C L I M AT E S C I E N C E
S P E C I A L

R E P O R T

Fourth National Climate Assessment | Volume I

C L I M AT E S C I E N C E
S P E C I A L

R E P O R T

Fourth National Climate Assessment | Volume IAvailable on-line at: <science2017.globalchange.gov>
This document responds to requirements of Section 106 of the U.S. Global Change Research Act of 1990
(P.L. 101-606, <http://www.globalchange.gov/about/legal-mandate>). It does not express any regulatory policies of the United States or any of its agencies, or make any findings of fact that could serve as
predicates of regulatory action. Agencies must comply with required statutory and regulatory processes before they could rely on any statements in the document or by the USGCRP as basis for regulatory
action.
This document was prepared in compliance with Section 515 of the Treasury and General Government Appropriations Act for Fiscal Year 2001 (P.L. 106-554) and information quality guidelines issued by the Department of Commerce / National Oceanic and Atmospheric Administration pursuant to Section 515 (<http://
www.cio.noaa.gov/services_programs/info_quality.html>). For purposes of compliance with Section 515,
this document is deemed a “highly influential scientific assessment” (HISA). The report graphics follow theISO 19115 standard which includes the necessary information to achieve reproducibility.
In all cases, permissions were secured by the U.S. Government to use and/or adapt copyrighted material contained in this document. High-resolution art is available at science2017.globalchange.gov/, withaccompanying captions providing source and credit information.
First published 2017

Recommended Citation for Report
USGCRP, 2017: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles,
D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change
Research Program, Washington, DC, USA, 470 pp., doi: 10.7930/J0J964J6.
Image Credit
Front Cover: Atmospheric rivers are relatively long, narrow regions in the atmosphere – like rivers in thesky – that transport most of the water vapor outside of the tropics. When an atmospheric river makes
landfall, extreme precipitation and flooding can often result. The cover features a natural-color image ofconditions over the northeastern Pacific on 20 February 2017, helping California and the American Westemerge from a 5-year drought in stunning fashion. Some parts of California received nearly twice asmuch rain in a single deluge as normally falls in the preceding 5 months (October–February). The visualization was generated by Jesse Allen (NASA Earth Observatory) using data from the Visible Infrared
Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) satellite.
Chapter Banners: Special thanks to the NASA Earth Observatory team for the non-captioned data products incorporated into chapter titles and web banners throughout the Climate Science Special Report.

CSSR Writing Team
Coordinating Lead Authors
Donald J. Wuebbles, National Science Foundation and
U.S. Global Change Research Program – University
of Illinois

David W. Fahey, NOAA Earth System ResearchLaboratory
Kathy A. Hibbard, NASA Headquarters

Lead Authors
Jeff R. Arnold, U.S. Army Corps of Engineers
Benjamin DeAngelo, NOAA Climate Program Office
Sarah Doherty, University of Washington
David R. Easterling, NOAA National Centers forEnvironmental Information
James Edmonds, Pacific Northwest National Laboratory
Timothy Hall, NASA Goddard Institute for Space Studies
Katharine Hayhoe, Texas Tech University
Forrest M. Hoffman, Oak Ridge National Laboratory
Radley Horton, Columbia University
Deborah Huntzinger, Northern Arizona University
Libby Jewett, NOAA Ocean Acidification Program
Thomas Knutson, NOAA Geophysical Fluid Dynamics
Lab
Robert E. Kopp, Rutgers University
James P. Kossin, NOAA National Centers forEnvironmental Information

Kenneth E. Kunkel, North Carolina State University
Allegra N. LeGrande, NASA Goddard Institute for Space Studies
L. Ruby Leung, Pacific Northwest National Laboratory
Wieslaw Maslowski, Naval Postgraduate School
Carl Mears, Remote Sensing Systems
Judith Perlwitz, NOAA Earth System Research Laboratory
Anastasia Romanou, Columbia University
Benjamin M. Sanderson, National Center for AtmosphericResearch
William V. Sweet, NOAA National Ocean Service
Patrick C. Taylor, NASA Langley Research Center
Robert J. Trapp, University of Illinois at Urbana-Champaign
Russell S. Vose, NOAA National Centers for Environmental
Information
Duane E. Waliser, NASA Jet Propulsion Laboratory
Michael F. Wehner, Lawrence Berkeley National Laboratory
Tristram O. West, DOE Office of Science

Review Editors
Linda O. Mearns, National Center for AtmosphericResearch
Ross J. Salawitch, University of Maryland

Christopher P. Weaver, USEPAContributing Authors
Richard Alley, Pennsylvania State University
C. Taylor Armstrong, NOAA Ocean Acidification
ProgramJohn Bruno, University of North Carolina
Shallin Busch, NOAA Ocean Acidification Program
Sarah Champion, North Carolina State University
Imke Durre, NOAA National Centers for Environmental
Information
Dwight Gledhill, NOAA Ocean Acidification Program
Justin Goldstein, U.S. Global Change Research Program– ICF
Boyin Huang, NOAA National Centers forEnvironmental Information

Hari Krishnan, Lawrence Berkeley National Laboratory
Lisa Levin, University of California – San Diego
Frank Muller-Karger, University of South Florida
Alan Rhoades, University of California – Davis
Laura Stevens, North Carolina State University
Liqiang Sun, North Carolina State University
Eugene Takle, Iowa State University
Paul Ullrich, University of California – Davis
Eugene Wahl, NOAA National Centers for Environmental
Information
John Walsh, University of Alaska – Fairbanks

Volume Editors
David J. Dokken, U.S. Global Change Research Program– ICF
David W. Fahey, National Oceanic and Atmospheric
Administration
Kathy A. Hibbard, National Aeronautics and Space
Administration

Thomas K. Maycock, Cooperative Institute for Climate andSatellites – North Carolina
Brooke C. Stewart, Cooperative Institute for Climate andSatellites – North Carolina
Donald J. Wuebbles, National Science Foundation and U.S.
Global Change Research Program – University of Illinois

Science Steering Committee
Benjamin DeAngelo, National Oceanic and Atmospheric
Administration
David W. Fahey, National Oceanic and Atmospheric
Administration
Kathy A. Hibbard, National Aeronautics and Space
Administration
Wayne Higgins, Department of Commerce

Jack Kaye, National Aeronautics and Space Administration
Dorothy Koch, Department of Energy
Russell S. Vose, National Oceanic and Atmospheric
Administration
Donald J. Wuebbles, National Science Foundation and U.S.
Global Change Research Program – University of Illinois

Subcommittee on Global Change ResearchWilliam Hohenstein, Department of Agriculture
Jack Kaye, National Aeronautics and Space
Administration
Dorothy Koch, Department of Energy
Andrew Miller, U.S. Environmental Protection Agency
David Reidmiller, U.S. Global Change Research Program
Trigg Talley, Department of State
Michael Van Woert, National Science Foundation

Ann Bartuska, Chair, Department of Agriculture
Virginia Burkett, Co-Chair, Department of the Interior
Gerald Geernaert, Vice-Chair, Department of Energy
Michael Kuperberg, Executive Director, U.S. Global
Change Research Program
John Balbus, Department of Health and Human Services
Bill Breed, U.S. Agency for International Development
Pierre Comizzoli, Smithsonian Institution
Wayne Higgins, Department of Commerce
Scott Harper, Department of Defense (Acting)Liaison to the Executive Office of the President
Kimberly Miller, Office of Management and Budget

Report Production Team
Bradley Akamine, U.S. Global Change Research Program Thomas K. Maycock, Cooperative Institute for Climate and– ICF
Satellites – North Carolina
Jim Biard, Cooperative Institute for Climate and Satellites
Deborah Misch, TeleSolv Consulting– North Carolina
Katie Reeves, U.S. Global Change Research Program – ICF
Andrew Buddenberg, Cooperative Institute for Climate
Deborah Riddle, NOAA National Centers for Environmental
and Satellites – North Carolina
Information
Sarah Champion, Cooperative Institute for Climate andReid Sherman, U.S. Global Change Research Program –Satellites – North Carolina
Straughan Environmental, Inc.
David J. Dokken, U.S. Global Change Research Program
Mara Sprain, LAC Group– ICF
Laura Stevens, Cooperative Institute for Climate and Satellites –Amrutha Elamparuthy, U.S. Global Change ResearchNorth Carolina
Program – Straughan Environmental, Inc.
Brooke C. Stewart, Cooperative Institute for Climate andJennifer Fulford, TeleSolv Consulting
Satellites – North Carolina
Jessicca Griffin, Cooperative Institute for Climate andLiqiang Sun, Cooperative Institute for Climate and Satellites –Satellites – North Carolina
North Carolina
Kate Johnson, ERT Inc.
Kathryn Tipton, U.S. Global Change Research Program – ICF
Angel Li, Cooperative Institute for Climate and Satellites
Sara Veasey, NOAA National Centers for Environmental– North Carolina
Information
Liz Love-Brotak, NOAA National Centers forEnvironmental Information
Administrative Lead Agency
Department of Commerce / National Oceanic and Atmospheric Administration

U.S. Global Change Research Programv

Climate Science Special Report

U.S. GLOBAL CHANGE RESEARCH PROGRAM
CLIMATE SCIENCE SPECIAL REPORT (CSSR)TABLE OF CONTENTS
Front Matter
About This Report ........................................................................................................................................................1
Guide to the Report .....................................................................................................................................................3
Executive Summary....................................................................................................................................................12
Chapters
1.

Our Globally Changing Climate ...............................................................................................................35

2.

Physical Drivers of Climate Change .........................................................................................................73

3.

Detection and Attribution of Climate Change ...................................................................................... 114

4.

Climate Models, Scenarios, and Projections ..........................................................................................133

5.

Large-Scale Circulation and Climate Variability ...................................................................................161

6.

Temperature Changes in the United States ............................................................................................185

7.

Precipitation Change in the United States .............................................................................................207

8.

Droughts, Floods, and Wildfires ..............................................................................................................231

9.

Extreme Storms ..........................................................................................................................................257

10.

Changes in Land Cover and Terrestrial Biogeochemistry ...................................................................277

11.

Arctic Changes and their Effects on Alaska and the Rest of the United States ................................303

12.

Sea Level Rise .............................................................................................................................................333

13.

Ocean Acidification and Other Ocean Changes ....................................................................................364

14.

Perspectives on Climate Change Mitigation .........................................................................................393

15.

Potential Surprises: Compound Extremes and Tipping Elements ..................................................... 411

AppendicesA.

Observational Datasets Used in Climate Studies ..................................................................................430

B.

Model Weighting Strategy ........................................................................................................................436

C.

Detection and Attribution Methodologies Overview...........................................................................443

D.

Acronyms and Units..................................................................................................................................452

E.

Glossary........................................................................................................................................................460

U.S. Global Change Research Programvi

Climate Science Special ReportAbout This Report
As a key part of the Fourth National Climate
Assessment (NCA4), the U.S. Global Change
Research Program (USGCRP) oversaw the
production of this stand-alone report of thestate of science relating to climate change andits physical impacts.

Much of this report is written at a level more
appropriate for a scientific audience, though
the Executive Summary is intended to be accessible to a broader audience.

The Climate Science Special Report (CSSR) is
designed to be an authoritative assessmentof the science of climate change, with a focus
on the United States, to serve as the foundation for efforts to assess climate-related risks
and inform decision-making about responses.
In accordance with this purpose, it does notinclude an assessment of literature on climatechange mitigation, adaptation, economic valuation, or societal responses, nor does it include
policy recommendations.

The National Oceanic and Atmospheric
Administration (NOAA) serves as the administrative lead agency for the preparationof NCA4. The CSSR Federal Science Steering
Committee (SSC)1 has representatives fromthree agencies (NOAA, the National Aeronautics and Space Administration [NASA], andthe Department of Energy [DOE]); USGCRP;2
and three Coordinating Lead Authors, all ofwhom were Federal employees during thedevelopment of this report. Following a public
notice for author nominations in March 2016,
the SSC selected the writing team, consistingof scientists representing Federal agencies,
national laboratories, universities, and theprivate sector. Contributing Authors wererequested to provide special input to the Lead
Authors to help with specific issues of theassessment.

Report Development, Review, andApproval Process

As Volume I of NCA4, CSSR serves several
purposes, including providing 1) an updated
detailed analysis of the findings of how climate change is affecting weather and climate
across the United States; 2) an executive summary and other CSSR materials that provide
the basis for the discussion of climate science
found in the second volume of the NCA4; and3) foundational information and projections
for climate change, including extremes, toimprove “end-to-end” consistency in sectoral,
regional, and resilience analyses within thesecond volume. CSSR integrates and evaluates
the findings on climate science and discusses
the uncertainties associated with these findings. It analyzes current trends in climatechange, both human-induced and natural,and projects major trends to the end of thiscentury. As an assessment and analysis of thescience, this report provides important input
to the development of other parts of NCA4,
and their primary focus on the human welfare, societal, economic, and environmental
elements of climate change.

The first Lead Author Meeting was held in
Washington, DC, in April 2016, to refine theoutline contained in the SSC-endorsed prospectus and to make writing assignments.
Over the course of 18 months before final
1
The CSSR SSC was charged with overseeing the development andproduction of the report. SSC membership was open to all USGCRP
agencies.
2
The USGCRP is made up of 13 Federal departments and agencies thatcarry out research and support the Nation’s response to global change.
The USGCRP is overseen by the Subcommittee on Global Change
Research (SGCR) of the National Science and Technology Council’s
Committee on Environment, Natural Resources, and Sustainability(CENRS), which in turn is overseen by the White House Office of Science and Technology Policy (OSTP). The agencies within USGCRP are
the Department of Agriculture, the Department of Commerce (NOAA),
the Department of Defense, the Department of Energy, the Department
of Health and Human Services, the Department of the Interior, the Department of State, the Department of Transportation, the Environmental Protection Agency, the National Aeronautics and Space Administration, the National Science Foundation, the Smithsonian Institution, andthe U.S. Agency for International Development.

1
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Climate Science Special Report

The Sustained National Climate
Assessment

publication, seven CSSR drafts were generated, with each successive iteration—from zeroto sixth-order drafts—undergoing additional
expert review, as follows: (i) by the writing
team itself (13–20 June 2016); (ii) by the SSC
convened to oversee report development (29July–18 August 2016); (iii) by the technicalagency representatives (and designees) comprising the Subcommittee on Global Change
Research (SGCR, 3–14 October 2016); (iv) by
the SSC and technical liaisons again (5–13
December 2016); (v) by the general publicduring the Public Comment Period (15 December 2016–3 February 2017) and an expert
panel convened by the National Academies of
Sciences, Engineering, and Medicine (NAS, 21
December 2016–13 March 2017);3 and (vi) by
the SGCR again (3–24 May 2017) to confirm
the Review Editor conclusions that all public
and NAS comments were adequately addressed. In October 2016, an 11-member corewriting team was tasked with capturing themost important CSSR key findings and generating an Executive Summary. Two additional
Lead Authors Meetings were held after major
review milestones to facilitate chapter teamdeliberations and consistency: 2–4 November 2016 (Boulder, CO) and 21–22 April 2017
(Asheville, NC). Literature cutoff dates were
enforced, with all cited material publishedby June 2017. The fifth-order draft including the Executive Summary was compiledin June 2017, and submitted to the Office of
Science and Technology Policy (OSTP). OSTP
is responsible for the Federal clearance process
prior to final report production and public
release. This published report represents the
final (sixth-order) draft.

The Climate Science Special Report has beendeveloped as part of the USGCRP’s sustained
National Climate Assessment (NCA) process.
This process facilitates continuous and transparent participation of scientists and stakeholders across regions and sectors, enabling
new information and insights to be assessedas they emerge. The Climate Science Special
Report is aimed at a comprehensive assessment of the science underlying the changes
occurring in Earth’s climate system, with a
special focus on the United States.

Sources Used in this Report
The findings in this report are based on a large

body of scientific, peer-reviewed research,
as well as a number of other publicly available sources, including well-established andcarefully evaluated observational and modeling datasets. The team of authors carefully
reviewed these sources to ensure a reliable
assessment of the state of scientific understanding. Each source of information was determined to meet the four parts of the quality
assurance guidance provided to authors (following the approach from NCA3): 1) utility,2) transparency and traceability, 3) objectivity,and 4) integrity and security. Report authors
assessed and synthesized information frompeer-reviewed journal articles, technical reports produced by Federal agencies, scientificassessments (such as the rigorously-reviewed
international assessments from the Intergovernmental Panel on Climate Change,1 reportsof the National Academy of Sciences and its
associated National Research Council, andvarious regional climate impact assessments,
conference proceedings, and government statistics (such as population census and energy
usage).

Author responses to comments submitted as part of the Public Comment Period and a USGCRP response to the review conducted by NAS
can be found on <science2017.globalchange.gov/downloads>.

3

2
U.S. Global Change Research Program

Climate Science Special ReportGuide to the Report
The following subsections describe the formatof the Climate Science Special Report and theoverall structure and features of the chapters.

the Northern Great Plains and Southern Great
Plains; and 2) The U.S. islands in the Caribbean are analyzed as a separate region apartfrom the Southeast.

Executive Summary
The Executive Summary describes the major
findings from the Climate Science Special
Report. It summarizes the overall findings andincludes some key figures and additional bullet points covering overarching and especially
noteworthy conclusions. The Executive Summary and the majority of the Key Findings
are written to be accessible to a wide range ofaudiences.

Chapter Text

Each chapter assesses the state of the science
for a particular aspect of the changing climate. The first chapter gives a summary of the
global changes occurring in the Earth’s climate system. This is followed in Chapter 2 bya summary of the scientific basis for climatechange. Chapter 3 gives an overview of theprocesses used in the detection and attributionof climate change and associated studies using
those techniques. Chapter 4 then discusses the
scenarios for greenhouse gases and particles
and the modeling tools used to study future
projections. Chapters 5 through 9 primarily
focus on physical changes in climate occurringin the United States, including those projected
to occur in the future. Chapter 10 provides
a focus on land use change and associatedfeedbacks on climate. Chapter 11 addresseschanges in Alaska in the Arctic, and how thelatter affects the United States. Chapters 12
and 13 discuss key issues connected with sea
level rise and ocean changes, including ocean
acidification, and their potential effects on
the United States. Finally, Chapters 14 and
15 discuss some important perspectives on
how mitigation activities could affect futurechanges in climate and provide perspectives
on what surprises could be in store for thechanging climate beyond the analyses alreadycovered in the rest of the assessment.

Chapters
Key Findings and Traceable Accounts

Each topical chapter includes Key Findings,
which are based on the authors’ expert judgment of the synthesis of the assessed literature. Each Key Finding includes a confidence
statement and, as appropriate, framing of key
scientific uncertainties, so as to better support
assessment of climate-related risks. (See “Documenting Uncertainty” below).
Each Key Finding is also accompanied by aTraceable Account that documents the supporting evidence, process, and rationale theauthors used in reaching these conclusions
and provides additional information onsources of uncertainty through confidence andlikelihood statements. The Traceable Accounts
can be found at the end of each chapter.
Regional Analyses

Throughout the report, the regional analysesof climate changes for the United States are
structured on 10 different regions as shownin Figure 1. There are differences from theregions used in the Third National Climate
Assessment2: 1) the Great Plains are split into

Throughout the report, results are presented
in United States customary units (e.g., degrees
Fahrenheit) as well as in the International System of Units (e.g., degrees Celsius).

3
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Climate Science Special Report

Alaska

Northwest

Northern
Great Plains

Northeast
MidwestWashington, DC

Southwest
Hawai‘iandPacific Islands

Southern
Great Plains

Southeast

Caribbean

Figure 1. Map of the ten regions of the United States used throughout the Climate Science Special Report. Regions
are similar to that used in the Third National Climate Assessment except that 1) the Great Plains are split into the Northern Great Plains and Southern Great Plains, and 2) the Caribbean islands have been split from the Southeast region.
(Figure source: adapted from Melillo et al. 20142).

periods. The 1901–1960 reference period isparticularly used for graphs that illustrate
past changes in climate conditions, whether
in observations or in model simulations. This60-year time period was also used for analyses in the Third National Climate Assessment(NCA32). The beginning date was chosenbecause earlier historical observations are
generally considered to be less reliable. Whilea 30-year base period is often used for climate
analyses, the choice of 1960 as the ending
date of this period was based on past changes
in human influences on the climate system.
Human-induced forcing exhibited a slow riseduring the early part of the last century but
then accelerated after 1960. Thus, these graphs
highlight observed changes in climate during
the period of rapid increase in human-caused

Reference Time Periods for Graphics

There are many different types of graphics
in the Climate Science Special Report. Someof the graphs in this report illustrate historical changes and future trends in climate
compared to some reference period, withthe choice of this period determined by the
purpose of the graph and the availability ofdata. The scientific community does not havea standard set of reference time periods forassessing the science, and these tend to be chosen differently for different reports and assessments. Some graphics are pulled from otherstudies using different time periods.
Where graphs were generated for this report(those not based largely on prior publications),
they are mostly based on one of two reference
4
U.S. Global Change Research Program

Climate Science Special Reportforcing and also reveal how well climate models simulate these observed changes.

possible, results beyond 2100 are shown. Notethat these time periods are different than thoseused in some of the graphics in NCA3. There
are also exceptions for graphics that are basedon existing publications.

Thus, a number of the graphs in the report areable to highlight the recent, more rapid changes relative to the early part of the century (thereference period) and also reveal how well the
climate models simulate observed changes. In
this report, this time period is used as the baseperiod in most maps of observed trends andall time-varying, area-weighted averages thatshow both observed and projected quantities.
For the observed trends, 1986–2015 is generally chosen as the most recent 30-year period(2016 data was not fully available until late inour development of the assessment).

For global results that may be dependent
on findings from other assessments (such as
those produced by the Intergovernmental
Panel on Climate Change, or IPCC), and forother graphics that depend on specific published work, the use of other time periods
was also allowed, but an attempt was madeto keep them as similar to the selected periods
as possible. For example, in the discussion ofradiative forcing, the report uses the standard
analyses from IPCC for the industrial era(1750 to 2011) (following IPCC 2013a1). And,of course, the paleoclimatic discussion of past
climates goes back much further in time.

The other commonly used reference period in this report is 1976–2005. The choiceof a 30-year period is chosen to account for
natural variations and to have a reasonablesampling in order to estimate likelihoods oftrends in extremes. This period is consistent
with the World Meteorological Organization’s
recommendation for climate statistics. This
period is used for graphs that illustrate projected changes simulated by climate models. The purpose of these graphs is to show
projected changes compared to a period thatallows stakeholders and decision makers tobase fundamental planning and decisions on
average and extreme climate conditions in a
non-stationary climate; thus, a recent available
30-year period was chosen.3 The year 2005 waschosen as an end date because the historicalperiod simulated by the models used in thisassessment ends in that year.

Model Results: Past Trends and Projected Futures

The NCA3 included global modeling resultsfrom both the CMIP3 (Coupled Model Intercomparison Project, 3rd phase) models usedin the 2007 international assessment4 and theCMIP5 (Coupled Model Intercomparison Project, Phase 5) models used in the more recent
international assessment.1 Here, the primaryresource for this assessment is the more recent
global model results and associated downscaled products from CMIP5. The CMIP5
models and the associated downscaled products are discussed in Chapter 4: Projections.
Treatment of Uncertainties: Likelihoods,
Confidence, and Risk Framing

Throughout this report’s assessment of thescientific understanding of climate change,
the authors have assessed to the fullest extent
possible the state-of-the-art understandingof the science resulting from the informationin the scientific literature to arrive at a seriesof findings referred to as Key Findings. The
approach used to represent the extent of un-

For future projections, 30-year periods areagain used for consistency. Projections are
centered around 2030, 2050, and 2085 with aninterval of plus and minus 15 years (for example, results for 2030 cover the period 2015–2045); Most model runs used here only projectout to 2100 for future scenarios, but where
5
U.S. Global Change Research Program

Climate Science Special Reportderstanding represented in the Key Findings
is done through two metrics:•

Confidence in the validity of a finding based on the type, amount, quality,
strength, and consistency of evidence (suchas mechanistic understanding, theory, data,
models, and expert judgment); the skill,
range, and consistency of model projections; and the degree of agreement within
the body of literature.

•

Likelihood, or probability of an effect or
impact occurring, is based on measuresof uncertainty expressed probabilistically(based on the degree of understanding or
knowledge, e.g., resulting from evaluating
statistical analyses of observations or model results or on expert judgment).

tically (based on statistical analysis of observations or model results or on expert judgment).
The authors used expert judgment basedon the synthesis of the literature assessed toarrive at an estimation of the likelihood thata particular observed effect was related tohuman contributions to climate change orthat a particular impact will occur within therange of possible outcomes. Model uncertainty is an important contributor to uncertainty
in climate projections, and includes, but is
not restricted to, the uncertainties introduced
by errors in the model’s representation of
the physical and bio-geochemical processes
affecting the climate system as well as in themodel’s response to external forcing.1
Where it is considered justified to report thelikelihood of particular impacts within therange of possible outcomes, this report takes
a plain-language approach to expressing theexpert judgment of the chapter team, basedon the best available evidence. For example,
an outcome termed “likely” has at least a 66%
chance of occurring (a likelihood greater thanabout 2 of 3 chances); an outcome termed“very likely,” at least a 90% chance (more than
9 out of 10 chances). See Figure 2 for a complete list of the likelihood terminology used inthis report.

The terminology used in the report associatedwith these metrics is shown in Figure 2. This
language is based on that used in NCA3,2 the
IPCC’s Fifth Assessment Report,1 and mostrecently the USGCRP Climate and Health assessment.5 Wherever used, the confidence andlikelihood statements are italicized.
Assessments of confidence in the Key Findings are based on the expert judgment of theauthor team. Authors provide supporting
evidence for each of the chapter’s Key Findings in the Traceable Accounts. Confidence isexpressed qualitatively and ranges from low
confidence (inconclusive evidence or disagreement among experts) to very high confidence(strong evidence and high consensus) (see Figure 2). Confidence should not be interpreted
probabilistically, as it is distinct from statistical
likelihood. See chapter 1 in IPCC1 for further
discussion of this terminology.

Traceable Accounts for each Key Finding1) document the process and rationale theauthors used in reaching the conclusions
in their Key Finding, 2) provide additional
information to readers about the quality of
the information used, 3) allow traceability toresources and data, and 4) describe the levelof likelihood and confidence in the Key Finding. Thus, the Traceable Accounts represent a
synthesis of the chapter author team’s judgment of the validity of findings, as determinedthrough evaluation of evidence and agreement in the scientific literature. The Traceable
Accounts also identify areas where data are

In this report, likelihood is the chance ofoccurrence of an effect or impact based on
measures of uncertainty expressed probabilis6
U.S. Global Change Research Program

Climate Science Special ReportConfidence Level

Likelihood

Very High

Virtually Certain

Strong evidence (establishedtheory, multiple sources, consistent
results, well documented andaccepted methods, etc.), highconsensus
High
Moderate evidence (several sources, some consistency, methods
vary and/or documentation limited,etc.), medium consensus
Medium99%–100%
Extremely Likely
95%–100%
Very Likely
90%–100%
Likely66%–100%

Suggestive evidence (a few sources, limited consistency, models
incomplete, methods emerging,etc.), competing schools of thought
Low

About as Likely as Not
33%–66%
Unlikely

Inconclusive evidence (limited
sources, extrapolations, inconsistent findings, poor documentation
and/or methods not tested, etc.),
disagreement or lack of opinions
among experts

0%–33%
Very Unlikely
0%–10%
Extremely Unlikely
0%–5%
Exceptionally Unlikely
0%–1%

Figure 2. Confidence levels and likelihood statements used in the report. (Figure source: adapted from USGCRP 20165
and IPCC 20131; likelihoods use the broader range from the IPCC assessment). As an example, regarding “likely,” a
66%–100% probability can be interpreted as a likelihood of greater than 2 out of 3 chances for the statement to becertain or true. Not all likelihoods are used in the report.

limited or emerging. Each Traceable Accountincludes 1) a description of the evidence base,2) major uncertainties, and 3) an assessment ofconfidence based on evidence.

Confidence and likelihood levels are based on
the expert judgment of the author team. They
determined the appropriate level of confidence or likelihood by assessing the available
literature, determining the quality and quantity of available evidence, and evaluating thelevel of agreement across different studies.
Often, the underlying studies provided theirown estimates of uncertainty and confidence
intervals. When available, these confidence
intervals were assessed by the authors in

All Key Findings include a description of confidence. Where it is considered scientificallyjustified to report the likelihood of particularimpacts within the range of possible outcomes, Key Findings also include a likelihood
designation.
7
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Climate Science Special Reportmaking their own expert judgments. For
specific descriptions of the process by which
the author team came to agreement on the Key
Findings and the assessment of confidence
and likelihood, see the Traceable Accounts ineach chapter.

lying outside this range, which neverthelesscannot be ruled out and may therefore be relevant for assessing overall risk. In some cases,
this involves an evaluation of the full range
of information contained in the ensemble ofclimate models used for this report, and inother cases this involves the consideration of
additional lines of scientific evidence beyond
the models.

In addition to the use of systematic language
to convey confidence and likelihood information, this report attempts to highlight aspectsof the science that are most relevant for supporting other parts of the Fourth National
Climate Assessment and its analyses of key
societal risks posed by climate change. This
includes attention to trends and changes in thetails of the probability distribution of futureclimate change and its proximate impacts (forexample, on sea level or temperature and precipitation extremes) and on defining plausible
bounds for the magnitude of future changes,
since many key risks are disproportionately determined by plausible low-probability,
high-consequence outcomes. Therefore, in
addition to presenting the expert judgment on
the “most likely” range of projected future climate outcomes, where appropriate, this report
also provides information on the outcomes

Complementing this use of risk-focusedlanguage and presentation around specificscientific findings in the report, Chapter 15:
Potential Surprises provides an overview ofpotential low probability/high consequence“surprises” resulting from climate change.
This includes its analyses of thresholds, also
called tipping points, in the climate systemand the compounding effects of multiple, interacting climate change impacts whose consequences may be much greater than the sum of
the individual impacts. Chapter 15 also highlights critical knowledge gaps that determine
the degree to which such high-risk tails andbounding scenarios can be precisely defined,
including missing processes and feedbacks.

8
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REFERENCES
1.

IPCC, 2013a: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on
Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. http://
www.climatechange2013.org/report/

2.

Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds.,
2014a: Climate Change Impacts in the United States:
The Third National Climate Assessment. U.S. Global
Change Research Program: Washington, D.C., 841
pp. http://dx.doi.org/10.7930/J0Z31WJ2

3.

Arguez, A. and R.S. Vose, 2011: The definition of thestandard WMO climate normal: The key to deriving
alternative climate normals. Bulletin of the American
Meteorological Society, 92, 699-704. http://dx.doi.
org/10.1175/2010BAMS2955.1

U.S. Global Change Research Program

9

4.

IPCC, 2007: Climate Change 2007: The Physical Science
Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on
Climate Change. Solomon, S., D. Qin, M. Manning, Z.
Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L.
Miller, Eds. Cambridge University Press, Cambridge.
U.K, New York, NY, USA, 996 pp. http://www.
ipcc.ch/publications_and_data/publications_ipcc_
fourth_assessment_report_wg1_report_the_physical_science_basis.htm

5.

USGCRP, 2016: The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment.
Crimmins, A., J. Balbus, J.L. Gamble, C.B. Beard, J.E.
Bell, D. Dodgen, R.J. Eisen, N. Fann, M.D. Hawkins,
S.C. Herring, L. Jantarasami, D.M. Mills, S. Saha,
M.C. Sarofim, J. Trtanj, and L. Ziska, Eds. U.S. Global
Change Research Program, Washington, DC, 312 pp.
http://dx.doi.org/10.7930/J0R49NQX

Climate Science Special ReportHighlights of the U.S. Global Change Research Program
Climate Science Special Report
The climate of the United States is strongly connected to the changing global climate. The statements
below highlight past, current, and projected climate changes for the United States and the globe.
Global annually averaged surface air temperature has increased by about 1.8°F (1.0°C) over the last 115
years (1901–2016). This period is now the warmest in the history of modern civilization. The last few
years have also seen record-breaking, climate-related weather extremes, and the last three years havebeen the warmest years on record for the globe. These trends are expected to continue over climate
timescales.
This assessment concludes, based on extensive evidence, that it is extremely likely that human activities, especially emissions of greenhouse gases, are the dominant cause of the observed warming
since the mid-20th century. For the warming over the last century, there is no convincing alternative
explanation supported by the extent of the observational evidence.
In addition to warming, many other aspects of global climate are changing, primarily in response to human activities. Thousands of studies conducted by researchers around the world have documented changes in surface, atmospheric, and oceanic temperatures; melting glaciers; diminishingsnow cover; shrinking sea ice; rising sea levels; ocean acidification; and increasing atmosphericwater vapor.
For example, global average sea level has risen by about 7–8 inches since 1900, with almost half(about 3 inches) of that rise occurring since 1993. Human-caused climate change has made a substantial contribution to this rise since 1900, contributing to a rate of rise that is greater than during any
preceding century in at least 2,800 years. Global sea level rise has already affected the United States;
the incidence of daily tidal flooding is accelerating in more than 25 Atlantic and Gulf Coast cities.
Global average sea levels are expected to continue to rise—by at least several inches in the next
15 years and by 1–4 feet by 2100. A rise of as much as 8 feet by 2100 cannot be ruled out. Sea level
rise will be higher than the global average on the East and Gulf Coasts of the United States.
Changes in the characteristics of extreme events are particularly important for human safety, infrastructure, agriculture, water quality and quantity, and natural ecosystems. Heavy rainfall is increasing in
intensity and frequency across the United States and globally and is expected to continue to increase. The largest observed changes in the United States have occurred in the Northeast.

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Climate Science Special ReportHeatwaves have become more frequent in the United States since the 1960s, while extreme cold
temperatures and cold waves are less frequent. Recent record-setting hot years are projected to become common in the near future for the United States, as annual average temperatures continue torise. Annual average temperature over the contiguous United States has increased by 1.8°F (1.0°C) forthe period 1901–2016; over the next few decades (2021–2050), annual average temperatures are
expected to rise by about 2.5°F for the United States, relative to the recent past (average from
1976–2005), under all plausible future climate scenarios.
The incidence of large forest fires in the western United States and Alaska has increased since
the early 1980s and is projected to further increase in those regions as the climate changes, withprofound changes to regional ecosystems.
Annual trends toward earlier spring melt and reduced snowpack are already affecting water resources in the western United States and these trends are expected to continue. Under higher scenarios, and assuming no change to current water resources management, chronic, long-duration hydrological drought is increasingly possible before the end of this century.
The magnitude of climate change beyond the next few decades will depend primarily on theamount of greenhouse gases (especially carbon dioxide) emitted globally. Without major reductions in emissions, the increase in annual average global temperature relative to preindustrial times
could reach 9°F (5°C) or more by the end of this century. With significant reductions in emissions, theincrease in annual average global temperature could be limited to 3.6°F (2°C) or less.
The global atmospheric carbon dioxide (CO2) concentration has now passed 400 parts per million(ppm), a level that last occurred about 3 million years ago, when both global average temperature and sea level were significantly higher than today. Continued growth in CO2 emissions over thiscentury and beyond would lead to an atmospheric concentration not experienced in tens to hundredsof millions of years. There is broad consensus that the further and the faster the Earth system is pushed
towards warming, the greater the risk of unanticipated changes and impacts, some of which are potentially large and irreversible.
The observed increase in carbon emissions over the past 15–20 years has been consistent with higher
emissions pathways. In 2014 and 2015, emission growth rates slowed as economic growth becameless carbon-intensive. Even if this slowing trend continues, however, it is not yet at a rate that would
limit global average temperature change to well below 3.6°F (2°C) above preindustrial levels.

Recommended Citation for Chapter
Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, B. DeAngelo, S. Doherty, K. Hayhoe, R. Horton, J.P. Kossin,
P.C. Taylor, A.M. Waple, and C.P. Weaver, 2017: Executive summary. In: Climate Science Special Report:
Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C.
Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp.
12-34, doi: 10.7930/J0DJ5CTG.
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Executive
Summary
Introduction
New observations and new research have increased our understanding of past, current, and future climate change since the Third U.S. National Climate Assessment (NCA3) was published in
May 2014. This Climate Science Special Report (CSSR) is designed to capture that new information and build on the existing body of science in order to summarize the current state of knowledge and provide the scientific foundation for the Fourth National Climate Assessment (NCA4).
Since NCA3, stronger evidence has emerged for continuing, rapid, human-caused warming of
the global atmosphere and ocean. This report concludes that “it is extremely likely that human
influence has been the dominant cause of the observed warming since the mid-20th century. For
the warming over the last century, there is no convincing alternative explanation supported by
the extent of the observational evidence.”
The last few years have also seen record-breaking, climate-related weather extremes, the threewarmest years on record for the globe, and continued decline in arctic sea ice. These trends are
expected to continue in the future over climate (multidecadal) timescales. Significant advances
have also been made in our understanding of extreme weather events and how they relate toincreasing global temperatures and associated climate changes. Since 1980, the cost of extreme
events for the United States has exceeded $1.1 trillion; therefore, better understanding of thefrequency and severity of these events in the context of a changing climate is warranted.
Periodically taking stock of the current state of knowledge about climate change and putting
new weather extremes, changes in sea ice, increases in ocean temperatures, and ocean acidification into context ensures that rigorous, scientifically-based information is available to inform
dialogue and decisions at every level. This climate science report serves as the climate science
foundation of the NCA4 and is generally intended for those who have a technical backgroundin climate science. In this Executive Summary, gray boxes present highlights of the main report.
These are followed by related points and selected figures providing more scientific details.
The summary material on each topic presents the most salient points of chapter findings andtherefore represents only a subset of the report’s content. For more details, the reader is referredto the individual chapters. This report discusses climate trends and findings at several scales:
global, nationwide for the United States, and for ten specific U.S. regions (shown in Figure 1 in
the Guide to the Report). A statement of scientific confidence also follows each point in the Executive Summary. The confidence scale is described in the Guide to the Report. At the end of the
Executive Summary and in Chapter 1: Our Globally Changing Climate, there is also a summary
box highlighting the most notable advances and topics since NCA3 and since the 2013 Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report.

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Global and U.S. Temperatures Continue to Rise
Long-term temperature observations are among the most consistent and widespread evidenceof a warming planet. Temperature (and, above all, its local averages and extremes) affects agricultural productivity, energy use, human health, water resources, infrastructure, natural ecosystems, and many other essential aspects of society and the natural environment. Recent data add
to the weight of evidence for rapid global-scale warming, the dominance of human causes, andthe expected continuation of increasing temperatures, including more record-setting extremes.
(Ch. 1)
Changes in Observed and Projected Global Temperature
The global, long-term, and unambiguous warming trend has continued during recent years. Since thelast National Climate Assessment was published, 2014 became the warmest year on record globally;
2015 surpassed 2014 by a wide margin; and 2016 surpassed 2015. Sixteen of the warmest years onrecord for the globe occurred in the last 17 years (1998 was the exception). (Ch. 1; Fig. ES.1)•

Global annual average temperature (as calculated from instrumental records over both landand oceans) has increased by more than 1.2°F (0.65°C) for the period 1986–2016 relative to
1901–1960; the linear regression change over the entire period from 1901–2016 is 1.8°F (1.0°C)(very high confidence; Fig. ES.1). Longer-term climate records over past centuries and millennia
indicate that average temperatures in recent decades over much of the world have been muchhigher, and have risen faster during this time period than at any time in the past 1,700 years
or more, the time period for which the global distribution of surface temperatures can be reconstructed (high confidence). (Ch. 1)

Global Temperatures Continue to Rise
Global Land and Ocean Temperature Anomalies

Surface Temperature Change

Annual2.0
1.6

Anomaly (°F)

1.2
0.8
0.4
0.0
−0.4
−0.8
1880

1900

1920

1940

1960

1980

Change in Temperature (ºF)

2000
−1.5 −1.0

Year

−0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Figure ES.1: (left) Global annual average temperature has increased by more than 1.2°F (0.7°C) for the period 1986–
2016 relative to 1901–1960. Red bars show temperatures that were above the 1901–1960 average, and blue bars
indicate temperatures below the average. (right) Surface temperature change (in °F) for the period 1986–2016 relative
to 1901–1960. Gray indicates missing data. From Figures 1.2. and 1.3 in Chapter 1.

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Many lines of evidence demonstrate that it is extremely likely that human influence has been
the dominant cause of the observed warming since the mid-20th century. Over the last century, there are no convincing alternative explanations supported by the extent of the observational evidence. Solar output changes and internal natural variability can only contribute
marginally to the observed changes in climate over the last century, and there is no convincing
evidence for natural cycles in the observational record that could explain the observed changes in climate. (Very high confidence) (Ch. 1)•

The likely range of the human contribution to the global mean temperature increase overthe period 1951–2010 is 1.1° to 1.4°F (0.6° to 0.8°C), and the central estimate of the observed
warming of 1.2°F (0.65°C) lies within this range (high confidence). This translates to a likely
human contribution of 92%–123% of the observed 1951–2010 change. The likely contributionsof natural forcing and internal variability to global temperature change over that period areminor (high confidence). (Ch. 3; Fig. ES.2)•

Natural variability, including El Niño events and other recurring patterns of ocean–atmosphere interactions, impact temperature and precipitation, especially regionally, over timescales of months to years. The global influence of natural variability, however, is limited to asmall fraction of observed climate trends over decades. (Very high confidence) (Ch. 1)

Human Activities Are the Primary Driver of Recent Global Temperature Rise

Radiative Forcing (W/m2), 1750–2011

3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

−0.5

Human-caused

Solar

Volcanic

Figure ES.2: Global annual average radiative forcing change from 1750 to 2011 due to human activities, changes in total solar
irradiance, and volcanic emissions. Black bars indicate the uncertainty in each. Radiative forcing is a measure of the influencea factor (such as greenhouse gas emissions) has in changing the global balance of incoming and outgoing energy. Radiative
forcings greater than zero (positive forcings) produce climate warming; forcings less than zero (negative forcings) produce
climate cooling. Over this time period, solar forcing has oscillated on approximately an 11-year cycle between −0.11 and +0.19
W/m2. Radiative forcing due to volcanic emissions is always negative (cooling) and can be very large immediately following
significant eruptions but is short-lived. Over the industrial era, the largest volcanic forcing followed the eruption of Mt. Tambora
in 1815 (−11.6 W/m2). This forcing declined to −4.5 W/m2 in 1816, and to near-zero by 1820. Forcing due to human activities,
in contrast, has becoming increasingly positive (warming) since about 1870, and has grown at an accelerated rate since about
1970. There are also natural variations in temperature and other climate variables which operate on annual to decadal timescales. This natural variability contributes very little to climate trends over decades and longer. Simplified from Figure 2.6 in
Chapter 2. See Chapter 2 for more details.

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Global climate is projected to continue to change over this century and beyond. The magnitude of climate change beyond the next few decades will depend primarily on the amount ofgreenhouse (heat-trapping) gases emitted globally and on the remaining uncertainty in thesensitivity of Earth’s climate to those emissions (very high confidence). With significant reductions in the emissions of greenhouse gases, the global annually averaged temperature rise
could be limited to 3.6°F (2°C) or less. Without major reductions in these emissions, the increase in annual average global temperatures relative to preindustrial times could reach 9°F(5°C) or more by the end of this century. (Ch. 1; Fig. ES.3)•

If greenhouse gas concentrations were stabilized at their current level, existing concentrations
would commit the world to at least an additional 1.1°F (0.6°C) of warming over this century
relative to the last few decades (high confidence in continued warming, medium confidence inamount of warming. (Ch. 4)

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Scenarios Used in this Assessment
Projections of future climate conditions use a range of plausible future scenarios. Consistent with previous
practice, this assessment relies on scenarios generated for the Intergovernmental Panel on Climate Change(IPCC). The IPCC completed its last assessment in 2013–2014, and its projections were based on updated scenarios, namely four “representative concentration pathways” (RCPs). The RCP scenarios are numbered according to changes in radiative forcing in 2100 relative to preindustrial conditions: +2.6, +4.5, +6.0 and +8.5 watts
per square meter (W/m2). Radiative forcing is a measure of the influence a factor (such as greenhouse gas
emissions) has in changing the global balance of incoming and outgoing energy. Absorption by greenhousegases (GHGs) of infrared energy radiated from the surface leads to warming of the surface and atmosphere.
Though multiple emissions pathways could lead to the same 2100 radiative forcing value, an associated pathway of CO2 and other human-caused emissions of greenhouse gases, aerosols, and air pollutants has been
selected for each RCP. RCP8.5 implies a future with continued high emissions growth, whereas the other RCPs
represent different pathways of mitigating emissions. Figure ES.3 shows these emissions pathways and thecorresponding projected changes in global temperature.

30
25
20

Projected Annual
Global Carbon Emissions

Projected Global Temperatures
10

Higher Scenario (RCP8.5)
Mid-high Scenario (RCP6.0)
Lower Scenario (RCP4.5)
Even Lower Scenario (RCP2.6)
Observed

Temperature Change (°F)

Annual Carbon Emissions (GtC)

Greater Emissions Lead to Significantly More Warming

15
10
5
0
−5
1900

1950

2000

2050

8
6
4
2
0

−2
1901

2100

RCP8.5
RCP4.5
RCP2.6
Observed

1951

2001

2051

2101

Figure ES.3: The two panels above show annual historical and a range of plausible future carbon emissions inunits of gigatons of carbon (GtC) per year (left) and the historical observed and future temperature change thatwould result for a range of future scenarios relative to the 1901–1960 average, based on the central estimate (lines)
and a range (shaded areas, two standard deviations) as simulated by the full suite of CMIP5 global climate models(right). By 2081–2100, the projected range in global mean temperature change is 1.1°–4.3°F under the even lower
scenario (RCP2.6; 0.6°–2.4°C, green), 2.4°–5.9°F under the lower scenario (RCP4.5; 1.3°–3.3°C, blue), 3.0°–6.8°F
under the mid-high scenario (RCP6.0; 1.6°–3.8°C, not shown) and 5.0°–10.2°F under the higher scenario (RCP8.5;
2.8°–5.7°C, orange). See the main report for more details on these scenarios and implications. Based on Figure
4.1 in Chapter 4.

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Changes in Observed and Projected U.S. Temperature
Annual average temperature over the contiguous United States has increased by 1.8°F (1.0°C) for theperiod 1901–2016 and is projected to continue to rise. (Very high confidence). (Ch. 6; Fig. ES.4)•

Annual average temperature over the contiguous United States has increased by 1.2°F (0.7°C)
for the period 1986–2016 relative to 1901–1960 and by 1.8°F (1.0°C) based on a linear regression for the period 1901–2016 (very high confidence). Surface and satellite data are consistentin their depiction of rapid warming since 1979 (high confidence). Paleo-temperature evidenceshows that recent decades are the warmest of the past 1,500 years (medium confidence). (Ch. 6)•

Annual average temperature over the contiguous United States is projected to rise (very highconfidence). Increases of about 2.5°F (1.4°C) are projected for the period 2021–2050 relative tothe average from 1976–2005 in all RCP scenarios, implying recent record-setting years may
be “common” in the next few decades (high confidence). Much larger rises are projected bylate century (2071–2100): 2.8°–7.3°F (1.6°–4.1°C) in a lower scenario (RCP4.5) and 5.8°–11.9°F(3.2°–6.6°C) in a higher scenario (RCP8.5) (high confidence). (Ch. 6; Fig. ES.4)•

In the United States, the urban heat island effect results in daytime temperatures 0.9°–7.2°F(0.5°–4.0°C) higher and nighttime temperatures 1.8°– 4.5°F (1.0°–2.5°C) higher in urban areas
than in rural areas, with larger temperature differences in humid regions (primarily in theeastern United States) and in cities with larger and denser populations. The urban heat islandeffect will strengthen in the future as the structure and spatial extent as well as population
density of urban areas change and grow (high confidence). (Ch. 10)

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Significantly More Warming Occurs Under
Higher Greenhouse
Gas
Concentration
Scenarios
Projected
Changes in
AnnualAverage Temperature
Mid 21st Century
Lower Scenario (RCP4.5)

Higher Scenario (RCP8.5)

Late 21st Century
Lower Scenario (RCP4.5)

Higher Scenario (RCP8.5)

Change in Temperature (°F)
2

4

6

8

10 12

14 16

18

Figure ES.4: These maps show the projected changes in annual average temperatures for mid- and late-21st century
for two future pathways. Changes are the differences between the average projected temperatures for mid-century(2036–2065; top), and late-century (2070–2099; bottom), and those observed for the near-present (1976–2005). See
Figure 6.7 in Chapter 6 for more details.

Many Temperature and Precipitation Extremes Are Becoming More Common
Temperature and precipitation extremes can affect water quality and availability, agricultural
productivity, human health, vital infrastructure, iconic ecosystems and species, and the likelihood of disasters. Some extremes have already become more frequent, intense, or of longer
duration, and many extremes are expected to continue to increase or worsen, presenting substantial challenges for built, agricultural, and natural systems. Some storm types such as hurricanes, tornadoes, and winter storms are also exhibiting changes that have been linked to climatechange, although the current state of the science does not yet permit detailed understanding.

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Observed Changes in Extremes
There have been marked changes in temperature extremes across the contiguous United States.
The number of high temperature records set in the past two decades far exceeds the number of low
temperature records. (Very high confidence) (Ch. 6, Fig. ES.5)•

The frequency of cold waves has decreased since the early 1900s, and the frequency of heatwaves has increased since the mid-1960s (the Dust Bowl era of the 1930s remains the peak
period for extreme heat in the United States). (Very high confidence). (Ch. 6)•

The frequency and intensity of extreme heat and heavy precipitation events are increasing inmost continental regions of the world (very high confidence). These trends are consistent withexpected physical responses to a warming climate. Climate model studies are also consistent
with these trends, although models tend to underestimate the observed trends, especially forthe increase in extreme precipitation events (very high confidence for temperature, high confidence for extreme precipitation). (Ch. 1)

Ratio of Daily Temperature Records

Record Warm Daily Temperatures Are Occurring More Often
7:1
6:1

More Daily Record Highs
More Daily Record Lows

5:1
4:1
3:1
2:1
1:1

1930 1940 1950 1960 1970 1980 1990 2000 2010
Year

Figure ES.5: Observed changes in the occurrence of record-setting daily temperatures in the contiguous United States.
Red bars indicate a year with more daily record highs than daily record lows, while blue bars indicate a year with morerecord lows than highs. The height of the bar indicates the ratio of record highs to lows (red) or of record lows to highs(blue). For example, a ratio of 2:1 for a blue bar means that there were twice as many record daily lows as daily record
highs that year. (Figure source: NOAA/NCEI). From Figure 6.5 in Chapter 6.

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Heavy precipitation events in most parts of the United States have increased in both intensity andfrequency since 1901 (high confidence). There are important regional differences in trends, with thelargest increases occurring in the northeastern United States (high confidence). (Ch. 7; Fig. ES.6)

Observed
Extreme Precipitation Has
Increased Change
Across Much of the United Statesin Heavy Precipitation

5-yr Maximum Daily Precipitation(1901–2016)

99th Percentile Precipitation(1958–2016)

NA

16

2

9
13

29

27

18

55

42

1

10
13

12

16

27

–11

NA

–12

NA

Number of 5-yr, 2 Day Events(1901–2016)

Number of 5-yr, 2 Day Events(1958–2016)

NA

27

11

13
21

30

74

63

92

53

−7

−2
40

11

58

NA

49

−33
NA

NA

Change (%)
<0

0-9

10-19

20-29

30-39

40+

Figure ES.6: These maps show the percentage change in several metrics of extreme precipitation by NCA4 region,
including (upper left) the maximum daily precipitation in consecutive 5-year periods; (upper right) the amount of precipitation falling in daily events that exceed the 99th percentile of all non-zero precipitation days (top 1% of all daily precipitation events); (lower left) the number of 2-day events with a precipitation total exceeding the largest 2-day amount thatis expected to occur, on average, only once every 5 years, as calculated over 1901–2016; and (lower right) the numberof 2-day events with a precipitation total exceeding the largest 2-day amount that is expected to occur, on average, only
once every 5 years, as calculated over 1958–2016. The number in each black circle is the percent change over theentire period, either 1901–2016 or 1958–2016. Note that Alaska and Hawai‘i are not included in the 1901–2016 maps
owing to a lack of observations in the earlier part of the 20th century. (Figure source: CICS-NC / NOAA NCEI). Basedon figure 7.4 in Chapter 7.

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Recent droughts and associated heat waves have reached record intensity in some regionsof the United States; however, by geographical scale and duration, the Dust Bowl era of the
1930s remains the benchmark drought and extreme heat event in the historical record. (Veryhigh confidence) (Ch. 8)•

Northern Hemisphere spring snow cover extent, North America maximum snow depth,snow water equivalent in the western United States, and extreme snowfall years in the southern and western United States have all declined, while extreme snowfall years in parts of thenorthern United States have increased. (Medium confidence). (Ch. 7)•

There has been a trend toward earlier snowmelt and a decrease in snowstorm frequency
on the southern margins of climatologically snowy areas (medium confidence). Winter storm
tracks have shifted northward since 1950 over the Northern Hemisphere (medium confidence).
Potential linkages between the frequency and intensity of severe winter storms in the UnitedStates and accelerated warming in the Arctic have been postulated, but they are complex, and,to some extent, contested, and confidence in the connection is currently low. (Ch. 9)•Tornado activity in the United States has become more variable, particularly over the 2000s,
with a decrease in the number of days per year with tornadoes and an increase in the numberof tornadoes on these days (medium confidence). Confidence in past trends for hail and severe
thunderstorm winds, however, is low (Ch. 9)

Projected Changes in Extremes•

The frequency and intensity of extreme high temperature events are virtually certain to increase in
the future as global temperature increases (high confidence). Extreme precipitation events will very
likely continue to increase in frequency and intensity throughout most of the world (high confidence).
Observed and projected trends for some other types of extreme events, such as floods, droughts,
and severe storms, have more variable regional characteristics. (Ch. 1)

Extreme temperatures in the contiguous United States are projected to increase even more than
average temperatures (very high confidence). (Ch. 6)•

Both extremely cold days and extremely warm days are expected to become warmer. Coldwaves are predicted to become less intense while heat waves will become more intense. The
number of days below freezing is projected to decline while the number above 90°F will rise.
(Very high confidence) (Ch. 6)•

The frequency and intensity of heavy precipitation events in the United States are projected
to continue to increase over the 21st century (high confidence). There are, however, important regional and seasonal differences in projected changes in total precipitation: the northern
United States, including Alaska, is projected to receive more precipitation in the winter andspring, and parts of the southwestern United States are projected to receive less precipitationin the winter and spring (medium confidence). (Ch. 7)

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The frequency and severity of landfalling “atmospheric rivers” on the U.S. West Coast (narrow streams of moisture that account for 30%–40% of the typical snowpack and annual precipitation in the region and are associated with severe flooding events) will increase as a resultof increasing evaporation and resulting higher atmospheric water vapor that occurs with increasing temperature. (Medium confidence) (Ch. 9)•

Projections indicate large declines in snowpack in the western United States and shifts tomore precipitation falling as rain than snow in the cold season in many parts of the central
and eastern United States (high confidence). (Ch. 7)•

Substantial reductions in western U.S. winter and spring snowpack are projected as the climate warms. Earlier spring melt and reduced snow water equivalent have been formally
attributed to human-induced warming (high confidence) and will very likely be exacerbated as
the climate continues to warm (very high confidence). Under higher scenarios, and assuming nochange to current water resources management, chronic, long-duration hydrological drought
is increasingly possible by the end of this century (very high confidence). (Ch. 8)

Future decreases in surface soil moisture from human activities over most of the United States arelikely as the climate warms under the higher scenarios. (Medium confidence) (Ch. 8)•

The human effect on recent major U.S. droughts is complicated. Little evidence is found fora human influence on observed precipitation deficits, but much evidence is found for a human influence on surface soil moisture deficits due to increased evapotranspiration caused byhigher temperatures. (High confidence) (Ch. 8)•

The incidence of large forest fires in the western United States and Alaska has increased since
the early 1980s (high confidence) and is projected to further increase in those regions as the climate warms, with profound changes to certain ecosystems (medium confidence). (Ch. 8)•

Both physics and numerical modeling simulations generally indicate an increase in tropical
cyclone intensity in a warmer world, and the models generally show an increase in the number of very intense tropical cyclones. For Atlantic and eastern North Pacific hurricanes andwestern North Pacific typhoons, increases are projected in precipitation rates (high confidence)and intensity (medium confidence). The frequency of the most intense of these storms is projected to increase in the Atlantic and western North Pacific (low confidence) and in the eastern
North Pacific (medium confidence). (Ch. 9)

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Box ES.1: The Connected Climate System: Distant Changes Affect theUnited States
Weather conditions and the ways they vary across regions and over the course of the year are influenced,
in the United States as elsewhere, by a range of factors, including local conditions (such as topography andurban heat islands), global trends (such as human-caused warming), and global and regional circulation patterns, including cyclical and chaotic patterns of natural variability within the climate system. For example,
during an El Niño year, winters across the southwestern United States are typically wetter than average, andglobal temperatures are higher than average. During a La Niña year, conditions across the southwestern United States are typically dry, and there tends to be a lowering of global temperatures (Fig. ES.7).
El Niño is not the only repeating pattern of natural variability in the climate system. Other important patterns include the North Atlantic Oscillation (NAO)/Northern Annular Mode (NAM), which particularly affects conditions
on the U.S. East Coast, and the North Pacific Oscillation (NPO) and Pacific North American Pattern (PNA), which
especially affect conditions in Alaska and the U.S. West Coast. These patterns are closely linked to other atmospheric circulation phenomena like the position of the jet streams. Changes in the occurrence of these patterns
or their properties have contributed to recent U.S. temperature and precipitation trends (medium confidence)
although confidence is low regarding the size of the role of human activities in these changes. (Ch. 5)
Understanding the full scope of human impacts on climate requires a global focus because of the interconnected nature of the climate system. For example, the climate of the Arctic and the climate of the continental
United States are connected through atmospheric circulation patterns. While the Arctic may seem remote tomost Americans, the climatic effects of perturbations to arctic sea ice, land ice, surface temperature, snow cover, and permafrost affect the amount of warming, sea level change, carbon cycle impacts, and potentially even
weather patterns in the lower 48 states. The Arctic is warming at a rate approximately twice as fast as the global average and, if it continues to warm at the same rate, Septembers will be nearly ice-free in the Arctic Ocean
sometime between now and the 2040s (see Fig. ES.10). The important influence of arctic climate change on
Alaska is apparent; the influence of arctic changes on U.S. weather over the coming decades remains an open
question with the potential for significant impact. (Ch. 11)
Changes in the Tropics can also impact the rest of the globe, including the United States. There is growing
evidence that the Tropics have expanded poleward by about 70 to 200 miles in each hemisphere over theperiod 1979–2009, with an accompanying shift of the subtropical dry zones, midlatitude jets, and storm tracks(medium to high confidence). Human activities have played a role in the change (medium confidence), althoughconfidence is presently low regarding the magnitude of the human contribution relative to natural variability(Ch. 5).
(continued on next page)

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Box ES.1 (continued)
Large-Scale Patterns of Natural Variability Affect U.S. Climate
Typical El Niño Winters

WarmerL

Low pressure
Drier
WetterExtended Pacific Jet
Stream, amplified
storm track

ColderTypical La Niña Winters

Variable Polar
Jet Stream

H

Blockinghigh pressure

ColderWetterDrier

Wetter
WarmerFigure ES.7: This figure illustrates the typical January–March weather anomalies and atmospheric circulation during
moderate to strong (top) El Niño and (bottom) La Niña. These influences over the United States often occur moststrongly during the cold season. From Figure 5.2 in Chapter 5.

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Oceans Are Rising, Warming, and Becoming More Acidic
Oceans occupy two-thirds of the planet’s surface and host unique ecosystems and species, including those important for global commercial and subsistence fishing. Understanding climate
impacts on the ocean and the ocean’s feedbacks to the climate system is critical for a comprehensive understanding of current and future changes in climate.
Global Ocean Heat
The world’s oceans have absorbed about 93% of the excess heat caused by greenhouse gas warming
since the mid-20th century, making them warmer and altering global and regional climate feedbacks.
(Very high confidence) (Ch. 13)•

Ocean heat content has increased at all depths since the 1960s and surface waters have warmed
by about 1.3° ± 0.1°F (0.7° ± 0.08°C) per century globally since 1900 to 2016. Under higher
scenarios, a global increase in average sea surface temperature of 4.9° ± 1.3°F (2.7° ± 0.7°C) is
projected by 2100. (Very high confidence). (Ch. 13)

Global and Regional Sea Level Rise
Global mean sea level (GMSL) has risen by about 7–8 inches (about 16–21 cm) since 1900, with about
3 of those inches (about 7 cm) occurring since 1993 (very high confidence). (Ch. 12)•

Human-caused climate change has made a substantial contribution to GMSL rise since 1900
(high confidence), contributing to a rate of rise that is greater than during any preceding century in at least 2,800 years (medium confidence). (Ch. 12; Fig. ES.8)•

Relative to the year 2000, GMSL is very likely to rise by 0.3–0.6 feet (9–18 cm) by 2030, 0.5–1.2
feet (15–38 cm) by 2050, and 1.0–4.3 feet (30–130 cm) by 2100 (very high confidence in lower bounds; medium confidence in upper bounds for 2030 and 2050; low confidence in upperbounds for 2100). Future emissions pathways have little effect on projected GMSL rise in thefirst half of the century, but significantly affect projections for the second half of the century(high confidence). (Ch. 12)

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Recent Sea Level Rise Fastest for Over 2,000 Years

Historical and Projected Global Mean Sea Level Rise

Global Mean
Sea Level (feet)

0.2
0.0
−0.2
−0.4
−0.6

Global mean sea level (feet)

−0.8
−500

0

500

1000
Year (CE)

1850

1900

1950
Year

1500

2000

8
6
4
2
0
1800

2000

2050

2100

Figure ES.8: The top panel shows observed and reconstructed mean sea level for the last 2,500 years. The bottom
panel shows projected mean sea level for six future scenarios. The six scenarios—spanning a range designed to informa variety of decision makers—extend from a low scenario, consistent with continuation of the rate of sea level rise overthe last quarter century, to an extreme scenario, assuming rapid mass loss from the Antarctic ice sheet. Note that therange on the vertical axis in the bottom graph is approximately ten times greater than in the top graph. Based on Figure12.2 and 12.4 in Chapter 12. See the main report for more details.

•

Emerging science regarding Antarctic ice sheet stability suggests that, for higher scenarios, a
GMSL rise exceeding 8 feet (2.4 m) by 2100 is physically possible, although the probability ofsuch an extreme outcome cannot currently be assessed. Regardless of emission pathway, it isextremely likely that GMSL rise will continue beyond 2100 (high confidence). (Ch. 12)•

Relative sea level rise in this century will vary along U.S. coastlines due, in part, to changes inEarth’s gravitational field and rotation from melting of land ice, changes in ocean circulation,and vertical land motion (very high confidence). For almost all future GMSL rise scenarios, relative sea level rise is likely to be greater than the global average in the U.S. Northeast and thewestern Gulf of Mexico. In intermediate and low GMSL rise scenarios, relative sea level rise
is likely to be less than the global average in much of the Pacific Northwest and Alaska. For
high GMSL rise scenarios, relative sea level rise is likely to be higher than the global averagealong all U.S. coastlines outside Alaska. Almost all U.S. coastlines experience more than global mean sea level rise in response to Antarctic ice loss, and thus would be particularly affectedunder extreme GMSL rise scenarios involving substantial Antarctic mass loss (high confidence).
(Ch. 12)

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Coastal Flooding•

As sea levels have risen, the number of tidal floods each year that cause minor impacts (alsocalled “nuisance floods”) have increased 5- to 10-fold since the 1960s in several U.S. coastal
cities (very high confidence). Rates of increase are accelerating in over 25 Atlantic and Gulf
Coast cities (very high confidence). Tidal flooding will continue increasing in depth, frequency,
and extent this century (very high confidence). (Ch. 12)“Nuisance Flooding” Increases Across the United States
365
Minor Tidal Floods (Days/Year)300

RCP8.5

RCP4.5

RCP2.6

Trend

Charleston, SC

200
100

365
300

San Francisco, CA

200
100
0
1960

1980

2000

2020

2040

2060

2080

2100

Figure ES. 9: Annual occurrences of tidal floods (days per year), also called sunny-day or nuisance flooding, haveincreased for some U.S. coastal cities. The figure shows historical exceedances (orange bars) for two of the locations—Charleston, SC and San Francisco, CA—and future projections through 2100. The projections are based upon the continuation of the historical trend (blue) and under median RCP2.6, 4.5 and 8.5 conditions. From Figure 12.5, Chapter 12.

•

Assuming storm characteristics do not change, sea level rise will increase the frequency andextent of extreme flooding associated with coastal storms, such as hurricanes and nor’easters(very high confidence). A projected increase in the intensity of hurricanes in the North Atlantic(medium confidence) could increase the probability of extreme flooding along most of the U.S.
Atlantic and Gulf Coast states beyond what would be projected based solely on relative sea
level rise. However, there is low confidence in the projected increase in frequency of intense Atlantic hurricanes, and the associated flood risk amplification, and flood effects could be offset
or amplified by such factors, such as changes in overall storm frequency or tracks. (Ch.12; Fig.
ES. 9)

Global Ocean Circulation•

The potential slowing of the Atlantic meridional overturning circulation (AMOC; of which
the Gulf Stream is one component)—as a result of increasing ocean heat content and freshwater-driven buoyancy changes—could have dramatic climate feedbacks as the ocean absorbs less heat and CO2 from the atmosphere. This slowing would also affect the climates of

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North America and Europe. Any slowing documented to date cannot be directly tied to human-caused forcing, primarily due to lack of adequate observational data and to challenges
in modeling ocean circulation changes. Under a higher scenario (RCP8.5), models show that
the AMOC weakens over the 21st century (low confidence). (Ch. 13)
Global and Regional Ocean Acidification
The world’s oceans are currently absorbing more than a quarter of the CO2 emitted to the atmosphereannually from human activities, making them more acidic (very high confidence), with potential
detrimental impacts to marine ecosystems. (Ch. 13)•

Higher-latitude systems typically have a lower buffering capacity against changing acidity,
exhibiting seasonally corrosive conditions sooner than low-latitude systems. The rate of acidification is unparalleled in at least the past 66 million years (medium confidence). Under thehigher scenario (RCP8.5), the global average surface ocean acidity is projected to increase by
100% to 150% (high confidence). (Ch. 13)•

Acidification is regionally greater than the global average along U.S. coastal systems as aresult of upwelling (e.g., in the Pacific Northwest) (high confidence), changes in freshwater
inputs (e.g., in the Gulf of Maine) (medium confidence), and nutrient input (e.g., in urbanizedestuaries) (high confidence). (Ch. 13)

Ocean Oxygen•

Increasing sea surface temperatures, rising sea levels, and changing patterns of precipitation,
winds, nutrients, and ocean circulation are contributing to overall declining oxygen concentrations at intermediate depths in various ocean locations and in many coastal areas. Overthe last half century, major oxygen losses have occurred in inland seas, estuaries, and in the
coastal and open ocean (high confidence). Ocean oxygen levels are projected to decrease by asmuch as 3.5% under the higher scenario (RCP8.5) by 2100 relative to preindustrial values (highconfidence). (Ch. 13)

Climate Change in Alaska and across the Arctic Continues to Outpace Global Climate
ChangeResidents of Alaska are on the front lines of climate change. Crumbling buildings, roads, andbridges and eroding shorelines are commonplace. Accelerated melting of multiyear sea ice
cover, mass loss from the Greenland Ice Sheet, reduced snow cover, and permafrost thawing
are stark examples of the rapid changes occurring in the Arctic. Furthermore, because elementsof the climate system are interconnected (see Box ES.1), changes in the Arctic influence climate
conditions outside the Arctic.

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Arctic Temperature Increases
Annual average near-surface air temperatures across Alaska and the Arctic have increased overthe last 50 years at a rate more than twice as fast as the global average temperature. (Very highconfidence) (Ch. 11)•

Rising Alaskan permafrost temperatures are causing permafrost to thaw and become morediscontinuous; this process releases additional carbon dioxide and methane resulting in additional warming (high confidence). The overall magnitude of the permafrost-carbon feedback
is uncertain (Ch.2); however, it is clear that these emissions have the potential to compromise
the ability to limit global temperature increases. (Ch. 11)•

Atmospheric circulation patterns connect the climates of the Arctic and the contiguous UnitedStates. Evidenced by recent record warm temperatures in the Arctic and emerging science,
the midlatitude circulation has influenced observed arctic temperatures and sea ice (high confidence). However, confidence is low regarding whether or by what mechanisms observed
arctic warming may have influenced the midlatitude circulation and weather patterns overthe continental United States. The influence of arctic changes on U.S. weather over the coming
decades remains an open question with the potential for significant impact. (Ch. 11)

Arctic Land Ice Loss•

Arctic land ice loss observed in the last three decades continues, in some cases accelerating(very high confidence). It is virtually certain that Alaska glaciers have lost mass over the last 50
years, with each year since 1984 showing an annual average ice mass less than the previous
year. Over the satellite record, average ice mass loss from Greenland was −269 Gt per year
between April 2002 and April 2016, accelerating in recent years (high confidence). (Ch. 11)

Arctic Sea Ice Loss
Since the early 1980s, annual average arctic sea ice has decreased in extent between 3.5% and 4.1%
per decade, has become thinner by between 4.3 and 7.5 feet, and is melting at least 15 more days
each year. September sea ice extent has decreased between 10.7% and 15.9% per decade. (Very highconfidence) (Ch. 11)•

Arctic sea ice loss is expected to continue through the 21st century, very likely resulting in nearly sea ice-free late summers by the 2040s (very high confidence). (Ch. 11)•

It is very likely that human activities have contributed to observed arctic surface temperature
warming, sea ice loss, glacier mass loss, and northern hemisphere snow extent decline (highconfidence). (Ch. 11)

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September Sea Ice Extent (million sq. miles)

Multiyear Sea Ice Has Declined Dramatically

3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

Slope=−13.3% ± 2.6% per decade
1979–2016 mean = 2.42 million sq. miles
1980

1985

1990

1995

Year

2000

2005

2010

2015

Figure ES.10: September sea ice extent and age shown for (top) 1984 and (middle) 2016, illustrating significant reductions in sea ice extent and age (thickness). The bar graph in the lower right of each panel illustrates the sea ice
area (unit: million km2) covered within each age category (> 1 year), and the green bars represent the maximum extent
for each age range during the record. The year 1984 is representative of September sea ice characteristics during the
1980s. The years 1984 and 2016 are selected as endpoints in the time series; a movie of the complete time series isavailable at http://svs.gsfc.nasa.gov/cgi-bin/details.cgi?aid=4489. (bottom) The satellite-era arctic sea ice areal extent
trend from 1979 to 2016 for September (unit: million mi2). From Figure 11.1 in Chapter 11.

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Limiting Globally Averaged Warming to 2°C (3.6°F) Will Require Major Reductions in
Emissions
Human activities are now the dominant cause of the observed trends in climate. For that reason,
future climate projections are based on scenarios of how human activities will continue to affectthe climate over the remainder of this century and beyond (see Sidebar: Scenarios Used in thisAssessment). There remains significant uncertainty about future emissions due to changing
economic, political, and demographic factors. For that reason, this report quantifies possible
climate changes for a broad set of plausible future scenarios through the end of the century. (Ch.
2, 4, 10, 14)

The observed increase in global carbon emissions over the past 15–20 years has been consistent
with higher scenarios (e.g., RCP8.5) (very high confidence). In 2014 and 2015, emission growth rates
slowed as economic growth became less carbon-intensive (medium confidence). Even if this slowing
trend continues, however, it is not yet at a rate that would limit the increase in the global averagetemperature to well below 3.6°F (2°C) above preindustrial levels (high confidence). (Ch. 4)•

Global mean atmospheric carbon dioxide (CO2) concentration has now passed 400 ppm, a level that last occurred about 3 million years ago, when global average temperature and sea level
were significantly higher than today (high confidence). Continued growth in CO2 emissions
over this century and beyond would lead to an atmospheric concentration not experienced intens of millions of years (medium confidence). The present-day emissions rate of nearly 10 GtC
per year suggests that there is no climate analog for this century any time in at least the last 50
million years (medium confidence). (Ch. 4)•

Warming and associated climate effects from CO2 emissions persist for decades to millennia. In the near-term, changes in climate are determined by past and present greenhouse gas
emissions modified by natural variability. Reducing net emissions of CO2 is necessary to limit
near-term climate change and long-term warming. Other greenhouse gases (e.g., methane)and black carbon aerosols exert stronger warming effects than CO2 on a per ton basis, butthey do not persist as long in the atmosphere (Ch. 2); therefore, mitigation of non-CO2 species
contributes substantially to near-term cooling benefits but cannot be relied upon for ultimate
stabilization goals. (Very high confidence) (Ch. 14)

Choices made today will determine the magnitude of climate change risks beyond the next few
decades. (Ch. 4, 14)•

Stabilizing global mean temperature to less than 3.6°F (2°C) above preindustrial levels requires
substantial reductions in net global CO2 emissions prior to 2040 relative to present-day values andlikely requires net emissions to become zero or possibly negative later in the century. After accounting for the temperature effects of non-CO2 species, cumulative global CO2 emissions muststay below about 800 GtC in order to provide a two-thirds likelihood of preventing 3.6°F (2°C) of

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warming. Given estimated cumulative emissions since 1870, no more than approximately 230 GtC
may be emitted in the future in order to remain under this temperature limit. Assuming global
emissions are equal to or greater than those consistent with the RCP4.5 scenario, this cumulative
carbon threshold would be exceeded in approximately two decades. (Ch. 14)•

Achieving global greenhouse gas emissions reductions before 2030 consistent with targets
and actions announced by governments in the lead up to the 2015 Paris climate conference
would hold open the possibility of meeting the long-term temperature goal of limiting global warming to 3.6°F (2°C) above preindustrial levels, whereas there would be virtually nochance if net global emissions followed a pathway well above those implied by country announcements. Actions in the announcements are, by themselves, insufficient to meet a 3.6°F(2°C) goal; the likelihood of achieving that depends strongly on the magnitude of global emissions reductions after 2030. (High confidence) (Ch. 14)•

Climate intervention or geoengineering strategies such as solar radiation management aremeasures that attempt to limit or reduce global temperature increases. Further assessmentsof the technical feasibilities, costs, risks, co-benefits, and governance challenges of climate
intervention or geoengineering strategies, which are as yet unproven at scale, are a necessary
step before judgments about the benefits and risks of these approaches can be made with highconfidence. (High confidence) (Ch. 14)•

In recent decades, land-use and land-cover changes have turned the terrestrial biosphere (soiland plants) into a net “sink” for carbon (drawing down carbon from the atmosphere), andthis sink has steadily increased since 1980 (high confidence). Because of the uncertainty in the
trajectory of land cover, the possibility of the land becoming a net carbon source cannot be
excluded (very high confidence). (Ch. 10)

There is a Significant Possibility for Unanticipated Changes
Humanity’s effect on the Earth system, through the large-scale combustion of fossil fuels andwidespread deforestation and the resulting release of carbon dioxide (CO2) into the atmosphere,
as well as through emissions of other greenhouse gases and radiatively active substances fromhuman activities, is unprecedented. There is significant potential for humanity’s effect on theplanet to result in unanticipated surprises and a broad consensus that the further and faster the
Earth system is pushed towards warming, the greater the risk of such surprises.
There are at least two types of potential surprises: compound events, where multiple extreme climate events occur simultaneously or sequentially (creating greater overall impact), and criticalthreshold or tipping point events, where some threshold is crossed in the climate system (that leads
to large impacts). The probability of such surprises—some of which may be abrupt and/or
irreversible—as well as other more predictable but difficult-to-manage impacts, increases as theinfluence of human activities on the climate system increases. (Ch. 15)

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Unanticipated and difficult or impossible-to-manage changes in the climate system are possible
throughout the next century as critical thresholds are crossed and/or multiple climate-related
extreme events occur simultaneously. (Ch. 15)•

Positive feedbacks (self-reinforcing cycles) within the climate system have the potential toaccelerate human-induced climate change and even shift the Earth’s climate system, in partor in whole, into new states that are very different from those experienced in the recent past(for example, ones with greatly diminished ice sheets or different large-scale patterns of atmosphere or ocean circulation). Some feedbacks and potential state shifts can be modeled andquantified; others can be modeled or identified but not quantified; and some are probably still
unknown. (Very high confidence in the potential for state shifts and in the incompleteness ofknowledge about feedbacks and potential state shifts). (Ch. 15)•

The physical and socioeconomic impacts of compound extreme events (such as simultaneousheat and drought, wildfires associated with hot and dry conditions, or flooding associatedwith high precipitation on top of snow or waterlogged ground) can be greater than the sumof the parts (very high confidence). Few analyses consider the spatial or temporal correlation
between extreme events. (Ch. 15)•

While climate models incorporate important climate processes that can be well quantified,they do not include all of the processes that can contribute to feedbacks (Ch. 2), compound extreme events, and abrupt and/or irreversible changes. For this reason, future changes outside
the range projected by climate models cannot be ruled out (very high confidence). Moreover, thesystematic tendency of climate models to underestimate temperature change during warm
paleoclimates suggests that climate models are more likely to underestimate than to overestimate the amount of long-term future change (medium confidence). (Ch. 15)

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Box ES.2: A Summary of Advances Since NCA3
Advances in scientific understanding and scientific approach, as well as developments in global policy, haveoccurred since NCA3. A detailed summary of these advances can be found at the end of Chapter 1: Our Globally Changing Climate. Highlights of what aspects are either especially strengthened or are emerging in thecurrent findings include•

Detection and attribution: Significant advances have been made in the attribution of the human influence
for individual climate and weather extreme events since NCA3. (Chapters 3, 6, 7, 8).

•

Atmospheric circulation and extreme events: The extent to which atmospheric circulation in the midlatitudes is changing or is projected to change, possibly in ways not captured by current climate models, is a
new important area of research. (Chapters 5, 6, 7).

•

Increased understanding of specific types of extreme events: How climate change may affect specifictypes of extreme events in the United States is another key area where scientific understanding has advanced. (Chapter 9).

•

High-resolution global climate model simulations: As computing resources have grown, multidecadalsimulations of global climate models are now being conducted at horizontal resolutions on the order of 15
miles (25 km) that provide more realistic characterization of intense weather systems, including hurricanes.
(Chapter 9).

•

Oceans and coastal waters: Ocean acidification, warming, and oxygen loss are all increasing, and scientificunderstanding of the severity of their impacts is growing. Both oxygen loss and acidification may be magnified in some U.S. coastal waters relative to the global average, raising the risk of serious ecological andeconomic consequences. (Chapters 2, 13).

•

Local sea level change projections: For the first time in the NCA process, sea level rise projections incorporate geographic variation based on factors such as local land subsidence, ocean currents, and changes inEarth’s gravitational field. (Chapter 12).

•

Accelerated ice-sheet loss: New observations from many different sources confirm that ice-sheet loss is
accelerating. Combining observations with simultaneous advances in the physical understanding of ice
sheets leads to the conclusion that up to 8.5 feet of global sea level rise is possible by 2100 under a higher
scenario (RCP8.5), up from 6.6 feet in NCA3. (Chapter 12).

•Low sea-ice areal extent: The annual arctic sea ice extent minimum for 2016 relative to the long-term record was the second lowest on record. The arctic sea ice minimums in 2014 and 2015 were also amongst
the lowest on record. Since 1981, the sea ice minimum has decreased by 13.3% per decade, more than 46%
over the 35 years. The annual arctic sea ice maximum in March 2017 was the lowest maximum areal extent
on record. (Chapter 11).

•

Potential surprises: Both large-scale state shifts in the climate system (sometimes called “tipping points”)
and compound extremes have the potential to generate unanticipated climate surprises. The further the
Earth system departs from historical climate forcings, and the more the climate changes, the greater the
potential for these surprises. (Chapter 15).

•

Mitigation: This report discusses some important aspects of climate science that are relevant to long-term
temperature goals and different mitigation scenarios, including those implied by government announcements for the Paris Agreement. (Chapters 4, 14).

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Our Globally
Changing Climate

KEY FINDINGS
1. The global climate continues to change rapidly compared to the pace of the natural variations in climate that have occurred throughout Earth’s history. Trends in globally averaged temperature, sea levelrise, upper-ocean heat content, land-based ice melt, arctic sea ice, depth of seasonal permafrost thaw,
and other climate variables provide consistent evidence of a warming planet. These observed trends
are robust and have been confirmed by multiple independent research groups around the world. (Veryhigh confidence)
2. The frequency and intensity of extreme heat and heavy precipitation events are increasing in most continental regions of the world (very high confidence). These trends are consistent with expected physicalresponses to a warming climate. Climate model studies are also consistent with these trends, althoughmodels tend to underestimate the observed trends, especially for the increase in extreme precipitation
events (very high confidence for temperature, high confidence for extreme precipitation). The frequency
and intensity of extreme high temperature events are virtually certain to increase in the future as global
temperature increases (high confidence). Extreme precipitation events will very likely continue to increase in frequency and intensity throughout most of the world (high confidence). Observed and projected trends for some other types of extreme events, such as floods, droughts, and severe storms, havemore variable regional characteristics.
3. Many lines of evidence demonstrate that it is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century. Formal detection and attribution
studies for the period 1951 to 2010 find that the observed global mean surface temperature warming
lies in the middle of the range of likely human contributions to warming over that same period. We
find no convincing evidence that natural variability can account for the amount of global warming observed over the industrial era. For the period extending over the last century, there are no convincing
alternative explanations supported by the extent of the observational evidence. Solar output changes
and internal variability can only contribute marginally to the observed changes in climate over the lastcentury, and we find no convincing evidence for natural cycles in the observational record that could
explain the observed changes in climate. (Very high confidence)
4. Global climate is projected to continue to change over this century and beyond. The magnitude ofclimate change beyond the next few decades will depend primarily on the amount of greenhouse(heat-trapping) gases emitted globally and on the remaining uncertainty in the sensitivity of Earth’s
climate to those emissions (very high confidence). With significant reductions in the emissions of greenhouse gases, the global annually averaged temperature rise could be limited to 3.6°F (2°C) or less.
Without major reductions in these emissions, the increase in annual average global temperatures relative to preindustrial times could reach 9°F (5°C) or more by the end of this century (high confidence).
(continued on next page)

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KEY FINDINGS (continued)
5. Natural variability, including El Niño events and other recurring patterns of ocean–atmosphere interactions, impact temperature and precipitation, especially regionally, over months to years. The global
influence of natural variability, however, is limited to a small fraction of observed climate trends over decades.
(Very high confidence)
6. Longer-term climate records over past centuries and millennia indicate that average temperatures inrecent decades over much of the world have been much higher, and have risen faster during this time
period, than at any time in the past 1,700 years or more, the time period for which the global distribution of surface temperatures can be reconstructed. (High confidence)

Recommended Citation for Chapter
Wuebbles, D.J., D.R. Easterling, K. Hayhoe, T. Knutson, R.E. Kopp, J.P. Kossin, K.E. Kunkel, A.N. LeGrande, C. Mears, W.V. Sweet, P.C. Taylor, R.S. Vose, and M.F. Wehner, 2017: Our globally changing climate.
In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey,
K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 35-72, doi: 10.7930/J08S4N35.

1.1 Introduction
Since the Third U.S. National Climate Assessment (NCA3) was published in May 2014, newobservations along multiple lines of evidence
have strengthened the conclusion that Earth’s
climate is changing at a pace and in a pattern
not explainable by natural influences. While
this report focuses especially on observed andprojected future changes for the United States,
it is important to understand those changes in
the global context (this chapter).

Rainfall patterns and storms are changing, andthe occurrence of droughts is shifting.
Many lines of evidence demonstrate that humanactivities, especially emissions of greenhousegases, are primarily responsible for the observed
climate changes in the industrial era, especiallyover the last six decades (see attribution analysis in Ch. 3: Detection and Attribution). Formal
detection and attribution studies for the period
1951 to 2010 find that the observed global meansurface temperature warming lies in the middleof the range of likely human contributions towarming over that same period. The Intergovernmental Panel on Climate Change concluded
that it is extremely likely that human influence
has been the dominant cause of the observed
warming since the mid-20th century.1 Over thelast century, there are no alternative explanations
supported by the evidence that are either credible or that can contribute more than marginallyto the observed patterns. There is no convincing
evidence that natural variability can account for
the amount of and the pattern of global warming

The world has warmed over the last 150 years,
especially over the last six decades, and thatwarming has triggered many other changes
to Earth’s climate. Evidence for a changing
climate abounds, from the top of the atmosphere to the depths of the oceans. Thousandsof studies conducted by tens of thousands ofscientists around the world have documentedchanges in surface, atmospheric, and oceanic
temperatures; melting glaciers; disappearing
snow cover; shrinking sea ice; rising sea level;
and an increase in atmospheric water vapor.

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observed over the industrial era.2, 3, 4, 5 Solar flux
variations over the last six decades have been
too small to explain the observed changes inclimate.6, 7, 8 There are no apparent natural cycles
in the observational record that can explain the
recent changes in climate (e.g., PAGES 2k Consortium 2013;9 Marcott et al. 2013;10 Otto-Bliesner
et al. 201611). In addition, natural cycles within
Earth’s climate system can only redistributeheat; they cannot be responsible for the observed
increase in the overall heat content of the climate
system.12 Any explanations for the observedchanges in climate must be grounded in understood physical mechanisms, appropriate inscale, and consistent in timing and direction withthe long-term observed trends. Known humanactivities quite reasonably explain what has happened without the need for other factors. Internalvariability and forcing factors other than humanactivities cannot explain what is happening, andthere are no suggested factors, even speculative
ones, that can explain the timing or magnitude
and that would somehow cancel out the role ofhuman factors.3, 13 The science underlying thisevidence, along with the observed and projectedchanges in climate, is discussed in later chapters,
starting with the basis for a human influence on
climate in Chapter 2: Physical Drivers of Climate
Change.

changes beyond about two weeks into the
future. Climate, on the other hand, is the statistics of weather—meaning not just averagevalues but also the prevalence and intensityof extremes—as observed over a period of decades. Climate emerges from the interaction,over time, of rapidly changing local weather
and more slowly changing regional and global
influences, such as the distribution of heat in
the oceans, the amount of energy reaching
Earth from the sun, and the composition of theatmosphere. See Chapter 4: Projections andlater chapters for more on climate projections.
Throughout this report, we include many
findings that further strengthen or add to theunderstanding of climate change relative tothose found in NCA3 and other assessmentsof the science. Several of these are highlighted
in an “Advances Since NCA3” box at the endof this chapter.

1.2 Indicators of a Globally Changing
ClimateHighly diverse types of direct measurements
made on land, sea, and in the atmosphere
over many decades have allowed scientists
to conclude with high confidence that global
mean temperature is increasing. Observational
datasets for many other climate variables support the conclusion with high confidence thatthe global climate is changing (also see EPA
201614).15, 16 Figure 1.1 depicts several of the observational indicators that demonstrate trends
consistent with a warming planet over the lastcentury. Temperatures in the lower atmosphereand ocean have increased, as have near-surface
humidity and sea level. Not only has ocean
heat content increased dramatically (Figure1.1), but more than 90% of the energy gained in
the combined ocean–atmosphere system overrecent decades has gone into the ocean.17, 18 Five
different observational datasets show the heat
content of the oceans is increasing.

Throughout this report, we also analyze
projections of future changes in climate. As
discussed in Chapter 4, beyond the next few
decades, the magnitude of climate change
depends primarily on cumulative emissions ofgreenhouse gases and aerosols and the sensitivity of the climate system to those emissions.
Predicting how climate will change in future
decades is a different scientific issue from predicting weather a few weeks from now. Local
weather is short term, with limited predictability, and is determined by the complicatedmovement and interaction of high pressure
and low pressure systems in the atmosphere;
thus, it is difficult to forecast day-to-day

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Indicators of Warming from Multiple Datasets
Indicators of Warming from Multiple Datasets

2

Land Surface Air Temperature:
4 Datasets

Anomaly (ºF)

1
0
−1
−2

Anomaly (ºF)−3
1.0

Sea Surface Temperature:
3 Datasets

0.5
0.0
−0.5
−1.0
−1.5
2 Sea Level:
4 Datasets
0

Anomaly (in)

−2
−4
−6
−8
1880

1900

Tropospheric Temperature:
5 Datasets

10
5

0.5
0.0
−0.5

−1.5

−20
6

N. Hemisphere Snow Cover:
1.5 1 Dataset

Ocean Heat Content:
5 Datasets

0.4
0.3

0.5
0.0
−0.5

1960

1980

2000

2020

Specific Humidity:
4 Datasets

0.2
0.1
0.0

−0.2
−0.3
−0.4
10
0

−10

Glacier Cumulative Mass Balance:
1 Dataset

−20

4

−30

3
2

Mar–AprAnnual2000−0.1

Arctic Sea Ice Extent:
5 1 Dataset

Extent (106 miles2)

1.0

1980
0.5

−5

−15

−1.5
1940

1960

0

−1.0

−1.0

1940

−10

2.0
Anomaly (106 miles2)

1920

Anomaly (1022 J)

Anomaly (ºF)

1.0

1860

Anomaly (g/kg)

1.5

Equivalent Depth in Water (ft)−1012020 1940−40−50

September
Annual
1960

−60
1980

2000

2020

−70
1940

1960

1980

2000

2020

Figure 1.1: This image shows observations globally from nine different variables that are key indicators of a warming
climate. The indicators (listed below) all show long-term trends that are consistent with global warming. In parentheses
are the number of datasets shown in each graph, the length of time covered by the combined datasets and their anomaly
reference period (where applicable), and the direction of the trend: land surface air temperature (4 datasets, 1850–2016
relative to 1976–2005, increase); sea surface temperature (3 datasets, 1850–2016 relative to 1976–2005, increase);sea level (4 datasets, 1880–2014 relative to 1996–2005, increase); tropospheric temperature (5 datasets, 1958–2016
relative to 1981–2005, increase); ocean heat content, upper 700m (5 datasets, 1950–2016 relative to 1996–2005, increase); specific humidity (4 datasets, 1973–2016 relative to 1980–2003, increase); Northern Hemisphere snow cover,March–April and annual (1 dataset, 1967–2016 relative to 1976–2005, decrease); arctic sea ice extent, September andannual (1 dataset, 1979–2016, decrease); glacier cumulative mass balance (1 dataset, 1980–2016, decrease). Moreinformation on the datasets can be found in the accompanying metadata. (Figure source: NOAA NCEI and CICS-NC,
updated from Melillo et al. 2014;144 Blunden and Arndt 201615).

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Basic physics tells us that a warmer atmosphere can hold more water vapor; this isexactly what is measured from satellite data.
At the same time, a warmer world means
higher evaporation rates and major changes
to the hydrological cycle (e.g., Kundzewicz
2008;19 IPCC 20131), including increases in theprevalence of torrential downpours. In addition, arctic sea ice, mountain glaciers, andNorthern Hemisphere spring snow cover haveall decreased. The relatively small increase in
Antarctic sea ice in the 15-year period from2000 through early 2016 appears to be best
explained as being due to localized naturalvariability (see e.g., Meehl et al. 2016;16 Ramsayer 201420); while possibly also related tonatural variability, the 2017 Antarctic sea ice
minimum reached in early March was thelowest measured since reliable records began
in 1979. The vast majority of the glaciers in theworld are losing mass at significant rates. The
two largest ice sheets on our planet—on theland masses of Greenland and Antarctica—areshrinking.

1901–1960 (Figure 1.2); see Vose et al.22 fordiscussion on how global annual average temperature is derived by scientists. The linear
regression change over the entire period from
1901–2016 is 1.8°F (1.0°C). Global average temperature is not expected to increase smoothly
over time in response to the human warming
influences, because the warming trend is superimposed on natural variability associatedwith, for example, the El Niño/La Niña oceanheat oscillations and the cooling effects of particles emitted by volcanic eruptions. Even so,
16 of the 17 warmest years in the instrumental
record (since the late 1800s) occurred in the
period from 2001 to 2016 (1998 was the exception). Global average temperature for 2016
has now surpassed 2015 by a small amount as
the warmest year on record. The year 2015 far
surpassed 2014 by 0.29°F (0.16°C), four times
greater than the difference between 2014 andthe next warmest year, 2010.23 Three of thefour warmest years on record have occurredsince the analyses through 2012 were reported
in NCA3.

Many other indicators of the changing climate
have been determined from other observations—for example, changes in the growing
season and the allergy season (see e.g., EPA
2016;14 USGCRP 201721). In general, the indicators demonstrate continuing changes in climate since the publication of NCA3. As withtemperature, independent researchers haveanalyzed each of these indicators and cometo the same conclusion: all of these changes
paint a consistent and compelling picture of a
warming planet.

A strong El Niño contributed to 2015’s record
warmth.15 Though an even more powerful El
Niño occurred in 1998, the global temperaturein that year was significantly lower (by 0.49°F[0.27°C]) than that in 2015. This suggests thathuman-induced warming now has a stronger
influence on the occurrence of record temperatures than El Niño events. In addition, the El
Niño/La Niña cycle may itself be affected by
the human influence on Earth’s climate system.3, 24 It is the complex interaction of natural
sources of variability with the continuously
growing human warming influence that is
now shaping Earth’s weather and, as a result,its climate.

1.3 Trends in Global Temperatures
Global annual average temperature (as calculated from instrumental records over bothland and oceans; used interchangeably with
global average temperature in the discussion below) has increased by more than 1.2°F(0.7°C) for the period 1986–2016 relative to

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Globally, the persistence of the warming overthe past 60 years far exceeds what can be accounted for by natural variability alone.1 That
does not mean, of course, that natural sources

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GlobalLand and Anomalies
Ocean Temperature Anomalies
Global Land and Ocean
Temperature
Annual2.0
1.6

Anomaly (°F)

1.2
0.8
0.4
0.0
−0.4
−0.8
1880

1900

1920

1940

1960

1980

2000

Year

Decadal1.6
1.4

Anomaly (°F)

1.2
1.0
0.8
0.6
0.4
0.2
0.0
−0.2
−0.4

1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
DecadeFigure 1.2: Top: Global annual average temperatures (as measured over both land andoceans) for 1880–2016 relative to the reference period of 1901–1960; red bars indicate
temperatures above the average over 1901–
1960, and blue bars indicate temperatures
below the average. Global annual averagetemperature has increased by more than 1.2°F(0.7°C) for the period 1986–2016 relative to
1901–1960. While there is a clear long-term
global warming trend, some years do not showa temperature increase relative to the previous
year, and some years show greater changes
than others. These year-to-year fluctuations intemperature are mainly due to natural sourcesof variability, such as the effects of El Niños,
La Niñas, and volcanic eruptions. Based on
the NCEI (NOAAGlobalTemp) dataset (updated from Vose et al.22) Bottom: Global averagetemperature averaged over decadal periods(1886–1895, 1896–1905, …, 1996–2005, except for the 11 years in the last period, 2006–2016). Horizontal label indicates midpoint yearof decadal period. Every decade since 1966–
1975 has been warmer than the previous decade. (Figure source: [top] adapted from NCEI
2016,23 [bottom] NOAA NCEI and CICS-NC).

of variability have become insignificant. They
can be expected to continue to contribute a
degree of “bumpiness” in the year-to-year
global average temperature trajectory, as well
as exert influences on the average rate ofwarming that can last a decade or more (seeBox 1.1).25, 26, 27

house gas concentrations, particularly at highnorthern latitudes, and over land as comparedto the ocean (see Figure 1.3). In general, winteris warming faster than summer (especially innorthern latitudes). Also, nights are warming
faster than days.29, 30 There is also some evidence of faster warming at higher elevations.31

Warming during the first half of the 1900s occurred mostly in the Northern Hemisphere.28
Recent decades have seen greater warming
in response to accelerating increases in green-

Most ocean areas around Earth are warming (see Ch. 13: Ocean Changes). Even in theabsence of significant ice melt, the ocean is
expected to warm more slowly given its larger

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Surface Temperature
SurfaceTemperatureChange
ChangeChange in Temperature (ºF)−1.5 −1.0

−0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Figure 1.3: Surface temperature change (in °F) for the period 1986–2015 relative to 1901–1960 from the NOAA
National Centers for Environmental Information’s (NCEI) surface temperature product. For visual clarity, statistical significance is not depicted on this map. Changes are generally significant (at the 90% level) over most land and ocean
areas. Changes are not significant in parts of the North Atlantic Ocean, the South Pacific Ocean, and the southeastern
United States. There is insufficient data in the Arctic Ocean and Antarctica for computing long-term changes (thosesections are shown in gray because no trend can be derived). The relatively coarse resolution (5.0° × 5.0°) of thesemaps does not capture the finer details associated with mountains, coastlines, and other small-scale effects (see Ch. 6:
Temperature Changes for a focus on the United States). (Figure source: updated from Vose et al. 201222).

Figure 1.4 shows the projected changes in
globally averaged temperature for a rangeof future pathways that vary from assuming
strong continued dependence on fossil fuelsin energy and transportation systems over the
21st century (the high scenario is Representative Concentration Pathway 8.5, or RCP8.5)
to assuming major emissions reduction (theeven lower scenario, RCP2.6). Chapter 4:
Projections describes the future scenarios andthe models of Earth’s climate system being
used to quantify the impact of human choices and natural variability on future climate.
These analyses also suggest that global surface
temperature increases for the end of the 21st
century are very likely to exceed 1.5°C (2.7°F)
relative to the 1850–1900 average for all projections, with the exception of the lowest part of
the uncertainty range for RCP2.6.1, 35, 36, 37

heat capacity, leading to land–ocean differences in warming (as seen in Figure 1.3). As a result, the climate for land areas often responds
more rapidly than the ocean areas, eventhough the forcing driving a change in climate
occurs equally over land and the oceans.1 A
few regions, such as the North Atlantic Ocean,
have experienced cooling over the last century, though these areas have warmed overrecent decades. Regional climate variability isimportant to determining potential effects ofclimate change on the ocean circulation (e.g.,
Hurrell and Deser 2009;32 Hoegh-Guldberg etal. 201433) as are the effects of the increasing
freshwater in the North Atlantic from meltingof sea and land ice.34

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Projected Global Temperatures
Projected
Global Temperatures
Temperature Change (°F)

1086RCP8.5
RCP4.5
RCP2.6
Observed

4
2
0

−2
1901

1951

2001

2051

2101

Figure 1.4: Multimodel simulated time series from 1900 to 2100 for the change in global annual mean surface temperature relative to 1901–1960 for a range of the Representative Concentration Pathways (RCPs; see Ch. 4: Projections formore information). These scenarios account for the uncertainty in future emissions from human activities (as analyzed
with the 20+ models from around the world used in the most recent international assessment1). The mean (solid lines) andassociated uncertainties (shading, showing ±2 standard deviations [5%–95%] across the distribution of individual models
based on the average over 2081–2100) are given for all of the RCP scenarios as colored vertical bars. The numbers ofmodels used to calculate the multimodel means are indicated. (Figure source: adapted from Walsh et al. 2014201).

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Box 1.1: Was there a “Hiatus” in Global Warming?
Natural variability in the climate system leads to year-to-year and decade-to-decade changes in global mean
temperature. For short enough periods of time, this variability can lead to temporary slowdowns or even reversals in the globally-averaged temperature increase. Focusing on overly short periods can lead to incorrect conclusions about longer-term changes. Over the past decade, such a slowdown led to numerous assertions abouta “hiatus” (a period of zero or negative temperature trend) in global warming over the previous 1.5 decades,
which is not found when longer periods are analyzed (see Figure 1.5).38 Thus the surface and tropospheric temperature records do not support the assertion that long-term (time periods of 25 years or longer) global warming has ceased or substantially slowed,39, 40 a conclusion further reinforced by recently updated and improved
datasets.26, 41, 42, 43

Temperature Anomaly (°F)

2.0
1.5
1.0
0.5

In Situ Surface Satellite Tropospheric
Temperature
Temperature
NOAA
UAH
STAR
GISS
HadCRUT4
RSS

0.0
−0.5

Running Trends (°F/Decade)

−1.0
−1.5
Surface, 12-Year Trends
Surface, 25-Year Trends
Satellite, 12-Year Trends
Satellite, 25-Year Trends

0.5

0.0

PDO Index

−0.5
2.0

Pacific Decadal Oscillation

1.0
0.0
−1.0
−2.0
1920

1940

1960

Year

1980

2000

2020

Figure 1.5: Panel A shows the annual mean temperature anomalies relative to a 1901–1960 baseline for global
mean surface temperature and global mean tropospheric temperature. Short-term variability is superposed on along-term warming signal, particularly since the 1960s. Panel B shows the linear trend of short (12-year) and longer (25-year) overlapping periods plotted at the time of the center of the trend period. For the longer period, trends
are positive and nearly constant since about 1975. Panel C shows the annual mean Pacific Decadal Oscillation(PDO) index. Short-term temperature trends show a marked tendency to be lower during periods of generally
negative PDO index, shown by the blue shading. (Figure source: adapted and updated from Trenberth 20153 andSanter et al. 2017;38 Panel B, © American Meteorological Society. Used with permission.)(continued on next page)

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Box 1.1 (continued)
For the 15 years following the 1997–1998 El Niño–Southern Oscillation (ENSO) event, the observed rate of temperature increase was smaller than the underlying long-term increasing trend on 30-year climate time scales,44even as other measures of global warming such as ocean heat content (see Ch. 13: Ocean Changes) and arctic
sea ice extent (see Ch. 12: Sea Level Rise) continued to change.45 Variation in the rate of warming on this timescale is not unexpected and can be the result of long-term internal variability in the climate system, or shortterm changes in climate forcings such as aerosols or solar irradiance. Temporary periods similar or larger inmagnitude to the current slowdown have occurred earlier in the historical record.
Even though such slowdowns are not unexpected, the slowdown of the early 2000s has been used as informal
evidence to cast doubt on the accuracy of climate projections from CMIP5 models, since the measured rate ofwarming in all surface and tropospheric temperature datasets from 2000 to 2014 was less than expected given
the results of the CMIP3 and CMIP5 historical climate simulations.38 Thus, it is important to explore a physicalexplanation of the recent slowdown and to identify the relative contributions of different factors.
Numerous studies have investigated the role of natural modes of variability and how they affected the flow ofenergy in the climate system of the post-2000 period.16, 46, 47, 48, 49 For the 2000–2013 time period, they find•In the Pacific Ocean, a number of interrelated features, including cooler than expected tropical ocean surface temperatures, stronger than normal trade winds, and a shift to the cool phase of the Pacific Decadal
Oscillation (PDO) led to cooler than expected surface temperatures in the Eastern Tropical Pacific, a region
that has been shown to have an influence on global-scale climate.49•

For most of the world’s oceans, heat was transferred from the surface into the deeper ocean,46, 47, 50, 51 causing a reduction in surface warming worldwide.

•

Other studies attributed part of the cause of the measurement/model discrepancy to natural fluctuations inradiative forcings, such as volcanic aerosols, stratospheric water vapor, or solar output.52, 53, 54, 55, 56

When comparing model predictions with measurements, it is important to note that the CMIP5 runs used anassumed representation of these factors for time periods after 2000, possibly leading to errors, especially in the
year-to-year simulation of internal variability in the oceans. It is very likely that the early 2000s slowdown wascaused by a combination of short-term variations in forcing and internal variability in the climate system, though
the relative contribution of each is still an area of active research .
Although 2014 already set a new high in globally averaged temperature record up to that time, in 2015–2016,
the situation changed dramatically. A switch of the PDO to the positive phase, combined with a strong El Niño
event during the fall and winter of 2015–2016, led to months of record-breaking globally averaged temperaturesin both the surface and satellite temperature records (see Figure 1.5),3 bringing observed temperature trends
into better agreement with model expectations (see Figure 1.6).
(continued on next page)

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Box 1.1 (continued)
On longer time scales, observed temperature changes and model simulations are more consistent. The observed
temperature changes on longer time scales have also been attributed to anthropogenic causes with high confidence (see Ch. 3: Detection and Attribution for further discussion).6 The pronounced globally averaged surface
temperature record of 2015 and 2016 appear to make recent observed temperature changes more consistent
with model simulations—including with CMIP5 projections that were (notably) developed in advance of occurrence of the 2015–2016 observed anomalies (Figure 1.6). A second important point illustrated by Figure 1.6 isthe broad overall agreement between observations and models on the century time scale, which is robust to theshorter-term variations in trends in the past decade or so. Continued global warming and the frequent settingof new high global mean temperature records or near-records is consistent with expectations based on model
projections of continued anthropogenic forcing toward warmer global mean conditions.

Global Mean Temperature Change

Figure 1.6: Comparison of global mean temperature anomalies (°F) from observations (through 2016) and theCMIP5 multimodel ensemble (through 2016), using the reference period 1901–1960. The CMIP5 multimodel
ensemble (orange range) is constructed from blended surface temperature (ocean regions) and surface air temperature (land regions) data from the models, masked where observations are not available in the GISTEMP dataset.27 The importance of using blended model data is shown in Richardson et al.42 The thick solid orange curveis the model ensemble mean, formed from the ensemble across 36 models of the individual model ensemblemeans. The shaded region shows the +/- two standard deviation range of the individual ensemble member annual
means from the 36 CMIP5 models. The dashed lines show the range from maximum to minimum values for eachyear among these ensemble members. The sources for the three observational indices are: HadCRUT4.5 (red):
http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html; NOAA (black): https://www.ncdc.noaa.
gov/monitoring-references/faq/anomalies.php; and GISTEMP (blue): https://data.giss.nasa.gov/pub/gistemp/gistemp1200_ERSSTv4.nc. (NOAA and HadCRUT4 downloaded on Feb. 15, 2017; GISTEMP downloaded on Feb.
10, 2017). (Figure source: adapted from Knutson et al. 201627).

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1.4 Trends in Global Precipitation

re-emitted to the surface, with the additionaltransport to the atmosphere coming by convection) must be balanced by increased latentheating, resulting in precipitation increases
of approximately 0.55% to 0.72% per °F (1%to 3% per °C).1, 61 Global atmospheric water
vapor should increase by about 6%–7% per °C
of warming based on the Clausius–Clapeyron
relationship (see Ch. 2: Physical Drivers of Climate Change); satellite observations of changes in precipitable water over oceans have been
detected at about this rate and attributed tohuman-caused changes in the atmosphere.62
Similar observed changes in land-based measurements have also been attributed to thechanges in climate from greenhouse gases.63

Annual averaged precipitation across global
land areas exhibits a slight rise (that is not statistically significant because of a lack of data
coverage early in the record) over the past
century (see Figure 1.7) along with ongoing increases in atmospheric moisture levels. Interannual and interdecadal variability is clearlyfound in all precipitation evaluations, owing
to factors such as the North Atlantic Oscillation (NAO) and ENSO—note that precipitation reconstructions are updated operationallyby NOAA NCEI on a monthly basis.57, 58
The hydrological cycle and the amount of
global mean precipitation is primarily controlled by the atmosphere’s energy budget
and its interactions with clouds.59 The amountof global mean precipitation also changes asa result of a mix of fast and slow atmospheric
responses to the changing climate.60 In thelong term, increases in tropospheric radiative
effects from increasing amounts of atmospheric CO2 (i.e., increasing CO2 leads to greater
energy absorbed by the atmosphere andEarlier studies suggested a climate change
pattern of wet areas getting wetter and dry
areas getting drier (e.g., Greve et al. 201464).
While Hadley Cell expansion should lead tomore drying in the subtropics, the poleward
shift of storm tracks should lead to enhanced
wet regions. While this high/low rainfall
behavior appears to be valid over ocean areas,

Annually-averaged Precipitation Trends

Change in Precipitation (inches)
−12

−10

−8

−6

−4

−2

0

2

4

6

8

10

12

Figure 1.7: Surface annually averaged precipitation change (in inches) for the period 1986–2015 relative to 1901–1960.
The data is from long-term stations, so precipitation changes over the ocean and Antarctica cannot be evaluated. The
trends are not considered to be statistically significant because of a lack of data coverage early in the record. The relatively
coarse resolution (0.5° × 0.5°) of these maps does not capture the finer details associated with mountains, coastlines, andother small-scale effects. (Figure source: NOAA NCEI and CICS-NC).

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1 | Our Globally Changing Climatechanges over land are more complicated. The
wet versus dry pattern in observed precipitation has only been attributed for the zonal
mean65, 66 and not regionally due to the large
amount of spatial variation in precipitation
changes as well as significant natural variability. The detected signal in zonal mean precipitation is largest in the Northern Hemisphere,with decreases in the subtropics and increases
at high latitudes. As a result, the observed increase (about 5% since the 1950s67, 68) in annual
averaged arctic precipitation have been detected and attributed to human activities.69

extreme weather and climate events. Extreme event attribution generally addresses
the question of whether climate change hasaltered the odds of occurrence of an extreme
event like one just experienced. Attributionof extreme weather events under a changing
climate is now an important and highly visible
aspect of climate science. As discussed in a
recent National Academy of Sciences (NAS)report,72 the science of event attribution is
rapidly advancing, including the understanding of the mechanisms that produce extreme
events and the development of methods thatare used for event attribution. Several otherreports and papers have reviewed the topic
of extreme event attribution.73, 74, 75 This reportbriefly reviews extreme event attribution
methodologies in practice (Ch. 3: Detection
and Attribution) and provides a number of examples within the chapters on various climate
phenomena (especially relating to the UnitedStates in Chapters 6–9).

1.5 Trends in Global Extreme Weather
Events
A change in the frequency, duration, and/or
magnitude of extreme weather events is one of
the most important consequences of a warming
climate. In statistical terms, a small shift in themean of a weather variable, with or without
this shift occurring in concert with a change
in the shape of its probability distribution, cancause a large change in the probability of a value relative to an extreme threshold (see Figure
1.8 in IPCC 20131).70 Examples include extreme
high temperature events and heavy precipitation events. Some of the other extreme events,
such as intense tropical cyclones, midlatitude
cyclones, lightning, and hail and tornadoes
associated with thunderstorms can occur as
more isolated events and generally have more
limited temporal and spatial observational
datasets, making it more difficult to study
their long-term trends. Detecting trends in thefrequency and intensity of extreme weather
events is challenging.71 The most intense events
are rare by definition, and observations may be
incomplete and suffer from reporting biases.
Further discussion on trends and projectionsof extreme events for the United States can befound in Chapters 6–9 and 11.

Extreme Heat and Cold
The frequency of multiday heat waves and extreme high temperatures at both daytime andnighttime hours is increasing over many of the
global land areas.1 There are increasing areasof land throughout our planet experiencing
an excess number of daily highs above given
thresholds (for example, the 90th percentile),with an approximate doubling of the world’s
land area since 1998 with 30 extreme heat days
per year.76 At the same time, frequencies ofcold waves and extremely low temperatures
are decreasing over the United States andmuch of the earth. In the United States, thenumber of record daily high temperatures has
been about double the number of record daily
low temperatures in the 2000s,77 and much of
the United States has experienced decreases of
5%–20% per decade in cold wave frequency.1, 75

An emerging area in the science of detection
and attribution has been the attribution of

The enhanced radiative forcing caused bygreenhouse gases has a direct influence on

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heat extremes by shifting distributions of daily
temperature.78 Recent work indicates changes in atmospheric circulation may also playa significant role (see Ch. 5: Circulation and
Variability). For example, a recent study foundthat increasing anticyclonic circulations partially explain observed trends in heat events
over North America and Eurasia, among othereffects.79 Observed changes in circulation may
also be the result of human influences on
climate, though this is still an area of active
research.

limited data, there is low confidence for any
significant current trends in river-flooding associated with climate change,89 but the magnitude and intensity of river flooding is projected to increase in the future.90 More on flooding
trends in the United States is in Chapter 8:
Droughts, Floods, and Wildfires.
Tornadoes and Thunderstorms
Increasing air temperature and moisture increase the risk of extreme convection, and thereis evidence for a global increase in severe thunderstorm conditions.91 Strong convection, alongwith wind shear, represents favorable conditions
for tornadoes. Thus, there is reason to expect
increased tornado frequency and intensity in a
warming climate.92 Inferring current changes
in tornado activity is hampered by changes inreporting standards, and trends remain highly
uncertain (see Ch. 9: Extreme Storms).84

Extreme Precipitation
A robust consequence of a warming climate is
an increase in atmospheric water vapor, which
exacerbates precipitation events under similar
meteorological conditions, meaning that whenrainfall occurs, the amount of rain falling inthat event tends to be greater. As a result, whatin the past have been considered to be extreme precipitation events are becoming more
frequent.1, 80, 81, 82 On a global scale, the observational annual-maximum daily precipitation
has increased by 8.5% over the last 110 years;
global climate models also derive an increasein extreme precipitation globally but tend tounderestimate the rate of the observed increase.80, 82, 83 Extreme precipitation events are
increasing in frequency globally over both wet
and dry regions.82 Although more spatiallyheterogeneous than heat extremes, numerous
studies have found increases in precipitation
extremes on many regions using a variety ofmethods and threshold definitions,84 and thoseincreases can be attributed to human-causedchanges to the atmosphere.85, 86 Finally, extreme precipitation associated with tropical
cyclones (TCs) is expected to increase in the
future,87 but current trends are not clear.84

Winter Storms
Winter storm tracks have shifted slightly
northward (by about 0.4 degrees latitude)
in recent decades over the Northern Hemisphere.93 More generally, extratropical cyclone
activity is projected to change in complex
ways under future climate scenarios, withincreases in some regions and seasons anddecreases in others. There are large model-to-model differences among CMIP5 climate
models, with some models underestimating
the current cyclone track density.94, 95
Enhanced arctic warming (arctic amplification), due in part to sea ice loss, reduces lower
tropospheric meridional temperature gradients, diminishing baroclinicity (a measure of
how misaligned the gradient of pressure isfrom the gradient of air density)—an important energy source for extratropical cyclones.
At the same time, upper-level meridionaltemperature gradients will increase due toa warming tropical upper troposphere anda cooling high-latitude lower stratosphere.

The impact of extreme precipitation trends on
flooding globally is complex because additional factors like soil moisture and changes
in land cover are important.88 Globally, due to

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While these two effects counteract each otherwith respect to a projected change in midlatitude storm tracks, the simulations indicatethat the magnitude of arctic amplification may
modulate some aspects (e.g., jet stream position, wave extent, and blocking frequency) of
the circulation in the North Atlantic region insome seasons.96

Tropical cyclone intensities are expected toincrease with warming, both on average andat the high end of the scale, as the range ofachievable intensities expands, so that themost intense storms will exceed the intensityof any in the historical record.102 Some studies
have projected an overall increase in tropical cyclone activity.103 However, studies withhigh-resolution models are giving a different
result. For example, a high-resolution dynamical downscaling study of global TC activity
under the lower scenario (RCP4.5) projects anincreased occurrence of the highest-intensity
tropical cyclones (Saffir–Simpson Categories
4 and 5), along with a reduced overall tropical
cyclone frequency, though there are considerable basin-to-basin differences.87 Chapter
9: Extreme Storms covers more on extreme
storms affecting the United States.

Tropical Cyclones
Detection and attribution of trends in past
tropical cyclone (TC) activity is hampered byuncertainties in the data collected prior to thesatellite era and by uncertainty in the relative contributions of natural variability andanthropogenic influences. Theoretical arguments and numerical modeling simulations
support an expectation that radiative forcing by greenhouse gases and anthropogenic
aerosols can affect TC activity in a variety ofways, but robust formal detection and attribution for past observed changes has not yet
been realized. Since the IPCC AR5,1 there is
new evidence that the locations where tropical cyclones reach their peak intensity havemigrated poleward in both the Northern andSouthern Hemispheres, in concert with theindependently measured expansion of thetropics.97 In the western North Pacific, thismigration has substantially changed the tropical cyclone hazard exposure patterns in theregion and appears to have occurred outsideof the historically measured modes of regional
natural variability.98

1.6 Global Changes in Land Processes
Changes in regional land cover have had
important effects on climate, while climatechange also has important effects on land
cover (also see Ch. 10: Land Cover).1 In somecases, there are changes in land cover that areboth consequences of and influences on global
climate change (e.g., declines in land ice andsnow cover, thawing permafrost, and insect
damage to forests).
Northern Hemisphere snow cover extent has
decreased, especially in spring, primarily due
to earlier spring snowmelt (by about 0.2 million
square miles [0.5 million square km]104, 105), andthis decrease since the 1970s is at least partiallydriven by anthropogenic influences.106 Snow cover reductions, especially in the Arctic region insummer, have led to reduced seasonal albedo.107

Whether global trends in high-intensity tropical cyclones are already observable is a topic ofactive debate. Some research suggests positive
trends,99, 100 but significant uncertainties remain(see Ch. 9: Extreme Storms).100 Other studies havesuggested that aerosol pollution has masked theincrease in TC intensity expected otherwise fromenhanced greenhouse warming.101, 102

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While global-scale trends in drought are
uncertain due to insufficient observations,regional trends indicate increased frequency
and intensity of drought and aridification onland cover in the Mediterranean108, 109 and West49

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Africa110, 111 and decreased frequency and intensity of droughts in central North America112
and northwestern Australia.110, 111, 113

Many studies have documented a lengthening
growing season, primarily due to the changing climate,120, 121, 122, 123 and elevated CO2 is expected to further lengthen the growing season
in places where the length is water limited.124
In addition, a recent study has shown an overall increase in greening of Earth in vegetatedregions,125 while another has demonstrated
evidence that the greening of Northern Hemisphere extratropical vegetation is attributable
to anthropogenic forcings, particularly risingatmospheric greenhouse gas levels.126 However, observations127, 128, 129 and models130, 131,
132
indicate that nutrient limitations and landavailability will constrain future land carbon
sinks.

Anthropogenic land-use changes, such as
deforestation and growing cropland extent,
have increased the global land surface albedo,
resulting in a small cooling effect. Effects ofother land-use changes, including modifications of surface roughness, latent heat flux,
river runoff, and irrigation, are difficult toquantify, but may offset the direct land-use
albedo changes.114, 115
Globally, land-use change since 1750 has
been typified by deforestation, driven by thegrowth in intensive farming and urban development. Global land-use change is estimatedto have released 190 ± 65 GtC (gigatonnesof carbon) through 2015.116, 117 Over the same
period, cumulative fossil fuel and industrial
emissions are estimated to have been 410 ±20 GtC, yielding total anthropogenic emissions of 600 ± 70 GtC, of which cumulativeland-use change emissions were about 32%.116,
117
Tropical deforestation is the dominant driverof land-use change emissions, estimated at
0.1–1.7 GtC per year, primarily from biomassburning. Global deforestation emissions ofabout 3 GtC per year are compensated byaround 2 GtC per year of forest regrowth insome regions, mainly from abandoned agricultural land.118, 119

Modifications to the water, carbon, and biogeochemical cycles on land result in both
positive and negative feedbacks to temperature increases.114, 133, 134 Snow and ice albedo
feedbacks are positive, leading to increased
temperatures with loss of snow and ice extent.
While land ecosystems are expected to have a
net positive feedback due to reduced natural
sinks of CO2 in a warmer world, anthropogenically increased nitrogen deposition may
reduce the magnitude of the net feedback.131,
135, 136
Increased temperature and reduced
precipitation increase wildfire risk and susceptibility of terrestrial ecosystems to pests anddisease, with resulting feedbacks on carbon
storage. Increased temperature and precipitation, particularly at high latitudes, drives up
soil decomposition, which leads to increased
CO2 and CH4 (methane) emissions.137, 138, 139,
140, 141, 142, 143
While some of these feedbacks arewell known, others are not so well quantified
and yet others remain unknown; the potential
for surprise is discussed further in Chapter 15:
Potential Surprises.

Natural terrestrial ecosystems are gaining
carbon through uptake of CO2 by enhanced
photosynthesis due to higher CO2 levels,
increased nitrogen deposition, and longer
growing seasons in mid- and high latitudes.
Anthropogenic atmospheric CO2 absorbed byland ecosystems is stored as organic matter inlive biomass (leaves, stems, and roots), deadbiomass (litter and woody debris), and soil
carbon.

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1.7 Global Changes in Sea Ice, Glaciers,and Land Ice

observed, suggesting that projected changes
are too conservative.1, 147, 160, 161 See Chapter 11:
Arctic Changes for further discussion of theimplications of changes in the Arctic.

