R3

135

0.35

A4

L10

O7

~135

~0.35

A4

L10

O7

76

0.46

0.28

4,750

4,050

4,909

4,375

2,935

L11

O8

R5

115

0.54

0.31

5,500

5,400

5,773

5,800

4,715

L11

O8

R5

~35

0.036

0.15

545

150

190

26

20

A5

L :12,13

O9

R3

161

0.024–0.034

0.21

5,310

1,780

4,230

845

265

A5

L :3,12,13

O3

R3

~35

0.019

0.0587

230

65

77

11

9

A5

L1

O9

R6

20

0.030

0.183

245

65

80

11

9

A5

L14

O10

R7

27.3

0.066

0.180

580

160

235

29

22

A5

L14

O10

R7

21

0.022

0.17

220

60

70

10

8 A5

L14

O9

R3

34

0.067

0.21

865

235

375

44

33

A5

L14

O9

R3

35.5

0.170

0.213

2,330

715

1,705

255

102

A5

L14

O10

R7

31

0.01

0.15

290

80

98

14

11

A5

O11

R3

65

0.039

0.23

1,350

370

660

72

51

A5

O9

R3

54

0.124

0.24

3,400

1,130

2,700

530

168

A5

O10

R7

98

0.022

0.20

1,870

530

1,120

121

74

A5

O5

R3

161

0.026

0.241

4,675

1,825

4,085

1,260

330

A5

L14

O10

R7

20

0.019

0.101

115

30

36

5

4 A5

L14

O10

R7

31

0.056

0.169

645

175

260

32

24

A5

L14

O10

R7

26

0.054

0.132

460

125

175

22

17

A5

L14

O10

R7

39

0.025

0.152

440

120

155

21

17

A5

L14

O10

R7

24

0.020

0.131

245

65

81

12

9

A5

L14

O10

R7

67

0.038

0.202

1,175

320

540

61

45

A5

L14

O10

R6

41

0.062

0.17

1,155

315

570

62

44

A5

O9

R3

68

0.017

0.173

1,010

275

435

51

38

A5

L14

O10

R7

103

0.019

0.15

1,295

355

680

72

50

A5

L15

O9

R8

110

0.024

0.206

2,645

765

1,740

205

108

A5

L14

O10

R7

30

0.022

0.0772

170

45

56

8

6 A5

L14

O10

R7

20

0.011

0.0594

55

15

17

3

2 A5

L14

O10

R7

L14

A.5

Appendix A | Appendix Table A-1
6

Industrial Designation or
Chemical Name

A.6

Chemical Formula

HCFC-141b

CH3CCl2F

HCFC-142

CH2ClCHF2

HCFC-142a

CH2FCHClF

HCFC-142b

CH3CClF2

HCFC-151

AtmosphericAbundance(2016) a

24.4 ppt

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

9.4

9.4

10.7

–

2.6

2.73

–

1.58

1.64

18

18

19.3

CH2ClCH2F

–

0.49

0.487

HCFC-151a

CH3CHClF

–

1.16

1.2

HCFC-221aa

CHCl2CCl2CCl2F

–

0.93

0.98

HCFC-221ab

CHClFCCl2CCl3

–

2.67

2.96

HCFC-221ba

CHCl2CClFCCl3

–

1.11

1.17

HCFC-221da

CCl3CHClCCl2F

–

3.29

3.71

HCFC-221ea

CCl3CHFCCl3

–

3.52

3.99

HCFC-222aa

CHCl2CCl2CClF2

–

1.11

1.17

HCFC-222ab

CHClFCCl2CCl2F

–

2.67

2.96

HCFC-222ac

CHF2CCl2CCl3

–

9.29

12.6

HCFC-222ba

CHCl2CClFCCl2F

–

1.11

1.17

HCFC-222bb

CHClFCClFCCl3

–

3.15

3.54

HCFC-222ca

CHCl2CF2CCl3

–

1.38

1.47

HCFC-222da

CCl2FCHClCCl2F

–

4.48

5.23

HCFC-222db

CCl3CHClCClF2

–

4.62

5.42

HCFC-222ea

CCl3CHFCCl2F

–

4.68

5.49

HCFC-223aa

CHCl2CCl2CF3

–

1.11

1.17

HCFC-223ab

CHClFCCl2CClF2

–

3.18

3.54

HCFC-223ac

CHF2CCl2CCl2F

–

9.29

12.6

HCFC-223ba

CHCl2CClFCClF2

–

1.39

1.47

HCFC-223bb

CHClFCClFCCl2F

–

3.18

3.54

HCFC-223bc

CHF2CClFCCl3

–

10.6

15.1

HCFC-223ca

CHCl2CF2CCl2F

–

1.38

1.47

HCFC-223cb

CHClFCF2CCl3

–

3.88

4.45

HCFC-223da

CCl2FCHClCClF2

–

6.48

7.86

HCFC-223db

CCl3CHClCF3

–

6.47

8.02

HCFC-223ea

CCl2FCHFCCl2F

–

6.28

7.74

HCFC-223eb

CCl3CHFCClF2

–

6.46

8.02

HCFC-224aa

CHClFCCl2CF3

–

3.15

3.54

HCFC-224ab

CHF2CCl2CClF2

–

11.3

15.1

HCFC-224ba

CHCl2CClFCF3

–

1.39

1.47

HCFC-224bb

CHClFCClFCClF2

–

4.1

4.45

22.2 ppt

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

72.3

0.069–0.102

0.16

2,590

800

1,900

285

114

A5

L :3,13

O4

R3

60

0.019

0.110

645

175

260

32

24

A5

L14

O10

R7

42

0.015

0.113

400

110

140

19

15

A5

L14

O10

R7

212

0.023-0.057

0.19

5,140

2,070

4,530

1,490

390

A5

L :3,13

O4

R3

20

0.008

0.0306

40

10

12

2

2

A5

L14

O10

R7

33

0.015

0.0629

200

55

66

9

7

A5

L14

O10

R7

20

0.027

0.183

140

38

46

7

5

L14

O10

R7

27

0.069

0.181

405

110

165

20

15

L14

O10

R7

20

0.032

0.174

160

44

53

8

6

L14

O10

R7

29

0.083

0.243

670

180

300

34

25

L14

O10

R7

30

0.088

0.219

640

175

295

33

24

L14

O10

R7

20

0.028

0.224

220

60

73

10

8

L14

O10

R7

27

0.061

0.234

560

150

225

28

21

L14

O10

R7

35

0.191

0.221

1,620

500

1,185

175

71

L14

O10

R7

20

0.028

0.210

210

56

68

10

8

L14

O10

R7

29

0.071

0.199

560

150

245

28

21

L14

O10

R7

22

0.034

0.205

250

68

86

12

10

L14

O10

R7

31

0.097

0.283

1,120

305

580

62

43

L14

O10

R7

31

0.100

0.265

1,080

295

570

61

41

L14

O10

R7

31

0.101

0.245

1,010

280

535

57

39

L14

O10

R7

20

0.024

0.195

205

56

68

10

8

L14

O10

R7

31

0.059

0.282

855

230

375

43

32

L14

O10

R7

35

0.164

0.289

2,265

695

1,660

245

99

L14

O10

R7

23

0.029

0.258

340

92

116

16

13

L14

O10

R7

31

0.059

0.235

710

195

310

36

27

L14

O10

R7

36

0.185

0.249

2,140

680

1,640

282

99

L14

O10

R7

22

0.029

0.234

310

83

105

15

12

L14

O10

R7

30

0.073

0.238

875

240

420

46

33

L14

O10

R7

37

0.111

0.313

1,850

525

1,155

129

74

L14

O10

R7

33

0.117

0.229

1,350

385

845

94

54

L14

O10

R7

33

0.114

0.282

1,620

460

1000

110

64

L14

O10

R7

33

0.117

0.262

1,540

440

965

108

62

L14

O10

R7

29

0.049

0.247

800

215

345

40

30

L14

O10

R7

45

0.141

0.306

2,945

960

2,305

430

142

L14

O10

R7

24

0.023

0.215

310

83

105

14

12

L14

O10

R7

51

0.047

0.283

1,180

320

585

64

45

L14

O10

R7

A.7

Appendix A | Appendix Table A-1
8

Industrial Designation or
Chemical Name

A.8

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

HCFC-224bc

CHF2CClFCCl2F

–

11.3

15.1

HCFC-224ca

CHCl2CF2CClF2

–

1.79

1.92

HCFC-224cb

CHClFCF2CCl2F

–

1.57

1.64

HCFC-224cc

CHF2CF2CCl3

–

12.5

19

HCFC-224da

CClF2CHClCClF2

–

10.4

12.3

HCFC-224db

CCl2FCHClCF3

–

9.39

12

HCFC-224ea

CCl2FCHFCClF2

–

9.16

11.6

HCFC-224eb

CCl3CHFCF3

–

8.88

11.9

HCFC-225aa

CHF2CCl2CF3

–

11.8

15.1

HCFC-225ba

CHClFCClFCF3

–

4.2

4.45

HCFC-225bb

CHF2CClFCClF2

–

15.9

19

HCFC-225ca

CHCl2CF2CF3

0.02 ppt

1.9

2.0

HCFC-225cb

CHClFCF2CClF2

0.04 ppt

5.9

6.3

HCFC-225cc

CHF2CF2CCl2F

–

14.1

19

HCFC-225da

CClF2CHClCF3

–

16.3

19.5

HCFC-225ea

CClF2CHFCClF2

–

15.3

18.1

HCFC-225eb

CCl2FCHFCF3

–

13.4

17.7

HCFC-226ba

CHF2CClFCF3

–

17

19

HCFC-226ca

CHClFCF2CF3

–

5.47

5.8

HCFC-226cb

CHF2CF2CClF2

–

21.6

24.7

HCFC-226da

CF3CHClCF3

–

27.7

32.6

HCFC-226ea

CClF2CHFCF3

–

24.9

28.8

HCFC-231aa

CHCl2CCl2CHClF

–

0.799

0.839

HCFC-231ab

CH2ClCCl2CCl2F

–

1.61

1.73

HCFC-231ac

CH2FCCl2CCl3

–

2.33

2.55

HCFC-231ba

CHCl2CClFCHCl2

–

0.56

0.586

HCFC-231bb

CH2ClCClFCCl3

–

2.54

2.8

HCFC-231da

CHCl2CHClCCl2F

–

0.54

0.557

HCFC-231db

CHClFCHClCCl3

–

1.34

1.43

HCFC-231ea

CHCl2CHFCCl3

–

0.76

0.799

HCFC-231fa

CCl2FCH2CCl3

–

6.26

7.71

HCFC-232aa

CHClFCCl2CHClF

–

1.65

1.77

HCFC-232ab

CHCl2CCl2CHF2

–

1.01

1.07

HCFC-232ac

CH2ClCCl2CClF2

–

2.56

2.8

HCFC-232ad

CH2FCCl2CCl2F

–

2.33

2.55

HCFC-232ba

CHCl2CClFCHClF

–

0.99

1.04

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

45

0.141

0.308

2,965

970

2,320

430

142

L14

O10

R7

28

0.028

0.262

480

130

173

23

18

L14

O10

R7

35

0.022

0.248

400

108

139

19

15

L14

O10

R7

37

0.174

0.314

3,215

1,090

2,600

550

165

L14

O10

R7

67

0.096

0.349

3,180

1,010

2,425

410

146

L14

O10

R7

43

0.119

0.285

2,420

745

1,780

265

107

L14

O10

R7

43

0.117

0.312

2,600

795

1,895

275

114

L14

O10

R7

35

0.126

0.235

1,915

580

1,380

195

83

L14

O10

R7

54

0.094

0.264

2,820

935

2,240

440

139

L14

O10

R7

74

0.025

0.254

1,175

320

590

65

45

L14

O10

R7

100

0.069

0.319

4,030

1,520

3,470

985

260

L14

O10

R7

44

0.025

0.220

470

127

170

22

18

A5

L3

O5

R3

101

0.033

0.290

1,860

525

1,110

120

73

A5

L3

O5

R3

55

0.110

0.344

4,080

1,455

3,410

835

230

L14

O10

R7

100

0.071

0.302

3,860

1,475

3,340

980

255

L14

O10

R7

99

0.068

0.340

4,210

1,560

3,590

975

260

L14

O10

R7

55

0.105

0.287

3,310

1,155

2,725

630

180

L14

O10

R7

161

0.019

0.267

3,790

1,480

3,310

1,020

265

L14

O10

R7

98

0.013

0.261

1,680

465

965

105

66

L14

O10

R7

174

0.022

0.341

5,370

2,385

4,915

1,970

540

L14

O10

R7

185

0.025

0.251

4,315

2,210

4,105

2,075

665

L14

O10

R7

180

0.023

0.307

5,095

2,455

4,775

2,195

650

L14

O10

R7

20

0.022

0.128

98

27

31

5

4

L14

O10

R7

23

0.042

0.180

280

75

98

13

10

L14

O10

R7

26

0.058

0.156

350

94

135

17

13

L14

O10

R7

20

0.015

0.114

62

17

19

3

2

L14

O10

R7

27

0.063

0.163

400

108

159

20

15

L14

O10

R7

20

0.015

0.136

70

19

22

3

3

L14

O10

R7

21

0.036

0.144

185

50

63

9

7

L14

O10

R7

20

0.021

0.131

96

26

30

4

4

L14

O10

R7

33

0.143

0.213

1,230

350

755

83

49

L14

O10

R7

245

0.036

0.177

300

82

106

14

11

L14

O10

R7

20

0.024

0.143

150

41

49

7

6

L14

O10

R7

29

0.053

0.222

590

160

235

29

22

L14

O10

R7

26

0.050

0.213

515

140

198

25

19

L14

O10

R7

20

0.023

0.162

165

45

54

8

6

L14

O10

R7

A.9

Appendix A | Appendix Table A-1
10

Industrial Designation or
Chemical Name

A.10

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

HCFC-232bb

CH2ClCClFCCl2F

–

2.56

2.8

HCFC-232bc

CH2FCClFCCl3

–

3.64

4.14

HCFC-232ca

CHCl2CF2CHCl2

–

0.70

0.737

HCFC-232cb

CH2ClCF2CCl3

–

4.47

5.21

HCFC-232da

CHCl2CHClCClF2

–

0.82

0.859

HCFC-232db

CHClFCHClCCl2F

–

1.51

1.61

HCFC-232dc

CHF2CHClCCl3

–

2.83

3.15

HCFC-232ea

CHCl2CHFCCl2F

–

0.83

0.872

HCFC-232eb

CHClFCHFCCl3

–

2.04

2.22

HCFC-232fa

CCl2FCH2CCl2F

–

9.23

12.5

HCFC-232fb

CCl3CH2CClF2

–

10.2

14.4

HCFC-233aa

CHClFCCl2CHF2

–

2.63

2.87

HCFC-233ab

CH2ClCCl2CF3

–

2.57

2.8

HCFC-233ac

CH2FCCl2CClF2

–

3.71

4.14

HCFC-233ba

CHClFCClFCHClF

–

2.1

2.23

HCFC-233bb

CHCl2CClFCHF2

–

1.27

1.34

HCFC-233bc

CH2ClCClFCClF2

–

4.75

5.21

HCFC-233bd

CH2FCClFCCl2F

–

3.71

4.14

HCFC-233ca

CHCl2CF2CHClF

–

1.27

1.34

HCFC-233cb

CH2ClCF2CCl2F

–

4.57

5.21

HCFC-233cc

CH2FCF2CCl3

–

6.26

7.71

HCFC-233da

CHCl2CHClCF3

–

0.896

0.939

HCFC-233db

CHClFCHClCClF2

–

2.37

2.52

HCFC-233dc

CHF2CHClCCl2F

–

3.55

3.96

HCFC-233ea

CHCl2CHFCClF2

–

0.982

1.03

HCFC-233eb

CHClFCHFCCl2F

–

2.32

2.51

HCFC-233ec

CHF2CHFCCl3

–

4.13

4.77

HCFC-233fa

CCl2FCH2CClF2

–

15.4

23.3

HCFC-233fb

CCl3CH2CF3

–

16.4

29.3

HCFC-234aa

CHF2CCl2CHF2

–

6.51

7.54

HCFC-234ab

CH2FCCl2CF3

–

3.76

4.14

HCFC-234ba

CHClFCClFCHF2

–

3.39

3.61

HCFC-234bb

CH2ClCClFCF3

–

4.84

5.21

HCFC-234bc

CH2FCClFCClF2

–

7.01

7.71

HCFC-234ca

CHClFCF2CHClF

–

2.74

2.9

HCFC-234cb

CHCl2CF2CHF2

–

1.65

1.74

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

29

0.053

0.222

590

160

235

29

22

L14

O10

R7

30

0.075

0.205

770

210

360

40

29

L14

O10

R7

20

0.017

0.130

95

26

30

4

4

L14

O10

R7

31

0.090

0.208

950

260

490

53

37

L14

O10

R7

20

0.019

0.178

151

41

48

7

6

L14

O10

R7

24

0.033

0.200

310

84

108

15

12

L14

O10

R7

28

0.060

0.184

540

145

225

27

20

L14

O10

R7

20

0.019

0.165

142

38

45

7

5

L14

O10

R7

25

0.045

0.183

390

104

143

19

15

L14

O10

R7

35

0.176

0.267

2,255

690

1,645

242

99

L14

O10

R7

36

0.194

0.249

2,255

710

1,710

280

103

L14

O10

R7

32

0.043

0.185

545

145

220

27

20

L14

O10

R7

31

0.042

0.194

560

150

225

27

21

L14

O10

R7

35

0.057

0.250

1,030

280

490

54

39

L14

O10

R7

38

0.031

0.202

475

128

178

23

18

L14

O10

R7

23

0.023

0.171

245

66

81

11

9

L14

O10

R7

53

0.057

0.261

1,365

375

730

78

53

L14

O10

R7

35

0.057

0.257

1,060

290

500

56

40

L14

O10

R7

23

0.023

0.174

245

67

83

12

9

L14

O10

R7

37

0.069

0.250

1,260

345

660

71

49

L14

O10

R7

33

0.100

0.246

1,650

465

1,020

112

66

L14

O10

R7

20

0.017

0.142

142

38

46

7

5

L14

O10

R7

40

0.034

0.238

630

170

245

31

24

L14

O10

R7

35

0.055

0.245

970

265

445

50

37

L14

O10

R7

20

0.019

0.183

200

54

65

9

8

L14

O10

R7

30

0.038

0.221

575

155

220

28

22

L14

O10

R7

31

0.068

0.236

1,080

295

540

58

41

L14

O10

R7

46

0.207

0.321

4,020

1,495

3,435

940

250

L14

O10

R7

37

0.247

0.204

2,635

1,010

2,285

675

175

L14

O10

R7

47

0.062

0.198

1,500

425

940

105

60

L14

O10

R7

41

0.039

0.215

980

265

465

51

37

L14

O10

R7

57

0.028

0.215

885

240

400

45

34

L14

O10

R7

67

0.035

0.218

1,265

350

680

73

49

L14

O10

R7

77

0.045

0.279

2,245

645

1,455

170

91

L14

O10

R7

51

0.025

0.205

685

185

280

34

26

L14

O10

R7

29

0.020

0.199

400

110

141

19

15

L14

O10

R7

A.11

Appendix A | Appendix Table A-1
12

Industrial Designation or
Chemical Name

A.12

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

HCFC-234cc

CH2ClCF2CClF2

–

9.46

10.6

HCFC-234cd

CH2FCF2CCl2F

–

6.64

7.71

HCFC-234da

CHClFCHClCF3

–

2.67

2.82

HCFC-234db

CHF2CHClCClF2

–

5.69

6.18

HCFC-234ea

CHCl2CHFCF3

–

1.06

1.11

HCFC-234eb

CHClFCHFCClF2

–

2.88

3.04

HCFC-234ec

CHF2CHFCCl2F

–

5.32

6.04

HCFC-234fa

CClF2CH2CClF2

–

31

43.4

HCFC-234fb

CCl2FCH2CF3

~45

45

98

HCFC-235ba

CHF2CClFCHF2

–

8.8

9.5

HCFC-235bb

CH2FCClFCF3

–

7.21

7.71

HCFC-235ca

CH2ClCF2CF3

–

9.82

10.6

HCFC-235cb

CHClFCF2CHF2

–

4.45

4.7

HCFC-235cc

CH2FCF2CClF2

–

14.2

15.7

HCFC-235da

CHF2CHClCF3

–

7.55

8.09

HCFC-235ea

CHClFCHFCF3

–

7.36

7.88

HCFC-235eb

CHF2CHFCClF2

–

3.18

3.33

HCFC-235fa

CClF2CH2CF3

–

61.7

88.6

HCFC-241aa

CH2ClCCl2CHClF

–

1.43

1.52

HCFC-241ab

CH2FCCl2CHCl2

–

0.77

0.803

HCFC-241ac

CH3CCl2CCl2F

–

5.18

6.18

HCFC-241ba

CH2ClCClFCHCl2

–

0.79

0.826

