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E nv ironmen t al A ss es s men t EPA/600/R-07/094F | April 2009 | ww w. epa.go v



# Assessment of the Impacts of Global Change on Regional U.S. Air Quality: A synthesis of climate change impacts on ground-level ozone

AN INTERIM REPORT OF THE U.S. EPA GLOBAL CHANGE RESEARCH PROGRAMEPA/600/R-07/094F April 2009



## Assessment of the Impacts of Global Change on Regional U.S. Air Quality: A Synthesis of Climate Change Impacts on Ground-Level Ozone

An Interim Report of the U.S. EPA Global Change Research Program

National Center for Environmental Assessment Office of Research and Development U.S. Environmental Protection Agency Washington, DC 20460DISCLAIMER

This document has been reviewed in acco rdance with U.S. Environmental Protection Agency policy and approved for publication. Men tion of trade names or commercial products

does not constitute endorsement or recommendation for use.

Preferred citation: U.S. Environmental Protection Agency (EPA). (2009) Assessment of the impacts of global change on regional U.S. air quality: a synthesis of climate change impacts on ground- level ozone. An Interim Report of the U.S. EPA Global Change Research Program. National Center for Environmental Assessment, Washington, DC; EPA/600/R-07/094F. Available from the National Technical Information Service, Springfield, VA, and online at http://www.epa.gov/ncea

.

iiTABLE OF CONTENTS

LIST OF TABLES ......................................................................................................................... vi LIST OF FIGURES ...................................................................................................................... vii LIST OF ABBREVIATIONS ........................................................................................................ ix FOREWORD

.............................................................................................................................. ... xi AUTHORS, CONTRIBUTORS, AND REVIEWERS ............................................................... xiii ACKNOWLEDGEMENTS ......................................................................................................... xvi EXECUTIVE SUMMARY ........................................................................................................ xvii SUMMARY OF POLICY RELEVANT FINDINGS ................................................................. xxii

1 INTRODUCTION TO THE PROBLEM .............................................................................. 1-1 1.1 INTRODUCTION ....................................................................................................... 1-1 1.2 MAJOR THEMES OF THE INTERIM ASSESSMENT REPORT ........................... 1-2 1.3 BACKGROUND ......................................................................................................... 1-4 1.3.1 Air Pollution ...................................................................................................... 1-4 1.3.2 Climate Change and Air Quality Linkages ....................................................... 1-4 1.3.2.1 Air Quality Impacts on Climate Change .......................................... 1-6 1.3.2.2 Climate Change Impacts on Air Quality .......................................... 1-6 1.4 DESIGN OF THE GLOBAL CHANGE AND AIR QUALITY ASSESSMENT ...... 1-8 1.4.1 Scope of the Assessment Effort ........................................................................ 1-9 1.4.2 What is Covered in this Report ....................................................................... 1-10 1.5 THE CLIENT COMMUNITIES ............................................................................... 1-11 1.5.1 EPA Office of Air and Radiation (OAR ), State, Tribal, and Local Air Quality Planners .............................................................................................. 1-11 1.5.2 U.S. Climate Change Science Program (CCSP) ............................................. 1-12 1.5.3 Climate Change Research Community ........................................................... 1-14 1.5.4 Air Quality Research Community .................................................................. 1-14 1.6 CONSIDERING UNCERTAINTY IN THE ASSESSMENT EFFORT .................. 1-15 1.7 STRUCTURE OF THIS REPORT ........................................................................... 1-16

2 OVERVIEW OF APPROACH .............................................................................................. 2-1 2.1 INTRODUCTION ....................................................................................................... 2-1 2.1.1 Process for Developing the Global Ch ange-Air Quality Assessment Effort ................................................................................................................. 2-2 2.2 WORKSHOP RECOMMENDATIONS ..................................................................... 2-2 2.2.1 Modeling ........................................................................................................... 2-2 2.2.2 Time Horizon Selected ..................................................................................... 2-5 2.2.3 Dual-Phase Assessment Approach ................................................................... 2-6 2.2.4 Research Priorities to Support Phase II ............................................................ 2-7 2.3 RESEARCH PARTNERSHIPS .................................................................................. 2-7

iiiTABLE OF CONTENTS (continued)

3 RESULTS AND SYNTHESIS .............................................................................................. 3-1 3.1 INTRODUCTION ....................................................................................................... 3-1 3.2 SUMMARY OF RESULTS FROM INDIVIDUAL GROUPS .................................. 3-1 3.2.1 GCTM-Focused Modeling Work ...................................................................... 3-2 3.2.1.1 Application of a Unified Aero sol-Chemistry-Climate GCM to Understand the Effects of Changing Climate and Global Anthropogenic Emissions on U.S. Air Quality: Harvard University ......................................................................................... 3-2 3.2.1.2 Impacts of Climate Change and Global Emissions on U.S. Air Quality: Development of an Integrated Modeling Framework and Sensitivity A ssessment: Carnegie Mellon University ......................................................................................... 3-4 3.2.2 Linked Global-Regional-Focused Modeling Work .......................................... 3-6 3.2.2.1 The Climate Impacts on Regional Air Quality (CIRAQ) Project: EPA .................................................................................... 3-6 3.2.2.2 Modeling Heat and Air Quality Impacts of Changing Urban Land Uses and Climate: Columbia University ................................ 3-7 3.2.2.3 Impacts of Global Climate and Emission Changes on U.S. Air Quality: University of Illinois ................................................... 3-8 3.2.2.4 Impact of Climate Change on U.S. Air Quality Using Multi-Scale Modeling with the MM5/SMOKE/CMAQ System: Washington State University .......................................................... 3-10 3.2.2.5 Guiding Future Air Quality Management in California: Sensitivity to Changing Climate—University of California, Berkeley .......................................................................................... 3-11 3.2.2.6 Sensitivity and Uncertainty Assessment of Global Climate Change Impacts on Ozone a nd Particulate Matter: Examination of Direct and Indirect, Emission-Induced Effects: GIT-NESCAUM-MIT ..................................................... 3-12 3.3 SYNTHESIS OF RESULTS ACROSS GROUPS .................................................... 3-13 3.3.1 Regional Modeling Results ............................................................................. 3-15 3.3.1.1 Modeling System Configurations , Simulations, and Evaluation ... 3-15 3.3.1.2 Changes in O 3 ................................................................................. 3-19 3.3.1.3 Changes in Drivers ......................................................................... 3-24 3.3.2 Global Modeling Results ................................................................................ 3-32 3.4 CHALLENGES AND LIMITATIONS OF THE MODEL-BASED APPROACH .............................................................................................................. 3-37 3.4.1 Inter-Model Variability and Model Evaluation .............................................. 3-38 3.4.2 The Role of Downscaling ............................................................................... 3-42 3.4.3 Uncertainties in Chemical Mechanisms .......................................................... 3-45 3.5 SYNTHESIS CONCLUSIONS AND FUTURE RESEARCH NEEDS .................. 3-47

ivTABLE OF CONTENTS (continued)

4 FUTURE DIRECTIONS ....................................................................................................... 4-1 4.1 PHASE II OF THE GLOBAL CHANGE AND AIR QUALITY ASSESSMENT ........................................................................................................... 4-1 4.2 EXTENDING THE MODELING SYSTEMS ............................................................ 4-1 4.2.1 Exploring Modeling Uncertainties .................................................................... 4-1 4.2.2 Additional Model Development ....................................................................... 4-2 4.2.3 Additional Pollutants—PM ............................................................................... 4-3 4.2.4 Additional Pollutants—Mercury ....................................................................... 4-4 4.3 COMBINED IMPACTS OF CLIMA TE AND EMISSIONS CHANGES: PRELIMINARY WORK ............................................................................................ 4-4 4.4 MODELING THE DRIVERS OF AIR POLLUTANT EMISSIONS ........................ 4-5 4.4.1 Economic Growth and Technology Choices .................................................... 4-7 4.4.2 Land Use and Transportation ............................................................................ 4-8 4.4.3 Emissions Changes Due to Changing Ecosystems: Biogenic VOCs ............... 4-8 4.4.4 Emissions Changes Due to Changing Ecosystems: Wildfires ......................... 4-9 4.4.5 Taking Integrated Emissions Scenarios Through to Future U.S. Regional Air Quality ......................................................................................... 4-9

REFERENCES ........................................................................................................................... R- 1

APPENDIX A: CURRENT U.S. REGI ONAL AIR QUALITY, ITS SENSITIVITY TO METEOROLOGY AND EARLY STUDIES OF THE EFFECT OF CLIMATE CHANGE ON AIR QUALITY ............................................... A-1

APPENDIX B: CHARACTERIZING A ND COMMUNICATING UNCERTAINTY: THE NOVEMBER 2006 WORKSHOP ...........................................................B-1

APPENDIX C: THE 2001 EPA GLOBAL CHANGE RESEARCH PROGRAM’S AIR QUALITY EXPERT WORKSHOP .................................................................C-1

APPENDIX D: U.S. EPA STAR GRANT RESEARCH CONTRIBUTING TO THE GCAQ ASSESSMENT .................................................................................... D-1

APPENDIX E: MODELING APPROACH FOR INTRAMURAL PROJECT ON CLIMATE IMPACTS ON REGIONAL AIR QUALITY................................ E-1

APPENDIX F: USING MARKAL TO GENERATE EMISSIONS GROWTH PROJECTIONS FOR THE EPA GLOBAL CHANGE RESEARCH PROGRAM’S AIR QUALITY ASSESSMENT .............................................. F-1

GLOSSARY OF CLIMATE AND AIR QUALITY TERMS .................................................... G-1

vLIST OF TABLES

3-1 The regional modeling systems wh ose results are discussed in Sections 3.3.1 and 3.3.2 ..................................................................................... 3-16

3-2 GCTM-only model simulations w hose results are discussed in Section 3.3.2 ....................................................................................................... 3-32

viLIST OF FIGURES

1-1 Schematic representation of the mu ltiple interactions between tropospheric chemical processes, biogeochemi cal cycles, and the climate system .................. 1-5

2-1 Links between global and regional climate and atmospheric chemistry processes with anthropogen ic activities governing air pollution emissions ........ 2-1

3-1 2050s-minus-present differences in simulated summer mean MDA8 O 3 concentrations (in ppb) for the (a) NERL ; (b) Illinois 1; (c ) Illinois 2; (d) WSU; and (e) GNM experiments (see Table 3-1) ............................................. 3-20

3-2 95 th percentile MDA8 O 3 concentration differences for the NERL experiment .......................................................................................................... 3-21

3-3 2050s-minus-present differences in simulated summer mean MDA8 O 3 concentrations (in ppb); reproduced fr om Figure 2 in Hogrefe et al. (2004b) ............................................................................................................... 3-22

3-4 Frequency of simulated summer mean MDA8 O 3 values exceeding 80 ppb in different regions from the NERL experiment; reproduced from Figure 11 in Nolte et al. (2008) ................................................................. 3-23

3-5 2050s-minus-present September-October compared to June-August differences in simulated mean MDA8 O 3 concentrations (in ppb); reproduced from Figure 6 in Nolte et al. (2008) ................................................ 3-24

3-6 2050s-minus-present differences in simulated summer mean near-surface air T (oC) for the (a) NERL; (b) Illinois 1; (c) Illinois 2; (d) WSU; and (e) GNM experiments .............................................................................................. 3-27

3-7 2050s-minus-present differences in simulated summer mean surface insolation (W m -2 ) for the (a) NERL; (b) Illinois 1; (c) Illinois 2; (d) WSU; and (e) GNM experiments ................................................................................. 3-28

3-8 2050s-minus-present differences in simulated summer mean biogenic VOC emissions (g Carbon m -2 day -1 ) for the (a) NERL; (b) Illinois 1; (c) Illinois 2; (d) WSU; and (e) GNM experiments ........................................... 3-30

3-9 2050s-minus-present differences in simulated summer (JJA) mean (a) MDA8 O 3 concentration (ppb); (b) near-s urface air temperature (oC); (c) surface insolation (W m -2 ); and (d) biogenic isoprene emissions (g Carbon m -2 day -1 ) for the Harvard global modeling experiment (see Table 3-2) ................................................................................................... 3-34

3-10 Same as Figure 3-9 but for the CMU global modeling experiment ................... 3-35

viiTABLE OF CONTENTS (continued)

3-11 The mean (top two panels) and stan dard deviation (bottom two panels) in future-minus-present MDA8 O 3 concentration differences across (left-hand panels) all seven experime nts (five regional and two global) shown in Figures 3-1, 3-9, and 3- 10 and, for comparison purposes, (right-hand panels) not including th e WSU experiment because it shows differences for July only, while the other experiments show JJA differences ................................................................................................... 3-49

3-12 Averages across the subregions s hown in Figure 3-13 for each of the simulations for (a) mean MDA8 O 3 (ppb); (b) near-surface air temperature (oC) ................................................................................................. 3-50

3-12 continued. Averages across the s ubregions shown in Figure 3-13 for each of the simulations for (c) surface insolation (W m -2 ); and (d) biogenic isoprene emissions (g Carbon m -2 sec -1 ) ............................................................ 3-51

3-13 The averaging subregions used in Figure 3-12

.................................................. 3-52

4-1 Integrated system of future climate, meteorology, and emissions scenarios ....... 4-6

viiiLIST OF ABBREVIATIONS

AGCM Atmospheric General Circulation Model AOGCM Atmosphere-Ocean General Circulation Model AQ air quality BC boundary conditions BEIS Biogenic Emissions Inventory System CAA Clean Air Act CAM Community Atmosphere Model CACM Caltech Atmospheric Chemistry Mechanism CICE The Los Alamos Sea Ice Model CCM3 Community Climate Model version 3 CCSM Community Climate System Model CSIM Community Sea Ice Model CLM Community Land Model CMAQ Community Multisc ale Air Quality Model CMIP Coupled Model Intercomparison Project CTM Chemical Transport Model EC elemental carbon ENSO El Niño-Southern Oscillation GCM General Circulation Model GCTM Global Chemical Transport Model GISS Goddard Institute for Space Studies GMAO Global Modeling and Assimilation Office HadCM3 Hadley Centre Coupled Model IC initial condition IGSM Integrated Global System Model LANL Los Alamos National Laboratory LWC liquid water content MDA8 Maximum Daily 8-hour Av erage Ozone Concentration MM Mesoscale Model MM5 Mesoscale Model (Version 5) MARKAL MARKet Allocation Model MOSIS Meteorology Office Surface Exchange Scheme MPMPO Model to Predict the Multiphase Partitioning of Organics NAAQS National Ambient Air Quality Standard NCAR National Center for Atmospheric Research NH 4 + ammonium ion NO 3 - nitrate ion OC organic carbon O 3 ozone OGCM Oceanic General Circulation Model PAN peroxyacetylnitrate PBL planetary boundary layer PCM Parallel Climate Model

ixLIST OF ABBREVIATIONS (continued)

PCTM PCM/CCSM Transition Model POP Parallel Ocean Program RACT reasonably availa ble control technology RCM Regional Climate Model RCMS Regional Climate Modeling System RCTM Regional Chemical Transport Model RH relative humidity RRF relative reduction factor PM 2.5 particulate matter with aerodynamic diameter below 2.5 μ m SIP State Implementation Plan SAPRC statewide air po llution research center SMOKE Sparse Matrix Operator Kernel Emissions SOA secondary organic aerosols SO 2 sulfur dioxide SO 4 = sulfate ion SRES special report on emissions scenarios SST sea surface temperature THC thermohaline circulation TKE turbulent kinetic energy UKMO United Kingdom Meteorology Office VOC volatile organic compound

xFOREWORD

The Global Change Research Program (GCR P) in EPA’s Office of Research and Development (ORD) is an assessment-oriented program with primary focus on evaluating the potential consequences of globa l change—particularly climate ch ange and climate variability— for air and water quality, aquatic ecosystems, a nd human health in the United States. The program investigates adaptation options to improve society’s ability to e ffectively respond to the risks presented by global change. The program also has begun to evaluate alternative strategies for reducing greenhouse gas emissions and the envi ronmental implications of those strategies. This initial report, entitled Assessment of the Im pacts of Global Change on Regional U.S. Air Quality: A Synthesis of Climat e Change Impacts on Ground-Level Ozone , was prepared by the GCRP to provide air quality managers and scientists with timely and useful information about the potential effects of clim ate change on air quality in the United States. It represents an integrated, multidisciplinary research and assess ment effort that includes contributions from multiple Laboratories and Centers in ORD, and it was done in part nership with EPA’s Office of Air and Radiation (OAR), which is interested in developing a foundation for considering the effects of climate change in the Agency’s air quality management programs. Additional contributors included partners in other federal agencies. I woul d like to thank the many people in ORD, OAR, the Regional Offices, the academic community, and our external review panel for their many contributions. The GCRP began an initial assessment of th e implications of climate change on air quality in 2000, because available scientific evid ence suggested that climate and air quality are closely coupled through atmospheri c chemical, radiative, and dyna mic processes. It was known that meteorology plays an essential role in whet her or not a metropolitan area meets the National Ambient Air Quality Standards (NAAQS) set by EPA for pollutants considered harmful to public health and the environment. It was al so known that a warming climate will lead to significant changes in regional meteorological patterns. However, it was not known how a changing climate will affect air quality for a gi ven region, and how climate change will affect a region’s ability to meet the NAAQS. The GCRP’s long-term climate change/air quality assessment goals therefore are the following:
1. Provide an answer to the basic question, “I s global change something we will have to account for when moving forward w ith U.S. air quality policy?”
2. Develop research tools and a knowledge base to answer science questions about the potential impacts of global cha nge on regional U.S. air quality.

xi3. Deliver to the air quality policy and management community an improved understanding of the behavior and complexities of the global change/air quality system as well as the strengths and limitations of the available scient ific tools and methods. 4. Provide a foundation for applying these scient ific insights and t ools to help answer specific policy and management questions. It is important to ascertain whether climate change should be considered in the formulation of future air quality policy. To do so, we must gain an understanding of the importance of climate change relative to other st ressors on air quality (e.g., changes in land-use) and the relative difficulty of coping with all stressors. However, this assessment design called for first providing insights about how air quality may respond to future changes in climate before tackling the additional complexiti es of incorporating potential future changes in anthropogenic emissions and long-range pollutant transport. This report is therefore an initial assessment that evaluates the effects of climate change al one on air quality across the United States. The assessment focuses primarily on the impact of climate change out to 2050 on groundlevel ozone, which is mainly a summertime polluta nt in the United States. Possible changes in “biogenic” emissions (i.e., emissions from natural sources), such as emissions of volatile organic carbon (VOC) from vegetation, were considered. Future assessment reports will focus on other regulated pollutants, including pa rticulate matter (PM) and mercur y, as well as on the combined
effects of both climate and human-caused emissi ons changes, to provide a more complete understanding of the range of possible im pacts of global chan ge on air quality. Caution must be exercised in interpreting the results presented here. First, this report does not address the question of whether regulatory standards for particular pollutants (e.g., ozone) should change because of climate change. Rather, the report sheds light on the question of whether climate change will make attainment of any standard—wherever it is set—more difficult. It presents scientific findings that, in combination with other information, will inform policymaking. Second, our understanding of the linka ges between climate and air quality is still at an early stage. There remains consider able uncertainty in cl imate modeling, and our knowledge of certain aspects of atmospheric chemis try are still lacking. With these caveats, this report includes results useful to national and regional air quality planners. This report represents a significant advan cement in our understanding of the possible impacts of climate change on region al air quality in the United St ates. It is our hope that the information contained in this report will enhance our ability as a nation to protect air quality and human health, even as our climate changes.
Lek Kadeli Acting Assistant Administrator Office of Research and Development

xiiAUTHORS, CONTRIBUTORS, AND REVIEWERS

Principal Authors

Ms. Anne Grambsch–National Center for Enviro nmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC 20460

Dr. Brooke L. Hemming–National Center for Envi ronmental Assessment, Office of Research and Development, U.S. Envir onmental Protection Agency, Research Triangle Park, NC 27711

Dr. Christopher P. Weaver–Natio nal Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC 20460

Contributing Authors

Dr. Alice Gilliland–Air Resources Laborat ory, National Oceanic and Atmospheric Administration in partnershi p with the National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Doug Grano–Office of Air Quality Planning a nd Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Sherri Hunt–National Center for Envir onmental Research, Office or Research and Development, U.S. Environmental Protection Agency, Washington, DC 20460

Dr. Tim Johnson–National Risk Management Res earch Laboratory, Office of Research and Development, U.S. Environmental Protecti on Agency, Research Triangle Park, NC 27711

Dr. Dan Loughlin–National Risk Management Re search Laboratory, Office or Research and Development, U.S. Environmental Protecti on Agency, Research Triangle Park, NC 27711

Dr. Darrell Winner–National Center for Envir onmental Research, Office or Research and Development, U.S. Environmental Protection Agency, Washington, DC 20460

Contributors

Dr. William G. Benjey–Air Resources Labor atory, National Oceanic and Atmospheric Administration in partnershi p with the National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Ellen J. Cooter–Air Resources Labor atory, National Oceanic and Atmospheric Administration in partnershi p with the National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

xiiiAUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)

Dr. Cynthia Gage–National Risk Management Re search Laboratory, Office or Research and Development, U.S. Environmental Protecti on Agency, Research Triangle Park, NC 27711

Dr. Chris Nolte–Air Resources Laboratory, Natio nal Oceanic and Atmospheric Administration in partnership with the National Exposure Resear ch Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Internal Reviewers

Mr. Bret Anderson–Region 7, U.S. Environmen tal Protection Agency, Kansas City, KS 66101

Ms. Louise Camalier–Office of Air Quality Planni ng and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Bill Cox–Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Ben DeAngelo–Office of Atmospheric Prog rams, Office of Air and Radiation, U.S. Environmental Protection Ag ency, Washington, DC 20460

Mr. Brian Eder–Air Resources Laboratory, Natio nal Oceanic and Atmospheric Administration in partnership with the National Exposure Resear ch Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Tyler Fox–Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Bryan Hubbell–Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Carey Jang–Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Dr. Terry Keating–Office of Policy Analysis and Review, Office of Air and Radiation, U.S. Environmental Protection Ag ency, Washington, DC 20460

Mr. Jim Ketcham-Colwill–Office of Policy Analys is and Review, Office of Air and Radiation, U.S. Environmental Protection Agency, Washington, DC 20460

Dr. Meredith Kurpius–Air Division, Region 9, U.S. Environmental Protection Agency, San Francisco, CA 94105

Ms. Anne McWilliams–Region 1, U.S. Environmental Protection Agency, Boston, MA 02114- 2023

xivAUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)

Dr. Andy Miller–National Risk Management Research Laboratory, Office or Research and Development, U.S. Environmental Protecti on Agency, Research Triangle Park, NC 27711

Ms. Madonna Narvaez–Region 10, U.S. Environmen tal Protection Agency, Seattle, WA 98101

Ms. Kathryn Parker–Office of Atmospheric Pr ograms, Office of Air and Radiation, U.S. Environmental Protection Ag ency, Washington, DC 20460

Ms. Sharon Philips–Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Frank Princiotta–National Ri sk Management Research Laborat ory, Office or Research and Development, U.S. Environmental Protecti on Agency, Research Triangle Park, NC 27711

Dr. ST Rao–Air Resources Labor atory, National Oceanic and At mospheric Administration in partnership with the National Exposure Resear ch Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Mr. Randy Robinson–Region 5, U.S. Environm ental Protection Agency, Chicago, IL 60604-3507

Mr. Jason Samenow–Office of Atmospheric Pr ograms, Office of Air and Radiation, U.S. Environmental Protection Ag ency, Washington, DC 20460

Dr. Ravi Srivastava–Office of Ai r Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

Ms. Sara Terry–Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711

xvACKNOWLEDGEMENTS

This interim report is part of a larger climat e change and air quality assessment that is being carried out by ORD’s Global Change Research Program. It was made possible because of the many people who helped plan and implemen t the assessment effort, participated in workshops, conducted research, and contributed ideas that shaped the final product. We are grateful for their support and encouragement. In particular, we wish to acknowledge the following: Doug McKinney, Michele Aston, Robe rt Gilliam, Bill Rhodes, Joseph DeCarolis, Carol Shay, Jenise Swall, Deb Mangis, R uby Leung, and the Science To Achieve Results (STAR) grantees. Many thanks as well to Jinhong Zhu and Xin-Zhong Liang who archived model output from the various pa rticipating groups and helped produce many of the figures in this report. Furthermore, we wish to tha nk our external review panel, David Allen, Russ Dickerson, Greg Huey, Alan Robock, Drew Shi ndell, Yuhang Wang, and Jason West, for their many valuable insights and critical contributions to improving the qua lity of this report. Finally, we are very grateful to the NCEA Global Change Research Program staff for their perspectives and support.

xviEXECUTIVE SUMMARY

It is increasingly recognized that the scie nce and policy communities need to explore the potential impact of long-term (multi-decadal), global climate change on regional air quality— specifically the possibility that such change may complicate air quality managers’ ability to attain their management goals. These concerns are grounded in information derived from observational studies, basic atmospheric chemis try, and modeling of short-term air pollution episodes. For example, these analyses have esta blished the major role th at weather patterns play in establishing conditions conducive to ozone (O 3 ) formation and accumulation, such as abundant sunshine, high temperatures, and stagnant air. It is now we ll understood that year-toyear variability in summer climat e is strongly correlated with th e number of days that exceed O 3 air quality standards. Historically, air pollution has been studied mostly in terms of immediate local and regional concerns, rather than as a global change issue. In 2001, the EPA Office of Research and Development’s Global Change Research Program in itiated an effort to increase understanding of the multiple complex interactions between long- term global climate change and atmospheric chemistry which have the potential to infl uence air pollution in the United States. The overall goal of the assessment effort is to enhance the ability of air quality policy makers and managers to consider global climate change in their decisions through this increased understanding. A phased approach has been used to systema tically achieve progress toward this overall goal. Phase I focused on building coupled modeli ng systems capable of capturing the range of processes from global climate to regional air quality and applying these systems to study the sensitivity of U.S. air quality to climat e change, with a particular emphasis on O 3 . Phase I considers the impact of climat e change on air quality in is olation, without including the interacting effects of changes in the emissions of pollutants from human systems. Phase II, in progress, focuses on the combined impacts of changing climate and changing human-caused air pollutant emissions. It builds on Phase I by: extending the modeling systems developed under Phase I to explore additional pollutants and proc esses; investigating more comprehensively the key modeling uncertainties uncovered in Phase I; and integrating changes in climate with the changes in emissions that might result from ch anges in air quality regul ations, population growth and economic development, changes in energy technologies, a nd land use change. This report provides a synthesis of the ma jor results from Phase I of the overall assessment. The findings presented here are interim, as Phase II is currently ongoing, and future reports will update and extend our knowledge of the potential impacts of global change on air quality.
xviiSpecifically, this report provides a synthesis of the EPA-funded m odeling studies that
have been carried out to date under Phase I, primarily for O 3 . Future interim reports will provide syntheses of additional topics, e.g., particulate matter. These projects have all adapted and combined existing tools from diverse fields, such as global climate models, global chemistry and transport models, regional meteorological models, and regional air quality models, into systems capable of carrying out numerical experiments to explore the sensi tivity of U.S. air quality to changes in global climate. These linked modeli ng systems have simulated nationwide changes in
O 3 concentrations, primarily for summertime, as a result of simulated climate change a few decades into the future. The numerical experime nts discussed in this report held human-caused
emissions of O 3 precursor pollutants constant at present- day levels, but allowed climate-sensitive natural emissions, like volatile organic compoun ds (VOCs) from vegetation, to vary in response to the simulated changes in climate. Coupling atmospheric chemical processes and the climate system presents considerable challenges because of the large number of physical , chemical, and biological processes involved, many of which are poorly understoo d, all interacting in complex ways. The types of modeling systems developed under this assessment permit the detailed exploration of the potential responses of air quality to climate change over the next few decades in a way that would be difficult or impossible with other approaches. For example, they permit the exploration of climate changes well outside of th e envelope of historical experi ence. In addition, they permit the systematic investigation of the multiple compe ting climate- and weather-related drivers of air quality interactions on the regional scale, which produce aggregate patterns of air quality change. This effort represents the first systematic attempt to use linked gl obal-to-regional climate and air quality modeling systems from multiple research groups to jointly investigate the regional dimensions of potential climate-induced air quality changes across the United States. The major findings from this suite of experiments are First, while these modeling studies cannot tell us what the future will hold, they demonstrate the potential for gl obal climate change to make U.S. air quality management more difficult, and therefore future air quality management decisions should begin to account for the impacts of climate change. Second, the science of modeling climate and at mospheric chemistry for the purposes of understanding the sensitivity of regio nal air quality to climate change is in its early stages. This effort highlights a number of uncertainties that limit the information that can be provided to support decision-making, as well as what work is needed (some currently underway) to begin addressing these uncertainties.

xviiiThe synthesis of scientific information in th is report supports the scientific community and air quality managers and policy makers by

• Providing an improved understanding of the ric hness and range of behaviors of the global change-regional air quality system;

• Providing an appreciation for the strengths a nd limitations of the scientific tools and methods used to develop this improved understanding;

• Creating the foundation for a suite of collabo rative activities between the scientific research and air quality policy and manageme nt communities to investigate specific air quality policy and management questions.

The two major findings rest on a foundation of a number of more detailed conclusions drawn from the modeling studies. In support of the first major finding

• For every region of the country, at least one (usually multiple) of the modeling groups found that simulated climate change caused increases in summertime O 3 concentrations.

