DRAFT EPA/600/R-07/094
DO NOT CITE OR QUOTE March 2008
Assessment of the Impacts of Global Change on Regional
U.S. Air Quality: A Preliminary Synthesis of Climate
Change Impacts on Ground-Level Ozone
An Interim Report of the U.S. EPA Global Change Research Program
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been formally released by the
U.S. Environmental Protection Agency and should not at this stage be construed to represent
Agency policy. It is being circulated for comment on its technical accuracy and policy
implications.
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC 20460
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DISCLAIMER
This document is distributed solely for the purpose of pre-dissemination peer review
under applicable information quality guidelines. It has not been formally disseminated by EPA.
It does not represent and should not be construed to represent any Agency determination or
policy. Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
This document is a draft for review purposes only and does not constitute Agency policy.
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TABLE OF CONTENTS
LIST OF TABLES vi
LIST OF FIGURES vii
LIST OF ABBREVIATIONS ix
FOREWORD xi
PREFACE xii
AUTHORS, CONTRIBUTORS, AND REVIEWERS xiii
ACKNOWLEDGEMENTS xvi
SUMMARY OF POLICY RELEVANT FINDINGS xvii
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
1.4. DESIGN OF THE GLOBAL CHANGE AND AIR QUALITY ASSESSMENT 1-6
1.5. THE CLIENT COMMUNITIES 1-7
1.5.1. EPA Office of Air and Radiation (OAR), State, Tribal, and Local
Air Quality Planners 1-7
1.5.2. U.S. Climate Change Science Program (CCSP) 1-8
1.5.3. Climate Change Research Community 1-10
1.5.4. Air Quality Research Community 1-10
1.6. CONSIDERING UNCERTAINTY IN THE ASSESSMENT EFFORT 1-11
1.7. STRUCTURE OF THIS REPORT 1-12
2. OVERVIEW OF APPROACH 2-1
2.1. INTRODUCTION 2-1
2.1.1. Process for Developing the Global Change-Air Quality Assessment Effort.... 2-1
2.2. WORKSHOP RECOMMENDATIONS 2-2
2.2.1. Modeling 2-2
2.2.2. Dual-Phase Assessment Approach 2-5
2.2.3. Time Horizon Selected 2-5
2.2.4. Research Priorities to Support Phase II 2-6
2.3. RESEARCH PARTNERSHIPS 2-7
3. RESULTS AND SYNTHESIS 3-1
3.1. INTRODUCTION 3-1
3.2. SUMMARY OF RESULTS FROM INDIVIDUAL GROUPS 3-2
3.2.1. GCTM-Focused Modeling Work 3-3
3.2.1.1. Application of a Unified Aerosol-Chemistry-Climate GCM
to Understand the Effects of Changing Climate and Global
Anthropogenic Emissions on U.S. Air Quality: Harvard
University 3-3
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 3-5
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TABLE OF CONTENTS (continued)
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 3-9
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 3-12
3.2.2.6. Sensitivity and Uncertainty Assessment of Global Climate
Change Impacts on Ozone and Particulate Matter:
Examination of Direct and Indirect, Emission-Induced
Effects: GIT-NESCAUM-MIT 3-13
3.3. SYNTHESIS OF RESULTS ACROSS GROUPS 3-14
3.3.1. Regional Modeling Results 3-15
3.3.1.1. Changes in O3 3-16
3.3.1.2. Changes in Drivers 3-18
3.3.2. Global Modeling Results 3-24
3.4. CHALLENGES AND LIMITATIONS OF THE MODEL-BASED
APPROACH 3-27
3.4.1. Inter-Model Variability and Model Evaluation 3-29
3.4.2. TheRoleofDownscaling 3-32
3.5. SYNTHESIS CONCLUSIONS AND FUTURE RESEARCH NEEDS 3-35
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 4-3
4.3. RELATIVE IMPACTS OF CLIMATE 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 4-6
4.4.2. Land Use and Transportation 4-7
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
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TABLE OF CONTENTS (continued)
REFERENCES R-l
APPENDIX A: GLOSSARY OF CLIMATE AND AIR QUALITY TERMS A-l
APPENDIX B: CURRENT U.S. REGIONAL AIR QUALITY, ITS SENSITIVITY
TO METEOROLOGY AND EARLY STUDIES OF THE EFFECT
OF CLIMATE CHANGE ON AIR QUALITY B-l
APPENDIX C: THE 2001 EPA GLOBAL CHANGE RESEARCH PROGRAM'S AIR
QUALITY EXPERT WORKSHOP C-l
APPENDIX D: U.S. EPA STAR GRANT RESEARCH CONTRIBUTING TO THE
GCAQ ASSESSMENT D-l
APPENDIX E: MODELING APPROACH FOR INTRAMURAL PROJECT ON
CLIMATE IMPACTS ON REGIONAL AIR QUALITY E-l
APPENDIX F: USING MARKAL TO GENERATE EMISSIONS GROWTH
PROJECTIONS FOR THE EPA GLOBAL CHANGE RESEARCH
PROGRAM'S AIR QUALITY ASSESSMENT F-l
APPENDIX G: CHARACTERIZING AND COMMUNICATING UNCERTAINTY:
THE NOVEMBER 2006 WORKSHOP G-l
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LIST OF TABLES
3-1. The GCM-RCM-RAQM model systems that produced the simulation
results discussed in sub-section 3.3.1 3-16
3-2. GCTM-only model simulations whose results are discussed in sub-
section 3.3.2 3-24
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LIST OF FIGURES
2-1. Links between global and regional climate and atmospheric chemistry
processes with anthropogenic activities governing air pollution emissions 2-1
3-1. 2050s-minus-present differences in simulated summer mean MDA8 Os
concentrations (in ppb) for the (a) NERL; (b) Illinois 1; (c) Illinois 2;
and (d) WSU experiments 3-40
3-2. Same as Figure 3-1 but for 95th percentile MDA8 Os concentration
differences 3-41
3-3. 2050s-minus-present differences in simulated summer mean MDA8
Os concentrations (in ppb); reproduced from Figure 2 in Hogrefe et al.
(2004b) 3-42
3-4. Frequency of simulated summer mean MDA8 Os values exceeding
80 ppb in different regions from the NERL experiment 3-43
3-5. 2050s-minus-present differences in simulated September-October
mean MDA8 Os concentrations (in ppb); reproduced from Figure 4
inNolteetal. (2007) 3-44
3-6. 2050s-minus-present differences in simulated summer mean (a)
MDA8 Os concentration (ppb); (b) near-surface air temperature (°C);
(c) surface insolation (W m"2); and (d) biogenic isoprene emissions
(tons day"1) for the NERL experiment 3-45
3-7. Same as Figure 3-6 but (a) shows 95th percentile MDA8 Os
concentration differences 3-46
3-8. Same as Figure 3-6 but for the Illinois 1 experiment 3-47
3-9. Same as Figure 3-8 (Illinois 1 experiment) but (a) shows 95th
percentile MDA8 Os concentration differences 3-48
3-10. Same as Figure 3-8 but for the Illinois 2 experiment 3-49
3-11. Same as Figure 3-10 (Illinois 2 experiment) but (a) shows 95th
percentile MDA8 Oj concentration differences 3-50
3-12. Same as Figure 3-6 but for the WSU experiment 3-51
3-13. Same as Figure 3-12 (WSU experiment) but (a) shows 95th
percentile MDA8 Os concentration differences 3-52
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LIST OF FIGURES (continued)
3-14. 2050s-minus-present differences in simulated summer (JJA) mean
O3 concentrations (in ppb) from the (a) Harvard 1; (b) Harvard 2; (c)
CMU; (d) Illinois 1; and (e) Illinois 2 global modeling experiments 3-53
3-15. 2050s-minus-present differences in simulated summer (JJA) mean (a)
MDA8 Os concentration (ppb); (b) near-surface air temperature (°C);
(c) surface insolation (W m"2); and (d) biogenic isoprene emissions
o O1
(10" g Carbon m" sec" ) for the Harvard 1 global modeling experiment 3-54
3-16. Same as Figure 3-15 but for the CMU global modeling experiment 3-55
4-1. Integrated system of future climate, meteorology, and emissions scenarios 4-6
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LIST OF ABBREVIATIONS
AGCM Atmospheric General Circulation Model
AOGCM Atmosphere-Ocean Global 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 Multiscale Air Quality Model
CMIP Coupled Model Intercomparison Project
CTM Chemical Transport Model
EC elemental carbon
ENSO El Nino-Southern Oscillation
GCM General Circulation Model
GCTM Global Chemical Transport Model
GISS Goddard Institute for Space Studies
GMAO Global Modeling and Assimilation Office
HadCMS Hadley Centre Coupled Model
1C initial condition
IGSM Integrated Global System Model
LANL Los Alamos National Laboratory
LWC liquid water content
MM Mesoscale Model
MM5 Mesoscale Model (Version 5)
MARKAL MARKet Allocation Model
MO SIS 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
NH4+ ammonium ion
NO3" nitrate ion
OC organic carbon
Os ozone
OGCM Oceanic General Circulation Model
PAN peroxyacetylnitrate
PEL planetary boundary layer
PCM Parallel Climate Model
PCTM PCM/CCSM Transition Model
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LIST OF ABBREVIATIONS (continued)
POP Parallel Ocean Program
RACT reasonably available control technology
RCM Regional Climate Model
RCMS Regional Climate Modeling System
RCTM Regional Chemical Transport Model
RH relative humidity
RRF relative reduction factor
PM2 5 particulate matter with aerodynamic diameter below 2.5 um
SIP State Implementation Plan
SAPRC statewide air pollution research center
SMOKE Sparse Matrix Operator Kernel Emissions
SOA secondary organic aerosols
SO2 sulfur dioxide
SO/f 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
This document is a draft for review purposes only and does not constitute Agency policy.
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FOREWORD
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This document is a draft for review purposes only and does not constitute Agency policy.
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PREFACE
This report was prepared by the Global Change Research Program in the National Center
for Environmental Assessment (NCEA) of the Office of Research and Development (ORD) at
the U.S. Environmental Protection Agency (EPA). It is intended for managers and scientists
working on air quality to provide them with information on the potential effects of climate
change on regional air quality in the United States. The Global Change Research Program
established a partnership with the agency's Office of Air and Radiation (OAR) to develop a
foundation for linking climate change to their air quality management programs.
With EPA's OAR and several Regional offices, EPA's ORD began an assessment effort
to increase our understanding of the multiple complex interactions between climate and
atmospheric chemistry. In the design of this program, the EPA recognized that three key
linkages inherent to the global change and air quality issue would need to be considered: those
across spatial scales, those across temporal scales, and those across disciplines. Developing the
modeling tools and knowledge base to achieve these linkages is a fundamental task of the
assessment.
The assessment design calls for first providing insight into possible air quality responses
to future climate changes before tackling the additional complexities of incorporating potential
future changes in anthropogenic emissions and long-range pollutant transport. This interim
report provides an update of the progress that has been made in applying climate and
atmospheric chemistry models to investigate 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 within the models (e.g., biogenic VOC
emissions). In addition, it provides a preliminary interpretation of what this improved scientific
understanding means for air quality management. Future assessment reports will cover the
combined impacts of changing climate and air pollutant emissions on air quality. The program
also plans to develop additional reports that focus on additional pollutants, including PM and
mercury.
The ultimate goal of the EPA Global Change Research Program's air quality assessment
effort is to provide air quality managers with the scientific information and tools to evaluate the
implications of global change for their programs and to enhance their ability to consider global
change in their decisions. This report is a preliminary step in that direction.
This document is a draft for review purposes only and does not constitute Agency policy.
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
Principal Authors
Ms. Anne Grambsch-National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Washington, DC 20460
Dr. Brooke L. Hemming-National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Christopher P. Weaver-National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Washington, DC 20460
Contributing Authors
Dr. Alice Gilliland-Air Resources Laboratory, National Oceanic and Atmospheric
Administration in partnership with the National Exposure Research Laboratory, U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
Mr. Doug Grano-Office of Air Quality Planning and Standards, Office of Air and Radiation,
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Sherri Hunt-National Center for Environmental Research, Office or Research and
Development, U.S. Environmental Protection Agency, Washington, DC 20460
Dr. Tim Johnson-National Risk Management Research Laboratory, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Dan Loughlin-National Risk Management Research Laboratory, Office or Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Darrell Winner-National Center for Environmental Research, Office or Research and
Development, U.S. Environmental Protection Agency, Washington, DC 20460
Contributors
Dr. William G. Benjey-Air Resources Laboratory, National Oceanic and Atmospheric
Administration in partnership with the National Exposure Research Laboratory, U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Ellen J. Cooter-Air Resources Laboratory, National Oceanic and Atmospheric
Administration in partnership with the National Exposure Research Laboratory, U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711
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AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)
Dr. Cynthia Gage-National Risk Management Research Laboratory, Office or Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. Chris Nolte-Air Resources Laboratory, National Oceanic and Atmospheric Administration in
partnership with the National Exposure Research Laboratory, U.S. Environmental Protection
Agency, Research Triangle Park, NC 27711
Internal Reviewers
Mr. Bret Anderson-Region 7, U.S. Environmental Protection Agency, Kansas City, KS 66101
Ms. Louise Camalier-Office of Air Quality Planning 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. Brian Eder-Air Resources Laboratory, National Oceanic and Atmospheric Administration in
partnership with the National Exposure Research 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 Agency, Washington, DC 20460
Mr. Jim Ketcham-Colwill-Office of Policy Analysis 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, CA94105
Ms. Anne McWilliams-Region 1, U.S. Environmental Protection Agency, Boston, MA 02114-
2023
Dr. Andy Miller-National Risk Management Research Laboratory, Office or Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
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AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)
Ms. MadonnaNarvaez-Region 10, U.S. Environmental Protection Agency, Seattle, WA 98101
Ms. Kathryn Parker-Office of Atmospheric Programs, Office of Air and Radiation, U.S.
Environmental Protection Agency, 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 Risk Management Research Laboratory, Office or Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Dr. ST Rao-Air Resources Laboratory, National Oceanic and Atmospheric Administration in
partnership with the National Exposure Research Laboratory, U.S. Environmental Protection
Agency, Research Triangle Park, NC 27711
Mr. Randy Robinson-Region 5, U.S. Environmental Protection Agency, Chicago, IL 60604-
3507
Mr. Jason Samenow-Office of Atmospheric Programs, Office of Air and Radiation, U.S.
Environmental Protection Agency, Washington, DC 20460
Dr. Ravi Srivastava-Office of Air 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
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ACKNOWLEDGEMENTS
This interim report is part of a larger climate 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 implement 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, Robert Gilliam, Bill Rhodes, Joseph DeCarolis,
Carol Shay, Jenise Swall, Ben DeAngelo, Deb Mangis, Ruby Leung, and the Science To
Achieve Results (STAR) grantees.
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SUMMARY OF POLICY RELEVANT FINDINGS
The recent Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment
Report (AR4) states, "Warming of the climate system 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 global average sea level" (IPCC, 2007). Directly relevant to EPA's
mission to protect human health and the environment 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 generation and the strength of
emissions from the biosphere, fires and dust. The sign and magnitude of these effects are highly
uncertain and will vary regionally." Climate change impacts have not yet been explicitly
considered in air quality program planning—accounting for them will be a critical challenge for
the air quality management system in the coming decades.
In partnership with EPA's Office of Air 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 chemistry. The ultimate goal of this assessment is
to enhance the ability of air quality managers to consider global change in their decisions
through improved characterization of the potential impacts of global change on air quality. An
integrated assessment framework was designed that leveraged the research and development
strengths within the EPA, within other agencies, and within the academic research community.
The assessment design calls for first developing insight into the range of possible air quality
responses to future climate changes alone (Phase I) before tackling the additional complexities of
integrating the effects of potential 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 explore 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, including direct meteorological impacts on atmospheric chemistry
and transport, and the effect of these meteorological changes on climate-sensitive natural
emissions of pollutant precursors. As such, it largely does not address the relative importance of
climate vs. anthropogenic emissions of air pollutants or the effectiveness of air quality
management efforts. Future assessment reports will focus on these additional dimensions.
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Two "grand challenges" have emerged in the course of developing and conducting this
assessment. The first arises from the Global Change Research Program's emphasis on decision
support, namely, to provide the best possible scientific basis for understanding potential climate
change impacts on air quality and air quality policies in a useful form and a timely manner as one
key set of inputs to help managers develop pollution control strategies. The second "grand
challenge" is to convey to the scientific research community the knowledge gaps that limit our
understanding of the problem and/or create barriers to the use and interpretation of scientific
information by decision makers.
It is possible to think of these challenges as informing two parallel, intersecting
"readings" of this report, one tuned to the perspective of a "science" audience and the other to
that of a "policy" audience. Each would highlight a distinct set of issues, perhaps grouping
broadly into 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?"
In the style of the IPCC Summary for Policy Makers, this Summary of Policy Relevant
Findings attempts to provide highlights for both of these audiences. For the scientific audience,
the additional insights generated as a result of synthesizing across the findings from multiple
research groups are presented. This synthesis improves our understanding of the potential for
climate change to impact air quality in different regions of the U.S. and the complex interplay
between air quality and its different climatic and meteorological drivers. It also points out
scientific and technical uncertainties to help guide future research efforts.
For the policy audience, the scientific findings presented in the Summary help provide a
qualitative answer to the question: "Is climate change something we will have to account for
when moving forward with U.S. air-quality policy?" Each of the synthesis conclusions and
supporting information presented here is followed by a discussion of potential links between
these conclusions and air quality policies. It is hoped that, by illuminating the subtleties and
complexities of the interactions between climate, meteorology, and air quality, and at the same
time by building an appreciation of the associated uncertainties, these findings can inform the
discussion concerning policy responses, and create a foundation for future, targeted efforts to
solve specific air quality management problems.
The discussion below summarizes information that has emerged from the assessment to
date. Most of the discussion centers on topics related to tropospheric ozone (63) since our
understanding of Os 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 model results discussed are for simulations that
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assumed no future changes in the anthropogenic emissions of precursor pollutants. Also, unless
otherwise indicated, "future" refers 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 63 (and PM) concentrations is
presented. With this information in hand, in the second sub-section, it is then possible to zero in
on those meteorological drivers important for air quality and highlight complexities in the
interaction between these drivers and pollutant concentrations, such as reinforcing or competing
effects of individual drivers. The third sub-section discusses climate change impacts on climate-
sensitive natural emissions of pollutant precursors. The fourth and fifth sub-sections discuss
important modeling uncertainties, and preliminary sensitivity tests comparing the first-order
impacts of climate and anthropogenic emissions changes, respectively, as previews of issues that
will receive more attention in the next phase of the assessment.
I. Impacts on Os (and PM) Concentrations
A. Climate change has the potential to produce significant increases in near-surface Os
concentrations in many areas of the U.S.
1. A large number of earlier observation- and model-based studies have demonstrated
connections between meteorological variability and 63 concentrations and
exceedances, implying the possibility of climate change leading to increasing Os
levels in some regions.
2. The new modeling studies discussed in this report show increases in summertime Os
concentrations over some substantial regions of the country as a result of simulated
2050 climate change. These results were obtained under the assumption of
anthropogenic emissions of precursor pollutants held constant at present-day levels
while allowing for changes in climate-sensitive natural emissions. The other regions
show little change, or, in limited areas, even slight decreases.
3. The increases are in the range 2-8 ppb for summertime-average maximum daily
8-hour (MDA8) O3.
4. The largest increases in Os concentrations in these simulations occur during peak
pollution events. (For example, the increases in 95th percentile of MDA8 O3 tend to
be significantly greater than those in summertime-mean MDA8 Os.)
5. There is greater agreement across simulations in these O3 changes for certain regions
than for others. For example, a loosely bounded area encompassing parts of the Mid-
Atlantic, Northeast, and lower Midwest tends to show at least some O3 increase
across most of the simulations. Other regions, notably the West Coast and the
Southeast/Gulf Coast, show conflicting results. For example, simulated future-minus-
present changes in MDA8 63 concentration in central California range from about -4
to +6 ppb across the modeling groups, with a similar range for the Gulf Coast of
Texas.
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6. As will be discussed in Sections II and III below, these disagreements in the spatial
patterns of future Os changes can largely be attributed to the wide variations across
simulations in the patterns of changes of key meteorological drivers (e.g., temperature
and cloud cover), along with the differing representations of isoprene nitrate
chemistry in the various model systems.
7. A subset of results also suggests that climate change effects on O3 grow continuously
over time, with evidence for significant impacts (in the same direction as described
above) emerging as early as the 2020s.
Relevance for air quality policy: These studies suggest that 63 nonattainment areas and
areas just below the Os National Ambient Air Quality Standards (NAAQS) should begin
to consider the impacts of climate change as they develop their attainment and
maintenance strategies, even for near-term planning horizons. In other words, they may
need to account for a "climate penalty" imposed on their control policies. Conflicting
results among simulations for certain regions of the country, for example the Southeast
and West, suggest that evaluations of the potential effectiveness of future controls will be
particularly sensitive to uncertainties in the modeling systems. The findings also indicate
that, where climate-change-induced increases in 63 do occur, damaging effects on
ecosystems, agriculture, and health will be especially pronounced, due to increases in the
frequency of extreme pollution events.
B. Climate change has the potential to push OB concentrations beyond the envelope of
natural interannual variability in many regions of the U.S. In addition, it has the
potential to lengthen the Os season.
1. Interannual variability in weather conditions plays an important role in determining
average O3 levels and exceedances in a given year. For example, statistical analyses
of current 63 observations show that, for several U.S. cities that have not attained the
current Os NAAQS, weather-related interannual variability can increase or decrease
observed mean Os concentrations by as much as 10 ppb from the 25-year
(1981-2006) mean.
2. The subset of modeling groups that examined multiple simulation years for both
present-day and future climate found that, in many regions, increases in summer 63
concentrations due to climate change were comparable in magnitude to, or even
greater than, simulated present-day interannual variability.
3. Similarly, a subset of the future climate simulations showed that, for parts of the
country with a defined summertime Os 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 control strategies, to capture the effects of both
natural meteorological variability and climate-induced changes. Air quality managers
may also need to plan to extend the season over which they monitor 63 concentrations
and be prepared to issue air quality alerts earlier in the spring and later into the fall.
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C. Increasing global near-surface humidity associated with climate change has
significant potential to decrease OB concentrations in remote areas with low ambient
NO x levels.
1. The global modeling studies described in this report simulate general decreases in O3
concentrations over remote areas with low NOX concentrations (e.g., oceans) as a
result of climate change. Consistent with current understanding of O3 chemistry, this
is due to increased O3 destruction in a more humid atmosphere.
2. This decrease is in contrast to the significant climate-related increases for many
already-polluted areas.
3. The relative impact of these changes in remote background O3 on simulated U.S. O3
concentrations is unclear. One potential influence pathway seen in some of the
modeling results is an increased mixing of clean air into coastal areas, via stronger
ocean-land flow combined with the reduced O3 concentrations over the oceans.
Relevance for air quality policy: Changes in O3 concentrations as a result of climate
change will depend, in part, on whether an area is clean or polluted, and/or on the degree
of influence of air masses from adjacent clean or polluted areas. For example, under low
NOX conditions, a reduced atmospheric lifetime for O3 in the future due to increased
humidity may imply reductions in the quantity of O3 transported downwind.
D. The potential impact of climate change on PM is less well understood than that on O*
Preliminary results from the modeling studies show a range of increases and decreases
in PM concentrations in different regions and for different component chemical
species in the same region.
1. Precipitation is a more important primary meteorological driver of PM than of O3,
due to its role in removing PM from the atmosphere (wet deposition). Precipitation is
particularly difficult to model and shows greater disagreement across simulations than
other variables.
2. Aerosol chemical processes, especially those concerning the formation of organic
aerosols, are not fully understood and therefore not well characterized in current
regional air quality models.
3. Preliminary simulation results suggest that, globally, PM generally decreases as a
result of simulated climate change, due to increased atmospheric humidity and
increased precipitation.
4. Regionally, simulated 2050 climate change produces both increases and decreases in
PM (on the order of a few percent), depending on region, with the largest increases in
the Midwest and Northeast.
5. This 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
competing directions.
6. Increase in wildfire frequency associated with a warmer climate has the potential to
increase PM levels in certain regions.
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Relevance for air quality policy: The more limited scientific understanding and greater
modeling uncertainties concerning the production and loss of PM highlight the need for
future research. Assessing the effects of a changing climate on PM on an airshed-by-
airshed basis may be helpful for considering the detailed chemical characteristics of local
PM, the possible range of changes in local precipitation, and the potential influence of
changing wildfire frequency.
II. Impacts on Meteorological Variables that Affect O3 Concentrations
A. Climate change has the potential to impact a number of meteorological variables
important for Os. Whether changes in these variables lead to increases, decreases, or
no change in OB concentrations in a given region depends on whether the effects of
these individual changes on OB act in concert or compete with each other.
1. The simulations discussed in this report all show significant future changes in
meteorological quantities such as temperature, cloud cover, humidity, precipitation,
wind speed and pattern, mixing depth, depth etc.
2. However, there is significant variability across simulations in the spatial patterns of
these future changes. For example, simulated future-minus-present changes in
summertime average temperature in central California range from about -1 to +2 °C
across the modeling groups, while changes in Texas ranged from about 0 to 3 °C.
3. As noted above in Section LA, these variations across simulations help explain the
disagreements in the spatial patterns of simulated future Os 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 O3 drivers in turn help create
the unique pattern of future 63 changes across regions seen for each simulation.
4. For example, the different simulations provide examples of regions, like parts of the
Northeast, Mid-Atlantic, and lower Midwest, where both temperature increased and
surface solar radiation increased (due to a decrease in cloudiness). These regions
tended to experience increases in future Os concentration. In contrast, regions where
the changes in these variables were in opposite directions tended to have mixed 63
results.
5. In general, variations in individual meteorological drivers are not independent of each
other. This is because these variables 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 the effects of long-term climate change on 63
are expressed.
Relevance for air quality policy: It is the interrelationships between the many
meteorological variables important for 63 that determine 63 concentrations at a particular
time and place. Evaluating the potential influence of climate change on air quality and
the potential effectiveness of future control strategies will require accounting for these
sometimes complex interactions. These complexities 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
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about the spatial patterns of future changes in meteorological variables and, hence, the
specific regional distributions of future O?, changes across the U.S.
B. There remains some disagreement across models of the effects of climate change on the
summertime mid-latitude storm tracks, with implications for the simulated frequency
and duration of synoptic stagnation events and resulting extreme Os episodes.
1. Global climate change is expected to produce changes in planetary-scale circulation
systems, thereby influencing regional weather patterns. For example, observations
suggest that the extratropical storm tracks have moved poleward over the last few
decades. A number of the modeling studies cited in the IPCC AR4 project that this
trend will continue into the future, resulting in significant changes in winds,
precipitation, and temperature patterns in mid-latitudes.
2. These types of changes have the potential to strongly affect summertime Os
concentrations over the northern portions of the U.S. Some of the modeling studies
discussed in this report simulate increases in the duration and frequency of extreme
63 events in the Midwest and Northeast that can be directly traced to the weaker
frontal systems and decreased frequency of surface cyclone activity due to a poleward
storm track shift. Others studies, however, do not seem to simulate these circulation
changes as strongly, and/or do not simulate the corresponding O3 increases.
3. Similarly, differences in simulations of the climate response of other key large-scale
circulation patterns, like the Bermuda High off the U.S. east coast, also can produce
significant differences in the amount and spatial distribution of simulated future 63.
Relevance for air quality policy: Understanding and accounting for changes in synoptic
stagnation events resulting from large-scale circulation changes is critical for
understanding potential changes in future Os concentrations in the northern portion of the
U.S. At present, modeling uncertainties persist, and further research is needed.
Consideration of historic patterns in local meteorology versus current observations may
help determine whether and where changes in stagnation should be addressed in city-
level air quality planning.
III. Impacts on Climate-Sensitive Natural Emissions of Os Precursors
A. Climate change has the potential to increase biogenic emissions of O3 precursors, but
significant uncertainties remain about the impact of these emissions changes on OB
concentrations in a given region. Increases in lightning NOx production may also be a
factor in future Os changes.
1. Earlier observational studies suggest that increases in biogenic emissions of volatile
organic compounds (VOCs) would occur in many regions as a result of the higher
temperatures associated with expected future climate change.
2. The modeling studies discussed in this report generally simulate increases in biogenic
VOC emissions over most of the country as a result of climate change, with
particularly substantial increases in certain regions, notably the Southeast.
3. However, these biogenic emissions increases do not necessarily correspond with 63
concentration increases, depending on the region and modeling system used.
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4. This appears to be because the response of 63 to changes in biogenic VOC emissions
depends strongly on how isoprene chemistry is represented in the models—models
that recycle isoprene nitrates back to NOX will tend to simulate significant Os
concentration increases in regions with biogenic emissions increases while models
that do not recycle isoprene nitrates will tend to simulate small increases or even 63
decreases.
5. Globally, an increase in the rate of natural production of NOX 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. O3 concentrations is a topic of ongoing research, these findings are
not highlighted in this report.
Relevance for air quality policy: Resolving uncertainties in the response of O3 to
biogenic emissions changes is critical for an improved understanding of potential climate
change impacts on 03. For example, evaluating the probable success of regional Os
control strategies in regions like the southeastern U.S. may be highly sensitive to this
uncertainty—additional anthropogenic emissions controls may need to be considered to
offset climate-induced increases in biogenic emissions, but only if there is a reasonable
expectation that these emissions increases will lead to Os increases. A better
understanding of the fate of isoprene nitrate is critical for resolving this issue. In
addition, local- and regional-scale O3 modeling does not typically consider NOX
production from lightning. Given potential future changes in lightning NOX emissions,
long-term air quality management strategies may need to account for growth in this
source as well.
IV. Modeling Uncertainties
A. Specific configuration choices made in the development and application of the
Integrated global-to-reglonal climate and air quality modeling systems used In this
assessment are key determinants of simulated future U.S. regional air quality. The
unique characteristics of the climate change problem present significant challenges for
uncertainty analysis of air quality Impacts.
1. As discussed in Section II above, there are large differences across modeling groups
and/or across different model configurations 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 Os.
2. These differences in simulated meteorology can largely be traced to differences in a
number of elements of model system configuration. Key elements include which
global climate model (GCM) was used to simulate future global climate change,
whether or not the output from this GCM was "downscaled" to much higher
resolution over the U.S. with a regional climate model (RCM), and which model
physical parameterization was used for representing cumulus convection.
3. Sensitivities of air quality-relevant meteorology to other parameterizations (e.g., for
turbulent mixing, radiative transfer, microphysics, and land-surface processes) may
also be important but have yet to be examined systematically.
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4. The specific techniques used to implement the downscaling of the GCM output with
an RCM may also significantly affect the results, but this issue is still to be examined
as well.
5. The choice of future greenhouse gas scenario used to drive a given future GCM
climate simulation is also a potential source of uncertainty, though in 2050, as
opposed to the end of the century, the range in greenhouse gas forcing across the
various IPCC scenarios used in this assessment is still relatively small.
6. Beyond qualitatively delineating the key sources of modeling uncertainty, however,
the complexities of the climate change-air quality problem will require a new
paradigm for assessing the uncertainties associated with future pollutant
concentrations. Many of the most important uncertainties are structural, arising rather
from a lack of fundamental scientific understanding than from insufficient
measurement of known parameters. In addition, the computational expense of running
coupled climate and air quality modeling systems hampers the application of many
traditional statistical analysis techniques. Therefore, new approaches will need to be
devised and applied if we are to significantly improve our understanding of the total
uncertainty associated with a particular air quality endpoint.
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 air quality. Interpretation of
the causes of simulated air quality changes will, in general, be highly sensitive to these
components. Additional efforts to understand and quantify these uncertainties, for
example as planned for Phase II, will aid in the interpretation of results produced by these
modeling systems. Furthermore, work is needed on new strategies for incorporating
information from climate models into uncertainty analysis while fully accounting for all
sources of uncertainty.
V. Relative Impacts of Climate and Anthropogenic Emissions Changes
A. Preliminary sensitivity tests suggest that the impacts of climate change on future U.S.
regional Os concentrations are potentially significant compared to future
anthropogenic emissions changes, but these relative impacts are highly sensitive to the
detailed assumptions about the magnitude and spatial distribution of emissions
changes.
1. A number of the modeling teams whose results are discussed in this report also
carried out simulations with modified future air pollutant emissions constructed using
spatially non-explicit scaling factors generally derived from the assumptions used to
formulate the various IPCC greenhouse gas emissions scenarios.
2. These highly preliminary tests found that the relative effects of climate and
anthropogenic precursor emissions changes are highly sensitive to the assumptions
about future emissions trajectories.
3. For example, simple scaling of future emissions to match the gross assumptions of the
IPCC Alb or Bl Special Report on Emissions Scenarios (SRES) scenario (IPCC,
2000) resulted in substantial reductions in NOX emissions in 2050, which in turn
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resulted in corresponding reductions in simulated future 63 concentrations that
dominated any O?, increases associated with climate change. In contrast, using future
emissions consistent with the weaker pollutant control assumptions in the "dirtier" A2
or AlFi scenarios tended to result in comparable magnitudes of the climate change
and emissions change effects.
4. The effects of climate and emissions changes were not, in general, additive. In other
words, the size of the climate penalty on air quality is highly dependent on the
emissions levels.
5. These results highlight the need for emissions scenarios with greater regional detail,
consistency between global and regional assumptions, and consistency between
greenhouse gases and precursor emissions. Meeting 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 extent that climate change may threaten the ability
of a region to attain or maintain air quality standards, additional controls (i.e., a climate
penalty) may be required. Preliminary results suggest that the magnitude of this penalty
could be significant in certain regions but also that it is highly dependent on detailed
assumptions about future emissions. Exploring 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 creation of improved scenarios of future emissions
that in turn will be integrated into the climate and air quality modeling systems to
produce more robust estimates of potential climate impacts on control policies.
This is an interim report, and therefore these findings should be considered to be
preliminary. Future reports will update, refine, and augment the synthesis across results
contained herein.