HCFC-241bb

CH3CClFCCl3

–

7.76

10

HCFC-241da

CHCl2CHClCHClF

–

0.56

0.581

HCFC-241db

CH2ClCHClCCl2F

–

0.53

0.549

HCFC-241dc

CH2FCHClCCl3

–

0.75

0.786

HCFC-241ea

CHCl2CHFCHCl2

–

0.42

0.429

HCFC-241eb

CH2ClCHFCCl3

–

1.05

1.11

HCFC-241fa

CHCl2CH2CCl2F

–

0.53

0.555

HCFC-241fb

CHClFCH2CCl3

–

1.48

1.59

HCFC-242aa

CHF2CCl2CH2Cl

–

2.13

2.29

HCFC-242ab

CH2FCCl2CHClF

–

1.78

1.91

HCFC-242ac

CH3CCl2CClF2

–

8.09

10

HCFC-242ba

CHClFCClFCH2Cl

–

1.99

2.11

HCFC-242bb

CHCl2CClFCH2F

–

1.03

1.09

HCFC-242bc

CH3CClFCCl2F

–

8.09

10

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

85

0.054

0.267

2,705

835

1,990

300

120

L14

O10

R7

48

0.063

0.281

2,160

615

1,370

155

87

L14

O10

R7

50

0.024

0.203

660

180

268

33

25

L14

O10

R7

72

0.039

0.271

1,820

510

1,070

115

71

L14

O10

R7

23

0.014

0.158

205

55

67

10

8

L14

O10

R7

52

0.026

0.241

845

230

355

42

32

L14

O10

R7

45

0.052

0.276

1,745

485

990

105

68

L14

O10

R7

108

0.132

0.347

6,225

3,405

6,005

3,330

1,170

L14

O10

R7

58

0.40

0.264

5,190

3,490

5,190

3,685

1,760

O9

R7

121

0.018

0.225

2,375

715

1,700

238

102

L14

O10

R7

110

0.017

0.237

2,140

620

1,410

167

87

L14

O10

R7

126

0.018

0.215

2,455

765

1,830

288

110

L14

O10

R7

85

0.014

0.234

1,375

375

710

76

53

L14

O10

R7

146

0.021

0.282

4,040

1,450

3,385

840

230

L14

O10

R7

112

0.017

0.227

2,130

620

1,430

175

88

L14

O10

R7

111

0.017

0.227

2,085

605

1,385

165

86

L14

O10

R7

69

0.012

0.274

1,160

315

510

59

44

L14

O10

R7

204

0.051

0.297

6,780

5,320

6,930

5,730

3,430

L14

O10

R7

24

0.035

0.116

187

51

64

9

7

L14

O10

R7

20

0.020

0.0937

81

22

25

4

3

L14

O10

R7

32

0.112

0.191

1,090

300

610

65

42

L14

O10

R7

20

0.020

0.121

108

29

34

5

4

L14

O10

R7

34

0.163

0.191

1,545

450

1,050

131

64

L14

O10

R7

20

0.014

0.100

63

17

19

3

2

L14

O10

R7

20

0.014

0.119

71

19

22

3

3

L14

O10

R7

20

0.019

0.115

97

26

31

5

4

L14

O10

R7

20

0.011

0.081

38

10

12

2

1

L14

O10

R7

20

0.027

0.125

147

40

48

7

6

L14

O10

R7

20

0.014

0.117

70

19

22

3

3

L14

O10

R7

22

0.037

0.152

255

69

87

12

10

L14

O10

R7

29

0.039

0.131

340

93

129

17

13

L14

O10

R7

27

0.034

0.132

290

78

103

14

11

L14

O10

R7

42

0.125

0.227

2,065

610

1,430

185

87

L14

O10

R7

37

0.033

0.151

370

100

135

18

14

L14

O10

R7

21

0.021

0.133

168

46

55

8

6

L14

O10

R7

42

0.125

0.244

2,220

655

1,535

199

93

L14

O10

R7

A.13

Appendix A | Appendix Table A-1
14

Industrial Designation or
Chemical Name

A.14

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

HCFC-242ca

CHCl2CF2CH2Cl

–

1.09

1.15

HCFC-242cb

CH3CF2CCl3

–

12.3

18.7

HCFC-242da

CHClFCHClCHClF

–

1.32

1.38

HCFC-242db

CHCl2CHClCHF2

–

0.73

0.761

HCFC-242dc

CH2ClCHClCClF2

–

1.2

1.25

HCFC-242dd

CH2FCHClCCl2F

–

0.83

0.871

HCFC-242ea

CHCl2CHFCHClF

–

0.72

0.756

HCFC-242eb

CH2ClCHFCCl2F

–

1.24

1.31

HCFC-242ec

CH2FCHFCCl3

–

1.7

1.84

HCFC-242fa

CHCl2CH2CClF2

–

0.74

0.768

HCFC-242fb

CHClFCH2CCl2F

–

1.61

1.71

HCFC-242fc

CHF2CH2CCl3

–

4.14

4.78

HCFC-243aa

CHF2CCl2CH2F

–

2.99

3.25

HCFC-243ab

CH3CCl2CF3

–

8.33

10

HCFC-243ba

CHF2CClFCH2Cl

–

3.63

3.88

HCFC-243bb

CHFClCClFCH2F

–

2.67

2.82

HCFC-243bc

CH3CClFCF2Cl

–

15.6

18.7

HCFC-243ca

CH2ClCF2CHClF

–

2.89

3.14

HCFC-243cb

CHCl2CF2CH2F

–

1.46

1.54

HCFC-243cc

CH3CF2CFCl2

19.5

18

27.1

HCFC-243da

CHF2CHClCHFCl

–

1.97

2.07

HCFC-243db

CH2ClCHClCF3

–

1.44

1.51

HCFC-243dc

CH2FCHClCF2Cl

–

2.03

2.13

HCFC-243ea

CHFClCHFCHFCl

–

1.57

1.64

HCFC-243eb

CHCl2CHFCHF2

–

0.90

0.938

HCFC-243ec

CH2ClCHFCF2Cl

–

1.7

1.78

HCFC-243ed

CH2FCHFCFCl2

–

2.03

2.17

HCFC-243fa

CHCl2CH2CF3

–

0.78

0.813

HCFC-243fb

CHFClCH2CF2Cl

–

2.24

2.36

HCFC-243fc

CHF2CH2CFCl2

–

5.07

5.73

HCFC-244ba

CH2FCClFCHF2

–

5.17

5.49

HCFC-244bb

CH3CClFCF3

–

16.6

18.7

HCFC-244ca

CH2ClCF2CHF2

–

6.39

6.82

HCFC-244cb

CH2FCF2CHFCl

–

4.02

4.24

HCFC-244cc

CH3CF2CF2Cl

–

31.2

38.1

HCFC-244da

CHF2CHClCHF2

–

3.88

4.09

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

22

0.022

0.144

193

52

63

9

7

L14

O10

R7

36

0.206

0.251

3,045

1,025

2,450

510

154

L14

O10

R7

29

0.024

0.147

238

64

80

11

9

L14

O10

R7

20

0.015

0.119

106

29

33

5

4

L14

O10

R7

28

0.023

0.170

250

68

83

12

9

L14

O10

R7

20

0.017

0.159

162

44

52

8

6

L14

O10

R7

20

0.015

0.119

106

29

33

5

4

L14

O10

R7

23

0.025

0.167

255

69

85

12

10

L14

O10

R7

24

0.034

0.174

365

98

129

17

14

L14

O10

R7

20

0.015

0.153

138

37

43

6

5

L14

O10

R7

26

0.031

0.203

400

109

140

19

15

L14

O10

R7

31

0.075

0.198

1000

270

500

54

38

L14

O10

R7

37

0.036

0.154

620

168

265

31

23

L14

O10

R7

49

0.085

0.205

2,095

625

1,465

195

89

L14

O10

R7

58

0.033

0.146

710

195

330

37

27

L14

O10

R7

50

0.027

0.161

580

157

235

29

22

L14

O10

R7

94

0.088

0.264

4,010

1,500

3,440

955

252

L14

O10

R7

37

0.035

0.182

710

192

295

35

27

L14

O10

R7

27

0.020

0.147

290

78

99

14

11

L14

O10

R7

54

0.19

0.315

5,130

2,060

4,540

1,495

390

O9

R6

42

0.022

0.162

430

116

158

21

16

L14

O10

R7

34

0.018

0.138

270

73

92

13

10

L14

O10

R7

43

0.023

0.203

555

150

206

27

21

L14

O10

R7

36

0.019

0.172

365

99

127

17

14

L14

O10

R7

21

0.014

0.141

170

46

55

8

6

L14

O10

R7

38

0.020

0.182

420

113

148

20

16

L14

O10

R7

32

0.026

0.215

590

159

218

28

22

L14

O10

R7

20

0.012

0.120

125

34

40

6

5

L14

O10

R7

45

0.024

0.231

700

189

265

34

26

L14

O10

R7

44.0

0.056

0.263

1,765

490

975

104

68

L14

O10

R7

90

0.017

0.173

1,310

360

730

78

51

L14

O10

R7

148

0.027

0.238

4,140

1,600

3,600

1,080

280

L14

O10

R7

101

0.018

0.173

1,580

450

985

109

63

L14

O10

R7

79

0.015

0.178

1,065

290

520

57

41

L14

O10

R7

173

0.039

0.277

6,120

3,360

5,905

3,290

1,160

L14

O10

R7

77

0.015

0.182

1,050

285

505

56

40

L14

O10

R7

A.15

Appendix A | Appendix Table A-1
16

Industrial Designation or
Chemical Name

A.16

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

HCFC-244db

CH2FCHClCF3

–

2.44

2.54

HCFC-244ea

CHF2CHFCHFCl

–

2.39

2.5

HCFC-244eb

CH2ClCHFCF3

–

2.04

2.12

HCFC-244ec

CH2FCHFCF2Cl

–

2.88

3.01

HCFC-244fa

CHFClCH2CF3

–

2.37

2.48

HCFC-244fb

CHF2CH2CF2Cl

–

7.76

8.35

HCFC-251aa

CH2FCCl2CH2Cl

–

1.26

1.34

HCFC-251ab

CH3CCl2CHFCl

–

1.73

1.85

HCFC-251ba

CH2ClCClFCH2Cl

–

1.34

1.4

HCFC-251bb

CH3CClFCHCl2

–

1.02

1.07

HCFC-251da

CH2ClCHClCHFCl

–

0.69

0.719

HCFC-251db

CH2FCHClCHCl2

–

0.40

0.416

HCFC-251dc

CH3CHClCFCl2

–

0.52

0.535

HCFC-251ea

CH2ClCHFCHCl2

–

0.47

0.489

HCFC-251eb

CH3CHFCCl3

–

0.68

0.709

HCFC-251fa

CHClFCH2CCl2H

–

0.33

0.339

HCFC-251fb

CH2ClCH2CCl2F

–

0.45

0.467

HCFC-251fc

CH2FCH2CCl3

–

0.65

0.676

HCFC-252aa

CH2FCCl2CH2F

–

1.94

2.07

HCFC-252ab

CH3CCl2CHF2

–

4.41

4.93

HCFC-252ba

CH2ClCClFCH2F

–

2.19

2.31

HCFC-252bb

CH3CClFCHClF

–

2.87

3.04

HCFC-252ca

CH2ClCF2CH2Cl

–

2.47

2.61

HCFC-252cb

CH3CF2CHCl2

–

1.19

1.25

HCFC-252da

CH2ClCHClCHF2

–

1.0

1.04

HCFC-252db

CH2FCHClCHClF

–

1.15

1.2

HCFC-252dc

CH3CHClCClF2

–

0.77

0.799

HCFC-252ea

CH2ClCHFCHClF

–

1.02

1.06

HCFC-252eb

CH2FCHFCHCl2

–

0.65

0.67

HCFC-252ec

CH3CHFCCl2F

–

0.84

0.882

HCFC-252fa

CHClFCH2CHClF

–

1.15

1.19

HCFC-252fb

CHCl2CH2CHF2

–

0.66

0.684

HCFC-252fc

CH2ClCH2CClF2

–

0.94

0.972

HCFC-252fd

CH2FCH2CCl2F

–

0.70

0.732

HCFC-253ba

CH2FCClFCH2F

–

3.66

3.86

HCFC-253bb

CH3CClFCHF2

–

7.85

8.46

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

57

0.012

0.164

600

162

235

29

23

L14

O10

R7

57

0.012

0.191

685

185

267

33

26

L14

O10

R7

51

0.011

0.151

460

125

170

22

17

L14

O10

R7

64

0.013

0.226

975

265

410

49

37

L14

O10

R7

56

0.012

0.185

655

178

255

32

25

L14

O10

R7

111

0.020

0.285

3,060

900

2,080

260

127

L14

O10

R7

23

0.028

0.0752

129

35

43

6

5

L14

O10

R7

27

0.037

0.110

260

70

93

12

10

L14

O10

R7

29

0.027

0.0951

173

47

59

8

7

L14

O10

R7

21

0.023

0.109

151

41

49

7

6

L14

O10

R7

20

0.016

0.0821

77

21

24

4

3

L14

O10

R7

20

0.009

0.0631

35

9

11

2

1

L14

O10

R7

20

0.012

0.122

85

23

26

4

3

L14

O10

R7

20

0.011

0.0776

50

14

15

2

2

L14

O10

R7

20

0.016

0.134

124

34

39

6

5

L14

O10

R7

20

0.008

0.0739

33

9

10

2

1

L14

O10

R7

20

0.011

0.107

66

18

20

3

2

L14

O10

R7

20

0.015

0.103

91

25

28

4

3

L14

O10

R7

31

0.029

0.105

310

83

113

15

12

L14

O10

R7

42

0.056

0.153

1,010

275

520

56

39

L14

O10

R7

44

0.027

0.0992

330

89

125

16

12

L14

O10

R7

51

0.032

0.147

640

173

265

32

24

L14

O10

R7

47

0.029

0.126

470

127

186

23

18

L14

O10

R7

24

0.019

0.146

265

71

87

12

10

L14

O10

R7

27

0.016

0.0897

135

37

44

6

5

L14

O10

R7

29

0.017

0.101

176

48

58

8

7

L14

O10

R7

22

0.013

0.149

174

47

55

8

7

L14

O10

R7

27

0.016

0.112

173

47

57

8

7

L14

O10

R7

20

0.011

0.0922

90

24

28

4

3

L14

O10

R7

20

0.015

0.175

223

60

71

10

8

L14

O10

R7

29

0.017

0.143

250

67

82

12

9

L14

O10

R7

20

0.011

0.114

113

31

35

5

4

L14

O10

R7

25

0.015

0.153

217

59

70

10

8

L14

O10

R7

20

0.012

0.155

164

45

52

8

6

L14

O10

R7

73

0.017

0.131

810

221

380

42

31

L14

O10

R7

108

0.024

0.184

2,260

665

1,545

195

94

L14

O10

R7

A.17

Appendix A | Appendix Table A-1
18

Industrial Designation or
Chemical Name

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

HCFC-253ca

CH2ClCF2CH2F

–

4.23

4.47

HCFC-253cb

CH3CF2CHClF

–

3.48

3.66

HCFC-253da

CH2FCHClCHF2

–

1.67

1.74

HCFC-253db

CH3CHClCF3

–

1.02

1.06

HCFC-253ea

CH2ClCHFCHF2

–

1.44

1.5

HCFC-253eb

CH2FCHFCHClF

–

1.5

1.56

HCFC-253ec

CH3CHFCClF2

–

1.13

1.17

HCFC-253fa

CHClFCH2CHF2

–

1.83

1.9

HCFC-253fb

CH2ClCH2CF3

–

1.05

1.09

HCFC-253fc

CH2FCH2CClF2

–

1.48

1.54

HCFC-261aa

CH3CCl2CH2F

–

1.06

1.11

HCFC-261ba

CH3CClFCH2Cl

–

2.19

2.31

HCFC-261da

CH2ClCHClCH2F

–

0.45

0.462

HCFC-261db

CH3CHClCHClF

–

0.47

0.478

HCFC-261ea

CH2ClCHFCH2Cl

–

0.54

0.554

HCFC-261eb

CH3CHFCHCl2

–

0.31

0.315

HCFC-261fa

CH2ClCH2CHClF

–

0.57

0.591

HCFC-261fb

CH2FCH2CHCl2

–

0.33

0.339

HCFC-261fc

CH3CH2CCl2F

–

0.61

0.638

HCFC-262ba

CH3CClFCH2F

–

3.4

3.59

HCFC-262ca

CH3CF2CH2Cl

–

3.2

3.33

HCFC-262da

CH2FCHClCH2F

–

0.92

0.956

HCFC-262db

CH3CHClCHF2

–

0.64

0.662

HCFC-262ea

CH2FCHFCH2Cl

–

0.83

0.856

HCFC-262eb

CH3CHFCHFCl

–

0.66

0.685

HCFC-262fa

CH2ClCH2CHF2

–

0.80

0.828

HCFC-262fb

CH2FCH2CHFCl

–

0.87

0.902

HCFC-262fc

CH3CH2CF2Cl

–

1.2

1.24

HCFC-271ba

CH3CClFCH3

–

5.0

5.37

HCFC-271da

CH3CHClCH2F

–

0.27

0.278

HCFC-271ea

CH3CHFCH2Cl

–

0.30

0.302

HCFC-271fa

CH2ClCH2CH2F

–

0.34

0.345

HCFC-271fb

CH3CH2CHClF

–

0.49

0.506

Hydrofluorocarbons

A.18

HFC-23CHF328.9 ppt

228

228

243

HFC-32CH2F2

10 ppt

5.4

5.4

5.5

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

79

0.018

0.135

960

265

485

52

37

L14

O10

R7

71

0.017

0.183

1,080

295

490

56

41

L14

O10

R7

44

0.012

0.118

335

91

118

16

13

L14

O10

R7

30

0.009

0.120

208

56

68

10

8

L14

O10

R7

39

0.011

0.113

275

75

95

13

10

L14

O10

R7

40

0.011

0.125

320

86

110

15

12

L14

O10

R7

33

0.009

0.183

350

95

116

16

13

L14

O10

R7

46

0.012

0.175

545

147

196

26

20

L14

O10

R7

31

0.009

0.121

215

58

70

10

8

L14

O10

R7

40

0.011

0.194

490

132

168

23

18

L14

O10

R7

23

0.020

0.0727

132

36

43

6

5

L14

O10

R7

44

0.031

0.0827

310

84

118

15

12

L14

O10

R7

20

0.009

0.0338

26

7

8

1

1

L14

O10

R7

20

0.009

0.0625

50

14

15

2

2

L14

O10

R7

20

0.010

0.0493

45

12

14

2

2

L14

O10

R7

20

0.006

0.0618

33

9

10

2

1

L14

O10

R7

20

0.011

0.0746

73

20

23

3

3

L14

O10

R7

20

0.006

0.0557

32

9

10

1

1

L14

O10

R7

20

0.012

0.137

145

39

45

7

5

L14

O10

R7

69

0.020

0.125

835

227

380

43

32

L14

O10

R7

66

0.019

0.117

730

197

320

37

27

L14

O10

R7

28

0.009

0.0587

107

29

34

5

4

L14

O10

R7

21

0.007

0.0813

103

28

32

5

4

L14

O10

R7

25

0.009

0.0657

107

29

34

5

4

L14

O10

R7

21

0.007

0.0982

128

35

40

6

5

L14

O10

R7

25

0.008

0.0858

135

37

43

6

5

L14

O10

R7

26

0.009

0.0991

170

46

54

8

6

L14

O10

R7

34

0.011

0.168

395

107

131

18

15

L14

O10

R7

83

0.028

0.106

1,225

340

675

72

47

L14

O10

R7

20

0.004

0.0261

17

5

5

1

1

L14

O10

R7

20

0.004

0.0330

23

6

7

1

1

L14

O10

R7

20

0.004

0.0284

22

6

7

1

1

L14

O10

R7

20

0.007

0.0652

75

20

23

3

3

L14

O10

R7

4420

0

0.18

11,085

12,69011,825

13,34013,150

A6

L3

R3

124

0

0.11

2,530

705

1,440

154

98

A6

L3

R3

A.19

Appendix A | Appendix Table A-1
20

Industrial Designation or
Chemical Name

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

2.8

2.8

2.9

31

30

32

9.7

10

10.5

14

14

14.1

3.5

3.6

3.70

51

51

57

146 days(114-335 days)

172 days(114-335 days)

172 days(114-335 days)

1.6

1.6

1.55

HFC-41

CH3F

HFC-125

CHF2CF3

HFC-134

CHF2CHF2

HFC-134a

CH2FCF3

HFC-143

CH2FCHF2

HFC-143a

CH3CF3

HFC-152

CH2FCH2F

HFC-152a

CH3CHF2

HFC-161

CH3CH2F

66 days(51–154 days)

80 days(51–154 days)

80 days(51–154 days)