Since NCA3,144 there have been significant
advances in the understanding of changes in
the cryosphere. Observations continue to show
declines in arctic sea ice extent and thickness,
Northern Hemisphere snow cover, and thevolume of mountain glaciers and continental ice
sheets.1, 145, 146, 147, 148, 149 Evidence suggests in manycases that the net loss of mass from the global
cryosphere is accelerating indicating significant
climate feedbacks and societal consequences.150,

In contrast to the Arctic, sea ice extent around
Antarctica has increased since 1979 by 1.2% to1.8% per decade.1 Strong regional differencesin the sea ice growth rates are found around
Antarctica but most regions (about 75%) show
increases over the last 30 years.162 The gain
in antarctic sea ice is much smaller than thedecrease in arctic sea ice. Changes in wind
patterns, ice–ocean feedbacks, and freshwater flux have contributed to antarctic sea ice
growth.162, 163, 164, 165

151, 152, 153, 154, 155

Arctic sea ice areal extent, thickness, andvolume have declined since 1979.1, 146, 147, 148, 156
The annual arctic sea ice extent minimum for2016 relative to the long-term record was thesecond lowest (2012 was the lowest) (http://nsidc.org/arcticseaicenews/). The arctic sea
ice minimum extents in 2014 and 2015 werealso among the lowest on record. Annually
averaged arctic sea ice extent has decreasedby 3.5%–4.1% per decade since 1979 withmuch larger reductions in summer and fall.1,
146, 148, 157
For example, September sea ice extent
decreased by 13.3% per decade between 1979
and 2016. At the same time, September multiyear sea ice has melted faster than perennial
sea ice (13.5% ± 2.5% and 11.5% ± 2.1% per
decade, respectively, relative to the 1979–2012
average) corresponding to 4–7.5 feet (1.3–2.3
meter) declines in winter sea ice thickness.1, 156
October 2016 serves as a recent example of theobserved lengthening of the arctic sea ice melt
season marking the slowest recorded arctic
sea ice growth rate for that month.146, 158, 159
The annual arctic sea ice maximum in March2017 was the lowest maximum areal extent onrecord (http://nsidc.org/arcticseaicenews/).

Since the NCA3,144 the Gravity Recovery andClimate Experiment (GRACE) constellation(e.g., Velicogna and Wahr 2013166) has provided a record of gravimetric land ice measurements, advancing knowledge of recent
mass loss from the global cryosphere. These
measurements indicate that mass loss from theAntarctic Ice Sheet, Greenland Ice Sheet, andmountain glaciers around the world continues
accelerating in some cases.151, 152, 154, 155, 167, 168 The
annually averaged ice mass from 37 global
reference glaciers has decreased every year
since 1984, a decline expected to continue evenif climate were to stabilize.1, 153, 169, 170
Ice sheet dynamics in West Antarctica arecharacterized by land ice that transitions tocoastal and marine ice sheet systems. Recent
observed rapid mass loss from West Antarctica’s floating ice shelves is attributed toincreased glacial discharge rates due to diminishing ice shelves from the surrounding
ocean becoming warmer.171, 172 Recent evidence
suggests that the Amundsen Sea sector is expected to disintegrate entirely151, 168, 172 raising
sea level by at least 1.2 meters (about 4 feet)and potentially an additional foot or more on
top of current sea level rise projections during

While current generation climate models
project a nearly ice-free Arctic Ocean in latesummer by mid-century, they still simulate
weaker reductions in volume and extent than

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this century (see Section 1.2.7 and Ch. 12: Sea
Level Rise for further details).173 The potential
for unanticipated rapid ice sheet melt and/or
disintegration is discussed further in Chapter
15: Potential Surprises.

about 3 inches (about 8 cm) of the global rise
since the early 1990s. Nearly two-thirds of thesea level rise measured since 2005 has resulted
from increases in ocean mass, primarily fromland-based ice melt; the remaining one-thirdof the rise is in response to changes in densityfrom increasing ocean temperatures.186

Over the last decade, the Greenland Ice Sheetmass loss has accelerated, losing 244 ± 6 Gt
per year on average between January 2003
and May 2013.1, 155, 174, 175 The portion of theGreenland Ice Sheet experiencing annual melt
has increased since 1980 including significant events.1, 176, 177, 178 A recent example, an
unprecedented 98.6% of the Greenland Ice
Sheet surface experienced melt on a single day
in July 2012.179, 180 Encompassing this event,
GRACE data indicate that Greenland lost
562 Gt of mass between April 2012 and April
2013—more than double the average annual
mass loss.

Global sea level rise and its regional variability forced by climatic and ocean circulation
patterns are contributing to significant increases in annual tidal-flood frequencies, which aremeasured by NOAA tide gauges and associated with minor infrastructure impacts to date;along some portions of the U.S. coast, frequency of the impacts from such events appears
to be accelerating (also see Ch. 12: Sea-Level
Rise).187, 188
Future projections show that by 2100, global
mean sea level is very likely to rise by 1.6–4.3
feet (0.5–1.3 m) under the higher scenario(RCP8.5), 1.1–3.1 feet (0.35–0.95 m) undera lower scenario (RCP4.5), and 0.8–2.6 feet
(0.24–0.79 m) under and even lower scenario(RCP2.6) (see Ch. 4: Projections for a description of the scenarios).189 Sea level will not rise
uniformly around the coasts of the UnitedStates and its oversea territories. Local sea level
rise is likely to be greater than the global average along the U.S. Atlantic and Gulf Coasts
and less than the global average in most of
the Pacific Northwest. Emerging science suggests these projections may be underestimates,
particularly for higher scenarios; a global meansea level rise exceeding 8 feet (2.4 m) by 2100
cannot be excluded (see Ch. 12: Sea Level Rise),and even higher amounts are possible as aresult of marine ice sheet instability (see Ch.
15: Potential Surprises). We have updated theglobal sea level rise scenarios for 2100 of Parris
et al.190 accordingly,191 and also extended to year
2200 in Chapter 12: Sea Level Rise. The scenarios are regionalized to better match the decisioncontext needed for local risk framing purposes.

In addition, permafrost temperatures andactive layer thicknesses have increased acrossmuch of the Arctic (also see Ch. 11: Arctic
Changes).1, 181, 182 Rising permafrost temperatures causing permafrost to thaw and become
more discontinuous raises concerns about
potential emissions of carbon dioxide andmethane.1 The potentially large contributionof carbon and methane emissions from permafrost and the continental shelf in the Arctic tooverall warming is discussed further in Chapter 15: Potential Surprises.

1.8 Global Changes in Sea Level
Statistical analyses of tide gauge data indicatethat global mean sea level has risen about 8–9
inches (20–23 cm) since 1880, with a rise rateof approximately 0.5–0.6 inches/decade from1901 to1990 (about 12–15 mm/decade; also see
Ch. 12: Sea Level Rise).183, 184 However, since
the early 1990s, both tide gauges and satellite
altimeters have recorded a faster rate of sea
level rise of about 1.2 inches/decade (approximately 3 cm/decade),183, 184, 185 resulting in

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1.9 Recent Global Changes Relative to
Paleoclimates

Global temperatures of the magnitude observed recently (and projected for the restof this century) are related to very different
forcings than past climates, but studies of past
climates suggest that such global temperatures
were likely last observed during the Eemian
period—the last interglacial—125,000 years
ago; at that time, global temperatures were,at their peak, about 1.8°F–3.6°F (1°C–2°C)
warmer than preindustrial temperatures.194
Coincident with these higher temperatures,
sea levels during that period were about 16–30
feet (6–9 meters) higher than modern levels195,
196(for further discussion on sea levels in thepast, see Ch. 12: Sea Level Rise).

Paleoclimate records demonstrate long-term
natural variability in the climate and overlap
the records of the last two millennia, referredto here as the “Common Era.” Before the emissions of greenhouse gases from fossil fuels
and other human-related activities became amajor factor over the last few centuries, thestrongest drivers of climate during the last
few thousand years had been volcanoes andland-use change (which has both albedo andgreenhouse gas emissions effects).192 Basedon a number of proxies for temperature (forexample, from tree rings, fossil pollen, corals, ocean and lake sediments, and ice cores),temperature records are available for the last
2,000 years on hemispherical and continental
scales (Figures 1.8 and 1.9).9, 193 High-resolution temperature records for North America
extend back less than half of this period,
with temperatures in the early parts of theCommon Era inferred from analyses of pollen and other archives. For this era, there is a
general cooling trend, with a relatively rapid
increase in temperature over the last 150–200
years (Figure 1.9, ). For context, global annual averaged temperatures for 1986–2015 arelikely much higher, and appear to have risen
at a more rapid rate during the last 3 decades,than any similar period possibly over the past
2,000 years or longer (IPCC1 makes a similarstatement, but for the last 1,400 years becauseof data quality issues before that time).

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Modeling studies suggest that the Eemian
period warming can be explained in part by
the hemispheric changes in solar insolationfrom orbital forcing as a result of cyclic changes in the shape of Earth’s orbit around thesun (e.g., Kaspar et al. 2005197), even thoughgreenhouse gas concentrations were similar
to preindustrial levels. Equilibrium climate
with modern greenhouse gas concentrations(about 400 ppm CO2) most recently occurred 3
million years ago during the Pliocene. During
the warmest parts of this period, global temperatures were 5.4°F–7.2°F (3°C–4°C) higher
than today, and sea levels were about 82 feet
(25 meters) higher.198

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Figure 1.8: Changes in the temperature of the Northern Hemisphere from surface observations (in red) and from proxies (in black; uncertainty range represented by shading) relative to 1961–1990 average temperature. If this graph wereplotted relative to 1901–1960 instead of 1961–1990, the temperature changes would be 0.47°F (0.26°C) higher. These
analyses suggest that current temperatures are higher than seen in the Northern Hemisphere, and likely globally, in atleast the last 1,700 years, and that the last decade (2006–2015) was the warmest decade on record. (Figure source:
adapted from Mann et al. 2008193).

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Proxy Temperature Reconstructions
0

200

400

600

800

1000

1200

1400

1600

1800

2000

3.6

Arctic - multiproxy0−3.6

Europe - multiproxy

3.6

0

−3.6
3.6

Asia - trees0

Δ Temperature (°F)

−3.6
3.6

HadCRUT4

North America - trees
North America - pollen

0

−3.6
3.6

South America - multiproxy

0

−3.6
3.6

Australasia - multiproxy0

Antarctica - glacier ice

−3.6
3.6

0

−3.6

0

200

400

600

800

1000

Year C.E.

1200

1400

1600

1800

2000

Figure 1.9: Proxy temperatures reconstructions for the seven regions of the PAGES 2k Network. Temperature anomalies are relative to the 1961–1990 reference period. If this graph were plotted relative to 1901–1960 instead of 1961–
1990, the temperature changes would 0.47°F (0.26°C) higher. Gray lines around expected-value estimates indicate
uncertainty ranges as defined by each regional group (see PAGE 2k Consortium9 and related Supplementary Information). Note that the changes in temperature over the last century tend to occur at a much faster rate than found in theprevious time periods. The teal values are from the HadCRUT4 surface observation record for land and ocean for the
1800s to 2000.202 (Figure source: adapted from PAGES 2k Consortium 20139).

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Box 1.2: Advances Since NCA3
This assessment reflects both advances in scientific understanding and approach since NCA3, as well as global
policy developments. Highlights of what aspects are either especially strengthened or are emerging in thefindings include•

Spatial downscaling: Projections of climate changes are downscaled to a finer resolution than the original global climate models using the Localized Constructed Analogs (LOCA) empirical statistical downscaling
model. The downscaling generates temperature and precipitation on a 1/16th degree latitude/longitude
grid for the contiguous United States. LOCA, one of the best statistical downscaling approaches, produces
downscaled estimates using a multi-scale spatial matching scheme to pick appropriate analog days fromobservations (Chapters 4, 6, 7).

•

Risk-based framing: Highlighting aspects of climate science most relevant to assessment of key societal risks
are included more here than in prior national climate assessments. This approach allows for emphasis ofpossible outcomes that, while relatively unlikely to occur or characterized by high uncertainty, would beparticularly consequential, and thus associated with large risks (Chapters 6, 7, 8, 9, 12, 15).

•

Detection and attribution: Significant advances have been made in the attribution of the human influence
for individual climate and weather extreme events since NCA3. This assessment contains extensive discussion of new and emerging findings in this area (Chapters 3, 6, 7, 8).

•

Atmospheric circulation and extreme events: The extent to which atmospheric circulation in the midlatitudes
is changing or is projected to change, possibly in ways not captured by current climate models, is a newimportant area of research. While still in its formative stages, this research is critically important because of
the implications of such changes for climate extremes including extended cold air outbreaks, long-duration
heat waves, and changes in storms and drought patterns (Chapters 5, 6, 7).

•

Increased understanding of specific types of extreme events: How climate change may affect specific typesof extreme events in the United States is another key area where scientific understanding has advanced.
For example, this report highlights how intense flooding associated with atmospheric rivers could increase
dramatically as the atmosphere and oceans warm or how tornadoes could be concentrated into a smaller
number of high-impact days over the average severe weather season (Chapter 9).

•

Model weighting: For the first time, maps and plots of climate projections will not show a straight average of
all available climate models. Rather, each model is given a weight based on their 1) historical performance
relative to observations and 2) independence relative to other models. Although this is a more accurate wayof representing model output, it does not significantly alter the key findings: the weighting produces very
similar trends and spatial patterns to the equal-weighting-of-models approach used in prior assessments(Chapters 4, 6, 7, Appendix B).

•

High-resolution global climate model simulations: As computing resources have grown, multidecadal simulations of global climate models are now being conducted at horizontal resolutions on the order of 15 miles(25 km) that provide more realistic characterization of intense weather systems, including hurricanes. Even
the limited number of high-resolution models currently available have increased confidence in projectionsof extreme weather (Chapter 9).

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Box 1.2 (continued)•

The so-called “global warming hiatus”: Since NCA3, many studies have investigated causes for the reported
slowdown in the rate of increase in near-surface global mean temperature from roughly 2000 through 2013.
The slowdown, which ended with the record warmth in 2014–2016, is understood to have been caused by
a combination of internal variability, mostly in the heat exchange between the ocean and the atmosphere,
and short-term variations in external forcing factors, both human and natural. On longer time scales, relevant to human-induced climate change, there is no hiatus, and the planet continues to warm at a steadypace as predicted by basic atmospheric physics and the well-documented increase in heat-trapping gases(Chapter 1).

•

Oceans and coastal waters: Ocean acidification, warming, and oxygen loss are all increasing, and scientificunderstanding of the severity of their impacts is growing. Both oxygen loss and acidification may be magnified in some U.S. coastal waters relative to the global average, raising the risk of serious ecological andeconomic consequences. There is some evidence, still highly uncertain, that the Atlantic Meridional Circulation (AMOC), sometimes referred to as the ocean’s conveyor belt, may be slowing down (Chapters 2, 13).

•

Local sea level change projections: For the first time in the NCA process, sea level rise projections incorporate geographic variation based on factors such as local land subsidence, ocean currents, and changes inEarth’s gravitational field (Chapter 12).

•

Accelerated ice-sheet loss: New observations from many different sources confirm that ice-sheet loss is accelerating. Combining observations with simultaneous advances in the physical understanding of ice sheets,
scientists are now concluding that up to 8.5 feet of global sea level rise is possible by 2100 under a higher
scenario, up from 6.6 feet in NCA3 (Chapter 12).

•Low sea-ice areal extent: The annual arctic sea ice extent minimum for 2016 relative to the long-term record
was the second lowest on record. The arctic sea ice minimums in 2014 and 2015 were also amongst thelowest on record. Since 1981, the sea ice minimum has decreased by 13.3% per decade, more than 46%
over the 35 years. The annual arctic sea ice maximum in March 2017 was the lowest maximum areal extent
on record. (Chapter 11).

•

Potential surprises: Both large-scale state shifts in the climate system (sometimes called “tipping points”)
and compound extremes have the potential to generate unanticipated surprises. The further Earth system
departs from historical climate forcings, and the more the climate changes, the greater the potential forthese surprises. For the first time in the NCA process we include an extended discussion of these potential
surprises (Chapter 15).

•

Mitigation: This report discusses some important aspects of climate science that are relevant to long-term
temperature goals and different mitigation scenarios, including those implied by government announcements for the Paris Agreement. (Chapters 4, 14).

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TRACEABLE ACCOUNTSKey Finding 1
The global climate continues to change rapidly compared to the pace of the natural variations in climatethat have occurred throughout Earth’s history. Trends
in globally averaged temperature, sea level rise, upper-ocean heat content, land-based ice melt, arctic sea
ice, depth of seasonal permafrost thaw, and other climate variables provide consistent evidence of a warming planet. These observed trends are robust and havebeen confirmed by multiple independent researchgroups around the world.

instigation and maintenance of reference quality observation networks such as the U.S. Climate Reference
Network (http://www.ncei.noaa.gov/crn/), enhanced
climate observational and data analysis capabilities,
and continued improvements in climate modeling all
have the potential to reduce uncertainties.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence that global climate ischanging and this change is apparent across a wide
range of observations, given the evidence base andremaining uncertainties. All observational evidence isconsistent with a warming climate since the late 1800s.
There is very high confidence that the global climatechange of the past 50 years is primarily due to humanactivities, given the evidence base and remaining uncertainties.1 Recent changes have been consistently
attributed in large part to human factors across a verybroad range of climate system characteristics.

Description of evidence base
The Key Finding and supporting text summarize extensive evidence documented in the climate science literature. Similar to statements made in previous national(NCA3)144 and international1 assessments.
Evidence for changes in global climate arises from multiple analyses of data from in-situ, satellite, and otherrecords undertaken by many groups over several decades. These observational datasets are used throughout this chapter and are discussed further in Appendix
1 (e.g., updates of prior uses of these datasets by Vose etal. 2012;22 Karl et al. 201526). Changes in the mean state
have been accompanied by changes in the frequency
and nature of extreme events (e.g., Kunkel and Frankson 2015;81 Donat et al. 201682). A substantial body ofanalysis comparing the observed changes to a broad
range of climate simulations consistently points to thenecessity of invoking human-caused changes to adequately explain the observed climate system behavior.
The influence of human impacts on the climate system
has also been observed in a number of individual climate variables (attribution studies are discussed in Ch.
3: Detection and Attribution and in other chapters).

Summary sentence or paragraph that integrates
the above information
The key message and supporting text summarizes extensive evidence documented in the climate sciencepeer-reviewed literature. The trends described in NCA3
have continued and our understanding of the observations related to climate and the ability to evaluate
the many facets of the climate system have increased
substantially.
Key Finding 2
The frequency and intensity of extreme heat and heavy
precipitation events are increasing in most continental regions of the world (very high confidence). These
trends are consistent with expected physical responses
to a warming climate. Climate model studies are also
consistent with these trends, although models tend
to underestimate the observed trends, especially forthe increase in extreme precipitation events (very highconfidence for temperature, high confidence for extreme precipitation). The frequency and intensity of extreme high temperature events are virtually certain to

Major uncertainties
Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and particularly regional, scales, and especially for extreme eventsand our ability to observe these changes at sufficient
resolution and to simulate and attribute such changes
using climate models. Innovative new approaches to

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increase in the future as global temperature increases(high confidence). Extreme precipitation events will very
likely continue to increase in frequency and intensity
throughout most of the world (high confidence). Observed and projected trends for some other types ofextreme events, such as floods, droughts, and severe
storms, have more variable regional characteristics.

peer-reviewed literature. The trends for extreme events
that were described in the NCA3 and IPCC assessments
have continued, and our understanding of the data andability to evaluate the many facets of the climate system have increased substantially.
Key Finding 3
Many lines of evidence demonstrate that it is extremely likely that human influence has been the dominantcause of the observed warming since the mid-20th century. Formal detection and attribution studies for the period 1951 to 2010 find that the observed global meansurface temperature warming lies in the middle of therange of likely human contributions to warming overthat same period. We find no convincing evidence thatnatural variability can account for the amount of global
warming observed over the industrial era. For the period
extending over the last century, there are no convincing
alternative explanations supported by the extent of theobservational evidence. Solar output changes and internal variability can only contribute marginally to theobserved changes in climate over the last century, andwe find no convincing evidence for natural cycles in theobservational record that could explain the observedchanges in climate. (Very high confidence)

Description of evidence base
The Key Finding and supporting text summarizes extensive evidence documented in the climate science
literature and are similar to statements made in previous national (NCA3)144 and international1 assessments.
The analyses of past trends and future projections in
extreme events and the fact that models tend to underestimate the observed trends are also well substantiated through more recent peer-reviewed literature as
well.75, 76, 81, 82, 83, 88, 90, 199
Major uncertainties
Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and particularly regional, scales, and especially for extreme eventsand our ability to simulate and attribute such changes using climate models. Innovative new approaches
to climate data analysis, continued improvements inclimate modeling, and instigation and maintenanceof reference quality observation networks such as the
U.S. Climate Reference Network (http://www.ncei.noaa.
gov/crn/) all have the potential to reduce uncertainties.

Description of evidence base
The Key Finding and supporting text summarizes extensive evidence documented in the climate science
literature and are similar to statements made in previous national (NCA3)144 and international1 assessments.
The human effects on climate have been well documented through many papers in the peer-reviewed
scientific literature (e.g., see Ch. 2: Physical Drivers of
Climate Change and Ch. 3: Detection and Attribution
for more discussion of supporting evidence).

Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence for the statements aboutpast extreme changes in temperature and precipitationand high confidence for future projections, based on theobservational evidence and physical understanding,that there are major trends in extreme events and significant projected changes for the future.

Major uncertainties
Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and particularly regional, scales, and especially for extreme eventsand our ability to simulate and attribute such changes
using climate models. The exact effects from land usechanges relative to the effects from greenhouse gas
emissions need to be better understood.

Summary sentence or paragraph that integrates
the above information
The Key Finding and supporting text summarizes extensive evidence documented in the climate science

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Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence for a major human influence on climate.

climate models. Of particular importance are remaining uncertainties in the understanding of feedbacks inthe climate system, especially in ice–albedo and cloud
cover feedbacks. Continued improvements in climate
modeling to represent the physical processes affecting
Earth’s climate system are aimed at reducing uncertainties. Monitoring and observation programs also canhelp improve the understanding needed to reduce uncertainties.

Summary sentence or paragraph that integrates
the above information
The key message and supporting text summarizes extensive evidence documented in the climate sciencepeer-reviewed literature. The analyses described in theNCA3 and IPCC assessments support our findings, andnew observations and modeling studies have further
substantiated these conclusions.

Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence for continued changes inclimate and high confidence for the levels shown in the
Key Finding.

Key Finding 4
Global climate is projected to continue to change overthis century and beyond. The magnitude of climatechange beyond the next few decades will depend primarily on the amount of greenhouse (heat-trapping)gases emitted globally and on the remaining uncertainty in the sensitivity of Earth’s climate to those emissions(very high confidence). With significant reductions in theemissions of greenhouse gases, the global annually averaged temperature rise could be limited to 3.6°F (2°C)
or less. Without major reductions in these emissions,the increase in annual average global temperatures
relative to preindustrial times could reach 9°F (5°C) or
more by the end of this century (high confidence).

Summary sentence or paragraph that integrates
the above information
The Key Finding and supporting text summarizes extensive evidence documented in the climate sciencepeer-reviewed literature. The projections that were described in the NCA3 and IPCC assessments support ourfindings, and new modeling studies have further substantiated these conclusions.
Key Finding 5
Natural variability, including El Niño events and otherrecurring patterns of ocean–atmosphere interactions,
impact temperature and precipitation, especially regionally, over months to years. The global influence of
natural variability, however, is limited to a small fractionof observed climate trends over decades.

Description of evidence base
The Key Finding and supporting text summarizes extensive evidence documented in the climate science
literature and are similar to statements made in previous national (NCA3)144 and international1 assessments.
The projections for future climate have been well documented through many papers in the peer-reviewed
scientific literature (e.g., see Ch. 4: Projections for descriptions of the scenarios and the models used).

Description of evidence base
The Key Finding and supporting text summarizes extensive evidence documented in the climate science
literature and are similar to statements made in previous national (NCA3)144 and international1 (IPCC 2013)assessments. The role of natural variability in climate
trends has been extensively discussed in the peer-reviewed literature (e.g., Karl et al. 2015;26 Rahmstorf et
al. 2015;34 Lewandowsky et al. 2016;39 Mears and Wentz
2016;41 Trenberth et al. 2014;200 Santer et al. 201738, 40, 68).

Major uncertainties
Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and particularly
regional, scales, and especially for extreme events andour ability to simulate and attribute such changes using

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Major uncertainties
Uncertainties still exist in the precise magnitude andnature of the full effects of individual ocean cycles andother aspects of natural variability on the climate system. Increased emphasis on monitoring should reduce
this uncertainty significantly over the next few decades.

Description of evidence base
The Key Finding and supporting text summarizes extensive evidence documented in the climate science
literature and are similar to statements made in previous national (NCA3)144 and international1 assessments.
There are many recent studies of the paleoclimate
leading to this conclusion including those cited in thereport (e.g., Mann et al. 2008;193 PAGE 2k Consortium
20139).

Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence, affected to some degree bylimitations in the observational record, that the role ofnatural variability on future climate change is limited.

Major uncertainties
Despite the extensive increase in knowledge in the lastfew decades, there are still many uncertainties in understanding the hemispheric and global changes in climate over Earth’s history, including that of the last few
millennia. Additional research efforts in this direction
can help reduce those uncertainties.

Summary sentence or paragraph that integrates
the above information
The Key Finding and supporting text summarizes extensive evidence documented in the climate sciencepeer-reviewed literature. There has been an extensive
increase in the understanding of the role of naturalvariability on the climate system over the last few decades, including a number of new findings since NCA3.

Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is high confidence for current temperatures to behigher than they have been in at least 1,700 years andperhaps much longer.

Key Finding 6
Longer-term climate records over past centuries andmillennia indicate that average temperatures in recent
decades over much of the world have been much higher, and have risen faster during this time period, thanat any time in the past 1,700 years or more, the time
period for which the global distribution of surface temperatures can be reconstructed.

U.S. Global Change Research Program

Summary sentence or paragraph that integrates
the above information
The Key Finding and supporting text summarizes extensive evidence documented in the climate sciencepeer-reviewed literature. There has been an extensive
increase in the understanding of past climates on ourplanet, including a number of new findings since NCA3.

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millennia. Proceedings of the National Academy of Sciences, 105, 13252-13257. http://dx.doi.org/10.1073/
pnas.0805721105

200. Trenberth, K.E., A. Dai, G. van der Schrier, P.D. Jones,
J. Barichivich, K.R. Briffa, and J. Sheffield, 2014: Global warming and changes in drought. Nature Climate
Change, 4, 17-22. http://dx.doi.org/10.1038/nclimate2067
201. Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. Wehner, J.
Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V.
Kharin, T. Knutson, F. Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. Climate Change Impacts in the United States: The
Third National Climate Assessment. Melillo, J.M., T.C.
Richmond, and G.W. Yohe, Eds. U.S. Global Change
Research Program, Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT

194. Turney, C.S.M. and R.T. Jones, 2010: Does the Agulhas Current amplify global temperatures during
super-interglacials? Journal of Quaternary Science, 25,
839-843. http://dx.doi.org/10.1002/jqs.1423
195. Dutton, A. and K. Lambeck, 2012: Ice volume and sea
level during the Last Interglacial. Science, 337, 216219. http://dx.doi.org/10.1126/science.1205749
196. Kopp, R.E., F.J. Simons, J.X. Mitrovica, A.C. Maloof,
and M. Oppenheimer, 2009: Probabilistic assessment of sea level during the last interglacial stage.
Nature, 462, 863-867. http://dx.doi.org/10.1038/
nature08686

202. Jones, P.D., D.H. Lister, T.J. Osborn, C. Harpham,
M. Salmon, and C.P. Morice, 2012: Hemispheric andlarge-scale land surface air temperature variations:
An extensive revision and an update to 2010. Journalof Geophysical Research, 117, D05127. http://dx.doi.

197. Kaspar, F., N. Kühl, U. Cubasch, and T. Litt, 2005: A
model-data comparison of European temperatures inthe Eemian interglacial. Geophysical Research Letters, 32,
L11703. http://dx.doi.org/10.1029/2005GL022456

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Physical Drivers of
Climate Change
KEY FINDINGS
1. Human activities continue to significantly affect Earth’s climate by altering factors that change its radiative balance. These factors, known as radiative forcings, include changes in greenhouse gases, small
airborne particles (aerosols), and the reflectivity of the Earth’s surface. In the industrial era, humanactivities have been, and are increasingly, the dominant cause of climate warming. The increase inradiative forcing due to these activities has far exceeded the relatively small net increase due to natural
factors, which include changes in energy from the sun and the cooling effect of volcanic eruptions.
(Very high confidence)
2. Aerosols caused by human activity play a profound and complex role in the climate system throughradiative effects in the atmosphere and on snow and ice surfaces and through effects on cloud formation and properties. The combined forcing of aerosol–radiation and aerosol–cloud interactions is negative (cooling) over the industrial era (high confidence), offsetting a substantial part of greenhouse gas
forcing, which is currently the predominant human contribution. The magnitude of this offset, globally
averaged, has declined in recent decades, despite increasing trends in aerosol emissions or abundancesin some regions (medium to high confidence).
3. The interconnected Earth–atmosphere–ocean system includes a number of positive and negative
feedback processes that can either strengthen (positive feedback) or weaken (negative feedback) thesystem’s responses to human and natural influences. These feedbacks operate on a range of time scales
from very short (essentially instantaneous) to very long (centuries). Global warming by net radiative
forcing over the industrial era includes a substantial amplification from these feedbacks (approximately a factor of three) (high confidence). While there are large uncertainties associated with some of these
feedbacks, the net feedback effect over the industrial era has been positive (amplifying warming) andwill continue to be positive in coming decades (very high confidence).

Recommended Citation for Chapter
Fahey, D.W., S.J. Doherty, K.A. Hibbard, A. Romanou, and P.C. Taylor, 2017: Physical drivers of climatechange. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J.,
D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 73-113, doi: 10.7930/J0513WCR.

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2.0 Introduction

ed with atmospheric composition (gases andaerosols) and cloud effects. We describe theprinciple radiative forcings and the variety of
feedback responses which serve to amplify
these forcings.

Earth’s climate is undergoing substantialchange due to anthropogenic activities (Ch. 1:
Our Globally Changing Climate). Understanding the causes of past and present climatechange and confidence in future projectedchanges depend directly on our ability tounderstand and model the physical driversof climate change.1 Our understanding ischallenged by the complexity and interconnectedness of the components of the climate
system (that is, the atmosphere, land, ocean,and cryosphere). This chapter lays out thefoundation of climate change by describing its
physical drivers, which are primarily associat-

incoming
Units (Wm-2) solar TOA

2.1 Earth’s Energy Balance and theGreenhouse Effect
The temperature of the Earth system isdetermined by the amounts of incoming(short-wavelength) and outgoing (both shortand long-wavelength) radiation. In the modern era, radiative fluxes are well-constrainedby satellite measurements (Figure 2.1). Abouta third (29.4%) of incoming, short-wavelength

thermal outgoing
TOA

solar reflected
TOA

340

100

(340, 341)

239

(96, 100)(236, 242)

atmosphericwindow

79(74, 91)

greenhousegases

latent heatsolar absorbed
atmosphere

solar
solar
reflected
down(179, 189) (22,26)
surfacesurface

185

24

161

(154, 166)

imbalance

0.6(0.2, 1.0)

solar absorbed
surface

84 20

398

342

(70, 85) (15, 25)(394, 400)

(338, 348)

sensibleheat

thermalup surface

thermaldown surface

evaporation

Figure 2.1: Global mean energy budget of Earth under present-day climate conditions. Numbers state magnitudesof the individual energy fluxes in watts per square meter (W/m2) averaged over Earth’s surface, adjusted within theiruncertainty ranges to balance the energy budgets of the atmosphere and the surface. Numbers in parentheses attached to the energy fluxes cover the range of values in line with observational constraints. Fluxes shown include thoseresulting from feedbacks. Note the net imbalance of 0.6 W/m2 in the global mean energy budget. The observational
constraints are largely provided by satellite-based observations, which have directly measured solar and infrared fluxes
at the top of the atmosphere over nearly the whole globe since 1984.217, 218 More advanced satellite-based measurements focusing on the role of clouds in Earth’s radiative fluxes have been available since 1998.219, 220 Top of Atmosphere(TOA) reflected solar values given here are based on observations 2001–2010; TOA outgoing longwave is based on
2005–2010 observations. (Figure source: Hartmann et al. 2013,221 Figure 2-11; © IPCC, used with permission).

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energy from the sun is reflected back to space,
and the remainder is absorbed by Earth’s
system. The fraction of sunlight scattered
back to space is determined by the reflectivity(albedo) of clouds, land surfaces (including
snow and ice), oceans, and particles in the atmosphere. The amount and albedo of clouds,
snow cover, and ice cover are particularly
strong determinants of the amount of sunlightreflected back to space because their albedos
are much higher than that of land and oceans.

but not limited to the average, near-surface
air temperature. Anthropogenic activities
have changed Earth’s radiative balance andits albedo by adding GHGs, particles (aerosols), and aircraft contrails to the atmosphere,
and through land-use changes. Changes in
the radiative balance (or forcings) producechanges in temperature, precipitation, andother climate variables through a complex
set of physical processes, many of which arecoupled (Figure 2.2). These changes, in turn,trigger feedback processes which can further
amplify and/or dampen the changes in radiative balance (Sections 2.5 and 2.6).

In addition to reflected sunlight, Earth loses
energy through infrared (long-wavelength)radiation from the surface and atmosphere.
Absorption by greenhouse gases (GHGs) of infrared energy radiated from the surface leads
to warming of the surface and atmosphere.
Figure 2.1 illustrates the importance of greenhouse gases in the energy balance of Earth’s
system. The naturally occurring GHGs in
Earth’s atmosphere—principally water vapor
and carbon dioxide—keep the near-surface airtemperature about 60°F (33°C) warmer than it
would be in their absence, assuming albedo isheld constant.2 Geothermal heat from Earth’s
interior, direct heating from energy production, and frictional heating through tidal
flows also contribute to the amount of energy available for heating Earth’s surface andatmosphere, but their total contribution is anextremely small fraction (< 0.1%) of that due
to net solar (shortwave) and infrared (longwave) radiation (e.g., see Davies and Davies
2010;3 Flanner 2009;4 Munk and Wunsch 1998,5
where these forcings are quantified).

In the following sections, the principal components of the framework shown in Figure 2.2
are described. Climate models are structured
to represent these processes; climate models
and their components and associated uncertainties, are discussed in more detail in Chapter 4: Projections.
The processes and feedbacks connectingchanges in Earth’s radiative balance to a
climate response (Figure 2.2) operate on alarge range of time scales. Reaching an equilibrium temperature distribution in responseto anthropogenic activities takes decades or
longer because some components of Earth’s
system—in particular the oceans and cryosphere—are slow to respond due to their large
thermal masses and the long time scale ofcirculation between the ocean surface and the
deep ocean. Of the substantial energy gainedin the combined ocean–atmosphere systemover the previous four decades, over 90% of
it has gone into ocean warming (see Box 3.1
Figure 1 of Rhein et al. 2013).6 Even at equilibrium, internal variability in Earth’s climate
system causes limited annual- to decadal-scalevariations in regional temperatures and otherclimate parameters that do not contribute tolong-term trends. For example, it is likely thatnatural variability has contributed between

Thus, Earth’s equilibrium temperature in themodern era is controlled by a short list of factors: incoming sunlight, absorbed and reflected sunlight, emitted infrared radiation, andinfrared radiation absorbed and re-emitted in
the atmosphere, primarily by GHGs. Changes in these factors affect Earth’s radiativebalance and therefore its climate, including

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SimplifiedConceptual
Conceptual Framework
Framework of
Simplified
ofthe
theClimate
ClimateSystem
SystemClimate forcing agents(Industrial-era changes)

CO2
Non-CO2 GHG

Feedback processes

Terrestrial
carbon uptake

PlanckOcean carbon
uptakeLapse rate

(CH4, O3, halocarbons, N2O)

Water vapor

Aerosols

PlanckLand surface
albedoLand use
Volcanic eruptions

Dynamic vegetation,
terrestrial ecosystems
Permafrost carbon

Atmosphericchemistry

Solar irradiance

Instantaneousradiative forcing

Ocean circulation,
ocean chemistry andbiochemistry, andweathering

Clouds

Effective
radiative forcing

Radiative
balance

Ocean
acidification

Snow and ice coverTemperaturechangeChanges in otherclimate variables

Ocean heatuptakeClimate impacts
Figure 2.2: Simplified conceptual modeling framework for the climate system as implemented in many climate models(Ch. 4: Projections). Modeling components include forcing agents, feedback processes, carbon uptake processes, andradiative forcing and balance. The lines indicate physical interconnections (solid lines) and feedback pathways (dashedlines). Principal changes (blue boxes) lead to climate impacts (red box) and feedbacks. (Figure source: adapted from
Knutti and Rugenstein 201582).

−0.18°F (−0.1°C) and 0.18°F (0.1°C) to changes
in surface temperatures from 1951 to 2010; bycomparison, anthropogenic GHGs have likely
contributed between 0.9°F (0.5°C) and 2.3°F(1.3°C) to observed surface warming over thissame period.7 Due to these longer time scale
responses and natural variability, changes
in Earth’s radiative balance are not realized
immediately as changes in climate, and evenin equilibrium there will always be variability
around mean conditions.

era. It is expressed as a change in net radiativeflux (W/m2) either at the tropopause or topof the atmosphere,8 with the latter nominallydefined at 20 km altitude to optimize observation/model comparisons.9 The instantaneous
RF is defined as the immediate change in net
radiative flux following a change in a climate
driver. RF can also be calculated after allowing
different types of system response: for example, after allowing stratospheric temperatures
to adjust, after allowing both stratosphericand surface temperature to adjust, or after
allowing temperatures to adjust everywhere(the equilibrium RF) (Figure 8.1 of Myhre et al.
20138).

2.2 Radiative Forcing (RF) and Effective
Radiative Forcing (ERF)
Radiative forcing (RF) is widely used to quantify a radiative imbalance in Earth’s atmosphere resulting from either natural changes
or anthropogenic activities over the industrial

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In this report, we follow the Intergovernmental Panel on Climate Change (IPCC) recom-

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2 | Physical Drivers of Climate Changemendation that the RF caused by a forcing
agent be evaluated as the net radiative fluxchange at the tropopause after stratospherictemperatures have adjusted to a new radiativeequilibrium while assuming all other variables(for example, temperatures and cloud cover)are held fixed (Box 8.1 of Myhre et al. 20138).
A change that results in a net increase in the
downward flux (shortwave plus longwave)
constitutes a positive RF, normally resulting ina warming of the surface and/or atmosphereand potential changes in other climate parameters. Conversely, a change that yields anincrease in the net upward flux constitutes anegative RF, leading to a cooling of the surface
and/or atmosphere and potential changes inother climate parameters.

to direct warming of the troposphere, which
produces horizontal temperature variations,variations in the vertical lapse rate, and changes in clouds and vegetation, and it includes
the microphysical effects of aerosols on cloud
lifetime. Rapid changes in land surface properties (temperature, snow and ice cover, andvegetation) are also included. Not included in
ERF are climate responses driven by changes
in sea surface temperatures or sea ice cover.
For forcing by aerosols in snow (Section 2.3.2),
ERF includes the effects of direct warming of
the snowpack by particulate absorption (forexample, snow-grain size changes). Changes
in all of these parameters in response to RF are
quantified in terms of their impact on radiative fluxes (for example, albedo) and includedin the ERF. The largest differences between RF
and ERF occur for forcing by light-absorbing
aerosols because of their influence on clouds
and snow (Section 2.3.2). For most non-aerosol
climate drivers, the differences between RF
and ERF are small.

RF serves as a metric to compare present, past,or future perturbations to the climate system(e.g., Boer and Yu 2003;10 Gillett et al. 2004;11
Matthews et al. 2004;12 Meehl et al. 2004;13
Jones et al. 2007;14 Mahajan et al. 2013;15 Shiogama et al. 201316). For clarity and consistency,
RF calculations require that a time period be
defined over which the forcing occurs. Here,this period is the industrial era, defined as
beginning in 1750 and extending to 2011, unless otherwise noted. The 2011 end date is that
adopted by the CMIP5 calculations, which are
the basis of RF evaluations by the IPCC.8

2.3 Drivers of Climate Change over the
Industrial Era
Climate drivers of significance over the industrial era include both those associated withanthropogenic activity and, to a lesser extent,
those of natural origin. The only significant
natural climate drivers in the industrial era
are changes in solar irradiance, volcanic
eruptions, and the El Niño–Southern Oscillation. Natural emissions and sinks of GHGs
and tropospheric aerosols have varied overthe industrial era but have not contributed
significantly to RF. The effects of cosmic rays
on cloud formation have been studied, but
global radiative effects are not consideredsignificant.18 There are other known driversof natural origin that operate on longer timescales (for example, changes in Earth’s orbit[Milankovitch cycles] and changes in atmospheric CO2 via chemical weathering of rock).
Anthropogenic drivers can be divided into a

A refinement of the RF concept introducedin the latest IPCC assessment17 is the use ofeffective radiative forcing (ERF). ERF for a
climate driver is defined as its RF plus rapid
adjustment(s) to that RF.8 These rapid adjustments occur on time scales much shorter than,for example, the response of ocean temperatures. For an important subset of climate
drivers, ERF is more reliably correlated withthe climate response to the forcing than is RF;
as such, it is an increasingly used metric when
discussing forcing. For atmospheric components, ERF includes rapid adjustments due

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Radiative Forcing of Climate Between 1750 and 2011
Radiative Forcing of Climate Between 1750 and 2011

Forcing agent

Anthropogenic

CO2
Well Mixed
Greenhouse Gases Other WMGHG
Ozone

Stratospheric

Stratospheric water
vapour from CH4
Surface Albedo

Land Use

Halocarbons
CH4

N2O

TroposphericBlack carbon
on snow

Contrails

Contrail induced cirrus

Aerosol-Radiation Interac.
Aerosol-Cloud Interac.

Natural

Total anthropogenic
Solar irradiance
−1

0

1
2
Radiative Forcing (W/m2)

3

Figure 2.3: Bar chart for radiative forcing (RF; hatched) and effective radiative forcing (ERF; solid) for the period
1750–2011, where the total ERF is derived from the Intergovernmental Panel on Climate Change’s Fifth Assessment
Report. Uncertainties (5% to 95% confidence range) are given for RF (dotted lines) and ERF (solid lines). Volcanic
forcing is not shown because this forcing is intermittent, exerting forcing over only a few years for eruptions during theindustrial era; the net forcing over the industrial era is negligible. (Figure source: Myhre et al. 2013,8 Figure 8-15; ©
IPCC, used with permission).

number of categories, including well-mixed
greenhouse gases (WMGHGs), short-lived climate forcers (SLCFs, which include methane,some hydrofluorocarbons [HFCs], ozone, andaerosols), contrails, and changes in albedo (forexample, land-use changes). Some WMGHGs
are also considered SLCFs (for example, methane). Figures 2.3–2.7 summarize features of theprincipal climate drivers in the industrial era.
Each is described briefly in the following.

1978,22 though proxy indicators of solar cycles
are available back to the early 1600s.23 Althoughthese variations amount to only 0.1% of the total solar output of about 1360 W/m2,24 relative
variations in irradiance at specific wavelengths
can be much larger (tens of percent). Spectral variations in solar irradiance are highest
at near-ultraviolet (UV) and shorter wavelengths,25 which are also the most important
wavelengths for driving changes in ozone.26, 27
By affecting ozone concentrations, variations
in total and spectral solar irradiance induce
discernible changes in atmospheric heating andchanges in circulation.21, 28, 29 The relationships
between changes in irradiance and changes
in atmospheric composition, heating, anddynamics are such that changes in total solar
irradiance are not directly correlated with theresulting radiative flux changes.26, 30, 31

2.3.1 Natural Drivers

Solar Irradiance
Changes in solar irradiance directly impact
the climate system because the irradiance is
Earth’s primary energy source.19 In the industrial era, the largest variations in total solar
irradiance follow an 11-year cycle.20, 21 Direct
solar observations have been available since

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The IPCC estimate of the RF due to changes
in total solar irradiance over the industrial
era is 0.05 W/m2 (range: 0.0 to 0.10 W/m2).8This forcing does not account for radiativeflux changes resulting from changes in ozone
driven by changes in the spectral irradiance.
Understanding of the links between changes
in spectral irradiance, ozone concentrations,
heating rates, and circulation changes has
recently improved using, in particular, satellite data starting in 2002 that provide solar
spectral irradiance measurements through the
UV26 along with a series of chemistry–climate
modeling studies.26, 27, 32, 33, 34 At the regional
scale, circulation changes driven by solar spectral irradiance variations may be significant
for some locations and seasons but are poorly
quantified.28 Despite remaining uncertainties,
there is very high confidence that solar radiance-induced changes in RF are small relativeto RF from anthropogenic GHGs over theindustrial era (Figure 2.3).8

es in ocean circulation patterns that last for
decades after major eruptions (for example,
Mt. Tambora in 1815).37, 38, 39, 40 In addition to
the direct RF, volcanic aerosol heats the stratosphere, altering circulation patterns, and depletes ozone by enhancing surface reactions,which further changes heating and circulation. The resulting impacts on advective heattransport can be larger than the temperature
impacts of the direct forcing.36 Aerosol fromboth explosive and non-explosive eruptions
also affects the troposphere through changes
in diffuse radiation and through aerosol–cloudinteractions. It has been proposed that major
eruptions might “fertilize” the ocean with sufficient iron to affect phyotoplankton production and, therefore, enhance the ocean carbon
sink.41 Volcanoes also emit CO2 and water
vapor, although in small quantities relative
to other emissions. At present, conservative
estimates of annual CO2 emissions from volcanoes are less than 1% of CO2 emissions fromall anthropogenic activities.42 The magnitudeof volcanic effects on climate depends on thenumber and strength of eruptions, the latitudeof injection and, for ocean temperature andcirculation impacts, the timing of the eruption
relative to ocean temperature and circulation
patterns.39, 40

Volcanoes
Most volcanic eruptions are minor events withthe effects of emissions confined to the troposphere and only lasting for weeks to months.
In contrast, explosive volcanic eruptions inject
substantial amounts of sulfur dioxide (SO2)and ash into the stratosphere, which lead tosignificant short-term climate effects (Myhre etal. 2013,8 and references therein). SO2 oxidizes
to form sulfuric acid (H2SO4) which condenses, forming new particles or adding mass topreexisting particles, thereby substantiallyenhancing the attenuation of sunlight transmitted through the stratosphere (that is, increasing aerosol optical depth). These aerosols
increase Earth’s albedo by scattering sunlightback to space, creating a negative RF that cools
the planet.35, 36 The RF persists for the lifetimeof aerosol in the stratosphere, which is a few
years, far exceeding that in the troposphere(about a week). The oceans respond to a negative volcanic RF through cooling and chang-

U.S. Global Change Research Program

Volcanic eruptions represent the largest
natural forcing within the industrial era. In
the last millennium, eruptions caused several
multiyear, transient episodes of negative RFof up to several W/m2 (Figure 2.6). The RF of
the last major volcanic eruption, Mt. Pinatubo
in 1991, decayed to negligible values later inthe 1990s, with the temperature signal lasting about twice as long due to the effects ofchanges in ocean heat uptake.37 A net volcanic RF has been omitted from the drivers ofclimate change in the industrial era in Figure
2.3 because the value from multiple, episodic eruptions is negligible compared with theother climate drivers. While future explosive

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volcanic eruptions have the potential to again
alter Earth’s climate for periods of several
years, predictions of occurrence, intensity, andlocation remain elusive. If a sufficient number of non-explosive eruptions occur over an
extended time period in the future, averagechanges in tropospheric composition or circulation could yield a significant RF.36(N2O). With atmospheric lifetimes of a decade
to a century or more, these gases have modestto-small regional variabilities and are circulated and mixed around the globe to yield smallinterhemispheric gradients. The atmosphericabundances and associated radiative forcingsof WMGHGs have increased substantially over the industrial era (Figures 2.4–2.6).
Contributions from natural sources of these
constituents are accounted for in the industrial-era RF calculations shown in Figure 2.6.

2.3.2 Anthropogenic Drivers

CO2 (ppm)

Principal Well-mixed Greenhouse Gases(WMGHGs)
The principal WMGHGs are carbon dioxide (CO2), methane (CH4), and nitrous oxide

400

400

360

360

320

320

280

280

240

240

200

200

CH4 (ppb)

160
800,000

600,000

400,000

200,000

0

160
1750

2000

2000

1750

1750

1500

1500

1250

1250

1000

1000

750

750

500

500

250
800,000

N2O (ppb)

CO2 has substantial global sources and sinks(Figure 2.7). CO2 emission sources have grown

600,000

400,000

200,000

0

250
1750

340

340

320

320

300

300

280

280

260

260

240

240

220

220

200

200

180
800,000

600,000 400,000 200,000
Years before 2015

0

180
1750

1800

1850

1900

1950

2000

1800

1850

1900

1950

2000

1800

1850

1900
Year

1950

2000

Figure 2.4: Atmospheric concentrations of CO2 (top), CH4 (middle), and N2O (bottom) over the last 800,000 years (leftpanels) and for 1750–2015 (right panels). Measurements are shown from ice cores (symbols with different colors fordifferent studies) and for direct atmospheric measurements (red lines). (Adapted from IPCC 2007,88 Figure SPM.1,© IPCC, used with permission; data are from https://www.epa.gov/climate-indicators/climate-change-indicators-atmospheric-concentrations-greenhouse-gases).

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in the industrial era primarily from fossil fuelcombustion (that is, coal, gas, and oil), cement
manufacturing, and land-use change from
activities such as deforestation.43 Carbonationof finished cement products is a sink of atmospheric CO2, offsetting a substantial fraction(0.43) of the industrial-era emissions from

cement production.44 A number of processes act to remove CO2 from the atmosphere,
including uptake in the oceans, residual land
uptake, and rock weathering. These combined processes yield an effective atmospheric
lifetime for emitted CO2 of many decades tomillennia, far greater than any other major

Change in Radiative Forcing(W/m2)

Radiative Forcing (W/m2)

Radiative Forcing (W/m2)

Radiative Forcing (W/m2)

Radiative
ForcingofofWell-mixed
Well-mixed Greenhouse
Radiative
Forcing
GreenhouseGases
Gases
1.5

a) All Anthropogenic Gases

CO2

1.0
0.5

CH4
Others

0.0
1850
1

1900

1950

N 2O

2000

b) Main Gases
CO2

10

−1

10

−2

CH4
N2O

1900

1950

2000

1950

2000

1950

2000

c) Minor Gases

10−2
10−3
10−4
1850
0.04

HFC

CFC

10−3
1850
10−1

HCFC

Halocarbon

CFC-11
CFC-12
HCFC-22
CFC-113
CF4
CH4CCI
CCI4
HFC-134aSF4
HFC-23

1900d) Rate of Change

0.03
0.02

Others
N 2O
CH4
CO2

0.01
0.00
1850

1900

Figure 2.5: (a) Radiative forcing (RF) from the major WMGHGs and groups of halocarbons (Others) from 1850 to 2011;(b) the data in (a) with a logarithmic scale; (c) RFs from the minor WMGHGs from 1850 to 2011 (logarithmic scale);(d) the annual rate of change ([W/m2]/year) in forcing from the major WMGHGs and halocarbons from 1850 to 2011.
(Figure source: Myhre et al. 2013,8 Figure 8-06; © IPCC, used with permission).

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Effective Radiative Forcing (W/m2)

Time Evolutionof ForcingsTime Evolutionof Forcings

2

Solar
Black carbon on snow
and contrails
Stratospheric water

Total ERF
Total anthropogenic ERF

Tropospheric ozone
Other WMGHG
Carbon dioxide0

Aerosol-radiation int.
Aerosol-cloud int.
Land use−2

Stratospheric ozone
Volcanic

−4

1750

1800

1850

1900
Year

1950

2000

Figure 2.6: Time evolution in effective radiative forcings (ERFs) across the industrial era for anthropogenic and naturalforcing mechanisms. The terms contributing to cumulative totals of positive and negative ERF are shown with colored
regions. The terms are labeled in order on the right-hand side with positive ERFs above the zero line and negative
ERFs below the zero line. The forcings from black-carbon-on-snow and contrail terms are grouped together into asingle term in the plot. Also shown are the cumulative sum of all forcings (Total; black dashed line) and of anthropogenic-only forcings (Total Anthropogenic; red dashed line). Uncertainties in 2011 ERF values are shown in the original
figure (Myhre et al. 2013,8 Figure 8-18). See the Intergovernmental Panel on Climate Change Fifth Assessment Report(IPCC AR5) Supplementary Material Table 8.SM.88for further information on the forcing time evolutions. Forcing numbers are provided in Annex II of IPCC AR5. The total anthropogenic forcing was 0.57 (0.29 to 0.85) W/m2 in 1950, 1.25
(0.64 to 1.86) W/m2 in 1980, and 2.29 (1.13 to 3.33) W/m2 in 2011. (Figure source: Myhre et al. 2013,8 Figure 8-18; ©
IPCC, used with permission).

GHG. Seasonal variations in CO2 atmosphericconcentrations occur in response to seasonalchanges in photosynthesis in the biosphere,
and to a lesser degree to seasonal variations inanthropogenic emissions. In addition to fossil
fuel reserves, there are large natural reservoirsof carbon in the oceans, in vegetation andsoils, and in permafrost.

ence in defining the global warming potential(GWP) of other GHGs and climate agents. The
GWP of a GHG is the integrated RF over a
specified time period (for example, 100 years)
from the emission of a given mass of the GHG
divided by the integrated RF from the same
mass emission of CO2.
The global mean methane concentration and
RF have also grown substantially in the industrial era (Figures 2.4 and 2.5). Methane is astronger GHG than CO2 for the same emission
mass and has a shorter atmospheric lifetime ofabout 12 years. Methane also has indirect climate effects through induced changes in CO2,
stratospheric water vapor, and ozone.45 The
100-year GWP of methane is 28–36, dependingon whether oxidation into CO2 is included
and whether climate-carbon feedbacks areaccounted for; its 20-year GWP is even higher(84–86) (Myhre et al. 20138 Table 8.7). With acurrent global mean value near 1840 parts per

In the industrial era, the CO2 atmospheric
growth rate has been exponential (Figure 2.4),
with the increase in atmospheric CO2 approximately twice that absorbed by the oceans.
Over at least the last 50 years, CO2 has shown
the largest annual RF increases among all
GHGs (Figures 2.4 and 2.5). The global average CO2 concentration has increased by 40%
over the industrial era, increasing from 278
parts per million (ppm) in 1750 to 390 ppm
in 2011;43 it now exceeds 400 ppm (as of 2016)(http://www.esrl.noaa.gov/gmd/ccgg/
trends/). CO2 has been chosen as the refer-

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40

CO2 flux (Gt CO2 /yr)

30

Fossil fuels
and industry

20
10

Land-use change
Land sink

0−10

Atmosphere

−20
−30

Ocean sink

−40

1880 1900 1920 1940 1960 1980 2000 2015
Figure 2.7: CO2 sources and sinks (GtCO2/yr) over the period 1870–2015. The partitioning of atmospheric emissions
among the atmosphere, land, and ocean is shown as equivalent negative emissions in the lower panel; of these, theland and ocean terms are sinks of atmospheric CO2. CO2 emissions from net land-use changes are mainly from deforestation. The atmospheric CO2 growth rate is derived from atmospheric observations and ice core data. The ocean
CO2 sink is derived from a combination of models and observations. The land sink is the residual of the other terms ina balanced CO2 budget and represents the sink of anthropogenic CO2 in natural land ecosystems. These terms only
represent changes since 1750 and do not include natural CO2 fluxes (for example, from weathering and outgassing
from lakes and rivers). (Figure source: Le Quére et al. 2016,135 Figure 3).

billion by volume (ppb), the methane concentration has increased by a factor of about 2.5
over the industrial era. The annual growthrate for methane has been more variable thanthat for CO2 and N2O over the past several
decades, and has occasionally been negative
for short periods.

negative trend in the methane annual growthrate starting in the mid-1980s and the rapid
increases in the annual rate in the last decade(Figure 2.4) reflect the complexity of the methane budget.43, 46, 47
Growth rates in the global mean nitrous
oxide (N2O) concentration and RF over theindustrial era are smaller than for CO2 andmethane (Figures 2.4 and 2.5). N2O is emitted
in the nitrogen cycle in natural ecosystems
and has a variety of anthropogenic sources,
including the use of synthetic fertilizers inagriculture, motor vehicle exhaust, and somemanufacturing processes. The current global
value near 330 ppb reflects steady growthover the industrial era with average increases
in recent decades of 0.75 ppb per year (Figure2.4).43 Fertilization in global food production is
responsible for about 80% of the growth rate.
Anthropogenic sources account for approximately 40% of the annual N2O emissions of
17.9 (8.1 to 30.7) TgN.43 N2O has an atmospheric lifetime of about 120 years and a GWP in the

Methane emissions, which have a variety ofnatural and anthropogenic sources, totaled
556 ± 56 Tg CH4 in 2011 based on top-down
analyses, with about 60% from anthropogenic
sources.43 The methane budget is complicatedby the variety of natural and anthropogenic
sources and sinks that influence its atmospheric concentration. These include the global abundance of the hydroxyl radical (OH),
which controls the methane atmospheric lifetime; changes in large-scale anthropogenic activities such as mining, natural gas extraction,
animal husbandry, and agricultural practices;and natural wetland emissions (Table 6.8,
Ciais et al. 201343). The remaining uncertainty
in the cause(s) of the approximately 20-year

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range 265–298 (Myhre et al. 20138 Table 8.7).
The primary sink of N2O is photochemical destruction in the stratosphere, which produces
nitrogen oxides (NOx) that catalytically destroy ozone (e.g., Skiba and Rees 201448). Small
indirect climate effects, such as the response of
stratospheric ozone, are generally not included in the N2O RF.

HFCs be controlled as a WMGHGs under theMontreal Protocol because of their large projected future abundances.50 In October 2016,
the Montreal Protocol adopted an amendment
to phase down global HFC production andconsumption, avoiding emissions equivalent
to approximately 105 Gt CO2 by 2100 based onearlier projections.50 The atmospheric growth
rates of some halocarbon concentrations are
significant at present (for example, SF6 andHFC-134a), although their RF contributions
remain small (Figure 2.5).

N2O is a component of the larger global budget of total reactive nitrogen (N) comprising
N2O, ammonia (NH3), and nitrogen oxides(NOx) and other compounds. Significant uncertainties are associated with balancing thisbudget over oceans and land while accounting for deposition and emission processes.43,
49
Furthermore, changes in climate parameters such as temperature, moisture, and CO2
concentrations are expected to affect the N2O
budget in the future, and perhaps atmosphericconcentrations.

Water Vapor
Water vapor in the atmosphere acts as a powerful natural GHG, significantly increasing
Earth’s equilibrium temperature. In the stratosphere, water vapor abundances are controlled by transport from the troposphere andfrom oxidation of methane. Increases in methane from anthropogenic activities therefore
increase stratospheric water vapor, producing
a positive RF (e.g., Solomon et al. 2010;51 Hegglin et al. 201452). Other less-important anthropogenic sources of stratospheric water vapor
are hydrogen oxidation,53 aircraft exhaust,54, 55
and explosive volcanic eruptions.56

Other Well-mixed Greenhouse Gases
Other WMGHGs include several categories ofsynthetic (i.e., manufactured) gases, including
chlorofluorocarbons (CFCs), halons, hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), andsulfur hexafluoride (SF6), collectively known
as halocarbons. Natural sources of these gasesin the industrial era are small compared toanthropogenic sources. Important examples
are the expanded use of CFCs as refrigerants
and in other applications beginning in the
mid-20th century. The atmospheric abundances of principal CFCs began declining in the
1990s after their regulation under the Montreal Protocol as substances that deplete stratospheric ozone (Figure 2.5). All of these gases
are GHGs covering a wide range of GWPs,
atmospheric concentrations, and trends. PFCs,
SF6, and HFCs are in the basket of gases covered under the United Nations Framework
Convention on Climate Change. The UnitedStates joined other countries in proposing that

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In the troposphere, the amount of water vapor
is controlled by temperature.57 Atmosphericcirculation, especially convection, limits thebuildup of water vapor in the atmospheresuch that the water vapor from direct emissions, for example by combustion of fossil
fuels or by large power plant cooling towers,
does not accumulate in the atmosphere but
actually offsets water vapor that would otherwise evaporate from the surface. Direct changes in atmospheric water vapor are negligible
in comparison to the indirect changes causedby temperature changes resulting from radiative forcing. As such, changes in troposphericwater vapor are considered a feedback in the
climate system (see Section 2.6.1 and Figure2.2). As increasing GHG concentrations warm

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the atmosphere, tropospheric water vapor
concentrations increase, thereby amplifying
the warming effect.57

weeks due to the general hygroscopic natureof primary and secondary particles and theubiquity of cloud and precipitation systems
in the troposphere. Particles that act as cloud
condensation nuclei (CCN) or are scavenged
by cloud droplets are removed from the troposphere in precipitation. The heterogeneity ofaerosol sources and locations combined withshort aerosol lifetimes leads to the high spatial and temporal variabilities observed in the
global aerosol distribution and their associated forcings.

Ozone
Ozone is a naturally occurring GHG in the
troposphere and stratosphere and is produced
and destroyed in response to a variety ofanthropogenic and natural emissions. Ozone
abundances have high spatial and temporalvariability due to the nature and variety ofthe production, loss, and transport processes
controlling ozone abundances, which adds
complexity to the ozone RF calculations. In
the global troposphere, emissions of methane,
NOx, carbon monoxide (CO), and non-methane volatile organic compounds (VOCs)
form ozone photochemically both near and
far downwind of these precursor source
emissions, leading to regional and global
positive RF contributions (e.g., Dentener et
al. 200558). Stratospheric ozone is destroyed
photochemically in reactions involving the
halogen species chlorine and bromine. Halogens are released in the stratosphere from thedecomposition of some halocarbons emitted
at the surface as a result of natural processes
and human activities.59 Stratospheric ozone
depletion, which is most notable in the polar
regions, yields a net negative RF.8

Aerosols from anthropogenic activities influence RF in three primary ways: throughaerosol–radiation interactions, through aerosol–cloud interactions, and through albedo
changes from absorbing-aerosol deposition
on snow and ice.60 RF from aerosol–radiationinteractions, also known as the aerosol “direct
effect,” involves absorption and scattering of
longwave and shortwave radiation. RF fromaerosol-cloud interactions, also known as thecloud albedo “indirect effect,” results fromchanges in cloud droplet number and size due
to changes in aerosol (cloud condensation nuclei) number and composition. The RF for the
global net aerosol–radiation and aerosol–cloudinteraction is negative.8 However, the RF is notnegative for all aerosol types. Light-absorbing
aerosols, such as black carbon, absorb sunlight, producing a positive RF. This absorption
warms the atmosphere; on net, this response isassessed to increase cloud cover and therefore
increase planetary albedo (the “semi-direct”
effect). This “rapid response” lowers the ERF
of atmospheric black carbon by approximately
15% relative to its RF from direct absorption
alone.61 ERF for aerosol–cloud interactions
includes this rapid adjustment for absorbing
aerosol (that is, the cloud response to atmospheric heating) and it includes cloud lifetime
effects (for example, glaciation and thermodynamic effects).60 Light-absorbing aerosols also
affect climate when present in surface snow by

Aerosols
Atmospheric aerosols are perhaps the mostcomplex and most uncertain component offorcing due to anthropogenic activities.8 Aerosols have diverse natural and anthropogenic
sources, and emissions from these sourcesinteract in non-linear ways.60 Aerosol types are
categorized by composition; namely, sulfate,black carbon, organic, nitrate, dust, and sea
salt. Individual particles generally include
a mix of these components due to chemicaland physical transformations of aerosols andaerosol precursor gases following emission.
Aerosol tropospheric lifetimes are days to

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lowering surface albedo, yielding a positive
RF (e.g., Flanner et al. 200962). For black carbon
deposited on snow, the ERF is a factor of threehigher than the RF because of positive feedbacks that reduce snow albedo and acceleratesnow melt (e.g., Flanner et al. 2009;62 Bond etal. 201361). There is very high confidence that the
RF from snow and ice albedo is positive.61

tive, depending on the balance of longwave(surface cooling or increases in water vapor)and shortwave (increased cloudiness) effects.65
Contrails
Line-shaped (linear) contrails are a special
type of cirrus cloud that forms in the wakeof jet-engine aircraft operating in the mid- toupper troposphere under conditions of highambient humidity. Persistent contrails, which
can last for many hours, form when ambient
humidity conditions are supersaturated withrespect to ice. As persistent contrails spreadand drift with the local winds after formation,
they lose their linear features, creating additional cirrus cloudiness that is indistinguishable from background cloudiness. Contrails
and contrail cirrus are additional forms ofcirrus cloudiness that interact with solar andthermal radiation to provide a global net positive RF and thus are visible evidence of an anthropogenic contribution to climate change.66

Land Surface
Land-cover changes (LCC) due to anthropogenic activities in the industrial era havechanged the land surface brightness (albedo), principally through deforestation andafforestation. There is strong evidence thatthese changes have increased Earth’s global
surface albedo, creating a negative (cooling)
RF of −0.15 ± 0.10 W/m2.8 In specific regions,however, LCC has lowered surface albedo
producing a positive RF (for example, throughafforestation and pasture abandonment).
In addition to the direct radiative forcing
through albedo changes, LCC also have indirect forcing effects on climate, such as altering
carbon cycles and altering dust emissions
through effects on the hydrologic cycle. These
effects are generally not included in the direct
LCC RF calculations and are instead included in the net GHG and aerosol RFs over theindustrial era. These indirect forcings may
be of opposite sign to that of the direct LCC
albedo forcing and may constitute a significant fraction of industrial-era RF driven byhuman activities.63 Some of these effects, suchas alteration of the carbon cycle, constitute climate feedbacks (Figure 2.2) and are discussed
more extensively in Chapter 10: Land Cover.
The increased use of satellite observations toquantify LCC has resulted in smaller negative
LCC RF values (e.g., Ju and Masek 201664).
In areas with significant irrigation, surface
temperatures and precipitation are affected bya change in energy partitioning from sensible
to latent heating. Direct RF due to irrigation is
generally small and can be positive or nega-

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2.4 Industrial-era Changes in Radiative
Forcing AgentsThe IPCC best-estimate values of present day
RFs and ERFs from principal anthropogenic
and natural climate drivers are shown in Figure 2.3 and in Table 2.1. The past changes in
the industrial era leading up to present day RF
are shown for anthropogenic gases in Figure
2.5 and for all climate drivers in Figure 2.6.
The combined figures have several striking features. First, there is a large range inthe magnitudes of RF terms, with contrails,
stratospheric ozone, black carbon on snow,
and stratospheric water vapor being small
fractions of the largest term (CO2). The sumof ERFs from CO2 and non-CO2 GHGs, tropospheric ozone, stratospheric water, contrails,
and black carbon on snow shows a gradual increase from 1750 to the mid-1960s and accelerated annual growth in the subsequent 50 years(Figure 2.6). The sum of aerosol effects, strato-

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Table 2.1. Global mean RF and ERF values in 2011 for the industrial era. a
Radiative Forcing Term

Radiative forcing (W/m2)

Effective radiative
forcing (W/m2) b

Well-mixed greenhouse gases(CO2, CH4, N2O, and halocarbons)+2.83 (2.54 to 3.12)+2.83 (2.26 to 3.40)

Tropospheric ozone+0.40 (0.20 to 0.60)

Stratospheric ozone−0.05 (−0.15 to +0.05)

Stratospheric water vapor from CH4+0.07 (+0.02 to +0.12)

Aerosol–radiation interactions−0.35 (−0.85 to +0.15)−0.45 (−0.95 to +0.05)

Aerosol–cloud interactions

Not quantified−0.45 (−1.2 to 0.0)

Surface albedo (land use)

−0.15 (−0.25 to −0.05)

Surface albedo (black carbon aerosol
on snow and ice)

+0.04 (+0.02 to +0.09)

Contrails+0.01 (+0.005 to +0.03)

Combined contrails and contrailinduced cirrus

Not quantified+0.05 (0.02 to 0.15)

Total anthropogenic

Not quantified+2.3 (1.1 to 3.3)

Solar irradiance+0.05 (0.0 to +0.10)abFrom IPCC8
RF is a good estimate of ERF for most forcing agents except black carbon on snow and ice
and aerosol–cloud interactions.

spheric ozone depletion, and land use show amonotonically increasing cooling trend for the
first two centuries of the depicted time series.
During the past several decades, however,this combined cooling trend has leveled off
due to reductions in the emissions of aerosols
and aerosol precursors, largely as a result oflegislation designed to improve air quality.67, 68
In contrast, the volcanic RF reveals its episodic, short-lived characteristics along withlarge values that at times dominate the total
RF. Changes in total solar irradiance over theindustrial era are dominated by the 11-year solar cycle and other short-term variations. The
solar irradiance RF between 1745 and 2005
is 0.05 (range of 0.0–0.1) W/m2,8 a very small
fraction of total anthropogenic forcing in 2011.
The large relative uncertainty derives frominconsistencies among solar models, which
all rely on proxies of solar irradiance to fit theindustrial era. In total, ERF has increased substantially in the industrial era, driven almost
completely by anthropogenic activities, with

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annual growth in ERF notably higher after the
mid-1960s.
The principal anthropogenic activities thathave increased ERF are those that increase net
GHG emissions. The atmospheric concentrations of CO2, CH4, and N2O are higher now
than they have been in at least the past 800,000
years.69 All have increased monotonically overthe industrial era (Figure 2.4), and are now
40%, 250%, and 20%, respectively, above theirpreindustrial concentrations as reflected in
the RF time series in Figure 2.5. Troposphericozone has increased in response to growth
in precursor emissions in the industrial era.
Emissions of synthetic GHGs have grown rapidly beginning in the mid-20th century, withmany bringing halogens to the stratosphereand causing ozone depletion in subsequent
decades. Aerosol RF effects are a sum overaerosol–radiation and aerosol–cloud interactions; this RF has increased in the industrial
era due to increased emissions of aerosol and87

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aerosol precursors (Figure 2.6). These global
aerosol RF trends average across disparate
trends at the regional scale. The recent leveling off of global aerosol concentrations is theresult of declines in many regions that were
driven by enhanced air quality regulations,particularly starting in the 1980s (e.g., Philipona et al. 2009;70 Liebensperger et al. 2012;71
Wild 201672). These declines are partially offset
by increasing trends in other regions, such as
much of Asia and possibly the Arabian Peninsula.73, 74, 75 In highly polluted regions, negative
aerosol RF may fully offset positive GHG RF,
in contrast to global annual averages in which
positive GHG forcing fully offsets negative
aerosol forcing (Figures 2.3 and 2.6).

representative of near-term climate change
from a GHG forcing. To estimate ECS, climate model runs have to simulate thousandsof years in order to allow sufficient time forocean temperatures to reach equilibrium.
In the IPCC’s Fifth Assessment Report, ECS
is assessed to be a factor of 1.5 or more greater than the TCR (ECS is 2.7°F to 8.1°F [1.5°C
to 4.5°C] and TCR is 1.8°F to 4.5°F [1.0°C to
2.5°C]76), exemplifying that longer time-scale
feedbacks are both significant and positive.
Confidence in the model-based TCR and ECS
values is increased by their agreement, within
respective uncertainties, with other methodsof calculating these metrics (Box 12.2 of Collins et al. 2013)77. The alternative methods include using reconstructed temperatures from
paleoclimate archives, the forcing/response
relationship from past volcanic eruptions,and observed surface and ocean temperaturechanges over the industrial era.77

2.5 The Complex Relationship between
Concentrations, Forcing, and Climate
Response
Climate changes occur in response to ERFs,
which generally include certain rapid responses to the underlying RF terms (Figure 2.2).
Responses within Earth’s system to forcing
can act to either amplify (positive feedback)or reduce (negative feedback) the original
forcing. These feedbacks operate on a rangeof time scales, from days to centuries. Thus,in general, the full climate impact of a given
forcing is not immediately realized. Of interest are the climate response at a given point
in time under continuously evolving forcingsand the total climate response realized for agiven forcing. A metric for the former, whichapproximates near-term climate change from a
GHG forcing, is the transient climate response(TCR), defined as the change in global meansurface temperature when the atmospheric
CO2 concentration has doubled in a scenario
of concentration increasing at 1% per year.
The latter is given by the equilibrium climate
sensitivity (ECS), defined as the change atequilibrium in annual and global mean surface temperature following a doubling of theatmospheric CO2 concentration.76 TCR is more

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While TCR and ECS are defined specifically for the case of doubled CO2, the climate
sensitivity factor, λ, more generally relates
the equilibrium surface temperature response(∆T) to a constant forcing (ERF) as given by∆T = λERF.76, 78 The λ factor is highly dependent on feedbacks within Earth’s system; all
feedbacks are quantified themselves as radiative forcings, since each one acts by affecting
Earth’s albedo or its greenhouse effect. Models
in which feedback processes are more positive(that is, more strongly amplify warming) tend
to have a higher climate sensitivity (see Figure9.43 of Flato et al.76). In the absence of feedbacks, λ would be equal to 0.54°F/(W/m2)(0.30°C/[W/m2]). The magnitude of λ for ERF
over the industrial era varies across models,
but in all cases λ is greater than 0.54°F/(W/m2), indicating the sum of all climate feedbacks tends to be positive. Overall, the global
warming response to ERF includes a substantial amplification from feedbacks, with a

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model mean λ of 0.86°F/(W/m2) (0.48°C/[W/m2]) with a 90% uncertainty range of ±0.23°F/(W/m2) (±0.13°C/[W/m2]) (as derived fromclimate sensitivity parameter in Table 9.5 of
Flato et al.76 combined with methodology of
Bony et al.79). Thus, there is high confidence thatthe response of Earth’s system to the industrial-era net positive forcing is to amplify that
forcing (Figure 9.42 of Flato et al.76).

aerosol precursor emissions is enhanced byin-atmosphere aerosol formation and chemical transformations, and by aerosol removal
in precipitation and surface deposition. Evenfor relatively uniformly distributed species(for example, WMGHGs), RF patterns are lesshomogenous than their concentrations. The
RF of a uniform CO2 distribution, for example, depends on latitude and cloud cover.83
With the added complexity and variabilityof regional forcings, the global mean RFs are
known with more confidence than the regional RF patterns. Forcing feedbacks in responseto spatially variable forcings also have variable geographic and temporal patterns.

The models used to quantify λ account for thenear-term feedbacks described below (Section 2.6.1), though with mixed levels of detailregarding feedbacks to atmospheric composition. Feedbacks to the land and ocean carbon sink, land albedo and ocean heat uptake,most of which operate on longer time scales(Section 2.6.2), are currently included on only
a limited basis, or in some cases not at all, inclimate models. Climate feedbacks are thelargest source of uncertainty in quantifying
climate sensitivity;76 namely, the responses
of clouds, the carbon cycle, ocean circulation
and, to a lesser extent, land and sea ice to surface temperature and precipitation changes.

Quantifying the relationship between spatial
RF patterns and regional and global climate
responses in the industrial era is difficult
because it requires distinguishing forcing responses from the inherent internal variabilityof the climate system, which acts on a rangeof time scales. The ability to test the accuracyof modeled responses to forcing patterns islimited by the sparsity of long-term observational records of regional climate variables. As
a result, there is generally very low confidence
in our understanding of the qualitative andquantitative forcing–response relationships at
the regional scale. However, there is medium
to high confidence in other features, such as
aerosol effects altering the location of the Inter
Tropical Convergence Zone (ITCZ) and thepositive feedback to reductions of snow and
ice and albedo changes at high latitudes.8, 60

The complexity of mapping forcings to climate responses on a global scale is enhanced
by geographic and seasonal variations inthese forcings and responses, driven in partby similar variations in anthropogenic emissions and concentrations. Studies show thatthe spatial pattern and timing of climate
responses are not always well correlated withthe spatial pattern and timing of a radiative
forcing, since adjustments within the climate
system can determine much of the response(e.g., Shindell and Faluvegi 2009;80 Crookand Forster 2011;81 Knutti and Rugenstein
201582). The RF patterns of short-lived climate
drivers with inhomogeneous source distributions, such as aerosols, tropospheric ozone,
contrails, and land cover change, are leading
examples of highly inhomogeneous forcings.
Spatial and temporal variability in aerosol and

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2.6 Radiative-forcing Feedbacks
2.6.1 Near-term Feedbacks

Planck Feedback
When the temperatures of Earth’s surface andatmosphere increase in response to RF, more
infrared radiation is emitted into the lower
atmosphere; this serves to restore radiativebalance at the tropopause. This radiative
feedback, defined as the Planck feedback, only89

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Cloud Feedbacks
An increase in cloudiness has two direct
impacts on radiative fluxes: first, it increases
scattering of sunlight, which increases Earth’s
albedo and cools the surface (the shortwave
cloud radiative effect); second, it increases
trapping of infrared radiation, which warms
the surface (the longwave cloud radiative
effect). A decrease in cloudiness has the opposite effects. Clouds have a relatively larger
shortwave effect when they form over dark
surfaces (for example, oceans) than over higher albedo surfaces, such as sea ice and deserts. For clouds globally, the shortwave cloudradiative effect is about −50 W/m2, and the
longwave effect is about +30 W/m2, yielding
a net cooling influence.89, 90 The relative magnitudes of both effects vary with cloud type aswell as with location. For low-altitude, thickclouds (for example, stratus and stratocumulus) the shortwave radiative effect dominates,
so they cause a net cooling. For high-altitude,
thin clouds (for example, cirrus) the longwave effect dominates, so they cause a net
warming (e.g., Hartmann et al. 1992;91 Chenet al. 200092). Therefore, an increase in low
clouds is a negative feedback to RF, while anincrease in high clouds is a positive feedback.
The potential magnitude of cloud feedbacks
is large compared with global RF (see Section
2.4). Cloud feedbacks also influence naturalvariability within the climate system and may
amplify atmospheric circulation patterns andthe El Niño–Southern Oscillation.93

partially offsets the positive RF while triggering other feedbacks that affect radiative balance. The Planck feedback magnitude is −3.20± 0.04 W/m2 per 1.8°F (1°C) of warming and is
the strongest and primary stabilizing feedbackin the climate system.84
Water Vapor and Lapse Rate Feedbacks
Warmer air holds more moisture (water
vapor) than cooler air—about 7% more per
degree Celsius—as dictated by the Clausius–Clapeyron relationship.85 Thus, as global temperatures increase, the total amount of water
vapor in the atmosphere increases, adding
further to greenhouse warming—a positive
feedback—with a mean value derived froma suite of atmosphere/ocean global climate
models (AOGCM) of 1.6 ± 0.3 W/m2 per 1.8°F(1°C) of warming (Table 9.5 of Flato et al.
2013).76 The water vapor feedback is responsible for more than doubling the direct climate
warming from CO2 emissions alone.57, 79, 84, 86
Observations confirm that global troposphericwater vapor has increased commensurate withmeasured warming (FAQ 3.2 and its Figure
1a in IPCC 2013).17 Interannual variations andtrends in stratospheric water vapor, while
influenced by tropospheric abundances, arecontrolled largely by tropopause temperaturesand dynamical processes.87 Increases in tropospheric water vapor have a larger warming
effect in the upper troposphere (where it iscooler) than in the lower troposphere, thereby decreasing the rate at which temperatures
decrease with altitude (the lapse rate). Warmer
temperatures aloft increase outgoing infrared
radiation—a negative feedback—with a meanvalue derived from the same AOGCM suiteof −0.6 ± 0.4 W/m2 per 1.8°F (1°C) warming.
These feedback values remain largely unchanged between recent IPCC assessments.17,
88
Recent advances in both observations andmodels have increased confidence that the net
effect of the water vapor and lapse rate feedbacks is a significant positive RF.76

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The net radiative effect of cloud feedbacks
is positive over the industrial era, with anassessed value of +0.27 ± 0.42 W/m2 per 1.8°F(1°C) warming.84 The net cloud feedback
can be broken into components, where thelongwave cloud feedback is positive (+0.24± 0.26 W/m2 per 1.8°F [1°C] warming) andthe shortwave feedback is near-zero (+0.14 ±0.40 W/m2 per 1.8°F [1°C] warming84), though
the two do not add linearly. The value of the

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shortwave cloud feedback shows a significant
sensitivity to computation methodology.84, 94,
95
Uncertainty in cloud feedback remains thelargest source of inter-model differences in calculated climate sensitivity.60, 84

freshwater input to the oceans, which in turncan affect ocean temperatures and circulation,
ocean–atmosphere heat exchange and moisture fluxes, and atmospheric circulation.69
The complete contribution of ice-sheet feedbacks on time scales of millennia are not generally included in CMIP5 climate simulations.
These slow feedbacks are also not thought tochange in proportion to global mean surface
temperature change, implying that the apparent climate sensitivity changes with time,making it difficult to fully understand climate
sensitivity considering only the industrial age.
This slow response increases the likelihood fortipping points, as discussed further in Chapter
15: Potential Surprises.

Snow, Ice, and Surface Albedo
Snow and ice are highly reflective to solar radiation relative to land surfaces and the ocean.
Loss of snow cover, glaciers, ice sheets, or sea
ice resulting from climate warming lowers
Earth’s surface albedo. The losses create thesnow–albedo feedback because subsequent
increases in absorbed solar radiation lead tofurther warming as well as changes in turbulent heat fluxes at the surface.96 For seasonal
snow, glaciers, and sea ice, a positive albedo
feedback occurs where light-absorbing aerosols are deposited to the surface, darkening
the snow and ice and accelerating the loss ofsnow and ice mass (e.g., Hansen and Nazarenko 2004;97 Jacobson 2004;98 Flanner et al. 2009;62
Skeie et al. 2011;99 Bond et al. 2013;61 Yang et al.
2015100).

The surface-albedo feedback is an important
influence on interannual variations in sea ice
as well as on long-term climate change. While
there is a significant range in estimates of thesnow-albedo feedback, it is assessed as positive,84, 109, 110 with a best estimate of 0.27 ± 0.06
W/m2 per 1.8°F (1°C) of warming globally.
Within the cryosphere, the surface-albedo
feedback is most effective in polar regions;94,
111
there is also evidence that polar surface-albedo feedbacks might influence the tropicalclimate as well.112

For ice sheets (for example, on Antarctica andGreenland—see Ch. 11: Arctic Changes), thepositive radiative feedback is further amplified by dynamical feedbacks on ice-sheet mass
loss. Specifically, since continental ice shelves
limit the discharge rates of ice sheets into theocean; any melting of the ice shelves accelerates the discharge rate, creating a positive
feedback on the ice-stream flow rate and total
mass loss (e.g., Holland et al. 2008;101 Schoof
2010;102 Rignot et al. 2010;103 Joughin et al.
2012104). Warming oceans also lead to accelerated melting of basal ice (ice at the base ofa glacier or ice sheet) and subsequent icesheet loss (e.g., Straneo et al. 2013;105 Thoma
et al. 2015;106 Alley et al. 2016;107 Silvano et al.
2016108). Feedbacks related to ice sheet dynamics occur on longer time scales than otherfeedbacks—many centuries or longer. Significant ice-sheet melt can also lead to changes in

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Changes in sea ice can also influence arctic
cloudiness. Recent work indicates that arctic
clouds have responded to sea ice loss in fall
but not summer.113, 114, 115, 116, 117 This has important implications for future climate change,
as an increase in summer clouds could offset
a portion of the amplifying surface-albedo
feedback, slowing down the rate of arctic
warming.
Atmospheric Composition
Climate change alters the atmospheric abundance and distribution of some radiatively
active species by changing natural emissions,91

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atmospheric photochemical reaction rates,
atmospheric lifetimes, transport patterns, ordeposition rates. These changes in turn alter
the associated ERFs, forming a feedback.118, 119,
120
Atmospheric composition feedbacks occur
through a variety of processes. Important
examples include climate-driven changes intemperature and precipitation that affect 1)
natural sources of NOx from soils and lightning and VOC sources from vegetation, allof which affect ozone abundances;120, 121, 1222) regional aridity, which influences surface
dust sources as well as susceptibility to wildfires; and 3) surface winds, which control theemission of dust from the land surface and theemissions of sea salt and dimethyl sulfide—anatural precursor to sulfate aerosol—from theocean surface.

2.6.2 Long-term Feedbacks

Terrestrial Ecosystems and Climate Change
Feedbacks
The cycling of carbon through the climate
system is an important long-term climate
feedback that affects atmospheric CO2 concentrations. The global mean atmospheric
CO2 concentration is determined by emissions
from burning fossil fuels, wildfires, and permafrost thaw balanced against CO2 uptake by
the oceans and terrestrial biosphere (Figures
2.2 and 2.7).43, 135 During the past decade, justless than a third of anthropogenic CO2 hasbeen taken up by the terrestrial environment,
and another quarter by the oceans (Le Quéréet al.135 Table 8) through photosynthesis andthrough direct absorption by ocean surface
waters. The capacity of the land to continue
uptake of CO2 is uncertain and depends on
land-use management and on responses of thebiosphere to climate change (see Ch. 10: Land
Cover). Altered uptake rates affect atmospheric CO2 abundance, forcing, and rates of climatechange. Such changes are expected to evolveon the decadal and longer time scale, thoughabrupt changes are possible.

Climate-driven ecosystem changes that alter
the carbon cycle potentially impact atmospheric CO2 and CH4 abundances (Section
2.6.2). Atmospheric aerosols affect clouds andprecipitation rates, which in turn alter aerosol removal rates, lifetimes, and atmosphericabundances. Longwave radiative feedbacks
and climate-driven circulation changes
also alter stratospheric ozone abundance.123
Investigation of these and other composition–climate interactions is an active area of
research (e.g., John et al. 2012;124 Pacifico et al.
2012;125 Morgenstern et al. 2013;126 Holmes etal. 2013;127 Naik et al. 2013;128 Voulgarakis etal. 2013;129 Isaksen et al. 2014;130 Dietmuller et
al. 2014;131 Banerjee et al. 2014132). While understanding of key processes is improving,
atmospheric composition feedbacks are absent
or limited in many global climate modelingstudies used to project future climate, though
this is rapidly changing.133 For some composition–climate feedbacks involving shorter-lived
constituents, the net effects may be near zeroat the global scale while significant at local toregional scales (e.g., Raes et al. 2010;120 Han etal. 2013134).

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Significant uncertainty exists in quantification of carbon-cycle feedbacks, with large
differences in the assumed characteristics ofthe land carbon-cycle processes in current
models. Ocean carbon-cycle changes in futureclimate scenarios are also highly uncertain.
Both of these contribute significant uncertainty to longer-term (century-scale) climate
projections. Basic principles of carbon cycle
dynamics in terrestrial ecosystems suggestthat increased atmospheric CO2 concentrations
can directly enhance plant growth rates and,
therefore, increase carbon uptake (the “CO2
fertilization” effect), nominally sequesteringmuch of the added carbon from fossil-fuelcombustion (e.g., Wenzel et al. 2016136). However, this effect is variable; sometimes plants
acclimate so that higher CO2 concentrations

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no longer enhance growth (e.g., Franks et al.
2013137). In addition, CO2 fertilization is often
offset by other factors limiting plant growth,such as water and or nutrient availability andtemperature and incoming solar radiationthat can be modified by changes in vegetation
structure. Large-scale plant mortality throughfire, soil moisture drought, and/or temperature changes also impact successional processes that contribute to reestablishment andrevegetation (or not) of disturbed ecosystems,
altering the amount and distribution of plants
available to uptake CO2. With sufficient disturbance, it has been argued that forests could,
on net, turn into a source rather than a sink ofCO2.138

and productivity. Recent advances in ESMs
are beginning to account for other importantfactors such as nutrient limitations.140, 141, 142
ESMs that do include carbon-cycle feedbacks
appear, on average, to overestimate terrestrial
CO2 uptake under the present-day climate143, 144
and underestimate nutrient limitations to CO2
fertilization.142 The sign of the land carbon-cycle feedback through 2100 remains unclear in
the newest generation of ESMs.142, 145, 146 Eleven
CMIP5 ESMs forced with the same CO2 emissions scenario—one consistent with RCP8.5
concentrations—produce a range of 795 to1145 ppm for atmospheric CO2 concentrationin 2100. The majority of the ESMs (7 out of11) simulated a CO2 concentration larger (by44 ppm on average) than their equivalent
non-interactive carbon cycle counterpart.146
This difference in CO2 equates to about 0.4°F(0.2°C) more warming by 2100. The inclusionof carbon-cycle feedbacks does not alter the
lower-end bound on climate sensitivity, but,in most climate models, inclusion pushes theupper bound higher.146

Climate-induced changes in the horizontal(for example, landscape to biome) and vertical(soils to canopy) structure of terrestrial ecosystems also alter the physical surface roughness
and albedo, as well as biogeochemical (carbon
and nitrogen) cycles and biophysical evapotranspiration and water demand. Combined,
these responses constitute climate feedbacks
by altering surface albedo and atmospheric
GHG abundances. Drivers of these changes interrestrial ecosystems include changes in thebiophysical growing season, altered seasonality, wildfire patterns, and multiple additional
interacting factors (Ch. 10: Land Cover).

Ocean Chemistry, Ecosystem, and Circulation
Changes
The ocean plays a significant role in climatechange by playing a critical role in controllingthe amount of GHGs (including CO2, water
vapor, and N2O) and heat in the atmosphere(Figure 2.7). To date most of the net energy
increase in the climate system from anthropogenic RF is in the form of ocean heat (seeBox 3.1 Figure 1 of Rhein et al. 2013).6 Thisadditional heat is stored predominantly (about60%) in the upper 700 meters of the ocean(see Ch. 12: Sea Level Rise and Ch. 13: Ocean
Changes).147 Ocean warming and climate-driven changes in ocean stratification and circulation alter oceanic biological productivity andtherefore CO2 uptake; combined, these feedbacks affect the rate of warming from radiative forcing.

Accurate determination of future CO2 stabilization scenarios depends on accounting forthe significant role that the land biosphereplays in the global carbon cycle and feedbacks
between climate change and the terrestrial
carbon cycle.139 Earth System Models (ESMs)
are increasing the representation of terrestrial carbon cycle processes, including plant
photosynthesis, plant and soil respirationand decomposition, and CO2 fertilization,with the latter based on the assumption thatan increased atmospheric CO2 concentration
provides more substrate for photosynthesis

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Marine ecosystems take up CO2 from theatmosphere in the same way that plants do on
land. About half of the global net primary production (NPP) is by marine plants (approximately 50 ± 28 GtC/year148, 149, 150). Phytoplankton NPP supports the biological pump, whichtransports 2–12 GtC/year of organic carbon
to the deep sea,151, 152 where it is sequesteredaway from the atmospheric pool of carbon for
200–1,500 years. Since the ocean is an important carbon sink, climate-driven changes in
NPP represent an important feedback because
they potentially change atmospheric CO2abundance and forcing.

they lead to altered patterns of ocean circulation, which impacts uptake of anthropogenic
heat and CO2.
Increased stratification inhibits surface mixing, high-latitude convection, and deep-water
formation, thereby potentially weakening
ocean circulations, in particular the Atlantic
Meridional Overturning Circulation (AMOC)(see also Ch. 13: Ocean Changes).157, 158 Reduced deep-water formation and slower overturning are associated with decreased heatand carbon sequestration at greater depths.
Observational evidence is mixed regardingwhether the AMOC has slowed over the past
decades to century (see Sect. 13.2.1 of Ch. 13:
Ocean Changes). Future projections show thatthe strength of AMOC may significantly decrease as the ocean warms and freshens and as
upwelling in the Southern Ocean weakens dueto the storm track moving poleward (see also
Ch. 13: Ocean Changes).159 Such a slowdownof the ocean currents will impact the rate at
which the ocean absorbs CO2 and heat fromthe atmosphere.

There are multiple links between RF-drivenchanges in climate, physical changes to theocean, and feedbacks to ocean carbon and heatuptake. Changes in ocean temperature, circulation, and stratification driven by climatechange alter phytoplankton NPP. Absorptionof CO2 by the ocean also increases its acidity,
which can also affect NPP and therefore thecarbon sink (see Ch. 13: Ocean Changes for amore detailed discussion of ocean acidification).

Increased ocean temperatures also accelerate
ice sheet melt, particularly for the Antarctic Ice
Sheet where basal sea ice melting is important relative to surface melting due to colder
surface temperatures.160 For the Greenland Ice
Sheet, submarine melting at tidewater margins
is also contributing to volume loss.161 In turn,changes in ice sheet melt rates change coldand freshwater inputs, also altering ocean
stratification. This affects ocean circulationand the ability of the ocean to absorb more
GHGs and heat.162 Enhanced sea ice export
to lower latitudes gives rise to local salinity
anomalies (such as the Great Salinity Anomaly163) and therefore to changes in ocean circulation and air–sea exchanges of momentum,
heat, and freshwater, which in turn affect theatmospheric distribution of heat and GHGs.

In addition to being an important carbon sink,
the ocean dominates the hydrological cycle,
since most surface evaporation and rainfall occur over the ocean.153, 154 The ocean componentof the water vapor feedback derives from therate of evaporation, which depends on surface
wind stress and ocean temperature. Climate
warming from radiative forcing also is associated with intensification of the water cycle(Ch. 7: Precipitation Change). Over decadal
time scales the surface ocean salinity has
increased in areas of high salinity, such as thesubtropical gyres, and decreased in areas oflow salinity, such as the Warm Pool region (seeCh. 13: Ocean Changes).155, 156 This increase instratification in select regions and mixing inother regions are feedback processes because

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Remote sensing of sea surface temperature andchlorophyll as well as model simulations andsediment records suggest that global phytoplankton NPP may have increased recently as
a consequence of decadal-scale natural climate
variability, such as the El Niño–Southern Oscillation, which promotes vertical mixing andupwelling of nutrients.150, 164, 165 Analyses of longer
trends, however, suggest that phytoplankton
NPP has decreased by about 1% per year overthe last 100 years.166, 167, 168 The latter results,
although controversial,169 are the only studiesof the global rate of change over this period.
In contrast, model simulations show decreasesof only 6.6% in NPP and 8% in the biologicalpump over the last five decades.170 Total NPP
is complex to model, as there are still areas of
uncertainty on how multiple physical factors
affect phytoplankton growth, grazing, andcommunity composition, and as certain phytoplankton species are more efficient at carbon
export.171, 172 As a result, model uncertainty is still
significant in NPP projections.173 While there arevariations across climate model projections, thereis good agreement that in the future there will be
increasing stratification, decreasing NPP, and adecreasing sink of CO2 to the ocean via biologicalactivity.172 Overall, compared to the 1990s, in 2090
total NPP is expected to decrease by 2%–16%and export production (that is, particulate flux tothe deep ocean) could decline by 7%–18% under
the higher scenario (RCP8.5).172 Consistent withthis result, carbon cycle feedbacks in the ocean
were positive (that is, higher CO2 concentrations
leading to a lower rate of CO2 sequestration tothe ocean, thereby accelerating the growth ofatmospheric CO2 concentrations) across the suiteof CMIP5 models.

organic matter available for microbial decomposition, releasing CO2 and methane to the atmosphere, providing additional radiative forcing
and accelerating warming. This process defines
the permafrost–carbon feedback. Combined data
and modeling studies suggest that this feedback
is very likely positive.174, 175, 176 This feedback was
not included in recent IPCC projections but is anactive area of research. Meeting stabilization or
mitigation targets in the future will require limits
on total GHG abundances in the atmosphere.
Accounting for additional permafrost-carbon
release reduces the amount of anthropogenic
emissions that can occur and still meet theselimits.177
The permafrost–carbon feedback in the higher
scenario (RCP8.5; Section 1.2.2 and Figure 1.4)
contributes 120 ± 85 Gt of additional carbon by
2100; this represents 6% of the total anthropogenic forcing for 2100 and corresponds to a global
temperature increase of +0.52° ± 0.38°F (+0.29°± 0.21°C).174 Considering the broader range offorcing scenarios (Figure 1.4), it is likely that thepermafrost–carbon feedback increases carbon
emissions between 2% and 11% by 2100. A key
feature of the permafrost feedback is that, once
initiated, it will continue for an extended period
because emissions from decomposition occur
slowly over decades and longer. In the coming
few decades, enhanced plant growth at high latitudes and its associated CO2 sink145 are expectedto partially offset the increased emissions frompermafrost thaw;174, 176 thereafter, decomposition
will dominate uptake. Recent evidence indicatesthat permafrost thaw is occurring faster than
expected; poorly understood deep-soil carbon
decomposition and ice wedge processes likely
contribute.178, 179 Chapter 11: Arctic Changes
includes a more detailed discussion of permafrost and methane hydrates in the Arctic. Futurechanges in permafrost emissions and the potential for even greater emissions from methane
hydrates in the continental shelf are discussed
further in Chapter 15: Potential Surprises.

Permafrost and Hydrates
Permafrost and methane hydrates contain large
stores of methane and (for permafrost) carbon in
the form of organic materials, mostly at northern high latitudes. With warming, this organic
material can thaw, making previously frozen

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TRACEABLE ACCOUNTSKey Finding 1
Human activities continue to significantly affect Earth’s
climate by altering factors that change its radiativebalance. These factors, known as radiative forcings,
include changes in greenhouse gases, small airborne
particles (aerosols), and the reflectivity of Earth’s surface. In the industrial era, human activities have been,
and are increasingly, the dominant cause of climate
warming. The increase in radiative forcing due to these
activities has far exceeded the relatively small net increase due to natural factors, which include changes inenergy from the sun and the cooling effect of volcanic
eruptions. (Very high confidence)

airborne research platforms, and from satellites. Bothdirect observations and modeling studies show large,
explosive eruptions affect climate parameters for years
to decades.36, 186 Over the industrial era, radiative forcing by volcanoes has been episodic and currently does
not contribute significantly to forcing trends. Observations indicate a positive but small increase in solar
input over the industrial era.8, 22, 23 Relatively higher
variations in solar input at shorter (UV) wavelengths25
may be leading to indirect changes in Earth’s radiativebalance through their impact on ozone concentrations
that are larger than the radiative impact of changes intotal solar irradiance,21, 26, 27, 28, 29 but these changes arealso small in comparison to anthropogenic greenhousegas and aerosol forcing.8 The finding of an increasingly
strong positive forcing over the industrial era is supported by observed increases in atmospheric temperatures (see Ch. 1: Our Globally Changing Climate) and byobserved increases in ocean temperatures (Ch. 1: Our
Globally Changing Climate and Ch. 13: Ocean Changes). The attribution of climate change to human activities is supported by climate models, which are able toreproduce observed temperature trends when RF fromhuman activities is included and considerably deviate
from observed trends when only natural forcings areincluded (Ch. 3: Detection and Attribution, Figure 3.1).

Description of evidence base
The Key Finding and supporting text summarizes extensive evidence documented in the climate science
literature, including in previous national (NCA3)180 andinternational17 assessments. The assertion that Earth’s
climate is controlled by its radiative balance is a well-established physical property of the planet. Quantification of the changes in Earth’s radiative balance comefrom a combination of observations and calculations.
Satellite data are used directly to observe changes inEarth’s outgoing visible and infrared radiation. Since
2002, observations of incoming sunlight include both
total solar irradiance and solar spectral irradiance.26
Extensive in situ and remote sensing data are used toquantify atmospheric concentrations of radiative forcing agents (greenhouse gases [e.g., Ciais et al. 2013;43
Le Quéré et al. 2016135] and aerosols [e.g., Bond et al.
2013;61 Boucher et al. 2013;60 Myhre et al. 2013;8 Jiao et
al. 2014;181 Tsigaridis et al. 2014;182 Koffi et al. 2016183])and changes in land cover,64, 184, 185 as well as the relevant properties of these agents (for example, aerosol
microphysical and optical properties). Climate models
are constrained by these observed concentrations andproperties. Concentrations of long-lived greenhousegases in particular are well-quantified with observations because of their relatively high spatial homogeneity. Climate model calculations of radiative forcing
by greenhouse gases and aerosols are supported byobservations of radiative fluxes from the surface, from

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Major uncertainties
The largest source of uncertainty in radiative forcing(both natural and anthropogenic) over the industrial era is quantifying forcing by aerosols. This finding
is consistent across previous assessments (e.g., IPCC
2007;88 IPCC 201317). The major uncertainties associatedwith aerosol forcing is discussed below in the Traceable
Accounts for Key Finding 2.
Recent work has highlighted the potentially larger roleof variations in UV solar irradiance, versus total solar irradiance, in solar forcing. However, this increase in solar forcing uncertainty is not sufficiently large to reduce
confidence that anthropogenic activities dominate industrial-era forcing.

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Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence that anthropogenic radiative forcing exceeds natural forcing over the industrial
era based on quantitative assessments of known radiative forcing components. Assessments of the naturalforcings of solar irradiance changes and volcanic activity show with very high confidence that both forcings are
small over the industrial era relative to total anthropogenic forcing. Total anthropogenic forcing is assessed
to have become larger and more positive during theindustrial era, while natural forcings show no similar
trend.

Description of evidence base
The Key Finding and supporting text summarize extensive evidence documented in the climate science literature, including in previous national (NCA3)180 and international17 assessments. Aerosols affect Earth’s albedo
by directly interacting with solar radiation (scatteringand absorbing sunlight) and by affecting cloud properties (albedo and lifetime).
Fundamental physical principles show how atmospheric aerosols scatter and absorb sunlight (aerosol–
radiation interaction), and thereby directly reduce incoming solar radiation reaching the surface. Extensive
in situ and remote sensing data are used to measureemission of aerosols and aerosol precursors from specific source types, the concentrations of aerosols in theatmosphere, aerosol microphysical and optical properties, and, via remote sensing, their direct impacts onradiative fluxes. Atmospheric models used to calculate
aerosol forcings are constrained by these observations(see Key Finding 1).

Summary sentence or paragraph that integrates
the above information
This key finding is consistent with that in the IPCC
Fourth Assessment Report (AR4)88 and Fifth Assessment Report (AR5);17 namely, anthropogenic radiative
forcing is positive (climate warming) and substantiallylarger than natural forcing from variations in solar input and volcanic emissions. Confidence in this finding
has increased from AR4 to AR5, as anthropogenic GHG
forcings have continued to increase, whereas solar forcing remains small and volcanic forcing near-zero overdecadal time scales.

In addition to their direct impact on radiative fluxes,
aerosols also act as cloud condensation nuclei. Aerosol–cloud interactions are more complex, with a strong
theoretical basis supported by observational evidence.
Multiple observational and modeling studies have concluded that increasing the number of aerosols in the atmosphere increases cloud albedo and lifetime, adding
to the negative forcing (aerosol–cloud microphysicalinteractions) (e.g., Twohy 2005;187 Lohmann and Feichter 2005;188 Quaas et al. 2009;189 Rosenfeld et al. 2014190).
Particles that absorb sunlight increase atmosphericheating; if they are sufficiently absorbing, the net effectof scattering plus absorption is a positive radiative forcing. Only a few source types (for example, from diesel
engines) produce aerosols that are sufficiently absorbing that they have a positive radiative forcing.61 Modeling studies, combined with observational inputs, haveinvestigated the thermodynamic response to aerosol
absorption in the atmosphere. Averaging over aerosol locations relative to the clouds and other factors,
the resulting changes in cloud properties represent a

Key Finding 2
Aerosols caused by human activity play a profound andcomplex role in the climate system through radiative effects in the atmosphere and on snow and ice surfaces
and through effects on cloud formation and properties.
The combined forcing of aerosol–radiation and aerosol–
cloud interactions is negative (cooling) over the industrial era (high confidence), offsetting a substantial part ofgreenhouse gas forcing, which is currently the predominant human contribution. The magnitude of this offset, globally averaged, has declined in recent decades,
despite increasing trends in aerosol emissions or abundances in some regions. (Medium to high confidence)

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negative forcing, offsetting approximately 15% of thepositive radiative forcing from heating by absorbing
aerosols (specifically, black carbon).61

anthropogenic aerosol emissions have changed andhow changing aerosol concentrations and composition
affect cloud properties (albedo and lifetime).60, 208 Froma theoretical standpoint, aerosol–cloud interactions arecomplex, and using observations to isolate the effects
of aerosols on clouds is complicated by the fact that
other factors (for example, the thermodynamic stateof the atmosphere) also strongly influence cloud properties. Further, changes in aerosol properties and theatmospheric thermodynamic state are often correlated
and interact in non-linear ways.209

Modeling and observational evidence both show thatannually averaged global aerosol ERF increased until
the 1980s and since then has flattened or slightly declined,191, 192, 193, 194 driven by the introduction of stronger
air quality regulations (Smith and Bond 2014; Fiore etal. 2015). In one recent study,195 global mean aerosol
RF has become less negative since IPCC AR5,8 due toa combination of declining sulfur dioxide emissions(which produce negative RF) and increasing black
carbon emissions (which produce positive RF). Withinthese global trends there are significant regional variations (e.g., Mao et al. 2014196), driven by both changes inaerosol abundance and changes in the relative contributions of primarily light-scattering and light-absorbing aerosols.68, 195 In Europe and North America, aerosol
ERF has significantly declined (become less negative)
since the 1980s.70, 71, 197, 198, 199, 200 In contrast, observations show significant increases in aerosol abundances over India,201, 202 and these increases are expected tocontinue into the near future.203 Several modeling andobservational studies point to aerosol ERF for China
peaking around 1990,204, 205, 206 though in some regionsof China aerosol abundances and ERF have continued
to increase.206 The suite of scenarios used for future climate projection (i.e., the scenarios shown in Ch. 1: Our
Globally Changing Climate, Figure 1.4) includes emissions for aerosols and aerosol precursors. Across thisrange of scenarios, globally averaged ERF of aerosols is
expected to decline (become less negative) in the coming decades,67, 192 reducing the current aerosol offset tothe increasing RF from GHGs.

Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence that aerosol radiative forcing is negative on a global, annually averaged basis,
medium confidence in the magnitude of the aerosol RF,
high confidence that aerosol ERF is also, on average,
negative, and low to medium confidence in the magnitude of aerosol ERF. Lower confidence in the magnitude of aerosol ERF is due to large uncertainties in theeffects of aerosols on clouds. Combined, we assess ahigh level of confidence that aerosol ERF is negative andsufficiently large to be substantially offsetting positive
GHG forcing. Improvements in the quantification ofemissions, in observations (from both surface-based
networks and satellites), and in modeling capability
give medium to high confidence in the finding that aerosol forcing trends are decreasing in recent decades.
Summary sentence or paragraph that integrates
the above information
This key finding is consistent with the findings of IPCC
AR58 that aerosols constitute a negative radiative forcing. While significant uncertainty remains in the quantification of aerosol ERF, we assess with high confidence
that aerosols offset about half of the positive forcing
by anthropogenic CO2 and about a third of the forcing
by all well-mixed anthropogenic GHGs. The fraction of
GHG forcing that is offset by aerosols has been decreasing over recent decades, as aerosol forcing has leveled
off while GHG forcing continues to increase.

Major uncertainties
Aerosol–cloud interactions are the largest source of
uncertainty in both aerosol and total anthropogenic
radiative forcing. These include the microphysical effects of aerosols on clouds and changes in clouds that
result from the rapid response to absorption of sunlightby aerosols. This finding, consistent across previous assessments (e.g., Forster et al. 2007;207 Myhre et al. 20138),is due to poor understanding of how both natural and

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Key Finding 3
The interconnected Earth–atmosphere–ocean climate
system includes a number of positive and negative
feedback processes that can either strengthen (positive
feedback) or weaken (negative feedback) the system’s
responses to human and natural influences. These
feedbacks operate on a range of time scales from very
short (essentially instantaneous) to very long (centuries). Global warming by net radiative forcing over theindustrial era includes a substantial amplification fromthese feedbacks (approximately a factor of three) (highconfidence). While there are large uncertainties associated with some of these feedbacks, the net feedback
effect over the industrial era has been positive (amplifying warming) and will continue to be positive in coming decades. (Very high confidence)

estimates of the cloud feedback as net positive. Feedback via changes in atmospheric composition is not
well-quantified but is expected to be small relative towater-vapor-plus-lapse-rate, snow, and cloud feedbacks at the global scale.120 Carbon cycle feedbacks
through changes in the land biosphere are currentlyof uncertain sign and have asymmetric uncertainties: they might be small and negative but could also
be large and positive.138 Recent best estimates of theocean carbon-cycle feedback are that it is positive withsignificant uncertainty that includes the possibility of anegative feedback for present-day CO2 levels.170, 211 The
permafrost–carbon feedback is very likely positive, andas discussed in Chapter 15: Potential Surprises, could
be a larger positive feedback in the longer term. Thus,in the balance of multiple negative and positive feedback processes, the preponderance of evidence is that
positive feedback processes dominate the overall radiative forcing feedback from anthropogenic activities.

Description of evidence baseThe variety of climate system feedbacks all depend
on fundamental physical principles and are knownwith a range of uncertainties. The Planck feedback isbased on well-known radiative transfer models. The
largest positive feedback is the water vapor feedback,which derives from the dependence of vapor pressure
on temperature. There is very high confidence that thisfeedback is positive, approximately doubling the direct
forcing due to CO2 emissions alone. The lapse rate feedback derives from thermodynamic principles. There isvery high confidence that this feedback is negative andpartially offsets the water vapor feedback. The water
vapor and lapse-rate feedbacks are linked by the factthat both are driven by increases in atmospheric water vapor with increasing temperature. Estimates of themagnitude of these two feedbacks have changed little
across recent assessments.60, 210 The snow- and ice-albedo feedback is positive in sign, with the magnitudeof the feedback dependent in part on the time scale ofinterest.109, 110 The assessed strength of this feedback
has also not changed significantly since IPCC 2007.88
Cloud feedbacks modeled using microphysical principles are either positive or negative, depending on thesign of the change in clouds with warming (increaseor decrease) and the type of cloud that changes (lowor high clouds). Recent international assessments60,
210
and a separate feedback assessment84 all give best

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Major uncertainties
Uncertainties in cloud feedbacks are the largest sourceof uncertainty in the net climate feedback (and therefore climate sensitivity) on the decadal to century timescale.60, 84 This results from the fact that cloud feedbacks
can be either positive or negative, depending not only
on the direction of change (more or less cloud) but also
on the type of cloud affected and, to a lesser degree,
the location of the cloud.84 On decadal and longer timescales, the biological and physical responses of theocean and land to climate change, and the subsequentchanges in land and oceanic sinks of CO2, contributesignificant uncertainty to the net climate feedback (Ch.
13: Ocean Changes). Changes in the Brewer–Dobson
atmospheric circulation driven by climate change andsubsequent effects on stratosphere–troposphere coupling also contribute to climate feedback uncertainty.77,
212, 213, 214, 215, 216

Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is high confidence that the net effect of all feedback processes in the climate system is positive, thereby amplifying warming. This confidence is based on

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consistency across multiple assessments, including
IPCC AR5 (IPCC 201317 and references therein), of themagnitude of, in particular, the largest feedbacks in the
climate system, two of which (water vapor feedback
and snow/ice albedo feedback) are definitively positivein sign. While significant increases in low cloud coverwith climate warming would be a large negative feedback to warming, modeling and observational studies
do not support the idea of increases, on average, in low
clouds with climate warming.
Summary sentence or paragraph that integrates
the above information
The net effect of all identified feedbacks to forcing is
positive based on the best current assessments andtherefore amplifies climate warming. Feedback uncertainties, which are large for some processes, areincluded in these assessments. The various feedback
processes operate on different time scales with carbon
cycle and snow– and ice–albedo feedbacks operating
on longer timelines than water vapor, lapse rate, cloud,and atmospheric composition feedbacks.

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202. Krishna Moorthy, K., S. Suresh Babu, M.R. Manoj,
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203. Pietikäinen, J.P., K. Kupiainen, Z. Klimont, R. Makkonen, H. Korhonen, R. Karinkanta, A.P. Hyvärinen,
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V.M. Kerminen, 2015: Impacts of emission reductions
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205. Li, J., Z. Han, and Z. Xie, 2013: Model analysis of
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207. Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R.
Betts, D.W. Fahey, J. Haywood, J. Lean, D.C. Lowe, G.
Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz, andR. Van Dorland, 2007: Ch. 2: Changes in atmosphericconstituents and in radiative forcing. Climate Change
2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report (AR4) of theIntergovernmental Panel on Climate Change. Solomon,S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B.
Averyt, M. Tignor, and H.L. Miller, Eds. Cambridge
University Press, Cambridge, UK. http://www.ipcc.
ch/publications_and_data/ar4/wg1/en/ch2.html
208. Carslaw, K.S., L.A. Lee, C.L. Reddington, K.J. Pringle, A. Rap, P.M. Forster, G.W. Mann, D.V. Spracklen,
M.T. Woodhouse, L.A. Regayre, and J.R. Pierce, 2013:
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Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi, and K.E. Taylor, 2007:
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Averyt, M. Tignor, and H.L. Miller, Eds. Cambridge
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211. Steinacher, M., F. Joos, T.L. Frölicher, L. Bopp, P. Cadule, V. Cocco, S.C. Doney, M. Gehlen, K. Lindsay,
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214. Li, F., J. Austin, and J. Wilson, 2008: The strength of
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215. Shepherd, T.G. and C. McLandress, 2011: A robust
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216. McLandress, C., T.G. Shepherd, M.C. Reader, D.A.
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217. Barkstrom, B.R., 1984: The Earth Radiation Budget
Experiment (ERBE). Bulletin of the American Meteorological Society, 65, 1170-1185. http://dx.doi.
org/10.1175/1520-0477(1984)065<1170:terbe>2.0.co;2218. Smith, G.L., B.R. Barkstrom, E.F. Harrison, R.B. Lee,and B.A. Wielicki, 1994: Radiation budget measurements for the eighties and nineties. Advances in Space
Research, 14, 81-84. http://dx.doi.org/10.1016/02731177(94)90351-4
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L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan,
B.J. Soden, P.W. Thorne, M. Wild, and P.M. Zhai,
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T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J.
Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, 159–254.
http://www.climatechange2013.org/report/full-report/

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Detection and Attributionof Climate Change

KEY FINDINGS
1. The likely range of the human contribution to the global mean temperature increase over the period
1951–2010 is 1.1° to 1.4°F (0.6° to 0.8°C), and the central estimate of the observed warming of 1.2°F(0.65°C) lies within this range (high confidence). This translates to a likely human contribution of 93%–123% of the observed 1951–2010 change. It is extremely likely that more than half of the global mean
temperature increase since 1951 was caused by human influence on climate (high confidence). The likely
contributions of natural forcing and internal variability to global temperature change over that period
are minor (high confidence).
2. The science of event attribution is rapidly advancing through improved understanding of the mechanisms that produce extreme events and the marked progress in development of methods that are used
for event attribution (high confidence).

Recommended Citation for Chapter
Knutson, T., J.P. Kossin, C. Mears, J. Perlwitz, and M.F. Wehner, 2017: Detection and attribution of climate change. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles,
D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change
Research Program, Washington, DC, USA, pp. 114-132, doi: 10.7930/J01834ND.

3.1 Introduction
Detection and attribution of climate change
involves assessing the causes of observedchanges in the climate system through systematic comparison of climate models andobservations using various statistical methods.
Detection and attribution studies are important for a number of reasons. For example,
such studies can help determine whether ahuman influence on climate variables (forexample, temperature) can be distinguishedfrom natural variability. Detection and attribution studies can help evaluate whether model
simulations are consistent with observed
trends or other changes in the climate system.
Results from detection and attribution studies

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can inform decision making on climate policy
and adaptation.
There are several general types of detection
and attribution studies, including: attributionof trends or long-term changes in climate
variables; attribution of changes in extremes;
attribution of weather or climate events;
attribution of climate-related impacts; and theestimation of climate sensitivity using observational constraints. Paleoclimate proxies canalso be useful for detection and attribution
studies, particularly to provide a longer-term
perspective on climate variability as a baselineon which to compare recent climate changes ofthe past century or so (for example, see Figure

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12.2 from Ch. 12: Sea Level Rise). Detection
and attribution studies can be done at various
scales, from global to regional.
Since the Intergovernmental Panel on Climate
Change (IPCC) Fifth Assessment Report (AR5)chapter on detection and attribution1 and theThird National Climate Assessment (NCA32),
the science of detection and attribution has
advanced, with a major scientific question
being the issue of attribution of extreme
events.3, 4, 5, 6 Therefore, the methods used inthis developing area of the science are briefly
reviewed in Appendix C: Detection and Attribution Methods, along with a brief overview
of the various general detection and attribution methodologies, including some recent
developments in these areas. Detection andattribution of changes in extremes in general
presents a number of challenges,7 including
limitations of observations, models, statisticalmethods, process understanding for extremes,
and uncertainties about the natural variability of extremes. Although the present report
does not focus on climate impacts on ecosystems or human systems, a relatively new anddeveloping area of detection and attribution
science (reviewed in Stone et al. 20138), concerns detecting and attributing the impacts ofclimate change on natural or human systems.
Many new developments in detection andattribution science have been fostered by theInternational Detection and Attribution Group(IDAG; http://www.image.ucar.edu/idag/
and http://www.clivar.org/clivar-panels/
etccdi/idag/international-detection-attribution-group-idag) which is an internationalgroup of scientists who have collaborated
since 1995 on “assessing and reducing uncertainties in the estimates of climate change.”
In the remainder of this chapter, we review
highlights of detection and attribution science,
particularly key attribution findings for therise in global mean temperature. However, as

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this is a U.S.-focused assessment, the reportas a whole will focus more on the detection
and attribution findings for particular regional
phenomena (for example, regional temperature, precipitation) or at least global-scale phenomena that are directly affecting the UnitedStates (for example, sea level rise). Most ofthese findings are contained in the individual phenomena chapters, rather than in thisgeneral overview chapter on detection andattribution. We provide summary links to thechapters where particular detection and attribution findings are presented in more detail.

3.2 Detection and Attribution of Global
Temperature Changes
The concept of detection and attribution is
illustrated in Figure 3.1, which shows a very
simple example of detection and attributionof global mean temperature. While more
powerful pattern-based detection and attribution methods (discussed later), and evengreater use of time averaging, can result inmuch stronger statements about detection andattribution, the example in Figure 3.1 serves
to illustrate the general concept. In the figure,observed global mean temperature anomalies(relative to a 1901–1960 baseline) are compared with anomalies from historical simulations of CMIP5 models. The spread of different individual model simulations (the blue
and orange shading) arises both from differences between the models in their responses tothe different specified climate forcing agents(natural and anthropogenic) and from internal(unforced) climate variability. Observed annual temperatures after about 1980 are shown
to be inconsistent with models that include
only natural forcings (blue shading) and areconsistent with the model simulations that include both anthropogenic and natural forcing(orange shading). This implies that the observed global warming is attributable in largepart to anthropogenic forcing. A key aspect ofa detection and attribution finding will be the115

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Global Mean Temperature Change(b)2

CMIP5 All-Forcing
GISTEMP Observed
HadCRUT4.5 Observed
NOAA Observed

Change in Temperature (°F)

Change in Temperature (°F)

(a)

1
0

2.5
2.0
1.5

CMIP5 Natural-Forcing Only
GISTEMP Observed
HadCRUT4.5 Observed
NOAA Observed

1.0
0.5
0.0

−0.5

−1

−1.0

1880 1900 1920 1940 1960 1980 2000 2020
1880 1900 1920 1940 1960 1980 2000 2020
Year
Year
Figure 3.1: Comparison of observed global mean temperature anomalies from three observational datasets to CMIP5
climate model historical experiments using: (a) anthropogenic and natural forcings combined, or (b) natural forcings
only. In (a) the thick orange curve is the CMIP5 grand ensemble mean across 36 models while the orange shading andouter dashed lines depict the ±2 standard deviation and absolute ranges of annual anomalies across all individual simulations of the 36 models. Model data are a masked blend of surface air temperature over land regions and sea surface
temperature over ice-free ocean regions to be more consistent with observations than using surface air temperaturealone. All time series (°F) are referenced to a 1901–1960 baseline value. The simulations in (a) have been extended
from 2006 through 2016 using projections under the higher scenario (RCP8.5). (b) As in (a) but the blue curves andshading are based on 18 CMIP5 models using natural forcings only. See legends to identify observational datasets.
Observations after about 1980 are shown to be inconsistent with the natural forcing-only models (indicating detectable
warming) and also consistent with the models that include both anthropogenic and natural forcing, implying that thewarming is attributable in part to anthropogenic forcing according to the models. (Figure source: adapted from Melillo
et al.2 and Knutson et al.19).

assessment of the adequacy of the models andobservations used for these conclusions, as
discussed and assessed in Flato et al.,9 Bindoff
et al.,1 and IPCC.10
The detection and attribution of global temperature change to human causes has been
one of the most important and visible findings over the course of the past global climatechange scientific assessments by the IPCC. The
first IPCC report11 concluded that a human influence on climate had not yet been detected,
but judged that “the unequivocal detection of
the enhanced greenhouse effect from observations is not likely for a decade or more.”
The second IPCC report12 concluded that “thebalance of evidence suggests a discernible
human influence on climate.” The third IPCC
report13 strengthened this conclusion to: “mostof the observed warming over the last 50 years
is likely to have been due to the increase ofgreenhouse gas concentrations.” The fourth

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IPCC report14 further strengthened the conclusion to: “Most of the observed increase inglobal average temperatures since the mid20th century is very likely due to the observed
increase in anthropogenic greenhouse gas concentrations.” The fifth IPCC report10 further
strengthened this to: “It is extremely likelythat more than half of the observed increasein global average surface temperature from1951 to 2010 was caused by the anthropogenic
increase in greenhouse gas concentrations andother anthropogenic forcings together.” These
increasingly confident statements have resulted from scientific advances, including better
observational datasets, improved models anddetection/attribution methods, and improved
estimates of climate forcings. Importantly, thecontinued long-term warming of the global climate system since the time of the first
IPCC report and the broad-scale agreement of
the spatial pattern of observed temperaturechanges with climate model projections of116

Climate Science Special Report3 | Detection and Attribution of Climate Change
greenhouse gas-induced changes as publishedin the late 1980s (e.g., Stouffer and Manabe
201715) give more confidence in the attributionof observed warming since 1951 as being due
primarily to human activity.

observed warming 5th to 95th percentile rangeof 0.59° to 0.71°C (1.1° to 1.3°F). The estimatedlikely contribution ranges for natural forcing
and internal variability were both much smaller (−0.1° to 0.1°C, or −0.2° to 0.2°F) than theobserved warming. The confidence intervals
that encompass the extremely likely range forthe anthropogenic contribution are wider thanthe likely range. Using these wider confidence
limits, the lower limit of attributable warming contribution range still lies above 50% of
the observed warming rate, and thus Bindoff
et al.1 concluded that it is extremely likely that
more than half of the global mean temperature increase since 1951 was caused by human
influence on climate. This assessment concurs
with the Bindoff et al.1 assessment of attributable warming and cooling influences.