Finally, it is important to emphasize that this 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 and a knowledge base to answer science questions
about the potential impacts of global change 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 science and policy audiences of this report. Such
activities would be aimed at answering specific air quality management questions and might
include, for example, the development of new tools and models explicitly for decision support
(rather than scientific research) that incorporate the new scientific and technical knowledge
gained as a result of this assessment. The initiation of such collaborative efforts would represent
a significant assessment outcome.
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1 1. INTRODUCTION TO THE PROBLEM
2
3 1.1. INTRODUCTION
4 The recent Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment
5 Report (AR4) found that "Warming of the climate system is unequivocal, as is now evident from
6 observations of increases in global average air and ocean temperatures, widespread melting of
7 snow and ice, and rising global average sea level" (IPCC, 2007). The IPCC also found that
8 "Most of the observed increase in globally averaged temperatures since the mid-20th century is
9 very likely due to the observed increase in anthropogenic greenhouse gas concentrations."
10 Furthermore, of particular importance for the U.S. Environmental Protection Agency's (EPA)
11 mission to protect human health and the environment was the IPCC's finding that "Future
12 climate change may cause significant air quality degradation by changing the dispersion rate of
13 pollutants, the chemical environment for ozone and aerosol generation and the strength of
14 emissions from the biosphere, fires and dust. The sign and magnitude of these effects are highly
15 uncertain and will vary regionally."
16 The National Research Council (NRC), in 2001, posed the question "To what extent will
17 the United States be in control of its own air quality in the coming decades?" noting that
18 "... changing climatic conditions could significantly affect the air quality in some regions of the
19 United States" (NRC, 2001). The NRC called for the expansion of weather and air quality
20 studies to include "studies of how air quality is affected by long-term climatic changes." To
21 address this concern, the EPA's Office of Research and Development (ORD) Global Change
22 Research Program initiated a research effort to increase our understanding of the multiple
23 complex interactions between climate and atmospheric chemistry. The ultimate goal of EPA's
24 air quality assessment is to enhance the ability of air quality managers to consider global change
25 in their decisions through improved characterization of the potential impacts of global change on
26 air quality.
27 This ultimate goal will be achieved via three distinct assessment sub-goals:
28 • To develop tools and a knowledge base to answer science questions about the impacts of
29 global change on air quality
30 • To deliver the general benefits to the air quality policy and management community that
31 derive from addressing these science questions, namely, an improved understanding of
32 the behavior and complexities of the global change-air quality system, an appreciation for
33 the strengths and limitations of the scientific tools and methods used to develop this
34 improved understanding, and an answer to the first and most basic "policy" question, "is
35 climate change something we will have to account for when moving forward with U.S.
36 air quality policy?"
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1 • To set the stage for determining how to apply these scientific insights and tools to help
2 answer specific, detailed policy and management questions.
3 This last sub-goal anticipates a separate activity, or set of activities, branching off from
4 this science assessment, that will coalesce around specific air quality decision support needs.
5 These activities might include, for example, developing new tools and models designed
6 explicitly for decision support (rather than for scientific research).
7 This interim assessment report provides an update on the progress toward these three sub-
8 goals. As will be discussed in more detail in sub-section 1.4 and Section 2 below, the assessment
9 design calls for first providing insight into possible air quality responses to future climate
10 changes before tackling the additional complexities of incorporating potential future changes in
11 anthropogenic emissions and long-range pollutant transport. Therefore, its primary focus is on
12 the potential changes in U.S. regional air quality due to global climate change alone, including
13 direct meteorological impacts on atmospheric chemistry and transport, and the effect of these
14 meteorological changes on climate-sensitive natural emissions of pollutant precursors. As such,
15 this interim report cannot fully address questions related to the relative importance of climate vs.
16 anthropogenic emissions of air pollutants or the effectiveness of air quality management efforts.
17 Future assessment reports will focus on these added dimensions.
18 The following sub-sections will present the major themes that run through this report,
19 provide background on the potential links between climate and air quality that motivate the
20 science questions underlying the assessment research, outline the structure and design of the
21 overall assessment, identify the assessment stakeholders, discuss issues related to handling
22 scientific uncertainty, and present a roadmap to the rest of the report.
23
24 1.2. MAJOR THEMES OF THE INTERIM ASSESSMENT REPORT
25 In the course of conducting this assessment, two "grand challenges" have emerged. The
26 first stems directly from the EPA Global Program's emphasis on decision support. The
27 challenge is to provide the best possible scientific basis for understanding the potential range of
28 impacts of climate change on air quality, and air quality policies, in a useful form and a timely
29 manner, as one important set of information inputs to help managers develop appropriate
30 pollution control strategies. Having these improved insights into the way the global change-air
31 quality system works may yield new options for addressing air quality issues or minimize the
32 potential for introducing policies with significant "unintended consequences." At the same time,
33 the complexity of the problem, and hence the data, models, and techniques used to address it,
34 means that many unanswered scientific questions and unresolved uncertainties will exist at a
35 given point in the decision-making timeline. These must be understood and accurately conveyed
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1 to policy makers so they have a sense of the levels of confidence underlying individual elements
2 of this scientific understanding and so they can appreciate the limits on the questions that science
3 can answer at a given moment in time (or will ever likely be able to answer).
4 The second "grand challenge" is to convey to the scientific research community the key
5 knowledge gaps that limit our understanding of the problem and/or create barriers to the use and
6 interpretation of scientific information by decision makers. These range from the sensitivity of
7 regional climate simulations to the parameterizations and methods used in downscaling to how
8 intricate details of the chemical mechanisms are represented in the models. For example, as will
9 be discussed in Section 3, there are a number of meteorological metrics that are crucial for
10 modeling regional air quality for which the climate modeling community has not yet
11 systematically evaluated the skill of their modeling systems. Similarly, future emissions
12 scenarios that are consistent across pollutants and geographic scales and that incorporate
13 important processes such as fire, land use, biogenic emissions, and technological change are
14 lacking, limiting the kinds of studies that can be accomplished at this time.
15 It is possible to think of these challenges as informing two parallel "readings" of this
16 report, one tuned to the perspective of a "science" audience and the other to that of a "policy"
17 audience. While these obviously intersect and overlap, each would highlight its own distinct set
18 of issues, falling broadly under two questions: "What do we know, scientifically, about the
19 climate change-air quality problem?" and "What might this knowledge mean for me, as an air
20 quality manager?"
21 For example, for the scientific audience, this report generates additional information by
22 synthesizing across the findings from multiple research groups. This synthesis improves our
23 understanding of the potential for climate change to impact air quality in different regions of the
24 U.S. and the complex interplay between air quality and its different climatic and meteorological
25 drivers. It also throws into relief scientific and technical uncertainties that will be helpful in
26 guiding future research efforts.
27 For the policy audience, the scientific findings presented in this report help answer the
28 "zeroeth-order" question raised above: "Is climate change something we will have to account for
29 when moving forward with U.S. air quality policy?" In addition, by illuminating the subtleties
30 and complexities of the interactions between climate, meteorology, and air quality, these findings
31 can inform thinking about policy responses. This knowledge can be carried forward into the next
32 phase of the assessment, which will consider added complications such as changes in
33 anthropogenic emissions drivers. Furthermore, this report provides a basis for evaluating the
34 relative robustness of these scientific findings in light of the uncertainties that surround them.
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1 Finally, all of these general insights create a foundation for targeted efforts to solve specific air
2 quality management problems.
3 1.3. BACKGROUND
4 Air pollution continues to be a widespread public health and environmental problem in
5 the U.S. The health effects of air pollution range from increased mortality to chronic effects on
6 respiratory and cardiovascular health. Air pollution also has been associated with increased use
7 of health care services, including visits to physicians and emergency rooms and admissions to
8 hospitals. Other effects include reduced visibility, damage to crops and buildings, and acidifying
9 deposition on soil and in water bodies, where the chemistry of the water and resident aquatic
10 species are affected.l The Clean Air Act calls for EPA to protect both human health and welfare,
11 and there is growing concern that global change may make it more difficult to reach these goals.
12 The NRC, in 2001, highlighted the linkages between climate and regional air quality and
13 the need for a comprehensive research strategy:
14
15 Air pollution is generally studied in terms of immediate local concerns rather than
16 as a long-term 'global change' issue. In the coming decades, however, rapid
17 population growth and urbanization in many regions of the world, as well as
18 changing climatic conditions, may expand the scope of air quality concerns by
19 significantly altering atmospheric composition over broad regional and even
20 global scales. ... Although air quality and climate are generally treated as separate
21 issues, they are closely coupled through atmospheric chemical, radiative, and
22 dynamical processes. ... A better understanding is needed in order to make
23 accurate estimates of future changes in climate and air quality and to evaluate
24 options for mitigating harmful changes.
25
26 More recently, the NRC (2004) identified climate change as an important new challenge
27 to the air quality management (AQM) system. The report concluded that "The AQM system
28 must be flexible and vigilant in the coming decades to ensure that pollution mitigation strategies
29 remain effective and sufficient as our climate changes."
30 Concerns about the impacts of climate change on air quality are grounded in information
31 derived from a wealth of observational studies, knowledge of basic atmospheric chemistry, and,
32 more recently, modeling studies (see Appendix 2 for more details about these lines of evidence).
33 For example, there have been many empirical analyses showing that weather patterns play a
34 major role in establishing conditions conducive to Os formation and accumulation, given
1 See, for example the Ozone Criteria Document, at
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149923, and the Paniculate Matter Criteria Document, at
http://cfpub.epa.gov/ncea/cfm/recordisplay .cfm?deid= 149923.
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1 sufficient levels of precursor pollutants such as nitrogen oxides (NOX) and volatile organic
2 compounds (VOCs): e.g., year-to-year variability in warm-season climate is strongly correlated
3 with variability in O?, exceedances. Generally speaking, meteorological conditions favorable to
4 high levels of 63 include sunshine, high temperatures, and stagnant air (NRC, 1991). However,
5 this NRC report also cautioned about the potential complexities of the problem arising from
6 interactions between key drivers, noting that, for example, the relationship between temperature
7 and O3 "cannot readily be extrapolated to a warmer climate because higher temperatures are
8 often correlated empirically with sunlight and meteorology."
9 A variety of statistical methods have been successfully applied to weather, 63, and other
10 data to obtain short-term air quality forecasts (U.S. EPA, 1999), estimate time trends (Thompson
11 et al., 2001; Bloomfield et al., 1995; Cox and Chu, 1993), and increase understanding of
12 underlying mechanisms (Sillman and Samson, 1995). There are substantially fewer observations
13 for particulate matter (PM), as monitoring networks have been in place for a much shorter time
14 period. This should improve over time as more data become available.
15 Two early modeling studies (Morris et al., 1995; U.S. EPA, 1989) of the effect of a
16 warming climate on U.S. Os levels considered a uniform 4°C increase in temperature across
17 horizontal, vertical, and temporal scales.2 The EPA study modeled specific episodes and
18 simulated changes in daily 1-hour maximum Os concentrations ranging from +3 to +20% for
19 Central California and from -2.4 to +8% for the Midwest and Southeast. Morris et al. (1995)
20 included the effect of warmer conditions on mobile source and biogenic emissions in their
21 simulation of a 4-day episode in the Northeast, simulating Os concentration increases of 15-25
22 parts per billion by volume (ppb) in much of the modeling domain above baseline daily one-hour
23 maximum concentrations of 110-120 ppb and 120-140 ppb (i.e., increases of 10-20%).
24 The results of these early studies suggested that regional air quality may be sensitive to a
25 warming climate, creating an additional challenge for air quality managers. However, as noted
26 by the authors, their studies were constrained by the limitations of the tools and data available at
27 the time. It was recognized that the relationship between climate change and air quality was not
28 a simple one of "higher temperatures equals worse air quality" (NRC, 1991; U.S. EPA, 1989).
29 The number of meteorological factors, and the complex interactions between and among them
30 and air pollutants (see Box 1-1), highlight the need to use sophisticated modeling tools and
31 experimental designs to help understand the multiple ways that climate change can affect
32 regional air quality. Fortunately, modeling capabilities have improved substantially since that
33 time and continue to improve.
2 Because of the technical hurdles existing at the time in adapting climate model output to be input to a
regional air quality model, the researchers elected to make this simplifying assumption.
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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
SO2 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, NOX, and O3, leading to more
or less dilution of polluted air in the boundary layer and altering the chemical transformation rates;
• The vegetative and soil emissions of hydrocarbons and NOX that are sensitive to temperature and light
levels, leading to changes in their concentrations;
• Deposition rates to vegetative surfaces 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.
4 1.4. DESIGN OF THE GLOBAL CHANGE AND AIR QUALITY ASSESSMENT
5 To address the need for an improved understanding of the potential impacts of global
6 change on U.S. regional air quality, building on the scientific understanding summarized in the
7 previous sub-section, an integrated assessment framework was designed that blends the research
8 and development strengths within the EPA with those of other agencies and the academic
9 research community. The assessment program was designed to provide the scientific
10 information and modeling capabilities to answer the following types of questions:3
11 • What are the effects of plausible future changes in climate, climate variability, and land-
12 use patterns on air quality, specifically ground-level Os and PM?
13 • What is the range of potential impacts of climate change on air quality relative to the
14 range of potential impacts of emissions changes due to pollution controls, technological
15 development, and land-use change?
16 • How might the effectiveness of air quality management be affected by climate change,
17 i.e., can changes in emissions, technology, and land use offset air quality changes due to
18 climate change?
19
3 These questions were adapted from the November 2002 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.
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1 As mentioned above, the program addresses these questions in two phases. The first
2 phase of the effort has focused on augmenting, linking, and applying existing climate and
3 atmospheric chemistry models to investigate the range of current and potential future
4 meteorological effects on air quality. It does not include changes in air pollutant emissions other
5 than those that are explicitly linked to meteorological variables and incorporated within the
6 models (e.g., biogenic VOC emissions). These modeling systems have so far been applied to a
7 limited range of greenhouse gas scenarios and alternative model specifications to begin to
8 address the first question. This report largely focuses on the results to date from this first phase,
9 with a major focus on 63.
10 The second phase of the program will focus on the combined impact of changing climate
11 and changing air pollutant emissions on air quality. It builds on the findings from the first phase
12 by extending the linked modeling systems developed therein, and also by exploring the scientific
13 uncertainties more comprehensively. Simultaneously, it integrates plausible, spatially detailed
14 scenarios of U.S. criteria pollutant emissions 50 years in the future with the climate and air
15 quality modeling efforts initiated in the first phase to address, in part, the last two questions. The
16 development of the tools to project technology, land use, and demographic changes needed to
17 derive these emissions scenarios is a critical aspect of this phase of the assessment. Future
18 assessment reports will cover the combined impacts of changing climate and air pollutant
19 emissions on air quality. The program also plans to develop additional reports that focus on
20 additional pollutants, including PM and mercury.
21
22 1.5. THE CLIENT COMMUNITIES
23 Section 1.2 referred to the two broadly defined themes, audiences, and readings of this
24 report that flow from the two "grand challenges." Though this conceptualization provides a
25 useful roadmap to the major purposes of the report, it is also important to identify specific groups
26 that are potential beneficiaries of the information contained herein, and that supply the audiences
27 and perspectives to which the report speaks. These include air quality managers, employees of
28 agencies working as part of the overall U.S. federal climate change research effort, and the
29 climate change and air quality research and modeling communities.
30
31 1.5.1. EPA Office of Air and Radiation (OAR), State, Tribal, and Local Air Quality
32 Planners
33 The EPA's Global Change Research Program engages in activities that support EPA's
34 mission to protect human health and the environment. As the specific focus of this report is air
35 quality, OAR is a major client for this work. Recent air quality regulations, such as the NOX
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1 State Implementation Plan (SIP) Call,4 Clean Air Interstate Rule (CAIR),5 Heavy Duty Highway
2 Diesel Rule,6 and Non-road Diesel Rule,7 are expected to bring many urban areas of the U.S.
3 into attainment with current PM and Os standards by 2015. However, as noted by the NRC
4 (2004),
5 The AQM system will need to ensure that pollution reduction strategies remain
6 effective as the climate changes, because some forms of air pollution, such as
7 ground-level ozone, might be exacerbated. In addition, emissions that contribute
8 to air pollution and climate change are fostered by similar anthropogenic
9 activities, that is, fossil fuel burning. Multi-pollutant approaches that include
10 reducing emissions contributing to climate warming as well as air pollution may
11 prove to be desirable.
12
13 Furthermore, air quality management involves policy decisions with consequences that
14 can last for decades. For example, policy guides the choices made for electricity production
15 investment and the emissions and fuel efficiencies of motor vehicles. Power plant and motor
16 vehicle fleet replacement involves very long lead-times (see, e.g., U.S. EPA, 1992). In this
17 context, it will be important to consider the air quality impacts of global change to identify
18 actions that accomplish air quality goals with the least long-term cost to society. Information
19 and tools supporting the creation of holistic, robust decisions are thus very much needed.
20 Similarly, information and tools supporting new and innovative approaches to existing and
21 emerging issues are needed as well. As introduced in Section 1.1, providing a foundation for
22 developing such decision support instruments that can be transferred to national, regional, state,
23 and local decision-makers is a critical goal of the overall air quality assessment effort.
24
25 1.5.2. U.S. Climate Change Science Program (CCSP)
26 The CCSP integrates federal research on climate change, as sponsored by 13 federal
27 agencies and overseen by the Office of Science and Technology Policy (OSTP), the Council on
28 Environmental Quality (CEQ), the National Economic Council (NEC), and the Office of
29 Management and Budget (OMB). The primary EPA role within the CCSP is to develop an
30 understanding of the potential consequences of global change on human health, ecosystems, and
31 socioeconomic systems in the U.S. Currently, EPA's ORD, within which the Global Change
4 "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.
5 "Clean Air Interstate Rule." U.S. EPA: Clean Air Rules of 2004. http://www.epa.gov/cair/.
6 "Clean Diesel Trucks, Buses, and Fuel: Heavy-Duty Engine and Vehicle Standards and Highway Diesel
Fuel Sulfur Control Requirements (the "2007 Heavy-Duty Highway Rule")." U.S. EPA.
http://www.epa.gov/otaq/highway-diesel/regs/2007-heavy-duty-highway.htm.
7 "Clean Air Nonroad Diesel - Tier 4 Final Rule." U.S. EPA. http://www.epa.gov/nonroad-
diesel/2004fr.htm.
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1 Research Program is located, is focusing on topics that include impacts on future water and air
2 quality, risks to coral reefs and watersheds, and impacts on biological criteria and aquatic
3 invasive species, as well as developing decision support methods and resources.
4 The impact of climate change on air quality is one of the overarching questions guiding
5 the Atmospheric Composition research element of the CCSP (CCSP, 2003; see Box 1-2). The
6 CCSP Atmospheric Composition Interagency Working Group coordinates research that focuses
7 on how the composition of the global atmosphere is altered by human activities and natural
8 phenomena and how such changes influence climate, Os, PM, ultraviolet radiation, pollutant
9 exposure, ecosystems, and human health. Atmospheric composition issues involving
10 interactions with climate variability and change—such as the potential effects of global climate
11 change on regional air quality—are important research topics. Several federal agencies,
12 including the National Oceanographic and Atmospheric Administration (NOAA), the National
13 Aeronautics and Space Administration (NASA), and the Department of Energy (DOE), are
14 involved in research activities in this area, including satellite observations, aircraft field
15 campaigns, laboratory studies, and global modeling studies. EPA contributes its expertise in
16 regional air quality modeling and anthropogenic emissions, along with research support in other
17 air quality-relevant topic areas.
18
19
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 influence 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 support 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 operational 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.
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1 In addition to contributing to efforts under the Atmospheric Composition element, the
2 scientific and technical accomplishments of the current assessment are enlarging the database of
3 information needed to address questions under a number of other CCSP elements (see Box 1-2).
4 Information from the ongoing air quality assessment will be included in the CCSP Synthesis and
5 Assessment Product 4.6: "Analyses of the effects of global change on human health and welfare
6 and human systems."
7
8 1.5.3. Climate Change Research Community
9 Understanding potential impacts of global change on U.S. air quality is a particularly
10 challenging task, given the varying climate regimes contained within the continental U.S. and the
11 3-dimensional modeling at high spatial and temporal resolution that is required to capture effects
12 of importance to policy planners. The larger climate change research community, including
13 other government science agencies and academia, plays a crucial role in the EPA Global Change
14 Research Program's research and development process by assuming the task of advancing the
15 capabilities of global and regional climate models and global and regional atmospheric chemistry
16 models. Beyond the many challenges of understanding potential future global climate change
17 itself, the problem of impacts on air quality adds additional dimensions. For example, the
18 climate modeling community has typically focused on long-term average meteorological
19 parameters on continental and planetary scales, while adverse regional air quality events are
20 often determined by finer-scale geographic and temporal variability. Successfully simulating the
21 impact of climate change on air quality requires advances in the climate sciences and climate
22 modeling, with particular attention to these spatial and temporal needs. The research synthesis
23 portion of this report (Section 3) presents an evaluation of the modeling studies conducted as part
24 of this assessment, studies that represent an initial step toward addressing this challenge.
25 In addition, the scientific results discussed in this report provide an important test of the
26 methodologies used for linking (downscaling) global and regional climate models, a key aspect
27 of climate impacts work in general. Further advances in meeting the demanding requirements of
28 simulating climate change impacts on U.S. air quality will improve our capabilities to assess
29 other global change impacts of great importance to the environmental policy community,
30 including impacts on water quality, aquatic ecosystems, water resources, agriculture, and forests,
31 in addition to the quantification of air quality-related human health effects.
32
33 1.5.4. Air Quality Research Community
34 Developing coupled climate and air quality modeling systems challenges the capabilities
35 of regional air quality models. Improvements in our ability to model chemistry of air pollution
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1 are needed in a number of areas to better understand the influence of climate change on air
2 quality. For example, enhancing linkages between climate/meteorology models and air quality
3 models, developing suitable initial and boundary conditions for all important chemical species,
4 and producing plausible future emission scenarios are all required. Comprehensive examinations
5 like this assessment effort also reveal key uncertainties in chemical mechanisms and processes
6 that can be used to prioritize future modeling improvements. Notable among these is the need to
7 introduce the ability to simulate two-way interactions between climate and chemistry: for
8 example, changes in the distribution of particulates as a result of climate or emissions changes
9 could have important impacts on the Earth's radiation budget, thereby further influencing
10 climate. Finally, the extremely large data files involved in this assessment effort have required
11 the development of automated data management and quality control tools and highlighted the
12 need for new data distribution systems.
13
14 1.6. CONSIDERING UNCERTAINTY IN THE ASSESSMENT EFFORT
15 The study of climate impacts on air quality is a still-emerging field of research.
16 Therefore, this report does not attempt to express the findings from the scientific synthesis in
17 terms of the probabilities ("likelihoods") of particular future events. Instead, the report provides
18 information to help evaluate the relative levels of confidence in the findings (see Box 1-3).
19 Findings for which multiple lines of evidence are presented, and for which there is general
20 agreement across these lines of evidence, should be viewed with higher confidence than findings
21 for which there is a paucity of observations or model simulation results or for which there are
22 competing interpretations of the results that are available. For example, as will be discussed in
23 Section 3, there is broad agreement across the modeling studies that simulated future climate
24 change leads to increases in biogenic VOC emissions in the southeast U.S., but there is
25 significant disagreement as to whether these emissions increases lead to increases in Os
26 concentrations due to uncertainty about how to model isoprene nitrate chemistry.
27 Section 3 provides a detailed discussion of the major uncertainties associated with the
28 coupled climate and air quality modeling systems upon which rests the science synthesis
29 presented in this report. Moving forward into the second phase of the assessment, the
30 complexity of the problem will grow when the dual dimensions of climate and emissions
31 changes are fully integrated. In anticipation of the challenges that multiple, interacting
32 categories of uncertainties will present for interpretation of the assessment findings, EPA
33 convened an expert workshop in November 2006 to begin the process of identifying a
34 comprehensive set of guiding principles to assist in evaluating uncertainty as the assessment
35 moves forward. Participants included experts in global and regional climate modeling,
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2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Box 1-3. Describing Uncertainty
Characterization of the uncertainty in a given finding, judgment, or prediction, and communication of this
uncertainty in clear, precise, objective language, are important components of scientific assessments. Large
global change assessment efforts, such as those conducted by the IPCC and CCSP, have produced general
guidance on handling uncertainty in assessment reports (see CCSP, 2007; IPCC, 2005). For example, a
fundamental principle is that basic differences between descriptions of uncertainty in terms of likelihood of an
outcome and level of confidence of the science underlying a finding must be recognized.
Likelihood is relevant when assessing the chance of defined future occurrence or outcome. When the maturity of
the scientific knowledge base warrants it, it is considered best practice to assign numerical probabilities to
qualifiers such as "probable," "possible," "likely," "unlikely," etc., to avoid differing interpretations among
people and contexts.
Level of confidence refers to the degree of belief in the
scientific community that available understanding,
models, and analyses are accurate, expressed by the
degree of consensus in the available evidence and its
interpretation. One way to think about the level of
confidence concept is to consider two attributes of the
state of knowledge underlying a given finding or
judgment: the amount of evidence available to support it
and the degree of consensus within the scientific
community about the interpretation of that available
evidence (see figure at right).
State of
Established but
incomplete
Speculative
Well established
Competing
explanations
Increasing evidence from observations, thecries, "node
socioeconomic modeling and emissions projection, atmospheric chemistry, regional air quality
modeling, and uncertainty analysis and communication, along with key stakeholders from OAR
and the EPA regions. The preliminary workshop findings suggested emphases on the following
issues: building a healthy, collaborative process involving both scientists and policy makers;
identifying formal uncertainty analysis techniques appropriate for complex, computationally
expensive linked climate and air quality modeling systems; evaluating the potential contributions
of complementary methods, such as expert elicitation; communications strategies; and the need
for future workshops to focus on specific technical issues. The workshop and its findings are
summarized in Appendix 7.
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 scientific findings of the assessment to date and
the issues of concern to air quality managers. Analogous to the approach taken in the IPCC
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1 Summary for Policymakers, OAR was substantially engaged in the writing of this section in
2 order to ensure the salience of the results for air quality policy.
3 Section 2 discusses in greater detail the design of the assessment effort, including the
4 process used to develop this design, key decisions made by the research team, research priorities,
5 and program capabilities. The focus on developing and applying linked global-to-regional
6 climate and air quality modeling systems is in recognition of the complexities of the global
7 change-air quality problem, including its multi-scale (i.e., from global to local; from decadal to
8 diurnal) dimensions.
9 Section 3 synthesizes the results emerging from the initial applications of these modeling
10 systems to the simulation of future U.S. air quality. It highlights the sensitivities in the climate-
11 air quality system and the uncertainties associated with the modeling tools.
12 Section 4 discusses the next phase of the assessment. It summarizes ongoing work that
13 seeks to increase our understanding of key modeling issues and develop new capabilities for
14 simulating future changes in anthropogenic emissions.
15 Appendix A has been provided to assist readers who are unfamiliar with the terms that are
16 frequently used in the discussion of climate and air quality research and policy. Appendix B
17 describes the meteorological variables to which U.S. air quality is known to be sensitive, e.g., the
18 basis for the anticipated effects of changing climate on future air quality. Appendix B also
19 discusses early research results on the role of climate in future air quality. Appendix C describes
20 the 2001 expert workshop convened by EPA NCEA to evaluate the research and assessment
21 framework developed by the EPA Global Change Research Program for identifying and
22 quantifying the effects of global change on U.S. regional air quality. Appendices D, E and F
23 expand upon the descriptions provided in the main report of the internal EPA ORD programs
24 contributing the GCRP assessment effort. Finally, Appendix G describes the 2006 workshop
25 convened by EPA NCEA to identify the essential issues that must be addressed in identifying
26 and communicating the uncertainties inherent in this assessment, and other complex, model-
27 based assessments.
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1 2. OVERVIEW OF APPROACH
2
3 2.1. INTRODUCTION
4 The NRC stated in 2001 that, "improving our understanding of linkages between climate,
5 atmospheric chemistry, and air quality and our ability to assess future states of the atmosphere
6 will require coupling local- and regional-scale air quality models with global-scale climate and
7 chemistry models" (NRC, 2001). The EPA's Global Change Research Program initiated a
8 research program designed to meet the "grand challenges" introduced in Section 1 that is
9 consistent with EPA's traditional "place-based" regional assessment approach, and that focuses
10 on spanning the breadth of issues from global-scale drivers of climate and air quality to
11 developing regional-scale inputs for air quality modeling.
12 In the design of this program, the
13 EPA recognized three key linkages
14 inherent to the global change and air
15 quality issue: those across spatial scales,
16 those across temporal scales, and those
17 across disciplines. The processes linking
18 global to regional scales, symbolized in
19 Figure 2-1, and the requirements for
20 modeling them, were identified as a first
21 step in the assessment design. Similarly,
22 while air quality is defined, studied, and
23 managed most readily on the synoptic
24 timescales associated with
25 meteorological and air quality episodes,
26 global climate change is manifested on timescales of decades and longer, imposing significant
27 research challenges to bridge this gap. Finally, given the inherently multi-disciplinary nature of
28 the problem, it was recognized that merging the efforts of the climate change, air quality,
29 emissions inventory, land use, energy, and transportation economics research communities
30 would be critical to bring about advances required for this assessment. Developing the modeling
31 tools and knowledge base to achieve these linkages is a fundamental task of the assessment.
32
33 2.1.1. Process for Developing the Global Change-Air Quality Assessment Effort
34 In 1997, the EPA's Global Program underwent a major redirection, including the
35 development of a new Strategic Plan in 1999. As part of that effort, the global change-air quality
Global
Air Quality
A
Global Change
i
Global
» Global
Meteorology
^^™
; - /
! // ^^
Regional Regional Regional
Boundary Meteorology — * Emissions 4
Conditions Fields Inventory
^ t 1 ^
'" Regional Air Quality
IPCC SRES
I
Regional Change
Scenarios
Technology
Population Growth
and Migration
Land-Use
AQand
GHG Policy
Economic
Growth
Figure 2-1. Links between global and regional
climate and atmospheric chemistry processes
with anthropogenic activities governing air
pollution emissions.
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1 assessment was designed. Specifically, a small workgroup was formed, made up of scientists
2 knowledgeable about various aspects of the issue, including atmospheric and emissions
3 modeling, technology, socioeconomics, climate modeling, and air quality programs. The
4 workgroup included members from all of the Labs and Centers involved in the EPA's Global
5 Change Research Program, and input from several offices within OAR was also solicited to help
6 guide the effort. An iterative process within the workgroup was used to define the purpose,
7 goals, and issues to be addressed; to identify appropriate EPA participants and stakeholders; and
8 to develop an initial conceptual framework for organizing the assessment effort, leading to a
9 white paper describing the proposed framework and timeline for accomplishing key milestones.
10 To review this draft framework and help EPA identify priority research needs, a
11 workshop was held in Research Triangle Park, North Carolina in December 2001 that brought
12 together technical experts from ORD and OAR, as well as invited international experts. The
13 goal of the workshop was to identify the important processes and inputs and to discuss the design
14 and implementation of the assessment. Participants included experts in climate modeling, air
15 quality modeling, anthropogenic emissions inventory development, and biogenic emissions
16 inventory development. The workshop agenda included presentations by a panel of experts on
17 regional climate modeling, future emissions inventory development, regional air quality
18 modeling, biogenic emissions and wildfires, and socioeconomic and technological change
19 projection methods. The workshop participants were assembled into four groups to discuss
20 specific issues related to the EPA Global Change Research Program's objectives: (1) the
21 Regional Climate Modeling Group, (2) the Emission Drivers and Anthropogenic Emissions
22 Group, (3) the Biogenic Emissions and Wildfires Group, and (4) the Air Quality Modeling
23 Group. Each examined charge questions about possible approaches, and each developed
24 recommendations for research required to meet the needs of the assessment. Here, the key
25 recommendations from the workshop that define the approach used in the assessment are
26 summarized (for further details see Appendix 3).
27
28 2.2. WORKSHOP RECOMMENDATIONS
29 2.2.1. Modeling
30 The three key conceptual linkages introduced above, i.e., across spatial scales, temporal
31 scales, and disciplines, are embodied in the foundational technical challenge of the assessment:
32 linking available modeling tools to span the climate, meteorology, air quality, and human
33 dimensions of the problem. As will be described in more detail below, the primary focus of this
34 2007 interim report is the potential for future climate change to impact air quality, independent
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1 of changes in anthropogenic emissions. The individual research communities use a number of
2 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
Global Climate Model (GCM): Comprehensive model of whole 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 spacing of 100-500 km to examine climate variables at
continental to global scales; most often applied in simulations of how long-term climate statistics evolve over
years, decades, or centuries in response to past or future changes in outside 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" reflects one current usage. Historically, "GCM" referred to the phrase
"General Circulation Model," terminology which is also still in use today.]
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 distributions of important gases and aerosols within the
atmosphere (e.g., O3, 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 U.S.); designed to capture more accurately role of fine-scale
forcings (e.g., topography, land-surface heterogeneity) and atmospheric processes (e.g., nonlinear dynamics of
fronts, development of convective rainfall systems) hard 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; driven at boundaries by gridded 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 chemical 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 operated on time and space scales characteristic of air pollution
episodes, i.e., a metropolitan area or larger region over period of a few days.
5
6 These different modeling tools have historically been developed for distinct purposes.
7 The assessment design reflects the need for bridging the gaps between these standard
8 applications to move toward more comprehensive, integrated systems capable of addressing the
9 breadth of the problem of potential climate change impacts on air quality.
10 As such, one core recommendation that emerged from the workshop was to use these
11 tools separately and in combination in multiple modeling approaches to investigate the relevant
12 space and time scales and physical/chemical processes governing the connections between
13 climate and air quality. These approaches are
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1 • Comprehensive modeling approach: This approach uses linked global and regional
2 climate and chemistry models to simulate fine regional details of present-day and future
3 air quality while simultaneously accounting for global drivers like changes in
4 anthropogenic emissions of greenhouse gases. Output from GCM simulations of long-
5 term climate change is used as input into a higher-resolution RCM, which "downscales"
6 the climate and meteorological variables to the scales required for input into an RAQM.
7 This approach is the most computationally expensive and methodologically complex,
8 with concerns such as the length of simulation required to extract a meaningful climate
9 change signal from interannual climate variability.