HFC-227ca

CF3CF2CHF2

28.2

30

32

HFC-227ea

CF3CHFCF3

36

36

37.5

HFC-236cb

CH2FCF2CF3

~13

13.4

14

HFC-236ea

CHF2CHFCF3

11.0

11.4

11.9

HFC-236fa

CF3CH2CF3

222

213

253

HFC-245ca

CH2FCF2CHF2

6.5

6.6

6.9

HFC-245cb

CF3CF2CH3

47.1

39.9

43

HFC-245ea

CHF2CHFCHF2

3.2

3.2

3.3

HFC-245eb

CH2FCHFCF3

3.2

3.2

3.3

HFC-245fa

CHF2CH2CF3

7.9

7.9

8.2

HFC-263fb

CH3CH2CF3

1.1

1.1

1.16

HFC-272ca

CH3CF2CH3

2.6

9

9.7

HFC-281ea

CH3CHFCH3

23 days(19–46 days)

27 days(19–46 days)

27 days(19–46 days)

HFC-329p

CHF2CF2CF2CF3

~30

32

34

HFC-338pcc

CHF2CF2CF2CHF2

12.9

13.5

14.0

HFC-356mcf

CH2FCH2CF2CF3

1.2

1.2

1.26

HFC-356mff

CF3CH2CH2CF3

8.3

8.5

8.9

HFC-365mfc

CH3CF2CH2CF3

0.94 ppt

8.7

8.9

9.3

HFC-43-10mee

CF3CHFCHFCF2CF3

0.27 ppt

16.1

17.0

17.9

HFC-458mfcf

CF3CH2CF2CH2CF3

22.9

23.8

25.5

HFC-55-10mcff

CF3CF2CH2CH2CF2CF3

7.5

7.7

8.0

HFC-52-13p

CHF2CF2CF2CF2CF2CF3

32.7

35.2

37.0

HFC-72-17p

CHF2CF2CF2CF2CF2CF2CF2CF3

–

23.8

24.9

–

1.4 days

1.4 days

20.4 ppt

90 ppt19.1 ppt

6.7 ppt

1.2 ppt

0.15 ppt

2.4 ppt

Unsaturated Hydrofluorocarbons
HFO-1123

A.20

CHF=CF2

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

65

0

0.02

430

116

177

21

16

595

0

0.23

6,280

3,450

6,040

3,350

1180

240

0

0.19

3,625

1,135

2,725

440

164

267

0

0.16

3,810

1,360

3,170

770

215

100

0

0.13

1,250

340

580

64

48

612

0

0.16

7,050

5,080

7,110

5,390

2,830

–

0

0.04

64

17

20

3.0

2.4

39

0

0.10

545

148

190

26

21

–

0

0.02

20

6

6

<1

<1

640

0

0.27

5,260

2,865

5,070

2,795

975

673

0

0.26

5,250

3,140

5,140

3,180

1,260

305

0

0.23

3,540

1,235

2,915

670

192

R3

270

0

0.30

4,190

1,370

3,290

620

202

R3

1350

0

0.24

6,785

7,680

7,230

8,090

7,870

R3

165

0

0.24

2,530

720

1,600

180

102

R3

550

0

0.24

6,340

4,000

6,280

4,150

1,800

R3

95

0

0.16

860

233

375

44

32

R3

90

0

0.20

1,070

290

460

54

40

R3

149

0

0.24

2,980

880

2,040

260

124

40

0

0.10

250

68

83

12

9.5

185

0

0.07

1,580

480

1,140

163

69

–

0

–

–

–

675

0

0.31

4,720

2,630

360

0

–

–

–

–

–

–

R9

40

0

–

–

–

–

–

–

R9

190

0

–

–

–

–

–

–

R9

190

0

0.22

2,660

810

1,915

271

115

A6

R3

365

0

0.359

3,770

1,470

3,295

1,015

265

A6

R10

375

0

–

–

–

–

–

–

R9

175

0

–

–

–

–

–

–

R9

710

0

–

–

–

–

–

–

L20

R9

525

0

–

–

–

–

–

–

L21

R9

–

0

0.0019

L22

R11

<1

–

<<1

–
4,565

<<1

–

2,595

<<1

A6

L :12,13

R3

L :3,13R3R3A6

L :3,13

R3
A6

L3

R3
R3

A6

L :3,13

R3
R3

A6

A6

L16

R3

L :3,17

R3

L3

R3
R3

L18
A6

935

<<1

R3

R3
R9

L19

R3

A.21

Appendix A | Appendix Table A-1
22

Industrial Designation or
Chemical Name

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

HFO-1132a

CH2=CF2

4.0 days(3–5.7 days)

4.6 days(3–5.7 days)

4.6 days(3–5.7 days)

HFO-1141

CH2=CHF

2.1 days(1.4–3.1 days)

2.5 days(1.4–3.1 days)

2.5 days(1.4–3.1 days)

HFO-1234ye(E)(E)-CHF=CFCHF2<5 days<5 days<5 days

HFO-1234ye(Z)(Z)-CHF=CFCHF2<5 days<5 days<5 days

HFO-1225ye(E)(E)-CF3CF=CHF

4.9 days(3.7–6.9 days)

5.7 days(3.7–6.9 days)

5.7 days(3.7–6.9 days)

HFO-1225ye(Z)(Z)-CF3CF=CHF

8.5 days(6.2–12 days)

10 days(6.2–12 days)

10 days(6.2–12 days)

2,3,3,4,4-pentafluorocyclobut-1-ene

c-CH=CFCF2CF2-–

270 days

270 days

HFO-1234ze(E)(E)-CF3CH=CHF

16.4 days(12.8–24 days)

19 days(12.8–24 days)

19 days(12.8–24 days)

HFO-1234ze(Z)(Z)-CF3CH=CHF

10.0 days

10.0 days

10.0 days

3,3,4,4-tetrafluorocyclobut-1-ene

c-CH=CHCF2CF2-–

84 days84 days

HFO-1234yf

CF3CF=CH2

10.5 days(8.4–16 days)

12 days(8.4–16 days)

12 days(8.4–16 days)

HFO-1261zf

CH2FCH=CH2

0.7 days(0.5–1.0 days)

0.8 days(0.5–1.0 days)

0.8 days(0.5–1.0 days)

HFO-1234yc

CF2=CFCH2F~2 days~2 days~2 days

HFO-1225zc

CF2=CHCF3~2 days~2 days~2 days

HFO-1234zc

CF2=CHCHF2<5 days<5 days<5 days

HFO-1336mzz(E)(E)-CF3CH=CHCF3–(~16–30 days)

122 days

122 days

HFO-1336mzz(Z)(Z)-CF3CH=CHCF3

22 days(16.3–32 days)

27 days(16.3–32 days)

27 days(16.3–32 days)

HFO-1243zf

CF3CH=CH2

7.0 days(5.5–11 days)

9 days
(5.5–11 days)

9 days
(5.5–11 days)

HFO-1345zf

C2F5CH=CH2

7.6 days(5.8–11.4 days)

9 days
(5.8–11.4 days)

9 days
(5.8–11.4 days)

HFO-1438mzz(E)(E)-CF3CH=CHCF2CF3–(16–30 days)

122 days

122 days

HFO-1447zf

CH2=CHCF2CF2CF3–(6–10 days)

9 days(6–10 days)

9 days(6–10 days)

3,3,4,4,5,5,6,6,6-Nonafluorohex-1-ene

C4F9CH=CH2

7.6 days

9 days9 days3,3,4,4,5,5,6,6,7,7,8,8,8Tridecafluorooct-1-ene
HFO-174-13fz

C6F13CH=CH2

7.6 days

9 days9 days3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,10Heptadecafluorodec-1-ene
HFO-194-17fz

C8F17CH=CH2

7.6 days

9 days9 daysHFO-1438ezy(E)(E)-(CF3)2CFCH=CHF–

43 days

43 days

Chlorocarbons and Hydrochlorocarbons

A.22

Methyl chloroform

CH3CCl3

2.6 ppt

5.0

5.0

6.1

Carbon tetrachloride

CCl4

80.5 ppt

26.0

32

–

Methyl chloride

CH3Cl

555 ppt

0.9

0.9

1.57

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

–

0

0.004

<1

<1

<1

<1

<1

R3

–

0

0.002

<1

<1

<1

<1

<1

R3

–

0

–

–

–

–

–

–

L23

R9

–

0

–

–

–

–

–

–

L23

R9

–

0

0.01

<1

<1

<1

<1

<1

R3

–

0

0.02

<1

<1

<1

<1

<1

R3

–

0

0.20

236

64

74

11

8.8

–

0

0.04

4

<1

<1

<1

<1

–

0

0.02

1

<1

<1

<1

<1

L21

R3

–

0

0.10

40.6

11

12

1.8

1.5

L24

R12

–

0

0.02

1

<1

<1

<1

<1

–

0

–

–

–

–

–

–

–

0

–

–

–

–

–

–

L23

R9

–

0

–

–

–

–

–

–

L23

R9

–

0

–

–

–

–

–

–

L23

R9

–

0

0.13

60

16

18

2.7

2.2

L25

R13

–

0

0.07

6

2

2

<1

<1

R3

–

0

0.01

<1

<1

<1

<1

<1

R3

–

0

0.01

<1

<1

<1

<1

<1

R3

–

0

–

–

–

–

–

–

–

0

–

–

–

–

–

–

–

0

0.03

<1

<1

<1

<1

<1

L27

R3

–

0

0.03

<1

<1

<1

<1

<1

L27

R3

–

0

0.03

<1

<1

<1

<1

<1

L27

R3

–

0

0.34

42

11

12

1.9

1.6

L28

R14

38

0.14–0.17

0.07

555

153

300

32

21

A8

L :3,13

O3

R3

44

0.89

0.174

3,790

2,110

3,670

2,080

750

A8

L :13,29

O3

R15

30.4

0.015

0.004

16

4.3

5.1

<1

<1

A8

L :3,13,30

O3

R15

L24

R12
R3

A7

R3
R9

L26

R9
R9

A.23

Appendix A | Appendix Table A-1
24

Industrial Designation or
Chemical Name

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) dMethylene chloride

CH2Cl2

34 ppt144 days(95–1070 days)

180 days(95–1070 days)

180 days(95–1070 days)

Chloroform

CHCl3

8.9 ppt

149 days(97–1145 days)

183 days(97–1145 days)

183 days(97–1145 days)

1,2-Dichloroethane

CH2ClCH2Cl

12.8 ppt(10.4–18.3)

65.0 days(41–555 days)

82 days(41–555 days)

82 days(41–555 days)

Chloroethane

CH3CH2Cl

39 days(26–280 days)

48 days(26–280 days)

48 days(26–280 days)

1-Chloropropane

CH3CH2CH2Cl

14 days(10–80 days)

16 days(10–80 days)

16 days(10–80 days)

2-Chloropropane

CH3CHClCH3

18 days(13–95 days)

22 days(13–95 days)

22 days(13–95 days)

Unsaturated Hydrochlorocarbons and Chlorocarbon
Chloroethene (vinyl chloride)

CH2=CHCl

1.5 days(0.9–2.2 days)

1.7 days(0.9–2.2 days)

1.7 days(0.9–2.2 days)

1,2-dichloroethene

CH2=CCl2

0.9 days(0.5–1.3 days)

1 days
(0.5–1.3 days)

1 days
(0.5–1.3 days)(E)-1,2-dichloroethene(E)-CClH=CClH–(3.2–6.7 days)

5.5 days(3.2–6.7 days)

5.5 days(3.2–6.7 days)(Z)-1,2-dichloroethene(Z)-CClH=CClH–(3.2–6.7 days)

5.2 days(3.2–6.7 days)

5.2 days(3.2–6.7 days)

Trichloroethene

CHCl=CCl2

0.3 ppt

4.9 days(3.3–7.1 days)

5.6 days(3.3–7.1 days)

5.6 days(3.3–7.1 days)

Perchloroethene

CCl2=CCl2

1.5 ppt

90 days(66–245 days)

110 days(66–245 days)

110 days(66–245 days)

Unsaturated Chlorofluorocarbons and Hydrochlorofluorocarbons
CFO-1113
Chlorotrifluoroethene

CF2=CFCl

1.4 days(0.8–2.1 days)

1.5 days(0.8–2.1 days)

1.5 days(0.8–2.1 days)

HCFO-1233zd(E)(E)-CF3CH=CHCl

26 days(21–39 days)

42.5 days(34–64 days)

42.5 days(34–64 days)

HCFO-1233zd(Z)(Z)-CF3CH=CHCl–~(20–40) days

13 days

13 days

HCFO-1233xf(2-chloro-3,3,3-fluoro-1-propene)

CF3CCl=CH2–~(20–40) days

42.5 days(34–64 days)

42.5 days(34–64 days)

CFO-1215yc(3-chloro-1,1,2,3,3-fluoro-1-propene)

CF2=CFCF2Cl~5 days~(3–7 days)~5 days~(3–7 days)~5 days~(3–7 days)

CFO-1316yff(4,4-dichloro-1,1,2,3,3,4-fluoro-1-butene)

CF2=CFCF2CFCl2~5 days~(3–7 days)~5 days~(3–7 days)~5 days~(3–7 days)

Bromocarbons, Hydrobromocarbons and Halons
Methyl bromide

A.24

CH3Br

6.8 ppt

0.8

0.8

1.8

Methylene bromide

CH2Br20.9 ppt(0.6–1.7)

123 days(80–890 days)

150 days(80–890 days)

150 days(80–890 days)

Bromoform

CHBr3

1.2 ppt(0.4–4.0)

24 days(15–88 days)

16 days(8–23 days)

57 days(15–88 days)

Halon-1201

CHBrF2

5.2

4.9

5.7

Halon-1202

CBr2F20.01 ppt

2.9

2.5

–

Halon-1211

CBrClF2

3.6 ppt

16.0

16

–

Halon-1301

CBrF3

3.3 ppt

65.0

72

–

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

–

–

0.028

37

10

11

1.7

1.4

A8

R15

–

–

0.07

66

18

20

3.0

2.2

A8

R15

–

–

0.01

5.1

1.4

1.5

<1

<1

A8

R3

–

–

0.004

1.8

<1

<1

<1

<1

–

–

–

–

–

0.005

0.9

<1

<1

<1

<1

R2

–

–

8.8e-4

<1

<<1

<<1

<<1

<<1

R2

–

–

1.17e-3

<<1

<<1

<<1

<<1

<<1

R2

–

<0.0003––<0.00032.95e-4

<<1

<<1

<<1

<<1

<<1

–

<0.0045.74e-3

<1

<1

<1

<<1

<<1

A9

–

–

0.053

21.7

5.9

6.5

1.0

0.8

A9

–

–

–

–

–

–

–

–

–

<0.0004

0.067

13.5

3.7

4.0

0.6

0.5

O12

R16

–

<0.0004

0.025

1.5

0.4

0.45

0.07

0.06

O12

R16

–

–

–

–

–

–

–

–

L31

R9

–

–

–

–

–

–

–

–

L32

R9

–

–

–

–

–

–

–

–

L32

R9

26.3

0.57

0.004

7.6

2

2.4

<1

<1

A9

L :3,13

O3

R3

–

3–4

0.01

5.3

1.4

1.6

<1

<1

A9

L2

O12

R3

–

1–5

0.003

<1

<1

<1

<<1

<<1

A9

L2

O12

R9

0.15

1,240

340

675

72

48

1.7

0.27

720

196

285

35

27

A9

L :2,3

O3

R3

41

6.9–7.7

0.29

4,590

1,750

3,950

1,130

300

A9

L :2,3

O3

R3

73.5

15.2–19.0

0.30

7,930

6,670

8,160

7,160

4,700

A9

L :2,3

O3

R3

35
36

–

–

–

–

–

–

–

R2

–

–

R9

–

O12

R9

O12

R2

O12

R2
R2

R9

L33

R3

A.25

Appendix A | Appendix Table A-1
26

Industrial Designation or
Chemical Name

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

Bromochloromethane

CH2BrCl

0.10 ppt(0.07–0.12)

137 days(89–1050 days)

165 days(89–1050 days)

165 days(89–1050 days)

Bromodichloromethane

CHBrCl2

0.3 ppt(0.1–0.9)78 days(38–250 days)

66 days(38–250 days)

95 days(56–460 days)

Dibromochloromethane

CHBr2Cl

0.3 ppt(0.1–0.8)

59 days(28–225 days)

59 days(28–225 days)

71 days(45–325 days)

bromoethane

CH3CH2Br

41 days(28–260 days)

50 days(30–260 days)

50 days(30–260 days)

1,2-dibromoethane

CH2BrCH2Br

70 days(44–590 days)

89 days(44–590 days)

89 days(44–590 days)

n-bromopropane

CH3CH2CH2Br

12.8 days(9–65 days)

15 days(9–65 days)

15 days(9–65 days)

Iso-bromopropane

CH3CHBrCH3

16.7 days(12–88 days)

20 days(12–88 days)

20 days(12–88 days)

Halon-2301

CH2BrCF3

3.4

3.2

3.2

Halon-2311 / Halothane

CHBrClCF3

1.0

1.0

1.1

Halon-2401

CHFBrCF3

2.9

2.9

3.1

Halon-2402 isomer

CF3CFBr2

–

2.5

–

Halon-2402

CBrF2CBrF2

20.0

28

–

0.010 ppt

0.42 ppt

Unsaturated Bromofluorocarbons
Bromothrifluoroethene

CFBr=CF2

1.4 days(0.9–2.0 days)

1.6 days(0.9–2.0 days)

1.6 days(0.9–2.0 days)

1-Bromo-2,2-fluoroethene

CHBr=CF2

2.3 days(1.5–3.4 days)

2.7 days(1.5–3.4 days)

2.7 days(1.5–3.4 days)

2-Bromo-3,3,3-fluoro-1-propene

CH2=CBrCF3

2.7 days(1.8–3.9 days)

3.2 days(1.8–3.9 days)

3.2 days(1.8–3.9 days)

2-bromo-3,3,4,4,4-fluoro-1-butene

CH2=CBrCF2CF3

3.1 days(2.0–4.6 days)

3.7 days(2.0–4.6 days)

3.7 days(2.0–4.6 days)

4-bromo-3,3,4,4-fluoro-1-butene

CH2=CHCF2CF2Br

6.5 days(4.7–9.5 days)

7.5 days(4.7–9.5 days)

7.5 days(4.7–9.5 days)–

4.5 days

4.5 days

500.0

569

–

Unsaturated Bromochlorofluorocarbons
4-bromo-3-chloro-3,4,4-trifluoro-1butene

CH2=CHCClFCBrF2

Fully Fluorinated Species

A.26

Nitrogen trifluoride

NF3

1.5 ppt

Perfluorotriethylamine

N(C2F5)3

–

>1000

–

Perfluorotripropylamine

N(C3F7)3

–

>1000

–

Perfluorotributylamine

N(C4F9)3

–

>1000

–

Perfluorotripentylamine

N(C5F11)3

–

>1000

–

Sulphur hexafluoride

SF6

8.9 ppt

3,200.0

3,200–(Trifluoromethyl)sulfur pentafluoride

SF5CF30.153 ppt

800.0

650–950–

PFC-14 (Perfluoromethane)

CF4

82.7 ppt

50,000.0

50,000–

PFC-116 (Perfluoroethane)

C2F6

4.6 ppt

10,000.0

10,000–Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yr

17

4.7

GTP
20-yr i

GTP
50-yr

5.2

Footnotes

GTP
100-yr0.8

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

–

–

0.022

0.6

A9

R2

–

–

–

–

–

–

–

–

A9

L2

R9

–

–

–

–

–

–

–

–

A9

L2

R9

–

<0.46

0.0060

1.7

0.5

0.5

<1

<1

–

–

0.012

3.7

1.0

1.1

0.17

0.14

–

<0.17

0.002

0.2

<1

<1

<<1

<<1

–

–

0.004

0.4

0.1

0.1

<1

<1

R2

–

–

0.14

620

167

270

31

23

R3

20

~1.6

0.13

151

41

49

7

6

45

–

0.19

675

184

285

34

25

L33

R3

–

–

–

–

–

–

–

–

L34

R9

41

15.7

0.31

3,920

2,030

3,730

1,900

615

–

–

–

–

–

–

–

–

–

–

–

–

–

–

<0.05

–

–

–

–

–

–

–

–

–

–

–

–

–

0.0135

<1

<1

<1

<<1

<<1

740

0

0.20

12,46015,75013,420

16,25018,035

–

0

0.68

8,150

10,6108,800

10,880

–

0

0.82

7,055

9,180

7,620

–

0

0.97

6,470

8,420

–

0

1.07

5,780

–

0

0.57

–

0

–
–

O12

R2
R2

O12

A9

A9

O13

L :3,13

R2

R3

O3

R3

–

O14

R9

–

–

O14

R9

–

–

–

O12

R9

–

–

–

–

O14

R9

–

–

–

–

O14

R9

O14

R17

L35

A10

L :3,36

R3

12,500

L37

R18

9,410

10,815

L37

R18

6,980

8,630

9,910

L37

R18

7,520

6,240

7,710

8,860

L37

R18

17,50023,50018,90023,80028,200

A10

L7

R3

0.59

13,50017,40014,50017,80020,200

A10

L38

R3

0

0.09

4,880

6,630

5,270

6,690

8,040

A10

L7

R3

0

0.25

8,210

11,1008,880

11,20013,500

A10

L7

R3

A10

A.27

Appendix A | Appendix Table A-1
28

Industrial Designation or
Chemical Name

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

3,000.03,000–

2,600.0

2,600

–

–

1.2

1.2

3,200.0

3,200–

PFC-c216 (Perfluorocyclopropane)

c-C3F6

PFC-218 (Perfluoropropane)