The IPCC AR5 presented an updated assessment of detection and attribution research at
the global to regional scale1 which is briefly
summarized here. Key attribution assessment
results from IPCC AR5 for global mean temperature are summarized in Figure 3.2, whichshows assessed likely ranges and midpoint
estimates for several factors contributing toincreases in global mean temperature. According to Bindoff et al.,1 the likely range of theanthropogenic contribution to global mean
temperature increases over 1951–2010 was
0.6° to 0.8°C (1.1° to 1.4°F), compared with theObserved
GHG
ANT
OA

NAT
Internal Variability
−1.5

−1.0

−0.5

0.0

0.5
(°F)

1.0

1.5

2.0

2.5

GHG - well-mixed greenhouse gases

OA - other anthropogenic forcings

ANT - all anthropogenic forcings combined

NAT - natural forcings

Figure 3.2: Observed global mean temperature trend (black bar) and attributable warming or cooling influences ofanthropogenic and natural forcings over 1951–2010. Observations are from HadCRUT4, along with observational uncertainty (5% to 95%) error bars.62 Likely ranges (bar-whisker plots) and midpoint values (colored bars) for attributable
forcings are from IPCC AR5.1. GHG refers to well-mixed greenhouse gases, OA to other anthropogenic forcings, NAT
to natural forcings, and ANT to all anthropogenic forcings combined. Likely ranges are broader for contributions fromwell-mixed greenhouse gases and for other anthropogenic forcings, assessed separately, than for the contributions
from all anthropogenic forcings combined, as it is more difficult to quantitatively constrain the separate contributions of
the various anthropogenic forcing agents. (Figure source: redrawn from Bindoff et al.;1 © IPCC. Used with permission.)

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Climate Science Special Report3 | Detection and Attribution of Climate Change
Apart from formal detection attribution studies such as those underlying the results above,
which use global climate model output andpattern-based regression methods, anthropogenic influences on global mean temperature
can also be estimated using simpler empiricalmodels, such as multiple linear regression/energy balance models (e.g., Canty et al. 201316;
Zhou and Tung 201317). For example, Figure
3.3 illustrates how the global mean surface
temperature changes since the late 1800s canbe decomposed into components linearly
related to several forcing variables (anthropogenic forcing, solar variability, volcanic forcing, plus an internal variability component,
here related to El Niño–Southern Oscillation).
Using this approach, Canty et al.16 also infera substantial contribution of anthropogenic
forcing to the rise in global mean temperaturesince the late 1800s. Stern and Kaufmann18 use
another method—Granger causality tests—
and again infer that “human activity is partially responsible for the observed rise in global
temperature and that this rise in temperature
also has an effect on the global carbon cycle.”
They also conclude that anthropogenic sulfate
aerosol effects may only be about half as large
as inferred in a number of previous studies.
Multi-century to multi-millennial-scale climate model integrations with unchanging
external forcing provide a means of estimating potential contributions of internal climate
variability to observed trends. Bindoff et al.1
conclude, based on multimodel assessments,that the likely range contribution of internalvariability to observed trends over 1951–2010
is about ±0.2°F, compared to the observed
warming of about 1.2°F over that period. A recent 5,200-year integration of the CMIP5 model having apparently the largest global mean
temperature variability among CMIP5 models
shows rare instances of multidecadal global
warming approaching the observed 1951–2010
warming trend.19 However, even that most

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extreme model cannot simulate century-scale
warming trends from internal variability thatapproach the observed global mean warming
over the past century. According to a multimodel analysis of observed versus CMIP5
modeled global temperature trends (Knutson
et al. 201320, Fig. 7a), the modeled naturalfluctuations (forced plus internal) would need
to be larger by about a factor of three for evenan unusual natural variability episode (95thpercentile) to approach the observed trend
since 1900. Thus, using present models there is
no known source of internal climate variability that can reproduce the observed warming
over the past century without including strong
positive forcing from anthropogenic greenhouse gas emissions (Figure 3.1). The modeled
century-scale trend due to natural forcings(solar and volcanic) is also minor (Figure3.1), so that, using present models, there is
no known source of natural variability thatcan reproduce the observed global warming
over the past century. One study21 comparing
paleoclimate data with models concluded thatcurrent climate models may substantially underestimate regional sea surface temperature
variability on multidecadal to multi-centennial timescales, especially at low latitudes. The
causes of this apparent discrepancy--whether
due to data issues, external forcings/response,or simulated internal variability issues--and its
implications for simulations of global temperature variability in climate models remainunresolved. Since Laepple and Huybers21 is asingle paleoclimate-based study and focuses
on regional, not global mean, temperature
variability, we have consequently not modified our conclusions regarding global temperature attribution from those contained in
Bindoff et al.,1 although further research on
this issue is warranted. In summary, we are
not aware of any convincing evidence thatnatural variability alone could have accounted
for the amount and timing of global warmingthat was observed over the industrial era.

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3 | Detection and Attribution of Climate Change
2.0
1.0
0.0
−1.0

Anomaly (°F)

Anomaly (°F)

Anomaly (°F)

0.2

Anomaly (°F)

Global Surface Temperature (CRU)

Solar Component

0.0
−0.2
0.0
−0.4
−0.8
0.4

Volcanic Component
Natural Variability

0.0
−0.4
2.0
1.5

Anthropogenic Component

1.0
0.5
0.0

1880 1900 1920 1940 1960 1980 2000
Year

Figure 3.3: Estimates of the contributions of several forcing factors and internal variability to global mean temperaturechange since 1870, based on an empirical approach using multiple linear regression and energy balance models. The
top panel shows global temperature anomalies (°F) from the observations62 in black with the multiple linear regression
result in red (1901–1960 base period). The lower four panels show the estimated contribution to global mean temperature
anomalies from four factors: solar variability; volcanic eruptions; internal variability related to El Niño/Southern Oscillation;
and anthropogenic forcing. The anthropogenic contribution includes a warming component from greenhouse gases concentrations and a cooling component from anthropogenic aerosols. (Figure source: adapted from Canty et al.16).

While most detection and attribution studies
focus on changes in temperature and othervariables in the historical record since about
1860 or later, some studies relevant to detection and attribution focus on changes overmuch longer periods. For example, geologicaland tide-based reconstructions of global meansea level (Ch. 12: Sea Level Rise, Figure 12.2b)
suggest that the rate of sea level rise in the lastcentury was faster than during any century
over the past ~2,800 years. As an example,
for Northern Hemisphere annual mean temperatures, Schurer et al.22 use detection andattribution fingerprinting methods along with

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paleoclimate reconstructions and millennial-scale climate model simulations from eightmodels to explore causes for temperature
variations from 850 AD to the present, including the Medieval Climate Anomaly (MCA,
around 900 to 1200 AD) and the Little Ice Age(LIA, around 1450 to 1800 AD). They conclude
that solar variability and volcanic eruptions
were the main causal factors for changes inNorthern Hemisphere temperatures from 1400
to 1900, but that greenhouse gas changes of
uncertain origin apparently contributed to thecool conditions during 1600–1800. Their study
provides further support for previous IPCC

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Climate Science Special Report3 | Detection and Attribution of Climate Changereport conclusions (e.g., IPCC 200714) that internal variability alone was extremely unlikely
to have been the cause of the recent observed
50- and 100-year warming trends. Andres andPeltier23 also inferred from millennial-scale
climate model simulations that volcanoes,
solar variability, greenhouse gases, and orbital
variations all contributed significantly to thetransition from the MCA to the LIA.
An active and important area of climate
research that involves detection and attribution science is the estimation of global climate
sensitivity, based on past observational constraints. An important measure of climate sensitivity, with particular relevance for climate
projections over the coming decades, is thetransient climate response (TCR), defined as
the rise in global mean surface temperature
at the time of CO2 doubling for a 1% per year
transient increase of atmospheric CO2. (Equilibrium climate sensitivity is discussed in Ch.
2: Physical Drivers of Climate Change). The
TCR of the climate system has an estimated range of 0.9° to 2.0°C (1.6° to 3.6°F) and
0.9° to 2.5°C (1.6° to 4.5°F), according to two
recent assessments (Otto et al.24 and Lewis andCurry25, respectively). Marvel et al.26 suggest,
based on experiments with a single climate
model, that after accounting for the different
efficacies of various historical climate forcing
agents, the TCR could be adjusted upwardfrom the Otto et al.24 and Lewis and Curry25
estimates. Richardson et al.27 report a best
estimate for TCR of 1.66°C (2.99 °F), with a 5%
to 95% confidence range of 1.0° to 3.3°C (1.8°
to 5.9°F). Furthermore, Richardson et al. conclude that the earlier studies noted above mayunderestimate TCR because the surface temperature dataset they used undersamples rapidly warming regions due to limited coverageand because surface water warms less thansurface air. Gregory et al.28 note, within CMIP5
models, that the TCR to the second doublingof CO2 (that is, from doubling to quadrupling)

U.S. Global Change Research Programis 40% higher than that for the first doubling.
They explore the various physical reasons forthis finding and conclude this may also leadto an underestimate of TCR in the empiricalobservation-based studies. In summary, estimation of TCR from observations continues tobe an active area of research with considerable
remaining uncertainties, as discussed above.
Even the low-end estimates for TCR cited
above from some recent studies (about 0.9ºC
or 1.6ºF) imply that the climate will continue
to warm substantially if atmospheric CO2
concentrations continue to increase over the
coming century as projected under a numberof future scenarios.

3.3 Detection and Attribution with a
United States Regional Focus
Detection and attribution at regional scales isgenerally more challenging than at the global
scale for a number of reasons. At the regional
scale, the magnitude of natural variability swings
are typically larger than for global means. If theclimate change signal is similar in magnitude at
the regional and global scales, this makes it moredifficult to detect anthropogenic climate changes
at the regional scale. Furthermore, there is lessspatial pattern information at the regional scalethat can be used to distinguish contributions
from various forcings. Other forcings that havetypically received less attention than greenhousegases, such as land-use change, could be more
important at regional scales than globally.29 Also,
simulated internal variability at regional scales
may be less reliable than at global scales (Bindoff
et al.1). While detection and attribution of changes in extremes (including at the regional scale)presents a number of key challenges,7 previous
studies (e.g., Zwiers et al. 201130) have demonstrated how detection and attribution methods,
combined with generalized extreme value distributions, can be used to detect a human influenceon extreme temperatures at the regional scale,including over North America.

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In IPCC AR5,1 which had a broader global
focus than this report, attributable humancontributions were reported for warming overall continents except Antarctica. Changes indaily temperature extremes throughout theworld; ocean surface and subsurface temperature and salinity sea level pressure patterns;
arctic sea ice loss; northern hemispheric snow
cover decrease; global mean sea level rise;and ocean acidification were all associatedwith human activity in AR5.1 IPCC AR5 also
reported medium confidence in anthropogenic
contributions to increased atmospheric specific humidity, zonal mean precipitation over
Northern Hemisphere mid to high latitudes,
and intensification of heavy precipitation overland regions. IPCC AR5 had weaker attribution conclusions than IPCC AR4 on somephenomena, including tropical cyclone anddrought changes.
Although the present assessment follows mostof the IPCC AR5 conclusions on detection andattribution of relevance to the United States,
we make some additional attribution assessment statements in the relevant chapters of
this report. Among the notable detection andattribution-relevant findings in this report are
the following (refer to the listed chapters forfurther details):•

Ch. 5: Circulation and Variability: The
tropics have expanded poleward by about
70 to 200 miles in each hemisphere overthe period 1979–2009, with an accompanying shift of the subtropical dry zones,
midlatitude jets, and storm tracks (medium
to high confidence). Human activities haveplayed a role in this change (medium confidence), although confidence is presently
low regarding the magnitude of the humancontribution relative to natural variability.

•

Ch. 6: Temperature Change: Detectable
anthropogenic warming since 1901 has

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occurred over the western and northern
regions of the contiguous United Statesaccording to observations and CMIP5
models (medium confidence), although overthe southeastern United States there has
been no detectable warming trend since1901. The combined influence of naturaland anthropogenic forcings on temperature extremes have been detected over large
subregions of North America (medium
confidence).
•

Ch. 7: Precipitation Change: For the continental United States, there is high confidence in the detection of extreme precipitation increases, while there is low confidence
in attributing the extreme precipitation
changes purely to anthropogenic forcing.
There is stronger evidence for a humancontribution (medium confidence) when
taking into account process-based understanding (for example, increased water
vapor in a warmer atmosphere).

•Ch. 8: Drought, Floods, and Wildfire:
While by some measures drought has
decreased over much of the continental
United States in association with longterm increases in precipitation, neither the
precipitation increases nor inferred drought
decreases have been confidently attributed to anthropogenic forcing. Detectablechanges—a mix of increases and decreases—in some classes of flood frequency
have occurred in parts of the United States,
although attribution studies have notestablished a robust connection between
increased riverine flooding and human-induced climate change. There is medium
confidence for a human-caused climatechange contribution to increased forest fire
activity in Alaska in recent decades andlow to medium confidence for a detectable
human climate change contribution in thewestern United States.

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Climate Science Special Report3 | Detection and Attribution of Climate Change•

Ch. 9: Extreme Storms: There is broad
agreement in the literature that humanfactors (greenhouse gases and aerosols)
have had a measurable impact on the observed oceanic and atmospheric variability
in the North Atlantic, and there is medium
confidence that this has contributed to theobserved increase in Atlantic hurricane activity since the 1970s. There is no consensus on the relative magnitude of humanand natural influences on past changes inhurricane activity.

•

Ch. 10: Land Cover: Modifications to land
use and land cover due to human activities
produce changes in surface albedo, latentand sensible heat, and atmospheric aerosol and greenhouse gas concentrations,accounting for an estimated 40% ± 16% of
the human-caused global radiative forcing
from 1850 to 2010 (high confidence).

•

Ch. 11: Arctic Changes: It is very likely that human activities have contributed to observed
arctic surface temperature warming, sea ice
loss, glacier mass loss, and Northern Hemisphere snow extent decline (high confidence).

•

Ch. 12: Sea Level Rise: Human-caused
climate change has made a substantial
contribution to global mean sea level rise
since 1900 (high confidence), contributing
to a rate of rise that is greater than during
any preceding century in at least 2,800
years (medium confidence).

•

Ch. 13: Ocean Changes: The world’s
oceans have absorbed about 93% of the excess heat caused by greenhouse warming
since the mid-20th Century. The world’s
oceans are currently absorbing more thana quarter of the carbon dioxide emitted
to the atmosphere annually from humanactivities, making them more acidic (veryhigh confidence).

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3.4 Extreme Event Attribution
Since the IPCC AR5 and NCA3,2 the attribution of extreme weather and climate events
has been an emerging area in the scienceof detection and attribution. Attribution of
extreme weather events under a changing
climate is now an important and highly visible
aspect of climate science. As discussed in the
recent National Academy of Sciences report,5
the science of event attribution is rapidly
advancing, including the understanding of themechanisms that produce extreme events andthe rapid progress in development of methods
used for event attribution.
When an extreme weather event occurs, thequestion is often asked: was this event causedby climate change? A generally more appropriate framing for the question is whether
climate change has altered the odds of occurrence of an extreme event like the one justexperienced. Extreme event attribution studies
to date have generally been concerned withanswering the latter question. In recent developments, Hannart et al.31 discuss the application of causal theory to event attribution, including discussion of conditions under whichstronger causal statements can be made, in
principle, based on theory of causality anddistinctions between necessary and sufficientcausality.
Several recent studies, including NAS,5 havereviewed aspects of extreme event attribution.3, 4, 6 Hulme4 and NAS5 discuss the motivations for scientists to be pursuing extreme
event attribution, including the need to informrisk management and adaptation planning.
Hulme4 categorizes event attribution studies/statements into general types, including those
based on: physical reasoning, statistical analysis of time series, fraction of attributable risk
(FAR) estimation (discussed in the Appendix), or those that rely on the philosophicalargument that there are no longer any purely

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natural weather events. The NAS5 reportoutlines two general approaches to event
attribution: 1) using observations to estimate achange in probability of magnitude of events,or 2) using model simulations to compare
an event in the current climate versus that ina hypothetical “counterfactual” climate notinfluenced by human activities. As discussed
by Trenberth et al.,32 Shepherd,33 and Horton
et al.,34 an ingredients-based or conditional attribution approach can also be used, when one
examines the impact of certain environmental
changes (for example, greater atmosphericmoisture) on the character of an extreme event
using model experiments, all else being equal.
Further discussion of methodologies is given
in Appendix C.
Examples of extreme event attribution studies are numerous. Many are cited by Hulme,4NAS,5 Easterling et al.,3 and there are manyfurther examples in an annual collection ofstudies of extreme events of the previous year,
published in the Bulletin of the American Meteorological Society.35, 36, 37, 38, 39
While an extensive review of extreme event
attribution is beyond the scope of this report,
particularly given the recent publication ofseveral assessments or review papers on the
topic, some general findings from the morecomprehensive NAS5 report are summarized
here:•

Confidence in attribution findings ofanthropogenic influence is greatest for
extreme events that are related to an aspectof temperature, followed by hydrologicaldrought and heavy precipitation, with little or no confidence for severe convective
storms or extratropical storms.

•

Event attribution is more reliable whenbased on sound physical principles, consistent evidence from observations, andU.S. Global Change Research Program

numerical models that can replicate theevent.
•

Statements about attribution are sensitive
to the way the questions are posed (that is,framing).

•

Assumptions used in studies must be
clearly stated and uncertainties estimatedin order for a clear, unambiguous interpretation of an event attribution to be possible.

The NAS report noted that uncertainties about
the roles of low-frequency natural variability
and confounding factors (for example, theeffects of dams on flooding) could be sourcesof difficulties in event attribution studies. In
addition, the report noted that attribution conclusions would be more robust in cases whereobserved changes in the event being examined
are consistent with expectations from model-based attribution studies. The report endorsed the need for more research to improveunderstanding of a number of important
aspects of event attribution studies, including
physical processes, models and their capabilities, natural variability, reliable long-term
observational records, statistical methods,
confounding factors, and future projections of
the phenomena of interest.
As discussed in Appendix C: Detection and
Attribution Methodologies, confidence istypically lower for an attribution-without-detection statement than for an attribution
statement accompanied by an established, detectable anthropogenic influence (for example,
a detectable and attributable long-term trend
or increase in variability) for the phenomenon
itself. An example of the former would be stating that a change in the probability or magnitude of a heat wave in the southeastern UnitedStates was attributable to rising greenhousegases, because there has not been a detectable

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century-scale trend in either temperature ortemperature variability in this region (e.g., Ch.
6: Temperature Change; Knutson et al. 201320).
To our knowledge, no extreme weather event
observed to date has been found to have zeroprobability of occurrence in a preindustrial
climate, according to climate model simulations.
Therefore, the causes of attributed extreme
events are a combination of natural variationsin the climate system compounded (or alleviated) by the anthropogenic change to the climate
system. Event attribution statements quantify the
relative contribution of these human and naturalcausal factors. In the future, as the climate change
signal gets stronger compared to natural variability, humans may experience weather events
which are essentially impossible to simulate in apreindustrial climate. This is already becoming
the case at large time and spatial scales, wherefor example the record global mean surface temperature anomaly observed in 2016 (relative to a
1901–1960 baseline) is essentially impossible for
global climate models to reproduce under preindustrial climate forcing conditions (for example,
see Figure 3.1).
The European heat wave of 200340 and Australia’s extreme temperatures and heat indicesof 2013 (e.g., Arblaster et al. 201441; King et al.
201442; Knutson et al. 201443; Lewis and Karoly 201444; Perkins et al. 201445) are examples
of extreme weather or climate events whererelatively strong evidence for a human contribution to the event has been found. Similarly,
in the United States, the science of event attribution for weather and climate extreme events
has been actively pursued since the NCA3.
For example, for the case of the recent California drought, investigators have attempted todetermine, using various methods discussed
in this chapter, whether human-caused climate change contributed to the event (seediscussion in Ch. 8: Droughts, Floods, andWildfires).

U.S. Global Change Research Program

As an example, illustrating different methods of attribution for an event in the UnitedStates, Hoerling et al.46 concluded that the 2011
Texas heat wave/meteorological drought was
primarily caused by antecedent and concurrent negative rainfall anomalies due mainly tonatural variability and the La Niña conditions
at the time of the event, but with a relatively
small (not detected) warming contribution
from anthropogenic forcing. The anthropogenic contribution nonetheless doubled thechances of reaching a new temperature recordin 2011 compared to the 1981–2010 reference
period, according to their study. Rupp et al.,47
meanwhile, concluded that extreme heat
events in Texas were about 20 times more
likely for 2008 La Niña conditions than similar conditions during the 1960s. This pair ofstudies illustrates how the framing of the attribution question can matter. For example, thestudies used different baseline reference periods to determine the magnitude of anomalies,
which can also affect quantitative conclusions,since using an earlier baseline period typicallyresults in larger magnitude anomalies (in agenerally warming climate). The Hoerling etal. analysis focused on both what caused mostof the magnitude of the anomalies as well aschanges in probability of the event, whereas Rupp et al. focused on the changes in theprobability of the event. Otto et al.48 showed
for the case of the Russian heat wave of 2010
how a different focus of attribution (fractionof anomaly explained vs. change in probability of occurrence over a threshold) can giveseemingly conflicting results, yet have no real
fundamental contradiction. In the illustrative case for the 2011 Texas heat/drought, weconclude that there is medium confidence thatanthropogenic forcing contributed to the heatwave, both in terms of a small contribution
to the anomaly magnitude and a significant
increase in the probability of occurrence of theevent.

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In this report, we do not assess or compile
all individual weather or climate extreme
events for which an attributable anthropogenic climate change has been claimed in a
published study, as there are now many suchstudies that provide this information. Some
event attribution-related studies that focus on
the United States are discussed in more detailin Chapters 6–9, which primarily examine
phenomena such as precipitation extremes,
droughts, floods, severe storms, and temperature extremes. For example, as discussed in
Chapter 6: Temperature Change (Table 6.3), a
number of extreme temperature events (warmanomalies) in the United States have been
partly attributed to anthropogenic influenceon climate.

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Traceable Accounts
Key Finding 1
The likely range of the human contribution to the global mean temperature increase over the period 1951–
2010 is 1.1° to 1.4°F (0.6° to 0.8°C), and the central estimate of the observed warming of 1.2°F (0.65°C) lies
within this range (high confidence). This translates toa likely human contribution of 93%–123% of the observed 1951-2010 change. It is extremely likely that morethan half of the global mean temperature increase since1951 was caused by human influence on climate (highconfidence). The likely contributions of natural forcing
and internal variability to global temperature changeover that period are minor (high confidence).
Description of evidence baseThis Key Finding summarizes key detection and attribution evidence documented in the climate science
literature and in the IPCC AR5,1 and references therein.
The Key Finding is essentially the same as the summary
assessment of IPCC AR5.
According to Bindoff et al.,1 the likely range of the anthropogenic contribution to global mean temperature increases over 1951–2010 was 1.1° to 1.4°F (0.6°
to 0.8°C, compared with the observed warming 5th to95th percentile range of 1.1° to 1.3°F (0.59° to 0.71°C).
The estimated likely contribution ranges for naturalforcing and internal variability were both much smaller (−0.2° to 0.2°F, or −0.1° to 0.1°C) than the observed
warming. The confidence intervals that encompass theextremely likely range for the anthropogenic contribution are wider than the likely range, but nonethelessallow for the conclusion that it is extremely likely that
more than half of the global mean temperature increase since 1951 was caused by human influence on
climate (high confidence).
The attribution of temperature increases since 1951 isbased largely on the detection and attribution analyses of Gillett et al.,49 Jones et al.,50 and considerationof Ribes and Terray,51 Huber and Knutti,52 Wigley andSanter,53 and IPCC AR4.54 The IPCC finding receives further support from alternative approaches, such as multiple linear regression/energy balance modeling16 andU.S. Global Change Research Programa new methodological approach to detection and attribution that uses additive decomposition and hypothesis testing,55 which infer similar attributable warming
results. Individual study results used to derive the IPCC
finding are summarized in Figure 10.4 of Bindoff et al.,1
which also assesses model dependence by comparing
results obtained from several individual CMIP5 models.
The estimated potential influence of internal variability is based on Knutson et al.20 and Huber and Knutti,52with consideration of the above references. Moreover,simulated global temperature multidecadal variability
is assessed to be adequate,1 with high confidence thatmodels reproduce global and Northern Hemispheretemperature variability across a range of timescales.9
Further support for these assessments comes fromassessments of paleoclimate data56 and increased confidence in physical understanding and models of theclimate system.10, 15 A more detailed traceable accountis contained in Bindoff et al.1 Post-IPCC AR5 supporting evidence includes additional analyses showing theunusual nature of observed global warming since thelate 1800s compared to simulated internal climate variability,19 and the recent occurrence of new record highglobal mean temperatures are consistent with model
projections of continued warming on multidecadalscales (for example, Figure 3.1).
Major uncertainties
As discussed in the main text, estimation of the transient climate response (TCR), defined as the global
mean surface temperature change at the time of CO2
doubling in a 1% per year CO2 transient increase experiment, continues to be an active area of research withconsiderable remaining uncertainties. Some detection
attribution methods use model-based methods together with observations to attempt to infer scaling
magnitudes of the forced responses based on regression methods (that is, they do not use the models’ climate sensitivities directly). However, if climate models
are significantly more sensitive to CO2 increases than
the real world, as suggested by the studies of Otto etal.24 and Lewis and Curry25 (though see differing conclusions from other studies in the main text), this could
lead to an overestimate of attributable warming esti-

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Climate Science Special Report3 | Detection and Attribution of Climate Change
mates, at least as obtained using some detection andattribution methods. In any case, it is important to better constrain the TCR to have higher confidence in general in attributable warming estimates obtained using
various methods.
The global temperature change since 1951 attributable
to anthropogenic forcings other than greenhouse gases has a wide estimated likely range (−1.1° to +0.2°F in
Fig. 3.1). This wide range is largely due to the considerable uncertainty of estimated total radiative forcing
due to aerosols (i.e., the direct effect combined with theeffects of aerosols on clouds57). Although more of therelevant physical processes are being included in models, confidence in these model representations remains
low.58 In detection/attribution studies there are substantial technical challenges in quantifying the separate attributable contributions to temperature change
from greenhouse gases and aerosols.1 Finally, there isa range of estimates of the potential contributions ofinternal climate variability, and some sources of uncertainty around modeled estimates (e.g., Laepple andHuybers 201421). However, current CMIP5 multimodel
estimates (likely range of ±0.2°F, or 0.1°C, over 60 years)
would have to increase by a factor of about three foreven half of the observed 60-year trend to lie within arevised likely range of potential internal variability (e.g.,
Knutson et al. 2013;20 Huber and Knutti 201252). Recently, Knutson et al.19 examined a 5,000-year integrationof the CMIP5 model having the strongest internal multidecadal variability among 25 CMIP5 models they examined. While the internal variability within this strongly varying model can on rare occasions produce 60-year
warmings approaching that observed from 1951–2010,
even this most extreme model did not produce any examples of centennial-scale internal variability warmingthat could match the observed global warming since
the late 1800s, even in a 5,000-year integration.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence that global temperature
has been increasing and that anthropogenic forcings
have played a major role in the increase observed over

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the past 60 years, with strong evidence from several
studies using well-established detection and attribution techniques. There is high confidence that the role ofinternal variability is minor, as the CMIP5 climate models as a group simulate only a minor role for internalvariability over the past 60 years, and the models havebeen assessed by IPCC AR5 as adequate for the purposeof estimating the potential role of internal variability.
If appropriate, estimate likelihood of impact orconsequence, including short description of basisof estimate
The amount of historical warming attributable to anthropogenic forcing has a very high likelihood of consequence, as it is related to the amount of future warming to be expected under various emission scenarios,
and the impacts of global warming are generally larger
for higher warming rates and higher warming amounts.
Summary sentence or paragraph that integrates
the above information
Detection and attribution studies, climate models,observations, paleoclimate data, and physical understanding lead to high confidence (extremely likely) that
more than half of the observed global mean warming
since 1951 was caused by humans, and high confidence
that internal climate variability played only a minor
role (and possibly even a negative contribution) in theobserved warming since 1951. The key message andsupporting text summarizes extensive evidence documented in the peer-reviewed detection and attribution
literature, including in the IPCC AR5.
Key Finding 2
The science of event attribution is rapidly advancing
through improved understanding of the mechanismsthat produce extreme events and the marked progressin development of methods that are used for event attribution (high confidence).
Description of evidence baseThis Key Finding paraphrases a conclusion of the National Academy of Sciences report5 on attribution of extreme weather events in the context of climate change.
That report discusses advancements in event attribu-

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Climate Science Special Report3 | Detection and Attribution of Climate Changetion in more detail than possible here due to space
limitations. Weather and climate science in generalcontinue to seek improved physical understanding of
extreme weather events. One aspect of improved understanding is the ability to more realistically simulate
extreme weather events in models, as the models embody current physical understanding in a simulation
framework that can be tested on sample cases. NAS5
provides references to studies that evaluate weather and climate models used to simulated extreme
events in a climate context. Such models can include
coupled climate models (e.g., Taylor et al. 2012;59 Flatoet al. 20139), atmospheric models with specified sea
surface temperatures, regional models for dynamical
downscaling, weather forecasting models, or statistical
downscaling models. Appendix C includes a brief description of the evolving set of methods used for event
attribution, discussed in more detail in references suchas NAS,5 Hulme,4 Trenberth et al.,32 Shepherd,33 Horton
et al.,34 Hannart,60 and Hannart et al.31, 61 Most of thismethodology as applied to extreme weather and climate event attribution, has evolved since the European
heat wave study of Stott et al.40
Major uncertainties
While the science of event attribution is rapidly advancing, studies of individual events will typically contain caveats. In some cases, attribution statements are
made without a clear detection of an anthropogenic
influence on observed occurrences of events similar tothe one in question, so that there is reliance on models to assess probabilities of occurrence. In such cases
there will typically be uncertainties in the model-based
estimations of the anthropogenic influence, in the estimation of the influence of natural variability on theevent’s occurrence, and even in the observational records related to the event (e.g., long-term records ofhurricane occurrence). Despite these uncertainties inindividual attribution studies, the science of event attribution is advancing through increased physical understanding and development of new methods of attribution and evaluation of models.

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Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence that weather and climate
science are advancing in their understanding of thephysical mechanisms that produce extreme events. For
example, hurricane track forecasts have improved inpart due to improved models. There is high confidence
that new methods being developed will help lead tofurther advances in the science of event attribution.
If appropriate, estimate likelihood of impact orconsequence, including short description of basisof estimate
Improving science of event attribution has a high likelihood of impact, as it is one means by which scientists canbetter understand the relationship between occurrenceof extreme events and long-term climate change. A further impact will be the improved ability to communicate
this information to the public and to policymakers forvarious uses, including improved adaptation planning.4, 5
Summary sentence or paragraph that integrates
the above information
Owing to the improved physical understanding of extreme weather and climate events as the science in thesefields progress, and owing to the high promise of newly
developed methods for exploring the roles of different
influences on occurrence of extreme events, there is highconfidence that the science of event attribution is rapidly
advancing.

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23. Andres, H.J. and W.R. Peltier, 2016: Regional influences of natural external forcings on the transition
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34. Horton, R.M., J.S. Mankin, C. Lesk, E. Coffel, and C.
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24. Otto, A., F.E.L. Otto, O. Boucher, J. Church, G. Hegerl, P.M. Forster, N.P. Gillett, J. Gregory, G.C.
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35. Herring, S.C., A. Hoell, M.P. Hoerling, J.P. Kossin,
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25. Lewis, N. and J.A. Curry, 2015: The implications forclimate sensitivity of AR5 forcing and heat uptake
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26. Marvel, K., G.A. Schmidt, R.L. Miller, and L.S. Nazarenko, 2016: Implications for climate sensitivity fromthe response to individual forcings. Nature Climate
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36. Herring, S.C., M.P. Hoerling, J.P. Kossin, T.C. Peterson, and P.A. Stott, 2015: Explaining Extreme
Events of 2014 from a Climate Perspective. Bulletinof the American Meteorological Society, 96, S1-S172.
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39. Peterson, T.C., P.A. Stott, and S. Herring, 2012: Explaining extreme events of 2011 from a climate
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48. Otto, F.E.L., N. Massey, G.J. van Oldenborgh, R.G.
Jones, and M.R. Allen, 2012: Reconciling two approaches to attribution of the 2010 Russian heatwave. Geophysical Research Letters, 39, L04702. http://dx.doi.org/10.1029/2011GL050422

40. Stott, P.A., D.A. Stone, and M.R. Allen, 2004: Humancontribution to the European heatwave of 2003. Nature, 432, 610-614. http://dx.doi.org/10.1038/nature03089

49. Gillett, N.P., J.C. Fyfe, and D.E. Parker, 2013: Attribution of observed sea level pressure trends to greenhouse gas, aerosol, and ozone changes. Geophysical Research Letters, 40, 2302-2306. http://dx.doi.
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41. Arblaster, J.M., E.-P. Lim, H.H. Hendon, B.C. Trewin, M.C. Wheeler, G. Liu, and K. Braganza, 2014:
Understanding Australia’s hottest September on record [in “Explaining Extreme Events of 2013 from a
Climate Perspective”]. Bulletin of the American Meteorological Society, 95 (9), S37-S41. http://dx.doi.
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42. King, A.D., D.J. Karoly, M.G. Donat, and L.V. Alexander, 2014: Climate change turns Australia’s 2013 Big
Dry into a year of record-breaking heat [in “Explaining Extreme Events of 2013 from a Climate Perspective”]. Bulletin of the American Meteorological Society,95 (9), S41-S45. http://dx.doi.org/10.1175/15200477-95.9.S1.1
43. Knutson, T.R., F. Zeng, and A.T. Wittenberg, 2014:
Multimodel assessment of extreme annual-mean
warm anomalies during 2013 over regions of Australia and the western tropical Pacific [in “Explaining
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45. Perkins, S.E., S.C. Lewis, A.D. King, and L.V. Alexander, 2014: Increased simulated risk of the hot Australian summer of 2012/13 due to anthropogenic
activity as measured by heat wave frequency andintensity [in “Explaining Extreme Events of 2013
from a Climate Perspective”]. Bulletin of the American
Meteorological Society, 95 (9), S34-S37. http://dx.doi.
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46. Hoerling, M., M. Chen, R. Dole, J. Eischeid, A. Kumar, J.W. Nielsen-Gammon, P. Pegion, J. Perlwitz,
X.-W. Quan, and T. Zhang, 2013: Anatomy of an extreme event. Journal of Climate, 26, 2811–2832. http://dx.doi.org/10.1175/JCLI-D-12-00270.1

50. Jones, G.S., P.A. Stott, and N. Christidis, 2013: Attribution of observed historical near surface temperature variations to anthropogenic and natural causes
using CMIP5 simulations. Journal of Geophysical Research, 118, 4001-4024. http://dx.doi.org/10.1002/
jgrd.50239
51. Ribes, A. and L. Terray, 2013: Application of regularised optimal fingerprinting to attribution. Part II: Application to global near-surface temperature. Climate
Dynamics, 41, 2837-2853. http://dx.doi.org/10.1007/
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52. Huber, M. and R. Knutti, 2012: Anthropogenic andnatural warming inferred from changes in Earth’s
energy balance. Nature Geoscience, 5, 31-36. http://dx.doi.org/10.1038/ngeo1327
53. Wigley, T.M.L. and B.D. Santer, 2013: A probabilistic
quantification of the anthropogenic component oftwentieth century global warming. Climate Dynamics,
40, 1087-1102. http://dx.doi.org/10.1007/s00382012-1585-8
54. Hegerl, G.C., F.W. Zwiers, P. Braconnot, N.P. Gillett,
Y. Luo, J.A.M. Orsini, N. Nicholls, J.E. Penner, and
P.A. Stott, 2007: Understanding and attributing climate change. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
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47. Rupp, D.E., P.W. Mote, N. Massey, C.J. Rye, R. Jones,
and M.R. Allen, 2012: Did human influence on climate make the 2011 Texas drought more probable? [in“Explaining Extreme Events of 2011 from a Climate
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56. Masson-Delmotte, V., M. Schulz, A. Abe-Ouchi, J.
Beer, A. Ganopolski, J.F. González Rouco, E. Jansen,
K. Lambeck, J. Luterbacher, T. Naish, T. Osborn, B.
Otto-Bliesner, T. Quinn, R. Ramesh, M. Rojas, X.
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M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia,
V. Bex, and P.M. Midgley, Eds. Cambridge University
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Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque,
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T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J.
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58. Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G.
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org/report/full-report/

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4

Climate Models, Scenarios,and Projections

KEY FINDINGS
1. If greenhouse gas concentrations were stabilized at their current level, existing concentrations would
commit the world to at least an additional 1.1°F (0.6°C) of warming over this century relative to thelast few decades (high confidence in continued warming, medium confidence in amount of warming).
2. Over the next two decades, global temperature increase is projected to be between 0.5°F and 1.3°F(0.3°–0.7°C) (medium confidence). This range is primarily due to uncertainties in natural sources of variability that affect short-term trends. In some regions, this means that the trend may not be distinguishable from natural variability (high confidence).
3. Beyond the next few decades, the magnitude of climate change depends primarily on cumulativeemissions of greenhouse gases and aerosols and the sensitivity of the climate system to those emissions (high confidence). Projected changes range from 4.7°–8.6°F (2.6°–4.8°C) under the higher scenario(RCP8.5) to 0.5°–1.3°F (0.3°–1.7°C) under the much lower scenario (RCP2.6), for 2081–2100 relative to
1986–2005 (medium confidence).
4. Global mean atmospheric carbon dioxide (CO2) concentration has now passed 400 ppm, a level thatlast occurred about 3 million years ago, when global average temperature and sea level were significantly higher than today (high confidence). Continued growth in CO2 emissions over this century
and beyond would lead to an atmospheric concentration not experienced in tens of millions of years(medium confidence). The present-day emissions rate of nearly 10 GtC per year suggests that there is no
climate analog for this century any time in at least the last 50 million years (medium confidence).
5. The observed increase in global carbon emissions over the past 15–20 years has been consistent withhigher scenarios (very high confidence). In 2014 and 2015, emission growth rates slowed as economic
growth has become less carbon-intensive (medium confidence). Even if this trend continues, however, it
is not yet at a rate that would limit the increase in the global average temperature to well below 3.6°F(2°C) above preindustrial levels (high confidence).
6. Combining output from global climate models and dynamical and statistical downscaling models
using advanced averaging, weighting, and pattern scaling approaches can result in more relevant androbust future projections. For some regions, sectors, and impacts, these techniques are increasing theability of the scientific community to provide guidance on the use of climate projections for quantifying regional-scale changes and impacts (medium to high confidence).
Recommended Citation for Chapter
Hayhoe, K., J. Edmonds, R.E. Kopp, A.N. LeGrande, B.M. Sanderson, M.F. Wehner, and D.J. Wuebbles,
2017: Climate models, scenarios, and projections. In: Climate Science Special Report: Fourth National Climate
Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 133-160, doi: 10.7930/
J0WH2N54.
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4 | Climate Models, Scenarios, and Projections
4.1 The Human Role in Future Climate
The Earth’s climate, past and future, is notstatic; it changes in response to both naturaland anthropogenic drivers (see Ch. 2: PhysicalDrivers of Climate Change). Human emissionsof carbon dioxide (CO2), methane (CH4), andother greenhouse gases now overwhelm theinfluence of natural drivers on the external
forcing of Earth’s climate (see Ch. 3: Detection
and Attribution). Climate change (see Ch. 1:
Our Globally Changing Climate) and ocean
acidification (see Ch. 13: Ocean Changes)
are already occurring due to the buildup of
atmospheric CO2 from human emissions in theindustrial era.1, 2
Even if existing concentrations could be immediately stabilized, temperature would continue to increase by an estimated 1.1°F (0.6°C)
over this century, relative to 1980–1999.3 Thisis because of the long timescale over whichsome climate feedbacks act (Ch. 2: PhysicalDrivers of Climate Change). Over the next
few decades, concentrations are projected toincrease and the resulting global temperature
increase is projected to range from 0.5°F to
1.3°F (0.3°C to 0.7°C). This range depends onnatural variability, on emissions of short-lived
species such as CH4 and black carbon that
contribute to warming, and on emissions ofsulfur dioxide (SO2) and other aerosols thathave a net cooling effect (Ch. 2: Physical Drivers of Climate Change). The role of emission
reductions of non-CO2 gases and aerosols inachieving various global temperature targets
is discussed in Chapter 14: Mitigation.
Over the past 15–20 years, the growth rate inatmospheric carbon emissions from humanactivities has increased from 1.5 to 2 parts
per million (ppm) per year due to increasing
carbon emissions from human activities thattrack the rate projected under higher scenarios, in large part due to growing contributions
from developing economies.4, 5, 6 One possible

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analog for the rapid pace of change occurring
today is the relatively abrupt warming of
9°–14°F (5°–8°C) that occurred during the Paleocene-Eocene Thermal Maximum (PETM),
approximately 55–56 million years ago.7, 8, 9, 10
However, emissions today are nearly 10 GtC
per year. During the PETM, the rate of maximum sustained carbon release was less than
1.1 GtC per year, with significant differences
in both background conditions and forcing
relative to today. This suggests that there is
no precise past analog any time in the last
66 million years for the conditions occurring
today.10, 11
Since 2014, growth rates of global carbon
emissions have declined, a trend cautiously attributed to declining coal use in China, despitelarge uncertainties in emissions reporting.12, 13
Economic growth is becoming less carbon-intensive, as both developed and emerging
economies begin to phase out coal and transition to natural gas and renewable, non-carbon
energy.14, 15
Beyond the next few decades, the magnitudeof future climate change will be primarily afunction of future carbon emissions and the
response of the climate system to those emissions. This chapter describes the scenariosthat provide the basis for the range of future
projections presented in this report: from thoseconsistent with continued increases in greenhouse gas emissions, to others that can only beachieved by various levels of emission reductions (see Ch. 14: Mitigation). This chapter also
describes the models used to quantify projected changes at the global to regional scaleand how it is possible to estimate the range in
potential climate change—as determined byclimate sensitivity, which is the response of
global temperature to a natural or anthropogenic forcing (see Ch. 2: Physical Drivers of
Climate Change)—that would result from agiven scenario.3

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4.2 Future Scenarios
Climate projections are typically presented fora range of plausible pathways, scenarios, ortargets that capture the relationships between
human choices, emissions, concentrations,and temperature change. Some scenarios are
consistent with continued dependence on
fossil fuels, while others can only be achieved
by deliberate actions to reduce emissions. The
resulting range reflects the uncertainty inherent in quantifying human activities (including
technological change) and their influence on
climate.
The first Intergovernmental Panel on Climate
Change Assessment Report (IPCC FAR) in
1990 discussed three types of scenarios: equilibrium scenarios, in which CO2 concentration
was fixed; transient scenarios, in which CO2
concentration increased by a fixed percentage
each year over the duration of the scenario;
and four brand-new Scientific Assessment(SA90) emission scenarios based on WorldBank population projections.16 Today, thatoriginal portfolio has expanded to encompass
a wide variety of time-dependent or transient
scenarios that project how population, energy
sources, technology, emissions, atmosphericconcentrations, radiative forcing, and/or global temperature change over time.
Other scenarios are simply expressed in termsof an end-goal or target, such as capping
cumulative carbon emissions at a specificlevel or stabilizing global temperature at or
below a certain threshold such as 3.6°F (2°C),
a goal that is often cited in a variety of scientific and policy discussions, most recently
the Paris Agreement.17 To stabilize climate
at any particular temperature level, however, it is not enough to halt the growth inannual carbon emissions. Global net carbon
emissions will eventually need to reach zero3
and negative emissions may be needed for agreater-than-50% chance of limiting warming

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below 3.6°F (2°C) (see also Ch. 14: Mitigation
for a discussion of negative emissions).18
Finally, some scenarios, like the “commitment” scenario in Key Finding 1 and the fixedCO2 equilibrium scenarios described above,
continue to explore hypothetical questions
such as, “what would the world look like,long-term, if humans were able to stabilize
atmospheric CO2 concentration at a given level?” This section describes the different typesof scenarios used today and their relevanceto assessing impacts and informing policy
targets.
4.2.1 Emissions Scenarios, Representative
Concentration Pathways, and Shared
Socioeconomic Pathways

The standard sets of time-dependent scenarios used by the climate modeling community
as input to global climate model simulations
provide the basis for the majority of the future
projections presented in IPCC assessment
reports and U.S. National Climate Assessments (NCAs). Developed by the integrated
assessment modeling community, these sets ofstandard scenarios have become more comprehensive with each new generation, as theoriginal SA90 scenarios19 were replaced by the
IS92 emission scenarios of the 1990s,20 whichwere in turn succeeded by the Special Reporton Emissions Scenarios in 2000 (SRES)21 andby the Representative Concentration Pathways in 2010 (RCPs).22
SA90, IS92, and SRES are all emission-based
scenarios. They begin with a set of storylinesthat were based on population projections
initially. By SRES, they had become much
more complex, laying out a consistent pictureof demographics, international trade, flow of
information and technology, and other social,technological, and economic characteristics offuture worlds. These assumptions were thenfed through socioeconomic and Integrated As-

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4 | Climate Models, Scenarios, and Projections
sessment Models (IAMs) to derive emissions.
For SRES, the use of various IAMs resulted in
multiple emissions scenarios corresponding
to each storyline; however, one scenario foreach storyline was selected as the representative “marker” scenario to be used as input toglobal models to calculate the resulting atmospheric concentrations, radiative forcing, andclimate change for the higher A1fi (fossil-intensive), mid-high A2, mid-low B2, and lower
B1 storylines. IS92-based projections wereused in the IPCC Second and Third Assessment Reports (SAR and TAR)23, 24 and the first
NCA.25 Projections based on SRES scenarios
were used in the second and third NCAs26, 27 aswell as the IPCC TAR and Fourth Assessment
Reports (AR4).24, 28
The most recent set of time-dependent scenarios, RCPs, builds on these two decades ofscenario development. However, RCPs differ
from previous sets of standard scenarios in atleast four important ways. First, RCPs are not
emissions scenarios; they are radiative forcing
scenarios. Each scenario is tied to one value: thechange in radiative forcing at the tropopause
by 2100 relative to preindustrial levels. The four
RCPs are numbered according to the change inradiative forcing by 2100: +2.6, +4.5, +6.0 and+8.5 watts per square meter (W/m2).29, 30, 31, 32
The second difference is that, starting fromthese radiative forcing values, IAMs are used
to work backwards to derive a range of emissions trajectories and corresponding policies
and technological strategies for each RCP thatwould achieve the same ultimate impact onradiative forcing. From the multiple emissions pathways that could lead to the same
2100 radiative forcing value, an associatedpathway of annual carbon dioxide and otheranthropogenic emissions of greenhouse gases,
aerosols, air pollutants, and other short-lived
species has been selected for each RCP to use
as input to future climate model simulations

U.S. Global Change Research Program(e.g., Meinshausen et al. 2011;33 Cubasch etal. 201334). In addition, RCPs provide climate
modelers with gridded trajectories of land use
and land cover.
A third difference between the RCPs and previous scenarios is that while none of the SRES
scenarios included a scenario with explicit policies and measures to limit climate forcing, all of
the three lower RCP scenarios (2.6, 4.5, and 6.0)
are climate-policy scenarios. At the higher endof the range, the RCP8.5 scenario corresponds
to a future where carbon dioxide and methane
emissions continue to rise as a result of fossil fuel use, albeit with significant declines inemission growth rates over the second half ofthe century (Figure 4.1), significant reduction inaerosols, and modest improvements in energy
intensity and technology.32 Atmospheric carbon
dioxide levels for RCP8.5 are similar to those of
the SRES A1FI scenario: they rise from current-day levels of 400 up to 936 ppm by the endof this century. CO2-equivalent levels (including emissions of other non-CO2 greenhousegases, aerosols, and other substances that affectclimate) reach more than 1200 ppm by 2100,
and global temperature is projected to increaseby 5.4°–9.9°F (3°–5.5°C) by 2100 relative to the
1986–2005 average. RCP8.5 reflects the upper
range of the open literature on emissions, but
is not intended to serve as an upper limit on
possible emissions nor as a business-as-usual orreference scenario for the other three scenarios.
Under the lower scenarios (RCP4.5 and
RCP2.6),29, 30 atmospheric CO2 levels remain
below 550 and 450 ppm by 2100, respectively.
Emissions of other substances are also lower;by 2100, CO2-equivalent concentrations that include all emissions from human activities reach
580 ppm under RCP4.5 and 425 ppm under
RCP2.6. RCP4.5 is similar to SRES B1, but the
RCP2.6 scenario is much lower than any SRES
scenario because it includes the option of using
policies to achieve net negative carbon dioxide

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emissions before the end of the century, while
SRES scenarios do not. RCP-based projections
were used in the most recent IPCC Fifth Assessment Report (AR5)3 and the third NCA27 andare used in this fourth NCA as well.
Within the RCP family, individual scenarios
have not been assigned a formal likelihood.
Higher-numbered scenarios correspond tohigher emissions and a larger and more rapid
global temperature change (Figure 4.1); therange of values covered by the scenarios waschosen to reflect the then-current range in the
open literature. Since the choice of scenario
constrains the magnitudes of future changes, most assessments (including this one; see
Ch. 6: Temperature Change) quantify futurechange and corresponding impacts under arange of future scenarios that reflect the uncertainty in the consequences of human choices
over the coming century.
Fourth, a broad range of socioeconomic scenarios were developed independently from the
RCPs and a subset of these were constrained,
using emissions limitations policies consistent
with their underlying storylines, to create five
Shared Socioeconomic Pathways (SSPs) withclimate forcing that matches the RCP values.
This pairing of SSPs and RCPs is designed to

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meet the needs of the impacts, adaptation, andvulnerability (IAV) communities, enablingthem to couple alternative socioeconomic
scenarios with the climate scenarios developed
using RCPs to explore the socioeconomic challenges to climate mitigation and adaptation.35
The five SSPs consist of SSP1 (“Sustainability”;
low challenges to mitigation and adaptation),
SSP2 (“Middle of the Road”; middle challenges
to mitigation and adaptation), SSP3 (“Regional
Rivalry”; high challenges to mitigation andadaptation), SSP4 (“Inequality”; low challenges
to mitigation, high challenges to adaptation),and SSP5 (“Fossil-fueled Development”; highchallenges to mitigation, low challenges to adaptation). Each scenario has an underlying SSP
narrative, as well as consistent assumptions regarding demographics, urbanization, economic
growth, and technology development. Only
SSP5 produces a reference scenario that is consistent with RCP8.5; climate forcing in the other
SSPs’ reference scenarios that don’t include
climate policy remains below 8.5 W/m2. In addition, the nature of SSP3 makes it impossible
for that scenario to produce a climate forcing as
low as 2.6 W/m2. While new research is under
way to explore scenarios that limit climate forcing to 2.0 W/m2, neither the RCPs nor the SSPs
have produced scenarios in that range.

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Emissions, Concentrations,andConcentrations,Temperature Projections
Emissions,and Temperature Projections

20
15

25
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(MtN2O/yr)

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700

A1T

B1

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600

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500

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300

300

12
10
8

Figure 4.1: The climate projections used in thisreport are based on the 2010 Representative
Concentration Pathways (RCP, right). They arelargely consistent with scenarios used in previous assessments, the 2000 Special Report on
Emission Scenarios (SRES, left). This figurecompares SRES and RCP annual carbon emissions (GtC per year, first row), annual methane
emissions (MtCH4 per year, second row), annual nitrous oxide emissions (MtN2O per year,
third row), carbon dioxide concentration in theatmosphere (ppm, fourth row), and global mean
temperature change relative to 1900–1960 as
simulated by CMIP3 models for the SRES scenarios and CMIP5 models for the RCP scenarios (°F, fifth row). Note that global mean temperature from SRES A1FI simulations are only
available from four global climate models. (Datafrom IPCC-DDC, IIASA, CMIP3, and CMIP5).

RCP8.5
RCP6.0
RCP4.5
RCP2.6

RCP8.5
RCP6.0
RCP4.5
RCP2.6500

30

Global Mean
Temperature Change (°F)

RCP Scenarios
30

A1FI
A2
A1B
B2
AIT
B1

25

Atmosperhic Carbon Dioxide
Concentration (parts per million)

Methane Emissions per Year
(MtCH4/yr)

Carbon Emissions per Year
(GtC/yr)

SRES Scenarios
30

12
A1fi
B1
Commitment

108

RCP8.5
RCP6.0
RCP4.5
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RCP8.5
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Commitment

RCP8.5
RCP4.5
RCP2.6

6

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0
2000 2020 2040 2060 2080 2100Year02000 2020 2040 2060 2080 2100
Year

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4.2.2 Alternative Scenarios

The emissions and radiative forcing scenarios
described above include a component of time:
how much will climate change, and by when?
Ultimately, however, the magnitude of human-induced climate change depends less on
the year-to-year emissions than it does on the
net amount of carbon, or cumulative carbon,
emitted into the atmosphere. The lower theatmospheric concentrations of CO2, the greater
the chance that eventual global temperaturechange will not reach the high end temperature projections, or possibly remain below
3.6°F (2°C) relative to preindustrial levels.
Cumulative carbon targets offer an alternative approach to expressing a goal designed
to limit global temperature to a certain level.
As discussed in Chapter 14: Mitigation, it ispossible to quantify the expected amount ofcarbon that can be emitted globally in order tomeet a specific global warming target such as
3.6°F (2°C) or even 2.7°F (1.5°C)—although if
current carbon emission rates of just under 10
GtC per year were to continue, the lower target would be reached in a matter of years. The
higher target would be reached in a matter of
decades (see Ch. 14: Mitigation).
Under a lower scenario (RCP4.5), global temperature change is more likely than not toexceed 3.6°F (2°C),3, 36 whereas under the evenlower scenario (RCP2.6), it is likely to remain
below 3.6°F (2°C).3, 37 While new research isunder way to explore scenarios consistent withlimiting climate forcing to 2.0 W/m2, a levelconsistent with limiting global mean surface
temperature change to 2.7°F (1.5°C), neither the
RCPs nor the SSPs have produced scenariosthat allow for such a small amount of temperature change (see also Ch. 14: Mitigation). 37
Future projections are most commonly summarized for a given future scenario (forexample, RCP8.5 or 4.5) over a range of future

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climatological time periods (for example,
temperature change in 2040–2079 or 2070–2099
relative to 1980–2009). While this approach has
the advantage of developing projections fora specific time horizon, uncertainty in future
projections is relatively high, incorporating
both the uncertainty due to multiple scenarios
as well as uncertainty regarding the responseof the climate system to human emissions.
These uncertainties increase the further out intime the projections go. Using these same transient, scenario-based simulations, however, it
is possible to analyze the projected changes
for a given global mean temperature (GMT)
threshold by extracting a time slice (typically
20 years) centered around the point in time atwhich that change is reached (Figure 4.2).
Derived GMT scenarios offer a way for the
public and policymakers to understand theimpacts for any given temperature threshold,as many physical changes and impacts havebeen shown to scale with global mean surface temperature, including shifts in average
precipitation, extreme heat, runoff, drought
risk, wildfire, temperature-related crop yieldchanges, and even risk of coral bleaching(e.g., NRC 2011;38 Collins et al. 2013;3 Frieler
et al. 2013;39 Swain and Hayhoe 201540). They
also allow scientists to highlight the effect of
global mean temperature on projected regional change by de-emphasizing the uncertainty
due to both climate sensitivity and futurescenarios.40, 41 This approach is less usefulfor those impacts that vary based on rate ofchange, such as species migrations, or where
equilibrium changes are very different fromtransient effects, such as sea level rise.
Pattern scaling techniques42 are based on asimilar assumption to GMT scenarios, namelythat large-scale patterns of regional change
will scale with global temperature change.
These techniques can be used to quantify
regional projections for scenarios that are not139

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4 | Climate Models, Scenarios, and Projections
6
10

RCP2.6
RCP4.5
RCP6.0
RCP8.5

8

5

6
3
4

2

2

0

GMT Anomalies (ºC)

GMT Anomalies (ºF)

4

1

2000

2020

2040
Year

2060

2080

0

Figure 4.2: Global mean temperature anomalies (°F) relative to 1976–2005 for four RCP scenarios, 2.6 (green), 4.5(yellow), 6.0 (orange), and 8.5 (red). Each line represents an individual simulation from the CMIP5 archive. Every RCPbased simulation with annual or monthly temperature outputs available was used here. The values shown here were
calculated in 0.5°C increments; since not every simulation reaches the next 0.5°C increment before end of century,
many lines terminate before 2100. (Figure source: adapted from Swain and Hayhoe 201540).

readily available in preexisting databases ofglobal climate model simulations, includingchanges in both mean and extremes (e.g., Fixet al. 201643). A comprehensive assessment
both confirms and constrains the validity ofapplying pattern scaling to quantify climate
response to a range of projected future changes.44 For temperature-based climate targets,these pattern scaling frames or GMT scenarios
offer the basis for more consistent comparisons across studies examining regional changeor potential risks and impacts.
4.2.3 Analogs from the Paleoclimate Record

Most CMIP5 simulations project transientchanges in climate through 2100; a few simulations extend to 2200, 2300, or beyond.
However, as discussed in Chapter 2: PhysicalDrivers of Climate Change, the long-term
impact of human activities on the carbon cycle

U.S. Global Change Research Programand Earth’s climate over the next few decades
and for the remainder of this century can only
be assessed by considering changes that occur
over multiple centuries and even millennia.38
In the past, there have been several examplesof “hothouse” climates where carbon dioxide
concentrations and/or global mean temperatures were similar to preindustrial, current,or plausible future levels. These periods are
sometimes referenced as analogs, albeit imperfect and incomplete, of future climate (e.g.,
Crowley 199010), though comparing climate
model simulations to geologic reconstructionsof temperature and carbon dioxide during
these periods suggests that today’s global climate models tend to underestimate the magnitude of change in response to higher CO2(see Ch. 15: Potential Surprises).

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The last interglacial period, approximately
125,000 years ago, is known as the Eemian.
During that time, CO2 concentration was
similar to preindustrial concentrations, around
280 ppm.45 Global mean temperature wasapproximately 1.8°–3.6°F (1°–2°C) higher thanpreindustrial temperatures,46, 47 although thepoles were significantly warmer 48, 49 and sea
level was 6 to 9 meters (20 to 30 feet) higher
than today.50 During the Pliocene, approximately 3 million years ago, long-term CO2
concentration was similar to today’s, around
400 ppm51—although this level was sustained
over long periods of time, whereas today the
global CO2 concentration is increasing rapidly.
At that time, global mean temperature wasapproximately 3.6°–6.3°F (2°–3.5°C) above
preindustrial, and sea level was somewhere
between 66 ± 33 feet (20 ± 10 meters) higher
than today.52, 53, 54
Under the higher scenario (RCP8.5), CO2concentrations are projected to reach 936 ppm
by 2100. During the Eocene, 35 to 55 million
years ago, CO2 levels were between 680 and
1260 ppm, or somewhere between two anda half to four and a half times higher thanpreindustrial levels.55 If Eocene conditions
are used as an analog, this suggests that if theCO2 concentrations projected to occur under
the RCP8.5 scenario by 2100 were sustained
over long periods of time, global temperatures
would be approximately 9°–14°F (5°–8°C)
above preindustrial temperatures.56 During
the Eocene, there were no permanent landbased ice sheets; Antarctic glaciation did not
begin until approximately 34 million years
ago.57 Calibrating sea level rise models against
past climate suggests that, under the RCP8.5
scenario, Antarctica could contribute 3 feet (1meter) of sea level rise by 2100 and 50 feet (15meters) by 2500.58 If atmospheric CO2 were
sustained at levels approximately two to threetimes above preindustrial for tens of thousands of years, it is estimated that Greenland

U.S. Global Change Research Programand Antarctic ice sheets could melt entirely,59
resulting in approximately 215 feet (65 meters)of sea level rise.60

4.3 Modeling Tools
Using transient scenarios such as SRES and
RCP as input, global climate models (GCMs)
produce trajectories of future climate change,
including global and regional changes intemperature, precipitation, and other physicalcharacteristics of the climate system (see also
Ch. 6: Temperature Change and Ch. 7: Precipitation Change).3, 61 The resolution of global
models has increased significantly since IPCC
FAR.19 However, even the latest experimental
high-resolution simulations, at 15–30 miles(25–50 km) per gridbox, are unable to simulate all of the important fine-scale processes
occurring at regional to local scales. Instead,downscaling methods are often used to correct
systematic biases, or offsets relative to observations, in global projections and translatethem into the higher-resolution information
typically required for impact assessments.
Dynamical downscaling with regional climate
models (RCMs) directly simulates the responseof regional climate processes to global change,
while empirical statistical downscaling models(ESDMs) tend to be more flexible and computationally efficient. Comparing the ability ofdynamical and statistical methods to reproduce
observed climate shows that the relative performance of the two approaches depends on
the assessment criteria.62 Although dynamicaland statistical methods can be combined into a
hybrid framework, many assessments still tend
to rely on one or the other type of downscaling,where the choice is based on the needs of theassessment. The projections shown in this report,
for example, are either based on the original
GCM simulations or on simulations that havebeen statistically downscaled using the LOcalized Constructed Analogs method (LOCA).63
This section describes the global climate models

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used today, briefly summarizes their development over the past few decades, and explains
the general characteristics and relative strengths
and weaknesses of the dynamical and statistical
downscaling.
4.3.1 Global Climate Models

Global climate models are mathematicalframeworks that were originally built on fundamental equations of physics. They accountfor the conservation of energy, mass, and momentum and how these are exchanged among
different components of the climate system.
Using these fundamental relationships, GCMs
are able to simulate many important aspects ofEarth’s climate: large-scale patterns of temperature and precipitation, general characteristics of storm tracks and extratropical cyclones, and observed changes in global mean
temperature and ocean heat content as a resultof human emissions.64
The complexity of climate models has grownover time, as they incorporate additional components of Earth’s climate system (Figure 4.3). For
example, GCMs were previously referred to as“general circulation models” when they included
only the physics needed to simulate the general circulation of the atmosphere. Today, global
climate models simulate many more aspects ofthe climate system: atmospheric chemistry andaerosols, land surface interactions including soil
and vegetation, land and sea ice, and increasingly even an interactive carbon cycle and/or
biogeochemistry. Models that include this lastcomponent are also referred to as Earth systemmodels (ESMs).
In addition to expanding the number of processes in the models and improving the treatment of existing processes, the total number of
GCMs and the average horizontal spatial resolution of the models have increased over time,
as computers become more powerful, andwith each successive version of the World Cli-

U.S. Global Change Research Programmate Research Programme’s (WCRP’s) Coupled Model Intercomparison Project (CMIP).
CMIP5 provides output from over 50 GCMs
with spatial resolutions ranging from about 30
to 200 miles (50 to 300 km) per horizontal sizeand variable vertical resolution on the orderof hundreds of meters in the troposphere or
lower atmosphere.
It is often assumed that higher-resolution,
more complex, and more up-to-date models
will perform better and/or produce morerobust projections than previous-generation
models. However, a large body of researchcomparing CMIP3 and CMIP5 simulations
concludes that, although the spatial resolutionof CMIP5 has improved relative to CMIP3,
the overall improvement in performance isrelatively minor. For certain variables, regions,and seasons, there is some improvement; forothers, there is little difference or even sometimes degradation in performance, as greater
complexity does not necessarily imply improved performance.65, 66, 67, 68 CMIP5 simulations do show modest improvement in model
ability to simulate ENSO,69 some aspects ofcloud characteristics,70 and the rate of arctic
sea ice loss,71 as well as greater consensus regarding projected drying in the southwestern
United States and Mexico.68
Projected changes in hurricane rainfall rates andthe reduction in tropical storm frequency are similar, but CMIP5-based projections of increases in
the frequency of the strongest hurricanes are generally smaller than CMIP3-based projections.72
On the other hand, many studies find little to no
significant difference in large-scale patterns ofchanges in both mean and extreme temperatureand precipitation from CMIP3 to CMIP5.65, 68, 73,
74
Also, CMIP3 simulations are driven by SRES
scenarios, while CMIP5 simulations are driven
by RCP scenarios. Although some scenarios havecomparable CO2 concentration pathways (Figure4.1), differences in non-CO2 species and aerosols

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Climate Modeling
AA Climate
ModelingTimeline
Timeline(When
Various
Components
Became(When Various Components BecameCommonly
CommonlyUsed)
Used)

1890s

Radiative
Transfer

1960s

Non-Linear
Hydrological
Fluid Dynamics
Cycle

Energy Balance Models

1970s

1990s

Sea Ice andLand Surface

Atmospheric
Chemistry

Atmosphere-Ocean General Circulation Models

2000s

Aerosols andVegetation

2010s

Biogeochemical
Cycles and Carbon
Earth System Models

Figure 4.3: As scientific understanding of climate has evolved over the last 120 years, increasing amounts of physics,
chemistry, and biology have been incorporated into calculations and, eventually, models. This figure shows when various processes and components of the climate system became regularly included in scientific understanding of global
climate calculations and, over the second half of the century as computing resources became available, formalized in
global climate models.

could be responsible for some of the differences
between the simulations.68 In NCA3, projections
were based on simulations from both CMIP3
and CMIP5. In this report, future projections are
based on CMIP5 alone.
GCMs are constantly being expanded to include
more physics, chemistry, and, increasingly, eventhe biology and biogeochemistry at work in the
climate system (Figure 4.3). Interactions within
and between the various components of theclimate system result in positive and negative
feedbacks that can act to enhance or dampen theeffect of human emissions on the climate system.
The extent to which models explicitly resolve
or incorporate these processes determines theirclimate sensitivity, or response to external forcing(see Ch. 2: Physical Drivers of Climate Change,
Section 2.5 on climate sensitivity, and Ch. 15: Potential Surprises on the importance of processes
not included in present-day GCMs).
Confidence in the usefulness of the future projections generated by global climate models isbased on multiple factors. These include thefundamental nature of the physical processes
they represent, such as radiative transfer or
geophysical fluid dynamics, which can be

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tested directly against measurements or theoretical calculations to demonstrate that model
approximations are valid (e.g., IPCC 199019).
They also include the vast body of literature
dedicated to evaluating and assessing model
abilities to simulate observed features of theearth system, including large-scale modes ofnatural variability, and to reproduce their net
response to external forcing that captures theinteraction of many processes which produce
observable climate system feedbacks (e.g., Flato et al. 201364). There is no better framework
for integrating our knowledge of the physicalprocesses in a complex coupled system like
Earth’s climate.
Given their complexities, GCMs typicallybuild on previous generations and therefore
many models are not fully independent fromeach other. Many share both ideas and modelcomponents or code, complicating the interpretation of multimodel ensembles that often
are assumed to be independent.75, 76 Consideration of the independence of different models
is one of the key pieces of information going
into the weighting approach used in this report (see Appendix B: Weighting Strategy).

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4.3.2 Regional Climate Models

Despite the differences in resolution, RCMs arestill subject to many of the same types of uncertainty as GCMs. Even the highest-resolution
RCM cannot explicitly model physical processes
that occur at even smaller scales than the model
is able to resolve; instead, parameterizations are
required. Similarly, RCMs might not include
a process or an interaction that is not yet wellunderstood, even if it is able to be resolved at the
spatial scale of the model. One additional sourceof uncertainty unique to RCMs arises fromthe fact that at their boundaries RCMs require
output from GCMs to provide large-scale circulation such as winds, temperature, and moisture;
the degree to which the driving GCM correctly
captures large-scale circulation and climate willaffect the performance of the RCM.80 RCMs canbe evaluated by directly comparing their output to observations; although this process canbe challenging and time-consuming, it is often
necessary to quantify the appropriate level ofconfidence that can be placed in their output.77

RCMs are computationally intensive, providing
a broad range of output variables that resolve
regional climate features important for assessing
climate impacts. The size of individual grid cells
can be as fine as 0.6 to 1.2 miles (1 to 2 km) per
gridbox in some studies, but more commonly
range from about 6 to 30 miles (10 to 50 km). At
smaller spatial scales, and for specific variables
and areas with complex terrain, such as coastlines or mountains, regional climate models havebeen shown to add value.78 As model resolution
increases, RCMs are also able to explicitly resolve some processes that are parameterized in
global models. For example, some models withspatial scales below 2.5 miles (4 km) are able todispense with the parameterization of convective precipitation, a significant source of error
and uncertainty in coarser models.79 RCMs canalso incorporate changes in land use, land cover,or hydrology into local climate at spatial scales
relevant to planning and decision-making at theregional level.

Studies have also highlighted the importanceof large ensemble simulations when quantifying regional change.81 However, due to their
computational demand, extensive ensemblesof RCM-based projections are rare. The largest ensembles of RCM simulations for North
America are hosted by the North American
Regional Climate Change Assessment Program (NARCCAP) and the North American
CORDEX project (NA-CORDEX). Thesesimulations are useful for examining patternsof change over North America and providing
a broad suite of surface and upper-air variables to characterize future impacts. Since
these ensembles are based on four simulations
from four CMIP3 GCMs for a mid-high SRES
scenario (NARCCAP) and six CMIP5 GCMs
for two RCP scenarios (NA-CORDEX), they
do not encompass the full range of uncertainty
in future projections due to human activities,
natural variability, and climate sensitivity.

Dynamical downscaling models are often referred to as regional climate models, since they
include many of the same physical processes
that make up a global climate model, but simulate these processes at higher spatial resolution
over smaller regions, such as the western or
eastern United States (Figure 4.4).77 Most RCM
simulations use GCM fields from pre-computed global simulations as boundary conditions.
This approach allows RCMs to draw from a
broad set of GCM simulations, such as CMIP5,
but does not allow for possible two-way feedbacks and interactions between the regionaland global scales. Dynamical downscaling canalso be conducted interactively through nestinga higher-resolution regional grid or model into
a global model during a simulation. Both approaches directly simulate the dynamics of theregional climate system, but only the second allows for two-way interactions between regionaland global change.

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Figure 4.4: CMIP5 global climate models typically operate at coarser horizontal spatial scales on the order of 30 to
200 miles (50 to 300 km), while regional climate models have much finer resolutions, on the order of 6 to 30 miles (10to 50 km). This figure compares annual average precipitation (in millimeters) for the historical period 1979–2008 using(a) a resolution of 250 km or 150 miles with (b) a resolution of 15 miles or 25 km to illustrate the importance of spatial
scale in resolving key topographical features, particularly along the coasts and in mountainous areas. In this case, both
simulations are by the GFDL HIRAM, an experimental high-resolution model. (Figure source: adapted from Dixon etal. 201686).

4.3.3 Empirical Statistical Downscaling Models

Empirical statistical downscaling models(ESDMs) combine GCM output with historicalobservations to translate large-scale predictors
or patterns into high-resolution projections
at the scale of observations. The observations
used in an ESDM can range from individual
weather stations to gridded datasets. As output, ESDMs can generate a range of products,from large grids to analyses optimized for a
specific location, variable, or decision-context.
Statistical techniques are varied, from thesimple difference or delta approaches used in
the first NCA (subtracting historical simulated
values from future values, and adding the resulting delta to historical observations)25 to theparametric quantile mapping approach usedin NCA2 and 3.26, 27, 82 Even more complex clustering and advanced mathematical modelingtechniques can rival dynamical downscaling
in their demand for computational resources(e.g., Vrac et al. 200783).
Statistical models are generally flexible andless computationally demanding than RCMs.
A number of databases using a variety of

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methods, including the LOcalized Constructed Analogs method (LOCA), provide statistically downscaled projections for a continuous
period from 1960 to 2100 using a large ensemble of global models and a range of higher andlower future scenarios to capture uncertainty
due to human activities. ESDMs are also effective at removing biases in historical simulated
values, leading to a good match between the
average (multidecadal) statistics of observed
and statistically downscaled climate at the
spatial scale and over the historical period of
the observational data used to train the statistical model. Unless methods can simultaneously downscale multiple variables, however,statistical downscaling carries the risk of altering some of the physical interdependences
between variables. ESDMs are also limited inthat they require observational data as input;
the longer and more complete the record, thegreater the confidence that the ESDM is being
trained on a representative sample of climaticconditions for that location. Application of
ESDMs to remote locations with sparse temporal and/or spatial records is challenging,
though in many cases reanalysis84 or evenmonthly satellite data85 can be used in lieu of145

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4 | Climate Models, Scenarios, and Projections
in situ observations. Lack of data availability
can also limit the use of ESDMs in applications that require more variables than temperature and precipitation. Finally, statisticalmodels are based on the key assumption thatthe relationship between large-scale weather
systems and local climate or the spatial pattern of surface climate will remain stationary
over the time horizon of the projections. This
assumption may not hold if climate change
alters local feedback processes that affect these
relationships.

vided by multiple models and scenarios, thenstatistical downscaling may be more appropriate than dynamical downscaling. However,even within statistical downscaling, selecting
an appropriate method for any given study
depends on the questions being asked (seeKotamarthi et al. 201677 for further discussion
on selection of appropriate downscaling methods). This report uses projections generated by
LOCA,63 which spatially matches model-simulated days, past and future, to analogs fromobservations.

ESDMs can be evaluated in three different
ways, each of which provides useful insight
into model performance.77 First, the model’s
goodness-of-fit can be quantified by comparing downscaled simulations for the historicalperiod with the identical observations used totrain the model. Second, the generalizabilityof the model can be determined by comparing downscaled historical simulations withobservations from a different time period
than was used to train the model; this is often
accomplished via cross-validation. Third andmost importantly, the stationarity of the model
can be evaluated through a “perfect model”
experiment using coarse-resolution GCM simulations to generate future projections, thencomparing these with high-resolution GCM
simulations for the same future time period.
Initial analyses using the perfect model approach have demonstrated that the assumption of stationarity can vary significantly by
ESDM method, by quantile, and by the timescale (daily or monthly) of the GCM input.86

4.3.4 Averaging, Weighting, and Selection of
Global Models

ESDMs are best suited for analyses that
require a broad range of future projectionsof standard, near-surface variables such astemperature and precipitation, at the scaleof observations that may already be used forplanning purposes. If the study needs to evaluate the full range of projected changes pro-

U.S. Global Change Research Program

The results of individual climate model simulations using the same inputs can differ fromeach other over shorter time scales ranging
from several years to several decades.87, 88
These differences are the result of normal,
natural variability, as well as the various
ways models characterize various small-scale
processes. Although decadal predictability isan active research area,89 the timing of specific natural variations is largely unpredictablebeyond several seasons. For this reason, multimodel simulations are generally averaged
to remove the effects of randomly occurring
natural variations from long-term trends andmake it easier to discern the impact of external
drivers, both human and natural, on Earth’s
climate. Multimodel averaging is typically thelast stage in any analysis, used to prepare figures showing projected changes in quantities
such as annual or seasonal temperature or precipitation (see Ch. 6: Temperature Change andCh. 7: Precipitation Change). While the effectof averaging on the systematic errors depends
on the extent to which models have similarerrors or offsetting errors, there is growing
recognition of the value of large ensembles
of climate model simulations in addressing
uncertainty in both natural variability andscientific modeling (e.g., Deser et al. 201287).

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Previous assessments have used a simple
average to calculate the multimodel ensemble.
This approach implicitly assumes each climate
model is independent from the others and of
equal ability. Neither of these assumptions,however, are completely valid. Some models
share many components with other models in
the CMIP5 archive, whereas others have beendeveloped largely in isolation.75, 76 Also, somemodels are more successful than others at
replicating observed climate and trends overthe past century, at simulating the large-scale
dynamical features responsible for creating
or affecting the average climate conditions
over a certain region, such as the Arctic or the
Caribbean (e.g., Wang et al. 2007;90 Wang et al.
2014;91 Ryu and Hayhoe 201492), or at simulating past climates with very different states
than present day.93 Evaluation of the success ofa specific model often depends on the variable
or metric being considered in the analysis,
with some models performing better thanothers for certain regions or variables. However, all future simulations agree that bothglobal and regional temperatures will increaseover this century in response to increasing
emissions of greenhouse gases from humanactivities.
Can more sophisticated weighting or model selection schemes improve the quality of
future projections? In the past, model weights
were often based on historical performance;
yet performance varies by region and variable, and may not equate to improved future
projections.65 For example, ranking GCMs
based on their average biases in temperature
gives a very different result than when thesame models are ranked based on their ability
to simulate observed temperature trends.94,
95
If GCMs are weighted in a way that does
not accurately capture the true uncertainty inregional change, the result can be less robust
than an equally-weighted mean.96 Although
the intent of weighting models is to increase

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the robustness of the projections, by giving
lesser weight to outliers a weighting scheme
may increase the risk of underestimating
the range of uncertainty, a tendency that has
already been noted in multi-model ensembles(see Ch. 15: Potential Surprises).
Despite these challenges, for the first timein an official U.S. Global Change Research
Program report, this assessment uses model weighting to refine future climate change
projections (see also Appendix B: Weighting
Strategy).97 The weighting approach is unique:it takes into account the interdependence ofindividual climate models as well as theirrelative abilities in simulating North American climate. Understanding of model history,
together with the fingerprints of particularmodel biases, has been used to identify model
pairs that are not independent. In this report,
model independence and selected global andNorth American model quality metrics are
considered in order to determine the weighting parameters.97 Evaluation of this approachshows improved performance of the weighted
ensemble over the Arctic, a region where model-based trends often differ from observations,
but little change in global-scale temperature
response and in other regions where modeled
and observed trends are similar, although
there are small regional differences in the statistical significance of projected changes. The
choice of metric used to evaluate models hasvery little effect on the independence weighting, and some moderate influence on the skill
weighting if only a small number of variables
are used to assess model quality. Because alarge number of variables are combined to
produce a comprehensive “skill metric,” themetric is not highly sensitive to any single
variable. All multimodel figures in this report
use the approach described in Appendix B:
Weighting Strategy.

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4.4 Uncertainty in Future Projections
The timing and magnitude of projected future climate change is uncertain due to theambiguity introduced by human choices (asdiscussed in Section 4.2), natural variability,
and scientific uncertainty,87, 98, 99 which includes
uncertainty in both scientific modeling andclimate sensitivity (see Ch. 2: Physical Driversof Climate Change). Confidence in projectionsof specific aspects of future climate change
increases if formal detection and attribution
analyses (Ch. 3: Detection and Attribution)
indicate that an observed change has been
influenced by human activities, and the projection is consistent with attribution. However,in many cases, especially at the regional scalesconsidered in this assessment, a human-forced
response may not yet have emerged from thenoise of natural climate variability but may be
expected to in the future (e.g., Hawkins andSutton 200998, 201199). In such cases, confidencein such “projections without attribution” may
still be significant under higher scenarios, if
the relevant physical mechanisms of change
are well understood.
Scientific uncertainty encompasses multiple
factors. The first is parametric uncertainty—
the ability of GCMs to simulate processes that
occur on spatial or temporal scales smaller
than they can resolve. The second is structural uncertainty—whether GCMs include andaccurately represent all the important physicalprocesses occurring on scales they can resolve.
Structural uncertainty can arise because a process is not yet recognized—such as “tipping
points” or mechanisms of abrupt change—or
because it is known but is not yet understoodwell enough to be modeled accurately—suchas dynamical mechanisms that are important
to melting ice sheets (see Ch. 15: Potential
Surprises). The third is climate sensitivity—ameasure of the response of the planet toincreasing levels of CO2, which is formallydefined in Chapter 2: Physical Drivers of Cli-

U.S. Global Change Research Programmate Change as the equilibrium temperaturechange resulting from a doubling of CO2 levels in the atmosphere relative to preindustrial
levels. Various lines of evidence constrain thelikely value of climate sensitivity to between
2.7°F and 8.1°F (1.5°C and 4.5°C;100 see Ch. 2:
Physical Drivers of Climate Change for further
discussion).
Which of these sources of uncertainty—human, natural, and scientific—is most important depends on the time frame and the variable considered. As future scenarios diverge(Figure 4.1), so too do projected changes in
global and regional temperatures.98 Uncertainty in the magnitude and sign of projectedchanges in precipitation and other aspects ofclimate is even greater. The processes that leadto precipitation happen at scales smaller thanwhat can be resolved by even high-resolution
models, requiring significant parameterization. Precipitation also depends on manylarge-scale aspects of climate, including atmospheric circulation, storm tracks, and moisture convergence. Due to the greater level ofcomplexity associated with modeling precipitation, scientific uncertainty tends to dominate
in precipitation projections throughout the entire century, affecting both the magnitude andsometimes (depending on location) the sign ofthe projected change in precipitation.99
Over the next few decades, the greater part of
the range or uncertainty in projected global andregional change will be the result of a combination of natural variability (mostly related to
uncertainty in specifying the initial conditionsof the state of the ocean)88 and scientific limitations in our ability to model and understandthe Earth’s climate system (Figure 4.5, Ch. 5:
Circulation & Variability). Differences in futurescenarios, shown in orange in Figure 4.5, represent the difference between scenarios, or humanactivity. Over the short term, this uncertainty isrelatively small. As time progresses, however,148

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4 | Climate Models, Scenarios, and Projections
differences in various possible future pathways
become larger and the delayed ocean response tothese differences begins to be realized. By about
2030, the human source of uncertainty becomesincreasingly important in determining the magnitude and patterns of future global warming.
Even though natural variability will continue

100

Fraction of Total Variance (%)

90

to occur, most of the difference between present
and future climates will be determined by choices that society makes today and over the next
few decades. The further out in time we look, thegreater the influence of these human choices areon the magnitude of future warming.

Internal variability

80

Scenario uncertainty

70
60
50
40
30
20

Scientific uncertainty

10
0

2020

2040

Year

2060

2080

2100

Figure 4.5: The fraction of total variance in decadal mean surface air temperature predictions explained by the threecomponents of total uncertainty is shown for the lower 48 states (similar results are seen for Hawai’i and Alaska, notshown). Orange regions represent human or scenario uncertainty, blue regions represent scientific uncertainty, andgreen regions represent the internal variability component. As the size of the region is reduced, the relative importanceof internal variability increases. In interpreting this figure, it is important to remember that it shows the fractional sources of uncertainty. Total uncertainty increases as time progresses. (Figure source: adapted from Hawkins and Sutton
200998).

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TRACEABLE ACCOUNTSKey Finding 1
If greenhouse gas concentrations were stabilized attheir current level, existing concentrations would commit the world to at least an additional 1.1°F (0.6°C) of
warming over this century relative to the last few decades (high confidence in continued warming, medium
confidence in amount of warming).

Summary sentence or paragraph that integrates
the above information
The key finding is based on the basic physical principles
of radiative transfer that have been well established for
decades to centuries; the amount of estimated warming for this hypothetical scenario is derived from Collins et al.3 which is in turn based on Meehl et al.28 using
CMIP3 models.

Description of evidence base
The basic physics underlying the impact of humanemissions on global climate, and the role of climate
sensitivity in moderating the impact of those emissions
on global temperature, has been documented sincethe 1800s in a series of peer-reviewed journal articles
that is summarized in a collection titled, “The Warming Papers: The Scientific Foundation for the Climate
Change Forecast”.101

Key Finding 2
Over the next two decades, global temperature increase is projected to be between 0.5°F and 1.3°F (0.3°–
0.7°C) (medium confidence). This range is primarily due
to uncertainties in natural sources of variability that
affect short-term trends. In some regions, this meansthat the trend may not be distinguishable from naturalvariability (high confidence).

The estimate of committed warming at constant atmospheric concentrations is based on IPCC AR5 WG1,
Chapter 12, section 12.5.2,3 page 1103 which is in turnderived from AR4 WG1, Chapter 10, section 10.7.1,28
page 822.

Description of evidence base
The estimate of projected near-term warming under continued emissions of carbon dioxide and othergreenhouse gases and aerosols was obtained directlyfrom IPCC AR5 WG1.61

Major uncertainties
The uncertainty in projected change under a commitment scenario is low and primarily the result of uncertainty in climate sensitivity. This key finding describes a
hypothetical scenario that assumes all human-caused
emissions cease and the Earth system responds only towhat is already in the atmosphere.

The statement regarding the sources of uncertainty
in near-term projections and regional uncertainty isbased on Hawkins and Sutton98, 99 and Deser et al.87, 88

Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
The statement has high confidence in the sign of futurechange and medium confidence in the amount of warming, based on the estimate of committed warming atconstant atmospheric concentrations from Collins etal.3 based on Meehl et al.28 for a hypothetical scenario
where concentrations in the atmosphere were fixed ata known level.

U.S. Global Change Research Program

Major uncertainties
As stated in the key finding, natural variability is the primary uncertainty in quantifying the amount of global
temperature change over the next two decades.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
The first statement regarding projected warming overthe next two decades has medium confidence in theamount of warming due to the uncertainties describedin the key finding. The second statement has high confidence, as the literature strongly supports the statementthat natural variability is the primary source of uncertainty over time scales of years to decades.87, 88, 89

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Summary sentence or paragraph that integrates
the above information
The estimated warming presented in this Key Finding isbased on calculations reported by Kirtman et al.61 The
key finding that natural variability is the most important uncertainty over the near-term is based on multiple
peer reviewed publications.
Key Finding 3
Beyond the next few decades, the magnitude of climate change depends primarily on cumulative emissions of greenhouse gases and aerosols and the sensitivity of the climate system to those emissions (highconfidence). Projected changes range from 4.7°–8.6°F(2.6°–4.8°C) under the higher scenario (RCP8.5) to
0.5°–1.3°F (0.3°–1.7°C) under the much lower scenario(RCP2.6), for 2081–2100 relative to 1986–2005 (medium
confidence).
Description of evidence base
The estimate of projected long-term warming under continued emissions of carbon dioxide and othergreenhouse gases and aerosols under the RCP scenarios was obtained directly from IPCC AR5 WG1.3
All credible climate models assessed in Chapter 9 of the
IPCC WG1 AR564 from the simplest to the most complex respond with elevated global mean temperature,
the simplest indicator of climate change, when atmospheric concentrations of greenhouse gases increase.
It follows then that an emissions pathway that tracks
or exceeds the higher scenario (RCP8.5) would lead tolarger amounts of climate change.
The statement regarding the sources of uncertainty inlong-term projections is based on Hawkins and Sutton.98, 99
Major uncertainties
As stated in the key finding, the magnitude of climatechange over the long term is uncertain due to humanemissions of greenhouse gases and climate sensitivity.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement

U.S. Global Change Research Program

The first statement regarding additional warming andits dependence on human emissions and climate sensitivity has high confidence, as understanding of the radiative properties of greenhouse gases and the existenceof both positive and negative feedbacks in the climate
system is basic physics, dating to the 19th century. The
second has medium confidence in the specific magnitude of warming, due to the uncertainties described in
the key finding.
Summary sentence or paragraph that integrates
the above information
The estimated warming presented in this key finding isbased on calculations reported by Collins et al.3 The key
finding that human emissions and climate sensitivity
are the most important sources of uncertainty over the
long-term is based on both basic physics regarding theradiative properties of greenhouse gases, as well as alarge body of peer reviewed publications.
Key Finding 4
Global mean atmospheric carbon dioxide (CO2) concentration has now passed 400 ppm, a level that last
occurred about 3 million years ago, when global average temperature and sea level were significantly higher
than today (high confidence). Continued growth in CO2emissions over this century and beyond would leadto an atmospheric concentration not experienced intens of millions of years (medium confidence). The present-day emissions rate of nearly 10 GtC per year suggests that there is no climate analog for this century
any time in at least the last 50 million years (medium
confidence).
Description of evidence base
The key finding is based on a large body of research
including Crowley,10 Schneider et al.,45 Lunt et al.,46 Otto-Bleisner et al.,47 NEEM,48 Jouzel et al.,49 Dutton et al.,53
Seki et al.,51 Haywood et al.,52 Miller et al.,54 Royer,56 Bowen et al.,7 Kirtland Turner et al.,8 Penman et al.,9 Zeebeet al.,11 and summarized in NRC38 and Masson-Delmotte
et al.102
Major uncertainties
The largest uncertainty is the measurement of past sea

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4 | Climate Models, Scenarios, and Projections
level, given the contributions of not only changes in land
ice mass, but also in solid earth, mantle, isostatic adjustments, etc. that occur on timescales of millions of years.
This uncertainty increases the further back in time wego; however, the signal (and forcing) size is also muchgreater. There are also associated uncertainties in precise
quantification of past global mean temperature and carbon dioxide levels. There is uncertainty in the age models used to determine rates of change and coincidenceof response at shorter, sub-millennial timescales.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
High confidence in the likelihood statement that pastglobal mean temperature and sea level rise were higher with similar or higher CO2 concentrations is based on
Masson-Delmotte et al.102 in IPCC AR5. Medium confidence that no precise analog exists in 66 million years
is based on Zeebe et al.11 as well as the larger body ofliterature summarized in Masson-Delmotte et al.102
Summary sentence or paragraph that integrates
the above information
The key finding is based on a vast body of literaturethat summarizes the results of observations, paleoclimate analyses, and paleoclimate modeling over the
past 50 years and more.

references in the text. This statement is based on Tans
and Keeling 2017;4 Raupach et al. 2007;5 Le Quéré et al.
2009;6 Jackson et al. 2016;12 Korsbakken et al. 201613
and personal communication with Le Quéré (2017).
The statement that the growth rate of carbon dioxide
increased over the past 15–20 years is based on thedata available here: https://www.esrl.noaa.gov/gmd/
ccgg/trends/gr.html
The evidence that actual emission rates track or exceed
the higher scenario (RCP8.5) is as follows. The actual
emission of CO2 from fossil fuel consumption and concrete manufacture over the period 2005–2014 is 90.11
Pg.104 The emissions consistent with RCP8.5 over thesame period assuming linear trends between years 2000,
2005, 2010, and 2020 in the specification is 99.24 Pg.
Actual emissions:
http://www.globalcarbonproject.org/ and Le Quéréet al.103
Emissions consistent with RCP8.5
http://tntcat.iiasa.ac.at:8787/RcpDb/dsd?Action=htmlpage&page=compare
The numbers for fossil fuel and industrial emissions(RCP) compared to fossil fuel and cement emissions (observed) in units of GtC are

Key Finding 5
The observed increase in global carbon emissions overthe past 15–20 years has been consistent with higher scenarios (very high confidence). In 2014 and 2015, emission
growth rates slowed as economic growth has become lesscarbon-intensive (medium confidence). Even if this trend
continues, however, it is not yet at a rate that would limit
the increase in the global average temperature to well below 3.6°F (2°C) above preindustrial levels (high confidence).
Description of Evidence Base
Observed emissions for 2014 and 2015 and estimatedemissions for 2016 suggest a decrease in the growthrate and possibly even emissions of carbon; this shift
is attributed primarily to decreased coal use in China
although with significant uncertainty as noted in the

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152

RCP8.5

ActualDifference

2005

7.97

8.23

0.26

2006

8.16

8.53

0.36

2007

8.35

8.78

0.42

2008

8.54

8.96

0.42

2009

8.74

8.87

0.14

2010

8.93

9.21

0.28

2011

9.19

9.54

0.36

2012

9.45

9.69

0.24

2013

9.71

9.82

0.11

2014

9.97

9.89

-0.08

2015

10.23

9.90

-0.34

total

99.24

101.41

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Major Uncertainties
Noneassessments is summarized in Kotamarthi et al.77 andsupported by Feser et al.78 and Prein et al.79

Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Very high confidence in increasing emissions over thelast 20 years and high confidence in the fact that recent emission trends will not be sufficient to avoid
3.6°F (2°C). Medium confidence in recent findings thatthe growth rate is slowing. Climate change scales withthe amount of anthropogenic greenhouse gas in theatmosphere. If emissions exceed those consistent with
RCP8.5, the likely range of changes in temperaturesand climate variables will be larger than projected.

Major uncertainties
Regional climate models are subject to the same structural and parametric uncertainties as global models, aswell as the uncertainty due to incorporating boundary
conditions. The primary source of error in applicationof empirical statistical downscaling methods is inappropriate application, followed by stationarity.

Summary sentence or paragraph that integrates
the above information
The key finding is based on basic physics relating emissions to concentrations, radiative forcing, and resulting
change in global mean temperature, as well as on IEA
data on national emissions as reported in the peer-reviewed literature.
Key Finding 6
Combining output from global climate models anddynamical and statistical downscaling models using
advanced averaging, weighting, and pattern scaling
approaches can result in more relevant and robust
future projections. For some regions, sectors, and impacts, these techniques are increasing the ability of thescientific community to provide guidance on the useof climate projections for quantifying regional-scalechanges and impacts (medium to high confidence).

Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Advanced weighting techniques have significantly improved over previous Bayesian approaches; confidence
in their ability to improve the robustness of multimodel
ensembles, while currently rated as medium, is likely togrow in coming years. Downscaling has evolved significantly over the last decade and is now broadly viewed
as a robust source for high-resolution climate projections that can be used as input to regional impact assessments.
Summary sentence or paragraph that integrates
the above information
Scientific understanding of climate projections, downscaling, multimodel ensembles, and weighting has
evolved significantly over the last decades to the extent that appropriate methods are now broadly viewed
as robust sources for climate projections that can beused as input to regional impact assessments.

Description of evidence base
The contribution of weighting and pattern scaling to
improving the robustness of multimodel ensemble
projections is described and quantified by a large bodyof literature as summarized in the text, including Sanderson et al.76 and Knutti et al.97 The state of the art of dynamical and statistical downscaling and the scientificcommunity’s ability to provide guidance regarding theapplication of climate projections to regional impact

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modeling: Progress, challenges, and prospects. Journal of the Meteorological Society of Japan. Series II, 82,
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81. Xie, S.-P., C. Deser, G.A. Vecchi, M. Collins, T.L. Delworth, A. Hall, E. Hawkins, N.C. Johnson, C. Cassou,
A. Giannini, and M. Watanabe, 2015: Towards predictive understanding of regional climate change.
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82. Stoner, A.M.K., K. Hayhoe, X. Yang, and D.J. Wuebbles, 2012: An asynchronous regional regression
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variables. International Journal of Climatology, 33, 24732494. http://dx.doi.org/10.1002/joc.3603
83. Vrac, M., M. Stein, and K. Hayhoe, 2007: Statistical
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84. Brands, S., J.M. Gutiérrez, S. Herrera, and A.S.
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85. Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis, and R. Nemani, 2013: Downscaled climate
projections suitable for resource management. Eos,Transactions, American Geophysical Union, 94, 321-323.
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86. Dixon, K.W., J.R. Lanzante, M.J. Nath, K. Hayhoe, A.
Stoner, A. Radhakrishnan, V. Balaji, and C.F. Gaitán,
2016: Evaluating the stationarity assumption in statistically downscaled climate projections: Is past
performance an indicator of future results? Climatic
Change, 135, 395-408. http://dx.doi.org/10.1007/
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87. Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012:
Uncertainty in climate change projections: The roleof internal variability. Climate Dynamics, 38, 527-546.
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88. Deser, C., R. Knutti, S. Solomon, and A.S. Phillips,
2012: Communication of the role of natural variability in future North American climate. Nature Climate
Change, 2, 775-779. http://dx.doi.org/10.1038/nclimate1562
89. Deser, C., A.S. Phillips, M.A. Alexander, and B.V.
Smoliak, 2014: Projecting North American climate
over the next 50 years: Uncertainty due to internalvariability. Journal of Climate, 27, 2271-2296. http://dx.doi.org/10.1175/JCLI-D-13-00451.1
90. Wang, M., J.E. Overland, V. Kattsov, J.E. Walsh, X.
Zhang, and T. Pavlova, 2007: Intrinsic versus forced
variation in coupled climate model simulations over
the Arctic during the twentieth century. Journal of
Climate, 20, 1093-1107. http://dx.doi.org/10.1175/
JCLI4043.1
91. Wang, C., L. Zhang, S.-K. Lee, L. Wu, and C.R.
Mechoso, 2014: A global perspective on CMIP5 climate model biases. Nature Climate Change, 4, 201-205.
http://dx.doi.org/10.1038/nclimate2118
92. Ryu, J.-H. and K. Hayhoe, 2014: Understanding thesources of Caribbean precipitation biases in CMIP3
and CMIP5 simulations. Climate Dynamics, 42, 32333252. http://dx.doi.org/10.1007/s00382-013-1801-1
93. Braconnot, P., S.P. Harrison, M. Kageyama, P.J.
Bartlein, V. Masson-Delmotte, A. Abe-Ouchi, B. Otto-Bliesner, and Y. Zhao, 2012: Evaluation of climate
models using palaeoclimatic data. Nature Climate
Change, 2, 417-424. http://dx.doi.org/10.1038/nclimate1456
94. Jun, M., R. Knutti, and D.W. Nychka, 2008: Local eigenvalue analysis of CMIP3 climate model errors.
Tellus A, 60, 992-1000. http://dx.doi.org/10.1111/
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95. Giorgi, F. and E. Coppola, 2010: Does the model regional bias affect the projected regional climate change? An analysis of global model projections. Climatic Change, 100, 787-795. http://dx.doi.
org/10.1007/s10584-010-9864-z
96. Weigel, A.P., R. Knutti, M.A. Liniger, and C. Appenzeller, 2010: Risks of model weighting in multimodelclimate projections. Journal of Climate, 23, 4175-4191.
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97. Knutti, R., J. Sedláček, B.M. Sanderson, R. Lorenz, E.M. Fischer, and V. Eyring, 2017: A climate
model projection weighting scheme accounting
for performance and interdependence. Geophysical Research Letters, 44, 1909-1918. http://dx.doi.
org/10.1002/2016GL072012
98. Hawkins, E. and R. Sutton, 2009: The potential tonarrow uncertainty in regional climate predictions.
Bulletin of the American Meteorological Society, 90, 10951107. http://dx.doi.org/10.1175/2009BAMS2607.1
99. Hawkins, E. and R. Sutton, 2011: The potential to narrow uncertainty in projections of regional precipitation change. Climate Dynamics, 37, 407-418. http://dx.doi.org/10.1007/s00382-010-0810-6
100. IPCC, 2013: Summary for policymakers. Climate
Change 2013: The Physical Science Basis. Contribution ofWorking Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F.,
D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley,
Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1–30. http://
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102. Masson-Delmotte, V., M. Schulz, A. Abe-Ouchi, J.
Beer, A. Ganopolski, J.F. González Rouco, E. Jansen,
K. Lambeck, J. Luterbacher, T. Naish, T. Osborn, B.
Otto-Bliesner, T. Quinn, R. Ramesh, M. Rojas, X.
Shao, and A. Timmermann, 2013: Information from
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M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia,
V. Bex, and P.M. Midgley, Eds. Cambridge University
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Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier,
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I. Harris, J. Hauck, T. Ilyina, A.K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer,
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Metzl, F. Millero, D.R. Munro, A. Murata, J.E.M.S.
Nabel, S. Nakaoka, Y. Nojiri, K. O’Brien, A. Olsen, T.
Ono, F.F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger,
R. Séférian, T. Steinhoff, B.D. Stocker, A.J. Sutton, T.
Takahashi, B. Tilbrook, I.T. van der Laan-Luijkx, G.R.
van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng, 2015: Global
carbon budget 2015. Earth System Science Data, 7, 349396. http://dx.doi.org/10.5194/essd-7-349-2015

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KEY FINDINGS
1. The tropics have expanded poleward by about 70 to 200 miles in each hemisphere over the period
1979–2009, with an accompanying shift of the subtropical dry zones, midlatitude jets, and storm tracks(medium to high confidence). Human activities have played a role in this change (medium confidence),although confidence is presently low regarding the magnitude of the human contribution relative tonatural variability.
2. Recurring patterns of variability in large-scale atmospheric circulation (such as the North Atlantic
Oscillation and Northern Annular Mode) and the atmosphere–ocean system (such as El Niño–Southern Oscillation) cause year-to-year variations in U.S. temperatures and precipitation (high confidence).
Changes in the occurrence of these patterns or their properties have contributed to recent U.S. temperature and precipitation trends (medium confidence), although confidence is low regarding the size ofthe role of human activities in these changes.
Recommended Citation for Chapter
Perlwitz, J., T. Knutson, J.P. Kossin, and A.N. LeGrande, 2017: Large-scale circulation and climate variability. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W.
Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research
Program, Washington, DC, USA, pp. 161-184, doi: 10.7930/J0RV0KVQ.

5.1 Introduction
The causes of regional climate trends cannot be
understood without considering the impact ofvariations in large-scale atmospheric circulation and an assessment of the role of internallygenerated climate variability. There are contributions to regional climate trends from changes
in large-scale latitudinal circulation, which is
generally organized into three cells in eachhemisphere—Hadley cell, Ferrell cell and Polarcell—and which determines the location of subtropical dry zones and midlatitude jet streams(Figure 5.1). These circulation cells are expected
to shift poleward during warmer periods,1, 2, 3, 4
which could result in poleward shifts in precipitation patterns, affecting natural ecosystems,
agriculture, and water resources.5, 6

U.S. Global Change Research Program

In addition, regional climate can be strongly
affected by non-local responses to recurring patterns (or modes) of variability of
the atmospheric circulation or the coupledatmosphere–ocean system. These modes ofvariability represent preferred spatial patterns
and their temporal variation. They accountfor gross features in variance and for teleconnections which describe climate links between
geographically separated regions. Modes ofvariability are often described as a productof a spatial climate pattern and an associatedclimate index time series that are identified
based on statistical methods like Principal
Component Analysis (PC analysis), which isalso called Empirical Orthogonal Function
Analysis (EOF analysis), and cluster analysis.

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Atmospheric Circulation
90°N

N

H

Polar Cell

Polar Easterlies

Plan view

L

60°N
Polar Jet Stream

Ferrel Cell

Westerlies

Expanding
Tropics H

30°N
Subtropical Jet Stream

Hadley Cell

NE Trade Winds

L

0°
SE Trade Winds

ExpandingH
Tropics

30°S
Subtropical Jet Stream
Westerlies

L

60°S
Polar Jet Stream
Polar Easterlies

N

H

Cross section view

90°S

60
Altitude in thousands of feet

Tropical Tropopause

50
40
30

Warm air rises

Cold air sinks
Subtropical
Jet Stream

Warm air rises

Cold
air sinks

Middle Tropopause

Tropopause

Polar
Jet Stream

Hadley Cell

20
10

90°N

H

Dry and stable

Ferrel Cell

Polar Cell

Easterlies

60°N

L

Westerlies

30°N
ExpandingH
Tropics
Dryand stable

NE Trade Winds

ITCZ
0°

L

Figure 5.1: (top) Plan and (bottom) cross-section schematic view representations of the general circulation of theatmosphere. Three main circulations exist between the equator and poles due to solar heating and Earth’s rotation: 1)
Hadley cell – Low-latitude air moves toward the equator. Due to solar heating, air near the equator rises vertically andmoves poleward in the upper atmosphere. 2) Ferrel cell – A midlatitude mean atmospheric circulation cell. In this cell,
the air flows poleward and eastward near the surface and equatorward and westward at higher levels. 3) Polar cell
– Air rises, diverges, and travels toward the poles. Once over the poles, the air sinks, forming the polar highs. At thesurface, air diverges outward from the polar highs. Surface winds in the polar cell are easterly (polar easterlies). A highpressure band is located at about 30° N/S latitude, leading to dry/hot weather due to descending air motion (subtropicaldry zones are indicated in orange in the schematic views). Expanding tropics (indicted by orange arrows) are associated with a poleward shift of the subtropical dry zones. A low pressure band is found at 50°–60° N/S, with rainy andstormy weather in relation to the polar jet stream bands of strong westerly wind in the upper levels of the atmosphere.
(Figure source: adapted from NWS 2016177).

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On intraseasonal to interannual time scales,
the climate of the United States is strongly
affected by modes of atmospheric circulation
variability like the North Atlantic Oscillation(NAO)/Northern Annular Mode (NAM),
North Pacific Oscillation (NPO), and Pacific/
North American Pattern (PNA).7, 8, 9 These
modes are closely linked to other atmospheric circulation phenomena like blocking
and quasi-stationary wave patterns and jet
streams that can lead to weather and climate
extremes.10 On an interannual time scale,coupled atmosphere–ocean phenomena like
El Niño–Southern Oscillation (ENSO) have aprominent effect.11 On longer time scales, U.S.
climate anomalies are linked to slow variations of sea surface temperature related to the
Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO).12, 13, 14
These modes of variability can affect the
local-to-regional climate response to external
forcing in various ways. The climate response
may be altered by the forced response of these
existing, recurring modes of variability.15
Further, the structure and strength of regional temperature and precipitation impacts ofthese recurring modes of variability may be
modified due to a change in the backgroundclimate.16 Modes of internal variability of theclimate system also contribute to observed
decadal and multidecadal temperature andprecipitation trends on local to regional scales,masking possible systematic changes due toan anthropogenic influence.17 However, there
are still large uncertainties in our understanding of the impact of human-induced climatechange on atmospheric circulation.4, 18 Furthermore, the confidence in any specific projectedchange in ENSO variability in the 21st century
remains low.19

U.S. Global Change Research Program

5.2 Modes of Variability: Past andProjected Changes
5.2.1 Width of the Tropics and Global Circulation

Evidence continues to mount for an expansionof the tropics over the past several decades,with a poleward expansion of the Hadley celland an associated poleward shift of the subtropical dry zones and storm tracks in eachhemisphere.5, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 The rate of
expansion is uncertain and depends on themetrics and data sources that are used. Recent
estimates of the widening of the global tropics
for the period 1979–2009 range between 1° and3° latitude (between about 70 and 200 miles) ineach hemisphere, an average trend of between
approximately 0.5° and 1.0° per decade.26
While the roles of increasing greenhouse gases
in both hemispheres,4, 30 stratospheric ozone
depletion in the Southern Hemisphere,31
and anthropogenic aerosols in the Northern Hemisphere32, 33 have been implicated ascontributors to the observed expansion, there
is uncertainty in the relative contributions of
natural and anthropogenic factors, and naturalvariability may currently be dominating.23, 34, 35
Most of the previous work on tropical expansion to date has focused on zonally averagedchanges. There are only a few recent studies that diagnose regional characteristics oftropical expansion. The findings depend on
analysis methods and datasets. For example,
a northward expansion of the tropics in mostregions of the Northern Hemisphere, including the Eastern Pacific with impact on drying
in the American Southwest, is found based on
diagnosing outgoing longwave radiation.36
However, other studies do not find a significant
poleward expansion of the tropics over the
Eastern Pacific and North America.37, 38 Thus,
while some studies associate the observed
drying of the U.S. Southwest with the poleward
expansion of the tropics,5, 39 regional impacts of
the observed zonally averaged changes in thewidth of the tropics are not understood.

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Due to human-induced greenhouse gas increases, the Hadley cell is likely to widen in the
future, with an accompanying poleward shift
in the subtropical dry zones, midlatitude jets,
and storm tracks.2, 4, 5, 40, 41, 42, 43 Large uncertainties remain in projected changes in non-zonal
to regional circulation components and related changes in precipitation patterns.18, 40, 44, 45
Uncertainties in projected changes in midlatitude jets are also related to the projected rateof arctic amplification and variations in thestratospheric polar vortex. Both factors could
shift the midlatitude jet equatorward, especially in the North Atlantic region.46, 47, 48, 49
5.2.2 El Niño–Southern Oscillation

El Niño–Southern Oscillation (ENSO) is a main
source of climate variability, with a two- toseven-year timescale, originating from coupled
ocean–atmosphere interactions in the tropical Pacific. Major ENSO events affect weather patterns
over many parts of the globe through atmospheric teleconnections. ENSO strongly affects precipitation and temperature in the United States with
impacts being most pronounced during the coldseason (Figure 5.2).11, 50, 51, 52, 53 A cooling trend of
the tropical Pacific Ocean that resembles La Niña
conditions contributed to drying in southwestern
North America from 1979 to 200654 and is found
to explain most of the decrease in heavy daily
precipitation events in the southern United Statesfrom 1979 to 2013.55
El Niño teleconnections are modulated by
the location of maximum anomalous tropical
Pacific sea surface temperatures (SST). Eastern Pacific (EP) El Niño events affect wintertemperatures primarily over the Great Lakes,
Northeast, and Southwest, while Central
Pacific (CP) events influence temperatures
primarily over the northwestern and southeastern United States.56 The CP El Niño alsoenhances the drying effect, but weakens thewetting effect, typically produced by traditional EP El Niño events on U.S. winterU.S. Global Change Research Program

precipitation.57 It is not clear whether observed decadal-scale modulations of ENSO
properties, including an increase in ENSO
amplitude58 and an increase in frequencyof CP El Niño events,59, 60 are due to internalvariability or anthropogenic forcing. Uncertainties in both the diagnosed distinct U.S.
climate effects of EP and CP events and causes for the decadal scale changes result fromthe limited sample size of observed ENSO
events in each category61, 62 and the relatively
short record of the comprehensive observations (since late 1970s) that would allow
the investigation of ENSO-related coupledatmosphere–ocean feedbacks.19 Furthermore,unforced global climate model simulations
show that decadal to centennial modulationsof ENSO can be generated without any change
in external forcing.63 A model study based onlarge, single-model ensembles of atmosphericand coupled atmosphere–ocean models findsthat external radiative forcing resulted in an
atmospheric teleconnection pattern that is
independent of ENSO-like variations during
the 1979–2014 period and is characterized by ahemisphere-scale increasing trend in heights.53
The representation of ENSO in climate models
has improved from CMIP3 to CMIP5 models,
especially in relation to ENSO amplitude.64, 65
However, CMIP5 models still cannot capture
the seasonal timing of ENSO events.66 Furthermore, they still exhibit errors in simulating key
atmospheric feedbacks, and the improvement
in ENSO amplitudes might therefore result
from error compensations.64 Limited observational records and the nonstationarity of tropical Pacific teleconnections to North America
on multidecadal time scales pose challenges
for evaluating teleconnections between ENSO
and U.S. climate in coupled atmosphere–ocean
models.61, 67 For a given SST forcing, however,
the atmospheric component of CMIP5 models
simulate the sign of the precipitation changeover the southern section of North America.68

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Typical El Niño Winters

WarmerL

Low pressure
Drier
WetterExtended Pacific Jet
Stream, amplified
storm track

ColderTypical La Niña Winters

Variable Polar
Jet Stream

H

ColderWetterBlockinghigh pressure

Drier

Wetter
WarmerFigure 5.2: El Niño- and La Niña-related winter features over North America. Shown are typical January to March
weather anomalies and atmospheric circulation during moderate to strong El Niño and La Niña conditions: (top) During
El Niño, there is a tendency for a strong jet stream and storm track across the southern part of the United States. The
southern tier of Alaska and the U.S. Pacific Northwest tend to be warmer than average, whereas the southern tier ofUnited States tends to be cooler and wetter than average. (bottom) During La Niña, there is a tendency of a very wavelike jet stream flow over the United States and Canada, with colder and stormier than average conditions across theNorth and warmer and less stormy conditions across the South. (Figure source: adapted from Lindsey 2016178).

Climate projections suggest that ENSO will remain a primary mode of natural climate variability in the 21st century.19 Climate models do
not agree, however, on projected changes in
the intensity or spatial pattern of ENSO.19 This
uncertainty is related to a model dependenceof simulated changes in the zonal gradient of
tropical Pacific sea surface temperature in a
warming climate.19 Model studies suggest an
eastward shift of ENSO-induced teleconnection patterns due to greenhouse gas-induced
climate change.69, 70, 71, 72 However, the impact
of such a shift on ENSO-induced climate
anomalies in the United States is not wellunderstood.72, 73

U.S. Global Change Research Program

In summary, there is high confidence that, in the
21st century, ENSO will remain a main sourceof climate variability over the United States
on seasonal to interannual timescales. There is
low confidence for a specific projected change in
ENSO variability.
5.2.3 Extra-tropical Modes of Variability andPhenomena

North Atlantic Oscillation and Northern
Annular Mode
The North Atlantic Oscillation (NAO), theleading recurring mode of variability in theextratropical North Atlantic region, describes
an opposing pattern of sea level pressure

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between the Atlantic subtropical high and the
Iceland/Arctic low. Variations in the NAO areaccompanied by changes in the location andintensity of the Atlantic midlatitude storm
track and blocking activity that affect climate
over the North Atlantic and surrounding
continents. A negative NAO phase is related to anomalously cold conditions and anenhanced number of cold outbreaks in theeastern United States, while a strong positive
phase of the NAO tends to be associated withabove-normal temperatures in this region.7, 74
The positive phase of the NAO is associatedwith increased precipitation frequency andpositive daily rainfall anomalies, including
extreme daily precipitation anomalies in thenortheastern United States.75, 76

er.7, 85 Climate model simulations show thatmultidecadal variations in the NAO induce
multidecadal variations in the strength of theAtlantic Meridional Overturning Circulation(AMOC) and poleward ocean heat transport in
the Atlantic, extending to the Arctic, with potential impacts on recent arctic sea ice loss andNorthern Hemisphere warming.85 However,other model simulations suggest that the NAO
and recent changes in Northern Hemisphereclimate were affected by recent variations in
the AMOC,86 for which enhanced freshwater
discharge from the Greenland Ice Sheet (GrIS)
may have been a contributing cause.87

The wintertime NAO/NAM index exhibits
pronounced variability on multidecadal timescales, with an increase from the 1960s to the1990s, a shift to a more negative phase sincethe 1990s due to a series of winters like 2009–2010 and 2010–2011 (which had exceptionally
low index values), and a return to more positive values after 2011.30 Decadal scale temperature trends in the eastern United States, including occurrences of cold outbreaks during
recent years, are linked to these changes in the
NAO/NAM.81, 82, 83, 84

Climate models are widely analyzed for theirability to simulate the spatial patterns of the
NAO/NAM and their relationship to temperature and precipitation anomalies over theUnited States.9, 65, 88 Climate models reproduce
the broad spatial and temporal features of
the NAO, although there are large differencesamong the individual models in the locationof the NAO centers of action and their averagemagnitude. These differences affect the agreement between observed and simulated climate
anomalies related to the NAO.9, 65 Climate
models tend to have a NAM pattern that ismore annular than observed,65, 88 resulting ina strong bias in the Pacific center of the NAM.
As a result, temperature anomalies over thenorthwestern United States associated with
the NAM in most models are of opposite signcompared to observation.88 Biases in the model
representation of NAO/NAM features arelinked to limited abilities of general circulation
models to reproduce dynamical processes, including atmospheric blocking,89 troposphere–
stratosphere coupling,90 and climatologicalstationary waves.90, 91

The NAO’s influence on the ocean occurs
through changes in heat content, gyre circulations, mixed layer depth, salinity, high-latitude deep water formation, and sea ice cov-

The CMIP5 models on average simulate a progressive shift of the NAO/NAM towards the
positive phase due to human-induced climatechange.92 However, the spread between model

The Northern Annular Mode/Arctic Oscillation (NAM/AO) is closely related to the NAO.
It describes a similar out-of-phase pressure
variation between mid- and high latitudes but
on a hemispheric rather than regional scale.77,
78
The time series of the NAO and NAM/AO
are highly correlated, with persistent NAO
and NAM/AO events being indistinguishable.79, 80

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simulations is larger than the projected multimodel increase,19 and there are uncertainties
related to future scenarios.9 Furthermore, it isfound that shifts between preferred periods ofpositive and negative NAO phase will continue to occur similar to those observed in thepast.19, 93 There is no consensus on the locationof changes of NAO centers among the global
climate models under future warming scenarios.9 Uncertainties in future projections of
the NAO/NAM in some seasons are linked tomodel spread in projected future arctic warming46, 47 (Ch. 11: Arctic Changes) and to how
models resolve stratospheric processes.19, 94
In summary, while it is likely that the NAO/
NAM index will become slightly more positive (on average) due to increases in GHGs,
there is low confidence in temperature andprecipitation changes over the United States
related to such variations in the NAO/NAM.
North Pacific Oscillation/West Pacific Oscillation
The North Pacific Oscillation (NPO) is a recurring mode of variability in the extratropicalNorth Pacific region and is characterized bya north-south seesaw in sea level pressure.
Effects of NPO on U.S. hydroclimate andmarginal ice zone extent in the arctic seas havebeen reported.8
The NPO is linked to tropical sea surface
temperature variability. Specifically, NPO
contributes to the excitation of ENSO events
via the “Seasonal Footprinting Mechanism”.95,
96
In turn, warm events in the central tropical
Pacific Ocean are suggested to force an NPOlike circulation pattern.97 There is low confidence in future projections of the NPO due tothe small number of modeling studies as wellas the finding that many climate models do
not properly simulate the observed linkages
between the NPO and tropical sea surface
temperature variability.19, 98

U.S. Global Change Research Program

Pacific/North American Pattern
The Pacific/North American (PNA) pattern isthe leading recurring mode of internal atmospheric variability over the North Pacificand the North American continent, especiallyduring the cold season. It describes a quadripole pattern of mid-tropospheric heightanomalies, with anomalies of similar sign located over the subtropical northeastern Pacificand northwestern North America and of the
opposite sign centered over the Gulf of Alaska
and the southeastern United States. The PNA
pattern is associated with strong fluctuationsin the strength and location of the East Asianjet stream. The positive phase of the PNA
pattern is associated with above average temperatures over the western and northwestern
United States, and below average temperatures across the south-central and southeastern United States, including an enhanced
occurrence of extreme cold temperatures.9, 99,
100
Significant negative correlation between the
PNA and winter precipitation over the Ohio
River Valley has been documented.9, 99, 101 The
PNA is related to ENSO events102 and also
serves as a bridge linking ENSO and NAO
variability.103
Climate models are able to reasonably represent the atmospheric circulation and climate
anomalies associated with the PNA pattern.
However, individual models exhibit differences compared to the observed relationship, due
to displacements of the simulated PNA centersof action and offsets in their magnitudes.9 Climate models do not show consistent location
changes of the PNA centers due to increases
in GHGs.9, 72 Therefore, there is low confidence
for projected changes in the PNA and the association with temperature and precipitation
variations over the United States.
Blocking and Quasi-Stationary Waves
Anomalous atmospheric flow patterns in theextratropics that remain in place for an ex-

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Climate Science Special Report5 | Large-Scale Circulation and Climate Variability
tended period of time (for example, blocking
and quasi-stationary Rossby waves)—and
thus affect a region with similar weather conditions like rain or clear sky for several days
to weeks—can lead to flooding, drought, heatwaves, and cold waves.10, 104, 105 Specifically,
blocking describes large-scale, persistent highpressure systems that interrupt the typicalwesterly flow, while planetary waves (Rossby
waves) describe large-scale meandering of theatmospheric jet stream.
A persistent pattern of high pressure in thecirculation off the West Coast of the UnitedStates has been associated with the recent
multiyear California drought106, 107, 108 (Ch. 8:
Droughts, Floods, and Wildfire). Blocking in
the Alaskan region, which is enhanced during
La Niña winters (Figure 5.2),109 is associatedwith higher temperatures in western Alaska
but shift to lower mean and extreme surface
temperatures from the Yukon southward to
the southern Plains.110 The anomalously cold
winters of 2009–2010 and 2010–2011 in the
United States are linked to the blocked (ornegative) phase of the NAO.111 Stationary
Rossby wave patterns may have contributed
to the North American temperature extremes
during summers like 2011.112 It has been suggested that arctic amplification has already led
to weakened westerly winds and hence more
slowly moving and amplified wave patterns
and enhanced occurrence of blocking113, 114(Ch. 11: Arctic Changes). While some studies
suggest an observed increase in the metricsof these persistent circulation patterns,113, 115
other studies suggest that observed changes
are small compared to atmospheric internalvariability.116, 117, 118
A decrease of blocking frequency with climatechange is found in CMIP3, CMIP5, and higher-resolution models.19, 119, 120 Climate models
robustly project a change in Northern Hemisphere winter quasi-stationary wave fields

U.S. Global Change Research Programthat are linked to a wetting of the North American West Coast,45, 121, 122 due to a strengtheningof the zonal mean westerlies in the subtropicalupper troposphere. However, CMIP5 models
still underestimate observed blocking activity
in the North Atlantic sector while they tend
to overestimate activity in the North Pacific,
although with a large intermodel spread.19
Most climate models also exhibit biases in the
representation of relevant stationary waves.44
In summary, there is low confidence in projectedchanges in atmospheric blocking and wintertime quasi-stationary waves. Therefore, ourconfidence is low on the association between
observed and projected changes in weather
and climate extremes over the United Statesand variations in these persistent atmosphericcirculation patterns.
5.2.4 Modes of Variability on Decadal to Multidecadal Time Scales

Pacific Decadal Oscillation (PDO) / Interdecadal
Pacific Oscillation (IPO)The Pacific Decadal Oscillation (PDO) was
first introduced by Mantua et al. 1997123 as
the leading empirical orthogonal function of
North Pacific (20°–70°N) monthly averaged
sea surface temperature anomalies.14 Interdecadal Pacific Oscillation (IPO) refers to thesame phenomenon and is based on Pacific-wide sea surface temperatures. PDO/IPO
lacks a characteristic timescale and represents
a combination of physical processes that span
the tropics and extratropics, including both
remote tropical forcing and local North Pacificatmosphere–ocean interactions.14 Consequently, PDO-related variations in temperatureand precipitation in the United States arevery similar to (and indeed may be causedby) variations associated with ENSO and thestrength of the Aleutian low (North PacificIndex, NPI), as shown in Figure 5.3. A PDO-related temperature variation in Alaska is also
apparent.124, 125

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Cold Season Relationshipbetween Climate Indices and Precipitation/Temperature Anomalies
Nov-Mar Precipitation

Nov-Mar Temperature

a)

PDO

d)

PDO

b)

ENSO

e)

ENSO

c)

NPI

f)

NPI

Correlation Coefficient−0.6 −0.5 −0.4 −0.3 −0.2−0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6

Figure 5.3: Cold season relationship between climate indices and U.S. precipitation and temperature anomalies determined from U.S. climate division data,179 for the years 1901–2014. November–March mean U.S. precipitation anomalies correlated with (a) the Pacific Decadal Oscillation (PDO) index, (b) the El Niño–Southern Oscillation (ENSO) index,
and (c) the North Pacific Index (NPI). November–March U.S. temperature anomalies correlated with (d) the PDO index,(e) the ENSO index, and (f) the NPI. United States temperature and precipitation related to the Pacific Decadal Oscillation are very similar to (and indeed may be caused by) variations associated with ENSO and the Aleutian low strength(North Pacific Index). (Figure source: Newman et al. 201614; © American Meteorological Society, used with permission).