10 • Intermediate modeling approach: This approach relies primarily on GCMs and GCTMs
11 to capture the broader impacts of climate change on air quality. The emphasis in this
12 approach is on the potential for increases or decreases in air pollution events as the
13 climate changes over a long simulation period. The results from such modeling work can
14 be used to guide the comprehensive modeling approach (e.g., by guiding the selection of
15 time periods for the higher-resolution simulations).
16 • Sensitivity approach: This approach applies detailed, state-of-the-art RAQMs at regional
17 and even urban scales. Rather than a dynamic linkage, air quality simulations are carried
18 out by varying key meteorological and emissions parameters to examine the sensitivity of
19 the air quality outputs over particular, identified meteorological and air quality episodes.
20 The sensitivity approach might permit use of more detailed descriptions of important
21 processes, i.e., aerosol processes.
22
23 Initially, the assessment team proposed to move forward primarily with the
24 Comprehensive approach. The workshop participants endorsed this plan as effective and
25 reasonable, but they also suggested the other two strategies to complement the Comprehensive
26 approach and add richness to the assessment.
27 Another key model-related discussion was the need to address uncertainty by sampling
28 over multiple GCMs, RCMs, GCTMs, RAQMs, and greenhouse gas emissions scenarios, as well
29 as the need to examine sensitivities to model parameterizations and downscaling methodologies.
30 A critical challenge is to quantify the uncertainty produced by the system of linked models
31 required to simulate changes in air quality driven by climate change. It was also acknowledged
32 that an important research gap was the evaluation of the climate models for their ability to
33 simulate air quality-relevant variables and air quality-relevant weather patterns at the appropriate
34 space and time scales.
35 Finally, the assessment team was urged to consider in more detail the role of
36 hemispheric-scale air pollutant transport and to support the development of appropriate initial
37 and boundary conditions for regional-scale air quality modeling efforts.
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1 2.2.2. Dual-Phase Assessment Approach
2 It is well-recognized that anthropogenic emissions levels are a dominant factor in
3 determining air quality, as evidenced by the dramatic improvements that took place with the
4 implementation of emissions controls beginning in the mid-20th Century in the U.S. and other
5 developed countries. Understanding how changes in air quality due to changing climate might
6 confound long-term management of these emissions for NAAQS attainment and maintenance is
7 a critical assessment goal. To more readily achieve this understanding, a second core
8 recommendation from the workshop was to investigate possible regional air quality responses to
9 future climate and meteorological changes alone, before tackling the additional complexities of
10 projecting changes in other aspects of the system, such as anthropogenic emissions and long-
11 range pollutant transport.
12 The assessment research program was, therefore, designed in two phases. Phase I
13 focuses on developing tools, capabilities, and a knowledge base, and then applying these in
14 research to address the impacts of climate change on air quality with anthropogenic emissions
15 held constant between present and future. Phase II builds on the insights from Phase I, by
16 extending the capabilities of the modeling systems developed therein (e.g., to more
17 comprehensively explore uncertainties, encompass additional pollutants, and investigate climate
18 and air quality feedbacks) and by adding the effects of changing patterns of anthropogenic
19 emissions (e.g., due to population, land-use, and energy and transportation technologies
20 changes). In this second phase, emissions will be projected into the future, accounting for factors
21 such as differential population growth and migration, economic growth, and technology change.
22 The major focus of this interim assessment report is the progress to date under Phase I,
23 presented in Section 3. The Phase II work will be the subject of follow-on reports. A summary
24 of research efforts already ongoing to support Phase II is provided in Section 4.
25
26 2.2.3. Time Horizon Selected
27 A key consideration is the timeframe for building future scenarios and carrying out future
28 climate and air quality simulations. It was decided to focus on a time horizon of roughly 2050 in
29 order to balance the following considerations:
30 Natural meteorological variability versus climate change: Because meteorology varies
31 from year-to-year, the signal from the changing climate needs to be relatively strong to discern
32 climatically driven effects on air quality. In its Third Assessment Report (TAR) (IPCC, 2001),
33 the IPCC projected that global average temperatures could increase from 1.4-5.8°C (2.5-10.4°F)
34 by 2100, and that the warming is expected to be larger than the global average for land areas in
35 the mid- and high latitude regions. These findings are consistent with the most updated
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1 projections from the IPCC AR4 (IPCC, 2007). This trend is expected to lead to intermediate
2 levels of warming in the intervening decades. For example, the U.S. National Assessment
3 (NAST, 2001) based their findings on average U.S. temperature increases of 0.5-2.0°F by 2025,
4 1.5-4.0°F by 2050, and 3.0-9.0°F by 2100. Therefore, the longer the timeframe, the stronger the
5 climate change signal captured relative to natural interannual and interdecadal variability.
6 Uncertainties in GCM climate projections: The IPCC AR4 (IPCC, 2007) documents
7 significantly greater divergence in the climate change projections for 2100 compared to 2050,
8 largely because the various driving greenhouse gas emissions scenarios from the IPCC Special
9 Report on Emissions Scenarios (SRES) (IPCC, 2000) have diverged relatively little by 2050.
10 Even though the climate change signal is stronger in 2100, the spread between model projections
11 created using different scenarios is not as wide. Choosing 2050 thus constrains somewhat one of
12 the potential sources of uncertainty in the assessment.
13 Uncertainties in the assumptions concerning long-term change in emissions drivers: The
14 uncertainty in projections of economic growth, patterns of land-use and land-cover change,
15 energy use, migration, transportation patterns, and technological development needed to develop
16 projections of anthropogenic emissions increases significantly over longer time horizons. An
17 assessment timeframe of, e.g., 2100, would likely be too speculative for practical application to
18 current air quality management planning.
19 Current EPA decision processes: In areas such as investment in electricity production,
20 motor vehicle emissions, and power plant and fleet replacement, the EPA already makes air
21 quality management decisions with long lead times of one to several decades. Therefore, a time
22 horizon of the next half-century for assessing the potential consequences of climate change on air
23 quality is consistent with this planning timescale.
24
25 2.2.4. Research Priorities to Support Phase II
26 Finally, we briefly summarize some key workshop recommendations on additional
27 research needed to support Phase II of the assessment.
28 Processes governing biogenic emissions: Algorithms will have to be developed that
29 describe chemical emissions of major vegetative species response to climate change for use in
30 current and biogenic emission forecasting. Projections of land-use changes will have to be
31 integrated with forest physiological models to project current and future biogenic VOC
32 emissions.
33 Wildfires: There is a need to develop methods to define fire emissions as a function of
34 fire intensity, extent, and frequency. Simultaneously, there is a need to develop methods to
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1 relate fire intensity, extent, and frequency to current and future land use, land management, fuel
2 loading, socioeconomic conditions, and climate.
3 Anthropogenic emissions projections: Plausible scenarios for future emissions need to be
4 developed that account for changes in urbanization, population growth, migration,
5 industrialization, fuel, technology, etc. Also needed is normalization of procedures for emissions
6 calculations across regions and countries and reconciliation between global and regional
7 emission inventories. Principles of downscaling socioeconomic scenarios to more detailed
8 geographic scales must be applied. There is also a need to incorporate feedbacks of climate
9 change on energy use, economic development, land use, and migration.
10 Air quality modeling: Improvements in our ability to model the chemistry of air pollution
11 in a number of areas will be required to more accurately simulate the influence of climate change
12 on air quality. These areas include representations of aerosol physical and chemical processes,
13 two-way linkages between climate/meteorology models and air quality models, the availability
14 of suitable initial and boundary conditions for all important chemical species, and stratosphere-
15 troposphere exchange.
16
17 2.3. RESEARCH PARTNERSHIPS
18 To implement the workshop recommendations and achieve the goals of the assessment,
19 the EPA's Global Change Research Program designed a joint intramural and extramural research
20 program. The goal is to harness the unique capabilities of the EPA research laboratories and the
21 academic community to build a broad program.
22 Within the EPA's intramural effort, the National Exposure Research Laboratory (NERL)
23 is the primary developer of the Community Multiscale Air Quality (CMAQ) model that predicts
24 air quality pollutant transport and fate (Byun and Schere, 2006). CMAQ, which, as of December
25 2006, has undergone three external peer reviews, is being used by the Office of Air Quality
26 Planning and Standards (OAQPS) within OAR for current rulemakings, as well as by the
27 research community for a range of research applications including climate and air quality
28 interactions. Via a partnership between EPA and NOAA, a team at NERL is charged under this
29 assessment with leading the development of a series of regional-scale air quality simulations
30 using CMAQ under current and future climate scenarios. This effort, the Climate Impacts on
31 Regional Air Quality (CIRAQ) project, was initiated in 2002 following the above-mentioned
32 workshop. This team provides the air quality modeling expertise to develop these simulations, to
33 interpret the sensitivity of air quality to the future climate changes simulated, and to consider
34 regulatory implications of potential changes in air quality.
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1 In addition, NERL researchers are key contributors to the development of models of
2 environmentally influenced emissions from the air-surface interface for regional and global
3 emissions inventories and application to air quality modeling, such as biogenic emissions (the
4 Biogenic Emission Inventory System; BEIS) and wildfire emissions (based on the Blue Sky
5 wildfire model). NERL was also the primary ORD collaborator in the development of the Sparse
6 Matrix Operator Kernel Emission (SMOKE) modeling system. SMOKE assembles input data
7 from anthropogenic emission inventories, and biogenic, mobile, and wildfire emission models
8 into the hourly, gridded, speciated form required by air quality models such as CMAQ. These
9 emissions models are needed for both retrospective and future air quality modeling scenarios.
10 More information on aspects of the NERL effort is contained in Appendix 5.
11 Simultaneously, researchers in the National Risk Management Research Laboratory
12 (NRMRL) are focused on evaluating the potential impact of technological evolution on future-
13 year air pollutant emissions, in coordination with the NERL efforts. This process involves
14 characterizing future energy demands and technologies, and using this information within energy
15 system models to estimate emissions over a wide range of alternative scenarios. In addition,
16 NRMRL researchers have developed a suite of analytical and visualization tools for examining
17 the flexibility available in meeting future emission targets and for evaluating sensitivity to
18 uncertainties in model parameters and inputs. NRMRL is applying these methods and tools to
19 examine the system-wide implications on fuel use and emissions of the penetration of new
20 transportation and electric generation technologies. This work directly addresses the need,
21 identified in the 2001 workshop, to develop realistic future emissions scenarios that are
22 regionally plausible and also consistent with assumptions about global trends. Together, NERL
23 and NRMRL have the expertise required to contribute crucially to both Phase I and Phase II of
24 the overall assessment. For additional information, see Appendix 6 and Section 4.
25 The assessment effort benefits from a strong collaboration with the extramural research
26 community. The EPA's National Center for Environmental Research (NCER), through its
27 competitive Science To Achieve Results (STAR) grants program, funded a number of leading
28 university research groups through the following Requests for Applications (RFAs):
29 • 2000: Assessing the Consequences of Interactions between Human Activities and a
30 Changing Climate
31 • 2002: Assessing the Consequences of Global Change for Air Quality: Sensitivity of U.S.
32 air quality to climate change and future global impacts
33 • 2003: Consequences of Global Change for Air Quality: Spatial Patterns in Air Pollution
34 Emissions
35 • 2004: Regional Development, Population Trend, and Technology Change Impacts on
3 6 Future Air Pollution Emissions
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1 • 2005: Fire, Climate and Air Quality
2 • 2006: Consequences of Global Change for Air Quality
3
4 These RFAs, most of which derive from the recommendations of the 2001 workshop,
5 encompass roughly 25 projects, totaling over $20 million, covering topics including projection of
6 population, development, and transportation trends; observations of biosphere-air quality
7 interactions; coupled climate and air quality modeling; and human health effects. Many of the
8 current projects involve collaboration across disciplines to link models. All of this is emblematic
9 both of the breadth of the issue and EPA's commitment to build and populate a comprehensive
10 framework to address it. Further details are provided in Appendix 4.
11 Finally, the National Center for Environmental Assessment (NCEA) has unique expertise
12 in preparing the air quality criteria documents upon which the NAAQS are based, conducting
13 environmental assessments, and performing synthetic analyses of the type presented in Section 3.
14 NCEA's global change assessment team has the primary responsibility for developing the reports
15 synthesizing the results of the broad inter-laboratory and extramural research effort represented
16 in this assessment.
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1 3. RESULTS AND SYNTHESIS
2
3 3.1. INTRODUCTION
4 As introduced in Sections 1 and 2, the problem and challenge of air quality is defined by
5 its local impacts combined with its global dimensions and the linkages across scales and
6 disciplines needed to address it. The purpose of this interim assessment report is to provide an
7 update on our progress toward the development of tools and a knowledge framework that
8 encompasses these linkages in the investigation of global change impacts on U.S. air quality.
9 EPA's assessment activities span the breadth of this topic, with each component of the overall
10 effort illuminating a different element of the framework.
11 Here, in Section 3, the focus is on results emerging from the subset of participating
12 intramural and extramural research groups that are currently producing model simulations of the
13 impacts of climate change on air quality, as part of Phase I of the assessment. This is a mid-
14 course overview of the findings to date from the several parallel efforts to build, test, and apply
15 individual versions of these linked climate and air quality modeling systems. Notably, this is the
16 first systematic effort to apply combined global and regional climate and air quality models to
17 investigations of climate change impacts on future regional air quality.
18 The material presented in this Section is intended to inform each of the two intertwining
19 readings introduced in Section 1, i.e., "science" and "policy," that run through the report and
20 reflect the two "grand challenges" of evaluating the state of the science and providing a
21 foundation on which effective decision support can be built.
22 The material in this Section maps onto these two readings in the following way. From a
23 scientific perspective the main goal is to assess the larger meaning of the various research
24 groups' model simulation results when examined all together. In other words, it is to provide a
25 preliminary synthesis by taking a broad view across this subset of assessment results. Therefore,
26 after brief summaries of activities and key findings to-date from each of the groups, the focus is
27 on inter-group comparisons of the results that are largely common to all or most. The aim is to
28 synthesize the simulated future air quality changes in different regions of the U.S., as well as the
29 dependence of these changes on different climatic drivers. By highlighting scientific and
30 technical uncertainties to which these findings are sensitive, the synthesis helps identify future
31 research needs.
32 From a policy perspective, this synthesis across scientific findings helps answer the
33 "zeroeth-order" question: "Is climate change something we will have to account for when
34 moving forward with U.S. air quality policy?" In addition, by illuminating the subtleties and
35 complexities of the interactions between climate, meteorology, and air quality, it helps build up
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1 intuition about the way the coupled system works. Finally, this Section provides an extended
2 discussion of the challenges and uncertainties associated with the modeling approach that
3 underpins the assessment, to create an improved understanding about the level of confidence in
4 the scientific findings, and an appreciation for the limits on what questions the science can
5 answer now, and may be able to answer in the future.
6 As the EPA's assessment activities continue, overall understanding will grow richer and
7 techniques will become more refined. Thus, it will be possible to build on the foundation
8 provided by this first attempt to interpret this evolving body of work.
9
10 3.2. SUMMARY OF RESULTS FROM INDIVIDUAL GROUPS
11 Results discussed throughout the rest of this Section are drawn from the intramural, EPA
12 work, as well as from several STAR-funded extramural initiatives. More detailed descriptions of
13 the experimental designs and results of the extramural (Appendix 4) and intramural (Appendix 5)
14 efforts are given in the appendices to this report.
15 The projects highlighted here largely share similar fundamental goals and approaches and
16 can be divided into two major groups: (1) those that, to date, have primarily used global climate
17 and chemistry models to focus on the large-scale changes in future U.S. air quality,8 and (2)
18 those that have used nested, high-resolution, global-to-regional modeling systems to focus on the
19 regional details of the potential future changes.9 All of these projects adapt existing modeling
20 tools (as described in Section 2) as components for assembling their systems, including GCTMs,
21 GCMs, RCMs, and RAQMs, along with emissions models and a number of boundary and initial
22 conditions datasets. They all apply these modeling systems in numerical experiments designed
23 broadly to investigate the impacts of future global climate change on U.S. air quality for present-
24 day and future time periods.
25 It is important to consider both the global model simulations and the downscaled regional
26 simulations together, because each method has its strengths and weaknesses. The global models
27 simulate the whole world in an internally self-consistent way across both climate and chemistry,
28 but because of computational demand must use coarse spatial resolution, thereby potentially
29 missing or misrepresenting key processes. Dynamical downscaling with an RCM dramatically
30 increases the resolution and process realism for the region of interest, but at the expense of
31 introducing artificial boundary conditions into the simulation. Section 3.4 below provides
8 The Harvard University and Carnegie Mellon University teams.
9 The EPA National Exposure Research Laboratory (NERL), 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.
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1 additional discussion of these relative advantages and trade-offs. Examining both sets of results
2 gives us a more complete picture of the overall climate-air quality system.
3 In addition to any similarities in approach, however, each project brings unique and
4 complementary differences in emphasis to these tasks. In aggregate, these differences add
5 greatly to the richness of the overall assessment. Below are brief summaries of selected key
6 themes and findings from each of these research efforts as a prelude to the more focused inter-
7 group comparisons of Section 3.3.
8
9 3.2.1. GCTM-Focused Modeling Work
10 3.2.1.1. Application of'a Unified Aerosol-Chemistry-Climate GCM to Understand the
11 Effects of Changing Climate and Global Anthropogenic Emissions on U.S. Air
12 Quality: Harvard University
13 In early work for this project, the Harvard research group examined the role of potential
14 changes in atmospheric circulation by carrying out GCM simulations, using the Goddard
15 Institute for Space Studies (GISS) GCM version II', for the period 1950-2052, with tracers
16 representing carbon monoxide (CO) and black carbon (BC) (Mickley et al., 2004). They based
17 the concentrations of greenhouse gases for the historical past on observations, while future
18 greenhouse gases followed the Alb IPCC SRES scenario. A key result from these simulations is
19 a future 10% decrease in the frequency of summertime mid-latitude surface cyclones moving
20 across southeastern Canada and a 20% decrease in cold surges from Canada into the Midwest.
21 Since these events typically clear air pollution in the Midwest and Northeast, pollution episodes
22 in these regions increase in duration (by 1-2 days) and intensity (by 5-10% in pollutant
23 concentration) in the future. These simulated future circulation changes are consistent with
24 findings from some other groups in the broader climate modeling community, and the Harvard
25 model also successfully reproduces the observed 40% decrease in North American cyclones from
26 1950-2000. However, as will be discussed in more detail below, other groups participating in
27 this assessment do not necessarily find the same decrease in future mid-latitude cyclones when
28 analyzing similar GCM outputs, or the same GCM outputs downscaled using an RCM (e.g., see
29 Leung and Gustafson, 2005).
30 Subsequent to this initial modeling effort, the Harvard group applied the GEOS-Chem
31 GCTM, driven by the next-generation GISS III GCM (Wu et al., 2007a), to the direct simulation
32 of 2050s Os (Wu et al., 2007b). For one set of simulations with this modeling system designed
33 to isolate the impacts of climate change alone on air quality, anthropogenic emissions of
34 precursor pollutants were held constant at present-day levels, while climate changed in response
35 to greenhouse gas increases under the IPCC Alb scenario. Climate-sensitive natural emissions,
36 e.g., of biogenic VOCs, were allowed to vary in response to the change in climate. In these
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1 simulations, they found that at global scales, future 63 averaged throughout the depth of the
2 troposphere increases, primarily due to increases in lightning (leading to additional NOX
3 production), but near the surface increases in water vapor generally caused Os decreases, except
4 over polluted continental regions. Focusing in more detail on the U.S., they found that the
5 response of Os to climate change varies by region. Their results show increases in mean
6 summertime Os concentrations of 2-5 ppb in the Northeast and Midwest, with little change in the
7 Southeast. The Harvard group also found that peak O3 pollution episodes are far more affected
8 by climate change than mean values, with effects exceeding 10 ppb in the Midwest and
9 Northeast. In contrast to this regional pattern of future 63 change, the Carnegie Mellon work
10 (described next) found a relatively smaller response in the Northeast and Midwest but a strong
11 increase in the Southeast, using some similar models and assumptions as the Harvard project
12 (although with a different IPCC greenhouse gas scenario and some key differences in the ocean
13 surface boundary condition). As will be discussed in greater detail below, the explanations for
14 these differences appear to reside in (1) differences in how the chemical mechanisms regulating
15 the reactions and transformation of biogenic VOC emissions are represented in the two modeling
16 systems and (2) possible differences in future simulated mid-latitude storm track changes.
17 In addition to these findings, this group used historically measured relationships between
18 temperature and the probability of Os concentrations above the air quality standard (e.g., see Lin
19 et al., 2001), together with statistically downscaled climate projections for the Northeast U.S.
20 from an ensemble of IPCC AR4 GCMs and scenarios, to project future 63 exceedances in the
21 region (Lin et al., 2007a). They found a doubling of the frequency of exceedances in the climate
22 of the 2050s if anthropogenic emissions were to remain constant. As will be discussed further
23 below, statistical relationships between observed 63 and temperature reflect both the direct
24 impact of temperature on Os chemistry and the often strong correlation between temperature and
25 other factors conducive to high 63 concentrations, such as clear skies, stagnant air, and increased
26 biogenic emissions. As such, they tend to be regionally and seasonally dependent. Work
27 exploring the use of these types of statistical approaches to project Os NAAQS exceedances (and
28 PM concentrations) is ongoing.
29 As a final part of this project, the Harvard group has developed, and is in the process of
30 testing, a linked global-to-regional system of models (including a GCM, GCTM, RCM, and
31 RAQM). This system will be applied to investigations of the effects of climate change, as well
32 as future changes in pollutant emissions and long-range transport, on regional-scale Os and PM
33 concentrations and mercury (Hg) deposition.
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1 Additional information on the Harvard research effort can be found in Appendix 5 and at:
2 • http://cfpub.epa.gov/ncer abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/6
3 157/report/O
4 • http: //www. as. harvard. edu/chemi stry/trop/gcap/
5
6 3.2.1.2. Impacts of Climate Change and Global Emissions on U.S. Air Quality:
1 Development of an Integrated Modeling Framework and Sensitivity Assessment:
8 Carnegie Mellon University
9 The Carnegie Mellon group performed global-scale simulations of atmospheric chemistry
10 under present and future (2050s) climate conditions using a "unified model," i.e., the GISS II'
11 model modified to incorporate tropospheric gas phase chemistry and aerosols. Ten years of both
12 present and future (following the A2 IPCC greenhouse gas emissions scenario) climate were
13 simulated, with anthropogenic air pollution emissions held at present-day levels to isolate the
14 effects of climate change. As in the Harvard project described above, the effects of changes in
15 climate-sensitive natural emissions were also included as part of the "climate" changes
16 simulated.
17 They found that a majority of the atmosphere near the Earth's surface experiences a
18 decrease in average Os concentrations under future climate with air pollution emissions held
19 constant, mainly due to the increase in humidity, which lowers 63 lifetimes (Racherla and
20 Adams, 2006). Further analysis of these results on a seasonal and regional basis found that,
21 while global near-surface Os decreases, a more complex response occurs in polluted regions.
22 Specifically, summertime 63 increases over Europe and North America, with larger increases for
23 the latter. A second key finding is that the frequency of extreme Os events increases in the
24 simulated future climate: over the eastern half of the U.S., where the largest simulated future 63
25 changes occurred, the greatest increases were at the high end of the O3 distribution, and there
26 was increased episode frequency that was statistically significant with respect to interannual
27 variability (Racherla and Adams, 2007). Additional analysis suggested that it is necessary to
28 simulate a minimum of 5 present-day and future years to separate a climate change response
29 from this interannual variability. These general results are broadly consistent with the Harvard
30 experiments described above. However, as also mentioned, there are important regional
31 differences in response between the two groups. These can likely largely be attributed to
32 differences in the modeled chemical mechanism for isoprene oxidation in the southeastern U.S.,
33 as well as possibly differences in the future simulation of the summertime storm track across the
34 northern part of the country. These issues will be discussed in more detail in the synthesis to
35 follow these summaries.
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1 The Carnegie Mellon team is also pursuing two complementary approaches in
2 conjunction with their global modeling efforts. First, they are investigating the sensitivity of Os,
3 PM, acid deposition, and visibility to individual meteorological parameters by performing a set
4 of sensitivity experiments using the PM Comprehensive Air Quality Model with Extensions
5 (PMCAMx) (e.g., see Dawson et al., 2007a, 2006). One key finding from this work is that 63
6 concentrations increased nearly linearly with temperature in the study region/period, and that a
7 2.5°C increase in temperature led to a 30% increase in the area exceeding the EPA 8-hour
8 standard. Second, they have now developed and tested a global-to-regional modeling system to
9 carry out higher-resolution investigations of the impacts of climate and anthropogenic emissions
10 changes on air quality (Dawson et al., 2007b).
11 Additional information on this research effort can be found in Appendix 5 and at:
12 • http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/6
13 240/report/O
14 • http ://www. ce. emu. edu/~adams/index.html
15 • http: //www. cheme. emu. edu/who/faculty/pandi s. html
16
17 3.2.2. Linked Global-Regional-Focused Modeling Work
18 3.2.2.1. The Climate Impacts on Regional Air Quality (CIRAQ) Project: EPA
19 In addition to the extramural projects described in this Section, an intramural modeling
20 study, the CIRAQ project, is being conducted at EPA NERL, as introduced in Section 2. Under
21 this project, the NERL team built a coupled global-to-regional climate and chemistry modeling
22 system covering the continental U.S. They used the output from a global climate simulation with
23 the GISS II' model (including a tropospheric Oj chemistry model) for 1950-2055, following the
24 Alb IPCC SRES greenhouse gas emissions scenario for the future simulation years (i.e., the
25 same simulation described in Mickley et al., 2004) as climate and chemical boundary conditions
26 for the regional climate and air quality simulations. The Penn State/NCAR Mesoscale Model
27 Version 5 (MM5) was used at DOE's Pacific Northwest National Laboratory (PNNL) to create
28 downscaled fields from this GCM simulation for the periods 1996-2005 and 2045-2055 (Leung
29 and Gustafson, 2005). The NERL group used this regionally downscaled meteorology to
30 simulate air quality for 5-year-long subsets of these present and future time periods with the
31 CMAQ model. Multiple years were simulated, in spite of the considerable computational
32 expense, to examine the role of interannual variability in the results.
33 A key element of this project was extensive evaluations of the simulated meteorological
34 variables, not just for long-term climate statistics (e.g., monthly and seasonal means), but of
35 synoptic-scale patterns that can be linked more directly to air quality episodes (Cooler et al.,
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1 2007a; Gilliam and Cooler, 2007; Gustafson and Leung, 2007). One important finding was that
2 the subtropical Bermuda High pressure system off the southeastern U.S. coast, a critical
3 component of eastern U.S. warm season weather patterns, was not well simulated in the
4 downscaled model runs, a result that is likely attributable to biases in the GCM, as will be
5 discussed further below. Another key finding was that, as mentioned above in the summary of
6 the Harvard project, the reduction in cyclones tracking across the northern U.S. found in Mickley
7 et al. (2004) was not as clearly present when this global model output was downscaled using
8 MM5 (Leung and Gustafson, 2005).
9 The NERL team also evaluated the CMAQ results against historical Os observations,
10 finding high biases in summertime Os related to the choice of chemical mechanism in CMAQ
11 between the Carbon Bond-IV (CB-IV) vs. the Statewide Air Pollution Research Center (SAPRC)
12 representations. In addition, they found Os biases related to biases in MM5-downscaled
13 meteorology. For example, the model under-predicted precipitation and over-predicted
14 temperature in the areas of the Midwest and Southeast where 63 was most over-predicted,
15 highlighting the strong control that meteorology can exert on O3.
16 In a set of future simulations with this global-to-regional climate and air quality modeling
17 system, for which anthropogenic emissions of precursor pollutants were held constant while
18 climate changed, the NERL group found increases in future summertime maximum daily 8-hour
19 (MDA8) Os concentrations of roughly 2-5 ppb in some areas (e.g., Northeast, Mid-Atlantic, and
20 Texas) compared to the present-day, though with strong regional variability and even decreases
21 in some regions (Nolte et al., 2007). This regional variability in future Os concentration changes
22 was associated primarily with changes in temperature, the amount of solar radiation reaching the
23 surface, and, to a lesser extent, climate-induced changes in biogenic emissions. The increases in
24 peak Os concentrations tended to be greater and cover larger areas than those in mean MDA8 Os.
25 These results will be discussed in more detail in the synthesis below. The NERL team also
26 found significant O3 increases in September and October over large portions of the country,
27 suggesting a possible extension of the Os season into the fall in the future.
28 Additional information on the NERL effort can be found in Appendix 6 and at
29 http ://www. epa. gov/asmdnerl/Climate/index.html.
30
31 3.2.2.2. Modeling Heat and Air Quality Impacts of Changing Urban Land Uses and
32 Climate: Columbia University
33 The Columbia group built a linked air quality modeling system based on the GISS
34 Atmosphere-Ocean (AO) GCM and the MM5 RCM and carried out simulations using two SRES
35 greenhouse gas scenarios (A2 and B2) for 5 summers each during the 1990s, 2020s, 2050s, and
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1 2080s, focusing on the eastern half of the continental U.S. Additional simulations using higher
2 resolution were carried out for the New York City metro area for particular meteorological/air
3 quality episodes. One important feature of the Columbia effort is that the team carried the air
4 quality modeling results through to an assessment of human health endpoints.
5 A key aspect of the Columbia team's work was the evaluation of the performance of this
6 coupled modeling system. They found that (1) dynamical downscaling with MM5 reduces
7 biases present in the GCM simulation, most strongly for temperature and less so for precipitation
8 and (2) there is a strong sensitivity of climate and Os to the choice of RCM parameterizations,
9 e.g., cumulus convection and Planetary Boundary Layer (PEL) schemes (e.g., see Lynn et al.,
10 2006a). In addition, the downscaled results were often quite different from those of the driving
11 GCM, including, for example, warmer summers. For Os, they found that their modeling system
12 was able to simulate synoptic and interannual variability reasonably well, including the
13 frequency and duration of extreme Os events, but underestimated variability on shorter time
14 scales (Hogrefe et al., 2004a).
15 In future climate change simulations (with anthropogenic emissions of air pollutants held
16 constant at present-day levels), the Columbia group found summertime Os increases of 2-8 ppb
17 across broad swathes of the Midwest and Mid-Atlantic (Hogrefe et al., 2004b). Significant
18 effects were already seen by the 2020s, with greater increases by the 2050s and 2080s. One
19 exception was certain geographic areas that experienced increases in mixed layer depths and
20 convective activity in the 2080s, changes that actually ended up decreasing Os, illustrating the
21 complexity of the climate-meteorology-Os relationship. In general, the spatial correlation of Os
22 increases with any one meteorological variable was not particularly strong in their results. Again
23 the largest future increases in Os were for the highest-concentration Os episodes, leading to large
24 increases in hypothetical exceedances concentrated in the Ohio Valley and the Mid-Atlantic
25 coast. They also found an increase in the duration of high-Os events. The effect of climate
26 change in 50 eastern U.S. cities, without considering future changes in air pollution emissions,
27 was to increase the number of days exceeding the 8-hour Os standard by 68% (Bell et al., 2007).
28 These model results also showed future increases in biogenic VOC emissions in most
29 places as a result of climate change, with the largest absolute increases in the southern and
30 southeastern parts of the U.S. While biogenic emissions changes were responsible for up to half
31 of the total climate effect on Os concentrations in some parts of the Ohio Valley and Mid-
32 Atlantic further to the north, they did not produce significant Os changes in these more southern
33 areas that experienced the largest changes in these emissions. The impact of how biogenic
34 emissions chemistry is represented in air quality modeling systems on simulated O3 is discussed
35 in more detail in the synthesis below.
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1 Finally, an analysis of the effects of land-use change on 63 (and heat waves) in the
2 smaller New York City metro region suggests that such changes could also have local impacts of
3 comparable magnitude to the climatic, emissions, and boundary conditions factors considered.
4 For more information on the Columbia team's efforts, see Appendix 5 and
5 • http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseacti on/display.abstractPetail/abstract/8
6 12/report/O
7 • http://www.mailman.hs.columbia.edu/ehs/research.html
8 • http ://www. geography .hunter. cuny. edu/luca/
9 • http://www.cmascenter.org/2003 workshop/session2/hogrefe abstract.pdf
10
11 3.2.2.3. Impacts of Global Climate and Emission Changes on U.S. Air Quality: University
12 of Illinois
13 The University of Illinois group focused on exploring and evaluating, as comprehensively
14 as possible, the capabilities and sensitivities of the tools and techniques underlying the full,
15 global-to-regional model-based approach to the problem. They concentrated on building a
16 system that accounts for global chemistry and climate, and regional meteorology and air quality,
17 capable of simulating effects of climate changes, emissions changes, and long-range transport
18 changes on regional air quality for the continental U.S. To capture a wider range of sensitivities,
19 they built different versions of this system, which combines multiple GCMs (PCM and the
20 Hadley Centre Model, HadCM3), SRES scenarios (AlFi, A2, Bl, B2), and convective
21 parameterizations (the Grell and Kain-Fritsch schemes) with the Model for OZone And Related
22 chemical Tracers (MOZART) GCTM, an MM5-based RCM known as CMM5, and the
23 SARMAP10 Air Quality Model (SAQM). They also made considerable efforts to evaluate both
24 climate and air quality variables with respect to historical observations and to understand the
25 implications of these evaluations for simulations of future changes.
26 Several important findings emerge from this group's model evaluation efforts. First, they
27 demonstrated that any individual GCM will likely have significant biases in temperature,
28 precipitation, and circulation patterns, as a result of both parameterizations and internal model
29 variability, so multi-model ensemble means will tend to be more accurate than individual models
30 (Kunkel and Liang, 2005). With proper attention, RCM downscaling can improve on these
31 GCM biases in climate variables over different temporal scales (e.g., diurnal, seasonal,
32 interannual), due to higher resolution and more comprehensive physics, and that furthermore the
33 RCM can produce future simulation results that differ significantly from those of the driving
10 SARMAP stands for the San Joaquin Valley Air Quality Study (SJVAQS)/Atmospheric Utility
Signatures, Predictions, and Experiments (AUSPEX) Regional Model Adaptation Project.