• These climate-induced increases, averaged ove r the summer season, we re in the range of approximately 2-8 parts per billion (ppb) for Maximum Daily 8-hour Average O 3 concentration, a key metric for regulating U.S. air quality.

• The climate sensitivity of O 3 was greatest for the peak po llution episodes that tend to occur over a number of days each summer, resu lting in substantially larger increases for these times than for the overall seasonal average.

While the results from the different research groups agreed on the above points, their modeling systems did not necessarily simulate the same regional patterns of climate-induced O 3 changes, with the individual simulations showing regions of little change, or even decreases, in
addition to the O 3 increases. This speaks to the second ma jor finding of this report, articulated above, of important modeling uncertainties. Certai n regions show greater ag reement than others: for example, there is very generally more agre ement on the spatial patterns of climate-induced
increases for the eastern half of the country than for the West, though parts of the Southeast show some of the strongest disagreements across th e modeling groups. These differences in the regional patterns of O 3 change result, in large part, from differences in how the different modeling systems, composed of different combin ations of climate models, chemistry models, greenhouse gas scenarios, and number of year s modeled, simulated the competing regional influences of changes in key meteorological dr ivers of air quality, especially the amount of sunlight reaching the surface and near-surface air temperature.

xixIn general, differences between climate si mulations tend to be more pronounced at the regional scales considered in this report than at the global scale. This is because of differences across models and simulations in the representa tion of large-scale circ ulation patterns that strongly affect regional meteorology, like the mid- latitude storm tracks and the subtropical high pressure systems. In addition, there are differe nces between models in how they capture smallscale processes, like clouds and precipitation, which al so are important for air quality. In the
studies discussed in this re port, these modeling uncertainti es strongly influenced the O 3 simulations, producing much of the difference in regional patterns of change between studies. For example, there were differences across mode ling groups in the regions of the country where simulated increases in cloud cover, and hence de creases in the amount of sunlight reaching the surface, partially counteracted the e ffects of warming temperatures on O 3 concentrations in these regions. This highlights current limitations in our ability to understand regional impacts of global climate change. The results from the modeling studies discu ssed in this report clearly show that a complex interplay between multiple meteorological factors drives regional O 3 changes. Simply considering a single variable, such as temperat ure, may not provide a sufficient basis for determining future air quality risks due to climate change in every region. Another important impact is that climate chan ge leads to changes in the natural emissions of VOCs., e.g., isoprene from vegetation. All of the modeling groups found climate-induced increases in these biogenic VOC emissions over most of the Un ited States, with especially pronounced increases in the Southeast. Howeve r, there are large disagreements across the different groups as to the degree to which these increases affect O 3 concentrations, with some simulations showing large O 3 increases while others show little change. An importa nt factor that helps explain these differences is the differing representation of isoprene nitrate chemistry in the different modeling systems, another key uncertainty in the science.
In addition to the issues disc ussed above, most of the groups examined the importance of year-to-year variability on thei r results to some degree. Th ese groups found that the climateinduced differences in O 3 concentrations are roughly the same size as present-day year-to-year variability. This implies that simulated future O 3 change can be strongly affected by the choice of present-day and future years to compare. It also implies that climate change has the potential to push O 3 concentrations in extreme years beyond the envelope of current natural year-to-year variability. Finally, while this report focuses mainly on summertime results, some of the modeling groups also found climate- induced increases in O 3 concentrations in some regions for the spring and fall, suggesting a possible future extension of the O 3 season that would present additional challenges for air quality managers.

xxMoving forward, this report has highlighted key areas for improving integrated climate and air quality modeling systems that can delive r improved information to meet evolving climate policy and air quality management decision support needs. These include:

• Using recent advances in global and regiona l models, parameterizations, and downscaling techniques to build more advanced coupled climate and air quality modeling systems;

• Developing ensembles of multiple modeling systems over many years of simulation to develop more robust results of air qua lity sensitivity to climate change;

• Carrying out more extensive evaluations of c limate models for their ability to represent processes (and timescales) that strongly influe nce regional air qualit y, such as regionalscale stagnation events.

• Carrying out more extensive evaluations of atmospheric chemistry models for their ability to represent certain chem ical pathways that lead to O 3 climate change sensitivity, such as the chemical fate of isoprene nitrate.

Finally, this report provides a preview of ongoing and upcom ing work under Phase II of the overall assessment: explori ng the uncertainties discussed above; extending the modeling systems to investigate the climate sensitivity of additional pollutants (i.e., particulate matter and mercury) in greater detail; expl oring feedbacks between chemistry and climate; and assessing the integrated effects of changes in climate and ch anges in emissions of pollutants by changes in human systems, such as population growth and migration, economic development, new regulations, energy use and technology, and land use.

xxiSUMMARY OF POLICY RELEVANT FINDINGS

The recent Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) states, “Warming of the climate sy stem is unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice, and rising globa l average sea level” (IPCC, 2007). Directly relevant to EPA’s mission to protect human health and the enviro nment is the IPCC finding that, “Future climate change may cause significant air quality degradation by changing the dispersion rate of pollutants, the chemical environment for ozone and aerosol generati on and the strength of emissions from the biosphere, fires and dust. Th e sign and magnitude of these effects are highly uncertain and will vary regionally.” Climate ch ange impacts have not yet been explicitly considered in air quality program planning—accoun ting for them will be a critical challenge for the air quality management system in the coming decades. In partnership with EPA’s Office of Ai r and Radiation (OAR) and several Regional offices, the EPA’s Office of Research and Development (ORD) Global Change Research Program began an assessment effort to increase scientific understanding of the multiple complex interactions between climate and atmospheric chem istry. The ultimate goal of this assessment is to enhance the ability of air qua lity managers to consider glob al change in their decisions through improved characterization of the potential impacts of global change on air quality. An integrated framework for the assessment wa s designed that leveraged the research and development strengths within the EPA, within ot her agencies, and within the academic research community. This framework calls for first deve loping insight into the range of possible air quality responses to future climate changes al one (Phase I) before tackling the additional complexities of integrating the effects of potentia l future changes in anthropogenic emissions and long-range pollutant transport with these climate-only impacts (Phase II). The core approach of the assessment is the development of integrated modeling systems capable of capturing these effects and applying them in simulations to ex plore the global change-air quality problem. This interim report provides an update on the progress in this first phase of the assessment. Its primary focus is on the potential changes in U.S. regional air quality due to global climate change alone, incl uding direct meteorological im pacts on atmospheric chemistry and transport, and the effect of these mete orological changes on clim ate-sensitive natural emissions of pollutant precursors. The aim in th is phase was to consider the effects of climate change in isolation, without accompanying chan ges in anthropogenic emissions of precursor pollutants expected to occur over the same timeframe. Future reports will explore the potential impacts when also considering possible ch anges in future air pollution emissions.

xxiiTwo “grand challenges” have emerged in th e course of developi ng and conducting this
assessment. The first arises from the Global Change Research Program’s emphasis on decision support, namely, to provide the best possible sc ientific basis for unders tanding potential climate change impacts on air quality and air quality polic ies in a useful form and a timely manner as one key set of inputs to help managers develop pollution control strategi es. The second “grand challenge” is to convey to the scientific rese arch community the knowledge gaps that limit our understanding of the problem and/or create barrie rs to the use and interp retation of scientific information by decision makers. The discussion below summarizes information that has emerged from the assessment to
date. Most of the discussion centers on topics related to tropospheric ozone (O 3 ) since our understanding of O 3 is more complete at this time than that of particulate matter (PM). Preliminary findings related to PM are presented where available. Unless otherwise indicated, to isolate the impacts of climate change, all mode l results discussed are for simulations that assumed no future changes in the anthropogenic emissions of precursor pollutants. Also, unless otherwise indicated, “future” refe rs to the time period around 2050. The organization of the rest of this Summary is as follows: In the first sub-section, what has been learned about possible impacts of climate change on O 3 (and PM) concentrations is presented. With this information in hand, in th e second sub-section, it is then possible to focus on those meteorological drivers important for ai r quality and highlight complexities in the interaction between these drivers and pollutant c oncentrations, such as reinforcing or competing
effects of individual drivers. The third sub-s ection discusses climate change impacts on climatesensitive natural emissions of po llutant precursors. The fourth and fifth sub-sections discuss important modeling uncertainties, and preliminary sensitivity te sts comparing the first-order impacts of climate and anthropogenic emissions cha nges, respectively, as previews of issues that
will receive more attention in th e next phase of the assessment.

I. Summary of Impacts on O 3 (and PM) Concentrations

A. Climate change has the potential to produce significant increases in near-surface O 3 concentrations throughout the United States.

1. A large number of earlier observation- and model-based studies have demonstrated connections between meteorol ogical variability and O 3 concentrations and exceedances, implying the possibility of climate change leading to increasing O 3 levels in some regions.

2. The new modeling studies discussed in this report show increases in summertime O 3 concentrations over substant ial regions of the country as a result of simulated 2050 climate change. These results were obta ined under the assumption of anthropogenic emissions of precursor pollutants held cons tant at present-day levels while allowing

xxiiifor some changes in climate-sensitive natu ral emissions. For nearly every region of the country, at least one (usu ally multiple) of the mode ling groups found that climate change caused increases in summertime O 3 concentrations.

3. Where these increases occur, the amount of increase in summertime average Maximum Daily 8-hour Average (MDA8) O 3 concentrations across all the modeling studies tends to fall in the range 2

− 8 ppb, as illustrated in the figures shown in Section 3.

4. The largest increases in O 3 concentrations in these simulations occur during peak pollution events. (For example, the increases in 95 th percentile of MDA8 O 3 tend to be significantly greater than t hose in summertime-mean MDA8 O 3 .)

5. Though in agreement on the above points, the different modeling systems did not necessarily simulate the same regi onal patterns of climate-induced O 3 changes, with the individual simulations showing some regions of little change, or even decreases, in addition to the O 3 increases.

6. As will be discussed in Sections II and III below, these disagreements in the spatial patterns of future O 3 changes can largely be attributed to the wide variations across simulations in the patterns of changes of ke y meteorological driv ers (e.g., temperature and cloud cover), along with the differi ng representations of key chemical mechanisms in the various model systems.

7. There is greater agreement across simulations in these O 3 changes for certain regions than for others. For example, there is generally more agreement on the spatial patterns of climate-induced in creases for the eastern half of the country than for the West, though parts of the Sout heast show some of the st rongest disagreements across the modeling groups.

8. A subset of results also suggests that climate change effects on O 3 grow continuously over time, with evidence for significant imp acts (in the same direction as described above) emerging as early as the 2020s. For example, the Columbia research group (which simulated only the eastern half of the United States) found significant summertime O 3 increases across broad swathes of the Midwest and Mid-Atlantic by the 2020s, with greater incr eases by the 2050s and 2080s. Relevance for air quality policy: These studies suggest th at EPA’s Office of Air Quality Planning and Standards should begin to consider climate change, for example, in the next update of EPA's ozone modeling guida nce, especially for planning horizons in 2020 and beyond. In other words, they may need to account for a “climate penalty” that could influence the amount of controls needed in some locations. Conflicting results among simulations for certain regions of the country suggest that evaluations of the potential effectiveness of future controls in t hose regions will be part icularly sensitive touncertainties in the modeling systems. The findings also indicate that, where climatechange-induced increases in O 3 do occur, damaging effects on ecosystems, agriculture, and health may be pronounced, due to increas es in the frequency of extreme pollution
events.

xxivB. Climate change has the potential to push O 3 concentrations in extreme years beyond the envelope of current natural year-to-year va riability. In addition, it has the potential to lengthen the O 3 season.
1. Interannual variability in weather conditions plays an important role in determining average O 3 levels and exceedances in a given year . For example, statistical analyses of current O 3 observations show that, for several U.S. cities that have not attained the current O 3 NAAQS, weather-related interannual va riability can increase or decrease observed mean O 3 concentrations by as much as 10 ppb from the 25-year (1981

− 2006) mean.

2. The subset of modeling groups that exam ined multiple simulation years for both present-day and future climate found that, in many regions, in creases in summer O 3 concentrations due to climate change were comparable in magnitude to, or even greater than, simulated presen t-day interannual variability.

3. Similarly, a subset of the future climate simulations showed that, for parts of the country with a defined summertime O 3 season, climate change expanded its duration into the fall and spring. Relevance for air quality policy: Multi-year simulations may be necessary to support the development of long-term air quality contro l strategies, to capture the effects of both natural meteorological variability and climat e-induced changes. Air quality managers may also need to plan to extend the season over whic h they monitor O 3 concentrations and be prepared to issue air quality alerts earlier in the spring and later into the fall.

C. Climate change is expected to cause a decrease in O 3 concentrations in remote areas with low ambient NO x levels.

1. The global modeling studies desc ribed in this report simula te general decreases in O 3 concentrations over remote areas with low NO x concentrations (e.g., oceans) as a result of climate change. Consiste nt with current understanding of O 3 chemistry, this is due to increased O 3 destruction in an atmosphe re with more water vapor.

2. This decrease is in contrast to the sign ificant climate-related increases for many already-polluted areas.

3. The relative impact of these changes in remote background O 3 on simulated U.S. O 3 concentrations is unclear. One potential influence pathway seen in some of the modeling results is an increas ed mixing of clean air into coastal areas, via stronger ocean-land flow combined with the reduced O 3 concentrations over the oceans. Relevance for air quality policy: Changes in O 3 concentrations as a result of climate change will depend, in part, on whether an ar ea is clean or polluted, and/or on the degree of influence of air masses from adjacent clean or polluted areas. For example, under low NO x conditions, a reduced atmospheric lifetime for O 3 in the future due to increased humidity may imply reductions in the quantity of O 3 transported downwind.

D. The potential impact of climate change on PM is less well understoo d than that on O 3 . Preliminary results from the modeling studie s show a range of increases and decreases in PM concentrations in different regi ons and for different component chemical species in the same region.

xxv1. Precipitation is a more important primary meteorological driver of PM than of O 3 , due to its role in removing PM from the atmosphere (wet deposition). Precipitation is particularly difficult to model and tends to show greater disagreement across simulations than other variables.

2. Aerosol chemical processes, especially those concerning the formation of organic aerosols and aerosol/cloud in teractions, are not fully understood and therefore not well characterized in current regional air quality models.

3. In addition, increase in wildfire frequency associated with a warmer climate has the potential to increase PM levels in certain regions, but the relative importance of this effect is not well characterized.

4. Preliminary simulation results suggest that the PM response reflects the combined climate change responses of the individual species that make up PM (e.g., sulfate, nitrate, ammonium, black carbon, organic carbon, etc.). Depending on the region, these individual responses can be in compe ting directions, producing either increases or decreases in PM (on the order of a few percent). Relevance for air quality policy: The more limited scientific understanding and greater modeling uncertainties concerni ng the production and loss of PM highlight the need for future research. Assessing the effects ofa changing climate on PM on an airshed-byairshed basis may be helpful for considering th e detailed chemical characteristics of local PM, the possible range of changes in local pr ecipitation, and the potential influence of changing wildfire frequency. An upcoming EP A report that is expected to incorporate new research findings will address the impacts of climate change on PM in more detail. II. Impacts on Meteorological Va riables that Directly Affect O 3 Concentrations

A. Climate change has the potential to im pact a number of meteorological variables important for O 3 . Whether changes in these variables lead to increases, decreases, or no change in O 3 concentrations in a given region depends on whether the effects of these individual changes on O 3 act in concert or compete with each other. This discussion of meteorological mechanisms is intended to provide additional detail to the general conclusions summarized in Section I above.

1. The simulations discussed in this report all show significant future changes in meteorological quantities such as temperat ure, cloud cover, humi dity, precipitation, wind speed and pattern, and mixing depth.

2. However, there is significant variability ac ross simulations in the spatial patterns of these future changes.

3. As noted above in Section I.A, these vari ations across simulations help explain the disagreements in the spatial patterns of simulated future O 3 changes. Each simulation produces its own unique pattern of changes in these key meteorological drivers. The combined effects of all of these changes in individual O 3 drivers in turn help create the unique pattern of future O 3 changes across regions seen for each simulation.

4. For example, the different simulations provide examples of regions where both temperature increased and surface solar radiation increased (due to a decrease in cloudiness). These regions tended to experience increases in future O 3 concentration.

xxviIn contrast, regions where the changes in these variables were in opposite directions tended to have mixed O 3 results.

5. In general, variations in individual meteor ological drivers are not independent of each other. This is because these variable s are linked through underlying atmospheric processes, and thus there will tend to be consistent variations across groups of variables as a result of specific changes in pressure and cloud patterns. It is through such changes in short-term weather that th e effects of long-term climate change on O 3 are expressed. Relevance for air quality policy: It is the interrelationships between the many meteorological variables important for O 3 that determine O 3 concentrations at a particular time and place. Evaluating the potential influe nce of climate change on air quality and the potential effectiveness of fu ture control strategies will require accounting for these sometimes complex interactions. These comple xities can best be appreciated through the use of integrated modeling systems capable of simulating interactions among drivers in a realistic and self-consistent way. Current modeling uncertainties lead to disagreements about the spatial patterns of future change s in meteorological variables and, hence, the specific regional distributions of future O 3 changes across the United States.

B. Global climate change is expected to produ ce changes in planetary-scale circulation systems, thereby influencing regional we ather patterns. These changes have the potential to strongly affect regional O 3 concentrations, since O 3 episodes are driven by synoptic meteorolog ical variability.

1. Observations suggest that the extratropi cal storm tracks have moved poleward over the last few decades. A number of recent modeling studies suggest that this trend could continue into the future (IPCC, 2007) , resulting in significant changes in winds, precipitation, and temperature patterns in mid-latitudes, with implications for the simulated frequency and duration of synoptic stagnation events a nd resulting extreme O 3 episodes.

2. Some of the modeling studies discussed in this report simulate increases in the duration and frequency of extreme O 3 events in the Midwest and Northeast that can be directly traced to the w eaker frontal systems and decr eased frequency of surface cyclone activity due to a poleward storm track shift.

3. There remains some disagreement across models of the effects of climate change on the summertime mid-latitude storm tracks a nd stagnation, however, as other studies discussed in this report do not seem to simu late these circulation changes as strongly, and/or do not simulate the corresponding O 3 increases.

4. Similarly, differences in simulations of th e climate response of other key large-scale circulation patterns, like th e Bermuda High off the U.S. east coast, also can produce significant differences in the amount and sp atial distribution of simulated future O 3
. Relevance for air quality policy: Changes in large-scale circulation systems can have a significant impact on O 3 throughout the country. For example, understanding and accounting for changes in synoptic stagnation events resulting from large-scale storm track shifts is critical for understanding potential changes in future O 3 concentrations in the northern portion of the United States. At present, modeling uncertainties persist, and

xxviifurther research is needed. Consideration of historic patterns in local meteorology versus current observations may help determine whet her and where changes in stagnation should be addressed in city-lev el air quality planning. III. Impacts on Climate-Sens itive Natural Emissions of O 3 Precursors

A. Climate change has the potential to increase biogenic emissions of O 3 precursors, but significant uncertainties remain about the impact of these emissions changes on O 3 concentrations in a given region. Increases in lightning NO x production may also be a factor in future O 3 changes. It is important to note that the modeling results discussed in this report do not account for all clim ate-sensitive natural emissions of chemical precursors, excluding, for example, oceanic d imethyl sulfide and sea-salt, mineral dust, methane from wetlands, an d wildfire emissions.

1. Earlier observational studies s uggest that increases in bi ogenic emissions of volatile organic compounds (VOCs) would occur in many regions as a result of the higher temperatures associated with e xpected future climate change.

2. The modeling studies discussed in this repor t generally simulate increases in biogenic VOC emissions over most of the country as a result of climate change, with particularly substantial increases in ce rtain regions, notab ly the Southeast.

3. However, these biogenic emissions increases do not necessarily correspond with large O 3 concentration increases, depending on th e region and modeling system used. One reason for this appears to be because the response of O 3 to changes in biogenic VOC emissions depends strongly on how isoprene chemistry is represented in the models.

4. Globally, an increase in the ra te of natural production of NO x by lightning is expected in a warmer and wetter climate. Some of the simulations discussed here examined this issue and did, in general, see future increases. As the significance of these results for regional U.S. O 3 concentrations is unclear given the research available at this time, these findings are not hi ghlighted in this report. Relevance for air quality policy: Resolving uncertainties in the response of O 3 to biogenic emissions changes is important to improve the understanding of potential climate change impacts on O 3 . For example, the success of regional O 3 control strategies in regions like the southeastern United States may be highly sensitive to this uncertainty—additional anthropogenic emissions c ontrols may need to be considered to offset climate-induced increases in biogeni c emissions, but only if these emissions increases will lead to large O 3 increases. A better understanding of the chemical reactions involving isoprene nitrate is critical for resolving this issue. Regional O 3 control strategies in areas wh ere biogenic VOC emissions are projected to increase due to climate change are likely to continue to be NO x -limited areas and thus continue to respond to NO x emissions decreases with O 3 concentration decrease s. In addition, localand regional-scale O 3 modeling does not typically consider NO x production from lightning. Given potential fu ture changes in lightning NO x emissions, long-term air quality management strategies may need to account for growth in this source as well.

xxviiiIV. Modeling Uncertainties

A. Simulated future U.S. regional air quality is highly sensitive to model configuration choices in the integrated global-to-regional climate and air quality modeling systems used in this assessment.

1. As discussed in Section II above, there ar e large differences across modeling groups, and/or across different model configurati ons used by the same group, in the specific spatial patterns of future simulated changes in meteorology that lead to differences in simulated future concentrations of O 3
. 2. These differences in simulated meteorology canlargely be traced to differences in a number of elements of m odel system configuration. Key elements include which global climate model (GCM) was used to simulate future global climate change, whether the output from this GCM was “dow nscaled” to much higher resolution over the United States with a regional climat e model (RCM), and which model physical parameterizations were used, for exampl e for representing cumulus convection.

3. Sensitivities of air quality-relevant meteorology to other parameterizations (e.g., for turbulent mixing, radiative transfer, micr ophysics, and land-surface processes) may also be important but have yet to be examined systematically.

4. The specific techniques used to implemen t the downscaling of the GCM output with an RCM may also significantly affect the result s, but this issue is still to be examined systematically as well.

5. As discussed above, there are also si gnificant sensitivities of simulated O 3 concentrations to uncertainties in the repr esentation of key chemi cal processes in the models.

6. The choice of future greenhouse gas scenario also affects the future GCM climate simulation, though in 2050, as opposed to th e end of the century, the range in greenhouse gas forcing across the various IPCC scenarios used in this assessment is still relatively small. Relevance for air quality policy: It is important to carefully select and describe the GCM, RCM, model physical parameterizations, and downscaling techniques used as part of any model-based analysis of potential future changes in ai r quality. Interpretation of the causes of simulated air quality changes wi ll, in general, be highly sensitive to these components. Additional efforts to understand and quantify the uncertainties associated with these components (as planned for Phase I I) will aid in the inte rpretation of results produced by these modeling systems. Furtherm ore, work is needed on new strategies for incorporating information from climate m odels into uncertainty analysis while accounting for all sources of uncertainty. V. Combined Impacts of Climate and Anthropogenic Emissions Changes

A. Preliminary work suggests that the impacts of climate change on future U.S. regional O 3 concentrations remain significant wh en also considering possible future anthropogenic O 3 precursor emissions changes. S everal major efforts to address the combined impacts are underway and will be the subject of another EPA Global Change Research Program report in 2012.

xxix1. A number of the modeling teams whose resu lts are discussed in this report also carried out simulations with modified future air pollutant emissions constructed using spatially non-explicit scaling factors genera lly derived from the assumptions used to formulate the various IPCC gree nhouse gas emissions scenarios.

2. These preliminary tests found that the comb ined effects of climate and anthropogenic precursor emissions changes are highly sens itive to the assumptions about future emissions trajectories.

3. For example, simple scaling of future emi ssions to match the gross assumptions of the IPCC A1B or B1 Special Report on Emissi ons Scenarios (SRES) scenario (IPCC, 2000) resulted in substan tial reductions of U.S. NO x emissions in 2050, which in turn resulted in corresponding reduc tions in simulated future O 3 concentrations. In contrast, using future emissions consiste nt with the weaker pollutant control assumptions in the “dirtier” A2 or A1Fi s cenarios tended to result in higher future O 3 concentrations.

4. The size of the climate change impact on air quality is highly dependent on the emissions levels. In other words, the eff ects of climate and emissions changes were not, in general, additive.

5. These results highlight the need for emissi ons scenarios with greater regional detail, consistency between global and regiona l assumptions, and consistency between greenhouse gases and precursor emissions. Me eting this need is a major focus of Phase II of the assessment effort. Relevance for air quality policy: While existing air quality controls will likely continue to produce significant benefits, to the exte nt that climate change may increase O 3 concentrations in some areas and therefore th reaten the ability of an area to attain or maintain air quality standard s, additional controls (i.e., a climate penalty) may be required. Preliminary results suggest that th e magnitudes of additional controls could be significant in certain regions but also th at they are highly dependent on detailed assumptions about future emissions. Expl oring these assumptions and improving our understanding of the fundamental emissions drivers, as part of Phase II of this assessment, is expected to lead to the crea tion of improved scenarios of future emissions that in turn will be integr ated into the climate and ai r quality modeling systems to produce more robust estimates of potentia l climate impacts on control policies. This is an interim report, and, therefore, these findings should be considered to be preliminary. Future reports will update, refi ne, and augment the synthesis contained herein. Finally, it is important to emphasize that th is assessment is a science assessment, not a policy assessment. In other words, the primary means by which this assessment will achieve its ultimate goal of enhancing the ability of air quality managers to consider global change in their decisions is through the development of tools an d a knowledge base to answer science questions about the potential impacts of global cha nge on air quality. The resulting improved understanding of the behavior and complexities of the system can then provide a basis for a suite of parallel, collaborative activities between the sc ience and policy audiences of this report. Such

xxxxxxi activities would be aimed at answering specific air quality management questions and might include, for example, the development of new t ools and models, designed with an explicit focus on decision support, that incorporate the new scie ntific and technical kno wledge gained as a
result of this assessment. The initiation of such collaborative efforts would represent a significant assessment outcome.1 INTRODUCTION TO THE PROBLEM

1.1 INTRODUCTION The recent Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) found that “Warming of the climate sy stem is unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice, and rising globa l average sea level” (IPCC, 2007). The IPCC also found that “Most of the observed increase in globally averaged temperatures since the mid-20 th century is very likely due to the observed increase in anthropogenic greenhouse ga s concentrations.” Furthermore, of particular importance for the U.S. Environmental Protection Agency’s (EPA) mission to protect human health and the envi ronment was the IPCC’s finding that “Future climate change may cause significant air quality degradation by changing the dispersion rate of pollutants, the chemical environment for ozone and aerosol generati on and the strength of emissions from the biosphere, fires and dust. Th e sign and magnitude of these effects are highly uncertain and will vary regionally.” The National Research Council (NRC), in 2001, posed the question “To what extent will the U.S. be in control of its own air qualit y in the coming decades?” noting that “...changing climatic conditions could significan tly affect the air quality in so me regions of the U.S.” (NRC, 2001). The NRC called for the expansion of weathe r and air quality studie s to include “studies of how air quality is affected by long-term clim atic changes.” To address this concern, the EPA’s Office of Research and Development (ORD ) Global Change Research Program initiated a research effort to increase our understanding of the multiple complex interactions between climate and atmospheric chemistry. The ultimate goal of EPA’s air quality assessment is to enhance the ability of air quality managers to consider global ch ange in their decisions through improved characterization of the potential impacts of global change on air quality. This ultimate goal will be achieved vi a three distinct assessment sub-goals:

• To develop tools and a knowledge base to answer science questions about the impacts of global change on air quality.

• To deliver the general benefits to the air quality policy and management community that derive from addressing these science ques tions, namely, an improved understanding of the behavior and complexities of the global ch ange-air quality system, an appreciation for the strengths and limitations of the scientific tools and me thods used to develop this improved understanding, and an answer to the first and most basic “policy” question, “is climate change something we will have to account for when moving forward with U.S. air quality policy?”

1-1• To set the stage for determining how to apply these sc ientific insights a nd tools to help answer specific, detailed po licy and management questions.

This last sub-goal anticipates a separate activity, or set of activities, branching off from this science assessment, that will coalesce around specific air qua lity decision support needs. These activities might include, for example, developing new tools and models designed explicitly for decision support (rather than primarily for scie ntific research). This interim assessment report provides an update on the progress toward these three sub-goals. As will be discussed in more detail in Section 1.4 below, and in Section 2, the assessment design calls for first providing insight into possible air qualit y responses to future climate changes before tackling the additional co mplexities of incorporating potential future changes in anthropogenic emissions and long-range pollutant transport. Therefore, its primary focus is on the potential changes in U.S. regiona l air quality due to globa l climate change alone, including direct meteorological impacts on atmos pheric chemistry and tran sport, and the effect of these meteorological changes on climate-sensitiv e natural emissions of pollutant precursors. As such, this interim report cannot fully address questions related to th e importance of changing future anthropogenic emissions of air pollutants. Meeting this need is a major focus of Phase II
of the assessment effort. The following sub-sections will present the major themes that run through this report, provide background on the potential links between climate and ai r quality that motivate the science questions underlying the assessment research, outline the structure and design of the overall assessment, identify the assessment stak eholders, discuss issues related to handling scientific uncertainty, and present a roadmap to the rest of the report.