The PDO does not show a long-term trend
either in SST reconstructions or in the ensemble mean of historical CMIP3 and CMIP5
simulations.14 Emerging science suggests thatexternally forced natural and anthropogenic
factors have contributed to the observed PDOlike variability. For example, a model study
finds that the observed PDO phase is affectedby large volcanic events and the variability
in incoming solar radiation.126 Aerosols fromanthropogenic sources could change theU.S. Global Change Research Program

temporal variability of the North Pacific SST
through modifications of the atmospheric circulation.127, 128 Furthermore, some studies showthat periods with near-zero warming trendsof global mean temperature and periods ofaccelerated temperatures could result from theinterplay between internally generated PDO/
IPO-like temperature variations in the tropical
Pacific Ocean and greenhouse gas-induced
ocean warming.129, 130

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Climate Science Special Report5 | Large-Scale Circulation and Climate Variability
Future changes in the spatial and temporalcharacteristics of PDO/IPO are uncertain.
Based on CMIP3 models, one study finds thatmost of these models do not exhibit significant
changes,98 while another study points out that
the PDO/IPO becomes weaker and more frequent by the end of the 21st century in somemodels.131 Furthermore, future changes in
ENSO variability, which strongly contributes
to the PDO/IPO,132 are also uncertain (Section
5.2.2). Therefore, there is low confidence in projected future changes in the PDO/IPO.
Atlantic Multidecadal Variability (AMV) / Atlantic Multidecadal Oscillation (AMO)
The North Atlantic Ocean region exhibits
coherent multidecadal variability that exerts
measurable impacts on regional climate forvariables such as U.S. precipitation12, 133, 134, 135
and Atlantic hurricane activity.13, 136, 137, 138, 139,
140
This observed Atlantic multidecadal variability, or AMV, is generally understood tobe driven by a combination of internal andexternal factors.12, 141, 142, 143, 144, 145, 146, 147, 148 The
AMV manifests in SST variability and patterns as well as synoptic-scale variability of
atmospheric conditions. The internal part ofthe observed AMV is often referred to as the
Atlantic Multidecadal Oscillation (AMO) andis putatively driven by changes in the strengthof the Atlantic Meridional Overturning Circulation (AMOC).142, 143, 149, 150 It is important tounderstand the distinction between the AMO,
which is often assumed to be natural (because of its putative relationship with natural
AMOC variability), and AMV, which simply
represents the observed multidecadal variability as a whole.
The relationship between observed AMV
and the AMOC has recently been called into
question and arguments have been made thatAMV can occur in the absence of the AMOC
via stochastic forcing of the ocean by coherent
atmospheric circulation variability, but this is

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presently a topic of debate.151, 152, 153, 154 Despite
the ongoing debates, it is generally acknowledged that observed AMV, as a whole, represents a complex conflation of natural internal variability of the AMOC, natural red-noise
stochastic forcing of the ocean by the atmosphere,146 natural external variability fromvolcanic events155, 156 and mineral aerosols,157
and anthropogenic forcing from greenhousegases and pollution aerosols.158, 159, 160, 161
As also discussed in Chapter 9: Extreme
Storms (in the context of Atlantic hurricanes),
determining the relative contributions of eachmechanism to the observed multidecadal
variability in the Atlantic is presently an active
area of research and debate, and no consensus
has yet been reached.146, 161, 162, 163, 164, 165, 166 Still,
despite the level of disagreement about therelative magnitude of human influences (particularly whether natural or anthropogenic
factors are dominating), there is broad agreement in the literature of the past decade or
so that human factors have had a measurable
impact on the observed AMV. Furthermore,
the AMO, as measured by indices constructedfrom environmental data (e.g., Enfield et al.
200112), is generally based on detrended SST
data and is then, by construction, segregatedfrom the century-scale linear SST trends thatare likely forced by increasing greenhousegas concentrations. In particular, removal of alinear trend is not expected to account for allof the variability forced by changes in sulfate
aerosol concentrations that have occurred overthe past century. In this case, increasing sulfate
aerosols are argued to cause cooling of Atlantic SST, thus offsetting the warming caused byincreasing greenhouse gas concentration. After the Clean Air Act and Amendments of the
1970s, however, a steady reduction of sulfate
aerosols is argued to have caused SST warming that compounds the warming from theongoing increases in greenhouse gas concentrations.160, 161 This combination of greenhouse170

Climate Science Special Report5 | Large-Scale Circulation and Climate Variability
gas and sulfate aerosol forcing, by itself, canlead to Atlantic multidecadal SST variability
that would not be removed by removing alinear trend.155
In summary, it is unclear what the statistically derived AMO indices represent, and it is
not readily supportable to treat AMO index
variability as tacitly representing natural variability, nor is it clear that the observed AMV istruly oscillatory in nature.167 There is a physical basis for treating the AMOC as oscillatory(via thermohaline circulation arguments),168
but there is no expectation of true oscillatory
behavior in the hypothesized external forcing
agents for the remaining variability. Detrending the SST data used to construct the AMO
indices may partially remove the century-scale
trends forced by increasing greenhouse gas
concentrations, but it is not adequate forremoving multidecadal variability forced
by aerosol concentration variability. There isevidence that natural AMOC variability has
been occurring for hundreds of years,149, 169,
170, 171, 172
and this has apparently played somerole in the observed AMV as a whole, but agrowing body of evidence shows that external
factors, both natural and anthropogenic, haveplayed a substantial additional role in the past
century.

5.3 Quantifying the Role of Internal
Variability on Past and Future U.S. Climate
Trends
The role of internal variability in masking
trends is substantially increased on regional
and local scales relative to the global scale,and in the extratropics relative to the tropics(Ch. 4: Projections). Approaches have beendeveloped to better quantify the externallyforced and internally driven contributions
to observed and future climate trends andvariability and further separate these contributions into thermodynamically and dynamically driven factors.17 Specifically, large “initial

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condition” climate model ensembles with 30
ensemble members and more93, 173, 174 and longcontrol runs175 have been shown to be usefultools to characterize uncertainties in climatechange projections at local/regional scales.
North American temperature and precipitation trends on timescales of up to a few
decades are strongly affected by intrinsic
atmospheric circulation variability.17, 173 For example, it is estimated that internal circulation
trends account for approximately one-third of
the observed wintertime warming over North
America during the past 50 years. In a few
areas, such as the central Rocky Mountains
and far western Alaska, internal dynamics
have offset the warming trend by 10%–30%.17
Natural climate variability superimposed
upon forced climate change will result in alarge range of possible trends for surface airtemperature and precipitation in the UnitedStates over the next 50 years (Figure 5.4).173
Climate models are evaluated with respect
to their proper simulation of internal decadal
variability. Comparing observed and simulated variability estimates at timescales longer
than 10 years suggest that models tend tooverestimate the internal variability in thenorthern extratropics, including over thecontinental United States, but underestimate
it over much of the tropics and subtropicalocean regions.93, 176 Such biases affect signalto-noise estimates of regional scale climatechange response and thus assessment ofinternally driven contributions to regional/
local trends.

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Climate Science Special Report5 | Large-Scale Circulation and Climate Variabilitya) Winter surface air temperature and sea level pressure

Temperature Change (ºF)
−12 −10 −8 −6 −4 −2 0

2

4

6

8

10 12

b) Winter precipitation and sea level pressure

Precipitation Change (in)−0.05 −0.03 −0.02 −0.01 0.00 0.01 0.02 0.03 0.05
Figure 5.4: (left) Total 2010–2060 winter trends decomposed into (center) internal and (right) forced components for twocontrasting CCSM3 ensemble members (runs 29 and 6) for (a) surface air temperature [color shading; °F/(51 years)] andsea level pressure (SLP; contours) and (b) precipitation [color shading; inches per day/(51 years)] and SLP (contours).
SLP contour interval is 1 hPa/(51 years), with solid (dashed) contours for positive (negative) values; the zero contour isthickened. The same climate model (CCSM3) simulates a large range of possible trends in North American climate overthe 2010–2060 period because of the influence of internal climate variability superposed upon forced climate trends. (Figure source: adapted from Deser et al. 2014;173 © American Meteorological Society, used with permission).

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Climate Science Special Report5 | Large-Scale Circulation and Climate Variability

TRACEABLE ACCOUNTSKey Finding 1
The tropics have expanded poleward by about 70 to
200 miles in each hemisphere over the period 1979–
2009, with an accompanying shift of the subtropicaldry zones, midlatitude jets, and storm tracks (medium
to high confidence). Human activities have played a rolein this change (medium confidence), although confidence is presently low regarding the magnitude of thehuman contribution relative to natural variability
Description of evidence base
The Key Finding is supported by statements of the Intergovernmental Panel on Climate Change’s Fifth Assessment Report24 and a large number of more recent
studies that examined the magnitude of the observed
tropical widening and various causes.5, 20, 22, 23, 25, 26, 27, 28, 29,
31
Additional evidence for an impact of greenhouse gas
increases on the widening of the tropical belt and poleward shifts of the midlatitude jets is provided by the
diagnosis of CMIP5 simulations.4, 40 There is emerging
evidence for an impact of anthropogenic aerosols on
the tropical expansion in the Northern Hemisphere.32,
33
Recent studies provide new evidence on the significance of internal variability on recent changes in thetropical width.23, 34, 35
Major uncertainties
The rate of observed expansion of tropics depends onwhich metric is used. The linkages between different
metrics are not fully explored. Uncertainties also result
from the utilization of reanalysis to determine trends
and from limited observational records of free atmosphere circulation, precipitation, and evaporation. The
dynamical mechanisms behind changes in the widthof the tropical belt (e.g., tropical–extratropical interactions and baroclinic eddies) are not fully understood.
There is also a limited understanding of how various
climate forcings, such as anthropogenic aerosols, affectthe width of tropics. The coarse horizontal and vertical resolution of global climate models may limit theability of these models to properly resolve latitudinalchanges in the atmospheric circulation. Limited observational records affect the ability to accurately estimate

U.S. Global Change Research Program

the contribution of natural decadal to multi-decadal
variability on observed expansion of the tropics.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Medium to high confidence that the tropics and related
features of the global circulation have expanded poleward is based upon the results of a large number of observational studies, using a wide variety of metrics anddata sets, which reach similar conclusions. A large number of studies utilizing modeling of different complexity and theoretical considerations provide compounding evidence that human activities, including increases
in greenhouse gases, ozone depletion, and anthropogenic aerosols, contributed to the observed poleward
expansion of the tropics. Climate models forced withthese anthropogenic drivers cannot explain the observed magnitude of tropical expansion and somestudies suggest a possibly large contribution of internal variability. These multiple lines of evidence lead tothe conclusion of medium confidence that human activities contributed to observed expansion of the tropics.
Summary sentence or paragraph that integrates
the above information
The tropics have expanded poleward in each hemisphere over the period 1979–2009 (medium to highconfidence) as shown by a large number of studies
using a variety of metrics, observations and reanalysis. Modeling studies and theoretical considerations
illustrate that human activities, including increases ingreenhouse gases, ozone depletion, and anthropogenic aerosols, cause a widening of the tropics. There is
medium confidence that human activities have contributed to the observed poleward expansion, taking into
account uncertainties in the magnitude of observed
trends and a possible large contribution of natural climate variability.
Key Finding 2
Recurring patterns of variability in large-scale atmospheric circulation (such as the North Atlantic Oscilla-

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Climate Science Special Report5 | Large-Scale Circulation and Climate Variabilitytion and Northern Annular Mode) and the atmosphere–
ocean system (such as El Niño–Southern Oscillation)
cause year-to-year variations in U.S. temperatures andprecipitation (high confidence). Changes in the occurrence of these patterns or their properties have contributed to recent U.S. temperature and precipitation
trends (medium confidence), although confidence is
low regarding the size of the role of human activities inthese changes.
Description of evidence base
The Key Finding is supported by a large number ofstudies that diagnose recurring patterns of variability
and their changes, as well as their impact on climate
over the United States. Regarding year-to-year variations, a large number of studies based on models andobservations show statistically significant associations
between North Atlantic Oscillation/Northern Annular
Mode and United States temperature and precipitation,7, 9, 74, 75, 76, 88 as well as El Niño–Southern Oscillation
and related U.S. climate teleconnections.11, 50, 51, 52, 53, 56, 57
Regarding recent decadal trends, several studies provide evidence for concurrent changes in the North Atlantic Oscillation/Northern Annular Mode and climate
anomalies over the United States.81, 82, 83, 84 Modelingstudies provide evidence for a linkage between cooling trends of the tropical Pacific Ocean that resemble
La Niña and precipitation changes in the southern United States.54, 55 Several studies describe a decadal modification of ENSO.58, 59, 60, 63 Modeling evidence is provided
that such decadal modifications can be due to internalvariability.63 Climate models are widely analyzed fortheir ability to simulate recurring patterns of variability
and teleconnections over the United States.9, 64, 65, 68, 88, 98
Climate model projections are also widely analyzed todiagnose the impact of human activities on NAM/NAO,
ENSO teleconnections, and other recurring modes ofvariability associated with climate anomalies.9, 19, 72, 92
Major uncertainties
A key uncertainty is related to limited observational records and our capability to properly simulate climate
variability on decadal to multidecadal timescales, aswell as to properly simulate recurring patterns of climate variability, underlying physical mechanisms, andU.S. Global Change Research Programassociated variations in temperature and precipitation
over the United States.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is high confidence that preferred patterns of variability affect U.S. temperature on a year-to-year timescale, based on a large number of studies that diagnoseobservational data records and long simulations. Thereis medium confidence that changes in the occurrenceof these patterns or their properties have contributed
to recent U.S. temperature and precipitation trends.
Several studies agree on a linkage between decadalchanges in the NAO/NAM and climate trends over theUnited States, and there is some modeling evidence fora linkage between a La Niña-like cooling trend over thetropical Pacific and precipitation changes in the southwestern United States. There is no robust evidence forobserved decadal changes in the properties of ENSO
and related United States climate impacts. Confidence
is low regarding the size of the role of human influencesin these changes because models do not agree on theimpact of human activity on preferred patterns of variability or because projected changes are small compared to internal variability.
Summary sentence or paragraph that integrates
the above information
Recurring modes of variability strongly affect temperature and precipitation over the United States oninterannual timescales (high confidence) as supported
by a very large number of observational and modelingstudies. Changes in some recurring patterns of variability have contributed to recent trends in U.S. temperature and precipitation (medium confidence). The causesof these changes are uncertain due to the limited observational record and because models exhibit somedifficulties simulating these recurring patterns of variability and their underlying physical mechanisms.

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Temperature Changes in the
United States

KEY FINDINGS
1. Annual average temperature over the contiguous United States has increased by 1.2°F (0.7°C) for theperiod 1986–2016 relative to 1901–1960 and by 1.8°F (1.0°C) based on a linear regression for the period 1895–2016 (very high confidence). Surface and satellite data are consistent in their depiction of rapid
warming since 1979 (high confidence). Paleo-temperature evidence shows that recent decades are thewarmest of the past 1,500 years (medium confidence).
2. There have been marked changes in temperature extremes across the contiguous United States. The
frequency of cold waves has decreased since the early 1900s, and the frequency of heat waves has increased since the mid-1960s. The Dust Bowl era of the 1930s remains the peak period for extreme heat.
The number of high temperature records set in the past two decades far exceeds the number of low
temperature records. (Very high confidence)
3. Annual average temperature over the contiguous United States is projected to rise (very high confidence). Increases of about 2.5°F (1.4°C) are projected for the period 2021–2050 relative to 1976–2005 in
all RCP scenarios, implying recent record-setting years may be “common” in the next few decades(high confidence). Much larger rises are projected by late century (2071–2100): 2.8°–7.3°F (1.6°–4.1°C) in alower scenario (RCP4.5) and 5.8°–11.9°F (3.2°–6.6°C) in the higher scenario (RCP8.5) (high confidence).
4. Extreme temperatures in the contiguous United States are projected to increase even more than average temperatures. The temperatures of extremely cold days and extremely warm days are both expected to increase. Cold waves are projected to become less intense while heat waves will become more
intense. The number of days below freezing is projected to decline while the number above 90°F willrise. (Very high confidence)

Recommended Citation for Chapter
Vose, R.S., D.R. Easterling, K.E. Kunkel, A.N. LeGrande, and M.F. Wehner, 2017: Temperature changes in
the United States. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles,
D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change
Research Program, Washington, DC, USA, pp. 185-206, doi: 10.7930/J0N29V45.

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Introduction
Temperature is among the most important
climatic elements used in decision-making.
For example, builders and insurers use temperature data for planning and risk management while energy companies and regulators
use temperature data to predict demand andset utility rates. Temperature is also a key
indicator of climate change: recent increases
are apparent over the land, ocean, and troposphere, and substantial changes are expected
for this century. This chapter summarizes
the major observed and projected changes innear-surface air temperature over the UnitedStates, emphasizing new data sets and model
projections since the Third National Climate
Assessment (NCA3). Changes are depictedusing a spectrum of observations, including
surface weather stations, moored ocean buoys,
polar-orbiting satellites, and temperature-sensitive proxies. Projections are based on global
models and downscaled products from CMIP5(Coupled Model Intercomparison Project
Phase 5) using a suite of Representative Concentration Pathways (RCPs; see Ch. 4: Projections for more on RCPs and future scenarios).

about 1970, and increased rapidly through2016. Because the increase was not constantover time, multiple methods were evaluatedin this report (as in NCA3) to quantify thetrend. All methods yielded rates of warmingthat were significant at the 95% level. The lowest estimate of 1.2°F (0.7°C) was obtained bycomputing the difference between the averagefor 1986–2016 (i.e., present-day) and the average for 1901–1960 (i.e., the first half of the lastcentury). The highest estimate of 1.8°F (1.0°C)
was obtained by fitting a linear (least-squares)
regression line through the period 1895–2016.
Thus, the temperature increase cited in thisassessment is 1.2°–1.8°F (0.7°–1.0°C).

6.1.1 Average Temperatures

This increase is about 0.1°F (0.06°C) less thanpresented in NCA3, and it results from theuse of slightly different periods in each report.
In particular, the decline in the lower boundstems from the use of different time periods
to represent present-day climate (NCA3 used
1991–2012, which was slightly warmer than
the 1986–2016 period used here). The decline
in the upper bound stems mainly from temperature differences late in the record (e.g., thelast year of data available for NCA3 was 2012,
which was the warmest year on record for thecontiguous United States).

The annual average temperature of the contiguous United States has risen since the startof the 20th century. In general, temperature
increased until about 1940, decreased until

Each NCA region experienced a net warming
through 2016 (Table 6.1). The largest changes were in the western United States, where
average temperature increased by more than
1.5°F (0.8°C) in Alaska, the Northwest, theSouthwest, and also in the Northern Great
Plains. As noted in NCA3, the Southeast had
the least warming, driven by a combination ofnatural variations and human influences.8 In
most regions, average minimum temperature
increased at a slightly higher rate than average
maximum temperature, with the Midwest having the largest discrepancy, and the Southwest
and Northwest having the smallest. This differential rate of warming resulted in a continuing

6.1 Historical Changes
Changes in average temperature are describedusing a suite of observational datasets. As
in NCA3, changes in land temperature areassessed using the nClimGrid dataset.1, 2
Along U.S. coastlines, changes in sea surface
temperatures are quantified using a new reconstruction3 that forms the ocean componentof the NOAA Global Temperature dataset.4
Changes in middle tropospheric temperatureare examined using updated versions of multiple satellite datasets.5, 6, 7

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Table 6.1. Observed changes in annual average temperature (°F) for each
National Climate Assessment region. Changes are the difference between
the average for present-day (1986–2016) and the average for the first half ofthe last century (1901–1960 for the contiguous United States, 1925–1960 for
Alaska, Hawai‘i, and the Caribbean). Estimates are derived from the nClimDiv
dataset1,2.
NCA Region

Change in Annual
Change in Annual
Average
Average Minimum
Maximum
Temperature
Temperature

Change in Annual
Average
TemperatureContiguous U.S.

1.23°F

1.06°F

1.41°F

Northeast

1.43°F

1.16°F

1.70°FSoutheast

0.46°F

0.16°F

0.76°F

Midwest1.26°F

0.77°F

1.75°F

Great Plains North

1.69°F

1.66°F

1.72°F

Great Plains South

0.76°F

0.56°F

0.96°FSouthwest

1.61°F

1.61°F

1.61°F

Northwest

1.54°F

1.52°F

1.56°F

Alaska1.67°F

1.43°F

1.91°F

Hawaii

1.26°F

1.01°F

1.49°F

Caribbean

1.35°F

1.08°F

1.60°Fdecrease in the diurnal temperature range thatis consistent with other parts of the globe.9
Annual average sea surface temperature also
increased along all regional coastlines (see Figure 1.3), though changes were generally smaller
than over land owing to the higher heat capacity of water. Increases were largest in Alaska(greater than 1.0°F [0.6°C]) while increases were
smallest (less than 0.5°F [0.3°C]) in coastal areasof the Southeast.
More than 95% of the land surface of thecontiguous United States had an increase inannual average temperature (Figure 6.1). In
contrast, only small (and somewhat dispersed)
parts of the Southeast and Southern Great
Plains experienced cooling. From a seasonal
perspective, warming was greatest and mostwidespread in winter, with increases of over
1.5°F (0.8°C) in most areas. In summer, warming was less extensive (mainly along the East
Coast and in the western third of the Nation),while cooling was evident in parts of theSoutheast, Midwest, and Great Plains.

U.S. Global Change Research Program

There has been a rapid increase in the average temperature of the contiguous UnitedStates over the past several decades. Thereis general consistency on this point between
the surface thermometer record from NOAA1
and the middle tropospheric satellite records from Remote Sensing Systems (RSS),5NOAA’s Center for Satellite Applications
and Research (STAR),7 and the University of
Alabama in Huntsville (UAH).6 In particular,
for the period 1979–2016, the rate of warmingin the surface record was 0.512°F (0.284°C)
per decade, versus trends of 0.455°F (0.253°C),
0.421°F (0.234°C), and 0.289°F (0.160 °C) per
decade for RSS version 4, STAR version 3, andUAH version 6, respectively (after accounting
for stratospheric influences). All trends are
statistically significant at the 95% level. For thecontiguous United States, the year 2016 was
the second-warmest on record at the surface
and in the middle troposphere (2012 was thewarmest year at the surface, and 2015 was thewarmest in the middle troposphere). Generally speaking, surface and satellite records

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Climate Science Special Report6 | Temperature Changes in the United States

Figure 6.1. Observed changes in annual, winter, and summer temperature (°F). Changes are the difference between
the average for present-day (1986–2016) and the average for the first half of the last century (1901–1960 for the contiguous United States, 1925–1960 for Alaska and Hawai‘i). Estimates are derived from the nClimDiv dataset.1, 2 (Figuresource: NOAA/NCEI).

do not have identical trends because they
do not represent the same physical quantity;
surface measurements are made using thermometers in shelters about 1.5 meters above
the ground whereas satellite measurements
are mass-weighted averages of microwave
emissions from deep atmospheric layers. The
UAH record likely has a lower trend because
it differs from the other satellite products inthe treatment of target temperatures from the
NOAA-9 satellite as well as in the correction
for diurnal drift.10
Recent paleo-temperature evidence confirms
the unusual character of wide-scale warming
during the past few decades as determinedfrom the instrumental record. The most important new paleoclimate study since NCA3
showed that for each of the seven continental regions, the reconstructed area-weighted

U.S. Global Change Research Program

average temperature for 1971–2000 was higher
than for any other time in nearly 1,400 years,11
although with significant uncertainty aroundthe central estimate that leads to this conclusion. Recent (up to 2006) 30-year smoothed
temperatures across temperate North America (including most of the continental United States) are similarly reconstructed as thewarmest over the past 1,500 years12 (Figure6.2). Unlike the PAGES 2k seven-continent
result mentioned above, this conclusion for
North America is robust in relation to theestimated uncertainty range. Reconstruction
data since 1500 for western temperate North
America show the same conclusion at the annual time scale for 1986–2005. This time period
and the running 20-year periods thereafter are
warmer than all possible continuous 20-year
sequences in a 1,000-member statistical reconstruction ensemble.13

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Climate Science Special Report6 | Temperature Changes in the United States

Anomalies (°C)(wrt 1904–1980 average)

0.6
0.3
0.0
−0.3
−0.6
−0.9
480

pollen

600

D1200

750

Instr

900

1SE

2 SE

1904–1980 mean

2000–2006 mean

1050 1200 1350 1500 1650 1800 1950
Year

Figure 6.2. Pollen-based temperature reconstruction for temperate North America. The blue curve depicts the pollen-based reconstruction of 30-year averages (as anomalies from 1904 to 1980) for the temperate region (30°–55°N,
75°–130°W). The red curve shows the corresponding tree ring-based decadal average reconstruction, which wassmoothed and used to calibrate the lower-frequency pollen-based estimate. Light (medium) blue zones indicate 2
standard error (1 standard error) uncertainty estimations associated with each 30-year value. The black curve shows
comparably smoothed instrumental temperature values up to 1980. The dashed black line represents the average temperature anomaly of comparably smoothed instrumental data for the period 2000–2006. (Figure source: NOAA NCEI).

6.1.2 Temperature Extremes

Shifts in temperature extremes are examined using
a suite of societally relevant climate change indices14, 15 derived from long-term observationsof daily surface temperature.16 The coldest andwarmest temperatures of the year are of particular relevance given their widespread use
in engineering, agricultural, and other sectoral
applications (for example, extreme annual
design conditions by the American Society ofHeating, Refrigeration, and Air Conditioning;plant hardiness zones by the U.S. Departmentof Agriculture). Cold waves and heat waves(that is, extended periods of below or above
normal temperature) are likewise of great
importance because of their numerous societal
and environmental impacts, which span fromhuman health to plant and animal phenology. Changes are considered for a spectrumof event frequencies and intensities, ranging
from the typical annual extreme to the 1-in-10
year event (an extreme that only has a 10%
chance of occurrence in any given year). The
discussion focuses on the contiguous UnitedStates; Alaska, Hawai‘i, and the Caribbean

U.S. Global Change Research Program

do not have a sufficient number of long-term
stations for a century-scale analysis.
Cold extremes have become less severe overthe past century. For example, the coldest
daily temperature of the year has increased
at most locations in the contiguous UnitedStates (Figure 6.3). All regions experienced net
increases (Table 6.2), with the largest rises in
the Northern Great Plains and the Northwest(roughly 4.5°F [2.5°C]), and the smallest in
the Southeast (about 1.0°F [0.6°C]). In general,
there were increases throughout the record,with a slight acceleration in recent decades(Figure 6.3). The temperature of extremely cold days (1-in-10 year events) generally
exhibited the same pattern of increases as thecoldest daily temperature of the year. Consistent with these increases, the number ofcool nights per year (those with a minimum
temperature below the 10th percentile for
1961–1990) declined in all regions, with muchof the West having decreases of roughly two
weeks. The frequency of cold waves (6-day periods with a minimum temperature below the189

Climate Science Special Report6 | Temperature Changes in the United States
Change in Coldest Temperature of the Year
1986–2016 Average Minus 1901–1960 Average

Change in Warmest Temperature of the Year
1986–2016 Average Minus 1901–1960 Average

4
0
−4
−8

−12
1900

Difference (°F)<−6−6 to −4−4 to −2
−2 to 00 to 2
2 to 44 to 6>6Warmest Temperature (°F)

Coldest Temperature (°F)

Difference (°F)<−6−6 to −4−4 to −2
−2 to 00 to 2
2 to 44 to 6
>6

1920

1940

1960

1980

2000

2020

104
102
100
98
1900

1920

1940

1960

1980

2000

2020

Figure 6.3. Observed changes in the coldest and warmest daily temperatures (°F) of the year in the contiguous UnitedStates. Maps (top) depict changes at stations; changes are the difference between the average for present-day (1986–2016) and the average for the first half of the last century (1901–1960). Time series (bottom) depict the area-weighted
average for the contiguous United States. Estimates are derived from long-term stations with minimal missing data in
the Global Historical Climatology Network–Daily dataset.16 (Figure source: NOAA/NCEI).

Table 6.2. Observed changes in the coldest andwarmest daily temperatures (°F) of the year for each
National Climate Assessment region in the contiguousUnited States. Changes are the difference between
the average for present-day (1986–2016) and the average for the first half of the last century (1901–1960).
Estimates are derived from long-term stations withminimal missing data in the Global Historical Climatology Network–Daily dataset.16
NCA Region

Change in Coldest
Day of the Year

Change inWarmest Dayof the Year

Northeast

2.83°F−0.92°F

Southeast

1.13°F−1.49°F

Midwest2.93°F−2.22°F

Great Plains
North

4.40°F−1.08°F

Great PlainsSouth

3.25°F−1.07°F

Southwest

3.99°F

0.50°F

Northwest

4.78°F−0.17°F

U.S. Global Change Research Program

10th percentile for 1961–1990) has fallen overthe past century (Figure 6.4). The frequency ofintense cold waves (4-day, 1-in-5 year events)
peaked in the 1980s and then reached record-low levels in the 2000s.17
Changes in warm extremes are more nuanced
than changes in cold extremes. For instance, thewarmest daily temperature of the year increased
in some parts of the West over the past century(Figure 6.3), but there were decreases in almost
all locations east of the Rocky Mountains. In fact,
all eastern regions experienced a net decrease(Table 6.2), most notably the Midwest (about
2.2°F [1.2°C]) and the Southeast (roughly 1.5°F[0.8°C]). The decreases in the eastern half of Nation, particularly in the Great Plains, are mainly
tied to the unprecedented summer heat of the
1930s Dust Bowl era, which was exacerbatedby land-surface feedbacks driven by springtime

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Climate Science Special Report6 | Temperature Changes in the United States

Cold Spells (Days)

12
10
8
6
4
2
1900 1920 1940 1960 1980 2000 2020

Warm Spells (Days)

24
20
16
12
8
4

Heat Wave Magnitude
Index daily

1900 1920 1940 1960 1980 2000 2020

86421900 1920 1940 1960 1980 2000 2020

Figure 6.4. Observed changes in cold and heat waves in the contiguous United States. The top panel depicts changes
in the frequency of cold waves; the middle panel depicts changes in the frequency of heat waves; and the bottom panel
depicts changes in the intensity of heat waves. Cold and heat wave frequency indices are defined in Zhang et al.,15 andthe heat wave intensity index is defined in Russo et al.14 Estimates are derived from long-term stations with minimal
missing data in the Global Historical Climatology Network–Daily dataset.16 (Figure source: NOAA/NCEI).

precipitation deficits and land mismanagement.18
However, anthropogenic aerosol forcing may
also have reduced summer temperatures in the
Northeast and Southeast from the early 1950s to
the mid-1970s,19 and agricultural intensification
may have suppressed the hottest extremes in the
Midwest.20 Since the mid-1960s, there has been
only a very slight increase in the warmest daily
temperature of the year (amidst large interannual
variability). Heat waves (6-day periods with a

U.S. Global Change Research Programmaximum temperature above the 90th percentile for 1961–1990) increased in frequency until
the mid-1930s, became considerably less common through the mid-1960s, and increased infrequency again thereafter (Figure 6.4). As withwarm daily temperatures, heat wave magnitude
reached a maximum in the 1930s. The frequency
of intense heat waves (4-day, 1-in-5 year events)
has generally increased since the 1960s in mostregions except the Midwest and the Great

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Climate Science Special Report6 | Temperature Changes in the United States
Plains.17, 21 Since the early 1980s (Figure 6.4), there
is suggestive evidence of a slight increase in theintensity of heat waves nationwide14 as well as
an increase in the concurrence of droughts andheat waves.22

Ratio of Daily Temperature Records

Changes in the occurrence of record-setting
daily temperatures are also apparent. Verygenerally, the number of record lows has
been declining since the late-1970s while thenumber of record highs has been rising.23 By
extension, there has been an increase in theratio of the number of record highs to record
lows (Figure 6.5). Over the past two decades,
the average of this ratio exceeds two (meaningthat twice as many high-temperature records
have been set as low-temperature records).
The number of new highs has surpassed thenumber of new lows in 15 of the last 20 years,
with 2012 and 2016 being particularly extreme(ratios of seven and five, respectively).

7:1
6:1

6.2 Detection and Attribution
6.2.1 Average Temperatures

While a confident attribution of global temperature increases to anthropogenic forcing has been
made,24 detection and attribution assessment
statements for smaller regions are generally
much weaker. Nevertheless, some detectable
anthropogenic influences on average temperature have been reported for North America andparts of the United States (e.g., Christidis et al.
2010;25 Bonfils et al. 2008;26 Pierce et al. 200927).
Figure 6.6 shows an example for linear trends
for 1901–2015, indicating a detectable anthropogenic warming since 1901 over the western andnorthern regions of the contiguous United States
for the CMIP5 multimodel ensemble—a condition that was also met for most of the individualmodels.28 The Southeast stands out as the only
region with no “detectable” warming since 1901;
observed trends there were inconsistent with
CMIP5 All Forcing historical runs.28 The cause

More Daily Record Highs
More Daily Record Lows

5:1
4:1
3:1
2:1
1:1

1930 1940 1950 1960 1970 1980 1990 2000 2010
Year

Figure 6.5. Observed changes in the occurrence of record-setting daily temperatures in the contiguous United States.
Red bars indicate a year with more daily record highs than daily record lows, while blue bars indicate a year with morerecord lows than highs. The height of the bar indicates the ratio of record highs to lows (red) or of record lows to highs(blue). For example, a ratio of 2:1 for a blue bar means that there were twice as many record daily lows as daily record
highs that year. Estimates are derived from long-term stations with minimal missing data in the Global Historical Climatology Network–Daily dataset.16 (Figure source: NOAA/NCEI).

U.S. Global Change Research Program

192

Climate Science Special Report6 | Temperature Changes in the United Statesof Annual
SurfaceTrends
Temperature
Trends (1901–2015)
Assessment Assessmentof Annual Surface
Temperature(1901–2015)a) Observed trend (1901–2015)

4
2
1.5
1
0.25
0

b) CMIP5 ensemble trend (1901–2015)

−0.25
−0.5
−1
−1.5
−2
−4

°F / 100 years

0.5

Figure 6.6. Detection and attribution assessment of trends in annual
average temperature (°F). Grid-box
values indicate whether linear trends
for 1901–2015 are detectable (that is,distinct from natural variability) and/or consistent with CMIP5 historical
All-Forcing runs. If the grid-box trend
is found to be both detectable andeither consistent with or greater than
the warming in the All-Forcing runs,
then the grid box is assessed as having a detectable anthropogenic contribution to warming over the period.
Gray regions represent grid boxes
with data that are too sparse for detection
and attribution. (Figure source: updated
from Knutson et al. 2013;28 © American
Meteorological Society. Used withpermission.)c) Summary trend assessment

Detectable anthro. increase,greater than modeled
Detectable anthro. increase,consistent with model
Detectable increase,less than modeled
No detectable trend; white
dots: consistent with model
White stippling:Obs. Consistent with All-Forcing
Insufficient dataof this “warming hole,” or lack of a long-termwarming trend, remains uncertain, though it
is likely a combination of natural and humancauses. Some studies conclude that changes inanthropogenic aerosols have played a crucialrole (e.g., Leibensperger et al. 2012;29, 30 Yu et al.
201431), whereas other studies infer a possible
large role for atmospheric circulation,32 internal
climate variability (e.g., Meehl et al. 2012;8 Knutson et al. 201328), and changes in land use (e.g.,Goldstein et al. 2009;33 Xu et al. 201534). Notably,
the Southeast has been warming rapidly since
the early 1960s.35, 36 In summary, there is medium
confidence for detectable anthropogenic warming over the western and northern regions of thecontiguous United States.

U.S. Global Change Research Program

6.2.2 Temperature Extremes

The Intergovernmental Panel on Climate
Change’s (IPCC’s) Fifth Assessment Report(AR5)24 concluded that it is very likely that human influence has contributed to the observedchanges in frequency and intensity of temperature extremes on the global scale since themid-20th century. The combined influence ofanthropogenic and natural forcings was also
detectable (medium confidence) over large
subregions of North America (e.g., Zwiers etal. 2011;37 Min et al. 201338). In general, however, results for the contiguous United States
are not as compelling as for global land areas,in part because detection of changes in U.S.
regional temperature extremes is affected by

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Climate Science Special Report6 | Temperature Changes in the United States
extreme temperature in the 1930s.17 Table 6.3
summarizes available attribution statements
for recent extreme U.S. temperature events.
As an example, the recent record or near-record high March–May average temperatures
occurring in 2012 over the eastern UnitedStates were attributed in part to external(natural plus anthropogenic) forcing;39 thecentury-scale trend response of temperature
to external forcing is typically a close approximation to the anthropogenic forcing response
alone. Another study found that although theextreme March 2012 warm anomalies over theUnited States were mostly due to natural variability, anthropogenic warming contributed tothe severity.40 Such statements reveal that bothnatural and anthropogenic factors influence
the severity of extreme temperature events.
Nearly every modern analysis of current extreme hot and cold events reveals some degreeof attributable human influence.

6.3 Projected Changes
6.3.1 Average Temperatures

Temperature projections are based on global model results and associated downscaled
products from CMIP5 using a suite of Representative Concentration Pathways (RCPs).
In contrast to NCA3, model weighting is
employed to refine projections of temperaturefor each RCP (Ch. 4: Projections; Appendix
B: Model Weighting). Weighting parameters
are based on model independence and skill
over North America for seasonal temperature
and annual extremes. Unless stated otherwise, all changes presented here represent theweighted multimodel mean. The weighting
scheme helps refine confidence and likelihood
statements, but projections of U.S. surface airtemperature remain very similar to those inNCA3. Generally speaking, extreme temperatures are projected to increase even more than
average temperatures.41

Table 6.3. Extreme temperature events in the United States for which attribution statements havebeen made. There are three possible attribution statements: “+” shows an attributable human-induced
increase in frequency or intensity, “−” shows an attributable human-induced decrease in frequency orintensity, “0” shows no attributable human contribution.
Study

PeriodRegionTypeStatement

52

Rupp et al. 2012
Angélil et al. 201753

Spring/Summer
2011

Texas

Hot

+
+

Hoerling et al. 201354

Summer 2011

Texas

Hot

+

Diffenbaugh and Scherer 2013
Angélil et al. 201753

July 2012

Northcentral
and Northeast

Hot

+
+

Cattiaux and Yiou 201356
Angélil et al. 201753

Spring 2012

East

Hot

0
+

Knutson et al. 2013b39
Angélil et al. 201753

Spring 2012

East

Hot

+
+

Jeon et al 201657

Summer 2011

Texas/
Oklahoma

Hot+

Dole et al. 2014 40

March 2012

Upper
MidwestHot+

Seager et al. 201458

2011–2014

California

Hot

+

59

Wolter et al. 2015

Winter 2014

MidwestCold−

Trenary et al. 2015

60

Winter 2014

East

Cold

0

55

U.S. Global Change Research Program

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Climate Science Special Report6 | Temperature Changes in the United States
The annual average temperature of the contiguous United States is projected to rise throughout
the century. Increases for the period 2021–2050
relative to 1976–2005 are projected to be about
2.5°F (1.4°C) for a lower scenario (RCP4.5) and
2.9°F (1.6°C) for the higher scenario (RCP8.5);
the similarity in warming reflects the similarity
in greenhouse gas concentrations during thisperiod (Figure 4.1). Notably, a 2.5°F (1.4°C) increase makes the near-term average comparable
to the hottest year in the historical record (2012).
In other words, recent record-breaking years
may be “common” in the next few decades. Bylate-century (2071–2100), the RCPs diverge significantly, leading to different rates of warming:
approximately 5.0°F (2.8°C) for RCP4.5 and 8.7°F(4.8°C) for RCP8.5. Likewise, there are different
ranges of warming for each scenario: 2.8°–7.3°F(1.6°–4.1°C) for RCP4.5 and 5.8°–11.9°F (3.2°–
6.6°C) for RCP8.5. (The range is defined here as
the difference between the average increase in
the three coolest models and the average increasein the three warmest models.) For both RCPs,
slightly greater increases are projected in summer than winter (except for Alaska), and average
maximums will rise slightly faster than averageminimums (except in the Southeast and Southern Great Plains).

U.S. Global Change Research Program

Statistically significant warming is projected
for all parts of the United States throughout
the century (Figure 6.7). Consistent with polar
amplification, warming rates (and spatial
gradients) are greater at higher latitudes. For
example, warming is largest in Alaska (morethan 12.0°F [6.7°C] in the northern half of thestate by late-century under RCP8.5), driven inpart by a decrease in snow cover and thus surface albedo. Similarly, northern regions of thecontiguous United States have slightly more
warming than other regions (roughly 9.0°F[5.5°C] in the Northeast, Midwest, and Northern Great Plains by late-century under RCP8.5;
Table 6.4). The Southeast has slightly lesswarming because of latent heat release fromincreases in evapotranspiration (as is alreadyevident in the observed record). Warming issmallest in Hawai‘i and the Caribbean (roughly 4.0°–6.0°F [2.2°–3.3°C] by late century under
RCP8.5) due to the moderating effects ofsurrounding oceans. From a sub-regional perspective, less warming is projected along thecoasts of the contiguous United States, again
due to maritime influences, although increases
are still substantial. Warming at higher elevations may be underestimated because the resolution of the CMIP5 models does not capture
orography in detail.

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Climate Science Special Report6 | Temperature Changes in the United States
Projected Changes
Projected
Changes ininAnnual
AnnualAverage
AverageTemperature
Temperature
Mid 21st Century
Lower Scenario (RCP4.5)

Higher Scenario (RCP8.5)

Late 21st Century
Lower Scenario (RCP4.5)

Higher Scenario (RCP8.5)

Change in Temperature (°F)
2

4

6

8

10 12

14 16

18

Figure 6.7. Projected changes in annual average temperatures (°F). Changes are the difference between the averagefor mid-century (2036–2065; top) or late-century (2070–2099, bottom) and the average for near-present (1976–2005).
Each map depicts the weighted multimodel mean. Increases are statistically significant in all areas (that is, more than
50% of the models show a statistically significant change, and more than 67% agree on the sign of the change45). (Figure source: CICS-NC and NOAA NCEI).

U.S. Global Change Research Program

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Climate Science Special Report6 | Temperature Changes in the United States
Table 6.4. Projected changes in annual average temperature (°F) for each National Climate Assessment region in
the contiguous United States. Changes are the difference between the average for mid-century (2036–2065) orlate-century (2071–2100) and the average for near-present (1976–2005) under the higher scenario (RCP8.5) anda lower scenario (RCP4.5). Estimates are derived from 32 climate models that were statistically downscaled using
the Localized Constructed Analogs technique.51 Increases are statistically significant in all areas (that is, more than
50% of the models show a statistically significant change, and more than 67% agree on the sign of the change45).
NCA Region

RCP4.5
Mid-Century(2036–2065)

RCP8.5
Mid-Century(2036–2065)

RCP4.5Late-Century(2071–2100)

RCP8.5Late-Century(2071–2100)

Northeast

3.98°F

5.09°F

5.27°F

9.11°FSoutheast

3.40°F

4.30°F

4.43°F

7.72°F

Midwest4.21°F

5.29°F

5.57°F

9.49°F

Great Plains North

4.05°F

5.10°F

5.44°F

9.37°F

Great Plains South

3.62°F

4.61°F

4.78°F

8.44°FSouthwest

3.72°F

4.80°F

4.93°F

8.65°F

Northwest

3.66°F

4.67°F

4.99°F

8.51°F

6.3.2 Temperature Extremes

Daily extreme temperatures are projected toincrease substantially in the contiguous United States, particularly under the higher scenario (RCP8.5). For instance, the coldest andwarmest daily temperatures of the year are expected to increase at least 5°F (2.8°C) in mostareas by mid-century,42 rising to 10°F (5.5°C) or
more by late-century.43 In general, there will belarger increases in the coldest temperatures ofthe year, especially in the northern half of the
Nation, whereas the warmest temperatures
will exhibit somewhat more uniform changes
geographically (Figure 6.8). By mid-century,
the upper bound for projected changes (i.e.,
the average of the three warmest models) is
about 2°F (1.1°C) greater than the weighted
multimodel mean. On a regional basis, annual
extremes (Table 6.5) are consistently projected
to rise faster than annual averages (Table 6.4).
Future changes in “very rare” extremes arealso striking; by late century, current 1-in-20
year maximums are projected to occur every
year, while current 1-in-20 year minimums are
not expected to occur at all.44
The frequency and intensity of cold waves is
projected to decrease while the frequency andU.S. Global Change Research Program

intensity of heat waves is projected to increase
throughout the century. The frequency of cold
waves (6-day periods with a minimum temperature below the 10th percentile) will decrease the most in Alaska and the least in the
Northeast while the frequency of heat waves(6-day periods with a maximum temperature
above the 90th percentile) will increase in all
regions, particularly the Southeast, Southwest,
and Alaska. By mid-century, decreases in thefrequency of cold waves are similar across
RCPs whereas increases in the frequency ofheat waves are about 50% greater in the higher scenario (RCP8.5) than the lower scenario(RCP4.5).45 The intensity of cold waves is projected to decrease while the intensity of heatwaves is projected to increase, dramatically sounder RCP8.5. By mid-century, both extreme
cold waves and extreme heat waves (5-day,
1-in-10 year events) are projected to havetemperature increases of at least 11.0°F (6.1°C)
nationwide, with larger increases in northern
regions (the Northeast, Midwest, Northern
Great Plains, and Northwest; Table 6.5).
There are large projected changes in the number of days exceeding key temperature thresholds throughout the contiguous United States.

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Climate Science Special Report6 | Temperature Changes in the United States

Figure 6.8. Projected changes in the coldest and warmest daily temperatures (°F) of the year in the contiguousUnited States. Changes are the difference between the average for mid-century (2036–2065) and the average fornear-present (1976–2005) under the higher scenario (RCP8.5). Maps in the top row depict the weighted multimodel
mean whereas maps on the bottom row depict the mean of the three warmest models (that is, the models with thelargest temperature increase). Maps are derived from 32 climate model projections that were statistically downscaled using the Localized Constructed Analogs technique.51 Increases are statistically significant in all areas (thatis, more than 50% of the models show a statistically significant change, and more than 67% agree on the sign of thechange45). (Figure source: CICS-NC and NOAA NCEI).

Table 6.5. Projected changes in temperature extremes (˚F) for each National Climate Assessment region in thecontiguous United States. Changes are the difference between the average for mid-century (2036–2065) and theaverage for near-present (1976–2005) under the higher scenario (RCP8.5). Estimates are derived from 32 climate
models that were statistically downscaled using the Localized Constructed Analogs technique.51 Increases are statistically significant in all areas (that is, more than 50% of the models show a statistically significant change, andmore than 67% agree on the sign of the change45).
NCA Region

Change in Coldest
Day of the Year

Change in Coldest
5-Day 1-in-10 Year
Event

Change in Warmest
Day of the Year

Change in Warmest 5-Day
1-in-10 Year Event

Northeast

9.51°F15.93°F

6.51°F12.88°F

Southeast

4.97°F

8.84°F

5.79°F11.09°F

Midwest9.44°F15.52°F

6.71°F13.02°F

Great Plains North

8.01°F12.01°F

6.48°F12.00°F

Great Plains South

5.49°F

9.41°F

5.70°F10.73°F

Southwest

6.13°F10.20°F

5.85°F11.17°F

Northwest

7.33°F10.95°F

6.25°F12.31°F

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For instance, there are about 20–30 more days
per year with a maximum over 90°F (32°C)
in most areas by mid-century under RCP8.5,
with increases of 40–50 days in much of theSoutheast (Figure 6.9). The upper bound for
projected changes is very roughly 10 days
greater than the weighted multimodel mean.
Consistent with widespread warming, there

are 20–30 fewer days per year with a minimum temperature below freezing in the northern and eastern parts of the nation, with decreases of more than 40–50 days in much theWest. The upper bound for projected changes
in freezing events is very roughly 10–20 days
fewer than the weighted multimodel mean inmany areas.

Projected Change in Number of Days Above 90°F
Mid 21st Century, Higher Scenario (RCP8.5)

Projected Change in Number of Days Below 32°F
Mid 21st Century, Higher Scenario (RCP8.5)

Weighted Multi-Model Mean

Weighted Multi-Model Mean

0

10

20

30

40

50

60

70

−70 −60 −50 −40 −30 −20 −10

Mean of Three Warmest Models

Mean of Three Warmest Models
0

10

20

30

40

50

60

0

−70 −60 −50 −40 −30 −20 −1070

0Figure 6.9. Projected changes in the number of days per year with a maximum temperature above 90°F and a minimum temperature below 32°F in the contiguous United States. Changes are the difference between the average formid-century (2036–2065) and the average for near-present (1976–2005) under the higher scenario (RCP8.5). Mapsin the top row depict the weighted multimodel mean whereas maps on the bottom row depict the mean of the threewarmest models (that is, the models with the largest temperature increase). Maps are derived from 32 climate model
projections that were statistically downscaled using the Localized Constructed Analogs technique.51 Changes are statistically significant in all areas (that is, more than 50% of the models show a statistically significant change, and morethan 67% agree on the sign of the change45). (Figure source: CICS-NC and NOAA NCEI).

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TRACEABLE ACCOUNTSKey Finding 1 Annual average temperature over thecontiguous United States has increased by 1.2°F (0.7°C)
for the period 1986–2016 relative to 1901–1960 and by
1.8°F (1.0°C) based on a linear regression for the period
1895–2016 (very high confidence). Surface and satellite
data are consistent in their depiction of rapid warming
since 1979 (high confidence). Paleo-temperature evidence shows that recent decades are the warmest of
the past 1,500 years (medium confidence).
Description of Evidence Base
The key finding and supporting text summarize extensive evidence documented in the climate science literature. Similar statements about changes exist in otherreports (e.g., NCA3;46 Global Climate Change Impacts in
the United States;47 SAP 1.1: Temperature trends in thelower atmosphere48).
Evidence for changes in U.S. climate arises from multiple analyses of data from in situ, satellite, and otherrecords undertaken by many groups over several decades. The primary dataset for surface temperaturesin the United States is nClimGrid,1, 2 though trends
are similar in the U.S. Historical Climatology Network,
the Global Historical Climatology Network, and otherdatasets. Several atmospheric reanalyses (e.g., 20th
Century Reanalysis, Climate Forecast System Reanalysis, ERA-Interim, Modern Era Reanalysis for Researchand Applications) confirm rapid warming at the surface
since 1979, with observed trends closely tracking theensemble mean of the reanalyses. Several recently improved satellite datasets document changes in middle
tropospheric temperatures.5, 6, 7 Longer-term changes
are depicted using multiple paleo analyses (e.g., Wahl
and Smerdon 2012;13 Trouet et al. 201312).
Major Uncertainties
The primary uncertainties for surface data relate tohistorical changes in station location, temperature
instrumentation, observing practice, and spatial sampling (particularly in areas and periods with low station
density, such as the intermountain West in the early
20th century). Satellite records are similarly impacted
by non-climatic changes such as orbital decay, diurnal

U.S. Global Change Research Program

sampling, and instrument calibration to target temperatures. Several uncertainties are inherent in temperature-sensitive proxies, such as dating techniquesand spatial sampling.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Very high (since 1895), High (for surface/satellite agreement since 1979), Medium (for paleo)
Likelihood of Impact
Extremely Likely
Summary sentence or paragraph that integrates
the above information
There is very high confidence in observed changes in average temperature over the United States based upon
the convergence of evidence from multiple data sources, analyses, and assessments.
Key Finding 2
There have been marked changes in temperature extremes across the contiguous United States. The frequency of cold waves has decreased since the early
1900s, and the frequency of heat waves has increased
since the mid-1960s. The Dust Bowl era of the 1930s remains the peak period for extreme heat. The number ofhigh temperature records set in the past two decades
far exceeds the number of low temperature records.
(Very high confidence)
Description of Evidence Base
The key finding and supporting text summarize extensive evidence documented in the climate science literature. Similar statements about changes have also been
made in other reports (e.g., NCA3;46 SAP 3.3: Weather andClimate Extremes in a Changing Climate;49 IPCC Special
Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation50).
Evidence for changes in U.S. climate arises from multiple analyses of in situ data using widely publishedclimate extremes indices. For the analyses presented

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here, the source of in situ data is the Global Historical
Climatology Network–Daily dataset,16 with changes
in extremes being assessed using long-term stations
with minimal missing data to avoid network-induced
variability on the long-term time series. Cold wave frequency was quantified using the Cold Spell Duration
Index,15 heat wave frequency was quantified using theWarm Spell Duration Index,15 and heat wave intensity
were quantified using the Heat Wave Magnitude Index
Daily.14 Station-based index values were averaged into
4° grid boxes, which were then area-averaged into atime series for the contiguous United States. Note thata variety of other threshold and percentile-based indices were also evaluated, with consistent results (e.g.,
the Dust Bowl was consistently the peak period for extreme heat). Changes in record-setting temperatures
were quantified as in Meehl et al. (2016).23
Major Uncertainties
The primary uncertainties for in situ data relate to historical changes in station location, temperature instrumentation, observing practice, and spatial sampling(particularly the precision of estimates of change inareas and periods with low station density, such as theintermountain West in the early 20th century).
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Very highLikelihood of Impact
Extremely likely
Summary sentence or paragraph that integrates
the above information
There is very high confidence in observed changes intemperature extremes over the United States based
upon the convergence of evidence from multiple data
sources, analyses, and assessments.
Key Finding 3
Annual average temperature over the contiguousUnited States is projected to rise (very high confidence). Increases of about 2.5°F (1.4°C) are projected

U.S. Global Change Research Program

for the period 2021–2050 relative to 1976–2005 in all
RCP scenarios, implying recent record-setting years
may be “common” in the next few decades (high confidence). Much larger rises are projected by late century(2071–2100): 2.8°–7.3°F (1.6°–4.1°C) in a lower scenario(RCP4.5) and 5.8°–11.9°F (3.2°–6.6°C) in a higher scenario (RCP8.5) (high confidence).
Description of Evidence Base
The key finding and supporting text summarize extensive evidence documented in the climate science
literature. Similar statements about changes have also
been made in other reports (e.g., NCA3;46 Global Climate
Change Impacts in the United States47). The basic physics
underlying the impact of human emissions on climate
has also been documented in every IPCC assessment.
Projections are based on global model results and associated downscaled products from CMIP5 for RCP4.5(lower scenario) and RCP8.5 (higher scenario). Model
weighting is employed to refine projections for each
RCP. Weighting parameters are based on model independence and skill over North America for seasonaltemperature and annual extremes. The multimodel
mean is based on 32 model projections that were statistically downscaled using the Localized Constructed
Analogs technique.51 The range is defined as the difference between the average increase in the three coolest
models and the average increase in the three warmest
models. All increases are significant (i.e., more than 50%of the models show a statistically significant change,
and more than 67% agree on the sign of the change45).
Major Uncertainties
Global climate models are subject to structural andparametric uncertainty, resulting in a range of estimates of future changes in average temperature. This ispartially mitigated through the use of model weighting
and pattern scaling. Furthermore, virtually every ensemble member of every model projection contains anincrease in temperature by mid- and late-century. Empirical downscaling introduces additional uncertainty(e.g., with respect to stationarity).

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Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Very high for projected change in annual average temperature; high confidence for record-setting years becoming the norm in the near future; high confidence
for much larger temperature increases by late century
under a higher scenario (RCP8.5).
Likelihood of Impact
Extremely likely
Summary sentence or paragraph that integrates
the above information
There is very high confidence in projected changes in average temperature over the United States based upon
the convergence of evidence from multiple model simulations, analyses, and assessments.
Key Finding 4
Extreme temperatures in the contiguous United States
are projected to increase even more than average temperatures. The temperatures of extremely cold days andextremely warm days are both expected to increase.
Cold waves are projected to become less intense while
heat waves will become more intense. The number ofdays below freezing is projected to decline while thenumber above 90°F will rise. (Very high confidence)
Description of Evidence Base
The key finding and supporting text summarize extensive evidence documented in the climate science literature (e.g., Fischer et al. 2013;42 Sillmann et al. 2013;43
Wuebbles et al. 2014;44 Sun et al. 201545). Similar statements about changes have also been made in othernational assessments (such as NCA3) and in reports by
the Climate Change Science Program (such as SAP 3.3:
Weather and Climate Extremes in a Changing Climate49).

temperature and annual extremes. The multimodel
mean is based on 32 model projections that were statistically downscaled using the Localized Constructed
Analogs technique.51 Downscaling improves on thecoarse model output, establishing a more geographically accurate baseline for changes in extremes and thenumber of days per year over key thresholds. The upper bound for projected changes is the average of thethree warmest models. All increases are significant (i.e.,
more than 50% of the models show a statistically significant change, and more than 67% agree on the signof the change45).
Major Uncertainties
Global climate models are subject to structural andparametric uncertainty, resulting in a range of estimates of future changes in temperature extremes. Thisis partially mitigated through the use of model weighting and pattern scaling. Furthermore, virtually every
ensemble member of every model projection contains
an increase in temperature by mid- and late-century.
Empirical downscaling introduces additional uncertainty (e.g., with respect to stationarity).
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Very highLikelihood of Impact
Extremely likely
Summary Sentence
There is very high confidence in projected changes intemperature extremes over the United States based
upon the convergence of evidence from multiple model simulations, analyses, and assessments.

Projections are based on global model results and associated downscaled products from CMIP5 for RCP4.5(lower scenario) and RCP8.5 (higher scenario). Model
weighting is employed to refine projections for each
RCP. Weighting parameters are based on model independence and skill over North America for seasonal

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41. Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T.
Fichefet, P. Friedlingstein, X. Gao, W.J. Gutowski,
T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J.
Weaver, and M. Wehner, 2013: Long-term climatechange: Projections, commitments and irreversibility.
Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K.
Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M.
Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,1029–1136. http://www.climatechange2013.org/report/full-report/
42. Fischer, E.M., U. Beyerle, and R. Knutti, 2013: Robust
spatially aggregated projections of climate extremes.
Nature Climate Change, 3, 1033-1038. http://dx.doi.
org/10.1038/nclimate2051
43. Sillmann, J., V.V. Kharin, F.W. Zwiers, X. Zhang, and
D. Bronaugh, 2013: Climate extremes indices in the
CMIP5 multimodel ensemble: Part 2. Future climate
projections. Journal of Geophysical Research Atmospheres, 118, 2473-2493. http://dx.doi.org/10.1002/
jgrd.50188
44. Wuebbles, D., G. Meehl, K. Hayhoe, T.R. Karl, K.
Kunkel, B. Santer, M. Wehner, B. Colle, E.M. Fischer,
R. Fu, A. Goodman, E. Janssen, V. Kharin, H. Lee, W.
Li, L.N. Long, S.C. Olsen, Z. Pan, A. Seth, J. Sheffield,
and L. Sun, 2014: CMIP5 climate model analyses:
Climate extremes in the United States. Bulletin of theAmerican Meteorological Society, 95, 571-583. http://dx.doi.org/10.1175/BAMS-D-12-00172.1
45. Sun, L., K.E. Kunkel, L.E. Stevens, A. Buddenberg,
J.G. Dobson, and D.R. Easterling, 2015: Regional Surface Climate Conditions in CMIP3 and CMIP5 for
the United States: Differences, Similarities, and Implications for the U.S. National Climate Assessment.
NOAA Technical Report NESDIS 144. National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service,
111 pp. http://dx.doi.org/10.7289/V5RB72KG
46. Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds.,
2014: Climate Change Impacts in the United States:
The Third National Climate Assessment. U.S. Global
Change Research Program: Washington, D.C., 841
pp. http://dx.doi.org/10.7930/J0Z31WJ2
47. Karl, T.R., J.T. Melillo, and T.C. Peterson, eds., 2009:
Global Climate Change Impacts in the United States.
Cambridge University Press: New York, NY, 189 pp.
http://downloads.globalchange.gov/usimpacts/
pdfs/climate-impacts-report.pdf

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48. CCSP, 2006: Temperature Trends in the Lower Atmosphere: Steps for Understanding and Reconciling Differences. A Report by the U.S. Climate Change Science
Program and the Subcommittee on Global Change Research. National Oceanic and Atmospheric Administration, Washington, D.C., 164 pp. http://www.
globalchange.gov/browse/reports/sap-11-temperature-trends-lower-atmosphere-steps-understanding-reconciling
49. CCSP, 2008: Weather and Climate Extremes in a Changing Climate - Regions of Focus - North America, Hawaii,
Caribbean, and U.S. Pacific Islands. A Report by the U.S.
Climate Change Science Program and the Subcommittee
on Global Change Research. Karl, T.R., G.A. Meehl,
C.D. Miller, S.J. Hassol, A.M. Waple, and W.L. Murray, Eds. Department of Commerce, NOAA’s National Climatic Data Center, Washington, D.C., 164 pp.
http://downloads.globalchange.gov/sap/sap3-3/
sap3-3-final-all.pdf
50. IPCC, 2012: Managing the Risks of Extreme Eventsand Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II
of the Intergovernmental Panel on Climate Change.
Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K.
Plattner, S.K. Allen, M. Tignor, and P.M. Midgley(Eds.). Cambridge University Press, Cambridge, UK
and New York, NY. 582 pp. https://www.ipcc.ch/
pdf/special-reports/srex/SREX_Full_Report.pdf
51. Pierce, D.W., D.R. Cayan, and B.L. Thrasher, 2014:
Statistical downscaling using Localized Constructed Analogs (LOCA). Journal of Hydrometeorology, 15, 2558-2585. http://dx.doi.org/10.1175/
jhm-d-14-0082.1
52. Rupp, D.E., P.W. Mote, N. Massey, C.J. Rye, R. Jones,
and M.R. Allen, 2012: Did human influence on climate make the 2011 Texas drought more probable? [in“Explaining Extreme Events of 2011 from a Climate
Perspective”]. Bulletin of the American MeteorologicalSociety, 93, 1052-1054. http://dx.doi.org/10.1175/
BAMS-D-12-00021.1
53. Angélil, O., D. Stone, M. Wehner, C.J. Paciorek, H.
Krishnan, and W. Collins, 2017: An independent assessment of anthropogenic attribution statements forrecent extreme temperature and rainfall events. Journal of Climate, 30, 5-16. http://dx.doi.org/10.1175/
JCLI-D-16-0077.1

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54. Hoerling, M., M. Chen, R. Dole, J. Eischeid, A. Kumar, J.W. Nielsen-Gammon, P. Pegion, J. Perlwitz,
X.-W. Quan, and T. Zhang, 2013: Anatomy of an extreme event. Journal of Climate, 26, 2811–2832. http://dx.doi.org/10.1175/JCLI-D-12-00270.1
55. Diffenbaugh, N.S. and M. Scherer, 2013: Likelihoodof July 2012 U.S. temperatures in pre-industrial andcurrent forcing regimes [in “Explaining Extreme
Events of 2013 from a Climate Perspective”]. Bulletin of the American Meteorological Society, 94 (9), S6-S9.
http://dx.doi.org/10.1175/BAMS-D-13-00085.1
56. Cattiaux, J. and P. Yiou, 2013: U.S. heat waves ofspring and summer 2012 from the flow analogue
perspective [in “Explaining Extreme Events of 2012
from a Climate Perspective”]. Bulletin of the American
Meteorological Society, 94 (9), S10-S13. http://dx.doi.
org/10.1175/BAMS-D-13-00085.1
57. Jeon, S., C.J. Paciorek, and M.F. Wehner, 2016: Quantile-based bias correction and uncertainty quantification of extreme event attribution statements. Weather and Climate Extremes, 12, 24-32. http://dx.doi.
org/10.1016/j.wace.2016.02.001
58. Seager, R., M. Hoerling, D.S. Siegfried, h. Wang, B.
Lyon, A. Kumar, J. Nakamura, and N. Henderson,
2014: Causes and Predictability of the 2011-14 California Drought. National Oceanic and Atmospheric
Administration, Drought Task Force Narrative Team,
40 pp. http://dx.doi.org/10.7289/V58K771F
59. Wolter, K., J.K. Eischeid, X.-W. Quan, T.N. Chase,
M. Hoerling, R.M. Dole, G.J.V. Oldenborgh, andJ.E. Walsh, 2015: How unusual was the cold winterof 2013/14 in the Upper Midwest? [in “Explaining
Extreme Events of 2014 from a Climate Perspective”]. Bulletin of the American Meteorological Society, 96 (12), S10-S14. http://dx.doi.org/10.1175/
bams-d-15-00126.1
60. Trenary, L., T. DelSole, B. Doty, and M.K. Tippett,
2015: Was the cold eastern US Winter of 2014 due
to increased variability? Bulletin of the American Meteorological Society, 96 (12), S15-S19. http://dx.doi.

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Precipitation Change
in the United States

KEY FINDINGS
1. Annual precipitation has decreased in much of the West, Southwest, and Southeast and increased inmost of the Northern and Southern Plains, Midwest, and Northeast. A national average increase of
4% in annual precipitation since 1901 is mostly a result of large increases in the fall season. (Medium
confidence)
2. Heavy precipitation events in most parts of the United States have increased in both intensity and frequency since 1901 (high confidence). There are important regional differences in trends, with the largest
increases occurring in the northeastern United States (high confidence). In particular, mesoscale convective systems (organized clusters of thunderstorms)—the main mechanism for warm season precipitation in the central part of the United States—have increased in occurrence and precipitation amounts
since 1979 (medium confidence).
3. The frequency and intensity of heavy precipitation events are projected to continue to increase over the
21st century (high confidence). Mesoscale convective systems in the central United States are expected
to continue to increase in number and intensity in the future (medium confidence). There are, however,important regional and seasonal differences in projected changes in total precipitation: the northern
United States, including Alaska, is projected to receive more precipitation in the winter and spring,and parts of the southwestern United States are projected to receive less precipitation in the winter andspring (medium confidence).
4. Northern Hemisphere spring snow cover extent, North America maximum snow depth, snow waterequivalent in the western United States, and extreme snowfall years in the southern and western United States have all declined, while extreme snowfall years in parts of the northern United States haveincreased (medium confidence). Projections indicate large declines in snowpack in the western UnitedStates and shifts to more precipitation falling as rain than snow in the cold season in many parts of thecentral and eastern United States (high confidence).

Recommended Citation for Chapter
Easterling, D.R., K.E. Kunkel, J.R. Arnold, T. Knutson, A.N. LeGrande, L.R. Leung, R.S. Vose, D.E. Waliser, and M.F. Wehner, 2017: Precipitation change in the United States. In: Climate Science Special Report:
Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C.
Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp.
207-230, doi: 10.7930/J0H993CC.

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7 | Precipitation Change in the United States
Introduction
Changes in precipitation are one of the mostimportant potential outcomes of a warming
world because precipitation is integral to thevery nature of society and ecosystems. Thesesystems have developed and adapted to thepast envelope of precipitation variations. Anylarge changes beyond the historical envelope
may have profound societal and ecologicalimpacts.
Historical variations in precipitation, as
observed from both instrumental and proxyrecords, establish the context around which
future projected changes can be interpreted,because it is within that context that systems
have evolved. Long-term station observations
from core climate networks serve as a primarysource to establish observed changes in bothmeans and extremes. Proxy records, which areused to reconstruct past climate conditions,are varied and include sources such as tree
ring and ice core data. Projected changes are
examined using the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite ofmodel simulations. They establish the likelihood of distinct regional and seasonal patternsof change.

7.1 Historical Changes
7.1.1 Mean Changes

Annual precipitation averaged across theUnited States has increased approximately
4% over the 1901–2015 period, slightly lessthan the 5% increase reported in the Third
National Climate Assessment (NCA3) overthe 1901–2012 period.1 There continue to beimportant regional and seasonal differences inprecipitation changes (Figure 7.1). Seasonally,national increases are largest in the fall, while
little change is observed for winter. Regional
differences are apparent, as the Northeast,
Midwest, and Great Plains have had increases

U.S. Global Change Research Program

while parts of the Southwest and Southeast
have had decreases. The slight decrease in
the change in annual precipitation across theUnited States since NCA3 appears to be theresult of the recent lingering droughts in thewestern and southwestern United States.2, 3However, the recent meteorological droughtin California that began in late 20114, 5 now appears to be largely over, due to the substantial
precipitation and snowpack the state received
in the winter of 2016–2017. The year 2015 was
the third wettest on record, just behind 1973
and 1983 (all of which were years marked
by El Niño events). Interannual variability is
substantial, as evidenced by large multiyear
meteorological and agricultural droughts in
the 1930s and 1950s.
Changes in precipitation differ markedly
across the seasons, as do regional patterns ofincreases and decreases. For the contiguousUnited States, fall exhibits the largest (10%)
and most widespread increase, exceeding 15%
in much of the Northern Great Plains, Southeast, and Northeast. Winter average for theUnited States has the smallest increase (2%),
with drying over most of the western United States as well as parts of the Southeast. In
particular, a reduction in streamflow in thenorthwestern United States has been linked
to a decrease in orographic enhancement ofprecipitation since 1950.6 Spring and summer
have comparable increases (about 3.5%) but
substantially different patterns. In spring, thenorthern half of the contiguous United Stateshas become wetter, and the southern half has
become drier. In summer, there is a mixtureof increases and decreases across the Nation.
Alaska shows little change in annual precipitation (+1.5%); however, in all seasons, central
Alaska shows declines and the panhandle
shows increases. Hawai‘i shows a decline ofmore than 15% in annual precipitation.

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Figure 7.1: Annual and seasonal changes in precipitation over the United States. Changes are the average for present-day (1986–2015) minus the average for the first half of the last century (1901–1960 for the contiguous UnitedStates, 1925–1960 for Alaska and Hawai‘i) divided by the average for the first half of the century. (Figure source: [toppanel] adapted from Peterson et al. 2013,78 © American Meteorological Society. Used with permission; [bottom four
panels] NOAA NCEI, data source: nCLIMDiv].

7.1.2 Snow

Changes in snow cover extent (SCE) in the
Northern Hemisphere exhibit a strong seasonal dependence.7 There has been little change
in winter SCE since the 1960s (when the firstsatellite records became available), while fall
SCE has increased. However, the decline inspring SCE is larger than the increase in fall
and is due in part to higher temperatures thatshorten the time snow spends on the groundin the spring. This tendency is highlighted by
the recent occurrences of both unusually highand unusually low monthly (October–June)SCE values, including the top 5 highest and top
5 lowest values in the 48 years of data. From2010 onward, 7 of the 45 highest monthly SCE
values occurred, all in the fall or winter (mostly in November and December), while 9 of
the 10 lowest May and June values occurred.

U.S. Global Change Research ProgramThis reflects the trend toward earlier springsnowmelt, particularly at high latitudes.8 An
analysis of seasonal maximum snow depth
for 1961–2015 over North America indicates astatistically significant downward trend of 0.11
standardized anomalies per decade and a trend
toward the seasonal maximum snow depth
occurring earlier—approximately one weekearlier on average since the 1960s.8 There has
been a statistically significant decrease over theperiod of 1930–2007 in the frequency of years
with a large number of snowfall days (years
exceeding the 90th percentile) in the southern
United States and the U.S. Pacific Northwest
and an increase in the northern United States.9
In the snow belts of the Great Lakes, lake effectsnowfall has increased overall since the early
20th century for Lakes Superior, Michigan-Huron, and Erie.10 However, individual studies for209

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7 | Precipitation Change in the United States
Lakes Michigan11 and Ontario12 indicate that
this increase has not been continuous. In bothcases, upward trends were observed until the
1970s/early 1980s. Since then, however, lakeeffect snowfall has decreased in these regions.
Lake effect snows along the Great Lakes areaffected greatly by ice cover extent and lakewater temperatures. As ice cover diminishes in
winter, the expectation is for more lake effectsnow until temperatures increase enough suchthat much of what now falls as snow instead
falls as rain.13, 14

observed to increase in intensity by about 6%
to 7% for each degree Celsius of temperature
increase, as dictated by the Clausius–Clapeyron relation. Figure 7.2 shows the observedchange in the 20-year return value of theseasonal maximum 1-day precipitation totals
over the period 1948–2015. A mix of increases
and decreases is shown, with the Northwest
showing very small changes in all seasons, thesouthern Great Plains showing a large increasein winter, and the Southeast showing a large
increase in the fall.

End-of-season snow water equivalent (SWE)—
especially important where water supply isdominated by spring snow melt (for example,
in much of the American West)—has declined
since 1980 in the western United States, basedon analysis of in situ observations, and is associated with springtime warming.15 Satellite
measurements of SWE based on brightness
temperature also show a decrease over this period.16 The variability of western United States
SWE is largely driven by the most extreme
events, with the top decile of events explaining 69% of the variability.17 The recent droughtin the western United States was highlighted
by the extremely dry 2014–2015 winter that
followed three previous dry winters. At Donner Summit, CA, (approximate elevation of
2,100 meters) in the Sierra Nevada Mountains,
end-of-season SWE on April 1, 2015, was thelowest on record, based on survey measurements back to 1910, at only 0.51 inches (1.3cm), or less than 2% of the long-term average.
This followed the previous record low in 2014.
The estimated return period of this drought
is at least 500 years based on paleoclimaticreconstructions.18

A U.S. index of extreme precipitation from
NCA3 was updated (Figure 7.3) through 2016.
This is the number of 2-day precipitation
events exceeding the threshold for a 5-yearrecurrence. The values were calculated byfirst arithmetically averaging the station data
for all stations within each 1° by 1° latitude/
longitude grid for each year and then averaging over the grid values across the contiguousUnited States for each year during the periodof 1896–2015. The number of events has been
well above average for the last three decades.
The slight drop from 2006–2010 to 2011–2016reflects a below-average number during thewidespread severe meteorological droughtyear of 2012, while the other years in thispentad were well above average. The index
value for 2015 was 80% above the 1901–1960
reference period average and the third highest
value in the 120 years of record (after 1998 and2008).

7.1.3 Observed changes in U.S. seasonal extreme
precipitation.

Extreme precipitation events occur when the
air is nearly completely saturated. Hence,extreme precipitation events are generally

U.S. Global Change Research Program

Maximum daily precipitation totals were
calculated for consecutive 5-year blocks
from 1901 (1901–1905, 1906–1910, 1911–1915,…, 2011–2016) for individual long-term stations. For each 5-year block, these values
were aggregated to the regional scale by firstarithmetically averaging the station 5-year
maximum for all stations within each 2° by
2° latitude/longitude grid and then averaging across all grids within each region to210

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7 | Precipitation Change in the United States

Observed Change
in Daily,
20-year
ReturnObserved
Changein Daily,
20-yearLevel
ReturnPrecipitation
Level Precipitation
WinterSpring

–0.02

+0.09
+0.04
+0.13

+0.25

+0.13

+0.08

+0.15

+0.19

+0.15
+0.15

+0.48

–0.02

+0.20

Summer

Fall

–0.04

+0.00

+0.16

+0.01

+0.23

+0.27
+0.27

+0.10
+0.04

+0.04
+0.09

+0.22

+0.41

+0.19

Change (inches)<0.0

0.0–0.10

0.11–0.20

0.21–0.30

0.31–0.40>0.40

Figure 7.2: Observed changes in the 20-year return value of the seasonal daily precipitation totals for the contiguousUnited States over the period 1948 to 2015 using data from the Global Historical Climatology Network (GHCN) dataset.
(Figure source: adapted from Kunkel et al. 2013;61 © American Meteorological Society. Used with permission.)

2-Day Precipitation Events
Exceeding 5-Year Recurrence Interval

2-Day Precipitation Events Exceeding 5-Year Recurrence Interval
Relative Number
of Extreme Events (%)

60

Pentad Average

40
20
0

2010

2000

1990

1980

1970

1960

1950

1940

1930

1920

1910

1900

−20
Year
Figure 7.3: Index of the number of 2-day precipitation events exceeding the station-specific threshold for a 5-yearrecurrence interval in the contiguous United States, expressed as a percentage difference from the 1901–1960 mean.
The annual values are averaged over 5-year periods, with the pentad label indicating the ending year of the period.
Annual time series of the number of events are first calculated at individual stations. Next, the grid box time series arecalculated as the average of all stations in the grid box. Finally, a national time series is calculated as the average ofthe grid box time series. Data source: GHCN-Daily. (Figure source: CICS-NC and NOAA NCEI).

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7 | Precipitation Change in the United Statescreate a regional time series. Finally, a trend
was computed for the resulting regional timeseries. The difference between these two
periods (Figure 7.4, upper left panel) indicatessubstantial increases over the eastern UnitedStates, particularly the northeastern UnitedStates with an increase of 27% since 1901. The
increases are much smaller over the western
United States, with the southwestern andnorthwestern United States showing little
increase.

Another index of extreme precipitation from
NCA3 (the total precipitation falling in the
top 1% of all days with precipitation) was
updated through 2016 (Figure 7.4, upper rightpanel). This analysis is for 1958–2016. There
are increases in all regions, with the largest
increases again in the northeastern UnitedStates. There are some changes in the values
compared to NCA3, with small increases insome regions such as the Midwest and Southwest and small decreases in others such as theNortheast, but the overall picture of changes is
the same.

Observed Change
in Heavy Precipitation

Observed Change in Heavy Precipitation
5-yr Maximum Daily Precipitation(1901–2016)

99th Percentile Precipitation(1958–2016)

NA

16

2

9
13

29

27

18

55

42

1

10
13

12

16

27

–11

NA

–12

NA

Number of 5-yr, 2 Day Events(1901–2016)

Number of 5-yr, 2 Day Events(1958–2016)

NA

27

11

13
21

30

74

63

92

53

−7

−2
40

11

58

NA

49

−33
NA

NA

Change (%)
<0

0-9

10-19

20-29

30-39

40+

Figure 7.4: These maps show the change in several metrics of extreme precipitation by NCA4 region, including (upper
left) the maximum daily precipitation in consecutive 5-year blocks, (upper right) the amount of precipitation falling indaily events that exceed the 99th percentile of all non-zero precipitation days, (lower left) the number of 2-day eventswith a precipitation total exceeding the largest 2-day amount that is expected to occur, on average, only once every 5
years, as calculated over 1901–2016, and (lower right) the number of 2-day events with a precipitation total exceeding
the largest 2-day amount that is expected to occur, on average, only once every 5 years, as calculated over 1958–2016.
The numerical value is the percent change over the entire period, either 1901–2016 or 1958–2016. The percentages
are first calculated for individual stations, then averaged over 2° latitude by 2° longitude grid boxes, and finally averaged over each NCA4 region. Note that Alaska and Hawai‘i are not included in the 1901–2016 maps owing to a lack ofobservations in the earlier part of the 20th century. (Figure source: CICS-NC and NOAA NCEI).

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The national results shown in Figure 7.3 weredisaggregated into regional values for two
periods: 1901–2016 (Figure 7.4, lower left
panel) and 1958–2016 (Figure 7.4, lower rightpanel) for comparison with Figure 7.4, upper
right panel. As with the other metrics, there
are large increases over the eastern half of theUnited States while the increases in the western United States are smaller and there are
actually small decreases in the Southwest.

warming will lead to a decrease in the numberof ETCs but an increase in the intensity of thestrongest ETCs. One factor potentially causing
this model ETC intensification is an increase inlatent heat release in these storms related to amoister atmosphere. Because of the idealized
nature of these simulations, the implicationsof these results for the real earth–atmospheresystem is uncertain. However, the increased
latent heat mechanism is likely to occur given
the high confidence in a future moister atmosphere. For eastern North America, CMIP5
simulations of the future indicate an increasein strong ETCs.19 Thus, it is possible that themost extreme precipitation events associatedwith ETCs may increase in the future.

There are differences in the magnitude ofchanges among the four different regional
metrics in Figure 7.4, but the overall pictureis the same: large increases in the eastern halfof the United States and smaller increases, orslight decreases, in the western United States.
7.1.4 Extratropical Cyclones and Mesoscale
Convective Systems

As described in Chapter 9: Extreme Storms,
there is uncertainty about future changes in
winter extratropical cyclones (ETCs).19 Thus,
the potential effects on winter extreme precipitation events is also uncertain. Summertime
ETC activity across North America has decreased since 1979, with a reduction of morethan 35% in the number of strong summertime
ETCs.20 Most climate models simulate littlechange over this same historical period, butthey project a decrease in summer ETC activity during the remainder of the 21st century.20
This is potentially relevant to extreme precipitation in the northeastern quadrant of theUnited States because a large percentage of theextreme precipitation events in this region arecaused by ETCs and their associated fronts.21
This suggests that in the future there may be
fewer opportunities in the summer for extreme precipitation, although increases in water vapor are likely to overcompensate for any
decreases in ETCs by increasing the likelihoodthat an ETC will produce excessive rainfall
amounts. A very idealized set of climate simulations22 suggests that substantial projected

U.S. Global Change Research Program

Mesoscale convective systems (MCSs), which
contribute substantially to warm season
precipitation in the tropics and subtropics,23
account for about half of rainfall in the central
United States.24 Schumacher and Johnson25
reported that 74% of all warm season extreme
rain events over the eastern two-thirds of theUnited States during the period 1999–2003
were associated with an MCS. Feng et al.26
found that large regions of the central United States experienced statistically significant
upward trends in April–June MCS rainfall of
0.4–0.8 mm per day (approximately 20%–40%)
per decade from 1979 to 2014. They further
found upward trends in MCS frequency of occurrence, lifetime, and precipitation amount,which they attribute to an enhanced west-toeast pressure gradient (enhanced Great Plains
low-level jet) and enhanced specific humidity
throughout the eastern Great Plains.
7.1.5 Detection and Attribution

Trends
Detectability of trends (compared to internal variability) for a number of precipitation
metrics over the continental United States has
been examined; however, trends identified for
the U.S. regions have not been clearly attribut-

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7 | Precipitation Change in the United States
ed to anthropogenic forcing.27, 28 One study
concluded that increasing precipitation trends
in some north-central U.S. regions and theextreme annual anomalies there in 2013 wereat least partly attributable to the combinationof anthropogenic and natural forcing.29
There is medium confidence that anthropogenic
forcing has contributed to global-scale intensification of heavy precipitation over land
regions with sufficient data coverage.30 Globalchanges in extreme precipitation have been
attributed to anthropogenically forced climate change,31, 32 including annual maximum
1-day and 5-day accumulated precipitation
over Northern Hemisphere land regionsand (relevant to this report) over the North
American continent.33 Although the UnitedStates was not separately assessed, the parts ofNorth America with sufficient data for analysis included the continental United Statesand parts of southern Canada, Mexico, andCentral America. Since the covered region was
predominantly over the United States, thesedetection/attribution findings are applicableto the continental United States.
Analyses of precipitation extreme changes
over the United States by region (20-year
return values of seasonal daily precipitation
over 1948–2015, Figure 7.2) show statisticallysignificant increases consistent with theoretical expectations and previous analyses.34 Further, a significant increase in the area affectedby precipitation extremes over North America
has also been detected.35 There is likely an anthropogenic influence on the upward trend inheavy precipitation,36 although models underestimate the magnitude of the trend. Extreme
rainfall from U.S. landfalling tropical cyclones
has been higher in recent years (1994–2008)
than the long-term historical average, evenaccounting for temporal changes in storm
frequency.10

U.S. Global Change Research Program

Based on current evidence, it is concluded
that detectable but not attributable increases in mean precipitation have occurred overparts of the central United States. Formal
detection-attribution studies indicate a humancontribution to extreme precipitation increases
over the continental United States, but confidence is low based on those studies alone dueto the short observational period, high naturalvariability, and model uncertainty.
In summary, based on available studies, it
is concluded that for the continental UnitedStates there is high confidence in the detectionof extreme precipitation increases, while there
is low confidence in attributing the extreme
precipitation changes purely to anthropogenic forcing. There is stronger evidence for ahuman contribution (medium confidence) when
taking into account process-based understanding (increased water vapor in a warmer atmosphere), evidence from weather and climate
models, and trends in other parts of the world.
Event Attribution
A number of recent heavy precipitation events
have been examined to determine the degree
to which their occurrence and severity can be
attributed to human-induced climate change.
Table 7.1 summarizes available attribution
statements for recent extreme U.S. precipitation events. Seasonal and annual precipitation
extremes occurring in the north-central andeastern U.S. regions in 2013 were examinedfor evidence of an anthropogenic influence on
their occurrence.29 Increasing trends in annual
precipitation were detected in the northerntier of states, March–May precipitation in theupper Midwest, and June–August precipitation in the eastern United States since 1900.
These trends are attributed to external forcing(anthropogenic and natural) but could not bedirectly attributed to anthropogenic forcing
alone. However, based on this analysis, it is214

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7 | Precipitation Change in the United States
Table 7.1. A list of U.S. extreme precipitation events for which attribution statements
have been made. In the far right column, “+” indicates that an attributable human-induced increase in frequency and/or magnitude was found, “−“ indicates that an attributable human-induced decrease in frequency and/or magnitude was found, “0” indicatesno attributable human contribution was identified. As in Tables 6.1 and 8.2, several of theevents were originally examined in the Bulletin of the American Meteorological Society’s(BAMS) State of the Climate Reports and reexamined by Angélil et al.76 In these cases,
both attribution statements are listed with the original authors first. Source: M. Wehner.
Event year andduration

RegionType

Attribution
statement

Knutson et al.
201429 / Angélil etal. 201776

ANN 2013

U.S. Northern Tier

Wet

+/0

Knutson et al.
201429 / Angélil etal. 201776

MAM 2013

U.S. Upper Midwest

Wet

+/+

Knutson et al.
201429 / Angélil etal. 201776

JJA 2013

Eastern U.S. Region

Wet

+/−

Edwards et al.
201477

October 4–5,
2013

South Dakota

Blizzard

0

Hoerling et al.
201437

September
10–14, 2013

ColoradoWet

0Pall et al. 201738

September
10–14, 2013

ColoradoWet+

3.66°F

4.67°F

4.99°F

8.51°F

Authors

Northwest

concluded that the probability of these kindsof extremes has increased due to anthropogenic forcing.
The human influence on individual storms
has been investigated with conflicting results.
For example, in examining the attributionof the 2013 Colorado floods, one study findsthat despite the expected human-induced
increase in available moisture, the GEOS-5
model produces fewer extreme storms in the
1983–2012 period compared to the 1871–1900
period in Colorado during the fall season; thestudy attributes that behavior to changes in
the large-scale circulation.37 However, anotherstudy finds that such coarse models cannot
produce the observed magnitude of precipitation due to resolution constraints.38 Based on ahighly conditional set of hindcast simulations

U.S. Global Change Research Program

imposing the large-scale meteorology and asubstantial increase in both the probability
and magnitude of the observed precipitation
accumulation magnitudes in that particularmeteorological situation, the study could not
address the question of whether such situations have become more or less probable.
Extreme precipitation event attribution isinherently limited by the rarity of the necessary meteorological conditions and the limited
number of model simulations that can be performed to examine rare events. This remains
an open and active area of research. However,based on these two studies, the anthropogenic
contribution to the 2013 Colorado heavy rainfall-flood event is unclear.
An event attribution study of the potential
influence of anthropogenic climate change on

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7 | Precipitation Change in the United States
the extreme 3-day rainfall event associatedwith flooding in Louisiana in August 201639
finds that such extreme rainfall events havebecome more likely since 1900. Model simulations of extreme rainfall suggest that anthropogenic forcing has increased the odds of sucha 3-day extreme precipitation event by 40% or
more.

7.2 Projections
Changes in precipitation in a warmer climate
are governed by many factors. Althoughenergy constraints can be used to understandglobal changes in precipitation, projecting regional changes is much more difficult becauseof uncertainty in projecting changes in thelarge-scale circulation that plays an importantrole in the formation of clouds and precipitation.40 For the contiguous United States(CONUS), future changes in seasonal average
precipitation will include a mix of increases, decreases, or little change, depending on
location and season (Figure 7.5). High-latitude
regions are generally projected to becomewetter while the subtropical zone is projected
to become drier. As the CONUS lies between
these two regions, there is significant uncertainty about the sign and magnitude of futureanthropogenic changes to seasonal precipitation in much of the region, particularly inthe middle latitudes of the Nation. However,
because the physical mechanisms controlling
extreme precipitation differ from those controlling seasonal average precipitation (Section
7.1.4), in particular atmospheric water vapor
will increase with increasing temperatures,confidence is high that precipitation extremes
will increase in frequency and intensity in the
future throughout the CONUS.

models with higher horizontal resolution
generally achieve better skill than the CMIP5
models in capturing the spatial patterns andmagnitude of winter precipitation in thewestern and southeastern United States (e.g.,
Mearns et al. 2012;41 Wehner 2013;42 Bacmeister et al. 2014;43 Wehner et al. 201444), leading
to improved simulations of snowpack andrunoff (e.g., Rauscher et al. 2008;45 Rasmussen
et al. 201146). Simulation of present and futuresummer precipitation remains a significant
challenge, as current convective parameterizations fail to properly represent the statistics
of mesoscale convective systems.47 As a result,high-resolution models that still require theparameterization of deep convection exhibit
mixed results.44, 48 Advances in computing
technology are beginning to enable regional
climate modeling at the higher resolutions(1–4 km), permitting the direct simulationof convective clouds systems (e.g., Ban et al.
201449) and eliminating the need for this classof parameterization. However, projections
from such models are not yet ready for inclusion in this report.
Important progress has been made by theclimate modeling community in providing
multimodel ensembles such as CMIP550 and
NARCCAP41 to characterize projection uncertainty arising from model differences andlarge ensemble simulations such as CESM-LE51
to characterize uncertainty inherent in the climate system due to internal variability. Theseensembles provide an important resource for
examining the uncertainties in future precipitation projections.

Global climate models used to project precipitation changes exhibit varying degrees offidelity in capturing the observed climatology
and seasonal variations of precipitation acrossthe United States. Global or regional climate

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7 | Precipitation Change in the United States
Projected Change (%) in Seasonal Precipitation
WinterSpringSummerFall

Change (%)<−30 −20 −10

010 20 >30

Figure 7.5: Projected change (%) in total seasonal precipitation from CMIP5 simulations for 2070–2099. The values
are weighted multimodel means and expressed as the percent change relative to the 1976–2005 average. These
are results for the higher scenario (RCP8.5). Stippling indicates that changes are assessed to be large compared tonatural variations. Hatching indicates that changes are assessed to be small compared to natural variations. Blank regions (if any) are where projections are assessed to be inconclusive. Data source: World Climate Research Program’s(WCRP’s) Coupled Model Intercomparison Project. (Figure source: NOAA NCEI).

7.2.1 Future Changes in U.S. Seasonal Mean
Precipitation

In the United States, projected changes in seasonal mean precipitation span the range fromprofound decreases to profound increases. In
many regions and seasons, projected changes in precipitation are not large comparedto natural variations. The general pattern ofchange is clear and consistent with theoretical
expectations. Figure 7.5 shows the weighted
CMIP5 multimodel average seasonal change
at the end of the century compared to thepresent under the higher scenario (RCP8.5;
see Ch. 4: Projections for discussion of RCPs).

U.S. Global Change Research Program

In this figure, changes projected with highconfidence to be larger than natural variations
are stippled. Regions where future changes areprojected with high confidence to be smaller
than natural variations are hatched. In winterand spring, the northern part of the country
is projected to become wetter as the global
climate warms. In the early to middle parts ofthis century, this will likely be manifested asincreases in snowfall.52 By the latter half of the
century, as temperature continues to increase,
it will be too warm to snow in many current
snow-producing situations, and precipitation
will mostly be rainfall. In the southwestern

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7 | Precipitation Change in the United States
United States, precipitation will decrease in
the spring but the changes are only a little
larger than natural variations. Many otherregions of the country will not experience significant changes in average precipitation. Thisis also the case over most of the country in thesummer and fall.

eastern United States was found using statistically downscaled CMIP5 simulations forced
with RCP8.5. By the end of the 21st century,
large areas that are currently snow dominatedin the cold season are expected to be rainfall
dominated.56

This pattern of projected precipitation changearises because of changes in locally available water vapor and weather system shifts. In the northern part of the continent, increases in water vapor, together with changes in circulation that are
the result of expansion of the Hadley cell, bringmore moisture to these latitudes while maintaining or increasing the frequency of precipitation-producing weather systems. This change inthe Hadley circulation (see Ch. 5: Circulation and
Variability for discussion of circulation changes)
also causes the subtropics, the region between
the northern and southern edges of the tropics
and the midlatitudes (about 35° of latitude), to bedrier in warmer climates as well as moving themean storm track northward and away from thesubtropics, decreasing the frequency of precipitation-producing systems. The combination ofthese two factors results in precipitation decreases in the southwestern United States, Mexico,and the Caribbean.53
Projected Changes In Snow
The Third National Climate Assessment54 projected reductions in annual snowpack of up
to 40% in the western United States based on
the SRES A2 emissions scenario in the CMIP3
suite of climate model projections. Recent
research using the CMIP5 suite of climate
model projections forced with a higher scenario (RCP8.5) and statistically downscaled forthe western United States continues to show
the expected declines in various snow metrics,
including snow water equivalent, the number of extreme snowfall events, and numberof snowfall days.55 A northward shift in therain–snow transition zone in the central and

U.S. Global Change Research Program

The Variable Infiltration Capacity (VIC) model
has been used to investigate the potential
effects of climate change on SWE. Declines in
SWE are projected in all western U.S. mountain ranges during the 21st century with thevirtual disappearance of snowpack in thesouthernmost mountains by the end of the
21st century under both the lower (RCP4.5)
and higher (RCP8.5) scenarios.57 The projected
decreases are most robust at the lower elevations of areas where snowpack accumulation
is now reliable (for example, the Cascades
and northern Sierra Nevada ranges). In theseareas, future decreases in SWE are largely
driven by increases in temperature. At higher (colder) elevations, projections are driven
more by precipitation changes and are thus
more uncertain.
7.2.2 Extremes

Heavy Precipitation Events
Studies project that the observed increase inheavy precipitation events will continue in the
future (e.g. Janssen et al. 2014,58 201659). Similar
to observed changes, increases are expected
in all regions, even those regions where total
precipitation is projected to decline, such as
the southwestern United States. Under thehigher scenario (RCP8.5) the number of extreme events (exceeding a 5-year return period)
increases by two to three times the historical
average in every region (Figure 7.6) by the endof the 21st century, with the largest increases
in the Northeast. Under the lower scenario(RCP4.5), increases are 50%–100%. Researchshows that there is strong evidence, both fromthe observed record and modeling studies, that
increased water vapor resulting from high-218

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7 | Precipitation Change in the United States
MidwestDecade2091–00

2081–90

2071–80

2061–70

2051–60

2041–50

2031–40

2021–30

2011–20

2091–00

South Great Plains

2081–90

North Great Plains

2071–80

Southeast

2061–70

Southwest

2051–60

Northeast

2041–50

Northwest

2031–40

2021–30

2 Day 5 Year
RCP4.5
RCP8.5
Standard Dev

2011–20

1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0

DecadeFigure 7.6: Regional extreme precipitation event frequency for a lower scenario (RCP4.5) (green; 16 CMIP5 models)
and the higher scenario (RCP8.5) (blue; 14 CMIP5 models) for a 2-day duration and 5-year return. Calculated for
2006–2100 but decadal anomalies begin in 2011. Error bars are ±1 standard deviation; standard deviation is calculated
from the 14 or 16 model values that represent the aggregated average over the regions, over the decades, and overthe ensemble members of each model. The average frequency for the historical reference period is 0.2 by definition andthe values in this graph should be interpreted with respect to a comparison with this historical average value. (Figuresource: Janssen et al. 201458).

er temperatures is the primary cause of theincreases.42, 60, 61 Additional effects on extreme
precipitation due to changes in dynamical
processes are poorly understood. However,atmospheric rivers (ARs), especially along theWest Coast of the United States, are projected toincrease in number and water vapor transport62
and experience landfall at lower latitudes63 by
the end of the 21st century.

U.S. Global Change Research Program

Projections of changes in the 20-year return
period amount for daily precipitation (Figure 7.7) using LOcally Constructed Analogs(LOCA) downscaled data also show large
percentage increases for both the middle andlate 21st century. A lower scenario (RCP4.5)
show increases of around 10% for mid-century
and up to 14% for the late century projections.
A higher scenario (RCP8.5) shows even larger increases for both mid- and late-century

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Projected Change
Projected
Changein Daily,
20-year Extreme
Precipitation
in Daily,
20-year
ExtremePrecipitation
Lower Emissions
Mid-century
+9

+8
+10

Late-century
+10

+10

+10

+13

+9

+9

+11

+19

+13

+10

+11
+13

+12

Higher Emissions

Mid-century

+14

+10

Late-century
+22

+16

+13

+20

+12

+20
+12

+13

+20

+21

Change (%)
0-4

5-9

10-14

15+

Figure 7.7: Projected change in the 20-year return period amount for daily precipitation for mid- (left maps) and late-21st
century (right maps). Results are shown for a lower scenario (top maps; RCP4.5) and for a higher scenario (bottom maps,
RCP8.5). These results are calculated from the LOCA downscaled data. (Figure source: CICS-NC and NOAA NCEI).

projections, with increases of around 20% bylate 21st century. No region in either scenario
shows a decline in heavy precipitation. The
increases in extreme precipitation tend toincrease with return level, such that increases
for the 100-year return level are about 30% by
the end of the century under a higher scenario(RCP8.5).
Projections of changes in the distributionof daily precipitation amounts (Figure 7.8)
indicate an overall more extreme precipitation
climate. Specifically, the projections indicate aslight increase in the numbers of dry days andthe very lightest precipitation days and a large
increase in the heaviest days. The number ofdays with precipitation amounts greater than
the 95th percentile of all non-zero precipita-

U.S. Global Change Research Programtion days increases by more than 25%. At thesame time, the number of days with precipitation amounts in the 10th–80th percentile range
decreases.
Most global climate models lack sufficient
resolution to project changes in mesoscaleconvective systems (MCSs) in a changing
climate.64 However, research by Cook et al.65
attempted to identify clues to changes indynamical forcing that create MCSs. To dothis, they examined the ability of 18 coupled
ocean–atmosphere global climate models(GCMs) to simulate potential 21st century
changes in warm-season flow and the associated U.S. Midwest hydrology resulting fromincreases in greenhouse gases. They selecteda subset of six models that best captured the220

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7 | Precipitation Change in the United States

Change in Number (%)

30
20
10
0

>95

90–95

80–90

70–80

60–70

50–60

40–50

30–40

20–30

10–20

0–10

No-Precip−10

Precipitation Percentile Interval

Figure 7.8: Projected change (percentage change relative to the 1976–2005 reference period average) in the numberof daily zero (“No-Precip”) and non-zero precipitation days (by percentile bins) for late-21st century under a higher
scenario (RCP8.5). The precipitation percentile bin thresholds are based on daily non-zero precipitation amounts fromthe 1976–2005 reference period that have been ranked from low to high. These results are calculated from the LOCA
downscaled data. (Figure source: CICS-NC and NOAA NCEI).

low-level flow and associated dynamics ofthe present-day climate of the central United States and then analyzed these models
for changes due to enhanced greenhousegas forcing. In each of these models, springtime precipitation increases significantly (by
20%–40%) in the upper Mississippi Valley anddecreases to the south. The enhanced moisture
convergence leading to modeled future climate rainfall increases in the U.S. Midwest iscaused by meridional convergence at 850 hPa,
connecting the rainfall changes with the Great
Plains Low-Level Jet intensification.66 This isconsistent with findings from Feng et al.26 in
the observational record for the period 1979–
2014 and by Pan et al.67 by use of a regional
climate model.

increase in all regions by up to five times inthe same areas that show the highest increases
in extreme precipitation rates.

Changes in intense hourly precipitation eventswere simulated by Prein et al.68 where theyfound the most intense hourly events (99.9
percentile) in the central United States increaseat the expense of moderately intense (97.5
percentile) hourly events in the warm season.
They also found the frequency of seasonal
hourly precipitation extremes is expected to

U.S. Global Change Research Program

Hurricane Precipitation
Regional model projections of precipitation from landfalling tropical cyclones over
the United States, based on downscaling of
CMIP5 model climate changes, suggest thatthe occurrence frequency of post-landfall
tropical cyclones over the United States willchange little compared to present day during
the 21st century, as the reduced frequency oftropical cyclones over the Atlantic domain ismostly offset by a greater landfalling fraction.
However, when downscaling from CMIP3
model climate changes, projections show a
reduced occurrence frequency over U.S. land,
indicating uncertainty about future outcomes.
The average tropical cyclone rainfall rates
within 500 km (about 311 miles) of the storm
center increased by 8% to 17% in the simulations, which was at least as much as expectedfrom the water vapor content increase factor
alone.

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Several studies have projected increases ofprecipitation rates within hurricanes overocean regions,69 particularly for the Atlanticbasin.70 The primary physical mechanism
for this increase is the enhanced water vapor
content in the warmer atmosphere, which
enhances moisture convergence into the storm
for a given circulation strength, although amore intense circulation can also contribute.71
Since hurricanes are responsible for many ofthe most extreme precipitation events in thesoutheastern United States,10, 21 such events arelikely to be even heavier in the future. In a setof idealized forcing experiments, this effect
was partly offset by differences in warming
rates at the surface and at altitude.72

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7 | Precipitation Change in the United States

TRACEABLE ACCOUNTSKey Finding 1
Annual precipitation has decreased in much of theWest, Southwest, and Southeast and increased in mostof the Northern and Southern Plains, Midwest, andNortheast. A national average increase of 4% in annual
precipitation since 1901 is mostly a result of large increases in the fall season. (Medium confidence)
Description of evidence base
The key finding and supporting text summarizes extensive evidence documented in the climate sciencepeer-reviewed literature. Evidence of long-term changes in precipitation is based on analysis of daily precipitation observations from the U.S. Cooperative Observer Network (http://www.nws.noaa.gov/om/coop/) andshown in Figure 7.1. Published work, such as the Third
National Climate Assessment,73 and Figure 7.1 show
important regional and seasonal differences in U.S. precipitation change since 1901.
Major uncertainties
The main source of uncertainty is the sensitivity of observed precipitation trends to the spatial distribution ofobserving stations and to historical changes in station
location, rain gauges, the local landscape, and observing practices. These issues are mitigated somewhat by
new methods to produce spatial grids through time.74
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Based on the evidence and understanding of the issues
leading to uncertainties, confidence is medium that average annual precipitation has increased in the UnitedStates. Furthermore, confidence is also medium that theimportant regional and seasonal differences in changes documented in the text and in Figure 7.1 are robust.
Summary sentence or paragraph that integrates
the above information
Based on the patterns shown in Figure 7.1 and numerous additional studies of precipitation changes in the
United States, there is medium confidence in the ob-

U.S. Global Change Research Program

served changes in annual and seasonal precipitation
over the various regions and the United States as awhole.
Key Finding 2
Heavy precipitation events in most parts of the UnitedStates have increased in both intensity and frequency
since 1901 (high confidence). There are important regional differences in trends, with the largest increases
occurring in the northeastern United States (high confidence). In particular, mesoscale convective systems (organized clusters of thunderstorms)—the main mechanism for warm season precipitation in the central partof the United States—have increased in occurrence andprecipitation amounts since 1979 (medium confidence).
Description of evidence base
The key finding and supporting text summarize extensive evidence documented in the climate sciencepeer-reviewed literature. Numerous papers have been
written documenting observed changes in heavy precipitation events in the United States, including thosecited in the Third National Climate Assessment and in
this assessment. Although station-based analyses (e.g.,Westra et al. 201334) do not show large numbers of statistically significant station-based trends, area averaging reduces the noise inherent in station-based dataand produces robust increasing signals (see Figures 7.2
and 7.3). Evidence of long-term changes in precipitation is based on analysis of daily precipitation observations from the U.S. Cooperative Observer Network(http://www.nws.noaa.gov/om/coop/) and shown in
Figures 7.2, 7.3, and 7.4.
Major uncertainties
The main source of uncertainty is the sensitivity of observed precipitation trends to the spatial distribution ofobserving stations and to historical changes in station
location, rain gauges, and observing practices. These issues are mitigated somewhat by methods used to produce spatial grids through gridbox averaging.

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Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Based on the evidence and understanding of the issues leading to uncertainties, confidence is high thatheavy precipitation events have increased in the United States. Furthermore, confidence is also high that theimportant regional and seasonal differences in changes documented in the text and in Figures 7.2, 7.3, and
7.4 are robust.
Summary sentence or paragraph that integrates
the above information
Based on numerous analyses of the observed recordin the United States there is high confidence in theobserved changes in heavy precipitation events, andmedium confidence in observed changes in mesoscaleconvective systems.
Key Finding 3
The frequency and intensity of heavy precipitation
events are projected to continue to increase over the
21st century (high confidence). Mesoscale convective
systems in the central United States are expected tocontinue to increase in number and intensity in the
future (medium confidence). There are, however, important regional and seasonal differences in projected changes in total precipitation: the northern UnitedStates, including Alaska, is projected to receive more
precipitation in the winter and spring, and parts of thesouthwestern United States are projected to receive
less precipitation in the winter and spring (medium
confidence).
Description of evidence base
Evidence for future changes in precipitation is basedon climate model projections and our understandingof the climate system’s response to increasing greenhouse gases and of regional mechanisms behind the
projected changes. In particular, Figure 7.7 documents
projected changes in the 20-year return period amountusing the LOCA data, and Figure 7.6 shows changes in 2
day totals for the 5-year return period using the CMIP5
suite of models. Each figure shows robust changes inextreme precipitation events as they are defined in

U.S. Global Change Research Program

the figure. However, Figure 7.5, which shows changes
in seasonal and annual precipitation, indicates whereconfidence in the changes is higher based on consistency between the models and that there are large areas where the projected change is uncertain.
Major uncertainties
A key issue is how well climate models simulate precipitation, which is one of the more challenging aspects ofweather and climate simulation. In particular, comparisons of model projections for total precipitation (fromboth CMIP3 and CMIP5, see Sun et al. 201575) by NCA3
region show a spread of responses in some regions (forexample, the Southwest) such that they are opposite
from the ensemble average response. The continental
United States is positioned in the transition zone between expected drying in the subtropics and wettingin the mid- and higher-latitudes. There are some differences in the location of this transition between CMIP3
and CMIP5 models and thus there remains uncertainty
in the exact location of the transition zone.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Based on evidence from climate model simulations andour fundamental understanding of the relationship ofwater vapor to temperature, confidence is high that
extreme precipitation will increase in all regions of theUnited States. However, based on the evidence andunderstanding of the issues leading to uncertainties,
confidence is medium that that more total precipitation is projected for the northern U.S. and less for theSouthwest.
Summary sentence or paragraph that integrates
the above information
Based on numerous analyses of model simulations andour understanding of the climate system there is highconfidence in the projected changes in precipitation extremes and medium confidence in projected changes intotal precipitation over the United States.

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Key Finding 4
Northern Hemisphere spring snow cover extent, North
America maximum snow depth, snow water equivalent in the western United States, and extreme snowfall years in the southern and western United States
have all declined, while extreme snowfall years in partsof the northern United States have increased (medium confidence). Projections indicate large declines insnowpack in the western United States and shifts tomore precipitation falling as rain than snow in the coldseason in many parts of the central and eastern UnitedStates (high confidence).
Description of evidence base
Evidence of historical changes in snow cover extent anda reduction in extreme snowfall years is consistent with
our understanding of the climate system’s response
to increasing greenhouse gases. Furthermore, climate
models continue to consistently show future declines
in snowpack in the western United States. Recent model projections for the eastern United States also confirm
a future shift from snowfall to rainfall during the coldseason in colder portions of the central and eastern
United States. Each of these changes is documented in
the peer-reviewed literature and are cited in the main
text of this chapter.

U.S. Global Change Research Program

Major uncertainties
The main source of uncertainty is the sensitivity ofobserved snow changes to the spatial distribution ofobserving stations and to historical changes in station
location, rain gauges, and observing practices, particularly for snow. Another key issue is the ability of climate
models to simulate precipitation, particularly snow. Future changes in the frequency and intensity of meteorological systems causing heavy snow are less certain
than temperature changes.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Given the evidence base and uncertainties, confidence
is medium that snow cover extent has declined in the
United States and medium that extreme snowfall years
have declined in recent years. Confidence is high thatwestern United States snowpack will decline in the future, and confidence is medium that a shift from snow
domination to rain domination will occur in the parts ofthe central and eastern United States cited in the text.
Summary sentence or paragraph that integrates
the above information
Based on observational analyses of snow cover, depth,
and water equivalent there is medium confidence in theobserved changes, and based on model simulations forthe future there is high confidence in snowpack declines
in the western United States and medium confidence inthe shift to rain from snow in the eastern United States.

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Kharin, T. Knutson, F. Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. Climate Change Impacts in the United States: The
Third National Climate Assessment. Melillo, J.M., T.C.
Richmond, and G.W. Yohe, Eds. U.S. Global Change
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10. Kunkel, K.E., D.R. Easterling, D.A.R. Kristovich, B.
Gleason, L. Stoecker, and R. Smith, 2010: Recent increases in U.S. heavy precipitation associated withtropical cyclones. Geophysical Research Letters, 37,
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Brooks, N.S. Diffenbaugh, R.M. Dole, J.P. Giovannettone, K. Guirguis, T.R. Karl, R.W. Katz, K. Kunkel, D.
Lettenmaier, G.J. McCabe, C.J. Paciorek, K.R. Ryberg,
S. Schubert, V.B.S. Silva, B.C. Stewart, A.V. Vecchia,
G. Villarini, R.S. Vose, J. Walsh, M. Wehner, D. Wolock, K. Wolter, C.A. Woodhouse, and D. Wuebbles,
2013: Monitoring and understanding changes in heatwaves, cold waves, floods and droughts in the United States: State of knowledge. Bulletin of the American Meteorological Society, 94, 821-834. http://dx.doi.
org/10.1175/BAMS-D-12-00066.1

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KEY FINDINGS
1. Recent droughts and associated heat waves have reached record intensity in some regions of theUnited States; however, by geographical scale and duration, the Dust Bowl era of the 1930s remains
the benchmark drought and extreme heat event in the historical record (very high confidence). While bysome measures drought has decreased over much of the continental United States in association withlong-term increases in precipitation, neither the precipitation increases nor inferred drought decreases
have been confidently attributed to anthropogenic forcing.
2. The human effect on recent major U.S. droughts is complicated. Little evidence is found for a human
influence on observed precipitation deficits, but much evidence is found for a human influence on surface soil moisture deficits due to increased evapotranspiration caused by higher temperatures. (Highconfidence)
3. Future decreases in surface (top 10 cm) soil moisture from anthropogenic forcing over most of theUnited States are likely as the climate warms under higher scenarios. (Medium confidence)
4. Substantial reductions in western U.S. winter and spring snowpack are projected as the climate
warms. Earlier spring melt and reduced snow water equivalent have been formally attributed to human-induced warming (high confidence) and will very likely be exacerbated as the climate continues towarm (very high confidence). Under higher scenarios, and assuming no change to current water resources management, chronic, long-duration hydrological drought is increasingly possible by the end of thiscentury (very high confidence).
5. Detectable changes in some classes of flood frequency have occurred in parts of the United States andare a mix of increases and decreases. Extreme precipitation, one of the controlling factors in flood statistics, is observed to have generally increased and is projected to continue to do so across the UnitedStates in a warming atmosphere. However, formal attribution approaches have not established a significant connection of increased riverine flooding to human-induced climate change, and the timing of
any emergence of a future detectable anthropogenic change in flooding is unclear. (Medium confidence)
6. The incidence of large forest fires in the western United States and Alaska has increased since the early
1980s (high confidence) and is projected to further increase in those regions as the climate warms, withprofound changes to certain ecosystems (medium confidence).
Recommended Citation for Chapter
Wehner, M.F., J.R. Arnold, T. Knutson, K.E. Kunkel, and A.N. LeGrande, 2017: Droughts, floods, andwildfires. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J.,
D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 231-256 doi: 10.7930/J0CJ8BNN.