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1 GCM (e.g., Liang et al., 2006). They found that the improvements in present-day climate
2 generally led directly to improvements in simulated air quality endpoints, though they also found
3 that the performance of their modeling system tended to be better for monthly and seasonal
4 average 63 concentrations than for multi-day high-Os episodes, reflecting the primary use for
5 which the driving climate models have been designed (Huang et al., 2007). In addition, they
6 found a high sensitivity of downscaled climate (and downscaling skill) to the convective scheme
7 chosen, with different parameterizations working better in different regions/regimes (Liang et al.,
8 2007b). This sensitivity strongly affects simulated air quality, for example by altering
9 meteorology and hence also biogenic emissions (Tao et al., 2007b). All of these findings are
10 consistent with, and expand considerably upon, the results from the Columbia project described
11 above.
12 Notably, the Illinois team also found that the different patterns of GCM biases with
13 respect to present-day observations in different simulations, as well as the way the RCM
14 downscaling altered these biases, were consistently reflected in the future GCM and GCM-RCM
15 differences as well. This suggests a strong link between the ability of a GCM or GCM-RCM
16 downscaling system to accurately reproduce present-day climate and the type of future climate it
17 simulates (Liang et al., 2007a).
18 In future simulations with their coupled global-to-regional modeling system completed to
19 date, based on PCM GCM simulations following both the AlFi and Bl SRES greenhouse gas
20 scenarios, the Illinois group found changes in 63 due to climate change alone (i.e., with
21 anthropogenic pollutant emissions held constant at present-day levels) that were of comparable
22 magnitude to those seen by the NERL and Columbia groups, though with differences in regional
23 spatial patterns (Tao et al., 2007a). These similarities and differences will be described in greater
24 detail in the synthesis below. The larger greenhouse gas concentrations, and hence greater
25 simulated climate change, associated with the AlFi scenario generally resulted in larger future
26 O3 increases than for the climate change simulation driven by the B1 scenario.
27 For more information on the Illinois group's efforts, see Appendix 5 and
28 • http://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/6
29 160/report/O
30 • http://www.sws.uiuc.edu/atmos/modeling/caqims/
31
32 3.2.2.4. Impact of Climate Change on U.S. Air Quality Using Multi-Scale Modeling with
3 3 the MM5/SMOKE/CMA Q System: Washington State University
34 Similar to the NERL, Columbia, and Illinois groups, the Washington State team
35 developed a combined global and regional climate and air quality modeling system to investigate
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1 changes in 63 (and PM). They used the PCM, MM5, and CMAQ models, and they focused on
2 the IPCC A2 scenario for future greenhouse gases. With this system, the Washington State
3 group investigated climate and air quality changes for the continental U.S. as a whole, and in
4 addition focused in more detail on two specific regions: the Pacific Northwest and the northern
5 Midwest. A key distinguishing feature of their effort is the attention to biogenic emissions and
6 the consideration of land cover changes (both vegetation cover and urban distributions), as well
7 as changes in the frequency of wildfires in their simulations. Evaluations of their coupled system
8 against observations indicated reasonable agreement with observed climatology and Os
9 concentrations in their two focus regions. They also examined wet and dry deposition rates and
10 found qualitatively similar results between modeled and measured rates in the Pacific Northwest.
11 In 10 years of simulated summertime Os under both present-day and future climate
12 conditions (with constant anthropogenic precursor pollutants), the Washington State group found
13 future Os increases in certain regions, most notably in the Northeast and Southwest, with smaller
14 increases or slight decreases in other regions (Chen et al., 2007). These climate change effects
15 were most pronounced when considering the extreme high end of the Oj concentration
16 distribution. The magnitude of the Os increases found by the Washington State group (i.e., a few
17 to several ppb) were roughly comparable to those found by the other regional modeling groups
18 already discussed, though again with differences in the specific regional spatial patterns of the
19 future changes, linked to differences in the spatial patterns of key Os drivers, discussed in more
20 detail in the synthesis below.
21 In addition, by accounting for plausible future changes in land-use distribution, they
22 simulated both net decreases and increases in biogenic emission capacity, depending on region:
23 i.e., they found that reductions in forested area in the Southeast and northern California due to
24 increases in development more than offset potential increased biogenic emissions due to climate
25 change, leading to reduction in MDA8 63 levels, while enhanced use of poplar plantations for
26 carbon sequestration significantly increased isoprene emissions in the Midwest and eastern U.S.,
27 leading to Os increases. Finally, they found that warmer and drier conditions in their future
28 simulations yielded increased occurrences of fire in the western states.
29 Additional information on this group's effort can be found in Appendix 5 and at
30 • http://cfpub.epa.gov/ncer abstracts/index.cfm/fuseaction/display.abstractPetail/abstract/6
31 229
32 • http://www.nwairquest.wsu.edu
33
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1 3.2.2.5. Guiding Future Air Quality Management in California: Sensitivity to Changing
2 Climate: University of California, Berkeley
3 Distinct from that of the other groups described above, the Berkeley group's research
4 focused in detail on central California, using a combination of model and observation-based
5 analyses to determine the effects on air quality of changes in temperature, humidity, atmospheric
6 mixing, and biogenic and anthropogenic emissions changes.
7 Specifically, the Berkeley group used CMAQ at very high resolution (4 km horizontal
8 grid spacing), driven by MM5, to investigate the effects of perturbations in these drivers on 63
9 concentrations during a 5-day O3 episode in the state (Steiner et al., 2006). They derived
10 plausible, spatially resolved future changes in summertime temperatures from two simulations
11 with the Community Climate Model version 3 (CCM3) GCM downscaled to a 40-km grid
12 spacing for the Western U.S.: one with a "pre-industrial" CO2 concentration of 280 parts per
13 million (ppm) and one representing a hypothetical 2050 climate with a doubled CC>2
14 concentration of 560 ppm (Snyder et al., 2002). The average August temperature difference
15 between these two downscaled simulations at each point in the domain was added to the MM5
16 meteorological output used to drive CMAQ. This temperature perturbation was applied in an
17 uncoupled manner so as not to affect other meteorological quantities such as wind speed and
18 boundary layer height, to isolate the impact of temperature changes on chemical reaction
19 kinetics. This imposed temperature increase was also used to derive perturbations of humidity
20 and biogenic VOC emissions for additional, separate sensitivity experiments. In addition to
21 these climate-based changes, the Berkeley group carried out simulations to investigate the
22 sensitivity of 63 to changes in anthropogenic NOX and VOC emissions, as well as to the inflow
23 of pollutants from outside the state.
24 They found that higher temperatures increased Os concentrations in this simulated
25 pollution episode both directly (through increased reaction rates) and indirectly (through
26 increases in biogenic emissions). Across all the different effects explored, they found that Os
27 sensitivity varied depending on proximity to the Pacific Coast (e.g., where impacts of increased
28 pollution at the inflow boundary are greatest), and on preexisting NOX or VOC levels (e.g., NOX-
29 saturated regions in central California appear to be most sensitive to climate-related changes).
30 The Berkeley team also conducted an observationally based study of the temperature
31 sensitivity of anthropogenic VOC emissions: the role of temperature in increasing fuel
32 evaporation was highlighted in this analysis (Rubin et al., 2006). Increased evaporation was
33 apparent in observed correlations between speciated VOCs and temperatures as they varied by
34 time of day and from day to day, with implications for the climate sensitivity of these emissions.
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1 Additional information about the Berkeley project can be found in Appendix 5 and at
2 http ://cfpub. epa. gov/ncer_ab stracts/index. cfm/fuseaction/di splay. ab stractDetail/ab stract/6231 /rep
3 ort/0
4
5 3.2.2.6. Sensitivity and Uncertainty Assessment of Global Climate Change Impacts on
6 Ozone and Particulate Matter: Examination of Direct and Indirect, Emission-
1 Induced Effects: GIT-NESCAUM-MIT
8 Similar to the NERL, Columbia, Washington State, and Illinois groups discussed above,
9 the GIT-NESCAUM-MIT group constructed a linked global-to-regional climate and air quality
10 modeling system to investigate the impacts of global change on regional U.S. 63 and PM
11 concentrations. Specifically, they used CMAQ, driven by present-day and future climate
12 simulations with the GISS II' GCM downscaled using MM5 (in fact, the same MM5-downscaled
13 GISS II' GCM simulations developed for the NERL project described above). However,
14 compared to these other groups, they had a unique focus on understanding the climate sensitivity
15 of regional air quality in the context of expected future pollutant emissions under the
16 implementation of current and future control strategies. This effort not only investigated Os, but
17 also PM and its speciated components of sulfates, nitrates, ammonium, and organics, in detail.
18 The strong, built-in link between the academic and air quality management communities
19 achieved via the inclusion of NESCAUM in the partnership is another strength of this program.
20 Their work to date attempts to determine if climate change will have significant impacts
21 on the efficacy of Os and PM emissions control strategies currently being considered in the U.S.
22 by focusing on (1) comparing the sensitivity of future regional U.S. air quality to changes in
23 emissions around present-day and projected future climate and emissions baselines and (2)
24 accounting for the effects of uncertainties in future climate on simulated future air quality to
25 evaluate the robustness of these results.
26 To address these issues, the GIT-NESCAUM-MIT team developed a detailed, spatially
27 resolved U.S. future air pollutant emissions inventory to understand the relative impacts of
28 climate change on future air quality in different emissions and control strategy regimes. To
29 accomplish this, they used the latest projection data available for the near future (to about 2020),
30 such as the EPA CAIR Inventory, and they extended point source emissions to 2050 using the
31 IMAGE11 model combined with the IPCC Alb emissions scenario (the same scenario used in the
32 GISS II' future climate simulations) and mobile source emissions from Mobile Source Emission
33 Factor Model version 6 (MOBILE6), projecting reductions of more than 50% in NOX and SC>2
34 emissions (Woo et al., 2007).
11 A Netherlands Environmental Assessment Agency modeling tool.
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1 A key finding from the GIT-NESCAUM-MIT work is that, overall, existing control
2 strategies should continue to be effective in an altered future climate, though with regional
3 variations in relative benefit (Tagaris et al., 2007). The magnitude of the "climate change
4 penalty" for controlling 63 (as defined by the Harvard group) is found to be consistent with the
5 work of Wu et al. (2007b). The spatial distribution and annual variation in the contribution of
6 precursors to 63 and PM formation under the combined future scenario of climate change and
7 emission controls remain similar to the baseline case, implying the continued effectiveness of
8 current control strategies. The findings further suggest, however, that compliance with air
9 quality standards in areas at or near the NAAQS in the future would be sensitive to the amount of
10 future climate change. Finally, an analysis of potential health impacts of these simulated future
11 air quality changes, using the environmental Benefits Mapping and Analysis Program
12 (BenMAP),12 is ongoing.
13 Additional information on the GIT-NESCAUM-MIT project can be found in Appendix 5
14 and at
15 • http://cfpub.epa.gov/ncer abstracts/index.cfm/fuseaction/display.abstractDetail/abstract/6
16 238/report/O
17 • http://www.ce.gatech.edu/~trussell/lamda/
18
19 3.3. SYNTHESIS OF RESULTS ACROSS GROUPS
20 This sub-section provides a synthetic analysis of results across the groups that have just
21 been introduced. As this is an interim report, the various projects are all at different stages:
22 many of the key results are just emerging. Therefore, as mentioned earlier, the major focus is the
23 particular subset of results completed to date which are largely common across groups, to
24 facilitate a synthesis. Nevertheless, even limiting discussion to this subset allows us to
25 effectively illustrate a number of key points to carry forward.
26 Specifically, then, the focus is on inter-group comparisons of future decade (~2050s) and
27 present-day simulations of summertime O3 under scenarios of climate change. The focus on
28 summer reflects the emphasis of the participating research groups on the primary season for Os
29 episodes and exceedances. All of the future simulations discussed in this sub-section held
30 anthropogenic emissions of precursor pollutants constant at present-day levels, but allowed
31 climate-sensitive natural emissions (e.g., of biogenic VOCs) to vary in response to the simulated
32 changes in climate.13 The organization is as follows: first, the Os results from the fully
12 See http://www.epa.gov/ttn/ecas/benmodels.html for more information.
13 -
Differences in IPCC SRES scenarios between the different simulations thus refer only to greenhouse gas
concentrations, and not precursor pollutants.
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1 downscaled, high-resolution regional model simulations are presented and compared; then,
2 complementary comparisons of differences in key meteorological variables (and biogenic
3 emissions) from these same simulations are provided to begin explaining these O?, results and to
4 highlight the sometimes complex interactions between 63 and its drivers; and finally, some
5 results from the global-model-only runs are presented to complement the regional model findings
6 and to illuminate more clearly certain important issues.
7 Most of the groups whose results make up this synthesis of the impacts of climate change
8 on Os have also carried out additional, in most cases highly preliminary, simulations designed to
9 investigate, to first-order, the effects of changes in climate relative to changes in worldwide
10 and/or U.S. anthropogenic emissions of precursor pollutants. The results from these simulations
11 are not included in the synthesis below to maintain the focus on first exploring climate change
12 impacts alone. However, these sensitivity studies provide useful insights that will help inform
13 the more detailed treatments of future emissions planned for Phase II, highlighting key
14 assumptions and uncertainties that will need to be addressed. Therefore, Section 4 contains a
15 brief summary of these analyses and findings.
16 Similarly, some of the groups have also completed simulations of potential future
17 changes in PM (and its component chemical species), but these results are not discussed here.
18 This is because the research effort and the level of scientific understanding are much more
19 mature at this time for climate and O?, than for climate and PM—there are far more Os results
20 from these projects to date to draw from, along with a greater knowledge base for interpreting
21 them. In addition, it is anticipated that many of the modeling-related issues revealed in the
22 examination of the Os results will likely apply to PM as well, though PM also poses unique
23 challenges for coupled climate-air quality modeling. Some discussion of progress toward
24 understanding climate change impacts on PM is also included in Section 4.
25
26 3.3.1. Regional Modeling Results
27 Table 3-1 lists the regional climate and Os modeling results available at the time of
28 writing this report. These simulations were carried out with linked systems consisting of a
29 GCM/GCTM, dynamical downscaling with an RCM, and regional-scale air quality calculations
30 with an RAQM. In aggregate, they cover a range of models, IPCC SRES scenarios, and
31 parameterizations (only the convective schemes are noted in Table 3-1).
32 All simulations cover the entire continental U.S. with their highest resolution grid, with
33 the exception of the Columbia group's MM5 and CMAQ runs, which cover the eastern half of
34 the country, and the Illinois group's SAQM runs, which use 30 km grid spacing over four sub-
35 regions of the country and 90 km everywhere else (their CMM5 runs use 30 km everywhere).
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1
2
3
4
5
6
7
8
9
10
11
12
13
Table 3-1. The GCM-RCM-RAQM model systems that produced the
simulation results discussed in sub-section 3.3.1 (not including the Berkeley
results, which follow a different experimental design but are also discussed
below). WSU stands for Washington State University and GNM stands for GIT-
NESCAUM-MIT. The SRES scenario listed refers only to greenhouse gas
concentrations, as all simulations discussed below held anthropogenic emissions
of Os precursor pollutant constant between present-day and future simulations.
The Illinois 1 and 2 runs have identical setups but are driven by the AlFi and the
Bl SRES greenhouse gas scenarios, respectively. The two grid cell sizes listed
for each group represent the horizontal grid spacing of the nested outer and inner
RCM domains. In the convection scheme column, BM stands for Berts-Miller
and KF for Kain-Fritsch.
Group
NERL
Columbia
Illinois 1
Illinois 2
WSU
GNM
GCM
GISS II'
GISS AO
PCM
PCM
PCM
GISS II'
SRES
Alb
A2
AlFi
Bl
A2
Alb
RCM
MM5
MM5
CMM5
CMM5
MM5
MM5
Grid Cell Size
108/36 km
108736km
90/30 km
90/30 km
108/3 6 km
108/3 6 km
Convection
Grell
BM
Grell
Grell
KF
Grell
RAQM
CMAQ
CMAQ
SAQM
SAQM
CMAQ
CMAQ
Period
5 sums/falls
5 summers
1 summer
1 summer
10 lulys
3 summers
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
For the Os plots shown below, these 30 km values in the sub-regions are overlaid on the
background map of 90 km values, and it is possible that there will be some minor discrepancies
at the boundaries of the sub-regions.
3.3.1.1. Changes in Oj
Figures 3-1 and 3-2 show summertime mean MDA8, and 95th percentile MDA8, Os
concentration differences between ~2050s and the present for four of the six simulations listed in
Table 3-1. The GIT-NESCAUM-MIT results (see Tagaris et al., 2007) resemble the NERL
results very closely, reflecting the fact that both groups used the same present-day and future
downscaled meteorology to drive the CMAQ model, and therefore are not reproduced here. The
Columbia results are shown separately in Figure 3-3 (reproduced from Hogrefe et al., 2004b)
because of their different spatial coverage. All plots discussed here show future minus present
differences. All Os values are in ppb.
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1 Key similarities between the results from the different groups emerge:
2 • In all the simulations, some substantial regions of the country show future increases in (X
3 concentrations of a few to several ppb under a future climate.
4 • Other, equally substantial, regions show little change in 63 concentrations, or even
5 decreases. The decreases tend to be smaller than the increases.
6 • These patterns of changing 63 concentrations in a future climate are accentuated in the
7 95th percentile MDA8 Oj compared to the mean MDA8 Oj. This basic result of larger
8 increases for high-O3 conditions appears in many different analyses across the different
9 groups (and was highlighted in the summaries above as well).
10
11 Key differences between groups emerge as well. Specifically, there are some broad
12 disagreements in the spatial patterns of change:
13 • As also discussed in Nolte et al. (2007), the NERL experiment (considering both mean
14 and 95th percentile MDA8) shows increases in Os concentration in the Mid-Atlantic and
15 parts of the Northeast, east Texas, and parts of California. It shows decreases in the
16 upper Midwest and Northwest. It shows little change elsewhere, including the Southeast.
17 • By contrast, the Illinois 1 experiment (see also Tao et al., 2007a) shows the strongest
18 increases in the Southeast, the Northwest, and the Mississippi Valley (as well as east
19 Texas, in agreement with NERL), with weaker increases in the upper Midwest. In
20 addition, the changes in this experiment tend to be larger than those from the NERL
21 experiment.
22 • The pattern from the Illinois 2 simulations is closer to that found by the NERL group, as
23 is the amplitude of the signal, though there are still differences.
24 • The WSU experiment (Chen et al., 2007) shows the largest increases in the Northeast,
25 parts of the Midwest, and desert Southwest, with decreases in California, the Southeast,
26 the Northwest, the Plains states, and east Texas. (Note that these results are for July only
27 as opposed to the entire summer.)
28
29 Certain regions show greater agreement across groups than others. For example, based
30 on Figures 3-1-3-3, a loosely bounded area encompassing parts of the Mid-Atlantic, Northeast,
31 and lower Midwest tends to show at least some 63 increase across most of the simulations. By
32 contrast, the West Coast and the Southeast/Gulf Coast are notable areas of disagreement, hinting
33 at some of the complexities underlying the interactions between climate and Os. Changes in
34 drivers that help explain these agreements and disagreements, and help illustrate these
35 complexities, will be presented and discussed shortly.
36 Note from Table 3-1 that there are differences in the number of present-day and future
37 years of simulation completed by the different groups so far. As introduced in Section 1, it is
38 well-recognized that interannual meteorological variability drives large year-to-year changes in
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1 Os, and each of the groups discussed here eventually aims to analyze interannual variability in
2 their simulations. Figure 3-4 (reproduced from Nolte et al. [2007]) illustrates two points. First,
3 for some regions, the average change in Os from the present to the 2050s as a result of climate
4 change is just as large as (and on top of) the year-to-year 63 variability that is of concern today.
5 Second, it highlights the need for simulating multiple years to increase the robustness of findings
6 about present-to-future changes. These results are consistent with those presented in Racherla
7 and Adams (2007) (based on their GCTM runs), who found that the magnitude of simulated
8 future changes in Os concentrations over the eastern U.S. even tended to be greater than the
9 magnitude of present-day interannual 63 variability, and that at least 5 years of simulation were
10 needed to fully separate the effects of climate change and interannual variability.
11 Finally, while mostly only summertime results have been examined to date, the NERL
12 group also considered the fall season, as mentioned earlier. They found strong future increases
13 in Os for September and October in a band stretching from Texas and the Southwest, across the
14 Plains states, and into the Upper Midwest (Figure 3-5, reproduced from Nolte et al., 2007). This
15 result is again consistent with Racherla and Adams (2007), who found an extension of the O3
16 season over the eastern U.S. into the spring and fall.
17
18 3.3.1.2. Changes in Drivers
19 There is already a great deal of regional variability in near-surface Os under current
20 climate conditions. For example, as introduced in Section 1, a large body of observational and
21 empirical work has helped us understand that concentrations tend to be especially great where
22 the emissions of precursor chemical species like VOCs and NOX are also large, and that,
23 furthermore, these pollutants tend to drive up 63 even more during the times when
24 meteorological conditions most favor strong net photochemical production—persistent high
25 pressure, stagnant air, lack of convection, clear skies, and warm temperatures—and vice versa.
26 It is for these reasons that the 63 NAAQS are most often exceeded during summertime hot spells
27 in places with large natural or anthropogenic precursor emissions (e.g., cities). To the extent that
28 climate change may alter weather patterns, and, hence, the frequency, duration, and intensity of
29 these episodes, for example, O3 concentrations could be significantly affected.
30 However, the causal chain linking (a) long-term global climate change, (b) changes in the
31 aspects of (often) short-term meteorological variability that most directly drive 63 concentration
32 changes of concern to air quality managers, and (c) any Os changes that ultimately result from
33 the interaction of these meteorological changes with the pollutants present in the environment
34 (which may themselves be sensitive to meteorology and climate) may not be straightforward.
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1 Changes in the 63 distribution of a given region due to climate change will reflect a balance
2 among competing changes in multiple factors.
3 For example, a number of meteorological variables have been identified as potentially
4 important, including
5 • Near-surface temperature
6 • Near-surf ace humidity
7 • Precipitation
8 • Cloud cover
9 • PEL height
10 • Near-surface wind speed and direction
11 • Ventilation and mixing due to convective events
12 • Ventilation and mixing due to synoptic-scale cyclones
13 • Ventilation and mixing due to coastal onshore flow.
14
15 These variables are not, in general, independent of each other. Instead, they will vary,
16 together or separately in different combinations, at different locations over different timescales,
17 in ways that may favor either increases or decreases in 63. For example, all other factors being
18 equal, increases in temperature at a given time and place might lead to increases in Os
19 concentration, but if these temperature increases are accompanied by increases in cloudiness, the
20 net result might be a decrease in O3 concentration. Box 3-1 provides a discussion of how one's
21 perception of the relationship between Os and its meteorological drivers can vary depending on
22 the timescale considered, using the temperature-Os relationship as an example. This provides
23 some additional context for interpreting these next modeling results to be presented.
24 The advantage of the type of model-based approach that is the focus of this Section, i.e.,
25 the strategy of linking climate, meteorological, and air quality models, is that such integrated
26 modeling systems are capable of capturing these complexities by representing the reinforcing
27 and competing interactions between variables in an internally self-consistent way. As such, they
28 help illuminate potentially non-obvious impacts of climate change on O3 that result from
29 synergistic interactions between the changes in key drivers.
30 Figures 3-6-3-13 display the future 63 changes from each of the simulations represented
31 in Figures 3-1 and 3-2, but now compared to average changes in two of the meteorological
32 drivers just under discussion: temperature and surface incoming solar radiation (typically
33 referred to as "insolation"). The insolation changes largely reflect changes in cloud cover. In
34 addition, each of these figures shows changes in mean biogenic VOC emissions (represented by
35 isoprene emissions for most of the simulations). As mentioned earlier, and well documented in
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Box 3-1. The Temperature-Os Relationship
As seen through the lenses of different meteorological/climatic timescales
Episode: The severity of a particular O3 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 O3 episode, they
significantly increased daily peak O3 concentrations. Temperature affects the kinetics of the O3-forming and
destroying chemical reactions. For example, in polluted environments, increasing temperature will tend to lead to
more NOX, and hence more O3, 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 O3 episode over California, found that temperature
perturbations consistent with plausible 2050s climate change led to increases in afternoon O3 concentrations of 1-
5 ppb across the state. Dawson et al. (2006) found similar effects of temperature modification when using the
PMCAMx model to simulate O3 concentrations during a week-long period over the eastern U.S.
Season: From the perspective of an entire season, however, mean O3 concentration and the number of O3
exceedances will likely depend at least as much on how many of these meteorological episodes that promote O3
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 give way to spells of hot, clear, dry, and stagnant
conditions will play a large role in determining whether it was a "high-O3" or "low-O3" summer. At this
timescale, temperature and O3 will also be positively correlated, but here the "temperature-O3" relationship exists
at least partly because temperature itself is highly correlated with these other meteorological conditions, like more
sunlight and less ventilation, that also favor increased O3 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 associations between higher temperatures, less cloudiness, and weaker mixing that
in aggregate would be likely to lead to O3 concentration increases. This would be true, for example, in the
regions where the IPCC AR4 (2007) suggests the possibility of increases in the frequency, duration, and intensity
of summertime heat waves. In other regions, however, climate change may lead to changes in these other
variables that do not favor increases in O3 concentrations. For example, a warmer world is likely, on average, to
be a wetter world. Both the Harvard and Carnegie Mellon GCTM results summarized earlier showed how
increases in humidity in their future simulations led to decreases in near-surface O3 in less-polluted regions (Wu
et al., 2007b; Dawson et al., 2006). Similarly, regions that experience increases in cloudiness (and hence
decreases in sunlight and O3 photo-production) in an altered future climate might have net O3 concentration
decreases, in spite of increased temperatures.
O
4
5 earlier work (e.g., Sillman and Samson, 1995, among many others), the emissions of these
6 important natural Os precursors are themselves also sensitive to meteorological variables,
7 including sunlight and temperature. Therefore, in conjunction with the direct forcing exerted on
8 Os processes by changes in meteorological variables, climate-induced changes in biogenic
9 emissions levels can lead to changes in 63 concentrations as well. As will be discussed again
10 below, this impact depends on the relative amounts of NOX and VOCs in the environment. For
11 example, Steiner et al. (2006) found significant Os concentration increases in the high-NOx San
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1 Francisco Bay area due to increases in biogenic VOC emissions, whereas even larger increases
2 in biogenic emissions over the Sierras actually produced slight Os decreases.
3 Other variables besides the ones shown in Figures 3-6-3-13 were also examined,
4 including average daily maximum temperature, precipitation, number of rainy days, and PEL
5 height. However, none of these additional comparisons are shown here because, at least at this
6 level of analysis, they do not seem to add a great deal to the explanatory power of temperature,
7 surface insolation, and biogenic emissions. This is likely due to the strong relationship among a
8 number of these variables that has already been discussed.
9 Finally, when interpreting the results shown below, it is important to recognize the
10 difference between what is presented in the figures, i.e., differences between monthly- or
11 seasonal-mean values of these variables derived from present-day and future climate simulations,
12 with the episode-focused literature that represents the more traditional approach to air quality
13 modeling to date. Recalling the discussion in Box 3-1, these long-term mean values encompass
14 not just changes in the meteorological conditions most related to 63 episodes, but the whole
15 spectrum of changes in regional climatology arising from global climate change. This issue is
16 revisited in Section 3.4 below, where the implications for interpreting such climate-air quality
17 modeling results are discussed.
18 As with the Os-only results shown in Figures 3-1-3-3, Figures 3-6-3-13 reveal some key
19 similarities between the different groups' results:
20 • In many regions with 63 increases or decreases, the O^ concentration changes seem to
21 correspond relatively well with combined changes in mean temperature and mean surface
22 insolation. For example, the NERL results show temperature and insolation increases in
23 the Mid-Atlantic and Texas corresponding with the 63 increases there, with 63 decreases
24 associated with the insolation decreases and local minimum in temperature increases in
25 the upper Midwest and the northern Plains. Similarly, insolation increases combined
26 with temperature increases match up reasonably well with the simulated Os concentration
27 increases in the Southeast and northern Plains in the Illinois 1 experiment, in Texas and
28 the southern Great Plains in the Illinois 2 experiment, and in the Northeast and desert
29 Southwest in the WSU experiment.
30 • In other regions, temperature and insolation vary in opposite directions, with mixed
31 effects on Os concentrations. For example, in the Illinois 1 simulations, in spite of
32 insolation decreases over much of the Northwest, the large increase in temperature seems
33 to drive Os increases there. This is similar to the situation in the WSU simulations in
34 parts of the Midwest. In contrast, in the NERL simulations, the effects of the temperature
35 increases in the Northwest seem to be offset by the effects of large decreases in insolation
36 there.
37 • In a small number of regions across the simulations, there is no strong correspondence
38 between 63 concentrations and either insolation or temperature (e.g., the areas around
39 Oklahoma in the Illinois 1 experiment and Nevada/Utah/Idaho in the Illinois 2
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1 experiment), suggesting that other forcing factors may be important, and/or that a
2 correspondence might exist, but only for different averaging periods and statistics of
3 these variables.
4 • Climate-induced biogenic emissions changes seem to contribute to the Ch concentration
5 changes but only in some regions.14 For example, temperature-driven increases in
6 biogenic emissions seem to have helped create the above-mentioned 63 increases in the
7 Northwest in the Illinois 1 experiment, and similarly in the Mid-Atlantic in the NERL
8 experiment, the Northeast in the Illinois 2 experiment, and the Southeast in the Illinois 1
9 experiment. Contrastingly, in parts of the Southeast and Mountain West in the NERL
10 experiment, emissions increase significantly but 63 concentrations do not change. Of
11 course, where there are strong correlations between biogenic emissions changes and Os
12 concentration changes, often there are similarly strong changes in insolation and/or
13 temperature, so separating the different effects is not always straightforward.
14
15 Again, the GIT-NESCAUM-MIT simulations (Tagaris et al., 2007) largely reproduce the
16 NERL results. As far as the results from the Columbia group, Hogrefe et al. (2004b) do not
17 report any single clear relationship across their study region between the spatial patterns of
18 future-minus-present 63 concentrations and a number of meteorological variables (e.g.,
19 temperature, wind speed, and mixed layer height), as mentioned in the summary in Section 3.2.
20 This is consistent with the potential for different competing effects in different regions illustrated
21 by the results shown here. They do note a strong sensitivity of future 63 changes to changes in
22 convective activity in certain areas, which may reflect the dependence on insolation found by the
23 other groups. With respect to biogenic emissions changes, they found the strongest increases in
24 emissions in the Southeast, similar to the results from the NERL and Illinois 1 and 2 experiments
25 but found that the largest Os concentration changes that could be attributed to biogenic emissions
26 changes occurred in parts of the Ohio Valley and coastal Mid-Atlantic.
27 Discerning the precise chemical pathways whereby Os responds to changes in biogenic
28 emissions, and how they vary as a function of region and climatic conditions, is an area of
29 ongoing scientific inquiry. Different air quality models employ different representations of these
30 pathways in their code. As such, differences between the simulated Os response to changes in
31 simulated biogenic emissions from different modeling systems is at this time a key source of
32 uncertainty in climate change impacts on future air quality, particularly in certain regions where
33 the effect of increasing VOC concentrations is highly dependent on NOX levels. This issue will
14 Note that the large decreases in biogenic emissions in California and the Southeast in the WSU
experiment are due to imposed changes in land use, not climate. This is a difference in the experimental design of
the WSU simulations. However, to the extent that these emissions decreases are associated with corresponding O3
decreases, in spite of temperature and insolation changes that would mostly seem to favor O3 increases, this suggests
a strong sensitivity of O3 to the emissions changes in these simulations (at least in the decreasing direction).
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1 be highlighted clearly below, in the intercomparison of the results from the global modeling
2 experiments.
3 The types of relationships between future summertime Os changes and future changes in
4 these three drivers, surface insolation, temperature, and biogenic emissions, as presented in
5 Figures 3-6-3-13, are also seen in the NERL simulation results for the fall season (not shown).
6 The correlations between the insolation, biogenic emissions, and 63 changes are particularly
7 strong, with temperature changes matching up well too in certain regions.
8 Finally, the various model evaluation studies carried out by some of the groups, as
9 described in the summaries in Section 3.2, provide a complementary perspective on the role of
10 these meteorological drivers in Os variability. Results from these evaluations of the modeling
11 systems with respect to historical observations of Os and meteorological variables tend to
12 reinforce the messages presented here. For example, Nolte et al. (2007) attribute part of the 63
13 biases over the eastern U.S. to the biases in temperature and precipitation present in their MM5
14 simulation, as documented in Leung and Gustafson (2005) and Gilliam and Cooler (2007). (It is
15 important to point out, though, that the use of the SAPRC instead of the CB-IV chemical
16 mechanism in CMAQ was in general responsible for a larger fraction of the Os biases than the
17 meteorological variables.) Similarly, Huang et al. (2007) showed how low or high biases in
18 simulated temperature over the Northeast and Midwest lead to Os concentration biases in the
19 same directions.
20 One way to summarize the simulation results presented in Figures 3-6-3-13 is to say that
21 O^ responds to the meteorological/emissions drivers in a qualitatively consistent manner across
22 the simulations from the different groups, but the regional patterns of relative changes in these
23 drivers is highly variable across these simulations. In other words, there are important
24 differences in the simulated future regional climate changes across groups that seem to drive the
25 differences in the regional patterns of 63 increases and decreases.
26 The differences in modeling systems among the groups, as documented in Table 3-1,
27 provide some indication of a number of possible contributing factors that might be responsible
28 for these differences in simulated future regional climate patterns, including:
29 • Differences in the GCM
30 • Differences in the SRES scenario
31 • Differences in the RCM
32 • Differences in the convection scheme
33 • Differences in the RAQM
34 • Differences in the amount of interannual variability captured
35
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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 evaluating their robustness in the context of the
existing scientific uncertainties. First, however, results from the global-model-only simulations
are considered, to enrich the perspective provided by the regional modeling results presented
above, and thus to achieve a fuller synthesis.