C3F8

PFC-c316 (Perfluorocyclobutene)

c-C4F6PFC-c318 (Perfluorocyclobutane)

c-C4F8

PFC-31-10 (Perfluorobutane)

n-C4F102,600.0

2,600–

PFC-c418 (Perfluorocyclopentene)

c-C5F831.0 days

1.1

1.1

PFC-41-12 (Perfluoropentane)

n-C5F124,100.0

4,100–

PFC-51-14 (Perfluorohexane)

n-C6F143,100.0

3,100–

PFC-61-16 (Perfluoroheptane)

n-C7F163,000.0

3,000–

PFC-71-18 (Perfluorooctane)

n-C8F18

3,000.0

3,000–

PFC-91-18 (isomer mixture)

C10F182,000.0

2,000–

PFC-c91-18(Z) (Perfluorodecalin(Z))(Z)-C10F18

2,000.0

2,000–

PFC-c91-18(E) (Perfluorodecalin(E))(E)-C10F18

2,000.0

2,000–

PFC-1114

CF2=CF2

1.1 days(0.7–1.6 days)

1.2 days(0.7–1.6 days)

1.2 days(0.7–1.6 days)

PFC-1216

CF3CF=CF2

4.9 days(3.3–7.1 days)

5.5 days(3.3–7.1 days)

5.5 days(3.3–7.1 days)

Perfluorobuta-1,3-diene

CF2=CFCF=CF2

1.1 days

1.1 days

1.1 days

Perfluorobut-1-ene

CF3CF2CF=CF2

6.0 days

6 days6 days

Perfluorobut-2-ene (71% (E) and 29% (Z)isomer blend)

CF3CF=CFCF3

31.0 days

31 days

31 days(E)-Perfluoro-2-butene(E)-CF3CF=CFCF3–

22 days

22 days(Z)-Perfluoro-2-butene(Z)-CF3CF=CFCF3–

35 days

35 days

Perfluoro(2-methyl-2-pentene)(CF3 )2C=CFCF2CF3–

192 days

192 days

0.63 ppt

1.44 ppt

0.148 ppt

Halogenated Ethers

A.28

HFE-125

CHF2OCF3

119.0

135

147

HFE-134 (HG-00)

CHF2OCHF2

25.4

26.9

28.4

HFE-143a

CH3OCF3

4.8

4.9

5.05

HFE-152a

CH3OCHF2

–

1.8

1.85

HFE-227ea

CF3CHFOCF3

46.7

54.8

58

HCFE-235ca2 (enflurane)

CHF2OCF2CHFCl

4.3

4.42

4.62

HCFE-235da2 (isoflurane)

CHF2OCHClCF3

3.5

3.5

3.7

HFE-236ca

CHF2OCF2CHF2

20.8

22.0

23.1

HFE-236ea2 (desflurane)

CHF2OCHFCF3

10.8

14.1

14.8

HFE-236fa

CF3CH2OCF3

~7.5

~7.5

~8

HFE-245cb2

CF3CF2OCH3

5.0

5.0

5.24

HFE-245fa1

CHF2CH2OCF3

6.6

~6.7

~7

0.11 ppt

3.2 ppt

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

–

0

0.23

6,850

9,200

7,400

9,310

11,000

A10

L39

R19

–

0

0.28

6,640

8,900

7,180

9,010

10,700

A10

L39

R3

–

0

0.25

425

115

141

20

16

L24

R12

–

0

0.32

7,110

9,540

7,680

9,660

11,500

A10

L7

R3

–

0

0.36

6,870

9,200

7,420

9,320

11,000

A10

L39

R3

–

0

0.28

322

87

106

15

12

L40

R20

–

0

0.41

6,350

8,550

6,860

8,650

10,300

A10

L7

R3

–

0

0.44

5,890

7,910

6,370

8,010

9,490

A10

L7

R3

–

0

0.50

5,830

7,820

6,290

7,920

9,380

A10

L39

R3

–

0

0.55

5,680

7,620

6,130

7,710

9,140

A10

L39

R3

–

0

0.55

5,390

7,190

5,820

7,290

8,570

L39

R3

–

0

0.56

5,430

7,240

5,860

7,340

8,630

L39

R3

–

0

0.48

4,720

6,290

5,090

6,380

7,500

L39

R3

–

0

0.002

<1

<1

<1

<1

<1

R3

–

0

0.01

<1

<1

<1

<1

<1

R3

–

0

0.003

<1

<1

<1

<1

<1

R3

–

0

0.02

<1

<1

<1

<1

<1

–

0

0.07

6

2

2

<1

<1

R3

–

0

0.068

5

1.3

1.4

<1

<1

R3

–

0

–

–

–

–

–

–

R9

–

0

–

–

–

–

–

–

R9

1665

0

0.41

12,615

12,98013,315

13,86011,960

R3

500

0

0.44

11,8405,965

11,215

5,530

1,735

R3

130

0

0.18

1,950

540

1,060

113

75

R3

56

0

–

–

–

–

–

–

R9

968

0

0.44

8,920

6,630

9,060

7,110

3,930

R3

100

0.04

0.41

2,190

600

1,125

121

84

86

0.03

0.42

1,800

490

820

93

68

436

0

0.56

9,855

4,420

9,050

3,700

1,020

316

0

0.45

6,440

2,300

5,385

1,320

365

196

0

0.36

3,350

980

2,240

273

138

134

0

0.33

2,360

655

1,280

136

91

168

0

0.31

2,960

845

1,880

214

119

L41

A11

R3

O13

R3

O13

R3
R19

A11

O13
L42

R3
R3
R3

L43

R3

A.29

Appendix A | Appendix Table A-1
30

Industrial Designation or
Chemical Name

A.30

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

HFE-245fa2

CHF2OCH2CF3

5.5

5.5

5.8

HFE-254cb1

CH3OCF2CHF2

2.5

2.5

2.62

HFE-254eb2

CH3OCHFCF3

88 days(69–200 days)

110 days(69–200 days)

110 days(69–200 days)

HFE-263fb2

CF3CH2OCH3

23 days(19–47 days)

28 days(19–47 days)

28 days(19–47 days)

HFE-263m1

CF3OCH2CH3

0.4~145 days~145 days

HFE-329mcc2

CHF2CF2OCF2CF3

~25

~25

~25

HFE-338mmz1(CF3 )2CHOCHF2

21.2

22.3

23.5

HFE-338mcf2

CF3CH2OCF2CF3

~7.5

~7.5

~8

HFE-347mmz1 (Sevoflurane)(CF3 )2CHOCH2F

~2

1.9

1.96

HFE-347mcc3 (HFE-7000)

CH3OCF2CF2CF3

5.0

5.1

5.3

HFE-347mcf2

CHF2CH2OCF2CF3

~6.6

~6.7

~7

HFE-347pcf2

CHF2CF2OCH2CF3

5.9

6.1

6.3

HFE-347mmy1(CF3 )2CFOCH3

3.7

3.7

3.8

HFE-347mcf

CHF2OCH2CF2CF3

5.6

5.8

6.0

HFE-356mec3

CH3OCF2CHFCF3

~3

2.5

2.62

HFE-356mff2

CF3CH2OCH2CF3

105 days(79–270 days)

128 days(79–270 days)

128 days(79–270 days)

HFE-356pcf2

CHF2CH2OCF2CHF2

~6

~6

~6

HFE-356pcf3

CHF2OCH2CF2CHF2

3.5

3.5

3.7

HFE-356pcc3

CH3OCF2CF2CHF2

~3

2.5

2.62

HFE-356mmz1(CF3 )2CHOCH3

61 days(49–128 days)

65 days(49–128 days)

65 days(49–128 days)

HFE-365mcf3

CF3CF2CH2OCH3

19.3 days(17–42 days)

25 days(17–42 days)

25 days(17–42 days)

HFE-365mcf2

CF3CF2OCH2CH3

0.6

219 days

219 days

HFE-374pc2

CHF2CF2OCH2CH3

64 days(49–128 days)

76 days(49–128 days)

76 days(49–128 days)

HFE-43-10pccc124(H-Galden 1040x, HG-11)

CHF2OCF2OC2F4OCHF2

13.5

14.1

14.7

HFE-449s1 (HFE-7100)

C4F9OCH3

4.7

4.8

5.0

n-HFE-7100

n-C4F9OCH3

4.7

4.8

5.0

i-HFE-7100

i-C4F9OCH3

4.7

4.8

5.0

HFE-54-11mecf

CF3CHFCF2OCH2CF2CF3

8.8

9.1

9.5

HFE-569sf2 (HFE-7200, isomer mix)

C4F9OC2H5

~0.8

0.8

0.8

n-HFE-7200

n-C4F9OC2H5

0.8

0.8

0.8

i-HFE-7200

i-C4F9OC2H5

0.8

0.63

0.65

HFE-236ca12 (HG-10)

CHF2OCF2OCHF2

25.0

26.5

28.0

HFE-338pcc13 (HG-01)

CHF2OCF2CF2OCHF2

12.9

13.4

14.0

0.16 ppt

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

145

0

0.36

2,910

810

1,670

179

114

R3

74

0

0.26

1,110

300

440

54

42

R3

–

0

–

–

–

0

0.04

6

1.6

1.8

<1

<1

L44

R3

–

0

0.13

102

28

31

4.7

3.8

L45

R3

580

0

0.53

6,960

3,360

6,520

3,010

895

L46

R3

440

0

0.44

6,000

2,715

5,525

2,290

635

196

0

0.44

3,180

930

2,120

259

131

46

0

0.32

685

185

250

33

26

135

0

0.35

1,970

545

1,090

116

76

R3

168

0

0.42

3,010

860

1,910

215

121

R3

155

0

0.48

3,170

890

1,930

211

126

R3

104

0

0.32

1,330

365

625

69

51

R3

150

0

–

–

–

–

–

–

R9

74

0

0.30

930

250

370

46

35

–

0

0.17

74

20

22

3.4

2.8

145

0

0.37

2,650

745

1,595

174

105

97

0

0.38

1,640

445

750

84

62

74

0

0.32

990

270

390

48

37

L48

R3

–

0

0.15

33

8.9

9.8

1.5

1.2

L35

R3

–

0

0.05

4.7

1.3

1.4

<1

<1

L50

R3

–

0

0.26

215

58

66

10

8

L51

R19

–

0

0.30

97

26

29

4.4

315

0

1.02

8,180

2,920

6,835

1,680

464

R3

128

0

0.36

1,530

420

825

88

59

R3

128

0

0.42

1,790

490

960

102

69

R3

128

0

0.35

1,490

410

800

85

57

R3

212

0

–

–

–

–

–

–

L52

R9

27

0

0.30

210

57

66

10

8

L53

R3

27

0

0.35

235

65

75

11

9

L53

R19

22

0

0.24

129

35

40

5.9

4.8

L53

R3

500

0

0.65

11,165

5,575

10,550

5,140

1,591

L54

R3

304

0

0.86

8,595

3,000

7,085

1,630

466

–

–

–

–

R9

R3
L47
A11

R3
O13

L48

R3

R3
R3

L49

R3
R3

3.6

R3

R3

A.31

Appendix A | Appendix Table A-1
32

Industrial Designation or
Chemical Name

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

HG-02(1,1’-Oxybis[2-(difluoromethoxy)-1,1,2,2-tetrafluoroethane)

HF2C(OCF2CF2 ) 2OCF2H

26

26.9

28.4

HG-03
(1,1,3,3,4,4,6,6,7,7,9,9,10,10,12,12-Hexadecafluoro-2,5,8,11-tetraoxadodecane)

HF2C(OCF2CF2 )3OCF2H

26

26.9

28.4

HG-04(1,1,3,3,4,4,6,6,7,7,9,9,10,10,12,12,1
3,13,15,15-Eicosafluoro-2,5,8,11,14pentaoxapentadecane)

HCF2O(CF2CF2O)4CF2H

26

26.9

28.4

HG-20

HF2C(OCF2 )2OCF2H

25.0

26.5

28.0

HG-21

HF2COCF2CF2OCF2OCF2OCF2H

13.5

13.4

14.0

HG-30

HF2C(OCF2 )3OCF2H

25.0

26.5

28.0

1-Ethoxy-1,1,2,2,3,3,3heptafluoro­propane

CF3CF2CF2OCH2CH3

0.8

0.75

0.77

Fluoroxene

CF3CH2OCH=CH2

3.6 days

3.6 days

3.6 days

1,1,2,2-Tetrafluoro-1-(fluoromethoxy)
ethaneCH2FOCF2CF2H

6.2

6.2

6.5

2-Ethoxy-3,3,4,4,5-pentafluorotetra­hydro2,5-bis[1,2,2,2-tetrafluoro-1(trifluoromethyl)ethyl]-furan

C12H5F19O2

1.0

0.81

0.83

Fluoro(methoxy)methane

CH3OCH2F

73.0 days

73 days73 days

Difluoro(methoxy)methane

CH3OCHF2

1.1

1.1

1.1

Fluoro(fluoromethoxy)methane

CH2FOCH2F

0.9

0.9

0.9

Difluoro(fluoromethoxy)methane

CH2FOCHF2

3.3

3.2

3.3

Trifluoro(fluoromethoxy)methane

CH2FOCF3

4.4

4.2

4.4

HG’-01

CH3OCF2CF2OCH3

2.0

1.7

1.74

HG’-02

CH3O(CF2CF2O) 2CH3

2.0

1.7

1.74

HG’-03

CH3O(CF2CF2O)3CH3

2.0

1.7

1.74

HFE-329me3

CF3CFHCF2OCF3

40.0

33.6

35.3

2-Chloro-1,1,2-trifluoro-1-methoxyethane

CH3OCF2CHFCl

1.4

1.43

1.49

PFPMIE(perfluoropolymethylisopropyl ether)

CF3OCF(CF3 )CF2OCF2OCF3

800.0

800

–

HFE-216

CF3OCF=CF2

8.4 days

1.6 days

1.6 days

Trifluoromethyl formate

HC(O)OCF3

<3.5

<3.5

3.7

Perfluoroethyl formate

HC(O)OCF2CF3

<3.5

<3.6

3.7

Perfluoropropyl formate

HC(O)OCF2CF2CF3

<2.6

<2.6

2.7

Perfluorobutyl formate

HC(O)OCF2CF2CF2CF3

3.0

<2.6

2.7

2,2,2-Trifluoroethyl formate

HC(O)OCH2CF3

0.4

200 days

204 days

3,3,3-Trifluoropropyl formate

HC(O)OCH2CH2CF3

0.3

99 days

110 days

1,2,2,2-Tetrafluoroethyl formate

HC(O)OCHFCF3

3.2

3.1

3.2

Fluoroesters

A.32

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

500

0

1.15

10,435

5,260

9,885

4,875

1,527

L54

R3

500

0

1.43

9,745

4,910

9,230

4,550

1,426

L54

R3

500

0

1.46

7,970

4,015

7,545

3,720

1,166

L54

R3

500

0

0.92

11,6305,810

10,995

5,350

1,658

L55

R19

304

0

1.71

10,925

3,815

9,005

2,070

592

L56

R19

500

0

1.65

16,5008,240

15,600

7,595

2,352

L55

R19

25

0

0.28

221

60

70

10

8.3

L51

R19

–

0

0.01

<1

<1

<1

<1

<1

L51

R19

153

0

0.34

3,040

855

1,860

205

121

L51

R19

28

0

0.49

165

45

52

7.6

6.2

L57

R3

–

0

0.07

46

13

14

2

2

L58

R19

35

0

0.17

515

139

168

24

19

L59

R19

30

0

0.19

470

127

150

22

18

L59

R19

92

0

0.30

2,155

585

950

109

82

L59

R19

120

0

0.33

2,620

715

1,315

142

100

L59

R19

51

0

0.29

685

185

245

33

26

L60

R19

51

0

0.56

770

210

275

37

29

L60

R3

51

0

0.76

740

200

260

35

28

L60

R3

680

0

0.48

6,900

3,955

6,715

3,955

1,485

L61

R3

42

0

0.21

455

123

156

22

17

L62

R3

–

0

0.65

7,500

9,710

8,070

9,910

11,300

L63

R3

–

0

0.02

<1

<1

<1

<1

<1

L64

R3

110

0

0.31

2,150

590

985

111

82

L65

R19

110

0

0.44

2,130

580

970

110

81

L66

R19

83

0

0.50

1,380

375

555

68

52

L66

R19

83

0

0.56

1,240

335

500

61

47

L66

R19

20

0

0.16

155

42

48

7

6

L67

R19

20

0

0.13

56

15

17

2.5

2.1

L68

R19

98

0

0.35

1,670

455

720

84

63

L69

R19

A.33

Appendix A | Appendix Table A-1
34

Industrial Designation or
Chemical Name

Chemical Formula

AtmosphericAbundance(2016) a

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

3.2

3.1

3.2

1,1,1,3,3,3-Hexafluoropropan-2-yl formate

HC(O)OCH(CF3 ) 2

Perfluorobutyl acetate

CH3C(O)OCF2CF2CF2CF3

21.9 days

22 days

22 days

Perfluoropropyl acetate

CH3C(O)OCF2CF2CF3

21.9 days

22 days

22 days

Perfluoroethyl acetate

CH3C(O)OCF2CF3

21.9 days

22 days

22 days

Trifluoromethyl acetate

CH3C(O)OCF3

21.9 days

22 days

22 days

Methyl carbonofluoridate

FCOOCH3

1.8

1.8

1.8

1,1-Difluoroethyl carbonofluoridate

FC(O)OCF2CH3

0.3

110 days

110 days

1,1-Difluoroethyl 2,2,2-trifluoroacetate

CF3C(O)OCF2CH3

0.3

110 days

110 days

Ethyl 2,2,2-trifluoroacetate

CF3C(O)OCH2CH3

21.9 days

22 days

22 days

2,2,2-Trifluoroethyl 2,2,2-trifluoroacetate

CF3C(O)OCH2CF3

54.8 days

180 days

180 days

Methyl 2,2,2-trifluoroacetate

CF3C(O)OCH3

0.6

1.0

1.0

Methyl 2,2-difluoroacetate

HCF2C(O)OCH3

40.1 days

124 days

124 days

Difluoromethyl 2,2,2-trifluoroacetate

CF3C(O)OCHF2

0.3

110 days

110 days

1,1,2-Trifluoro-2-(trifluoromethoxy)ethane

CHF2CHFOCF3

9.8

9.0

9.3

1-Ethoxy-1,1,2,3,3,3-hexafluoropropane

CF3CHFCF2OCH2CH3

0.4

147 days

150 days

1,1,1,2,2,3,3-Heptafluoro-3-(1,2,2,2tetrafluoroethoxy)-propane

CF3CF2CF2OCHFCF3

67.0

59.4

62

1,1,2,2-Tetrafluoro-3-methoxypropane

CHF2CF2CH2OCH3

14.2 days

26 days

26 days

3,3,3-Trifluoro-propanal

CF3CH2CHO

2 days

5 days

5 days3,3,3-Trifluoropropan-1-ol

CF3CH2CH2OH

12 days

15 days

15 days

2,2,3,3,3-Pentafluoropropan-1-ol

CF3CF2CH2OH

0.3

172 days

172 days

4,4,4-Trifluorobutan-1-ol

CF3(CH2 ) 2CH2OH

4 days

5.4 days

5.4 days

2,2,3,3,4,4,5,5-Octafluorocyclopentanol-(CF2 )4CH(OH)-

0.3

110 days

110 days

1,1,1,3,3,3-Hexafluoropropan-2-ol(CF3 ) 2CHOH

1.9

1.9

1.95

3,3,4,4,5,5,6,6,7,7,7Undecafluoroheptan-1-ol

CF3(CF2 )4CH2CH2OH

20 days

17 days

17 days

3,3,4,4,5,5,6,6,7,7,8,8,9,9,9Pentadecafluorononan-1-ol

CF3(CF2 )6CH2CH2OH

20 days

17 days

17 days

3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,11Nonadecafluoroundeca-1-o1

CF3(CF2 )8CH2CH2OH

20 days

17 days

17 days

2,2,3,3,4,4,4-Heptafluorobutan-1-ol

CF3CF2CF2CH2OH

0.6

0.55

0.55

2,2,3,3-Tetrafluoro-1-propanol

CHF2CF2CH2OH

91.2 days

93 days

93 days

2,2,3,4,4,4-Hexafluoro-1-butanol

CF3CHFCF2CH2OH

112 days(85–280 days)

134 days(85–280 days)

134 days(85–280 days)