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8.1 Drought
The word “drought” brings to mind abnormally dry conditions. However, the meaningof “dry” can be ambiguous and lead to confusion in how drought is actually defined. Threedifferent classes of droughts are defined by
NOAA and describe a useful hierarchal set
of water deficit characterization, each withdifferent impacts. “Meteorological drought”
describes conditions of precipitation deficit.
“Agricultural drought” describes conditions of soil moisture deficit. “Hydrologicaldrought” describes conditions of deficit inrunoff.1 Clearly these three characterizationsof drought are related but are also different
descriptions of water shortages with different target audiences and different timescales. In particular, agricultural drought isof concern to producers of food while hydrological drought is of concern to water systemmanagers. Soil moisture is a function of bothprecipitation and evapotranspiration. Because
potential evapotranspiration increases withtemperature, anthropogenic climate change
generally results in drier soils and often lessrunoff in the long term. In fact, under thehigher scenario (RCP8.5; see Ch. 4: Projections
for a description of the RCP scenarios) at theend of the 21st century, no region of the planet
is projected to experience significantly higher
levels of annual average surface soil moisture
due to the sensitivity of evapotranspiration
to temperature, even though much higher
precipitation is projected in some regions.2
Seasonal and annual total runoff, on theother hand, are projected to either increase or
decrease, depending on location and season
under the same conditions,2 illustrating thecomplex relationships between the various
components of the hydrological system. Meteorological drought can occur on a range oftime scales, in addition to seasonal or annual
time scales. “Flash droughts” can result fromjust a few weeks of dry weather,3 and the paleoclimate record contains droughts of several

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decades. Hence, it is vital to describe precisely
the definition of drought in any public discussion to avoid confusion due to this complexity.
As the climate changes, conditions currently
considered “abnormally” dry may become relatively “normal” in those regions undergoingaridification, or extremely unlikely in thoseregions becoming wetter. Hence, the reference
conditions defining drought may need to bemodified from those currently in practice.
8.1.1 Historical Context

The United States has experienced all three
types of droughts in the past, always driven,at least in some part, by natural variations
in seasonal and/or annual precipitation
amounts. As the climate changes, we can expect that human activities will alter the effectof these natural variations. The “Dust Bowl”
drought of the 1930s is still the most significant meteorological and agricultural drought
experienced in the United States in terms of
its geographic and temporal extent. However,even though it happened prior to most of thecurrent global warming, human activities exacerbated the dryness of the soil by the farming practices of the time.4 Tree ring archives
reveal that such droughts (in the agriculturalsense) have occurred occasionally over thelast 1,000 years.5 Climate model simulations
suggest that droughts lasting several years to
decades occur naturally in the southwestern
United States.6 The Intergovernmental Panelon Climate Change Fifth Assessment Report(IPCC AR5)7 concluded “there is low confidence in detection and attribution of changesin (meteorological) drought over global land
areas since the mid-20th century, owing toobservational uncertainties and difficulties
in distinguishing decadal-scale variability
in drought from long-term trends.” As they
noted, this was a weaker attribution statement than in the Fourth Assessment Report,8
which had concluded “that an increased riskof drought was more likely than not due to232

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8 | Droughts, Floods, and Wildfires
anthropogenic forcing during the second halfof the 20th century.” The weaker statement in
AR5 reflected additional studies with conflicting conclusions on global drought trends(e.g., Sheffield et al. 2012;9 Dai 201310). Western
North America was noted as a region where
determining if observed recent droughts were
unusual compared to natural variability wasparticularly difficult. This was due to evidence
from paleoclimate proxies of cases of central
U.S. droughts during the past 1,000 years thatwere longer and more intense than historical
U.S. droughts.11 Drought is, of course, directly connected to seasonal precipitation totals.
Figure 7.1 shows detectable observed recentchanges in seasonal precipitation. In fact, theincreases in observed summer and fall precipitation are at odds with the projections inFigure 7.5. As a consequence of this increased
precipitation, drought statistics over the entire CONUS have declined.3, 12 Furthermore,
there is no detectable change in meteorological drought at the global scale.9 However, anumber of individual event attribution studies
suggest that if a drought occurs, anthropogenic temperature increases can exacerbate
soil moisture deficits (e.g., Seager et al. 2015;13
Trenberth et al. 201414). Future projections of
the anthropogenic contribution to changes indrought risk and severity must be consideredin the context of the significant role of naturalvariability.
8.1.2 Recent Major U.S. Droughts

Meteorological and Agricultural Drought
The United States has suffered a number ofvery significant droughts of all types since2011. Each of these droughts was a result ofdifferent persistent, large-scale meteorologicalpatterns of mostly natural origins, with varying degrees of attributable human influence.
Table 8.1 summarizes available attribution
statements for recent extreme U.S. droughts.
Statements about meteorological drought are
decidedly mixed, revealing the complexities

U.S. Global Change Research Programin interpreting the low tail of the distributionof precipitation. Statements about agricultural
drought consistently maintain a human influence if only surface soil moisture measures are
considered. The single agricultural drought
attribution study at root depth comes to the
opposite conclusion.15 In all cases, these attribution statements are examples of attribution
without detection (see Appendix C). The absence of moisture during the 2011 Texas/Oklahoma drought and heat wave was found to bean event whose likelihood was enhanced by
the La Niña state of the ocean, but the humaninterference in the climate system still doubled
the chances of reaching such high temperatures.16 This study illustrates that the effect ofhuman-induced climate change is combinedwith natural variations and can compoundor inhibit the realized severity of any given
extreme weather event.
The Great Plains/Midwest drought of 2012
was the most severe summer meteorological drought in the observational record forthat region.17 An unfortunate string of threedifferent patterns of large-scale meteorology
from May through August 2012 precluded thenormal frequency of summer thunderstorms
and was not predicted by the NOAA seasonal
forecasts.17 Little influence of the global sea
surface temperature (SST) pattern on meteorological drought frequency has been found inmodel simulations.17 No evidence of a humancontribution to the 2012 precipitation deficit in
the Great Plains and Midwest is found in numerous studies.17, 18, 19 However, an alternative
view is that the 2012 central U.S. drought can
be classified as a “heat wave flash drought”,20
a type of rapidly evolving drought that has
decreased in frequency over the past century.3
Also, an increase in the chances of the unusually high temperatures seen in the UnitedStates in 2012, partly associated with resultant
dry summer soil moisture anomalies, wasattributed to the human interference with the233

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climate system,21 indicating the strong feedback between lower soil moisture and higher
surface air temperatures during periods of low
precipitation. One study found that most, but

not all, of the 2012 surface moisture deficit in
the Great Plains was attributable to the precipitation deficit.22 That study also noted that
Great Plains root depth and deeper soil mois-

Table 8.1. A list of U.S. droughts for which attribution statements have been made. In the last column, “+” indicates that an attributable human-induced increase in frequency and/or magnitude was found, “−” indicates thatan attributable human-induced decrease in frequency and/or magnitude was found, “0” indicates no attributable
human contribution was identified. As in Tables 6.2 and 7.1, several of the events were originally examined in the
Bulletin of the American Meteorological Society’s (BAMS) State of the Climate Reports and reexamined by Angélil
et al.18 In these cases, both attribution statements are listed with the original authors first. (Source: M. Wehner)
Authors

Event Year and Duration

Region or StateType

Attribution Statement

Rupp et al. 2012130/ Angélil et al.
201718MAMJJA 2011

Texas

Meteorological+/+

Hoerling et al.
201316

2012

Texas

Meteorological+

Rupp et al. 201319/ Angélil et al.
201718MAMJJA 2012

CO, NE, KS, OK, IA,
MO, AR & IL

Meteorological

0/0

Rupp et al. 201319/ Angélil et al.
201718

MAM 2012

CO, NE, KS, OK, IA,
MO, AR & IL

Meteorological

0/0

Rupp et al. 201319/ Angélil et al.
201718JJA 2012

CO, NE, KS, OK, IA,
MO, AR & IL

Meteorological0/+

Hoerling et al.
201417MJJA 2012

Great Plains/Midwest

Meteorological0

Swain et al. 201424/ Angélil et al.
201718

ANN 2013

California

Meteorological+/+

Wang andSchubert 201429/ Angélil et al.
201718

JS 2013

California

Meteorological0/+

Knutson et al.
2014131 / Angélil etal. 201718

ANN 2013

California

Meteorological0/+

Knutson et al.
2014131 / Angélil etal. 201718

MAM 2013

U.S. Southern Plains
regionMeteorological0/+

Diffenbaugh et al.
201528

2012–2014

California

Agricultural+

Seager et al.
201513

2012–2014

California

Agricultural+

Cheng et al. 201615

2011–2015

California

Agricultural−

Mote et al. 201631

2015

Washington, Oregon, California

Hydrological (snowwater equivalent)+

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ture was higher than normal in 2012 despite
the surface drying, due to wet conditions inprior years, indicating the long time scales
relevant below the surface.22
The recent California drought, which began
in 2011, is unusual in different respects. In
this case, the precipitation deficit from 2011 to2014 was a result of the “ridiculously resilient ridge” of high pressure. This very stablehigh pressure system steered storms towards
the north, away from the highly engineered
California water resource system.13, 23, 24 A
slow-moving high sea surface temperature(SST) anomaly, referred to as “The Blob”—
was caused by a persistent ridge that weakened the normal cooling mechanisms for that
region of the upper ocean.25 Atmosphericmodeling studies showed that the ridge thatcaused The Blob was favored by a pattern of
persistent tropical SST anomalies that were
warm in the western equatorial Pacific andsimultaneously cool in the far eastern equatorial Pacific.23, 26 It was also favored by reduced
arctic sea ice and from feedbacks with The
Blob’s SST anomalies.27 These studies alsosuggest that internal variability likely played aprominent role in the persistence of the 2013–2014 ridge off the west coast of North America. Observational records are not long enoughand the anomaly was unusual enough thatsimilarly long-lived patterns have not been often seen before. Hence, attribution statements,
such as that about an increasing anthropogenic influence on the frequency of geopotential
height anomalies similar to 2012–2014 (e.g.,
Swain et al. 201424), are without associateddetection (Ch. 3: Detection and Attribution).
A secondary attribution question concerns theanthropogenic precipitation response in thepresence of this SST anomaly. In attribution
studies with a prescribed 2013 SST anomaly, aconsistent increase in the human influence on
the chances of very dry California conditions
was found.18

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Anthropogenic climate change did increase
the risk of the high temperatures in California
in the winters of 2013–2014 and 2014–2015,
especially the latter,13, 28, 29 further exacerbating the soil moisture deficit and the associated stress on irrigation systems. This raises
the question, as yet unanswered, of whether
droughts in the western United States are
shifting from precipitation control30 to temperature control. There is some evidence tosupport a relationship between mild winter and/or warm spring temperatures anddrought occurrence,31 but long-term warming
trends in the tropical and North Pacific do notappear to have led to trends toward less precipitation over California.32 An anthropogenic
contribution to commonly used measures ofagricultural drought, including the Palmer
Drought Severity Index (PDSI), was found in
California28, 33 and is consistent with previous
projections of changes in PDSI10, 34, 35 and withan attribution study.36 Due to its simplicity,
the PDSI has been criticized as being overly
sensitive to higher temperatures and thus may
exaggerate the human contribution to soil dryness.37 In fact, this study also finds that formulations of potential evaporation used in more
complicated hydrologic models are similarly
biased, undermining confidence in the magnitude but not the sign of projected surface soil
moisture changes in a warmer climate. Seager
et al.13 analyzed climate model output directly,
finding that precipitation minus evaporationin the southwestern United States is projected
to experience significant decreases in surface
water availability, leading to surface runoff
decreases in California, Nevada, Texas, andthe Colorado River headwaters even in thenear term. However, the criticisms of PDSI
also apply to most of the CMIP5 land surface model evapotranspiration formulations.
Analysis of soil moisture in the CMIP5 models
at deeper levels is complicated by the wide
variety in sophistication of their component
land models. A pair of studies reveals less

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sensitivity at depth-to-surface air temperature increases than at near-surface levels.15, 38
Berg et al.39 adjust for the differences in landcomponent model vertical treatments, finding
projected change in vertically integrated soil
moisture down to 3 meters depth is mixed,with projected decreases in the Southwest
and in the south-central United States, but
increases over the northern plains. Nonetheless, the warming trend has led to declines ina number of indicators, including Sierra snow
water equivalent, that are relevant to hydrological drought.30 Attribution of the California
drought and heat wave remains an interesting
and controversial research topic.
In summary, there has not yet been a formal
identification of a human influence on pastchanges in United States meteorologicaldrought through the analysis of precipitation
trends. Some, but not all, U.S. meteorologicaldrought event attribution studies, largely inthe “without detection” class, exhibit a human
influence. Attribution of a human influence onpast changes in U.S. agricultural drought arelimited both by availability of soil moistureobservations and a lack of subsurface modeling studies. While a human influence on
surface soil moisture trends has been identified with medium confidence, its relevance toagriculture may be exaggerated.
Runoff And Hydrological Drought
Several studies focused on the Colorado Riverbasin in the United States that used moresophisticated runoff models driven by the
CMIP3 models40, 41, 42, 43, 44 showed that annual
runoff reductions in a warmer western Unites
States climate occur through a combinationof evapotranspiration increases and precipitation decreases, with the overall reduction
in river flow exacerbated by human water
demands on the basin’s supply. Reduced U.S.
snowfall accumulations in much warmer

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future climates are virtually certain as frozen
precipitation is replaced by rain regardless ofthe projected changes in total precipitation
amounts discussed in Chapter 7: Precipitation
Change (Figure 7.6). The profound change inthe hydrology of snowmelt-driven flows in
the western United States is well documented.
Earlier spring runoff45 reduced the fraction of
precipitation falling as snow46 and the snowpack water content at the end of winter,47, 48
consistent with warmer temperatures. Formal
detection and attribution (Ch. 3: Detection
and Attribution) of the observed shift towards
earlier snowmelt-driven flows in the western
United States reveals that the shift is detectably different from natural variability andattributable to anthropogenic climate change.49
Similarly, observed declines in the snow waterequivalent in the region have been formally
attributed to anthropogenic climate change50
as have temperature, river flow, and snowpack.41, 51 As a harbinger, the unusually low
western U.S. snowpack of 2015 may become
the norm.31
In the northwestern United States, long-term
trends in streamflow have seen declines, withthe strongest trends in drought years52 thatare attributed to a decline in winter precipitation.53 These reductions in precipitation arelinked to decreased westerly wind speeds
in winter over the region. Furthermore,
the trends in westerlies are consistent with
CMIP5-projected wind speed changes due
to a decreasing meridional temperature andpressure gradients rather than low-frequency
climate variability modes. Such precipitation
changes have been a primary source of change
in hydrological drought in the Northwest overthe last 60 years54 and are in addition to changes in snowpack properties.
We conclude with high confidence that theseobserved changes in temperature controlled

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8 | Droughts, Floods, and Wildfires
aspects of western U.S. hydrology are likely aconsequence of human changes to the climate
system.
8.1.3 Projections of Future Droughts

The future changes in seasonal precipitation
shown in Chapter 7: Precipitation Change(Figure 7.6) indicate that the southwestern
United States may experience chronic future precipitation deficits, particularly in thespring. In much warmer climates, expansionof the tropics and subtropics, traceable tochanges in the Hadley circulation, cause shifts
in seasonal precipitation that are particularly
evident in such arid and semi-arid regions andincrease the risk of meteorological drought.
However, uncertainty in the magnitude andtiming of future southwestern drying is high.
We note that the weighted and downscaled
projections of Figure 7.6 exhibit significantly
less drying and are assessed to be less significant in comparison to natural variations thanthe original unweighted CMIP5 projections.34
Western U.S. hydrological drought is currently controlled by the frequency and intensityof extreme precipitation events, particularly
atmospheric rivers, as these events represent
the source of nearly half of the annual watersupply and snowpack for the western coastal
states.55, 56 Climate projections indicate greater
frequency of atmospheric rivers in the future(e.g., Dettinger 2011;55 Warner et al. 2015;57 Gao
et al. 2015;58 see further discussion in Ch. 9:
Extreme Storms). Sequences of these extreme
storms have played a critical role in ending
recent hydrological droughts along the U.S.
West Coast.59 However, as winter temperatures increase, the fraction of precipitation falling as snow will decrease, potentially disrupting western U.S. water management practices.
Significant U.S. seasonal precipitation deficits
are not confidently projected outside of theSouthwest. However, future higher tempera-

U.S. Global Change Research Programtures will likely lead to greater frequencies andmagnitudes of agricultural droughts throughout the continental United States as the resulting increases in evapotranspiration outpace
projected precipitation increases.2 Figure 8.1
shows the weighted multimodel projectionof the percent change in near-surface soil
moisture at the end of the 21st century under the higher scenario (RCP8.5), indicatingwidespread drying over the entire continental
United States. Previous National Climate Assessments34, 60 have discussed the implicationof these future drier conditions in the context
of the PDSI, finding that the future normal
condition would be considered drought at
the present time, and that the incidence of“extreme drought” (PDSI < −4) would besignificantly increased. However, as described
below, the PDSI may overestimate future soil
moisture drying.
This projection is made “without attribution”(Ch. 4: Projections), but confidence that future soils will generally be drier at the surface is medium, as the mechanisms leading
to increased evapotranspiration in a warmer
climate are elementary scientific facts. However, the land surface component models in the
CMIP5 climate models vary greatly in their sophistication, causing the projected magnitude
of both the average soil moisture decrease andthe increased risk for agricultural drought tobe less certain. The weighted projected seasonal decreases in surface soil moisture are generally towards drier conditions, even in regions
and seasons where precipitation is projected toexperience large increases (Figure 7.6) due toincreases in the evapotranspiration associatedwith higher temperature. Drying is assessed tobe large relative to natural variations in muchof the CONUS region in the summer. Significant spring and fall drying is also projected in
the mountainous western states, with potential implications for forest and wildfire risk.
Also, the combination of significant summer

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8 | Droughts, Floods, and Wildfires
and fall drying in the midwestern states has
potential agricultural implications. The largest
percent changes are projected in the southwestern United States and are consistent inmagnitude with an earlier study of the Colorado River Basin using more sophisticatedmacroscale hydrological models.42

depth of typical U.S. crops. Cook et al.38 findthat future drying at a depth of 30 cm will beless than at 2 cm, but still significant and comparable to a modified PDSI formulation. Fewof the CMIP5 land models have detailed ecological representations of evapotranspiration
processes, causing the simulation of the soil
moisture budget to be less constrained thanreality.61 Over the western United States, unrealistically low elevations in the CMIP5 models
due to resolution constraints present a further
challenge in interpreting evapotranspiration
changes. Nonetheless, Figure 8.1 shows a projected drying of surface soil moisture acrossnearly all of the coterminous United States inall seasons, even in regions and seasons where

In this assessment, we limit the direct CMIP5
weighted multimodel projection of soil moisture shown in Figure 8.1 to the surface (defined as the top 10 cm of the soil), as the land
surface component sub-models vary greatly
in their representation of the total depth of thesoil. A more relevant projection to agricultural
drought would be the soil moisture at the root

Projected Change (mm) in Soil Moisture, End of Century, Higher Emissions
WinterSpringSummerFall

Change (mm)<−4 −3 −2 −1

0

1

2

3

>4

Figure 8.1: Projected end of the 21st century weighted CMIP5 multimodel average percent changes in near surface
seasonal soil moisture (mrsos) under the higher scenario (RCP8.5). Stippling indicates that changes are assessed tobe large compared to natural variations. Hashing indicates that changes are assessed to be small compared to naturalvariations. Blank regions (if any) are where projections are assessed to be inconclusive (Appendix B). (Figure source:
NOAA NCEI and CICS-NC).

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8 | Droughts, Floods, and Wildfires
precipitation is projected to increase, consistent with increased evapotranspiration due toelevated temperatures.38
Widespread reductions in mean snowfall
across North America are projected by the
CMIP5 models.62 Together with earlier snowmelt at altitudes high enough for snow,
disruptions in western U.S. water delivery
systems are expected to lead to more frequenthydrological drought conditions.40, 41, 50, 63, 64
Due to resolution constraints, the elevationof mountains as represented in the CMIP5
models is too low to adequately represent theeffects of future temperature on snowpacks.
However, increased model resolution has been
demonstrated to have important impacts on
future projections of snowpack water content
in warmer climates and is enabled by recent
advances in high performance computing.65
Figure 8.2 and Table 8.2 show a projectionof changes in western U.S. mountain winter(December, January, and February) hydrology obtained from a different high-resolution
atmospheric model at the middle and end of
the 21st century under the higher scenario(RCP8.5). These projections indicate dramatic
reductions in all aspects of snow66 and are similar to previous statistically downscaled pro-Historicaljections.67, 68 Table 8.2 reveals that the reductions in snow water equivalent accelerate in
the latter half of this century under the higher
scenario (RCP8.5) and with substantial variations across the western United States. Changes in snow residence time, an alternative
measure of snowpack relevant to the timing of
runoff, is also shown to be sensitive to elevation, with widespread reductions across thisregion.69 Given the larger projected increases
in temperature at high altitudes compared toadjacent lower altitudes70 and the resulting
changes in both snowpack depth and melt
timing in very warm future scenarios suchas RCP8.5, and assuming no change to water
resource management practices, several important western U.S. snowpack reservoirs effectively disappear by 2100 in this dynamical
projection, resulting in chronic, long-lastinghydrological drought. This dramatic statement
is also supported by two climate model studies: a multimodel statistical downscaling of
the CMIP5 RCP8.5 ensemble that finds large
areal reductions in snow-dominated regionsof the western United States by mid-century
and complete elimination of snow-dominatedregions in certain watersheds,68 and a large ensemble simulation of a global climate model.71

Mid-Century

End-Century

mm
0

80

160

240

Figure 8.2: Projected changes in winter (DJF) snow water equivalent at the middle and end of this century under
the higher scenario (RCP8.5) from a high-resolution version of the Community Atmospheric Model, CAM5.66 (Figuresource: H. Krishnan, LBNL).

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8 | Droughts, Floods, and Wildfires
Table 8.2. Projected changes in western U.S. mountain range winter (DJF) snow-related hydrology variables at the
middle and end of this century. Projections are for the higher scenario (RCP8.5) from a high-resolution version of
the Community Atmospheric Model, CAM5.66
Mountain Range

Snow Water
Equivalent (% Change)

Snow Cover(% Change)

Snowfall (% Change)

Surface Temperature(change in K)

2050

2100

2050

2100

2050

2100

2050

2100

Cascades

−41.5

−89.9

−21.6

−72.9

−10.7

−50.0

0.9

4.1

Klamath−50.75

−95.8

−38.6

−89.0

−23.1

−78.7

0.8

3.5

Rockies

−17.3

−65.1

−8.2

−43.1

1.7

−8.2

1.4

5.5

Sierra Nevada

−21.8

−89.0

−21.9

−77.7

−4.7

−66.6

1.1

4.5

Wasatch and Uinta

−18.9

−78.7

−14.2

−61.4

4.1

−34.6

1.8

6.1

Western USA

−22.3

−70.1

−12.7

−51.5

−1.6

−21.4

1.3

5.2

As earlier spring melt and reduced snow water equivalent have been formally attributed tohuman-induced warming, substantial reductions in western U.S. winter and spring snowpack are projected (with attribution) to be very
likely as the climate continues to warm (veryhigh confidence). Under higher scenarios andassuming no change to current water-resources management, chronic, long-duration hydrological drought is increasingly possible by theend of this century (very high confidence).

8.2 Floods
Flooding damage in the United States cancome from flash floods of smaller rivers andcreeks, prolonged flooding along major rivers,
urban flooding unassociated with proximity
to a riverway, coastal flooding from storm
surge which may be exacerbated by sea levelrise, and the confluence of coastal storms
and inland riverine flooding from the same
precipitation event (Ch. 12: Sea Level Rise).
Flash flooding is associated with extreme precipitation somewhere along the river which
may occur upstream of the regions at risk.
Flooding of major rivers in the United Stateswith substantial winter snow accumulations
usually occurs in the late winter or spring andcan result from an unusually heavy seasonal
snowfall followed by a “rain on snow” eventor from a rapid onset of higher temperatures

U.S. Global Change Research Programthat leads to rapid snow melting within theriver basin. In the western coastal states, mostflooding occurs in conjunction with extreme
precipitation events referred to as “atmospheric rivers” (see Ch. 9: Extreme Storms),72,
73
with mountain snowpack being vulnerable
to these typically warmer-than-normal storms
and their potential for rain on existing snow
cover.74 Hurricanes and tropical storms are
an important driver of flooding events in theeastern United States. Changes in streamflow
rates depend on many factors, both humanand natural, in addition to climate change.
Deforestation, urbanization, dams, floodwater management activities, and changes inagricultural practices can all play a role inpast and future changes in flood statistics.
Projection of future changes is thus a complex
multivariate problem.34
The IPCC AR57 did not attribute changes inflooding to anthropogenic influence nor report
detectable changes in flooding magnitude,
duration, or frequency. Trends in extreme
high values of streamflow are mixed acrossthe United States.34, 75, 76 Analysis of 200 U.S.
stream gauges indicates areas of both increasing and decreasing flooding magnitude77 but
does not provide robust evidence that thesetrends are attributable to human influences.
Significant increases in flood frequency have240

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been detected in about one-third of stream
gauge stations examined for the central United States, with a much stronger signal of
frequency change than is found for changes
in flood magnitude in these gauges.78 Thisapparent disparity with ubiquitous increases
in observed extreme precipitation (Figure 7.2)
can be partly explained by the seasonality ofthe two phenomena. Extreme precipitation
events in the eastern half of the CONUS arelarger in the summer and fall when soil moisture and seasonal streamflow levels are low
and less favorable for flooding.79 By contrast,
high streamflow events are often larger in thespring and winter when soil moisture is highand snowmelt and frozen ground can enhance
runoff.80 Furthermore, floods may be poorly
explained by daily precipitation characteristics alone; the relevant mechanisms are more
complex, involving processes that are seasonally and geographically variable, including theseasonal cycles of soil moisture content andsnowfall/snowmelt.81
Recent analysis of annual maximum streamflow shows statistically significant trends inthe upper Mississippi River valley (increasing)and in the Northwest (decreasing).44 In fact,
across the midwestern United States, statistically significant increases in flooding are well
documented.78, 82, 83, 84, 85, 86, 87, 88 These increases
in flood risk and severity are not attributed
to 20th century changes in agricultural practices87, 89 but instead are attributed mostly tothe observed increases in precipitation shownin Figures 7.1 through 7.4.78, 84, 89, 90 Trends inmaximum streamflow in the northeastern
United States are less dramatic and less spatially coherent,44, 80 although one study foundmostly increasing trends91 in that region, consistent with the increasing trends in observed
extreme precipitation in the region (Ch. 6:
Temperature Change).34, 80

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The nature of the proxy archives complicates the reconstruction of past flood eventsin a gridded fashion as has been done withdroughts. However, reconstructions of pastriver outflows do exist. For instance, it has
been suggested that the mid-20th century water allocations from the Colorado River weremade during one of the wettest periods of thepast five centuries.92 For the eastern UnitedStates, the Mississippi River has undergone
century-scale variability in flood frequency—
perhaps linked to the moisture availability inthe central United States and the temperature
structure of the Atlantic Ocean.93
The complex mix of processes complicates theformal attribution of observed flooding trends
to anthropogenic climate change and suggeststhat additional scientific rigor is needed inflood attribution studies.94 As noted above,
precipitation increases have been found tostrongly influence changes in flood statistics.
However, in U.S. regions, no formal attribution of precipitation changes to anthropogenic forcing has been made so far, so indirect
attribution of flooding changes is not possible.
Hence, no formal attribution of observed
flooding changes to anthropogenic forcing has
been claimed.78
A projection study based on coupling an ensemble of regional climate model output to ahydrology model95 finds that the magnitude of
future very extreme runoff (which can lead toflooding) is decreased in most of the summer
months in Washington State, Oregon, Idaho,
and western Montana but substantially increases in the other seasons. Projected weighted increases in extreme runoff from the coastto the Cascade Mountains are particularly
large in that study during the fall and winterwhich are not evident in the weighted seasonal averaged CMIP5 runoff projections.2 For
the West Coast of the United States, extremely
heavy precipitation from intense atmospheric

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river storms is an important factor in flood
frequency and severity.55, 96 Projections indicate
greater frequency of heavy atmospheric rivers
in the future (e.g., Dettinger et al. 2011;96 Warner et al. 2015;57 Gao et al. 2015;58 see further
discussion in Ch. 9: Extreme Storms). Translating these increases in atmospheric riverfrequency to their impact on flood frequency
requires a detailed representation of westernstates topography in the global projection
models and/or via dynamic downscaling toregional models and is a rapidly developing
science. In a report prepared for the Federal
Insurance and Mitigation Administration of
the Federal Emergency Management Agency,
a regression-based approach of scaling rivergauge data based on seven commonly usedclimate change indices from the CMIP3 database97 found that at the end of the 21st century
the 1% annual chance floodplain area would
increase in area by about 30%, with largerchanges in the Northeast and Great Lakes
regions and smaller changes in the central partof the country and the Gulf Coast.98
Urban flooding results from heavy precipitation events that overwhelm the existing
sewer infrastructure’s ability to convey the
resulting stormwater. Future increases in dailyand sub-daily extreme precipitation rates will
require significant upgrades to many communities’ storm sewer systems, as will sea level
rise in coastal cities and towns.99, 100
No studies have formally attributed (see Ch. 3:
Detection and Attribution) long-term changes
in observed flooding of major rivers in the
United States to anthropogenic forcing. We
conclude that there is medium confidence that
detectable (though not attributable to anthropogenic forcing changes) increases in flood
statistics have occurred in parts of the central
United States. Key Finding 3 of Chapter 7:
Precipitation Change states that the frequency
and intensity of heavy precipitation events are

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projected to continue to increase over the 21st
century with high confidence. Given the connection between extreme precipitation and flooding, and the complexities of other relevant factors, we concur with the IPCC Special Reporton Extremes (SREX) assessment of “medium
confidence (based on physical reasoning) thatprojected increases in heavy rainfall would
contribute to increases in local flooding insome catchments or regions”.101
Existing studies of individual extreme flooding events are confined to changes in the locally responsible precipitation event and havenot included detailed analyses of the events’hydrology. Gochis et al.102 describe the massive floods of 2013 along the Colorado front
range, estimating that the streamflow amounts
ranged from 50- to 500-year return values
across the region. Hoerling et al.17 analyzed
the 2013 northeastern Colorado heavy multiday precipitation event and resulting flood,finding little evidence of an anthropogenic
influence on its occurrence. However, Pall etal.103 challenge their event attribution methodology with a more constrained study and findthat the thermodynamic response of precipitation in this event due to anthropogenic forcing
was substantially increased. The Pall et al.103
approach does not rule out that the likelihoodof the extremely rare large-scale meteorological pattern responsible for the flood may havechanged.

8.3 Wildfires
A global phenomenon with natural (lightning)and human-caused ignition sources, wildfire
represents a critical ecosystem process. Recent decades have seen a profound increasein forest fire activity over the western UnitedStates and Alaska.104, 105, 106, 107 The frequencyof large wildfires is influenced by a complex
combination of natural and human factors.
Temperature, soil moisture, relative humidity,
wind speed, and vegetation (fuel density) are

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0 300 600

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2008

1984
1992
2000
2008
Arizona-New Mexico Mountains

1992

2000

0

MTBS Fires
1984–2011
300 km

1984
1992
2000
Warm Deserts

2008

1984

2008

0 40 80

0 20 50

1984
1992
2000
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0 40 80

2008

1984
1992
2000
2008
Mediterranean California

1984

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0 100

0 40 80

1984
1992
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2008
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1984
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1984
1992
2000
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0 30 60

Number of Large Fires per Ecoregion

040

Number of Large Fires per Ecoregion

2000
2008
1984
1992
Sierra/Klamath/Cascade Mtns

Rocky Mountains

2008

1992

2000

Figure 8.3: Trends in the annual number of large fires in the western United States for a variety of ecoregions. The
black lines are fitted trend lines. Statistically significant at a 10% level for all regions except the Snake Plain/Columbian
Plateau, Basin and Range, and Mediterranean California regions. (Figure source: Dennison et al.108).

important aspects of the relationship between
fire frequency and ecosystems. Forest management and fire suppression practices canalso alter this relationship from what it was in
the preindustrial era. Changes in these control
parameters can interact with each other incomplex ways with the potential for tipping
points—in both fire frequency and in ecosystem properties—that may be crossed as theclimate warms.
Figure 8.3 shows that the number of large
fires has increased over the period 1984–2011,
with high statistical significance in 7 out of
10 western U.S. regions across a large varietyof vegetation, elevation, and climatic types.108
State-level fire data over the 20th century109 indicates that area burned in the western UnitedStates decreased from 1916 to about 1940, wasat low levels until the 1970s, then increased
into the more recent period. Modeled increases in temperatures and vapor pressure deficits
due to anthropogenic climate change have increased forest fire activity in the western United States by increasing the aridity of forest fu-

U.S. Global Change Research Program

els during the fire season.104 Increases in these
relevant climatic drivers were found to be
responsible for over half the observed increase
in western U.S. forest fuel aridity from 1979 to2015 and doubled the forest fire area over theperiod 1984–2015.104 Littell et al. (2009, 2010,2016)109, 110, 111 found that two climatic mechanisms affect fire in the western United States:
increased fuel flammability driven by warmer,
drier conditions and increased fuel availability
driven by antecedent moisture. Littell et al.111
found a clear link between increased drought
and increased fire risk. Yoon et al.112 assessedthe 2014 fire season, finding an increased riskof fire in California. While fire suppression
practices can also lead to a significant increasein fire risk in lower-elevation and drier forest
types, this is less important in higher-elevationand moister forests.113, 114, 115 Increases in future
forest fire extent, frequency, and intensity
depend strongly on local ecosystem properties
and will vary greatly across the United States.
Westerling et al.116 projected substantial increases in forest fire frequency in the Greater
Yellowstone ecosystem by mid-century under243

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the older SRES A2 emissions scenario, andfurther stated that years without large fires in
the region will become extremely rare. Stavros et al.117 projected increases in very large
fires (greater than 50,000 acres) across thewestern United States by mid-century underboth the lower and higher scenarios (RCP4.5
and RCP8.5, respectively). Likewise, Prestemon et al.118 projected significant increases inlightning-ignited wildfire in the Southeast by
mid-century but with substantial differences
between ecoregions. However, other factors,
related to climate change such as water scarcity or insect infestations may act to stifle future
forest fire activity by reducing growth or otherwise killing trees leading to fuel reduction.110
Historically, wildfires have been less frequentand of smaller extent in Alaska comparedto the rest of the globe.119, 120 Shortened landsnow cover seasons and higher temperatures
have made the Arctic more vulnerable towildfire.119, 120, 121 Total area burned and thenumber of large fires (those with area greaterthan 1,000 square km or 386 square miles) in
Alaska exhibits significant interannual anddecadal scale variability from influences ofatmospheric circulation patterns and controlled burns, but have likely increased since1959.122 The most recent decade has seen an
unusually large number of severe wildfire
years in Alaska, for which the risk of severe
fires has likely increased by 33%–50% as a result of anthropogenic climate change123 and is
projected to increase by up to a factor of fourby the end of the century under the mid-highscenario (RCP6.0).121 Historically less flammable tundra and cooler boreal forest regions
could shift into historically unprecedented firerisk regimes as a consequence of temperatures
increasing above the minimum thresholds
required for burning. Alaska’s fire season is
also likely lengthening—a trend expected tocontinue.119, 124 Thresholds in temperature andprecipitation shape Arctic fire regimes, andU.S. Global Change Research Program

projected increases in future lightning activity
imply increased vulnerability to future climatechange.119, 121 Alaskan tundra and forest wildfires will likely increase under warmer anddrier conditions124, 125 and potentially result ina transition into a fire regime unprecedented
in the last 10,000 years.126 Total area burned is
projected to increase between 25% and 53% by
the end of the century.127
Boreal forests and tundra contain large storesof carbon, approximately 50% of the total
global soil carbon.128 Increased fire activity
could deplete these stores, releasing themto the atmosphere to serve as an additional source of atmospheric CO2 and alter the
carbon cycle if ecosystems change from higher
to lower carbon densities.126, 128 Additionally,
increased fires in Alaska may also enhance thedegradation of Alaska’s permafrost, blackening the ground, reducing surface albedo, andremoving protective vegetation.
Both anthropogenic climate change and thelegacy of land use/management have an
influence on U.S. wildfires and are subtly andinextricably intertwined. Forest management
practices have resulted in higher fuel densities in most U.S. forests, except in the Alaskanbush and the higher mountainous regionsof the western United States. Nonetheless,there is medium confidence for a human-caused
climate change contribution to increased forest
fire activity in Alaska in recent decades with alikely further increase as the climate continues
to warm, and low to medium confidence for a
detectable human climate change contribution
in the western United States based on existingstudies. Recent literature does not containa complete robust detection and attribution
analysis of forest fires including estimates ofnatural decadal and multidecadal variability, as described in Chapter 3: Detection and
Attribution, nor separate the contributions toobserved trends from climate change and for-244

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est management. These assessment statements
about attribution to human-induced climatechange are instead multistep attribution statements (Ch. 3: Detection and Attribution) basedon plausible model-based estimates of anthropogenic contributions to observed trends. The
modeled contributions, in turn, are based on
climate variables that are closely linked to firerisk and that, in most cases, have a detectable
human influence, such as surface air temperature and snow melt timing.

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TRACEABLE ACCOUNTSKey Finding 1
Recent droughts and associated heat waves havereached record intensity in some regions of the UnitedStates; however, by geographical scale and duration,
the Dust Bowl era of the 1930s remains the benchmark
drought and extreme heat event in the historical record (very high confidence). While by some measures
drought has decreased over much of the continental
United States in association with long-term increases
in precipitation, neither the precipitation increases nor
inferred drought decreases have been confidently attributed to anthropogenic forcing.
Description of evidence base
Recent droughts are well characterized and describedin the literature. The Dust Bowl is not as well documented, but available observational records support the key
finding. The last sentence is an “absence of evidence”
statement and does not imply “evidence of absence” offuture anthropogenic changes. The inferred decreases
in some measures of U.S. drought or types of drought(heat wave/flash droughts) are described in Andreadis
and Lettenmaier12 and Mo and Lettenmaier3.
Major uncertainties
Record-breaking temperatures are well documented
with low uncertainty.129 The magnitude of the Dust
Bowl relative to present times varies with location. Uncertainty in the key finding is affected by the quality of
pre-World War II observations but is relatively low.
Assessment of confidence based on evidence andagreement
Precipitation is well observed in the United States, leading to very high confidence.
Summary sentence or paragraph that integrates
the above information
The key finding is a statement that recent U.S. droughts,
while sometimes long and severe, are not unprecedented in the historical record.

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Key Finding 2
The human effect on recent major U.S. droughts is complicated. Little evidence is found for a human influenceon observed precipitation deficits, but much evidence
is found for a human influence on surface soil moisture
deficits due to increased evapotranspiration caused byhigher temperatures (high confidence).
Description of evidence baseObservational records of meteorological drought
are not long enough to detect statistically significant
trends. Additionally, paleoclimatic evidence suggeststhat major droughts have occurred throughout the
distant past. Surface soil moisture is not well observed
throughout the CONUS, but numerous event attribution studies attribute enhanced reduction of surface
soil moisture during dry periods to anthropogenic
warming and enhanced evapotranspiration. Sophisticated land surface models have been demonstrated toreproduce the available observations and have allowed
for century scale reconstructions.
Major uncertainties
Uncertainties stem from the length of precipitation
observations and the lack of surface moisture observations.
Assessment of confidence based on evidence andagreement
Confidence is high for widespread future surface soil moisture deficits, as little change is projected for future summer
and fall average precipitation. In the absence of increased
precipitation (and in some cases with it), evapotranspiration increases due to increased temperatures will lead toless soil moisture overall, especially near the surface.
Summary sentence or paragraph that integrates
the above information
The precipitation deficit portion of the key finding is a conservative statement reflecting the conflicting and limited
event attribution literature on meteorological drought.
The soil moisture portion of the key finding is limited tothe surface and not the more relevant root depth and is
supported by the studies cited in this chapter.

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Key Finding 3
Future decreases in surface (top 10 cm) soil moisturefrom anthropogenic forcing over most of the UnitedStates are likely as the climate warms under the higher
scenarios. (Medium confidence)
Description of evidence baseFirst principles establish that evaporation is at least
linearly dependent on temperatures and accounts formuch of the surface moisture decrease as temperature increases. Plant transpiration for many non-desert
species controls plant temperature and responds toincreased temperature by opening stomata to release
more water vapor. This water comes from the soil atroot depth as the plant exhausts its stored water supply (very high confidence). Furthermore, nearly all CMIP5
models exhibit U.S. surface soil moisture drying at theend of the century under the higher scenario (RCP8.5),
and the multimodel average exhibits no significant annual soil moisture increases anywhere on the planet.2
Major uncertainties
While both evaporation and transpiration changes areof the same sign as temperature increases, the relative
importance of each as a function of depth is less wellquantified. The amount of transpiration varies considerably among plant species, and these are treated withwidely varying degrees of sophistication in the land
surface components of contemporary climate models.
Uncertainty in the sign of the anthropogenic change ofroot depth soil moisture is low in regions and seasonsof projected precipitation decreases (Ch. 7: Precipitation Changes). There is moderate to high uncertainty
in the magnitude of the change in soil moisture at all
depths and all regions and seasons. This key finding isa “projection without attribution” statement as such adrying is not part of the observed record. Projectionsof summertime mean CONUS precipitation exhibit
no significant change. However, recent summertime
precipitation trends are positive, leading to reduced
agricultural drought conditions overall.12 While statistically significant increases in precipitation have been
identified over parts of the United States, these trends
have not been clearly attributed to anthropogenic forcing (Ch. 7: Precipitation Change). Furthermore, North

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American summer temperature increases under thehigher scenario (RCP8.5) at the end of the century are
projected to be substantially more than the current observed (and modeled) temperature increase. Becauseof the response of evapotranspiration to temperature
increases, the CMIP5 multimodel average projection
is for drier surface soils even in those high latitude regions (Alaska and Canada) that are confidently projected to experience increases in precipitation. Hence, in
the CONUS region, with little or no projected summertime changes in precipitation, we conclude that surface
soil moisture will likely decrease.
Assessment of confidence based on evidence andagreement
CMIP5 and regional models support the surface soil
moisture key finding. Confidence is assessed as “medium” as this key finding—despite the high level ofagreement among model projections—because of difficulties in observing long-term changes in this metric,
and because, at present, there is no published evidence
of detectable long-term decreases in surface soil moisture across the United States.
Summary sentence or paragraph that integrates
the above information
In the northern United States, surface soil moisture (top
10 cm) is likely to decrease as evaporation outpaces
increases in precipitation. In the Southwest, the combination of temperature increases and precipitation
decreases causes surface soil moisture decreases to bevery likely. In this region, decreases in soil moisture at
the root depth are likely.
Key Finding 4
Substantial reductions in western U.S. winter and springsnowpack are projected as the climate warms. Earlier
spring melt and reduced snow water equivalent havebeen formally attributed to human induced warming(high confidence) and will very likely be exacerbated as
the climate continues to warm (very high confidence).
Under higher scenarios, and assuming no change to current water resources management, chronic, long-duration hydrological drought is increasingly possible by theend of this century (very high confidence).

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Description of evidence baseFirst principles tell us that as temperatures rise, minimum snow levels also must rise. Certain changes inwestern U.S. hydrology have already been attributed
to human causes in several papers following Barnett
et al.41 and are cited in the text. The CMIP3/5 models
project widespread warming with future increases inatmospheric GHG concentrations, although these areunderestimated in the current generation of global climate models (GCMs) at the high altitudes of the western United States due to constraints on orographic representation at current GCM spatial resolutions.
CMIP5 models were not designed or constructed for direct projection of locally relevant snowpack amounts.
However, a high-resolution climate model, selected for
its ability to simulate western U.S. snowpack amounts
and extent, projects devastating changes in the hydrology of this region assuming constant water resource management practices.66 This conclusion is also supported
by a statistical downscaling result shown in Figure 3.1 ofWalsh et al.34 and Cayan et al.67 and by the more recent
statistical downscaling study of Klos et al.68.
Major uncertainties
The major uncertainty is not so much “if” but rather“how much” as changes to precipitation phases (rainor snow) are sensitive to temperature increases that inturn depend on greenhouse gas (GHG) forcing changes. Also, changes to the lower-elevation catchments
will be realized prior to those at higher elevations that,even at 25 km, are not adequately resolved. Uncertainty in the final statement also stems from the usage of
one model but is tempered by similar findings from statistical downscaling studies. However, this simulation isa so-called “prescribed temperature” experiment withthe usual uncertainties about climate sensitivity wired
in by the usage of one particular ocean temperaturechange. Uncertainty in the equator-to-pole differential
ocean warming rate is also a factor.
Assessment of confidence based on evidence andagreement
All CMIP5 models project large-scale warming in thewestern United States as GHG forcing increases. Warm-

U.S. Global Change Research Programing is underestimated in most of the western UnitedStates due to elevation deficiencies that are a consequence of coarse model resolution.
Summary sentence or paragraph that integrates
the above information
Warmer temperatures lead to less snow and more rain if
total precipitation remains unchanged. Projected winter/spring precipitation changes are a mix of increases
in northern states and decreases in the Southwest. In
the northern Rocky Mountains, snowpack is projected to decrease even with a projected precipitation increase due to this phase change effect. This will lead to,at the very least, profound changes to the seasonal andsub-seasonal timing of the western U.S. hydrologicalcycle even where annual precipitation remains nearly
unchanged with a strong potential for water shortages.
Key Finding 5
Detectable changes in some classes of flood frequency
have occurred in parts of the United States and are a
mix of increases and decreases. Extreme precipitation,
one of the controlling factors in flood statistics, is observed to have generally increased and is projected tocontinue to do so across the United States in a warming
atmosphere. However, formal attribution approaches
have not established a significant connection of increased riverine flooding to human-induced climatechange, and the timing of any emergence of a future
detectible anthropogenic change in flooding is unclear. (Medium confidence)
Description of evidence baseObserved changes are a mix of increases and decreases
and are documented by Walsh et al.34 and other studies
cited in the text. No attribution statements have been
made.
Major uncertainties
Floods are highly variable both in space and time. The
multivariate nature of floods complicates detection
and attribution.

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Assessment of confidence based on evidence andagreement
Confidence is limited to medium due to both the lackof an attributable change in observed flooding to date
and the complicated multivariate nature of flooding.
However, confidence is high in the projections of increased future extreme precipitation, the principal
driver (among several) of many floods. It is unclear
when an observed long-term increase in U.S. riverine
flooding will be attributed to anthropogenic climatechange. Hence, confidence is medium in this part of the
key message at this time.
Summary sentence or paragraph that integrates
the above information
The key finding is a relatively weak statement reflecting the lack of definitive detection and attribution ofanthropogenic changes in U.S. flooding intensity, duration, and frequency.
Key Finding 6
The incidence of large forest fires in the western UnitedStates and Alaska has increased since the early 1980s(high confidence) and is projected to further increasein those regions as the climate warms with profoundchanges to certain ecosystems (medium confidence).
Description of evidence baseStudies by Dennison et al. (western United States)108
and Kasischke and Turetsky (Alaska)122 document theobserved increases in fire statistics. Projections of Westerling et al. (western United States)116 and Young et al.121
and others (Alaska) indicate increased fire risk. Theseobservations and projections are consistent with drying due to warmer temperatures leading to increased
flammability and longer fire seasons.

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Major uncertainties
Analyses of other regions of the United States, which
also could be subject to increased fire risk, do notseem to be readily available. Likewise, projections of
the western U.S. fire risk are of limited areas. In termsof attribution, there is still some uncertainty as to how
well non-climatic confounding factors such as forestry management and fire suppression practices havebeen accounted for, particularly for the western UnitedStates. Other climate change factors, such as increased
water deficits and insect infestations, could reduce fuelloads, tending towards reducing fire frequency and/or
intensity.
Assessment of confidence based on evidence andagreement
Confidence is high in the observations due to solid observational evidence. Confidence in projections would
be higher if there were more available studies coveringa broader area of the United States and a wider rangeof ecosystems.
Summary sentence or paragraph that integrates
the above information
Wildfires have increased over parts of the western United States and Alaska in recent decades and are projected to continue to increase as a result of climate change.
As a result, shifts in certain ecosystem types may occur.

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Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T.
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105. Higuera, P.E., J.T. Abatzoglou, J.S. Littell, and P. Morgan, 2015: The changing strength and nature of fire-climate relationships in the northern Rocky Mountains,
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107. Westerling, A.L., H.G. Hidalgo, D.R. Cayan, and T.W.
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M.A. Moritz, 2014: Large wildfire trends in thewestern United States, 1984–2011. GeophysicalResearch Letters, 41, 2928-2933.
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org/10.1002/2014GL059576
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Westerling, 2009: Climate and wildfire area burnedin western U.S. ecoprovinces, 1916-2003. Ecological Applications, 19, 1003-1021.
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Lutz, R.A. Norheim, and M.M. Elsner, 2010: Forest
ecosystems, disturbance, and climatic change inWashington State, USA. Climatic Change, 102, 129158. http://dx.doi.org/10.1007/s10584-010-9858-x
111. Littell, J.S., D.L. Peterson, K.L. Riley, Y. Liu, andC.H. Luce, 2016: A review of the relationships between drought and forest fire in the United States.
Global Change Biology, 22, 2353-2369. http://dx.doi.
org/10.1111/gcb.13275
112. Yoon, J.-H., B. Kravitz, P.J. Rasch, S.-Y.S. Wang, R.R.
Gillies, and L. Hipps, 2015: Extreme fire season in
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113. Harvey, B.J., 2016: Human-caused climate change is
now a key driver of forest fire activity in the western United States. Proceedings of the National Academy of Sciences, 113, 11649-11650. http://dx.doi.
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114. Schoennagel, T., T.T. Veblen, and W.H. Romme, 2004:
The interaction of fire, fuels, and climate across Rocky
Mountain forests. BioScience, 54, 661-676. http://dx.doi.org/10.1641/0006-3568(2004)054[0661:TIOFFA]2.0.CO;2115. Stephens, S.L., J.K. Agee, P.Z. Fulé, M.P. North, W.H.
Romme, T.W. Swetnam, and M.G. Turner, 2013:
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116. Westerling, A.L., M.G. Turner, E.A.H. Smithwick,
W.H. Romme, and M.G. Ryan, 2011: Continued
warming could transform Greater Yellowstone fire
regimes by mid-21st century. Proceedings of the National Academy of Sciences, 108, 13165-13170. http://dx.doi.org/10.1073/pnas.1110199108

106. Running, S.W., 2006: Is global warming causing
more, larger wildfires? Science, 313, 927-928. http://dx.doi.org/10.1126/science.1130370

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117.

Stavros, E.N., J.T. Abatzoglou, D. McKenzie,
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the likelihood of very large wildland fires under a changing climate in the contiguous Western United States. Climatic Change, 126, 455-468.
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limits of the past 10,000 years. Proceedings of the
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Flannigan, M., B. Stocks, M. Turetsky, and M.
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McGuire, A.D., L.G. Anderson, T.R. Christensen,
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Partain, J.L., Jr., S. Alden, U.S. Bhatt, P.A. Bieniek, B.R. Brettschneider, R. Lader, P.Q. Olsson, T.S. Rupp, H. Strader, R.L.T. Jr., J.E. Walsh,
A.D. York, and R.H. Zieh, 2016: An assessmentof the role of anthropogenic climate change in
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Sanford, T., R. Wang, and A. Kenwa, 2015: The
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http://dx.doi.org/10.1071/WF14167

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KEY FINDINGS
1. Human activities have contributed substantially to observed ocean–atmosphere variability in the Atlantic Ocean (medium confidence), and these changes have contributed to the observed upward trend in
North Atlantic hurricane activity since the 1970s (medium confidence).
2. Both theory and numerical modeling simulations generally indicate an increase in tropical cyclone(TC) intensity in a warmer world, and the models generally show an increase in the number of very
intense TCs. For Atlantic and eastern North Pacific hurricanes and western North Pacific typhoons,increases are projected in precipitation rates (high confidence) and intensity (medium confidence). The
frequency of the most intense of these storms is projected to increase in the Atlantic and western North
Pacific (low confidence) and in the eastern North Pacific (medium confidence).
3. Tornado activity in the United States has become more variable, particularly over the 2000s, with adecrease in the number of days per year with tornadoes and an increase in the number of tornadoes
on these days (medium confidence). Confidence in past trends for hail and severe thunderstorm winds,
however, is low. Climate models consistently project environmental changes that would putatively
support an increase in the frequency and intensity of severe thunderstorms (a category that combines
tornadoes, hail, and winds), especially over regions that are currently prone to these hazards, but confidence in the details of this projected increase is low.
4. There has been a trend toward earlier snowmelt and a decrease in snowstorm frequency on the southern margins of climatologically snowy areas (medium confidence). Winter storm tracks have shifted
northward since 1950 over the Northern Hemisphere (medium confidence). Projections of winter storm
frequency and intensity over the United States vary from increasing to decreasing depending on
region, but model agreement is poor and confidence is low. Potential linkages between the frequency
and intensity of severe winter storms in the United States and accelerated warming in the Arctic havebeen postulated, but they are complex, and, to some extent, contested, and confidence in the connection is currently low.
5. The frequency and severity of landfalling “atmospheric rivers” on the U.S. West Coast (narrow streams
of moisture that account for 30%–40% of the typical snowpack and annual precipitation in the region
and are associated with severe flooding events) will increase as a result of increasing evaporation andresulting higher atmospheric water vapor that occurs with increasing temperature. (Medium confidence)

Recommended Citation for Chapter
Kossin, J.P., T. Hall, T. Knutson, K.E. Kunkel, R.J. Trapp, D.E. Waliser, and M.F. Wehner, 2017: Extreme
storms. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W.
Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research
Program, Washington, DC, USA, pp. 257-276, doi: 10.7930/J07S7KXX.

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9.1 Introduction
Extreme storms have numerous impacts onlives and property. Quantifying how broadscale average climate influences the behaviorof extreme storms is particularly challenging,in part because extreme storms are comparatively rare short-lived events and occur within
an environment of largely random variability.
Additionally, because the physical mechanisms linking climate change and extreme
storms can manifest in a variety of ways, eventhe sign of the changes in the extreme storms
can vary in a warming climate. This makes
detection and attribution of trends in extreme
storm characteristics more difficult than detection and attribution of trends in the larger
environment in which the storms evolve (e.g.,
Ch. 6: Temperature Change). Projecting changes in severe storms is also challenging becauseof model constraints in how they capture andrepresent small-scale, highly local physics.
Despite the challenges, good progress is being
made for a variety of storm types, such as
tropical cyclones, severe convective storms(thunderstorms), winter storms, and atmospheric river events.

9.2 Tropical Cyclones (Hurricanes andTyphoons)
Detection and attribution (Ch. 3: Detection and
Attribution) of past changes in tropical cyclone(TC) behavior remain a challenge due to thenature of the historical data, which are highly
heterogeneous in both time and among the various regions that collect and analyze the data.1,
2, 3
While there are ongoing efforts to reanalyze
and homogenize the data (e.g., Landsea etal. 2015;4 Kossin et al. 20132), there is still low
confidence that any reported long-term (multidecadal to centennial) increases in TC activity
are robust, after accounting for past changes
in observing capabilities [which is unchanged
from the Intergovernmental Panel on Climate
Change Fifth Assessment Report (IPCC AR5)assessment statement5]. This is not meant toU.S. Global Change Research Program

imply that no such increases have occurred, butrather that the data are not of a high enoughquality to determine this with much confidence. Furthermore, it has been argued thatwithin the period of highest data quality (sincearound 1980), the globally observed changes inthe environment would not necessarily support
a detectable trend in tropical cyclone intensity.2
That is, the trend signal has not yet had time torise above the background variability of naturalprocesses.
Both theory and numerical modeling simulations (in general) indicate an increase in TC
intensity in a warmer world, and the models
generally show an increase in the number ofvery intense TCs.6, 7, 8, 9, 10 In some cases, climate
models can be used to make attribution statements about TCs without formal detection(see also Ch. 3: Detection and Attribution). For
example, there is evidence that, in addition to
the effects of El Niño, anthropogenic forcing
made the extremely active 2014 Hawaiian
hurricane season substantially more likely,
although no significant rising trend in TC frequency near Hawai‘i was detected.11
Changes in frequency and intensity are notthe only measures of TC behavior that may beaffected by climate variability and change, andthere is evidence that the locations where TCs
reach their peak intensity has migrated poleward over the past 30 years in the Northernand Southern Hemispheres, apparently in concert with environmental changes associatedwith the independently observed expansionof the tropics.12 The poleward migration in
the western North Pacific,13 which includes anumber of U.S. territories, appears particularly consistent among the various available TC
datasets and remains significant over the past
60–70 years after accounting for the known
modes of natural variability in the region (Figure 9.1). The migration, which can substantially change patterns of TC hazard exposure and258

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mortality risk, is also evident in 21st century
Coupled Model Intercomparison Project Phase
5 (CMIP5) projections following the RCP8.5
emissions trajectories, suggesting a possible
link to human activities. Further analysis comparing observed past TC behavior with climate model historical forcing runs (and withmodel control runs simulating multidecadalinternal climate variability alone) are needed
to better understand this process, but it is expected that this will be an area of heightened
future research.

Departure from Average Latitude (°)

In the Atlantic, observed multidecadal variability of the ocean and atmosphere, which TCs areshown to respond to, has been ascribed (Ch. 3:
Detection and Attribution) to natural internalvariability via meridional overturning ocean
circulation changes,14 natural external variability caused by volcanic eruptions15, 16 and Saharan dust outbreaks,17, 18 and anthropogenic ex-

ternal forcing via greenhouse gases and sulfate
aerosols.19, 20, 21 Determining the relative contributions of each mechanism to the observed
multidecadal variability in the Atlantic, andeven whether natural or anthropogenic factors
have dominated, is presently a very active areaof research and debate, and no consensus has
yet been reached.22, 23, 24, 25, 26, 27 Despite the levelof disagreement about the relative magnitudeof human influences, there is broad agreementthat human factors have had an impact on theobserved oceanic and atmospheric variability
in the North Atlantic, and there is medium confidence that this has contributed to the observed
increase in hurricane activity since the 1970s.
This is essentially unchanged from the IPCC
AR5 statement,6 although the post-AR5 literature has only served to further support thisstatement.28 This is expected to remain an active
research topic in the foreseeable future.

3
2
1
0
−1
−2
−3

0.21°± 0.13°per decade
1945 1955 1965 1975 1985 1995 2005 2015
Year

Figure 9.1: Poleward migration, in degrees of latitude, of the location of annual mean tropical cyclone (TC) peak
lifetime intensity in the western North Pacific Ocean, after accounting for the known regional modes of interannual(El Niño–Southern Oscillation; ENSO) and interdecadal (Pacific Decadal Oscillation; PDO) variability. The time seriesshows residuals of the multivariate regression of annually averaged latitude of TC peak lifetime intensity onto the mean
Niño-3.4 and PDO indices. Data are taken from the Joint Typhoon Warning Center (JTWC). Shading shows 95% confidence bounds for the trend. Annotated values at lower right show the mean migration rate and its 95% confidence
interval in degrees per decade for the period 1945–2013. (Figure source: adapted from Kossin et al. 2016;13 © American
Meteorological Society. Used with permission.)

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The IPCC AR5 consensus TC projections forthe late 21st century (IPCC Figure 14.17)8
include an increase in global mean TC intensity, precipitation rate, and frequency ofvery intense (Saffir-Simpson Category 4–5)
TCs, and a decrease, or little change, in global TC frequency. Since the IPCC AR5, somestudies have provided additional support forthis consensus, and some have challenged
an aspect of it. For example, a recent study9
projects increased mean hurricane intensities
in the Atlantic Ocean basin and in most, but
not all, other TC-supporting basins (see Table
3 in Knutson et al. 20159). In their study, theglobal occurrence of Saffir–Simpson Category
4–5 storms was projected to increase significantly, with the most significant basin-scalechanges projected for the Northeast Pacificbasin, potentially increasing intense hurricane
risk to Hawai‘i (Figure 9.2) over the coming
century. However, another recent (post-AR5)
study proposed that increased thermal stratification of the upper ocean in CMIP5 climate
warming scenarios should substantiallyreduce the warming-induced intensification ofTCs estimated in previous studies.29 Follow-up
studies, however, estimate that the effect ofsuch increased stratification is relatively small,
reducing the projected intensification of TCs
by only about 10%–15%.30, 31
Another recent study challenged the IPCC
AR5 consensus projection of a decrease, or
little change, in global tropical cyclone frequency by simulating increased global TC
frequency over the 21st century under thehigher scenario (RCP8.5).32 However, anothermodeling study has found that neither direct analysis of CMIP5-class simulations, nor
indirect inferences from the simulations (suchas those of Emanuel 201332), could reproduce
the decrease in TC frequency projected in a
warmer world by high-resolution TC-permitting climate models,33 which adds uncertainty
to the results of Emanuel.32

U.S. Global Change Research Program

In summary, despite new research that challenges one aspect of the AR5 consensus forlate 21st century-projected TC activity, it remains likely that global mean tropical cyclone
maximum wind speeds and precipitation rates
will increase; and it is more likely than not thatthe global frequency of occurrence of TCs
will either decrease or remain essentially thesame. Confidence in projected global increases
of intensity and tropical cyclone precipitation rates is medium and high, respectively, as
there is better model consensus. Confidence isfurther heightened, particularly for projected
increases in precipitation rates, by a robust
physical understanding of the processes thatlead to these increases. Confidence in projected increases in the frequency of very intense
TCs is generally lower (medium in the eastern
North Pacific and low in the western North Pacific and Atlantic) due to comparatively fewerstudies available and due to the competing
influences of projected reductions in overall
storm frequency and increased mean intensity
on the frequency of the most intense storms.
Both the magnitude and sign of projectedchanges in individual ocean basins appears to
depend on the large-scale pattern of changes
to atmospheric circulation and ocean surface
temperature (e.g., Knutson et al. 20159). Projections of these regional patterns of change—
apparently critical for TC projections—are
uncertain, leading to uncertainty in regional
TC projections.

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a)

b)

45°N
30°N
15°N
0°
15°S
30°S
45°S

45°N
30°N
15°N
0°
15°S
30°S
45°S

Present Day Simulation: 244 Cat 4–5 storms

Storm Category
0°30°E 60°E 90°E 120°E 150°E 180° 150°W120°W 90°W 60°W 30°W

0°

RCP4.5 Late 21st Century: 313 Cat 4–5 storms

0°30°E 60°E 90°E 120°E 150°E 180° 150°W120°W 90°W 60°W 30°W

TS
HR1
HR2
HR3
HR4
HR5

0°

Figure 9.2: Tracks of simulated Saffir–Simpson Category 4–5 tropical cyclones for (a) present-day or (b) late-21st-century conditions, based on dynamical downscaling of climate conditions from the CMIP5 multimodel ensemble (lower
scenario; RCP4.5). The tropical cyclones were initially simulated using a 50-km grid global atmospheric model, buteach individual tropical cyclone was re-simulated at higher resolution using the GFDL hurricane model to provide more
realistic storm intensities and structure. Storm categories or intensities are shown over the lifetime of each simulated
storm, according to the Saffir–Simpson scale. The categories are depicted by the track colors, varying from tropicalstorm (blue) to Category 5 (black; see legend). (Figure source: Knutson et al. 2015;9 © American Meteorological Society. Used with permission.)Box 9.1: U.S. Landfalling Major Hurricane “Drought”
Hurricane Harvey made landfall as a major hurricane (Saffir–Simpson Category 3 or higher) in Texas in 2017,
breaking what has sometimes been colloquially referred to as the “hurricane drought.” Prior to Harvey, the last
major hurricane to make landfall in the continental United States was Wilma in 2005. The 11-year (2006–2016)absence of U.S. major hurricane landfall events is unprecedented in the historical records dating back to the mid19th century and has occurred in tandem with average to above-average basin-wide major hurricane counts.
Was the 11-year absence of U.S. landfalling major hurricanes due to random luck, or were there systematicchanges in climate that drove this?
One recent study indicates that the absence of U.S. landfalling major hurricanes cannot readily be attributed toany sustained changes in the climate patterns that affect hurricanes.34 Based on a statistical analysis of the historical North Atlantic hurricane database, the study found no evidence of a connection between the number ofmajor U.S. landfalls from one year to the next and concluded that the 11-year absence of U.S. landfalling major
hurricanes was random. A subsequent recent study did identify a systematic pattern of atmosphere/ocean conditions that vary in such a way that conditions conducive to hurricane intensification in the deep tropics occur inconcert with conditions conducive to weakening near the U.S. coast.35 This result suggests a possible relationshipbetween climate and hurricanes; increasing basin-wide hurricane counts are associated with a decreasing fraction of major hurricanes making U.S. landfall, as major hurricanes approaching the U.S. coast are more likely to

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Box 9.1 (continued)
weaken during active North Atlantic hurricane periods (such as the present period). It is unclear to what degree
this relationship has affected absolute hurricane landfall counts during the recent active hurricane period fromthe mid-1990s, as the basin-wide number and landfalling fraction are in opposition (that is, there are more major hurricanes but a smaller fraction make landfall as major hurricanes). It is also unclear how this relationshipmay change as the climate continues to warm. Other studies have identified systematic interdecadal hurricane
track variability that may affect landfalling hurricane and major hurricane frequency.36, 37, 38
Another recent study39 shows that the extent of the absence is sensitive to uncertainties in the historical dataand even small variations in the definition of a major hurricane, which is somewhat arbitrary. It is also sensitive
to the definition of U.S. landfall, which is a geopolitical-border-based constraint and has no physical meaning.
In fact, many areas outside of the U.S. border have experienced major hurricane landfalls in the past 11 years.
In this sense, the frequency of U.S. landfalling major hurricanes is not a particularly robust metric with whichto study questions about hurricane activity and its relationship with climate variability. Furthermore, the 11year absence of U.S. landfalling major hurricanes is not a particularly relevant metric in terms of coastal hazard
exposure and risk. For example, Hurricanes Ike (2008), Irene (2011), Sandy (2012), and most recently Hurricane
Matthew (2016) brought severe impacts to the U.S. coast despite not making landfall in the United States whileclassified as major hurricanes. In the case of Hurricane Sandy, extreme rainfall and storm surge (see also Ch.
12: Sea Level Rise) during landfall caused extensive destruction in and around the New York City area, despite
Sandy’s designation as a post-tropical cyclone at that time. In the case of Hurricane Matthew, the center camewithin about 40 miles of the Florida coast while Matthew was a major hurricane, which is close enough to significantly impact the coast but not close enough to break the “drought” as it is defined.
In summary, the absence of U.S. landfalling major hurricanes from Wilma in 2005 to Harvey in 2017 was anomalous. There is some evidence that systematic atmosphere/ocean variability has reduced the fraction of hurricanes making U.S. landfall since the mid-1990s, but this is at least partly countered by increased basin-wide
numbers, and the net effect on landfall rates is unclear. Moreover, there is a large random element, and the metric itself suffers from lack of physical basis due to the arbitrary intensity threshold and geopolitically based constraints. Additionally, U.S. coastal risk, particularly from storm surge and freshwater flooding, depends strongly
on storm size, propagation speed and direction, and rainfall rates. There is some danger, in the form of evoking
complacency, in placing too much emphasis on an absence of a specific subset of hurricanes.

9.3 Severe Convective Storms(Thunderstorms)
Tornado and severe thunderstorm events
cause significant loss of life and property:
more than one-third of the $1 billion weather
disasters in the United States during the past
25 years were due to such events, and, relative
to other extreme weather, the damages fromconvective weather hazards have undergone
the largest increase since 1980.40 A particularchallenge in quantifying the existence andintensity of these events arises from the data

U.S. Global Change Research Programsource: rather than measurements, the occurrence of tornadoes and severe thunderstorms
is determined by visual sightings by eyewitnesses (such as “storm spotters” and law
enforcement officials) or post-storm damage
assessments. The reporting has been susceptible to changes in population density, modifications to reporting procedures and training, theintroduction of video and social media, and soon. These have led to systematic, non-meteorological biases in the long-term data record.

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Nonetheless, judicious use of the report
database has revealed important information
about tornado trends. Since the 1970s, theUnited States has experienced a decrease in
the number of days per year on which tornadoes occur, but an increase in the numberof tornadoes that form on such days.41 One
important implication is that the frequency ofdays with large numbers of tornadoes—tornado outbreaks—appears to be increasing (Figure 9.3). The extent of the season over whichsuch tornado activity occurs is increasing as
well: although tornadoes in the United States
are observed in all months of the year, anearlier calendar-day start to the season of highactivity is emerging. In general, there is moreinterannual variability, or volatility, in tornado
occurrence (see also Elsner et al. 201542).43
Evaluations of hail and (non-tornadic) thunderstorm wind reports have thus far been lessrevealing. Although there is evidence of anincrease in the number of hail days per year,
the inherent uncertainty in reported hail size
reduces the confidence in such a conclusion.44

Thunderstorm wind reports have proven tobe even less reliable, because, as compared totornadoes and hail, there is less tangible visualevidence; thus, although the United States haslately experienced several significant thunderstorm wind events (sometimes referred to as“derechos”), the lack of studies that explore
long-term trends in wind events and the uncertainties in the historical data preclude any
robust assessment.
It is possible to bypass the use of reportsby exploiting the fact that the temperature,humidity, and wind in the larger vicinity—or “environment”—of a developing thunderstorm ultimately control the intensity,
morphology, and hazardous tendency of thestorm. Thus, the premise is that quantifications of the vertical profiles of temperature,humidity, and wind can be used as a proxy for
actual severe thunderstorm occurrence. In particular, a thresholded product of convective
available potential energy (CAPE) and vertical wind shear over a surface-to-6 km layer(S06) constitutes one widely used means of

200
180
160
140
120
100
80
60
40
20
0

10
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6
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1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 20100Days per Yearwith more than 30 Tornadoes

Days per Yearwith at Least 1 Tornado

Annual Tornado Activity in the U.S. (1955–2013)
Annual Tornado Activity in the U.S. (1955–2013)

Figure 9.3: Annual tornado activity in the United States over the period 1955–2013. The black squares indicate thenumber of days per year with at least one tornado rated (E)F1 or greater, and the black circles and line show thedecadal mean line of such tornado days. The red triangles indicate the number of days per year with more than 30 tornadoes rated (E)F1 or greater, and the red circles and line show the decadal mean of these tornado outbreaks. (Figuresource: redrawn from Brooks et al. 201441).

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representing the frequency of severe thunderstorms.45 This environmental-proxy approach
avoids the biases and other issues with eyewitness storm reports and is readily evaluatedusing the relatively coarse global datasets andglobal climate models. It has the disadvantage
of assuming that a thunderstorm will necessarily form and then realize its environmental
potential.
Upon employing global climate models(GCMs) to evaluate CAPE and S06, a consistent finding among a growing number ofproxy-based studies is a projected increase in
the frequency of severe thunderstorm environments in the United States over the mid- tolate 21st century.46, 47, 48, 49, 50, 51 The most robust
projected increases in frequency are over the
U.S. Midwest and southern Great Plains,during March-April-May (MAM).46 Based on
the increased frequency of very high CAPE,
increases in storm intensity are also projected
over this same period (see also Del Genio et al.
200752).
Key limitations of the environmental proxyapproach are being addressed through the applications of high-resolution dynamical downscaling, wherein sufficiently fine model grids
are used so that individual thunderstorms
are explicitly resolved, rather than implicitly
represented (as through environmental proxies). The individually modeled thunderstorms
can then be quantified and assessed in termsof severity.53, 54, 55 The dynamical-downscaling results have thus far supported the basic
findings of the environmental proxy studies, particularly in terms of the seasons andgeographical regions projected to experiencethe largest increases in severe thunderstorm
occurrence.46
The computational expense of high-resolution
dynamical downscaling makes it difficult togenerate model ensembles over long time

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periods, and thus to assess the uncertaintyof the downscaled projections. Because these
dynamical downscaling implementations focus on the statistics of storm occurrence rather
than on faithful representations of individual
events, they have generally been unconcernedwith specific extreme convective events inhistory. So, for example, such downscaling
does not address whether the intensity of anevent like the Joplin, Missouri, tornado of May
22, 2011, would be amplified under projected
future climates. Recently, the “pseudo-global
warming” (PGW) methodology (see Schäret al. 199656), which is a variant of dynamical
downscaling, has been adapted to addressthese and related questions. As an example,
when the parent “supercell” of select historicaltornado events forms under the climate conditions projected during the late 21st century,
it does not evolve into a benign, unorganized
thunderstorm but instead maintains its supercellular structure.57 As measured by updraft
strength, the intensity of these supercells
under PGW is relatively higher, although
not in proportion to the theoretical intensity
based on the projected higher levels of CAPE.
The adverse effects of enhanced precipitation
loading under PGW has been offered as one
possible explanation for such shortfalls in projected updraft strength.

9.4 Winter Storms
The frequency of large snowfall years has
decreased in the southern United States
and Pacific Northwest and increased in thenorthern United States (see Ch. 7: Precipitation Change). The winters of 2013/2014 and2014/2015 have contributed to this trend.
They were characterized by frequent storms
and heavier-than-normal snowfalls in the
Midwest and Northeast and drought in thewestern United States. These were related toblocking (a large-scale pressure pattern withlittle or no movement) of the wintertime circulation in the Pacific sector of the Northern

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Hemisphere (e.g., Marinaro et al. 201558) that
put the midwestern and northeastern United States in the primary winter storm track,
while at the same time reducing the numberof winter storms in California, causing severe
drought conditions.59 While some observational studies suggest a linkage between blocking
affecting the U.S. climate and enhanced arctic
warming (arctic amplification), specifically for
an increase in highly amplified jet stream patterns in winter over the United States,60 otherstudies show mixed results.61, 62, 63 Therefore, adefinitive understanding of the effects of arctic
amplification on midlatitude winter weather remains elusive. Other explanations havebeen offered for the weather patterns of recent
winters, such as anomalously strong Pacifictrade winds,64 but these have not been linked
to anthropogenic forcing (e.g., Delworth et al.
201565).
Analysis of storm tracks indicates that there
has been an increase in winter storm frequency and intensity since 1950, with a slight shift
in tracks toward the poles.66, 67, 68 Current global climate models (CMIP5) do in fact predict
an increase in extratropical cyclone (ETC) frequency over the eastern United States, including the most intense ETCs, under the higher
scenario (RCP8.5).69 However, there are large
model-to-model differences in the realism of
ETC simulations and in the projected changes.
Moreover, projected ETC changes have large
regional variations, including a decreased
total frequency in the North Atlantic, further
highlighting the complexity of the response to
climate change.

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9.5 Atmospheric Rivers
The term “atmospheric rivers” (ARs) refers
to the relatively narrow streams of moisturetransport that often occur within and acrossmidlatitudes70 (Figure 9.4), in part because
they often transport as much water as in the
Amazon River.71 While ARs occupy less than
10% of the circumference of Earth at any given
time, they account for 90% of the poleward
moisture transport across midlatitudes (amore complete discussion of precipitation
variability is found in Ch. 7: Precipitation
Change). In many regions of the world, theyaccount for a substantial fraction of the precipitation,72 and thus water supply, often delivered in the form of an extreme weather andprecipitation event (Figure 9.4). For example,
ARs account for 30%–40% of the typical snowpack in the Sierra Nevada mountains andannual precipitation in the U.S. West Coast
states73, 74—an essential summertime source ofwater for agriculture, consumption, and ecosystem health. However, this vital source ofwater is also associated with severe flooding—with observational evidence showing a close
connection between historically high streamflow events and floods with landfalling AR
events—in the west and other sectors of theUnited States.75, 76, 77 More recently, research has
also demonstrated that ARs are often found tobe critical in ending droughts in the western
United States.78

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495 mmin 24 h50°N (a) 13 Oct 2009 p.m. composite Atmospheric
river
40°N
30°N
20°N
10°N
Eq
50°N (b) 7 Nov 2006 a.m. composite
Atmospheric
40°N
river

625 mmin 72 h

30°N
20°N
10°N
Eq

IWV (cm)

1

2

3

5

4

7

6

AR Frequency and IVT

90°N
60°N
30°N
EQ
30°S

Percentage of Total

60°S
90°S

0
60°E

120°E
3

180°
%

6
50°W

9

120°W

60°W

10

20

30

40

50

60

70

0°

500kg/m/s
12Extreme-Precipitation Events at US Coop Stations, 1950–2008

Primarily Due
to AtmosphericRiver Events

40°W

30°W

R-CAT 1: 200 < P< 300 mm
R-CAT 2: 300 < P< 400 mm
R-CAT 3: 400 < P< 500 mm
R-CAT 4: P > 500 mm

120°W

105°W

90°W

75°W

Figure 9.4: (upper left) Atmospheric rivers depicted in Special Sensor Microwave Imager (SSM/I) measurements of
SSM/I total column water vapor leading to extreme precipitation events at landfall locations. (middle left) Annual meanfrequency of atmospheric river occurrence (for example, 12% means about 1 every 8 days) and their integrated vapor
transport (IVT).72 (bottom) ARs are the dominant synoptic storms for the U.S. West Coast in terms of extreme precipitation93 and (right) supply a large fraction of the annual precipitation in the U.S. West Coast states.73 [Figure source: (upper and middle left) Ralph et al. 2011,94 (upper right) Guan and Waliser 2015,72 (lower left) Ralph and Dettinger 2012,93(lower right) Dettinger et al. 2011;73 left panels, © American Meteorological Society. Used with permission.]

Given the important role that ARs play in thewater supply of the western United States andtheir role in weather and water extremes in thewest and occasionally other parts of the United States (e.g., Rutz et al. 201479), it is critical toexamine how climate change and the expected
intensification of the global water cycle andatmospheric transports (e.g., Held and Soden
2006;80 Lavers et al. 201581) are projected to impact ARs (e.g., Dettinger and Ingram 201382).

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Under climate change conditions, ARs may
be altered in a number of ways, namely theirfrequency, intensity, duration, and locations.
In association with landfalling ARs, any ofthese would be expected to result in impacts
on hazards and water supply given the discussion above. Assessments of ARs in climatechange projections for the United States havebeen undertaken for central California from
CMIP3,73 and a number of studies have been

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done for the West Coast of North America,83, 84,
85, 86, 87
and these studies have uniformly shownthat ARs are likely to become more frequentand intense in the future. For example, one recent study reveals a large increase of AR days
along the West Coast by the end of the 21st
century under the higher scenario (RCP8.5),
with fractional increases between 50% and600%, depending on the seasons and landfall
locations.83 Results from these studies (and Lavers et al. 201388 for ARs impacting the United
Kingdom) show that these AR changes werepredominantly driven by increasing atmospheric specific humidity, with little discernible change in the low-level winds. The higher
atmospheric water vapor content in a warmer
climate is to be expected because of an increase in saturation water vapor pressure with
air temperature (Ch. 2: Physical Drivers of
Climate Change). While the thermodynamic
effect appears to dominate the climate change
impact on ARs, leading to projected increases
in ARs, there is evidence for a dynamical effect(that is, location change) related to the projected poleward shift of the subtropical jet thatdiminished the thermodynamic effect in thesouthern portion of the West Coast of North
America.83

show qualitatively similar projected increases
while also providing evidence that the models represent AR frequency, transports, andspatial distributions relatively well comparedto observations.84, 85 A caveat associated withdrawing conclusions from any given study or
differences between two is that they typicallyuse different detection methodologies thatare typically tailored to a regional setting (cf.
Guan and Waliser 201572). Additional researchis warranted to examine these storms froma global perspective, with additional andmore in-depth, process-oriented diagnostics/
metrics. Stepping away from the sensitivities
associated with defining atmospheric rivers,
one study examined the intensification of theintegrated vapor transport (IVT), which is easily and unambiguously defined.81 That study
found that for the higher scenario (RCP8.5),
multimodel mean IVT and the IVT associatedwith extremes above 95% percentile increaseby 30%–40% in the North Pacific. These results, along with the uniform findings of thestudies above examining projected changes
in ARs for western North America and theUnited Kingdom, give high confidence that thefrequency of AR storms will increase in association with rising global temperatures.

Presently, there is no clear consensus onwhether the consistently projected increases
in AR frequency and intensity will translate
to increased precipitation in California. Thisis mostly because previous studies did not
examine this explicitly and because the model
resolution is poor and thus the topography ispoorly represented, and the topography is a
key aspect of forcing the precipitation out of
the systems.89 The evidence for considerable
increases in the number and intensity of ARs
depends (as do all climate variability studies
based on dynamical models) on the model
fidelity in representing ARs and their interactions with the global climate/circulation.
Additional confidence comes from studies that

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TRACEABLE ACCOUNTSKey Finding 1
Human activities have contributed substantially to observed ocean–atmosphere variability in the Atlantic
Ocean (medium confidence), and these changes havecontributed to the observed upward trend in North Atlantic hurricane activity since the 1970s (medium confidence).
Description of evidence base
The Key Finding and supporting text summarizes extensive evidence documented in the climate science
literature and is similar to statements made in previous national (NCA3)90 and international91 assessments.
Data limitations are documented in Kossin et al. 20132
and references therein. Contributions of natural andanthropogenic factors in observed multidecadal variability are quantified in Carslaw et al. 2013;22 Zhang etal. 2013;27 Tung and Zhou 2013;26 Mann et al. 2014;23
Stevens 2015;25 Sobel et al. 2016;24 Walsh et al. 2015.10
Major uncertainties
Key remaining uncertainties are due to known andsubstantial heterogeneities in the historical tropical
cyclone data and lack of robust consensus in determining the precise relative contributions of natural andanthropogenic factors in past variability of the tropicalenvironment.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Confidence in this finding is rated as medium. Although
the range of estimates of natural versus anthropogenic
contributions in the literature is fairly broad, virtually all
studies identify a measurable, and generally substantial, anthropogenic influence. This does constitute aconsensus for human contribution to the increases intropical cyclone activity since 1970.
Summary sentence or paragraph that integrates
the above information
The key message and supporting text summarizes extensive evidence documented in the climate sciencepeer-reviewed literature. The uncertainties and points

U.S. Global Change Research Programof consensus that were described in the NCA3 and IPCC
assessments have continued.
Key Finding 2
Both theory and numerical modeling simulations generally indicate an increase in tropical cyclone (TC) intensity in a warmer world, and the models generally
show an increase in the number of very intense TCs. For
Atlantic and eastern North Pacific hurricanes and western North Pacific typhoons, increases are projected inprecipitation rates (high confidence) and intensity (medium confidence). The frequency of the most intense of
these storms is projected to increase in the Atlantic andwestern North Pacific (low confidence) and in the eastern North Pacific (medium confidence).
Description of evidence base
The Key Finding and supporting text summarizes extensive evidence documented in the climate science
literature and is similar to statements made in previous national (NCA3)90 and international91 assessments.
Since these assessments, more recent downscalingstudies have further supported these assessments(e.g., Knutson et al. 20159), though pointing out thatthe changes (future increased intensity and tropical
cyclone precipitation rates) may not occur in all ocean
basins.
Major uncertainties
A key uncertainty remains in the lack of a supporting
detectable anthropogenic signal in the historical data
to add further confidence to these projections. As such,confidence in the projections is based on agreement
among different modeling studies and physical understanding (for example, potential intensity theory
for tropical cyclone intensities and the expectation ofstronger moisture convergence, and thus higher precipitation rates, in tropical cyclones in a warmer environment containing greater amounts of environmental
atmospheric moisture). Additional uncertainty stems
from uncertainty in both the projected pattern andmagnitude of future sea surface temperatures.9268

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Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Confidence is rated as high in tropical cyclone rainfall
projections and medium in intensity projections since
there are a number of publications supporting theseoverall conclusions, fairly well-established theory,
general consistency among different studies, varying
methods used in studies, and still a fairly strong consensus among studies. However, a limiting factor for
confidence in the results is the lack of a supporting detectable anthropogenic contribution in observed tropical cyclone data.

regions that are currently prone to these hazards, but
confidence in the details of this projected increase is low.

There is low to medium confidence for increased occurrence of the most intense tropical cyclones for mostocean basins, as there are relatively few formal studies
that focus on these changes, and the change in occurrence of such storms would be enhanced by increased
intensities, but reduced by decreased overall frequencyof tropical cyclones.

The third statement is based on projections from awide range of climate models, including GCMs andRCMs, run over the past 10 years (e.g., see the review by
Brooks 201392). The evidence is derived from an “environmental-proxy” approach, which herein means thatsevere thunderstorm occurrence is related to the occurrence of two key environmental parameters: CAPE
and vertical wind shear. A limitation of this approach isthe assumption that the thunderstorm will necessarily
form and then realize its environmental potential. This
assumption is indeed violated, albeit at levels that varyby region and season.

Summary sentence or paragraph that integrates
the above information
Models are generally in agreement that tropical cyclones will be more intense and have higher precipitation rates, at least in most ocean basins. Given theagreement between models and support of theory andmechanistic understanding, there is medium to highconfidence in the overall projection, although there issome limitation on confidence levels due to the lack of
a supporting detectable anthropogenic contribution
to tropical cyclone intensities or precipitation rates.
Key Finding 3
Tornado activity in the United States has become morevariable, particularly over the 2000s, with a decrease in
the number of days per year with tornadoes and an increase in the number of tornadoes on these days (medium confidence). Confidence in past trends for hail andsevere thunderstorm winds, however, is low. Climate
models consistently project environmental changes thatwould putatively support an increase in the frequency
and intensity of severe thunderstorms (a category thatcombines tornadoes, hail, and winds), especially over

U.S. Global Change Research Program

Description of evidence base
Evidence for the first and second statement comes fromthe U.S. database of tornado reports. There are wellknown biases in this database, but application of an intensity threshold [greater than or equal to a rating of 1
on the (Enhanced) Fujita scale], and the quantificationof tornado activity in terms of tornado days instead of
raw numbers of reports are thought to reduce these biases. It is not known at this time whether the variability
and trends are necessarily due to climate change.

Major uncertainties
Regarding the first and second statements, there is stillsome uncertainty in the database, even when the data
are filtered. The major uncertainty in the third statement equates to the aforementioned limitation (that is,
the thunderstorm will necessarily form and then realize
its environmental potential).
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Medium: That the variability in tornado activity has increased.
Medium: That the severe-thunderstorm environmental
conditions will change with a changing climate, but
Low: on the precise (geographical and seasonal) realization of the environmental conditions as actual severe
thunderstorms.

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Summary sentence or paragraph that integrates
the above information
With an established understanding of the data biases,
careful analysis provides useful information about pastchanges in severe thunderstorm and tornado activity.
This information suggests that tornado variability has
increased in the 2000s, with a concurrent decrease in
the number of days per year experiencing tornadoes
and an increase in the number of tornadoes on thesedays. Similarly, the development of novel applicationsof climate models provides information about possible
future severe storm and tornado activity, and althoughconfidence in these projections is low, they do suggestthat the projected environments are at least consistent
with environments that would putatively support anincrease in frequency and intensity of severe thunderstorms.
Key Finding 4
There has been a trend toward earlier snowmelt anda decrease in snowstorm frequency on the southern
margins of climatologically snowy areas (medium confidence). Winter storm tracks have shifted northward
since 1950 over the Northern Hemisphere (medium
confidence). Projections of winter storm frequency andintensity over the United States vary from increasing
to decreasing depending on region, but model agreement is poor and confidence is low. Potential linkages
between the frequency and intensity of severe winterstorms in the United States and accelerated warming in
the Arctic have been postulated, but they are complex,
and, to some extent, contested, and confidence in theconnection is currently low.
Description of evidence base
The Key Finding and supporting text summarizes evidence documented in the climate science literature.
Evidence for changes in winter storm track changes are
documented in a small number of studies.67, 68 Futurechanges are documented in one study,69 but there arelarge model-to-model differences. The effects of arctic
amplification on U.S. winter storms have been studied,but the results are mixed,60, 61, 62, 63 leading to considerable uncertainties.

U.S. Global Change Research Program

Major uncertainties
Key remaining uncertainties relate to the sensitivity ofobserved snow changes to the spatial distribution ofobserving stations and to historical changes in station
location and observing practices. There is conflicting
evidence about the effects of arctic amplification on
CONUS winter weather.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is high confidence that warming has resulted inearlier snowmelt and decreased snowfall on the warm
margins of areas with consistent snowpack based on anumber of observational studies. There is medium confidence that Northern Hemisphere storm tracks haveshifted north based on a small number of studies. Thereis low confidence in future changes in winter storm frequency and intensity based on conflicting evidence
from analysis of climate model simulations.
Summary sentence or paragraph that integrates
the above information
Decreases in snowfall on southern and low elevation
margins of currently climatologically snowy areas arelikely but winter storm frequency and intensity changes are uncertain.
Key Finding 5
The frequency and severity of landfalling “atmosphericrivers” on the U.S. West Coast (narrow streams of moisture that account for 30%–40% of the typical snowpack
and annual precipitation in the region and are associated with severe flooding events) will increase as aresult of increasing evaporation and resulting higher
atmospheric water vapor that occurs with increasing
temperature (medium confidence).
Description of evidence base
The Key Finding and supporting text summarizes evidence documented in the climate science literature.
Evidence for the expectation of an increase in the frequency and severity of landfalling atmospheric riverson the U.S. West Coast comes from the CMIP-based

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climate change projection studies of Dettinger et al.
2011;73 Warner et al. 2015;87 Payne and Magnusdottir
2015;85 Gao et al. 2015;83 Radić et al. 2015;86 and Hagos
et al. 2016.84 The close connection between atmospheric rivers and water availability and flooding is basedon the present-day observation studies of Guan et al.
2010;74 Dettinger et al. 2011;73 Ralph et al. 2006;77 Neiman et al. 2011;76 Moore et al. 2012;75 and Dettinger
2013.78
Major uncertainties
A modest uncertainty remains in the lack of a supporting detectable anthropogenic signal in the historical
data to add further confidence to these projections.
However, the overall increase in atmospheric rivers projected/expected is based to a very large degree on thevery high confidence that the atmospheric water vapor
will increase. Thus, increasing water vapor coupled withlittle projected change in wind structure/intensity still
indicates increases in the frequency/intensity of atmospheric rivers. A modest uncertainty arises in quantifying the expected change at a regional level (for example, northern Oregon vs. southern Oregon) given that
there are some changes expected in the position of thejet stream that might influence the degree of increasefor different locations along the West Coast. Uncertainty in the projections of the number and intensity of ARs
is introduced by uncertainties in the models’ ability torepresent ARs and their interactions with climate.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Confidence in this finding is rated as medium based on
qualitatively similar projections among different studies.
Summary sentence or paragraph that integrates
the above information
Increases in atmospheric river frequency and intensity
are expected along the U.S. West Coast, leading to thelikelihood of more frequent flooding conditions, withuncertainties remaining in the details of the spatial
structure of theses along the coast (for example, northern vs. southern California).

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frontal wave. Monthly Weather Review, 139, 1169-1189.
http://dx.doi.org/10.1175/2010mwr3596.1

86. Radić, V., A.J. Cannon, B. Menounos, and N. Gi,
2015: Future changes in autumn atmospheric river
events in British Columbia, Canada, as projected by
CMIP5 global climate models. Journal of GeophysicalResearch Atmospheres, 120, 9279-9302. http://dx.doi.
org/10.1002/2015JD023279
87. Warner, M.D., C.F. Mass, and E.P. Salathé Jr., 2015:
Changes in winter atmospheric rivers along theNorth American West Coast in CMIP5 climate models. Journal of Hydrometeorology, 16, 118-128. http://dx.doi.org/10.1175/JHM-D-14-0080.1
88. Lavers, D.A., R.P. Allan, G. Villarini, B. LloydHughes, D.J. Brayshaw, and A.J. Wade, 2013: Futurechanges in atmospheric rivers and their implications
for winter flooding in Britain. Environmental Research
Letters, 8, 034010. http://dx.doi.org/10.1088/17489326/8/3/034010

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10

Changes in Land Coverand Terrestrial Biogeochemistry

KEY FINDINGS
1. Changes in land use and land cover due to human activities produce physical changes in land surface
albedo, latent and sensible heat, and atmospheric aerosol and greenhouse gas concentrations. The
combined effects of these changes have recently been estimated to account for 40% ± 16% of the human-caused global radiative forcing from 1850 to present day (high confidence). In recent decades, land
use and land cover changes have turned the terrestrial biosphere (soil and plants) into a net “sink” forcarbon (drawing down carbon from the atmosphere), and this sink has steadily increased since 1980(high confidence). Because of the uncertainty in the trajectory of land cover, the possibility of the land
becoming a net carbon source cannot be excluded (very high confidence).
2. Climate change and induced changes in the frequency and magnitude of extreme events (e.g.,droughts, floods, and heat waves) have led to large changes in plant community structure with subsequent effects on the biogeochemistry of terrestrial ecosystems. Uncertainties about how climate change
will affect land cover change make it difficult to project the magnitude and sign of future climate feedbacks from land cover changes (high confidence).
3. Since 1901, regional averages of both the consecutive number of frost-free days and the length ofthe corresponding growing season have increased for the seven contiguous U.S. regions used in thisassessment. However, there is important variability at smaller scales, with some locations actuallyshowing decreases of a few days to as much as one to two weeks. Plant productivity has not increased
commensurate with the increased number of frost-free days or with the longer growing season due toplant-specific temperature thresholds, plant–pollinator dependence, and seasonal limitations in waterand nutrient availability (very high confidence). Future consequences of changes to the growing season
for plant productivity are uncertain.
4. Recent studies confirm and quantify that surface temperatures are higher in urban areas than insurrounding rural areas for a number of reasons, including the concentrated release of heat frombuildings, vehicles, and industry. In the United States, this urban heat island effect results in daytime
temperatures 0.9°–7.2°F (0.5°–4.0°C) higher and nighttime temperatures 1.8°– 4.5°F (1.0°–2.5°C) higher
in urban areas, with larger temperature differences in humid regions (primarily in the eastern UnitedStates) and in cities with larger and denser populations. The urban heat island effect will strengthen in
the future as the structure, spatial extent, and population density of urban areas change and grow (highconfidence).

Recommended Citation for Chapter
Hibbard, K.A., F.M. Hoffman, D. Huntzinger, and T.O. West, 2017: Changes in land cover and terrestrial
biogeochemistry. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles,
D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change
Research Program, Washington, DC, USA, pp. 277-302, doi: 10.7930/J0416V6X.

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Climate Science Special Report10 | Changes in Land Cover and Terrestrial Biogeochemistry10.1 Introduction
Direct changes in land use by humans arecontributing to radiative forcing by altering
land cover and therefore albedo, contributing toclimate change (Ch. 2: Physical Drivers of Climate Change). This forcing is spatially variablein both magnitude and sign; globally averaged, it
is negative (climate cooling; Figure 2.3). Climatechanges, in turn, are altering the biogeochemistryof land ecosystems through extended growing
seasons, increased numbers of frost-free days,
altered productivity in agricultural and forested
systems, longer fire seasons, and urban-induced
thunderstorms.1, 2 Changes in land use and land
cover interact with local, regional, and global

climate processes.3 The resulting ecosystem
responses alter Earth’s albedo, the carbon cycle,
and atmospheric aerosols, constituting a mixof positive and negative feedbacks to climatechange (Figure 10.1 and Chapter 2, Section
2.6.2).4, 5 Thus, changes to terrestrial ecosystems or land cover are a direct driver of climatechange and they are further altered by climatechange in ways that affect both ecosystem productivity and, through feedbacks, the climate
itself. The following sections describe advances
since the Third National Climate Assessment(NCA3)6 in scientific understanding of land
cover and associated biogeochemistry and theirimpacts on the climate system.

LULCC

1) Atmospheric:increase

1) Atmospheric composition
2) Land surface albedo
3) Sensible heat flux4) Latent heat and water
vapor fluxes

Some decomp.

Also NH3
Dust, ammonium

BC, OC, SO4

Rice cultivation

Deforest- Irrigation(Agriculture)ation
Hydrologicalcycle changes

Natural Environment

Forest regrowth

Forests

Crop regrowth

Wetlands

decrease

4)

SOA

Veg. regrowth

Veg. regrowth

Naturalfire cycle3)

Decomposition

2)

CO2
CH4
N2O
NOx
Aerosols

Ag. wasteburning

Wildfire
regimeshift

Pasture Reforestation

Wood
harvestSome C stored
in wood/paper
products

Anthropogenic Land Use and Land Cover Change

Figure 10.1: This graphical representation summarizes land–atmosphere interactions from natural and anthropogenic
land-use and land-cover change (LULCC) contributions to radiative forcing. Emissions and sequestration of carbon andfluxes of nitrogen oxides, aerosols, and water shown here were used to calculate net radiative forcing from LULCC.
(Figure source: Ward et al. 20145).

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Climate Science Special Report10 | Changes in Land Cover and Terrestrial Biogeochemistry10.2 Terrestrial Ecosystem Interactions
with the Climate System
Other chapters of this report discuss changes
in temperature (Ch. 6: Temperature Change),precipitation (Ch. 7: Precipitation Change),hydrology (Ch. 8: Droughts, Floods, andWildfires), and extreme events (Ch. 9: Extreme
Storms). Collectively, these processes affect thephenology, structure, productivity, and biogeochemical processes of all terrestrial ecosystems, and as such, climate change will alter
land cover and ecosystem services.
10.2.1 Land Cover and Climate Forcing

Changes in land cover and land use have long
been recognized as important contributors
to global climate forcing (e.g., Feddema et al.
20057). Historically, studies that account forthe contribution of the land cover to radiative
forcing have accounted for albedo forcings
only and not those from changes in land
surface geophysical properties (e.g., planttranspiration, evaporation from soils, plantcommunity structure and function) or in aerosols. Physical climate effects from land-coveror land-use change do not lend themselves
directly to quantification using the traditionalradiative forcing concept. However, a framework to attribute the indirect contributions ofland cover to radiative forcing and the climate
system—including effects on seasonal andinterannual soil moisture and latent/sensible
heat, evapotranspiration, biogeochemical cycle (CO2) fluxes from soils and plants, aerosol
and aerosol precursor emissions, ozone precursor emissions, and snowpack—was reported in NRC.8 Predicting future consequences ofchanges in land cover on the climate system
will require not only the traditional calculations of surface albedo but also surface net
radiation partitioning between latent andsensible heat exchange and the effects ofresulting changes in biogeochemical trace gas
and aerosol fluxes. Future trajectories of land
use and land cover change are uncertain and

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will depend on population growth, changes
in agricultural yield driven by the competing
demands for production of fuel (i.e., bioenergy crops), food, feed, and fiber as well asurban expansion. The diversity of future land
cover and land use changes as implemented
by the models that developed the Representative Concentration Pathways (RCPs) toattain target goals of radiative forcing by 2100
is discussed by Hurtt et al.9 For example, thehigher scenario (RCP8.5)10 features an increaseof cultivated land by about 185 million hectares from 2000 to 2050 and another 120 million
hectares from 2050 to 2100. In the mid-highscenario (RCP6.0)—the Asia Pacific Integrated
Model (AIM),11 urban land use increases due
to population and economic growth whilecropland area expands due to increasing fooddemand. Grassland areas decline while total
forested area extent remains constant throughout the century.9 The Global Change Assessment Model (GCAM), under a lower scenario(RCP4.5), preserved and expanded forested
areas throughout the 21st century. Agricultural land declined slightly due to this afforestation, yet food demand is met through crop
yield improvements, dietary shifts, production
efficiency, and international trade.9, 12 As withthe higher scenario (RCP8.5), the even lower
scenario (RCP2.6)13 reallocated agricultural
production from developed to developing
countries, with increased bioenergy production.9 Continued land-use change is projectedacross all RCPs (2.6, 4.5, 6.0, and 8.5) and is
expected to contribute between 0.9 and 1.9
W/m2 to direct radiative forcing by 2100.5 The
RCPs demonstrate that land-use management
and change combined with policy, demographic, energy technological innovations andchange, and lifestyle changes all contribute tofuture climate (see Ch. 4: Projections for moredetail on RCPs).14
Traditional calculations of radiative forcing byland-cover change yield small forcing values

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Climate Science Special Report10 | Changes in Land Cover and Terrestrial Biogeochemistry(Ch. 2: Physical Drivers of Climate Change)
because they account only for changes insurface albedo (e.g., Myhre and Myhre 2003;15
Betts et al. 2007;16 Jones et al. 201517). Recent
assessments (Myhre et al. 20134 and references
therein) are beginning to calculate the relative contributions of land-use and land-coverchange (LULCC) to radiative forcing in addition to albedo and/or aerosols.5 Radiative
forcing data reported in this chapter are largely
from observations (see Table 8.2 in Myhre et al.
20134). Ward et al.5 performed an independent
modeling study to partition radiative forcing
from natural and anthropogenic land use andland cover change and related land management activities into contributions from carbon
dioxide (CO2), methane (CH4), nitrous oxide(N2O), aerosols, halocarbons, and ozone (O3).

zero due to regrowth of forests which offsets
the release of CO2 from fire.18

The more extended effects of land–atmosphereinteractions from natural and anthropogenic
land-use and land-cover change (LULCC;
Figure 10.1) described above have recently
been reviewed and estimated by atmosphericconstituent (Figure 10.2).4, 5 The combined albedo and greenhouse gas radiative forcing forland-cover change is estimated to account for
40% ± 16% of the human-caused global radiative forcing from 1850 to 2010 (Figure 10.2).5
These calculations for total radiative forcing(from LULCC sources and all other sources)
are consistent with Myhre et al. 20134 (2.23 W/
m2 and 2.22 W/m2 for Ward et al. 20145 andMyhre et al. 20134, respectively). The contributions of CO2, CH4, N2O, and aerosols/O3/
albedo effects to total LULCC radiative forcing
are about 47%, 34%, 15%, and 4%, respectively,
highlighting the importance of non-albedo
contributions to LULCC and radiative forcing.
The net radiative forcing due specifically tofire—after accounting for short-lived forcing
agents (O3 and aerosols), long-lived greenhouse gases, and land albedo change both
now and in the future—is estimated to be nearA recent analysis shows large-scale greening in the Arctic and boreal regions of North
America and browning in the boreal forestsof eastern Alaska for the period 1984–2012.23
Satellite observations and ecosystem models
suggest that biogeochemical interactions ofcarbon dioxide (CO2) fertilization, nitrogen(N) deposition, and land-cover change are responsible for 25%–50% of the global greeningof the Earth and 4% of Earth’s browning between 1982 and 2009.24, 25 While several studies
have documented significant increases in therate of green-up periods, the lengthening of
the growing season (Section 10.3.1) also alters
the timing of green-up (onset of growth) andbrown-down (senescence); however, where
ecosystems become depleted of water resources as a result of a lengthening growing season,
the actual period of productive growth can betruncated.26

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10.2.2 Land Cover and Climate Feedbacks

Earth system models differ significantly inprojections of terrestrial carbon uptake,19 withlarge uncertainties in the effects of increasing
atmospheric CO2 concentrations (i.e., CO2
fertilization) and nutrient downregulation
on plant productivity, as well as the strengthof carbon cycle feedbacks (Ch. 2: PhysicalDrivers of Climate Change).20, 21 When CO2
effects on photosynthesis and transpiration
are removed from global gridded crop models, simulated response to climate across themodels is comparable, suggesting that model
parameterizations representing these processes remain uncertain.22

Large-scale die-off and disturbances resulting
from climate change have potential effects
beyond the biogeochemical and carbon cycle
effects. Biogeophysical feedbacks can strengthen or reduce climate forcing. The low albedo

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Radiative Forcing (W/m2) between 1850 and 2010

3

CO2

N2O CH4

Aerosols
DirectIndirect

AlbedoLand

Halo-Snow/ carbons
ice

2

1

0

SUM

LULCC
Non-LULCC
Total Anthropogenic (2011)(IPCC AR5)

−1
Figure 10.2: Anthropogenic radiative forcing (RF) contributions, separated by land-use and land-cover change (LULCC) and non-LULCC sources (green and maroon bars, respectively), are decomposed by atmospheric constituent toyear 2010 in this diagram, using the year 1850 as the reference. Total anthropogenic RF contributions by atmosphericconstituent4 (see also Figure 2.3) are shown for comparison (yellow bars). Error bars represent uncertainties for total
anthropogenic RF (yellow bars) and for the LULCC components (green bars).5 The SUM bars indicate the net RF when
all anthropogenic forcing agents are combined. (Figure source: Ward et al. 20145).

of boreal forests provides a positive feedback,but those albedo effects are mitigated intropical forests through evaporative cooling;
for temperate forests, the evaporative effects
are less clear.27 Changes in surface albedo,
evaporation, and surface roughness can havefeedbacks to local temperatures that are larger
than the feedback due to the change in carbon
sequestration.28 Forest management frameworks (e.g., afforestation, deforestation, andavoided deforestation) that account for biophysical (e.g., land surface albedo and surface
roughness) properties can be used as climate
protection or mitigation strategies.29
10.2.3 Temperature ChangeInteractions between temperature changes,
land cover, and biogeochemistry are more
complex than commonly assumed. Previous research suggested a fairly direct relationship between increasing temperatures,
longer growing seasons (see Section 10.3.1),

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increasing plant productivity (e.g., Walsh etal. 201430), and therefore also an increase in
CO2 uptake. Without water or nutrient limitations, increased CO2 concentrations and warm
temperatures have been shown to extend thegrowing season, which may contribute tolonger periods of plant activity and carbon
uptake, but do not affect reproduction rates.31
However, a longer growing season can also
increase plant water demand, affecting regional water availability, and result in conditions
that exceed plant physiological thresholds
for growth, producing subsequent feedbacks
to radiative forcing and climate. These consequences could offset potential benefits of alonger growing season (e.g., Georgakakos etal. 201432; Hibbard et al. 201433). For instance,increased dry conditions can lead to wildfire(e.g., Hatfield et al. 2014;34 Joyce et al. 2014;35
Ch. 8: Droughts, Floods and Wildfires) andurban temperatures can contribute to urban-induced thunderstorms in the southeast-

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Climate Science Special Report10 | Changes in Land Cover and Terrestrial Biogeochemistry
ern United States.36 Temperature benefits ofearly onset of plant development in a longer
growing season can be offset by 1) freeze
damage caused by late-season frosts; 2) limits
to growth because of shortening of the photoperiod later in the season; or 3) by shorter
chilling periods required for leaf unfolding by
many plants.37, 38 MODIS data provided insight into the coterminous U.S. 2012 drought,
when a warm spring reduced the carbon cycle
impact of the drought by inducing earlier
carbon uptake.39 New evidence points tolonger temperature-driven growing seasonsfor grasslands that may facilitate earlier onsetof growth, but also that senescence is typicallyearlier.40 In addition to changing CO2 uptake,higher temperatures can also enhance soil decomposition rates, thereby adding more CO2
to the atmosphere. Similarly, temperature, as
well as changes in the seasonality and intensity of precipitation, can influence nutrient andwater availability, leading to both shortages
and excesses, thereby influencing rates andmagnitudes of decomposition.1
10.2.4 Water Cycle Changes

The global hydrological cycle is expected to intensify under climate change as a consequenceof increased temperatures in the troposphere.
The consequences of the increased water-holding capacity of a warmer atmosphere include
longer and more frequent droughts and lessfrequent but more severe precipitation eventsand cyclonic activity (see Ch. 9: Extreme
Storms for an in-depth discussion of extreme
storms). More intense rain events and storms
can lead to flooding and ecosystem disturbances, thereby altering ecosystem function
and carbon cycle dynamics. For an extensive
review of precipitation changes and droughts,
floods, and wildfires, see Chapters 7 and 8 inthis report, respectively.
From the perspective of the land biosphere,
drought has strong effects on ecosystem

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productivity and carbon storage by reducing photosynthesis and increasing the risk of
wildfire, pest infestation, and disease susceptibility. Thus, droughts of the future willaffect carbon uptake and storage, leading to
feedbacks to the climate system (Chapter 2,
Section 2.6.2; also see Chapter 11 for Arctic/
climate/wildfire feedbacks).41 Reduced productivity as a result of extreme drought events
can also extend for several years post-drought(i.e., drought legacy effects).42, 43, 44 In 2011, themost severe drought on record in Texas led
to statewide regional tree mortality of 6.2%,
or nearly nine times greater than the averageannual mortality in this region (approximately
0.7%).45 The net effect on carbon storage wasestimated to be a redistribution of 24–30 TgCfrom the live to dead tree carbon pool, whichis equal to 6%–7% of pre-drought live tree carbon storage in Texas state forestlands.45 Another way to think about this redistribution is thatthe single Texas drought event equals approximately 36% of annual global carbon losses
due to deforestation and land-use change.46
The projected increases in temperatures andin the magnitude and frequency of heavy
precipitation events, changes to snowpack,and changes in the subsequent water availability for agriculture and forestry may leadto similar rates of mortality or changes in land
cover. Increasing frequency and intensity ofdrought across northern ecosystems reduces
total observed organic matter export, has led
to oxidized wetland soils, and releases storedcontaminants into streams after rain events.47
10.2.5 Biogeochemistry

Terrestrial biogeochemical cycles play a keyrole in Earth’s climate system, including byaffecting land–atmosphere fluxes of many
aerosol precursors and greenhouse gases, including carbon dioxide (CO2), methane (CH4),
and nitrous oxide (N2O). As such, changes
in the terrestrial ecosphere can drive climatechange. At the same time, biogeochemical

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cycles are sensitive to changes in climate andatmospheric composition.
Increased atmospheric CO2 concentrations
are often assumed to lead to increased plant
production (known as CO2 fertilization) andlonger-term storage of carbon in biomass andsoils. Whether increased atmospheric CO2
will continue to lead to long-term storage ofcarbon in terrestrial ecosystems depends onwhether CO2 fertilization simply intensifies
the rate of short-term carbon cycling (for example, by stimulating respiration, root exudation, and high turnover root growth), howwater and other nutrients constrain CO2 fertilization, or whether the additional carbon isused by plants to build more wood or tissues
that, once senesced, decompose into longlived soil organic matter. Under increased CO2
concentrations, plants have been observed to
optimize water use due to reduced stomatal
conductance, thereby increasing water-use efficiency.48 This change in water-use efficiency
can affect plants’ tolerance to stress and specifically to drought.49 Due to the complex interactions of the processes that govern terrestrial
biogeochemical cycling, terrestrial ecosystem
responses to increasing CO2 levels remain
one of the largest uncertainties in long-termclimate feedbacks and therefore in predicting
longer-term climate change (Ch. 2: PhysicalDrivers of Climate Change).
Nitrogen is a principal nutrient for plant
growth and can limit or stimulate plant productivity (and carbon uptake), depending on
availability. As a result, increased nitrogen
deposition and natural nitrogen-cycle responses to climate change will influence theglobal carbon cycle. For example, nitrogen
limitation can inhibit the CO2 fertilization response of plants to elevated atmospheric CO2(e.g., Norby et al. 2005;50 Zaehle et al. 201051).
Conversely, increased decomposition of soilorganic matter in response to climate warm-

U.S. Global Change Research Programing increases nitrogen mineralization. This
shift of nitrogen from soil to vegetation can
increase ecosystem carbon storage.46, 52 Whilethe effects of increased nitrogen deposition
may counteract some nitrogen limitation on
CO2 fertilization, the importance of nitrogen in
future carbon–climate interactions is not clear.
Nitrogen dynamics are being integrated into
the simulation of land carbon cycle modeling,
but only two of the models in CMIP5 included
coupled carbon–nitrogen interactions.53
Many factors, including climate, atmospheric
CO2 concentrations, and nitrogen deposition rates influence the structure of the plantcommunity and therefore the amount andbiochemical quality of inputs into soils.54, 55,
56
For example, though CO2 losses from soils
may decrease with greater nitrogen deposition, increased emissions of other greenhousegases, such as methane (CH4) and nitrous
oxide (N2O), can offset the reduction in CO2.57
The dynamics of soil organic carbon under the
influence of climate change is poorly understood and therefore not well represented inmodels. As a result, there is high uncertainty
in soil carbon stocks in model simulations.58, 59
Future emissions of many aerosol precursors
are expected to be affected by a number of climate-related factors, in part because of changes in aerosol and aerosol precursors from theterrestrial biosphere. For example, volatileorganic compounds (VOCs) are a significant
source of secondary organic aerosols, and biogenic sources of VOCs exceed emissions fromthe industrial and transportation sectors.60
Isoprene is one of the most important biogenic
VOCs, and isoprene emissions are strongly
dependent on temperature and light, as wellas other factors like plant type and leaf age.60
Higher temperatures are expected to lead toan increase in biogenic VOC emissions. Atmospheric CO2 concentration can also affect isoprene emissions (e.g., Rosenstiel et al. 200361).

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Changes in biogenic VOC emissions canimpact aerosol formation and feedbacks withclimate (Ch. 2: Physical Drivers of Climate
Change, Section 2.6.1; Feedbacks via changes
in atmospheric composition). Increased biogenic VOC emissions can also impact ozone
and the atmospheric oxidizing capacity.62
Conversely, increases in nitrogen oxide (NOx)pollution produce tropospheric ozone (O3),which has damaging effects on vegetation. For
example, a recent study estimated yield losses
for maize and soybean production of up to 5%
to 10% due to increases in O3.63
10.2.6 Extreme Events and DisturbanceThis section builds on the physical overview
provided in earlier chapters to frame how
the intersections of climate, extreme events,
and disturbance affect regional land coverand biogeochemistry. In addition to overall
trends in temperature (Ch. 6: Temperature
Change) and precipitation (Ch. 7: Precipitation
Change), changes in modes of variability suchas the Pacific Decadal Oscillation (PDO) andthe El Niño–Southern Oscillation (ENSO) (Ch.
5: Circulation and Variability) can contributeto drought in the United States, which leads tounanticipated changes in disturbance regimes
in the terrestrial biosphere (e.g., Kam et al.
201464). Extreme climatic events can increase
the susceptibility of ecosystems to invasiveplants and plant pests by promoting transportof propagules into affected regions, decreasing
the resistance of native communities to establishment, and by putting existing native species at a competitive disadvantage.65 For example, drought may exacerbate the rate of plantinvasions by non-native species in rangelands
and grasslands.45 Land-cover changes suchas encroachment and invasion of non-native
species can in turn lead to increased frequency
of disturbance such as fire. Disturbance events
alter soil moisture, which, in addition to being
affected by evapotranspiration and precipitation (Ch. 8: Droughts, Floods, and Wildfires),

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is controlled by canopy and rooting architecture as well as soil physics. Invasive plants
may be directly responsible for changes in fire
regimes through increased biomass, changes in the distribution of flammable biomass,
increased flammability, and altered timing offuel drying, while others may be “fire followers” whose abundances increase as a resultof shortening the fire return interval (e.g.,Lambert et al. 201066). Changes in land cover
resulting from alteration of fire return intervals, fire severity, and historical disturbance
regimes affect long-term carbon exchange
between the atmosphere and biosphere (e.g.,
Moore et al. 201645). Recent extensive diebacks
and changes in plant cover due to drought
have interacted with regional carbon cycle
dynamics, including carbon release frombiomass and reductions in carbon uptake fromthe atmosphere; however, plant regrowth
may offset emissions.67 The 2011–2015 meteorological drought in California (describedin Ch. 8: Droughts, Floods, and Wildfires),combined with future warming, will lead to
long-term changes in land cover, leading toincreased probability of climate feedbacks(e.g., drought and wildfire) and in ecosystemshifts.68 California’s recent drought has also
resulted in measureable canopy water losses,
posing long-term hazards to forest health andbiophysical feedbacks to regional climate.44,
69, 70Multiyear or severe meteorological andhydrological droughts (see Ch. 8: Droughts,
Floods, and Wildfires for definitions) can also
affect stream biogeochemistry and riparian
ecosystems by concentrating sediments andnutrients.67
Changes in the variability of hurricanes andwinter storm events (Ch. 9: Extreme Storms)
also affect the terrestrial biosphere, as shownin studies comparing historic and future (projected) extreme events in the western UnitedStates and how these translate into changes inregional water balance, fire, and streamflow.

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Composited across 10 global climate models(GCMs), summer (June–August) water-balance deficit in the future (2030–2059) increases
compared to that under historical (1916–2006)
conditions. Portions of the Southwest thathave significant monsoon precipitation andsome mountainous areas of the Pacific Northwest are exempt from this deficit.71 Projectionsfor 2030–2059 suggest that extremely low
flows that have historically occurred (1916–
2006) in the Columbia Basin, upper Snake River, southeastern California, and southwestern
Oregon are less likely to occur. Given the historical relationships between fire occurrence
and drought indicators such as water-balance
deficit and streamflow, climate change canbe expected to have significant effects on fireoccurrence and area burned.71, 72, 73
Climate change in the northern high latitudes
is directly contributing to increased fire occurrence (Ch. 11: Arctic Changes); in the coterminous United States, climate-induced changes
in fires, changes in direct human ignitions,and land-management practices all significantly contribute to wildfire trends. Wildfires
in the western United States are often ignited
by lightning, but management practices suchas fire suppression contribute to fuels and amplify the intensity and spread of wildfire. Fires
initiated from unintentional ignition, such as
by campfires, or intentional human-caused ignitions are also intensified by increasingly dry
and vulnerable fuels, which build up with fire
suppression or human settlements (See also
Ch. 8: Droughts, Floods, and Wildfires).

10.3 Climate Indicators and Agriculturaland Forest Responses
Recent studies indicate a correlation between
the expansion of agriculture and the global
amplitude of CO2 uptake and emissions.74, 75
Conversely, agricultural production is increasingly disrupted by climate and extreme
weather events, and these effects are expected

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to be augmented by mid-century and beyond
for most crops.76, 77 Precipitation extremes put
pressure on agricultural soil and water assets
and lead to increased irrigation, shrinking
aquifers, and ground subsidence.
10.3.1 Changes in the Frost-Free and Growing
Seasons

The concept that longer growing seasons are
increasing productivity in some agriculturaland forested ecosystems was discussed in the
Third National Climate Assessment (NCA3).6
However, there are other consequences to alengthened growing season that can offset
gains in productivity. Here we discuss theseemerging complexities as well as other aspects of how climate change is altering andinteracting with terrestrial ecosystems. The
growing season is the part of the year in which
temperatures are favorable for plant growth.
A basic metric by which this is measured isthe frost-free period. The U.S. Department ofAgriculture Natural Resources Conservation
Service defines the frost-free period using a
range of thresholds. They calculate the average date of the last day with temperature
below 24°F (−4.4°C), 28°F (−2.2°C), and 32°F
(0°C) in the spring and the average date of thefirst day with temperature below 24°F, 28°F,
and 32°F in the fall, at various probabilities.
They then define the frost-free period at threeindex temperatures (32°F, 28°F, and 24°F), alsowith a range of probabilities. A single temperature threshold (for example, temperature
below 32°F) is often used when discussing
growing season; however, different plant cover-types (e.g., forest, agricultural, shrub, andtundra) have different temperature thresholds for growth, and different requirements/
thresholds for chilling.34, 78 For the purposes ofthis report, we use the metric with a 32°F (0°C)
threshold to define the change in the numberof “frost-free” days, and a temperature threshold of 41°F (5°C) as a first-order measure of285

Climate Science Special Report10 | Changes in Land Cover and Terrestrial Biogeochemistry
how the growing season length has changed
over the observational record.78

increased plant productivity over most of theUnited States.25

The NCA3 reported an increase in the growing season length of as much as several weeks
as a result of higher temperatures occurring
earlier and later in the year (e.g., Walsh et al.
2014;30 Hatfield et al. 2014;34 Joyce et al. 201435).
NCA3 used a threshold of 32°F (0°C) (i.e., thefrost-free period) to define the growing season. An update to this finding is presented in
Figures 10.3 and 10.4, which show changes
in the frost-free period and growing season,
respectively, as defined above. Overall, thelength of the frost-free period has increasedin the contiguous United States during thepast century (Figure 10.3). However, growing
season changes are more variable: growing
season length increased until the late 1930s,
declined slightly until the early 1970s, increased again until about 1990, and remained
quasi-stable thereafter (Figure 10.4). This
contrasts somewhat with changes in thelength of the frost-free period presented in
NCA3, which showed a continuing increase
after 1980. This difference is attributable to thetemperature thresholds used in each indicator
to define the start and end of these periods.
Specifically, there are now more frost-free days
(32°F threshold) in winter than the growing
season (41°F threshold).

Consistent with increases in growing season
length and the coldest temperature of the year,
plant hardiness zones have shifted northwardin many areas.79 The widespread increase intemperature has also impacted the distribution of other climate zones in parts of the United States. For instance, there have been moderate changes in the range of the temperateand continental climate zones of the eastern
United States since 195080 as well as changes
in the coverage of some extreme climate zones
in the western United States. In particular, thespatial extent of the “alpine tundra” zone has
decreased in high-elevation areas,81 while theextent of the “hot arid” zone has increased in
the Southwest.82

The lengthening of the growing season has
been somewhat greater in the northern andwestern United States, which experienced
increases of 1–2 weeks in many locations. In
contrast, some areas in the Midwest, Southern
Great Plains, and the Southeast had decreases of a week or more between the periods
1986–2015 and 1901–1960.2 These differencesreflect the more general pattern of warming
and cooling nationwide (Ch. 6: Temperature
Changes). Observations and models haveverified that the growing season has generally

U.S. Global Change Research Program

The period over which plants are actually productive, that is, their true growing season, is afunction of multiple climate factors, including
air temperature, number of frost-free days,
and rainfall, as well as biophysical factors,
including soil physics, daylight hours, andthe biogeochemistry of ecosystems.83 Temperature-induced changes in plant phenology, like flowering or spring leaf onset, could
result in a timing mismatch (phenologicalasynchrony) with pollinator activity, affecting seasonal plant growth and reproduction
and pollinator survival.84, 85, 86, 87 Further, while
growing season length is generally referred
to in the context of agricultural productivity,
the factors that govern which plant types willgrow in a given location are common to allplants whether they are in agricultural, natural, or managed landscapes. Changes in boththe length and the seasonality of the growing
season, in concert with local environmental
conditions, can have multiple effects on agricultural productivity and land cover.

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In the context of agriculture, a longer growing
season could allow for the diversification of
cropping systems or allow multiple harvests
within a growing season. For example, shifts
in cold hardiness zones across the contiguousUnited States suggest widespread expansionof thermally suitable areas for the cultivationof cold-intolerant perennial crops88 as well as
for biological invasion of non-native plants
and plant pests.89 However, changes in available water, conversion from dry to irrigatedfarming, and changes in sensible and latent
heat exchange associated with these shifts
need to be considered. Increasingly dry conditions under a longer growing season canalter terrestrial organic matter export andcatalyze oxidation of wetland soils, releasing
stored contaminants (for example, copper andnickel) into streamflow after rainfall.47 Similarly, a longer growing season, particularly
in years where water is limited, is not due towarming alone, but is exacerbated by higher
atmospheric CO2 concentrations that extend
the active period of growth by plants.31 Longer
growing seasons can also limit the types ofcrops that can be grown, encourage invasive
species encroachment or weed growth, or in-crease demand for irrigation, possibly beyond
the limits of water availability. They could also
disrupt the function and structure of a region’s
ecosystems and could, for example, alter therange and types of animal species in the area.

(a)

(b)

Observed Increase in Frost-Free Season Length+17

A longer and temporally shifted growing
season also affects the role of terrestrial ecosystems in the carbon cycle. Neither seasonality of growing season (spring and summer)
nor carbon, water, and energy fluxes should
be interpreted separately when analyzing theimpacts of climate extremes such as drought(Ch. 8: Droughts, Floods, and Wildfires).39, 90
Observations and data-driven model studies
suggest that losses in net terrestrial carbon
uptake during record warm springs followed
by severely hot and dry summers can belargely offset by carbon gains in record-exceeding warmth and early arrival of spring.39
Depending on soil physics and land cover, acool spring, however, can deplete soil water
resources less rapidly, making the subsequent
impacts of precipitation deficits less severe.90
Depletion of soil moisture through early plant
activity in a warm spring can potentially amplify summer heating, a typical lagged direct
Projected Changes in Frost-free Season Length

+10

+11

+9

+17
+4

+7

Change in Annual Number of Days

Change in Annual Number of Days
0–4

5–9

10–14

15+

0 10 20 30 40 50 60 70 80 90

Figure 10.3: (a) Observed changes in the length of the frost-free season by region, where the frost-free season isdefined as the number of days between the last spring occurrence and the first fall occurrence of a minimum temperature at or below 32°F. This change is expressed as the change in the average number of frost-free days in 1986–2015
compared to 1901–1960. (b) Projected changes in the length of the frost-free season at mid-century (2036–2065 ascompared to 1976–2005) under the higher scenario (RCP8.5). Gray indicates areas that are not projected to experience a freeze in more than 10 of the 30 years (Figure source: (a) updated from Walsh et al. 2014;30 (b) NOAA NCEI and
CICS-NC, data source: LOCA dataset).