1.2 MAJOR THEMES OF THE IN TERIM ASSESSMENT REPORT In the course of conducting this assessment, two “grand challenges” have emerged. The first stems directly from the EPA Global Ch ange Research Program’s emphasis on decision support. The challenge is to provide the best possible scientific basis for understanding the potential range of impacts of climate change on ai r quality, and air quality policies, in a useful form and a timely manner, as one important set of information inputs to help managers develop appropriate pollution control stra tegies. Having these improved insights into the way the global change-air quality system works may yield new options for addressing air quality issues or minimize the potential for introducing policies with significant unintended consequences. At the same time, the complexity of the problem, and hence the data, models, and techniques used to address it, means that many unanswered scientif ic questions and unresolved uncertainties will exist at a given point in the d ecision-making timeline. These mu st be understood and accurately

1-2conveyed to policy makers so they have a sense of the levels of confiden ce underlying individual
elements of this sc ientific understanding. The second “grand challenge” is to convey to the scientific research community the key knowledge gaps that limit our understanding of the problem and/or create barriers to the use and interpretation of scientific information by decision makers. These range from the sensitivity of regional climate simulations to the parameteriza tions and methods used in downscaling to how
the intricate details of the chemical mechanisms are represented in the models. For example, as will be discussed in Section 3, there are a number of meteorological metrics that are crucial for modeling regional air quality for which th e climate modeling community has not yet systematically evaluated the skill of their m odeling systems. Similarly, future emissions scenarios that are consistent across pollutant s and geographic scales and that incorporate important processes such as fire, land use, biogenic emissions, and technological change are lacking, limiting the kinds of studies that can be accomplished at this time. It is possible to think of these challenges as informing two parallel “readings” of this report, one tuned to the perspec tive of a “science” audience and th e other to that of a “policy” audience. While these obviously intersect and ov erlap, each would highlight its own distinct set of issues, falling broadly under two questions: “What do we know, scientifically, about the climate change-air quality problem?” and “What might this knowledge mean for me, as an air quality manager?” For example, for the scientific audience, th is report generates a dditional information by synthesizing across the findings from multiple re search groups. This synthesis improves our understanding of the potential for climate change to impact air quality in different regions of the United States and the complex interplay between air quality and its di fferent climatic and meteorological drivers. It also th rows into relief scientific and technical uncertainties that will be helpful in guiding future research efforts. For the policy audience, the scie ntific findings presented in this report begin to answer the question raised above: “Is climate change something we will have to account for when moving forward with U.S. air quality policy?” In addition, by illuminating the subtleties and complexities of the interactions between climat e, meteorology, and air quality, these findings can inform thinking about policy responses. This kno wledge can be carried forward into the next phase of the assessment, which will consider added complications such as changes in anthropogenic emissions drivers. Furthermore, this report provides a basis for evaluating the
relative robustness of these scien tific findings in light of the un certainties that surround them. Finally, all of these general insights create a foundation for targeted efforts to solve specific air quality management problems.

1-31.3 BACKGROUND

1.3.1 Air Pollution EPA's mission is to protect human health and the environment. To achieve this mission, EPA implements a variety of programs unde r the Clean Air Act that reduce ambient concentrations of air pollutants, including thos e that cause smog, haze, and acid rain. Pollutants such as ozone (O 3 ) are not emitted directly into the atmosphere: instead they are created by chemical reactions between nitrogen oxides (NO x ) and volatile organic compounds (VOCs) in the presence of heat and sunlight. VOCs are em itted from a variety of sources, including motor vehicles, chemical plants, refineries, factor ies, consumer and commercial products, other industries, and natural (biogenic) sources. NO x is emitted from motor vehicles, power plants, other sources of combustion, and natural sources including lightning and bi ological processes in
the soil. EPA’s efforts have been successf ul: between 1980 and 2007, emissions of VOCs and
NO x decreased by 50 and 39 percent respectively, even though gross domestic product increased 124 percent, vehicle miles travel ed increased 103 per cent, and energy consumption increased 30 percent (U.S. EPA, 2008; see also http://www.epa.gov/airtrends/sixpoll.html ). Air pollution, however, continue s to be a widespread public health and environmental problem in the United States. In 2007, approxi mately 158 million people lived in counties that
exceeded at least one of the National Ambient Air Quality Standards (NAAQS). The health effects of air pollution range from increased mo rtality to chronic effects on respiratory and cardiovascular health (e.g., see Je rrett et al., 2009). Air pollution also has been associated with increased use of health care services, includi ng visits to physicians and emergency rooms and admissions to hospitals. Other e ffects include reduced visibility, damage to crops and buildings, and acidifying deposition on soil and in water b odies, where the chemistry of the water and resident aquatic species are affected. 1 Moreover, there is growi ng concern that global change may make it more difficult to reach these goals. The air quality assessment effort itself does not address health and other effects. However, it will provide information that will be used in the Global Program’s climate and air quality health assessments, the first of which focuses on O 3
.

1.3.2 Climate Change and Air Quality Linkages The NRC, in 2001, highlighted the linkages betw een climate and region al air quality and the need for a comprehensive research strategy: Air pollution is generally studied in terms of immediate local concerns rather than as a long-term 'global change' issue. In the coming decades, however, rapid

1 See, for example the Ozone Criteria Document, at http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=149923 , and the Particulate Matter Criteria Document, at http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=149923

. 1-4population growth and urbanization in ma ny regions of the world, as well as changing climatic conditions, may expand the scope of air quality concerns by significantly altering atmo spheric composition over broad regional and even global scales. ... Although air quality and c limate are generally treated as separate issues, they are closely coupled throu gh atmospheric chemical, radiative, and dynamical processes. ... A better understan ding is needed in order to make accurate estimates of future changes in climate and air quality and to evaluate options for mitigating harmful changes.

Coupling atmospheric chemical processes and the climate system remains a challenge to the science and modeling commun ities, however, because a large number of physical, chemical, and biological processes are invo lved (see Figure 1-1), and many of these are poorly understood.

Figure 1-1

. Schematic representation of th e multiple interactions between tropospheric chemical processes, bi ogeochemical cycles, and the climate system. RF represents radiative forcing, UV re presents ultraviolet radiation, and IR represents infrared radiation. The red arrows, discussed below in Section 1.4.1, represent the scope of EPA’s assessm ent effort. (Adapted from IPCC, 2007.) Atmospheric Concentrations CH 4 VOCs

CO

Pollutant Emissions

• Fossil Fuel Combustion

• Biomass Burning

• Industrial Emissions

• Emissions Relating to Land

‐ use Change 

### Climate Nitrogen Cycle Emissions Environmental Concentrations Atmospheric Concentrations 

## O 3

Ecosystem Nutrients Æ Biogenic Emissions Lightning, Precipitation, Temperature RF (IR) CH

4 , VOC Emissions

Rates RF (UV + IR) O 3 Production O 3 Production

Carbon Oxidation (via OH) Temperature, Humidity, Dynamics

N 2 O, RF NO x , HNO 3 Lifetimes

Atmospheric Concentrations CH 4 VOCs

CO Atmospheric Concentrations CH 4 VOCs

CO Atmospheric Concentrations CH 4 VOCs

CO

Pollutant Emissions

• Fossil Fuel Combustion

• Biomass Burning

• Industrial Emissions

• Emissions Relating to Land

‐ use Change Pollutant Emissions

• Fossil Fuel Combustion

• Biomass Burning

• Industrial Emissions

• Emissions Relating to Land

‐ use Change 

### Climate Nitrogen Cycle Emissions Environmental Concentrations Nitrogen Cycle

Emissions Environmental Concentrations Nitrogen Cycle

Emissions Environmental Concentrations Atmospheric Concentrations 

## O 3

Ecosystem Nutrients Æ Biogenic Emissions Lightning, Precipitation, Temperature RF (IR) CH

4 , VOC Emissions

Rates RF (UV + IR) O 3 Production O 3 Production

Carbon Oxidation (via OH) Temperature, Humidity, Dynamics

N 2 O, RF NO x , HNO 3 Lifetimes

1-51.3.2.1 Air Quality Impacts on Climate Change Prior to the mid-1970s, anthropogenic clim ate change was largely viewed as a
CO 2 -driven phenomena. This picture began to change with a series of papers on non-CO 2 reactive gases including:

• The impact of Chlorofluorocarbons (CFCs) on the greenhouse effect (e.g., Ramanathan, 1975);

• The impact of NO x on stratospheric O 3 (a strong greenhouse gas) (e.g., Crutzen, 1972);

• The identification of methane (CH 4 ) and nitrous oxide (N 2 O) as greenhouse gases (e.g., Wang et al., 1976); and

• The contribution of tropospheric O 3 (and therefore CO, VOCs, and NO x ) to global warming (e.g., Fishman et al., 1980).

A World Meteorological report published in 1985 concluded that trace gases other than
CO 2 contributed as much anthropogenic climate forcing as CO 2 since the indus trial revolution (Ramanathan et al., 1985), and our understandin g of the multiple strong and complex links between climate and air quality have con tinued to evolve (Ramanathan and Feng, 2009). Air pollution emissions can also affect con centrations of the hydr oxyl radical (OH), the primary cleansing agent of the lower atmosphere, with increases in NO x tending to elevate OH levels, while increases in CO have the opposite effect. Changes in OH affect the lifetime and
thus the concentrations of r eactive greenhouse gases such as CH 4 , HFCs, and HCFCs (NRC, 2001). The nitrogen cycle itself plays a key role in climate and atmospheric chemistry, contributing N 2 O (a greenhouse gas), NO x (an O 3 precursor, with indirect effects on CH 4 via shortening of atmospheric lifetime), and ammonia (NH 3 ), which contributes to the formation of sulphate and nitrate aerosols (IPCC, 2007). Aerosol particles affect climate by scattering a nd absorbing radiation (t he “direct effect”) and through their impact on clouds (the “indirect e ffect”). Aerosols inte ract with clouds and precipitation in a variety of ways: e.g., by acti ng as cloud condensation nu clei and/or ice nuclei; through effects on the albedo or re flectivity of the cloud; and by im pacting cloud lifetimes. Such effects can change precipitation patterns as well as cloud extent and opti cal properties (CCSP, 2009).

1.3.2.2 Climate Change Impacts on Air Quality Concerns about the impacts of climate cha nge on air quality are grounded in information
derived from a wealth of obser vational studies, knowledge of ba sic atmospheric chemistry, and, more recently, modeling studies (see Appendix A for mo re details about these lines of evidence).

1-6For example, there have been many empirical an alyses showing that we ather patterns play a major role in establishing conditions conducive to O 3 formation and accumulation, given sufficient levels of precursor pollu tants such as nitrogen oxides (NO x ) and volatile organic compounds (VOCs): e.g., year-to-y ear variability in warm-season climate is strongly correlated with variability in O 3 exceedances. Generally speaking, me teorological conditions favorable to high levels of O 3 include sunshine, high temperatures, a nd stagnant air (NRC, 1991). However, this NRC report also cautioned about the potential complexities of the problem arising from interactions between key drivers, noting that, fo r example, the relationship between temperature
and O 3 “cannot readily be extrapolated to a warmer climate because higher temperatures are often correlated empirically w ith sunlight and meteorology.” A variety of statistical me thods have been successfully applied to weather, O 3 , and other data to obtain short-term air quality forecasts (U.S. EPA, 1999), estimate time trends (Thompson et al., 2001; Bloomfield et al ., 1996; Cox and Chu, 1993; Camalie r et al., 2007), and increase understanding of underlying mechanisms (Sillman and Samson, 1995). There are substantially fewer observations for particulate matter (PM), as monitoring networks have been in place for a much shorter time period. This should improve over time as more data become available. Two early modeling studies (Morris et al., 1995 ; U.S. EPA, 1989) of the effect of a warming climate on U.S. O 3 levels considered a uniform 4° C increase in temperature across horizontal, vertical, and temporal scales. 2 The EPA study modeled specific episodes and simulated changes in daily 1-hour maximum O 3 concentrations ranging from +3 to +20% for
Central California and from

− 2.4 to +8% for the Midwest and S outheast. Morris et al. (1995)
included the effect of warmer conditions on m obile source and biogenic emissions in their simulation of a 4-day episode in the Northeast, simulating O 3 concentration increases of
15

− 25 parts per billion by volume (ppb) in much of the modeling domain above baseline daily
one-hour maximum concentrations of 110

− 120 ppb and 120

− 140 ppb (i.e., increases of
10

− 20%). The results of these early studies suggested th at regional air quality may be sensitive to a warming climate, creating an additional challenge for air quality managers. However, as noted by the authors, their studies were constrained by the limitations of the tools and data available at the time. It was recognized that the relationship between climate change and air quality was not a simple one of “higher temperatures equals wo rse air quality” (NRC, 1991; U.S. EPA, 1989). The number of meteorological factors, and the complex interactions between and among them and air pollutants (see Box 1-1), highlight the need to use so phisticated modeling tools and experimental designs to help understand the mu ltiple ways that climate change can affect

2 Because of the technical hurdles existing at the time in adapting climat e model output to be input to a regional air quality model, the researchers elected to make this simplifying assumption.
1-7regional air quality. Fortunately, modeling capab ilities have improved substantially since that time and continue to improve.

Box 1-1. Climate Change Factors Important for Regional Air Quality Adapted from U.S. EPA (1989)

Changes in the following affect air quality:

• The average maximum or minimum temperature and/or changes in their spatial distribution and duration leading to a change in reaction rate coefficients and the solubility of gases in cloud water solution;

• The frequency and pattern of cloud cover leading to a change in reaction rates and rates of conversion of SO 2 to sulfate aerosols, l eading to acid deposition;

• The frequency and intensity of stagnation episodes or a change in the mixing layer leading to more or less mixing of polluted air with background air;

• Background boundary layer concentrations of water vapor, hydrocarbons, NO x , and O 3 , leading to more or less dilution of polluted air in the boundary laye r and altering the chemical transformation rates;

• The vegetative and soil emissions of hydrocarbons and NO x that are sensitive to temperature and light levels, leading to changes in their concentrations;

• Deposition rates to vegetative su rfaces whose absorption of pollutants is a function of moisture, temperature, light intensity, and other factors, leading to changes in concentrations; and

• Circulation and precipitation patterns leading to a change in the abundance of pollutants deposited locally versus those exported off the continent.

1.4 DESIGN OF THE GLOBAL CHANGE AND AIR QUALITY ASSESSMENT To address the need for an improved unders tanding of the potentia l impacts of global change on U.S. regional air quality, building on the scientific understa nding summarized above, an integrated assessment framework was designe d that blends the research and development strengths within the EPA with those of other agencies and the academic research community. The assessment program was designed to provide the scientific information and modeling capabilities to answer the following types of questions: 3

• What are the effects of plausible future cha nges in climate, climate variability, and landuse patterns on air qualit y, specifically ground-level O 3 and PM?

• What is the range of potential impacts of climate change on air quality relative to the range of potential impacts of emissions cha nges due to pollution c ontrols, technological development, and land-use change?

• How might the effectiveness of air quality management be affected by climate change, i.e., can changes in emissions, technology, and land use offset air quality changes due to climate change?

3 These questions were adapted from the November 2 002 EPA Global Change Research Program Research Strategy (EPA/600/R-02/087), which can be found at: http://www.epa.gov/ncea/ pdfs/glblstrtgy.pdf

. 1-81.4.1 Scope of the Assessment Effort The discussion in Section 1.3.1 is not a comp rehensive description of all the potential linkages between climate and atmospheric chemistr y. Instead, it is meant to highlight the fact that these linkages are complex, involve nonlinear coupling among nu merous processes, and that many of these are not well quantified. The scientific enterprise required to elucidate all of the linkages exceeds the resources available to the EPA’s Global Change Research Program. Accordingly, the Program elected to focus its efforts on the impact of climate change on regional air quality (the red arrows emanating from the “Climate” box in Figure 1-1) to inform and support EPA’s air quality programs. The NRC (2004) identified climate change as an important new challenge to the air quality ma nagement (AQM) system. The report concluded that “The AQM system must be flexible and vi gilant in the coming decades to ensure that pollution mitigation strategies remain effective and sufficient as our climate changes.” Focusing on climate effects on air quality al so takes advantage of the consid erable expertise within EPA in regional air quality modeling. Other federa l agencies have active research programs investigating other aspects of Figure 1-1, such as the feedback effects of aerosols and atmospheric chemistry on the climate system. The Atmospheric Composition research element of the U.S. Climate Change Science Program (CCSP) coordinates research on atmospheric chemistry and climate system intera ctions across the federal government. 4 The assessment addresses its questions in two phases. Phase I of the effort focuses on augmenting, linking, and applying existing climat e and atmospheric chemistry models to investigate the range of current and potential future meteorological effects on air quality. It does not include changes in air pollutant emissions other than those that are explicitly linked to meteorological variables and incorporated wi thin the models (e.g., biogenic VOC emissions, evaporative emissions, lightning NO x , depending on the modeling system). Phase II of the assessment focuses on the combined impact of changing climate and changing air pollutant emissions on air quality. It builds on the findings from the first phase by extending the linked modeling systems developed th erein, and also by expl oring the scientific uncertainties more comprehensively. Simultaneousl y, it integrates plausible, spatially detailed scenarios of U.S. criteria pollutant emissions 50 years in the future with the climate and air quality modeling efforts initiated in the first phase. The development of the tools to create plausible scenarios of technology, land use, and demographic changes needed to derive these emissions scenarios is a critical aspe ct of this phase of the assessment.

4 See http://www.usgcrp.gov/usgcrp/ProgramElements/atmosphere.htm

. 1-91.4.2 What is Covered in this Report The problem and challenge of air quality is de fined by its local impacts combined with its global dimensions and the linkages across scales and disciplines needed to address it. The purpose of this interim assessment report is to provide an update on our progress toward the development of tools and a knowledge framewor k that encompasses these linkages in the investigation of global change impacts on U.S. ai r quality. It is not intended to provide a comprehensive assessment of the literature. There have been several recent state-of-the-science reviews that provide such assessments (e.g., s ee IPCC, 2007 Chapter 7; Jacob and Winner, 2009; U.K. Royal Society, 2008). By design, the emphasis in this report is on EPA, and EPA-funded, work carried out under the EPA Global Change Research Program’s assessment. In Section 3, the focus is on results emerging from the subset of participating intramural and extramural research groups that are currently produci ng model simulations of the impacts of climate change on air quality, as part
of Phase I of the assessment. This is a mid-course overview of the findings to date from the several parallel efforts to build, test, and apply individual versions of these linked climate and air quality modeling systems. Notably, this is the fi rst systematic effort to apply combined global and regional climate and air quali ty models to investigations of potential climate change impacts on future U.S. regional air quality. Though the fo cus is on EPA, and EPA-funded, research, this body of work does in fact represent the large majo rity of the research to date in the area of applying these types of linked modeling systems to the problem of regional U.S. air quality (e.g., see Jacob and Winner, 2009). From a scientific perspective, the main goal is to assess the larger meaning of the various research groups’ model simulation results when examined all together. The aim is to synthesize the simulated air quality changes in different regions of the United States, as well as the dependence of these changes on different climatic drivers. By highlighting scientific and
technical uncertainties to which these findings are sensitive, the synthesis helps identify future
research needs. From a policy perspective, this synthesis acro ss scientific findings begins to answer the question: “Is climate change something we will have to account for when moving forward with U.S. air quality policy?” In addition, by illumi nating the subtleties and complexities of the interactions between climate, meteorology, and air quality, it he lps build up intuition about the way the coupled system works. Section 3 also provides an extended disc ussion of the challenges
and uncertainties associated with the modeling approach that underp ins the assessment, to create an improved understanding about the level of co nfidence in the scientific findings, and an appreciation for the limits on what questions th e science can answer now, and may be able to
answer in the future.

1-10Changes in anthropogenic forcing (i.e., foss il fuels, biomass burning, and land use) are not covered in this report. However, they will be addressed in the second phase of the assessment effort. Section 4 provides an overv iew of Phase II. O ngoing activities include investigation of modeling uncerta inties (for example, through the use of ensemble approaches), additional model development (for example, the incorporation of dynamic vegetation sub-models), and examination of additional pollu tants including PM and mercury. Preliminary results for PM are provided, but a more comprehensive presentation awaits future assessment reports focusing on these additional pollutants. Future assessment reports also will cover the combined impacts of changing climate and air pollu tant emissions on air quality. Initial results
from combined climate and emissions sensitiv ity studies and ongoing work on the drivers of emissions changes—e.g., technology, population growth and geographic distribution, economic growth, and land use—are also described in Section 4.

1.5 THE CLIENT COMMUNITIES

Section 1.2 referred to the two broadly defined themes, audiences, and readings of this report that flow from the two “grand challenge s.” Though this conceptualization provides a useful roadmap to the major purposes of the report, it is also important to identify specific groups that are potential beneficiaries of the information contained herei n, and that supply the audiences and perspectives to which the re port speaks. These include air quality managers, employees of agencies working as part of the overall U.S. fe deral climate change research effort, and the climate change and air quality research and modeling communities.

1.5.1 EPA Office of Air and Radi ation (OAR), State, Tribal, and Local Air Quality Planners The EPA’s Global Change Research Program engages in activities that support EPA’s mission to protect human health and the environment. As the specific focus of this report is air quality, OAR is a major client for this work. Recent air quality regulations, such as the NO x State Implementation Plan (SIP) Call, 5 Clean Air Interstate Rule (CAIR), 6 Heavy Duty Highway Diesel Rule, 7 and Non-road Diesel Rule, 8 are expected to bring ma ny urban areas of the United

5 “Finding of Significant Contribution and Rulemakings for Certain States in the Ozone Transport Assessment Group Region for the Purposes of Reducing Regional Transport of Ozone (“NOx SIP Call”).” U.S. EPA Technology Transfer Network: O3 Implementation.
6 “Clean Air Interstate Rule.” U.S. EPA: Clean Air Rules of 2004. http://www.epa.gov/cair/

. 7 “Clean Diesel Trucks, Buses, and Fuel: Heavy-Duty Engine and Vehicle Sta ndards and Highway Diesel Fuel Sulfur Control Requirements (the “2007 Heavy-Duty Highway Rule”).” U.S. EPA. http://www.epa.gov/otaq/highway-diese l/regs/2007-heavy-duty-highway.htm

. 8 “Clean Air Nonroad Di esel – Tier 4 Final Rule.” U.S. EPA. http://www.epa.gov/nonroad-diesel/2004fr.htm

. 1-11States into attainment with current PM and O 3 standards by 2015. However, as noted by the NRC (2004), The AQM system will need to ensure that pollution reduction strategies remain effective as the climate changes, becaus e some forms of air pollution, such as ground-level ozone, might be exacerbated. In addition, emissions that contribute to air pollution and climate change are fostered by similar anthropogenic activities, that is, fossil fuel burning. Multi-pollutant approaches that include reducing emissions contributing to climat e warming as well as air pollution may prove to be desirable.

Furthermore, air quality management involve s policy decisions with consequences that can last for decades. For example, policy guides the choices made for electricity production
investment and the emissions and fuel efficienci es of motor vehicles. Power plant and motor vehicle fleet replacement involves very long lead -times (see, e.g., U.S. EPA, 1992). In this context, it will be important to consider the air quality impacts of global change to identify actions that accomplish air quality goals with the least long-term cost to society. Information and tools supporting the creation of holistic, robust decisions ar e thus very much needed. Similarly, information and tools supporting ne w and innovative approaches to existing and emerging issues are needed as well. As in troduced in Section 1.1 a bove, providing a foundation for developing such decision support instruments th at can be transferred to national, regional, state, and local decision-makers is a critical goal of the overall air qua lity assessment effort.

1.5.2 U.S. Climate Change Science Program (CCSP) The CCSP integrates federal research on c limate change, as sponsored by 13 federal agencies and overseen by the Office of Scie nce and Technology Policy (OSTP), the Council on Environmental Quality (CEQ), the National Economic Council (NEC), and the Office of Management and Budget (OMB). The primary EP A role within the CCSP is to develop an understanding of the potential cons equences of global change on human health, ecosystems, and socioeconomic systems in the United States. Currently, EPA’s ORD, within which the Global Change Research Program is located, is focusing on topics that include impacts on future water and air quality, risks to coral reefs and watersheds, and impacts on biological criteria and aquatic invasive species, as well as developi ng decision support methods and resources. The impact of climate change on air quality is one of the overarching questions guiding the Atmospheric Composition research element of the CCSP (CCSP, 2003; see Box 1-2). The CCSP Atmospheric Composition Interagency Worki ng Group coordinates re search that focuses on how the composition of the global atmosphere is altered by human activities and natural phenomena and how such changes influence climate, O 3 , PM, ultraviolet radiation, pollutant exposure, ecosystems, and human health. Atmospheric composition issues involving

1-12interactions with climate variab ility and change—such as the poten tial effects of global climate change on regional air quality—a re important research topics . Several federal agencies, including the National Oceanographic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administ ration (NASA), and the Department of Energy (DOE), are involved in research activities in this area, including satellite obser vations, aircraft field campaigns, laboratory studies, and global modeling studies. EPA contributes its expertise in regional air quality modeling and anthropogenic emissions, along with research support in other air quality-relevant topic areas.

Box 1-2. Contributions to CCSP

The EPA Global Change Research Program Air Quality Assessment addresses a number of CCSP research and development elements, as described in the CCSP strategic plan (CCSP, 2003), including

Chapter 3. Atmospheric Composition Question 3.3 : What are the effects of regional pollution on the global atmosphere and the effects of global climate and chemical changes on regional air quality and atmospheric chemical inputs to ecosystems? Question 3.5 : What are the couplings and feedback mechanisms among climate change, air pollution, and ozone layer depletion, and their relationship to the health of humans and ecosystems?

Chapter 9. Human Contributions and Responses to Environmental Change Question 9.2 : What are the current and potential future impacts of global environmental variability and change on human welfare, what factors infl uence the capacity of human societies to respond to change, and how can resilience be increased and vulnerability reduced? Question 9.4 : What are the potential human health effects of global environmental change, and what climate, socioeconomic, and environmental information is needed to assess the cumulative risk to health from these effects?

Chapter 11. Decision Support Resources Development Goal 11.1 : Prepare scientific syntheses and assessments to su pport informed discussion of climate variability and change issues by decision-makers, stakeholders, the media, and the general public. Goal 11.2 : Develop resources to support adaptive management and planning for responding to climate variability and climate change, and transition these resources from research to op erational application. Goal 11.3 : Develop and evaluate methods (scenario evaluations, integrated analyses, alternative analytical approaches) to support climate change policymaking and demonstrate these methods with case studies.

In addition to contributing to efforts under the Atmospheric Composition element, the scientific and technical accomplishments of the current assessment are enlarging the database of information needed to address questions under a nu mber of other CCSP elements (see Box 1-2). Information from the ongoing air quality assess ment is included in the CCSP Synthesis and Assessment Product 4.6: “Analyses of the effects of global change on human health and welfare and human systems” (CCSP, 2008).

1-131.5.3 Climate Change Research Community Understanding potential impacts of global cha nge on U.S. air quality is a particularly challenging task, given the varying climate regi mes contained within the continental United
States and the 3-dimensional mode ling at high spatial and temporal resolution that is required to capture effects of importance to policy planners. The larger climate change research community, including other government science agencies a nd academia, plays a crucial role in the EPA Global Change Research Program’s research a nd development process by assuming the task of advancing the capabilities of global and regional climate mo dels and global and regional atmospheric chemistry models. Beyond the many challenges of understanding potential future global climate change itself, the problem of impacts on air quality adds additional dimensions. For example, the global climate modeling co mmunity has typically focused on long-term average meteorological parameters on continenta l and planetary scales, while adverse regional air quality events are often determined by fine r-scale geographic and te mporal variability. Successfully simulating the impact of climate ch ange on air quality requires advances in the climate sciences and climate modeling, with partic ular attention to thes e spatial and temporal needs. The research synthesis po rtion of this report (Section 3) looks across the modeling studies conducted as part of this assessment, studies that represent an initial step toward addressing this challenge. In addition, the modeling work in this asse ssment provides an important test of some methodologies used for linking (downscaling) globa l and regional climate models, a key aspect of climate impacts work in general. Further advances in meeting the demanding requirements of simulating climate change impacts on U.S. air quality will improve our capabilities to assess other global change impacts of great importa nce to the environmental policy community, including impacts on water quality, aquatic ecosystems, water resour ces, agriculture, and forests, in addition to the quantification of air quality-related human health effects.

1.5.4 Air Quality Research Community Developing coupled climate a nd air quality modeling system s challenges th e capabilities of regional air quality models. Improvements in our ability to model chemistry of air pollution
are needed in a number of areas to better unders tand the influence of climate change on air quality. For example, enhancing linkages betw een climate/meteorology m odels and air quality models, developing suitable init ial and boundary conditions for all important chemical species, and producing plausible future emission scenarios are all required. Comprehensive examinations
like this assessment effort also reveal key uncertainties in chem ical mechanisms and processes that can be used to prioritize future modeling im provements. Notable among these is the need to introduce the ability to simulate two-way inter actions between climate and chemistry: for

1-14example, changes in the distribut ion of particulates as a result of climate or emissions changes could have important impacts on the Earth’s radiation budget, thereby further influencing climate. Finally, the extremely large data files involved in this assessment effort have required the development of automated data management and quality control tool s and highlighted the need for new data distribution systems.