3.3.2. Global Modeling Results
Table 3-2 lists the groups that have results from GCTM simulations presently available.
Table 3-2. GCTM-only model simulations whose results are discussed in
sub-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 results 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.
Group
Harvard 1
Harvard 2
CMU
Illinois 1
Illinois 2
GCTM
GEOS-Chem
GISS IF
GISS IF
MOZART
MOZART
GCM and SRES Scenario
GISS III Alb
GISS IF Alb
GISS IF A2 SSTs
PCMAlFi
PCMB1
Grid Cell Size
4° lat x 5° Ion
4° lat x 5° Ion
4° lat x 5° Ion
2.8° lat x 2.8° Ion
2.8° lat x 2.8° Ion
Period
5 summers/falls
5 summers
10 summers/falls
5 summers
5 summers
20
21
22
23
24
25
26
27
28
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., 2007b), 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
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1 near future. Here, a somewhat more limited inter-group comparison than for the regional
2 modeling results is presented, with the goal of illustrating a few specific points.
3 First, Figure 3-14 shows the future-minus-present mean summertime O?, concentrations
4 over the U.S. from all five GCTM simulations listed in Table 3-2. Although the resolution is
5 much coarser than in the regional simulations, the two main conclusions from the regional results
6 shown in Figures 3-1 and 3-2 still hold. Namely, large regions of the country show future Os
7 concentration increases of a few to several ppb, and there are significant disagreements in the
8 spatial patterns of these changes between the simulations.
9 A more detailed comparison of the Harvard 1 (see also Wu et al., 2007b) and CMU (see
10 also Racherla and Adams, 2006) results helps illustrate particularly well two critical insights: the
11 potential importance for simulated future O?, of large-scale circulation changes, and the
12 importance of how isoprene chemistry is represented in the model. Figure 3-15 shows the mean
13 MDA8 Os changes from the Harvard 1 experiment, along with accompanying changes in
14 temperature, insolation, and biogenic emissions, while Figure 3-16 shows the same quantities for
15 the CMU experiment.
16 In the Harvard 1 results (shown in Figure 3-15), the largest Os increases are mostly in a
17 sweeping pattern from the central U.S., across the Plains states and the Midwest, and extending
18 into the Northeast. In contrast to the regional model results shown in Figures 3-6-3-13, there is
19 no immediately obvious spatial correlation between the changes in Os and those of any of the
20 driver variables. The insolation increase in the Midwest matches, to some degree, the pattern of
21 Os increase there, but the largest temperature, insolation, and biogenic emissions increases occur
22 in the southern part of the country, where there are much smaller changes in 03. This weak
23 relationship also holds for a number of other variables considered (e.g., precipitation, PEL
24 height, etc.) but not shown.
25 Figure 3-16 shows a distinctly different regional pattern of change. In the CMU
26 experiment, the major increase in future O3 concentration is instead centered on the Southeast
27 and Gulf Coast, with any increases progressively lessening up the coast through the Mid-Atlantic
28 and into the Northeast, and minimal 63 changes in the Midwest and Plains states. As already
29 mentioned, based on the analyses conducted so far, the differences in these results can seemingly
30 mostly be explained by two factors: (1) differences in the future simulation of the summertime
31 storm track across the northern part of the country and (2) differences in the modeled chemical
32 mechanism for isoprene oxidation in the southeastern U.S.
33 As explained in Wu et al. (2007b), there are two distinct dynamical shifts from the
34 present to the future climate in the Harvard 1 experiment: a decrease in summertime cyclones
35 tracking across the upper part of the country, resulting in a decrease in cloudiness and
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1 precipitation over the upper Midwest (as reflected in the insolation changes shown in Figure
2 3-15), and a northward shift of the Bermuda High, resulting in a decrease in convective activity
3 over the Gulf Coast, Texas, and the southern Great Plains. All other factors being equal, both
4 shifts might be expected to contribute to 63 concentration increases in their respective regions.
5 In this context, the spatial pattern of Os concentration increases in Figure 3-15a is
6 certainly consistent with the decrease in cyclones in the north in the Harvard 1 experiment, as
7 suggested in Wu et al. (2007b). This decrease in storm track activity does not seem to be
8 present, or is not present as strongly, however, in the CMU simulations, consistent with the
9 relatively small 63 changes in these same regions in Figure 3-16a. Racherla and Adams (2007)
10 examined the distribution of sea-level pressure anomalies in their present-day and future
11 simulations and found only relatively small (and not statistically significant) changes in these
12 regions. However, an important caveat is that they did not carry out the type of more detailed
13 pollutant tracer experiments performed for Mickley et al. (2004) that might have revealed more
14 pronounced changes in cyclone activity.
15 Acknowledging this qualification, it seems plausible that differences in simulated future
16 large-scale circulation patterns explain the differences in future Os changes simulated by the two
17 groups for the northern part of the country. What is the explanation for the even larger
18 difference in simulated future Os changes in the southern half?
19 Differences in how isoprene chemistry is captured in the modeling systems of the two
20 groups, leading to differences in how 63 responds to the climate-induced changes in biogenic
21 VOC emissions, can likely explain most of the remaining differences. The spatial patterns of
22 future-minus-present changes in isoprene emissions shown in Figures 3-15d and 3-16d are
23 qualitatively similar, with the largest increases centered on the Southeast and Gulf Coast regions
24 for both groups. Examining the CMU results in Figure 3-16, it appears that increases in
25 temperature and decreases in cloud cover (and hence increases in insolation) have combined to
26 lead to increases in both isoprene emissions and O3 concentrations in this region. An additional
27 simulation with future meteorology but scaled-back isoprene emissions has confirmed that it is in
28 fact the enhanced 63 chemical production resulting from these enhanced emissions that is largely
29 responsible for the simulated future Os increases (Racherla and Adams, 2007). Contrast this
30 with the Harvard 1 results shown in Figure 3-15, where, in spite of the large increase in future
31 emissions over the Southeast and Gulf Coast, there are only weak changes in 63 concentrations
32 there. Even the especially large increases in temperature and insolation that accompany these
33 biogenic emissions changes in Texas and Louisiana do not seem to appreciably increase future
34 O3 concentrations.
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1 This striking difference between the two sets of results is likely traceable to the modeled
2 isoprene nitrate chemistry. While increased emissions of biogenic VOCs are often associated
3 with increases in O?, concentrations, these increased emissions can also lead to decreases in Os
4 concentrations via different pathways. For example, it is thought that high concentrations of
5 isoprene can reduce Os amounts through direct ozonolysis and can also suppress Os production
6 in NOx-limited regimes (e.g., rural areas) by sequestering NOX in isoprene nitrates (e.g., see Fiore
7 et al., 2005). In the Harvard 1 modeling system, increasing isoprene emissions results in little
8 change, or even decreases in Os amounts, largely because the model chemistry represents these
9 isoprene nitrates as a "terminal" sink for NOX. In the absence of additional NOX, the small
10 change in Os concentrations in Texas and the Gulf Coast, in spite of the strongly favorable
11 climate changes there, likely is due to this suppressing effect of isoprene.15 By contrast, in the
12 CMU modeling system, the isoprene nitrates that form are assumed to react rapidly with OH and
13 Os and "recycle" NOX back to the atmosphere with 100% efficiency. This NOX then becomes
14 available to help create 63 again, tending to favor greater 63 concentrations in regions of greater
15 biogenic VOC emissions. It is this effect that dominates the impact of climate change on O3 in
16 the CMU results. Constraining the precise pathways whereby isoprene, NOX, and Os are linked
17 is the subject of ongoing research (e.g., see Horowitz et al., 2007), and as such will remain an
18 important source of uncertainty in the modeling systems.
19 Before concluding with a summary of the synthesis points that have emerged, the
20 following sub-section provides some additional discussion of outstanding issues related to
21 modeling the linked climate-air quality system and the complexities and scientific uncertainties
22 inherent therein.
23
24 3.4. CHALLENGES AND LIMITATIONS OF THE MODEL-BASED APPROACH
25 All of the results shown in this section are model-based. This emphasis on model studies
26 has been built, from the beginning, into the framework and implementation of the assessment.
27 This sub-section spends some time outlining the challenges, limitations, and areas of uncertainty
28 associated with this model-based approach to provide context for a meaningful interpretation of
29 this synthesis. This discussion helps delineate areas of needed future research to build on our
30 understanding of the climate change-air quality problem, and it aims to convey how the findings
31 presented above might be sensitive to the various modeling uncertainties.
15 An additional point to make in this discussion is that, in the Harvard 1 simulations, enhanced ventilation
and mixing also plays a role in partially offsetting expected climate-induced O3 concentration increases in some
near-coastal regions. This results from the combination of the humidity-driven decreases in O3 over the oceans
reported in Wu et al. (2007b) (and also Racherla and Adams [2006]), and perhaps also stronger onshore flow due to
an increase in the summertime land-ocean heating contrast. Lin et al. (2007b) report similar effects in their
simulations of future O3 over U.S. and China.
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1 The central concern of this section is the use of linked systems of global and regional
2 climate and air quality models to investigate potential future changes in Os that may occur due to
3 climate change. These complex modeling systems are extremely valuable scientific tools, as
4 they allow for the exploration of nonlinearities, feedbacks, threshold effects, and in general
5 surprising behaviors that only emerge when the various components are linked together. They
6 also, to a degree, encapsulate current scientific understanding of how a wide range of chemical,
7 physical, and dynamical processes interact with each other; i.e., they provide a useful snapshot of
8 the state of the science.
9 Because of the complexity of the system they mean to mirror, however, at any moment
10 they necessarily embody only an incomplete representation. This results from technical
11 challenges, such as limitations on computing power, as well as from a fundamental lack of
12 understanding of certain processes.
13 Furthermore, different versions of these modeling systems, for example as developed by
14 different groups, will sample different parts of the space of possible representations. One
15 strength of the current assessment effort is the distribution of results across multiple groups and
16 models. Therefore, it is possible to consider different combinations over a range of models,
17 scenarios, and parameterizations, as summarized in Tables 3-1 and 3-2. It is also important to
18 emphasize, however, that, because of the enormous computational burden of these modeling
19 systems as applied to this problem, at this point it is only a small subset of the available range
20 that has been sampled here (e.g., four GCMs, four SRES scenarios, essentially one RCM, three
21 convection schemes, etc.). Expanding the scope to include additional models, scenarios, and
22 parameterizations, along with multiple combinations of each, might further broaden the
23 distribution of projected regional 63 changes. Alternatively, such new results might reinforce
24 previous findings.
25 Therefore, any synthesis conclusions are subject to revisions pending results from future
26 investigations. However, this preliminary synthesis makes it possible to identify some of the key
27 modeling-related sensitivities that are likely to determine our ability to accurately simulate
28 climate change-driven 63 changes, as summarized in the following questions:
29 • What kinds of differences do different GCMs (under different greenhouse gas emissions
30 scenarios) simulate in the climate, and especially in the weather patterns that matter most
31 for air quality?
32 • How do RCMs translate these climate and meteorological changes down to the regional
33 scales that are desired?
34 • How are important chemical mechanisms represented in the climate-air quality modeling
35 systems?
36
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1 The third point has been already been highlighted, to an extent. Therefore, the rest of this
2 sub-section will focus on the first two issues.
O
4 3.4.1. Inter-Model Variability and Model Evaluation
5 The IPCC AR4 (IPCC, 2007) summarizes current understanding of variations in future
6 global climate simulations. The spread across models, groups, and scenarios is the result of
7 differences in exogenous forcings, like natural volcanic or solar changes or changes in
8 anthropogenic emissions of greenhouse gases and aerosols. This spread also results from
9 internal model variability and nonlinear behavior that reflect the inherently chaotic nature of the
10 atmospheric and oceanic circulations. Finally, it arises from model configuration differences due
11 to different choices for dealing with resolution constraints, numerical approximations, and lack
12 of perfect understanding of processes or perfect observations of key parameters. The impact of
13 these factors is reflected in the range of average climates, and regional spatial distributions of
14 climate characteristics, simulated by the different GCMs that are featured here.
15 The significance of these inter-model/scenario differences varies depending on the lens
16 provided by the particular problem of interest. For air quality in general, and Os specifically, a
17 critical question is: "What kind of changes do models simulate in the weather patterns that
18 matter most for air quality?" The results shown in Figures 3-14-3-16 illustrate some of the
19 uncertainties associated with this question. Physical and dynamical arguments suggest that
20 future decreases in the equator-to-pole temperature gradient should drive poleward shifts in the
21 mid-latitude storm tracks, and that this may lead to decreases in the frequency of cyclone
22 ventilation of pollutants in the Northeast and Midwest. The results from the Harvard 1 (and
23 Harvard 2) experiment show this clearly, while those from the CMU experiment do not seem to.
24 Taking a broader perspective across many models and groups, the IPCC AR4 states
25
26 Central and northern regions of North America are under the influence of mid-
27 latitude cyclones. Projections by AOGCMs [Atmosphere-Ocean Global
28 Circulation Models] generally indicate a slight poleward shift in storm tracks, an
29 increase in the number of strong cyclones but a reduction in medium-strength
30 cyclones over Canada and poleward of 70°N (IPCC, 2007).
31
32 However, the agreement across groups is by no means absolute. Furthermore, the IPCC
33 report states
34
35 Results from a systematic analysis of AMIP-2 simulations (Hodges, 2004;
36 Stratton and Pope, 2004) indicate that models run with observed SSTs are capable
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1 of producing storm tracks located in about the right locations, but nearly all show
2 some deficiency in the distribution and level of cyclone activity (IPCC, 2007).
O
4 Recent increases in model resolution and other improvements have led to improvements
5 in simulations of present-day storm tracks, and may eventually lead to a stronger consensus on
6 the likely magnitude and direction of future climate-induced changes over the U.S. At this time,
7 however, current levels of uncertainty probably do not allow us to say much more than (1) the
8 number and intensity of summertime cyclones passing over the northern U.S. is a key factor in
9 determining air quality there and (2) the occurrence of fewer and weaker cyclones is a plausible
10 consequence of global climate change.
11 This discussion about cyclones suggests a broader question: how should the scientific
12 community evaluate the performance of these modeling systems for the task at hand? It is not
13 possible to answer this question comprehensively here, but it is possible to place some general
14 issues with which the climate modeling community continuously struggles in the context of the
15 specific problem of climate change impacts on air quality.
16 First, all groups carry out evaluations of their modeling systems compared to historical
17 observations. The key is to conduct these evaluations for the variables, and statistics of those
18 variables, that are most relevant for the problem of interest. As discussed above in Section 3.3,
19 "air quality," from a health, environmental, and regulatory perspective, is largely determined by
20 episodes that occur during specific, sporadic weather events, so what is most important to know
21 is how well available modeling tools simulate these events and how well they can predict future
22 changes. At present, however, the focus of the climate modeling community is still largely on
23 long-term mean values of variables like temperature, precipitation, and cloud cover. These
24 quantities can be important in situ drivers of air quality on short timescales, but more effort is
25 needed to understand how changes in atmospheric flow patterns are reflected in the changes in
26 these long-term means. There is a need to address questions like: "Did a simulated temperature
27 change in a given region result from an across-the-board change in baseline temperature during
28 all weather regimes, or instead from a change in the frequency of occurrence of one particular
29 weather pattern (e.g., the afternoon sea breeze, synoptic-scale anticyclones, or mesoscale
30 convective systems)?" Climatological averages of variables like temperature will only have
31 explanatory power for air quality to the extent that they reflect the changes in the most relevant
32 circulation patterns, as opposed to being obscured by "noise" that is less related to air quality
33 (e.g., increases in nighttime average temperature).
34 The current situation reflects the relatively youthful state of coupled climate and air
35 quality science. The application of climate models to air quality represents a significant
36 challenge for the climate modeling community. One path forward is to make it standard practice
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1 to conduct in-depth evaluations of global and regional climate models for additional variables
2 and metrics more relevant for air quality. As Gustafson and Leung (2007) state,
O
4 Our ability to address these questions relies critically on the ability of climate
5 models in simulating the meteorological conditions needed to realistically
6 simulate air quality. Because of the nonlinear nature of atmospheric chemistry
7 and its dependence on difficult to model variables, such as precipitation and the
8 planetary boundary layer (PEL) height, biases in variables considered acceptable
9 for other downscaling applications may not be appropriate for this new
10 application. An additional challenge in air quality assessment is the required
11 knowledge of the three dimensional structures of the atmosphere, which are not
12 needed for most other assessments.
13
14 New efforts carried out under the auspices of this assessment, as summarized in Leung
15 and Gustafson (2005), Gilliam and Cooler (2007), Cooler et al. (2007a, b), and Gustafson and
16 Leung (2007) represent significant advances in this area and provide useful insights moving
17 forward.
18 Second, it is important to remember that, for the problem under consideration here,
19 accurately reproducing present-day conditions is not interesting in and of itself, but is interesting
20 for what it might imply for simulating and understanding future changes. The connection
21 between the two is not necessarily straightforward. Again from the IPCC AR4: "What does the
22 accuracy of a climate model's simulation of past or contemporary climate say about the accuracy
23 of its projections of climate change? This question is just beginning to be addressed..." (IPCC,
24 2007: Ch. 8).
25 Given a particular variable, and statistic of that variable, to be evaluated, there are two
26 sources of error in any future-minus-present comparison: the bias in the present-day simulation,
27 and some (hypothetical) bias in simulating the future conditions. The modeling community
28 typically makes two implicit assumptions about these sources, but these assumptions are
29 potentially contradictory. First, there is the assumption that these two errors are correlated, i.e.,
30 the better the modeling system is at reproducing present-day observations, the better it will be at
31 reproducing future climate shifts. This could lead logically to the conclusion that a model
32 system that does a poor job of simulating the present will likely be even worse at getting the
33 "correct" future-minus-present changes. However, it is often simultaneously asserted that
34 looking at differences between simulated future and present results will yield accurate insights,
35 i.e., that the biases should be similar in the present and future simulations and thus will cancel.
36 Barring improbable coincidences, these two assumptions can only be reconciled if a third
37 assumption also holds: namely, that most of the biases in the present-day simulation come from
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1 error sources that will not impact the model's ability to capture the future changes, i.e., the
2 present-day biases will simply be carried along to the future. The validity of this assumption for
3 a highly nonlinear system like climate must be tested. Again, research carried out for this
4 assessment is contributing to this need. For example, Liang et al. (2007a) showed how GCM
5 (and downscaled RCM) biases with respect to historical observations are consistently propagated
6 into future simulations, empirically linking the ability of a modeling system to accurately
7 reproduce present-day climate to the types of future climate changes it predicts.
8
9 3.4.2. TheRoleofDownscaling
10 As described in Section 2, this assessment has been built, in part, around dynamical
11 downscaling, i.e., the use of an RCM to derive higher-resolution meteorology from a GCM
12 simulation for a particular sub-region of the globe. This is in recognition of the dual need to be
13 regionally explicit, so as to connect more closely with the priorities of policy makers, while at
14 the same time capturing the inherently global scale of the climate drivers. As noted, this is really
15 the first systematic attempt to apply these techniques to air quality impacts work, and valuable
16 lessons are being learned.
17 The fundamental task of dynamical downscaling is to maximize the "value retained"
18 from the GCM and the "value added" by the RCM. In other words, successful downscaling will
19 take advantage of the things the RCM does well in simulating weather and climate, by virtue of
20 its high resolution, without sacrificing too much of what the GCM does well, by virtue of its
21 global extent. From the results presented above, it is clear that changes in both large-scale
22 circulation patterns and local-scale forcings are crucial drivers of Os changes. A given modeling
23 system will be able to accurately simulate changes in 63 only to the extent that it can accurately
24 capture both.
25 Because of its higher resolution, the RCM develops small-scale features that the GCM
26 cannot. These features develop for three primary reasons (see, e.g., Denis et al., 2002):
27 • finer-scale representations of surface characteristics, like topography, water bodies,
28 vegetation, soil moisture, and land use, that lead to local-scale circulation systems like
29 sea and lake breezes and mountain-valley flows;
30 • nonlinearities in the fluid dynamics equations that lead to the development of fronts and
31 other mesoscale features;
32 • hydrodynamic instabilities arising from shear or buoyancy forcing that create turbulent
33 eddies and convection and are more accurately represented with higher resolution.
34
35 RCMs therefore add the most value by more accurately simulating near-surface
36 meteorological fields, as well as extreme conditions (e.g., cyclone low pressure, intense
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1 precipitation, high winds). These advantages make it possible to significantly improve on
2 regional biases in temperature and precipitation present in GCM simulations (e.g., see Liang et
3 al., 2006), and these improvements can lead directly to improved simulations of O^.
4 RCM performance is highly sensitive, however, to the physical parameterizations used,
5 as already summarized above. For example, Liang et al. (2006, 2004a, b) and Lynn et al.
6 (2006a, b) found strong sensitivities of temperature and precipitation to the convection scheme
7 chosen. These meteorological sensitivities drive corresponding sensitivities in simulated air
8 quality (e.g., Kunkel et al., 2007; Tao et al., 2007b). In addition, sensitivities of air quality to
9 PEL, radiation, microphysics, and land-surface schemes may also be important, but these have
10 yet to be examined as systematically.
11 Along with the physical parameterizations, the other major sensitivity of the RCM is the
12 application of the large-scale boundary conditions from the GCM, i.e., the actual
13 "implementation" of the dynamical downscaling that links the GCM with the RCM. By itself, an
14 RCM cannot simulate the large-scale circulation of the atmosphere because the drivers are
15 planetary in scale (e.g., the difference in net radiation between equator and poles), necessitating a
16 global domain. So, for example, an RCM cannot generate dynamical systems like the mid-
17 latitude storm tracks, which instead must be supplied by a GCM. It is in the context of this
18 GCM-provided large-scale circulation that the smaller-scale features described above evolve.
19 This leads to the basic question of dynamical downscaling: how best to close the system? In
20 other words, what is the optimal method for importing information from the GCM into the RCM
21 so as to preserve any desired features of the large-scale circulation patterns without
22 compromising the ability of the RCM to develop realistic smaller scales?
23 The most common practice has been to assimilate the GCM fields into a narrow strip at
24 the lateral boundaries of the RCM domain. This technique is commonly referred to as "lateral
25 nudging," and follows Davies (1976). Everywhere else in the domain, the RCM develops its
26 own solution, which it is hoped will evolve consistently within the envelope defined by the GCM
27 flow at the boundaries. This approach is widely used and has yielded valuable results in a
28 number of different applications across the field of regional climate modeling. It is the approach
29 that is used in all the downscaling work contributing to this report. The major perceived
30 advantage of this approach is that it allows for the possibility of the RCM correcting biases not
31 only in the relatively fine-scale, near-surface temperature and precipitation features, but also in
32 continental-scale circulation patterns. For example, Gustafson and Leung (2007) illustrate how a
33 better representation of the Rockies leads to improvements in the overall flow patterns over the
34 U.S. when MM5 is used to downscale the GISS II' GCM simulation.
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1 Recent work (see Rockel et al., 2007; Miguez-Macho et al., 2005; Castro et al., 2005;
2 Miguez-Macho et al., 2004; von Storch et al., 2000), however, suggests that this lateral nudging
3 approach can be problematic and introduce additional biases of its own. Specifically, if the
4 RCM captures the energy of the large-scale flow only through assimilation at its lateral
5 boundaries, two problems can arise. First, the energy of the large-scale circulation can be
6 progressively lost as a result of several factors as it makes its way into the domain from the RCM
7 boundaries. This lost energy cannot be re-supplied by the RCM, since, as already noted, the
8 drivers are planetary in scale. A potential consequence, then, is weaker large-scale circulation
9 features in the RCM compared to the GCM. Second, the large-scale flow field can be modified
10 significantly as it makes its way across the RCM domain. This can cause problems at the RCM
11 boundaries that, in turn, can introduce artificial flow features back in the main body of the model
12 domain. For example, the jet stream entering the western boundary of the RCM domain will
13 encounter the steeper (because higher-resolution) Rockies and be deflected, so that by the time it
14 reaches the eastern boundary, it will not be consistent with the GCM boundary condition there.
15 Both of these problems are more pronounced with larger RCM domains and coarser RCM
16 resolution.16
17 One method for handling these problems is so-called "spectral nudging," i.e., nudging
18 applied not at the lateral boundaries at all spatial scales, but instead applied at all locations in the
19 RCM domain (above the PEL at least) but only for the longest waves that are resolved in the
20 GCM (see Miguez-Macho [2004] and von Storch et al. [2000] for descriptions of the technique).
21 At this time, whether lateral nudging or spectral nudging is preferable is just becoming an active
22 research question: "Does one take the large-scale flow field of the GCM as "truth" and force the
23 RCM to conform to it as closely as possible, or does one instead allow the RCM to evolve a
24 more independent circulation?" Therefore, the implications for simulating air quality are as yet
25 unclear, since the downscaled simulations carried out to date for this assessment have all used
26 the lateral nudging approach.
27 Given what we do know at this time about dynamical downscaling, however, the
28 following should be considerations when interpreting the regional air quality results presented in
29 this section:
30 • The RCM may not faithfully capture important features of the large-scale circulation
31 patterns present in the driving GCM. In particular, the large-scale flow might be too
32 weak in the RCM, leading to a proportionally too-strong influence of more local-scale
16 To date, these two potential pitfalls of lateral nudging have mostly been investigated for RCM
simulations driven by global reanalysis data and not GCM output, and there may be differences between the two in
the impact on the downscaled fields.
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1 forcing, like convection. Alternatively, there might be artificial flow features introduced
2 by discrepancies between the RCM and GCM at the boundaries.
3 • Even if the RCM reproduces the GCM's large-scale circulation very closely, it may still
4 simulate different air quality patterns because of differences in the way it simulates
5 convective clouds and rainfall, or other fine-scale processes, embedded within this large-
6 scale flow.
7 Either or both of these considerations may help explain why, as mentioned previously,
8 the influence of a shift in the storm track present in the Mickley et al. (2004) GCM experiment
9 does not show up as clearly when this same GCM simulation is downscaled using MM5 (Nolte
10 et al., 2007; Leung and Gustafson, 2005). Precisely attributing these differences between the
11 downscaled results and the driving global simulation remains a key task in the furthering of our
12 understanding of the impacts of global climate change on regional air quality, and it remains the
13 subj ect of ongoing investigation.
14 In any case, the strong influence of the GCM-simulated climate on the downscaled results
15 is inescapable, regardless of the methodological details. Gustafson and Leung (2007) emphasize
16 that the GCM chosen will strongly impact any downstream regional air quality findings. Gilliam
17 and Cooler (2007) and Cooler et al. (2007a, b) show clearly that much of the bias in the NERL
18 group's regional simulations for the eastern U.S. can be traced directly to an incorrect
19 northeastward displacement of the Bermuda High in the driving GISS II' GCM simulation. This
20 and similar results, then, underscore again the discussion from above: quantifying the biases and
21 characteristics of the individual global model simulations being relied upon for representing
22 future climate change is of critical importance for the problem of global change impacts on air
23 quality.
24
25 3.5. SYNTHESIS CONCLUSIONS AND FUTURE RESEARCH NEEDS
26 This section concludes by collecting and summarizing the major points that have
27 emerged from the scientific synthesis. These help address the goals of this report by addressing
28 questions like "What new findings are emerging from the body of work that EPA has made
29 possible?" and "What have we learned about our ability to simulate potential future changes in
30 U.S. regional air quality due to climate change?"
31 Specifically:
32
33 • All the simulations, both those carried out using global models and those
34 carried out using regional downscaling systems, show increases in
35 summertime 63 concentrations over some substantial regions of the
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1 country as a result of simulated climate change. The other regions show
2 little change, or, in limited areas, even slight decreases.
O
4 • For summertime-mean MDA8 Os, these increases are in the 2-8 ppb
5 range.
6
7 • The largest increases in Os concentrations in these simulations occur
8 during peak pollution events. For example, the increases in 95th
9 percentile MDA8 Os tend to be significantly greater than those for
10 summertime-mean MDA8 Os.
11
12 • Certain regions show greater agreement in Os concentration changes
13 across simulations than others. For example, a loosely bounded area
14 encompassing parts of the Mid-Atlantic, Northeast, and lower Midwest
15 tends to show at least some Os increase across most of the simulations,
16 whereas there are substantial disagreements across simulations for the
17 West Coast and the Southeast/Gulf Coast.
18
19 • Helping to explain these differences in the regional patterns of Os changes
20 are the wide variations across the different simulations in the patterns of
21 mean changes in key meteorological drivers, such as temperature and
22 surface insolation.
23
24 • The different simulations provide examples of regions where simulated
25 future changes in meteorological variables seem to have reinforcing
26 effects on Os, and regions in which meteorological changes seem to have
27 competing effects. For example, regions where the future-minus-present
28 changes in simulated temperature and insolation are in the same direction
29 as each other tend to experience Os concentration changes in a similar
30 direction. Temperature and insolation varying in opposite directions tends
31 to correspond with mixed Os changes.
32
33 • The global modeling results highlight the importance of changes in large-
34 scale circulation patterns for modifying these drivers. Whether or not a
35 given modeling system simulates changes in key circulation features, like
36 the mid-latitude storm track or the Bermuda High, has a strong impact on
37 the simulated future Os changes.
38
39 • Other factors to which the patterns in the simulated meteorological
40 variables appear to be highly sensitive include the choice of convection
41 scheme and whether or not the global model outputs are dynamically
42 downscaled with an RCM.
43
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1 • Across nearly all simulations, climate change is associated with simulated
2 increases in biogenic VOC emissions over most of the U.S., with
3 especially pronounced increases in the Southeast.
4
5 • These biogenic emissions increases do not necessarily correspond with Os
6 concentration increases, however, depending on the region and modeling
7 system. This appears to be because, as highlighted by the global modeling
8 results, the response of 63 to changes in biogenic emissions depends
9 sensitively on how isoprene chemistry is represented in the models—
10 models that recycle isoprene nitrates back to NOX will tend to simulate
11 significant Os concentration increases in regions with biogenic emissions
12 increases, while models that do not recycle isoprene nitrates will tend to
13 simulate small changes, or even Os decreases.
14
15 • Based on the results from a small subset of the simulations that examined
16 multiple years of model runs, future-minus-present increases in Os
17 concentrations can be just as great, or greater than, present-day interannual
18 variability in these simulations. This suggests that climate change has the
19 potential to push 63 concentrations beyond the envelope of natural
20 variability. It also highlights the fact that the amount of future-minus-
21 present change in Os concentration simulated will likely depend strongly
22 on the choice of present and future simulated years to compare, and that
23 multi-year simulations are desirable for producing findings that are more
24 robust.
25
26 • Similarly, a small subset of the simulations suggest that, for parts of the
27 country with a distinct summertime Os season, climate change has the
28 potential to lead to an extension into the fall and spring.
29
30 These findings should be interpreted as speaking to the question, "How does the system
31 work?" rather than the question, "What will happen in the future?" They provide insight into the
32 subtleties and complexities of the interactions between climate, meteorology, and air quality,
33 thereby helping to build intuition about the richness, and range of behaviors, of the climate-air
34 quality system. They also illustrate how valuable the modeling systems developed for this
35 assessment can be for exploring this problem.
36 This improved system understanding, combined with a clear appreciation of the
37 important uncertainties, opens the doors to a wide range of future applications based on this
38 knowledge and these tools. For example, the results of modeling experiments have the potential
39 to provide guidance as to whether, for example, statistical relationships based on historical
40 observations of O3 and temperature will serve as accurate approximations of the effects of
41 climate change in a given region. Other applications might include evaluating the potential for
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1 unintended consequences of a particular policy choice, e.g., whether tree plantations for carbon
2 sequestration might harm air quality in a given region in the face of future climate change.
3 In addition, these findings highlight a number of areas where further research is needed:
4 1. First, as has been emphasized throughout, an improved understanding of how well
5 models simulate the large-scale circulation patterns that are important for air quality is
6 needed. This issue was being considered at least as early as 1991, when the NRC pointed
7 out that whether a GCM simulated a persistent high or low pressure pattern over a given
8 region had the potential to counteract any increase in 63 associated with warmer
9 temperatures, through changes in other meteorological drivers (NRC, 1991). The NRC
10 also pointed out in this report that no two GCMs simulate the same shifts in pressure
11 patterns in response to increases in greenhouse gases. As discussed above in Section 3.4,
12 these kinds of disagreements among models persist today.
13 2. As a related point, there is a need for an improved understanding of how well RCMs can
14 downscale changes in these GCM-simulated circulation patterns, as well as a need for
15 more insight into the sensitivity of these downscaled regional simulations to model
16 parameterizations, including convection schemes, but also expanding to PEL, radiative
17 transfer, microphysics, and land-surface schemes.
18 3. Recalling the discussion surrounding Box 3-1, a critical component of addressing points
19 1 and 2 above will be extending efforts, initiated in this first phase of the assessment, to
20 evaluate the GCM- and RCM-based systems for the meteorological variables, and
21 especially the temporal statistics of the meteorology, most appropriate for air quality: for
22 example, long-term average changes in the frequency, duration, and intensity of
23 stagnation episodes driven by synoptic-scale variability. This will need to include
24 outputting and analyzing the required quantities, at the required temporal frequency, from
25 the models, as well as further analyses of historical observational data.
26 4. Development and refinement of techniques for systematically exploring the effects of the
27 modeling uncertainties are also needed, including ensemble methods, techniques for
28 blending ensemble approaches with dynamical downscaling, and reduced form models.
29 5. An issue raised in a small subset of the results discussed in this section is whether or not
30 the possible future extension of the Os season into the spring and fall is robust across
31 more simulations. Additional simulations that go beyond summertime are needed to
32 address this.
33 6. Another issue arising from a small subset of the results is the question of interannual
34 variability. Particularly in the regional modeling results, to date there is disparity in the
35 number of years simulated across the different groups. Moving forward, more precise
36 quantification of the magnitude of mean future Os changes relative to interannual
37 variability, as well as the potential for future increases or decreases in interannual
38 variability itself, is needed.
39
40 Moving beyond meteorology, the results to date also suggest important gaps in our
41 understanding of issues related to chemistry and emissions:
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1 1. More research is needed into the links between climate, biogenic emissions, and Oi. The
2 results presented here highlight the importance of correctly representing isoprene nitrate
3 chemistry in models to accurately capture the response of Os to changes in emissions.