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Deviation from Average (Days)

15
10
5
0
−5

−10
1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Year
Figure 10.4: The length of the growing season in the contiguous 48 states compared with a long-term average (1895–
2015), where “growing season” is defined by a daily minimum temperature threshold of 41°F. For each year, the line
represents the number of days shorter or longer than the long-term average. The line was smoothed using an 11-year
moving average. Choosing a different long-term average for comparison would not change the shape of the data overtime. (Figure source: Kunkel 20162).

effect of an extremely warm spring.42 Ecosystem responses to the phenological changes oftiming and extent of growing season and subsequent biophysical feedbacks are therefore
strongly dependent on the timing of climate
extremes (Ch. 8: Droughts, Floods, and Wildfires; Ch. 9: Extreme Storms).90
The global Coupled Model Intercomparison Project Phase 5 (CMIP5) analyses did
not explicitly explore future changes to thegrowing season length. Many of the projectedchanges in North American climate are generally consistent across CMIP5 models, butthere is substantial inter-model disagreementin projections of some metrics important toproductivity in biophysical systems, including the sign of regional precipitation changes
and extreme heat events across the northern
United States.91
10.3.2 Water Availability and Drought

Drought is generally parameterized in mostagricultural models as limited water availability and is an integrated response of both
meteorological and agricultural drought, as
described in Chapter 8: Droughts, Floods,

U.S. Global Change Research Programand Wildfires. However, physiological aswell as biophysical processes that influence
land cover and biogeochemistry interact withdrought through stomatal closure induced
by elevated atmospheric CO2 levels.48, 49 Thishas direct impacts on plant transpiration,
atmospheric latent heat fluxes, and soil moisture, thereby influencing local and regional
climate. Drought is often offset by management through groundwater withdrawals,
with increasing pressure on these resources
to maintain plant productivity. This results inindirect climate effects by altering land surface
exchange of water and energy with the atmosphere.92
10.3.3 Forestry Considerations

Climate change and land-cover change inforested areas interact in many ways, suchas through changes in mortality rates driven
by changes in the frequency and magnitudeof fire, insect infestations, and disease. In
addition to the direct economic benefits offorestry, unquantified societal benefits include
ecosystem services, like protection of watersheds and wildlife habitat, and recreation andhuman health value. United States forests and288

Climate Science Special Report10 | Changes in Land Cover and Terrestrial Biogeochemistry
related wood products also absorb and store
the equivalent of 16% of all CO2 emitted byfossil fuel burning in the United States eachyear.6 Climate change is expected to reduce thecarbon sink strength of forests overall.
Effective management of forests offers theopportunity to reduce future climate change—for example, as given in proposals for Reduced Emissions from Deforestation andforest Degradation (REDD+; https://www.
forestcarbonpartnership.org/what-redd) indeveloping countries and tropical ecosystems (see Ch. 14: Mitigation)—by capturing
and storing carbon in forest ecosystems andlong-term wood products.93 Afforestation in
the United States has the potential to captureand store 225 million tons of additional carbon
per year from 2010 to 2110.94, 95 However, theprojected maturation of United States forests96
and land-cover change, driven in particularby the expansion of urban and suburban areas
along with projected increased demands forfood and bioenergy, threaten the extent of forests and their carbon storage potential.97
Changes in growing season length, combinedwith drought and accompanying wildfire arereshaping California’s mountain ecosystems.
The California drought led to the lowest
snowpack in 500 years, the largest wildfiresin post-settlement history, greater than 23%
stress mortality in Sierra mid-elevation forests,
and associated post-fire erosion.69 It is anticipated that slow recovery, possibly to different
ecosystem types, with numerous shifts to species’ ranges will result in long-term changes toland surface biophysical as well as ecosystem
structure and function in this region (http://
www.fire.ca.gov/treetaskforce/).69
While changes in forest stocks, composition, andthe ultimate use of forest products can influence
net emissions and climate, the future net changes
in forest stocks remain uncertain.9, 27, 98, 99, 100 This

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uncertainty is due to a combination of uncertainties in future population size, population
distribution and subsequent land-use change,
harvest trends, wildfire management practices(for example, large-scale thinning of forests), andthe impact of maturing U.S. forests.

10.4 Urban Environments and Climate
ChangeUrban areas exhibit several characteristics that
affect land-surface and geophysical attributes,
including building infrastructure (rougher,
more uneven surfaces compared to rural or
natural systems), increased emissions and concentrations of aerosols and other greenhousegasses, and increased anthropogenic heatsources.101, 102 The understanding that urban
areas modify their surrounding environmenthas been accepted for over a century, but themechanisms through which this occurs have
only begun to be understood and analyzed formore than 40 years.102, 103 Prior to the 1970s, themajority of urban climate research was observational and descriptive,104 but since that time,
more importance has been given to physicaldynamics that are a function of land surface(for example, built environment and change
to surface roughness); hydrologic, aerosol,and other greenhouse gas emissions; thermal
properties of the built environment; and heatgenerated from human activities (Seto et al.
2016105 and references therein).
There is now strong evidence that urban environments modify local microclimates, with
implications for regional and global climatechange.102, 104 Urban systems affect various
climate attributes, including temperature,
rainfall intensity and frequency, winter precipitation (snowfall), and flooding. New observational capabilities—including NASA’s dual
polarimetric radar, advanced satellite remotesensing (for example, the Global Precipitation
Measurement Mission-GPM), and regionalized, coupled land–surface–atmospheric289

Climate Science Special Report10 | Changes in Land Cover and Terrestrial Biogeochemistrymodeling systems for urban systems—are
now available to evaluate aspects of daytime
and nighttime temperature fluctuations;urban precipitation; contribution of aerosols;
how the urban built environment impacts theseasonality and type of precipitation (rain orsnow) as well as the amount and distributionof precipitation; and the significance of theextent of urban metropolitan areas.101, 102, 106, 107
The urban heat island (UHI) is characterized
by increased surface and canopy temperatures as a result of heat-retaining asphalt andconcrete, a lack of vegetation, and anthropogenic generation of heat and greenhousegasses.107 The heat gain due to the storage
capacity of urban built structures, reductions
in local evapotranspiration, and anthropogenically generated heat alter the spatio-temporal
pattern of temperature and leads to the UHI
phenomenon. The UHI physical processes
that affect the climate system include generation of heat storage in buildings during theday, nighttime release of latent heat storageby buildings, and sensible heat generated byhuman activities, include heating of buildings,
air conditioning, and traffic.108
The strength of the effect is correlated with thespatial extent and population density of urban
areas; however, because of varying definitionsof urban vs. non-urban, impervious surface
area is a more objective metric for estimating
the extent and intensity of urbanization.109
Based on land surface temperature measurements, on average, the UHI effect increases
urban temperature by 5.2°F (2.9°C), but it has
been measured at 14.4°F (8°C) in cities built
in areas dominated by temperate forests.109
In arid regions, however, urban areas can be
more than 3.6°F (2°C) cooler than surrounding
shrublands.110 Similarly, urban settings lose
up to 12% of precipitation through impervious surface runoff, versus just over 3% loss
to runoff in vegetated regions. Carbon losses

U.S. Global Change Research Programfrom the biosphere to the atmosphere throughurbanization account for almost 2% of thecontinental terrestrial biosphere total, a significant proportion given that urban areas only
account for around 1% of land in the UnitedStates.110 Similarly, statistical analyses of therelationship between climate and urban land
use suggest an empirical relationship between
the patterns of urbanization and precipitation
deficits during the dry season. Causal factors for this reduction may include changes
to runoff (for example, impervious-surface
versus natural-surface hydrology) that extend
beyond the urban heat island effect and energy-related aerosol emissions.111
The urban heat island effect is more significant during the night and during winter thanduring the day, and it is affected by the shape,size, and geometry of buildings in urban centers as well as by infrastructure along gradients from urban to rural settlements.101, 105, 106
Recent research points to mounting evidence
that urbanization also affects cycling of water,
carbon, aerosols, and nitrogen in the climate
system.106
Coordinated modeling and observational
studies have revealed other mechanisms bywhich the physical properties of urban areas
can influence local weather and climate. It
has been suggested that urban-induced windconvergence can determine storm initiation;
aerosol concentrations and composition theninfluence the amount of cloud water and ice
present in the clouds. Aerosols can also influence updraft and downdraft intensities, theirlife span, and surface precipitation totals.107 A
pair of studies investigated rainfall efficiency
in sea-breeze thunderstorms and found that
integrated moisture convergence in urban
areas influenced storm initiation and mid-level moisture, thereby affecting precipitation
dynamics.112, 113

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According to the World Bank, over 81% of theUnited States population currently resides inurban settings.114 Climate mitigation efforts
to offset UHI are often stalled by the lack ofquantitative data and understanding of thespecific factors of urban systems that contribute to UHI. A recent study set out to quantitatively determine contributors to the intensityof UHI across North America.115 The study
found that population strongly influenced
nighttime UHI, but that daytime UHI varied
spatially following precipitation gradients.
The model applied in this study indicated thatthe spatial variation in the UHI signal wascontrolled most strongly by impacts on theatmospheric convection efficiency. Becauseof the impracticality of managing convection
efficiency, results from Zhao et al.115 support
albedo management as an efficient strategy to
mitigate UHI on a large scale.

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TRACEABLE ACCOUNTSKey Finding 1
Changes in land use and land cover due to human activities produce physical changes in land surface albedo, latent and sensible heat, and atmospheric aerosoland greenhouse gas concentrations. The combined
effects of these changes have recently been estimated to account for 40% ± 16% of the human-caused
global radiative forcing from 1850 to present day (highconfidence). In recent decades, land use and land cover changes have turned the terrestrial biosphere (soiland plants) into a net “sink” for carbon (drawing downcarbon from the atmosphere), and this sink has steadily
increased since 1980 (high confidence). Because of theuncertainty in the trajectory of land cover, the possibility of the land becoming a net carbon source cannot be
excluded (very high confidence).
Description of evidence baseTraditional methods that estimate albedo changes forcalculating radiative forcing due to land-use change
were identified by NRC.8 That report recommended
that indirect contributions of land-cover change to climate-relevant variables, such as soil moisture, greenhouse gas (e.g., CO2 and water vapor) sources and sinks,
snow cover, aerosols, and aerosol and ozone precursor
emissions also be considered. Several studies havedocumented physical land surface processes such as
albedo, surface roughness, sensible and latent heat exchange, and land-use and land-cover change that interact with regional atmospheric processes (e.g., Marotz etal. 1975;116 Barnston and Schickendanz 1984;117 Alpert
and Mandel 1986;118 Pielke and Zeng 1989;119 Feddema
et al. 2005;7 Pielke et al. 2007120); however, traditional
calculations of radiative forcing by land-cover change
in global climate model simulations yield small forcing
values (Ch. 2: Physical Drivers of Climate Change) because they account only for changes in surface albedo(e.g., Myhre and Myhre 2003;15 Betts et al. 2007;16 Joneset al. 201517).
Recent studies that account for the physical as well asbiogeochemical changes in land cover and land use
radiative forcing estimated that these drivers contribute 40% of present radiative forcing due to land-use/

U.S. Global Change Research Program

land-cover change (0.9 W/m2).4, 5 These studies utilized
AR5 and follow-on model simulations to estimatechanges in land-cover and land-use climate forcing
and feedbacks for the greenhouse gases—carbon dioxide, methane, and nitrous oxide—that contributeto total anthropogenic radiative forcing from land-use
and land-cover change.4, 5 This research is grounded inlong-term observations that have been documented
for over 40 years and recently implemented into global Earth system models.4, 20 For example, IPCC 2013:
Summary for Policymakers states: “From 1750 to 2011,
CO2 emissions from fossil fuel combustion and cement
production have released 375 [345 to 405] GtC to theatmosphere, while deforestation and other land-use
changes are estimated to have released 180 [100 to260] GtC. This results in cumulative anthropogenic
emissions of 555 [470 to 640] GtC.”121 IPCC 2013, Working Group 1, Chapter 14 states for North America: “Insummary, it is very likely that by mid-century the anthropogenic warming signal will be large compared tonatural variability such as that stemming from the NAO,
ENSO, PNA, PDO, and the NAMS in all North America
regions throughout the year”.122
Major uncertainties
Uncertainty exists in the future land-cover and landuse change as well as uncertainties in regional calculations of land-cover change and associated radiative
forcing. The role of the land as a current sink has very
high confidence; however, future strength of the land
sink is uncertain.96, 97 The existing impact of land systems on climate forcing has high confidence.4 Based on
current RCP scenarios for future radiative forcing targets ranging from 2.6 to 8.5 W/m2, the future forcing
has lower confidence because it is difficult to estimatechanges in land cover and land use into the future.14
Compared to 2000, the CO2-eq. emissions consistent
with RCP8.5 more than double by 2050 and increase by
three by 2100.10 About one quarter of this increase isdue to increasing use of fertilizers and intensificationof agricultural production, giving rise to the primarysource of N2O emissions. In addition, increases in livestock population, rice production, and enteric fermentation processes increase CH4 emissions.10 Therefore,

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if existing trends in land-use and land-cover change
continue, the contribution of land cover to forcing will
increase with high confidence. Overall, future scenarios
from the RCPs suggest that land-cover change basedon policy, bioenergy, and food demands could lead tosignificantly different distribution of land cover types(forest, agriculture, urban) by 2100.9, 10, 11, 12, 13, 14
Summary sentence or paragraph that integrates
the above information
The key finding is based on basic physics and biophysical models that have been well established for decades with regards to the contribution of land albedo
to radiative forcing (NRC 2005). Recent assessments
specifically address additional biogeochemical contributions of land-cover and land-use change to radiative
forcing.4, 8 The role of current sink strength of the land isalso uncertain.96, 97 The future distribution of land coverand contributions to total radiative forcing are uncertain and depend on policy, energy demand and foodconsumption, dietary demands.14
Key Finding 2
Climate change and induced changes in the frequency and magnitude of extreme events (e.g., droughts,
floods, and heat waves) have led to large changes inplant community structure with subsequent effects
on the biogeochemistry of terrestrial ecosystems. Uncertainties about how climate change will affect land
cover change make it difficult to project the magnitude
and sign of future climate feedbacks from land coverchanges (high confidence).
Description of evidence base
From the perspective of the land biosphere, drought
has strong effects on ecosystem productivity and carbon storage by reducing microbial activity and photosynthesis and by increasing the risk of wildfire, pest
infestation, and disease susceptibility. Thus, future
droughts will affect carbon uptake and storage, leading to feedbacks to the climate system.41 Reduced productivity as a result of extreme drought events can also
extend for several years post-drought (i.e., drought legacy effects).42, 43, 44 Under increased CO2 concentrations,plants have been observed to optimize water use due

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to reduced stomatal conductance, thereby increasingwater-use efficiency.48 This change in water-use efficiency can affect plants’ tolerance to stress and specifically to drought.49
Recent severe droughts in the western United States(Texas and California) have led to significant mortality
and carbon cycle dynamics (http://www.fire.ca.gov/
treetaskforce/).45, 69 Carbon redistribution through mortality in the Texas drought was around 36% of global carbon losses due to deforestation and land usechange.46
Major uncertainties
Major uncertainties include how future land-use/
land-cover changes will occur as a result of policy
and/or mitigation strategies in addition to climatechange. Ecosystem responses to phenological changes are strongly dependent on the timing of climate
extremes.90 Due to the complex interactions of the processes that govern terrestrial biogeochemical cycling,terrestrial ecosystem response to increasing CO2 levels
remains one of the largest uncertainties in long-termclimate feedbacks and therefore in predicting longer-term climate change effects on ecosystems (e.g.,
Swann et al. 201649).
Summary sentence or paragraph that integrates
the above information
The timing, frequency, magnitude, and extent of climate extremes strongly influence plant community
structure and function, with subsequent effects onterrestrial biogeochemistry and feedbacks to the climate system. Future interactions between land coverand the climate system are uncertain and depend on
human land-use decisions, the evolution of the climate
system, and the timing, frequency, magnitude, and extent of climate extremes.
Key Finding 3
Since 1901, regional averages of both the consecutive
number of frost-free days and the length of the corresponding growing season have increased for the sevencontiguous U.S. regions used in this assessment. However, there is important variability at smaller scales, with

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Climate Science Special Report10 | Changes in Land Cover and Terrestrial Biogeochemistrysome locations actually showing decreases of a few
days to as much as one to two weeks. Plant productivity has not increased commensurate with the increased
number of frost-free days or with the longer growing
season due to plant-specific temperature thresholds,
plant–pollinator dependence, and seasonal limitations
in water and nutrient availability (very high confidence).
Future consequences of changes to the growing season for plant productivity are uncertain.
Description of evidence base
Data on the lengthening and regional variability of
the growing season since 1901 were updated by Kunkel.2 Many of these differences reflect the more general pattern of warming and cooling nationwide (Ch. 6:
Temperature Changes). Without nutrient limitations,increased CO2 concentrations and warm temperatures
have been shown to extend the growing season, which
may contribute to longer periods of plant activity andcarbon uptake but do not affect reproduction rates.31
However, other confounding variables that coincidewith climate change (for example, drought, increased
ozone, and reduced photosynthesis due to increased
or extreme heat) can offset increased growth associated with longer growing seasons26 as well as changes
in water availability and demand for water (e.g., Georgakakos et al. 2014;32Hibbard et al. 201433). Increased
dry conditions can lead to wildfire (e.g., Hatfield et al.
2014;34 Joyce et al. 2014;35 Ch. 8: Droughts, Floods andWildfires) and urban temperatures can contribute to urban-induced thunderstorms in the southeastern United States.36 Temperature benefits of early onset of plantdevelopment in a longer growing season can be offset
by 1) freeze damage caused by late-season frosts; 2)limits to growth because of shortening of the photoperiod later in the season; or 3) by shorter chilling periods
required for leaf unfolding by many plants.37, 38
Major uncertainties
Uncertainties exist in future response of the climate
system to anthropogenic forcings (land use/land cover as well as fossil fuel emissions) and associated feedbacks among variables such as temperature and precipitation interactions with carbon and nitrogen cycles
as well as land-cover change that impact the length of

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the growing season (Ch. 6: Temperature Changes andCh. 8: Droughts, Floods and Wildfires).26, 31, 34
Summary sentence or paragraph that integrates
the above information
Changes in growing season length and interactions
with climate, biogeochemistry, and land cover werecovered in 12 chapters of NCA36 but with sparse assessment of how changes in the growing season might
offset plant productivity and subsequent feedbacks tothe climate system. This key finding provides an assessment of the current state of the complex nature of thegrowing season.
Key Finding 4
Recent studies confirm and quantify higher surface
temperatures in urban areas than in surrounding rural
areas for a number of reasons, including the concentrated release of heat from buildings, vehicles, and industry. In the United States, this urban heat island effectresults in daytime temperatures 0.9°–7.2°F (0.5°–4.0°C)
higher and nighttime temperatures 1.8°– 4.5°F (1.0°–
2.5°C) higher in urban areas, with larger temperature
differences in humid regions (primarily in the eastern
United States) and in cities with larger and denser populations. The urban heat island effect will strengthen
in the future as the structure, spatial extent, and population density of urban areas change and grow (highconfidence).
Description of evidence baseUrban interactions with the climate system have been
investigated for more than 40 years.102, 103 The heat gain
due to the storage capacity of urban built structures,
reduction in local evapotranspiration, and anthropogenically generated heat alter the spatio-temporal pattern of temperature and leads to the well-known urban
heat island (UHI) phenomenon.101, 105, 106 The urban heatisland (UHI) effect is correlated with the extent of impervious surfaces, which alter albedo or the saturationof radiation.109 The urban-rural difference that defines
the UHI is greatest for cities built in temperate forest
ecosystems.109 The average temperature increase is
2.9°C, except for urban areas in biomes with arid andsemiarid climates.109, 110

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Major uncertainties
The largest uncertainties about urban forcings or feedbacks to the climate system are how urban settlements
will evolve and how energy consumption and efficiencies, and their interactions with land cover and water,
may change from present times.10, 14, 33, 105
Summary sentence or paragraph that integrates
the above information
Key Finding 4 is based on simulated and satellite land
surface measurements analyzed by Imhoff et al.109.
Bounoua et al.,110 Shepherd,107 Seto and Shepherd,106
Grimmond et al.,101 and Seto et al.105 provide specificreferences with regard to how building materials andspatio-temporal patterns of urban settlements influence radiative forcing and feedbacks of urban areas tothe climate system.

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97. McKinley, D.C., M.G. Ryan, R.A. Birdsey, C.P. Giardina, M.E. Harmon, L.S. Heath, R.A. Houghton, R.B.
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Climate Science Special Report11Arctic Changes and their Effects on
Alaska and the Rest of the United States

KEY FINDINGS
1. Annual average near-surface air temperatures across Alaska and the Arctic have increased over thelast 50 years at a rate more than twice as fast as the global average temperature (very high confidence).
2. Rising Alaskan permafrost temperatures are causing permafrost to thaw and become more discontinuous; this process releases additional carbon dioxide and methane, resulting in an amplifying feedback
and additional warming (high confidence). The overall magnitude of the permafrost–carbon feedback is
uncertain; however, it is clear that these emissions have the potential to compromise the ability to limit
global temperature increases.
3. Arctic land and sea ice loss observed in the last three decades continues, in some cases accelerating(very high confidence). It is virtually certain that Alaska glaciers have lost mass over the last 50 years,
with each year since 1984 showing an annual average ice mass less than the previous year. Based on
gravitational data from satellites, average ice mass loss from Greenland was −269 Gt per year between
April 2002 and April 2016, accelerating in recent years (high confidence). Since the early 1980s, annual
average arctic sea ice has decreased in extent between 3.5% and 4.1% per decade, become thinner by
between 4.3 and 7.5 feet, and began melting at least 15 more days each year. September sea ice extent
has decreased between 10.7% and 15.9% per decade (very high confidence). Arctic-wide ice loss is expected to continue through the 21st century, very likely resulting in nearly sea ice-free late summers by
the 2040s (very high confidence).
4. It is very likely that human activities have contributed to observed arctic surface temperature warming,
sea ice loss, glacier mass loss, and Northern Hemisphere snow extent decline (high confidence).
5. Atmospheric circulation patterns connect the climates of the Arctic and the contiguous United States.
Evidenced by recent record warm temperatures in the Arctic and emerging science, the midlatitude
circulation has influenced observed arctic temperatures and sea ice (high confidence). However, confidence is low regarding whether or by what mechanisms observed arctic warming may have influenced
the midlatitude circulation and weather patterns over the continental United States. The influence ofarctic changes on U.S. weather over the coming decades remains an open question with the potential
for significant impact.
Recommended Citation for Chapter
Taylor, P.C., W. Maslowski, J. Perlwitz, and D.J. Wuebbles, 2017: Arctic changes and their effects on Alaska and the rest of the United States. In: Climate Science Special Report: Fourth National Climate Assessment,
Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)].
U.S. Global Change Research Program, Washington, DC, USA, pp. 303-332, doi: 10.7930/J00863GK.

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11.1 Introduction

Improved data quality and increased observational coverage would help address societally
relevant arctic science questions.

Climate changes in Alaska and across the
Arctic continue to outpace changes occurring
across the globe. The Arctic, defined as thearea north of the Arctic Circle, is a vulnerable and complex system integral to Earth’s
climate. The vulnerability stems in part fromthe extensive cover of ice and snow, where thefreezing point marks a critical threshold thatwhen crossed has the potential to transformthe region. Because of its high sensitivity toradiative forcing and its role in amplifying
warming,1 the arctic cryosphere is a key indicator of the global climate state. Accelerated
melting of multiyear sea ice, mass loss fromthe Greenland Ice Sheet (GrIS), reduction ofterrestrial snow cover, and permafrost degradation are stark examples of the rapid Arctic-wide response to global warming. These
local arctic changes influence global sea level,
ocean salinity, the carbon cycle, and potentially atmospheric and oceanic circulation
patterns. Arctic climate change has altered theglobal climate in the past2 and will influence
climate in the future.

Despite these challenges, our scientific knowledge is sufficiently advanced to effectively
inform policy. This chapter documents significant scientific progress and knowledge
about how the Alaskan and arctic climate haschanged and will continue to change.

11.2 Arctic Changes
11.2.1 Alaska and Arctic Temperature

As an arctic nation, United States’ decisions
regarding climate change adaptation andmitigation, resource development, trade,
national security, transportation, etc., dependon projections of future Alaskan and arctic climate. Aside from uncertainties due tonatural variability, scientific uncertainty, andhuman activities including greenhouse gas
emissions (see Ch. 4: Projections), additional
unique uncertainties in our understanding ofarctic processes thwart projections, including
mixed-phase cloud processes;3 boundary layer
processes;4 sea ice mechanics;4 and ocean currents, eddies, and tides that affect the advection of heat into and around the Arctic Ocean.5,
6
The inaccessibility of the Arctic has made itdifficult to sustain the high-quality observations of the atmosphere, ocean, land, and ice
required to improve physically-based models.

U.S. Global Change Research Program

Surface temperature—an essential component of the arctic climate system—drives andsignifies change, fundamentally controllingthe melting of ice and snow. Further, thevertical profile of boundary layer temperature
modulates the exchange of mass, energy, andmomentum between the surface and atmosphere, influencing other components such as
clouds.7, 8 Arctic temperatures exhibit spatial
and interannual variability due to interactions
and feedbacks between sea ice, snow cover, atmospheric heat transports, vegetation, clouds,
water vapor, and the surface energy budget.9,
10, 11
Interannual variations in Alaskan temperatures are strongly influenced by decadal
variability like the Pacific Decadal Oscillation(Ch. 5: Circulation and Variability).12, 13 However, observed temperature trends exceed thisvariability.
Arctic surface and atmospheric temperatures
have substantially increased in the observational record. Multiple observation sources,
including land-based surface stations since atleast 1950 and available meteorological reanalysis datasets, provide evidence that arctic
near-surface air temperatures have increased
more than twice as fast as the global average.14,
15, 16, 17, 18
Showing enhanced arctic warming
since 1981, satellite-observed arctic averagesurface skin temperatures have increased by
1.08° ± 0.13°F (+0.60° ± 0.07°C) per decade.19

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As analyzed in Chapter 6: Temperature
Change (Figure 6.1), strong near-surface airtemperature warming has occurred across
Alaska exceeding 1.5°F (0.8°C) over the last 30
years. Especially strong warming has occurred
over Alaska’s North Slope during autumn.
For example, Utqiagvik’s (formally Barrow)warming since 1979 exceeds 7°F (3.8°C) in
September, 12°F (6.6°C) in October, and 10°F
(5.5°C) in November.20
Enhanced arctic warming is a robust featureof the climate response to anthropogenic
forcing.21, 22 An anthropogenic contribution toarctic and Alaskan surface temperature warming over the past 50 years is very likely.23, 24, 25,
26, 27
One study argues that the natural forcing
has not contributed to the long-term arctic
warming in a discernable way.27 Also, otheranthropogenic forcings (mostly aerosols) havelikely offset up to 60% of the high-latitude
greenhouse gas warming since 1913,27 suggesting that arctic warming to date would havebeen larger without the offsetting influenceof aerosols. Other studies argue for a moresignificant contribution of natural variability
to observed arctic temperature trends24, 28 andindicate that natural variability alone cannot
explain observed warming. It is very likely that
arctic surface temperatures will continue toincrease faster than the global mean throughthe 21st century.25, 26, 27, 29
11.2.2 Arctic Sea Ice Change

Arctic sea ice strongly influences Alaskan,arctic, and global climate by modulatingexchanges of mass, energy, and momentum
between the ocean and the atmosphere. Variations in arctic sea ice cover also influence
atmospheric temperature and humidity, wind
patterns, clouds, ocean temperature, thermal
stratification, and ecosystem productivity.7, 10,30, 31, 32, 33, 34, 35, 36, 37
Arctic sea ice exhibits significant interannual, spatial, and seasonal variability driven by atmospheric wind patterns

U.S. Global Change Research Programand cyclones, atmospheric temperature andhumidity structure, clouds, radiation, sea ice
dynamics, and the ocean. 38, 39, 40, 41, 42, 43, 44
Overwhelming evidence indicates that thecharacter of arctic sea ice is rapidly changing.
Observational evidence shows Arctic-wide
sea ice decline since 1979, accelerating ice loss
since 2000, and some of the fastest loss along
the Alaskan coast.19, 20, 45, 46 Although sea ice
loss is found in all months, satellite observations show the fastest loss in late summer andautumn.45 Since 1979, the annual average arctic sea ice extent has very likely decreased at a
rate of 3.5%–4.1% per decade.19, 37 Regional sea
ice melt along the Alaskan coasts exceeds thearctic average rates with declines in the Beaufort and Chukchi Seas of −4.1% and −4.7% per
decade, respectively.20 The annual minimum
and maximum sea ice extent have decreased
over the last 35 years by −13.3% ± 2.6% and−2.7% ± 0.5% per decade, respectively.47 The
ten lowest September sea ice extents over thesatellite period have all occurred in the last ten
years, the lowest in 2012. The 2016 September
sea ice minimum tied with 2007 for the second
lowest on record, but rapid refreezing resulted
in the 2016 September monthly average extent
being the fifth lowest. Despite the rapid initialrefreezing, sea ice extent was again in record
low territory during fall–winter 2016/2017
due to anomalously warm temperatures in themarginal seas around Alaska,47 contributing
to a new record low in winter ice-volume (seehttp://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly).48
Other important characteristics of arctic sea ice
have also changed, including thickness, age,and volume. Sea ice thickness is monitored
using an array of satellite, aircraft, and vessel
measurements.37, 45 The mean thickness of thearctic sea ice during winter between 1980 and2008 has decreased between 4.3 and 7.5 feet
(1.3 and 2.3 meters).37 The age distribution

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United States

3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

Slope=−13.3% ± 2.6% per decade
1979–2016 mean = 2.42 million sq. miles
1980

1985

1990

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1995

Year

2000

2005

306

2010

2015

Figure 11.1: September sea ice extent
and age shown for (a) 1984 and (b) 2016,
illustrating significant reductions in sea
ice extent and age (thickness). Bar graphin the lower right of each panel illustrates
the sea ice area (unit: million km2) covered within each age category (>1 year),and the green bars represent the maximum value for each age range during therecord. The year 1984 is representative ofSeptember sea ice characteristics during
the 1980s. The years 1984 and 2016 areselected as endpoints in the time series;
a movie of the complete time series isavailable at http://svs.gsfc.nasa.gov/cgibin/details.cgi?aid=4489. (c) Shows thesatellite-era arctic sea ice areal extent
trend from 1979 to 2016 for September(unit: million mi2). [Figure source: Panels(a),(b): NASA Science Visualization Studio; data: Tschudi et al. 2016;49 Panel (c)data: Fetterer et al. 2016209].

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United Statesof sea ice has become younger since 1988.
In March 2016, first-year (multi-year) sea ice
accounted for 78% (22%) of the total extent,
whereas in the 1980s first-year (multi-year)
sea ice accounted for 55% (45%).47 Moreover,
ice older than four years accounted for 16%of the March 1985 icepack but accounted for
only 1.2% of the icepack in March 2016, indicating significant changes in sea ice volume.47
The top two panels in Figure 11.1 show the
September sea ice extent and age in 1984 and2016, illustrating significant reductions in sea
ice age.49 While these panels show only two
years (beginning point and ending point) of
the complete time series, these two years are
representative of the overall trends discussed
and shown in the September sea ice extent
time series in the bottom panel of Fig 11.1.
Younger, thinner sea ice is more susceptible
to melt, therefore reductions in age and thickness imply a larger interannual variability ofextent.

80 years;37 however, one recent study suggeststhat it may have increased since 1979.53

Sea ice melt season—defined as the numberof days between spring melt onset and fall
freeze-up—has lengthened Arctic-wide byat least five days per decade since 1979, withlarger regional changes.46, 50 Some of the largest
observed changes in sea ice melt season (Figure 11.2) are found along Alaska’s northern
and western coasts, lengthening the melt season by 20–30 days per decade and increasing
the annual number of ice-free days by morethan 90.50 Summer sea ice retreat along coastal
Alaska has led to longer open water seasons,making the Alaskan coastline more vulnerable
to erosion.51, 52 Increased melt season length
corresponds to increased absorption of solar
radiation by the Arctic Ocean during summer and increases upper ocean temperature,
delaying fall freeze-up. Overall, this process
significantly contributes to reductions in arctic
sea ice.42, 46 Wind-driven sea ice export throughthe Fram Strait has not increased over the last

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It is very likely that there is an anthropogenic
contribution to the observed arctic sea ice decline since 1979. A range of modeling studies
analyzing the September sea ice extent trends
in simulations with and without anthropogenic forcing conclude that these declines cannot
be explained by natural variability alone.54, 55,
56, 57, 58, 59
Further, observational-based analyses
considering a range of anthropogenic and natural forcing mechanisms for September sea ice
loss reach the same conclusion.60 Considering
the occurrence of individual September sea ice
anomalies, internal climate variability alone
very likely could not have caused recently observed record low arctic sea ice extents, suchas in September 2012.61, 62 The potential contribution of natural variability to arctic sea ice
trends is significant.55, 63, 64 One recent study28
indicates that internal variability dominatesarctic atmospheric circulation trends, accounting for 30%–50% of the sea ice reductions since
1979, and up to 60% in September. However,previous studies indicate that the contributions from internal variability are smaller than
50%.54, 55 This apparent significant contribution of natural variability to sea ice decline
indicates that natural variability alone cannot
explain the observed sea ice decline and isconsistent with the statement that it is very
likely there is an anthropogenic contribution tothe observed arctic sea ice decline since 1979.
Continued sea ice loss is expected across the
Arctic, which is very likely to result in latesummers becoming nearly ice-free (areal
extent less than 106 km2 or approximately 3.9 ×105 mi2) by the 2040s.21, 65 Natural variability,66
future scenarios, and model uncertainties64, 67,
68
all influence sea ice projections. One study
suggests that internal variability alone accounts for a 20-year prediction uncertainty in

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United States
Trends in Sea Ice Melt Season
1979-2013

180°

90°E

90°W
50°N

<−75 −70 −60 −50 −40 −30 −20 −10
Trend(days/decade)

0

10

20

Figure 11.2: A 35-year trend in arctic sea ice melt season length, in days per decade, from passive microwave satellite
observations, illustrating that the sea ice season has shortened by more than 60 days in coastal Alaska over the last 30
years. (Figure source: adapted from Parkinson 201450).

the timing of the first occurrence of an icefree summer, whereas differences between ahigher scenario (RCP8.5) and a lower scenario(RCP4.5) add only 5 years.63 Projected September sea ice reductions by 2081–2100 range
from 43% for an even lower scenario (RCP2.6)
to 94% for RCP8.5.21 However, September sea
ice projections over the next few decades are
similar for the different anthropogenic forcing associated with these scenarios; scenario
dependent sea ice loss only becomes apparent
after 2050. Another study69 indicates that the
total sea ice loss scales roughly linearly with
CO2 emissions, such that an additional 1,000
GtC from present day levels corresponds to

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ice-free conditions in September. A key message from the Third National Climate Assessment (NCA3)70 was that arctic sea ice is disappearing. The fundamental conclusion of thisassessment is unchanged; additional researchcorroborates the NCA3 statement.
11.2.3 Arctic Ocean and Marginal Seas

Sea Surface Temperature
Arctic Ocean sea surface temperatures (SSTs)
have increased since comprehensive records
became available in 1982. Satellite-observed
Arctic Ocean SSTs, poleward of 60°N, exhibit
a trend of 0.16° ± 0.02°F (0.09° ± 0.01°C) per
decade.19 Arctic Ocean SST is controlled by a308

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United States
Salinity
Arctic Ocean salinity influences the freezing
temperature of sea ice (less salty water freezes
more readily) and the density profile representing the integrated effects of freshwatertransport, river runoff, evaporation, and sea
ice processes. Arctic Ocean salinity exhibits multidecadal variability, hampering theassessment of long-term trends.78 Emerging
evidence suggests that the Arctic Ocean andmarginal sea salinity has decreased in recent
years despite short-lived regional salinity
increases between 2000 and 2005.71 Increased
river runoff, rapid melting of sea and land
ice, and changes in freshwater transport haveinfluenced observed Arctic Ocean salinity.71, 79

combination of factors, including solar radiation and energy transport from ocean currents and atmospheric winds. Summertime
Arctic Ocean SST trends and patterns strongly
couple with sea ice extent; however, clouds,
ocean color, upper-ocean thermal structure,
and atmospheric circulation also play a role.40,
71
Along coastal Alaska, SSTs in the Chukchi
Sea exhibit a statistically significant (95% confidence) trend of 0.9° ± 0.5°F (0.5° ± 0.3°C) per
decade.72
Arctic Ocean temperatures also increased at
depth.71, 73 Since 1970, Arctic Ocean Intermediate Atlantic Water—located between 150 and
900 meters—has warmed by 0.86° ± 0.09°F(0.48° ± 0.05°C) per decade; the most recent
decade being the warmest.73 The observed
temperature level is unprecedented in the last
1,150 years for which proxy indicators provide records.74, 75 The influence of Intermediate
Atlantic Water warming on future Alaska andarctic sea ice loss is unclear.38, 76
Alaskan Sea Level Rise
The Alaskan coastline is vulnerable to sea level
rise (SLR); however, strong regional variability exists in current trends and future projections. Some regions are experiencing relative
sea level fall, whereas others are experiencing
relative sea level rise, as measured by tidegauges that are part of NOAA’s National Water
Level Observation Network. These tide gauge
data show sea levels rising fastest along thenorthern coast of Alaska but still slower than
the global average, due to isostatic rebound(Ch. 12: Sea Level Rise).77 However, considerable uncertainty in relative sea level rise exists
due to a lack of tide gauges; for example, notide gauges are located between Bristol Bay and
Norton Sound or between Cape Lisburne andPrudhoe Bay. Under almost all future scenarios, SLR along most of the Alaskan coastline isprojected to be less than the global average (Ch.
12: Sea Level Rise).

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Ocean Acidification
Arctic Ocean acidification is occurring at afaster rate than the rest of the globe (see also Ch.
13: Ocean Changes).80 Coastal Alaska and its
ecosystems are especially vulnerable to ocean
acidification because of the high sensitivity ofArctic Ocean water chemistry to changes in sea
ice, respiration of organic matter, upwelling, andincreasing river runoff.80 Sea ice loss and a longer
melt season contribute to increased vulnerabilityof the Arctic Ocean to acidification by lowering
total alkalinity, permitting greater upwelling, andinfluencing the primary production characteristics in coastal Alaska.81, 82, 83, 84, 85, 86 Global-scalemodeling studies suggest that the largest andmost rapid changes in pH will continue along
Alaska’s coast, indicating that ocean acidification
may increase enough by the 2030s to significantly
influence coastal ecosystems.80
11.2.4 Boreal Wildfires

Alaskan wildfire activity has increased in recent decades. This increase has occurred bothin the boreal forest87 and in the arctic tundra,88
where fires historically were smaller and lessfrequent. A shortened snow cover season andhigher temperatures over the last 50 years89
make the Arctic more vulnerable to wildfire.87,

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United States
Total area burned and the number of large
fires (those with area greater than 1,000 km2 or
386 mi2) in Alaska exhibit significant interannual and decadal variability, from influencesof atmospheric circulation patterns and controlled burns, but have likely increased since1959.91 The most recent decade has seen an
unusually large number of years with anomalously large wildfires in Alaska.92 Studies
indicate that anthropogenic climate change
has likely lengthened the wildfire season andincreased the risk of severe fires.93 Further,wildfire risks are expected to increase throughthe end of the century due to warmer, drier
conditions.90, 94 Using climate simulations toforce an ecosystem model over Alaska (Alaska
Frame-Based Ecosystem Code, ALFRESCO),
the total area burned is projected to increase
between 25% and 53% by 2100.95 A transition
into a regime of fire activity unprecedented
in the last 10,000 years is possible.96 We conclude that there is medium confidence for ahuman-caused climate change contribution toincreased forest fire activity in Alaska in recent
decades. See Chapter 8: Drought, Floods, andWildfires for more details.
88, 90A significant amount of the total global soil
carbon is found in the boreal forest and tundra
ecosystems, including permafrost.97, 98, 99 Increased fire activity could deplete these stores,
releasing them to the atmosphere to serve as
an additional source of atmospheric CO2.97, 100
Increased fires may also enhance the degradation of Alaska’s permafrost by blackening the
ground, reducing surface albedo, and removing protective vegetation.101, 102, 103, 104
11.2.5 Snow CoverSnow cover extent has significantly decreased
across the Northern Hemisphere and Alaska
over the last decade (see also Ch. 7: Precipitation
Change and Ch. 10: Land Cover).105, 106 Northern Hemisphere June snow cover decreased bymore than 65% between 1967 and 2012,37, 107 at

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a trend of −17.2% per decade since 1979.89 Junesnow cover dipped below 3 million square km(approximately 1.16 million square miles) for thefifth time in six years between 2010 and 2015, athreshold not crossed in the previous 43 yearsof record.89 Early season snow cover in May,
which affects the accumulation of solar insolation
through the summer, has also declined at −7.3%
per decade, due to reduced winter accumulationfrom warmer temperatures. Regional trends insnow cover duration vary, with some showing
earlier onsets while others show later onsets.89 In
Alaska, the 2016 May statewide snow coverageof 595,000 square km (approximately 372,000
square miles) was the lowest on record datingback to 1967; the snow coverage of 2015 was thesecond lowest, and 2014 was the fourth lowest.
Human activities have very likely contributed
to observed snow cover declines over the last
50 years. Attribution studies indicate that observed trends in Northern Hemisphere snow
cover cannot be explained by natural forcing
alone, but instead require anthropogenic
forcing.24, 106, 108 Declining snow cover is expected to continue and will be affected by boththe anthropogenic forcing and evolution ofarctic ecosystems. The observed tundra shrub
expansion and greening109, 110 affects melt by
influencing snow depth, melt dynamics, andthe local surface energy budget. Nevertheless,model simulations show that future reductions in snow cover influence biogeochemical
feedbacks and warming more strongly thanchanges in vegetation cover and fire in the
North American Arctic.111
11.2.6 Continental Ice Sheets and Mountain
Glaciers

Mass loss from ice sheets and glaciers influences sea level rise, the oceanic thermohaline
circulation, and the global energy budget.
Moreover, the relative contribution of GrIS toglobal sea level rise continues to increase, exceeding the contribution from thermal expan-

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United States

Cumulative Climatic Mass Balance (kg/m2)

Dramatic changes have occurred across GrIS,
particularly at its margins. GrIS average annual mass loss from January 2003 to May 2013
was −244 ± 6 Gt per year (approximately 0.26
inches per decade sea level equivalent).113 One
study indicates that ice mass loss from Greenland was −269 Gt per year between April
2002 and April 2016.47 Increased surface melt,
runoff, and increased outlet glacier discharge
from warmer air temperatures are primarycontributing factors.114, 115, 116, 117, 118 The effects ofwarmer air and ocean temperatures on GrIS
can be amplified by ice dynamical feedbacks,
such as faster sliding, greater calving, and increased submarine melting.116, 119, 120, 121 Shallow
ocean warming and regional ocean and atmospheric circulation changes also contribute tomass loss.122, 123, 124 The underlying mechanismsof the recent discharge speed-up remain unclear;125, 126 however, warmer subsurface ocean

and atmospheric temperatures118, 127, 128 andmeltwater penetration to the glacier bed125, 129very likely contribute.
Annual average ice mass from Arctic-wide
glaciers has decreased every year since 1984,112,
130, 131with significant losses in Alaska, especially over the past two decades (Figure11.3).37, 132 Figure 11.4 illustrates observedchanges from U.S. Geological Survey repeat
photography of Alaska’s Muir Glacier, retreating more than 4 miles between 1941 and
2004, and its tributary the Riggs Glacier. Total
glacial ice mass in the Gulf of Alaska region
has declined steadily since 2003.113 NASA’s
Gravity Recovery and Climate Experiment(GRACE) indicates mass loss from the northern and southern parts of the Gulf of Alaska
region of −36 ± 4 Gt per year and −4 ± 3 Gt per
year, respectively.113 Studies suggest an anthropogenic imprint on imbalances in Alaskan
glaciers, indicating that melt will continue
through the 21st century.112, 133, 134 Multiple
datasets indicate that it is virtually certain that
Alaskan glaciers have lost mass over the last
50 years and will continue to do so.135

1×104

400

0

200

−1×104−2×104−3×1040
Pan-Arctic
Alaska (solid: WGMS dashed: GRACE)
N. Scandinavia
Arctic Canada
Iceland
Svalbard
1960

1970

1980
1990
Year

−200

2000

2010

Cumulative Mass Loss (Gl)sion (see Ch. 12: Sea Level Rise). Observational and modeling studies indicate that GrIS andglaciers in Alaska are out of mass balance withcurrent climate conditions and are rapidly
losing mass.37, 112 In recent years, mass loss has
accelerated and is expected to continue.112, 113−400

Figure 11.3: Time series of the cumulative climatic mass balance (units: kg/m2) in five arctic regions and for the
Pan-Arctic from the World Glacier Monitoring Service (WGMS;210 Wolken et al.;211 solid lines, left y-axis), plus Alaskan
glacial mass loss observed from NASA GRACE113 (dashed blue line, right y-axis). (Figure source: Harig and Simons
2016113 and Wolken et al. 2016;211 © American Meteorological Society, used with permission.)

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United States
(a)

1941

(b)

2004
Figure 11.4: Two northeast-looking photographs of the Muir Glacier located in southeastern Alaska taken from a
Glacier Bay Photo station in (a) 1941 and (b) 2004. U.S. Geological Survey repeat photography allows the tracking ofglacier changes, illustrating that between 1941 and 2004 the Muir Glacier has retreated more than 4 miles to the northwest and out of view. Riggs Glacier (in view) is a tributary to Muir Glacier and has retreated by as much as 0.37 miles
and thinned by more than 0.16 miles. The photographs also illustrate a significant change in the surface type between
1941 and 2004 as bare rock in the foreground has been replaced by dense vegetation (Figure source: USGS 2004212).

Midlatitude circulation influences arctic
climate and climate change.11, 136, 137, 138, 139, 140, 141,
142, 143, 144, 145
Record warm arctic temperaturesin winter 2016 resulted primarily from thetransport of midlatitude air into the Arctic,
demonstrating the significant midlatitude influence.146 Emerging science demonstrates thatwarm, moist air intrusions from midlatitudes
results in increased downwelling longwave
radiation, warming the arctic surface and hindering wintertime sea ice growth.139, 141, 147, 148

weather and climate extremes has become an
active research area.137, 146 Several pathways
have been proposed (see references in Cohenet al.149 and Barnes and Screen150): reduced
meridional temperature gradient, a more sinuous jet-stream, trapped atmospheric waves,modified storm tracks, weakened stratospheric polar vortex. While modeling studies link a
reduced meridional temperature gradient tofewer cold temperature extremes in the continental United States,151, 152, 153, 154 other studies
hypothesize that a slower jet stream may amplify Rossby waves and increase the frequency
of atmospheric blocking, causing more persistent and extreme weather in midlatitudes.155

The extent to which enhanced arctic surface
warming and sea ice loss influence the largescale atmospheric circulation and midlatitude

Multiple observational studies suggest thatthe concurrent changes in the Arctic andNorthern Hemisphere large-scale circula-11.3 Arctic Feedbacks on the Lower 48 andGlobally
11.3.1 Linkages between Arctic Warming andLower Latitudes

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United Statestion since the 1990s did not occur by chance,
but were caused by arctic amplification.149,
150, 156
Reanalysis data suggest a relationshipbetween arctic amplification and observedchanges in persistent circulation phenomena
like blocking and planetary wave amplitude.155, 157, 158 The recent multi-year California
drought serves as an example of an event
caused by persistent circulation phenomena(see Ch. 5: Circulation and Variability and Ch.
8: Drought, Floods, and Wildfires).159, 160, 161
Robust empirical evidence is lacking because
the arctic sea ice observational record is too
short162 or because the atmospheric response
to arctic amplification depends on the priorstate of the atmospheric circulation, reducing
detectability.146 Furthermore, it is not possible
to draw conclusions regarding the direction of
the relationship between arctic warming andmidlatitude circulation based on empiricalcorrelation and covariance analyses alone. Observational analyses have been combined withmodeling studies to test causality statements.
Studies with simple models and Atmospheric
General Circulation Models (AGCMs) provide evidence that arctic warming can affectmidlatitude jet streams and location of storm
tracks.137, 146, 150 In addition, analysis of CMIP5
models forced with increasing greenhousegases suggests that the magnitude of arctic
amplification affects the future midlatitude jet
position, specifically during boreal winter.163
However, the effect of arctic amplification on
blocking is not clear (Ch. 5: Circulation and
Variability).164
Regarding attribution, AGCM simulations
forced with observed changes in arctic sea ice
suggest that the sea ice loss effect on observed
recent midlatitude circulation changes andwinter climate in the continental UnitedStates is small compared to natural large-scaleatmospheric variability.142, 144, 154, 165 It is argued,
however, that climate models do not properly

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reproduce the linkages between arctic amplification and lower latitude climate due tomodel errors, including incorrect sea ice–atmosphere coupling and poor representation ofstratospheric processes.137, 166
In summary, emerging science demonstrates
a strong influence of the midlatitude circulation on the Arctic, affecting temperatures andsea ice (high confidence). The influence of arctic
changes on the midlatitude circulation andweather patterns are an area of active research.
Currently, confidence is low regarding whether
or by what mechanisms observed arctic warming may have influenced midlatitude circulation and weather patterns over the continental
United States. The nature and magnitude ofarctic amplification’s influence on U.S. weather over the coming decades remains an open
question.
11.3.2 Freshwater Effects on Ocean Circulation

The addition of freshwater to the Arctic Ocean
from melting sea ice and land ice can influenceimportant arctic climate system characteristics, including ocean salinity, altering ocean
circulation, density stratification, and sea ice
characteristics. Observations indicate thatriver runoff is increasing, driven by land ice
melt, adding freshwater to the Arctic Ocean.167
Melting arctic sea and land ice combined with
time-varying atmospheric forcing79, 168 control
Arctic Ocean freshwater export to the North
Atlantic. Large-scale circulation variability inthe central Arctic not only controls the redistribution and storage of freshwater in the Arctic79
but also the export volume.169 Increased freshwater fluxes can weaken open ocean convection and deep water formation in the Labrador
and Irminger seas, weakening the Atlantic meridional overturning circulation (AMOC).170,
171
AMOC-associated poleward heat transport
substantially contributes to North Americanand continental European climate; any AMOC
slowdown could have implications for global

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United Statesclimate change as well (see Ch. 15: Potential
Surprises).172, 173 Connections to subarctic ocean
variations and the Atlantic Meridional Overturning Circulation have not been conclusively established and require further investigation (see Ch. 13: Ocean Changes).
11.3.3 Permafrost–Carbon Feedback

Alaska and arctic permafrost characteristics
have responded to increased temperaturesand reduced snow cover in most regions sincethe 1980s.130 The permafrost warming ratevaries regionally; however, colder permafrost
is warming faster than warmer permafrost.37,
174
This feature is most evident across Alaska,
where permafrost on the North Slope is warming more rapidly than in the interior. Permafrost temperatures across the North Slope atvarious depths ranging from 39 to 65 feet (12to 20 meters) have warmed between 0.3° and
1.3°F (0.2° and 0.7°C) per decade over the observational period (Figure 11.5).175 Permafrost
active layer thickness increased across muchof the Arctic while showing strong regional

30

variations.37, 130, 176 Further, recent geologic survey data indicate significant permafrost thaw
slumping in northwestern Canada and acrossthe circumpolar Arctic that indicate significant
ongoing permafrost thaw, potentially priming
the region for more rapid thaw in the future.177
Continued degradation of permafrost and atransition from continuous to discontinuous
permafrost is expected over the 21st century.37,
178, 179

Permafrost contains large stores of carbon.
Though the total contribution of these carbon
stores to global methane emission is uncertain,
Alaska’s permafrost contains rich and vulnerable organic carbon soils.99, 179, 180 Thus, warming Alaska permafrost is a concern for the
global carbon cycle as it provides a possibility
for a significant and potentially uncontrollable
release of carbon, complicating the ability tolimit global temperature increases. Current
methane emissions from Alaskan arctic tundra
and boreal forests contribute a small fractionof the global methane (CH4) budget.181 Howev-

Wrigley 1 (12.0m)
Normal Wells (12.0m)

Ground Temperature (°F)

Chandalar Shelf (20.0m)
25

20

Old Man (15.0m)
Happy Valley (20.0m)
Gailbrath Lake (20.0m)
Franklin Bluffs (20.0m)

15Deadhorse (20.0m)
West Dock (20.0m)

10
1975

1980

1985

1990

1995
Year

2000

2005

2010

2015

Figure 11.5: Time series of annual mean permafrost temperatures (units: °F) at various depths from 39 to 65 feet (12 to
20 meters) from 1977 through 2015 at several sites across Alaska, including the North Slope continuous permafrost region (purple/blue/green shades), and the discontinuous permafrost (orange/pink/red shades) in Alaska and northwestern Canada. Solid lines represent the linear trends drawn to highlight that permafrost temperatures are warming fasterin the colder, coastal permafrost regions than the warmer interior regions. (Figure Source: adapted from Romanovsky
et al. 2016;175 © American Meteorological Society, used with permission.)

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United Stateser, gas flux measurements have directly measured the release of CO2 and CH4 from arctic
permafrost.182 Recent measurements indicatethat cold season methane emissions (aftersnowfall) are greater than summer emissions
in Alaska, and methane emissions in uplandtundra are greater than in wetland tundra.183
The permafrost–carbon feedback represents
the additional release of CO2 and CH4 fromthawing permafrost soils providing additionalradiative forcing, a source of a potential surprise (Ch. 15: Potential Surprises).184 Thawing
permafrost makes previously frozen organic
matter available for microbial decomposition,
producing CO2 and CH4. The specific condition under which microbial decomposition
occurs, aerobic or anaerobic, determines the
proportion of CO2 and CH4 released. Thisdistinction has potentially significant implications, as CH4 has a 100-year global warming
potential 35 times that of CO2.185 Emerging
science indicates that 3.4 times more carbon isreleased under aerobic conditions than anaerobic conditions, and 2.3 times more carbon
after accounting for the stronger greenhouseeffect of CH4.186 Additionally, CO2 and CH4
production strongly depends on vegetationand soil properties.184
Combined data and modeling studies indicate a positive permafrost–carbon feedback
with a global sensitivity between −14 and −19
GtC per °C (approximately −25 to −34 GtC per°F) soil carbon loss187, 188 resulting in a total 120± 85 GtC release from permafrost by 2100 and
an additional global temperature increase of
0.52° ± 0.38°F (0.29° ± 0.21°C) by the permafrost–carbon feedback.189 More recently, Chadburn et al.190 infer a −4 million km2 per °C (orapproximately 858,000 mi2 per °F) reduction inpermafrost area to globally averaged warming at stabilization by constraining climate
models with the observed spatial distributionof permafrost; this sensitivity is 20% higher

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than previous studies. In the coming decades,enhanced high-latitude plant growth and its
associated CO2 sink should partially offset theincreased emissions from permafrost thaw;179,
189, 191
thereafter, decomposition is expected to dominate uptake. Permafrost thaw isoccurring faster than models predict due topoorly understood deep soil, ice wedge, andthermokarst processes.188, 192, 193 Additionally,
uncertainty stems from the surprising uptakeof methane from mineral soils.194 There is highconfidence in the positive sign of the permafrost–carbon feedback, but low confidence in the
feedback magnitude.
11.3.4 Methane Hydrate Instability

Significant stores of CH4, in the form of methane hydrates (also called clathrates), lie within
and below permafrost and under the global
ocean on continental margins. The estimatedtotal global inventory of methane hydrates
ranges from 500 to 3,000 GtC195, 196, 197 witha central estimate of 1,800 GtC.198 Methane
hydrates are solid compounds formed at highpressures and cold temperatures, trapping
methane gas within the crystalline structure ofwater. Methane hydrates within upper continental slopes of the Pacific, Atlantic, and Gulf
of Mexico margins and beneath the Alaskan
arctic continental shelf may be vulnerable tosmall increases in ocean temperature.197, 198, 199,
200, 201, 202, 203

Rising sea levels and warming oceans havea competing influence on methane hydratestability.199, 204 Studies indicate that the temperature effect dominates and that the overall
influence is very likely a destabilizing effect.198
Projected warming rates for the 21st century
Arctic Ocean are not expected to lead to sudden or catastrophic destabilization of seafloor
methane hydrates.205 Recent observations indicate increased CH4 emission from the arctic
seafloor near Svalbard; however, these emissions are not reaching the atmosphere.198, 206

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TRACEABLE ACCOUNTSKey Finding 1
Annual average near-surface air temperatures across
Alaska and the Arctic have increased over the last 50
years at a rate more than twice as fast as the global average temperature. (Very high confidence)
Description of evidence base
The Key Finding is supported by observational evidence from ground-based observing stations, satellites, and data-model temperature analyses from multiple sources and independent analysis techniques.14, 15,
16, 17, 18, 19, 20
For more than 40 years, climate models havepredicted enhanced arctic warming, indicating a solid
grasp on the underlying physics and positive feedbacks
driving the accelerated arctic warming.1, 21, 22 Lastly, similar statements have been made in NCA3,70 IPCC AR5,17
and in other arctic-specific assessments such as the
Arctic Climate Impacts Assessment207 and Snow, Water,
Ice and Permafrost in the Arctic.130
Major Uncertainties
The lack of high quality and restricted spatial resolutionof surface and ground temperature data over many
arctic land regions and essentially no measurements
over the Central Arctic Ocean hamper the ability tobetter refine the rate of arctic warming and completely restrict our ability to quantify and detect regional
trends, especially over the sea ice. Climate models generally produce an arctic warming between two to threetimes the global mean warming. A key uncertainty is
our quantitative knowledge of the contributions fromindividual feedback processes in driving the accelerated arctic warming. Reducing this uncertainty will help
constrain projections of future arctic warming.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Very high confidence that the arctic surface and air temperatures have warmed across Alaska and the Arctic at
a much faster rate than the global average is provided
by the multiple datasets analyzed by multiple independent groups indicating the same conclusion. Additionally, climate models capture the enhanced warming in

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the Arctic, indicating a solid understanding of the underlying physical mechanisms.
If appropriate, estimate likelihood of impact orconsequence, including short description of basisof estimate
It is very likely that the accelerated rate of arctic warming will have a significant consequence for the UnitedStates due to accelerated land and sea ice melt driving
changes in the ocean including sea level rise threatening our coastal communities and freshening of sea water that is influencing marine ecology.
Summary sentence or paragraph that integrates
the above information
Annual average near-surface air temperatures across
Alaska and the Arctic have increased over the last 50
years at a rate more than twice the global average. Observational studies using ground-based observing stations and satellites analyzed by multiple independentgroups support this finding. The enhanced sensitivityof the arctic climate system to anthropogenic forcing
is also supported by climate modeling evidence, indicating a solid grasp on the underlying physics. These
multiple lines of evidence provide very high confidence
of enhanced arctic warming with potentially significant
impacts on coastal communities and marine ecosystems.
Key Finding 2
Rising Alaskan permafrost temperatures are causing
permafrost to thaw and become more discontinuous;
this process releases additional carbon dioxide andmethane, resulting in an amplifying feedback and additional warming (high confidence). The overall magnitude of the permafrost–carbon feedback is uncertain;
however, it is clear that these emissions have the potential to compromise the ability to limit global temperature increases.
Description of evidence base
The Key Finding is supported by observational evidence of warming permafrost temperatures and a
deepening active layer, in situ gas measurements and316

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United Stateslaboratory incubation experiments of CO2 and CH4 release, and model studies.37, 179, 186, 187, 188, 192, 193 Alaska andarctic permafrost characteristics have responded to increased temperatures and reduced snow cover in mostregions since the 1980s, with colder permafrost warming faster than warmer permafrost.37, 130, 175 Large carbon
soil pools (more than 50% of the global below-groundorganic carbon pool) are locked up in the permafrost
soils,180 with the potential to be released. Thawing permafrost makes previously frozen organic matter available for microbial decomposition. In situ gas flux measurements have directly measured the release of CO2
and CH4 from arctic permafrost.182, 183 The specific conditions of microbial decomposition, aerobic or anaerobic,
determines the relative production of CO2 and CH4. Thisdistinction is significant as CH4 is a much more powerful greenhouse gas than CO2.185 However, incubation
studies indicate that 3.4 times more carbon is released
under aerobic conditions than anaerobic conditions,leading to a 2.3 times the stronger radiative forcing
under aerobic conditions.186 Combined data and modeling studies suggest a global sensitivity of the permafrost–carbon feedback warming global temperaturesin 2100 by 0.52° ± 0.38°F (0.29° ± 0.21°C) alone.189 Chadburn et al.190 infer the sensitivity of permafrost area to
globally averaged warming to be 4 million km2 by constraining a group of climate models with the observed
spatial distribution of permafrost; this sensitivity is 20%
higher than previous studies. Permafrost thaw is occurring faster than models predict due to poorly understood deep soil, ice wedge, and thermokarst processes.188, 192, 193, 208 Additional uncertainty stems from thesurprising uptake of methane from mineral soils194 anddependence of emissions on vegetation and soil properties.184 The observational and modeling evidence
supports the Key Finding that the permafrost–carbon
cycle is positive.
Major uncertainties
A major limiting factor is the sparse observations ofpermafrost in Alaska and remote areas across the Arctic.
Major uncertainties are related to deep soil, ice wedging, and thermokarst processes and the dependence
of CO2 and CH4 uptake and production on vegetationand soil properties. Uncertainties also exist in relevant

U.S. Global Change Research Program

soil processes during and after permafrost thaw, especially those that control unfrozen soil carbon storage
and plant carbon uptake and net ecosystem exchange.
Many processes with the potential to drive rapid permafrost thaw (such as thermokarst) are not included incurrent earth system models.
Assessment of confidence based on evidence andagreement, including short description of nature ofevidence and level of agreement
There is high confidence that permafrost is thawing,
becoming discontinuous, and releasing CO2 and CH4.
Physically-based arguments and observed increases
in CO2 and CH4 emissions as permafrost thaws indicatethat the feedback is positive. This confidence level isjustified based on observations of rapidly changing
permafrost characteristics.
If appropriate, estimate likelihood of impact orconsequence, including short description of basisof estimate
Thawing permafrost very likely has significant impacts
to the global carbon cycle and serves as a source of CO2
and CH4 emission that complicates the ability to limit
global temperature increases.
Summary sentence or paragraph that integrates
the above information
Permafrost is thawing, becoming more discontinuous,and releasing CO2 and CH4. Observational and modeling evidence indicates that permafrost has thawed
and released additional CO2 and CH4 indicating that thepermafrost–carbon cycle feedback is positive accounting for additional warming of approximately 0.08º to
0.50ºC on top of climate model projections. Although
the magnitude of the permafrost–carbon feedback is
uncertain due to a range of poorly understood processes (deep soil and ice wedge processes, plant carbon uptake, dependence of uptake and emissions onvegetation and soil type, and the role of rapid permafrost thaw processes, such as thermokarst), emerging
science and the newest estimates continue to indicatethat this feedback is more likely on the larger side of therange. Impacts of permafrost thaw and the permafrost

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United States
carbon feedback complicates our ability to limit global
temperature increases by adding a currently unconstrained radiative forcing to the climate system.
Key Finding 3
Arctic land and sea ice loss observed in the last three
decades continues, in some cases accelerating (veryhigh confidence). It is virtually certain that Alaska glaciers have lost mass over the last 50 years, with eachyear since 1984 showing an annual average ice massless than the previous year. Based on gravitational datafrom satellites, average ice mass loss from Greenland
was −269 Gt per year between April 2002 and April
2016, accelerating in recent years (high confidence).
Since the early 1980s, annual average arctic sea ice has
decreased in extent between 3.5% and 4.1% per decade, become thinner by between 4.3 and 7.5 feet, andbegan melting at least 15 more days each year. September sea ice extent has decreased between 10.7% and15.9% per decade (very high confidence). Arctic-wide iceloss is expected to continue through the 21st century,very likely resulting in nearly sea ice-free late summers
by the 2040s (very high confidence).
Description of evidence base
The Key Finding is supported by observational evidence
from multiple ground-based and satellite-based observational techniques (including passive microwave, laser and radar altimetry, and gravimetry) analyzed by independent groups using different techniques reachingsimilar conclusions.19, 37, 45, 47, 112, 113, 134, 135 Additionally, theU.S. Geological Survey repeat photography databaseshows the glacier retreat for many Alaskan glaciers(Figure 11.4: Muir Glacier). Several independent model
analysis studies using a wide array of climate models
and different analysis techniques indicate that sea ice
loss will continue across the Arctic, very likely resulting in late summers becoming nearly ice-free by the2040s.21, 59, 65
Major uncertainties
Key uncertainties remain in the quantification andmodeling of key physical processes that contribute to
the acceleration of land and sea ice melting. Climate
models are unable to capture the rapid pace of ob-

U.S. Global Change Research Program

served sea and land ice melt over the last 15 years; amajor factor is our inability to quantify and accurately
model the physical processes driving the accelerated
melting. The interactions between atmospheric circulation, ice dynamics and thermodynamics, clouds,
and specifically the influence on the surface energy
budget are key uncertainties. Mechanisms controlling
marine-terminating glacier dynamics—specifically theroles of atmospheric warming, seawater intrusions under floating ice shelves, and the penetration of surface
meltwater to the glacier bed—are key uncertainties inprojecting Greenland Ice Sheet melt.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence that arctic sea and land ice
melt is accelerating and mountain glacier ice mass is
declining given the multiple observational sources andanalysis techniques documented in the peer-reviewed
climate science literature.
If appropriate, estimate likelihood of impact orconsequence, including short description of basisof estimate
It is very likely that accelerating arctic land and sea ice
melt impacts the United States. Accelerating Arctic
Ocean sea ice melt increases coastal erosion in Alaska
and makes Alaskan fisheries more susceptible to ocean
acidification by changing Arctic Ocean chemistry.
Greenland Ice Sheet and Alaska mountain glacier melt
drives sea level rise threatening coastal communities in
the United States and worldwide, influencing marine
ecology, and potentially altering the thermohaline circulation.
Summary sentence or paragraph that integrates
the above information
Arctic land and sea ice loss observed in the last three
decades continues, in some cases accelerating. A diverse range of observational evidence from multiple
data sources and independent analysis techniques provide consistent evidence of substantial declines in arctic sea ice extent, thickness, and volume since at least
1979, mountain glacier melt over the last 50 years, and318

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United States
accelerating mass loss from Greenland. An array of different models and independent analyses indicate that
future declines in ice across the Arctic are expected resulting in late summers in the Arctic becoming ice freeby the 2040s.
Key Finding 4
It is very likely that human activities have contributed toobserved arctic surface temperature warming, sea ice
loss, glacier mass loss, and Northern Hemisphere snow
extent decline (high confidence).
Description of evidence base
The Key Finding is supported by many attribution studies using a wide array of climate models documenting
the anthropogenic influence on arctic temperature, sea
ice, mountain glaciers, and snow extent.23, 24, 25, 26, 27, 29,
54, 55, 56, 57, 58, 59, 61, 62, 106, 108, 133
Observation-based analyses
also support an anthropogenic influence.60, 69 Najafi etal.27 show that the greenhouse warming signal in the
Arctic could be even stronger, as a significant portionof greenhouse gas induced warming (approximately60%) has been offset by anthropogenic aerosol emissions. The emerging science of extreme event attribution indicates that natural variability alone could nothave caused the recently observed record low arctic
sea ice extents, such as in September 2012.61, 62 Natural variability in the Arctic is significant,63, 64 however
the majority of studies indicate that the contribution
from individual sources of internal variability to observed trends in arctic temperature and sea ice are lessthan 50%28, 54, 55 and alone cannot explain the observed
trends over the satellite era. This Key Finding marks anincreased confidence relative to the IPCC AR524 moving from likely to very likely. In our assessment, the newunderstanding of the anthropogenic forcing,27 its relationship to arctic climate change,69 arctic climate variability,28, 63, 64 and especially extreme event attribution
studies61, 62 reaffirms previous studies and warrants theincreased likelihood of an anthropogenic influence on
arctic climate change. Multiple lines evidence, independent analysis techniques, models, and studies support the Key Finding.

U.S. Global Change Research Program

Major uncertainties
A major limiting factor in our ability to attribute arctic
sea ice and glacier melt to human activities is the significant natural climate variability in the Arctic. Longer
data records and a better understanding of the physical mechanisms that drive natural climate variability in
the Arctic are required to reduce this uncertainty. Another major uncertainty is the ability of climate models
to capture the relevant physical processes and climatechanges at a fine spatial scale, especially those at theland and ocean surface in the Arctic.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is high confidence that human activities have contributed to arctic surface temperature warming, sea ice
loss since 1979, glacier mass loss, and Northern Hemisphere snow extent given multiple independent analysis techniques from independent groups using manydifferent climate models indicate the same conclusion.
If appropriate, estimate likelihood of impact orconsequence, including short description of basisof estimate
Arctic sea ice and glacier mass loss impacts the United States by affecting coastal erosion in Alaska and key
Alaskan fisheries through an increased vulnerability
to ocean acidification. Glacier mass loss is a significant
driver of sea level rise threatening coastal communities
in the United States and worldwide, influencing marine
ecology, and potentially altering the Atlantic Meridional Overturning Circulation.172
Summary sentence or paragraph that integrates
the above information
Evidenced by the multiple independent studies, analysis techniques, and the array of different climate models used over the last 20 years, it is very likely that humanactivities have contributed to arctic surface temperature warming, sea ice loss since 1979, glacier massloss, and Northern Hemisphere snow extent decline
observed across the Arctic. Key uncertainties remain in
the understanding and modeling of arctic climate variability; however, many independent studies indicate

319

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11 | Arctic Changes and their Effects on Alaska and the Rest of the United Statesthat internal variability alone cannot explain the trends
or extreme events observed in arctic temperature andsea ice over the satellite era.
Key Finding 5
Atmospheric circulation patterns connect the climatesof the Arctic and the contiguous United States. Evidenced by recent record warm temperatures in the
Arctic and emerging science, the midlatitude circulation has influenced observed arctic temperatures andsea ice (high confidence). However, confidence is low
regarding whether or by what mechanisms observed
arctic warming may have influenced the midlatitude
circulation and weather patterns over the continental
United States. The influence of arctic changes on U.S.
weather over the coming decades remains an open
question with the potential for significant impact.
Description of evidence base
The midlatitude circulation influences the Arctic
through the transport of warm, moist air, altering theArctic surface energy budget.138, 142, 143, 144 The intrusionof warm, moist air from midlatitudes increases downwelling longwave radiation, warming the arctic surface and hindering wintertime sea ice growth.139, 147
Emerging research provides a new understanding of
the importance of synoptic time scales and the episodic nature of midlatitude air intrusions.139, 141, 148 The
combination of recent observational and model-based
evidence as well as the physical understanding of themechanisms of midlatitude circulation effects on arctic
climate supports this Key Finding.
In addition, research on the impact of arctic climate on
midlatitude circulation is rapidly evolving, including
observational analysis and modeling studies. Multiple
observational studies provide evidence for concurrent
changes in the Arctic and Northern Hemisphere largescale circulation changes.149, 150, 156 Further, modelingstudies demonstrate that arctic warming can influence
the midlatitude jet stream and storm track.137, 146, 150, 163
However, attribution studies indicate that the observed
midlatitude circulation changes over the continental
United States are smaller than natural variability andare therefore not detectable in the observational re-

U.S. Global Change Research Program

cord.142, 144, 154, 165 This disagreement between independent studies using different analysis techniques andthe lack of understanding of the physical mechanism(s)supports this Key Finding.
Major uncertainties
A major limiting factor is our understanding and modeling of natural climate variability in the Arctic. Longer
data records and a better understanding of the physicalmechanisms that drive natural climate variability in the
Arctic are required to reduce this uncertainty. The inability of climate models to accurately capture interactions between sea ice and the atmospheric circulationand polar stratospheric processes limits our current understanding.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
High confidence in the impact of midlatitude circulation
on arctic changes from the consistency between observations and models as well as a solid physical understanding.
Low confidence on the detection of an impact of arctic
warming on midlatitude climate is based on short observational data record, model uncertainty, and lack ofphysical understanding.
Summary sentence or paragraph that integrates
the above information
The midlatitude circulation has influenced observed arctic temperatures, supported by recent observational andmodel-based evidence as well as the physical understanding from emerging science. In turn, confidence is
low regarding the mechanisms by which observed arctic
warming has influenced the midlatitude circulation andweather patterns over the continental United States, due
to the disagreement between numerous studies and alack of understanding of the physical mechanism(s). Resolving the remaining questions requires longer data
records and improved understanding and modeling ofphysics in the Arctic. The influence of arctic changes on
U.S. weather over the coming decades remains an open
question with the potential for significant impact.

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187. Koven, C.D., D.M. Lawrence, and W.J. Riley, 2015:
Permafrost carbon−climate feedback is sensitive
to deep soil carbon decomposability but not deep
soil nitrogen dynamics. Proceedings of the National
Academy of Sciences, 112, 3752-3757. http://dx.doi.
org/10.1073/pnas.1415123112
188. Koven, C.D., E.A.G. Schuur, C. Schädel, T.J. Bohn,
E.J. Burke, G. Chen, X. Chen, P. Ciais, G. Grosse,
J.W. Harden, D.J. Hayes, G. Hugelius, E.E. Jafarov,
G. Krinner, P. Kuhry, D.M. Lawrence, A.H. MacDougall, S.S. Marchenko, A.D. McGuire, S.M. Natali, D.J.
Nicolsky, D. Olefeldt, S. Peng, V.E. Romanovsky,
K.M. Schaefer, J. Strauss, C.C. Treat, and M. Turetsky,
2015: A simplified, data-constrained approach to estimate the permafrost carbon–climate feedback. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373, 20140423.
http://dx.doi.org/10.1098/rsta.2014.0423
189. Schaefer, K., H. Lantuit, E.R. Vladimir, E.A.G.
Schuur, and R. Witt, 2014: The impact of the permafrost carbon feedback on global climate. Environmental Research Letters, 9, 085003. http://dx.doi.
org/10.1088/1748-9326/9/8/085003
190. Chadburn, S.E., E.J. Burke, P.M. Cox, P. Friedlingstein, G. Hugelius, and S. Westermann, 2017: An
observation-based constraint on permafrost loss as afunction of global warming. Nature Climate Change,
7, 340-344. http://dx.doi.org/10.1038/nclimate3262
191. Friedlingstein, P., P. Cox, R. Betts, L. Bopp, W.v. Bloh,
V. Brovkin, P. Cadule, S. Doney, M. Eby, I. Fung, G.
Bala, J. John, C. Jones, F. Joos, T. Kato, M. Kawamiya, W. Knorr, K. Lindsay, H.D. Matthews, T. Raddatz,
P. Rayner, C. Reick, E. Roeckner, K.-G. Schnitzler, R.
Schnur, K. Strassmann, A.J. Weaver, C. Yoshikawa,and N. Zeng, 2006: Climate–carbon cycle feedback
analysis: Results from the C4MIP model intercomparison. Journal of Climate, 19, 3337-3353. http://dx.doi.
org/10.1175/JCLI3800.1
192. Fisher, J.B., M. Sikka, W.C. Oechel, D.N. Huntzinger,
J.R. Melton, C.D. Koven, A. Ahlström, M.A. Arain, I.
Baker, J.M. Chen, P. Ciais, C. Davidson, M. Dietze,
B. El-Masri, D. Hayes, C. Huntingford, A.K. Jain, P.E.
Levy, M.R. Lomas, B. Poulter, D. Price, A.K. Sahoo, K.
Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C.E. Miller, 2014: Carbon
cycle uncertainty in the Alaskan Arctic. Biogeosciences, 11, 4271-4288. http://dx.doi.org/10.5194/bg-114271-2014
193. Liljedahl, A.K., J. Boike, R.P. Daanen, A.N. Fedorov,
G.V. Frost, G. Grosse, L.D. Hinzman, Y. Iijma, J.C. Jorgenson, N. Matveyeva, M. Necsoiu, M.K. Raynolds,
V.E. Romanovsky, J. Schulla, K.D. Tape, D.A. Walker,
C.J. Wilson, H. Yabuki, and D. Zona, 2016: Pan-Arctic
ice-wedge degradation in warming permafrost andits influence on tundra hydrology. Nature Geoscience,
9, 312-318. http://dx.doi.org/10.1038/ngeo2674

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194. Oh, Y., B. Stackhouse, M.C.Y. Lau, X. Xu, A.T. Trugman, J. Moch, T.C. Onstott, C.J. Jørgensen, L. D’Imperio, B. Elberling, C.A. Emmerton, V.L. St. Louis,
and D. Medvigy, 2016: A scalable model for methane consumption in Arctic mineral soils. Geophysical Research Letters, 43, 5143-5150. http://dx.doi.
org/10.1002/2016GL069049
195. Archer, D., 2007: Methane hydrate stability and anthropogenic climate change. Biogeosciences, 4, 521544. http://dx.doi.org/10.5194/bg-4-521-2007
196. Piñero, E., M. Marquardt, C. Hensen, M. Haeckel,
and K. Wallmann, 2013: Estimation of the global inventory of methane hydrates in marine sediments
using transfer functions. Biogeosciences, 10, 959-975.
http://dx.doi.org/10.5194/bg-10-959-2013
197. Ruppel, C.D. Methane hydrates and contemporary climate change. Nature Education Knowledge, 2011. 3.
198. Ruppel, C.D. and J.D. Kessler, 2017: The interaction of climate change and methane hydrates. Reviews of Geophysics, 55, 126-168. http://dx.doi.
org/10.1002/2016RG000534
199. Bollmann, M., T. Bosch, F. Colijn, R. Ebinghaus,
R. Froese, K. Güssow, S. Khalilian, S. Krastel, A.
Körtzinger, M. Langenbuch, M. Latif, B. Matthiessen,
F. Melzner, A. Oschlies, S. Petersen, A. Proelß, M.
Quaas, J. Reichenbach, T. Requate, T. Reusch, P. Rosenstiel, J.O. Schmidt, K. Schrottke, H. Sichelschmidt,
U. Siebert, R. Soltwedel, U. Sommer, K. Stattegger, H.
Sterr, R. Sturm, T. Treude, A. Vafeidis, C.v. Bernem,
J.v. Beusekom, R. Voss, M. Visbeck, M. Wahl, K. Wallmann, and F. Weinberger, 2010: World Ocean Review:
Living With the Oceans. maribus gGmbH, 232 pp.
http://worldoceanreview.com/wp-content/downloads/wor1/WOR1_english.pdf
200. Brothers, L.L., B.M. Herman, P.E. Hart, and C.D.
Ruppel, 2016: Subsea ice-bearing permafrost on the
U.S. Beaufort Margin: 1. Minimum seaward extent
defined from multichannel seismic reflection data.
Geochemistry, Geophysics, Geosystems, 17, 4354-4365.
http://dx.doi.org/10.1002/2016GC006584
201. Johnson, H.P., U.K. Miller, M.S. Salmi, and E.A.
Solomon, 2015: Analysis of bubble plume distributions to evaluate methane hydrate decomposition on the continental slope. Geochemistry, Geophysics, Geosystems, 16, 3825-3839. http://dx.doi.
org/10.1002/2015GC005955
202. Ruppel, C.D., B.M. Herman, L.L. Brothers, and P.E.
Hart, 2016: Subsea ice-bearing permafrost on the U.S.
Beaufort Margin: 2. Borehole constraints. Geochemistry, Geophysics, Geosystems, 17, 4333-4353. http://dx.doi.org/10.1002/2016GC006582
203. Skarke, A., C. Ruppel, M. Kodis, D. Brothers, and
E. Lobecker, 2014: Widespread methane leakage
from the sea floor on the northern US Atlantic margin. Nature Geoscience, 7, 657-661. http://dx.doi.
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204. Hunter, S.J., D.S. Goldobin, A.M. Haywood, A.
Ridgwell, and J.G. Rees, 2013: Sensitivity of the
global submarine hydrate inventory to scenarios of
future climate change. Earth and Planetary Science
Letters, 367, 105-115. http://dx.doi.org/10.1016/j.
epsl.2013.02.017
205. Kretschmer, K., A. Biastoch, L. Rüpke, and E. Burwicz,
2015: Modeling the fate of methane hydrates underglobal warming. Global Biogeochemical Cycles, 29, 610625. http://dx.doi.org/10.1002/2014GB005011
206. Graves, C.A., L. Steinle, G. Rehder, H. Niemann,
D.P. Connelly, D. Lowry, R.E. Fisher, A.W. Stott, H.
Sahling, and R.H. James, 2015: Fluxes and fate of dissolved methane released at the seafloor at the landward limit of the gas hydrate stability zone offshore
western Svalbard. Journal of Geophysical Research
Oceans, 120, 6185-6201. http://dx.doi.org/10.1002/
2015JC011084
207. ACIA, 2005: Arctic Climate Impact Assessment.
ACIA Secretariat and Cooperative Institute for Arctic
Research, 1042 pp. http://www.acia.uaf.edu/pages/scientific.html
208. Hollesen, J., H. Matthiesen, A.B. Møller, and B. Elberling, 2015: Permafrost thawing in organic Arctic soils
accelerated by ground heat production. Nature Climate Change, 5, 574-578. http://dx.doi.org/10.1038/
nclimate2590
209. Fetterer, F., K. Knowles, W. Meier, and M. Savoie,
2016, updated daily: Sea Ice Index, Version 2. National Snow and Ice Data Center, Boulder, CO.
210. WGMS, 2016: Fluctuations of Glaciers Database.
World Glacier Monitoring Service, Zurich, Switzerland.
211. Wolken, G., M. Sharp, L.M. Andreassen, A. Arendt,
D. Burgess, J.G. Cogley, L. Copland, J. Kohler, S.
O’Neel, M. Pelto, L. Thomson, and B. Wouters, 2016:[The Arctic] Glaciers and ice caps outside Greenland[in “State of the Climate in 2015”]. Bulletin of the American Meteorological Society, 97, S142-S145. http://dx.
doi.org/10.1175/2016BAMSStateoftheClimate.1
212. USGS, 2004: Repeat Photography of Alaskan Glaciers: Muir Glacier (USGS Photograph by Bruce F.
Molnia). Department of the Interior, U.S. Geological
Survey. https://www2.usgs.gov/climate_landuse/
glaciers/repeat_photography.asp

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12
KEY FINDINGS

Sea Level Rise

1. Global mean sea level (GMSL) has risen by about 7–8 inches (about 16–21 cm) since 1900, with about 3of those inches (about 7 cm) occurring since 1993 (very high confidence). Human-caused climate change
has made a substantial contribution to GMSL rise since 1900 (high confidence), contributing to a rate ofrise that is greater than during any preceding century in at least 2,800 years (medium confidence).
2. Relative to the year 2000, GMSL is very likely to rise by 0.3–0.6 feet (9–18 cm) by 2030, 0.5–1.2 feet
(15–38 cm) by 2050, and 1.0–4.3 feet (30–130 cm) by 2100 (very high confidence in lower bounds; medium
confidence in upper bounds for 2030 and 2050; low confidence in upper bounds for 2100). Future pathways
have little effect on projected GMSL rise in the first half of the century, but significantly affect projections for the second half of the century (high confidence). Emerging science regarding Antarctic ice sheet
stability suggests that, for high emission scenarios, a GMSL rise exceeding 8 feet (2.4 m) by 2100 is
physically possible, although the probability of such an extreme outcome cannot currently be assessed.
Regardless of pathway, it is extremely likely that GMSL rise will continue beyond 2100 (high confidence).
3. Relative sea level (RSL) rise in this century will vary along U.S. coastlines due, in part, to changes inEarth’s gravitational field and rotation from melting of land ice, changes in ocean circulation, and vertical land motion (very high confidence). For almost all future GMSL rise scenarios, RSL rise is likely to begreater than the global average in the U.S. Northeast and the western Gulf of Mexico. In intermediateand low GMSL rise scenarios, RSL rise is likely to be less than the global average in much of the PacificNorthwest and Alaska. For high GMSL rise scenarios, RSL rise is likely to be higher than the global
average along all U.S. coastlines outside Alaska. Almost all U.S. coastlines experience more than global
mean sea level rise in response to Antarctic ice loss, and thus would be particularly affected under
extreme GMSL rise scenarios involving substantial Antarctic mass loss (high confidence).
4. As sea levels have risen, the number of tidal floods each year that cause minor impacts (also called“nuisance floods”) have increased 5- to 10-fold since the 1960s in several U.S. coastal cities (very highconfidence). Rates of increase are accelerating in over 25 Atlantic and Gulf Coast cities (very high confidence). Tidal flooding will continue increasing in depth, frequency, and extent this century (very highconfidence).

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12 | Sea Level RiseKEY FINDINGS (continued)
5. Assuming storm characteristics do not change, sea level rise will increase the frequency and extent of
extreme flooding associated with coastal storms, such as hurricanes and nor’easters (very high confidence). A projected increase in the intensity of hurricanes in the North Atlantic (medium confidence)
could increase the probability of extreme flooding along most of the U.S. Atlantic and Gulf Coast states
beyond what would be projected based solely on RSL rise. However, there is low confidence in the projected increase in frequency of intense Atlantic hurricanes, and the associated flood risk amplificationand flood effects could be offset or amplified by such factors as changes in overall storm frequency ortracks.

Recommended Citation for Chapter
Sweet, W.V., R. Horton, R.E. Kopp, A.N. LeGrande, and A. Romanou, 2017: Sea level rise. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard,
D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington,
DC, USA, pp. 333-363, doi: 10.7930/J0VM49F2.

12.1 Introduction

Sea level rise is closely linked to increasing
global temperatures. Thus, even as uncertainties remain about just how much sea level may
rise this century, it is virtually certain that sea
level rise this century and beyond will posea growing challenge to coastal communities,
infrastructure, and ecosystems from increased(permanent) inundation, more frequent and extreme coastal flooding, erosion of coastal landforms, and saltwater intrusion within coastal
rivers and aquifers. Assessment of vulnerability
to rising sea levels requires consideration ofphysical causes, historical evidence, and projections. A risk-based perspective on sea level risepoints to the need for emphasis on how changing sea levels alter the coastal zone and interact
with coastal flood risk at local scales.
This chapter reviews the physical factors driving changes in global mean sea level (GMSL)
and those causing additional regional variations in relative sea level (RSL). It presents
geological and instrumental observations ofhistorical sea level changes and an assessmentof the human contribution to sea level change.
It then describes a range of scenarios for fu-

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ture levels and rates of sea level change, andthe relationship of these scenarios to the Representative Concentration Pathways (RCPs).
Finally, it assesses the impact of changes in sea
level on extreme water levels.
While outside the scope of this chapter, it isimportant to note the myriad of other potential impacts associated with RSL rise, wave
action, and increases in coastal flooding. These
impacts include loss of life, damage to infrastructure and the built environment, salinization of coastal aquifers, mobilization of pollutants, changing sediment budgets, coastal
erosion, and ecosystem changes such as marsh
loss and threats to endangered flora and fauna.1 While all of these impacts are inherently
important, some also have the potential toinfluence local rates of RSL rise and the extent
of wave-driven and coastal flooding impacts.
For example, there is evidence that wave
action and flooding of beaches and marshes
can induce changes in coastal geomorphology,
such as sediment build up, that may iteratively modify the future flood risk profile ofcommunities and ecosystems.2

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12.2 Physical Factors Contributing to Sea
Level Rise

reciprocally, it rises by greater than the global
average far from the melting ice sheet.

Sea level change is driven by a variety ofmechanisms operating at different spatial andtemporal scales (see Kopp et al. 20153 for areview). GMSL rise is primarily driven by two
factors: 1) increased volume of seawater due
to thermal expansion of the ocean as it warms,and 2) increased mass of water in the ocean
due to melting ice from mountain glaciers andthe Antarctic and Greenland ice sheets.4 The
overall amount (mass) of ocean water, andthus sea level, is also affected to a lesser extent by changes in global land-water storage,
which reflects changes in the impoundment ofwater in dams and reservoirs and river runoff
from groundwater extraction, inland sea andwetland drainage, and global precipitation
patterns, such as occur during phases of the El
Niño–Southern Oscillation (ENSO).4, 5, 6, 7, 8

Third, the Earth’s mantle is still moving in
response to the loss of the great North American (Laurentide) and European ice sheets of theLast Glacial Maximum; the associated changes in the height of the land, the shape of theocean basin, and the Earth’s gravitational fieldgive rise to glacial-isostatic adjustment (Figure
12.1e). For example, in areas once covered by
the thickest parts of the great ice sheets of theLast Glacial Maximum, such as in Hudson Bay
and in Scandinavia, post-glacial rebound of theland is causing RSL to fall. Along the flanks of
the ice sheets, such as along most of the east
coast of the United States, subsidence of thebulge that flanked the ice sheet is causing RSL
to rise.

Sea level and its changes are not uniform
globally for several reasons. First, atmosphere–
ocean dynamics—driven by ocean circulation,
winds, and other factors—are associated withdifferences in the height of the sea surface,as are differences in density arising from the
distribution of heat and salinity in the ocean.
Changes in any of these factors will affect sea
surface height. For example, a weakening of the
Gulf Stream transport in the mid-to-late 2000s
may have contributed to enhanced sea level
rise in the ocean environment extending to thenortheastern U.S. coast,9, 10, 11 a trend that manymodels project will continue into the future.12
Second, the locations of land ice melting andland water reservoir changes impart distinct
regional “static-equilibrium fingerprints” onsea level, based on gravitational, rotational, andcrustal deformation effects (Figure 12.1a–d).13
For example, sea level falls near a melting ice
sheet because of the reduced gravitational
attraction of the ocean toward the ice sheet;

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Finally, a variety of other factors can cause local
vertical land movement. These include natural
sediment compaction, compaction caused by
local extraction of groundwater and fossil fuels,and processes related to plate tectonics, such as
earthquakes and more gradual seismic creep(Figure 12.1f).14, 15Compared to many climate variables, the trend
signal for sea level change tends to be large
relative to natural variability. However, at interannual timescales, changes in ocean dynamics,
density, and wind can cause substantial sea
level variability in some regions. For example,
there has been a multidecadal suppression ofsea level rise off the Pacific coast16 and large
year-to-year variations in sea level along theNortheast U.S. coast.17 Local rates of landheight change have also varied dramaticallyon decadal timescales in some locations, suchas along the western Gulf Coast, where rates
of subsurface extraction of fossil fuels andgroundwater have varied over time.18

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a)

GIS

b)

WAIS

c)

EAIS

d)

Median GlaciersFeet/Century
<−0.4

e)

−0.2

0

0.2

0.4

RSL rates due to GIA

0.6

f)

0.8

−0.5

0

1.2

1.4

RSL rates due to all
non-climatic processes

Feet/Century
<−1.0

1

Feet/Century
0.5

>1.0

<−1.5

−1.0

−0.5

0

0.5

>1.0

Figure 12.1: (a–d) Static-equilibrium fingerprints of the relative sea level (RSL) effect of land ice melt, in units of feetof RSL change per feet of global mean sea level (GMSL) change, for mass loss from (a) Greenland, (b) West Antarctica, (c) East Antarctica, and (d) the median projected combination of melting glaciers, after Kopp et al.3, 76 (e) Model
projections of the rate of RSL rise due to glacial-isostatic adjustment (units of feet/century), after Kopp et al.3 (f) Tide
gauge-based estimates of the non-climatic, long term contribution to RSL rise, including the effects of glacial isostatic
adjustment, tectonics, and sediment compaction (units of feet/century).76 (Figure source: (a)–(d) Kopp et al. 2015,3 (e)adapted from Kopp et al. 2015;3 (f) adapted from Sweet et al. 201771).

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12.3 Paleo Sea Level
Geological records of temperature and sea
level indicate that during past warm periods
over the last several millions of years, GMSL
was higher than it is today.19, 20 During theLast Interglacial stage, about 125,000 years
ago, global average sea surface temperature
was about 0.5° ± 0.3°C (0.9° ± 0.5°F) above thepreindustrial level [that is, comparable to theaverage over 1995–2014, when global mean
temperature was about 0.8°C (1.4°F) above
the preindustrial levels].21 Polar temperatures
were comparable to those projected for 1°–2°C(1.8°–3.6°F) of global mean warming above thepreindustrial level. At this time, GMSL wasabout 6–9 meters (about 20–30 feet) higher
than today (Figure 12.2a).22, 23 This geologicalbenchmark may indicate the probable longterm response of GMSL to the minimum magnitude of temperature change projected for thecurrent century.
Similarly, during the mid-Pliocene warm
period, about 3 million years ago, global mean
temperature was about 1.8°–3.6°C (3.2°–6.5°F)
above the preindustrial level.24 Estimates of
GMSL are less well constrained than during
the Last Interglacial, due to the smaller number of local geological sea level reconstruction
and the possibility of significant vertical landmotion over millions of years.20 Some reconstructions place mid-Pliocene GMSL at about
10–30 meters (about 30–100 feet) higher than
today.25 Sea levels this high would requirea significantly reduced Antarctic ice sheet,highlighting the risk of significant Antarctic
ice sheet loss under such levels of warming(Figure 12.2a).

different salinity-sensitive microfossils within salt marsh sediments.27 During the main
portion of the deglaciation, from about 17,000
to 8,000 years ago, GMSL rose at an averagerate of about 12 mm/year (0.5 inches/year).28
However, there were periods of faster rise. For
example, during Meltwater Pulse 1a, lastingfrom about 14,600 to 14,300 years ago, GMSL
may have risen at an average rate about 50
mm/year (2 inches/year).29
Since the disappearance of the last remnantsof the North American (Laurentide) Ice Sheetabout 7,000 years ago30 to about the start of the20th century, however, GMSL has been relatively stable. During this period, total GMSL
rise is estimated to have been about 4 meters(about 13 feet), most of which occurred between 7,000 and 4,000 years ago.28 The Third
National Climate Assessment (NCA3) noted,
based on a geological data set from North
Carolina,31 that the 20th century GMSL rise
was much faster than at any time over the past
2,000 years. Since NCA3, high-resolution sea
level reconstructions have been developed for
multiple locations, and a new global analysis of such reconstructions strengthens thisfinding.32 Over the last 2,000 years, prior to theindustrial era, GMSL exhibited small fluctuations of about ±8 cm (3 inches), with a significant decline of about 8 cm (3 inches) between
the years 1000 and 1400 CE coinciding with
about 0.2°C (0.4°F) of global mean cooling.32
The rate of rise in the last century, about 14
cm/century (5.5 inches/century), was greaterthan during any preceding century in at least
2,800 years (Figure 12.2b).32

For the period since the Last Glacial Maximum, about 26,000 to 19,000 years ago,26 geologists can produce detailed reconstructions ofsea levels as well as rates of sea level change.
To do this, they use proxies such as the heights
of fossil coral reefs and the populations of

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(a)

1000

900

CO2

2100

CO2 (ppm)

800
700

Temperature relative to preindustrial

600
500

300

40

CO2

CO2

CO2

1890

200

30

CO2

2017

6–9m

10–30m20
100

Sea level (m)400

1.8–3.6°C

0.8–2°C

1°C?
?

Present

MIS 5ePliocene

125,000
3,000,000
Years before present

0.2

Historical Global Mean Sea Level
5
0
−5
−10
−15
−20

0.0
−0.2
−0.4
−0.6
−0.8
−500

0

500

1000
Year (CE)

1500

Global Mean
Sea Level (cm)Global Mean
Sea Level (feet)

(b)

2000

Figure 12.2: (a) The relationship between peak global mean temperature, atmospheric CO2, maximum global meansea level (GMSL), and source(s) of meltwater for two periods in the past with global mean temperature comparable toor warmer than present. Light blue shading indicates uncertainty of GMSL maximum. Red pie charts over Greenlandand Antarctica denote fraction, not location, of ice retreat. Atmospheric CO2 levels in 2100 are shown under RCP8.5. (b)
GMSL rise from −500 to 1900 CE, from Kopp et al.’s32 geological and tide gauge-based reconstruction (blue), from 1900
to 2010 from Hay et al.’s33 tide gauge-based reconstruction (black), and from 1992 to 2015 from the satellite-based
reconstruction updated from Nerem et al.35 (magenta). (Figure source: (a) adapted from Dutton et al. 201520 and (b)Sweet et al. 201771).

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12.4 Recent Past Trends (20th and 21stCenturies)

being negative (perhaps as much as about −0.6mm/year [−0.02 inches/year]) during the period of heavy dam construction in the middleof the last century, and turning positive in the
1990s as groundwater withdrawal came todominate.8 On decadal timescales, precipitation variability can dominate human-inducedchanges in land water storage; recent satellite-gravity estimates suggest that, over 2002–
2014, a human-caused land-water contribution
to GMSL of 0.4 mm/year (0.02 inches/year)
was more than offset by −0.7 mm/year (−0.03
inches/year) due to natural variability.5

12.4.1 Global Tide Gauge Network and Satellite
Observations

A global tide gauge network provides the century-long observations of local RSL, whereas
satellite altimetry provides broader coverageof sea surface heights outside the polar regions starting in 1993. GMSL can be estimatedthrough statistical analyses of either data set.
GMSL trends over the 1901–1990 period varyslightly (Hay et al. 2015:33 1.2 ± 0.2 mm/year[0.05 inches/year]; Church and White 2011:34
1.5 ± 0.2 mm/year [0.06 inches/year]) withdifferences amounting to about 1 inch over 90
years. Thus, these results indicate about 11–14
cm (4–5 inches) of GMSL rise from 1901 to
1990.
Tide gauge analyses indicate that GMSL rose
at a considerably faster rate of about 3 mm/year (0.12 inches/year) since 1993,33, 34 a result
supported by satellite data indicating a trendof 3.4 ± 0.4 mm/year (0.13 ± 0.02 inches/year)
over 1993–2015 (update to Nerem et al. 201035).
These results indicate an additional GMSL
rise of about 7 cm (about 3 inches) since 1990
(Figure 12.2b, Figure 12.3a) and about 16–21
cm (about 7–8 inches) since 1900. Satellite (altimetry and gravity) and in situ water column(Argo floats) measurements show that, since2005, about one third of GMSL rise has beenfrom steric changes (primarily thermal expansion) and about two thirds from the additionof mass to the ocean, which represents agrowing land-ice contribution (compared tosteric) and a departure from the relative contributions earlier in the 20th century (Figure
12.3a).4, 36, 37, 38, 39, 40

Comparison of results from a variety of approaches supports the conclusion that a substantial fraction of GMSL rise since 1900 is attributable to human-caused climate change.32,
41, 42, 43, 44, 45, 46, 47, 48
For example, based on thelong term historical relationship between
temperature and rate of GMSL change, Kopp
et al.32 found that GMSL rise would extremely
likely have been less than 59% of observed in
the absence of 20th century global warming,and that it is very likely that GMSL has been
higher since 1960 than it would have been
without 20th century global warming (Figure
12.3b). Similarly, using a variety of models forindividual components, Slangen et al.41 foundthat about 80% of the GMSL rise they simulated for 1970–2005 and about half of that which
they simulated for 1900–2005 was attributable
to anthropogenic forcing.

In addition to land ice, the mass-addition
contribution also includes net changes in
global land-water storage. This term varied insign over the course of the last century, withhuman-induced changes in land-water storage

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Global Mean Sea Level Budget

(a)

2$"
$#

1

%&'()*+,-'+./

Change in Global Mean
Sea Level (inches)

3

#)3
#)4
#)2
#
!#)2
!#)4
!#)3

%&'()*+,-'+./

Global mean sea level
Global mean ocean mass
Global mean steric sea level
Ocean mass + steric sea level$0## $0"# $1## $1"# 2###

#)3
#)4
#)2
#
!#)2
!#)4
!#)3

"

0#

1995

2000

2005

2010

2015

!"

Observed
Model - Historical
Model - No warming

Change in Global Mean
Sea Level (cm)

15
10

6
4

5

2

0

0

−5

(c)

1800

1850

1900
Year

Change in Global Mean
Sea Level (inches)

(b)

−2
2000

1950

Change in Sea Surface Height, 1993–2015

inches/decade

−1

0

1

2

3

4

Figure 12.3: (a) Contributions of ocean mass changes from land ice and land water storage (measured by satellite
gravimetry) and ocean volume changes (or steric, primarily from thermal expansion measured by in situ ocean profilers)
and their comparison to global mean sea level (GMSL) change (measured by satellite altimetry) since 1993. (b) An
estimate of modeled GMSL rise in the absence of 20th century warming (blue), from the same model with observed
warming (red), and compared to observed GMSL change (black). Heavy/light shading indicates the 17th–83rd and5th–95th percentiles. (c) Rates of change from 1993 to 2015 in sea surface height from satellite altimetry data; updated
from Kopp et al.3 using data updated from Church and White.34 (Figure source: (a) adapted and updated from Leulietteand Nerem 2016,40 (b) adapted from Kopp et al. 201632 and (c) adapted and updated from Kopp et al. 20153).

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Over timescales of a few decades, ocean–atmosphere dynamics drive significant variability in sea surface height, as can be observedby satellite (Figure 12.3c) and in tide gauge
records that have been adjusted to accountfor background rates of rise due to long term
factors like glacio-isostatic adjustments. For
example, the U.S. Pacific Coast experienced
a slower-than-global increase between about
1980 and 2011, while the western tropical Pacific experienced a faster-than-global increasein the 1990s and 2000s. This pattern was associated with changes in average winds linked
to the Pacific Decadal Oscillation (PDO)16, 49,
50
and appears to have reversed since about
2012.51 Along the Atlantic coast, the U.S.
Northeast has experienced a faster-than-global
increase since the 1970s, while the U.S. Southeast has experienced a slower-than-global
increase since the 1970s. This pattern appears
to be tied to changes in the Gulf Stream,10, 12, 52,53
although whether these changes represent
natural variability or a long-term trend remains uncertain.54
12.4.2 Ice Sheet Gravity and Altimetry and Visual
Observations

Since NCA3, Antarctica and Greenland havecontinued to lose ice mass, with mounting evidence accumulating that mass loss is accelerating. Studies using repeat gravimetry (GRACE
satellites), repeat altimetry, GPS monitoring,and mass balance calculations generally agree
on accelerating mass loss in Antarctica.55, 56, 57, 58
Together, these indicate a mass loss of roughly
100 Gt/year (gigatonnes/year) over the last
decade (a contribution to GMSL of about 0.3
mm/year [0.01 inches/year]). Positive accumulation rate anomalies in East Antarctica,
especially in Dronning Maud Land,59 havecontributed to the trend of slight growth there(e.g., Seo et al. 2015;57 Martín-Español et al.
201658), but this is more than offset by massloss elsewhere, especially in West Antarctica
along the coast facing the Amundsen Sea,60, 61

U.S. Global Change Research Program

Totten Glacier in East Antarctica,62, 63 and along
the Antarctic Peninsula.57, 58, 64 Floating ice
shelves around Antarctica are losing mass at
an accelerating rate.65 Mass loss from floating
ice shelves does not directly affect GMSL, butdoes allow faster flow of ice from the ice sheet
into the ocean.
Estimates of mass loss in Greenland basedon mass balance from input-output, repeat
gravimetry, repeat altimetry, and aerial imagery as discussed in Chapter 11: Arctic Changes reveal a recent acceleration.66 Mass loss
averaged approximately 75 Gt/year (about
0.2 mm/year [0.01 inches/year] GMSL rise)from 1900 to 1983, continuing at a similar rateof approximately 74 Gt/year through 2003
before accelerating to 186 Gt/year (0.5 mm/year [0.02 inches/year] GMSL rise) from 2003
to 2010.67 Strong interannual variability does
exist (see Ch. 11: Arctic Changes), such as
during the exceptional melt year from April
2012 to April 2013, which resulted in mass lossof approximately 560 Gt (1.6 mm/year [0.06
inches/year]).68 More recently (April 2014–
April 2015), annual mass losses have resumed
the accelerated rate of 186 Gt/year.67, 69 Mass
loss over the last century has reversed thelong-term trend of slow thickening linked tothe continuing evolution of the ice sheet fromthe end of the last ice age.70

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12.5 Projected Sea Level Rise
12.5.1 Scenarios of Global Mean Sea Level Rise

No single physical model is capable of accurately representing all of the major processes
contributing to GMSL and regional/local RSL
rise. Accordingly, the U.S. Interagency Sea
Level Rise Task Force (henceforth referred
to as “Interagency”)71 has revised the GMSL
rise scenarios for the United States and now
provides six scenarios that can be used forassessment and risk-framing purposes (Figure
12.4a; Table 12.1). The low scenario of 30 cm(about 1 foot) GMSL rise by 2100 is consistent
with a continuation of the recent approximately 3 mm/year (0.12 inches/year) rate of risethrough to 2100 (Table 12.2), while the fiveother scenarios span a range of GMSL rise be-

tween 50 and 250 cm (1.6 and 8.2 feet) in 2100,
with corresponding rise rates between 5 mm/year (0.2 inches/year) to 44 mm/year (1.7
inches/year) towards the end of this century(Table 12.2). The highest scenario of 250 cm
is consistent with several literature estimatesof the maximum physically plausible level of
21st century sea level rise (e.g., Pfeffer et al.
2008,72 updated with Sriver et al. 201273 estimates of thermal expansion and Bamber andAspinall 201374 estimates of Antarctic contribution, and incorporating land water storage,
as discussed in Miller et al. 201375 and Kopp
et al. 201476). It is It is also consistent with thehigh end of recent projections of Antarctic ice
sheet melt discussed below.77 The Interagency

8
6
4
2
0
1800

(b)

1850

1900

1950
Year

2000

2050

RCP 2.6
RCP 4.5
RCP 8.5

Global meansea level (feet)

(a)

2100

Projected Relative Sea Level Change for 2100
under the Intermediate Scenario

Change in Sea Level (feet)
<0

1

2

3

4

5

>6

Figure 12.4: (a) Global mean sea level (GMSL) rise from 1800 to 2100, based on Figure 12.2b from 1800 to 2015, thesix Interagency71 GMSL scenarios (navy blue, royal blue, cyan, green, orange, and red curves), the very likely ranges
in 2100 for different RCPs (colored boxes), and lines augmenting the very likely ranges by the difference between the
median Antarctic contribution of Kopp et al.76 and the various median Antarctic projections of DeConto and Pollard.77(b) Relative sea level (RSL) rise (feet) in 2100 projected for the Interagency Intermediate Scenario (1-meter [3.3 feet]
GMSL rise by 2100) (Figure source: Sweet et al. 201771).

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Table 12.1. The Interagency GMSL rise scenarios in meters (feet) relative to 2000. All values are 19-year averages of GMSL centered at the identified year. To convert from a 1991–
2009 tidal datum to the 1983–2001 tidal datum, add 2.4 cm (0.9 inches).
Scenario

2020

2030

2050

2100

Low

0.06 (0.2)

0.09 (0.3)

0.16 (0.5)

0.30 (1.0)

Intermediate-Low

0.08 (0.3)

0.13 (0.4)

0.24 (0.8)

0.50 (1.6)

Intermediate

0.10 (0.3)

0.16 (0.5)

0.34 (1.1)

1.0 (3.3)

Intermediate-High

0.10 (0.3)

0.19 (0.6)

0.44 (1.4)

1.5 (4.9)

High

0.11 (0.4)

0.21 (0.7)

0.54 (1.8)

2.0 (6.6)

Extreme0.11 (0.4)

0.24 (0.8)

0.63 (2.1)

2.5 (8.2)

Table 12.2. Rates of GMSL rise in the Interagency scenarios in mm/year (inches/year). All
values represent 19-year average rates of change, centered at the identified year.
Scenario

2020

2030

2050

2090

Low

3 (0.1)3 (0.1)3 (0.1)3 (0.1)

Intermediate-Low

5 (0.2)5 (0.2)5 (0.2)5 (0.2)

Intermediate

6 (0.2)7 (0.3)10 (0.4)

15 (0.6)

Intermediate-High

7 (0.3)10 (0.4)

15 (0.6)

24 (0.9)High

8 (0.3)13 (0.5)

20 (0.8)35 (1.4)

Extreme10 (0.4)

15 (0.6)

25 (1.0)44 (1.7)

Table 12.3. Interpretations of the Interagency GMSL rise scenarios
ScenarioInterpretation

Continuing current rate of GMSL rise, as
Low calculated since 1993
Low end of very likely range under RCP2.6
Modest increase in rate
Middle of likely range under RCP2.6
Intermediate-Low
Low end of likely range under RCP4.5
Low end of very likely range under RCP8.5
High end of very likely range under RCP4.5
High end of likely range under RCP8.5
Intermediate
Middle of likely range under RCP4.5 when
accounting for possible ice cliff instabilities
Slightly above high end of very likely range
under RCP8.5
Intermediate-High
Middle of likely range under RCP8.5 when
accounting for possible ice cliff instabilities
High end of very likely range under RCP8.5
High when accounting for possible ice cliff
instabilities
ExtremeU.S. Global Change Research Program

Consistent with estimates of physicallypossible “worst case”

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12 | Sea Level Rise
GMSL scenario interpretations are shown in
Table 12.3.
The Interagency scenario approach is similar
to local RSL rise scenarios of Hall et al.78 used
for all coastal U.S. Department of Defense
installations worldwide. The Interagency
approach starts with a probabilistic projection
framework to generate time series and regional projections consistent with each GMSL rise
scenario for 2100.76 That framework combines probabilistic estimates of contributions
to GMSL and regional RSL rise from ocean
processes, cryospheric processes, geologicalprocesses, and anthropogenic land-water
storage. Pooling the Kopp et al.76 projections
across even lower, lower, and higher scenarios(RCP2.6, 4.5, and 8.5), the probabilistic projections are filtered to identify pathways consistent with each of these 2100 levels, with themedian (and 17th and 83rd percentiles) pickedfrom each of the filtered subsets.
12.5.2 Probabilities of Different Sea Level Rise
Scenarios

Several studies have estimated the probabilities of different amounts of GMSL rise under
different pathways (e.g., Church et al. 2013;4
Kopp et al. 2014;76 Slangen et al. 2014;79 Jevrejeva et al. 2014;80 Grinsted et al. 2015;81 Kopp
et al. 2016;32 Mengel et al. 2016;82 Jackson andJevrejeva 201683) using a variety of methods,
including both statistical and physical models.
Most of these studies are in general agreementthat GMSL rise by 2100 is very likely to be
between about 25–80 cm (0.8–2.6 feet) underan even lower scenario (RCP2.6), 35–95 cm(1.1–3.1 feet) under a lower scenario (RCP4.5),
and 50–130 cm (1.6–4.3 feet) under a higher
scenario (RCP8.5), although some projections
extend the very likely range for RCP8.5 as highas 160–180 cm (5–6 feet) (Kopp et al. 2014,76
sensitivity study).80, 83 Based on Kopp et al.,76
the probability of exceeding the amount of
GMSL in 2100 under the Interagency scenarios
is shown in Table 12.4.
The Antarctic projections of Kopp et al.,76 the
GMSL projections of which underlie Table
12.4, are consistent with a statistical-physicalmodel of the onset of marine ice sheet instability calibrated to observations of ongoingretreat in the Amundsen Embayment sectorof West Antarctica.84 Ritz et al.’s84 95th percentile Antarctic contribution to GMSL of 30 cm
by 2100 is comparable to Kopp et al.’s76 95th
percentile projection of 33 cm under the higher
scenario (RCP8.5). However, emerging sciencesuggests that these projections may understate
the probability of faster-than-expected ice
sheet melt, particularly for high-end warming
scenarios. While these probability estimates

Table 12.4. Probability of exceeding the Interagency GMSL scenarios in2100 per Kopp et al.76 New evidence regarding the Antarctic ice sheet,
if sustained, may significantly increase the probability of the intermediate-high, high, and extreme scenarios, particularly under the higher
scenario (RCP8.5), but these results have not yet been incorporated into
a probabilistic analysis.
Scenario

RCP2.6

RCP4.5

RCP8.5

Low

94%

98%

100%

Intermediate-Low

49%

73%

96%

Intermediate

2%

3%

17%

Intermediate-High

0.4%

0.5%

1.3%

High

0.1%

0.1%

0.3%

Extreme

0.05%

0.05%

0.1%

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12 | Sea Level Riseare consistent with the assumption that therelationship between global temperature and
GMSL in the coming century will be similar tothat observed over the last two millennia,32, 85
emerging positive feedbacks (self-amplifying
cycles) in the Antarctic Ice Sheet especially86,
87
may invalidate that assumption. Physicalfeedbacks that until recently were not incorporated into ice sheet models88 could add
about 0–10 cm (0–0.3 feet), 20–50 cm (0.7–1.6
feet) and 60–110 cm (2.0–3.6 feet) to central
estimates of current century sea level rise
under even lower, lower, and higher scenarios(RCP2.6, RCP4.5 and RCP8.5, respectively).77
In addition to marine ice sheet instability,
examples of these interrelated processes include ice cliff instability and ice shelf hydrofracturing. Processes underway in Greenland
may also be leading to accelerating high-end
melt risk. Much of the research has focusedon changes in surface albedo driven by themelt-associated unmasking and concentrationof impurities in snow and ice.69 However, ice
dynamics at the bottom of the ice sheet may
be important as well, through interactions
with surface runoff or a warming ocean. As
an example of the latter, Jakobshavn Isbræ,Kangerdlugssuaq Glacier, and the Northeast
Greenland ice stream may be vulnerable tomarine ice sheet instability.66
12.5.3 Sea Level Rise after 2100

2.5 meter (1.0–8.2 feet) range spanned by thesix Interagency GMSL scenarios in year 2100
increases to about 0.4–9.7 meters (1.3–31.8feet), as shown in Table 12.5. These six scenarios imply average rates of GMSL rise over the
first half of the next century of 1.4 mm/year(0.06 inch/year), 4.6 mm/yr (0.2 inch/year),
16 mm/year (0.6 inch/year), 32 mm/year (1.3
inches/year), 46 mm/yr (1.8 inches/year) and
60 mm/year (2.4 inches/year), respectively.
Excluding the possible effects of still emerging
science regarding ice cliffs and ice shelves, it isvery likely that by 2200 GMSL will have risen
by 0.3–2.4 meters (1.0–7.9 feet) under an evenlower scenario (RCP2.6), 0.4–2.7 meters (1.3–
8.9 feet) under a lower scenario (RCP4.5), and
1.0–3.7 meters (3.3–12 feet) under the higher
scenario (RCP8.5).76

GMSL rise will not stop in 2100, and so it isuseful to consider extensions of GMSL rise
projections beyond this point. By 2200, the 0.3–

Under most projections, GMSL rise will also
not stop in 2200. The concept of a “sea level rise commitment” refers to the long-term
projected sea level rise were the planet’s temperature to be stabilized at a given level (e.g.,
Levermann et al. 2013;89 Golledge et al. 201590).
The paleo sea level record suggests that even2°C (3.6°F) of global average warming above
the preindustrial temperature may represent
a commitment to several meters of rise. One
modeling study suggesting a 2,000-year commitment of 2.3 m/°C (4.2 feet/°F)89 indicatesthat emissions through 2100 would lock in a
likely 2,000-year GMSL rise commitment of
about 0.7–4.2 meters (2.3–14 feet) under aneven lower scenario (RCP2.6), about 1.7–5.6

Table 12.5. Post-2100 extensions of the Interagency GMSL rise scenarios in meters (feet)
Scenario

2100

2120

2150

2200

Low

0.30 (1.0)

0.34 (1.1)

0.37 (1.2)

0.39 (1.3)

Intermediate-Low

0.50 (1.6)

0.60 (2.0)

0.73 (2.4)

0.95 (3.1)

Intermediate

1.0 (3.3)

1.3 (4.3)

1.8 (5.9)

2.8 (9.2)

Intermediate-High

1.5 (4.9)

2.0 (6.6)

3.1 (10)5.1 (17)High

2.0 (6.6)

2.8 (9.2)

4.3 (14)7.5 (25)

Extreme2.5 (8.2)

3.6 (12)5.5 (18)

9.7 (32)

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12 | Sea Level Rise
meters (5.6–19 feet) under a lower scenario(RCP4.5), and about 4.3–9.9 meters (14–33
feet) under the higher scenario (RCP8.5).91
However, as with the 21st century projections,emerging science regarding the sensitivity of
the Antarctic Ice Sheet may increase the estimated sea level rise over the next millennium,
especially for a higher scenario.77 Large-scale
climate geoengineering might reduce thesecommitments,92, 93 but may not be able to avoid
lock-in of significant change.94, 95, 96, 97 Oncechanges are realized, they will be effectively
irreversible for many millennia, even if humans artificially accelerate the removal of CO2
from the atmosphere.77

on the process-level projections of the Interagency GMSL scenarios, several key regional
patterns are apparent in future U.S. RSL rise as
shown for the Intermediate (1 meter [3.3 feet]
GMSL rise by 2100 scenario) in Figure 12.4b.

The 2,000-year commitment understates thefull sea level rise commitment, due to the longresponse time of the polar ice sheets. Paleo sea
level records (Figure 12.2a) suggest that 1°C ofwarming may already represent a long-term
commitment to more than 6 meters (20 feet)of GMSL rise.20, 22, 23 A 10,000-year modelingstudy98 suggests that 2°C warming represents
a 10,000-year commitment to about 25 meters(80 feet) of GMSL rise, driven primarily bya loss of about one-third of the Antarctic ice
sheet and three-fifths of the Greenland ice
sheet, while 21st century emissions consistent
with a higher scenario (RCP8.5) represent a
10,000-year commitment to about 38 meters(125 feet) of GMSL rise, including a complete
loss of the Greenland ice sheet over about
6,000 years.

3. RSL rise is additionally augmented in the
Northeast by the effects of glacial isostatic
adjustment.

12.5.4 Regional Projections of Sea Level Change

Because the different factors contributing to
sea level change give rise to different spatial
patterns, projecting future RSL change at
specific locations requires not just an estimateof GMSL change but estimates of the different
processes contributing to GMSL change—eachof which has a different associated spatial pattern—as well as of the processes contributing
exclusively to regional or local change. Based

U.S. Global Change Research Program

1. RSL rise due to Antarctic Ice Sheet melt
is greater than GMSL rise along all U.S.
coastlines due to static-equilibrium effects.
2. RSL rise due to Greenland Ice Sheet melt
is less than GMSL rise along the coastlineof the continental United States due tostatic-equilibrium effects. This effect is
especially strong in the Northeast.

4. The Northeast is also exposed to rise due
to changes in the Gulf Stream and reductions in the Atlantic meridional overturning circulation (AMOC). Were the AMOC
to collapse entirely—an outcome viewed
as unlikely in the 21st century—it could
result in as much as approximately 0.5
meters (1.6 feet) of additional regional sea
level rise (see Ch. 15: Potential Surprises
for further discussion).99, 100
5. The western Gulf of Mexico and parts of
the U.S. Atlantic Coast south of New Yorkare currently experiencing significant RSL
rise caused by the withdrawal of groundwater (along the Atlantic Coast) and ofboth fossil fuels and groundwater (along
the Gulf Coast). Continuation of thesepractices will further amplify RSL rise.
6. The presence of glaciers in Alaska andtheir proximity to the Pacific Northwest
reduces RSL rise in these regions, due
to both the ongoing glacial isostatic adjustment to past glacier shrinkage and to346

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12 | Sea Level Rise
the static-equilibrium effects of projected
future losses.
7. Because they are far from all glaciers andice sheets, RSL rise in Hawai‘i and other
Pacific islands due to any source of melting land ice is amplified by the static-equilibrium effects.

12.6 Extreme Water Levels
12.6.1 Observations

Coastal flooding during extreme high-water
events has become deeper due to local RSL
rise and more frequent from a fixed-elevation
perspective.78, 101, 102, 103 Trends in annual frequencies surpassing local emergency preparedness thresholds for minor tidal flooding(i.e., “nuisance” levels of about 30–60 cm [1–2feet]) that begin to flood infrastructure andtrigger coastal flood “advisories” by NOAA’s
National Weather Service have increased 5- to
10-fold or more since the 1960s along the U.S.
coastline,104 as shown in Figure 12.5a. Locations experiencing such trend changes (based
upon fits of flood days per year of Sweet andPark 2014105) include Atlantic City and Sandy
Hook, NJ; Philadelphia, PA; Baltimore andAnnapolis, MD; Norfolk, VA; Wilmington,
NC; Charleston, SC; Savannah, GA; Mayport
and Key West, FL; Port Isabel, TX, La Jolla,
CA; and Honolulu, HI. In fact, over the last
several decades, minor tidal flood rates havebeen accelerating within several (more than25) East and Gulf Coast cities with establishedelevation thresholds for minor (nuisance)
flood impacts, fastest where elevation thresholds are lower, local RSL rise is higher, andextreme variability less.104, 105, 106
Trends in extreme water levels (for example,
monthly maxima) in excess of mean sea levels(for example, monthly means) exist, but are
not commonplace.48, 101, 107, 108, 109 More common
are regional time dependencies in high-waterprobabilities, which can co-vary on an interan-

U.S. Global Change Research Programnual basis with climatic and other patterns.101,
110, 111, 112, 113, 114, 115
These patterns are often associated with anomalous oceanic and atmosphericconditions.116, 117 For instance, the probabilityof experiencing minor tidal flooding is compounded during El Niño periods along portions of the West and Mid-Atlantic Coasts105
from a combination of higher sea levels andenhanced synoptic forcing and storm surge
frequency.112, 118, 119, 120
12.6.2 Influence of Projected Sea Level Rise on
Coastal Flood Frequencies

The extent and depth of minor-to-major
coastal flooding during high-water events
will continue to increase in the future as local
RSL rises.71, 76, 78, 105, 121, 122, 123, 124, 125 Relative tofixed elevations, the frequency of high-water
events will increase the fastest where extreme
variability is less and the rate of local RSL
rise is higher.71, 76, 105, 121, 124, 126 Under the RCPbased probabilistic RSL projections of Kopp
et al. 2014,76 at tide gauge locations along thecontiguous U.S. coastline, a median 8-foldincrease (range of 1.1- to 430-fold increase)is expected by 2050 in the annual number offloods exceeding the elevation of the current
100-year flood event (measured with respect
to a 1991–2009 baseline sea level).124 Under
the same forcing, the frequency of minor
tidal flooding (with contemporary recurrence
intervals generally <1 year104) will increase
even more so in the coming decades105, 127
and eventually occur on a daily basis (Figure
12.5b). With only about 0.35 m (<14 inches) ofadditional local RSL rise (with respect to theyear 2000), annual frequencies of moderatelevel flooding—those locally with a 5-year recurrence interval (Figure 12.5c) and associatedwith a NOAA coastal flood warning of serious
risk to life and property—will increase 25-foldat the majority of NOAA tide gauge locations
along the U.S. coastline (outside of Alaska) byor about (±5 years) 2080, 2060, 2040, and 2030
under the Interagency Low, Intermediate-Low,

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(b)

1960

1980

2000

2010

NOAA

RCP2.6

Trend

200
100
365
San Francisco, CA
300
200
0
1960 1980 2000 2020 2040 2060 2080 2100

auges

Tide G(d)

Water Level Height (ft) above Average Highest Tide

Water Level Height (feet)
0

RCP4.5365
Charleston, SC
300

100
HI
CA
TX
FL
FL

1990

FL
SC
VA
MD
DE
NJ
NY
NY
RI

(c)

1970

RCP8.5

Minor Tidal Floods (Days/Year)

90
80
70
60
50
40
30
20
10
0
1950

Days per Year

(a)

1

2

3

Decade the 5-year Event Becomes a 0.2-year Event
under the Intermediate Scenario

Decade4>52020 2030 2040 2050 2060 2070 2080 2090 2100 <2200

Figure 12.5: (a) Tidal floods (days per year) exceeding NOAA thresholds for minor impacts at 28 NOAA tide gauges
through 2015. (b) Historical exceedances (orange), future projections through 2100 based upon the continuation of thehistorical trend (blue), and future projections under median RCP2.6, 4.5 and 8.5 conditions, for two of the locations—Charleston, SC and San Francisco, CA. (c) Water level heights above average highest tide associated with a local
5-year recurrence probability, and (d) the future decade when the 5-year event becomes a 0.2-year (5 or more times peryear) event under the Interagency Intermediate scenario; black dots imply that a 5-year to 0.2-year frequency change
does not unfold by 2200 under the Intermediate scenario. (Figure source: (a) adapted from Sweet and Marra 2016,165(b) adapted from Sweet and Park 2014,105 (c) and (d) Sweet et al. 201771).