2-Fluoroethanol

CH2FCH2OH

20.4 days

16 days

16 days

2,2-Difluoroethanol

CHF2CH2OH

40 days61 days61 days

2,2,2-Trifluoroethanol

CF3CH2OH

0.3

167 days

167 days

Halogenated Alcohols

A.34

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

Footnotes

GTP
100-yr

A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

96

0

0.33

1,175

320

510

59

44

L68

R19

–

0

0.12

6

2

2

<1

<1

L70

R19

–

0

0.11

6

2

2

<1

<1

L71

R19

–

0

0.10

8

2

2

<1

<1

L71

R19

–

0

0.07

8

2

2

<1

<1

L71

R19

58

0

0.07

350

95

126

17

13

L69

R19

–

0

0.17

90

24

27

4.1

3.4

L72

R19

–

0

0.27

103

28

31

4.7

3.9

L68

R19

–

0

0.05

5

1

1

<1

<1

L73

R19

–

0

0.15

85

23

26

3.9

3.2

L73

R19

34

0

0.18

315

86

103

15

12

L73

R19

–

0

0.05

35

9

10

1.6

1.3

L73

R19

–

0

0.24

99

27

30

5

4

L68

R19

250

0

0.35

3,765

1,145

2,720

390

164

L74

R3

20

0

0.19

88

24

27

4

3

L74

R3

1400

0

0.58

7,750

5,980

7,910

6,435

3,755

L74

R3

–

0

0.03

3

0.9

1

<1

<1

L75

R3

–

0

0.004

<1

<1

<1

<1

<1

L76

R3

–

0

0.02

1.6

<1

<1

<1

<1

L77

R3

–

0

0.14

99

27

30

4.6

3.7

R3

–

0

0.01

<1

<1

<1

<1

<1

R3

–

0

0.16

47

13

14

2

2

62

0

0.26

670

182

245

32

25

–

0

0.06

2

<1

<1

<1

<1

L79

R3

–

0

0.07

1.8

<1

<1

<1

<1

L79

R3

–

0

0.05

<1

<1

<1

<1

<1

L79

R3

–

0

0.20

124

34

38

5.7

4.6

L80

R3

–

0

0.11

48

13

14

2

1.8

L81

R3

–

0

0.19

86

23

26

4.0

3.2

R3

–

0

0.02

3

0.8

0.9

<1

<1

R3

–

0

0.04

18

5

5

0.8

0.7

R3

–

0

0.10

103

28

31

4.7

3.8

R3

L78

R3
R3

A.35

Appendix A | Appendix Table A-1
36

WMO (2014)
Total
Lifetime(years) b

Total
Lifetime(years) c

Tropospheric(OH
Reactive loss)
Lifetime(years) d

CF3CF2CF2CF2CH2OH

142 days(111–330 days)

172 days(111–330 days)

172 days(111–330 days)

NOVEC-1230, FK-5-1-12
Perfluoro(2-methyl-3-pentanone)

CF3CF2C(O)CF(CF3) 2

7.0 days(7–14 days)

7 days(7–14 days)–

NOVEC-774
Tetradecafluoro-2,4dimethylpentan-3-one(CF3)2CFC(O)CF(CF3)2

–

–

–

Perfluoro(2-methyl-3-hexanone)

CF3CF2CF2C(O)CF(CF3)2

–

–

–

7 days
(3.5–9.6 days)<14 days(3.5–14 days)

197 days

Industrial Designation or
Chemical Name

2,2-3,3,4,4,5,5,5-fluoro-1-pentanol

Chemical Formula

AtmosphericAbundance(2016) a

Halogenated Ketones

Iodocarbons
0.8 ppt(0.3–2.1)

Methyl iodide

CH3I

Trifluoroiodomethane

CF3I

4 days
(0.7–4 days)<5 days(0.7–5 days)

3.0

Bromoiodomethane

CH2BrI–≤60 mins

145 days

Chloroiodomethane

CH2ClI–<100 mins

145 days

Diiodomethane

CH2I2

–

≤5 mins

4 daysIodoethane

CH3CH2I

4 days
(2.4–13.9 days)<4 days(2.4–13.9 days)

52 days(13–94 days)

n-iodopropane

CH3CH2CH2I–<2 days

15 daysi-iodopropane

CH3CHICH3–<1 day13 days1-iodo-heptafluoropropane

CF3CF2CF2I<2 days<2 days

3.0

Special Compounds

A.36

Carbonyl fluoride

COF2–(5–10 days)

7 days(5–10 days)–

Phosphorus tribromide

PBr3–(<0.01 days)–(<0.01 days)–

Ammonia

NH3––(Few days)–(Few days)

Carbonyl Sulfide

COS

505 ppt

Sulfuryl fluoride

SO2F2

2.3 ppt

–
36.0

2
36.0

110 days–>300

Appendix Table A-1 | Appendix A
Stratospheric
Lifetime(years) e
ODP f

Radiative
Efficiency(W m-2 ppb-1) g

GWP
20-yr hGWP
100-yrGTP
20-yr i

GTP
50-yr

GTP
100-yr

–

–

–

–

–

Footnotes
A: Abundance O: ODP
L: Lifetime
R: RE, GWP, & GTP

–

0

–

–

0

0.03

<1

<1

<1

<1

<1

L82

R3

–

0

–

–

–

–

–

–

L83

R21

–

0

–

–

–

–

–

–

L83

R21

–

<0.42

6.0e-4

<<1

<<1

<<1

<<1

<<1

–

<0.09

–

–

–

–

–

–

–

–

–

–

–

–

<0.07

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

<0.04

–

0

–

8.0e-3

–

–

–

0

–

1.4e-3

<1

<1

<1

<1

<1

0

–

5.7e-3

43

12

16

2.0

1.6

630

0

0.20

6,840

4,090

6,690

4,140

1,650

<<1

<<1

<<1
–

–

<1
–

L :20,84,
85,86

O12

R2

–

A12

L :84,
85,86

O12

R22

–

–

A12

L :84,
87,88

O15

R22

–

–

A12

L :84,
87,88

O12

R22

A12

L :84,
87,89

O15

R2

–

L :84,86,
87,90

O15

R22

–

L :84,
87,90

O15

R22

L :84,87,
90

O15

R2

L :84,91

O16

R23

<<1

<<1
–

<1
–

A12

<<1

<<1

<<1

<<1
–

<1
–

R9

<<1
–

<1
–

<1

L92

–

L92

R2
O14

R9

L82

R2

A10

L93

R2

A10

L :13,94

R3

A.37

Appendix A | Appendix Table A-1

Table Heading Footnotes38a

The data given in the abundance column are only intended to provide a “snapshot” of the compound’s ­atmosphericabundance. The chapter and table(s) cited in the compound’s footnote provides the analysis details, previously ­reported abundances and trends, and references. This summary does not represent a comprehensive survey ofthe abundance of all molecules included in the table. Compounds included in this table fall into several different­abundance classifications: (1) compounds with known sources and global observations for which values are reported
in the table, (2) compounds with known sources, but with only local or regional observations for which some values
are reported in the table (note that the abundances for very short-lived substances (VSLSs) may represent local observations that can vary with location, altitude, and season), (3) compounds with natural and/or man-made sources thatare not addressed in the report chapters, and (4) compounds with no presently known sources or observations.

b

Total lifetime reported in WMO (2014).

c

Atm
Total lifetime (τTotal) is defined as the combination of the total atmospheric lifetime ( τ Total), which includes tropospheric loss (OH reaction and UV photolysis) and stratospheric loss (reactions with OH and O(1D) and UV photolysis),with the lifetimes due to ocean and soil uptake. Except where noted in the footnotes, tropospheric loss due to Cl atom
reaction is not ­included. Mesospheric loss processes are negligible except for very long-lived compounds as noted in
the footnotes.

τ

1

1

=

Atm
Total

τ Trop1=

τ Trop1

τ Strat1=

τ Total

A.38

=

τ

1
Strat
OH

1

τ

+

τ Strat

1

+

Trop
OH

+

+
Atm

τ Total1τ+τ Meso1

τ Trophv

1
Strat
O( 1 D)

1

1

τ Ocean++1

τ Strathv
1

τ Soild

The tropospheric partial lifetime due reaction with the OH radical was calculated relative to the lifetime for CH3CCl3(6.1 years) using a temperature of 272 K. OH reaction rate coefficients are taken from Burkholder et al. (2015) unlessstated otherwise in the footnote. Lifetimes for very short-lived substances (VSLSs) are reported, although their local
lifetimes will depend on the time and location of their emission. A representative range of VSLS local lifetimes takenfrom WMO (2014) Chapter 1 (Tables 1-5, 1-11) are given in parenthesis where available. The tropospheric OH partial
lifetime for CH3CCl3 (6.1 years) was calculated from an overall lifetime of 5.0 years derived from the AGAGE and NOAA
networks using a stratospheric partial lifetime of 38 years and an ocean partial lifetime of 94 years (Prinn et al., 2005).

e

The stratospheric partial lifetime was estimated based on atmospheric model calculations, where available, and empirical relationships for the OH, O(1D), and photolysis partial lifetimes. Stratospheric lifetimes are not reported for very
short-lived substances (VSLSs). The minimum stratospheric partial lifetime was taken to be 20 years.
*

Stratospheric OH reactive loss partial lifetimes were estimated based on the empirical correlation derived
Trop
using 2-D model results reported in Ko et al. (2013): log10 ( τ Strat
OH ) = 1.528 + 0.901 × log10 ( τ OH ).

*The O(1D) lifetime was based on measured or estimated reaction rate coefficients (kreactive, cm3 molecule-1s-1) and estimated using the empirical lifetime relationship; τ(years) = 3.7 × 10-8/kreactive. Where experimental data were not available, the O(1D) reactivity was estimated using the activity relationship for H
atom and Cl atom abstraction given in Baasandorj et al. (2013).

*

Stratospheric photolysis partial lifetimes were taken from model calculations or based on the empiricalestimates given in Orkin et al. (2013) or for the HCFCs from Papanastasiou et al. (2018).

Appendix Table A-1 | Appendix A
fAtmospheric model calculated and semi-empirical values as cited in the footnotes.

g

Radiative efficiency (RE) values were calculated using the empirical approach given in Hodgenbrog et al. (2013). The
values reported in this table are lifetime adjusted using the exponential expression reported in Hodgenbrog et al.,(2013) for compounds primarily removed by UV photolysis in the stratosphere and the S shaped curve for compounds
primarily removed by reaction with the OH radical. The values also include a +10% stratospheric temperature change­adjustment.

hGlobal warming potentials (GWPs) are calculated relative to CO2.

i

Global temperature change potentials (GTPs) calculated relative to CO2 using the parameterization given in Supplementary Material Section S8.12 and references therein in IPCC (2013).

Abundance Footnotes
A1.	
A2.	
A3.	
A4.	
A5.	
A6.	

See Chapter 6.	
See Chapter 1, Section 1.5.2 and Table 1-8.	
See Chapter 1, Section 1.5.1 and Table 1-8.	
See Chapter 1, Section 1.2.1 and Table 1-1.	
See Chapter 1, Section 1.2.5 and Table 1-1.
See Chapter 2, Section 2.3 and Table 2-3.	

A7.	 See Chapter 2, Section 2.3.5.
A8.	 See Chapter 1, Section 1.2 and Tables 1-1, 1-2, 1-3 and 1-4.
A9.	 See Chapter 1, Section 1.2 and 1.3 and Tables 1-1 and 1-4.
A10.	 See Chapter 1, Section 1.5 and Table 1-8.
A11.	 See Chapter 1, Section 1.5.
A12.	 See Chapter 1, Section 1.3 and Table 1-4.

Lifetime Footnotes
L1.	
L2.	

Total lifetime reported in WMO (2014).
Tropospheric photolysis partial lifetimes
Molecule

FormulaLifetime (years)

Reference

Nitrous oxide

N2O14,600SPARC Lifetime Report (Ko et al., 2013) *Carbon tetrachloride

CCl4

1,230

SPARC Lifetime Report (Ko et al., 2013) *CFC-11

CCl3F

1,770

SPARC Lifetime Report (Ko et al., 2013) *CFC-12

CCl2F2

12,500SPARC Lifetime Report (Ko et al., 2013) *CFC-112CCl2FCCl2F

2,280

Davis et al. (2016)

CFC-112a

CCl3CClF2

1,190

Davis et al. (2016)

CFC-113

CCl2FCClF2

8,120

SPARC Lifetime Report (Ko et al., 2013) *CFC-113a

CCl3CF3

1,480

Davis et al. (2016)

CFC-114

CClF2CClF2

19,600SPARC Lifetime Report (Ko et al., 2013) *CFC-114a

CCl2FCF3

8,300

Davis et al. (2016)(E)-R316c(E)-1,2-c-C4F6Cl2

3,600

Papadimitriou et al. (2013b)(Z)-R316c(Z)-1,2-c-C4F6Cl2

10,570

Papadimitriou et al. (2013b)

Bromodichloromethane

CHBrCl2

222 days

WMO (2014) Table 1-5

Dibromochloromethane

CHBr2Cl

160 days

WMO (2014) Table 1-5

Methylene bromide

CH2Br2

13.7

WMO (2014) Table 1-5

Bromoform

CHBr3~23 days

Papanastasiou et al. (2014)

Halon-1202

CBr2F22.74

Papanastasiou et al. (2013)

Halon-1211

CBrClF2

27.2

Papanastasiou et al. (2013)

Halon-1301

CBrF3

4,050

SPARC Lifetime Report (Ko et al., 2013) **Halon-2402

CBrF2CBrF2

85.5

Papanastasiou et al. (2013)* Model mean given in SPARC Table 5.6 scaled to recommended total lifetime.
** Model mean given in SPARC Table 5.6 scaled to CBrF3 UV cross section reported by Bernard et al. (2015).
A.39

Appendix A | Appendix Table A-1
L3.	Atmospheric lifetimes recommended in the SPARC40
Lifetime report (Ko et al., 2013). Note, in some cases there areslight differences between the combination of the partial lifetimes and the recommended total atmospheric lifetime, which was derived from multi-model results and field observations.
L4.	
OH rate coefficient taken from Atkinson (2003).
L5.	
OH rate coefficient taken from Le Calve et al. (1997).
L6.	
Room temperature OH rate coefficient taken from Wallington et al. (1997) and E/R assumed to be 1000 K.
L7.	
Total lifetime is the best estimate taken from Ravishankara et al. (1993) that includes mesospheric loss due to­Lyman-α (121.567 nm) photolysis. Electron reactive loss was included for SF6.
L8.	
Stratospheric partial lifetime was taken from the 2-D model calculations in Davis et al. (2016). These values arein agreement with the values reported in Laube et al. (2014) [59 (43–95) years for CFC-112, 51 (32–113) years for­CFC-112a, and 59 (31–305) years for CFC-113a (scaled to a CFC-11 lifetime of 52 years)], but of higher precision.
L9.	
The total lifetime includes mesospheric loss due to Lyman-α (121.567 nm) photolysis.
L10.	
Lifetime taken from Kloss et al. (2014).
L11.	
Stratospheric partial lifetime taken from 2-D model calculations in Papadimitriou et al. (2013b).
L12.	
k(OH) in the NASA/JPL (Burkholder et al., 2015) data evaluation was updated since the previous assessment resulting in a slight change in the OH tropospheric loss partial lifetime from that given in WMO (2014).
L13.	
Ocean and soil loss partial lifetimes
Molecule

FormulaSoil Lifetime(years)Reference

Ocean Lifetime(years)Reference

Methyl ­chloride CH3Cl

4.2

Hu et al. (2012)12

Hu et al. (2013)

Methyl­­bromide

CH3Br

3.35

Montzka and­Reimann et al.
(WMO, 2014)3.1

Hu et al. (2012)

Carbon­tetrachloride

CCl4

HCFC-21

CHCl2F–

673

Yvon-Lewis and Butler (2002)

HCFC-22

CHClF2–

1,174

Yvon-Lewis and Butler (2002)

HCFC-124

CHClFCF3–

1,855

Yvon-Lewis and Butler (2002)

HCFC-141b

CH3CCl2F–

9,190

Yvon-Lewis and Butler (2002)

HCFC-142b

CH3CClF2–

122,200

Yvon-Lewis and Butler (2002)

HFC-41

CH3F

–

1,340

Yvon-Lewis and Butler (2002)

HFC-125

CHF2CF3–

10,650

Yvon-Lewis and Butler (2002)

HFC-134a

CH2FCF3–

5,909

Yvon-Lewis and Butler (2002)

HFC-152a

CH3CHF2–

1,958

Yvon-Lewis and Butler (2002)

Methyl­chloroform

CH3CCl3–

94

Yvon-Lewis and Butler (2002)

Sulfuryl­fluoride

SO2F2

–

40

Mühle et al. (2009)

Rhew and Happell
375 (288–536)* (2016),
SPARC (2016)

183 (147–241)* Butler et al. (2016)* Possible range of lifetime given in parenthesis.
L14.	
L15.	
L16.	
L17.	
A.40

Lifetimes taken from Papanastasiou et al. (2018) where k(OH) was calculated using the structure activity relationship (SAR) of DeMore (1996) and stratospheric lifetime estimated as described in footnote e.
2-D model calculated stratospheric lifetime reported in McGillen et al. (2015).
OH reactivity assumed the same as CHF2CF3 (HFC-125).
Stratospheric partial lifetime calculated using 2-D model with OH and O(1D) rate coefficients recommended in
SPARC lifetime report, Chapter 3 (Ko et al., 2013).

Appendix Table A-1 | Appendix A
OH reactivity calculated using the structure activity relationships of DeMore (1996) with an assumed E/R of 1700 K.
OH reactivity calculated using the room temperature rate coefficient reported by Young et al. (2009a) with an­assumed E/R of 1700 K.
L20.	
OH reactivity taken from IUPAC evaluation (Ammann et al., 2017).
L21.	
OH reactivity taken from Chen et al. (2011).
L22.	
The OH rate coefficient data were taken from Baasandorj and Burkholder (2016).
L23.	
No experimental data available for the OH reaction and the lifetimes were estimated based on reactivity trends offluorinated ethenes and propenes.
L24.	
The OH rate coefficient was taken from Jia et al. (2013).
L25.	
The OH rate coefficient data were taken from Baasandorj et al. (2018).
L26.	
Assumed to be the same as HFO-1336mzz(E).
L27.	
OH reactivity was calculated using the room temperature rate coefficient reported by Sulbaek Andersen et al.
(2005) with the E/R obtained for the OH + CH2=CHCF3 reaction, -170 K.
L28.	
OH reactivity was calculated using the rate coefficient data from Papadimitriou and Burkholder (2016).
L29.	
Lifetimes recommended in the SPARC CCl4 report (2016).
L30.	
Lifetime due to reaction with Cl-atom of 259 years taken from the SPARC lifetime report, Chapter 5 model-mean(Ko et al., 2013).
L31.	
Local lifetime estimated as similar to that of (E)-CF3CH=CHCl.
L32.	
Local lifetime estimated as similar to that of CF3CF=CF2.
L33.	
Stratospheric photolysis lifetime was estimated using the empirical relationship given in Orkin et al. (2013).
L34.	
Lifetime estimated to be similar to that of CBr2F2.
L35.	
OH rate coefficient taken from Orkin et al. (2017).
L36.	
Tropospheric (84,150 years) and mesospheric (2,531 years) lifetimes taken from 2-D model calculations in Papadimitriou et al. (2013a).
L37.	
Estimated total lifetime lower-limit.
L38.	
Total lifetime range taken from Takahashi et al. (2002) including mesospheric loss due to Lyman-α (121.567 nm)
photolysis, dissociative electron attachment, and solar proton event loss processes.
L39.	
Total lifetime estimate based on the increase in Lyman-α (121.567 nm) absorption cross section (increased photolysis rate) with increasing number of –CF2- groups in the perfluorocarbon.
L40.	
OH rate coefficient taken from Zhang et al. (2017).
L41.	
Room temperature OH rate coefficient taken from Young et al. (2009b) with the E/R assumed to be the same as for
the OH + CF3CF=CF2 reaction, -415 K.
L42.	
Tropospheric OH partial lifetime estimated from that for CF3CH2OCF2CHF2 by adjusting for the reactivity contribution of –CF2CHF2 determined from the reactivity of CF3CF2OCF2CHF2.
L43.	
Tropospheric OH partial lifetime estimated from those for CF3OCH3 and CHF2CH2CF3.
L44.	
Room temperature OH rate coefficient taken from Oyaro et al. (2005) with an assumed E/R of 500 K.
L45.	
Estimated OH reactivity.
L46.	 Tropospheric OH partial lifetime estimated to be greater than that of CHF2CF2OCHF2 and less than that of­CHF2CF2CF2CF3.
L47.	
Tropospheric OH partial lifetime estimated to be the same as for CF3OCH2CF3.
L48.	
Tropospheric OH partial lifetime estimated to be the same as for CH3OCF2CHF2.
L49.	
Tropospheric OH partial lifetime estimated from the sum of the OH partial lifetimes of CF3CF2OCF2CHF2 and­CF3CF2OCH2CHF2.
L50.	Room temperature OH rate coefficient taken from Oyaro et al. (2004) with an assumed E/R of 500 K.
L51.	
Tropospheric OH partial lifetime estimated in Bravo et al. (2011b).
L52.	
Tropospheric OH partial lifetime calculated using the OH rate coefficient from Chen et al. (2005).
L53.	
Tropospheric OH partial lifetime calculated using the room temperature OH rate coefficient from Christensen et al.
(1998) with an assumed E/R of 1000 K.
L54.	
OH reactivity assumed to be similar to that of HCF2OCF2H.
L55.	
OH reactivity assumed to be similar to HG-10.
L56.	
OH reactivity assumed to be similar to HG-01.
L57.	
Tropospheric OH partial lifetime calculated using the room temperature OH rate coefficient from Javadi et al.
(2007) with an assumed E/R of 1000 K.
L18.	
L19.	