4 Furthermore, improving biogenic emissions inventories and process models of the
5 response of biogenic emissions to climate and atmospheric composition changes should
6 also be a priority.
7 2. As already discussed, while some of the groups have also carried out simulations of PM,
8 in addition to Os, the focus in this section is only on the O?, results. Our understanding of
9 how to represent PM chemistry in modeling systems is more limited, and there are a
10 number of additional complexities surrounding PM, including the fact that it consists of
11 multiple species, and that precipitation is a more important primary meteorological driver
12 for PM than for Os, an issue because the uncertainties in modeling precipitation are much
13 greater than in modeling, for example, temperature. Much additional research is needed
14 on simulating the potential impacts of climate change on PM. Brief summaries of the
15 ongoing work on PM under this assessment, as well as on emissions and chemistry
16 issues, is provided next, in Section 4.
17 Finally, there are a wide range of issues related to anthropogenic emissions of precursor
18 pollutant that will become important as the assessment moves into its next phase. Building on
19 the modeling experiments discussed here, one major consideration is that much additional work
20 is needed to construct emissions scenarios that are realistic and internally self-consistent across
21 both greenhouse gases and precursor pollutants. These and other issues will also be discussed in
22 Section 4.
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1
2
4
B
5
2
«%
-5
-6
ppb _
b. Illinois 1
(JJA)
c. Illinois 2
(JJA)
•-20
N-12
-4
-4
•"-20
,, w ^
a. NERL
(JJA)
o
—a
d. WSU
(July)
Figure 3-1. 2050s-minus-present differences in simulated summer mean
MDA8 O3 concentrations (in ppb) for the (a) NERL; (b) Illinois 1; (c) Illinois
2; and (d) WSU experiments.
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8
S
2
-2
-5
-8
"ppb
a. NERL
(JJA)
l
2
3
4
b. Illinois 1
(JJA)
c. Illinois 2
•20
[U
-4
•-4
—12
B--20
r
16
£1
0
-<•»
-a
I?
d. WSU
(July)
-th
Figure 3-2. Same as Figure 3-1 but for 95 percentile MDA8 O3
concentration differences.
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1
2
3
4
Figure 3-3. 2050s-minus-present differences in simulated summer mean
MDA8 Os concentrations (in ppb); reproduced from Figure 2 in Hogrefe et
al. (2004b).
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Midwest
Mf -
Northeast
1
2
3
4
5
6
7
8
9
: J
JHTI
Pacific
ww-
'--.1 -
•:w -
1
"— k-
D :t
0 1
• *C
m
Mountain West
South
Figure 3-4. Frequency of simulated summer mean MDA8 Os values
exceeding 80 ppb in different regions from the NERL experiment. 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 represent 2050s climate and
decreases in anthropogenic Os precursor emissions; reproduced from Figure 9 in
Nolte et al. (2007).
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Change in MDA8 Ozone (Sep-Oct)
1
2
4
Figure 3-5. 2050s-minus-present differences in simulated
September-October mean MDA8 Os concentrations (in ppb);
reproduced from Figure 4 in Nolte et al. (2007).
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B
5
2
-2
-5
-8
ppb _
b. T
c. Insolation
5
3
1
-1
-3
-9
1
2
3
4
5
a. MDA8
O3
d. Isoprene
Figure 3-6. 2050s-minus-present differences in simulated summer
mean (a) MDA8 O3 concentration (ppb); (b) near-surface air
temperature (°C); (c) surface insolation (W m 2); and (d) biogenic
isoprene emissions (tons day"1) for the NERL experiment.
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8
5
2
-2
-S
-&
'ppb
b. T
c. Insolation
5
3
1
1
3
S
1
2
3
4
a. 95th
MDA8
03
d. Isoprene
-th
Figure 3-7. Same as Figure 3-6 but (a) shows 95 percentile MDA8
Os concentration differences.
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1
2
4
b. T
c. Insolation
•*•»
a. MDA8
O3
5*0
—W
d. Isoprene
Figure 3-8. Same as Figure 3-6 but for the Illinois 1 experiment. (Isoprene
emissions differences are given in g s"1).
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I
2
3
4
b. T
c. Insolation
I -20
;rj-fr>_P-'-^^m
\ T m
\ - ^^;
a. 95th
MDA8
03
K
'SO
W/m"?
d. Isoprene
Figure 3-9. Same as Figure 3-8 (Illinois I experiment) but (a) shows
-th
95 percentile MDA8 Os concentration differences.
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
n
b. T
c. Insolation
-PO
a. MDA8
O3
o
-2
20
W/rtT2
d. Isoprene
Figure 3-10. Same as Figure 3-8 but for the Illinois 2 experiment.
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1
2
4
b. T
c. Insolation
-so
of*
a. 95th
MDA8
03
K
10
'SO
d. Isoprene
Figure 3-11. Same as Figure 3-10 (Illinois 2 experiment) but (a) shows
-th
95 percentile MDA8 Os concentration differences.
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1
2
3
K20
_ 12
-4
--4
-12
-20
b. T
(JJA)
c. Insolation
(JJA)
•T500
j-300
-100
—100
N--300
^--500
• :
a. MDA8
03
(July)
PI
-1
d. BVOC
(July)
(g C/grid/s)
Figure 3-12. Same as Figure 3-6 but for the WSU experiment. (Biogenic
emissions differences are given in g Carbon grid cell"1 sec"1).
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1
2
3
U:
•20
•12
-4
-•4
-12
-20
b. T
(JJA)
c. Insolation
(JJA)
•r
j-300
-100
--100
300
500
a. 95th
MDA8
03
(July)
--1
--3
1-50
1-30
-10
'--10
r30
•--50
d. BVOC
(July)
(g C/grid/s)
Figure 3-13. Same as Figure 3-12 (WSU experiment) but (a) shows
-th
95 percentile MDA8 Os concentration differences.
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a. Harvard 1
b. Harvard 2
I
3
1
-1
-3
ppb
1
2
3
c. CMU
d. Illinois 1
-1
-2
e. Illinois 2
1C
B
fS
4
2
0
-2
Figure 3-14. 2050s-minus-present differences in simulated summer (JJA)
mean Os concentrations (in ppb) from the (a) Harvard 1; (b) Harvard 2; (c)
CMU; (d) Illinois 1; and (e) Illinois 2 global modeling experiments.
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1
2
3
4
5
3
1
-1
-3
b. T
c. Insolation
9
3
-3
-9
a. MDA8
O3
3,5
3,0
2.5
2.0
1.5
6
2
-2
d. Isoprene
(10e-8g C/m2/s)
Figure 3-15. 2050s-minus-present differences in simulated summer (JJA) mean (a)
MDA8 O3 concentration (ppb); (b) near-surface air temperature (°C); (c) surface
insolation (W m"2); and (d) biogenic isoprene emissions (10~8 g Carbon m"2 sec"1) for
the Harvard 1 global modeling experiment.
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l
2
3
4
5
I
4
2
0
I
-2
-4
b. T
c. Cloud cover
I a/
10000
5000
0
-5000
a. MDA8
03
fl
I
0.8
0.4
0.0
d. Isoprene
(9/s)
Figure 3-16. Same as Figure 3-15 but for the CMU global modeling
experiment. (Biogenic isoprene emissions differences are given in g sec"1).
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1 4. FUTURE DIRECTIONS
2
3 4.1. PHASE II OF THE GLOBAL CHANGE AND AIR QUALITY ASSESSMENT
4 As outlined in Section 2, Phase II of the assessment program requires a transition from
5 climate-only studies to an evaluation of the integrated effects of changes in climate and changes
6 in anthropogenic air pollutant emissions. Simplistic assumptions about future U.S. emissions are
7 of limited usefulness for evaluating the possible range of climate change impacts on air quality at
8 scales that are of interest for planning and management. Therefore, EPA ORD has initiated
9 several projects that are developing new methods and modeling tools for creating regional-scale
10 emissions projections for the U.S. These projects recognize that the important drivers of future
11 changes in air pollutant emissions are linked. For example, economic factors influence
12 population migration which, in turn, affects land use, thereby affecting air pollutant emissions
13 via choices in transportation modalities. To realistically represent the feedbacks among the
14 drivers of air pollutant emissions, modeling systems must be developed that capture these links
15 between underlying processes.
16 Phase II of the air quality assessment will also build upon the insights gained in Phase I
17 from the efforts of the contributing research teams in producing climate change-only air quality
18 simulations, including the effects of particular modeling choices. This Section, therefore, begins
19 by highlighting efforts underway to improve the climate-air quality modeling systems, and
20 planned efforts to develop efficient approaches for evaluating the impact of uncertainties on
21 model outputs. An overview of the projects focused on devising modeling tools to capture the
22 processes governing the underlying drivers of air pollutant emissions, and the links between
23 them, follows. Air pollutant emissions scenarios will eventually be shared with the climate-air
24 quality modeling teams, who will, in turn, simulate the integrated effects of climate and
25 emissions changes on regional U.S. air quality.
26
27 4.2. EXTENDING THE MODELING SYSTEMS
28 Section 3 concluded with a discussion of modeling uncertainties and research needs to be
29 addressed. Ongoing and upcoming activities designed to achieve these improvements and
30 needed advances in modeling capability are discussed in the following subsections.
31
32 4.2.1. Exploring Modeling Uncertainties
33 Ensemble modeling techniques are being applied to more fully explore the effects on
34 model outputs of uncertainties in the global-to-regional climate and air quality modeling
35 systems. This involves blending multiple alternative GCMs, RCMs, and RAQMs with multiple
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1 emissions scenarios and model physical parameterizations (including both PEL and convection
2 schemes). In addition, some of the work will explore the use of Bayesian weighting of ensemble
3 members based on their skill in representing both observed climate and air quality, as a means of
4 reducing the number of ensemble members required for capturing the probable range of future
5 climate changes.
6 Several modeling teams plan to evaluate potential changes in the length and timing of
7 annual O3 seasons under a changed climate. To better capture and characterize changes in
8 interannual variability in different climate regimes, simulations of additional present-day and
9 future years with the global-to-regional modeling systems are also planned.
10 Finally, the groups discussed in Section 3 that carried out global scale-only simulations
11 are in the process of conducting comparable studies using downscaled global-to-regional
12 modeling systems. The application of these new systems to simulations of future regional
13 climate and air quality will expand the range of models, scenarios, and methodologies in the
14 assessment. Added to the results obtained to date, these new simulations have the potential to
15 increase the level of confidence in key conclusions made in this report.
16
17 4.2.2. Additional Model Development
18 Substantial uncertainly remains in the modeling of current biogenic VOC emissions.
19 EPA ORD is currently supporting studies to better define the processes governing biogenic
20 emissions to improve their representation in regional air quality modeling systems. These
21 studies include work to identify and quantify species-dependent emissions sensitivities to
22 temperature and other meteorological variables, to changes in forest composition in response to
23 changing climate, and to changes in ambient CC>2 concentrations, based on observations and
24 biochemical modeling.
25 The accumulating body of new scientific insights is being used to design biogenic
26 emissions models with greater process realism. These models are also being extended to include
27 complementary capabilities, such as dynamic vegetation sub-models to capture the two-way
28 coupling between land cover and climate. These improvements will assist in increasing our
29 understanding of the potential role of biogenic emissions changes in global change-related
30 impacts on air quality.
31 The importance of feedbacks between climate change and regional air quality is not
32 presently well-understood. Should climate change produce significant changes in aerosol
33 chemistry and composition, or substantial changes in tropospheric Os, those perturbations could
34 feed back onto the Earth's radiation budget, possibly driving further changes in climate. Other
35 research efforts within the assessment program include an investigation of the importance of
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1 these two-way feedbacks between climate change and air quality. To explore this question,
2 NERL is expanding the pollutant chemistry represented in the Weather Research and Forecast
3 Model with Chemistry (WRF/Chem). Simultaneously, an extramural effort funded by the STAR
4 program is directly linking WRF with the CMAQ model in a combined WRF-CMAQ system.
5 Both will be applied in studies of future climate and air quality. Downscaling GCM simulations
6 of future climate using WRF/Chem and WRF-CMAQ will allow for the assessment of possible
7 long-term impacts of global change on regional air quality while accounting for feedbacks
8 between meteorology, air quality, and radiation in a unified modeling framework.
9
10 4.2.3. Additional Pollutants—PM
11 Some of the groups whose Os results are featured in Section 3 have also carried out
12 simulations of PM. Because of the additional complexities and uncertainties associated with PM
13 and its response to climate change, these results were not incorporated into the synthesis.
14 However, a few preliminary results suggest that
15 • Globally, PM generally decreases as a result of simulated climate change (with
16 anthropogenic emissions held constant), due to increased atmospheric humidity and/or
17 increased precipitation;
18 • Regionally, simulated climate change produces both increases and decreases in PM (on
19 the order of a few percent) in 2050, depending on the region of the U.S., with the largest
20 increases in the Midwest and Northeast;
21 • The responses of the individual species that make up net PM (e.g., sulfate, nitrate,
22 ammonium, black carbon, organic carbon, etc.) to climate change are highly variable,
23 depending on the chemistry and transport characteristics of each species;
24 • Key uncertainties to which simulated PM is sensitive include model precipitation, model
25 aerosol chemistry, volatilization of semi-volatile PM species, such as nitrate and
26 secondary organic aerosol (SOA), and assumed future air pollution emissions.
27
28 Building on these findings, work underway, both within EPA and funded through the
29 STAR program, is continuing to explore the impacts of climate and emissions changes on PM in
30 coupled climate and air quality modeling systems. Efforts to improve the relevant aerosol
31 chemistry in these models, as well as to introduce the capability of two-way coupling between
32 chemistry and meteorology (as noted above) are also underway.
33
34 4.2.4. Additional Pollutants—Mercury
35 Some of the modeling groups already highlighted in this report, in conjunction with
36 several new groups, will also be extending our understanding of the impact of global change on
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1 air pollution to mercury (Hg). Climate change can potentially impact a number of atmospheric
2 processes that help determine the fate of Hg, including heterogeneous oxidation of gas-phase Hg,
3 dry deposition of elemental, reactive gas-phase and particulate Hg, and Hg chemistry in the
4 presence of fog, clouds, and photochemical smog.
5 These groups will use both models and observational datasets to explore Hg chemistry
6 and transport as a function of climate and emissions changes. The focus will be on present and
7 future Hg distribution for the U.S. as a whole, as well as for particular regions, e.g., the Great
8 Lakes, Florida. In addition, this work will be aimed at improving the Hg chemistry in the linked
9 climate and air quality modeling systems by incorporating additional reactions and refining
10 existing representations.
11
12 4.3. RELATIVE IMPACTS OF CLIMATE AND EMISSIONS CHANGES:
13 PRELIMINARY WORK
14 Several of the modeling teams that produced the simulations discussed in Section 3 also
15 conducted preliminary evaluations of the relative effects of changes in anthropogenic air
16 pollutant precursor emissions and changes in climate on regional U.S. quality. The general
17 approach taken was to assume that, rather than remaining constant at the NEI1999-2000 levels,
18 future U.S. emissions of pollutant precursors, i.e., NOX, SC>2, VOCs, and CO, scaled in ways that
19 were consistent with the IPPC SRES scenarios.
20 The major findings that emerged from these sensitivity studies are as follows: First, that
21 the relative effects of climate and anthropogenic precursor emissions changes are much more
22 sensitive to the assumptions about future emissions trajectories than differences in simulated
23 climate across models and groups. For example, simple scaling of future emissions to match the
24 gross assumptions of the IPCC Alb or Bl SRES scenario resulted in substantial reductions in
25 NOX emissions, with corresponding reductions in simulated future Os that dominated any
26 increases associated with climate change. In contrast, using future emissions consistent with the
27 weaker pollutant control assumptions in the "dirtier" A2 or AlFi scenarios tended to result in
28 comparable magnitudes of the climate change and emissions change effects. Second, the effects
29 of climate and emissions changes are not, in general, additive. In other words, the degree of
30 "climate penalty" on air quality is itself highly dependent on the emissions levels.
31 Therefore, these results highlight the need for additional work to develop more
32 sophisticated, regionally detailed scenarios of U.S. anthropogenic precursor pollutants that
33 account for population, economic, energy, and transportation changes, along with work to
34 improve the representation of natural emissions sensitive to climate and land-use changes. These
35 efforts are highlighted in the next sub-section.
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1 4.4. MODELING THE DRIVERS OF AIR POLLUTANT EMISSIONS
2 Human activities, such as population growth and migration, economic growth, land use,
3 and technology change are key drivers affecting emissions. Changes in human activity patterns
4 impact pollutant emissions across the globe, and, combined with global scale circulation
5 patterns, influence the long-range transport of air pollution into the U.S.
6 There is a gap in our understanding of how these factors will interact to influence air
7 quality at urban and regional scales in the U.S. In addition, while human activities generate the
8 largest share of the U.S. air pollutant emissions burden, biogenic and wildfire emissions also
9 contribute to the degradation of regional-scale air quality. The vegetation composition and
10 biomass density of forest ecosystems help determine both the emissions of biogenic VOCs and
11 the intensity and frequency of wildfires. These properties are sensitive, to varying degrees, to
12 changing climate and to local and regional development. Future progress will require integrating
13 population growth and land-use models with economic forecasts, technology models, travel
14 demand models, mobile source models, and forest composition and wildfire process models to
15 create emissions modeling systems that can be used to blend comprehensive scenarios of future
16 air pollution emissions with those of future climate and meteorology changes (Figure 4-1).
17 As described in Section 2, evaluating the combined air quality impacts of changing
18 anthropogenic emissions levels, changing biogenic and wildfire emissions levels, and changing
19 climate is a critical goal of Phase II of the air quality assessment effort. To accomplish this, the
20 assessment program has undertaken a significant research effort to develop and/or apply the
21 necessary emissions projection tools. The following sub-sections highlight efforts underway to
22 investigate the critical processes leading to pollutant emissions changes and to incorporate this
23 information into modeling tools capable of realistically simulating long-term emissions changes.
24 A growing U.S. population can be expected to lead to increased energy and transportation
25 service demands, potentially leading to increased pollutant emissions, depending on control
26 strategies implemented. In addition, internal migration of the U.S. population could redistribute
27 pollutant emissions geographically.
28 The Cohort-Component methodology17 is being used to develop a range of scenarios of
29 future U.S. population. These scenarios build on the Census Bureau's population projections,
30 systematically incorporating assumptions to express the differences captured in the IPCC SRES
31 storylines. The migration component of the demographic model uses a regression-based
32 "gravity" model that depends on the functional connectivity of each county to all others and
33 amenity values to estimate production and attraction values for domestic migration. This effort
34
17 For example, see http://www.census.gov/population/www/projections/aboutproj.html.
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1
2
3
4
5
Scenario
Assumptions
National-level population S
economic growth
Technology availability and cost
Air quality and GHG policies
Population
Growth &
Migration
Economic
Growth
Land-Use
Change
Emissions
growth npiits
Emissions
Characterization
Regional
Meteorology
Emissions
inventory
Background
pollutant
concentrations
Pollutant
concentrations
'Economic
inpacts
Figure 4-1. Integrated system of future climate, meteorology, and emissions
scenarios. Population growth, migration, and land use.
7 is exploring the wide range of assumptions at national, state, and local scales in the U.S. that are
8 consistent with the general SRES storylines.
9 Future development patterns will result in changes in both the quantity and location of
10 pollutant emissions. The demographic-migration model described above is being coupled with a
11 spatial allocation-type land-use model to develop urban and exurban growth projections
12 consistent with the SRES storylines. The potential of these land-use scenarios for spatially
13 allocating emission sources is under investigation.
14
15 4.4.1. Economic Growth and Technology Choices
16 Absent additional air pollution controls and/or improvements in technologies, economic
17 growth would be expected to increase emissions. Other trends, like further transformation from
18 a manufacturing-based to a service-based economy, can also lead to changes in domestic
19 emissions. A range of plausible economic scenarios to capture these factors is needed as part of
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1 an integrated evaluation of human-driven change in future emissions. Several models have been
2 employed by OAR in policymaking, and the EPA's Global Change Research Program is
3 planning to evaluate them (and others) for application in the Phase II assessment effort.
4 Changes in future anthropogenic emissions cannot be understood apart from the
5 development, deployment, and use of energy and transportation technologies. To assist in
6 defining those relationships, a Market Allocation (MARKAL) energy-systems modeling
7 framework has been developed to examine the most emission-intensive sectors of the U.S.
8 economy: transportation and electric power production. MARKAL maps the energy economy
9 from primary energy sources, through their refining and transformation processes, to the point at
10 which a variety of technologies (e.g., classes of light-duty personal vehicles, heat pumps, or gas
11 furnaces) service end-use energy demands (e.g., projected vehicle miles traveled, space heating).
12 A large linear programming model, MARKAL determines the least-cost pattern of technology
13 investment and use required to meet specified demands, and then calculates the resulting criteria
14 pollutant and greenhouse gas emissions. Preliminary scenarios of potential future emissions and
15 emissions growth factors for energy system technologies, such as combustion technologies in the
16 electricity generation, transportation, industrial, residential, and commercial sectors, have been
17 generated for the U.S. Particular attention has been paid to alternative-fuel vehicles (e.g.,
18 ethanol-gasoline, plug-in gasoline-electric hybrids, hydrogen fuel cell) and analyses to date show
19 that different technology development and penetration scenarios can have greatly differing
20 emissions consequences.
21 Research has also been conducted on the response of electricity consumption to warming
22 from climate change, capacity siting and dispatch decisions, and characterization of emerging
23 energy generation technologies in terms of cost and cost projections and learning parameters.
24 This modeling system has been used to analyze the effect of climate change upon the temporal
25 and spatial distributions of NOX emissions in the Mid-Atlantic and Midwest power markets. An
26 additional study investigates air quality consequences from the broad adoption of ethanol-
27 gasoline, plug-in gasoline-electric hybrids, and wind-electrolysis-hydrogen fuel-cell vehicles.
28 The consequence of this technology shift will be explored for Los Angeles, the Central Valley,
29 and Atlanta over the next 50 years.
30
31 4.4.2. Land Use and Transportation
32 A critical and previously unexplored dimension in projecting air quality in response to
33 human factors is the spatial distribution of the emissions projected to result from land-use and
34 transportation choices. Several studies of the connection between socioeconomic forces, land-
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1 use planning and development patterns, policy design, and future air quality are underway as part
2 of the assessment's research program. Specific studies include
3 • In Washington DC, development and application of a flexible modeling framework to
4 estimate long-term mobile sources emissions;
5 • In Chicago, an examination of the consequences of continued deindustrialization of U.S.
6 manufacturing and its impact on the city's manufacturing-heavy metro area;
7 • In the Upper Midwest, a study of the air quality changes associated with a "smart
8 growth" land-use and development policy over the next 25 to 50 years;
9 • In the San Joaquin Valley, CA, investigation of the effect on emissions from combined
10 changes in economics, land-use, water constraints, transportation, and stationary sources;
11 • In the Charlotte, NC metro area, an examination of the influence of development patterns
12 (e-g-, transit oriented development, dense mixed-use development, development
13 supportive of non-motorized transportation modes for non-work trips, neo-traditional
14 suburbs, new urban core development, and redevelopment) on the spatial characteristics
15 and quantity of emissions;
16 • In Austin, TX, a comparison of emissions, air quality, and exposures from an integrated
17 transportation-land-use model with four urban growth scenarios developed through a
18 regional "visioning" initiative known as Envision Central Texas;
19 • In the Puget Sound region, a project to integrate an activity-based travel model
20 component and a network assignment component into a land-use model (UrbanSim) and
21 to tightly couple this system to air emissions models.
22
23 4.4.3. Emissions Changes Due to Changing Ecosystems: Biogenic VOCs
24 Changing amounts and distributions of biogenic emissions due to land-use and climate
25 changes is potentially a key factor for future air quality. Past studies have shown that emissions
26 of VOCs from forest ecosystems can cause increases in pollution in near-urban and suburban
27 areas. In one example, VOC emissions from forests near Atlanta entirely offset the effects of the
28 policies put in place to reduce mobile-source emissions.
29 As described above, substantial uncertainty remains in modeling biogenic emissions. As
30 part of the assessment effort, EPA is supporting studies on the VOC-emitting species in the
31 current climate. Fundamental scientific questions are being addressed concerning the chemical
32 and physical properties of primary and secondary organic aerosols (POAs, SOAs), the identity of
33 the biogenic VOCs that form SOAs, and the sensitivity of VOCs, POAs, and SOAs emission and
34 formation rates to changes in environmental conditions.
35
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1 4.4.4. Emissions Changes Due to Changing Ecosystems: Wildfires
2 Fires, both natural and anthropogenic, have significant impacts on U.S. air quality,
3 especially on PM concentrations. Recent studies show that fires in North America can have
4 important effects on U.S. visibility and air quality on an episodic basis. Climate variability
5 influences the extent and intensity of fires, e.g., moist years followed by dry years produce very
6 favorable conditions for wildfires. Climate change, which is very likely to increase the
7 frequency of precipitation in some areas, drought in other areas, and produce higher temperatures
8 in general, may enhance future fire frequency, extent, and intensity regionally.
9 Therefore, along with better model representations of the effects of climate change on
10 biogenic VOC emissions, simulations of the effects of climate on air quality should also consider
11 changing levels in wildfire-generated Os and PM precursor emissions. Three modeling studies
12 are underway that integrate the complex interactions of fire, climate, and air quality and are
13 exploring important uncertainties. Two groups are focusing on the U.S. Southeast as a test case,
14 with the third working to evaluate wildfire changes across the continental U.S. as a whole. All
15 three teams are working to develop integrated models that account for fire-related changes in
16 ecosystems in a warming climate, such as the extent of vegetative cover and fuel characteristics.
17 State-level fire statistics, along with ground and satellite observations, will be used to evaluate
18 the performance of the modeling systems. In addition, the continental-scale study will develop a
19 climatology of plume heights from forest fires since 2000, and will relate plume heights to area
20 burned for use in the climate change scenarios.
21
22 4.4.5. Taking Integrated Emissions Scenarios Through to Future U.S. Regional Air
23 Quality
24 As shown in Figure 4-1, Phase II of the assessment will involve integrating these
25 demographic, land-use, economics, transportation and energy models to produce a series of
26 future emissions scenarios as input for the integrated climate and regional air quality models
27 developed in Phase I of the program. Building on the improved understanding from the work
28 already accomplished, and the new insights that will emerge in the near future, an important task
29 will be to identify a subset of emission scenarios that capture the range of desired assumptions
30 and outcomes to explore the critical questions of interest in the integrated climate and emissions
31 modeling efforts. Conducting a series of sensitivity test simulations over shorter time periods, so
32 that a wider range of emissions scenarios can be tested, will likely be a key aspect of the research
33 design. The results from these sensitivity tests will provide guidance on which set of scenarios
34 offers sufficient representation of the range of plausible emissions changes for the future.
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REFERENCES
Aw, J; MJ, Kleeman. (2003) Evaluating the first-order effect of intraannual temperature variability on urban air
pollution. J Geophys Res 108:4365, doi:10.1029/2002JD002688.
Bell, ML; Goldberg, R; Hogrefe, C; et al. (2007) Climate change, ambient ozone, and health in 50 U.S. cities.
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APPENDICES
APPENDIX A GLOSSARY OF CLIMATE AND AIR QUALITY TERMS A-l
APPENDIX B U. S. Air Quality: Its Sensitivity to Meteorology and Early Studies of the Effect
of Climate Change B-l
B.I. INTRODUCTION B-ll
B.2. THE LINKS BETWEEN METEOROLOGY, BIOGENIC AND
EVAPORATIVE EMISSIONS, AND AIR QUALITY B-l
B.2.1. Surface Temperature B-2
B.2.2. Temperature Effects on Anthropogenic VOC Emissions B-2
B.2.3. Temperature Effects on Biogenic Emissions B-2
B.2.4. Temperature and Aerosol Thermodynamics B-3
B.2.5. Atmospheric Stability B-3
B.2.6. Mixing Height B-3
B.2.7. Humidity B-4
B.2.8. Wind Speed and Direction B-4
B.2.9. Cloud Cover and Precipitation B-5
B.3. REGIONAL PATTERNS IN THE O3 CONCENTRATION RESPONSE
TO METEOROLOGY B-5
B.4. CLIMATE CHANGE AND U.S. AIR QUALITY: EARLY AND
EXTERNAL STUDIES B-6
B.5. REFERENCES B-9
APPENDIX C THE 2001 EPA GCRP AIR QUALITY EXPERT WORKSHOP C-1
C.I. INTRODUCTION C-l
C.2. SUMMARY OF WORKSHOP RECOMMENDATIONS C-2
C.2.1. Recommendations from the Regional Climate Modeling Group C-2
C.2.2. Recommendations from the Biogenic and Fire Emissions Group C-5
C.2.3. Recommendations from the Emission Drivers and Anthropogenic
Emissions Group C-7
C.2.4. Recommendations from the Air Quality Modeling Group C-8
C.3. REFERENCE C-9
APPENDIX D U. S. EPA STAR GRANT RESEARCH CONTRIBUTING TO
THE GCAQ ASSESSMENT D-l
D.I. STAR SOLICITATIONS D-l
D. 1.1. Assessing the Consequences of Interactions between Human
Activities and a Changing Climate D-l
D. 1.2. Assessing the Consequences of Global Change for Air Quality:
Sensitivity of U.S. Air Quality to Climate Change and Future Global
Impacts D-2
D. 1.3. Consequences of Global Change for Air Quality: Spatial Patterns
in Air Pollution Emissions D-2
D.I.3.1. University of Colorado at Boulder D-4
D.I.3.2. University of North Carolina at Chapel Hill D-4
D.I.3.3. University of Texas at Austin D-4
D.I.3.4. University of Illinois at Urbana D-5
D.I.3.5. University of New Hampshire D-5
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D.I.3.6. Resources for the Future D-5
D.I.4. Regional Development, Population Trend, and Technology Change
Impacts on Future Air Pollution Emissions D-6
D.I.4.1. University of Wisconsin-Madison D-6
D.I.4.2. Georgia Institute of Technology D-6
D.I.4.3. University of California - Davis D-7
D.l.4.4. Johns Hopkins University D-8
D.l.4.5. State University of New York at Buffalo D-8
D.l.4.6. University of North Carolina at Chapel Hill D-9
D.I.4.7. University of Washington-Seattle D-9
D.I.4.8. University of Texas at Austin D-9
D.I.5. Fire, Climate and Air Quality D-10
D.I.5.1. Georgia Institute of Technology D-10
D.I.5.2. Harvard University D-ll
D.I.5.3. University of North Carolina at Chapel Hill D-ll
D.I.6. Consequences of Global Change for Air Quality D-12
D.I.6.1. University of California - Davis D-12
D.I.6.2. University of Illinois at Urbana D-13
D.I.6.3. University of Wisconsin - Madison D-14
D.I.6.4. Desert Research Institute D-14
D.I.6.5. Stanford University D-15
D.I.6.6. University of Michigan D-15
D.I.6.7. North Carolina State University D-15
D.I.6.8. Harvard University D-16
D.I.6.9. Carnegie Mellon University D-16
D.I.6.10. Washington State University D-17
APPENDIX E MODELING APPROACH FOR INTRAMURAL PROJECT ON
CLIMATE IMPACTS ON REGIONAL AIR QUALITY E-l
E.I. REFERENCES E-8
APPENDIX F USING MARKAL TO GENERATE EMIS SIGNS GROWTH
PROJECTIONS FOR THE EPA GCRP AIR QUALITY ASSESSMENT F-l
F.I. INTRODUCTION F-l
F.I.I. Background F-l
F.I.2. Conceptual Framework F-2
F. 1.3. Intramural Emissions Modeling Effort for the 2010 Assessment
Report F-5
F.2. ENERGY SYSTEM MODELING F-7
F.2.1. The MARKAL Energy System Model F-7
F.3. APPLICATION F-12
F.3.1. Scenario Analysis F-12
F.3.2. Illustrative Application F-12
F.3.3. Reference Case F-l3
F.3.4. Discussion F-l 9
F.4. GENERATION OF INPUTS TO THE AIR QUALITY ASSESSMENT F-20
F.5. REFERENCES F-21
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APPENDIX G CHARACTERIZING AND COMMUNICATING UNCERTAINTY:
THE NOVEMBER 2006 WORKSHOP G-l
G.I. INTRODUCTION G-l
G.2. WORKSHOP GOALS, PARTICIPANTS, AND STRUCTURE G-l
G.3. PRELIMINARY FINDINGS G-3
G.3.1. General Findings G-4
G.3.2. Findings on Technical Issues Specific to the Global Change-
Air Quality Modeling Systems G-5
G.3.3. Findings on Communication Strategies G-5
G.4. REFERENCES G-6
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1 APPENDIX A
2 GLOSSARY OF CLIMATE AND AIR QUALITY TERMS
O
4 Aerosols — Solid or liquid particles suspended within the atmosphere. Examples are sulfate
5 particles, which reflect light, and black carbon particles, which absorb light.
6
7 Anthropogenic Emissions — Gaseous and particulate pollutants (or precursors to pollutants) that
8 are released into the atmosphere as a consequence of human activities.
9
10 Anthropogenic Secondary Organic Aerosols -- Secondary organic aerosols that are formed from
11 anthropogenic precursors.
12
13 Atmospheric Processes -- Processes affecting the formation, removal, and distribution of energy,
14 momentum, gases, aerosols, and clouds within the earth's atmosphere as a
15 function of time and space. Examples include gas-phase chemistry,
16 heterogeneous chemistry, aqueous-phase chemistry, gas-to-particle conversion,
17 radiative transfer, nucleation of particles, evaporation of particles, wet and dry
18 deposition, formation of clouds, emissions, and horizontal and vertical transport
19 processes.
20
21 Attainment Area -- A geographic area in which levels of a given criteria air pollutant fall below
22 the health-based primary national ambient air quality standard (NAAQS) for the
23 pollutant. An area may have on acceptable level for one criteria air pollutant and
24 unacceptable levels for others. Thus, an area could be both attainment and non-
25 attainment at the same time. Attainment areas are defined using federal pollutant
26 limits set by EPA.