1.6 CONSIDERING UNCERTAINTY IN THE ASSESSMENT EFFORT Characterization of the uncertainty in a given finding, judgment, or prediction, and communication of this uncertainty in clear, precise, objective la nguage, are important components of scientific assessments. Large glob al change assessment ef forts, such as those conducted by the IPCC and CCSP, have produced general guidance on handling uncertainty in assessment reports (see CCSP, 2009; IPCC, 2005). Fo r example, a fundamental principle is that basic differences between descriptions of uncerta inty in terms of likelih ood of an outcome and
level of confidence of th e science underlying a findi ng must be recognized. Likelihood is relevant when assessing the ch ance of defined future occurrence or outcome. When the maturity of the scientific k nowledge base warrants it, it is considered best practice to assign numerical probab ilities to qualifiers such as “p robable,” “possible,” “likely,” “unlikely,” etc., to avoid differing inte rpretations among people and contexts. Level of confidence refers to the degree of belief in the scientific community that available understanding, models, and analys es are accurate, expressed by the degree of
consensus in the available eviden ce and its interpretation. One way to think about the level of confidence concept is to consider two attribut es of the state of know ledge underlying a given finding or judgment: the amount of evidence availa ble to support it and th e degree of consensus within the scientific community about the interpretation of that available evidence. The study of climate impacts on air quality is a still-emerging field of research. In addition, the modeling studies disc ussed herein were designed to be sensitivity studies, not predictions. Therefore, this repo rt does not attempt to express the findings from the scientific synthesis in terms of the probabilities (“likelihood s”) of particular future events. Instead, the report provides information to help evaluate the re lative levels of confidence in the findings. Findings for which multiple lines of evidence ar e presented, and for which there is general agreement across these lines of evidence, should be viewed with higher confidence than findings for which there is a paucity of observations and/ or model simulation results or for which there are competing interpretations of the results that ar e available. For example, as will be discussed in Section 3, there is broad agreement across th e modeling studies, consis tent with scientific understanding from theory and observations, that simulated future climate change leads to increases in biogenic VOC emissions in the sout heastern United States, but there is significant

1-15disagreement as to whether these emissions increases lead to large increases in O 3 concentrations due to uncertainty about how to re present isoprene nitrate chemistry. Section 3 provides a detailed discussion of th e major uncertainties associated with the coupled climate and air quality modeling sy stems upon which rests the science synthesis presented in this report. Moving forward in to the second phase of the assessment, the complexity of the problem will grow when the multiple dimensions of climate and emissions changes are fully integrated. In anticipati on of the challenges that multiple, interacting categories of uncertainties will present for interpretation of the assessment findings, EPA convened an expert workshop in November 2006 to begin the process of identifying a set of guiding principles to assist in evaluating uncertainty as the assessment moves forward. Participants included experts in global and regional climate modeling, socioeconomic modeling and emissions projection, atmospheric chemistry, regional air quality modeling, and uncertainty analysis and communication, along with key stak eholders from OAR and the EPA regions. The workshop findings suggested emphases on th e following issues: building a healthy, collaborative process involving bot h scientists and policy makers; identifying formal uncertainty analysis techniques appropriate for complex, co mputationally expensive linked climate and air quality modeling systems; evaluating the poten tial contributions of complementary methods, such as expert elicitation; communications strategi es; and the need for future workshops to focus on specific technical issues. The workshop and its findings are summarized in Appendix B.

1.7 STRUCTURE OF THIS REPORT This report presents the progress made toward the overall assessment goals. It is divided into five sections (including this one): The Summary of Policy Relevant Findings , which precedes this section, seeks to draw some preliminary connecting lines between the scie ntific findings of the assessment to date and
the issues of concern to air quality managers. Analogous to the approach taken in the IPCC Summary for Policymakers, OAR was substantially engaged in the writing of this section in
order to ensure the salience of the results for air quality policy.
Section 2 discusses in greater detail the design of the assessment effort, including the process used to develop this design, key decisions made by the research team, research priorities, and program capabilities. The focus on deve loping and applying linke d global-to-regional climate and air quality modeling systems is in recognition of the complexities of the global change-air quality problem, including its multi-scal e (i.e., from global to local; from decadal to diurnal) dimensions.
Section 3 synthesizes the results emerging from th e initial applications of these modeling systems to the simulation of U.S. air quality under potential future climate change. It highlights

1-161-17 the sensitivities in the climate-air quality system and the uncertainties associated with the modeling tools.
Section 4 discusses the next phase of the assessm ent. It summarizes ongoing work that seeks to increase our understanding of key mode ling issues and develop new capabilities for simulating future changes in anthropogenic emissions. Appendix A describes the meteorological variables to which U.S. air quality is known to be sensitive, e.g., the basis for the anticipated effects of changing climate on future air quality. Appendix A also discusses early research results on th e role of climate in future air quality. Appendix B describes the 2006 workshop convened by EPA NCEA to identify the essential
issues that must be addressed in identifying and communicating the uncertainties inherent in this assessment, and other complex, model-based assessments. Appendix C describes the 2001 expert workshop convened by EPA NCEA to evaluate the research and assessment framework developed by the EPA Global Change Research Program for identifying and quantifying the effects of global change on U.S. regional air quality. Finally, Appendices D, E, and F expand upon the descriptions provided in the main report of the internal EPA ORD programs contributing the GCRP assessment effort. A glossa ry has been provided to assist readers who are unfamiliar with the terms that are frequently used in the discussion of climate and air quality research and policy.2-1 2 OVERVIEW OF APPROACH

2.1 INTRODUCTION The NRC stated in 2001 that, “improving our understanding of linkages between climate, atmospheric chemistry, and air quality and our abili ty to assess future states of the atmosphere will require coupling local- and regional-scale ai r quality models with global-scale climate and chemistry models” (NRC, 2001). The EPA’s Global Change Research Program initiated a research program designed to meet the “grand challenges” introduced in Section 1 that is consistent with EPA’s traditional “place-based” regional assessment approach, and that focuses on spanning the breadth of issues from global-scale drivers of climate and air quality to developing regional-scale input s for air quality modeling. In the design of this program, the EPA r ecognized three key linkages inherent to the global change and air quality issue: those across spatial scales, those across temporal scales, and those across disciplines. The processes linking gl obal to regional scales , symbolized in Figure 2-1, and the requirements for modeling them, were identified as a first step in the assessment design. Similarly, while air qua lity is defined, studied, and managed most readily on the synoptic timescales associated with meteorological and air qu ality episodes, global climate change is manifested on timescales of decades and longer, imposing significant research challenges to bridge this gap. Finally, given the inherently multi-disciplinary nature of the problem, it was recognized that merging the efforts of the climate change, air quality, emissions inventory, land use, energy, and transportation
economics research
communities would be critical to bring about advances required for this assessment. Developing the modeling tools and knowledge base to achieve these linkages is a
fundamental task of the

assessment. Figure 2-1. Links between glob al and regional climate and atmospheric chemistry processes with anthropogenic activities governing air pollution emissions. The dashed arrows represent feedbacks not considered as part of this assessment. Regional Meteorology

Regional Emissions Regional Air Quality Regional Boundary Conditions Meteorological Chemical Global Emissions

Global Air Quality Global Meteorology Global Change Scenarios (IPCC SRES)

Regional Change Scenarios

• Economic Growth

• Technology

• Population Growth and Migration

• AQ and GHG Policy Regional Meteorology

Regional Emissions Regional Air Quality Regional Boundary Conditions Meteorological Chemical Global Emissions

Global Air Quality Global Meteorology Global Change Scenarios (IPCC SRES)

Regional Change Scenarios

• Economic Growth

• Technology

• Population Growth and Migration

• AQ and GHG Policy Regional Meteorology

Regional Emissions Regional Air Quality Regional Boundary Conditions Meteorological Chemical Regional Boundary Conditions Meteorological Chemical Global Emissions

Global Air Quality Global Meteorology Global Air Quality Global Meteorology Global Change Scenarios (IPCC SRES) Global Change

Scenarios (IPCC SRES)

Regional Change Scenarios

• Economic Growth

• Technology

• Population Growth and Migration

• AQ and GHG Policy2.1.1 Process for Developing the Global Ch ange-Air Quality Assessment Effort In 1997, the EPA’s Global Program underwen t a major redirection, including the development of a new Strategic Plan in 1999. As pa rt of that effort, the global change-air quality assessment was designed. Specifically, a small workgroup was formed, ma de up of scientists knowledgeable about various aspects of the i ssue, including atmospheric and emissions modeling, technology, socioeconomics, climate modeling, and air quality programs. The workgroup included members from all of the Labs and Centers involved in the EPA’s Global Change Research Program, and input from several offices within OAR was also solicited to help guide the effort. An iterative process within the workgroup was used to define the purpose, goals, and issues to be addressed; to identify appropriate EPA participan ts and stakeholders; and to develop an initial conceptu al framework for organizing the assessment effort, leading to a white paper describing the proposed framework and timeline for accomplishing key milestones. To review this draft framework and help EPA identify priority research needs, a workshop was held in Research Triangle Par k, North Carolina in December 2001 that brought together technical expe rts from ORD and OAR, as well as i nvited internationa l experts. The goal of the workshop was to identify the important processes and inputs and to discuss the design and implementation of the assessment. Particip ants included experts in climate modeling, air quality modeling, anthropogenic emissions inventory development, and biogenic emissions inventory development. The workshop agenda included presentations by a panel of experts on regional climate modeling, future emissions i nventory development, regional air quality modeling, biogenic emissions and wildfires, and socioeconomic and technological change projection methods. The workshop participants were assembled into four groups to discuss specific issues related to the EPA Global Cha nge Research Program’s objectives: (1) the Regional Climate Modeling Group, (2) the Emi ssion Drivers and Anthropogenic Emissions Group, (3) the Biogenic Emissions and Wildfi res Group, and (4) the Air Quality Modeling Group. Each examined charge questions a bout possible approaches, and each developed recommendations for research required to meet the needs of the assessment. Here, the key recommendations from the workshop that define the approach used in the assessment are summarized (for further de tails see Appendix C).

2.2 WORKSHOP RECOMMENDATIONS

2.2.1 Modeling The three key conceptual linkages introduced ab ove, i.e., across spatial scales, temporal scales, and disciplines, are embodied in the found ational technica l challenge of the assessment: linking available modeling tools to span th e climate, meteorology, air quality, and human dimensions of the problem. As will be described in more detail below, the primary focus of this

2-22007 interim report is the potential for future clim ate change to impact air quality, independent of changes in anthropogenic emissions. The i ndividual research commun ities use a number of different types of models, described in Box 2-1, to study the various aspects of this sub-problem.

Box 2-1. Climate and Chemistry Modeling Tools

General Circulation Model (GCM): Comprehensive model of Earth system, including components that simulate 3-D flow in atmosphere and ocean, exchange of energy and water with land and ocean surface, and growing and melting of ice sheets and sea ice, ultimately in response to amount of solar energy received over time across planet; typically operated with horizontal grid sp acing of 100-500 km to ex amine climate variables at continental to global scales; most often applied in simu lations of how long-term clim ate statistics evolve over years, decades, or centuries in response to past or future changes in outsid e forcings (e.g., variations in solar input, volcanic aerosols, and changes in anthropogenic greenhouse gas emissions). [Note: The use of “GCM” as an acronym for “Global Climate Model” and “Global Circulation Model” reflects current usage as well.]

Global Chemistry and Transport Model (GCTM): Type of model that blends representations of chemical reactions and physical chemical transformations with meteorology supplied either from gridded observational analyses or a GCM simulation; applied to study how transport by winds, deposition onto or emissions from surface, and atmospheric chemistry control long-term dist ributions of important gases and aerosols within the atmosphere (e.g., O 3 , carbon monoxide, sulfates, and black carbon, among many others); chemistry/transport can also be built directly into a GCM for similar applications.

Regional Climate Model (RCM): Similar to a high-resolution (e.g., 10-50 km) version of a GCM but only applied to limited area of globe (e.g., continental United States); designed to capture more accurately role of finescale forcings (e.g., topography, land-surface heteroge neity) and atmospheric proce sses (e.g., nonlinear dynamics of fronts, development of convective rainfall systems) ha rd to represent at coarse scales of a GCM; derived primarily from weather prediction models but including some additional features that allow simulations longer than typical several-day timescale of weather forecasts; dr iven at boundaries by gridde d analyses of observational data or output from a GCM to study in greater detail how long-term, large-scale climate variability is expressed in weather events over shorter timescales and in particular locations.

Regional Air Quality Model (RAQM): Developed to account for impact of meteorological transport and mixing, atmospheric chemistry, and surface deposition/ emission of multiple chemi cal species, particularly regulated pollutants; most often applied by air quality management community to evaluate impact of control strategies and practices; also frequently used in research mode to develop improved understanding of chemical and physical interactions in atmosphere; typically operate d on time and space scales ch aracteristic of air pollution episodes, i.e., a metropolitan area or larg er region over period of a few days.

These different modeling tools have historically been deve loped for distinct purposes. The assessment design reflects the need for bridging the gaps between these standard applications to move toward more comprehensiv e, integrated systems capable of addressing the breadth of the problem of potential climate change impacts on air quality. As such, one core recommendation that emer ged from the workshop was to use these tools separately and in combination in multiple modeling approaches to investigate the relevant space and time scales and physical/chemical processes governing the connections between climate and air quality. These approaches are

2-3• Comprehensive modeling approach: This a pproach uses linked global and regional climate and chemistry models to simulate fine regional details of present-day and future air quality while simultaneously accounting for global drivers like changes in anthropogenic emissions of greenhouse gases. Output from GCM simulations of longterm climate change is used as input in to a higher-resolution RCM, which “downscales” the climate and meteorological variables to th e scales required for input into an RAQM. This approach is the most computationally expensive and methodologically complex, with concerns such as the length of simula tion required to extract a meaningful climate change signal from inte rannual climate variability.

• Intermediate modeling approach: This a pproach relies primarily on GCMs and GCTMs to capture the broader impacts of climate ch ange on air quality. The emphasis in this approach is on the potential fo r increases or decreases in air pollution events as the climate changes over a long si mulation period. The results from such modeling work can be used to guide the comprehensive modeling approach (e.g., by guidi ng the selection of time periods for the highe r-resolution simulations).

• Sensitivity approach: This approach applies de tailed, state-of-the-art RAQMs at regional and even urban scales. Rather than a dynami c linkage, air quality simulations are carried out by varying key meteorological and emissions parameters to examine the sensitivity of the air quality outputs over particular, identi fied meteorological and air quality episodes. The sensitivity approach might permit use of more detailed descriptions of important processes, i.e., aerosol processes.

Initially, the assessment team proposed to move forward primarily with the Comprehensive approach. The workshop particip ants endorsed this plan as effective and reasonable, but they also suggest ed the other two strategies to complement the Comprehensive approach and add richness to the assessment. Another key model-related disc ussion was the need to address uncertainty by sampling over multiple GCMs, RCMs, GCTMs, RAQMs, as we ll as the need to examine sensitivities to model parameterizations and downscaling methodologies . A critical challeng e is to quantify the uncertainty produced by the system of linked models required to simulate changes in air quality driven by climate change. It was also acknowle dged that an important research gap was the evaluation of the climate models for their ability to simulate air quality-relevant variables and air quality-relevant weather pa tterns at the appropriate space and time scales. Finally, the assessment team was urged to consider in more de tail the role of hemispheric-scale air pollutant transport and to support the deve lopment of appropriate initial and boundary conditions for regional-s cale air quality modeling efforts.

2-42.2.2 Time Horizon Selected A key consideration is the timeframe for building future scenarios and carrying out future climate and air quality simulations. It was d ecided to focus on a time horizon of roughly 2050 in order to balance the following considerations: Natural meteorological variabi lity versus climate change: Because meteorology varies from year-to-year, the signal from the changing c limate needs to be relatively strong to discern climatically driven effects on air quality. In its Third Asse ssment Report (TAR) (IPCC, 2001), the IPCC projected that global average temperatures could increase from 1.4

− 5.8oC (2.5

− 10.4oF) by 2100, and that the warming is expected to be la rger than the global average for land areas in the mid- and high latitude regions. These fi ndings are consistent with the most updated projections from the IPCC AR4 (IPCC, 2007). This trend is expected to lead to intermediate levels of warming in the intervening decades. For example, the U.S. National Assessment (NAST, 2001) based their findings on av erage U.S. temperature increases of 0.5

− 2.0oF by 2025,
1.5

− 4.0oF by 2050, and 3.0

− 9.0oF by 2100. Therefore, the longer the timeframe, the stronger the climate change signal captured relative to natural interannual and interdecadal variability. Uncertainties in GCM climate projections: The IPCC AR4 (IPCC, 2007) documents significantly greater divergence in the climate change projections for 2100 compared to 2050, largely because the various driving greenhouse gas emissions scenarios from the IPCC Special Report on Emissions Scenarios (SRES) (IPCC, 2000) have diverged rela tively little by 2050. Even though the climate change signal is strong er in 2100, the spread between model projections created using different scenarios is not as wide . Choosing 2050 thus constrains somewhat one of the potential sources of uncer tainty in the assessment. Uncertainties in the assumpti ons concerning long-term chang e in emissions drivers: The uncertainty in projections of economic growth, patterns of land-use and land-cover change, energy use, migration, transportation patterns, an d technological development needed to develop projections of anthropogenic emissions increases significantly over longer time horizons. An assessment timeframe of, e.g., 2100, would likely be too speculative for practical application to current air quality management planning. Current EPA decision processes: In areas such as investment in electricity production, motor vehicle emissions, and power plant and fleet replacement, the EPA already makes air quality management decisions with long lead times of one to several decades. Therefore, a time horizon of the next half-century for assessing the potential conseque nces of climate change on air quality is consistent with this planning timescale.

2-52.2.3 Dual-Phase Assessment Approach It is well recognized that anthropogenic emissions leve ls are a dominant factor in determining air quality, as evidenced by the dr amatic improvements that took place with the implementation of emissions controls beginning in the mid-20 th Century in the United States and other developed countries. Understanding how ch anges in air quality due to changing climate might confound long-term management of these emissions for NAAQS attainment and maintenance is a critical assessment goal. To mo re readily achieve this understanding, a second core recommendation from the workshop was to investigate possible regional air quality responses to future climate and meteorological changes alone, before tackling the additional complexities of accounting for changes in other aspects of the system, such as anthropogenic emissions and long-range pollutant transport. The assessment research program was, ther efore, designed in two phases. Phase I focuses on developing tools, capab ilities, and a knowledge base , and then applying these in research to address the impacts of climate change on air quality with anthropogenic emissions held constant between present and future. Ph ase II builds on the insights from Phase I, by extending the capabilities of the modeling systems developed therein (e.g., to more comprehensively explore uncertainties, encompass additional pollutants, and investigate climate and air quality feedbacks) and by adding the effects of changing patterns of anthropogenic emissions (e.g., due to population, land-use, and energy and transpor tation technologies changes). In this second phase, emissions will be projected into the future, accounting for factors such as differential population growth and migr ation, economic growth, and technology change. As described in Section 1.4.2, the major focus of this interim asse ssment report is the progress to date under Phase I, presented in Sect ion 3. The Phase II work will be the subject of follow-on reports. A summary of research effo rts already ongoing to support Phase II is provided in Section 4. One of the key challenges in executing th e comprehensive approach described in
Section 2.2.1 lies as much with maintaining logical consistency in linking the many models as
with the technical difficulties of simulating changes to 2050. The O 3 simulations in Phase I, reflecting the climate in 2050, have been acco mplished while holding air pollution emissions constant at present-day levels. In the strictest sense, this therefore introduces an internal inconsistency, i.e., between emissions of gr eenhouse gases and those of conventional air pollutants, which in reality are coupled. The model results from this first phase serve as sensitivity tests—to determine the potential eff ect on climate-induced meteorological changes on air quality and to better understand the charac teristics of the linked modeling systems—and cannot be construed in any way as future predictions.

2-62.2.4 Research Priorities to Support Phase II Finally, we briefly summarize some key workshop recommendations on additional research needed to support Phase II of the assessment. Processes governing biogenic emissions: Algorithms will have to be developed that describe chemical emissions of major vegetative species response to climate change for use in current and biogenic emission fore casting. Projections of land-use changes will have to be integrated with forest physiological models to project current and future biogenic VOC
emissions. Wildfires: There is a need to develop methods to define fire emissions as a function of fire intensity, extent, and fre quency. Simultaneously, there is a need to develop methods to relate fire intensity, extent, a nd frequency to current and future land use, land management, fuel loading, socioeconomic conditions, and climate. Anthropogenic emissions projections: Plausible scenarios for future emissions need to be developed that account for changes in urbanization, population growth, migration, industrialization, fuel, technology, etc. Also needed is normalization of procedures for emissions calculations across regions and countries and reconciliation between global and regional emission inventories. Principles of downscali ng socioeconomic scenarios to more detailed geographic scales must be applied. There is al so a need to incorporate feedbacks of climate change on energy use, economic devel opment, land use, and migration. Air quality modeling: Improvements in our ability to model the chemistry of air pollution in a number of areas will be required to more accura tely simulate the influence of climate change on air quality. These areas include representation s of aerosol physical and chemical processes, two-way linkages between climate/meteorology mode ls and air quality mode ls, the availability of suitable initial and boundary conditions for al l important chemical species, and stratospheretroposphere exchange.

2.3 RESEARCH PARTNERSHIPS To implement the workshop recommendations an d achieve the goals of the assessment, the EPA’s Global Change Research Program designe d a joint intramural and extramural research program. The goal is to harness the unique capabili ties of the EPA research laboratories and the academic community to build a broad program. Within the EPA’s intramural effort, the Na tional Exposure Research Laboratory (NERL) is the primary developer of the Community Multiscale Air Quality (CMAQ) model that predicts air quality pollutant transport and fate (Byun a nd Schere, 2006). CMAQ, which, as of December 2006, has undergone three external peer reviews, is being used by the Office of Air Quality Planning and Standards (OAQPS) within OAR for current ru lemakings, as well as by the

2-7research community for a range of research applications including cl imate and air quality interactions. Via a partnership between EPA and NOAA, a team at NERL is charged under this assessment with leading the development of a seri es of regional-scale air quality simulations using CMAQ under current and future climate scen arios. This effort, the Climate Impacts on Regional Air Quality (CIRAQ) project, was initiated in 2002 following the above-mentioned workshop. This team provides the air quality mode ling expertise to develop these simulations, to interpret the sensitivity of air quality to the future climate changes simulated, and to consider regulatory implications of potential changes in air quality. In addition, NERL researchers are key contri butors to the development of models of environmentally influenced emissions from th e air-surface interface for regional and global emissions inventories and applic ation to air quality modeling, such as biogenic emissions (the Biogenic Emission Inventory System; BEIS) and wildfire emissions (based on the Blue Sky wildfire model). NERL was also the primary OR D collaborator in the development of the Sparse Matrix Operator Kernel Emission (SMOKE) mo deling system. SMOKE assembles input data from anthropogenic emission inventories, and bi ogenic, mobile, and wildfire emission models into the hourly, gridded, speciated form required by air quality models such as CMAQ. These emissions models are needed for both retrospectiv e and future air quality modeling scenarios. More information on aspects of the NERL effort is contained in Appendix E. Simultaneously, researchers in the National Risk Manage ment Research Laboratory (NRMRL) are focused on evaluating the potential impact of technological evolution on futureyear air pollutant emissions, in coordination wi th the NERL efforts. This process involves characterizing future energy demands and technol ogies, and using this information within energy system models to estimate emissions over a wide range of alternative scenarios. In addition, NRMRL researchers have developed a suite of an alytical and visualiza tion tools for examining the flexibility available in meeting future emi ssion targets and for evaluating sensitivity to uncertainties in model parameters and inputs. NRMRL is applying these methods and tools to examine the system-wide implications on fuel use and emissions of the penetration of new transportation and electric gene ration technologies. This work directly addresses the need, identified in the 2001 workshop, to develop real istic future emissions scenarios that are regionally plausible and also cons istent with assumptions about global trends. Together, NERL and NRMRL have the expertise required to contri bute crucially to both Phase I and Phase II of
the overall assessment. For additional in formation, see Appendix F and Section 4.
The assessment effort benefits substantia lly from a strong coll aboration with the extramural research community. The EPA’s National Center for Environmental Research (NCER), through its competitive Science To Achiev e Results (STAR) grants program, funded a

2-82-9 number of leading university research groups through the following Requests for Applications (RFAs):

• 2000: Assessing the Consequences of Interactions between Human Activities and a Changing Climate

• 2002: Assessing the Consequences of Global C hange for Air Quality: Sensitivity of U.S. air quality to climate change and future global impacts

• 2003: Consequences of Global Change for Air Q uality: Spatial Patterns in Air Pollution Emissions

• 2004: Regional Development, Population Trend, and Technology Change Impacts on Future Air Pollution Emissions

• 2005: Fire, Climate and Air Quality

• 2006: Consequences of Global Change for Air Quality

These RFAs, most of which derive fro m the recommendations of the 2001 workshop, encompass roughly 25 projects, totaling over $20 m illion, covering topics in cluding projection of population, development, and transportation tren ds; observations of biosphere-air quality interactions; coupled climate and air quality mode ling; and human health effects. Many of the current projects involve co llaboration across disciplines to link models. All of this is emblematic both of the breadth of the issue and EPA’s commitment to build and populate a comprehensive
framework to address it. Further de tails are provided in Appendix D. Finally, the National Center for Environmen tal Assessment (NCEA) has unique expertise in preparing the air quality criteria documents upon which the NAAQS are based, conducting environmental assessments, and performing synthetic analyses of the type presented in Section 3. NCEA’s global change assessment team has the pr imary responsibility for developing the reports synthesizing the results of the br oad inter-laboratory and extramural research effort represented
in this assessment.3 RESULTS AND SYNTHESIS

3.1 INTRODUCTION The goal of this section is to synthesi ze the EPA, and EPA-funded, climate and air quality modeling research that has emerged in Phas e I of the assessment. The material presented here is intended to map onto each of the two inte rtwining readings introduced in Section 1, i.e., “science” and “policy,” that run through the repo rt and reflect the two “grand challenges” of evaluating the state of the science and provi ding a foundation on which effective decision support can be built. Section 3.2 provides brief summar ies of activities and key findings to date from each of the participating modeling groups. Section 3.3 at tempts to assess the la rger meaning of the groups’ results when they are examined all toge ther, focusing on inter-group comparisons of the simulation outputs that are larg ely common to all (or most)—it provides a preliminary synthesis by taking a broad view across this subset of assessment result s. Section 3.4 discusses the challenges and uncertainties associated with the modeling approach that underpins the

assessment.
As the EPA’s assessment activities continue , overall understanding will grow richer and techniques will become more refined. Thus , it will be possible to build on the foundation provided by this first attempt to in terpret this evolving body of work.

3.2 SUMMARY OF RESULTS FROM INDIVIDUAL GROUPS Results discussed throughout the rest of this section are drawn from the intramural, EPA work, as well as from several STAR-funded extramur al initiatives. More de tailed descriptions of the experimental designs and re sults of the extramur al (Appendix D) and intramural (Appendix E) efforts are given in the appendices to this report. The projects highlighted here largely share similar fundament al goals and approaches and can be divided into two major groups: (1) those th at, to date, have primarily used global climate and chemistry models to focus on the large- scale changes in future U.S. air quality, 9 and (2) those that have used nested, high-resolution, glob al-to-regional modeling systems to focus on the regional details of the po tential future changes. 10 All of these projects adapt existing modeling tools (as described in Section 2) as components for assembling their systems, including GCTMs,

9 The Harvard University and Carnegie Mellon University teams.
10 The EPA National Exposure Research Laboratory (NER L), Columbia University, University of Illinois, Washington State University, University of California, Berkeley, and Georgia Institute of Technology (GIT)- Northeast States for Coordinated Air Use Management (NESCAUM)-Massachusetts Institute of Technology (MIT)
teams. 3-1GCMs, RCMs, and RAQMs, along with emissions models and a number of boundary and initial conditions datasets. They all apply these mode ling systems in numerical experiments designed broadly to investigate the impacts of future gl obal climate change on U.S. air quality for presentday and future time periods. It is important to consider both the global model simulations and the downscaled regional simulations together, because each method has its strengths and weaknesses. The global models simulate the whole world in an internally self -consistent way across both climate and chemistry, but because of computational demand must use coarse spatial resolution, thereby potentially missing or misrepresenting key processes. D ynamical downscaling with an RCM dramatically increases the resolution and process realism for the region of interest, but at the expense of introducing lateral boundary conditions into th e simulation. Section 3.4 provides additional discussion of these relative advantages and trade-o ffs. Examining both sets of results gives us a more complete picture of the ove rall climate-air quality system. In addition to any similarities in appro ach, however, each project brings unique and complementary differences in emphasis to these tasks. In aggregate, these differences add greatly to the richness of the overall assessment. Below are brief summaries of selected key themes and findings from each of these research efforts as a prelude to the more focused intergroup comparisons of Section 3.3.