A.41

Appendix A | Appendix Table A-1
42 structure activity relationship estimated OH rate coefficientTropospheric OH partial lifetime calculated using thein Urata et al. (2003).
L59.	
OH rate coefficient calculated theoretically in Blowers et al. (2008).
L60.	
Tropospheric OH partial lifetime calculated using the room temperature OH rate coefficient from Sulbaek Andersen et al. (2004) with an assumed E/R of 1000 K.
L61.	
Tropospheric OH partial lifetime calculated using the room temperature OH rate coefficient from Wallington et al.
(2004) with an assumed E/R of 1000 K.
L62.	
Tropospheric OH partial lifetime calculated using the OH rate coefficient from Tokuhashi et al. (1999).
L63.	
Lifetime estimated in Young et al. (2006).
L64.	
Tropospheric OH partial lifetime calculated using the room temperature OH rate coefficient from Mashino et al.
(2000) with an assumed E/R of -400 K.
L65.	
Tropospheric OH partial lifetime calculated using the OH rate coefficient from Chen et al. (2004a).
L66.	
Tropospheric OH partial lifetime calculated using the OH rate coefficient from Chen et al. (2004b). There is no experimental data available for perfluorobutyl formate and it is assumed to be similar to perfluoroporopyl formate.
L67.	
Tropospheric OH partial lifetime calculated using the room temperature OH rate coefficient from Oyaro et al.
(2004) with an assumed E/R of 500 K.
L68.	
Lifetime estimated in Bravo et al. (2011a).
L69.	
Tropospheric OH partial lifetime calculated using the OH rate coefficient from Chen et al. (2006).
L70.	
Tropospheric OH partial lifetime estimated in Christensen et al. (1998) based on comparison with Cl atom ­reactivity.
L71.	
OH reactivity assumed to be the same as for perfluorobutyl acetate.
L72.	
OH reactivity assumed to be the same as for CF3C(O)OCH2CH3.
L73.	
Tropospheric OH partial lifetime calculated using the room temperature OH rate coefficient from Blanco and ­Teruel(2007) with an assumed E/R of 1000 K.
L74.	
Tropospheric OH partial lifetime calculated using the room temperature OH rate coefficient from Oyaro et al.
(2005) with an assumed E/R of 1500 K.
L75.	
Tropospheric OH partial lifetime calculated using the room temperature OH rate coefficient from Oyaro et al.
(2004) with an assumed E/R of 1000 K.
L76.	
Tropospheric OH partial lifetime calculated using the OH rate coefficient from Antiñolo et al. (2010).
L77.	
OH rate coefficient taken from Antiñolo et al. (2011).
L78.	
OH reactivity estimated by comparison with other fluoroalcohols.
L79.	
OH reactivity calculated using the room temperature rate coefficient reported by Ellis et al. (2003) with an assumed
E/R of 1000 K.
L80.	
OH rate coefficient taken from Bravo et al. (2010).
L81.	
OH rate coefficient taken from Antiñolo et al. (2012).
L82.	
Tropospheric photolysis is the dominant loss process for perfluoroketones (Jackson et al., 2011; Taniguchi et al., 2003).
L83.	
OH reactivity assumed to be similar to that of NOVEC-1230.
L84.	
Lifetime primarily determined by UV photolysis with a decreasing local lifetime with increasing altitude.
L85.	
Lifetime estimates taken from the 3-D model simulations of Youn et al. (2010).
L86.	
Lifetime range is representative of the variation in local photolysis partial lifetime with time and location of­emission.
L87.	
Photolysis lifetimes taken from Mössinger et al. (1998) for CH2BrI and Roehl et al. (1997) for CH2ClI, CH3CH2I,­CH3CH2CH2I, CH3CHICH3.
L88.	
OH reactivity assumed to be similar to that of CH2Br2.
L89.	
OH reactivity taken from Zhang et al. (2011).
L90.	
OH reactivity taken from Zhang et al. (2012)
L91.	
Photolysis and OH reactivity assumed the same as for CF3I.
L92.	
Heterogeneous processing is the predominate removal process.
L93.	Lifetime reported in Brühl et al. (2012).
L94.	
Lifetimes taken from Papadimitrou et al. (2008) and Mühle et al. (2009).

L58.	

ODP Footnotes
O1.	See Chapter 1, Section 1.5.1.
A.42

Appendix Table A-1 | Appendix A
O2.	
O3.	
O4.	
O5.	
O6.	
O7.	
O8.	
O9.	
O10.	
O11.	
O12.	

O13.	
O14.	
O15.	
O16.	

Negligible and assigned a value of zero.
ODP taken from WMO (2014) Table 5.2.
A greater ODP value was reported from the 2-D model calculations in Davis et al. (2016): 0.95 (CFC-113), 0.78 (­CFC114), 0.44 (CFC-115), 1.01 (CFC-12), and 1.06 (CCl4).
Taken from Montreal Protocol.
ODP taken from the 2-D model calculations in Davis et al. (2016). The semi-empirical ODP reported in Laube et al.
(2014) is consistent with the latter Davis et al. (2016) value, but has a larger uncertainty range.
ODP taken from Kloss et al. (2014).
ODP taken from the 2-D model calculations in Papadimitriou et al. (2013b).
Semi-empirical ODP calculated using empirical relationship of the fractional release factor with stratospheric lifetime given in Papanastasiou et al. (2018).
Taken from Papanastasiou et al. (2018).
ODP taken from WMO (2011).
Upper-limit of ODPs of short-lived substances reported in the studies of Brioude et al. (2010) (C2H5Br, CH2CBrCF3,
n-C3H7Br, C2HCl3, CCl3CHO, CH3I, CF3I, C3F7I, CH2ClI, CHBr3), Wuebbles and co-workers (Patten et al., 2011; Patten
and Wuebbles, 2010; Wuebbles et al., 2011; Wuebbles et al., 2009; Youn et al., 2010) (C3H7Br, C2HCl3, C2Cl4, HFO1233zd, (E)-CHCl=CHCl, CF3I, and CH3I), and Tegtmeier et al. (2012) (CH2Br2, CHBr3). The derived ODPs in thesestudies were shown to be strongly dependent on the region and season of the substance emission with the greatest values obtained for emissions in the Indian subcontinent.
Taken from Langbein et al. (1999).
Value not available.
Assumed to be <0.02 for surface emission.
Assumed to be the same as for CF3I.

RE, GWP, and GTP Footnotes
R1.	
R2.	
R3.	
R4.	
R5.	
R6.	
R7.	
R8.	
R9.	
R10.	
R11.	
R12.	
R13.	
R14.	
R15.	
R16.	
R17.	
R18.	
R19.	

Radiative metrics taken from IPCC (2013).
RE calculated using the room temperature infrared absorption spectrum reported in the Pacific Northwest N­ ational
Laboratory (PNNL) database, https://secure2.pnl.gov/nsd/nsd.nsf (Sharpe et al., 2004).
RE taken from recommendation given in Hodnebrog et al. (2013), which was based on a combination of literature
review of experimental data and re-analysis.
Radiative metrics calculated using the infrared absorption spectrum reported in Davis et al. (2016) and the lifetime
reported here.
Radiative metrics calculated using the infrared absorption spectrum reported in Papadimitriou et al. (2013b) andthe lifetime reported here.
Radiative metrics calculated using the theoretically calculated infrared absorption spectrum in Papanastasiou etal. (2018) and lifetimes given here.
Radiative metrics calculated using the theoretically calculated infrared absorption spectrum and lifetimes ­reported
in Papanastasiou et al. (2018).
Radiative metrics calculated using the infrared spectrum and lifetimes reported in McGillen et al. (2015).
Radiative efficiency not available.
Radiative metrics calculated using the RE reported in Le Bris et al. (2018) and the lifetime reported here.
Radiative metrics calculated using the infrared spectrum in Baasandorj and Burkholder (2016).
Radiative metrics calculated using the RE reported in Jai et al. (2013) with lifetime and stratospheric temperaturecorrections applied here using the lifetimes reported here.
Radiative metrics calculated using the infrared spectrum in Baasandorj et al. (2018).
Radiative metrics calculated using the infrared spectrum in Papadimitriou and Burkholder (2016).
Radiative metrics calculated using the infrared spectrum in Wallington et al. (2016).
Radiative metrics calculated using the infrared spectrum in Gierczak et al. (2014).
Radiative metrics calculated using the infrared spectrum in Orkin et al. (2017).
Infrared absorption spectrum taken from Bernard et al. (2018).
RE taken from recommendation given in Hodnebrog et al. (2013), which was based on theoretically calculated
infrared absorption spectra and analysis.
A.43

Appendix A | Appendix Table A-1
44Zhang et al. (2017) and the lifetime reported here.
R20.	
Radiative metrics calculated using the RE reported in
R21.	
Assumed to be similar to NOVEC-1230.
R22.	
Assumed negligible.
R23.	Assumed to be similar to that of CF3I.

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A.49

Appendix B
Chemical Formulae and Nomenclature
Reactive Halogen-Containing SpeciesCl
atomic chlorineBratomic bromine
total inorganic chlorine
Brytotal inorganic bromineClymolecular chlorine
Br2
molecular bromine
Cl2
ClO
chlorine monoxide
BrO
bromine monoxide(ClO + 2 ClOOCl)
Br2O
dibromine monoxideClOxdichlorine peroxide (ClO dimer)
BrOx(Br, BrO, BrONO2,
Cl2O2, ClOOCl
				 HOBr, ....)
chlorine nitrate
BrONO2, BrNO3 bromine nitrate
ClONO2, ClNO3
HClhydrogen chloride (hydrochloric acid)
HBrhydrogen bromide
HOClhypochlorous acid
HOBr
hypobromous acidF
atomic fluorine
molecular fluorine
F2
total inorganic fluorine
Fy
HFhydrogen fluoride (hydrofluoric acid)
F + FO
FOx
		

I
I2
Iy
IO
IOx

atomic iodine
molecular iodine
total inorganic iodine
iodine monoxide
iodine radicals

Other Reactive SpeciesOatomic oxygenHatomic hydrogenO(3P)atomic oxygen (ground state)
H2molecular hydrogen1
O( D)atomic oxygen (first excited state) OH
hydroxyl radical
O2
molecular oxygenHO2hydroperoxyl radical
O3
ozone
H2O
water
Ox
odd oxygen (O, O(1D), O3)
HOx
odd hydrogen (H,				 OH, HO2, H2O2)
			atomic nitrogen
HNO2, HONO
nitrous acidN
N2
molecular nitrogen
HOONO
pernitrous acid
N2O
nitrous oxide
HNO3nitric acidnitric oxide
HNO4, HOONO2 peroxynitric acidNO
nitrogen dioxide
NH3ammoniaNO2
NO3
nitrogen trioxide, nitrate radical
NH4NO3ammonium nitrate
N2O5
dinitrogen pentoxide
NOxnitrogen oxides				 (NO + NO2)

B.1

Appendix B | Chemical Formulae and Nomenclature

Other Reactive Species (continued)
HNO3•3H2O

nitric acid trihydrate condensate
NOytotal reactive nitrogen(NAT)			(NO, NO2, NO3, N2O5,
			ClONO2, HNO4,
				HNO3)
atomic sulfur		S
sulfur dioxide
H2Shydrogen sulfide
SO2
H2SO4
sulfuric acidCS2
carbon disulfide
CH3SCH3
dimethyl sulfide (DMS)
COS, OCS
carbonyl sulfide			Ccarbon atom		COcarbon monoxideCO2carbon dioxide
methyl radical
CH3CH3ethaneCH3
CH4methane
CH3CH2CH3
propaneCH3OH
methyl alcohol, methanol
CH3CH2CH2CH3
butaneCH2O
formaldehyde
CF3C(O)OH, CF3CO2H trifluoroacetic acid (TFA)
			
CaCO3
TiO2
Al2O3

calcite, calcium carbonate		titanium dioxide		
aluminum oxide		Note: Table A-1 in Appendix A provides an extensive listing of chemical names and formulas, including many ozone depleting substances, their replacements, and other substances of interest to the Montreal Protocol.

B.2

Appendix C2018 Ozone Assessment Acronym Dictionary
A1baseline (or most likely) halocarbon scenario of the Ozone Assessment
A1-2006
baseline (or most likely) halocarbon scenario of the 2006 Ozone Assessment
A1-2010baseline (or most likely) halocarbon scenario of the 2010 Ozone Assessment
A1B
scenario of the IPCC Special Report on Emissions Scenarios (SRES)
A5
Article 5 countries of the Montreal Protocol
AGAGE
Advanced Global Atmospheric Gases Experiment
AAO
Antarctic oscillationAC
air conditioning
ACC
Antarctic Circumpolar Current
ACCMIP
Atmospheric Chemistry and Climate Model Intercomparison Project
ACE-FTS
Fourier Transform Spectrometer instrument on the		 Atmospheric Chemistry Experiment satellite
AGAGE
Advanced Global Atmospheric Gases Experiment (atmospheric monitoring surface sites)
AGWP
absolute GWP, which is the radiative forcing integrated over a given time horizon,
		 resulting from a pulse emission of the gas
AMIP
Atmospheric Model Intercomparison Project
AMSU
Advanced Microwave Sounding Unit (satellite-based instrument)
AO
Arctic oscillation
AoA
age of stratospheric air
AR5
IPCC Fifth Assessment Report
ASL
Amundsen Sea Low
ATAL
Asian Tropopause Aerosol Layer
ATLASa Lagrangian chemistry and transport model (CTM)
ATT
Cross-Calibrated Multi-Platform products
CCMVal
CDM
CFCs
CFSR
CGAA
CIMS
CLAES
CLaMS
CMEs
CMIP
CONTRAST
CPT
CR-AVE
CSIRO
CTM
CUE

Chemistry-Climate Model Validation Activity (e.g. CCMVal-2 = Phase 2 of CCMVal)
Clean Development Mechanism of the Kyoto Protocol
chlorofluorocarbons
Climate Forecast System Reanalysis
Cape Grim Air Archive (atmospheric monitoring surface sites)
Chemical Ionization Mass Spectrometer instrument
Cryogenic Limb Array Etalon Spectrometry (satellite-based instrument)
Chemical Lagrangian Model of the Stratosphere
coronal mass ejections
Climate Model Intercomparison Project (e.g. CMIP5 = Phase 5 of CMIP)
Convective Transport of Active Species in the Tropics (aircraft-based field campaign)
cold point tropopause
Costa Rica Aura Validation Experiment (aircraft-based field campaign)
Commonwealth Scientific and Industrial Research Organisation
chemistry transport modelCritical use exemption

C.1

Appendix C | 2018 Ozone Assessment Acronym Dictionary

C.2

DJF
DOAS
DU

2
December-January-February
Differential Optical Absorption Spectroscopy instrument
Dobson Units

ECMWF
ECS
EDGAR
EESC
EHF
ENSO
ENVISAT
EOFs
EPA
EPP
ERA
EAR-40ERA-Interim

European Centre for Medium-Range Weather Forecasts (forecast model)
equilibrium climate sensitivity
Emissions Database for Global Atmospheric Research
Equivalent Effective Stratospheric Chlorine
eddy heat flux
El Niño–Southern Oscillation
Environmental Satellite
empirical orthogonal functions
Environmental Protection Agency
energetic particle precipitation
ECMWF Re-Analysis (a global atmospheric reanalysis data product)
ECMWF 40-year Re-Analysis
ECMWF Interim Re-Analysis

FPH
FRF
FTIR

frost point hygrometer
fractional release factor
Fourier transform infrared

GAW
GC-ECD
GC-MS
GCM
GDP
GeoMIPGEOSCCM
GHG
GOME
GOMOS
GOZCARDSGPS
GSG
GtCO2-eq
GTO
GTP
GWP

Global Atmosphere Watch programme of WMO
gas chromatography-electron capture detection instrument
gas chromatography-mass spectrometry instrument
general circulation modelgross domestic product
Geoengineering Model Intercomparison Project
Goddard Earth Observing System Chemistry Climate Modelgreenhouse gas
Global Ozone Monitoring Experiment spectrometer (satellite-based instrument)
Global Ozone Monitoring by Occultation of Stars (satellite-based instrument)
Global OZone Chemistry And Related trace gas Data records for the Stratosphereglobal positioning system
GOME-SCIAMACHY-GOME-2 merged dataset
gigatonnes of carbon dioxide equivalent
the GOME-type Total Ozone column ozone productglobal temperature potential
global warming potential

HALOE
HF
HCFC
HCFO
HFC
HFE
HFO
HIAPERHIPPO

HALogen Occultation Experiment (satellite-based instrument)
hydrogen fluoridehydrochlorofluorocarbonhydrochlorofluoroolefinhydrofluorocarbon
halogenated ether
hydrofluoroolefin
High-performance Instrumented Airborne Platform for Environmental Research
HIAPER Pole-to-Pole Observations (aircraft-based field campaign)2018 Ozone Assessment Acronym Dictionary | Appendix C

HTOC
IASI
IGAC
IHD
ILT
IPCC

Halon Technical Options Committee
Infrared Atmospheric Sounding Interferometer (satellite-based instrument)International Global Atmospheric Chemistry projectinterhemispheric difference
independent linear trend
Intergovernmental Panel on Climate Change

JJA

June-July-August

LCCP
LIMS
LOTUS
LS
LZRH

Life Cycle Climate Performance
Limb Infrared Monitor of the Stratosphere (satellite-based instrument)
Long-term Ozone Trends and Uncertainties in the Stratosphere (a SPARC activity)
lower stratospherelevel of zero radiative heating

MAC
mobile air conditioning
MAGICC6
Model for the Assessment of Greenhouse Gas Induced Climate Change
MAM
March-April-May
MBL
marine boundary layer
MERRA
Modern Era Retrospective-analysis for Research and Applications
		 (e.g. MERRA-2 = version 2 of MERRA)
MIPAS
Michelson Interferometer for Passive Atmospheric Sounding
MIPAS-Bballoon-based Michelson Interferometer for Passive Atmospheric Sounding
MLF
Montreal Protocol’s Multilateral Fund
MLR
multiple linear regression
MLS
Microwave Limb Sounder (satellite-based instrument)
MMM
multi-model meanMODmerged ozone dataset
MSU
Microwave Sounding Unit (satellite-based instrument)
MZM
monthly zonal mean
NAM
NAO
NASA
NAT
NCEP
NCEP/CFSR
NDACC
NDCs
NH
NIES
NIK
NOAA
NRLSSI

Northern Annular Mode
North Atlantic oscillation
National Aeronautics and Space Administration (United States)
nitric acid trihydrate
National Centers for Environmental Prediction (NOAA; United States)
NCEP Climate Forecast System Reanalysis (data product)
Network for the Detection of Atmospheric Composition Change
Nationally Determined Contributions within the Paris Agreement within the UNFCCC
Northern Hemisphere
National Institute for Environmental Studies (Japan)
not-in-kind
National Oceanic and Atmospheric Administration (United States)
Naval Research Laboratory Spectral Solar Irradiance model

OCS
ODP
ODS
OMD

carbonyl sulfide (also COS)
ozone depletion potential
ozone-depleting substanceozone hole mass deficit
C.3

Appendix C | 2018 Ozone Assessment Acronym Dictionary

OMI
OMPS
OPCs
OSIRIS

4
Ozone Monitoring Instrument (satellite-based
instrument)
Ozone Mapping Profiler Suite (satellite-based instrument)
optical particle counters
Optical Spectrograph and InfraRed Imaging System (satellite-based instrument)

PCE
PCO
PFC
PG
PGI
Pre-AVE
PSC
PWLT
PWT

perchloroethylene, also known as tetrachloroethylene
partial column ozone
perfluorocarbon
product gas
product gas injection
Pre- Aura Validation Experiment (field campaign)polar stratospheric cloudpiecewise linear trend
piecewise trend

QBO
QPS

Quasi-Biennial Oscillation
quarantine and pre-shipment

RCP
RF

Representative Concentration Pathway (used by IPCC)
radiative forcing

S-RIP
WCRP SPARC Reanalysis Intercomparison Project
SABER
Sounding of the Atmosphere using Broadband Emission Radiometry (satellite-based instrument)
SAGE
Stratospheric Aerosol and Gas Experiment (satellite-based instrument)
SAM
southern annular mode
SAP
UNEP Scientific Assessment Panel to the Parties of the Montreal Protocol
SATIRE-S
Spectral and Total Irradiance Reconstruction—Satellite era model
SBUV / SBUV MOD /SBUV COH
Solar Backscatter Ultraviolet (satellite-based instrument) / Merged Ozone Data (MOD) product /		 SBUV Cohesive dataset
SCIAMACHY
SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY
		 (satellite-based instrument)
SCISAT
SCIence SATelliteSGsource gas
SGI
source gas injection
SH
Southern Hemisphere
SHADOZ
Southern Hemisphere Additional Ozonesondes
SHIVA
Stratospheric ozone Halogen Impacts in a Varying Atmosphere (field experiment)
SLIMCAT
Single-Layer Isentropic Model of Chemistry and Transport
SLR
sea level rise
SNAP
Significant New Alternatives Policy
SON
September-October-November
SOR
solar ozone response
SORCE
Solar Radiation and Climate Experiment
SPARC
Stratospheric Processes And their Role in Climate (project of WCRP)
SPEs
solar proton events
SRES
Special Report on Emissions Scenarios (used by IPCC)
SSI
solar spectral irradiance
SSP
Shared Socioeconomic Pathway
C.4