Intermediate, and Intermediate-High GMSL
scenarios, respectively.71 Figure 12.5d, whichshows the decade in which the frequency ofsuch moderate level flooding will increase 25fold under the Interagency Intermediate Scenario, highlights that the mid- and Southeast
Atlantic, western Gulf of Mexico, California,
and the Island States and Territories are mostsusceptible to rapid changes in potentiallydamaging flood frequencies.
12.6.3 Waves and Impacts

The combination of a storm surge at high tidewith additional dynamic effects from waves128,
129
creates the most damaging coastal hydraulic conditions.130 Simply with higher-than-nor-

U.S. Global Change Research Program

mal sea levels, wave action increases thelikelihood for extensive coastal erosion131, 132,133
and low-island overwash.134 Wave runup
is often the largest water level component
during extreme events, especially along island
coastlines where storm surge is constrained bybathymetry.78, 121, 123 On an interannual basis,wave impacts are correlated across the Pacific
Ocean with phases of ENSO.135, 136 Over thelast half century, there has been an increasing
trend in wave height and power within the
North Pacific Ocean137, 138 that is modulated by the PDO.137, 139 Resultant increases inwave run-up have been more of a factor than
RSL rise in terms of impacts along the U.S.
Northwest Pacific Coast over the last several

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decades.140 In the Northwest Atlantic Ocean,no long-term trends in wave power have been
observed over the last half century,141 thoughhurricane activity drives interannual variability.142 In terms of future conditions this century,
increases in mean and maximum seasonal
wave heights are projected within parts of thenortheast Pacific, northwest Atlantic, and Gulf
of Mexico.138, 143, 144, 145
12.6.4 Sea Level Rise, Changing Storm Characteristics, and Their Interdependencies

Northeast,146 whereas others project decreases
in strong extratropical storms in some regions(e.g., Zappa et al. 2013147).

Future probabilities of extreme coastal floods
will depend upon the amount of local RSL
rise, changes in coastal storm characteristics,
and their interdependencies. For instance,
there have been more storms producing
concurrent locally extreme storm surge andrainfall (not captured in tide gauge data) along
the U.S. East and Gulf Coasts over the last 65
years, with flooding further compounded by
local RSL rise.166 Hemispheric-scale extratropical cyclones may experience a northward shift
this century, with some studies projecting an
overall decrease in storm number (Colle et al.
2015117 and references therein). The researchis mixed about strong extratropical storms;
studies find potential increases in frequency
and intensity in some regions, like within the

U.S. Global Change Research Program

For tropical cyclones, model projections for
the North Atlantic mostly agree that intensities and precipitation rates will increase thiscentury (see Ch. 9: Extreme Storms), althoughsome model evidence suggests that trackchanges could dampen the effect in the U.S.
Mid-Atlantic and Northeast.148 Assuming other storm characteristics do not change, sea level rise will increase the frequency and extent
of extreme flooding associated with coastal
storms, such as hurricanes and nor’easters.
A projected increase in the intensity of hurricanes in the North Atlantic could increase theprobability of extreme flooding along most of
the U.S. Atlantic and Gulf Coast states beyond
what would be projected based solely on
RSL rise.110, 149, 150, 151 In addition, RSL increases
are projected to cause a nonlinear increasein storm surge heights in shallow bathymetry environments152, 153, 154, 155, 156 and extend
wave propagation and impacts landward.152,
153
However, there is low confidence in themagnitude of the increase in intensity andthe associated flood risk amplification, and it
could be offset or amplified by other factors,
such as changes in storm frequency or tracks(e.g., Knutson et al. 2013,157 2015158).

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TRACEABLE ACCOUNTSKey Finding 1
Global mean sea level (GMSL) has risen by about 7–8
inches (about 16–21 cm) since 1900, with about 3 ofthose inches (about 7 cm) occurring since 1993 (veryhigh confidence). Human-caused climate change has
made a substantial contribution to GMSL rise since
1900 (high confidence), contributing to a rate of rise thatis greater than during any preceding century in at least
2,800 years (medium confidence).
Description of evidence baseMultiple researchers, using different statistical approaches, have integrated tide gauge records to estimate GMSL rise since the late nineteenth century (e.g.,
Church and White 2006,159 2011;34 Hay et al. 2015;33 Jevrejeva et al. 2009).42 The most recent published rateestimates are 1.2 ± 0.233 or 1.5 ± 0.234 mm/year over
1901–1990. Thus, these results indicate about 11–14
cm (4–5 inches) of GMSL rise from 1901 to 1990. Tide
gauge analyses indicate that GMSL rose at a considerably faster rate of about 3 mm/year (0.12 inches/year)
since 1993,33, 34 a result supported by satellite data indicating a trend of 3.4 ± 0.4 mm/year (0.13 inches/year)
over 1993–2015 (update to Nerem et al. 201035) (Figure
12.3a). These results indicate an additional GMSL riseof about 7 cm (about 3 inches) rise since 1990. Thus,
total GMSL rise since 1900 is about 16–21 cm (about
7–8 inches).
The finding regarding the historical context of the 20th
century change is based upon Kopp et al.32, who conducted a meta-analysis of geological RSL reconstructions spanning the last 3,000 years from 24 locations
around the world as well as tide gauge data from 66
sites and the tide gauge based GMSL reconstruction of
Hay et al.33 By constructing a spatio-temporal statisticalmodel of these data sets, they identified the common
global sea level signal over the last three millennia andits uncertainties. They found a 95% probability thatthe average rate of GMSL change over 1900–2000 was
greater than during any preceding century in at least
2,800 years.

U.S. Global Change Research Program

The finding regarding the substantial human contribution is based upon several lines of evidence. Kopp
et al.,32 based on the long term historical relationshipbetween temperature and the rate of sea level change,
found that it is extremely likely that GMSL rise would
have been <59% of observed in the absence of 20th
century global warming, and that it is very likely that
GMSL has been higher since 1960 than it would havebeen without 20th century global warming. Using avariety of models for individual components, Slangen
et al.41 found that 69% ± 31% out of the 87% ± 20%
of GMSL rise over 1970–2005 that their models simulated was attributable to anthropogenic forcing, andthat 37% ± 38% out of 74% ± 22% simulated was attributable over 1900–2005. Jevrejeva et al.,42 using therelationship between forcing and GMSL over 1850 and2001 and CMIP3 models, found that ~75% of GMSL risein the 20th century is attributable to anthropogenic
forcing. Marcos and Amores,45 using CMIP5 models,
found that ~87% of ocean heat uptake since 1970 in
the top 700 m of the ocean has been due to anthropogenic forcing. Slangen et al.,46 using CMIP5, foundthat anthropogenic forcing was required to explain
observed thermosteric SLR over 1957–2005. Marzeion
et al.47 found that 25% ± 35% of glacial loss over 1851–
2010, and 69% ± 24% over 1991–2010, was attributable
to anthropogenic forcing. Dangendorf et al.,43 basedon time series analysis, found that >45% of observed
GMSL trend since 1900 cannot (with 99% probability)
be explained by multi-decadal natural variability. Becker et al.,44 based on time series analysis, found a 99%
probability that at least 1.0 or 1.3 mm/year of GMSL riseover 1880–2010 is anthropogenic.
Major uncertainties
Uncertainties in reconstructed GMSL change relate to
the sparsity of tide gauge records, particularly before
the middle of the twentieth century, and to different
statistical approaches for estimating GMSL change
from these sparse records. Uncertainties in reconstructed GMSL change before the twentieth century
also relate to the sparsity of geological proxies for sea

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12 | Sea Level Riselevel change, the interpretation of these proxies, andthe dating of these proxies. Uncertainty in attribution
relates to the reconstruction of past changes and themagnitude of unforced variability.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Confidence is very high in the rate of GMSL rise since
1900, based on multiple different approaches to estimating GMSL rise from tide gauges and satellite altimetry. Confidence is high in the substantial human contribution to GMSL rise since 1900, based on both statisticaland physical modeling evidence. It is medium that themagnitude of the observed rise since 1900 is unprecedented in the context of the previous 2,800 years, basedon meta-analysis of geological proxy records.
Summary sentence or paragraph that integrates
the above information
This key finding is based upon multiple analyses of tidegauge and satellite altimetry records, on a meta-analysis
of multiple geological proxies for pre-instrumental sea
level change, and on both statistical and physical analyses of the human contribution to GMSL rise since 1900.
Key Finding 2
Relative to the year 2000, GMSL is very likely to rise by
0.3–0.6 feet (9–18 cm) by 2030, 0.5–1.2 feet (15–38 cm)
by 2050, and 1.0–4.3 feet (30–130 cm) by 2100 (very highconfidence in lower bounds; medium confidence in upperbounds for 2030 and 2050; low confidence in upper bounds
for 2100). Future pathways have little effect on projected
GMSL rise in the first half of the century, but significantly affect projections for the second half of the century(high confidence). Emerging science regarding Antarctic
ice sheet stability suggests that, for high emission scenarios, a GMSL rise exceeding 8 feet (2.4 m) by 2100 is
physically possible, although the probability of such an
extreme outcome cannot currently be assessed. Regardless of pathway, it is extremely likely that GMSL rise willcontinue beyond 2100 (high confidence).

U.S. Global Change Research Program

Description of evidence base
The lower bound of the very likely range is based ona continuation of the observed approximately 3 mm/year rate of GMSL rise. The upper end of the very likely
range is based upon estimates for the higher scenario (RCP8.5) from three studies producing fully probabilistic projections across multiple RCPs. Kopp et al.
201476 fused multiple sources of information accounting for the different individual process contributing to
GMSL rise. Kopp et al. 201632 constructed a semi-empirical sea level model calibrated to the Common Era
sea level reconstruction. Mengel et al.82 constructed a
set of semi-empirical models of the different contributing processes. All three studies show negligible RCP
dependence in the first half of this century, becoming
more prominent in the second half of the century. A
sensitivity study by Kopp et al. 2014,76 as well as studies by Jevrejeva et al.80 and by Jackson and Jevrejeva,83
used frameworks similar to Kopp et al. 201632 but incorporated directly an expert elicitation study on ice sheet
stability.74 (This study was incorporated in Kopp et al.
2014’s76 main results with adjustments for consistency
with Church et al. 20134). These studies extend the very
likely range for the higher scenario (RCP8.5) as high as
160–180 cm (5–6 feet) (Kopp et al. 2014,76 sensitivity
study).80, 83
To estimate the effect of incorporating the DeContoand Pollard77 projections of Antarctic ice sheet melt,
we note that Kopp et al. (2014)’s76 median projectionof Antarctic melt in 2100 is 4 cm (1.6 inches) (RCP2.6),
5 cm (2 inches) (RCP4.5), or 6 cm (2.4 inches) (RCP8.5).
By contrast, DeConto and Pollard’s77 ensemble mean
projections are (varying the assumptions for the sizeof Pliocene mass loss and the bias correction in theAmundsen Sea) 2–14 cm (0.1–0.5 foot) for an evenlower scenario (RCP2.6), 26–58 cm (0.9–1.9 feet) for alower scenario (RCP4.5), and 64–114 cm (2.1–3.7 ft) forthe higher scenario (RCP8.5). Thus, we conclude that
DeConto and Pollard’s77 projection would lead to a –10cm (−0.1–0.3 ft) increase in median RCP2.6 projections,a 21–53 cm (0.7–1.7 feet) increase in median RCP4.5
projections, and a 58–108 cm (1.9–3.5 feet) increase inmedian RCP8.5 projections.

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12 | Sea Level Risetinuation of the historical trend over the last quarter
century would indicate. There is medium confidence inthe upper end of very likely ranges for 2030 and 2050.
Due to possibly large ice sheet contributions, there is
low confidence in the upper end of very likely ranges
for 2100. Based on multiple projection methods, there
is high confidence that differences between emission
scenarios are small before 2050 but significant beyond
2050.

Very likely ranges, 2030 relative to 2000 in cm (feet)
Kopp et al.
(2014)76

Kopp et al.
(2016)32

Mengel etal. (2016)82

RCP8.511–18(0.4–0.6)

8–15(0.3–0.5)

7–12(0.2–0.4)

RCP4.510–18(0.3–0.6)

8–15(0.3–0.5)

7–12(0.2–0.4)

RCP2.610–18(0.3–0.6)

8–15(0.3–0.5)

7–12(0.2–0.4)

Summary sentence or paragraph that integrates
the above information
This key finding is based upon multiple methods for
estimating the probability of future sea level change
and on new modeling results regarding the stability ofmarine based ice in Antarctica.

Very likely ranges, 2050 relative to 2000 in cm (feet)
Kopp et al.
(2014)76

Kopp et al.
(2016)32

Mengel etal. (2016)82

RCP8.521–38(0.7–1.2)

16–34(0.5–1.1)

15–28(0.5–0.9)

RCP4.518–35(0.6–1.1)

15–31(0.5–1.0)

14–25(0.5–0.8)

RCP2.618–33(0.6–1.1)

14–29(0.5–1.0)

13–23(0.4–0.8)

Very likely ranges, 2100 relative to 2000 in cm (feet)
Kopp et al.
(2014)76

Kopp et al.
(2016)32

Mengel etal. (2016)82

RCP8.5

55–121(1.8–4.0)

52–131(1.7–4.3)

57–131(1.9–4.3)

RCP4.536–93(1.2–3.1)

33–85(1.1–2.8)

37–77(1.2–2.5)

RCP2.629–82(1.0–2.7)

24–61(0.8–2.0)

28–56(0.9–1.8)

Major uncertainties
Since NCA3, multiple different approaches have beenused to generate probabilistic projections of GMSL rise,
conditional upon the RCPs. These approaches are in
general agreement. However, emerging results indicatethat marine-based sectors of the Antarctic Ice Sheet are
more unstable than previous modeling indicated. The
rate of ice sheet mass changes remains challenging toproject.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence that future GMSL rise overthe next several decades will be at least as fast as a con-

U.S. Global Change Research Program

Key Finding 3
Relative sea level (RSL) rise in this century will varyalong U.S. coastlines due, in part, to changes in Earth’s
gravitational field and rotation from melting of land
ice, changes in ocean circulation, and vertical land motion (very high confidence). For almost all future GMSL
rise scenarios, RSL rise is likely to be greater than the
global average in the U.S. Northeast and the western
Gulf of Mexico. In intermediate and low GMSL rise scenarios, RSL rise is likely to be less than the global average in much of the Pacific Northwest and Alaska. For
high GMSL rise scenarios, RSL rise is likely to be higher
than the global average along all U.S. coastlines outside Alaska. Almost all U.S. coastlines experience morethan global-mean sea-level rise in response to Antarctic
ice loss, and thus would be particularly affected under
extreme GMSL rise scenarios involving substantial Antarctic mass loss (high confidence).
Description of evidence base
The processes that cause geographic variability in RSLchange are reviewed by Kopp et al. 3 Long tide gauge
data sets show the RSL rise caused by vertical land motion due to glacio-isostatic adjustment and fluid withdrawal along many U.S. coastlines.160, 161 These observations are corroborated by glacio-isostatic adjustment
models, by GPS observations, and by geological data
(e.g., Engelhart and Horton 2012162). The physics of the352

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12 | Sea Level Rise
gravitational, rotational and flexural “static-equilibrium
fingerprint” response of sea level to redistribution ofmass from land ice to the oceans is well established.13,
163
GCM studies indicate the potential for a Gulf Stream
contribution to sea level rise in the U.S. Northeast.12, 164
Kopp et al.76 and Slangen et al.46 accounted for land motion (only glacial isostatic adjustment for Slangen et al.),fingerprint, and ocean dynamic responses. Comparing
projections of local RSL change and GMSL change inthese studies indicate that local rise is likely to be greater than the global average along the U.S. Atlantic andGulf Coasts and less than the global average in most of
the Pacific Northwest. Sea level rise projections in thisreport are developed by an Interagency Sea Level Rise
Task Force.71
Major uncertainties
Since NCA3, multiple authors have produced global orregional studies synthesizing the major process thatcauses global and local sea level change to diverge.
The largest sources of uncertainty in the geographic
variability of sea level change are ocean dynamic sea
level change and, for those regions where sea level fingerprints for Greenland and Antarctica differ from theglobal mean in different directions, the relative contributions of these two sources to projected sea levelchange.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Because of the enumerated physical processes, there isvery high confidence that RSL change will vary across
U.S. coastlines. There is high confidence in the likely
differences of RSL change from GMSL change under
different levels of GMSL change, based on projections
incorporating the different relevant processes.
Summary sentence or paragraph that integrates
the above information
The part of the key finding regarding the existenceof geographic variability is based upon a broader observational, modeling, and theoretical literature. The
specific differences are based upon the scenarios described by the Interagency Sea Level Rise Task Force.71

U.S. Global Change Research Program

Key Finding 4
As sea levels have risen, the number of tidal floods eachyear that cause minor impacts (also called “nuisancefloods”) have increased 5- to 10-fold since the 1960s inseveral U.S. coastal cities (very high confidence). Rates ofincrease are accelerating in over 25 Atlantic and Gulf
Coast cities (very high confidence). Tidal flooding willcontinue increasing in depth, frequency, and extent
this century (very high confidence).
Description of evidence base
Sweet et al.104 examined 45 NOAA tide gauge locations
with hourly data since 1980 and Sweet and Park105 examined a subset of these (27 locations) with hourly
data prior to 1950, all with a National Weather Service
elevation threshold established for minor “nuisance”
flood impacts. Using linear or quadratic fits of annual number of days exceeding the minor thresholds,
Sweet and Park105 find increases in trend-derived values between 1960 and 2010 greater than 10-fold at 8
locations, greater than 5-fold at 6 locations, and greater than 3-fold at 7 locations. Sweet et al.,104 Sweet andPark,105 and Ezer and Atkinson106 find that annual minor tidal flood frequencies since 1980 are accelerating along locations on the East and Gulf Coasts (>25
locations104) due to continued exceedance of a typicalhigh-water distribution above elevation thresholds forminor impacts.
Historical changes over the last 60 years in flood probabilities have occurred most rapidly where RSL rates
were highest and where tide ranges and extreme variability is less (Sweet and Park 2014). In terms of future
rates of changes in extreme event probabilities relative
to fixed elevations, Hunter,126 Tebaldi et al.,121 Kopp etal.,76 Sweet and Park105 and Sweet et al.71 all find that locations with less extreme variability and higher RSL rise
rates are most prone.
Major uncertainties
Minor flooding probabilities have been only assessed
where a tide gauge is present with >30 years of data
and where a NOAA National Weather Service elevationthreshold for impacts has been established. There arelikely many other locations experiencing similar flood-353

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12 | Sea Level Riseing patterns, but an expanded assessment is not possible at this time.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence that exceedance probabilities of high tide flooding at dozens of local-specificelevation thresholds have significantly increased overthe last half century, often in an accelerated fashion,and that exceedance probabilities will continue to increase this century.
Summary sentence or paragraph that integrates
the above information
This key finding is based upon several studies finding
historic and projecting future changes in high-waterprobabilities for local-specific elevation thresholds forflooding.
Key Finding 5
Assuming storm characteristics do not change, sealevel rise will increase the frequency and extent of extreme flooding associated with coastal storms, suchas hurricanes and nor’easters (very high confidence). A
projected increase in the intensity of hurricanes in the
North Atlantic (medium confidence) could increase theprobability of extreme flooding along most of the U.S.
Atlantic and Gulf Coast states beyond what would be
projected based solely on RSL rise. However, there is
low confidence in the projected increase in frequency
of intense Atlantic hurricanes, and the associated floodrisk amplification and flood effects could be offset or
amplified by such factors as changes in overall storm
frequency or tracks.
Description of evidence base
The frequency, extent, and depth of extreme
event-driven (for example, 5- to 100-year event probabilities) coastal flooding relative to existing infrastructure will continue to increase in the future as local RSL
rises.71, 76, 78, 103, 121, 122, 123, 124 Extreme flood probabilities
will increase regardless of change in storm characteristics, which may exacerbate such changes. Model-based
projections of tropical storms and related major storm

U.S. Global Change Research Program

surges within the North Atlantic mostly agree that intensities and frequencies of the most intense storms
will increase this century.110, 149, 150, 151, 157 However, theprojection of increased hurricane intensity is more robust across models than the projection of increased
frequency of the most intense storms, since a numberof models project a substantial decrease in the overall
number of tropical storms and hurricanes in the North
Atlantic. Changes in the frequency of intense hurricanes depends on changes in both the overall frequency of tropical cyclones storms and their intensities.
High-resolution models generally project an increase
in mean hurricane intensity in the Atlantic (e.g., Knutson et al. 2013157). In addition, there is model evidence
for a change in tropical cyclone tracks in warm years
that minimizes the increase in landfalling hurricanes in
the U.S. Mid-Atlantic or Northeast.148
Major uncertainties
Uncertainties remain large with respect to the precisechange in future risk of a major coastal impact at a specific location from changes in the most intense tropical
cyclone characteristics and tracks beyond changes imposed from local sea level rise.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is low confidence that the flood risk at specificlocations will be amplified from a major tropical storm
this century.
Summary sentence or paragraph that integrates
the above information
This key finding is based upon several modeling studies of future hurricane characteristics and associatedincreases in major storm surge risk amplification.

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130. Moritz, H., K. White, B. Gouldby, W. Sweet, P. Ruggiero, M. Gravens, P. O’Brien, H. Moritz, T. Wahl,
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140. Ruggiero, P., 2013: Is the intensifying wave climateof the U.S. Pacific Northwest increasing flooding
and erosion risk faster than sea-level rise? Journal of
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P. Liu, and M. Zhang, 2013: Historical evaluationand future prediction of eastern North Americanand western Atlantic extratropical cyclones in the
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150. Little, C.M., R.M. Horton, R.E. Kopp, M. Oppenheimer, and S. Yip, 2015: Uncertainty in twenty-first-century CMIP5 sea level projections. Journalof Climate, 28, 838-852. http://dx.doi.org/10.1175/
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org/10.1038/nclimate2736

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13

Ocean Acidification and Other
Ocean Changes

KEY FINDINGS
1. The world’s oceans have absorbed about 93% of the excess heat caused by greenhouse gas warming
since the mid-20th century, making them warmer and altering global and regional climate feedbacks.
Ocean heat content has increased at all depths since the 1960s and surface waters have warmed byabout 1.3° ± 0.1°F (0.7° ± 0.08°C) per century globally since 1900 to 2016. Under a higher scenario, a
global increase in average sea surface temperature of 4.9° ± 1.3°F (2.7° ± 0.7°C) by 2100 is projected,
with even higher changes in some U.S. coastal regions. (Very high confidence)
2. The potential slowing of the Atlantic meridional overturning circulation (AMOC; of which the
Gulf Stream is one component)—as a result of increasing ocean heat content and freshwater driven
buoyancy changes—could have dramatic climate feedbacks as the ocean absorbs less heat and CO2
from the atmosphere. This slowing would also affect the climates of North America and Europe. Any
slowing documented to date cannot be directly tied to anthropogenic forcing primarily due to lack ofadequate observational data and to challenges in modeling ocean circulation changes. Under a higher
scenario (RCP8.5) in CMIP5 simulations, the AMOC weakens over the 21st century by 12% to 54%(low confidence).
3. The world’s oceans are currently absorbing more than a quarter of the CO2 emitted to the atmosphere annually from human activities, making them more acidic (very high confidence), with potential
detrimental impacts to marine ecosystems. In particular, higher-latitude systems typically have alower buffering capacity against pH change, exhibiting seasonally corrosive conditions sooner than
low-latitude systems. Acidification is regionally increasing along U.S. coastal systems as a result of
upwelling (for example, in the Pacific Northwest) (high confidence), changes in freshwater inputs (forexample, in the Gulf of Maine) (medium confidence), and nutrient input (for example, in agricultural
watersheds and urbanized estuaries) (high confidence). The rate of acidification is unparalleled in atleast the past 66 million years (medium confidence). Under the higher scenario (RCP8.5), the global
average surface ocean acidity is projected to increase by 100% to 150% (high confidence).
4. Increasing sea surface temperatures, rising sea levels, and changing patterns of precipitation, winds,
nutrients, and ocean circulation are contributing to overall declining oxygen concentrations at intermediate depths in various ocean locations and in many coastal areas. Over the last half century, major
oxygen losses have occurred in inland seas, estuaries, and in the coastal and open ocean (high confidence). Ocean oxygen levels are projected to decrease by as much as 3.5% under the higher scenario(RCP8.5) by 2100 relative to preindustrial values (high confidence).

Recommended Citation for Chapter
Jewett, L. and A. Romanou, 2017: Ocean acidification and other ocean changes. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J.
Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC,
USA, pp. 364-392, doi: 10.7930/J0QV3JQB.
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13.0 A Changing Ocean
Anthropogenic perturbations to the global
Earth system have included important alterations in the chemical composition, temperature, and circulation of the oceans. Some ofthese changes will be distinguishable from thebackground natural variability in nearly halfof the global open ocean within a decade, with
important consequences for marine ecosystems and their services.1 However, the timeframe for detection will vary depending on
the parameter featured.2, 3

13.1 Ocean Warming
13.1.1 General Background

Approximately 93% of excess heat energy
trapped since the 1970s has been absorbed
into the oceans, lessening atmospheric warming and leading to a variety of changes inocean conditions, including sea level rise andocean circulation (see Ch. 2: Physical Drivers of Climate Change, Ch. 6: Temperature
Change, and Ch. 12: Sea Level Rise in thisreport).1, 4 This is the result of the high heat capacity of seawater relative to the atmosphere,
the relative area of the ocean compared to theland, and the ocean circulation that enables
the transport of heat into deep waters. Thislarge heat absorption by the oceans moderates
the effects of increased anthropogenic greenhouse emissions on terrestrial climates whilealtering the fundamental physical propertiesof the ocean and indirectly impacting chemical properties such as the biological pumpthrough increased stratification.1, 5 Although
upper ocean temperature varies over shortand medium timescales (for example, seasonal
and regional patterns), there are clear longterm increases in surface temperature andocean heat content over the past 65 years.4, 6, 7
13.1.2 Ocean Heat Content

warming can be documented and compared
between particular regions, ocean basins, anddepths. However, for years prior to the 1970s,
estimates of ocean uptake are confined to theupper ocean (up to 700 m) due to sparse spatial and temporal coverage and limited vertical capabilities of many of the instruments
in use. OHC estimates are improved for time
periods after 1970 with increased sampling
coverage and depth.4, 8 Estimates of OHC havebeen calculated going back to the 1950s using averages over longer time intervals (i.e.,
decadal or 5-year intervals) to compensate forsparse data distributions, allowing for clear
long-term trends to emerge (e.g., Levitus et al.
20127).
From 1960 to 2015, OHC significantly increased for both 0–700 and 700–2,000 m
depths, for a total ocean warming of about
33.5 ± 7.0 × 1022 J (a net heating of 0.37 ± 0.08
W/m2; Figure 13.1).6 During this period,
there is evidence of an acceleration of ocean
warming beginning in 1998,9 with a totalheat increase of about 15.2 × 1022 J.6 Robust
ocean warming occurs in the upper 700 m
and is slow to penetrate into the deep ocean.
However, the 700–2,000 m depths constitutean increasing portion of the total ocean energy budget as compared to the surface ocean(Figure 13.1).6 The role of the deep ocean(below 2,000 m [6,600 ft]) in ocean heat uptake
remains uncertain, both in the magnitude but
also the sign of the uptake.10, 11 Penetrationof surface waters to the deep ocean is a slow
process, which means that while it takes only
about a decade for near-surface temperatures
to respond to increased heat energy, the deep
ocean will continue to warm, and as a result
sea levels will rise for centuries to millennia
even if all further emissions cease.4

Ocean heat content (OHC) is an ideal variable
to monitor changing climate as it is calculated using the entire water column, so ocean

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15

Ocean heat content (1022 Joules)

10
5
0
−5
−10
−15
−20

0−700m700−2000m
0−2000m

−25
−30
−35
1950

1960

1970

1980
1990
Year

2000

2010

Figure 13.1: Global Ocean heat content change time series. Ocean heat content from 0 to 700 m (blue), 700 to 2,000m (red), and 0 to 2,000 m (dark gray) from 1955 to 2015 with an uncertainty interval of ±2 standard deviations shownin shading. All time series of the analysis performed by Cheng et al.6 are smoothed by a 12-month running mean filter,
relative to the 1997–2005 base period. (Figure source: Cheng et al. 20176).

Several sources have documented warming inall ocean basins from 0–2,000 m depths over the
past 50 years (Figure 13.2).6, 7, 12 Annual fluctuations in surface temperatures and OHC are
attributed to the combination of a long-term
secular trend and decadal and smaller timescale variations, such as the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal
Oscillation (AMO) (Ch. 5: Circulation & Variability; Ch. 12: Sea Level Rise).13, 14 The transport of heat to the deep ocean is likely linked tothe strength of the Atlantic Meridional Overturning Circulation (see Section 13.2.1), where
the Atlantic and Southern Ocean accounts forthe dominant portion of total OHC change atthe 700–2,000 m depth.6, 8, 9, 15 Decadal variabil-

U.S. Global Change Research Program

ity in ocean heat uptake is mostly attributed
to ENSO phases (with El Niños warming and
La Niñas cooling). For instance, La Niña conditions over the past decade have led to colder
ocean temperatures in the eastern tropical
Pacific.6, 8, 9, 16 For the Pacific and Indian Oceans,the decadal shifts are primarily observed in
the upper 350 m depth, likely due to shallow
subtropical circulation, leading to an abrupt
increase of OHC in the Indian Ocean carriedby the Indonesian throughflow from the Pacific
Ocean over the last decade.9 Although there isnatural variability in ocean temperature, there
remain clear increasing trends due to anthropogenic influences.

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20

OHC (0–2000 m) 1022 J

15
10
5

Indian Ocean
North Atlantic
Tropical/subtropical Atlantic
North PacificTropical/subtropical PacificSouthern oceans

0
−5

−10
−15
1960

1900

1980

1990

2000

2010

Figure 13.2: Ocean heat content changes from 1960 to 2015 for different ocean basins for 0 to 2,000 m depths. Time
series is relative to the 1997–1999 base period and smoothed by a 12-month running filter by Cheng et al.6 The curves
are additive, and the ocean heat content changes in different ocean basins are shaded in different colors (Figuresource: Cheng et al. 20176).

13.1.3 Sea Surface Temperature and U.S. Regional
Warming

In addition to OHC, sea surface temperature(SST) measurements are widely available.
SST measurements are useful because 1) the
measurements have been taken over 150 years(albeit using different platforms, instruments,
and depths through time); 2) SST reflects thelower boundary condition of the atmosphere;
and 3) SST can be used to predict specificregional impacts of global warming on terrestrial and coastal systems.15, 17, 18 Globally,
surface ocean temperatures have increased by
1.3° ± 0.1°F (0.70° ± 0.08°C) per century from1900 to 2016 for the Extended Reconstructed
Sea Surface Temperature version 4 (ERSST v4)
record.19 All U.S. coastal waters have warmed
by more than 0.7°F (0.4°C) over this period
as shown in both Table 13.1 and Chapter 6:
Temperature Change, Figure 6.6. During thepast 60 years, the rates of increase of SSTs forthe coastal waters of three U.S. regions wereabove the global average rate. These included the waters around Alaska, the Northeast,and the Southwest (Table 13.1). Over thelast decade, some regions have experienced

U.S. Global Change Research Program

increased high ocean temperature anomalies.
SST in the Northeast has warmed faster than
99% of the global ocean since 2004, and a peaktemperature for the region in 2012 was part ofa large “ocean heat wave” in the Northwest
Atlantic that persisted for nearly 18 months.20,
21
Projections indicate that the Northeast willcontinue to warm more quickly than otherocean regions through the end of the century.22 In the Northwest, a resilient ridge ofhigh pressure over the North American West
Coast suppressed storm activity and mixing,
which intensified heat in the upper ocean in aphenomenon known as “The Blob”.23 Anomalously warm waters persisted in the coastal
waters of the Alaskan and Pacific Northwest
from 2013 until 2015. Under a higher scenario(RCP8.5), SSTs are projected to increase by anadditional 4.9°F (2.7°C) by 2100 (Figure 13.3),
whereas for a lower scenario (RCP4.5) the SST
increase would be 2.3°F (1.3°C).24 In all U.S.
coastal regions, the warming since 1901 is
detectable compared to natural variability andattributable to anthropogenic forcing, according to an analysis of the CMIP5 models (Ch. 6:
Temperature Change, Figure 6.5).

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Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes
Table 13.1. Historical sea surface temperature trends (°C per century) and projected
trends by 2080 (°C) for eight U.S. coastal regions and globally. Historical temperature
trends are presented for the 1900–2016 and 1950–2016 periods with 95% confidence
level, observed using the Extended Reconstructed Sea Surface Temperature version 4(ERSSTv4).19 Global and regional predictions are calculated for lower and higher scenarios(RCP4.5 and RCP8.5, respectively) with 80% spread of all the CMIP5 members comparedto the 1976–2005 period.151 The historical trends were analyzed for the latitude and longitude in the table, while the projected trends were analyzed for the California Current instead of the Northwest and Southwest separately and for the Bering Sea in Alaska (NOAA).
RegionLatitude andLongitude

GlobalHistorical Trend(°C/100 years)

Projected Trend
2080 (relative to
1976–2005 climate) (°C)

1900–2016

1950–2016

RCP4.5

RCP8.50.70 ± 0.08

1.00 ± 0.11

1.3 ± 0.6

2.7 ± 0.7

2.5 ± 0.6

3.7 ± 1.0

1.7 ± 0.4

2.8 ± 0.6

Alaska50°–66°N,
150°–170°W

0.82 ± 0.26

1.22 ± 0.59

Northwest(NW)

40°–50°N,
120°–132°W

0.64 ± 0.30

0.68 ± 0.70

Southwest(SW)

30°–40°N,
116°–126°W

0.73 ± 0.33

1.02 ± 0.79

Hawaii (HI)18°–24°N,
152°–162°W

0.58 ± 0.19

0.46 ± 0.39

1.6 ± 0.4

2.8 ± 0.6

Northeast (NE)36°–46°N,
64°–76°W

0.63 ± 0.31

1.10 ± 0.71

2.0 ± 0.3

3.2 ± 0.6

Southeast (SE)24°–34°N,
64°–80°W

0.40 ± 0.18

0.13 ± 0.34

1.6 ± 0.3

2.7 ± 0.4

Gulf of Mexico(GOM)

20°–30°N,
80°–96°W

0.52 ± 0.14

0.37 ± 0.27

1.6 ± 0.3

2.8 ± 0.3

Caribbean

10°–20°N,
66°–86°W

0.76 ± 0.15

0.77 ± 0.32

1.5 ± 0.4

2.6 ± 0.3

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Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes

CMIP5
ENSMN
Anomaly
CMIP5
ENSMN RCP8.5
RCP8.5 Anomaly(2050–2099)–(1956–2005)(2050–2099)−(1956–2005)°C
0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3
Figure 13.3: Projected changes in sea surface temperature (°C) for the coastal United States under the higher scenario (RCP8.5). Projected anomalies for the 2050–2099 period are calculated using a comparison from the average sea
surface temperatures over 1956–2005. Projected changes are examined using the Coupled Model Intercomparison
Project Phase 5 (CMIP5) suite of model simulations. (Figure source: NOAA).

13.1.4 Ocean Heat Feedback

The residual heat not taken up by the oceansincreases land surface temperatures (approximately 3%) and atmospheric temperatures(approximately 1%), and melts both land andsea ice (approximately 3%), leading to sea
level rise (see Ch. 12: Sea Level Rise).4, 6, 25 The
meltwater from land and sea ice amplifies
further subsurface ocean warming and ice
shelf melting, primarily due to increased thermal stratification, which reduces the ocean’s
efficiency in transporting heat to deep waters.4
Surface ocean stratification has increased
by about 4% during the period 1971–201026
due to thermal heating and freshening fromincreased freshwater inputs (precipitationand evaporation changes and land and sea ice
melting). The increase of ocean stratification
will contribute to further feedback of ocean
warming and, indirectly, mean sea level. In
addition, increases in stratification are associated with suppression of tropical cyclone intensification,27 retreat of the polar ice sheets,28
and reductions of the convective mixing at
higher latitudes that transports heat to the

U.S. Global Change Research Program

deep ocean through the Atlantic Meridional
Overturning Circulation.29 Ocean heat uptake
therefore represents an important feedback
that will have a significant influence on future
shifts in climate (see Ch. 2: Physical Drivers of
Climate Change).

13.2 Ocean Circulation
13.2.1 Atlantic Meridional Overturning Circulation

The Atlantic Meridional Overturning Circulation (AMOC) refers to the three-dimensional,
time-dependent circulation of the Atlantic
Ocean, which has been a high priority topic of study in recent decades. The AMOC
plays an important role in climate throughits transport of heat, freshwater, and carbon(e.g., Johns et al. 2011;30 McDonagh et al.
2015;31 Talley et al. 201632). AMOC-associatedpoleward heat transport substantially contributes to North American and continental
European climate (see Ch. 5: Circulation and
Variability). The Gulf Stream, in contrast toother western boundary currents, is expected
to slow down because of the weakening of
the AMOC, which would impact the Euro-369

Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes
pean climate.33 Variability in the AMOC has
been attributed to wind forcing on intra-annual time scales and to geostrophic forces oninterannual to decadal timescales.34 Increased
freshwater fluxes from melting Arctic Sea andland ice can weaken open ocean convection
and deep-water formation in the Labrador
and Irminger Seas, which could weaken the
AMOC (Ch. 11: Arctic Changes; also see Ch.
5, Section 5.2.3: North Atlantic Oscillation andNorthern Annular Mode).29, 33
While one recent study has suggested that the
AMOC has slowed since preindustrial times29
and another suggested slowing on faster timescales,35 there is at present insufficient observational evidence to support a finding of longterm slowdown of AMOC strength over the
20th century4 or within the last 50 years34 as
decadal ocean variability can obscure longterm trends. Some studies show long-term
trends,36, 37 but the combination of sparse data
and large seasonal variability may also leadto incorrect interpretations (e.g., Kanzow
et al. 201038). Several recent high resolution
modeling studies constrained with the limited existing observational data39 and/or withreconstructed freshwater fluxes40 suggest thatthe recently observed AMOC slowdown at
26°N (off the Florida coast) since 2004 (e.g., as
described in Smeed et al. 201435) is mainly due
to natural variability, and that anthropogenic
forcing has not yet caused a significant AMOC
slowdown. In addition, direct observations of
the AMOC in the South Atlantic fail to unambiguously demonstrate anthropogenic trends(e.g., Dong et al. 2015;41 Garzoli et al. 201342).
Under a higher scenario (RCP8.5) in CMIP5
simulations, it is very likely that the AMOC
will weaken over the 21st century. The projected decline ranges from 12% to 54%,43 with therange width reflecting substantial uncertainty
in quantitative projections of AMOC behavior.
In lower scenarios (like RCP4.5), CMIP5 mod-U.S. Global Change Research Program

els predict a 20% weakening of the AMOC
during the first half of the 21st century and astabilization and slight recovery after that.44
The projected slowdown of the AMOC will
be counteracted by the warming of the deep
ocean (below 700 m [2,300 ft]), which willtend to strengthen the AMOC.45 The situation is further complicated due to the known
bias in coupled climate models related to thedirection of the salinity transport in models
versus observations, which is an indicator of
AMOC stability (e.g., Drijhout et al. 2011;46
Bryden et al. 2011;47 Garzoli et al. 201342). Someargue that coupled climate models should
be corrected for this known bias and that
AMOC variations could be even larger than
the gradual decrease most models predict if
the AMOC were to shut down completely and“flip states”.48 Any AMOC slowdown could
result in less heat and CO2 absorbed by theocean from the atmosphere, which is a positive feedback to climate change (also see Ch. 2:
Physical Drivers of Climate Change).49, 50, 51
13.2.2 Changes in Salinity Structure

As a response to warming, increased atmospheric moisture leads to stronger evaporation or precipitation in terrestrial and oceanic
environments and melting of land and sea ice.
Approximately 80% of precipitation/evaporation events occur over the ocean, leading topatterns of higher salt content or freshwater
anomalies and changes in ocean circulation(see Ch. 2: Physical Drivers of Climate Change
and Ch. 6: Temperature Change).52 Over 1950–
2010, average global amplification of the surface salinity pattern amounted to 5.3%; where
fresh regions in the ocean became fresher andsalty regions became saltier.53 However, thelong-term trends of these physical and chemical changes to the ocean are difficult to isolate
from natural large-scale variability. In particular, ENSO displays particular salinity andprecipitation/evaporation patterns that skew
the trends. More research and data are neces-

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Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes
sary to better model changes to ocean salinity.
Several models have shown a similar spatial
structure of surface salinity changes, including
general salinity increases in the subtropicalgyres, a strong basin-wide salinity increasein the Atlantic Ocean, and reduced salinity inthe western Pacific warm pools and the North
Pacific subpolar regions.52, 53 There is also astronger distinction between the upper salty
thermocline and fresh intermediate depth
through the century. The regional changes insalinity to ocean basins will have an overall
impact on ocean circulation and net primary
production, leading to corresponding carbon
export (see Ch. 2: Physical Drivers of Climate
Change). In particular, the freshening of the
Arctic Ocean due to melting of land and sea
ice can lead to buoyancy changes which could
slow down the AMOC (see Section 13.2.1).
13.2.3 Changes in Upwelling

Significant changes to ocean stratification andcirculation can also be observed regionally,
along the eastern ocean boundaries and at theequator. In these areas, wind-driven upwelling brings colder, nutrient- and carbon-richwater to the surface; this upwelled water ismore efficient in heat and anthropogenic CO2
uptake. There is some evidence that coastal
upwelling in mid- to high-latitude easternboundary regions has increased in intensity
and/or frequency,54 but in more tropical areasof the western Atlantic, such as in the Caribbean Sea, it has decreased between 1990 and2010.55, 56 This has led to a decrease in primary
productivity in the southern Caribbean Sea.55
Within the continental United States, the California Current is experiencing fewer (by about
23%–40%) but stronger upwelling events.57, 58,
59
Stronger offshore upwelling combined withcross-shelf advection brings nutrients fromthe deeper ocean but also increased offshoretransport.60 The net nutrient load in the coastal
regions is responsible for increased productivity and ecosystem function.

U.S. Global Change Research Program

IPCC 2013 concluded that there is low confidence in the current understanding of how
eastern upwelling systems will be alteredunder future climate change because of the obscuring role of multidecadal climate variability.26 However, subsequent studies show thatby 2100, upwelling is predicted to start earlier
in the year, end later, and intensify in three ofthe four major eastern boundary upwellingsystems (not in the California Current).61 In
the California Current, upwelling is projected
to intensify in spring but weaken in summer,with changes emerging from the envelope ofnatural variability primarily in the second halfof the 21st century.62 Southern Ocean upwelling will intensify while the Atlantic equatorial upwelling systems will weaken.57, 61 The
intensification is attributed to the strengthening of regional coastal winds as observations already show,58 and model projections
under the higher scenario (RCP8.5) estimate
wind intensifying near poleward boundaries(including northern California Current) andweakening near equatorward boundaries (including southern California Current) for the
21st century.61, 63

13.3 Ocean Acidification
13.3.1 General BackgroundIn addition to causing changes in climate,increasing atmospheric levels of carbon dioxide (CO2) from the burning of fossil fuels
and other human activities, including changes in land use, have a direct effect on ocean
carbonate chemistry that is termed ocean
acidification.64, 65 Surface ocean waters absorb
part of the increasing CO2 in the atmosphere,
which causes a variety of chemical changes inseawater: an increase in the partial pressureof CO2 (pCO2,sw), dissolved inorganic carbon(DIC), and the concentration of hydrogen andbicarbonate ions and a decrease in the concentration of carbonate ions (Figure 13.4). In brief,
CO2 is an acid gas that combines with water
to form carbonic acid, which then dissociates

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Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes
to hydrogen and bicarbonate ions. Increasing
concentrations of seawater hydrogen ions
result in a decrease of carbonate ions throughtheir conversion to bicarbonate ions. The concentration of carbonate ions in seawater affects
saturation states for calcium carbonate compounds, which many marine species use tobuild their shells and skeletons. Ocean acidity
refers to the concentration of hydrogen ions inocean seawater regardless of ocean pH, which
is fundamentally basic (e.g., pH > 7). Ocean

400

surface waters have become 30% more acidic
over the last 150 years as they have absorbed
large amounts of CO2 from the atmosphere,66
and anthropogenically sourced CO2 is gradually invading into oceanic deep waters. Since
the preindustrial period, the oceans have absorbed approximately 29% of all CO2 emitted
to the atmosphere.67 Oceans currently absorb
about 26% of the human-caused CO2 anthropogenically emitted into the atmosphere.67

Carbon Dioxide

350

pH

325

Air
Sea

300
270
8.15
8.10
260
8.05
250
8.00
240
7.95
230
7.90
220
7.85
210
7.80 *calculated from discrete water samples
1980 1985 1990 1995 2000 2005 2010 2015
Year

CO32−(µmol/kg)

ppm

375

Figure 13.4: Trends in surface (< 50 m) ocean carbonate chemistry calculated from observations obtained at the
Hawai‘i Ocean Time-series (HOT) Program in the North Pacific over 1988–2015. The upper panel shows the linked
increase in atmospheric (red points) and seawater (blue points) CO2 concentrations. The bottom panel shows a decline in seawater pH (black points, primary y-axis) and carbonate ion concentration (green points, secondary y-axis).
Ocean chemistry data were obtained from the Hawai‘i Ocean Time-series Data Organization & Graphical System(HOT-DOGS, http://hahana.soest.hawaii.edu/hot/hot-dogs/index.html). (Figure source: NOAA).

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Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes
13.3.2 Open Ocean Acidification

Surface waters in the open ocean experiencechanges in carbonate chemistry reflective oflarge-scale physical oceanic processes (see Ch.
2: Physical Drivers of Climate Change). These
processes include both the global uptake ofatmospheric CO2 and the shoaling of naturally acidified subsurface waters due to verticalmixing and upwelling. In general, the rateof ocean acidification in open ocean surface
waters at a decadal time-scale closely approximates the rate of atmospheric CO2 increase.68
Large, multidecadal phenomena such as the
Atlantic Multidecadal Oscillation and Pacific
Decadal Oscillation can add variability to theobserved rate of change.68
13.3.3 Coastal Acidification

Coastal shelf and nearshore waters are influenced by the same processes as open ocean
surface waters such as absorption of atmospheric CO2 and upwelling, as well as a number of additional, local-level processes, including freshwater, nutrient, sulfur, and nitrogen
inputs.69, 70 Coastal acidification generally exhibits higher-frequency variability and shortterm episodic events relative to open-ocean
acidification.71, 72, 73, 74 Upwelling is of particularimportance in coastal waters, especially along
the U.S. West Coast. Deep waters that shoal
with upwelling are enriched in CO2 due to uptake of anthropogenic atmospheric CO2 whenlast in contact with the atmosphere, coupledwith deep water respiration processes andlack of gas exchange with the atmosphere.65,
75
Freshwater inputs to coastal waters change
seawater chemistry in ways that make it more
susceptible to acidification, largely by freshening ocean waters and contributing varying
amounts of dissolved inorganic carbon (DIC),
total alkalinity (TA), dissolved and particulateorganic carbon, and nutrients from riverine
and estuarine sources. Coastal waters of theEast Coast and mid-Atlantic are far more influenced by freshwater inputs than are Pacific

U.S. Global Change Research Program

Coast waters.76 Coastal waters can episodicallyexperience riverine and glacial melt plumes
that create conditions in which seawater candissolve calcium carbonate structures.77, 78
While these processes have persisted historically, climate-induced increases in glacial
melt and high-intensity precipitation events
can yield larger freshwater plumes thanhave occurred in the past. Nutrient runoff
can increase coastal acidification by creating
conditions that enhance biological respiration.
In brief, nutrient loading typically promotes
phytoplankton blooms, which, when they die,are consumed by bacteria. Bacteria respire
CO2 and thus bacterial blooms can result inacidification events whose intensity depends
on local hydrographic conditions, including
water column stratification and residence
time.72 Long-term changes in nutrient loading,
precipitation, and/or ice melt may also impartlong-term, secular changes in the magnitude
of coastal acidification.
13.3.4 Latitudinal Variation

Ocean carbon chemistry is highly influenced by water temperature, largely because
the solubility of CO2 in seawater increases
as water temperature declines. Thus, cold,
high-latitude surface waters can retain more
CO2 than warm, lower-latitude surface waters.76, 79 Because carbonate minerals also more
readily dissolve in colder waters, these waters
can more regularly become undersaturated
with respect to calcium carbonate whereby
mineral dissolution is energetically favored.
This chemical state, often referred to as seawater being “corrosive” to calcium carbonate,
is important when considering the ecologicalimplications of ocean acidification as many
species make structures such as shells andskeletons from calcium carbonate. Seawaterconditions undersaturated with respect tocalcium carbonate are common at depth, butcurrently and historically rare at the surface
and near-surface.80 Some high-latitude surface

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Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes
and near-surface waters now experience suchcorrosive conditions, which are rarely documented in low-latitude surface or near-surface
systems. For example, corrosive conditions
at a range of ocean depths have been documented in the Arctic and northeastern Pacific
Oceans.74, 79, 81, 82 Storm-induced upwelling
could cause undersaturation in tropical areas in the future.83 It is important to note that
low-latitude waters are experiencing a greater
absolute rate of change in calcium carbonate
saturation state than higher latitudes, thoughthese low-latitude waters are not approaching
the undersaturated state except within nearshore or some benthic habitats.84
13.3.5 Paleo Evidence

Evidence suggests that the current rate ofocean acidification is the fastest in the last 66
million years (the K-Pg boundary) and possibly even the last 300 million years (whenthe first pelagic calcifiers evolved providing
proxy information and also a strong carbonate
buffer, characteristic of the modern ocean).85, 86
The Paleo-Eocene Thermal Maximum (PETM;
around 56 million years ago) is often referenced as the closest analogue to the present,
although the overall rate of change in CO2
conditions during that event (estimated between 0.6 and 1.1 GtC/year) was much lower
than the current increase in atmospheric CO2of 10 GtC/year.86, 87 The relatively slower rateof atmospheric CO2 increase at the PETM likely led to relatively small changes in carbonate
ion concentration in seawater compared withthe contemporary acidification rate, due to theability of rock weathering to buffer the changeover the longer time period.86 Some of the presumed acidification events in Earth’s history
have been linked to selective extinction events
suggestive of how guilds of species may respond to the current acidification event.85

U.S. Global Change Research Program

13.3.6 Projected Changes

Projections indicate that by the end of the
century under higher scenarios, such as SRES
A1FI or RCP8.5, open-ocean surface pH will
decline from the current average level of 8.1
to a possible average of 7.8 (Figure 13.5).1
When the entire ocean volume is considered
under the same scenario, the volume of waters
undersaturated with respect to calcium carbonate could expand from 76% in the 1990s to
91% in 2100, resulting in a shallowing of thesaturation horizons—depths below which undersaturation occurs.1, 88 Saturation horizons,
which naturally vary among ocean basins,influence ocean carbon cycles and organisms
with calcium carbonate structures, especiallyas they shoal into the zones where most biota
lives.81, 89 As discussed above, for a varietyof reasons, not all ocean and coastal regions
will experience acidification in the same way
depending on other compounding factors.
For instance, recent observational data fromthe Arctic Basin show that the Beaufort Sea
became undersaturated, for part of the year,with respect to aragonite in 2001, while othercontinental shelf seas in the Arctic Basin are
projected to do so closer to the middle of the
century (e.g., the Chukchi Sea in about 2033
and Bering Sea in about 2062).90 Deviationfrom the global average rate of acidification
will be especially true in coastal and estuarine
areas where the rate of acidification is influenced by other drivers than atmospheric CO2,
some of which are under the control of local
management decisions (for example, nutrient
pollution loads).

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SurfacepH
pHin
in 2090s
2090s (RCP8.5,
changesfrom 1990s)
Surface(RCP8.5,changesfrom 1990s)

−0.2
−0.3
−0.4
−0.5
Figure 13.5: Predicted change in sea surface pH in 2090–2099 relative to 1990–1999 under the higher scenario(RCP8.5), based on the Community Earth System Models–Large Ensemble Experiments CMIP5 (Figure source:
adapted from Bopp et al. 201324).

13.4 Ocean Deoxygenation
13.4.1 General Background

Oxygen is essential to most life in the ocean,governing a host of biogeochemical and biological processes. Oxygen influences metabolic, physiological, reproductive, behavioral,
and ecological processes, ultimately shaping
the composition, diversity, abundance, anddistribution of organisms from microbes towhales. Increasingly, climate-induced oxygen
loss (deoxygenation) associated with ocean
warming and reduced ventilation to deep waters has become evident locally, regionally, andglobally. Deoxygenation can also be attributed
to anthropogenic nutrient input, especially inthe coastal regions, where the nutrients canlead to the proliferation of primary production
and, consequently, enhanced drawdown ofdissolved oxygen by microbes.91 In addition,
acidification (Section 13.2) can co-occur withdeoxygenation as a result of warming-enhanced biological respiration.92 As aerobic
organisms respire, O2 is consumed and CO2 is
produced. Understanding the combined effectof both low O2 and low pH on marine ecosystems is an area of active research.93 Warming
also raises biological metabolic rates which,in combination with intensified coastal andestuarine stratification, exacerbates eutrophication-induced hypoxia. We now see earlier

U.S. Global Change Research Program

onset and longer periods of seasonal hypoxia
in many eutrophic sites, most of which occur
in areas that are also warming.91
13.4.2 Climate Drivers of Ocean Deoxygenation

Global ocean deoxygenation is a direct effectof warming. Ocean warming reduces thesolubility of oxygen (that is, warmer water canhold less oxygen) and changes physical mixing (for example, upwelling and circulation)of oxygen in the oceans. The increased temperature of global oceans accounts for about
15% of current global oxygen loss,94 althoughchanges in temperature and oxygen are notuniform throughout the ocean.15 Warming also
exerts direct influence on thermal stratificationand enhances salinity stratification throughice melt and climate change-associated precipitation effects. Intensified stratification leads
to reduced ventilation (mixing of oxygen into
the ocean interior) and accounts for up to 85%
of global ocean oxygen loss.94 Effects of ocean
temperature change and stratification on oxygen loss are strongest in intermediate or mode
waters at bathyal depths (in general, 200–3,000m) and also nearshore and in the open ocean;these changes are especially evident in tropicaland subtropical waters globally, in the Eastern
Pacific,95 and in the Southern Ocean.94

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Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes
There are also other, less direct effects of global
temperature increase. Warming on land reduces terrestrial plant water efficiency (througheffects on stomata; see Ch. 8: Drought, Floods,
and Wildfires, Key Message 3), leading togreater runoff, on average, into coastal zones(see Ch. 8: Drought, Floods, and Wildfires forother hydrological effects of warming) andfurther enhancing hypoxia potential because
greater runoff can mean more nutrient transport (See Ch. 2: Physical Drivers of Climate
Change).96, 97 Estuaries, especially ones withminimal tidal mixing, are particularly vulnerable to oxygen-depleted dead zones from theenhanced runoff and stratification. Warming
can induce dissociation of frozen methanein gas hydrates buried on continental margins, leading to further drawdown of oxygen
through aerobic methane oxidation in thewater column.98 On eastern ocean boundaries,
warming can enhance the land–sea temperature differential, causing increased upwelling
due to higher winds with (a) greater nutrient
input leading to production, sinking, decay,and biochemical drawdown of oxygen and (b)upwelling of naturally low-oxygen, high-CO2
waters onto the upper slope and shelf environments.58, 65 However, in the California Current,
upwelling intensification has occurred only inthe poleward regions (north of San Francisco),
and the drivers may not be associated withland–sea temperature differences.63 Taken together, the effects of warming are manifested
as low-oxygen water in open oceans are beingtransported to and upwelled along coastal
regions. These low-oxygen upwelled waters
are then coupled with eutrophication-induced
hypoxia, further reducing oxygen content incoastal areas.
Changes in precipitation, winds, circulation,
airborne nutrients, and sea level can also
contribute to ocean deoxygenation. Projected increases in precipitation in some regions
will intensify stratification, reducing vertical

U.S. Global Change Research Program

mixing and ventilation, and intensify nutrient
input to coastal waters through excess runoff,
which leads to increased algal biomass andconcurrent dissolved oxygen consumption via
community respiration.99 Coastal wetlands
that might remove these nutrients before theyreach the ocean may be lost through rising sea
level, further exacerbating hypoxia.97 Someobservations of oxygen decline are linked
to regional changes in circulation involving
low-oxygen water masses. Enhanced fluxes ofairborne iron and nitrogen are interacting withnatural climate variability and contributing tofertilization, enhanced respiration, and oxygen
loss in the tropical Pacific.100
13.4.3 Biogeochemical Feedbacks of Deoxygenation to Climate and Elemental Cycles

Climate patterns and ocean circulation have alarge effect on global nitrogen and oxygen cycles, which in turn affect phosphorus and trace
metal availability and generate feedbacks
to the atmosphere and oceanic production.
Global ocean productivity may be affectedby climate-driven changes below the tropicaland subtropical thermocline which control
the volume of suboxic waters (< 5 micromolarO2), and consequently the loss of fixed nitrogen through denitrification.101, 102 The extent
of suboxia in the open ocean also regulates
the production of the greenhouse gas nitrous
oxide (N2O); as oxygen declines, greater N2O
production may intensify global warming,as N2O is about 310 times more effective attrapping heat than CO2 (see Ch. 2: PhysicalDrivers of Climate Change, Section 2.3.2).103, 104
Production of hydrogen sulfide (H2S, which ishighly toxic) and intensified phosphorus recycling can occur at low oxygen levels.105 Other
feedbacks may emerge as oxygen minimum
zone (OMZ) shoaling diminishes the depths ofdiurnal vertical migrations by fish and invertebrates, and as their huge biomass and associated oxygen consumption deplete oxygen.106

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13.4.4 Past Trends

Over hundreds of millions of years, oxygen
has varied dramatically in the atmosphereand ocean and has been linked to biodiversity
gains and losses.107, 108 Variation in oxygenation in the paleo record is very sensitive toclimate—with clear links to temperature andoften CO2 variation.109 OMZs expand and contract in synchrony with warming and cooling
events, respectively.110 Episodic climate events
that involve rapid temperature increases overdecades, followed by a cool period lasting a
few hundred years, lead to major fluctuationsin the intensity of Pacific and Indian Ocean
OMZs (i.e., DO of < 20 µM). These events areassociated with rapid variations in North Atlantic deep water formation.111 Ocean oxygen
fluctuates on glacial-interglacial timescales of
thousands of years in the Eastern Pacific.112, 113
13.4.5 Modern Observations (last 50+ years)

Long-term oxygen records made over thelast 50 years reflect oxygen declines in inlandseas,114, 115, 116 in estuaries,117, 118 and in coastal waters.119, 120, 121, 122 The number of coastal,eutrophication-induced hypoxic sites in the
United States has grown dramatically over the
past 40 years.123 Over larger scales, global syntheses show hypoxic waters have expanded
by 4.5 million km2 at a depth of 200 m,95 withwidespread loss of oxygen in the Southern

U.S. Global Change Research Program

Ocean,94 Western Pacific,124 and North Atlantic.125 Overall oxygen declines have been greater in coastal oceans than in the open ocean126
and often greater inshore than offshore.127
The emergence of a deoxygenation signal inregions with naturally high oxygen variability
will unfold over longer time periods (20–50
years from now).128
13.4.6 Projected Changes

Global Models
Global models generally agree that ocean
deoxygenation is occurring; this finding is
also reflected in in situ observations frompast 50 years. Compilations of 10 Earth System models predict a global average loss ofoxygen of −3.5% (higher scenario, RCP8.5)
to −2.4% (lower scenario, RCP4.5) by 2100,
but much stronger losses regionally, and inintermediate and mode waters (Figure 13.6).24
The North Pacific, North Atlantic, Southern
Ocean, subtropical South Pacific, and South
Indian Oceans all are expected to experience
deoxygenation, with O2 decreases of as muchas 17% in the North Pacific by 2100 for the
RCP8.5 pathway. However, the tropical Atlantic and tropical Indian Oceans show increasing
O2 concentrations. In the many areas where
oxygen is declining, high natural variability
makes it difficult to identify anthropogenicallyforced trends.128

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Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes
Projected
Projected Change
Change inin Dissolved
Dissolved Oxygen
Oxygenmmol/m3−200 −185 −170 −155 −140 −125 −110 −95 −80 −65 −50 −35 −20 −5

5

20 35

50 65 80 95 110 125 140 155 170 185 200

Figure 13.6: Predicted change in dissolved oxygen on the σθ = 26.5 (average depth of approximately 290 m) potential
density surface, between the 1981–2000 and 2081–2100, based on the Community Earth System Models–Large Ensemble Experiments (Figure source: redrawn from Long et al. 2016128).

Regional Models
Regional models are critical because many
oxygen drivers are local, influenced by bathymetry, winds, circulation, and fresh waterand nutrient inputs. Most eastern boundary
upwelling areas are predicted to experience
intensified upwelling to 2100,61 although on
the West Coast projections for increasing
upwelling for the northern California Current
occur only north of San Francisco (see Section
13.2.3).
Particularly notable for the western UnitedStates, variation in trade winds in the eastern
Pacific Ocean can affect nutrient inputs, leading to centennial periods of oxygen decline oroxygen increase distinct from global oxygen
decline.129 Oxygen dynamics in the Eastern
Tropical Pacific are highly sensitive to equatorial circulation changes.130
Regional modeling also shows that year-toyear variability in precipitation in the central
United States affects the nitrate–N flux by theMississippi River and the extent of hypox-

U.S. Global Change Research Programia in the Gulf of Mexico.131 A host of climate
influences linked to warming and increased
precipitation are predicted to lower dissolved
oxygen in Chesapeake Bay.132

13.5 Other Coastal Changes
13.5.1 Sea Level Rise

Sea level is an important variable that affects
coastal ecosystems. Global sea level rose rapidly at the end of the last glaciation, as glaciersand the polar ice sheets thinned and melted attheir fringes. On average around the globe, sealevel is estimated to have risen at rates exceeding 2.5 mm/year between about 8,000 and
6,000 years before present. These rates steadilydecreased to less than 2.0 mm/year throughabout 4,000 years ago and stabilized at lessthan 0.4 mm/year through the late 1800s.
Global sea level rise has accelerated again
within the last 100 years, and now averages
about 1 to 2 mm/year.133 See Chapter 12: Sea
Level Rise for more thorough analysis of how
sea level rise has already and will affect the
U.S. coasts.

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13.5.2 Wet and Dry Deposition

Dust transported from continental desert regions to the marine environment deposits nutrients such as iron, nitrogen, phosphorus, andtrace metals that stimulate growth of phytoplankton and increase marine productivity.134
U.S. continental and coastal regions experience large dust deposition fluxes originatingfrom the Saharan desert to the East and fromCentral Asia and China to the Northwest.135
Changes in drought frequency or intensity resulting from anthropogenically forced climatechange, as well as other anthropogenic activities such as agricultural practices and landuse changes may play an important role in
the future viability and strength of these dust
sources (e.g., Mulitza et al. 2010136).
Additionally, oxidized nitrogen, released
during high-temperature combustion overland, and reduced nitrogen, released from
intensive agriculture, are emitted in high population areas in North America and are carriedaway and deposited through wet or dry deposition over coastal and open ocean ecosystems
via local wind circulation. Wet deposition ofpollutants produced in urban areas is known
to play an important role in changes of ecosystem structure in coastal and open oceansystems through intermediate changes in thebiogeochemistry, for instance in dissolved
oxygen or various forms of carbon.137
13.5.3 Primary Productivity

Marine phytoplankton represent about halfof the global net primary production (NPP)(approximately 50 ± 28 GtC /year), fixing
atmospheric CO2 into a bioavailable form forutilization by higher trophic levels (see also
Ch. 2: Physical Drivers of Climate Change).138,
139
As such, NPP represents a critical component in the role of the oceans in climate feedback. The effect of climate change on primary
productivity varies across the coasts depending on local conditions. For instance, nutrients

U.S. Global Change Research Programthat stimulate phytoplankton growth are
impacted by various climate conditions, suchas increased stratification which limits thetransport of nutrient-rich deep water to thesurface, changes in circulation leading to variability in dry and wet deposition of nutrients
to coasts, and altered precipitation/evaporation which changes runoff of nutrients fromcoastal communities. The effect of the multiple
physical factors on NPP is complex and leads
to model uncertainties.140 There is considerable variation in model projections for NPP,
from estimated decreases or no changes, to thepotential increases by 2100.141, 142, 143 Simulations from nine Earth system models projected
total NPP in 2090 to decrease by 2%–16% andexport production (that is, particulate flux tothe deep ocean) to drop by 7%–18% as compared to 1990 (RCP8.5).142 More information
on phytoplankton species response and associated ecosystem dynamics is needed as any
reduction of NPP and the associated export
production would have an impact on carbon
cycling and marine ecosystems.
13.5.4 Estuaries

Estuaries are critical ecosystems of biological,
economic, and social importance in the UnitedStates. They are highly dynamic, influenced
by the interactions of atmospheric, freshwater,terrestrial, oceanic, and benthic components.
Of the 28 national estuarine research reservesin the United States and Puerto Rico, all arebeing impacted by climate change to varying
levels.144 In particular, sea level rise, saltwater intrusion, and the degree of freshwater
discharge influence the forces and processes
within these estuaries.145 Sea level rise andsubsidence are leading to drowning of existing
salt marshes and/or subsequent changes in
the relative area of the marsh plain, if adaptive
upslope movement is impeded due to urbanization along shorelines. Several model scenarios indicate a decline in salt marsh habitat
quality and an accelerated degradation as the379

Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes
rate of sea level rise increases in the latter halfof the century.146, 147 The increase in sea level aswell as alterations to oceanic and atmosphericcirculation can result in extreme wave conditions and storm surges, impacting coastal
communities.144 Additional climate change
impacts to the physical and chemical estuarine
processes include more extreme sea surface
temperatures (higher highs and lower lows
compared to the open ocean due to shallower
depths and influence from land temperatures),changes in flow rates due to changes in precipitation, and potentially greater extents ofsalinity intrusion.

U.S. Global Change Research Program

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Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes

TRACEABLE ACCOUNTSKey Finding 1
The world’s oceans have absorbed about 93% of theexcess heat caused by greenhouse gas warming since
the mid-20th century, making them warmer and altering global and regional climate feedbacks. Ocean heat
content has increased at all depths since the 1960s
and surface waters have warmed by about 1.3° ± 0.1°F(0.7° ± 0.08°C) per century globally since 1900 to 2016.
Under a higher scenario, a global increase in averagesea surface temperature of 4.9° ± 1.3°F (2.7° ± 0.7°C) by
2100 is projected, with even higher changes in some
U.S. coastal regions. (Very high confidence)
Description of evidence base
The key finding and supporting text summarizes theevidence documented in climate science literature, including Rhein et al. 2013.4 Oceanic warming has been
documented in a variety of data sources, most notably the World Ocean Circulation Experiment (WOCE)(http://www.nodc.noaa.gov/woce/wdiu/) and Argo
databases (https://www.nodc.noaa.gov/argo/) and
Extended Reconstructed Sea Surface Temperature(ERSST) v4 (https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v4). There is particular confidence in calculated warming for the time period since1971 due to increased spatial and depth coverage andthe level of agreement among independent SST observations from satellites, surface drifters and ships,
and independent studies using differing analyses,
bias corrections, and data sources.6, 7, 11 Other observations such as the increase in mean sea level rise (seeCh. 12: Sea Level Rise) and reduced Arctic/Antarctic
ice sheets (see Ch. 11: Arctic Changes) further confirm
the increase in thermal expansion. For the purpose ofextending the selected time periods back from 1900
to 2016 and analyzing U.S. regional SSTs, the ERSST
version 4 (ERSSTv4)19 is used. For the centennial timescale changes over 1900–2016, warming trends in all
regions are statistically significant with the 95% confidence level. U.S. regional SST warming is similar between calculations using ERSSTv4 in this report andthose published by Belkin,148 suggesting confidence inthese findings. The projected increase in SST is based

U.S. Global Change Research Programon evidence from the latest generation of Earth System
Models (CMIP5).
Major uncertainties
Uncertainties in the magnitude of ocean warming stemfrom the disparate measurements of ocean temperature over the last century. There is low uncertainty inwarming trends of the upper ocean temperature from
0–700 m depth, whereas there is more uncertainty
for deeper ocean depths of 700–2,000 m due to theshort record of measurements from those areas. Dataon warming trends at depths greater than 2,000 m areeven more sparse. There are also uncertainties in thetiming and reasons for particular decadal and interannual variations in ocean heat content and the contributions that different ocean basins play in the overall
ocean heat uptake.
Summary sentence or paragraph that integrates
the above information
There is very high confidence in measurements thatshow increases in the ocean heat content and warming of the ocean, based on the agreement of different
methods. However, long-term data in total ocean heatuptake in the deep ocean are sparse leading to limited
knowledge of the transport of heat between and within ocean basins.
Key Finding 2
The potential slowing of the Atlantic Meridional Overturning Circulation (AMOC; of which the Gulf Stream is
one component)—as a result of increasing ocean heat
content and freshwater driven buoyancy changes—
could have dramatic climate feedbacks as the ocean
absorbs less heat and CO2 from the atmosphere.51 This
slowing would also affect the climates of North America and Europe. Any slowing documented to date cannot be directly tied to anthropogenic forcing primarily
due to lack of adequate observational data and to challenges in modeling ocean circulation changes. Undera higher scenario (RCP8.5) in CMIP5 simulations, theAMOC weakens over the 21st century by 12% to 54%(low confidence).

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Description of evidence base
Investigations both through direct observations andmodels since 20134 have raised significant concerns
about whether there is enough evidence to determine
the existence of an overall slowdown in the AMOC. As
a result, more robust international observational campaigns are underway currently to measure AMOC circulation. Direct observations have determined a statistically significant slowdown at the 95% confidence level at
26°N (off Florida; see Baringer et al. 2016149) but modeling studies constrained with observations cannot attribute this to anthropogenic forcing.39 The study29 which
seemed to indicate broad-scale slowing has since been
discounted due to its heavy reliance on sea surface temperature cooling as proxy for slowdown rather than actual direct observations. Since Rhein et al. 2013,4 more
observations have led to increased statistical confidence
in the measurement of the AMOC. Current observation
trends indicate the AMOC slowing down at the 95% confidence level at 26°N and 41°N but a more limited in situ
estimate at 35°S, shows an increase in the AMOC.35, 149
There is no one collection spot for AMOC-related data,
but the U.S. Climate Variability and Predictability Program (US CLIVAR) has a U.S. AMOC priority focus area
and a webpage with relevant data sites (https://usclivar.
org/amoc/amoc-time-series).
The IPCC 2013 WG1 projections indicate a high likelihood of AMOC slowdown in the next 100 years, however overall understanding is limited by both a lack of
direct observations (which is being remedied) and alack of model skill to resolve deep ocean dynamics. As aresult, this key finding was given an overall assessmentof low confidence.
Major uncertainties
As noted, uncertainty about the overall trend of the
AMOC is high given opposing trends in northern andsouthern ocean time series observations. Although
earth system models do indicate a high likelihoodof AMOC slowdown as a result of a warming, climate
projections are subject to high uncertainty. This uncertainty stems from intermodel differences, internalvariability that is different in each model, uncertainty
in stratification changes, and most importantly uncer-

U.S. Global Change Research Program

tainty in both future freshwater input at high latitudes
as well as the strength of the subpolar gyre circulation.
Summary sentence or paragraph that integrates
the above information
The increased focus on direct measurements of the
AMOC should lead to a better understanding of 1)
how it is changing and its variability by region, and 2)
whether those changes are attributable to climate drivers through both model improvements and incorporation of those expanded observations into the models.
Key Finding 3
The world’s oceans are currently absorbing more than aquarter of the CO2 emitted to the atmosphere annuallyfrom human activities, making them more acidic (veryhigh confidence), with potential detrimental impacts tomarine ecosystems. In particular, higher-latitude systems typically have a lower buffering capacity against
pH change, exhibiting seasonally corrosive conditions
sooner than low-latitude systems. Acidification is regionally increasing along U.S. coastal systems as a result of upwelling (for example, in the Pacific Northwest)(high confidence), changes in freshwater inputs (forexample, in the Gulf of Maine) (medium confidence),
and nutrient input (for example, in agricultural watersheds and urbanized estuaries) (high confidence). The
rate of acidification is unparalleled in at least the past
66 million years (medium confidence). Under the higher scenario (RCP8.5), the global average surface ocean
acidity is projected to increase by 100% to 150% (highconfidence).
Description of evidence base
Evidence on the magnitude of the ocean sink is obtained from multiple biogeochemical and transport
ocean models and two observation-based estimatesfrom the 1990s for the uptake of the anthropogenic CO2. Estimates of the carbonate system (DIC andalkalinity) were based on multiple survey cruises in
the global ocean in the 1990s (WOCE – now GO-SHIP,JGOFS). Coastal carbon and acidification surveys havebeen executed along the U.S. coastal large marine ecosystem since at least 2007, documenting significantly
elevated pCO2 and low pH conditions relative to oce-382

Climate Science Special Report13 | Ocean Acidification and Other Ocean Changes
anic waters. The data are available from the National
Centers for Environmental Information (https://www.
ncei.noaa.gov/). Other sources of biogeochemical bottle data can be found from HOT-DOGS ALOHA (http://hahana.soest.hawaii.edu/hot/hot-dogs) or CCHDO(https://cchdo.ucsd.edu/). Rates of change associatedwith the Palaeocene-Eocene Thermal Maximum (PETM,
56 million years ago) were derived using stable carbon
and oxygen isotope records preserved in the sedimentary record from the New Jersey shelf using time series analysis and carbon cycle–climate modelling. Thisevidence supports a carbon release during the onsetof the PETM over no less than 4,000 years, yielding amaximum sustained carbon release rate of less than 1.1
GtC per year.86 The projected increase in global surface
ocean acidity is based on evidence from ten of the latest generation earth system models which include six
distinct biogeochemical models that were included in
the latest IPCC AR5 2013.
Major uncertainties
In 2014 the ocean sink was 2.6 ± 0.5 GtC (9.5 GtCO2),
equivalent to 26% of the total emissions attributed tofossil fuel use and land use changes.67 Estimates of the
PETM ocean acidification event evidenced in the geological record remains a matter of some debate within
the community. Evidence for the 1.1 GtC per year cited by Zeebe et al.,86 could be biased as a result of brief
pulses of carbon input above average rates of emissions were they to transpire over timescales ≲ 40 years.

Summary sentence or paragraph that integrates
the above information
There is very high confidence in evidence that the oceansabsorb about a quarter of the carbon dioxide emitted
in the atmosphere and hence become more acidic. The
magnitude of the ocean carbon sink is known at a highconfidence level because it is estimated using a series ofdisparate data sources and analysis methods, while themagnitude of the interannual variability is based only
on model studies. There is medium confidence that thecurrent rate of climate acidification is unprecedented in
the past 66 million years. There is also high confidence
that oceanic pH will continue to decrease.

U.S. Global Change Research Program

Key Finding 4
Increasing sea surface temperatures, rising sea levels,and changing patterns of precipitation, winds, nutrients, and ocean circulation are contributing to overall declining oxygen concentrations at intermediate
depths in various ocean locations and in many coastal
areas. Over the last half century, major oxygen losses have occurred in inland seas, estuaries, and in the
coastal and open ocean (high confidence). Ocean oxygen levels are projected to decrease by as much as
3.5% under the higher scenario (RCP8.5) by 2100 relative to preindustrial values (high confidence).
Description of evidence base
The key finding and supporting text summarizes theevidence documented in climate science literatureincluding Rhein et al. 2013,4 Bopp et al. 2013,24 andSchmidtko et al. 2017.150 Evidence arises from extensive
global measurements of the WOCE after 1989 and individual profiles before that.94 The first basin-wide dissolved oxygen surveys were performed in the 1920s.150
The confidence level is based on globally integrated O2
distributions in a variety of ocean models. Although
the global mean exhibits low interannual variability, regional contrasts are large.
Major uncertainties
Uncertainties (as estimated from the intermodel
spread) in the global mean are moderate mainly because ocean oxygen content exhibits low interannual
variability when globally averaged. Uncertainties inlong-term decreases of the global averaged oxygen
concentration amount to 25% in the upper 1,000 m
for the 1970–1992 period and 28% for the 1993–2003
period. Remaining uncertainties relate to regional variability driven by mesoscale eddies and intrinsic climate
variability such as ENSO.
Summary sentence or paragraph that integrates
the above information
Major ocean deoxygenation is taking place in bodies
of water inland, at estuaries, and in the coastal and theopen ocean (high confidence). Regionally, the phenomenon is exacerbated by local changes in weather, ocean
circulation, and continental inputs to the oceans.

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14

Perspectives on Climate
Change Mitigation

KEY FINDINGS
1. Reducing net emissions of CO2 is necessary to limit near-term climate change and long-term warming.
Other greenhouse gases (for example, methane) and black carbon aerosols exert stronger warming
effects than CO2 on a per ton basis, but they do not persist as long in the atmosphere; therefore, mitigation of non-CO2 species contributes substantially to near-term cooling benefits but cannot be relied
upon for ultimate stabilization goals. (Very high confidence)
2. Stabilizing global mean temperature to less than 3.6°F (2°C) above preindustrial levels requires substantial reductions in net global CO2 emissions prior to 2040 relative to present-day values and likely
requires net emissions to become zero or possibly negative later in the century. After accounting for thetemperature effects of non-CO2 species, cumulative global CO2 emissions must stay below about 800
GtC in order to provide a two-thirds likelihood of preventing 3.6°F (2°C) of warming. Given estimatedcumulative emissions since 1870, no more than approximately 230 GtC may be emitted in the future toremain under this temperature threshold. Assuming global emissions are equal to or greater than thoseconsistent with the RCP4.5 scenario, this cumulative carbon threshold would be exceeded in approximately two decades. (High confidence)
3. Achieving global greenhouse gas emissions reductions before 2030 consistent with targets and actions
announced by governments in the lead up to the 2015 Paris climate conference would hold open thepossibility of meeting the long-term temperature goal of limiting global warming to 3.6°F (2°C) above
preindustrial levels, whereas there would be virtually no chance if net global emissions followed apathway well above those implied by country announcements. Actions in the announcements are,by themselves, insufficient to meet a 3.6°F (2°C) goal; the likelihood of achieving that goal depends
strongly on the magnitude of global emissions reductions after 2030. (High confidence)
4. Further assessments of the technical feasibilities, costs, risks, co-benefits, and governance challenges ofclimate intervention or geoengineering strategies, which are as yet unproven at scale, are a necessary
step before judgments about the benefits and risks of these approaches can be made with high confidence. (High confidence)

Recommended Citation for Chapter
DeAngelo, B., J. Edmonds, D.W. Fahey, and B.M. Sanderson, 2017: Perspectives on climate change mitigation. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W.
Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research
Program, Washington, DC, USA, pp. 393-410, doi: 10.7930/J0M32SZG.

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14 | Perspectives on Climate Change Mitigation
Introduction
This chapter provides scientific context for
key issues regarding the long-term mitigationof climate change. As such, this chapter first
addresses the science underlying the timing ofwhen and how CO2 and other greenhouse gas(GHG) mitigation activities that occur in thepresent affect the climate of the future. When
do we see the benefits of a GHG emission
reduction activity? Chapter 4: Projections provides further context for this topic. Relatedly,
the present chapter discusses the significanceof the relationship between net cumulative
CO2 emissions and eventual global warming
levels. The chapter reviews recent analysesof global emissions pathways associatedwith preventing 3.6°F (2°C) or 2.7°F (1.5°C)
of warming relative to preindustrial times.
And finally, this chapter briefly reviews thestatus of climate intervention proposals andhow these types of mitigation actions could
possibly play a role in avoiding future climatechange.

14.1 The Timing of Benefits from
Mitigation Actions
14.1.1 Lifetime of Greenhouse Gases and Inherent
Delays in the Climate System

Carbon dioxide (CO2) concentrations in theatmosphere are directly affected by humanactivities in the form of CO2 emissions. Atmospheric CO2 concentrations adjust to humanemissions of CO2 over long time scales, spanning from decades to millennia.1, 2 The IPCC
estimated that 15% to 40% of CO2 emitted until 2100 will remain in the atmosphere longer
than 1,000 years.1 The persistence of warming
is longer than the atmospheric lifetime of CO2
and other GHGs, owing in large part to thethermal inertia of the ocean.3 Climate change
resulting from anthropogenic CO2 emissions,and any associated risks to the environment,
human health and society, are thus essentiallyirreversible on human timescales.4 The worldis committed to some degree of irreversible

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warming and associated climate change resulting from emissions to date.
The long lifetime in the atmosphere of CO22and some other key GHGs, coupled with thetime lag in the response of the climate system
to atmospheric forcing,5 has timing implications for the benefits (i.e., avoided warming orrisk) of mitigation actions. Large reductions inemissions of the long-lived GHGs are estimated to have modest temperature effects inthe near term (e.g., over one to two decades)
because total atmospheric concentration levels
require long periods to adjust,6 but are necessary in the long term to achieve any objectiveof preventing warming of any desired magnitude. Near-term projections of global meansurface temperature are therefore not strongly
influenced by changes in near-term emissions
but rather dominated by natural variability,
the Earth system response to past and current
GHG emissions, and by model spread (i.e.,the different climate outcomes associated withdifferent models using the same emissions
pathway).7 Long-term projections of global
surface temperature (after mid-century), on
the other hand, show that the choice of global
emissions pathway, and thus the long-term
mitigation pathway the world chooses, is thedominant source of future uncertainty in climate outcomes.3, 8Some studies have nevertheless shown thepotential for some near-term benefits of mitigation. For example, one study found that, evenat the regional scale, heat waves would alreadybe significantly more severe by the 2030s in a
non-mitigation scenario compared to a moderate mitigation scenario.9 The mitigation of
non-CO2 GHGs with short atmospheric lifetimes (such as methane, some hydrofluorocarbons [HFCs], and ozone) and black carbon (anaerosol that absorbs solar radiation; see Ch. 2:
Physical Drivers of Climate Change), collectively referred to as short-lived climate pollutants

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14 | Perspectives on Climate Change Mitigation(SLCPs), has been highlighted as a particular
way to achieve more rapid climate benefits(e.g., Zaelke and Borgford-Parnell 201510).
SLCPs are substances that not only have an atmospheric lifetime shorter (for example, weeks
to a decade) than CO2 but also exert a stronger
radiative forcing (and hence temperature effect)
compared to CO2 on a per ton basis.11 For thesereasons, mitigation of SLCP emissions produces more rapid radiative responses. In the caseof black carbon, with an atmospheric lifetime ofa few days to weeks,12 emissions (and therefore
reductions of those emissions) produce strong
regional effects. Mitigation of black carbon andmethane also generate direct health co-benefits.13, 14 Reductions and/or avoidances of SLCP
emissions could be a significant contribution
to staying at or below a 3.6°F (2°C) increaseor any other chosen global mean temperature
increase.15, 16, 17, 18 The recent Kigali Amendment
to the Montreal Protocol seeks to phase down
global HFC production and consumption inorder to avoid substantial GHG emissions incoming decades. Stringent and continuous
SLCP mitigation could potentially increase
allowable CO2 budgets for avoiding warming
beyond any desired future level, by up to 25%
under certain scenarios.18 However, given that
economic and technological factors tend to couple CO2 and many SLCP emissions to varying
degrees, significant SLCP emissions reductions
would be a co-benefit of CO2 mitigation.

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14.1.2 Stock and Stabilization: Cumulative CO2 andthe Role of Other Greenhouse Gases

Net cumulative CO2 emissions in the industrial era will largely determine long-term, global
mean temperature change. A robust feature ofmodel climate change simulations is a nearly linear relationship between cumulative
CO2 emissions and global mean temperature
increases, irrespective of the details and exact
timing of the emissions pathway (see Figure
14.1; see also Ch. 4: Projections). Limiting andstabilizing warming to any level implies that
there is a physical upper limit to the cumulative amount of CO2 that can be added tothe atmosphere.3 Eventually stabilizing the
global temperature requires CO2 emissions
to approach zero.19 Thus, for a 3.6°F (2°C) or
any desired global mean warming goal, an
estimated range of cumulative CO2 emissions
from the current period onward can be calculated. The key sources of uncertainty for any
compatible, forward looking CO2 budget associated with a given future warming objective
include the climate sensitivity, the responseof the carbon cycle including feedbacks (forexample, the release of GHGs from permafrost
thaw), the amount of past CO2 emissions, andthe influence of past and future non-CO2 species.3, 19 Increasing the probability that any given temperature goal will be reached therefore
implies tighter constraints on cumulative CO2
emissions. Relatedly, for any given cumulative
CO2 budget, higher emissions in the near term
imply the need for steeper reductions in thelong term.

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Cumulative total anthropogenic CO2 emissions from 1870 (GtCO2)
1000
2000
3000
4000
5000
6000
7000
8000

Temperature anomaly relative to 1861–1880 (°C)

5

2100

4

2100

3
2100
2050

2

2050
2050
2030

1

2050
2100

2030

2010

1950

0

RCP2.6
RCP4.5
RCP6.0
RCP8.5

2000
1980

Historical
RCP range
1% yr−1 CO2
1% yr −1 CO2 range

1890

0

500
1000
1500
2000
Cumulative total anthropogenic CO2 emissions from 1870 (GtC)

2500

Figure 14.1: Global mean temperature change for a number of scenarios as a function of cumulative CO2 emissions
from preindustrial conditions, with time progressing along each individual line for each scenario. (Figure source: IPCC
2013;42 ©IPCC. Used with permission).

Between 1870 and 2015, human activities,
primarily the burning of fossil fuels and deforestation, emitted about 560 GtC in the form of
CO2 into the atmosphere.20 According to best
estimates in the literature, 1,000 GtC is thetotal cumulative amount of CO2 that could beemitted yet still provide a two-thirds likelihood of preventing 3.6°F (2°C) of global mean
warming since preindustrial times.3, 21 That
estimate, however, ignores the additional radiative forcing effects of non-CO2 species (thatis, the net positive forcing resulting from theforcing of other well-mixed GHGs, including
halocarbons, plus the other ozone precursor
gases and aerosols). Considering both historical and projected non-CO2 effects reduces theestimated cumulative CO2 budget compatible
with any future warming goal,18 and in thecase of 3.6°F (2°C) it reduces the aforemen-

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tioned estimate to 790 GtC.3 Given this morecomprehensive estimate, limiting the global
average temperature increase to below 3.6°F(2°C) means approximately 230 GtC more
CO2 could be emitted globally. To illustrate, if
one assumes future global emissions follow
a pathway consistent with the lower scenario(RCP4.5), this cumulative carbon thresholdis exceeded by around 2037, while under
the higher scenario (RCP8.5) this occurs byaround 2033. To limit the global average temperature increase to 2.7°F (1.5°C), the estimated cumulative CO2 budget is about 590 GtC(assuming linear scaling with the compatible
3.6°F (2°C) budget that also considers non-CO2
effects), meaning only about 30 GtC more of
CO2 could be emitted. Further emissions of 30
GtC (in the form of CO2) are projected to occur
in the next few years (Table 14.1).

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Table 14.1: Dates illustrating when cumulative CO2 emissions thresholds associated with eventual warming of 3.6°F or 2.7°F above preindustrial levels might be reached. RCP4.5 and RCP8.5
refer, respectively, to emissions consistent with the lower and higher scenarios used throughout this report. The estimated cumulative CO2 emissions (measured in Gigatons (Gt) of carbon)
associated with different probabilities (e.g., 66%) of preventing 3.6°F (2°C) of warming arefrom the IPCC.3 The cumulative emissions compatible with 2.7°F (1.5°C) are linearly derived
from the estimates associated with 3.6°F (2°C). The cumulative CO2 estimates take into account the additional net warming effects associated with past and future non-CO2 emissions
consistent with the RCP scenarios. Historical CO2 emissions from 1870–2015 (including fossil
fuel combustion, land use change, and cement manufacturing) are from Le Quéré et al.20 See
Traceable Accounts for further details.
Dates by when cumulative carbon emissions (GtC) since 1870 reach amountcommensurate with 3.6°F (2°C), when accounting for non-CO2 forcings

66% = 790 GtC

50% = 820 GtC

33% = 900 GtC

RCP4.5

2037

2040

2047

RCP8.5

2033

2035

2040

Dates by when cumulative carbon emissions (GtC) since 1870 reach amountcommensurate with 2.7°F (1.5°C), when accounting for non-CO2 forcings

66% = 593 GtC

50% = 615 GtC

33% = 675 GtC

RCP4.5

2019

2021

2027

RCP8.5

2019

2021

2025

14.2 Pathways Centered Around 3.6°F(2°C)
The idea of a 3.6°F (2°C) goal can be found in
the scientific literature as early as 1975. Nordhaus22 justified it by simply stating, “If there
were global temperatures more than 2 or 3°C
above the current average temperature, thiswould take the climate outside of the rangeof observations which have been made overthe last several hundred thousand years.”
Since that time, the concept of a 3.6°F (2°C)
goal gained attention in both scientific andpolicy discourse. For example, the Stockholm
Environment Institute23 published a reportstating that 3.6°F (2°C) “can be viewed as an
upper limit beyond which the risks of grave
damage to ecosystems, and of non-linear
responses, are expected to increase rapidly.”And in 2007, the IPCC Fourth Assessment
Report stated, among other things: “Confidence has increased that a 1 to 2°C increasein global mean temperature above 1990 levels(about 1.5 to 2.5°C above pre-industrial) poses

U.S. Global Change Research Programsignificant risks to many unique and threatened systems including many biodiversity
hotspots.” Most recently, the Paris Agreementof 2015 took on the long-term goal of “holding
the increase in the global average temperature
to well below 2°C above pre-industrial levels
and pursuing efforts to limit the temperature
increase to 1.5°C above pre-industrial levels.”
Many countries announced GHG emissions
reduction targets and related actions (formallycalled Intended Nationally Determined Contributions [INDCs]) in the lead up to the Paris
meeting; these announcements addressed
emissions through 2025 or 2030 and take awide range of forms. A number of studies
have generated projections of future GHG
emissions based on these announcements andevaluated whether, if implemented, the resulting emissions reductions would limit the increase in global average temperatures to 3.6°F(2°C) above preindustrial levels. In June 2017,
the United States announced its intent to withdraw from the Paris Agreement. The scenarios

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14 | Perspectives on Climate Change Mitigationassessed below were published prior to thisannouncement and therefore do not reflect the
implications of this announcement.
Estimates of global emissions and temperature
implications from emissions pathways consistent with targets and actions announced bygovernments in the lead up to the 2015 Parisclimate conference24, 25, 26, 27, 28 generally findthat 1) these targets and actions would reduce
GHG emissions growth by 2030 relative toa situation where these goals did not exist,
though emissions are still not expected to belower in 2030 than in 2015; and 2) the targets
and actions would be a step towards limiting
global mean temperature increase to 3.6°F(2°C), but by themselves, would be insufficientfor this goal. According to one study, emissions pathways consistent with governments’announcements imply a median warming of
4.7°–5.6°F (2.6°–3.1°C) by 2100, though year
2100 temperature estimates depend on assumed emissions between 2030 and 2100.24 For
example, Climate Action Tracker,26 using alternative post-2030 assumptions, put the range at
5.9°–7.0°F (3.3°–3.9°C).
Emissions pathways consistent with the targets and actions announced by governmentsin the lead up to the 2015 Paris conference

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have been evaluated in the context of thelikelihood of global mean surface temperature change (Figure 14.2). It was found thatthe likelihood of limiting the global mean
temperature increase to 3.6°F (2°C) or lesswas enhanced by these announced actions,but depended strongly on assumptions about
subsequent policies and measures. Under ascenario in which countries maintain the samepace of decarbonization past 2030 as they
announced in their first actions (leading up
to 2025 or 2030) there is some likelihood (lessthan 10%) of preventing a global mean surface
temperature change of 3.6°F (2°C) relative topreindustrial levels; this scenario thus holds
open the possibility of achieving this goal,
whereas there would be virtually no chance
if emissions climbed to levels above those
implied by country announcements (Figure14.2).27 Greater emissions reductions beyond
2030 (based on higher decarbonization rates
past 2030) increase the likelihood of limiting
warming to 3.6°F (2°C) or lower to about
30%, and almost eliminate the likelihood of a
global mean temperature increase greater than
7°F (4°C). Scenarios that assume even greater emissions reductions past 2030 would benecessary to have at least a 50% probability oflimiting warming to 3.6°F (2°C)27 as discussed
and illustrated further below.

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Global CO2 emissions from energy
and industry (GtCO2/year)(a)

Emissions pathways

(b)

80
70
60

Reference-No
policyReference-Low
policy

IPCC AR5baseline range

50
INDCs

40

Paris-Continued
ambition

30

Temperature probabilities
Projected
warming until
2100 relative
to preindustrial
levels
1–1.5°C
1.5–2°C
2–3°C
3–4°C
>4°C

20
10
0

Paris-Increased
ambition

IPCC AR5 2°C≥ 50% chance range

Illustrative 50%−101990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

0 10 20 30 40 50 60 70 80 90 100
Likelihood of projected warming until 2100(%)

Figure 14.2: Global CO2 emissions and probabilistic temperature outcomes of government announcements associated with the lead up to the Paris climate conference. (a) Global CO2 emissions from energy and industry (includes
CO2 emissions from all fossil fuel production and use and industrial processes such as cement manufacture that also
produce CO2 as a byproduct) for emissions pathways following no policy, current policy, meeting the governments’ announcements with constant country decarbonization rates past 2030, and meeting the governments’ announcementswith higher rates of decarbonization past 2030. INDCs refer to Intended Nationally Determined Contributions which is
the term used for the governments’ announced actions in the lead up to Paris. (b) Likelihoods of different levels of increase in global mean surface temperature during the 21st century relative to preindustrial levels for the four scenarios.
Although (a) shows only CO2 emissions from energy and industry, temperature outcomes are based on the full suiteof GHG, aerosol, and short-lived species emissions across the full set of human activities and physical Earth systems.
(Figure source: Fawcett et al. 201527).

There is a limited range of pathways whichenable the world to remain below 3.6°F (2°C)
of warming (see Figure 14.3), and almost all
but the most rapid near-term mitigation pathways are heavily reliant on the implementation of CO2 removal from the atmosphere laterin the century or other climate intervention,
discussed below. If global emissions are in linewith the first round of announced government
actions by 2030, then the world likely needs
to reduce effective GHG emissions to zero by

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2080 and be significantly net negative by theend of the century (relying on as yet unproventechnologies to remove GHGs from the atmosphere) in order to stay below 3.6°F (2°C) of
warming. Avoiding 2.7°F (1.5°C) of warming
requires more aggressive action still, with net
zero emissions achieved by 2050 and net negative emissions thereafter. In either case, fasternear-term emissions reductions significantly
decrease the requirements for net negativeemissions in the future.

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RCP8.5
Unconditional INDCs
Conditional INDCs

50

25

RCP2.6
Fastest mitigation

0

Slowest mitigation−25
2000 2015 2030 2050
2075
Year

Effective CO2 emissions (GtCO2e/yr)

(c)

2100

2125

66% chance of peaking ≤ 2°C warming
75

RCP8.5

Unconditional INDCs
Conditional INDCs

50

25

RCP2.60−25
2000 2015 2030 2050
2075
Year

2100

2125

Effective CO2 emissions (GtCO2e/yr)

75

66% chance of exceeding 1.5°C
for no more than 50 years(b)75

RCP8.5

Unconditional INDCs
Conditional INDCs

50

25

RCP2.60−25
2000 2015 2030 2050
2075
Year

2100

2125

66% chance of exceeding 2°C
for no more than 50 years(d)Effective CO2 emissions (GtCO2e/yr)

Effective CO2 emissions (GtCO2e/yr)(a) 66% chance of peaking ≤ 1.5°C warming

75

RCP8.5

Unconditional INDCs
Conditional INDCs

50

25

RCP2.60−25
2000 2015 2030 2050
2075
Year

2100

2125

Figure 14.3: Global emissions pathways for GHGs, expressed as CO2-equivalent emissions, which would be consistent with different temperature goals (relative to preindustrial temperatures). INDCs refer to Intended Nationally Determined Contributions which is the term used for the governments’ announced actions in the lead up to Paris. (a) showsa set of pathways where global mean temperatures would likely (66%) not exceed 2.7°F (1.5°C). A number of pathways
are consistent with the goal, ranging from the red curve (slowest near-term mitigation with large negative emissions
requirements in the future) to the black curve with rapid near-term mitigation and less future negative emissions. (b)shows similar pathways with a 66% chance of exceeding 2.7°F (1.5°C) for only 50 years, where (c) and (d) show similar
emission pathways for 3.6°F (2°C). (Figure source: Sanderson et al. 201625).

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14.3 The Potential Role of Climate
Intervention in Mitigation Strategies
Limiting the global mean temperature increase
through emissions reductions or adapting
to the impacts of a greater-than-3.6°F (2°C)
warmer world have been acknowledged asseverely challenging tasks by the international
science and policy communities. Consequently, there is increased interest by some scientists
and policy makers in exploring additional
measures designed to reduce net radiative
forcing through other, as yet untested actions,which are often referred to as geoengineering or climate intervention (CI) actions. CI
approaches are generally divided into two
categories: carbon dioxide removal (CDR)29
and solar radiation management (SRM).30
CDR and SRM methods may have future roles
in helping meet global temperature goals.
Both methods would reduce global averagetemperature by reducing net global radiative
forcing: CDR through reducing atmospheric
CO2 concentrations and SRM through increasing Earth’s albedo.
The evaluation of the suitability and advisability of potential CI actions requires adecision framework that includes importantdimensions beyond scientific and technicalconsiderations. Among these dimensions to
be considered are the potential developmentof global and national governance and oversight procedures, geopolitical relations, legal
considerations, environmental, economic
and societal impacts, ethical considerations,and the relationships to global climate policy
and current GHG mitigation and adaptation
actions. It is clear that these social science andother non-physical science dimensions arelikely to be a major part of the decision framework and ultimately control the adoption andeffectiveness of CI actions. This report only
acknowledges these mostly non-physical scientific dimensions and must forego a detailed
discussion.

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By removing CO2 from the atmosphere, CDR
directly addresses the principal cause ofclimate change. Potential CDR approaches include direct air capture, currently well-understood biological methods on land (for example, afforestation), less well-understood andpotentially risky methods in the ocean (forexample, ocean fertilization), and accelerated
weathering (for example, forming calcium carbonate on land or in the oceans).29 While CDR
is technically possible, the primary challenge
is achieving the required scale of removal ina cost-effective manner, which in part presumes a comparison to the costs of other, more
traditional GHG mitigation options.31, 32 In
principle, at large scale, CDR could measurably reduce CO2 concentrations (that is, cause
negative emissions). Point-source capture (asopposed to CO2 capture from ambient air) andremoval of CO2 is a particularly effective CDR
method. The climate value of avoided CO2
emissions is essentially equivalent to that of
the atmospheric removal of the same amount.
To realize sustained climate benefits from
CDR, however, the removal of CO2 from theatmosphere must be essentially permanent—
at least several centuries to millennia. In
addition to high costs, CDR has the additional
limitation of long implementation times.
By contrast, SRM approaches offer the only
known CI methods of cooling Earth within a
few years after inception. An important limitation of SRM is that it would not address damage to ocean ecosystems from increasing ocean
acidification due to continued CO2 uptake.
SRM could theoretically have a significant
global impact even if implemented by a small
number of nations, and by nations that are notalso the major emitters of GHGs; this could beviewed either as a benefit or risk of SRM.30
Proposed SRM concepts increase Earth’s albedo through injection of sulfur gases or aerosols into the stratosphere (thereby simulating

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the effects of explosive volcanic eruptions)or marine cloud brightening through aerosol
injection near the ocean surface. Injection ofsolid particles is an alternative to sulfur andyet other SRM methods could be deployed inspace. Studies have evaluated the expected
effort and effectiveness of various SRM methods.30, 33 For example, model runs were performed in the GeoMIP project using the full
CMIP5 model suite to illustrate the effect of
reducing top-of-the-atmosphere insolation tooffset climate warming from CO2.34 The idealized runs, which assumed an abrupt, globally-uniform insolation reduction in a 4 × CO2
atmosphere, show that temperature increases
are largely offset, most sea ice loss is avoided,
average precipitation changes are small, andnet primary productivity increases. However,important regional changes in climate variables are likely in SRM scenarios as discussed
below.
As global ambitions increase to avoid or
remove CO2 emissions, probabilities of large
increases in global temperatures by 2100 are
proportionately reduced.27 Scenarios in whichlarge-scale CDR is used to meet a 3.6°F (2°C)
limit while allowing business-as-usual consumption of fossil fuels are likely not feasible
with present technologies. Model SRM scenarios have been developed that show reductions
in radiative forcing up to 1 W/m2 with annual
stratospheric injections of 1 Mt of sulfur fromaircraft or other platforms.35, 36 Preliminary
studies suggest that this could be accomplished at an implementation cost as low asa few billion dollars per year using current
technology, enabling an individual country or
subnational entity to conduct activities having
significant global climate impacts.
SRM scenarios could in principle be designed
to follow a particular radiative forcing trajectory, with adjustments made in response
to monitoring of the climate effects.37 SRM

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could be used as an interim measure to avoid
peaks in global average temperature andother climate parameters. The assumption isoften made that SRM measures, once implemented, must continue indefinitely in order
to avoid the rapid climate change that would
occur if the measures were abruptly stopped.
SRM could be used, however, as an interim
measure to buy time for the implementationof emissions reductions and/or CDR, andSRM could be phased out as emissions reductions and CDR are phased in, to avoid abruptchanges in radiative forcing.37
SRM via marine cloud brightening derives
from changes in cloud albedo from injection ofaerosols into low-level clouds, primarily over
the oceans. Clouds with smaller and more
numerous droplets reflect more sunlight thanclouds with fewer and larger droplets. Current
models provide more confidence in the effectsof stratospheric injection than in marine cloudbrightening and in achieving scales large
enough to reduce global forcing.30
CDR and SRM have substantial uncertainties
regarding their effectiveness and unintended
consequences. For example, CDR on a large
scale may disturb natural systems and haveimportant implications for land-use changes. For SRM actions, even if the reduction in
global average radiative forcing from SRM
was exactly equal to the radiative forcing from
GHGs, the regional and temporal patterns ofthese forcings would have important differences. While SRM could rapidly lower global
mean temperatures, the effects on precipitation patterns, light availability, crop yields,acid rain, pollution levels, temperature gradients, and atmospheric circulation in response
to such actions are less well understood. Also,
the reduction in sunlight from SRM may
have effects on agriculture and ecosystems. In
general, restoring regional preindustrial temperature and precipitation conditions through402

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SRM actions is not expected to be possible
based on ensemble modeling studies.38 As
a consequence, optimizing the climate andgeopolitical value of SRM actions would likely
involve tradeoffs between regional temperature and precipitation changes.39 Alternatively,intervention options have been proposed toaddress particular regional impacts.40
GHG forcing has the potential to push theclimate farther into unprecedented states forhuman civilization and increase the likelihoodof “surprises” (see Ch. 15: Potential Surprises).
CI could prevent climate change from reaching a state with more unpredictable consequences. The potential for rapid changes upon
initiation (or ceasing) of a CI action would
require adaptation on timescales significantly
more rapid than what would otherwise benecessary. The NAS29, 30 and the Royal Society41
recognized that research on the feasibilities
and consequences of CI actions is incomplete
and call for continued research to improveknowledge of the feasibility, risks, and benefitsof CI techniques.

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TRACEABLE ACCOUNTSKey Finding 1
Reducing net emissions of CO2 is necessary to limit
near-term climate change and long-term warming.
Other greenhouse gases (for example, methane) andblack carbon aerosols exert stronger warming effects
than CO2 on a per ton basis, but they do not persist as
long in the atmosphere; therefore, mitigation of nonCO2 species contributes substantially to near-termcooling benefits but cannot be relied upon for ultimate
stabilization goals. (Very high confidence)
Description of evidence base
Joos et al.2 and Ciais et al. (see Box 6.1 in particular)1
describe the climate response of CO2 pulse emissions,and Solomon et al.,4 NRC,19 and Collins et al.3 describe
the long-term warming and other climate effects associated with CO2 emissions. Paltsev et al.8 and Collins etal.3 describe the near-term vs. long-term nature of climate outcomes resulting from GHG mitigation. Myhreet al.11 synthesize numerous studies detailing information about the radiative forcing effects and atmospheric lifetimes of all GHGs and aerosols (see in particularAppendix 8A therein). A recent body of literature has
emerged highlighting the particular role that non-CO2
mitigation can play in providing near-term coolingbenefits (e.g., Shindell et al. 2012;17 Zaelke and Borgford-Parnell 2015;10 Rogelj et al. 201518). For each of theindividual statements made in Key Finding 1, there are
numerous literature sources that provide consistentgrounds on which to make these statements with very
high confidence.
Major uncertainties
The Key Finding is comprised of qualitative statementsthat are traceable to the literature described above andin this chapter. Uncertainties affecting estimates of theexact timing and magnitude of the climate response
following emissions (or avoidance of those emissions)of CO2 and other GHGs involve the quantity of emissions, climate sensitivity, some uncertainty about theremoval time or atmospheric lifetime of CO2 and other
GHGs, and the choice of model carrying out future simulations. The role of black carbon in climate change is

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more uncertain compared to the role of the well-mixed
GHGs (see Bond et al. 201312).
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
Key Finding 1 is comprised of qualitative statements basedon a body of literature for which there is a high level ofagreement. There is a well-established understanding,
based in the literature, of the atmospheric lifetime andwarming effects of CO2 vs. other GHGs after emission, andin turn how atmospheric concentration levels respond following the emission of CO2 and other GHGs.
Summary sentence or paragraph that integrates
the above information
The qualitative statements contained in Key Finding 1
reflect aspects of fundamental scientific understanding, well grounded in the literature, that provide a relevant framework for considering the role of CO2 andnon-CO2 species in mitigating climate change.
Key Finding 2
Stabilizing global mean temperature to less than 3.6°F(2°C) above preindustrial levels requires substantial reductions in net global CO2 emissions prior to 2040 relative to present-day values and likely requires net emissions to become zero or possibly negative later in the
century. After accounting for the temperature effects
of non-CO2 species, cumulative global CO2 emissions
must stay below about 800 GtC in order to provide a
two-thirds likelihood of preventing 3.6°F (2°C) of warming. Given estimated cumulative emissions since 1870,
no more than approximately 230 GtC may be emitted
in the future to remain under this temperature threshold. Assuming global emissions are equal to or greaterthan those consistent with the RCP4.5 scenario, thiscumulative carbon threshold would be exceeded in approximately two decades. (High confidence)
Description of evidence base
Key Finding 2 is a case study, focused on a pathway associated with 3.6°F (2°C) of warming, based on the moregeneral concepts described in the chapter. As such, the404

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evidence for the relationship between cumulative CO2
emissions and global mean temperature response3, 19, 21
also supports Key Finding 3.
Numerous studies have provided best estimates of cumulative CO2 compatible with 3.6°F (2°C) of warming
above preindustrial levels, including a synthesis by
the IPCC.3 Sanderson et al.25 provide further recent evidence to support the statement that net CO2 emissions
would need to approach zero or become negative laterin the century in order to avoid this level of warming.
Rogelj et al. 201518 and the IPCC3 demonstrate that theconsideration of non-CO2 species has the effect of further constraining the amount of cumulative CO2 emissions compatible with 3.6°F (2°C) of warming.
Table 14.1 shows the IPCC estimates associated withdifferent probabilities (66% [the one highlighted in Key
Finding 2], 50%, and 33%) of cumulative CO2 emissions
compatible with warming of 3.6°F (2°C) above preindustrial levels, and the cumulative CO2 emissions compatible with 2.7°F (1.5°C) are in turn linearly derived
from those, based on the understanding that cumulative emissions scale linearly with global mean temperature response. The IPCC estimates take into accountthe additional radiative forcing effects—past and future—of non-CO2 species based on the emissions
pathways consistent with the RCP scenarios (available
here: https://tntcat.iiasa.ac.at/RcpDb/dsd?Action=htmlpage&page=about#descript).
The authors calculated the dates shown in Table 14.1,
which supports the last statement in Key Finding 2,
based on Le Quéré et al.20 and the publicly available
RCP database. Le Quéré et al.20 provide the widely usedreference for historical global, annual CO2 emissions
from 1870 to 2015 (land-use change emissions were
estimated up to year 2010 so are assumed to be constant between 2010 and 2015). Future CO2 emissions
are based on the lower and higher scenarios (RCP4.5
and RCP8.5, respectively); annual numbers between
model-projected years (2020, 2030, 2040, etc.) are linearly interpolated.

U.S. Global Change Research Program

Major uncertainties
There are large uncertainties about the course of future
CO2 and non-CO2 emissions, but the fundamental pointthat CO2 emissions need to eventually approach zeroor possibly become net negative to stabilize warming
below 3.6°F (2°C) holds regardless of future emissions
scenario. There are also large uncertainties about themagnitude of past (since 1870 in this case) CO2 andnon-CO2 emissions, which in turn influence the uncertainty about compatible cumulative emissions fromthe present day forward. Further uncertainties regarding non-CO2 species, including aerosols, include theirradiative forcing effects. The uncertainty in achieving
the temperature targets for a given emissions pathway
is, in large part, reflected by the range of probabilities
shown in Table 14.1.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence in the first statement of
Key Finding 2 because it is based on a number of sources with a high level of agreement. The role of non-CO2
species in particular introduces uncertainty in the second statement of Key Finding 2 regarding compatible
cumulative CO2 emissions that take into account pastand future radiative forcing effects of non-CO2 species;
though this estimate is based on a synthesis of numerous studies by the IPCC. The last statement of Key
Finding 2 is straightforward based on the best available
estimates of historical emissions in combination withthe widely used future projections of the RCP scenarios.
Summary sentence or paragraph that integrates
the above information
Fundamental scientific understanding of the climate
system provides a framework for considering potential pathways for achieving a target of preventing 3.6°F(2°C) of warming. There are uncertainties about cumulative CO2 emissions compatible with this goal, in largepart because of uncertainties about the role of non-CO2
species, but it appears, based on past emissions and future projections, that the cumulative carbon thresholdfor this goal could be reached or exceeded in about
two decades.

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Key Finding 3
Achieving global greenhouse gas emissions reductions
before 2030 consistent with targets and actions announced by governments in the lead up to the 2015
Paris climate conference would hold open the possibility of meeting the long-term temperature goal of limiting global warming to 3.6°F (2°C) above preindustrial
levels, whereas there would be virtually no chance if
global net emissions followed a pathway well above
those implied by country announcements. Actions in
the announcements are, by themselves, insufficient
to meet a 3.6°F (2°C) goal; the likelihood of achievingthat goal depends strongly on the magnitude of global
emissions reductions after 2030. (High confidence)

Summary sentence or paragraph that integrates
the above information
Different analyses have estimated the implications for
global mean temperature of the emissions reductions
consistent with the actions announced by governments in the lead up to the 2015 Paris climate conference and have reached similar conclusions. Assuming
emissions reductions indicated in these announcements are achieved, along with a range of climate sensitivities, these contributions provide some likelihoodof meeting the long-term goal of limiting global warming to well below 3.6°F (2°C) above preindustrial levels,
but much depends on assumptions about what happens after 2030.

Description of evidence base
The primary source supporting this key finding is Fawcett et al.;27 it is also supported by Rogelj et al.,24 Sanderson et al.,25 and the Climate Action Tracker.26 Each ofthese analyses evaluated the global climate implicationsof the aggregation of the individual country contributions thus far put forward under the Paris Agreement.

Key Finding 4
Further assessments of the technical feasibilities, costs,
risks, co-benefits, and governance challenges of climate
intervention or geoengineering strategies, which are as
yet unproven at scale, are a necessary step before judgments about the benefits and risks of these approaches
can be made with high confidence. (High confidence)

Major uncertainties
The largest uncertainty lies in the assumption ofachieving emissions reductions consistent with theannouncements prior to December 2015; these reductions are assumed to be achieved but could either beover- or underachieved. This in turn creates uncertainty
about the extent of emissions reductions that would beneeded after the first round of government announcements in order to achieve the 2°C or any other target.
The response of the climate system, the climate sensitivity, is also a source of uncertainty; the Fawcett et al.
analysis used the IPCC AR5 range, 1.5° to 4.5°C.

Description of evidence base
Key Finding 4 contains qualitative statements based on
the growing literature addressing this topic, includingfrom such bodies as the National Academy of Sciences
and the Royal Society, coupled with judgment by theauthors about the future interest level in this topic.

Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is high confidence in this key finding because anumber of analyses have examined the implications ofthese announcements and have come to similar conclusions, as captured in this key finding.

U.S. Global Change Research Program

Major uncertainties
The major uncertainty is how public perception andinterest among policymakers in climate intervention
may change over time, even independently from theperceived level of progress made towards reducing CO2
and other GHG emissions over time.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is high confidence that climate intervention strategies may gain greater attention, especially if efforts toslow the buildup of atmospheric CO2 and other GHGs
are considered inadequate by many in the scientificand policy communities.

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Summary sentence or paragraph that integrates
the above information
The key finding is a qualitative statement based on thegrowing literature on this topic. The uncertainty moving forward is the comfort level and desire among numerous stakeholders to research and potentially carryout these climate intervention strategies, particularly
in light of how progress by the global community to
reduce GHG emissions is perceived.

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Potential Surprises: Compound
Extremes and Tipping Elements

KEY FINDINGS
1. Positive feedbacks (self-reinforcing cycles) within the climate system have the potential to accelerate
human-induced climate change and even shift the Earth’s climate system, in part or in whole, into
new states that are very different from those experienced in the recent past (for example, ones withgreatly diminished ice sheets or different large-scale patterns of atmosphere or ocean circulation).
Some feedbacks and potential state shifts can be modeled and quantified; others can be modeled or
identified but not quantified; and some are probably still unknown. (Very high confidence in the potential for state shifts and in the incompleteness of knowledge about feedbacks and potential state shifts).
2. The physical and socioeconomic impacts of compound extreme events (such as simultaneous heatand drought, wildfires associated with hot and dry conditions, or flooding associated with high precipitation on top of snow or waterlogged ground) can be greater than the sum of the parts (very highconfidence). Few analyses consider the spatial or temporal correlation between extreme events.
3. While climate models incorporate important climate processes that can be well quantified, they do
not include all of the processes that can contribute to feedbacks, compound extreme events, andabrupt and/or irreversible changes. For this reason, future changes outside the range projected byclimate models cannot be ruled out (very high confidence). Moreover, the systematic tendency of climate models to underestimate temperature change during warm paleoclimates suggests that climate
models are more likely to underestimate than to overestimate the amount of long-term future change(medium confidence).

Recommended Citation for Chapter
Kopp, R.E., K. Hayhoe, D.R. Easterling, T. Hall, R. Horton, K.E. Kunkel, and A.N. LeGrande, 2017: Potential surprises – compound extremes and tipping elements. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, andT.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 411-429, doi:
10.7930/J0GB227J.

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15.1 Introduction
The Earth system is made up of many components that interact in complex ways across a
broad range of temporal and spatial scales. As
a result of these interactions the behavior of
the system cannot be predicted by looking at
individual components in isolation. Negative
feedbacks, or self-stabilizing cycles, within
and between components of the Earth system
can dampen changes (Ch. 2: Physical Driversof Climate Change). However, their stabilizing effects render such feedbacks of lessconcern from a risk perspective than positive
feedbacks, or self-reinforcing cycles. Positive
feedbacks magnify both natural and anthropogenic changes. Some Earth system components, such as arctic sea ice and the polar ice
sheets, may exhibit thresholds beyond whichthese self-reinforcing cycles can drive thecomponent, or the entire system, into a radically different state. Although the probabilitiesof these state shifts may be difficult to assess,their consequences could be high, potentiallyexceeding anything anticipated by climate
model projections for the coming century.
Humanity’s effect on the Earth system,
through the large-scale combustion of fossil
fuels and widespread deforestation and theresulting release of carbon dioxide (CO2) into
the atmosphere, as well as through emissionsof other greenhouse gases and radiatively
active substances from human activities, isunprecedented (Ch. 2: Physical Drivers of
Climate Change). These forcings are driving
changes in temperature and other climate
variables. Previous chapters have covered avariety of observed and projected changes insuch variables, including averages and extremes of temperature, precipitation, sea level,and storm events (see Chapters 1, 4–13).
While the distribution of climate model
projections provides insight into the range ofpossible future changes, this range is limited

U.S. Global Change Research Programby the fact that models do not include or fully
represent all of the known processes and components of the Earth system (e.g., ice sheets or
arctic carbon reservoirs),1 nor do they include
all of the interactions between these components that contribute to the self-stabilizing andself-reinforcing cycles mentioned above (e.g.,
the dynamics of the interactions between ice
sheets, the ocean, and the atmosphere). They
also do not include currently unknown processes that may become increasingly relevant
under increasingly large climate forcings. This
limitation is emphasized by the systematictendency of climate models to underestimate
temperature change during warm paleoclimates (Section 15.5). Therefore, there is significant potential for humanity’s effect on theplanet to result in unanticipated surprises anda broad consensus that the further and fasterthe Earth system is pushed towards warming,
the greater the risk of such surprises.
Scientists have been surprised by the Earthsystem many times in the past. The discoveryof the ozone hole is a clear example. Prior to groundbreaking work by Molina andRowland2, chlorofluorocarbons (CFCs) wereviewed as chemically inert; the chemistry bywhich they catalyzed stratospheric ozone depletion was unknown. Within eleven years of
Molina and Rowland’s work, British Antarctic
Survey scientists reported ground observations showing that spring ozone concentrations in the Antarctic, driven by chlorine fromhuman-emitted CFCs, had fallen by about
one-third since the late 1960s.3 The problem
quickly moved from being an “unknown
unknown” to a “known known,” and by 1987,
the Montreal Protocol was adopted to phaseout these ozone-depleting substances.
Another surprise has come from arctic sea
ice. While the potential for powerful positive
ice-albedo feedbacks has been understoodsince the late 19th century, climate models

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have struggled to capture the magnitude ofthese feedbacks and to include all the relevant
dynamics that affect sea ice extent. As of 2007,
the observed decline in arctic sea ice fromthe start of the satellite era in 1979 outpaced
the declines projected by almost all the models used by the Intergovernmental Panel on
Climate Change’s Fourth Assessment Report(AR4),4 and it was not until AR4 that the IPCC
first raised the prospect of an ice-free summer
Arctic during this century.5 More recent studies are more consistent with observations andhave moved the date of an ice-free summer
Arctic up to approximately mid-century (seeCh. 11: Arctic Changes).6 But continued rapid
declines—2016 featured the lowest annually
averaged arctic sea ice extent on record, andthe 2017 winter maximum was also the lowest
on record—suggest that climate models may
still be underestimating or missing relevant
feedback processes. These processes could
include, for example, effects of melt ponds,
changes in storminess and ocean wave impacts, and warming of near surface waters.7, 8, 9

system that can be stable in multiple different
states and can be “tipped” between thesestates by small changes in forcing, amplified
by positive feedbacks. Examples of potential
tipping elements include ice sheets, modesof atmosphere–ocean circulation like the El
Niño–Southern Oscillation, patterns of ocean
circulation like the Atlantic meridional overturning circulation, and large-scale ecosystems
like the Amazon rainforest.10, 11 While compound extremes and tipping elements constitute at least partially “known unknowns,”
the paleoclimate record also suggests thepossibility of “unknown unknowns.” Thesepossibilities arise in part from the tendency of
current climate models to underestimate past
responses to forcing, for reasons that may or
may not be explained by current hypotheses(e.g., hypotheses related to positive feedbacks
that are unrepresented or poorly represented
in existing models).