27
28 Biogenic Emissions — Emissions of gaseous and particulate pollutants and precursors to
29 pollutants from natural sources, such as plants and trees.
30
31 Clean Air Act -- The original Clean Air Act was passed in 1963, but the national air pollution
32 control program is actually based on the 1970 version of the law. The 1990 Clean
33 Air Act Amendments are the most far-reaching revisions of the 1970 law. In this
34 summary, the 1990 amendments are referred to as the 1990 Clean Air Act.
35
36 Climate — The long-term average weather of a region, including typical weather patterns, the
37 frequency and intensity of storms, cold spells, and heat waves. Climate is not the
38 same as weather; it is the average pattern of weather for a particular region.
39 Climatic elements include precipitation, temperature, humidity, sunshine, wind
40 velocity, phenomena such as fog, frost, and hail storms, and other measures of the
41 weather.
42
43 Climate Forcing -- The earth's climate changes when the amount of energy stored by the climate
44 system is varied. The most significant changes occur when the global energy
45 balance between incoming energy from the sun and outgoing heat from the earth
46 is upset. There are a number of natural mechanisms that can upset this balance,
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1 for example fluctuations in the earth's orbit, variations in ocean circulation, and
2 changes in the composition of the atmosphere. Changes in the composition of the
3 atmosphere can occur due to man-made pollution, through emissions of
4 greenhouse gases. By altering the global energy balance, such mechanisms
5 "force" the climate to change. Consequently, scientists call them "climate
6 forcing" mechanisms.
7
8 Climate Change -- Changes in long-term trends in the climate, such as changes in average
9 temperatures. In Intergovernmental Panel on Climate Change (IPCC) usage,
10 climate change refers to any change in climate over time, whether due to natural
11 variability or as a result of human activity. In United Nations Framework
12 Convention on Climate Change usage, climate change refers to a change in
13 climate that is attributable directly or indirectly to human activity that alters
14 atmospheric composition.
15
16 Climate System — The global climate system is made up of the atmosphere, the oceans, the ice
17 sheets (cryosphere), living organisms (biosphere) and the soils, sediments and
18 rocks (geosphere), which all affect the movement of heat, momentum, and
19 moisture, around the earth's surface.
20
21 Climate Variability — Deviations of climate statistics over a given period of time (such as a
22 specific month, season, or year) from the long-term climate statistics relating to
23 the corresponding period.
24
25 Criteria Pollutants — Under the federal Clean Air Act, EPA has identified six major air
26 pollutants that have adverse effects on public health and the environment called
27 "criteria air pollutants:" ozone, carbon monoxide, nitrogen dioxide, sulfur
28 dioxide, particulate matter, and lead. EPA has set National Ambient Air Quality
29 Standards for each of these criteria pollutants to protect public health and the
30 environment.
31
32 Downscaling — Methods to obtain high spatial resolution data from a coarser scale atmospheric
33 or coupled oceanic-atmospheric circulation model run on the global domain.
34 Downscaling can be achieved using fine spatial scale (mesoscale) meteorological
35 models (referred to as "dynamical downscaling") or statistical relationships
36 ("statistical downscaling").
37
38 Emissions — Release of substances (e.g., greenhouse gases) into the atmosphere or the
39 substances themselves.
40
41 Energy Security — The stable supply of energy resources to the main consumers. Increasingly,
42 energy security is viewed as a much broader concept that extends to the
43 extraction, transport, and sale of energy.
44
45 General Circulation Model (GCM) — A computer model of the basic dynamics, physics of and
46 internal interactions of the global climate system (including the atmosphere and
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1 oceans) and their interactions. GCMs used to simulate climate variability and
2 change.
3 Global Warming Potential (GWP) -- A system of multipliers devised to enable warming effects
4 of different gases to be compared. The cumulative warming effect, over a
5 specified time period, of an emission of a mass unit of CC>2 is assigned the value
6 of 1. Effects of emissions of a mass unit of non-CC>2 greenhouse gases are
7 estimated as multiples. For example, over the next 100 years, a gram of methane
8 in the atmosphere is currently estimated as having 23 times the warming effect as
9 a gram of carbon dioxide; methane's 100-year GWP is thus 23.
10 Greenhouse Effect — The insulating effect of atmospheric greenhouse gases (e.g., water vapor,
11 carbon dioxide, methane, etc.) that keeps the earth's temperature about 60°F
12 warmer than it would be otherwise.
13 Greenhouse Gas (GHG) — Any gas that contributes to the "greenhouse effect."
14 Indirect Effects —As opposed to direct effects of aerosol particles on radiative forcing due to the
15 scattering and absorption of light, indirect effects are due to the ability of some
16 particles to act as cloud condensation nuclei. This changes the number of droplets
17 in clouds and their size distribution, which alters precipitation, cloud extent and
18 lifetime. Because of the reflection of solar radiation by clouds and other
19 interactions between clouds and radiation, there is an indirect forcing of the global
20 system from aerosols via their effects on clouds. Another potential indirect
21 forcing involves the heterogeneous chemistry involving aerosols and greenhouse
22 gases.
23
24 Intergovernmental Panel on Climate Change (IPCC) - The IPCC was established in 1988 by
25 the World Meteorological Organization and the UN Environment Program. The
26 IPCC is responsible for providing the scientific and technical foundation for the
27 United Nations Framework Convention on Climate Change, primarily through the
28 publication of periodic assessment reports.
29
30 Mean Climate — The average of climate variables over a spatial domain or temporal period. For
31 example, the mean sea surface temperature is a measure of climate change. A
32 mean precipitation over a 5-year period may be calculated for a future scenario to
33 average out the year-to-year variability.
34
35 Mesoscale — A spatial dimension ranging from 2 to 2000 km. This is the typical spatial scales of
36 urban air pollution, local winds, thunderstorms, etc.
37
38 Meteorology — The science that deals with the phenomena of the atmosphere, especially weather
39 and weather conditions. Weather is the day-to-day changes in temperature, air
40 pressure, moisture, wind, cloudiness, rainfall, and sunshine.
41
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1 Negative Feedback -- A process that results in a reduction in the response of a system to an
2 external influence. For example, increased plant productivity in response to
3 global warming would be a negative feedback on warming because the additional
4 growth would act as a sink for CO2, reducing the atmospheric CO2 concentration.
5
6 Nonattainment Area -- A geographic area in which the level of a criteria air pollutant is higher
7 than the level allowed by the federal standards. A single geographic area may
8 have acceptable levels of one criteria air pollutant but unacceptable levels of one
9 or more other criteria air pollutants; thus, an area can be both attainment and
10 nonattainment at the same time. It has been estimated that 60% of Americans live
11 in nonattainment areas.
12
13 Non-Radiative Forcing — A process or change that leads to energy redistribution within the
14 global climate system, but does not directly affect the energy budget of the
15 atmosphere. Processes that induce non-radiative forcing usually operate over vast
16 time scales (107 to 109 years) and mainly affect the climate through their influence
17 over the geometry of the earth's surface, such as location and size of mountain
18 ranges and position of the ocean basins.
19
20 Positive Feedback -- A process that results in an amplification of the response of a system to an
21 external influence. For example, increased atmospheric water vapor in response
22 to global warming would be a positive feedback on warming, because water vapor
23 is, itself, a GHG. Increases in water vapor in association with increases in
24 greenhouse gases would cause greater warming than would occur if water vapor
25 remained constant.
26
27 Radiative Forcing -- Changes in the energy balance of the earth-atmosphere system in response
28 to a change in factors such as greenhouse gases, land-use change, or solar
29 radiation. The climate system inherently attempts to balance incoming (e.g.,
30 light) and outgoing (e.g., heat) radiation. Positive radiative forcings increase the
31 temperature of the lower atmosphere, which in turn increases temperatures at the
32 earth's surface. Negative radiative forcings cool the lower atmosphere. Radiative
33 forcing is most commonly measured in units of watts per square meter (W/m2).
34
35 Regional Scale -- A geospatial scale in the global climate-air quality field that is relative rather
36 than absolute. For applications of global circulation models, examples of regions
37 may be North America, Africa, or South Pacific Ocean. For applications within
38 the continental U.S., examples of regions may be Northeastern U.S., the Upper
39 Midwest, or the Pacific Northwest.
40
41 Secondary Organic Aerosols (SOA) — Carbonaceous aerosols that are not emitted but produced
42 in the atmosphere. Typically, precursor gases (such as aromatic hydrocarbons,
43 monoterpenes) undergo chemical reactions, condensation, and other atmospheric
44 processes to form SOA.
45
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1 Sequestration — The removal of atmospheric CC>2, either through biological processes (e.g.,
2 plants and trees), or geological processes through storage of CC>2 in underground
3 reservoirs.
4
5 Sinks — Any process, activity, or mechanism that results in the net removal of greenhouse gases,
6 aerosols, or precursors of greenhouse gases from the atmosphere.
7
8 SRES Scenarios -- A suite of emissions scenarios developed by the Intergovernmental Panel on
9 Climate Change in its Special Report on Emissions Scenarios (SRES). These
10 scenarios were developed to explore a range of potential future greenhouse gas
11 emissions pathways over the 21st century and their subsequent implications for
12 global climate change.
13
14 State Implementation Plan (SIP) — A detailed description of the programs a state will use to
15 carry out its responsibilities under the Clean Air Act. A SIP is a collection of the
16 regulations used by a state to reduce air pollution. The Clean Air Act requires
17 that EPA approve each SIP. Members of the public are given opportunities to
18 participate in review and approval of SIPs.
19
20 Stratosphere — The region of the Earth's atmosphere 10-50 km above the surface of the planet.
21
22 Thermohaline Circulation (THC) -- A 3-dimensional pattern of ocean circulation that is driven
23 by wind, heat, and changes in salinity. Thermohaline Circulation is responsible
24 for distributing energy, as heat, and matter, as dissolved solids and gases,
25 throughout the global ocean-atmosphere climate system. In the Atlantic, wind-
26 driven surface currents transport warm tropical surface water northward where it
27 cools and then sinks into the deep ocean. The deep ocean current is driven south,
28 beneath the tropical oceans, eventually warming and rising to the surface in the
29 North Pacific. Global warming is projected to increase sea-surface temperatures,
30 which may slow the THC process by reducing the sinking of cold water in the
31 North Atlantic. In addition, ocean salinity influences water density, and, thus,
32 decreases in sea-surface salinity from the melting of ice caps and glaciers may
33 also slow THC. Other terms for THC include, "the ocean conveyor belt," "the
34 great ocean conveyer," "the global conveyor belt," and "the meridional
35 overturning circulation."
36
37 Troposphere — The region of the atmosphere 0 to approximately 10 km above the earth's
38 surface.
39
40 Tropospheric Ozone -- Ozone in the lower atmosphere (troposphere) or near ground is
41 considered to be one of the pressing air quality issues. Most ground-level ozone
42 is formed indirectly by the action of sunlight on volatile organic compounds in the
43 presence of nitrogen dioxide and, as such, is a secondary pollutant. There are no
44 direct man-made emissions of ozone to the atmosphere. During photochemical
45 smog episodes, levels can rise to over 100 ppb. Ozone episodes are likely to
46 develop following sustained periods of warmth and calm weather. Once formed,
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1 ozone is scavenged by nitric oxide, usually present in urban areas as a result of
2 traffic fumes and less so in the countryside. Consequently, ozone usually occurs
3 in higher concentrations during summer than winter, and in urban rather than rural
4 areas. Background levels of ozone are usually less than 15 ppb but can be as high
5 as 60 ppb.
6
7 Tropopause -- The transitional region between the stratosphere and the troposphere.
8
9 Weather — Weather is the specific condition of the atmosphere at a particular place and time. It
10 is measured in terms of such things as wind, temperature, humidity, atmospheric
11 pressure, cloudiness, and precipitation. In most places, weather can change from
12 hour-to-hour, day-to-day, and season-to-season.
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1 APPENDIX B
2 U.S. AIR QUALITY: ITS SENSITIVITY TO METEOROLOGY AND
3 EARLY STUDIES OF THE EFFECT OF CLIMATE CHANGE
4
5 B.I. INTRODUCTION
6 This appendix offers information that can serve as a point of reference for evaluating the
7 significance of the projections of meteorological and air quality change discussed in this report.
8 The first section addresses the role of meteorology in determining air quality, followed by a
9 discussion of the observed regional patterns in ozone (O3) concentrations and meteorological
10 sensitivities. The discussion draws from the open literature and extensive summaries found in
11 the U.S. EPA Air Quality Criteria Documents for O3 and Paniculate Matter (U.S. EPA, 2006,
12 2004). This appendix concludes with a survey of the climate and air quality literature from
13 earlier modeling efforts and more recent studies conducted independently from the EPA GCRP
14 air quality program.
15
16 B.2. THE LINKS BETWEEN METEOROLOGY, BIOGENIC AND EVAPORATIVE
17 EMISSIONS, AND AIR QUALITY
18 The link between meteorology and extreme ground-level PM and O3 concentrations is
19 well understood by the air quality management community. The earliest recorded incidences of
20 extreme PM concentrations in London took place in wintertime during periods with low
21 temperature, fog, and low wind speeds (stagnant conditions) (Brimblecombe, 1987). However,
22 the relationship between meteorology and air quality can be complex. Observations of urban O3
23 concentrations as a function of ground-level temperature provide an example of this complexity.
24 However, the relationships between O3 concentrations and any specific predictor are location-
25 specific, e.g., relationships observed in one area may not be readily extrapolated to another.
26 In addition to temperature, other factors, such as wind speed and direction, humidity, and
27 precipitation frequency, are also known to be important determinants of air quality. Very often,
28 however, individual meteorological variables are closely associated with other air quality-
29 relevant meteorological properties, making simple sensitivity relationships difficult to establish.
30 For example, high surface temperatures are often associated with clear skies and strong inversion
31 layers, making it difficult to establish a causal relationship between any of the given factors and
32 high O3 concentrations. Nevertheless, strong relationships between pollutant concentrations and
33 simple, easily measured meteorological variables, i.e., temperature and wind speed, have been
34 derived and can inform an analysis of the potential impacts of a warming climate on air quality.
35 This section discusses the links between specific meteorological variables and air quality.
36 Ozone and PM are often similarly affected by changes in these variables. Therefore, the links
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1 between meteorology, 63, and PM are discussed together. Exceptions, such as the distinctive
2 role of precipitation in determining ambient PM concentrations, are noted.
O
4 B.2.1. Surface Temperature
5 Local Os formation depends on atmospheric conditions such as the availability of solar
6 ultraviolet radiation capable of initiating photolysis reactions, air temperatures, and the
7 concentrations of chemical precursors. Daily maximum temperature is one of the strongest
8 predictors for O3 pollution (Cox and Chu, 1996; U.S. EPA, 2003; Anderson et al., 2001;
9 Vukovich and Sherwell, 2003).
10 Secondary pollutants, including ozone (O3) and other photochemical oxidants, particulate
11 sulfate, nitrate, ammonium, and secondary organic aerosols (SOA), are formed in the ambient
12 atmosphere via chemical reactions that take place in the gas phase, on particle surfaces, or in
13 cloud droplets. In many cases, the chemical rate constants for these reactions are temperature
14 sensitive. Furthermore, high surface temperatures are often associated with high levels of solar
15 radiation, e.g., clear skies, leading to increased photochemical smog production. High ambient
16 temperatures can also influence the emissions of anthropogenic and biogenic volatile organic
17 compounds (VOCs) —important precursors of both 63 and PM.
18
19 B.2.2. Temperature Effects on Anthropogenic VOC Emissions
20 There are direct and indirect effects of climate change on anthropogenic emissions.
21 Direct effects are typically related to the enhanced evaporation of volatile chemicals at higher
22 temperatures. In particular, VOC emissions from fugitive sources and mobile sources (U.S.
23 EPA, 2002) are expected to increase with temperature. Evaporative emissions of the VOCs
24 found in fuel occur during fuel transfer processes and from storage tank and fuel line leakage.
25 This source, accounting for nearly half of all evaporative emissions, contributes significantly to
26 the U.S. ground-level ozone problem.
27
28 B.2.3. Temperature Effects on Biogenic Emissions
29 Biogenic VOCs serve as precursors for both Os and secondary organic PM2.5. Isoprene
30 has been shown to produce low yields of organic PM2.5 (Kroll et al., 2005). However, since
31 isoprene is the most abundant hydrocarbon emitted into the atmosphere after methane, even low
32 yields can produce significant levels of organic PM2.5. Isoprene and terpenoid compounds,
33 another source of secondary organic aerosols, are emitted by vegetation. These emissions
34 increase exponentially with temperatures up to a species-dependent limit in the range of 35-40°C
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1 (e.g., Geron et al., 1994; Constable et al., 1999; Sanderson et al., 2003; Lathiere et al., 2006;
2 Steiner et al., 2006.).
3 B.2.4. Temperature and Aerosol Thermodynamics
4 For semi-volatile particulate species (nitrate, ammonium, secondary organic aerosols),
5 climate change could be expected to affect gas/particle partitioning. First, gas/particle
6 equilibrium may shift towards the gas phase at higher temperatures because the saturation vapor
7 pressures of semi-volatile compounds increase with temperature. Thermodynamics dictates that
8 the saturation vapor pressure, which is the capacity of air to hold vapors of a trace gas, increases
9 with increasing temperature. Second, temperature and relative humidity (RH) affect the water
10 content of particles. Aw and Kleeman (2003) modeled the formation of secondary particles in an
11 environment at elevated temperatures and found that even though the production of some
12 condensable gases (e.g., HNOs) is increased, the partition of condensable material to the particle
13 phase is suppressed when the temperature is increased by 2-5°C. As a result, both the total mass
14 and size distributions of particles are predicted to decrease.
15
16 B.2.5. Atmospheric Stability
17 Dry deposition is a function of the aerodynamic resistance of the bulk atmosphere, the
18 quasi-laminar sublayer resistance near the surface, and the chemical-specific surface resistance
19 for the gas. The fall velocity of a particle due to gravity and the aerodynamic and laminar
20 sublayer resistance control the overall dry deposition velocity of a particle. Changes in climate
21 can affect the aerodynamic resistance, which depends on the atmospheric stability. Changing
22 temperature and RH can affect the size of particles due to gas-particle partitioning, hence altering
23 their fall velocities.
24
25 B.2.6. Mixing Height
26 Mixing conditions are governed by both synoptic scale pressure systems and local diurnal
27 temperature and humidity changes. The development of the mixing layer is an important
28 controlling factor for air pollution episodes. Stable conditions that typically occur at night, over
29 water or during winter, significantly limit the amount of vertical mixing of pollutants, whereas
30 unstable conditions typical of warm daytime conditions enhance vertical mixing.
31 The city of Los Angeles is a well-studied example of the air quality consequences of a
32 strong inversion layer. The confluence of a strong temperature inversion with high summertime
33 temperatures effectively creates a closed, heated reaction vessel that amplifies the photochemical
34 production of secondary pollutants, like 63. (Jacobson, 2002) Other western cities within the
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1 U.S. are subject to strong inversions, especially those located adjacent to mountains, such as Salt
2 Lake City and Denver.
O
4 B.2.7. Humidity
5 Water vapor participates in the suppression of Os formation by reacting with the O(1D)
6 radical, the most important precursor to tropospheric Os formation. Relative humidity is also a
7 predictor of PM in the southwestern U.S. (Wise and Comrie, 2005). High humidity conditions
8 lead to the partitioning of water into particles. Additional particle water facilitates further
9 partitioning of gas-phase water-soluble compounds into the particle phase (e.g., Liao et al.,
10 2006). When aqueous-phase oxidants, such as peroxides and hydroxyl radical, are present,
11 aqueous-phase oxidation reactions can lead to acidic compounds that alter the particle pH,
12 further enhancing the partitioning of water-soluble compounds.
13
14 B.2.8. Wind Speed and Direction
15 High winds are typically associated with ventilated conditions and disperse air pollution
16 near source areas. However, strong winds can also enhance transport of polluted air to
17 downwind locations. Lower winds are typically associated with stagnant conditions, and
18 stagnant conditions have been found to be associated with high PM and 63 concentrations (e.g.,
19 Pun and Seigneur, 1999; Ellis et al., 2000; Pun et al., 2000). Therefore, wind speeds play a role
20 in the accumulation of air pollutants (e.g., Gebhart et al., 2001, Wise and Comrie, 2005). In
21 addition, changing wind patterns associated with climate change can affect the frequency with
22 which pollution plumes are carried to a specific location (e.g., Mickley et al., 2004).
23 Land-sea breezes affect the concentration and dispersal of pollutants in coastal zone
24 cities. However, the presence of mountain barriers limits mixing (as in Los Angeles) and results
25 in a higher frequency and duration of days with high Os concentrations.
26 Ozone concentrations in southern urban areas (such as Houston, TX and Atlanta, GA)
27 tend to decrease with increasing wind speed. In northern cities (such as Chicago,
28 IL; New York, NY; Boston, MA; and Portland, ME), the average Os concentrations over the
29 metropolitan areas increase with wind speed, indicating that transport of 63 and its precursors
30 from upwind areas is important (Schichtel and Husar, 2001).
31 Resuspension of dust and previously deposited particles increases with increasing wind
32 speeds. Emissions of sea salt particles are a strong function of wind speed (Gong et al., 1997).
33
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1 B.2.9. Cloud Cover and Precipitation
2 Global climate change may alter the distribution of clouds (Stevenson et al., 2005).
3 Changing cloud distributions will correspondingly alter photochemical oxidation rates in the
4 areas affected.
5 Wet deposition of PM is a function of the form and amount of precipitation. At locations
6 where climate change alters the precipitation pattern (rain vs. snow), frequency, and intensity,
7 removal of particles and soluble gases by wet deposition may be increased or reduced (e.g.,
8 Langer et al., 2005; Sanderson et al., 2006).
9
10 B.3. REGIONAL PATTERNS IN THE O3 CONCENTRATION RESPONSE TO
11 METEOROLOGY
12 While the time series is too short to provide insight into the long-term role of climate in
13 determining Os concentrations, statistical analyses of the U.S. Os observational dataset have
14 shown consistent spatial patterns in the relationship between meteorological variables and 63
15 production.
16 These patterns can serve as a useful reference from which to interpret the climate-based
17 projections presented in this report. The variability in annual and seasonal meteorology reduces
18 the predictability of air quality, introducing a noisy "background" on the temporal record of
19 observed air pollution concentrations. This background noise makes detection of the long-term
20 effect of emissions control programs difficult. The air quality science and regulatory community
21 has applied a variety of statistical techniques to the problem of removing meteorological noise
22 from the air quality record, with a high level of success (Cox and Chu, 1993).
23 In addition to isolating the downward trend in O3 levels, consistent with declining
24 precursor emissions, from the variable background, the statistical analyses of the air quality
25 record have revealed regionally-oriented air quality sensitivities to specific meteorological
26 variables. The results of these studies suggest a major role for synoptic-scale, as well as local -
27 scale, meteorology in determining air quality. The studies discussed below identified distinctive,
28 regionally specific meteorological sensitivities in regional pollutant concentrations. Useful
29 insights into the impacts of climate change on regional air quality may be found in the
30 comparison of these observed patterns to those synoptic-scale changes projected to occur under
31 different GHG emissions scenarios.
32 Eder et al. (1994) used a cluster analysis to identify seven meteorological regimes in the
33 Eastern half of the U.S. that affect 63, each of which can be represented by a multivariate
34 regression model based on temperature, wind speed and direction, pressure, cloud cover, dew
35 point, solar insolation, mixing height, and upper air temperature, dew point, and wind speed and
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1 direction. Camalier and Cox (2007), using an alternative approach and observational data taken
2 from the U.S. EPA AQS database, have also identified a series of distinctive regions that are
3 distinguished by the relative sensitivity of Os concentration sensitivities to different
4 meteorological variables. Figure B-3 provides a map of these regions and the two most
5 important variables related to Os air quality for each region.
6 Lehman et al. (2004) analyzed the AQS database of daily 8-hr maximum 63
7 concentrations collected in the EPA AQS database for 1,090 stations in the eastern half of the
8 U.S. for the 1993 to 2002 period. They applied a rotated principle component analysis to a
9 reasonably complete, spatially representative, non-urban subset of the database in order to
10 identify coherent, regionally oriented patterns in Os concentrations. Five spatially homogenous
11 regions were identified: the U.S. Northeast, Great Lakes, Mid-Atlantic, Southwest (including
12 Alabama, Louisiana, Texas, Oklahoma), and Florida. The Mid-Atlantic region displayed the
13 highest mean concentration (52 ppb) of all of the regions analyzed, followed by the Great Lakes,
14 Southwest, and Northeast regions with around 47 ppb. The average concentration derived for
15 Florida was 41 ppb. The authors found strong correlations in measured concentrations among
16 stations within the same region, suggesting that the geospatial patterns of pollutant emissions and
17 meteorological activity may also have a regional orientation. These results suggest that these
18 regions may define natural domains for regional scale modeling studies of the influence of Os (as
19 well as PM) on climate.
20 Camalier et al. (2007) also identified a north-south gradient in the eastern U.S. with
21 respect to the importance of changes in temperature and humidity on Os concentrations. Their
22 result suggests that the northeastern U.S. is more susceptible to temperature-induced increases in
23 63. It has been suggested that the effect may be attributed to the fact that, currently, the
24 Northeast is subject to a greater range of possible temperature changes during a typical Os
25 season—including periods of lower-than-average temperatures—resulting in a regional capacity
26 for additional warm, high O3 days. The characteristically warmer temperatures and narrower
27 range in temperature variation in temperatures in the Southeast is consistent with the observed
28 lower 63 sensitivity to temperature. (See Figure B-4)
29
30 B.4. CLIMATE CHANGE AND U.S. AIR QUALITY: EARLY AND EXTERNAL
31 STUDIES
32 Early studies of the potential effect of a warming climate, specifically on U.S. ozone
33 levels, include an evaluation of the consequences of a hypothetical 4°C increase in temperature
34 across horizontal, vertical, and temporal scales (Morris et al., 1989; Morris et al, 1995). The
35 Morris et al. (1989) study modeled specific episodes and projected increased 63 concentrations
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1 ranging from 3-20% in a simulation of Central California and from -2.4-8% for simulations of
2 the
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Transport Direction, Max Temp
Max temp. Humidity
Humidity, Max temp
Humidity, Transport
Transport Direction, Humidity
Figure B-3. Patterns of Os sensitivity to wind
direction, temperature, and humidity, in the eastern
portion of the U.S.
Ozone Response to Temperature
(Positive response)
Ozone Response to Relative Humidity
(Negative response)
Figure B-4. Trends in ambient Os concentration sensitivities with
respect to temperature and humidity.
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o
en
10 20 30
Max Temperature (°C)
40 60 SO 100
Relative Humidity (%)
(D
I
O
O)
s
o
0 400 800 1200
Transport Distance (km)
0 100 200 300
Transport Origin (deg)
2 Figure B-5. Ozone response to changes in the four
3 important meteorological parameters affecting ozone for
4 Cleveland, OH. Panel A: the Os response to temperature is
5 relatively small for temperatures below ~20°C but is strongly
6 positive above 20°C. Panel B: Decreasing Os with increasing
7 relative humidity reflects increased cloudiness and atmospheric
8 instability. Panel C: Os concentrations are inversely
9 proportional to transport distance, e.g., the distance traveled by
10 the incoming air mass over the previous 24-hours, reflecting
11 the dilution effect of higher wind speed. For Cleveland, the
12 largest increment in ozone occurs when transport winds are
13 from a southwesterly direction (i.e., 200 to 250 deg).
14
15
16 Midwest and Southeast. Morris et al. (1995) included the effect of warmer conditions on mobile
17 source and biogenic emissions in their simulation of a 4-day episode in the Northeast. In that
18 simulation, Os concentrations increased 15-25 ppb in much of the modeling domain, above
19 baseline concentrations of 110-120 ppb range and 120-140 ppb range (10-20% increase).
20 In a recently study, Murazaki and Hess (2006), employing an approach that is similar to
21 one of the approaches used by GCRP-supported STAR researchers, modeled global-scale
22 atmospheric chemistry driven by a climate modeled for an IPCC Al SRES scenario. They fixed
23 U.S. emissions at 1990 levels and projected U.S. surface Os levels for the 2090-2100 timeframe
24 to estimate the effect of a changing climate. The impact of climate change on U. S. surface Os
25 levels is investigated. They found that the response of O3 to climate change in polluted regions
26 is not the same as in remote regions, i.e., a 0-2 ppbv decrease in background Os in the future
27 simulation over the U.S. but an increase in Os produced internally within the U.S. of up to 6
28 ppbv. They attributed the decrease in background Os to a future decrease in the lifetime of Os in
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1 low NOX regions. They also noted that the decrease in background 63 roughly cancels any
2 increase observed for the Western U.S. and concluded that the Eastern U.S. will be most
3 impacted by climate-induced Os increases, i.e., upwards of 5 ppbv. They predicted that in the
4 future over the northeastern U.S., up to 12 additional days each year will exceed the maximum
5 daily 8-hour averaged Os limit of 80 ppbv. They attribute the net future increases in Os that they
6 detected in their model results to various climatic factors including changes in temperature, water
7 vapor, clouds, transport, and lightning NOx.
8 Other efforts to relate climate change and air quality have used the sensitivity approach,
9 where important meteorological parameters known to impact air quality are perturbed one at a
10 time. Several groups studied the response of Os and PM to increased temperature (e.g., Aw and
11 Kleeman, 2003; Kleinman and Lipfert, 1996; Sillman and Samson, 1995). Aw and Kleeman
12 (2003) found that within the Los Angeles basin, daily Os maximum concentrations are not very
13 sensitive to temperature in areas with abundant NOX emissions but increase by 7 to 16 ppb at
14 downwind locations. Sillman and Samson (1995) studied the response of 63 to temperature in
15 other urban and polluted rural environments and suggested that the increase in O3 is due to
16 increased peroxyacetylnitrate (PAN) dissociation at higher temperatures. Thermal degradation
17 of PAN releases NOX, allowing it to participate in photochemical 63 production.
18 Due to the role of NOX chemistry in urban areas, the response of Os to climate change in
19 urban areas may be different from the response of rural or background Os where NOX chemistry
20 is less important. Aw and Kleeman (2003) investigated the formation of secondary particles in
21 an environment with elevated temperatures and found that even though the production of some
22 condensable gases (e.g., HNOs) is increased, the partition of condensable material to the particle
23 phase is suppressed when the temperature is increased by 2-5°C. As a result, both mass and size
24 of particles are predicted to decrease.
25
26 B.5. REFERENCES
27 Anderson, HR; Derwent, RG; Stedman, J. (2001) Air pollution and climate change. In: McMichael, AJ; Kovats, RS,
28 eds. Health effects of climate change in the UK. London, UK: Department of Health; pp.193-201. Available online
29 at http://www.dh.gov.uk/assetRoot/04/06/89/15/04068915.pdf (accessed 29 Aug 2006).
3 0 Aw, J; Kleeman, MJ. (2003) Evaluating the first-order effect of intraanual temperature variability on urban
31 pollution. J Geophys Res 108(D12):4365, doi: 10.1029/2002JD002688.
32 Brimblecombe, P. (1987) "The Big Smoke: A History of Air Pollution in London Since Medieval Times,"
3 3 Routledge Kegan & Paul.
34 Camalier, L; Cox, WM; Dolwick, P. (2007) The effects of meteorology on ozone in urban areas and their use in
35 assessing ozone trends. Atmospheric Environment 41:7127-7137.
36 Constable, JVH; Guenther, AB; Schimel, DS; et al. (1999) Modelling changes in VOC emissions in response to
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1 climate change in the continental United States. Glob Change Biol 5(7):791-806.
2 Cox, W.M., Chu, S., 1993. Meteorologically adjusted ozone trends in urban areas: a probabilistic approach.
3 Atmospheric Environment 27B: 425-434.
4
5 Cox, WM; Chu, SH. (1996) Assessment of interannual ozone variation in urban areas from a climatological
6 perspective. Atmos Environ 30(14):2615-2629.
7 Eder, BK; Davis, JM; Bloomfield, P. (1994) An automated classification scheme designed to better elucidate the
8 dependence of ozone on meteorology. J Appl Meteorol 33(10): 1182-1199.
9 Ellis, AW; Hildebrandt, ML; Thomas, WM; et al. (2000) Analysis of the climatic mechanisms contributing to the
10 summertime transport of lower atmospheric ozone across metropolitan Phoenix, Arizona, USA. Climate Res
11 15:13-31. Available online at http://www.int-res.com/articles/cr/15/c015p013.pdf.
12 Gebhart, KA; Kreidenweis, SM; Malm, WC. (2001) Back trajectory analysis of fine paniculate matter measured at
13 Big Bend National Park in the historical database and the 1996 scoping study. Sci Total Environ 276(1-3): 185-204.
14 Geron, CD; Guenther, AB; Pierce, TE. (1994) An improved model for estimating emissions of volatile organic
15 compounds from forests in the eastern United States. J Geophys Res 99(D6): 12773-12792.
16 Gong SL; Barrie, LA; Blanchet, J-P. (1997) Modeling sea-salt aerosols in the atmosphere 1. Model development. J
17 Geophys Res 102(D3):3805-3818.
18 Jacobson, MZ. (2002) Atmospheric pollution: history, science, and regulation. Cambridge, UK: Cambridge
19 University Press.
20 Kleinman, LI; Lipfert, FW. (1996) Metropolitan New York in the greenhouse: air quality and health effects. Ann
21 NY Acad. Sci 790:91-110.
22 Kroll JH; Ng NL; Murphy SM; Flagan RC; Seinfeld JH (2005) Secondary organic aerosol formation from isoprene
23 photooxidation under high-NOx conditions. Geophys Res Lett 32(18): L18808.
24 Langner, J; Bergstrom, R; Foltescu, V. (2005) Impact of climate change on surface ozone and deposition of sulphur
25 and nitrogen in Europe. Atmos Environ. 39(6): 1129-1141.
26 Lathiere, J; Hauglustaine, DA: Friend, AD; et al. (2006) Impact of climate variability and land use changes on
27 global biogenic volatile organic compound emissions. Atmos Chem Phys 6:2129-2146. Available online at
28 http://www.atmos-chem-phys.net/6/2129/2006/acp-6-2129-2006.pdf.