2018 Ozone Assessment Acronym Dictionary | Appendix C

SST
SSU
SSW
STAR
STE
STT
Suomi NPPSWOOSHSWV
SZA

sea surface temperature
Stratospheric Sounding Unit (satellite-based instrument)
sudden stratospheric warming
The NOAA Center for Satellite Applications and Researchstratosphere-troposphere exchange
stratosphere-to-troposphere transport
Suomi National Polar-orbiting Partnership weather satellite
Stratospheric Water and OzOne Satellite Homogenized (merged data record)
stratospheric water vapor
solar zenith angle

TC4
Tropical Composition, Cloud and Climate Coupling (aircraft-based field experiment)
TCE
trichloroethene, trichloroethylene
TCO
total column ozone
TEAP
UNEP Technology and Economic Assessment Panel to the Parties of the Montreal Protocol
TELIS
TEtrahertz and submillimeter LImb Sounder (satellite-based instrument)
TES
Tropospheric Emission Spectrometer
TFA
trifluoroacetic acid
TOAR
Tropospheric Ozone Assessment Report
TOMCAT
Toulouse Off-line Model of Chemistry and Transport
TOMS
Total Ozone Mapping Spectrometer (satellite-based instrument)TP
tropopause pressure
TransCom-VSLS A multi-model intercomparison of halogenated very short-lived substances
TSI
total solar irradiance
TTL
tropical tropopause layer
UARS
UCI
UEA
UNEP
UNFCCC
US
UT
UTLS
UV

Upper Atmosphere Research Satellite
University of California Irvine
University of East Anglia
United Nations Environment Programme
United Nations Framework Convention on Climate Change
upper stratosphere
upper troposphere
upper troposphere/lower stratosphereultraviolet

VEI
VSL SG
VSLS

Volcanic Explosivity Index
Very Short-Lived Source Gas
very short-lived substance

WACCM
WCRP
WMO
WOUDC

Whole Atmosphere Community Climate Model
World Climate Research Programme
World Meteorological Organization
World Ozone and Ultraviolet Radiation Data Centre of WMO/GAW

C.5

Appendix D
List of Authors, Contributors, and Reviewers
Assessment Co-chairs
David W. Fahey
Paul A. Newman
John A. Pyle
Bonfils Safari

NOAA Earth System Research Laboratory, Chemical Sciences Division
NASA Goddard Space Flight Center
University of Cambridge and the National Centre for Atmospheric Science
University of Rwanda, College of Science and Technology, Kigali

USA
USA
UK
RwandaAssessment Scientific Steering Committee
David W. Fahey
Paul A. Newman
John A. Pyle
Bonfils Safari
Martyn P. Chipperfield
David J. Karoly
Doug Kinnison
Malcolm K. Ko
Michelle Santee

NOAA Earth System Research Laboratory, Chemical Sciences Division
NASA Goddard Space Flight Center
University of Cambridge and the National Centre for Atmospheric Science
University of Rwanda, College of Science and Technology, Kigali
University of Leeds
Climate Science Centre, Commonwealth Scientific andIndustrial Research Organisation (CSIRO), Aspendale, Victoria
National Center for Atmospheric Research (NCAR)
NASA Langley Research CenterNASA Jet Propulsion Laboratory (JPL)

USA
USA
UK
RwandaUK
Australia
USA
USA
USA

Assessment Coordinator
Sarah J. Doherty

University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division

USA

Coordinating Lead Authors
Chapter 1: Update on Ozone-Depleting Substances (ODSs) and
Other Gases of Interest to the Montreal Protocol
Andreas Engel
Matthew Rigby

Goethe Universität, Frankfurt
University of Bristol, School of Chemistry

Germany
UK

Chapter 2: Hydrofluorocarbons (HFCs)
Stephen A. Montzka
Guus J.M. Velders

NOAA Earth System Research Laboratory, Global Monitoring Division
USA
Dutch National Institute for Public Health and Environment (RIVM),
Bilthoven & Utrecht University
The Netherlands

Chapter 3: Update on Global Ozone: Past, Present, and Future
D.1

Appendix D | List of Authors, Contributors, and Reviewers

Peter Braesicke

Jessica Neu

Institute of Meteorology and Climate Research (IMK)
Atmospheric Trace Gases and Remote Sensing (ASF)
Karlsruher Institut für Technologie (KIT)NASA Jet Propulsion Laboratory (JPL)

Germany
USA

Chapter 4: Polar Stratospheric Ozone: Past, Present, and Future
Ulrike Langematz
Matthew B. Tully

Freie Universität Berlin, Berlin
Bureau of Meteorology, Melbourne

Germany
Australia

Chapter 5: Stratospheric Ozone Changes and Climate
Alexey Yu. Karpechko
Amanda C. Maycock

Finnish Meteorological Institute, Helsinki
University of Leeds

FinlandUK

Chapter 6: Scenarios and Information for Policymakers
Lucy J. Carpenter
John S. Daniel

University of YorkNOAA Earth System Research Laboratory, Chemical Sciences Division

UK
USA

Scientific Assessment of Ozone Depletion: 2018: Appendix A Table
James B. Burkholder

NOAA Earth System Research Laboratory, Chemical Sciences DivisionUSA

20 Questions and Answers About the Ozone Layer
Sarah J. Doherty

David W. Fahey
Michaela I. Hegglin
Ross J. Salawitch

University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
NOAA Earth System Research Laboratory, Chemical Sciences Division
University of Reading
Department of Atmospheric and Oceanic Science &Department of Chemistry and Biochemistry,
Earth System Science Interdisciplinary Center,
University of Maryland, College Park

USA
USA
UK

USA

Co-Authors
Chapter 1: Update on Ozone-Depleting Substances (ODSs) and
Other Gases of Interest to the Montreal Protocol
James B. Burkholder
Rafael P. Fernandez
Lucien Froidevaux
Bradeley D. Hall
Ryan Hossaini
Takuya Saito
Martin K. Vollmer
Bo YaoNOAA Earth System Research Laboratory, Chemical Sciences Division
Consejo Nacional de Investigaciones Cientificas y Técnicas (CONICET)
NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
NOAA Earth System Research Laboratory, Global Monitoring Division
Lancaster Environment Centre, Lancaster University
National Institute for Environmental Studies (NIES)
Center for Environmental Measurement and Analysis
Swiss Federal Laboratories for Materials Science and Technology (EMPA)
China Meteorological Administration (CMA)
Meteorological Observation Centre (MOC)

Chapter 2: Hydrofluorocarbons (HFCs)
D.2

USA
Argentina
USA
USA
UK
Japan
Switzerland
ChinaList of Authors, Contributors, and Reviewers | Appendix D

Paul B. Krummel

Climate Science Centre, Commonwealth Scientific and Industrial
Research Organisation (CSIRO) Oceans and Atmosphere
Australia
Scripps Institution of Oceanography, University of California, San Diego
USA
National Institute of Standards and Technology (NIST)USA
Department of Oceanography, School of Earth System Sciences
Kyungpook National University
South Korea
Lawrence Berkeley National Laboratory
USA
Technology and Econominc Assessment Panel (TEAP) of the Montreal Protocol
The Chemours Company
USA

Jens Mühle
Vladimir Orkin
Sunyoung Park
Nihar ShahHelen Walter-Terrinoni

Chapter 3: Update on Global Ozone: Past, Present, and Future
Vitali Fioletov
Sophie Godin-Beekmann

Daan Hubert
Irina Petropavlovskikh

Masato Shiotani
Björn-Martin Sinnhuber

Environment and Climate Change Canada (ECCC)
Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS),
Centre National de la Recherche Scientifique (CNRS),
Sorbonne University
Royal Belgian Institute for Space Aeronomy (BIRA-IASB)
University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Global Monitoring Division
Research Institute for Sustainable Humanosphere, Kyoto University
Institute of Meteorology and Climate Research (IMK)
Atmospheric Trace Gases and Remote Sensing (ASF)
Karlsruher Institut für Technologie (KIT)

CanadaFranceBelgium

USA
Japan

Germany

Chapter 4: Polar Stratospheric Ozone: Past, Present, and Future
Natalia Calvo
Martin Dameris
A. T. J. de Laat
Andrew Klekociuk
Rolf Müller
Paul Young

Universidad Complutense de Madrid
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Institut für Physik der Atmosphäre
Royal Netherlands Meteorological Institute
Australian Antarctic Division
Forschungszentrum Jülich GmbH
Lancaster University

Spain
Germany
The Netherlands
Australia
Germany
UK

Chapter 5: Stratospheric Ozone Changes and Climate
Marta Abalos
Hideharu Akiyoshi
Julie Arblaster
Chaim Garfinkel
Karen H. Rosenlof
Michael Sigmond

Universidad Complutense de Madrid
National Institute for Environmental Studies (NIES)
Monash University
The Hebrew University of Jerusalem
NOAA Earth System Research Laboratory, Chemical Sciences Division
Environment and Climate Change Canada (ECCC)

Spain
Japan
Australia
IsraelUSA
CanadaChapter 6: Scenarios and Information for Policymakers
Eric L. Fleming
Tatsuya Hanaoka
Jianxin Hu
A.R. Ravishankara
Martin N. Ross
Simone Tilmes
Timothy J. Wallington

Science Systems and Applications, Inc. & NASA Goddard Space Flight Center
National Institute for Environmental Studies
Peking University
Colorado State University, Fort Collins, Colorado
The Aerospace Corporation
National Center for Atmospheric Research (NCAR)
Ford Motor Company

USA
Japan
China
USA
USA
USA
USA
D.3

Appendix D | List of Authors, Contributors, and Reviewers

Donald J. Wuebbles

University of Illinois

USA

Contributing Authors
Chapter 1: Update on Ozone-Depleting Substances (ODSs) and
Other Gases of Interest to the Montreal Protocol
Elliot L. AtlasPeter F. Bernath
Donald R. Blake
Geoff Dutton
Paul B. Krummel
Johannes C. Laube
Emmanuel Mahieu
Stephen A. Montzka
Jens Mühle
Gerald Nedoluha
Simon O’Doherty
David E. Oram
Klaus Pfeilsticker
Ronald G. Prinn
Birgit Quack
Isobel J. Simpson
Ray F. Weiss

University of Miami, Rosenstiel School of Marine andAtmospheric Science, Department of Atmospheric Sciences
USA
Old Dominion University, Department of Chemistry and Biochemistry &University of Waterloo, Department of Chemistry
USA/Canada
University of California, Irvine, Department of Chemistry
USA
NOAA Earth System Research Laboratory, Global Monitoring Division
USA
Climate Science Centre, Commonwealth Scientific and Industrial
Research Organisation (CSIRO) Oceans and Atmosphere
Australia
University of East Anglia, School of Environmental Sciences
UK
University of Liège, Institute of Astrophysics and Geophysics
Belgium
NOAA Earth System Research Laboratory, Global Monitoring Division
USA
Scripps Institution of Oceanography, University of California, San Diego
USA
Naval Research Laboratory
USA
University of Bristol, School of Chemistry
UK
University of East Anglia, School of Environmental Sciences andNational Centre for Atmospheric Sciences
UK
University of Heidelberg, Institut für Umweltphysik
Germany
Massachusetts Institute of Technology (MIT), Center for Global Change Science
USA
GEOMAR Helmholtz Centre for Ocean Research Kiel
Germany
University of California, Irvine, Department Of Chemistry
USA
Scripps Institution of Oceanography, University of California, San Diego
USA

Chapter 2: Hydrofluorocarbons (HFCs)
Peter F. Bernath
Chris Boone
Lei HuMichael J. Kurylo
Emma Leedham Elvidge
Michela Maione
Benjamin R. Miller

Simon O’Doherty
Matthew Rigby
Isobel J. Simpson
Martin K. Vollmer
Ray F. Weiss
D.4

Old Dominion University, Department of Chemistry and Biochemistry &University of Waterloo, Department of Chemistry
USA/Canada
Department of Chemistry, University of Waterloo
CanadaUniversity of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Global Monitoring Division
USA
Universities Space Research Association
Goddard Earth Sciences, Technology, and Research (USRA/GESTAR)
USA
Center for Ocean and Atmospheric Sciences
School of Environmental Sciences, University East Anglia
UK
Univerità di Urbino and National Research Council
Italy
University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Global Monitoring Division
USA
University of Bristol, School of Chemistry
UK
University of Bristol, School of Chemistry
UK
University of California, Irvine, Department of Chemistry
USA
Swiss Federal Laboratories for Materials Science and Technology (EMPA) Switzerland
Scripps Institution of Oceanography, University of California, San Diego
USAList of Authors, Contributors, and Reviewers | Appendix D

Chapter 3: Update on Global Ozone: Past, Present, and Future
William Ball

Kai-Lan Chang
Robert Damadeo
Sandip Dhomse
Stacey M. Frith
Audrey Gaudel

Birgit Hassler
Stefanie Kremser
Stergios Misios
Olaf Morgenstern
Ross J. Salawitch

Viktoria F. Sofieva
Kleareti Tourpali
Olga Tweedy
Daniel Zawada

Institute for Atmospheric and Climate Science,ETH Zürich & Physikalisch-Meteorologisches
Observatorium Davos World Radiation Center
Switzerland
NOAA Earth System Research Laboratory, Global Monitoring Division
USA
NASA Langley Research Center
USA
School of Earth and Environment, University of Leeds
UK
Science Systems and Applications, Inc. & NASA Goddard Space Flight Center
USA
University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
Institut für Physik der Atmosphäre (IPA)
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Germany
Bodeker Scientific
New ZealandAtmospheric Physics Clarendon Laboratory, University of Oxford
UK
National Institute of Water and Atmospheric Research Ltd (NIWA)
New ZealandDepartment of Atmospheric and Oceanic Science &Department of Chemistry and Biochemistry,
Earth System Science Interdisciplinary Center,
University of Maryland, College ParkUSA
Finnish Meteorological Institute, Helsinki
FinlandLaboratory of Atmospheric Physics, Aristotle University of Thessaloniki
GreeceJohns Hopkins University, Department of Earth and Planetary Sciences
USA
University of Saskatchewan, Department of Physics and Engineering Physics Canada

Chapter 4: Polar Stratospheric Ozone: Past, Present, and Future
Simon Alexander
Sandip Dhomse
Bernd FunkePeter von der GathenJens-Uwe Grooß
Stefanie Kremser
Gloria L. Manney
Sergej Molleker
Eric Nash
Michael C. Pitts
Alfonso Saiz-López
Franziska Schmidt
Miriam Sinnhuber
Reinhold Spang
Ines Tritscher
Mark Weber

Australian Antarctic Division
Australia
University of Leeds
UK
Instituto de Astrofísica de Andalucía, CSIC
Spain
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research
Germany
Forschungszentrum Jülich GmbHGermany
Bodeker Scientific
New Zealand
NorthWest Research Associates & New Mexico Institute of Mining and Technology USA
Max-Planck-Institut for Chemistry
Germany
Science Systems and Applications, Inc.
USA
NASA Langley Research Center
USA
Institute of Physical Chemistry Rocasolano
Spain
Freie Universität Berlin, Institut für Meteorologie
Germany
Karlsruher Institut für Technologie (KIT)
Germany
Forschungszentrum Jülich GmbHGermany
Forschungszentrum Jülich GmbHGermany
Institute of Environmental Physics, Universität Bremen
Germany

Chapter 5: Stratospheric Ozone Changes and Climate
Valentina Aquila
Antara Banerjee
Andreas Chrysanthou
David Ferreira

American University, Washington, DC
Columbia University
University of Leeds
University of Reading

USA
USA
UK
UK
D.5

Appendix D | List of Authors, Contributors, and Reviewers

Hella GarnyNathan P. Gillett
Peter Landschützer
Eun-Pa Lim
Michael J. Mills
William J. Randel
Eric Ray

William Seviour
Seok-Woo Son
Neil Swart

Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Canadian Centre for Climate Modelling and Analysis andEnvironment and Climate Change Canada (ECCC)
Max-Planck-Institut für Meteorologie
Bureau of Meteorology
Atmospheric Chemistry Observations & Modeling (ACOM)
National Center for Atmospheric Research (NCAR)
Atmospheric Chemistry Observations & Modeling (ACOM)
National Center for Atmospheric Research (NCAR)
University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
Johns Hopkins University
Seoul National University
Environment and Climate Change Canada (ECCC)

Germany
CanadaGermany
Australia
USA
USA

USA
USA
South Korea
CanadaChapter 6: Scenarios and Information for Policymakers
James B. Burkholder
Andreas EngelØivind Hodnebrog
Ryan Hossaini
Lambert J.M. Kuijpers
Stephen A. Montzka
Ross J. Salawitch

Walter Tribett
Guus J.M. Velders
Helen Walter-Terrinoni

NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
Goethe Universität, Frankfurt
Germany
Center for International Climate Research (CICERO)
NorwayLancaster Environment Centre, Lancaster University
UK
Eindhoven University of Technology, A/genT Consultancy
The Netherlands
NOAA Earth System Research Laboratory, Global Monitoring Division
USA
Department of Atmospheric and Oceanic Science &Department of Chemistry and Biochemistry,
Earth System Science Interdisciplinary Center,
University of Maryland, College Park
USA
University of Maryland, College Park
USA
Dutch National Institute for Public Health and Environment (RIVM),
Bilthoven & Utrecht University
The Netherlands
Technology and Econominc Assessment Panel (TEAP) of the Montreal Protocol
The Chemours Company
USA

Scientific Assessment of Ozone Depletion: 2018: Appendix TablesØivind Hodnebrog
Vladimir L. Orkin

Center for International Climate Research (CICERO)
National Institute of Standards and Technology

NorwayUSA

Review Editors
Chapter 1: Update on Ozone-Depleting Substances (ODSs) and
Other Gases of Interest to the Montreal Protocol
Qing Liang
Stefan Reimann

NASA Goddard Space Flight Center
Swiss Federal Laboratories for Materials Science and Technology (EMPA)USA
Switzerland

Chapter 2: Hydrofluorocarbons (HFCs)
Lambert J.M. Kuijpers
Bill Sturges
D.6

Eindhoven University of Technology, A/genT Consultancy
School of Environmental Sciences, University of East Anglia

The Netherlands
UKList of Authors, Contributors, and Reviewers | Appendix D

Chapter 3: Update on Global Ozone: Past, Present, and Future
Wolfgang Steinbrecht

Meteorological Observatory Hohenpeissenberg
Deutscher Wetterdienst (DWD)
Institute of Environmental Physics, Universität Bremen

Mark Weber

Germany
Germany

Chapter 4: Polar Stratospheric Ozone: Past, Present, and Future
William J. Randel

Atmospheric Chemistry Observations & Modeling (ACOM)
National Center for Atmospheric Research (NCAR)
Massachusetts Institute of Technology (MIT)

Susan Solomon

USA
USA

Chapter 5: Stratospheric Ozone Changes and Climate
Chiara Cagnazzo
Lorenzo Polvani

Italian National Research Council, RomeColumbia University, New York

Italy
USA

Chapter 6: Scenarios and Information for Policymakers
Neil Harris
Michael Prather

Cranfield University
University of California, Irvine

UK
USA

Reviewers
Georgios Amanatidis
Mads P. Sulbæk Andersen
Valentina Aquila
Tim Arnold
Matthew Ashfold
Pieter Aucamp
Alkiviadis F. Bais
William Ball

Slimane Bekki
Germar Bernhard
P.K. Bhartia
Nicola Blake
Greg Bodeker
Adam Bourassa
John Burrows
Amy Butler

Pablo Canziani
Andrew Charlton-Perez
Cathy Clerbaux
Melanie Coldewey-Egbers
Robert Damadeo

European Parliament
Belgium
California State University, Northridge & University of Copenhagen
USA/Denmark
American University, Washington, D.C.
USA
National Physical Laboratory & University of Edinburgh
UK
University of Nottingham Malaysia Campus
Malaysia
Environmental Effects Assessment Panel (EEAP) /Ptersa Environmental Consultants
South Africa
Aristotle University of Thessaloniki, Laboratory of Atmospheric Physics
GreeceInstitute for Atmospheric and Climate Science,ETH Zürich & Physikalisch-Meteorologisches
Observatorium Davos World Radiation Centre
Switzerland
Centre National de la Recherche Scientifique (CNRS)FranceBiospherical Instruments, Inc.
USA
NASA Goddard Space Flight Center
USA
University of California, Irvine, Department of Chemistry
USA
Bodeker Scientific
New Zealand
University of Saskatchewan
CanadaUniversity of Bremen
Germany
University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
Consejo Nacional de Investigaciones Cientificas y Técnicas (CONICET)
Universidad Tecnológica Nacional, Buenos Aires (UTN.BA)
Argentina
University of Reading
UK
Centre National de la Recherche Scientifique (CNRS) &Université Pierre et Marie Curie
FranceDeutsches Zentrum für Luft- und Raumfahrt (DLR)
Germany
NASA Langley Research CenterUSA
D.7