3.2.1 GCTM-Focused Modeling Work

3.2.1.1 Application of a Unified Aerosol-Chem istry-Climate GCM to Understand the Effects of Changing Climate and Global Anthropogenic Emissions on U.S. Air Quality: Harvard University In early work for this project, the Harvard re search group examined the role of potential changes in atmospheric circulation by carry ing out GCM simulations, using the Goddard Institute for Space Studies (GISS) GCM version II ′ , for the period 1950

− 2052, with tracers representing carbon monoxide (C O) and black carbon (BC) (Mickl ey et al., 2004). They based the concentrations of greenhouse gases for the historical past on observations, while future greenhouse gases followed the A1b IPCC SRES scenar io. A key result from these simulations is a future 10% decrease in the frequency of su mmertime mid-latitude surface cyclones moving
across southeastern Canada and a 20% decrease in cold surges from Canada into the Midwest. Since these events typically clear air pollution in the Midwest and Northeast, pollution episodes in these regions increa se in duration (by 1

− 2 days) and intensity (by 5

− 10% in pollutant concentration) in the future. These simulated future circulation change s are consistent with findings from some other groups in the broader climate modeling community, and the Harvard model also successfully reproduces the observed 40% decrease in North American cyclones from

3-21950

− 2000. These results are supported, and expa nded upon, by more recent work from this group, e.g., see Leibensperger et al. (2008), who found that the frequency of mid-latitudes cyclones tracking across eastern North America in the southern climatological storm track was a strong predictor of the frequency of summertime po llution episodes in the eastern United States for the period 1980

− 2006. In addition, they found a decrea sing trend over this period in the number of cyclones in this stor m track that they attributed to greenhouse warming, consistent with a number of other observational and modeling studies. However, as will be discussed in more detail below, other gr oups, including those participating in this assessment, do not necessarily find the same decrease in future mi d-latitude cyclones when analyzing similar GCM outputs, or even the same GCM outputs dow nscaled using an RCM (e.g., see Leung and Gustafson, 2005). Subsequent to the initial mode ling effort described in Mick ley et al. (2004), the Harvard group applied the GEOS-Chem GCTM, driven by the GISS III GCM (Wu et al., 2007), to the
direct simulation of 2050s O 3 air quality over the United Stat es (Wu et al., 2008a) and global tropospheric O 3 and the policy-relevant background O 3 over the United States (Wu et al., 2008b).
For one set of simulations with this modeling system designed to isolate the impacts of climate change alone on air quality, anthropogenic emissions of precursor pollutants were held constant at present-day levels, while climate changed in response to greenhouse gas increases under the IPCC A1b scenario (Wu et al., 2008a). Climate- sensitive natural emissions, e.g., of biogenic VOCs, were allowed to vary in response to the ch ange in climate. In these simulations, they found that at global scales, future O 3 averaged throughout the depth of the troposphere increases, primarily due to increases in lig htning (leading to additional NO x production), but near the surface increases in water vapor generally caused O 3 decreases, except over polluted continental regions. Focusing in more detail on the Unite d States, they found th at the response of O 3 to climate change varies by region. Their resu lts show increases in mean summertime O 3 concentrations of 2

− 5 ppb in the Northeast and Midwest, with little change in the Southeast. The Harvard group also found that peak O 3 pollution episodes are far more affected by climate change than mean values, with effects exceeding 10 ppb in the Midwest and Northeast. In contrast to this regional pattern of future U.S. O 3 change, the Carnegie Mellon work (described next) found a relatively smaller respon se in the Northeast a nd Midwest but a strong increase in the Southeast, using some similar models and assumptions as the Harvard project (although with a different IPCC greenhouse gas scenar io and some key differences in the ocean surface boundary condition). As will be discussed in greater detail below, the explanations for these differences appear to resi de in (1) differences in how th e chemical mechanisms regulating
the reactions and transformation of biogenic VOC emissions are re presented in the two modeling systems and (2) possible differences in future simulated mid-latitude storm track changes.

3-3In addition to these findings, this group used historically measured relationships between temperature and the probability of O 3 concentrations above the air quality standard (e.g., see Lin et al., 2001), together with statistically downscaled climate projections for the Northeast United
States from an ensemble of IPCC AR4 GC Ms and scenarios, to project future O 3 exceedances in the region (Lin et al., 2007). They found a doubling of the frequency of exceedances in the climate of the 2050s if anthropogenic emissions were to remain constant. As will be discussed further below, statistical rela tionships between observed O 3 and temperature reflect both the direct impact of temperature on O 3 chemistry and the often strong correlation between temperature and other fact ors conducive to high O 3 concentrations, such as clear skies, stagnant air, and increased biogenic emissions. As su ch, they tend to be regionally and seasonally dependent. Work exploring the use of these t ypes of statistical appr oaches to project O 3 NAAQS exceedances (and PM concentrations) is ongoing. As a final part of this project, the Harvard group has developed, and is in the process of testing, a linked global-to-regional system of models (including a GCM, GCTM, RCM, and RAQM). This system will be applied to investigations of the effects of climate change, as well as future changes in pollutant emissions and long-range transpor t, on regional-scale O 3 and PM concentrations and mercury (Hg) deposition.
Additional information on the Harvard research effort can be found in Appendix D and at

• http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/6 157/report/0

• http://www.as.harvard.edu/ chemistry/trop/gcap/

3.2.1.2 Impacts of Climate Change and Global Emissions on U.S. Air Quality: Development of an Integrated Modeling Framework and Sensitivity Assessment: Carnegie Mellon University The Carnegie Mellon group performed global-sc ale simulations of atmospheric chemistry under present and future (2050s) climate conditions using a “unified model,” i.e., the GISS II ′ model modified to incorporate tr opospheric gas phase chemistry a nd aerosols. Ten years of both present and future climate were simulated, following the A2 IPCC greenhouse gas emissions scenario, with anthropogenic air pollution emissions held at present-day levels to isolate the effects of climate change. As in the Harvard pr oject described above, the effects of changes in
certain climate-sensitive natural emissions were also included as part of the “climate” changes
simulated. They found that a majority of the atmosphe re near the Earth’s surface experiences a decrease in average O 3 concentrations under future climate with air pollution emissions held constant, mainly due to the incr ease in humidity, which lowers O 3 lifetimes (Racherla and

3-4Adams, 2006). Further analysis of these resu lts on a seasonal and regional basis found that, while global near-surface O 3 decreases, a more complex resp onse occurs in polluted regions. Specifically, summertime O 3 increases over Europe and North Am erica, with larger increases for the latter. A second key finding is that the frequency of extreme O 3 events increases in the simulated future climate: over the eastern half of the United States, where the largest simulated
future O 3 changes occurred, the greatest increa ses were at the high end of the O 3 distribution, and there was increased episode frequency that wa s statistically significant with respect to interannual variability (Racherla an d Adams, 2008). They further s uggested that it is necessary to simulate a minimum of five present-day a nd future years to separate a climate change response from this interannual variability. These general results are broadly consistent with the Harvard experiments described above. However, as also mentioned, there are important regional differences in response between the two groups. Thes e can largely be attributed to differences in the modeled chemical mechanism for isoprene oxidation in the southeastern United States, as well as possibly differences in the future simulation of the summertime storm track across the northern part of the country. These issues will be discussed in more deta il in the synthesis to
follow these summaries. The Carnegie Mellon team is also purs uing two complementary approaches in conjunction with their global modeling efforts. Firs t, they are investigating the sensitivity of O 3 , PM, acid deposition, and visibility to individual meteorological parameters by performing a set of sensitivity experiments using the PM Compre hensive Air Quality Model with Extensions (PMCAMx) (e.g., see Dawson et al., 2007a, b). One key finding from this work is that O 3 concentrations increased nearly linearly with temperature in the study region/period, and that a 2.5oC increase in temperature led to a 30% increase in the area exceeding the EPA 8-hour standard. Second, they have now developed and te sted a global-to-regional modeling system to carry out higher-resolution inve stigations of the impacts of cl imate and anthropogenic emissions changes on air quality (Dawson et al., 2008).
Additional information on this research e ffort can be found in Appendix D and at

• http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/6 240/report/0

• http://www.ce.cmu.edu/~adams/index.html

• http://www.cheme.cmu.edu/who/faculty/pandis.html

3-53.2.2 Linked Global-Regional-Focused Modeling Work

3.2.2.1 The Climate Impacts on Regional Air Quality (CIRAQ) Project: EPA In addition to the extramural projects descri bed in this section, an intramural modeling study, the CIRAQ project, is bei ng conducted at EPA NERL, as in troduced in Section 2. Under this project, the NERL team built a coupled gl obal-to-regional climate and chemistry modeling system covering the continental United States. They used the output from a global climate
simulation with the GISS II ′ model (including a tropospheric O 3 chemistry model) for
1950

− 2055, following the A1b IPCC SRES greenhouse gas emissions scenario for the future simulation years (i.e., the same simulation descri bed in Mickley et al., 2004) as climate and chemical boundary conditions for the regional c limate and air quality simulations. The Penn
State/NCAR Mesoscale Model Ve rsion 5 (MM5) was used at DOE ’s Pacific Northwest National Laboratory (PNNL) to create downscaled fields from this GCM simulation for the periods
1996

− 2005 and 2045

− 2055 (Leung and Gustafson, 2005). The NERL group used this regionally downscaled meteorology to simulate air quality for 5-year-long subsets of these present and future time periods with the CMAQ model. Multiple years were simulated, in spite of the considerable computational expense, to examine the role of interannual variability in the results. A key element of this project was extensive evaluations of the simulated meteorological variables, not just for long-ter m climate statistics (e.g., monthl y and seasonal means), but of synoptic-scale patterns that can be linked more directly to air quality episodes (Cooter et al., 2005; Gilliam et al., 2006; Gustafson and Le ung, 2007). One important finding was that the subtropical Bermuda High pressure system off the southeastern United States coast, a critical component of eastern United States warm season weather patterns, was not well simulated in the downscaled model runs, a result that is likely at tributable to biases in the GCM, as will be
discussed further below. Anot her key finding was that, as men tioned above in the summary of the Harvard project, the reduction in cyclones tr acking across the northern United States found in Mickley et al. (2004) was not as clearly presen t when this global model output was downscaled using MM5 (Leung and Gustafson, 2005). The NERL team also evaluated the CMAQ results against historical O 3 observations, finding high biases in summertime O 3 related to the choice of chemical mechanism in CMAQ between the Carbon Bond-IV (CB-IV) vs. the Stat ewide Air Pollution Research Center (SAPRC) representations. In addition, they found O 3 biases related to bi ases in MM5-downscaled meteorology. For example, the model under-pre dicted precipitation and over-predicted temperature in the areas of th e Midwest and Southeast where O 3 was most over predicted, highlighting the strong control th at meteorology can exert on O 3
. In a set of future simulations with this gl obal-to-regional climate and air quality modeling system, for which anthropogenic emissions of pr ecursor pollutants were held constant while

3-6climate changed, the NERL group found increases in future summertime maximum daily 8-hour (MDA8) O 3 concentrations of roughly 2

− 5 ppb in some areas (e.g., Northeast, Mid-Atlantic, and Gulf Coast) compared to the present-day, t hough with strong regional variability and even decreases in some regions (Nolte et al., 2008). This regional variability in future O 3 concentration changes was associated primarily w ith changes in temperature, the amount of solar radiation reaching the surface, a nd, to a lesser extent, climate-induced changes in biogenic emissions. The increases in peak O 3 concentrations tended to be greater and cover larger areas
than those in mean MDA8 O 3 . These results will be discusse d in more detail in the synthesis below. The NERL team also found significant O 3 increases in September and October over large portions of the country, suggesti ng a possible extension of the O 3 season into the fall in the
future. Additional information on the NERL effort can be found in Appendix E and at http://www.epa.gov/asmdnerl/Climate/index.html

.

3.2.2.2 Modeling Heat and Air Quality Impacts of Changing Urban Land Uses and Climate: Columbia University The Columbia group built a linked air qua lity modeling system based on the GISS Atmosphere-Ocean (AO) GCM (Russell et al ., 1995) and the MM5 RCM and carried out simulations using two SRES greenhouse gas scenarios (A2 and B2) for 5 summers each during the 1990s, 2020s, 2050s, and 2080s, focusing on the eastern half of the contin ental United States. Additional simulations using higher resolution we re carried out for the New York City metro area for particular meteorological/a ir quality episodes. One important feature of the Columbia
effort is that the team carried the air quality modeling results through to an assessment of human health endpoints. A key aspect of the Columbia team’s work wa s the evaluation of the performance of this coupled modeling system. They found that (1) dynamical downscaling with MM5 reduces biases present in the GCM simulation, most strongly for temperature and less so for precipitation and (2) there is a strong sensitivity of climate to the choice of RCM parameterizations, e.g., the cumulus convection scheme (e.g., see Lynn et al ., 2004). In addition, the downscaled results were often quite different from those of th e driving GCM, including, for example, warmer
summers. For O 3 , they found that their modeling system was able to simulate synoptic and interannual variability reasonably well, including the fre quency and duration of extreme O 3 events, but underestimated variability on shorter time s cales (Hogrefe et al., 2004a). In future climate change simulations (with anthropogenic emissions of air pollutants held constant at present-day levels), the Columbia group found summertime O 3 increases of 2

− 8 ppb
across broad swathes of the Midwest and Mid- Atlantic (Hogrefe et al., 2004b). Significant

3-7effects were already seen by the 2020s, with greater increas es by the 2050s and 2080s. One exception was certain geographic areas that expe rienced increases in mixed layer depths and convective activity in the 2080s, changes that actually ended up decreasing O 3 , illustrating the complexity of the climate-meteorology-O 3 relationship. In general, the spatial correlation of O 3 increases with any one meteorological variable was not particularly strong in their results. Again the largest future increases in O 3 were for the highest-concentration O 3 episodes, leading to large increases in hypothetical exceedances concentrated in the Ohio Valley and the Mid-Atlantic coast. They also found an incr ease in the duration of high-O 3 events. The effect of climate change in 50 eastern U.S. cities, without cons idering future changes in air pollution emissions, was to increase the number of days exceeding the 8-hour O 3 standard by 68% (Bell et al., 2007).
These model results also showed future in creases in biogenic VOC emissions in most places as a result of climate chan ge, with the largest absolute increases in the southern and southeastern parts of the United States. While biogenic emissions changes were responsible for up to half of the total climate effect on O 3 concentrations in some pa rts of the Ohio Valley and Mid-Atlantic further to the north, they did not produce significant O 3 changes in these more southern areas that experienced the largest ch anges in these emissions. The impact of how biogenic emissions chemistry is represented in air quality modeling systems on simulated O 3 is
discussed in more detail in the synthesis below. Finally, an analysis of the e ffects of land-use change on O 3 (and heat waves) in the smaller New York City metro region suggests that such changes could also have local impacts of comparable magnitude to the climatic, emissi ons, and boundary conditions factors considered (Civerolo et al., 2007). For more information on the Columbia team’s efforts, see Appendix D and

• http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/8 12/report/0

• http://www.mailman.hs.columbia.edu/ehs/research.html

• http://www.geography.h unter.cuny.edu/luca/

• http://www.cmascenter.org/2003_works hop/session2/hogref e_abstract.pdf

3.2.2.3 Impacts of Global Climate and Emission Changes on U.S. Air Quality: University of Illinois The University of Illinois group focused on e xploring and evaluating, as comprehensively as possible, the capabilities a nd sensitivities of the tools a nd techniques underlying the full, global-to-regional model-based approach to th e problem. They concentrated on building a system that accounts for global chemistry and c limate, and regional meteorology and air quality, capable of simulating effects of climate change s, emissions changes, and long-range transport

3-8changes on regional air quality for the continenta l United States (Huang et al., 2007; 2008). To capture a wider range of sensit ivities, they built different ve rsions of this system, which combines multiple GCMs (PCM and the Hadley Centre Model, HadCM3), SRES scenarios (A1Fi, A2, B1, B2), and convective parameterizati ons (the Grell and Kain-Fritsch schemes) with the Model for OZone And Related chemical Tracers (MOZART) GCTM, a modified version of the MM5 RCM (referred to as CMM5), and the SARMAP 11 Air Quality Model (SAQM). They also made considerable efforts to evaluate both climate and air quality variables with respect to
historical observations and to understand the implications of thes e evaluations for simulations of future changes. Several important findings emerge from this gr oup’s model evaluation efforts. First, they demonstrated that any individual GCM will likely have significant biases in temperature, precipitation, and circulation patte rns, as a result of both parame terizations and internal model variability, so multi-model ensemble means will tend to be more accurate than individual models (Kunkel and Liang, 2005). With proper attent ion, RCM downscaling can improve on these
GCM biases in climate variab les over different temporal sc ales (e.g., diurnal, seasonal, interannual), due to higher resolution and more comprehensive physics, and that furthermore the RCM can produce future simulations of temper ature and precipitation patterns that differ significantly from those of th e driving GCM (e.g., Liang et al., 2006). They found that the improvements in present-day climate generally le d directly to improvements in simulated air quality endpoints, though they also found that the performance of their modeling system tended to be better for monthly and seasonal average O 3 concentrations than for multi-day high-O 3 episodes, reflecting the primary use for which th e driving climate models have been designed (Huang et al., 2007). In addition, they found a high sensitivity of downscaled climate (and downscaling skill) to the convective scheme chos en, with different para meterizations working better in different regions/regimes (Liang et al., 2007). This sensitivity strongly affects simulated air quality, for example by altering meteorology and hence al so biogenic emissions (Tao et al., 2008). All of these findings are c onsistent with, and expa nd considerably upon, the results from the Columbia project described above. Notably, the Illinois team also found that the different patt erns of GCM biases with respect to present-day observat ions in different simulations , as well as the way the RCM downscaling altered these biases, were consistent ly reflected in the future GCM and GCM-RCM
differences as well. This s uggests a strong link between the ability of a GCM or GCM-RCM downscaling system to accurately reproduce present-day climate and the type of future climate it simulates (Liang et al., 2008).

11 SARMAP stands for the San Joaquin Valley Air Quality Study (SJVAQS)/Atmospheric Utility Signatures, Predictions, and Experiments (AUSPEX) Regional Model Adaptation Project.
3-9In future simulations with their coupled gl obal-to-regional modeling system completed to date, based on PCM GCM simulations following both the A1Fi and B1 SRES greenhouse gas scenarios, the Illinois group found changes in O 3 due to climate change alone (i.e., with anthropogenic pollutant emissions he ld constant at present-day leve ls) that were of comparable magnitude to those seen by the NERL and Colu mbia groups, though with di fferences in regional spatial patterns (Tao et al., 2007). These similari ties and differences will be described in greater detail in the synthesis below. The larger greenhouse gas concentra tions, and hence greater simulated climate change, associated with the A1Fi scenario generally resulted in larger future
O 3 increases than for the climate change simulation driven by the B1 scenario. For more information on the Illinois group’s efforts, see Appendix D and

• http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/6 160/report/0

• http://www.sws.uiuc.edu/atmos/modeling/caqims/

3.2.2.4 Impact of Climate Change on U.S. Air Quality Using Multi-Scale Modeling with the MM5/SMOKE/CMAQ System: Washington State University Similar to the NERL, Columbia, and Illi nois groups, the Washington State team developed a combined global and regional climate and air quality modeling system to investigate changes in O 3 (and PM) (Chen et al., 2009; Avise et al., 2009). They used the PCM, MM5, and CMAQ models, and they focused on the IPCC A2 scenario for future greenhouse gases. With this system, the Washington State group investigated climate and air quality changes for the continental United States as a whole, and in addition focused in more detail on two specific regions: the Pacific Northwest a nd the northern Midwest. A key distinguishing feature of their effort is the attention to bioge nic emissions and the considera tion of land cover changes (both vegetation cover and urban distributions), as well as changes in the frequency of wildfires in their simulations. Evaluations of their coupled system against observati ons indicated reasonable agreement with observed climatology and O 3 concentrations in their two focus regions. They also examined wet and dry deposition rates and found qualitatively similar results between
modeled and measured rates in the Pacific Northwest. In five years of simulated summertime O 3 under both present-day and future climate conditions (with constant anthropogenic precur sor pollutants), the Wa shington State group found
future O 3 increases in certain regions, most notably in the Northeast and Southwest, with smaller increases or slight decreases in other regions (Avise et al., 2009) . These climate change effects were most pronounced when consider ing the extreme high end of the O 3 concentration distribution. The magnitude of the O 3 increases found by the Washi ngton State group (i.e., a few to several ppb) were roughly comparable to those found by the other regional modeling groups

3-10already discussed, though again with differences in the specific re gional spatial patterns of the future changes, linked to differences in the spatial patterns of key O 3 drivers, discussed in more detail in the synthesis below. In addition, by accounting for plausible future changes in land-use distribution, they
simulated both net decreases and increases in biogenic emission capacity, depending on region: i.e., they found that reductions in forested area in the Southeast and West due to increases in development more than offset potential increa sed biogenic emissions due to climate change, leading to reduction in MDA8 O 3 levels, while enhanced use of poplar plantations for carbon sequestration significantly incr eased isoprene emissions in th e Midwest and eastern United States, leading to O 3 increases. Finally, they found that warmer and drier conditions in their future simulations yielded increased occu rrences of fire in the western states. Additional information on this group’s e ffort can be found in Appendix D and at

• http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/6 229

• http://www.nwairquest.wsu.edu

3.2.2.5 Guiding Future Air Quality Management in California: Sensitivity to Changing Climate—University of California, Berkeley Distinct from that of the other groups desc ribed above, the Berkeley group’s research focused in detail on central California, usi ng a combination of model and observation-based analyses to determine the effect s on air quality of changes in te mperature, humidity, atmospheric mixing, and biogenic and anthr opogenic emissions changes. Specifically, the Berkeley group used CMAQ at very high resolution (4 km horizontal grid spacing), driven by MM5, to investigate the effects of perturbations in these drivers on O 3 concentrations during a 5-day O 3 episode in the state (Steiner et al., 2006). They derived plausible, spatially resolved future changes in summertime temperatures from two simulations with the Community Climate Model version 3 (CCM3) GCM downscaled to a 40 km grid spacing for the western United States : one with a “pre-industrial” CO 2 concentration of 280 parts per million (ppm) and one representing a hypothetical 2050 climate with a doubled CO 2 concentration of 560 ppm (Snyder et al., 2002). The average August temperature difference
between these two downscaled simulations at e ach point in the domain was added to the MM5 meteorological output used to dr ive CMAQ. This temperature pe rturbation was applied in an uncoupled manner so as not to affect other mete orological quantities su ch as wind speed and boundary layer height, to isolate the impact of temperature changes on chemical reaction kinetics. This imposed temperature increase was al so used to derive pert urbations of humidity and biogenic VOC emissions for additional, separa te sensitivity experiments. In addition to

3-11these climate-based changes, the Berkeley group carried out simulations to investigate the sensitivity of O 3 to changes in anthropogenic NO x and VOC emissions, as well as to the inflow of pollutants from outside the state. They found that higher temperatures increased O 3 concentrations in this simulated pollution episode both directly (through increa sed reaction rates) a nd indirectly (through increases in biogenic emissions). Across all the different effects explored, they found that O 3 sensitivity varied depending on proximity to th e Pacific Coast (e.g., where impacts of increased pollution at the inflow boundary ar e greatest), and on preexisting NO x or VOC levels (e.g.,
NO x -saturated regions in central California appe ar to be most sensitive to climate-related changes). The Berkeley team also conducted an obser vationally based study of the temperature sensitivity of anthropogenic VOC emissions: th e role of temperatur e in increasing fuel evaporation was highlighted in th is analysis (Rubin et al., 20 06). Increased evaporation was apparent in observed correlations between speciated VOCs and temp eratures as they varied by time of day and from day to day, with implications for the climate sensitivity of these emissions. Additional information about the Berkeley project can be found in Appendix D and at http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/6231/rep
ort/0

.

3.2.2.6 Sensitivity and Uncertainty Assessment of Global Climate Change Impacts on Ozone and Particulate Matter: Examination of Direct and Indirect, EmissionInduced Effects: GIT-NESCAUM-MIT Similar to the NERL, Columbia, Washington St ate, and Illinois groups discussed above, the GIT-NESCAUM-MIT group constructed a linke d global-to-regional climate and air quality modeling system to investigate the impact s of global change on regional U.S. O 3 and PM concentrations (Tagaris et al., 2007; Liao et al., 2007). Specifica lly, they used CMAQ, driven by present-day and future climate simulations with the GISS II ′ GCM downscaled using MM5 (the
same MM5-downscaled GISS II ′ GCM simulations developed for the NERL project described above). However, compared to these other gr oups, they had a unique focus on understanding the climate sensitivity of regional air quality in the co ntext of expected future pollutant emissions under the implementation of curre nt and future control strate gies. This effort not only investigated O 3 , but also PM and its speciated component s of sulfates, nitrates, ammonium, and organics, in detail. A strong, built-in link between the academic and regional air quality management communities is achieved via the inclusion of NESCAUM in the partnership. Their work to date attempts to determine if climate change will ha ve significant impacts on the efficacy of O 3 and PM emissions control strategies currently being considered in the

3-12United States by focusing on (1) comparing the sensit ivity of future regional U.S. air quality to changes in emissions around present-day and proj ected future climate and emissions baselines and (2) accounting for the effects of uncertainties in future climate on simulated future air quality
to evaluate the robustness of thes e results (see Liao et al., 2009). To address these issues, the GIT-NESCAUM-M IT team developed a detailed, spatially resolved U.S. future air pollutant emissions inventory to understand th e relative impacts of climate change on future air quali ty in different emissions and control strategy regimes. To accomplish this, they used the latest projection da ta available for the near future (to about 2020), such as the EPA CAIR Inventory, and they ex tended point source emissions to 2050 using the
IMAGE 12 model combined with the IPCC A1b emissions scenario (the same scenario used in the
GISS II ′ future climate simulations) and mobile s ource emissions from Mobile Source Emission Factor Model version 6 (MOBILE6), projec ting reductions of more than 50% in NO x and SO 2 emissions (Woo et al., 2007). A key finding from the GIT-NESCAUM-MIT wo rk is that, overall, existing control strategies should continue to be effective in an altered future climate, though with regional
variations in relative benefit (Tagaris et al., 2007). The magnitude of the “climate change penalty” for controlling O 3 (as defined by the Harvard group) is found to be consistent with the work of Wu et al. (2008a). Th e spatial distribution and annual va riation in the contribution of precursors to O 3 and PM formation under the combined fu ture scenario of climate change and
emission controls remain similar to the baseline case, implying the continued effectiveness of current control strategies. The findings furt her suggest, however, that compliance with air quality standards in areas at or near the NAAQS in the future w ould be sensitive to the amount of future climate change. Finally, an analysis of potential health impacts of these simulated future air quality changes, using th e environmental Benefits Ma pping and Analysis Program (BenMAP), 13 is ongoing.
Additional information on the GIT-NESCAUM-M IT project can be found in Appendix D
and at

• http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/6 238/report/0

• http://www.ce.gatech.edu/~trussell/lamda/

3.3 SYNTHESIS OF RESULTS ACROSS GROUPS This sub-section synthesizes findings across the global a nd regional modeling results from the groups that have just been introdu ced, focusing on nationwide changes in summertime

12 A Netherlands Environmental Assessment Agency modeling tool.
13 See http://www.epa.g ov/ttn/ecas/benmodels.html for more information.
3-13O 3 concentrations due to simulated climate ch ange a few decades into the future. Other pollutants are not addressed here. As already men tioned, the major focus is the particular subset of results completed to date which are largely common across groups, to facilitate a synthesis. Nevertheless, even limiting discussi on to this subset allows us to effectively illustrate a number of key points to carry forward. Specifically, then, the focus is on inter-group comparisons of future decade (~2050s) and present-day simulations of summertime O 3 under scenarios of climate change. The emphasis on summer reflects that of the pa rticipating research groups, i. e., on the primary season for O 3 episodes and exceedances. All of the future si mulations discussed in this sub-section held anthropogenic emissions of precursor pollutants constant at present-da y levels, but allowed climate-sensitive natural emissions (e.g., of biogeni c VOCs) to vary in response to the simulated changes in climate. 14 The organization is as follows: first, the O 3 results from the fully downscaled, high-resolution regional model simu lations are presented and compared; then, comparisons of differences in key meteorologica l variables (and biogenic emissions) from these same simulations are provided to begin explaining these O 3 results and to highlight the sometimes complex interactions between O 3 and its drivers; and fina lly, some results from the global-model-only runs are presented to comp lement the regional model findings and to illuminate more clearly certain important issues. Most of the groups whose results make up this synthesis of the impact s of climate change
on O 3 have also carried out additional, in most cas es highly preliminary, simulations designed to investigate, to first-order, the effects of cha nges in climate relative to changes in worldwide and/or U.S. anthropogenic emissions of precursor pollutants. The results from these simulations are not included in the synthesis below to main tain the focus on first exploring climate change impacts alone. However, these sensitivity studies provide useful insights that will help inform
the more detailed treatments of future emi ssions planned for Phase II, highlighting key assumptions and uncertainties that will need to be addressed. Therefore, Section 4 contains a brief summary of these analyses and findings. Similarly, some of the groups have also completed simulations of potential future changes in PM (and its component chemical species ), but these results ar e not discussed here. This is because the research effort and the level of scientific understanding are much more
mature at this time for climate and O 3 than for climate and PM—there are far more O 3 results from these projects to date to draw from, al ong with a greater knowledge base for interpreting them. In addition, it is anticipat ed that many of the modeling-re lated issues revealed in the
examination of the O 3 results will likely appl y to PM as well, though PM also poses unique

14 Differences in IPCC SRES scenarios between the different simulations thus refer only to greenhouse gas concentrations, and not precursor pollutants.
3-143-15 challenges for coupled climate-air quality m odeling. Some discussion of progress toward understanding climate change impacts on PM is al so included in Section 4, and a future report focusing on PM is anticipated.