15.2 Risk Quantification and Its Limits
Quantifying the risk of low-probability,
high-impact events, based on models or observations, usually involves examining the tailsof a probability distribution function (PDF).
Robust detection, attribution, and projectionof such events into the future is challenged bymultiple factors, including an observational
record that often does not represent the fullrange of physical possibilities in the climate
system, as well as the limitations of the statistical tools, scientific understanding, andmodels used to describe these processes.12

This chapter focuses primarily on two typesof potential surprises. The first arises frompotential changes in correlations between
extreme events that may not be surprisingon their own but together can increase thelikelihood of compound extremes, in which
multiple events occur simultaneously or in
rapid sequence. Increasingly frequent compound extremes—either of multiple types of
events (such as paired extremes of droughts
and intense rainfall) or over greater spatial or
temporal scales (such as a drought occurringin multiple major agricultural regions aroundthe world or lasting for multiple decades)—are often not captured by analyses that focus
solely on one type of extreme.
The second type of surprise arises from self-reinforcing cycles, which can give rise to “tipping elements”—subcomponents of the Earth

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The 2013 Boulder, Colorado, floods and theDust Bowl of the 1930s in the central UnitedStates are two examples of extreme events
whose magnitude and/or extent are unprecedented in the observational record. Statistical
approaches such as Extreme Value Theory canbe used to model and estimate the magnitudeof rare events that may not have occurred in
the observational record, such as the “1,000-

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year flood event” (i.e., a flood event with a
0.1% chance of occurrence in any given year)(e.g., Smith 198713). While useful for many
applications, these are not physical models:
they are statistical models that are typically
based on the assumption that observed patterns of natural variability (that is, the sample
from which the models derive their statistics)
are both valid and stationary beyond theobservational period. Extremely rare events
can also be assessed based upon paleoclimate
records and physical modeling. In the paleoclimatic record, numerous abrupt changes
have occurred since the last deglaciation,
many larger than those recorded in the instrumental record. For example, tree ring recordsof drought in the western United States show
abrupt, long-lasting megadroughts that weresimilar to but more intense and longer-lasting
than the 1930s Dust Bowl.14

on physical climate predictions over the last
20 years found a tendency for scientific assessments such as those of the IPCC to under-predict rather than over-predict changes thatwere subsequently observed.18

15.3 Compound Extremes

Since models are based on physics rather thanobservational data, they are not inherently
constrained to any given time period or setof physical conditions. They have been used
to study the Earth in the distant past andeven the climate of other planets (e.g., Lunt
et al. 2012;15 Navarro et al. 201416). Looking
to the future, thousands of years’ worth ofsimulations can be generated and exploredto characterize small-probability, high-risk
extreme events, as well as correlated extremes(see Section 15.3). However, the likelihoodthat such model events represent real risks islimited by well-known uncertainties in climate
modeling related to parameterizations, model
resolution, and limits to scientific understanding (Ch. 4: Projections). For example, conventional convective parameterizations in global
climate models systematically underestimate
extreme precipitation.17 In addition, models often do not accurately capture or even include
the processes, such as permafrost feedbacks,
by which abrupt, non-reversible change may
occur (see Section 15.4). An analysis focusing

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An important aspect of surprise is the potential for compound extreme events. Thesecan be events that occur at the same time orin sequence (such as consecutive floods inthe same region) and in the same geographic
location or at multiple locations within a given
country or around the world (such as the 2009
Australian floods and wildfires). They may
consist of multiple extreme events or of events
that by themselves may not be extreme but together produce a multi-event occurrence (suchas a heat wave accompanied by drought19). It
is possible for the net impact of these events
to be less than the sum of the individual
events if their effects cancel each other out.
For example, increasing CO2 concentrations
and acceleration of the hydrological cycle may
mitigate the future impact of extremes in grossprimary productivity that currently impact the
carbon cycle.20 However, from a risk perspective, the primary concern relates to compound
extremes with additive or even multiplicative
effects.
Some areas are susceptible to multiple types ofextreme events that can occur simultaneously.
For example, certain regions are susceptible toboth flooding from coastal storms and riverine
flooding from snow melt, and a compound
event would be the occurrence of both simultaneously. Compound events can also result
from shared forcing factors, including natural
cycles like the El Niño–Southern Oscillation(ENSO); large-scale circulation patterns, suchas the ridge observed during the 2011–2017
California drought (e.g., Swain et al. 201621;
see also Ch. 8: Droughts, Floods, and Wildfires); or relatively greater regional sensitivity

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to global change, as may occur in “hot spots”
such as the western United States.22 Finally,
compound events can result from mutually
reinforcing cycles between individual events,
such as the relationship between drought andheat, linked through soil moisture and evaporation, in water-limited areas.23

Another compound event frequently discussed in the literature is the increase in wildfire risk resulting from the combined effects ofhigh precipitation variability (wet seasons followed by dry), elevated temperature, and low
humidity. If followed by heavy rain, wildfires
can in turn increase the risk of landslides anderosion. They can also radically increase emissions of greenhouse gases, as demonstrated by
the amount of carbon dioxide produced by theFort McMurray fires of May 2016—more than
10% of Canada’s annual emissions.

In a changing climate, the probability ofcompound events can be altered if there is anunderlying trend in conditions such as mean
temperature, precipitation, or sea level thatalters the baseline conditions or vulnerabilityof a region. It can also be altered if there is achange in the frequency or intensity of individual extreme events relative to the changing
mean (for example, stronger storm surges,
more frequent heat waves, or heavier precipitation events).
The occurrence of warm/dry and warm/
wet conditions is discussed extensively in theliterature; at the global scale, these conditions
have increased since the 1950s,24 and analysis
of NOAA’s billion-dollar disasters illustrates
the correlation between temperature andprecipitation extremes during the costliest
climate and weather events since 1980 (Figure 15.1, right). In the future, hot summers
will become more frequent, and although it
is not always clear for every region whether
drought frequency will change, droughts inalready dry regions, such as the southwestern
United States, are likely to be more intense
in a warmer world due to faster evaporation
and associated surface drying.25, 26, 27 For otherregions, however, the picture is not as clear.
Recent examples of heat/drought events (inthe southern Great Plains in 2011 or in California, 2012–2016) have highlighted the inadequacy of traditional univariate risk assessment
methods.28 Yet a bivariate analysis for the contiguous United States of precipitation deficits
and positive temperature anomalies finds no
significant trend in the last 30 years.29

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A third example of a compound event involves flooding arising from wet conditions
due to precipitation or to snowmelt, which
could be exacerbated by warm temperatures.
These wet conditions lead to high groundwater levels, saturated soils, and/or elevatedriver flows, which can increase the risk offlooding associated with a given storm days oreven months later.23
Compound events may surprise in two ways.
The first is if known types of compound
events recur, but are stronger, longer-lasting,and/or more widespread than those experienced in the observational record or projectedby model simulations for the future. One example would be simultaneous drought eventsin different agricultural regions across thecountry, or even around the world, that challenge the ability of human systems to provide
adequate affordable food. Regions that lackthe ability to adapt would be most vulnerable
to this risk (e.g., Fraser et al. 201330). Anotherexample would be the concurrent and more
severe heavy precipitation events that haveoccurred in the U.S. Midwest in recent years.
After record insurance payouts following theevents, in 2014 several insurance companies,
led by Farmers Insurance, sued the city of
Chicago and surrounding counties for failing to adequately prepare for the impacts ofa changing climate. Although the suit was

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15 | Potential Surprises: Compound Extremes and Tipping Elements
dropped later that same year, their point was
made: in some regions of the United States,
the insurance industry is not able to cope with
the increasing frequency and/or concurrenceof certain types of extreme events.
The second way in which compound events
could surprise would be the emergence of
new types of compound events not observedin the historical record or predicted by model simulations, due to model limitations (interms of both their spatial resolution as well as
their ability to explicitly resolve the physicalprocesses that would result in such compound

Arctic Sea Ice
MethanePermafrost Hydrates
CarbonAmazonRainforest

AtmosphericSuperrotation

HOT

15

Maximum Temperature Anomaly (°F)

Atlantic Deep
Water
Formation

Coral Reefs

El Niño-Southern Oscillation

20

Greenland
Ice Sheet

Boreal Forest

events), an increase in the frequency of such
events from human-induced climate change,or both. An example is Hurricane Sandy,
where sea level rise, anomalously high ocean
temperatures, and high tides combined tostrengthen both the storm and the magnitudeof the associated storm surge.31 At the sametime, a blocking ridge over Greenland—afeature whose strength and frequency may
be related to both Greenland surface melt
and reduced summer sea ice in the Arctic (seealso Ch. 11: Arctic Changes)32—redirected thestorm inland to what was, coincidentally, an
exceptionally high-exposure location.

10
5
0

DRY

WET

−5
−10
−15

Instability of Marine-based
Antarctic Ice

Drought
Fire

COLD

−20
−50

−30

−10

0

10

30

50

Precipitation Anomaly (%)

Figure 15.1: (left) Potential climatic tipping elements affecting the Americas (Figure source: adapted from Lenton et al.
200810). (right) Wildfire and drought events from the NOAA Billion Dollar Weather Events list (1980–2016), and associated temperature and precipitation anomalies. Dot size scales with the magnitude of impact, as reflected by the cost of
the event. These high-impact events occur preferentially under hot, dry conditions.

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15.4 Climatic Tipping Elements

state shift may occur rapidly, but the state shiftitself may take decades, centuries, or evenmillennia to play out. Often the forcing thatcommits a tipping element to a shift in stateis unknown. Sometimes, it is even unclear
whether a proposed tipping element actuallyexhibits tipping behavior. Through a combination of physical modeling, paleoclimate
observations, and expert elicitations, scientists
have identified a number of possible tipping
elements in atmosphere–ocean circulation, thecryosphere, the carbon cycle, and ecosystems(Figure 15.1, left; Table 15.1).

Different parts of the Earth system exhibit
critical thresholds, sometimes called “tipping
points” (e.g., Lenton et al. 2008;10 Collins et al.
2013;25 NRC 2013;33 Kopp et al. 201611). These
parts, known as tipping elements, have the potential to enter into self-amplifying cycles thatcommit them to shifting from their current
state into a new state: for example, from one
in which the summer Arctic Ocean is coveredby ice, to one in which it is ice-free. In somepotential tipping elements, these state shifts
occur abruptly; in others, the commitment to a

Table 15.1: Potential tipping elements (adapted from Kopp et al. 201611).
Candidate Climatic
Tipping Element

State Shift

Main Impact Pathways

Atlantic meridional
overturning
circulation

Major reduction in strength

Regional temperature and precipitation; global
mean temperature; regional sea level

El Niño–Southern
Oscillation

Increase in amplitude

Regional temperature and precipitation

Equatorial
atmosphericsuperrotation

Initiation

Cloud cover; climate sensitivity

Regional North
Atlantic Ocean
convection

Major reduction in strength

Regional temperature and precipitation

Antarctic Ice Sheet

Major decrease in ice volume

Sea level; albedo; freshwater forcing on ocean
circulation

Arctic sea ice

Major decrease in summertime
and/or perennial area

Regional temperature and precipitation; albedo

Greenland Ice Sheet

Major decrease in ice volume

Sea level; albedo; freshwater forcing on ocean
circulation

Methane hydrates

Massive release of carbon

Greenhouse gas emissions

Permafrost carbon

Massive release of carbon

Greenhouse gas emissions

Amazon rainforest

Dieback, transition to grasslands

Greenhouse gas emissions; biodiversity

Boreal forest

Dieback, transition to grasslands

Greenhouse gas emissions; albedo; biodiversity

Coral reefs

Die-off

Biodiversity

Atmosphere–ocean
circulation

Cryosphere

Carbon cycle

Ecosystem

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One important tipping element is the Atlantic
meridional overturning circulation (AMOC), a
major component of global ocean circulation.
Driven by the sinking of cold, dense water in
the North Atlantic near Greenland, its strengthis projected to decrease with warming due
to freshwater input from increased precipitation, glacial melt, and melt of the Greenland
Ice Sheet (see also discussion in Ch. 11: Arctic
Changes).34 A decrease in AMOC strength isprobable and may already be culpable for the“warming hole” observed in the North Atlantic,34, 35 although it is still unclear whether thisdecrease represents a forced change or internal variability.36 Given sufficient freshwater
input, there is even the possibility of complete
AMOC collapse. Most models do not predict
such a collapse in the 21st century,33 although
one study that used observations to bias-correct climate model simulations found that CO2
concentrations of 700 ppm led to a AMOC
collapse within 300 years.37
A slowing or collapse of the AMOC would
have several consequences for the UnitedStates. A decrease in AMOC strength would
accelerate sea level rise off the northeastern
United States,38 while a full collapse could
result in as much as approximately 1.6 feet (0.5m) of regional sea level rise,39, 40 as well as acooling of approximately 0°–4°F (0°–2°C) overthe country.37, 41 These changes would occur in
addition to preexisting global and regional sea
level and temperature change. A slowdownof the AMOC would also lead to a reductionof ocean carbon dioxide uptake, and thus anacceleration of global-scale warming.42
Another tipping element is the atmospheric–oceanic circulation of the equatorial Pacificthat, through a set of feedbacks, drives the state
shifts of the El Niño–Southern Oscillation. Thisis an example of a tipping element that alreadyshifts on a sub-decadal, interannual timescale,

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primarily in response to internal noise. Climate
model experiments suggest that warming will
reduce the threshold needed to trigger extremely strong El Niño and La Niña events.43,
44
As evident from recent El Niño and La Niña
events, such a shift would negatively impact
many regions and sectors across the UnitedStates (for more on ENSO impacts, see Ch. 5:
Circulation and Variability).
A third potential tipping element is arctic
sea ice, which may exhibit abrupt state shifts
into summer ice-free or year-round ice-freestates.45, 46 As discussed above, climate models
have historically underestimated the rate ofarctic sea ice loss. This is likely due to insufficient representation of critical positive
feedbacks in models. Such feedbacks could
include: greater high-latitude storminess andocean wave penetration as sea ice declines;
more northerly incursions of warm air andwater; melting associated with increasingwater vapor; loss of multiyear ice; and albedo
decreases on the sea ice surface (e.g., Schröder
et al. 2014;7 Asplin et al. 2012;8 Perovich et al.
20089). At the same time, however, the point at
which the threshold for an abrupt shift would
be crossed also depends on the role of naturalvariability in a changing system; the relative
importance of potential stabilizing negative
feedbacks, such as more efficient heat transfer from the ocean to the atmosphere in fall
and winter as sea declines; and how sea ice inother seasons, as well as the climate system
more generally, responds once the first “icefree” summer occurs (e.g., Ding et al. 201747).
It is also possible that summer sea ice may not
abruptly collapse, but instead respond in amanner proportional to the increase in temperature.48, 49, 50, 51 Moreover, an abrupt decreasein winter sea ice may result simply as thegradual warming of Arctic Ocean causes it tocross a critical temperature for ice formation,rather than from self-reinforcing cycles.52

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Two possible tipping elements in the carbon
cycle also lie in the Arctic. The first is buriedin the permafrost, which contains an estimated 1,300–1,600 GtC (see also Ch. 11: Arctic
Changes).53 As the Arctic warms, about 5–15%
is estimated to be vulnerable to release in thiscentury.53 Locally, the heat produced by thedecomposition of organic carbon could serve
as a positive feedback, accelerating carbon
release.54 However, the release of permafrost
carbon, as well as whether that carbon is
initially released as CO2 or as the more potent
greenhouse gas CH4, is limited by many factors, including the freeze–thaw cycle, the ratewith which heat diffuses into the permafrost,
the potential for organisms to cycle permafrost carbon into new biomass, and oxygen
availability. Though the release of permafrost
carbon would probably not be fast enough totrigger a runaway self-amplifying cycle leading to a permafrost-free Arctic,53 it still has thepotential to significantly amplify both local
and global warming, reduce the budget ofhuman-caused CO2 emissions consistent with
global temperature targets, and drive continued warming even if human-caused emissions
stopped altogether.55, 56
The second possible arctic carbon cycle tipping element is the reservoir of methane hydrates frozen into the sediments of continental
shelves of the Arctic Ocean (see also Ch. 11:
Arctic Changes). There is an estimated 500 to
3,000 GtC in methane hydrates,57, 58, 59 with amost recent estimate of 1,800 GtC (equivalently, 2,400 Gt CH4).60 If released as methane rather than CO2, this would be equivalent to about
82,000 Gt CO2 using a global warming potential of 34.61 While the existence of this reservoir
has been known and discussed for several
decades (e.g., Kvenvolden 198862), only recently has it been hypothesized that warming
bottom water temperatures may destabilize
the hydrates over timescales shorter than mil-

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lennia, leading to their release into the watercolumn and eventually the atmosphere (e.g.,Archer 2007;57 Kretschmer et al. 201563). Recent
measurements of the release of methane fromthese sediments in summer find that, whilemethane hydrates on the continental shelf andupper slope are undergoing dissociation, theresulting emissions are not reaching the ocean
surface in sufficient quantity to affect the atmospheric methane budget significantly, if at
all.60, 64 Estimates of plausible hydrate releases
to the atmosphere over the next century are
only a fraction of present-day anthropogenic
methane emissions.60, 63, 65
These estimates of future emissions frompermafrost and hydrates, however, neglect thepossibility that humans may insert themselves
into the physical feedback systems. With anestimated 53% of global fossil fuel reserves in
the Arctic becoming increasingly accessible ina warmer world,66 the risks associated withthis carbon being extracted and burned, further exacerbating the influence of humans on
global climate, are evident.67, 68 Of less concern
but still relevant, arctic ocean waters themselves are a source of methane, which could
increase as sea ice decreases.69
The Antarctic and Greenland Ice Sheets areclear tipping elements. The Greenland Ice
Sheet exhibits multiple stable states as a resultof feedbacks involving the elevation of the ice
sheet, atmosphere-ocean-sea ice dynamics,
and albedo.70, 71, 72, 73 At least one study suggeststhat warming of 2.9ºF (1.6°C) above a preindustrial baseline could commit Greenland toan 85% reduction in ice volume and a 20 foot(6 m) contribution to global mean sea level
over millennia.71 One 10,000-year modelingstudy74 suggests that following the higher
RCP8.5 scenario (see Ch. 4: Projections) overthe 21st century would lead to complete lossof the Greenland Ice Sheet over 6,000 years.

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In Antarctica, the amount of ice that sits on
bedrock below sea level is enough to raise
global mean sea level by 75.5 feet (23 m).75 This
ice is vulnerable to collapse over centuries tomillennia due to a range of feedbacks involving ocean-ice sheet-bedrock interactions.74, 76, 77,
78, 79, 80
Observational evidence suggests that ice
dynamics already in progress have committed
the planet to as much as 3.9 feet (1.2 m) worthof sea level rise from the West Antarctic Ice
Sheet alone, although that amount is projected
to occur over the course of many centuries.81,
82
Plausible physical modeling indicates that,
under the higher RCP8.5 scenario, Antarctic
ice could contribute 3.3 feet (1 m) or more to
global mean sea level over the remainder ofthis century,83 with some authors arguing that
rates of change could be even faster.84 Over
10,000 years, one modeling study suggeststhat 3.6°F (2°C) of sustained warming could
lead to about 70 feet (25 m) of global mean sea
level rise from Antarctica alone.74
Finally, tipping elements also exist in largescale ecosystems. For example, boreal forests
such as those in southern Alaska may expand
northward in response to arctic warming.
Because forests are darker than the tundra
they replace, their expansion amplifies regional warming, which in turn accelerates
their expansion.85 As another example, coral
reef ecosystems, such as those in Florida, aremaintained by stabilizing ecological feedbacks
among corals, coralline red algae, and grazing
fish and invertebrates. However, these stabilizing feedbacks can be undermined by warming, increased risk of bleaching events, spreadof disease, and ocean acidification, leading toabrupt reef collapse.86 More generally, many
ecosystems can undergo rapid regime shifts in
response to a range of stressors, including climate change (e.g., Scheffer et al. 2001;87 Folke
et al. 200488).

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15.5 Paleoclimatic Hints of Additional
Potential Surprises
The paleoclimatic record provides evidence
for additional state shifts whose driving
mechanisms are as yet poorly understood.
As mentioned, global climate models tend tounderestimate both the magnitude of global
mean warming in response to higher CO2
levels as well as its amplification at high latitudes, compared to reconstructions of temperature and CO2 from the geological record.
Three case studies—all periods well predating
the first appearance of Homo sapiens around
200,000 years ago89—illustrate the limitationsof current scientific understanding in capturing the full range of self-reinforcing cycles that
operate within the Earth system, particularly
over millennial time scales.
The first of these, the late Pliocene, occurred
about 3.6 to 2.6 million years ago. Climate
model simulations for this period systematically underestimate warming north of 30°N.90
During the second of these, the middle Miocene (about 17–14.5 million years ago), models
also fail to simultaneously replicate global
mean temperature—estimated from proxies to be approximately 14° ± 4°F (8° ± 2°C)
warmer than preindustrial—and the approximately 40% reduction in the pole-to-equator temperature gradient relative to today.91
Although about one-third of the global mean
temperature increase during the Miocene canbe attributed to changes in geography andvegetation, geological proxies indicate CO2
concentrations of around 400 ppm,91, 92 similar
to today. This suggests the possibility of as yet
unmodeled feedbacks, perhaps related to asignificant change in the vertical distributionof heat in the tropical ocean.93
The last of these case studies, the early Eocene,occurred about 56–48 million years ago. This
period is characterized by the absence of permanent land ice, CO2 concentrations peaking

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around 1,400 ± 470 ppm,94 and global temperatures about 25°F ± 5°F (14°C ± 3°C) warmer
than the preindustrial.95 Like the late Pliocene
and the middle Miocene, this period also
exhibits about half the pole-to-equator temperature gradient of today.15, 96 About one-thirdof the temperature difference is attributable
to changes in geography, vegetation, and ice
sheet coverage.95 However, to reproduce boththe elevated global mean temperature and the
reduced pole-to-equator temperature gradient,climate models would require CO2 concentrations that exceed those indicated by the proxyrecord by two to five times15—suggesting once
again the presence of as yet poorly understoodprocesses and feedbacks.
One possible explanation for this discrepancy
is a planetary state shift that, above a particular CO2 threshold, leads to a significant increase in the sensitivity of the climate to CO2.
Paleo-data for the last 800,000 years suggest
a gradual increase in climate sensitivity with
global mean temperature over glacial-interglacial cycles,97, 98 although these results are
based on a time period with CO2 concentra-

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tions lower than today. At higher CO2 levels,
one modeling study95 suggests that an abruptchange in atmospheric circulation (the onset of equatorial atmospheric superrotation)
between 1,120 and 2,240 ppm CO2 could leadto a reduction in cloudiness and an approximate doubling of climate sensitivity. However,the critical threshold for such a transition ispoorly constrained. If it occurred in the past at
a lower CO2 level, it might explain the Eocene
discrepancy and potentially also the Miocene
discrepancy: but in that case, it could alsopose a plausible threat within the 21st century
under the higher RCP8.5 scenario.
Regardless of the particular mechanism, thesystematic paleoclimatic model-data mismatch for past warm climates suggests thatclimate models are omitting at least one, andprobably more, processes crucial to future
warming, especially in polar regions. For thisreason, future changes outside the range projected by climate models cannot be ruled out,
and climate models are more likely to underestimate than to overestimate the amount oflong-term future change.

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TRACEABLE ACCOUNTSKey Finding 1
Positive feedbacks (self-reinforcing cycles) within the
climate system have the potential to accelerate human-induced climate change and even shift the Earth’s
climate system, in part or in whole, into new states thatare very different from those experienced in the recent
past (for example, ones with greatly diminished ice
sheets or different large-scale patterns of atmosphereor ocean circulation). Some feedbacks and potential
state shifts can be modeled and quantified; others canbe modeled or identified but not quantified; and someare probably still unknown. (Very high confidence in thepotential for state shifts and in the incompleteness ofknowledge about feedbacks and potential state shifts).
Description of evidence baseThis key finding is based on a large body of scientific
literature recently summarized by Lenton et al.,10 NRC,33
and Kopp et al.11 As NRC33 (page vii) states, “A study ofEarth’s climate history suggests the inevitability of ‘tipping points’—thresholds beyond which major and rapid changes occur when crossed—that lead to abruptchanges in the climate system” and (page xi), “Can all
tipping points be foreseen? Probably not. Some will
have no precursors, or may be triggered by naturally
occurring variability in the climate system. Some will
be difficult to detect, clearly visible only after they havebeen crossed and an abrupt change becomes inevitable.” As IPCC AR5 WG1 Chapter 12, section 12.5.525 further states, “A number of components or phenomena
within the Earth system have been proposed as potentially possessing critical thresholds (sometimes referred
to as tipping points) beyond which abrupt or nonlinear
transitions to a different state ensues.” Collins et al.25
further summarizes critical thresholds that can be modeled and others that can only be identified.
Major uncertainties
The largest uncertainties are 1) whether proposed
tipping elements actually undergo critical transitions;2) the magnitude and timing of forcing that will be
required to initiate critical transitions in tipping elements; 3) the speed of the transition once it has been
triggered; 4) the characteristics of the new state that re-

U.S. Global Change Research Programsults from such transition; and 5) the potential for new
tipping elements to exist that are yet unknown.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence in the likelihood of the existence of positive feedbacks, and the tipping elements
statement is based on a large body of literature published over the last 25 years that draws from basic physics, observations, paleoclimate data, and modeling.
There is very high confidence that some feedbacks can bequantified, others are known but cannot be quantified,and others may yet exist that are currently unknown.
Summary sentence or paragraph that integrates
the above information
The key finding is based on NRC33 and IPCC AR5 WG1Chapter 12 section 12.5.5,25 which made a thorough assessment of the relevant literature.
Key Finding 2
The physical and socioeconomic impacts of compound
extreme events (such as simultaneous heat and drought,
wildfires associated with hot and dry conditions, or flooding associated with high precipitation on top of snow orwaterlogged ground) can be greater than the sum of theparts (very high confidence). Few analyses consider the
spatial or temporal correlation between extreme events.
Description of evidence baseThis key finding is based on a large body of scientific
literature summarized in the 2012 IPCC Special Reporton Extremes.23 The report’s Summary for Policymakers(page 6) states, “exposure and vulnerability are key determinants of disaster risk and of impacts when risk is realized... extreme impacts on human, ecological, or physical systems can result from individual extreme weather
or climate events. Extreme impacts can also result from
non-extreme events where exposure and vulnerability
are high or from a compounding of events or their impacts. For example, drought, coupled with extreme heatand low humidity, can increase the risk of wildfire.”

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Major uncertainties
The largest uncertainties are in the temporal congruence of the events and the compounding nature of
their impacts.
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence that the impacts of multiple events could exceed the sum of the impacts ofevents occurring individually.
Summary sentence or paragraph that integrates
the above information
The key finding is based on the 2012 IPCC SREX report,
particularly section 3.1.3 on compound or multiple
events, which presents a thorough assessment of therelevant literature.
Key Finding 3
While climate models incorporate important climate
processes that can be well quantified, they do not include all of the processes that can contribute to feedbacks, compound extreme events, and abrupt and/or
irreversible changes. For this reason, future changes
outside the range projected by climate models cannot
be ruled out (very high confidence). Moreover, the systematic tendency of climate models to underestimate
temperature change during warm paleoclimates suggests that climate models are more likely to underestimate than to overestimate the amount of long-term
future change (medium confidence).

U.S. Global Change Research Program

Description of evidence baseThis key finding is based on the conclusions of IPCC
AR5 WG1,99 specifically Chapter 9;1 the state of the artof global models is briefly summarized in Chapter 4:
Projections of this report. The second half of this key
finding is based upon the tendency of global climate
models to underestimate, relative to geological reconstructions, the magnitude of both long-term global
mean warming and the amplification of warming athigh latitudes in past warm climates (e.g., Salzmann
et al. 2013;90 Goldner et al. 2014;91 Caballeo and Huber
2013;95 Lunt et al. 201215).
Major uncertainties
The largest uncertainties are structural: are the models
including all the important components and relationships necessary to model the feedbacks and if so, arethese correctly represented in the models?
Assessment of confidence based on evidence andagreement, including short description of natureof evidence and level of agreement
There is very high confidence that the models are incomplete representations of the real world; and there is medium confidence that their tendency is to under- rather
than over-estimate the amount of long-term futurechange.
Summary sentence or paragraph that integrates
the above information
The key finding is based on the IPCC AR5 WG1 Chapter 9,1 as well as systematic paleoclimatic model/datacomparisons.

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Climate Science Special ReportAppendix A

Observational Datasets
Used in Climate Studies
Recommended Citation for Chapter
Wuebbles, D.J., 2017: Observational datasets used in climate studies. In: Climate Science Special Report:
Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C.
Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp.
430-435, doi: 10.7930/J0BK19HT.

Climate Datasets
Observations, including those from satellites, mobile platforms, field campaigns, andground-based networks, provide the basisof knowledge on many temporal and spatial
scales for understanding the changes occurring in Earth’s climate system. These observations also inform the development, calibration,and evaluation of numerical models of thephysics, chemistry, and biology being usedin analyzing past changes in climate and formaking future projections. As all observational data collected by support from Federal
agencies are required to be made available freeof charge with machine readable metadata,
everyone can access these products for theirpersonal analysis and research and for informing decisions. Many of these datasets areaccessible through web services.
Many long-running observations worldwide
have provided us with long-term records
necessary for investigating climate change andits impacts. These include important climate
variables such as surface temperature, sea ice
extent, sea level rise, and streamflow. Perhaps
one of the most iconic climatic datasets, thatof atmospheric carbon dioxide measured
at Mauna Loa, Hawai‘i, has been recorded

U.S. Global Change Research Program

since the 1950s. The U.S. and Global Historical Climatology Networks have been used
as authoritative sources of recorded surface
temperature increases, with some stations
having continuous records going back many
decades. Satellite radar altimetry data (forexample, TOPEX/JASON1 & 2 satellite data)
have informed the development of the University of Colorado’s 20+ year record of global
sea level changes. In the United States, the
USGS (U.S. Geological Survey) National Water
Information System contains, in some instances, decades of daily streamflow records which
inform not only climate but land-use studies
as well. The U.S. Bureau of Reclamation and
U.S. Army Corp of Engineers have maintained
data about reservoir levels for decades whereapplicable. Of course, datasets based on shorter-term observations are used in conjunction
with longer-term records for climate study,
and the U.S. programs are aimed at providing
continuous data records. Methods have beendeveloped and applied to process these dataso as to account for biases, collection method,
earth surface geometry, the urban heat islandeffect, station relocations, and uncertainty(e.g., see Vose et al. 2012;1 Rennie et al. 2014;2
Karl et al. 20153).

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Even observations not designed for climate
have informed climate research. These include
ship logs containing descriptions of ice extent,
readings of temperature and precipitation
provided in newspapers, and harvest records.
Today, observations recorded both manuallyand in automated fashions inform research
and are used in climate studies.
The U.S Global Change Research Program(USGCRP) has established the Global Change
Information System (GCIS) to better coordinate and integrate the use of federal information products on changes in the global environment and the implications of those changes
for society. The GCIS is an open-source, webbased resource for traceable global change
data, information, and products. Designed
for use by scientists, decision makers, and the
public, the GCIS provides coordinated links
to a select group of information products
produced, maintained, and disseminated bygovernment agencies and organizations. Currently the GCIS is aimed at the datasets usedin Third National Climate Assessment (NCA3)
and the USGCRP Climate and Health Assessment. It will be updated for the datasets usedin this report (The Climate Science Special
Report, CSSR).
Temperature and Precipitation Observational
Datasets
For analyses of surface temperature or precipitation, including determining changes overthe globe or the United States, the starting
point is accumulating observations of surface
air temperature or precipitation taken at observing stations all over the world, and, in thecase of temperature, sea surface temperatures(SSTs) taken by ships and buoys. These are
direct measurements of the air temperature,sea surface temperature, and precipitation.
The observations are quality assured to exclude clearly erroneous values. For tempera-

U.S. Global Change Research Program

ture, additional analyses are performed on thedata to correct for known biases in the way
the temperatures were measured. These biases
include the change to the observations that
result from changes in observing practices orchanges in the location or local environmentof an observing station. One example is with
SSTs where there was a change in practicefrom throwing a bucket over the side of theship, pulling up seawater and measuring
the temperature of the water in the bucket tomeasuring the temperature of the water in theengine intake. The bucket temperatures are
systematically cooler than engine intake waterand must be corrected.
For evaluating the globally averaged temperature, data are then compared to a long-term
average for the location where the observations were taken (e.g., a 30-year average for anindividual observing station) to create a deviation from that average, commonly referred
to as an anomaly. Using anomalies allows
the spatial averaging of stations in different
climates and elevations to produce robust estimates of the spatially averaged temperature or
precipitation for a given area.
To calculate the temperature or precipitation
for a large area, like the globe or the UnitedStates, the area is divided into “grid boxes”
usually in latitude/longitude space. For example, one common grid size has 5° x 5° latitude/
longitude boxes, where each side of a grid box
is 5° of longitude and 5° of latitude in length.
All data anomalies in a given grid box are averaged together to produce a gridbox average.
Some grid boxes contain no observations, but
nearby grid boxes do contain observations,so temperatures or precipitation for the gridboxes with no observations are estimated as afunction of the nearby grid boxes with observations for that date.

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Climate Science Special ReportAppendix A | Observational Datasets Used in Climate Studies
Calculating the temperature or precipitation
value for the larger area, either the globe or
the United States, is done by averaging thevalues for all the grid boxes to produce one
number for each day, month, season, or year
resulting in a time series. The time series ineach of the grid boxes are also used to calculate long-term trends in the temperature or
precipitation for each grid box. This provides
a picture of how temperatures and precipitation are changing in different locations.
Evidence for changes in the climate of theUnited States arises from multiple analyses ofdata from in situ, satellite, and other records
undertaken by many groups over several
decades. The primary dataset for surface
temperatures and precipitation in the United States is nClimGrid,4, 5 though trends are
similar in the U.S. Historical Climatology
Network, the Global Historical Climatology
Network, and other datasets. For temperature,
several atmospheric reanalyses (e.g., 20th
Century Reanalysis, Climate Forecast System
Reanalysis, ERA-Interim, and Modern Era
Reanalysis for Research and Applications)confirm rapid warming at the surface since
1979, with observed trends closely tracking

the ensemble mean of the reanalyses.1 Several
recently improved satellite datasets documentchanges in middle tropospheric temperatures.6, 7, 8 Longer-term changes are depictedusing multiple paleo analyses (e.g., Wahl andSmerdon 2012;9 Trouet et al. 201310).
Satellite Temperature Datasets
A special look is given to the satellite temperature datasets because of controversies
associated with these datasets. Satellite-borne
microwave sounders such as the Microwave
Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU) instruments
operating on NOAA polar-orbiting platforms
take measurements of the temperature of thicklayers of the atmosphere with near global
coverage. Because the long-term data record
requires the piecing together of measurements
made by 16 different satellites, accurate instrument intercalibration is of critical importance.
Over the mission lifetime of most satellites, theinstruments drift in both calibration and local
measurement time. Adjustments to counterthe effects of these drifts need to be developed
and applied before a long-term record can beassembled. For tropospheric measurements,

Global Tropospheric
Temperature Anomaly (°F)

1.5
RSSv4.0
UAHv6.0
STARv4
RSSv3.3
UAHv5.6
STARv3

1.0
0.5
0.0
−0.5
1980

1990

2000

2010

Year
Figure A.1: Annual global (80°S–80°N) mean time series of tropospheric temperature for five recent datasets (seebelow). Each time series is adjusted so the mean value for the first three years is zero. This accentuates the differencesin the long-term changes between the datasets. (Figure source: Remote Sensing Systems).

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Climate Science Special ReportAppendix A | Observational Datasets Used in Climate Studies
Table A.1.: Global Trends in Temperature Total Troposphere (TTT)
since 1979 and 2000 (in °F per decade).
DatasetTrend (1979–2015) (°F/
Decade)Trend (2000–2015)(°F/Decade)

RSS V4.0

0.301

0.198

UAH V6Beta5

0.196

0.141

STAR V4.0

0.316

0.157

RSS V3.3

0.208

0.105

UAH V5.6

0.176

0.211

STAR V3.0

0.286

0.061

the most challenging of these adjustments isthe adjustment for drifting measurement time,
which requires knowledge of the diurnal cycle
in both atmospheric and surface temperature.
Current versions of the sounder-based datasets account for the diurnal cycle by either
using diurnal cycles deduced from model
output11, 12 or by attempting to derive thediurnal cycle from the satellite measurements
themselves (an approach plagued by samplingissues and possible calibration drifts).13, 14 Recently a hybrid approach has been developed,
RSS Version 4.0,6 that results in an increased
warming signal relative to the other approach-U.S. Global Change Research Program

es, particularly since 2000. Each of these methods has strengths and weaknesses, but none
has sufficient accuracy to construct an unassailable long-term record of atmospheric temperature change. The resulting datasets show
a greater spread in decadal-scale trends than
do the surface temperature datasets for thesame period, suggesting that they may be lessreliable. Figure A.1 shows annual time series
for the global mean tropospheric temperaturefor some recent versions of the satellite datasets. These data have been adjusted to remove
the influence of stratospheric cooling.15 Linear
trend values are shown in Table A.1.

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Climate Science Special ReportAppendix A | Observational Datasets Used in Climate Studies

DATA SOURCES
All Satellite Data are “Temperature Total Troposphere” time series calculated from TMT and TLS(1.1*TMT) - (0.1*TLS). This combination reduces the effectof the lower stratosphere on the tropospheric temperature.
(Fu, Qiang et al. “Contribution of stratospheric cooling tosatellite-inferred tropospheric temperature trends.” Nature
429.6987 (2004): 55-58.)

UAH. UAH Version 6.0Beta5. Yearly (yyyy) text filesof TMT and TLS are available fromhttps://www.nsstc.uah.edu/data/msu/v6.0beta/tmt/
https://www.nsstc.uah.edu/data/msu/v6.0beta/tls/
Downloaded 5/15/2016.

UAH. UAH Version 5.6. Yearly (yyyy) text files of
TMT and TLS are available fromhttp://vortex.nsstc.uah.edu/data/msu/t2/
http://vortex.nsstc.uah.edu/data/msu/t4/
Downloaded 5/15/2016.

RSS. RSS Version 4.0.
ftp://ftp.remss.com/msu/data/netcdf/RSS_Tb_Anom_Maps_
ch_TTT_V4_0.nc
Downloaded 5/15/2016

RSS. RSS Version 3.3.
ftp://ftp.remss.com/msu/data/netcdf/RSS_Tb_Anom_Maps_
ch_TTT_V3.3.nc
Downloaded 5/15/2016

NOAA STAR. Star Version 3.0.
ftp://ftp.star.nesdis.noaa.gov/pub/smcd/emb/mscat/data/
MSU_AMSU_v3.0/Monthly_Atmospheric_Layer_Mean_Temperature/Merged_Deep-Layer_Temperature/NESDIS-STAR_
TCDR_MSU-AMSUA_V03R00_TMT_S197811_E201709_
C20171002.nc
ftp://ftp.star.nesdis.noaa.gov/pub/smcd/emb/mscat/data/
MSU_AMSU_v3.0/Monthly_Atmospheric_Layer_Mean_Temperature/Merged_Deep-Layer_Temperature/NESDIS-STAR_
TCDR_MSU-AMSUA_V03R00_TLS_S197811_E201709_
C20171002.nc
Downloaded 5/18/2016.

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Vose, R.S., M. Squires, D. Arndt, I. Durre, C. Fenimore, K. Gleason, M.J. Menne, J. Partain, C.N. Williams Jr., P.A. Bieniek, and R.L. Thoman, 2017: Deriving historical temperature and precipitation timeseries for Alaska climate divisions via climatologically aided interpolation. Journal of Service Climatology
10, 20. https://www.stateclimate.org/sites/default/
files/upload/pdf/journal-articles/2017-Ross-etal.
pdf

6.

Mears, C.A. and F.J. Wentz, 2016: Sensitivity of satellite-derived tropospheric temperature trends to thediurnal cycle adjustment. Journal of Climate, 29, 36293646. http://dx.doi.org/10.1175/JCLI-D-15-0744.1

7.

Spencer, R.W., J.R. Christy, and W.D. Braswell, 2017:
UAH Version 6 global satellite temperature products: Methodology and results. Asia-Pacific Journalof Atmospheric Sciences, 53, 121-130. http://dx.doi.
org/10.1007/s13143-017-0010-y

U.S. Global Change Research Program

8.

Zou, C.-Z. and J. Li, 2014: NOAA MSU Mean Layer Temperature. National Oceanic and Atmospheric
Administration, Center for Satellite Applications
and Research, 35 pp. http://www.star.nesdis.noaa.
gov/smcd/emb/mscat/documents/MSU_TCDR_CATBD_Zou_Li.pdf

9.

Wahl, E.R. and J.E. Smerdon, 2012: Comparative performance of paleoclimate field and index reconstructions derived from climate proxies and noise-only
predictors. Geophysical Research Letters, 39, L06703.
http://dx.doi.org/10.1029/2012GL051086

10. Trouet, V., H.F. Diaz, E.R. Wahl, A.E. Viau, R. Graham, N. Graham, and E.R. Cook, 2013: A 1500-yearreconstruction of annual mean temperature for temperate North America on decadal-to-multidecadal
time scales. Environmental Research Letters, 8, 024008.
http://dx.doi.org/10.1088/1748-9326/8/2/024008
11. Mears, C.A. and F.J. Wentz, 2009: Construction of theRemote Sensing Systems V3.2 atmospheric temperature records from the MSU and AMSU microwave
sounders. Journal of Atmospheric and Oceanic Technology, 26, 1040-1056. http://dx.doi.org/10.1175/
2008JTECHA1176.1
12. Zou, C.-Z., M. Gao, and M.D. Goldberg, 2009: Error structure and atmospheric temperature trends
in observations from the microwave sounding unit.
Journal of Climate, 22, 1661-1681. http://dx.doi.
org/10.1175/2008JCLI2233.1
13. Christy, J.R., R.W. Spencer, W.B. Norris, W.D. Braswell, and D.E. Parker, 2003: Error estimates of version 5.0 of MSU–AMSU bulk atmospheric temperatures. Journal of Atmospheric and Oceanic Technology,
20, 613-629. http://dx.doi.org/10.1175/1520-0426(
2003)20<613:EEOVOM>2.0.CO;2
14. Po-Chedley, S., T.J. Thorsen, and Q. Fu, 2015: Removing diurnal cycle contamination in satellite-derived tropospheric temperatures: Understanding
tropical tropospheric trend discrepancies. Journal of
Climate, 28, 2274-2290. http://dx.doi.org/10.1175/
JCLI-D-13-00767.1
15. Fu, Q. and C.M. Johanson, 2005: Satellite-derived vertical dependence of tropical tropospheric temperature trends. Geophysical Research Letters, 32, L10703.
http://dx.doi.org/10.1029/2004GL022266

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Climate Science Special ReportAppendix B

Model Weighting
Strategy
Recommended Citation for Chapter
Sanderson, B.M. and M.F. Wehner, 2017: Model weighting strategy. In: Climate Science Special Report:
Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C.
Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp.
436-442, doi: 10.7930/J06T0JS3.

Introduction
This document briefly describes a weighting
strategy for use with the Climate Model Intercomparison Project, Phase 5 (CMIP5) multimodel archive in the Fourth National Climate
Assessment (NCA4). This approach considers
both skill in the climatological performanceof models over North America and the interdependency of models arising from common
parameterizations or tuning practices. The
method exploits information relating to theclimatological mean state of a number of projection-relevant variables as well as long-term
metrics representing long-term statistics ofweather extremes. The weights, once computed, can be used to simply compute weighted
mean and significance information from anensemble containing multiple initial condition members from co-dependent models ofvarying skill.
Our methodology is based on the concepts
outlined in Sanderson et al. 2015,1 and thespecific application to the NCA4 is also described in that paper. The approach produces
a single set of model weights that can be used
to combine projections into a weighted mean
result, with significance estimates which also
treat the weighting appropriately.

U.S. Global Change Research Program

The method, ideally, would seek to have two
fundamental characteristics:•

If a duplicate of one ensemble member isadded to the archive, the resulting meanand significance estimate for future change
computed from the ensemble should notchange.

•If a demonstrably unphysical model isadded to the archive, the resulting meanand significance estimates should also notchange.

MethodThe analysis requires an assessment of bothmodel skill and an estimate of intermodel
relationships—for which intermodel root
mean square difference is taken as a proxy.
The model and observational data used here
is for the contiguous United States (CONUS),
and most of Canada, using high-resolution
data where available. Intermodel distances arecomputed as simple root mean square differences. Data is derived from a number of meanstate fields and a number of fields that represent extreme behavior—these are listed inTable B.1. All fields are masked to only include
information from CONUS/Canada.

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Climate Science Special ReportAppendix B | Model Weighting Strategy
The root mean square error (RMSE) between
observations and each model can be used to
produce an overall ranking for model simu-

lations of the North American climate. Figure
B.1 shows how this metric is influenced bydifferent component variables.

Table B.1: Observational datasets used as observations.
Field

Description

SourceReference

Years

TS

Surface Temperature(seasonal)

Livneh,Hutchinson(Hopkinson et al. 2012;3 Hutchinson etal. 2009;4 Livneh et al. 20135)

1950–2011PR

Mean Precipitation(seasonal)

Livneh,Hutchinson(Hopkinson et al. 2012;3 Hutchinson etal. 2009;4 Livneh et al. 20135)

1950–2011

RSUT

TOA Shortwave Flux
(seasonal)

CERES-EBAF(Wielicki et al. 19966)

2000–2005

RLUT

TOA Longwave Flux
(seasonal)

CERES-EBAF(Wielicki et al. 19966)

2000–2005

T

Vertical Temperature
Profile (seasonal)

AIRS*(Aumann et al. 20037)

2002–2010

RH

Vertical Humidity Profile (seasonal)

AIRS(Aumann et al. 20037)

2002–2010

PSL

Surface Pressure(seasonal)

ERA-40(Uppala et al. 20058)

1970–2000

Tnn

Coldest Night

Livneh,Hutchinson(Hopkinson et al. 2012;3 Hutchinson etal. 2009;4 Livneh et al. 20135)

1950–2011

Txn

Coldest Day

Livneh,Hutchinson(Hopkinson et al. 2012;3 Hutchinson etal. 2009;4 Livneh et al. 20135)

1950–2011

Tnx

Warmest Night

Livneh,Hutchinson(Hopkinson et al. 2012;3 Hutchinson etal. 2009;4 Livneh et al. 20135)

1950–2011

Txx

Warmest day

Livneh,Hutchinson(Hopkinson et al. 2012;3 Hutchinson etal. 2009;4 Livneh et al. 20135)

1950–2011seasonal max. 5-day
total precip.

Livneh,Hutchinson(Hopkinson et al. 2012;3 Hutchinson etal. 2009;4 Livneh et al. 20135)

1950–2011

rx5dayU.S. Global Change Research Program

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Climate Science Special ReportAppendix B | Model Weighting Strategy
North America Skill (all seasons)
psl

1.4

rlut

1.3

rsut

1.2

tas
ta

1.1

hur

1

rx5day

0.9

tnn

0.8

tnx
txn

0.7

txx

0.6

CESM1_CAM5
ACCESS1_0
CCSM4
MPI_ESM_P
CESM1_BGC
HadGEM2_ES
ACCESS1_3
MPI_ESM_LR
MPI_ESM_MR
MRI_CGCM3
MRI_ESM1
MIROC5
CMCC_CMS
HadGEM2_CC
CMCC_CM
HadGEM2_AO
GFDL_CM3
inmcm4
CNRM_CM5
NorESM1_M
IPSL_CM5A_MR
GISS_E2_R_p1
IPSL_CM5A_LR
bcc_csm1_1_m
GISS_E2_R_p2
HadCM3CSIRO_Mk3_6_0
BNU_ESM
GISS_E2_H_p1
MIROC4h
GISS_E2_H_p2
CanESM2
IPSL_CM5B_LR
bcc_csm1_1
GFDL_ESM2M
GFDL_ESM2G
CESM1_FASTCHEM
FGOALS_g2
CMCC_CESMMIROC_ESM_CHEM
MIROC_ESM

Combined

Distance from obs (relative to CMIP mean)

1.5

pr

0.5

Figure B.1: A graphical representation of the intermodel distance matrix for CMIP5 and a set of observed values. Eachrow and column represents a single climate model (or observation). All scores are aggregated over seasons (individual
seasons are not shown). Each box represents a pairwise distance, where warm (red) colors indicate a greater distance.
Distances are measured as a fraction of the mean intermodel distance in the CMIP5 ensemble. (Figure source: Sanderson et al. 20172).

1.3

1.1

0.9

0.7

1.8

1.6
1

0.
9

0.7

1.4

1.2
0.

0.

7

5

1.0

1.1

0.9

0.8

3

0.

North American Skill Weight

3

1.

0.5

1.

0.7

0.5

0.6

0.3

0.5

0.4

0.3
0.1

0.4

0.5

0.6

0.7

0.8

0.9

1.0

HadGEM2_AO
HadGEM2_CC
HadGEM2_ES
IPSL_CM5B_LR
MRI_CGCM3
MRI_ESM1
inmcm4
CMCC_CESM
CMCC_CM
CMCC_CMSGFDL_CM3
GFDL_ESM2GGFDL_ESM2M
IPSL_CM5A_LR
IPSL_CM5A_MR
MPI_ESM_LR
MPI_ESM_MR
MPI_ESM_P
CESM1_CAM5
NorESM1_M
bcc_csm1_1_m
MIROC_ESM
MIROC_ESM_CHEM
MIROC4h
CNRM_CM5CSIRO_Mk3_6_0
MIROC5ACCESS1_0
ACCESS1_3
CCSM4
CESM1_BGC
CESM1_FASTCHEM
CanESM2
BNU_ESMbcc_csm1_1
GISS_E2_H_p1
GISS_E2_H_p2
GISS_E2_R_p1
GISS_E2_R_p2
FGOALS_g2
HadCM3
Combined Weight

Figure B.2: Model skill and independence weights for the CMIP5 archive evaluated over the North American domain.
Contours show the overall weighting, which is the product of the two individual weights. (Figure source: Sanderson
et al. 20172).

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438

Climate Science Special ReportAppendix B | Model Weighting Strategy
Models are downweighted for poor skill if
their multivariate combined error is significantly greater than a “skill radius” term,
which is a free parameter of the approach. The
calibration of this parameter is determinedthrough a perfect model study.2 A pairwise
distance matrix is computed to assess intermodel RMSE values for each model pair in
the archive, and a model is downweighted for
dependency if there exists another model witha pairwise distance to the original model significantly smaller than a “similarity radius.”
This is the second parameter of the approach,which is calibrated by considering known
relationships within the archive. The resulting
skill and independence weights are multiplied
to give an overall “combined” weight—illustrated in Figure B.2 for the CMIP5 ensemble
and listed in Table B.2.
The weights are used in the Climate Science
Special Report (CSSR) to produce weighted
mean and significance maps of future change,
where the following protocol is used:•

Stippling—large changes, where theweighted multimodel average change isgreater than double the standard deviationof the 20-year mean from control simulations runs, and 90% of the weight corresponds to changes of the same sign.

•

Hatching—No significant change, where
the weighted multimodel average change
is less than the standard deviation of the 20year means from control simulations runs.

•

Whited out—Inconclusive, where theweighted multimodel average change isgreater than double the standard deviationof the 20-year mean from control runs andless than 90% of the weight corresponds tochanges of the same sign.

U.S. Global Change Research Program

We illustrate the application of this method
to future projections of precipitation change
under the higher scenario (RCP8.5) in Figure
B.3. The weights used in the report are chosen to be conservative, minimizing the risk ofoverconfidence and maximizing out-of-sample predictive skill for future projections. This
results (as in Figure B.3) in only modest differences in the weighted and unweighted maps.
It is shown in Sanderson et al. 20172 that amore aggressive weighting strategy, or one focused on a particular variable, tends to exhibit
a stronger constraint on future change relative
to the unweighted case. It is also notable that
tradeoffs exist between skill and replication in
the archive (evident in Figure B.2), such thatthe weighting for both skill and uniquenesshas a compensating effect. As such, mean
projections using the CMIP5 ensemble are notstrongly influenced by the weighting. However, the establishment of the weighting strategy
used in the CSSR provides some insurance
against a potential case in future assessments
where there is a highly replicated, but poorly
performing model.

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Climate Science Special ReportAppendix B | Model Weighting Strategy
Table B.2: Uniqueness, skill, and combined weights for CMIP5.
Uniqueness Weight

Skill Weight

Combined

ACCESS1-0

0.60

1.69

1.02

ACCESS1-3

0.78

1.40

1.09

BNU-ESM

0.88

0.77

0.68

CCSM4

0.43

1.57

0.68

CESM1-BGC

0.44

1.46

0.64

CESM1-CAM5

0.72

1.80

1.30

CESM1-FASTCHEM

0.76

0.50

0.38

CMCC-CESM

0.98

0.36

0.35

CMCC-CM

0.89

1.21

1.07

CMCC-CMS

0.59

1.23

0.73

CNRM-CM5

0.94

1.08

1.01

CSIRO-Mk3-6-0

0.95

0.77

0.74

CanESM2

0.97

0.65

0.63

FGOALS-g2

0.97

0.39

0.38

GFDL-CM3

0.81

1.18

0.95

GFDL-ESM2G

0.74

0.59

0.44

GFDL-ESM2M

0.72

0.60

0.43

GISS-E2-H-p1

0.38

0.74

0.28

GISS-E2-H-p2

0.38

0.69

0.26

GISS-E2-R-p1

0.38

0.97

0.37

GISS-E2-R-p2

0.37

0.89

0.33

HadCM3

0.98

0.89

0.87

HadGEM2-AO

0.52

1.19

0.62

HadGEM2-CC

0.50

1.21

0.60

HadGEM2-ES

0.43

1.40

0.61

IPSL-CM5A-LR

0.79

0.92

0.72

IPSL-CM5A-MR

0.83

0.99

0.82

IPSL-CM5B-LR

0.92

0.63

0.58

MIROC-ESM

0.54

0.28

0.15

MIROC-ESM-CHEM

0.54

0.32

0.17

MIROC4h

0.97

0.73

0.71

MIROC5

0.89

1.24

1.11

MPI-ESM-LR

0.35

1.38

0.49

MPI-ESM-MR

0.38

1.37

0.52

MPI-ESM-P

0.36

1.54

0.56

MRI-CGCM3

0.51

1.35

0.68

MRI-ESM1

0.51

1.31

0.67

NorESM1-M

0.83

1.06

0.88

bcc-csm1-1

0.88

0.62

0.55

bcc-csm1-1-m

0.90

0.89

0.80

inmcm4

0.95

1.13

1.08

U.S. Global Change Research Program

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Climate Science Special ReportAppendix B | Model Weighting Strategy(a) Unweighted
best estimate

80
60
40
20
0

200

220

240

260

280

300

320

300

320

(b) Independence Weighted
best estimate

80
60
40
20
0

80

200

220

240

260

280

(c) Skill+Independence Weighted
best estimate

60
40
20
0

200

220

240

260

280

300

320

−3
–2
–1
0
1
2
3
Precipition change in mm/day (2080–2100)-(1980–2000)
Figure B.3: Projections of precipitation change over North America in 2080–2100, relative to 1980–2000 under the higher
scenario (RCP8.5). (a) Shows the simple unweighted CMIP5 multimodel average, using the significance methodology
from IPCC9; (b) shows the weighted results as outlined in Section 3 for models weighted by uniqueness only; and (c)shows weighted results for models weighted by both uniqueness and skill. (Figure source: Sanderson et al. 20172).

U.S. Global Change Research Program

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Climate Science Special ReportAppendix B | Model Weighting Strategy
REFERENCES
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Sanderson, B.M., R. Knutti, and P. Caldwell, 2015: A
representative democracy to reduce interdependency
in a multimodel ensemble. Journal of Climate, 28, 51715194. http://dx.doi.org/10.1175/jcli-d-14-00362.1

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Sanderson, B.M., M. Wehner, and R. Knutti, 2017:
Skill and independence weighting for multi-model
assessment. Geoscientific Model Development, 10, 23792395. http://dx.doi.org/10.5194/gmd-10-2379-2017

3.

Hopkinson, R.F., M.F. Hutchinson, D.W. McKenney,
E.J. Milewska, and P. Papadopol, 2012: Optimizing
input data for gridding climate normals for Canada.
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Hutchinson, M.F., D.W. McKenney, K. Lawrence, J.H.
Pedlar, R.F. Hopkinson, E. Milewska, and P. Papadopol, 2009: Development and testing of Canada-wide
interpolated spatial models of daily minimum–maximum temperature and precipitation for 1961–2003.
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Livneh, B., E.A. Rosenberg, C. Lin, B. Nijssen, V.
Mishra, K.M. Andreadis, E.P. Maurer, and D.P. Lettenmaier, 2013: A long-term hydrologically based
dataset of land surface fluxes and states for the conterminous United States: Update and extensions.
Journal of Climate, 26, 9384-9392. http://dx.doi.
org/10.1175/jcli-d-12-00508.1

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Wielicki, B.A., B.R. Barkstrom, E.F. Harrison, R.B. Lee
III, G.L. Smith, and J.E. Cooper, 1996: Clouds and theEarth’s Radiant Energy System (CERES): An Earthobserving system experiment. Bulletin of the American Meteorological Society, 77, 853-868. http://dx.doi.
org/10.1175/1520-0477(1996)077<0853:catere>2.0.
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Aumann, H.H., M.T. Chahine, C. Gautier, M.D. Goldberg, E. Kalnay, L.M. McMillin, H. Revercomb, P.W.
Rosenkranz, W.L. Smith, D.H. Staelin, L.L. Strow,
and J. Susskind, 2003: AIRS/AMSU/HSB on the
Aqua mission: Design, science objectives, data products, and processing systems. IEEE Transactions on
Geoscience and Remote Sensing, 41, 253-264. http://dx.doi.org/10.1109/tgrs.2002.808356

8.

Uppala, S.M., P.W. KÅllberg, A.J. Simmons, U. Andrae, V.D.C. Bechtold, M. Fiorino, J.K. Gibson, J.
Haseler, A. Hernandez, G.A. Kelly, X. Li, K. Onogi,
S. Saarinen, N. Sokka, R.P. Allan, E. Andersson, K.
Arpe, M.A. Balmaseda, A.C.M. Beljaars, L.V.D. Berg,
J. Bidlot, N. Bormann, S. Caires, F. Chevallier, A. Dethof, M. Dragosavac, M. Fisher, M. Fuentes, S. Hagemann, E. Hólm, B.J. Hoskins, L. Isaksen, P.A.E.M.
Janssen, R. Jenne, A.P. McNally, J.F. Mahfouf, J.J.
Morcrette, N.A. Rayner, R.W. Saunders, P. Simon, A.
Sterl, K.E. Trenberth, A. Untch, D. Vasiljevic, P. Viterbo, and J. Woollen, 2005: The ERA-40 re-analysis.
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IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
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Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. http://
www.climatechange2013.org/report/

442

Climate Science Special ReportAppendix C

Detection and Attribution
Methodologies Overview
Recommended Citation for Chapter
Knutson, T., 2017: Detection and attribution methodologies overview. In: Climate Science Special Report:
Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C.
Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp.
443-451, doi: 10.7930/J0319T2J.

C.1 Introduction and Conceptual
Framework
In this appendix, we present a brief overview
of the methodologies and methodologicalissues for detection and attribution of climatechange. Attributing an observed change or an
event partly to a causal factor (such as anthropogenic climate forcing) normally requiresthat the change first be detectable.1 A detectable
observed change is one which is determined
to be highly unlikely to occur (less than about
a 10% chance) due to internal variability alone,
without necessarily being ascribed to a causal
factor. An attributable change refers to a change
in which the relative contribution of causal
factors has been evaluated along with an assignment of statistical confidence (e.g., Bindoff
et al. 2013;2 Hegerl et al. 20101).
As outlined in Bindoff et al.,2 the conceptual
framework for most detection and attribution
studies consists of four elements: 1) relevant
observations; 2) the estimated time history of
relevant climate forcings (such as greenhousegas concentrations or volcanic activity); 3) amodeled estimate of the impact of the climate
forcings on the climate variables of interest;and 4) an estimate of the internal (unforced)
variability of the climate variables of inter-

U.S. Global Change Research Program

est—that is, the changes that can occur due
to natural unforced variations of the ocean,
atmosphere, land, cryosphere, and other elements of the climate system in the absence of
external forcings. The four elements above canbe used together with a detection and attribution framework to assess possible causes ofobserved changes.

C.2 Fingerprint-Based Methods
A key methodological approach for detection
and attribution is the regression-based “fingerprint” method (e.g., Hasselmann 1997;3
Allen and Stott 2003;4 Hegerl et al. 2007;5
Hegerl and Zwiers 2011;6 Bindoff et al. 20132),
where observed changes are regressed onto amodel-generated response pattern to a particular forcing (or set of forcings), and regression
scaling factors are obtained. When a scaling
factor for a forcing pattern is determined to besignificantly different from zero, a detectablechange has been identified. If the uncertainty
bars on the scaling factor encompass unity,
the observed change is consistent with themodeled response, and the observed change
can be attributed, at least in part, to the associated forcing agent, according to this methodology. Zwiers et al.7 showed how detection
and attribution methods could be applied

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Climate Science Special ReportAppendix C | Detection and Attribution Methodologies Overview
to the problem of changes in daily temperature extremes at the regional scale by using ageneralized extreme value (GEV) approach.
In their approach, a time-evolving pattern of
GEV location parameters (i.e., “fingerprint”)
from models is fit to the observed extremes asa means of detecting and attributing changes in the extremes to certain forcing sets (forexample, anthropogenic forcings).

forcings, are interpreted as having an attributable anthropogenic contribution, subject tocaveats regarding uncertainties in observations, climate forcings, modeled responses,
and simulated internal climate variability. This
simpler method is useful for assessing trends
over smaller regions such as sub-regions of theUnited States (see the example given in Figure
6.5 for regional surface temperature trends).

A recent development in detection/attribution
methodology8 uses hypothesis testing andan additive decomposition approach rather
than linear regression of patterns. The new
approach makes use of the magnitudes of
responses from the models rather than using
the model patterns and deriving the scaling
factors (magnitudes of responses) from regression. The new method, in a first application,
gives very similar attributable anthropogenic
warming estimates to the earlier methods asreported in Bindoff et al.2 and shown in Figure
3.2. Some further methodological developments for performing optimal fingerprint
detection and attribution studies are proposedin Hannart,9 who, for example, focuses on thepossible use of raw data in analyses without
the use of dimensional reductions, such as
projecting the data onto a limited number ofbasis functions, such as spherical harmonics,
before analysis.

Delsole et al.12 introduced a method of identifying internal (unforced) variability in climate
data by decomposing variables by time scale,using a measure of their predictability. They
found that while such internal variability
could contribute to surface temperature trendsof 30-years’ duration or less, and could be
responsible for the accelerated global warming during 1977–2008 compared to earlier
decades, the strong (approximately 0.8°C, or
1.4°F) warming trend seen in observations
over the past century was not explainable bysuch internal variability. Constructed circulation analogs13, 14 is a method used to identify
the part of observed surface temperaturechanges that is due to atmospheric circulation
changes alone.

C.3 Non-Fingerprint Based Methods
A simpler detection/attribution/consistency
calculation, which does not involve regression and pattern scaling, compares observed
and simulated time series to assess whether observations are consistent with naturalvariability simulations or with simulations
forced by both natural and anthropogenic
forcing agents.10, 11 Cases where observations
are inconsistent with model simulations using
natural forcing only (a detectable change),
while also being consistent with models thatincorporate both anthropogenic and natural

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The time scale by which climate change signals will become detectable in various regions
is a question of interest in detection andattribution studies, and methods of estimating
this have been developed and applied (e.g.,
Mahlstein et al. 2011;15 Deser et al. 201216).
These studies illustrate how natural variability
can obscure forced climate signals for decades,
particularly for smaller (less than continental)
space scales.
Other examples of detection and attribution
methods include the use of multiple linear
regression with energy balance models (e.g.,
Canty et al. 201317) and Granger causality tests(e.g., Stern and Kaufmann 201418). These are
typically attempting to relate forcing time

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series, such as the historical record of atmospheric CO2 since 1860, to a climate response
measure, such as global mean temperature orocean heat content, but without using a full
coupled climate model to explicitly estimate
the response of the climate system to forcing(or the spatial pattern of the response to forcing). Granger causality, for example, explores
the lead–lag relationships between different
variables to infer causal relationships between
them and attempts to control for any influenceof a third variable that may be linked to theother two variables in question.

C.4 Multistep Attribution and Attribution
without Detection
A growing number of climate change and extreme event attribution studies use a multistep
attribution approach,1 based on attribution ofa change in climate conditions that are closely
related to the variable or event of interest. In
the multistep approach, an observed change
in the variable of interest is attributed to achange in climate or other environmental conditions, and then the changes in the climate orenvironmental conditions are separately attributed to an external forcing, such as anthropogenic emissions of greenhouse gases. As
an example, some attribution statements forphenomena such as droughts or hurricane activity—where there are not necessarily detectable trends in occurrence of the phenomenon
itself—are based on models and on detectedchanges in related variables such as surface
temperature, as well as an understanding of
the relevant physical processes linking surface
temperatures to hurricanes or drought. For
example, some studies of the recent California
drought (e.g., Mao et al. 2015;19 Williams et al.
201520) attribute a fraction of the event to anthropogenic warming or to long-term warming based on modeling or statistical analysis,
although without claiming that there was adetectable change in the drought frequency ormagnitude.

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The multistep approach and model simulations are both methods that, in principle, canallow for attribution of a climate change or achange in the likelihood of occurrence of anevent to a causal factor without necessarily detecting a significant change in the occurrence
rate of the phenomenon or event itself (though
in some cases, there may also be a detectablechange in the variable of interest). For example, Murakami et al.21 used model simulations
to conclude that the very active hurricane
season observed near Hawai‘i in 2014 was atleast partially attributable to anthropogenic
influence; they also show that there is no clear
long-term detectable trend in historical hurricane occurrence near Hawai‘i in available
observations. If an attribution statement ismade where there is not a detectable change inthe phenomenon itself (for example, hurricane
frequency or drought frequency) then thisstatement is an example of attribution without
detection. Such an attribution without detection can be distinguished from a conventional
single-step attribution (for example, global
mean surface temperature) where in the lattercase there is a detectable change in the variable of interest (or the scaling factor for a forcing pattern is significantly different from zeroin observations) and attribution of the changes
in that variable to specific external forcing
agents. Regardless of whether a single-stepor multistep attribution approach is used, orwhether there is a detectable change in thevariable of interest, attribution statementswith relatively higher levels of confidence areunderpinned by a thorough understanding of
the physical processes involved.
There are reasons why attribution without
detection statements can be appropriate,
despite the lower confidence typically associated with such statements as compared
to attribution statements that are supported
by detection of a change in the phenomenon
itself. For example, an event of interest may be

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so rare that a trend analysis for similar events
is not practical. Including attribution without
detection events in the analysis of climatechange impacts reduces the chances of a false
negative, that is, incorrectly concluding thatclimate change had no influence on a given
extreme events22 in a case where it did havean influence. However, avoiding this type oferror through attribution without detection
comes at the risk of increasing the rate of false
positives, where one incorrectly concludes
that anthropogenic climate change had acertain type of influence on an extreme event
when in fact it did not have such an influence(see Box C.1).

in two climate states: one state with anthropogenic influence (where the probability isp1) and the other state without anthropogenic
influence (where the probability is p0). Thenthe ratio (p1/p0) describes how much more orless likely the event is in the modeled climate
with anthropogenic influence compared to amodeled hypothetical climate without anthropogenic influences. Another common
metric used with this approach is the fractionof attributable risk (FAR), defined as FAR =1 – (p0/p1). Further refinements on such an
approach using causal theory are discussed in
Hannart et al. 29

C.5 Extreme Event Attribution
Methodologies
Since the release of the Intergovernmental
Panel on Climate Change’s Fifth Assessment
Report (IPCC AR5) and the Third National
Climate Assessment (NCA3),23 there have beenfurther advances in the science of detection
and attribution of climate change. An emerging area in the science of detection and attribution is the attribution of extreme weather andclimate events.24, 25, 26 According to Hulme,27
there are four general types of attribution
methods that are applied in practice: physicalreasoning, statistical analysis of time series,
fraction of attributable risk (FAR) estimation,and the philosophical argument that there are
no longer any purely natural weather events.
As discussed in a recent National Academyof Sciences report,24 possible anthropogenic
influence on an extreme event can be assessedusing a risk-based approach, which examines
whether the odds of occurrence of a type of
extreme event have changed, or through an
ingredients-based or conditional attribution
approach.

In the conditional or ingredients-based approach,24, 30, 31, 32 an investigator may look forchanges in occurrence of atmospheric circulation and weather patterns relevant to theextreme event, or at the impact of certain environmental changes (for example, greater atmospheric moisture) on the character of an extreme event. Conditional or ingredients-based
attribution can be applied to extreme eventsor to climate changes in general. An exampleof the ingredients-based approach and morediscussion of this type of attribution method isgiven in Box C.2.
Hannart et al.29 have discussed how causal theory can also be applied to attribution
studies in order to distinguish between necessary and sufficient causation. Hannart et al.33
further propose methodologies to use data
assimilation systems, which are now used
operationally to update short-term numerical
weather prediction models, for detection andattribution. They envision how such systems
could be used in the future to implement
near-real time systematic causal attribution of
weather and climate-related events.

In the risk-based approach,24, 27, 28 one typicallyuses a model to estimate the probability (p) of
occurrence of a weather or climate event with-

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Box C.1. On the Use of Significance Levels and Significance Tests in
Attribution Studies
In detection/attribution studies, a detectable observed change is one which is determined to be highly unlikely
to occur (less than about a 10% chance) due to internal variability alone. Some frequently asked questions concern the use of such a high statistical threshold (significance level) in attribution studies. In this box, we respondto several such questions received in the public review period.
Why is such a high degree of confidence (for example, statistical significance at p level of 0.05) typically required
before concluding that an attributable anthropogenic component to a climate change or event has been detected? For example, could attribution studies be reframed to ask whether there is a 5% or more chance that anthropogenic climate change contributed to the event?
This question is partly related to the issue of risk avoidance. For example, if there is a particular climate change
outcome that we wish to avoid (for example, global warming of 3°C, or 10°C, or a runaway greenhouse) then one
can use the upper ranges of confidence intervals of climate model projections as guidance, based on available
science, for avoiding such outcomes. Detection/attribution studies typically deal with smaller changes than climate projections over the next century or more. For detection/attribution studies, researchers are confronting
models with historical data to explore whether or not observed climate change signals are emerging from thebackground of natural variability. Typically, the emergent signal is just a small fraction of what is predicted by
the models for the coming century under continued strong greenhouse gas emission scenarios. Detecting thata change has emerged from natural variability is not the same as approaching a threshold to be avoided, unlessthe goal is to ensure no detectable anthropogenic influence on climate. Consequently, use of a relative strong
confidence level (or p-value of 0.05) for determining climate change detection seems justified for the particularcase of climate change detection, since one can also separately use risk-avoidance strategies or probability criteria to avoid reaching certain defined thresholds (for example, a 2°C global warming threshold).
A related question concerns ascribing blame for causing an extreme event. For example, if a damaging hurricane
or typhoon strikes an area and causes much damage, affected residents may ask whether human-caused climatechange was at least partially to blame for the event. In this case, climate scientists sometimes use the “Fractionof Attributable Risk” framework, where they examine whether the odds of some threshold event occurring havebeen increased due to anthropogenic climate change. This is typically a model-based calculation, where theprobability distribution related to the event in question is modeled under preindustrial and present-day climate
conditions, and the occurrence rates are compared for the two modeled distributions. Note that such an analysis
can be done with or without the detection of a climate change signal for the occurrence of the event in question.
In general, cases where there has been a detection and attribution of changes in the event in question to humancauses, then the attribution of increased risk to anthropogenic forcing will be relatively more confident.
The question of whether it is more appropriate to use approaches that incorporate a high burden of statisticalevidence before concluding that anthropogenic forcings contributed significantly (as in traditional detection/
attribution studies) versus using models to estimate anthropogenic contributions when there may not even bea detectable signal present in the observations (as in some Fraction of Attributable Risk studies) may depend on
what type of error or scenario one most wants to avoid. In the former case, one is attempting to avoid the errorof concluding that anthropogenic forcing has contributed to some observed climate change, when in fact, it later
turns out that anthropogenic forcing has not contributed to the change. In the second case, one is attempting
to avoid the “error” of concluding that anthropogenic forcing has not contributed significantly to an observed

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climate change or event when (as it later comes to be known) anthropogenic forcing had evidently contributed
to the change, just not at a level that was detectable at the time compared to natural variability.
What is the tradeoff between false positives and false negatives in attribution statistical testing, and how is it
decided which type of error one should focus on avoiding?
As discussed above, there are different types of errors or scenarios that we would ideally like to avoid. However,the decision of what type of analysis to do may involve a tradeoff where one decides that it is more important
to avoid either falsely concluding that anthropogenic forcing has contributed, or to avoid falsely concluding thatanthropogenic forcing had not made a detectable contribution to the event. Since there is no correct answer
that can apply in all cases, it would be helpful if, in requesting scientific assessments, policymakers provide some
guidance about which type of error or scenario they would most desire be avoided in the analyses and assessments in question.
Since substantial anthropogenic climate change (increased surface temperatures, increased atmospheric water
vapor, etc.) has already occurred, aren’t all extreme events affected to some degree by anthropogenic climatechange?
Climate scientists are aware from modeling experiments that very tiny changes to initial conditions in model
simulations lead to very different realizations of internal climate variability “noise” in the model simulations.
Comparing large samples of this random background noise from models against observed changes is one way totest whether the observed changes are statistically distinguishable from internal climate variability. In any case,this experience also teaches us that any anthropogenic influence on climate, no matter how tiny, has some effecton the future trajectory of climate variability, and thus could affect the timing and occurrence of extreme events.
More meaningful questions are: 1) Has anthropogenic forcing produced a statistically significant change in theprobability of occurrence of some class of extreme event? 2) Can we determine with confidence the net sign ofinfluence of anthropogenic climate change on the frequency, intensity, etc., of a type of extreme event? 3) Can
climate scientists quantify (with credible confidence intervals) the effect of climate change on the occurrence
frequency, the intensity, or some other aspect of an observed extreme event?

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Box C.2 Illustration of Ingredients-based Event Attribution: The Case of
Hurricane Sandy
To illustrate some aspects of the conditional or ingredients-based attribution approach, the case of Hurricane
Sandy can be considered. If one considers Hurricane Sandy’s surge event, there is strong evidence that sea levelrise, at least partly anthropogenic in origin (see Ch. 12: Sea Level Rise), made Sandy’s surge event worse, all otherfactors being equal.34 The related question of whether anthropogenic climate change increased the risk of anevent like Sandy involves not just the sea level ingredient to surge risk but also whether the frequency and/or
intensity of Sandy-like storms has increased or decreased as a result of anthropogenic climate change. This latterquestion is more difficult and is briefly reviewed here.
A conditional or ingredients-based attribution approach, as applied to a hurricane event such as Sandy, may assume that the weather patterns in which the storm was embedded—and the storm itself—could have occurredin a preindustrial climate, and the event is re-simulated while changing only some aspects of the large-scale
environment (for example, sea surface temperatures, atmospheric temperatures, and moisture) by an estimatedanthropogenic climate change signal. Such an approach thus explores whether anthropogenic climate change
to date has, for example, altered the intensity of a Hurricane Sandy-like storm, assuming the occurrence of a
Sandy-like storm in both preindustrial and present-day climates. Modeling studies show, as expected, that theanomalously warm sea surface temperatures off the U.S. East Coast during Sandy led to a substantially more
intense simulated storm than under present-day climatological conditions.35 However, these anomalous sea surface temperatures and other environmental changes are a mixture of anthropogenic and natural influences, andso it is not generally possible to infer the anthropogenic component from such experiments. Another study36
modeled the influence of just the anthropogenic changes to the thermodynamic environment (including sea
surface temperatures, atmospheric temperatures, and moisture perturbations) and concluded that anthropogenic climate change to date had caused Hurricane Sandy to be about 5 hPa more intense, but that this modeledchange was not statistically significant at the 95% confidence level. A third study used a statistical–dynamical
model to compare simulated New York City-area tropical cyclones in pre-anthropogenic and anthropogenic time
periods.34 It concluded that there have been anthropogenically induced increases in the types of tropical cyclones that cause extreme surge events in the region, apart from the effects of sea level rise, such as increased
radius of maximum winds in the anthropogenic era. However, the statistical–dynamical model used in the study
simulates an unusually large increase in global tropical cyclone activity in 21st century projections37 compared toother tropical cyclone modeling studies using dynamical models—a number of which simulate future decreases
in late 21st century tropical storm frequency in the Atlantic basin (e.g., Christensen et al. 201338). This range of
uncertainty among various model simulations of Atlantic tropical cyclone activity under climate change imply
that there is low confidence in determining the net impact to date of anthropogenic climate change on the riskof Sandy-like events, though anthropogenic sea level rise, all other things equal, has increased the surge risk.
In summary, while there is agreement that sea level rise alone has caused greater storm surge risk in the New
York City area, there is low confidence on whether a number of other important determinants of storm surge
climate risk, such as the frequency, size, or intensity of Sandy-like storms in the New York region, have increased
or decreased due to anthropogenic warming to date.

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Acronyms and Unitsdoi: 10.7930/J0ZC811D
AGCM

atmospheric general circulation model

AIS

Antarctic Ice Sheet

AMO

Atlantic Multidecadal Oscillation

AMOC

Atlantic meridional overturning circulation

AMSU

Advanced Microwave Sounding Unit

AO

Arctic Oscillation

AOD

aerosol optical depthARatmospheric riverAW

Atlantic Water

BAMS

Bulletin of the American Meteorological SocietyBCblack carbon

BCE

Before Common Era

CAM5Community Atmospheric Model, Version 5

CAPE

convective available potential energy

CCN

cloud condensation nuclei

CCSM3

Community Climate System Model, Version 3

CDR

carbon dioxide removal

CE

Common Era

CENRS

Committee on Environment, Natural Resources, and Sustainability (National
Science and Technology Council, White House)

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CESM-LE

Community Earth System Model Large Ensemble Project

CFCs

chlorofluorocarbons

CIclimate intervention

CMIP5

Coupled Model Intercomparison Project, Fifth Phase (also CMIP3 and CMIP6)

CONUS

contiguous United States

CP

Central Pacific

CSSR

Climate Science Special ReportDIC

dissolved inorganic carbon

DJF

December-January-February

DoD SERDP

U.S. Department of Defense, Strategic Environmental Research andDevelopment Program

DOE

U.S. Department of Energy

EAIS

East Antarctic Ice Sheet

ECSequilibrium climate sensitivity

ENSO

El Niño–Southern Oscillation

EOF analysis

empirical orthogonal function analysis

EP

Eastern Pacific

ERF

effective radiative forcing

ESD

empirical statistical downscaling

ESDM

empirical statistical downscaling model

ESM

Earth System Model

ESS

Earth system sensitivity

ETC

extratropical cyclone

ETCCDI

Expert Team on Climate Change Detection Indices

GBI

Greenland Blocking Index

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GCIS

Global Change Information System

GCM

global climate model

GeoMIP

Geoengineering Model Intercomparison Project

GFDL HiRAM

Geophysical Fluid Dynamics Laboratory, global HIgh Resolution Atmospheric
Model (NOAA)

GHCN

Global Historical Climatology Network (National Centers for Environmental
Information, NOAA)

GHG

greenhouse gas

GMSL

global mean sea level

GMT

global mean temperatureGPS

global positioning system

GRACE

Gravity Recovery and Climate Experiment

GrIS

Greenland Ice Sheet

GWP

global warming potential

HadCM3

Hadley Centre Coupled Model, Version 3

HadCRUT4

Hadley Centre Climatic Research Unit Gridded Surface Temperature Dataset 4

HCFCs

hydrochlorofluorocarbons

HFCs

hydrofluorocarbons

HOT

Hawai‘i Ocean Time-series

HOT-DOGS

Hawai‘i Ocean Time-series Data Organization & Graphical System

HURDAT2

revised Atlantic Hurricane Database (National Hurricane Center, NOAA)

IAM

integrated assessment model

IAV

impacts, adaptation, and vulnerability

INMCM

Institute for Numerical Mathematics Climate Model

IPCC

Intergovernmental Panel on Climate Change

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IPCC AR5

Fifth Assessment Report of the IPCC; also SPM—Summary for Policymakers,
and WG1, WG2, WG3—Working Groups 1–3

IPOInterdecadal Pacific Oscillation

IVT

integrated vapor transport

JGOFS

U.S. Joint Global Ocean Flux Study

JJA

June-July-August

JTWC

Joint Typhoon Warning Center

LCC

land-cover changes

LULCC

land-use and land-cover change

MAM

March-April-May

MSU

Microwave Sounding Unit

NAM

Northern Annular Mode

NAO

North Atlantic Oscillation

NARCCAP

North American Regional Climate Change Assessment Program (World
Meteorological Organization)

NAS

National Academy of Sciences

NASA

National Aeronautics and Space Administration

NCA

National Climate Assessment

NCA3

Third National Climate Assessment

NCA4

Fourth National Climate Assessment

NCEI

National Centers for Environmental Information (NOAA)

NDC

nationally determined contribution

NOAA

National Oceanic and Atmospheric Administration

NPI

North Pacific Index

NPO

North Pacific oscillation

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NPP

net primary production

OMZs

oxygen minimum zones

OSTP

Office of Science and Technology Policy (White House)

PCA

principle component analysis

PDO

Pacific Decadal Oscillation

PDSI

Palmer Drought Severity Index

PETM

Paleo-Eocene Thermal Maximum

PFCs

perfluorocarbons

PGW

pseudo-global warming

PNA

Pacific North American Pattern

RCM

regional climate models

RCP

Representative Concentration Pathway

RFradiative forcing

RFaci

aerosol–cloud interaction (effect on RF)

RFariaerosol–radiation interaction (effect on RF)

RMSE

root mean square error

RSL

relative sea level

RSS

remote sensing systems

S06

surface-to-6 km layer

SCEsnow cover extent

SGCR

Subcommittee on Global Change Research (National Science and Technology
Council, White House)

SLCF

short-lived climate forcer

SLCP

short-lived climate pollutant

SLR

sea level rise

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SOC

soil organic carbon

SRES

IPCC Special Report on Emissions Scenarios

SREX

IPCC Special Report on Managing the Risks of Extreme Events and Disasters
to Advance Climate Change Adaptation

SRM

solar radiation management

SSC

Science Steering Committee

SSI

solar spectral irradiance

SSP

Shared Socioeconomic Pathway

SST

sea surface temperature

STAR

Center for Satellite Applications and Research (NOAA)

SWCRE

shortwave cloud radiative effect (on radiative fluxes)

LWCRE

longwave cloud radiative effect (on radiative fluxes)TA

total alkalinityTC

tropical cyclone

TCR

transient climate response

TCRE

transient climate response to cumulative carbon emissions

TOPEX/JASON1,2

Topography Experiment/Joint Altimetry Satellite Oceanography Network
satellites (NASA)

TSI

total solar irradiance

TTT

temperature total troposphere

UAH

University of Alabama, HuntsvilleUHI

urban heat island (effect)

UNFCCC

United Nations Framework Convention on Climate Change

USGCRP

U.S. Global Change Research Program

USGS

U.S. Geological Survey

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UV

ultraviolet

VOCsvolatile organic compounds

WAIS

West Antarctic Ice Sheet

WCRP

World Climate Research Programme

WMGHG

well-mixed greenhouse gas

WOCE

World Ocean Circulation Experiment (JGOFS)

Abbreviations and Units
C

carbonCOcarbon monoxide

CH4methanecm

centimetersCO2carbon dioxide°Cdegrees Celsius°F

degrees Fahrenheit

GtC

gigatonnes of carbon

hPA

hectopascal

H2Shydrogen sulfide

H2SO4

sulfuric acidkm

kilometersmmetersmm

millimetersMt

megatonμatm

microatmosphereNnitrogen

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N2O

nitrous oxide

NOxnitrogen oxides

O2

molecular oxygen

O3

ozone

OH

hydroxyl radical

PgC

petagrams of carbon

ppb

parts per billion

ppm

parts per million

SF6

sulfur hexafluoride

SO2

sulfur dioxide

TgC

teragrams of carbon

W/m2

Watts per meter squared

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Glossary Terms
doi: 10.7930/J0TM789P

Abrupt climate change

The total effective radiative forcing due to both
aerosol–cloud and aerosol–radiation interactions
is denoted aerosol effective radiative forcing(ERFari+aci). See also aerosol–radiation interaction. (condensed from IPCC AR5 WGI Annex
III: Glossary)

Change in the climate system on a timescale
shorter than the timescale of the responsible
forcing. In the case of anthropogenic forcing
over the past century, abrupt change occurs
over decades or less. Abrupt change need not be
externally forced. (CSSR, Ch. 15)

Aerosol–cloud interaction

A process by which a perturbation to aerosol
affects the microphysical properties and evolution of clouds through the aerosol role as cloud
condensation nuclei or ice nuclei, particularly inways that affect radiation or precipitation; suchprocesses can also include the effect of clouds
and precipitation on aerosol. The aerosol perturbation can be anthropogenic or come from somenatural source. The radiative forcing from suchinteractions has traditionally been attributed
to numerous indirect aerosol effects, but in thisreport, only two levels of radiative forcing (oreffect) are distinguished:
The radiative forcing (or effect) due to aerosol–cloud interactions (RFaci) is the radiative
forcing (or radiative effect, if the perturbation
is internally generated) due to the change innumber or size distribution of cloud droplets
or ice crystals that is the proximate result of anaerosol perturbation, with other variables (inparticular total cloud water content) remaining
equal. In liquid clouds, an increase in cloud
droplet concentration and surface area would
increase the cloud albedo. This effect is also
known as the cloud albedo effect, first indirect
effect, or Twomey effect. It is a largely theoretical concept that cannot readily be isolated inobservations or comprehensive process models
due to the rapidity and ubiquity of rapid adjustments. This is contrasted with the effective
radiative forcing (or effect) due to aerosol–cloudinteractions (ERFaci)

Aerosol–radiation interaction (RFari)

The radiative forcing (or radiative effect, if
the perturbation is internally generated) of anaerosol perturbation due directly to aerosol–
radiation interactions, with all environmental
variables remaining unaffected. It is traditionally known in the literature as the direct aerosol
forcing (or effect).
The total effective radiative forcing due to both
aerosol–cloud and aerosol–radiation interactions
is denoted aerosol effective radiative forcing(ERFari+aci). See also aerosol-cloud interaction.
(condensed from IPCC AR5 WGI Annex III:
Glossary)

Agricultural drought
See drought.

Albedo

The fraction of solar radiation reflected by asurface or object, often expressed as a percentage. Snow-covered surfaces have a high albedo,
the albedo of soils ranges from high to low, andvegetation-covered surfaces and oceans have a
low albedo. The Earth’s planetary albedo varies
mainly through varying cloudiness, snow, ice,leaf area, and land-cover changes. (IPCC AR5WGI Annex III: Glossary)

Altimetry

A technique for measuring the height of the
Earth’s surface with respect to the geocenter ofthe Earth within a defined terrestrial reference

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Biological pumpframe (geocentric sea level). (IPCC AR5 WGIAnnex III: Glossary)

The suite of biologically mediated processes
responsible for transporting carbon against a
concentration gradient from the upper ocean tothe deep ocean. (Passow and Carlson, 2012)

Anticyclonic circulation

Fluid motion having a sense of rotation about
the local vertical opposite to that of the earth’s
rotation; that is, clockwise in the Northern
Hemisphere, counterclockwise in the Southern
Hemisphere, and undefined at the equator. It
is the opposite of cyclonic circulation. (AMS
glossary).

Blocking

Associated with persistent, slow-moving highpressure systems that obstruct the prevailingwesterly winds in the middle and high latitudes
and the normal eastward progress of extratropical transient storm systems. It is an importantcomponent of the intraseasonal climate variability in the extratropics and can cause long-lived
weather conditions such as cold spells in winterand heat waves in summer. (IPCC AR5 WGIAnnex III: Glossary)Atlantic meridional overturning circulation(AMOC)
See Meridional overturning circulation(MOC).
Atmospheric blocking
See Blocking.

Carbon dioxide fertilization

The enhancement of the growth of plants as aresult of increased atmospheric CO2 concentration. (IPCC AR5 WGI Annex III: Glossary)

Atmospheric river

A long, narrow, and transient corridor of strong
horizontal water vapor transport that is typically associated with a low-level jet stream aheadof the cold front of an extratropical cyclone.
The water vapor in atmospheric rivers is supplied by tropical and/or extratropical moisturesources. Atmospheric rivers frequently leadto heavy precipitation where they are forced
upward—for example, by mountains or byascent in the warm conveyor belt. Horizontal
water vapor transport in the midlatitudes occurs
primarily in atmospheric rivers and is focusedin the lower troposphere. (AMS glossary).

Baroclinicity

A set of techniques that aim to remove CO2 directly from the atmosphere by either (1) increasing natural sinks for carbon or (2) using chemical engineering to remove the CO2, with theintent of reducing the atmospheric CO2 concentration. CDR methods involve the ocean, land
and technical systems, including such methods
as iron fertilization, large-scale afforestation anddirect capture of CO2 from the atmosphere using
engineered chemical means. (truncated version
from IPCC AR5 WGI Annex III: Glossary)

Climate engineering

The state of stratification in a fluid in whichsurfaces of constant pressure (isobaric) intersect
surfaces of constant density (isosteric). (AMS
glossary).

Bias correction method

Carbon dioxide removal

See geoengineering.

Climate intervention
See geoengineering.

One of two main statistical approaches used toalleviate the limitations of global and regional
climate models, in which the statistics of thesimulated model outputs are adjusted to thoseof the observation data. (The other approach isempirical/stochastic downscaling, describedunder downscaling). The rescaled variables canremove the effects of systematic errors in climate
model outputs. (derived from Kim et al., 2015)

Climate sensitivity

In Intergovernmental Panel on Climate Change(IPCC) reports, equilibrium climate sensitivity(units: °C) refers to the equilibrium (steady state)change in the annual global mean surface temperature following a doubling of the atmosphericequivalent carbon dioxide concentration. The
effective climate sensitivity (units: °C) is an estimate of the global mean surface temperature re-461
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tative Concentration Pathways. (edited from
IPCC AR5 WGII Annex II: Glossary).

sponse to doubled carbon dioxide concentrationthat is evaluated from model output or observations for evolving non-equilibrium conditions.
It is a measure of the strengths of the climate
feedbacks at a particular time and may vary with
forcing history and climate state, and therefore
may differ from equilibrium climate sensitivity.
The transient climate response (units: °C) is thechange in the global mean surface temperature,
averaged over a 20-year period centered at thetime of atmospheric carbon dioxide doubling, ina climate model simulation in which CO2 increases at 1% per year. It is a measure of the strengthand rapidity of the surface temperature response
to greenhouse gas forcing. (IPCC AR5 WGIAnnex III: Glossary)

Compound event

An event that consists of 1) two or more extreme
events occurring simultaneously or successively,2) combinations of extreme events with underlying conditions that amplify the impact of theevents, or 3) combinations of events that are
not themselves extremes but lead to an extreme
event or impact when combined. The contributing events can be of similar or different types.
(CSSR, Ch. 15, drawing upon SREX 3.1.3)

Critical threshold

A threshold that arises within a system asa result of the amplifying effects of positive
feedbacks. The crossing of a critical threshold
commits the system to a change in state. (CSSR,
Ch. 15)Cloud radiative effect

The radiative effect of clouds relative to theidentical situation without clouds (previously
called cloud radiative forcing). (drawn from
IPCC AR5 WGI Annex III: Glossary)

Cryosphere

Clouds can act as a greenhouse ingredient towarm the Earth by trapping outgoing longwave infrared radiative flux at the top of theatmosphere (the longwave cloud radiative
effect [LWCRE]). Clouds can also enhance theplanetary albedo by reflecting shortwave solar
radiative flux back to space to cool the Earth (theshortwave cloud radiative effect [SWCRE]).
The net effect of the two competing processes
depends on the height, type, and the opticalproperties of the clouds. (edited from NOAA,
Geophysical Fluid Dynamics Laboratory)CMIP

The Coupled Model Intercomparison Project is astandard experimental protocol for studying theoutput of coupled atmosphere–ocean generalcirculation models (AOGCMs). Phases three andfive (CMIP3 and CMIP5, respectively) coordinated and archived climate model simulations
based on shared model inputs by modelinggroups from around the world. The CMIP3
multi-model data set includes projections using
the SRES scenarios drawn from the Intergovernmental Panel on Climate Change’s Special
Report on Emissions Scenarios. The CMIP5
dataset includes projections using the Represen-

All regions on and beneath the surface of the
Earth and ocean where water is in solid form,
including sea ice, lake ice, river ice, snow cover,glaciers and ice sheets, and frozen ground(which includes permafrost). (IPCC AR5 WGIAnnex III: Glossary)

Cyclonic circulation

Fluid motion in the same sense as that of theearth, that is, counterclockwise in the Northern
Hemisphere, clockwise in the Southern Hemisphere, undefined at the equator. (AMS glossary).

Denitrification

As used in this report, refers to the loss of fixed
nitrogen in the ocean through biogeochemical
processes. (CSSR, Ch. 13).

Deoxygenation
See hypoxia.

Downscaling

A method that derives local- to regional-scale(10–100 km) information from larger-scalemodels or data analyses. Two main methods
exist. Dynamical downscaling uses the output
of regional climate models, global models withvariable spatial resolution, or high-resolution
global models. Empirical/statistical downscal-

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ing methods develop statistical relationshipsthat link the large-scale atmospheric variables
with local/regional climate variables. In allcases, the quality of the driving model remains
an important limitation on the quality of thedownscaled information. (IPCC AR5 WGIAnnex III: Glossary)

has two phases: the warm oceanic phase, El
Niño, accompanies high air surface pressurein the western Pacific, while the cold phase, La
Niña, accompanies low air surface pressure in
the western Pacific. Each phase generally lastsfor 6 to 18 months. ENSO events occur irregularly, roughly every 3 to 7 years. The extremes ofthis climate pattern’s oscillations cause extreme
weather (such as floods and droughts) in many
regions of the world. (USGCRP)

Drought

A period of abnormally dry weather longenough to cause a serious hydrological imbalance. Drought is a relative term; therefore,
any discussion in terms of precipitation deficit
must refer to the particular precipitation-related
activity that is under discussion. For example,
shortage of precipitation during the growing
season impinges on crop production or ecosystem function in general (due to soil moisturedrought, also termed agricultural drought),
and during the runoff and percolation season
primarily affects water supplies (hydrologicaldrought). Storage changes in soil moisture andgroundwater are also affected by increases in
actual evapotranspiration in addition to reductions in precipitation. A period with an abnormal precipitation deficit is defined as a meteorological drought. (IPCC AR5 WGI Annex III:
Glossary)

Empirical/statistical downscaling
See downscaling.

Equivalent carbon dioxide concentration

The concentration of carbon dioxide that would
cause the same radiative forcing as a given
mixture of carbon dioxide and other forcing
components. Those values may consider only
greenhouse gases, or a combination of greenhouse gases and aerosols. Equivalent carbon
dioxide concentration is a metric for comparing
radiative forcing of a mix of different greenhouse gases at a particular time but does not
imply equivalence of the corresponding climatechange responses nor future forcing. There is
generally no connection between equivalentcarbon dioxide emissions and resulting equivalent carbon dioxide concentrations. (IPCC AR5WGI Annex III: Glossary)

Dynamical downscaling
See downscaling.

Earth System Model

Eutrophication

A coupled atmosphere–ocean general circulation model in which a representation of the
carbon cycle is included, allowing for interactive
calculation of atmospheric CO2 or compatible
emissions. Additional components (for example, atmospheric chemistry, ice sheets, dynamic
vegetation, nitrogen cycle, but also urban orcrop models) may be included. (IPCC AR5 WGIAnnex III: Glossary)

Over-enrichment of water by nutrients suchas nitrogen and phosphorus. It is one of theleading causes of water quality impairment. The
two most acute symptoms of eutrophication
are hypoxia (a state of oxygen depletion) andharmful algal blooms. (IPCC AR5 WGII Annex
II: Glossary).

Extratropical cyclone

Effective radiative forcing
See radiative forcing.

El Niño–Southern Oscillation

A natural variability in ocean water surfacepressure that causes periodic changes in ocean
surface temperatures in the tropical Pacific
Ocean. El Niño–Southern Oscillation (ENSO)

A large-scale (of order 1,000 km) storm in the
middle or high latitudes having low centralpressure and fronts with strong horizontal
gradients in temperature and humidity. A major
cause of extreme wind speeds and heavy precipitation especially in wintertime. (IPCC AR5 WGIAnnex III: Glossary)

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Feedbacks

An interaction between processes in the climate
system, in which the result of an initial process
triggers changes in a second process that in turninfluences the initial one. A positive feedback
magnifies the original process, while a negative
feedback attenuates or diminishes it. Positive
feedbacks are sometimes referred to as “vicious”
or “virtuous” cycles, depending on whether
their effects are viewed as harmful or beneficial.
(CSSR, Ch. 15)

Geoengineering

A broad set of methods and technologies that
aim to deliberately alter the climate system inorder to alleviate the impacts of climate change(also known as climate intervention (National
Academy of Sciences) or climate engineering).
Most, but not all, methods seek to either 1)
reduce the amount of absorbed solar energy
in the climate system (Solar Radiation Management) or 2) increase net carbon sinks fromthe atmosphere at a scale sufficiently large toalter climate (Carbon Dioxide Removal). Scaleand intent are of central importance. Two key
characteristics of geoengineering methods ofparticular concern are that they use or affectthe climate system (e.g., atmosphere, land, or
ocean) globally or regionally and/or could havesubstantive unintended effects that cross national boundaries. (adapted from IPCC AR5 WGIAnnex III: Glossary)sides. A glacier is maintained by accumulationof snow at high altitudes, balanced by melting
at low altitudes and/or discharge into the sea.
An ice mass of the same origin as glaciers, but ofcontinental size, is an ice sheet, defined further
below. (IPCC AR5 WGI Annex III: Glossary)

Global mean sea level

The average of relative sea level or of sea surface
height across the ocean.

Global warming potential (GWP)

An index, based on radiative properties ofgreenhouse gases, measuring the radiative forcing following a pulse emission of a unit massof a given greenhouse gas in the present-day
atmosphere integrated over a chosen time horizon, relative to that of carbon dioxide. The GWP
represents the combined effect of the differing
times these gases remain in the atmosphere andtheir relative effectiveness in causing radiative
forcing. (truncated from IPCC AR5 WGI Annex
III: Glossary)

Gravimetry

Measurement of the Earth’s gravitational field.
Using satellite data from the Gravity Recovery
and Climate Experiment (GRACE), measurements of the mean gravity field help scientists
better understand the structure of the solid
Earth and learn about ocean circulation. Monthly measurements of time-variable gravity can beused to study ground water fluctuations, sea ice,
sea level rise, deep ocean currents, ocean bottompressure, and ocean heat flux. (modified fromNASA Earth Observatory on the GRACE project)

Glacial isostatic adjustment (GIA)

The deformation of the Earth and its gravity
field due to the response of the earth–oceansystem to changes in ice and associated waterloads. It includes vertical and horizontal deformations of the Earth’s surface and changes ingeoid due to the redistribution of mass during
the ice–ocean mass exchange. GIA is currently
contributing to relative sea level rise in much of
the continental United States. (IPCC AR5 WGIAnnex III: Glossary)

Greenhouse gas (GHG)

Greenhouse gases are those gaseous constituentsof the atmosphere, both natural and anthropogenic, that absorb and emit radiation at specificwavelengths within the spectrum of terrestrial
radiation emitted by the Earth’s surface, theatmosphere itself, and by clouds. This property
causes the greenhouse effect. Water vapor (H2O),
carbon dioxide (CO2), nitrous oxide (N2O),
methane (CH4), and ozone (O3) are the primary
greenhouse gases in the Earth’s atmosphere.
Moreover, there are a number of entirely human-made greenhouse gases in the atmosphere,
such as the halocarbons and other chlorine- andGlacier

A perennial mass of land ice that originatesfrom compressed snow, shows evidence of pastor present flow (through internal deformation
and/or sliding at the base), and is constrainedby internal stress and friction at the base and464

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Irreversible

bromine-containing substances, dealt with
under the Montreal Protocol. Beside CO2, N2O,
and CH4, the Kyoto Protocol dealt with thegreenhouse gases sulfur hexafluoride (SF6), hydrofluorocarbons (HFCs), and perfluorocarbons(PFCs). (adapted from IPCC AR5 WGI Annex
III: Glossary)

Changes in components of the climate systemthat either cannot be reversed, or can only bereversed on timescales much longer than thetimescale over which the original forcing occurred. (CSSR, Ch. 15)

Longwave cloud radiative effect (LWCRE)

Hydrological drought

See cloud radiative effect.

See drought.

Meridional overturning circulation (MOC)

Hypoxia

Meridional (north–south) overturning circulation in the ocean quantified by zonal (east–west)
sums of mass transports in depth or density
layers. In the North Atlantic, away from the subpolar regions, the Atlantic MOC (AMOC, whichis in principle an observable quantity) is often
identified with the thermohaline circulation(THC), which is a conceptual and incomplete
interpretation. It must be borne in mind that the
AMOC is also driven by wind, and can also include shallower overturning cells such as occur
in the upper ocean in the tropics and subtropics,in which warm (light) waters moving poleward
are transformed to slightly denser waters andsubducted equatorward at deeper levels. (adapted from IPCC AR5 WGI Annex III: Glossary)

Deficiency of oxygen in water bodies, which
can be a symptom of eutrophication (nutrient
overloading). Deoxygenation (the process ofremoving oxygen) leads to hypoxia, and the expansion of oxygen minimum zones (IPCC AR5
WGII Annex II: Glossary supplemented withother sources).

Ice sheet

A mass of land ice of continental size that is
sufficiently thick to cover most of the underlying bed, so that its shape is mainly determinedby its dynamics (the flow of the ice as it deforms
internally and/or slides at its base). An ice sheet
flows outward from a high central ice plateau
with a small average surface slope. The margins usually slope more steeply, and most ice
is discharged through fast flowing ice streams
or outlet glaciers, in some cases into the sea or
into ice shelves floating on the sea. There are
only two ice sheets in the modern world, one on
Greenland and one on Antarctica. During glacial
periods there were others, including the Laurentide Ice Sheet in North America, whose loss
is the primary driver of glacial isostatic adjustment in the United States today. (adapted from
IPCC AR5 WGI Annex III: Glossary)

Meridional temperature gradient

North–South temperature variation

Meteorological drought
See drought.

Mode waterIce wedge

Common features of the subsurface in permafrost regions, ice wedges develop by repeatedfrost cracking and ice vein growth over hundreds to thousands of years. Ice wedge formation causes the archetypal polygonal patterns
seen in tundra across the Arctic landscape.
(adapted from Liljedal et al., 2016)

Instantaneous radiative forcing
See radiative forcing.

Water of exceptionally uniform properties
over an extensive depth range, caused in most
instances by convection. Mode waters represent
regions of water mass formation; they are notnecessarily water masses in their own right but
contribute significant volumes of water to otherwater masses. Because they represent regionsof deep sinking of surface water, mode water
formation regions are atmospheric heat sources. Subantarctic Mode Water is formed during
winter in the subantarctic zone just north of
the subantarctic front and contributes to thelower temperature range of central water; onlyin the extreme eastern Pacific Ocean does it
obtain a temperature low enough to contributeto Antarctic Intermediate Water. Subtropical465
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Mode Water is mostly formed through enhanced
subduction at selected locations of the subtropics and contributes to the upper temperature
range of central water. Examples of Subtropical Mode Water are the 18°C water formed in
the Sargasso Sea, Madeira Mode Water formedat the same temperature but in the vicinity ofMadeira, and 13°C water formed not by surface
processes but through mixing in Agulhas Current eddies as they enter the Benguela Current.
(AMS glossary).

position. In nitrogen mineralization, organic nitrogen from decaying plant and animal residues(proteins, nucleic acids, amino sugars and urea)
is converted to ammonia (NH3) and ammonium(NH4+) by biological activity. (IPCC AR5 WGIAnnex III: Glossary)

Ocean acidification

The process by which ocean waters havebecome more acidic due to the absorption ofhuman-produced carbon dioxide, which interacts with ocean water to form carbonic acid andlower the ocean’s pH. Acidity reduces the capacity of key plankton species and shelled animals
to form and maintain shells. (USGCRP)

Model ability/model skill

Representativeness of the ability of a climate
model to reproduce historical climate observational data.

Model bias

Ocean stratification

The existence or formation of distinct layers orlaminae in the ocean identified by differencesin thermal or salinity characteristics (e.g., densities) or by oxygen or nutrient content. (adaptedfrom AMS glossary).

Systematic error in model output that over- or
under-emphasizes particular model mechanismor results.

Model ensemble

Also known as a multimodel ensemble (MME),
a group of several different global climate
models (GCMs) used to create a large numberof climate simulations. An MME is designed toaddress structural model uncertainty between
different climate models, rather than parametric
uncertainty within any one particular model.
(UK Met Office, Climate Projections, Glossary)

Oxygen minimum zones (OMZs)

The midwater layer (200–1,000 m) in the open
ocean in which oxygen saturation is the lowest
in the ocean. The degree of oxygen depletion
depends on the largely bacterial consumption oforganic matter, and the distribution of the OMZs
is influenced by large-scale ocean circulation. In
coastal oceans, OMZs extend to the shelves andmay also affect benthic ecosystems. OMZs can
expand through a process of deoxygenation.
(supplemented version of IPCC AR5 WGII
Annex II: Glossary).

Model independence

An analysis of the degree to which models aredifferent from one another. Also is used as aninterpretation of an ensemble as constituting
independent samples of a distribution which
represents our collective understanding of theclimate system. (summarized based on Annan
and Hargreaves, 2017)

Pacific Decadal Oscillation

The pattern and time series of the first empiricalorthogonal function of sea surface temperature
over the North Pacific north of 20°N. The PDO
broadened to cover the whole Pacific Basin is
known as the Interdecadal Pacific Oscillation.
The PDO and IPO exhibit similar temporal evolution. (IPCC AR5 WGI Annex III: Glossary)

Nationally determined contributions (NDCs)
See Paris Agreement.

Negative feedbacks
See feedbacks.

Parameterization

In climate models, this term refers to the technique of representing processes that cannot be
explicitly resolved at the spatial or temporal
resolution of the model (sub-grid scale processes) by relationships between model-resolved

Nitrogen mineralization

Mineralization/remineralization is the conversion of an element from its organic form to an
inorganic form as a result of microbial decom466

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Petagramlarger-scale variables and the area- or time-averaged effect of such subgrid scale processes.
(IPCC AR5 WGI Annex III: Glossary)

One petagram (Pg) = 1015 grams or 1012 kilograms. A petagram is the same as a gigaton,
which is a billion metric tons, where 1 metric
ton is 1,000 kg. Estimated 2014 global fossil
fuel emissions were 9.855 Pg = 9.855 Gt = 9,855
million metric tons of carbon. (CDIAC – Carbon
Dioxide Information Center: Boden et al., 2017)

Parametric uncertainty
See uncertainty.

Paris Agreement

An international climate agreement with thecentral aim to hold global temperature rise thiscentury well below 2°C above preindustrial
levels and to pursue efforts to limit the temperature increase even further to 1.5°C. For the
first time, all parties are required to put forwardemissions reductions targets, and to strengthen
those efforts in the years ahead as the Agreement is assessed every five years. Each country’s
proposed mitigation target (the intended nationally determined contribution [INDC]) becomes
an official nationally determined contribution(NDC) when the country ratifies the agreement.
The Paris Agreement was finalized on December 12, 2015, at the 21st Conference of Parties(COP 21) of the United National Framework
Convention on Climate Change (UNFCCC).
“Paris” entered into force on November 4, 2016,
after ratification by 55 countries that account for
at least 55% of global emissions). The agreement
had a total of 125 national parties by early 2017.
(summarized/edited from UNFCCC)

Positive feedbacks
See feedbacks.

Proxy

A way to indirectly measure aspects of climate.
Biological or physical records from ice cores, treerings, and soil boreholes are good examples ofproxy data. (USGCRP)

Radiative forcing

The change in the net (downward minus
upward) radiative flux (expressed in W/m2) atthe tropopause or top of atmosphere due to achange in an external driver of climate change,such as a change in the concentration of carbon
dioxide or in the output of the Sun. Sometimes
internal drivers are still treated as forcings eventhough they result from the alteration in climate,for example aerosol or greenhouse gas changes
in paleoclimates. The traditional radiative forcing is computed with all tropospheric properties
held fixed at their unperturbed values, andafter allowing for stratospheric temperatures, if
perturbed, to readjust to radiative–dynamical
equilibrium. Radiative forcing is instantaneous
if no change in stratospheric temperature isaccounted for. The radiative forcing once rapid
adjustments are accounted for is the effective
radiative forcing. Radiative forcing is not to be
confused with cloud radiative forcing, which
describes an unrelated measure of the impact
of clouds on the radiative flux at the top of theatmosphere. (truncated from IPCC AR5 WGIAnnex III: Glossary)

Pattern scaling

A simple and computationally cheap method to
produce climate projections beyond the scenarios run with expensive global climate models(GCMs). The simplest technique has known
limitations and assumes that a spatial climate
anomaly pattern obtained from a GCM can bescaled by the global mean temperature anomaly.
(Herger et al., 2015)

Permafrost

Ground that remains at or below freezing for atleast two consecutive years. (USGCRP)

Permafrost active layer

Relative sea level

The height of the sea surface, measured withrespect to the height of the underlying land.
Relative sea level changes in response to bothchanges in the height of the sea surface andchanges in the height of the underlying land.

The layer of ground that is subject to annual
thawing and freezing in areas underlain by permafrost. (IPCC AR5 WGI Annex III: Glossary)

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Representative Concentration Pathways

Saffir-Simpson hurricane scale

Scenarios that include time series of emissions
and concentrations of the full suite of greenhouse gases and aerosols and chemically active
gases, as well as land use/land cover. The
word “representative” signifies that each RCP
provides only one of many possible scenariosthat would lead to the specific radiative forcing
characteristics. The term “pathway” emphasizes
that not only the long-term concentration levels
are of interest, but also the trajectory taken overtime to reach that outcome. RCPs usually referto the portion of the concentration pathway
extending up to 2100. Four RCPs produced from
Integrated Assessment Models were selectedfrom the published literature for use in theIntergovernmental Panel on Climate Change’s
Fifth Assessment Report: RCP2.6, a pathway
where radiative forcing peaks at approximately
3 W/m2 before 2100 and then declines; RCP4.5
and RCP6.0, two intermediate stabilization
pathways in which radiative forcing is stabilized at approximately 4.5 W/m2 and 6.0 W/m2,
respectively, after 2100; and RCP8.5, a high pathway
for which radiative forcing reaches greater than
8.5 W/m2 by 2100 and continues to rise for someamount of time (truncated and adapted from
IPCC AR5 WGI Annex III: Glossary, excluding
discussion of extended concentration pathways)

A classification scheme for hurricane intensity based on the maximum surface windspeed and the type and extent of damage doneby the storm. The wind speed categories are
as follows: 1) 33–42 m/s (65–82 knots or 74–95
mph); 2) 43–49 m/s (83– 95 knots or 96–110
mph); 3) 50–58 m/s (96–113 knots or 111–129
mph); 4) 59–69 m/s (114–134 knots or 130–156
mph); and 5) 70 m/s (135 knots or 156 mph) andhigher. These categories are used routinely byweather forecasters in North America to characterize the intensity of hurricanes for the public.
(adapted from AMS glossary).

Saturation

The condition in which vapor pressure is equal
to the equilibrium vapor pressure over a planesurface of pure liquid water, or sometimes ice.
(AMS glossary).

Scenarios

Plausible descriptions of how the future may
develop based on a coherent and internallyconsistent set of assumptions about key driving forces (e.g., rate of technological change,
prices) and relationships. Note that scenarios areneither predictions nor forecasts, but are usefulto provide a view of the implications of developments and actions. (IPCC AR5 WGI Annex III:
Glossary)

Rossby waves

Rossby waves, also known as planetary waves,naturally occur in rotating fluids. Within the
Earth’s ocean and atmosphere, these waves
form as a result of the rotation of the planet.
These waves affect the planet’s weather andclimate. Oceanic Rossby waves are huge, undulating movements of the ocean that stretch
horizontally across the planet for hundreds ofkilometers in a westward direction. Atmospheric Rossby waves form primarily as a result of the
Earth’s geography. Rossby waves help transfer
heat from the tropics toward the poles and cold
air toward the tropics in an attempt to return
the atmosphere to balance. They also help locate
the jet stream and mark out the track of surface
low pressure systems. The slow motion of thesewaves often results in fairly long, persistent
weather patterns. (adapted from NOAA National Ocean Service)

Sea level pressure

The atmospheric pressure at mean sea level,
either directly measured or, most commonly,
empirically determined from the observed station pressure. In regions where the Earth’s
surface is above sea level, it is standard observational practice to reduce the observed surfacepressure to the value that would exist at a pointat sea level directly below if air of a temperature corresponding to that actually present at
the surface were present all the way down to
sea level. In actual practice, the mean temperature for the preceding 12 hours is employed,rather than the current temperature. This
“reduction of pressure to sea level” is responsible for many anomalies in the pressure field inmountainous areas on the surface synoptic
chart. (AMS glossary).

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Shared Socioeconomic Pathways

A basis for emissions and socioeconomic scenarios, an SSP is one of a collection of pathways that
describe alternative futures of socioeconomic
development in the absence of climate policy
intervention. The combination of SSP-basedsocioeconomic scenarios and Representative
Concentration Pathway (RCP)-based climate
projections can provide a useful integrative frame
for climate impact and policy analysis. (updated
from IPCC AR5 WGIII Annex I: Glossary).

can exceed the global mean value by up to about
30%. (draws on Hay et al., 2012)

Structural model uncertainty
See uncertainty.

Teleconnection

A statistical association between climate variables at widely separated, geographically fixed
spatial locations. Teleconnections are causedby large spatial structures such as basin-wide
coupled modes of ocean–atmosphere variability,
Rossby wave-trains, midlatitude jets and storm
tracks, etc. (IPCC AR5 WGI Annex III: Glossary)

Shortwave cloud radiative effect (SWCRE)
See cloud radiative effect.

Snow water equivalent

Thermohaline circulation (THC)

The depth of liquid water that would result if
a mass of snow melted completely. (IPCC AR5WGI Annex III: Glossary)

Large-scale circulation in the ocean that transforms low-density upper ocean waters to higher-density intermediate and deep waters andreturns those waters back to the upper ocean.
The circulation is asymmetric, with conversion
to dense waters in restricted regions at highlatitudes and the return to the surface involving
slow upwelling and diffusive processes overmuch larger geographic regions. The THC isdriven by high densities at or near the surface,caused by cold temperatures and/or high salinities, but despite its suggestive though common
name, is also driven by mechanical forces suchas wind and tides. Frequently, the name THC
has been used synonymously with the Meridional Overturning Circulation. (IPCC AR5 WGIAnnex III: Glossary)

Solar radiation management (SRM)

The intentional modification of the Earth’s
shortwave radiative budget with the aim to
reduce climate change according to a given
metric (for example, surface temperature,
precipitation, regional impacts, etc). Artificial
injection of stratospheric aerosols and cloudbrightening are two examples of SRM techniques. Methods to modify some fast-responding elements of the longwave radiative budget(such as cirrus clouds), although not strictly
speaking SRM, can be related to SRM. See alsogeoengineering. (edited from IPCC AR5 WGIAnnex III: Glossary)

Static-equilibrium (sea level change)
fingerprint

Thermokarst

The near-instantaneous pattern of relative sea
level change associated with changes in the
distribution of mass at the surface of the Earth,for example due to the melting of ice on land.
Near a shrinking ice sheet (within ~2,000 km of
the margin), sea level will fall due to both crustal uplift and the reduction of the gravitational
pull on the ocean from the ice sheet. Close to theice sheet, this fall can be an order of magnitude
greater than the equivalent rise in global meansea level associated with the meltwater addition
to the ocean. Far from the ice sheet, sea level will
generally rise with greater amplitude as the distance from the ice sheet increases, and this rise

The process by which characteristic landforms
result from the thawing of ice-rich permafrost or
the melting of massive ground ice. (IPCC AR5WGI Annex III: Glossary)Threshold

The value of a parameter summarizing asystem, or a process affecting a system, at which
qualitatively different system behavior emerges.
Beyond this value, the system may not conformto statistical relationships that described it previously. For example, beyond a threshold level ofocean acidification, wide-scale collapse of coralecosystems may occur. (CSSR, Ch. 15)

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Tipping elements

statements (for example, reflecting the judgmentof a team of experts) (cut from IPCC AR5 WGII
Annex II: Glossary).

Systems with critical thresholds, beyond whichsmall perturbations in forcing can—as a result of
positive feedbacks—lead to large, nonlinear, andirreversible shifts in state. In the climate system, atipping element is a subcomponent of the climate
system (typically at a spatial scale of approximately 1,000 km or larger). (CSSR, Ch. 15)

Given that no model can represent the world
with complete accuracy, structural model uncertainty refers to how well the physical processesof the real world are represented in the structureof a model. Different modeling research groups
will represent the climate system in different
ways, and to some extent this decision is asubjective judgement. The use of climate model
ensembles can address the uncertainty of differently structured models. (adapted from UK Met
Office, Climate Projections, Glossary)

Tipping point

The critical threshold of a tipping element.
Some limit its use to critical thresholds inwhich both the commitment to change and thechange itself occur without a significant lag,while others also apply it to situations where acommitment occurs rapidly, but the committedchange may play out over centuries and evenmillennia. (CSSR, Ch. 15)

In contrast, parametric uncertainty refers to incomplete knowledge about real world processes
in a climate model. A parameter is well-specified in that it has a true value, even if this value
is unknown. Such empirical quantities can bemeasured, and the level of uncertainty about
them can be represented in probabilistic terms.
(adapted from Morgan and Henrion, 1990, pp
50-52)

Transient climate response
See climate sensitivity.

Tropopause

The boundary between the troposphere and thestratosphere. (IPCC AR5 WGI Annex III: Glossary)

Uncertainty

A state of incomplete knowledge that can result
from a lack of information or from disagreement
about what is known or even knowable. It may
have many types of sources, from imprecisionin the data to ambiguously defined concepts or
terminology, or uncertain projections of humanbehavior. Uncertainty can therefore be represented by quantitative measures (for example,
a probability density function) or by qualitative

Urban heat island effect

The relative warmth of a city compared withsurrounding rural areas, associated with changes in runoff, effects on heat retention, and changes in surface albedo. (IPCC AR5 WGI Annex III:
Glossary)

Zonal mean

Data average along a latitudinal circle on the
globe.

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