Appendix D | List of Authors, Contributors, and Reviewers

Sean Davis

Mohamdou Diallo
Anne Douglass
Richard Eckman
Paul Fraser
Lucien Froidevaux
Jan Fuglestvedt
Ann M. Gabriel
Hella Garny
Jimmy Gasore
Edwin P. GerberAndrew Gettleman
Nathan P. Gillett
William Goetzler
Jens-Uwe Grooß
Joanna Haigh
Alain HauchecornePeter Hitchcock
Lei HuNathalie Huret
Imre M. Jánosi
Julie Jones
Yasuko Kasai
Jooil Kim
Jeff Knight
Michael J. Kurylo
Susan Gabriela Lakkis

Johannes C. Laube
Kathy Law

Sunday Leonard
Pu LinJintai Lin
D.8

University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
Institute for Energy and Climate Research Stratosphere (IEK-7) &Forschungszentrum Jülich GmbHGermany
NASA Goddard Space Flight Center
USA
NASA Headquarters
USA
Climate Science Centre, Commonwealth Scientific andIndustrial Research Organization (CSIRO)
Australia
NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
USA
Center for International Climate Research (CICERO)
NorwayDepartment of the Environment and Energy
Australia
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Germany
University of Rwanda, College of Science and Technology
Department of Physics, Kigali
RwandaCourant Institute of Mathematical Sciences, New York University
USA
National Center for Atmospheric Research (NCAR)
USA
Canadian Centre for Climate Modelling and Analysis andEnvironment and Climate Change Canada (ECCC)
CanadaNavigant Consulting, Inc.
USA
Forschungszentrum Jülich GmbHGermany
Imperial College London
UK
Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS),
Centre National de la Recherche Scientifique (CNRS)/IPSL
Université de Versailles Staint Quentin-en-Yvelines (UVSQ), UPMCFranceLaboratoire de Meteorologie Dynamique (LMD)
Ecole polytechnique, Palaiseau
FranceUniversity of Colorado, Cooperative Institute for Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Global Monitoring DivisionUSA
LPC2E Centre National de la Recherche Scientifique (CNRS)FranceELTE Eötvös Loránd University, Budapest
Hungary
Department of Geography, University of Sheffield
UK
National Institute of Information and Communications (NICT), Tokyo
Japan
Scripps Institution of Oceanography, University of California, San DiegoUSA
Met Office Hadley Center
UK
Universities Space Research Association
Goddard Earth Sciences, Technology, and Research) (USRA/GESTAR)
USA
Pontificia Universidad Católica Argentina, Facultad de Ingeniería yCiencias Agrarias (UCA); Universidad Tecnológica Nacional
Unidad de Investigación y Desarrollo de las Ingenierías
Facultad Regional Buenos Aires (UTN.BA)
Argentina
University of East Anglia, School of Environmental Sciences
UK
Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS),
Centre National de la Recherche Scientifique (CNRS)/IPSL
Université de Versailles Staint Quentin-en-Yvelines (UVSQ), UPMCFranceSTAP-GEF, UN Environment
Kenya
Program in Atmospheric and Oceanic Sciences, Princeton University &NOAA Geophysical Fluid Dynamics Laboratory
USA
Peking University
ChinaList of Authors, Contributors, and Reviewers | Appendix D

Nathaniel Livesey
Sasha Madronich
Michela Maione
Martin R. Manning
Katja Matthes
Archie McCulloch
Richard L. McKenzie
Johan Mellqvist
Pauline M. Midgley
Olaf Morgenstern
Vaishali Naik
Hiroaki Naoe
Simon O’Doherty
Luke OmanDavid E. Oram
Andrew Orr
Prabir Patra
Andrea Pazmiño

Judith Perlwitz

Thomas Peter
Damaris Kirsch Pinheiro
Giovanni Pitari
Michael C. Pitts
Marta Pizano
Felix Ploeger
David Plummer
Jean-Pierre Pommereau

Robert W. Portmann

V. Ramaswamy
Marilyn Raphael
Piera Raspollini
Claire Reeves
Andy Reisinger
James Renwick
Rob Rhew
Harald Rieder
Martin Riese

NASA Jet Propulsion Laboratory (JPL), California Institute of Technology
USA
National Center for Atmospheric Research (NCAR)USA
Univerità di Urbino and National Research Council
Italy
Victoria University of Wellington
New Zealand
GEOMAR Helmholtz Centre for Ocean Research Kiel &Christian-Albrechts-Universität zu Kiel
Germany
Atmospheric Chemistry Research Group, University of Bristol
UK
National Institute of Water and Atmospheric Research Ltd (NIWA Lauder) New Zealand
Chalmers University of Technology
SwedenIndependent Consultant
Germany
National Institute of Water and Atmospheric Research Ltd (NIWA)
New Zealand
NOAA Geophysical Fluid Dynamics Laboratory
USA
Meteorological Research InstituteJapan
University of Bristol, School of Chemistry
UK
NASA Goddard Space Flight Center
USA
University of East Anglia, School of Environmental Sciences andNational Centre for Atmospheric Sciences
UK
British Antarctic Survey (BAS)UK
JAMSTEC (Japan Agency for Marine-Earth Science & Technology)
Japan
Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS),
Centre National de la Recherche Scientifique (CNRS)
Université de Versailles Staint Quentin-en-Yvelines (UVSQ)FranceUniversity of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Physical Sciences DivisionUSA
ETH Zürich		
Switzerland
UFSM - Federal University of Santa Maria
BrazilUniversità dell’Aquila - Department of Physical and Chemical Sciences
Italy
NASA Langley Research Center
USA
Consultant		Colombia
Institute of Energy and Climate Research (IEK-7)
Forschungszentrum Jülich GmbHGermany
Environment and Climate Change Canada (ECCC)
CanadaLaboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS),
Centre national de la recherche scientifique (CNRS)
Université de Saint Quentin (UVSQ)FranceUniversity of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
NOAA Geophysical Fluid Dynamics Laboratory
USA
University of California, Los Angeles
USA
Istituto di Fisica Applicata “Nello Carrara”
Consiglio Nazionale delle Ricerche (IFAC-CNR)
Italy
University of East Anglia
UK
New Zealand Agricultural Greenhouse Gas Research Centre
New ZealandVictoria University of Wellington
New Zealand
University of California, Berkeley
USA
University of Graz, Wegener Center for Climate and Global Change
Austria
Forschungszentrum Jülich GmbHGermany
D.9

Appendix D | List of Authors, Contributors, and Reviewers

Alan Robock
Karen H. Rosenlof
Alfonso Saiz-López
Ross J. Salawitch

Kaoru Sato
Adam Scaife
Sue Schauffler
Robyn Schofield
Jonathan Shanklin
Rajendra Shende
Keith Shine
Rajiv Singh
Miriam Sinnhuber
Karen Smith
Keith Solomon
Gabi Stiller
Susan Strahan
Tove Svendby
David Tarasick
Larry W. Thomason
Roland van der A
Carolina Vera

Jean-Paul VernierThomas von Clarmann
Christian von Savigny
Krzysztof Wargan
Ray F. WeissDavid Wilmouth
Stephen Wilson
Caradee Wright
Yangyang Xu
Shigeo Yoden
Yoko Yokouchi
Adrián Yuchechen

Durwood Zaelke

D.10

Rutgers University
USA
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
Institute of Physical Chemistry Rocasolano
Spain
Department of Atmospheric and Oceanic Science &Department of Chemistry and Biochemistry,
Earth System Science Interdisciplinary Center,
University of Maryland, College ParkUSA
Department of Earth and Planetary Science, University of TokyoJapan
Met Office Hadley Center (MOHC)UK
National Center for Atmospheric Research (NCAR)USA
University of Melbourne
Australia
British Antarctic Survey (BAS)
UK
Technology, Education, Research and Rehabilitation for the 		
Environment (TERRE) Policy Centre
India
Department of Meteorology, University of ReadingUK
Honeywell InternationalUSA
Karlsruher Institut für Technologie (KIT)
Germany
University of Toronto ScarboroughCanadaUN Environment, Ozone Secretariat &Center for Toxicology, University of Guelph
Kenya/Canada
Karlsruher Institut für Technologie (KIT)
Institute of Meteorology and Climate Research (IMK)
Germany
NASA Goddard Space Flight Center
USA
Norwegian Institute for Air Research (NILU)
NorwayEnvironment and Climate Change Canada (ECCC)
CanadaNASA Langley Research Center
USA
Royal Netherlands Meteorological Institute (RNMI)The Netherlands
Universidad Tecnológica Nacional, Buenos Aires (UTN.BA)
Centro de Investigaciones del Mar y de la Atmoósfera (CIMA)
Consejo Nacional de Investigaciones Cientificas y Técnicas (CONICET)
Argentina
NASA Langley Research Center
USA
Karlsruher Institut für Technologie (KIT)
Institute of Meteorology and Climate Research (IMK)
Germany
University of Greifswald
Germany
Science Systems and Applications (SSAI) & Global Modeling and Assimilation Office		
NASA Goddard Space Flight Center
USA
Scripps Institution of Oceanography, Univesity of California, San Diego
USA
Harvard University
USA
Centre for Atmospheric Chemistry, University of Wollongong
Australia
South African Medical Research Council and University of Pretoria
South Africa
Texas A&M University
USA
Kyoto University
Japan
National Institute for Environmental Studies (visiting scientist)
Japan
Universidad Tecnológica Nacional, Buenos Aires (UTN.BA)
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
Unidad de Investigación y Desarrollo de las Ingenierías (UIDI)
Argentina
Institute for Governance and Sustainable Development (IGSD)
USA

List of Authors, Contributors, and Reviewers | Appendix D

Sponsoring Organizations and LiaisonsTina Birmpili
Geir Braathen
Vincent-Henri Peuch
Jack KayeDavid W. Fahey
David Dokken

UN Environment, Ozone Secretariat
World Meteorological Organization (WMO)
European Commission, Liaison
European Centre for Medium-Range Weather Forecasts (ECMWF)
NASA Headquarters
NOAA Earth System Research Laboratory, Chemical Sciences Division
United States Global Change Research Program (USGCRP)

Kenya
Switzerland
UK
USA
USA
USA

Organizational LiaisonsPieter Aucamp
Marco Gonzáles
Lambert J.M. Kuijpers
Bella Maranion
Helen Walter-Terrinoni
Sophia Mylona

Environmental Effects Assessment Panel (EEAP) of the Montreal Protocol &Ptersa Environmental Consultants
South Africa
Senior member Technology and Economic Assessment Panel (TEAP) of theMontreal Protocol & Former Executive Secretary, Ozone Secretariat
Costa Rica
Eindhoven Univeisty of Technology, A/genT Consultancy
The Netherlands
Co-Chair of the Technology and Economic Assessment Panel (TEAP) of theMontreal Protocol & U.S. Environmental Protection Agency (EPA)
USA
Technology and Econominc Assessment Panel (TEAP) of the Montreal Protocol
The Chemours Company
USA
UN Environment, Ozone Secretariat
Kenya

Final Author Team Meeting
16-20 July 2018

Les Diablerets, Switzerland
Steve Andersen
Pieter Aucamp
Tina Birmpili
Geir Braathen

by Jean-Pierre Pommereau - June 2010

Institute for Governance & Sustainable Development (IGSD)
Environmental Effects Assessment Panel (EEAP) &Ptersa Environmental Consultants
UN Environment, Ozone Secretariat
World Meteorological Organization (WMO)

USA
South Africa
Kenya
Switzerland
D.11

Appendix D | List of Authors, Contributors, and Reviewers

Peter Braesicke

Chiara Cagnazzo
Lucy J. Carpenter
Martyn P. Chipperfield
Debra Dailey-Fisher
Martin Dameris
John S. Daniel
Sarah J. Doherty

Andreas EngelDavid W. Fahey
Paul Fraser
Sophie Godin-Beekmann

Marco Gonzáles
Neil Harris
Michaela I. Hegglin
Jianxin Hu
Kenneth W. Jucks
Elizabeth M. Juvera
David J. Karoly
Alexey Yu. Karpechko
Doug Kinnison
Ronda KnottMalcolm J. Ko
Lambert J.M. Kuijpers
Michael J. Kurylo
Ulrike Langematz
Qing Liang
Bella Maranion
Amanda C. Maycock
Pauline Midgley
Suzette Milano-Schoser
Stephen A. Montzka
Olaf Morgenstern
Rolf Müller
D.12

Institute of Meteorology and Climate Research (IMK)
Atmospheric Trace Gases and Remote Sensing (ASF)
Karlsruher Institut für Technologie (KIT)
Germany
Italian National Research Council , Rome
Italy
University of YorkUK
University of Leeds
UK
NOAA Earth System Research Laboratory, Chemical Sciences DivisionUSA
Deutsches Zentrum für Luft- und Raumfahrt (DLR)
Institut für Physik der Atmosphäre
Germany
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
Goethe Universität, Frankfurt
Germany
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
Climate Science Centre, Commonwealth Scientific andIndustrial Research Organization (CSIRO)
Australia
Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS),
Centre National de la Recherche Scientifique (CNRS),
Sorbonne University
FranceSenior member Technology and Economic Assessment Panel (TEAP)
Former Executive Secretary, Ozone Secretariat
Costa Rica
Cranfield University
UK
University of Reading
UK
Peking University
ChinaNASA Headquarters
USA
Bay Area Environmental Research Institute (BAERI) &NASA Earth Science Project OfficeUSA
Climate Science Centre, Commonwealth Scientific andIndustrial Research Organisation (CSIRO), Aspendale, Victoria
Australia
Finnish Meteorological Institute, Helsinki
FinlandNational Center for Atmospheric Research (NCAR)
USA
Science and Technology Corporation
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
NASA Langley Research Center
USA
Eindhoven University of Technology, A/genT Consultancy
The Netherlands
Universities Space Research Association
Goddard Earth Sciences, Technology, and Research USRA/GESTAR)
USA
Freie Universität Berlin, Berlin
Germany
NASA Goddard Space Flight Center
USA
Technology and Economic Assessment Panel (TEAP) &U.S. Environmental Protection Agency
USA
University of Leeds
UK
Independent Consultant
Germany
Science and Technology Corporation
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
NOAA Earth System Research Laboratory, Global Monitoring Division
USA
National Institute of Water and Atmospheric Research Ltd (NIWA)
New Zealand
Forschungszentrum Jülich GmbHGermany

List of Authors, Contributors, and Reviewers | Appendix D

Sophia Mylona
Jessica Neu
Paul A. Newman
Tyeisha B. Philson
David Plummer
Lorenzo Polvani
Michael Prather
John A. Pyle
A.R. Ravishankara
Claire Reeves
Stefan Reimann
Ann M. Reiser
Matthew Rigby
Karen H. Rosenlof
Bonfils Safari
Ross J. Salawitch

Michelle Santee
Rajendra Shende
Michael SigmondSusan Solomon
Wolfgang Steinbrecht
Bill Sturges
Simone Tilmes
Matthew B. Tully
Guus J.M. Velders
Timothy J. Wallington
Helen Walter-Terrinoni
Mark Weber
Ray F. Weiss
Donald J. Wuebbles

UN Environment, Ozone Secretariat
Kenya
NASA Jet Propulsion Laboratory (JPL)USA
NASA Goddard Space Flight Center
USA
Science Systems and Applications, Inc. & NASA Earth System Project OfficeUSA
Environment and Climate Change Canada (ECCC)
CanadaColumbia University, New YorkUSA
University of California, Irvine
USA
University of Cambridge and the National Centre for Atmospheric Science
UK
Colorado State University, Fort Collins, Colorado
USA
University of East Anglia
UK
Swiss Federal Laboratories for Materials Science and Technology (EMPA) Switzerland
NOAA Earth System Research Laboratory, Office of the Executive Director
USA
University of Bristol, School of Chemistry
UK
NOAA Earth System Research Laboratory, Chemical Sciences Division
USA
University of Rwanda, College of Science and Technology, Kigali
RwandaDepartment of Atmospheric and Oceanic Science &Department of Chemistry and Biochemistry,
Earth System Science Interdisciplinary Center,
University of Maryland, College Park
USA
NASA Jet Propulsion Laboratory (JPL)USA
Technology, Education, Research and Rehabilitation for the 		
Environment (TERRE) Policy Centre
India
Environment and Climate Change Canada (ECCC)
CanadaMassachusetts Institute of Technology (MIT)USA
Meteorological Observatory Hohenpeissenberg
Deutscher Wetterdienst (DWD)
Germany
School of Environmental Sciences, University of East Anglia
UK
National Center for Atmospheric Research (NCAR)
USA
Bureau of Meteorology, Melbourne
Australia
Dutch National Institute for Public Health and Environment (RIVM),
Bilthoven & Utrecht University
The Netherlands
Ford Motor Company
USA
Technology and Econominc Assessment Panel (TEAP) of the Montreal Protocol
The Chemours Company
USA
Institute of Environmental Physics, Universität Bremen
Germany
Scripps Institution of Oceanography, University of California, San Diego
USA
University of Illinois
USA

Graphic Design and Layout
Debra Dailey-Fisher
Nada Derek
F. Dennis Dickerson
Netta Kasher
Sydnee Masias
Ann M. Reiser

NOAA Earth System Research Laboratory, Chemical Sciences Division
Climate Science Centre, Commonwealth Scientific andIndustrial Research Organisation (CSIRO) Oceans and AtmosphereRespond Grafiks
The Hebrew University of Jerusalem
Science and Technology Corporation
NOAA Office of the Chief Administrative Officer
NOAA Earth System Research Laboratory, Office of the Executive Director

USA
Australia
USA
Israel
USA
USA
D.13

Appendix D | List of Authors, Contributors, and Reviewers

Albert Romero

NOAA Office of the Chief Administrative Officer

USA

Editorial TeamSarah J. Doherty

Tiffany Means
Brooke C. Stewart
Andrea McCarrick
Debra Dailey-Fisher
Ann M. Reiser

University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
North Carolina State University and NOAA Cooperative Institute for Climate andSatellites -NC, National Centers for Environmental Information (NCEI)
North Carolina State University and NOAA Cooperative Institute for Climate andSatellites -NC, National Centers for Environmental Information (NCEI)
North Carolina State University and NOAA Cooperative Institute for Climate andSatellites -NC, National Centers for Environmental Information (NCEI)NOAA Earth System Research Laboratory, Chemical Sciences Division
NOAA Earth System Research Laboratory, Office of the Executive Director

USA
USA
USA
USA
USA
USA

Administrative Support
Debra Dailey-Fisher
Elizabeth M. Juvera
Ronda Knott
Suzette Milano-Schoser
Tyeisha B. Philson
Ann M. Reiser
Kathy A. Thompson

NOAA Earth System Research Laboratory, Chemical Sciences Division
Bay Area Environmental Research Institute (BAERI) &NASA Earth Science Project Office (ESPO)
Science and Technology Corporation
NOAA Earth System Research Laboratory, Chemical Sciences Division
Science and Technology Corporation
NOAA Earth System Research Laboratory, Chemical Sciences Division
Science Systems and Applications, Inc. (SSAI) &NASA Earth System Project Office (ESPO)NOAA Earth System Research Laboratory, Office of the Executive Director
Science Systems and Applications, Inc.

USA
USA
USA
USA
USA
USA
USA

Computing, Technical, and Network Support
Catherine Burgdorf Rasco

Jenny Fox
Mike Mascola
Douglas Ohlhorst
Richard Tisinai

D.14

University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
NOAA Earth System Research Laboratory, Chemical Sciences Division
Cherokee Services GroupNOAA Earth System Research Laboratory, Office of the Executive Director
NOAA Earth System Research Laboratory, Chemical Sciences Division
University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division

USA
USA
USA
USA

USA

List of Authors, Contributors, and Reviewers | Appendix D

Reference Research and Editing
Debra Dailey-Fisher
Jenn Kinkade

Ann M. Szczurek Davis

Sydnee Masias
Suzette Milano-Schoser
Ann M. Reiser

NOAA Earth System Research Laboratory, Chemical Sciences Division
University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
University of Colorado, Cooperative Institute for
Research in Environmental Sciences at
NOAA Earth System Research Laboratory, Chemical Sciences Division
Science and Technology Corporation
NOAA Office of the Chief Administrative Officer
Science and Technology Corporation
NOAA Earth System Research Laboratory, Chemical Sciences Division
NOAA Earth System Research Laboratory, Office of the Executive Director

USA

USA

USA
USA
USA
USA

D.15

World Meteorological Organization
Global Ozone Research and Monitoring Project—Report No. 58

SCIENTIFIC ASSESSMENT OFOZONE DEPLETION: 2018