3.3.1 Regional Modeling Results

3.3.1.1 Modeling System Configurations, Simulations, and Evaluation

Table 3-1 lists the re gional climate and O 3 modeling results discussed in this section.
These simulations were carried out with linked systems cons isting of a GCM/GCTM, dynamical downscaling with an RCM, and regional-scale air quality calculations with an RAQM. In aggregate, they cover a range of models, IPCC SRES scenarios of future greenhouse gas emissions, climate and meteorological model physical parameterizations, and chemical
mechanisms. The principal comparison in this section is across the regional modeling experiments listed in Table 3-1 that have regional simulati on domains covering the entire continental United States. These are the NERL, University of Il linois (Illinois 1 and Illi nois 2), Washington State (WSU), and Georgia Tech-NESCAUM-MIT (GNM) sets of simulations. Results from the Berkeley and Columbia simulations, conducted for subsets of the country, are referred to in the
course of the text to re inforce particular findings. Note th at the NERL and GNM sets both relied
on the same MM5-downscaled GISS III climate simulations, though GNM is for three summers versus five for NERL. They also differed in th eir development of their emissions inventories.
Note also that Illinois 1 and Illinois 2 are id entical except for the greenhouse gas emissions scenario used in the GCM simulation of future global climate, with Illinois 1 using the IPCC SRES A1Fi and Illinois 2 using B1. The many additional details of each of these sets of numerical experiments can be found in the references cited in Ta ble 3-1 (and further references therein). It is important to reiterate that the differen ces in IPCC SRES scenarios for the simulations
listed in Table 3-1 refer only to greenhouse gas concentrations, a nd not precursor pollutants. As emphasized previously in this repor t, all of the results shown here are from simulations that held anthropogenic emissions of precursor pollutants, as well as other relevant chemical species (e.g.,
CH 4 ) constant at present-day levels. Climate-sensitive natural emissions, such as biogenic VOCs, evaporative emissions, and lightning NO x (depending on the modeling system used), were allowed to change in response to the si mulated climate change, with the biogenic VOCs being the dominant impact. Land use and land cove r also remained consta nt. Finally, potential impacts of changes in O 3 concentrations on plant producti vity and carbon uptake were not included (e.g., see Sitch et al., 2007).Table 3-1. The regional modeling systems whoseresults are discussed in Sections 3.3.1 and 3.3.2.The regionalresolution listed for each group representsthe horizontal grid spacing of the regionalair quality simulation (also correspondingto the innermost nested grid of the RCM). The Illinois AQM runs use 30 km grid spacing over four sub-regions of the countryand 90 km everywhere else (their CMM5 runsuse 30 km everywhere). Therefore, for the O3results shown below, these 30km values in the sub-regions are overlaid on the background map of 90 km values, introducing some minor contouringdiscrepancies at the boundaries of the sub-regions.

3-16BerkeleyaColumbiabNERLcIllinois 1dIllinois 2dWSUeGNMfDomainCent. CAEast. U.S.Cont. U.S.Cont. U.S.Cont. U.S.Cont. U.S.Cont. U.S.Simulation Period1 August5 JJAs5 JJAs1 JJA1 JJA5 Julys3 JJAsGCMCCM3GISS AOGISS IIIPCMPCMPCMGISS IIIGlobal Resolution2.8o × 2.8o4o × 5o4o × 5o2.8o × 2.8o2.8o × 2.8o2.8o × 2.8o4o × 5oGHG Scenario2 × CO2A2A1bA1FiB1A2A1bRCMMM5MM5MM5CMM5gCMM5gMM5MM5RegionalResolution4 km36 km36 km90/30 km90/30 km36 km36 kmConvectionSchemeN/ABetts-MillerGrellGrellGrellKain-FritschGrellRAQMCMAQCMAQCMAQAQMhAQMhCMAQCMAQChemicalMechanismiSAPRC99jCB-IVkSAPRC99RADM2lRADM2SAPRC99SAPRC99Climate SensitiveEmissionsBVOCsBVOCs;EvaporativemBVOCs;EvaporativemBVOCs;EvaporativemBVOCs;EvaporativemBVOCs;EvaporativemBVOCs;EvaporativemaFor more details, see Steiner et al. (2006).bFor more details, see Hogrefe et al. (2004a,b)—the GISS AOmodel refers to the model of Russell et al. (1995).3-17cFor more details, see Leung and Gustafson (2005); Nolte et al. (2008).dFor more details, see Liang et al. (2006); Huang et al. (2007; 2008); Tao et al. (2007).eFor more details, see Chen et al. (2009); Avise et al. (2009).fFor more details, see Tagaris et al. (2007);Liao et al. (2007); Woo et al. (2007).gCMM5 is based on the standard MM5, but with modifications to the buffer zone, oceaninterface, and cloud-radiation interactions.hAQM has been adapted from the SARMAP model, incorporating a faster, more accurate numerical solver for gas-phase chemistry.iNote that the SAPRC99 and RADM2 chemical mechanisms recycle isoprene nitrate, while the CB-IV mechanism does not.jFor more details, see Carter (2000).kFor more details, see Gery et al. (1989).lFor more details, see Stockwell et al. (1990).mThrough the SMOKE emissions modeling system, e.g., see Houyoux et al. (2000).Cent. = Central; East. = Eastern; Cont. = Continental; U.S. = United States.All of these modeling systems have been evaluated to some degree with respect to
historical observations of both climate and chemistry. Each of the individual modeling components making up the coupled system is we ll established in their respective research communities, and has undergone extensive testi ng and evaluation, though not necessarily for the particular variables, and statisti cs, most appropriate for coupled climate and air quality research. For example, the CMAQ model has been extensively evaluated against observations for operational air quality forecasting evaluations (e.g., see Eder and Yu, 2006; Eder et al., 2006), as well as for the purposes of examining issues such as the sensitivity of simulated O 3 concentration to nudging of meteorological fiel ds and subsequent impact on O 3 biases (e.g., see Otte, 2008). Similar claims may be made for the other globa l and regional climate and chemistry modeling
components. Evaluation of the coupled modeling systems, built out of these individual components, is at an early stage. Each of the modeling team s has performed a number of evaluations of their coupled climate and air quality systems using st ation observations of me teorological variables and ozone concentrations (e.g., from EPA’s Air Quality System database 15 ) for various historical time periods. Details of these evaluations ca n be found in the references cited above, and
additional references therein fo r the individual modeling component s. For example, the NERL group compared their combin ed GCM-RCM-RAQM MDA8 O 3 distributions with AQS observations nationally, finding reasonable agreem ent comparable to th at found for uncoupled CMAQ simulations. In general, they found the smallest biases in th e northeastearn United States, and at the high end of the O 3 distribution. They attrib uted these biases to both meteorological and chemical mechanism factors. Beyond providing insight into the performance a nd biases of the modeling systems, these evaluation studies also provide a number of importa nt insights that complement the simulations of climate change impacts on O 3 that will be discussed shortly, e .g., on the role of meteorological
drivers or alternative chemical mechanisms in O 3 variability. For exampl e, Nolte et al. (2008) attribute a portion of the O 3 biases over the eastern United St ates that they observe in their coupled system to the biases in temperatur e and precipitation present in the MM5 regional climate used to drive their ozone simulation (s ee also Leung and Gustafson, 2005). They also found, in sensitivity studies, differences in simulated O 3 using the SAPRC vs. the CB-IV chemical mechanism in CMAQ (see also Faraji et al., 2008). Similarly, Huang et al. (2007) showed how low or high biases in simulated temp erature over the Northeast and Midwest lead to
O 3 concentration biases in the same directions.

15 http://www.epa.gov/ttn/airs/airsaqs/

. 3-183.3.1.2 Changes in O 3 Figure 3-1 shows summertime mean MDA8 O 3 concentration differences between simulated future and present-day climates fo r the regional modeling experiments listed in Table 3-1 that have model domains covering the en tire continental United States. These are the NERL, Illinois 1, Illinois 2, WSU, and GNM simu lations. Results from the Berkeley and Columbia simulations, conducted for subsets of the country, are referred to in the course of the text to reinforce partic ular findings. MDA8 O 3 is selected because of its direct relevance to U.S. air quality standards. All plots discussed here show future minus pres ent differences. All O 3 values are in ppb.

Key similarities between the results from the different groups emerge:

• For all the present/future simulation pairs, so me substantial regions of the country show future increases in O 3 concentrations of roughly 2

− 8 ppb under a future climate.

• Other regions show little change in O 3 concentrations, or even decreases, though the decreases tend to be less pronounced than the increases.

• These patterns of O 3 differences are accentuated in the 95 th percentile MDA8 O 3 (shown in Figure 3-2 for the NERL experiment, as one example of this result) compared to the mean MDA8 O 3
.

The basic result of larger climate sensitivity of O 3 concentrations for high-O 3 conditions (e.g., 95 th percentile MDA8 O 3 ) is one of the most robust find ings of this synthesis—it holds across all the modeling groups and appears in many different analyses carried out by these groups. These more detailed results can be found in the papers cited in Table 3-1. This is significant, because these high-O 3 episodes are of particular c oncern for air quality managers. Some pronounced differences in the broad spat ial patterns of cha nge across experiments emerge as well. For example, the NERL and GNM simulations show increases in O 3 concentration in the Mid-Atlantic and parts of the Northeast, Gulf Coast, and parts of the West. They also show decreases in the upper Midwes t and Northwest and lit tle change elsewhere, including the Southeast. By contrast, the Illinoi s 1 experiment shows the strongest increases in the Southeast, the Northwest, and the Mississippi Valley (as well as the Gulf Coast, in agreement with NERL), with weaker increases in the upper Midwest. In addition, th ese changes tend to be larger than those from the NERL experiment. The WSU experiment shows the largest increases

3-19Figure 3-1. 2050s-minus-present differe nces in simula ted summer mean MDA8 O 3 concentrations (in ppb) for the (a) NERL; (b) Illinois 1; (c) Illinois 2; (d) WSU; and (e) GNM experiments (see Table 3-1).

3-20Figure 3-2. 95 th percentile MDA8 O 3 concentration differences for the NERL experiment.

in the Northeast, parts of the Mi dwest, and desert Sout hwest, with decreases in some parts of the West, the Southeast, the Northwest, the Plains stat es, and the Gulf Coast. As is to be expected, the NERL and GNM patterns are quite similar, with differences primarily reflecting the averaging over five vs. three summers, respectiv ely. This highlights the potential importance of interannual variability in driving differences between modeling groups, as will be discussed
further below. Certain regions show greater agreement ac ross experiments than others. Figure 3-1 illustrates that a loosely bounded area, encompassi ng parts of the Mid-Atlantic, Northeast, and lower Midwest, tends to show at least some O 3 increase across all the simulations. By contrast,
the West and the Southeast/Gulf Coast are areas of greater disagreement, hinting at some of the complexities underlying the intera ctions between climate and O 3 . Even for these regions, however, at least some of the models (here and in Section 3.3. 2) show substantial climateinduced O 3 increases. Changes in drivers that help explain these agreements and disagreements, and help illustrate these complexities, will be presented and discussed shortly. All of these findings are genera lly consistent with results fr om the earlier Columbia study (see Hogrefe et al., 2004b). Figure 3-3 shows fu ture-minus-present climate summertime mean

3-21MDA8 O 3 concentration difference for their modeli ng domain, covering the eastern half of the
United States.

Figure 3-3. 2050s-minus-present differe nces in simula ted summer mean MDA8 O 3 concentrations (in ppb); reproduced from Figure 2 in Hogrefe et al. (2004b).

Note from Table 3-1 that there are differe nces in the number of years of simulation completed by the different groups so far. As introduced in Section 1, and discussed further below, it is well recognized that interannual meteorological variabil ity drives large year-to-year changes in O 3 (e.g., see White et al., 2007; Leibensper ger et al., 2008; Jacob and Winner, 2009). All of the modeling groups eventually aim to anal yze interannual variability in their simulations. In this context, Figure 3-4 (reproduced from Nolte et al., 2008) illustrates two points. First, for some regions, the average change in O 3 from the present to the 2050s as a result of climate change is just as large as (a nd on top of) the year-to-year O 3 variability that is of concern today. In other words, climate change has the potential to push O 3 concentrations in extreme years beyond the envelope of natural interannual variability. Secon d, it highlights the need for simulating multiple years to increase the robustne ss of findings about present-to-future changes.
These results are consistent with those presented in Racherla and Adams (2008) (based on their GCTM runs), who found that the magnit ude of simulated future changes in O 3 concentrations over the eastern United States tended to be greate r than the magnitude of present-day interannual
3-22O 3 variability, and that at least 5 years of simulation were needed to fully separate the effects of climate change and interannual variability.

presents 2050s climate and decreases in anthropogenic

3 precursor emissions.

und

g

e extension of the O 3 season for som regions of the United St ates under future climate change. Figure 3-4. Frequency of si mulated summer mean MDA8 O 3 values exceeding 80 ppb in different regions from the NERL experiment; reproduced from Figure 11 in Nolte et al. (2008). Each bar represents 1 year. The leftmost group of bars corresponds to present-day climate, the center group to 2050s climate with anthropogenic emissions held constant at present-day values, and the rightmost group re O

Finally, while this analysis focuses on summe rtime results, a few of the groups also fo strong increases in O 3 concentrations in their future comp ared to present climate simulations over certain regions of the count ry (e.g., Nolte et al., 2008; Avis e et al., 2009; Racherla and Adams, 2008). Figure 3-5 (reproduced from Nolte et al., 2008) illustrate s this point, showin September-October O 3 increases in a band stretching from the Southwest, across the Plains states, and into the Upper Midwest. These results suggest a possibl

e

3-23Figure 3-5. 2050s-min us-present Septem ber-October compared to JuneAugust differences in simulated mean MDA8 O 3 concentrations (in ppb); reproduced from Figure 6 in Nolte et al. (2008).

3.3.1.3 Changes in Drivers There is already a great deal of regional variability in near-surface O 3 under current climate conditions. For example, as introduced in Section 1, a large body of observational and empirical work has helped us understand that con centrations tend to be especially great where the emissions of precursor ch emical species like VOCs and NO x are also large, and that, furthermore, these pollutants tend to drive up O 3 even more during the times when meteorological conditions most favor strong net photochemical production—persistent high pressure, stagnant air, lack of convection, clear skies, and warm temperatures—and vice versa.
It is for these reasons that the O 3 NAAQS are most often exceeded during summertime hot spells in places with large natural or anthropogenic prec ursor emissions (e.g., cities). To the extent that climate change may alter weather patterns, and, hence, the frequency, duration, and intensity of these episodes, for example, O 3 concentrations could be significantly affected. However, the causal chain linking (a) long-term global climate change , (b) changes in the aspects of (often) short-term meteorological vari ability that most directly drive near-surface O 3 concentration changes of concern to air quality managers, and (c) any O 3 changes that ultimately result from the interaction of these meteorologi cal changes with the pollutants present in the environment (which may themselves be sensi tive to meteorology and climate) may not be straightforward. Changes in the O 3 distribution of a given region due to climate change will reflect a balance among competing changes in multiple factors. For example, a number of meteorological vari ables have been identified as potentially important, including

• Near-surface temperature

• Near-surface humidity

3-24• Precipitation

• Cloud cover

• Planetary Boundary Layer (PBL) height

• Near-surface wind speed and direction

• Ventilation and mixing due to convective events

• Ventilation and mixing due to synoptic-scale cyclones

• Ventilation and mixing due to coastal onshore flow.

These variables are not, in general, indepe ndent of each other. Instead, they vary, together or separately in different combinations, at differe nt locations over different timescales, in ways that may favor either increases or decreases in O 3 . For example, all other factors being equal, increases in temperature at a given time and place might lead to increases in O 3 concentration, but if these temperature increases are accompanied by increases in cloudiness, the net result might be a decrease in O 3 concentration. Box 3-1 provides a discussion of how one’s perception of the relationship between O 3 and its meteorological drivers can vary depending on the timescale considered, using the temperature-O 3 relationship as an example. This provides some additional context for interpreting these next modeling results to be presented. This issue is revisited in Section 3.4 below, where the imp lications for interpreting long-term mean climate change-air quality modeli ng results are discussed. The advantage of the type of model-based approa ch that is the focus of this section, i.e., the strategy of linking climate, meteorology, and ai r quality models, is th at such integrated modeling systems are capable of capturing these complexities by representing the reinforcing and competing interactions between variables in an internally self-consistent way. As such, they help illuminate potentially non-obvious impacts of climate change on O 3 that result from synergistic interactions between the changes in key drivers. Figures 3-6 and 3-7 display the average future -minus-present differences in near-surface air temperature and surface incoming solar radiation (typically referred to as “insolation”), which are two of the most critical meteorological drivers of ground-level O 3 . The insolation changes largely reflect changes in cloud cover. Other va riables besides the two shown in Figures 3-6 and 3-7 were also examined, including average daily maximum temperature, pr ecipitation, number of rainy days, and PBL height. However, none of these additional comparisons are shown here because, at least at this level of analysis, they do not seem to add a great deal to the explanatory power of temperature and surf ace insolation (and, as will be discussed below, biogenic VOC emissions). This is likely due to the strong correlations among these variables already been
discussed.

3-25Box 3-1. The Temperature-O 3 Relationship As seen through the lenses of differe nt meteorological/climatic timescales

Episode: The severity of a particular O 3 episode lasting one or a few days can depend strongly on temperature. For example, Aw and Kleeman (2003) found that, by increasing temperature (but without modifying the other meteorological variables) in an air quality model simulation of a southern California O 3 episode, they significantly increased daily peak O 3 concentrations. Temperature affects the kinetics of the O 3 -forming and destroying chemical reactions. For example, in polluted environments, increasing temperature will tend to lead to more NO x , and hence more O 3 , via a decrease in peroxyacetylnitrate (PAN) production. The new results from the Berkeley and Carnegie Mellon groups described in Section 3.2 have yielded similar insights. Steiner et al. (2006), in their very high-resolution simulations of a 5-day O 3 episode over California, found that temperature perturbations consistent with plausible 2050s cl imate change led to increases in afternoon O 3 concentrations of 1- 5 ppb across the state. Dawson et al . (2007b) found similar effects of temperature modification when using the PMCAMx model to simulate O 3 concentrations during a week-long period over the eastern U.S.

Season: From the perspective of an entire season, however, mean O 3 concentration and the number of O 3 exceedances will likely depend at least as much on how many of these mete orological episodes that promote O 3 formation occur, and how long they last, as on how hot it is during them. In other words, how often in a given summer that cool, cloudy, rainy, and windy conditions gi ve way to spells of hot, clear, dry, and stagnant conditions will play a large role in determining whether it was a “high-O 3 ” or “low-O 3 ” summer. At this timescale, temperature and O 3 will also be positively correlated, but here the “temperature-O 3 ” relationship exists at least partly because temperature itself is highly correl ated with these other meteor ological conditions, like more sunlight and less ventilation, that also favor increased O 3 concentrations.

Long-Term Climate Change: On the multi-decadal timescales of global climate change, however, the relationship between temperature and these other meteorological drivers may or may not play out in the same way that is characteristic of seasonal timescales. In some regions, climate change may indeed have the effect of producing long-term average associatio ns between higher temperatures, less cloudiness, and weaker mixing that in aggregate would be likely to lead to O 3 concentration increases. This w ould be true, for example, in the regions most at risk for increases in the frequency, duration, and intensity of summertime heat waves (e.g., see Meehl and Tebaldi, 2004; IPCC, 2007). In other regions, however, climate change may lead to changes in these other variables that do not favor increases in O 3 concentrations. For example, a warmer world is likely, on average, to be a wetter world. Bo th the Harvard and Carnegie Mellon GCTM results summarized earlier showed how increases in humidity in their future simulations led to decreases in near-surface O 3 in less-polluted regions (Wu et al., 2008a; Racherla and Adams, 2006). Similarly, regions that e xperience increases in cloudiness (and hence decreases in sunlight and O 3 photo-production) in an altered future climate might have net O 3 concentration decreases, in spite of increased temperatures.

Combined with the O 3 results shown above in Figure 3-1, Figures 3-6, and 3-7 reveal some key similarities in the relationships between O 3 and meteorological drivers among the
different model studies:

• First, in many regions the O 3 concentration changes (Figure 3-1) seem to correspond relatively well with combined changes in mean temperature (Figure 3-6) and mean surface insolation (Figure 3-7). For ex ample, the NERL results show the O 3 increases corresponding with temperature and insolation increases in the Mid-Atlantic and Gulf Coast and O 3 decreases associated with the inso lation decreases and the local minimum in temperature increases in the uppe r Midwest and the northern Plains.

3-26Figure 3-6. 2050s-minus-present di fferences in simulated summer mean near-surface air T (oC) for the (a) NERL; (b) Illinois 1; (c) Illinois 2; (d) WSU; and (e) GNM experiments.

3-27Figure 3-7. 2050s-minus-present di fferences in simulated summer mean surface insolation (W m -2 ) for the (a) NERL; (b) Illinois 1; (c) Illinois 2; (d) WSU; and (e) GNM experiments.

• In other regions, temperature and insolati on vary in opposite directions, with mixed impacts on O 3 concentrations. For example, in the Illinois 1 simulations, in spite of insolation decreases over much of the Northwest, the large increase in temperature there seems to drive O 3 increases.

In a small number of regions across the simulations, there is no strong correspondence
between O 3 concentrations and either insolation or temperature (e.g., the areas around Oklahoma

3-28in the Illinois 1 experiment and Nevada/Utah/Ida ho in the Illinois 2 experiment), suggesting that other forcing factors may be important, and/or that a correspondence mi ght exist, but only for different averaging periods and statistics of these variables.
The differences between the NERL and GNM results are consistent with this last bullet. For example, in the Plains states, GNM shows greater O 3 decreases, consistent with the difference in temperature and insolation trends resulting from the difference in the number of
summers simulated. Again, as discussed above and in Box 3-1, when interpreting these m onthly- or seasonalmean results it is important to recognize they en compass not just change s in the meteorological
conditions most related to O 3 episodes, but the whole spect rum of changes in regional climatology arising from global climate change. Considering the results from the Columbia group, Hogrefe et al . (2004b) do not report any single clear relationship acr oss their study region between th e spatial patterns of futureminus-present O 3 concentrations and a number of mete orological variables (e.g., temperature, wind speed, and mixed layer height), as menti oned in the summary in Section 3.2. This is consistent with the potential for different competi ng effects in different regions illustrated by the results shown here. They do note a strong sensitivity of future O 3 changes to changes in convective activity in certain areas, which may reflect the dependence on insolation found by the other groups. Figure 3-8 shows the patterns of changes in mean biogenic VOC emissions across the simulations. As documented in earlier work (e .g., Chameides et al., 1988; Roselle et al., 1991; Guenther et al., 1994; Pierce et al., 1998; Fuente s et al., 2000; Purves et al., 2004; among others), the emissions of these important natural O 3 precursors are themselves sensitive to meteorology, including sunlight and temperature. Therefore, in conjunction w ith the direct forcing exerted on
O 3 processes by changes in meteorology, climate-i nduced changes in biogenic emissions levels can lead to changes in O 3 concentrations as well (see also Zhang et al., 2008). As will be discussed again below, in the cont ext of the global modeling results, this impact depends in part
on the relative amounts of NO x and VOCs in the environment. Fo r example, Steiner et al. (2006) found significant O 3 concentration increases in the high-NO x San Francisco Bay area due to increases in biogenic VOC emissions, whereas even larger increases in biogenic emissions over the Sierras actually produced slight O 3 decreases. The climate-induced biogenic emissions change s shown in Figure 3-8 seem to contribute
to the O 3 concentration changes, but only in some regions, and not wholly consistently across model studies. For example, temperature-driven increases in biogenic emissions may contribute
to the above-mentioned O 3 increases in the Northwest in the Illinois 1 experiment, the Mid-Atlantic in the NERL and GNM experiments, the Northeast in the Illi nois 2 experiment, and

3-29Figure 3-8. 2050s-minus-present differe nces in simula ted summer mean biogenic VOC emissions (g Carbon m -2 day -1 ) for the (a) NERL; (b) Illinois 1; (c) Illinois 2; (d) WSU; and (e) GNM experiments.

the Southeast in the Illin ois 1 experiment. Contrastingly, in parts of the Southeast and Mountain West in the NERL and GNM experiments, emissions increase significantly but O 3 concentrations do not change. Notably, the WSU si mulation shows large decreases in O 3 in some of the parts of
the Southeast and Gulf Coast where increases in VOC emissions are the str ongest, a result that is partially attributed to increases in precip itation, and hence reduced photo-production. Where there are strong correlations between biogenic emissions changes and O 3 concentration changes, often there are similarly strong changes in inso lation and/or temperature, so separating the different effects is not always straightforward. The earlier work by Hogrefe et al. (2004b) found
3-30the strongest increases in emissions in the Southe ast, similar to the resu lts from the NERL and Illinois 1 and 2 experiments, but found that the largest O 3 concentration cha nges that could be attributed to biogenic emissions changes occurred instead in part s of the Ohio Valley and coastal
Mid-Atlantic. Discerning the precise chemical pathways whereby O 3 responds to changes in biogenic emissions, and how they vary as a function of re gion and climatic conditi ons, is an area of ongoing scientific inquiry. Different air quality models employ diffe rent representations of these pathways in their code. As such, differences between the simulated O 3 response to changes in simulated biogenic emissions from different mode ling systems is at this time a key source of uncertainty in climate change impacts on future air quality, particularly in certain regions where the effect of increasing VOC concen trations is highly dependent on NO x levels. This issue will be highlighted further in Secti on 3.3.2 below, in the intercompa rison of the results from the global modeling experiments. One way to summarize the aggregate results pres ented in Figures 3-1 and 3-6 to 3-8 is to say that O 3 responds to the meteorological/emissions driv ers in a qualitatively consistent manner across the simulations, but the regional patterns of relative changes in these drivers is highly
variable across these same simulations. In other words, there are important differen ces in the simulated future regional climate changes across groups that seem to drive the differences in the regional patterns of O 3 increases (and decreases). The differences in modeli ng systems among the groups, as documented in Table 3-1, provide some indication of a number of possible contributing factors that might be responsible for these differences in simulate d future regional climate patterns, including

• Differences in the driving GCM

• Differences in the SRES greenhouse gas scenario

• Differences in the RCM (and/or model physi cal parameterizations) used to simulate regional meteorology

• Differences in the RAQM (and/or chemical mechanisms)

• Differences in the amount of interannual variability captured

These issues of inter-group differences, and the sensitivity of simulation results to modeling methodology, are discussed in greater detail in Section 3.4 below, to provide additional guidance on interpreting the findings and evalua ting their robustness in the context of the existing scientific uncertainties.

3-31The findings presented here, in Sections 3.3.1.2 and 3.3.1.3, are generally consistent with the limited number of regional climate and air qu ality modeling experiments recently carried out for Europe. For example, Forkel and Knoche (2006) simulated changes in near-surface O 3 concentrations between the 1990s and the 2030s over Southern Germany under climate change but no change in anthropogenic emissions. They found a 10 percent increase in average daily
maximum O 3 during summer (approximately 2

− 6 ppb, depending on location in the model domain). Langner et al. (2005), in a set of regional modeling experiments, found climate change-induced increases in April-September O 3 concentrations during the mid-21 st century compared to the present over Southern and Ce ntral Europe, with decreases over Northern Europe, and that these changes were significant with respect to interannual variability. Meleux et al. (2007) found higher summertime O 3 concentrations under future climate conditions over Europe, due primarily to increased temperat ures, decreased cloudine ss and precipitation, and increases in biogenic VOC emissi ons. They also found large regi onal variability in these O 3 changes. Finally, Szopa and Ha uglustaine (2007) found worsening O 3 conditions over Europe as a result of anticipated climate change in 2030, th ough this was sensitive to the choice of global and regional emissions change scenarios.

3.3.2 Global Modeling Results Table 3-2 lists the groups that have results from GCTM simulations available at the time of developing this report.

Table 3-2. GCTM-only model simulati ons whose results are discussed in Section 3.3.2. CMU stands for Carnegie Mellon University. The two Harvard runs use different GCMs with the same SRES greenhouse gas scenario. The two Illinois runs have identical setups but are driven with different SRES scenarios. As with the regional modeling system re sults discussed above, anthropogenic emissions of precursor pollutants were held constant across present-day and future simulations, while natural climate-sensitive emissions were allowed to change.

Harvard 1 a Harvard 2 b CMU c Illinois 1 d Illinois 2 d

Simulation Period 5 summer/falls 5 summers 10 summers/falls 5 summers 5 summers

GCM GISS III GISS II ′ GISS II ′ PCM PCM

Resolution 4o × 5o 4o × 5o 4o × 5o 2.8o × 2.8o 2.8o × 2.8o

GHG Scenario A1b A1b A2 A1Fi B1

GCTM GEOS-Chem GISS II ′ e GISS II ′ e MOZART v.4 MOZART v.4

3-32Chemical Mechanism GEOS-Chem f Harvard Trop Chem Model g Harvard Trop Chem Model MOZART v.4 h MOZART v.4

Climate Sensitive Emissions BVOCs; Lightning and soil NO x BVOCs; Lightning and soil NO x BVOCs; Lightning and soil NO x BVOCs; Lightning and soil NO x BVOCs; Lightning and soil NO x

a For more details, see Wu et al. (2007); Wu et al. (2008a; 2008b)
b For more details, see Mickley et al. (2004)

c For more details, see Racher la and Adams (2006; 2008)
d For more details, see Tao et al. (2007); Lin et al. (2008); Huang et al. (2008)

e The GISS II ′ model was coupled to the Harvard tropospheric O 3 -NO x -hydrocarbon chemical model (Mickley et al., 1999)
f For more details, see http://homepages.see.leeds.ac.uk/~lecm je/GEOS-CHEM/GEOS-CHEM_Chemistry.htm

. g For more details, see Mickley et al. (1999)
h For more details, see Horowitz et al. (2003) and http://gctm.acd.ucar.edu/mozart/models/m4/index.shtml

.

All of these GCM/GCTM simulations are also associated with regional downscaling and air quality modeling efforts. The Illinois GCM/ GCTM runs are the same ones used to provide climatic and chemical boundary conditions for the Illinois 1 and 2 regional simulations listed in Table 3-1 and described above (see also Lin et al., 2008), and the Harvard 2 run is the same one used to drive the NERL regional simulations (see also Mickley et al., 2004). The Harvard 1 and CMU simulations will similarly eventually be used to drive RCM and RAQM models—these groups have developed and tested full global-to-regional systems, with results expected in the near future. Here, a somewhat more limite d inter-group comparison than for the regional modeling results is presente d, with the goal of illustrating a few specific points. In a global context, the results from these si mulations are generally consistent with other GCTM climate change experiments (e.g., see Mu razaki and Hess, 2006; Stevenson et al., 2006; Zeng et al., 2008): e.g., d ecreases in background O 3 concentrations in clean environments (e.g., the oceans), due to increased water vapor con centrations, and increases regionally over the polluted continents. A comparison of results across all of these simulations for the United States in particular (not shown) supports the most general conclusions from the re gional modeling studies: i.e., large regions of the country show future O 3 concentration increases of a few to several ppb, and there can be significant differences in the spa tial patterns of these ch anges between different modeling experiments. The purpose of this sub-s ection is to highlight a comparison between two of these simulation sets—Harvard 1 (see also Wu et al., 2008a) and CMU (see also Racherla and Adams, 2006)—because these results illustrate pa rticularly well two critical insights: the potential importance for simulated future O 3 of large-scale circulation changes, and the potential importance of how isoprene chemistry is represented in the modeling systems.

3-33Figure 3-9 shows the mean MDA8 O 3 changes from the Harvard 1 experiment, along with accompanying changes in temperature, insola tion, and biogenic emissions. In these results, the largest O 3 increases are mostly in a sweeping patte rn from the central United States, across the Plains states and the Midwest, and extending into the Northeast. In contrast to the regional model results shown above, there is not as obviou s a spatial correlation be tween the changes in
O 3 and those of any one of the driver variables. The insolation increase in the Midwest matches, to some degree, the pattern of O 3 increase there, but the largest temperature, insolation, and biogenic emissions increases occu r in the southern part of th e country, where there are much smaller changes in O 3 . This weak relationship also holds for a number of other variables considered but not shown (e.g., precipitation, PBL height, etc.). In Figure 3-10, which shows the same quanti ties for the CMU experiment, a different regional pattern of change emerges. Here, the major increases in future O 3 concentrations are
instead centered on the Gulf Coast a nd eastern seaboard, with minimal O 3 changes in the upper
Midwest and northern Plains states.

Figure 3-9. 2050s-minus-present di fferences in simulated summer (JJA) mean (a) MDA8 O 3 concentration (ppb); (b) near-surface air temperature (oC); (c) surface insolation (W m -2 ); and (d) biogenic isoprene emissions (g Carbon m -2 day -1 ) for the Harvard global modeling experiment (see Table 3-2).

3-34Figure 3-10. Same as Figure 3-9 but for the CMU global modeling experiment.

The differences between these two sets of re sults can seemingly mostly be explained by two factors: (1) differences in the future simulation of the summertime storm track across the northern part of the country and (2) differences in the response of O 3 to changes in biogenic
VOC emissions in the southeastern United States. As explained in Wu et al. (2008a), there ar e two distinct dynami cal shifts from the present to the future climate in the Harvard 1 experiment: a decrease in summertime cyclones tracking across the upper part of the United States, resulting in a decrease in cloudiness and precipitation over the upper Mi dwest (as reflected in the insolation changes shown in Figure 3-9), and a northward shift of the Berm uda High, resulting in a decrease in convective activity over the Gulf Coast and the southern Grea t Plains. All other factors being equal, both shifts might be expected to contribute to O 3 concentration increases in their respective regions. In this context, the spatial pattern of O 3 concentration increases in Figure 3-9a is certainly consistent with the decrease in cyclones in the north in the Harvard 1 expe riment, as suggested in Wu et al. (2008a) and originally posited in Mickle y et al. (2004), i.e., that the decrease in cold surges in the simulated future climate leads to a decrease in the cleari ng of pollutants from the

3-35boundary layer (see also Murazaki and Hess, 2006) . Racherla and Adams (2008), on the other hand, examined the distribution of sea-level pres sure anomalies in the present-day and future CMU simulations and found only relatively small changes in these regions. These results suggest that storm track activity does not decrease in the future in this CMU model simulation, but a more detailed analysis of the storm tracks in this model may be needed (Leibensperger et al., 2008). Acknowledging this qualification, it seems plausible that differe nces in simulated future large-scale circulation patterns expl ain the differences in future O 3 changes simulated by the two groups for the northern part of the country. Wh at is the explanation for the even larger
difference in simulated future O 3 changes in the southern half?
The difference in simulated future O 3 changes in the southern ha lf of the country likely
arises because of differences in how O 3 responds to the climate-induced changes in biogenic VOC emissions in modeling systems used in the Harvard 1 and CMU experiments. The spatial patterns of future-minus-present changes in isoprene emissions shown in Figures 3-9d and 3-10d are qualitatively similar, with the largest incr eases centered on the Southeast and Gulf Coast regions for both groups. Examining the CMU results in Figure 3-10, it appe ars that increases in temperature and decreases in cloud cover (and hen ce increases in insolation) have combined to
lead to increases in both isoprene emissions and O 3 concentrations in this region. An additional CMU simulation with future meteorology but scaled -back isoprene emissions has confirmed that
the enhanced O 3 chemical production resulting from these enhanced emissions are largely responsible for the simulated future O 3 increases (Racherla and Adams, 2008).
Contrast this with the Harvard 1 result s, which show only weak changes in O 3 concentrations over the Southeast an d Gulf Coast, in spite of the large increase in future biogenic VOC emissions. Even the especially large in creases in temperature and insolation that accompany these biogenic emissions changes in the Gulf Coast region do not seem to increase appreciably future O 3 concentrations. One factor to which this striking difference between the two sets of results might be traced is the modeled isoprene nitrate chemis try. While increased emissions of biogenic VOCs
are often associated with increases in O 3 concentrations, these increased emissions can also lead
to decreases in O 3 concentrations via different pathways. For example, high concentrations of isoprene can reduce O 3 amounts through direct ozonolys is and can also suppress O 3 production in NOx-limited regimes (e.g., rural areas) by sequestering NO x in isoprene nitrates (e.g., see Fiore et al., 2005). In the modeling system used fo r the Harvard 1 simulations, it is plausible that increasing isoprene emissions results in li ttle change, or ev en decreases in O 3 amounts, largely because the model chemistry repr esents these isoprene nitrates as a “terminal” sink for NO x . In
the absence of additional NO x , the small change in O 3 concentrations in the Gulf Coast, in spite

3-36of the strongly favorable climat e changes there, could be explai ned by this suppressing effect of isoprene. By contrast, in the CMU modeling syst em, the isoprene nitrates are assumed to react rapidly with OH and O 3 and “recycle” NO x back to the atmosphere with 100% efficiency. This
NO x then becomes available to help create O 3 again, tending to favor greater O 3 concentrations in regions of greater biogenic VOC emissions, and dominating the impact of climate change on
O 3 in the CMU results. This comparison strongly illustrates the importance of understanding the underlying
details of the chemical mechanism of O 3 formation. Constraining the precise pathways whereby isoprene, NO x , and O 3 are linked is the subject of ongoing research (e.g., see Horowitz et al., 2007), and as such remains an important source of uncertainty in the modeling systems. However, there are a number of other important uncertainties associated with the choice of chemical mechanisms, as will be discussed further in Section 3.4. Finally, in the Harvard 1 simulations, enhanced ventilation and mixi ng also plays a role in partially offsetting ex pected climate-induced O 3 concentration increases in some near-coastal regions. This results from the combinati on of the humidity-driven decreases in O 3 over the oceans reported in Wu et al. (2008b) (and also Racherla and Adams, 2006), and perhaps also stronger onshore flow due to an in crease in the summertime land-oc ean heating contrast. Lin et al. (2008) report similar effects in their simulations of future O 3 over United States and China. Before concluding with a summary of the synthesis points that have emerged, the following sub-section provides some additional discussion of outstandi ng issues related to modeling the linked climate-air quality system and the complexities and scientific uncertainties
inherent therein.

3.4 CHALLENGES AND LIMITATIONS OF THE MODEL-BASED APPROACH

All of the results shown in this section ar e model-based. This emphasis on model studies has been built, from the beginning, into the fram ework and implementation of the assessment. This sub-section spends some time outlining the challenges, limitations, and areas of uncertainty associated with this model-based approach to pr ovide context for a meaningful interpretation of this synthesis. This discussion helps delineate areas of needed future research to build on our understanding of the climate cha nge-air quality problem, and it aims to convey how the findings presented above might be sensitive to the various modeling uncertainties.
The central concern of this section is the use of linked systems of global and regional climate and air quality models to inve stigate potential future changes in O 3 that may occur due to climate change. These complex modeling systems are extremely valuable scientific tools, as they allow for the exploration of nonlinearities, feedbacks, threshold effects, and in general surprising behaviors that only emerge when the various components are linked together. They

3-37also, to a degree, encapsulate cu rrent scientific understanding of how a wide range of chemical, physical, and dynamical processes intera ct with each other; i.e., they provide a useful snapshot of
the state of the science. Because of the complexity of the system th ey mean to mirror, however, at any moment they necessarily embody only an incomplete representation. This results from technical challenges, such as limitations on computing pow er, as well as from a fundamental lack of understanding of cer tain processes. Furthermore, different versions of these modeling systems, for example as developed by different groups, will sample different parts of the space of possible representations. The current
assessment effort shows the distribution of re sults across multiple groups and linked modeling system. Therefore, it is possible to consider different combinations over a range of models, scenarios, and parameterizations, as summarized in Tables 3-1 and 3-2. It is also important to emphasize, however, that, because of the enor mous computational burden of these modeling systems as applied to this problem, at this point it is only a very small subset of the available range that has been sampled here (e.g., a few GCMs and SRES scenarios, essentially one RCM, three regional model convection schemes, etc. ). Expanding the scope to include additional models, scenarios, and parameterizations, along with multiple combinations of each, might further broaden the distribution of projected regional O 3 changes. Alternatively, such new results might reinforce previous findings. Therefore, any synthesis conclusions are subj ect to revisions pending results from future investigations. However, this preliminary synthe sis makes it possible to identify some of the key modeling-related sensitivities that are likely to determine our ability to accurately simulate climate change-driven O 3 changes, as summarized in the following questions:

• What kinds of differences do different GCMs (under differe nt greenhouse gas emissions scenarios) simulate in the climate, and especially in the weather patterns that matter most for air quality?

• How do RCMs translate these climate and mete orological changes dow n to the regional scales that are desired?

• How are important chemical mechanisms repr esented in the climate-air quality modeling systems?

3.4.1 Inter-Model Variability and Model Evaluation The IPCC AR4 (IPCC, 2007) summarizes curren t understanding of variations in future global climate simulations. The spread across m odels, groups, and scenarios is the result of differences in exogenous forcings, like natura l volcanic or solar changes or changes in

3-38anthropogenic emissions of greenhouse gases and aerosols. This spread also results from internal model variability and nonlinear behavior th at reflect the inherently chaotic nature of the atmospheric and oceanic circulations. Finally, it arises from model confi guration differences due to different choices for dealing with resolution constraints, nu merical approximations, and lack of perfect understanding of proce sses or perfect observations of key parameters. The impact of these factors is reflected in the range of averag e climates, and regional sp atial distributions of climate characteristics, simulated by the different GCMs that are featured here. The significance of these inter-model/scenari o differences varies depending on the lens provided by the particular problem of intere st. For air quality in general, and O 3 specifically, a critical question is “What kind of changes do models simulate in the weather patterns that matter most for air quality?” The re sults shown in Figures 3-9 and 3-10 illustrate some of the uncertainties associated with this question. Physical and dynamical arguments suggest that future decreases in the equator-to-pole temperat ure gradient should drive poleward shifts in the mid-latitude storm tracks, and that this may l ead to decreases in the frequency of cyclone ventilation of pollutants in the Northeast a nd Midwest. The results from the Harvard 1 experiment show this clearly, while those from the CMU experiment do not seem to. Taking a broader perspective across many models and groups, the IPCC AR4 states

Central and northern regions of Nort h America are under the influence of mid-latitude cyclones. Projections by AOGCMs [Atmosphere-Ocean Global Circulation Models] generally indicate a slight poleward shift in storm tracks, an increase in the number of strong cycl ones but a reduction in medium-strength cyclones over Canada and poleward of 70°N (IPCC, 2007).

However, the agreement across groups is by no means absolute. Furthermore, the IPCC
report states

Results from a systematic analysis of AMIP-2 simulations (Hodges, 2004; Stratton and Pope, 2004) indicate that m odels run with observed SSTs are capable of producing storm tracks located in about the right locations, but nearly all show some deficiency in the distribution an d level of cyclone activity (IPCC, 2007).

Recent increases in model resolution and ot her improvements have led to improvements in simulations of present-day storm tracks, and may eventually lead to a stronger consensus on the likely magnitude and direction of future clim ate-induced changes over the United States. At this time, however, current levels of uncertainty probably do not allow us to say much more than (1) the number and intensity of summertime cycl ones passing over the northern United States is

3-39a key factor in determining air quality there and (2) the occurren ce of fewer and weaker cyclones is a plausible consequence of global climate change. This discussion about cyclones suggests a br oader question: how should the scientific community evaluate the performan ce of these modeling systems for th e task at hand? It is not possible to answer this question comprehensivel y here, but it is possible to place some general issues with which the climate modeling community continuously struggles in the context of the specific problem of climate change impacts on air quality. First, all groups carry out ev aluations of their modeling syst ems compared to historical observations. The key is to conduct these evaluati ons for the variables, an d statistics of those variables, that are most relevant for the problem of interest. As discussed in various places in this report, “air quality,” from a health, envir onmental, and regulatory perspective in the United States, has been largely determined by episodes that occur during specific, sporadic weather events. Therefore, what is most importan t to know is how well av ailable modeling tools simulate these events and how well they can pred ict future changes. At present, however, the focus of the climate modeling community is still largely on long-term mean values of variables like temperature, precipitation, and cloud cover. These quantities can be important in situ drivers of air quality on short timescales, but more e ffort is needed to understand how changes in atmospheric flow patterns are reflected in the cha nges in these long-term means. There is a need to address questions like “Did a simulated temper ature change in a give n region result from an across-the-board change in baseline temperature during all weather regimes, or instead from a change in the frequency of occu rrence of one particular weathe r pattern (e.g., the afternoon sea breeze, synoptic-scale anticyclones, or meso scale convective systems)?” Climatological averages of variables like temperature will only have explanatory power for air quality to the extent that they reflect the cha nges in the most relevant circulat ion patterns, as opposed to being obscured by “noise” that is less related to air quality (e.g., increases in nighttime average temperature). The current situation reflects the relatively youthful state of coupled climate and air quality science. The application of climate models to air quality represents a significant challenge for the climate modeling community. One path forward is to ma ke it standard practice to conduct in-depth evaluations of global and re gional climate models for additional variables and metrics more relevant for air qualit y. As Gustafson and Leung (2007) state,

Our ability to address these questions reli es critically on the ability of climate models in simulating the meteorological conditions needed to realistically simulate air quality. Because of the n onlinear nature of atmospheric chemistry and its dependence on difficult to model variables, such as precipitation and the planetary boundary layer (PBL) height, bias es in variables considered acceptable

3-40for other downscaling applications ma y not be appropriate for this new application. An additional challenge in air quality assessment is the required knowledge of the three dimensional struct ures of the atmosphere, which are not needed for most other assessments.

New efforts carried out under the auspices of this assessment, as summarized in Leung and Gustafson (2005), Gilliam et al. (2006) , and Gustafson and Leung (2007) represent significant advances in this area and provide useful insights moving forward. Second, it is important to remember that, for the problem under consideration here, accurately reproducing present-day c onditions is not interesting in a nd of itself, but is interesting for what it might imply for simulating and unde rstanding future changes. The connection between the two is not necessarily straightforw ard. Again from the IPCC AR4: “What does the accuracy of a climate model’s simulation of past or contemporary climate say about the accuracy of its projections of climate ch ange? This question is just be ginning to be addressed...” (IPCC, 2007: Ch. 8). Given a particular variable, and statistic of that variable, to be evaluated, there are two sources of error in any future-minus-present comp arison: the bias in the present-day simulation, and some (hypothetical) bias in simulating th e future conditions. The modeling community typically makes two implicit assumptions about these sources, but these assumptions are potentially contradictory. First, there is the a ssumption that these two erro rs are correlated, i.e., the better the modeling system is at reproducing pr esent-day observations, the better it will be at reproducing future climate shifts . This could lead logically to the conclusion that a model system that does a poor job of si mulating the present will likely be even worse at getting the “correct” future-minus-present changes. Howeve r, it is often simultaneously asserted that looking at differences between simulated future and present results will yield accurate insights, i.e., that the biases should be similar in the pres ent and future simulations and thus will cancel. Barring improbable coincidences, these two assumptions can onl y be reconcile d if a third assumption also holds: namely, that most of the biases in the present-day simulation come from error sources that will not impact the model’s ab ility to capture the future changes, i.e., the present-day biases will simply be carried along to the future. The validity of this assumption for a highly nonlinear system like climate must be te sted. Again, research carried out for this assessment is contributing to this need. For ex ample, Liang et al. ( 2008) showed how GCM (and downscaled RCM) biases with respect to historical observations are consiste ntly propagated into future simulations, empirically linking the ability of a modeling system to accurately reproduce present-day climate to the types of future climate changes it predicts.

3-413.4.2 The Role of Downscaling As described in Section 2, this assessme nt has been built, in part, around dynamical downscaling, i.e., the use of an RCM to deri ve higher-resolution me teorology from a GCM simulation for a particular sub-region of the globe. This is in recognition of the dual need to be regionally explicit, so as to connect more closel y with the priorities of policy makers, while at the same time capturing the inherently global scale of the climate driver s. As noted, this is really the first systematic attempt to apply these tec hniques to air quality impacts work, and valuable lessons are being learned. The fundamental task of dynamical downscalin g is to maximize the “value retained” from the GCM and the “value added” by the RCM. In other words, successful downscaling will take advantage of the things the RCM does well in simulating weather and climate, by virtue of its high resolution, without sacrif icing too much of what the GC M does well, by virtue of its global extent. From the results presented above , it is clear that cha nges in both large-scale circulation patterns and local-scale fo rcings are crucial drivers of O 3 changes. A given modeling system will be able to accurately simulate changes in O 3 only to the extent that it can accurately capture both. Because of its higher resolution, the RCM deve lops small-scale features that the GCM cannot. These features develop for three pr imary reasons (see, e .g., Denis et al., 2002):

• finer-scale representations of surface charac teristics, like topography, water bodies, vegetation, soil moisture, and land use, that l ead to local-scale ci rculation systems like sea and lake breezes and mountain-valley flows;

• nonlinearities in the fluid dynamics equations th at lead to the development of fronts and other mesoscale features;

• hydrodynamic instabilities arisi ng from shear or buoyancy forc ing that create turbulent eddies and convection and ar e more accurately represente d with higher resolution.

RCMs therefore add the most value by more accurately simulating near-surface meteorological fields, as well as extreme c onditions (e.g., cyclone low pressure, intense precipitation, high winds). These advantages make it possible to si gnificantly improve on regional biases in temperature and precipitation present in GCM simulations (e.g., see Liang et al., 2006), and these improvements can lead directly to improved simulations of O 3
. RCM performance is highly sensitive, however, to the physical parameterizations used, as already summarized above. For example, Li ang et al. (2006; 2004a,b) and Lynn et al. (2004; 2007) found strong sensitivities of temperature and precipitation to th e convection scheme chosen. These meteorological se nsitivities drive corresponding se nsitivities in simulated air

3-42quality (e.g., Kunkel et al., 2007; Tao et al., 2008). In addition, sensitivities of air quality to PBL, radiation, microphysics, and land-surface sche mes may also be important, but these have yet to be examined as systematically in this assessment. Along with the physical parameterizations, the other major sensitivity of the RCM is the application of the large-s cale boundary conditions from the GCM, i.e., the actual “implementation” of the dynamical downscaling that links the GCM with the RCM. By itself, an RCM cannot simulate the large-scale circulati on of the atmosphere because the drivers are planetary in scale (e.g., the differe nce in net radiation between equator and pol es), necessitating a global domain. So, for example, an RCM cannot generate dynamical systems like the mid-latitude storm tracks, which instead must be s upplied by a GCM. It is in the context of this GCM-provided large-scale circula tion that the smaller-scale feat ures described above evolve. This leads to the basic question of dynamical downscaling: how be st to close the system? In other words, what is the optimal method for im porting information from the GCM into the RCM so as to preserve any desired features of the large-scale circul ation patterns without compromising the ability of the RCM to develop realistic smaller scales? The most common practice has been to assimila te the GCM fields into a narrow strip at
the lateral boundaries of the RCM domain. This technique is commonly re ferred to as “lateral nudging,” and follows Davies (1976). Everywhere else in the domain, the RCM develops its own solution, which it is hoped will evolve consistently within the envelope defined by the GCM flow at the boundaries. This approach is wide ly used and has yielded valuable results in a number of different applications across the field of regional climat e modeling. It is the approach that is used in all the downscaling work cont ributing to this report. The major perceived advantage of this approach is that it allows fo r the possibility of the RCM correcting biases not only in the relatively fine-scale, near-surface temp erature, and precipitation features, but also in
continental-scale circula tion patterns. For example, Gustaf son and Leung (2007) illustrate how a better representation of the Rockies leads to im provements in the overall flow patterns over the
United States when MM5 is used to downscale the GISS II ′ GCM simulation. Recent work (see Rockel et al., 2008; Miguez- Macho et al., 2004, 2005; Castro et al., 2005; von Storch et al., 2000), how ever, suggests that this late ral nudging approach can be problematic and introduce additional biases of its own. Specifically, if the RCM captures the energy of the large-scale flow only through assi milation at its lateral boundaries, two problems can arise. First, the energy of the large-scale circ ulation can be progressively lost as a result of several factors as it makes its way into the domain from the RCM boundaries. This lost energy cannot be re-supplied by the RCM, since, as alrea dy noted, the drivers are planetary in scale. A potential consequence, then, is weaker large-scal e circulation features in the RCM compared to the GCM. Second, the large-scale flow field can be modified significantly as it makes its way

3-43across the RCM domain. This can cause probl ems at the RCM boundaries that, in turn, can
introduce artificial flow features back in the main body of the model domain. For example, the jet stream entering the western boundary of the RCM domain will encounte r the steeper (because higher-resolution) Rockies and be deflected, so that by the time it reac hes the eastern boundary, it will not be consistent with the GCM boundary condition there. Both of these problems are more pronounced with larger RCM do mains and coarser RCM resolution. 16 One method for handling these problems is so-called “spectral nudging,” i.e., nudging applied not at the lateral boundaries at all spatial scales, but instead applied at all locations in the RCM domain (above the PBL at least) but only fo r the longest waves that are resolved in the GCM (see Miguez-Macho et al., 2004 and von Stor ch et al., 2000 for descriptions of the technique). At this time, wh ether lateral nudging or spectral nudging is preferable is just becoming an active research questio n: does one take the large-sc ale flow field of the GCM as
“truth” and force the RCM to conform to it as cl osely as possible, or does one instead allow the RCM to evolve a more independent circulation? Therefore, the implications for simulating air quality are as yet unclear, sin ce the downscaled simulations carried out to date for this assessment have all used the lateral nudging approach. Given what we do know at this time about dynamical downscaling, however, the following should be considerations when interpreti ng the regional air quality results presented in
this section:

• The RCM may not faithfully capture important features of the large-scale circulation patterns present in the driving GCM. In particular, the la rge-scale flow might be too weak in the RCM, leading to a proportionally too-strong influence of more local-scale forcing, like convection. Alternatively, there might be artificial flow features introduced by discrepancies between the RCM and GCM at the boundaries.

• Even if the RCM reproduces the GCM’s large-sc ale circulation very closely, it may still simulate different air quality patterns becau se of differences in the way it simulates convective clouds and rainfall, or other fine-s cale processes, embedded within this largescale flow.

Either or both of these considerations may help explain why, as mentioned previously, the influence of a shift in the storm track presen t in the Mickley et al. (2004) GCM experiment does not show up as clearly when this same GCM simulation is downsca led using MM5 (Nolte et al., 2008; Leung and Gustafson, 2005). Precisely attributing these differences between the
downscaled results and the drivi ng global simulation remains a key task in the furthering of our

16 To date, these two potential pitfalls of latera l nudging have mostly been investigated for RCM simulations driven by global reanalys is data and not GCM output, and there may be differences between the two in the impact on the downscaled fields.
3-44understanding of the impacts of gl obal climate change on regional air quality, and it remains the subject of ongoing investigation. In any case, the strong influe nce of the GCM-simulated clim ate on the downscaled results is inescapable, regardless of the methodologica l details. Gustafson and Leung (2007) emphasize that the GCM chosen will strongly impact any down stream regional air quality findings. Nolte et al. (2008) show clearly that a large portion of th e bias in the NERL grou p’s regional simulations for the eastern United States can be traced directly to an incorrect northeastward displacement of the Bermuda High in the driving GISS II ′ GCM simulation. This and similar results, then, underscore again the discussion from above: quan tifying the biases and ch aracteristics of the individual global model simulations being relied upon for representing future climate change is of critical importance for th e problem of global change impacts on air quality.

3.4.3 Uncertainties in Chemical Mechanisms

The differences in simulated O 3 as a function of isoprene chemistry, as discussed in Section 3.3.2, highlight the im portance of the choice of regional air pollution modeling mechanisms in investigating the potential for climate-induced changes in ai r quality. Differences
in simulated O 3 concentrations among modeling studies may be attributed, in part, to differences in the choice of photochemical mechanism. Each mechanism, in turn, will have characteristic
uncertainties as well as biases in simulated O 3 concentrations, due to unc ertain information about the chemical reactions represented by the m odel mechanism, and the simplifying assumptions used to optimize computational speed. For example, Atkinson (2000) liste d several sources of scientific uncertainty in air quality photochemical modeling mechanisms. Note that most of these uncertainties relate to the oxidation of biogenic compounds:

• Quantitative knowledge of the rate constants and mechanism of the reactions of organic peroxy (RO 2 ) radicals with NO, HO 2 radicals, and other RO 2 and NO 3 radicals

• Additional data concerning the organic nitrat es yields from the reactions of organic peroxy radicals with NO as a function of temperature and pressure

• Knowledge of reaction rates of alkoxy radicals for decomposition, isomerization and reaction with O 2 , especially alkoxy radicals other th an those formed from alkanes and alkenes (for example, from hydroxyl-compounds , ethers, glycol et hers, and esters)

• The detailed mechanisms of the reactions of O 3 with alkenes and VOCs containing >C=C< bonds (this involves understanding the reactions of the initially energy-rich biradicals and of the thermalized bi radicals formed in these reactions)

3-45• Studies of the thermal decompositions of othe r atmospherically-important reactions of the higher PANs, including, for example, CH 2 =C(CH 3 )C(O)OONO 2 formed in the atmospheric photo-oxidation of isoprene

• Further understanding of the products and mech anisms of the reactions of monoterpenes and oxygenated VOCs (including 2-methyl-3-but en-2-ol) emitted fr om vegetation with OH radicals, NO 3 radicals and O 3

• Improved knowledge of the mechanisms and pr oducts of the reacti ons of OH-aromatic adducts with O 2 and NO 2

• An improved understanding of the tropos pheric chemistry of many oxygenated VOCs formed as first-generation products of VOC photooxidations, including but not limited to carbonyls (including unsaturated dicarbonyl s), di-unsaturated dicarbonyls, and unsaturated epoxy-carbonyls), hydroperoxides, and esters

• A quantitative understanding of the reacti on sequences leading to products which gas/particle partition and lead to secondary aerosol formation

Compounding the uncertainties created by incomp lete information about the chemical pathways, and their associated ra te constants, is the practical necessity for abbreviating the overall chemical reaction scheme to improve the computational speed of the air quality model. A photochemical oxidation mechanism that explic itly treated all of the known atmospheric
reactions would have to include more than 20,000 reactions and several thousand organic reactants and products (Dodge, 2000). Reaction sc hemes like those used by the research teams participating in the EPA assessment program have been streamlined to minimize the number of reaction steps, either by lumping several relevant organic compounds into classes that are given “average” reaction rates, or by i gnoring reaction pathways that a ppear to be unimportant in determining the concentrations of the targeted pollutant. These design choices are made through a process of evaluation against observational data. Mechanisms may also be fine-tuned to produce output that better fits the ambient data by adjusting the reaction rate constants within the laboratory-established experiment al uncertainty range. Furtherm ore, differences exist amongst models in the size of the time steps used for calculating pollutant con centrations. Given the complex, nonlinear, nature of O 3 production, these differences tend to result in differences in predicted O 3 concentrations among models. See also Fi ne et al. (2003) for additional discussion of many of these issues. A number of intercomparison studies of phot ochemical mechanisms have been reported in the air quality literature: e.g., see Russell and Dennis (2000), Ji menez et al. (2003), and Faraji et al. (2008), among many others. For example, Jimenez et al. (2008) compared box model calculations, with identical inputs and boundary conditions, for several state-of-the-art photochemical mechanisms. They found that the calculated average and maximum O 3

3-46concentrations, along with the concentrations for nine related chemical species, varied widely between these mechanisms. Gilliland et al. (200 8) evaluated the performance of the CB-IV and
SAPRC99 chemical mechanisms in a study of model response to NO x reductions associated with the implementation of the NO x SIP Call. They found that CB-IV significantly underestimates
the contribution of O 3 (and its precursors) from long-range transport, and it is less successful than the SAPRC99 mechanism at capturing th e effects of meteorol ogical changes on O 3 concentrations. Variability in simulated O 3 concentrations among photochemical mechanisms does not necessarily imply that any one mechanism is in correct: rather, that each may have been optimized for different local or regional c onditions. Given the necessity of simplifying photochemical mechanisms for the sake of comput ational efficiency, future studies of climate change-induced air quality change might reasona bly include photochemical mechanisms that have been tailored to perform best under a range of well defined conditions consistent with the emissions, meteorological, and land-u se conditions under consideration.
Additional detailed discussions of bot h well understood and highly uncertain O 3 photochemistry can be found in the U.S. EPA Air Quality Criteria Document for O 3 (U.S. EPA, 2006).

3.5 SYNTHESIS CONCLUSIONS AND FUTURE RESEARCH NEEDS This section concludes by collecting and summarizing the major points that have emerged from the scientific synthesis. These he lp address the goals of this report by addressing questions like “What new findings are emerging from the body of work that EPA has made possible?” and “What have we lear ned about our ability to simula te potential future changes in U.S. regional air quality due to climate change?” Specifically,

• Across all of the modeling experiments, global and regional, carried out by the different groups, simulated global climate change causes increases in summertime O 3 concentrations over substantia l regions of the country. Fo r nearly every region of the country, at least one (usually multiple) of th e modeling groups found that climate change caused increases in summertime O 3 concentrations.

• For summertime-mean MDA8 O 3 , the increases are in the 2

− 8 ppb range.

• The largest increases in O 3 concentrations in these simulations occur during peak pollution events. For example, the increases in 95 th percentile MDA8 O 3 tend to be significantly greater than those for summertime-mean MDA8 O 3
.

• Though in agreement on the above points, the different modeling systems did not necessarily simulate the same regi onal patterns of climate-induced O 3 changes, with the individual simulations showing some regions of little change, or even decreases, in addition to the O 3 increases.

3-47• These differences in the regional patterns of O 3 changes result from variations across the simulations in the patterns of mean changes in key meteorological drivers, such as temperature and surface insolation. The m odeling experiments provide examples of regions where simulated future changes in meteorological variables either have reinforcing or competing effects on O 3 concentrations. Figure 3-11 shows the mean and standard deviation in futu re-minus-present MDA8 O 3 differences across all seven sets of simulation results displayed in Section 3.3.

• For example, regions where the changes in simulated temperature and insolation are in the same direction tend to experience O 3 concentration changes in a similar direction, while temperature and insolation varying in opposite directions tends to correspond with mixed O 3 changes. Figure 3-12 shows regional averages across MDA8 O 3 concentration differences and the differences in these driv ers for the sub-regions shown in Figure 3-13.

• Large-scale circulation patterns play an important role in modifying these local meteorological drivers. For example, how a given modeling system simulates changes in key circulation features, like the mid-latit ude storm track or the Bermuda High, has a strong impact on the simulated future O 3 concentrations.

• Other factors to which the patterns in the si mulated meteorological variables appear to be highly sensitive include the choice of convect ion scheme and whether or not the global model outputs are dynamically downscaled with an RCM.

• Certain regions show greater agreement than ot hers. For example, there is very generally more agreement on the spatial patterns of clim ate-induced increases for the eastern half of the country than for the West, though pa rts of the Southeast show some of the strongest disagreements across the modeling grou ps. Even for these regions, however, at least some of the models show substantial climate-induced O 3 increases.

• Across nearly all simulations, climate change is associated with simulated increases in biogenic VOC emissions over most of the United States, with especially pronounced increases in the Southeast.

• These biogenic emissions increases do not necessarily correspond with O 3 concentration increases, however, depending on the region and modeling system.

• One factor in this, as highlighted by the globa l modeling results, is that the response of O 3 to changes in biogenic emissions may depe nd sensitively on how isoprene chemistry is represented in the model. Models that recycle isoprene nitrates back to NO x may tend to simulate greater O 3 concentration increases in regions with biogenic emissions increases than models for which isoprene nitrate is a terminal sink for NO x .

• Interannual variability plays a critical role in determini ng seasonal-average O 3 levels in a given year. Some of the modeling groups found that, in some regions of the United States, the average increase in MDA8 O 3 concentrations from th e present to the 2050s as a result of climate change was as large as the present-day year-to- year variability. In other words, climate change has the potential to push O 3 concentrations in extreme years beyond the envelope of natu ral interannual variability.

3-483-49 Figure 3-11. The mean (top two panels) and standard deviation (bottom two panels) in future-minus-present MDA8 O 3 concentration differences across (left-hand panels) all seven experiment s (five regional and two global) shown in Figures 3-1, 3-9, and 3-10 and, fo r comparison purposes, (right-hand panels) not including the WSU experime nt because it shows differences for July only, while the other exp eriments show JJA differences. 

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3-50SWDOWN

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Figure 3-12 continued. Averages across the subregions shown in Figure 3-13 for each of the simulations fo r (c) surface insolation (W m -2 ); and (d) biogenic isoprene emissions (g Carbon m -2 sec -1 ).

3-51Figure 3-13. The averaging subregions used in Figure 3-12.

• It also highlights the fact that the amount of future-minus-present change in O 3 concentration simulated will likely depend strongly on the choice of present and future simulated years to compare, and that multi- year simulations are desirable for producing findings that are more robust.

• In addition, while this analysis focuses on su mmertime results, some of the groups also found increases in O 3 concentrations in some regions in the spring and fall, suggesting the possibility of an extension of the O 3 season under climate change.

These findings should be interpreted as speak ing to the question, “How does the system work?” rather than the question, “W hat will happen in the future?” They provide insight into the subtleties and complexities of the interactions between climate, meteorology, and air quality, thereby helping to build intuition about the richne ss, and range of behaviors, of the climate-air quality system. They also illustrate how valuable the modeling systems developed for this assessment can be for exploring this problem. This improved system understanding, combin ed with a clear appreciation of the important uncertainties, opens the doors to a wide range of future applications based on this knowledge and these tools. For ex ample, the results of modeling experiments have the potential

3-52to provide guidance as to whether, for example, statistical relationshi ps based on historical
observations of O 3 and temperature will serve as accurate approximations of the effects of climate change in a given region. Other applic ations might include evaluating the potential for unintended consequences of a pa rticular policy choice, e.g., whet her tree plantations for carbon sequestration might harm air quality in a given region in the face of future climate change. In addition, these findings highlight a number of areas where further research is needed:

1. First, as has been emphasized throug hout, an improved understanding of how well models simulate the large-scale circulation pa tterns that are importa nt for air quality is needed. This issue was being considered at least as early as 1991, when the NRC pointed out that whether a GCM simulated a persistent high or low pressure pattern over a given region had the potential to counteract any increase in O 3 associated with warmer temperatures, through changes in other mete orological drivers (NRC, 1991). The NRC also pointed out in this repor t that GCMs do not in general simulate the same shifts in pressure patterns in response to increases in greenhouse gases. As discussed above in Section 3.4, these kinds of disagreeme nts among models persist today.

2. As a related point, there is a need for an improved unders tanding of how well RCMs can downscale changes in these GCM-simulated circ ulation patterns, as well as a need for more insight into the sensitivity of these downscaled regional simulations to model parameterizations, including convection scheme s, but also expanding to PBL, radiative transfer, microphysics, and land-surface schemes.

3. Recalling the discussion surrounding Box 3-1, a cr itical component of addressing points 1 and 2 above will be extending efforts, initiate d in this first phase of the assessment, to evaluate the GCM- and RCM-based system s for the meteorological variables, and especially the temporal statistics of the mete orology, most appropriate for air quality: for example, long-term average changes in the frequency, duration, and intensity of stagnation episodes driven by synoptic-scale variability. This will need to include outputting and analyzing the requ ired quantities, at the requi red temporal frequency, from the models, as well as further analys es of historical observational data.

4. Development and refinement of techniques for systematically exploring the effects of the modeling uncertainties are also needed, in cluding ensemble methods, techniques for blending ensemble approaches with dynami cal downscaling, and reduced form models.

5. An issue raised in a small subset of the result s discussed in this sec tion is whether or not the possible future extension of the O 3 season into the spring and fall is robust across more simulations. Additional simulations that go beyond summertime are needed to address this.

6. Another issue arising from a sm all subset of the results is the question of interannual variability. Particularly in th e regional modeling results, to date there is disparity in the number of years simulated across the different groups. Moving forward, more precise quantification of the magn itude of mean future O 3 changes relative to interannual variability, as well as the pot ential for future increases or decreases in interannual variability itself, is needed.

3-53Moving beyond meteorology, the results to date also suggest important gaps in our understanding of issues relate d to chemistry and emissions:

1. More research is needed into the links between climate, biogenic emissions, and O 3 . The results presented here highli ght the importance of correctly representing isoprene nitrate chemistry in models to accura tely capture the response of O 3 to changes in emissions. In addition, there are other uncertainties in ch emical mechanisms with the potential to influence climate change-air quality impacts that require further study.

2. Improving biogenic emissions inve ntories and process models of the response of biogenic emissions to climate and atmospheric com position changes should also be a priority.

3. Changes in deposition velocity as a function of the impact of changing CO 2 concentrations on stomatal conductance could also be incorporated into the modeling systems.
4. An overarching issue that has not been fully addressed to date is whether or not the overall O 3 chemical regime change as a func tion of climate change, and/or global atmospheric composition change (e.g., as a function of changing concentrations of CH 4 and other species).

5. As already discussed, while some of the groups have also carried out simulations of PM, in addition to O 3 , the focus in this section is only on the O 3 results. Our u nderstanding of how to represent PM chemistry in modeling systems is more limited, and there are a number of additional complexities surrounding PM , including the fact that it consists of multiple species, and that precipitation is a more important primary meteorological driver for PM than for O 3 , an issue because the uncertainties in modeling precipitation are much greater than in modeling, for example, temperat ure. Much additional research is needed on simulating the potential impacts of climat e change on PM. Brief summaries of the ongoing work on PM under this assessment, as well as on emissions and chemistry issues, is provided next, in Section 4.

Furthermore, there are a wide range of i ssues related to anthropogenic emissions of precursor pollutants that will become important as the assessment moves into its next phase. These include the impacts of changes, on future emissions in the United States (and worldwide),
in:

• Energy use

• Land use

• Agricultural practices

• Transportation patterns

• Demographics

• Technology

3-543-55 Building on the modeling experiments discusse d here, one major consideration is that
much additional work is needed to construct emi ssions scenarios that are realistic and internally self-consistent across both greenhous e gases and precursor pollutant s. These and other issues will feature prominently in Phase II of the a ssessment, and they are previewed in Section 4. Finally, there are a number of issues for the air quality management community to consider, related to the potentia l for scientific research to pr ovide improved decision support.
These include how best to inform the scientific community about the specific air quality metrics
to focus on in research that would best inform management activities, as well as how best to
address mismatches between the timescales of air quality management and long-term global climate change.4 FUTURE DIRECTIONS

4.1 PHASE II OF THE GLOBAL CHANG E AND AIR QUALITY ASSESSMENT As outlined in Section 2, Phase II of the assessment program requires a transition from climate-only studies to an evaluation of the integr ated effects of changes in climate and changes in anthropogenic air pollutant emissions. Simplis tic assumptions about future U.S. emissions are of limited usefulness for evaluating the possible ra nge of climate change impacts on air quality at scales that are of interest for planning and management. Therefore, EPA ORD has initiated several projects that are devel oping new methods and modeling t ools for creating regional-scale emissions projections for the United States. Thes e projects recognize that the important drivers of future changes in air pollutant emissions ar e linked. For example, economic factors influence population migration which, in turn , affects land use, thereby a ffecting air pollutant emissions via choices in transportation modalities. To re alistically represent the feedbacks among the drivers of air pollutant emissions , modeling systems must be deve loped that capture these links between underlyi ng processes. Phase II of the air quality assessment will also build upon the insights gained in Phase I
from the efforts of the contribu ting research teams in producing climate change-only air quality simulations, including the effects of particular modeling choices. This section, therefore, begins by highlighting efforts underway to improve th e climate-air quality modeling systems, and planned efforts to develop efficient approaches for evaluating the impact of uncertainties on model outputs. An overview of the projects fo cused on devising modeling tools to capture the processes governing the underlyi ng drivers of air pollutant em issions, and the links between them, follows. Air pollutant emissions scenarios will eventually be shared with the climate-air quality modeling teams, who will, in turn, simulate the integrated effects of climate and emissions changes on regional U.S. air quality.

4.2 EXTENDING THE MO DELING SYSTEMS

Section 3 concluded with a discussion of m odeling uncertainties and research needs to be addressed. Ongoing and upcoming activities de signed to achieve these improvements and needed advances in modeling capability ar e discussed in the following subsections.

4.2.1 Exploring Modeling Uncertainties Ensemble modeling techniques are being appl ied to more fully explore the effects on model outputs of uncertainties in the global-to-regional c limate and air quality modeling systems. This involves blending multiple alte rnative GCMs, RCMs, and RAQMs with multiple

4-1emissions scenarios and model physical paramete rizations (including bo th PBL and convection schemes). In addition, some of the work will expl ore the use of Bayesian weighting of ensemble members based on their skill in representing both observed climate and air quality, as a means of reducing the number of ensemble members require d for capturing the probable range of future climate changes. Adding new GCMs, RCMs, and RAQ Ms to the suite used in Phase I is also an important element of this work. Several modeling teams plan to evaluate poten tial changes in the length and timing of
annual O 3 seasons under a changed climate. To be tter capture and characterize changes in interannual variability in different climate regi mes, simulations of additional present-day and future years with the global-to-regional modeling systems are also planned. Finally, the groups discussed in Section 3 that carried out global sc ale-only simulations are in the process of conducting comparable studies using downscal ed global-to-regional modeling systems. The applica tion of these new systems to simulations of future regional climate and air quality will also expand the range of models, scenarios, and methodologies in the assessment. Added to the results obtained to date, these new simulations have the potential to
increase the level of confidence in, and/or add nuance to, key conc lusions made in this report.

4.2.2 Additional Model Development Substantial uncertainty remains in the m odeling of current biogenic VOC emissions. EPA ORD is currently supporting studies to bett er define the processes governing biogenic emissions to improve their repr esentation in regional air quali ty modeling systems. These studies include work to identify and quantify species-dependent emissions sensitivities to temperature and other meteorological variables, to changes in forest composition in response to changing climate, and to changes in ambient CO 2 concentrations, based on observations and biochemical modeling. The accumulating body of new scientific insi ghts is being used to design biogenic emissions models with greater process realism. These models are also being extended to include complementary capabilities, such as dynamic ve getation sub-models to capture the two-way coupling between land cover and climate. Thes e improvements will assist in increasing our understanding of the poten tial role of biogenic emissions ch anges in global change-related impacts on air quality. The importance of feedbacks between climate change and regional air quality is not presently well understood. Should climate change produce significant changes in aerosol chemistry and composition, or substantial changes in tropospheric O 3 , those perturbations could
feed back onto the Earth’s radia tion budget, possibly driving furthe r changes in climate. Other research efforts within the assessment program include an investigati on of the importance of

4-2these two-way feedbacks between climate change and air qualit y. To explore this question, NERL is expanding the pollutant chemistry repr esented in the Weather Research and Forecast Model with Chemistry (WRF/Chem). Simultaneous ly, an extramural effort funded by the STAR program is directly linking WRF with the CM AQ model in a combined WRF-CMAQ system. Both will be applied in studies of future clim ate and air quality. Downscaling GCM simulations of future climate using WRF/Chem and WRF- CMAQ will allow for the assessment of possible long-term impacts of global change on regiona l air quality while a ccounting for feedbacks between meteorology, air quality, and radi ation in a unified modeling framework.

4.2.3 Additional Pollutants—PM Some of the groups whose O 3 results are featured in Sect ion 3 have also carried out
simulations of PM. Because of the additional co mplexities and uncertainties associated with PM and its response to climate change, these results were not incorporated into the synthesis. However, a few preliminary results suggest that

• Globally, PM generally decreases as a resu lt of simulated climate change (with anthropogenic emissions held constant), due to increased atmospheric humidity and/or increased precipitation;

• Regionally, simulated climate change produces both increases and decreases in PM (on the order of a few percent) in 2050, depending on the region of the United States, with the largest increases in th e Midwest and Northeast;

• The responses of the individual species that make up net PM (e.g., sulfate, nitrate, ammonium, black carbon, organi c carbon, etc.) to climate change are highly variable, depending on the chemistry and trans port characteristics of each species;

• Key uncertainties to which simulated PM is sensitive include m odel precipitation, model aerosol chemistry, aerosol-cloud interactions, volat ilization of semi-volatile PM species, such as nitrate and secondary organic aeros ol (SOA), and assumed future air pollution emissions.

Building on these findings, work underwa y, both within EPA and funded through the STAR program, is continuing to explore the imp acts of climate and emissions changes on PM in coupled climate and air quality modeling systems. Efforts to improve the relevant aerosol chemistry in these models, as well as to intr oduce the capability of tw o-way coupling between chemistry and meteorology (as noted above) are also underway. In addition, substantial work is being done outside the EPA sphere that is expected to contri bute knowledge and techniques as
the assessment moves forward.

4-34.2.4 Additional Pollutants—Mercury Some of the modeling groups already highlight ed in this report, in conjunction with several new groups, will also be extending our unde rstanding of the impact of global change on air pollution to mercury (Hg). Climate change can potentially impact a number of atmospheric processes that help determine the fate of Hg, including heterogeneous oxi dation of gas-phase Hg, dry deposition of elemental, r eactive gas-phase and particulate Hg, and Hg chemistry in the presence of fog, clouds, and photochemical smog. These groups will use both models and observational datasets to explore Hg chemistry and transport as a function of climate and emissions changes. The focus will be on present and future Hg distribution for the Unit ed States as a whole, as well as for particular regions, e.g., the Great Lakes, Florida. In addi tion, this work will be aimed at improving the Hg chemistry in the linked climate and air quality modeling systems by incorporating additional reactions and refining existing representations.

4.3 COMBINED IMPACTS OF CLIMATE AND EMISSIONS CHANGES: PRELIMINARY WORK Several of the modeling teams that produced th e simulations discusse d in Section 3 also conducted preliminary evaluations of the combin ed effects of changes in anthropogenic air pollutant precursor emissions and changes in climate on regional U.S. quality. The general approach taken was to assume that, rather than remaining constant at the NEI 1999

− 2000 levels, future U.S. emissions of po llutant precursors, i.e., NO x , SO 2 , VOCs, and CO, scaled in ways that
were consistent with the IPCC SRES scenarios. The major findings that emerged from these sensitivity studies are as follows: First, that the combined effects of climate and anthropogeni c precursor emissions changes are much more sensitive to the assumptions about future emissi ons trajectories than differences in simulated climate across models and groups. For example, si mple scaling of future emissions to match the gross assumptions of the IPCC A1b or B1 SRES scenario resulted in substantial reductions in
NO x emissions, with corresponding reductions in simulated future O 3 that dominated any increases associated with climate change. In cont rast, using future emissions consistent with the weaker pollutant control assumptions in the “dir tier” A2 or A1Fi scenarios tended to result in climate and emissions producing changes of comp arable magnitudes. Second, the effects of climate and emissions changes are not, in genera l, additive. In other words, the degree of “climate penalty” on air quality is itself highly dependent on the emissions levels. Therefore, these results highlight the need for additional work to develop more sophisticated, regionally detailed scenarios of U.S. anthropogenic precursor pollutants that account for population, economic, energy, and tran sportation changes, along with work to

4-4improve the representation of natural emissions se nsitive to climate and land-use changes. These efforts are highlighted in the next sub-section.

4.4 MODELING THE DRIVERS OF AIR POLLUTANT EMISSIONS Human activities, such as population growth and migration, economic growth, land use, and technology change are key dr ivers affecting emissions. Cha nges in human activity patterns impact pollutant emissions across the globe, a nd, combined with global scale circulation patterns, influence the long-ra nge transport of air polluti on into the United States. There is a gap in our understa nding of how these factors will interact to influence air quality at urban and regional scales in the Un ited States. In addition, while human activities generate the largest share of the U.S. air po llutant emissions burden, biogenic and wildfire emissions also contribute to the degradation of regional-scale air qua lity. The vegetation composition and biomass density of forest ecosy stems help determine both the emissions of biogenic VOCs and the intensity and frequency of wildfires. These properties are sensitive, to varying degrees, to changing climate and to loca l and regional development. Future progress will require integrating population growth a nd land-use models with economic forecasts, technology models, travel demand models, mobile source models, and forest composition and wildfire process models to create emissions modeling systems that can be used to blend comprehensive scenarios of future air pollution emissions with those of future climate and meteorology changes (Figure 4-1). As described in Section 2, evaluating the combined air quality impacts of changing anthropogenic emissions levels, changing biogenic and wildfire emissions levels, and changing climate is a critical goal of Ph ase II of the air quality assessment effort. To accomplish this, the assessment program has undertaken a significant re search effort to develop and/or apply the necessary emissions projection tools. The follo wing sub-sections highlight efforts underway to investigate the critical processe s leading to pollutant emissions changes and to incorporate this information into modeling tools capable of realis tically simulating long-term emissions changes. A growing U.S. population can be expected to lead to increased energy and transportation service demands, potentially leading to incr eased pollutant emissions, depending on control strategies implemented. In addi tion, internal migration of the U.S. population could redistribute pollutant emissions geographically. The Cohort-Component methodology 17 is being used to develo p a range of scenarios of future U.S. population. These scenarios build on the Census Bureau’s population projections, systematically incorporating assumptions to expr ess the differences captured in the IPCC SRES

17 For example, see http://www.census.gov/population/www/projections/aboutproj.html

. 4-5Figure 4-1. Integrated system of fu ture climate, meteorology, and emissions scenarios. Population growth, migratio n, and land use. The dashed blue lines represent feedbacks.

storylines. The migration component of the demographic model uses a regression-based “gravity” model that depends on the functional connectivity of each county to all others and amenity values to estimate production and attracti on values for domestic mi gration. This effort is exploring the wide range of a ssumptions at national, state, and local scales in the United States that are consistent with the general SRES storylines. Future development patterns will result in changes in both the qua ntity and location of pollutant emissions. The demographic-migration model described above is being coupled with a spatial allocation-type land-use model to de velop urban and exurba n growth projections consistent with the SRES storylines. The pot ential of these land-use scenarios for spatially allocating emission sources is under investigation.

4-64.4.1 Economic Growth and Technology Choices Absent additional air pollution controls a nd/or improvements in technologies, economic growth would be expected to increase emissions. Other trends, like further transformation from a manufacturing-based to a service-based econo my, can also lead to changes in domestic emissions. A range of plausible economic scenarios to capture these factors is needed as part of an integrated evaluation of human-driven change in future emissions. Several models have been employed by OAR in policymaking, and the EP A’s Global Change Research Program is planning to evaluate them (and others) for a pplication in the Phase II assessment effort. Changes in future anthropogenic emissions cannot be understood apart from the development, deployment, and use of energy and transportation technologies. To assist in defining those relationships, a Market A llocation (MARKAL) ener gy-systems modeling framework has been developed to examine the most emission-intensive sectors of the U.S. economy: transportation and electric power production. MARKAL maps the energy economy from primary energy sources, through their refining and transformation processes, to the point at which a variety of technologies (e.g., classes of light-duty persona l vehicles, heat pumps, or gas furnaces) service end-use energy demands (e.g., proj ected vehicle miles trav eled, space heating). A large linear programming mode l, MARKAL determines the l east-cost pattern of technology investment and use required to meet specified de mands, and then calculates the resulting criteria pollutant and greenhouse gas emissions. Preliminar y scenarios of potential future emissions and emissions growth factors for energy system tec hnologies, such as combustion technologies in the electricity generation, transportatio n, industrial, residential, and commercial sectors, have been generated for the United States. Particular attention has been paid to alternative-fuel vehicles (e.g., ethanol-gasoline, plug-in gaso line-electric hybrids, hydrogen fuel cell) and analyses to date show that different technology development and penetration scenarios can have greatly differing emissions consequences.
Research has also been conducted on the res ponse of electricity consumption to warming from climate change, capacity siting and disp atch decisions, and char acterization of emerging energy generation technologies in terms of cost and cost projections a nd learning parameters. This modeling system has been used to analyze the effect of climate change upon the temporal and spatial distributions of NO x emissions in the Mid-Atlantic and Midwest power markets. An additional study investigates air quality conse quences from the broad adoption of ethanolgasoline, plug-in gasolin e-electric hybrids, and wind-electroly sis-hydrogen fuel-cell vehicles. The consequence of this technol ogy shift will be explored for Lo s Angeles, the Central Valley, and Atlanta over the next 50 years.

4-74.4.2 Land Use and Transportation A critical and previously une xplored dimension in projecti ng air quality in response to human factors is the spatial dist ribution of the emissions projecte d to result from land-use and transportation choices. Several studies of the connection between socioeconomic forces, landuse planning and development patterns, policy desi gn, and future air quality are underway as part of the assessment’s research program. Specific studies include

• In Washington DC, development and applica tion of a flexible modeling framework to estimate long-term mobile sources emissions;

• In Chicago, an examination of the consequen ces of continued deindus trialization of U.S. manufacturing and its impact on the c ity’s manufacturing-heavy metro area;

• In the Upper Midwest, a study of the air qua lity changes associated with a “smart growth” land-use and development po licy over the next 25 to 50 years;

• In the San Joaquin Valley, CA, investigation of the effect on emissions from combined changes in economics, land-use, water constrai nts, transportation, and stationary sources;

• In the Charlotte, NC metro area, an examinati on of the influence of development patterns (e.g., transit oriented development, dense mixed-use development, development supportive of non-motorized transportation m odes for non-work trips, neo-traditional suburbs, new urban core development, and redevelopment) on the sp atial charac teristics and quantity of emissions;

• In Austin, TX, a comparison of emissions, air quality, and exposures from an integrated transportation-land-use model with four urban growth scenarios developed through a regional “visioning” initiative know n as Envision Central Texas;

• In the Puget Sound region, a project to in tegrate an activity-b ased travel model component and a network assignment component into a land-use model (UrbanSim) and to tightly couple this system to air emissions models.

4.4.3 Emissions Changes Due to Changing Ecosystems: Biogenic VOCs Changing amounts and distributions of biogeni c emissions due to land-use and climate changes is potentially a key factor for future air quality, as discussed throu ghout this report. Past
studies have shown that emissi ons of VOCs from forest ecosystems can cause increases in pollution in near-urban and suburban areas. In one example, VOC emissions from forests near Atlanta entirely offset the effects of the polic ies put in place to reduce mobile-source emissions. As described above, substantial uncertainty remains in modeling biogenic emissions. As part of the assessment effort, EPA is suppor ting studies on the VOC-emitting species in the
current climate. Fundamental sc ientific questions ar e being addressed conc erning the chemical and physical properties of primary and secondary organic aerosols (POAs, SOAs), the identity of

4-8the biogenic VOCs that form SOAs, and the sens itivity of VOCs, POAs, and SOAs emission and formation rates to changes in environmental co nditions. In addition, much research is being done outside the EPA sphere that is expected to contribute new findings to the assessment as it
moves forward.

4.4.4 Emissions Changes Due to Chan ging Ecosystems: Wildfires Fires, both natural and anthropogenic, have significant impacts on U.S. air quality, especially on PM concentrations. Recent studies show that fires in North America can have important effects on U.S. visibility and air qual ity on an episodic basis. Climate variability influences the extent and intensity of fires, e.g., moist years followed by dry years produce very
favorable conditions for wildfires. Climate ch ange, which is very likely to increase the frequency of precipitation in some areas, drought in other areas, and produce higher temperatures in general, may enhance future fire fr equency, extent, and in tensity regionally. Therefore, along with better model representations of the effects of climate change on biogenic VOC emissions, simulations of the effects of climate on air quality should also consider changing levels in wildfire-generated O 3 and PM precursor emissions. Three modeling studies are underway that integrat e the complex interacti ons of fire, climate, and air quality and are exploring important uncertainties. Two groups are focusing on the U. S. Southeast as a test case, with the third working to evaluate wildfire ch anges across the continental United States as a whole. All three teams are working to develop integrated models that account for fire-related changes in ecosystems in a warming climate, such as the extent of vegetative cover and fuel characteristics. State-level fire statistics, along with ground and satell ite observations, will be used to evaluate the performance of the modeli ng systems. In addition, the continental-scale study will develop a climatology of plume height s from forest fires since 2000, and will relate plume heights to area burned for use in the climate change scenarios.

4.4.5 Taking Integrated Emissions Scenarios Through to Future U.S. Regional Air Quality As shown in Figure 4-1, Phase II of the assessment will involve integrating these demographic, land-use, economics, transportation and energy models to produce a series of future emissions scenarios as input for the inte grated climate and region al air quality models developed in Phase I of the program. Buildi ng on the improved understanding from the work already accomplished, and the new insights that will emerge in the near future, an important task will be to identify a subset of emission scenario s that capture the range of desired assumptions and outcomes to explore the critical questions of interest in the integrated climate and emissions modeling efforts. Conducting a series of sensitivity test simulations over s horter time periods, so

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