29 Lehman J, Swinton K, Bortnick S, Hamilton C, Baldridge E, Eder B, Cox B (2004) Spatio-temporal characterization
30 of tropospheric ozone across the eastern United States. Atmos Environ. 38 (26): 4357-4369.
31 Liao, H; Chen, W-T; Seinfeld, JH. (2006) Role of climate change in global predictions of future tropospheric ozone
32 and aerosols. J Geophys Res. 111:D 12304, doi: 10.1029/2005JD006852.
33 Mickley, LJ; Jacob, DJ; Field, BD; et al. (2004) Effects of future climate change on regional air pollution episodes
34 in the United States. Geophys Res Lett 3LL24103, doi:10.1029/2004GL021216.
35 Morris RE, Gery MS, Liu Mk, Moore GE, Daly C, Greenfield SM. (1989) Sensitivity of a regional oxidant model
36 to variation in climate parameters. In: The Potential Effects of Global Climate Change on the United States (Smith
37 JB, Tirpak DA eds). US Environmental Protection Agency, Office of Policy, Planning and Evaluation, Washington
38 DC.
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1 Morris RE; Guthrie PD; Knopes CA. (1995) Photochemical modeling analysis under global warming conditions.
2 In: Proceedings of the 88th Air & Waste Management Association Annual Meeting and Exhibition, Paper No. 95-
3 WP-74B.02. Pittsburgh PA, Air & Waste Management Association.
4 Murazaki, K; Hess, P. (2006) How does climate change contribute to surface ozone change over the United States?
5 J GeophysRes 111:D05301, doi: 10.1029/2005JD005873.
6 Pun, BK; Louis, J-F; Pai, P; et al. (2000) Ozone formation in the California San Joaquin Valley: a critical
7 assessment of modeling and data needs. J Air Waste Manage Assoc 50(6):961-971.
8 Pun, BK; Seigneur, C. (1999) Understanding paniculate matter formation in the California San Joaquin Valley:
9 conceptual model and data needs - adequacy and validation of meteorological measurements aloft during IMS95.
10 Atmos Environ 33(29):4865-4875.
11 Sanderson, MG; Jones, CD; Collins, WJ; et al. (2003) Effect of climate change on isoprene emissions and surface
12 ozone levels. Geophys Res Lett 30(18): 1936, doi:10.1029/2003GL017642.
13 Sanderson, MG; Collins, WJ; Johnson, CE; et al. (2006) Present and future acid deposition to ecosystems: The effect
14 of climate chang. Atmos Environ 40(7):1275-1283.
15 Schichtel, BA; Husar, RB (2001) Eastern North American transport climatology during high- and low-ozone days
16 Atmos Environ 35(6): 1029-1038.
17 Sillman, SP; Samson, J. (1995) Impact of temperature on oxidant photochemistry in urban, polluted rural, and
18 remote environments. J Geophys Res 100(D6): 11479-11508.
19 Steiner, AL; Tonse, S; Cohen, RC; et al. (2006) Influence of future climate and emissions on regional air quality in
20 California. J GeophysRes 111:D18303, doi:10.1029/2005JD006935.
21 Stevenson, DS; Doherty, RM; Sanderson, MG; et al. (2005) Impacts of climate change and variability on
22 tropospheric ozone and its precursors. Faraday Discuss 130:41-57.
23 U.S. EPA (Environmental Protection Agency). (2002) Sensitivity analysis of MOBILE6.0. Office of Transportation
24 and Air Quality, Washington DC; EPA/420-R/02/035. Available online at
25 http://www.epa.gov/oms/models/mobile6/r02035.pdf.
26 U.S. EPA (Environmental Protection Agency. (2003) Guidelines for developing an air quality (Ozone and PM25)
27 forecasting program. AIRNow Program, Research Triangle Park, NC; EPA/456/R-03/002.
28
29 U.S. EPA (Environmental Protection Agency). (2004) Air quality criteria for paniculate matter. National Center for
30 Environmental Assessment, Research Triangle Park, NC; EPA/600/P-99/002aF-bF. Available online at
31 http://cfpub.epa. gov/ncea/cfm/recordisplay.cfm?deid=87903.
32 U.S. EPA (Environmental Protection Agency) (2006). Air quality criteria for ozone and related photochemical
33 oxidants. National Center for Environmental Assessment, Research Triangle Park, NC; EPA/600/R-05/004aF-cF.
34 Available online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid= 149923.
3 5 Vukovich, FM: Sherwell, J. (2003) An examination of the relationship between certain meteorological parameters
36 and surface ozone variations in the Baltimore-Washington corridor. Atmos Environ 37(7):971-981.
37 Wise, EK; Comrie, AC. (2005) Meteorologically adjusted urban air quality trends in the Southwestern United States.
38 Atmos Environ 39:2969-2980.
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1 APPENDIX C
2 THE 2001 EPA GCRP AIR QUALITY EXPERT WORKSHOP
O
4 C.I. INTRODUCTION
5 A meaningful assessment of the impacts of global change requires a reasonably well-
6 resolved understanding of the relevant processes and physical and chemical links between
7 global, regional, and local scales. The atmospheric sciences community has begun to recognize
8 that climate and air quality are linked through atmospheric chemical, radiative, and dynamic
9 processes at multiple scales. The results of a limited number of studies of the relationship
10 between weather and ozone concentrations, the effects of temperature on atmospheric chemistry,
11 and the sensitivity of emissions to weather and land-use suggest that global change could
12 adversely affect air quality. However, the community's understanding of the many climate/air
13 quality links is still very limited. A better definition of these links is required for
14 • Estimates of future changes in climate and air quality;
15 • Assessment of impacts; and
16 • Identification of effective policies and technologies for reducing adverse effects.
17
18 In 2001, the National Research Council concluded "Improving our understanding of the
19 interactions between climate and air quality will depend primarily on developing more
20 sophisticated modeling tools; in particular, it will require the ability to couple local- and
21 regional-scale air quality models (which cover spatial scales of a few hundred meters to hundreds
22 of kilometers) with global-scale climate and chemistry models" (NRC, 2001). In addition, tools
23 for simulating other pertinent aspects of global change occurring within the U.S., such as
24 changes in population migration and land-use, or energy and transportation technologies, are
25 needed to prepare future modeling scenarios that would be relevant to U. S. air quality.
26 Furthermore, given the importance of natural and anthropogenic change in determining the
27 frequency of wildfires and the substantial role that wildfires can play in regional air quality, a
28 means of modeling the effect of global change on U.S. wildfire frequency is also needed for the
29 proj ection of future air quality.
30 The first step towards accomplishing the goal of assessing global change impacts on
31 regional air quality was the development of an assessment framework. The assessment
32 framework guides activities undertaken by the EPA Global Change Research Program to
33 establish the capability to analyze the relationship between global change and air quality.
34 Initially the Program used existing tools and models, supplemented by additional analyses as
35 needed to define missing components, to implement the assessment framework. However, it was
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1 also recognized that research was needed to fill knowledge gaps and enhance our ability to
2 conduct such assessments.
3 To evaluate the feasibility of assessing climate impacts on air quality and identify key
4 research gaps, the Program hosted a workshop in Research Triangle Park, North Carolina, in
5 December 2001. The workshop drew on the technical expertise of staff from the Office of
6 Research and Development (ORD) and the Office of Air and Radiation (OAR), and an array of
7 invited international experts. Working groups were formulated to address a set of questions
8 prepared by EPA concerning the current science and the capabilities of available modeling tools
9 for regional climate, biogenic and fire emissions, anthropogenic emissions and their drivers, and
10 air quality. Each group identified research and development needs and then prepared and
11 presented recommendations to EPA on how to proceed in designing an assessment-oriented
12 scientific research and modeling effort.
13
14 C.2. SUMMARY OF WORKSHOP RECOMMENDATIONS
15 Recommendations for research that were developed by the four workshop groups and that
16 are required to meet the EPA/ORD objectives on assessing the impact of global climate change
17 on regional air quality follow.
18
19 C.2.1. Recommendations from the Regional Climate Modeling Group
20 (1) Define climate model output variables needed/desired for air quality modeling
21 analysis.
22 Most studies to date using a regional climate model (RCM) have been designed to
23 address data needs for agricultural or hydrologic impact assessments. Hence, output data have
24 been typically saved for variables directly related to temperature, precipitation,
25 evapotranspiration, soil moisture, and surface runoff. The group felt that most current datasets
26 would probably not be adequate to meet the needs of air quality modelers.
27 (2) Survey air quality models to identify important variables or statistical aspects
28 (frequencies, persistence, and amplitude) that need to be reproduced by
29 RCMs).
30 Past studies with regional climate simulations typically evaluated simulation aspects that
31 are important for hydrologic or agricultural assessment (e.g., temperature and precipitation). It is
32 less clear what aspects of the regional simulation are important for air quality assessment. RCM
33 outputs to date have typically been saved at spatial grid resolutions of about 50 km and at time
34 intervals of a few hours to a day. Most analyses of model output emphasize the surface variables
35 or lowest atmospheric layer in the model. Air quality models typically require higher time
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1 resolution (hourly or even shorter time intervals) and 3-dimensional data from the surface to the
2 top of the atmospheric mixed layer, if not higher.
3 (3) Identify appropriate time and space scales for coordinating regional climate
4 and air quality simulations.
5 Current RCMs typically operate on a spatial grid resolution of 50 km while current air
6 quality models may operate on a range of spatial grid resolutions, from 100 km to as fine as 2
7 km, depending upon the specific application. Thus, linking RCM outputs to the input needs of
8 air quality models will require careful consideration of both space and time scales in order to
9 provide simulations that are long enough to be climatologically representative yet at time and
10 space resolutions that are computationally (and financially) feasible.
11 (4) Determine the RCM configurations that are important to air quality
12 assessment (e.g., vertical/horizontal resolution, boundary conditions, model
13 domain).
14 Once the appropriate scales of inter-model linkage have been identified, a set of
15 specifications will be developed for configuring the RCM and for conducting multiple
16 simulations ("ensembles") with one or more RCM to appropriately characterize climate variance
17 in air quality model input considerations.
18 (5) Conduct diagnostic studies on variables identified in recommendation #2 to
19 determine the degree of fidelity in RCM simulations.
20 The breakout group noted examples in previous RCM studies of discrepancies between
21 model outputs and observations that were often related to inadequate parameterizations of
22 physical processes in the model, to insufficient resolution, to insufficient data, to uncertainties in
23 scientific understanding, etc. There could also be instances of RCMs "getting the right answer
24 for the wrong reason," thereby creating uncertainties when applying the model to assess effects
25 of future climate changes.
26 (6) Conduct model inter-comparisons for variables important to air quality to
27 identify and quantify model biases and uncertainties (e.g., forcing, nesting,
28 performance, and inter-model uncertainties)
29 It was noted that some RCM inter-comparison studies that have been conducted in the
30 Project to Inter-compare Regional Climate Simulations project (led by Iowa State University and
31 involving more than a dozen modeling groups from around the world). Another study performed
32 by the Electric Power Research Institute (EPRI) described some extensions of the approach that
33 have compared RCMs directly with statistical downscaling methods. But all of these studies
34 have concentrated on model performance in simulating basic meteorological variables
35 (especially temperature and precipitation), which interests the agricultural or hydrologic
36 community and not necessarily the air quality community.
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1 (7) Develop approaches for selecting "meaningful episodes" (days to weeks for
2 specifically selected U.S. regions) out of long (10 years plus) regional climate
3 model simulations.
4 Air quality standards are typically expressed as short-term (hourly or daily) averages that
5 are not to be exceeded more than a specified number of times per year. Air quality model
6 simulations are typically run over time periods of a few days to a few weeks to identify air
7 quality episodes. It was shown that ensemble climate change simulations of current and future
8 conditions suggest large interannual and decadal variability in future climate and among
9 ensemble members. Multiple, long-term simulations are therefore needed to represent
10 meaningful future climate scenarios. This poses a serious challenge to air quality assessment
11 since physically based air quality models are extremely computationally intensive (as compared
12 to global or regional climate modeling). An alternative approach to long-term simulation is to
13 develop future climate scenarios based on extracting episodes from regional simulations that
14 capture major changes in synoptic events that are significant to air quality. Such episodes may
15 represent changes in intensity or frequency of stagnation, atmospheric inversion, or El Nino-
16 Southern Oscillation (ENSO) cycles, etc.
17 (8) Identify the appropriate RCM ensembles needed to characterize climate
18 variance for air quality modeling purposes (both multiple simulations and
19 multiple models).
20 To appropriately characterize the climate variance simulated among models, a new round
21 of RCM simulations will be required, involving multiple RCMs (research recommendation #8).
22 The research recommendations so far have focused on linking RCMs to air quality simulations of
23 specific episodes over relatively short time duration. However, linking air quality assessments to
24 multiple climate change scenarios, and for the many ensembles of simulations necessary to
25 quantify probabilities of climatic and air quality risks, could far exceed the computing resources
26 available to RCM modelers. The hydrological impacts community has addressed a need for
27 long-term assessments through the successful application of statistical downscaling-based
28 climate models to proj ecting precipitation and river runoff extremes.
29 (9) Investigate the usefulness of applying statistically downscaled climate models
30 coupled to statistical air quality models for long-term assessments.
31 Statistical downscaling models for climate parameter inputs are presently used in
32 operational forecasting models for seasonal and interannual basin hydrology on a site-specific
33 basis due to their very efficient computation and successful calibration. Process-based air quality
34 models, however, require extensive descriptions of the 3-dimensional structure of the atmosphere
35 and there are insufficient observational data for developing statistical downscaling for most
36 meteorological variables important for air quality assessment. However, it may be worth
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1 exploring whether statistical downscaling may, in some way, be useful for providing climate
2 inputs for empirical air quality models.
3 (10) Investigate the importance of incorporating "full chemistry" into an RCM for
4 defining background inputs and determining the importance of feedbacks of
5 chemistry into climate.
6 The members of the breakout group viewed the development of the "Ultimate" model—
7 an RCM incorporating full chemistry—as a vision for the future. While the CMAQ advertises its
8 "Plug and Play" capabilities to test quickly (and easily?) alternative chemical schemes, two-way
9 coupling of CMAQ-type models with climate models is more complicated. Early work on
10 incorporating chemical modules into global climate models has met with rather mixed success
11 and often with a very high "cost" penalty in terms of dramatically increased computing times.
12 Under these circumstances, the breakout group recommended a "go slow" approach toward
13 adding full chemistry to RCMs by determining inputs needs and the potential significance of
14 chemical feedbacks on climate.
15
16 C.2.2. Recommendations from the Biogenic and Fire Emissions Group
17 (1) There is a need to develop algorithms that describe chemical emissions of
18 major vegetative species' response to climate change for use in current and
19 biogenic emission forecasting.
20 Changes in vegetative growth, yield, and water use have been the foci of research efforts
21 to understand climate change impacts on natural and domestic woody and herbaceous vegetation.
22 Basic research is needed to better understand the physiological impacts of climate change on
23 vegetation chemical emissions. An improved knowledge of species-level response to climate
24 change is needed before complete terrestrial emission budget cycle is possible.
25 (2) Research is required to integrate land-use/land-cover projection changes with
26 forest physiological models to project current and future changes in VOC
27 emissions.
28 Both an understanding of climate change impacts on plant physiology and on land-
29 use/land-cover are needed to generate VOC budgets and balances in terrestrial ecosystems.
30 While plant physiological studies provide a measure of VOC contribution per vegetation type,
31 land-use/land-cover data are needed to scale the predictions individual emissions to the regional
32 or continental scale.
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1 (3) There is a need to develop methods to define fire emissions as a function of fire
2 intensity, extent, and frequency.
3 A good program for monitoring major fire intensity, extent, and frequency currently
4 exists within the federal government. However, research to link monitored data with emission
5 by vegetation type is lacking. Additional development of the relationships between the emission
6 by-products of vegetation combustion and fire monitoring program data are needed before an
7 accurate estimate of fire impacts to atmospheric emissions can be completed.
8 (4) There is a need to develop methods to relate fire intensity, extent, and
9 frequency to current land-use, land management, fuel loading, and climate.
10 The current federal fire-monitoring program is designed to track fire intensity, extent, and
11 frequency. However, scientists have only a coarse understanding of how monitored data relates
12 to land-use, land management, fuel loading, and climate. An improved understanding of the
13 drivers of fire intensity, extent, and frequency are needed for use in fire emission projection
14 scenarios.
15 (5) There is a need to develop methods to relate and apply fire intensity, extent,
16 and frequency to the future socio-economic/climatic scenarios.
17 Future land-use change, economic conditions, and climate will likely be primary drivers
18 of fire emission through 2050. Therefore, socio-economic and climate change models need to be
19 developed and applied to national scale fire emission models before a complete biogenic
20 emission budget is possible.
21 (6) Research must be performed concerning current and future emissions from
22 animal husbandry and fertilizer application.
23 Animal husbandry and agricultural fertilizer applications are major sources of emissions.
24 Some data exist regarding the current type and extent of animal husbandry and fertilizer
25 application type and rates across the continent. However, there is a lack of understanding in
26 relating climate, soil, vegetation, and animal conditions with emission patterns. Additionally,
27 current animal husbandry and fertilizer application practices may change in the future. Both an
28 improved understanding of environmental interaction and projections of future practices are
29 needed to estimate emission inputs of future animal husbandry and fertilizer application before a
30 complete terrestrial emission budget can be completed.
31 (7) Research must be performed to understand drivers of current and of future
32 rates of ammonia and VOC deposition (e.g., soil moisture, ammonia gas to
33 particulate).
34 The concentrations of atmospheric gases are dependent on the current atmospheric
35 concentration, inputs, and outputs from the system. The majority of research focuses on changes
36 in atmospheric gases inputs. However, understandings of deposition of gases from the
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1 atmosphere are equally needed for the development of an atmospheric emission balance.
2 Deposition of ammonia and VOC are two important gases that have both atmospheric and
3 terrestrial impacts. A better understanding of ammonia and VOC deposition would be useful in
4 predicting future emissions and for use in examining terrestrial impacts.
5
6 C.2.3. Recommendations from the Emission Drivers and Anthropogenic Emissions Group
7 (1) There is a need to perform research on feedbacks of regional climate change
8 on population migration and economic activity.
9 Regional climate change could have significant impacts on climate sensitive economic
10 sectors such as agriculture and forestry. Current national/regional economic models are not
11 likely to address these impacts. Furthermore, the ability to adapt to regional climate change may
12 vary by regions and economic sectors. Regional climate change may also change the relative
13 attractiveness of certain regions within the U.S. and influence the migration of population.
14 Present research addressing these issues is limited and further research is required.
15 (2) There is a need to perform research on feedbacks on energy use and emissions
16 from energy use due to regional climate change.
17 Regional climate change will likely impact energy use especially for heating and cooling.
18 These changes will impact emissions at the commercial, industrial, and domestic levels due to
19 direct use of energy but also impact emissions from electricity generation plants. These changes
20 could increase the frequency and magnitude of episodes with high emissions and poor air
21 quality. Furthermore, changes in water supply and land-use can impact strategies for utilization
22 of biomass to meet energy needs. Present research addressing these issues is limited and further
23 research is required.
24 (3) There is a need to perform research on feedbacks from climate change on
25 biogenic emissions.
26 The impact of changes in regional climate of biogenic emissions needs to be quantified.
27 Regional climate change can lead to migration of species, which can change emissions. New
28 approaches to scale national/regional economic, demographic, and energy model results to more
29 detailed geographic resolution are required. Current approaches are relatively simple and may
30 not reflect key trends.
31 (4) Additional research is required on land-use models.
32 Land-use change represents one of the most important factors influencing future air
33 quality. Population growth combined with increased wealth; changes in transportation, energy,
34 and communication technology; regional migration; use of personal verse public transport; and
35 lifestyles can lead to significant changes in local drivers for emissions sources along with
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1 emission factors. The current modeling capability to address these types of changes is limited
2 and not focused on the longer-term structural effects that may happen in the future.
3 (5) New research is required on agent-oriented models.
4 New research into agent-oriented models may provide opportunities to address land-use
5 change, migration, and other issues listed above. Research into these models and application of
6 these models to this problem is limited and further research is required.
7 C.2.4. Recommendations from the Air Quality Modeling Group
8 (1) Group recommends three, complimentary modeling approaches: A
9 comprehensive modeling approach, an intermediate modeling approach, and a
10 sensitivity approach.
11 The comprehensive approach uses linked, dynamic models to simulate air quality. The
12 meteorological chain links a GCM to an RCM. The downscaled, meteorological output from the
13 RCM is linked to a regional air quality model (RAQ) model. A global chemical transport model
14 (GCTM) would produce chemical boundary condition information for the RAQ model. This
15 approach raises concerns, however, about the length of simulation required to achieve a climate
16 signal above the climate variability. Previous regional climate downscaling suggests that
17 simulations over 10-20 years may be required to rigorously meet statistical requirements.
18 However, the computational resources required for this length of simulation greatly exceed the
19 available resources for the near future. Thus, the group considered serious explorations of
20 methods to avoid 20-year simulations and yet produce meaningful results.
21 The intermediate approach uses a GCTM or coarse scale RAQ model to simulate the
22 impact on air quality due to long-lived GHGs over the 50 years from the present to 2050. The
23 emphasis in this approach will be to explore the change in high air pollution events as the climate
24 changes. It will be important to incorporate the ocean response to the changing climate in order
25 to perform this simulation properly. One set of simulation will hold air pollutant emissions
26 constant over the simulation (besides corrections for temperature and other climate variables). In
27 another set, plausible scenarios of emissions for 2050 can be simulated to compare the
28 magnitudes of the climate influence on the emissions changes. The results will guide the
29 comprehensive modeling approach and may allow selection of episodes using statistical
30 sampling techniques to avoid 20 years of simulation. The intermediate approach may also
31 provide coarse estimates of the impact of climate change on air quality in Hawaii and Alaska.
32 The sensitivity approach would focus on the application of detailed, state-of-the-art urban
33 and regional air quality models. Rather than a dynamic linkage, the RAQ simulations would
34 vary key parameters to examine the sensitivity of air quality. The issue of climate variability is
35 removed through varying parameters such as temperature to define the potential responses. The
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1 sensitivity approach permits use of more detailed descriptions of important processes (i.e.,
2 aerosol processes and chemical speciation of fine particulate matter). It also enables detailed
3 comparison to observational data. Therefore, simulations including EPA Supersite locations
4 might prove extremely valuable.
5 (2) It is recommended that more than one GCM be used to explore the range of
6 possible future climates.
7 Future climate on a regional scale is highly uncertain. Any analysis will need to grapple
8 with the uncertainty and climate variability.
9 (3) Close interaction between the regional climate modeling and synoptic
10 climatology communities is recommended to characterize large-scale
11 meteorological events that effect air quality, which would be used to guide the
12 development of air quality modeling scenarios.
13 The variability of the climate signal, as well as air pollution episodes, will determine the
14 desired sampling time periods. Computational resource requirements are likely to constrain the
15 length of detailed, fine-scale, air-quality simulations to significantly less than the desired length
16 to find the climate signal in the noise due to climate variability.
17 (4) It is recommended that plausible scenarios for future emissions be developed.
18 Plausible scenarios for future emissions are crucial to the policy relevance of the air
19 quality modeling simulations. Exogenous socio-economic changes may have a much larger
20 impact than climate change on air quality 50 years into the future. Techniques to explore the
21 root causes of air quality changes, such as simulations holding emissions constant, will need to
22 be utilized to produce significant results for policy decisions.
23 (5) It is essential that the air quality modeling results from CMAQ be improved.
24 These improvements should be targeted towards areas of magnified importance due to the
25 novel aspects of this effort.
26 (6) Quantify the uncertainty produced by the chain of linked models required to
27 make an air quality prediction due to climate change.
28 The linkage of a chain of dynamic models of different scale requires care in maintaining
29 consistency especially in consideration of nesting schemes and dynamics. The uncertainty
30 introduced by the chain of linked models should be quantified through observational data
31 whenever feasible.
32
33 C.3. REFERENCE
34 NRC (National Research Council). (2001) Global air quality: an imperative for long-term observational strategies.
35 Committee on Atmospheric Chemistry, Washington, DC: National Academy Press; 41 pp. Available online at
3 6 http://www-nacip.ucsd.edu/NRCAtmosChemCommRpt.pdf.
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3
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5
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12
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15
16
17
APPENDIX D
U.S. EPA STAR GRANT RESEARCH CONTRIBUTING TO THE GCAQ ASSESSMENT
D.I. STAR SOLICITATIONS
The Science To Achieve Results (STAR) program plays a major role in the study of the
impacts of climate change on air quality. Through the STAR competitive process, EPA ORD
has funded several leading university research groups to investigate the various aspects of the
impact of global change on air quality. Additional information about the STAR program can be
found at http://www.epa.gov/ncer/, which provides links to more detailed descriptions and
progress reports for the projects summarized here. Table D-l lists the Requests for Applications
(RFAs) that address various aspects of climate impacts on air quality. Results from awards that
were made in 2000 and 2002 are discussed in greater detail in Section 3 of the main report.
Research from awards made in 2003-2006 is ongoing and described briefly in Section 4; a
synopsis and summary of these awards follow.
Table D-l. Requests for applications for the global program (STAR)
Year
2000
2002
2003
2004
2004
2006
RFA Title
Assessing the Consequences of Interactions between Human Activities and a
Changing Climate
Assessing the Consequences of Global Change for Air Quality: Sensitivity of
U.S. Air Quality to Climate Change and Future Global Impacts
Consequences of Global Change for Air Quality: Spatial Patterns in Air
Pollution Emissions
Regional Development, Population Trend, and Technology Change Impacts on
Future Air Pollution Emissions
Fire, Climate and Air Quality
Consequences of Global Change for Air Quality
18
19
20
21
22
23
24
D.I.I. Assessing the Consequences of Interactions between Human Activities and a
Changing Climate
The purpose of this RFA was to foster the development of models that enable assessors to
consider the effects of human activities in tandem with the effects of climate change and climate
variability. Two of the four proposals selected for funding explored the impacts of climate
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1 change and air quality. These projects confirmed the new direction of the program and provided
2 important early results. (See Table D-2)
O
4 Table D-2. 2000 STAR grant recipients: assessing the consequences of
5 interactions between human activities and a changing climate
6
Institution
Johns Hopkins
University
Columbia University
Title
Implications of Climate Change for Regional Air Pollution,
Effects and Energy Consumption Behavior
Modeling Heat and Air Quality Impacts of Changing Urban
and Climate
Health
Land Uses
7
8
9 D.1.2. Assessing the Consequences of Global Change for Air Quality: Sensitivity of U.S.
10 Air Quality to Climate Change and Future Global Impacts
11 The focus of this RFA was on linking global and regional models and/or on increasing
12 understanding of the sensitivities of air quality to climate change. The six projects funded under
13 this solicitation (Table D-3) are described in more detail in Section 3 of the report.
14
15 D.1.3. Consequences of Global Change for Air Quality: Spatial Patterns in Air Pollution
16 Emissions
17 The focus of this RFA was on the development of methods for creating plausible North
18 American emission scenarios for use in assessments of climate change impacts on regional air
19 quality. Of particular interest were changes in the spatial distribution of stationary, mobile, and
20 biogenic emissions over the longer timeframes used in global change assessments (e.g., 50+
21 years or more). For example, the physical characteristics and patterns of land development in a
22 region can affect air quality by influencing travel mode choices, trips, trip speed, number of
23 miles driven, and, therefore, mobile source emissions. Similarly, emissions from stationary air
24 pollution sources, such as power plants and factories, will also be affected by the characteristics
25 and patterns of land development. In addition, economic growth, changes in the composition of
26 economic output (e.g., the gross domestic product or GDP), and technological change have the
27 potential to affect both the total amount and spatial distribution of stationary source emissions.
28 Finally, changes in land use, vegetation, and climate can influence the natural emission of
29 volatile organic compounds (VOC), carbon monoxide, and oxides of nitrogen. Six proposals
30 were funded under this RFA (Table D-4), four of which focused on biogenic emissions.
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2
3
4
Table D-3. 2002 STAR grant recipients: assessing the consequences of global
change for air quality: sensitivity of U.S. air quality to climate change and
future global impacts
Institution
Harvard University
Georgia Institute of
Technology
Carnegie Mellon
University
Washington State
University
University of Illinois
at Urbana
University of
California - Berkeley
Title
Application of a Unified Aerosol-Chemistry-Climate GCM to
Understand the Effects of Changing Climate and Global Anthropogenic
Emissions on U.S. Air Quality
Sensitivity and Uncertainty Assessment of Global Climate Change
Impacts on Ozone and Parti culate Matter: Examination of Direct and
Indirect, Emission-Induced Effects
Impacts of Climate Change and Global Emissions on U.S. Air Quality:
Development of an Integrated Modeling Framework and Sensitivity
Assessment
Impact of Climate Change on U.S. Air Quality Using Multi-scale
Modeling with the MM5/SMOKE/CMAQ System
Impacts of Global Climate and Emission Changes on U.S. Air Quality
Guiding Future Air Quality Management in California: Sensitivity to
Changing Climate
5
6
7
Table D-4. 2003 STAR grant recipients: consequences of global change for
air quality: spatial patterns in air pollution emissions
Institution
University of
Colorado at Boulder
University of North
Carolina at Chapel
Hill
University of Texas at
Austin
University of Illinois
at Urbana
University of New
Hampshire
Resources for the
Future
Title
New Biogenic VOC Emission Models
Reduced Atmospheric Methane Consumption by Temperate Forest
Soils Under Elevated Atmospheric CC^: Causative Factors
Impacts of Climate Change and Land Cover Change on Biogenic
Volatile Organic Compounds (BVOCs) Emissions in Texas
Development and Evaluation of a Methodology for Determining Air
Pollution Emissions Relative to Geophysical and Societal Changes
A Coupled Measurement-Modeling Approach to Improve Biogenic
Emission Estimates: Application to Future Air Quality Assessments
An Integrated Framework for Estimating Long-Term Mobile Source
Emissions Linking Land Use, Transportation, and Economic Behavior
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1 D.l.3.1. University of Colorado at Boulder
2 Although many studies have shown that emissions of VOCs from forest ecosystems
3 cause an increase in air pollution, current air pollution models lack fundamental insight into the
4 biochemical mechanisms in plants that produce these compounds. Specifically, it is not possible
5 to accurately predict whether the emission of these compounds and their influence on air quality
6 will change if the climate of the earth or the atmospheric concentration of CC>2 changes in the
7 future. The CU-Boulder group is conducting experiments to elucidate the biochemical processes
8 that cause the emission of these compounds and is describing their response to temperature and
9 CO2 change. The focus of their studies is on isoprene and acetaldehyde, two of the most
10 commonly-emitted compounds from U.S. forests.
11
12 D. 1.3.2. University of North Carolina at Chapel Hill
13 The overall aim of the UNC-Chapel Hill research is to determine the duration and
14 underlying cause(s) for the decline in atmospheric methane consumption in a CO2-enriched
15 forest. The Duke Forest Free Air Carbon Dioxide Enrichment (FACE) site is used to (1)
16 quantify the dynamics of soil-atmosphere exchange of methane, (2) quantify the impact of CC>2
17 enrichment on the exudation of dissolved organic compounds from roots of the loblolly pine into
18 the rhizosphere, and the effects of these compounds on the rates of methane oxidation in soils,
19 (3) quantify the dissolved organic compounds and ions from throughfall precipitation as a
20 supplement to root exudates and the effects of these compounds on rates of methane oxidation in
21 soils, and (4) evaluate the impact of CC>2 enrichment on soil physical and biogeochemical
22 properties central to atmospheric methane consumption, including effective diffusivity, microbial
23 community structure, the soil locus of methanotrophic activity, and physiological characteristics
24 of the methane-oxidizing community.
25
26 D. 1.3.3. University of Texas at A ustin
27 Climate change can influence the emissions of biogenic VOCs (BVOCs) directly (i.e.,
28 changes in solar radiation and air temperature, among other variables, affect the vegetation's
29 capability to release BVOCs) or indirectly (climate change-induced changes in vegetation
30 species and their prevalence, thereby modulating the emission rates of BVOC). In addition,
31 human-driven land use change will also impact BVOC emissions. The UT-Austin group is
32 coupling climate models, biogenic emission models, air quality models, and anthropogenic land-
33 use models to quantify direct and indirect effects of climate change on biogenic emissions and to
34 predict future air quality trends, using Texas as a case study.
35
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1 D.l.3.4. University of Illinois at Urbana
2 The University of Illinois is developing an Emissions Inventory Modeling System
3 (EIMS) that uses econometric models and emission development tools to formulate future
4 emission inventories for different climate change scenarios in the format used for the National
5 Emissions Inventories (NEI). Changes in population, economy, policies and regulations,
6 technological development, transportation systems, energy systems, landscape and land use, and
7 vegetation and land cover are being considered within the development of the EIMS capability.
8 For the initial development and testing of the modeling system, the focus is on the Chicago area,
9 where the econometric modeling is most highly refined. During the later stages of the research,
10 the methods will be extended to the entire Midwest to demonstrate the wider applicability of the
11 techniques.
12
13 D.l.3.5. University of New Hampshire
14 This investigation is focused on the northeastern U.S. with overall objectives to (1)
15 predict changes in regional climate that will influence natural biogenic emissions to the
16 atmosphere and air quality, (2) quantify the impact of regional climate change on plant
17 ecosystem composition, (3) estimate the regional impact of a changing plant ecosystem on
18 biogenic emissions, and (4) estimate the impact of changes in regional climate and plant
19 ecosystem on aerosol loading, Os, NOx, hydrocarbons, and the oxidative capacity of the
20 atmosphere.
21
22 D.l.3.6. Resources for the Future
23 The interactions between transportation, land use, and vehicle ownership decisions are
24 fundamental to understanding future mobile source emissions. Furthermore, the importance of
25 these interactions increases for issues that require a long planning horizon, such as climate
26 change. The aim of the proposed research is to create a flexible modeling framework to estimate
27 long-term mobile source emissions in a metropolitan region; a framework that reflects the
28 importance of geographic specificity, technological change, and especially behavioral
29 adjustments by consumers. The development of the framework will provide insight into the
30 sensitivity of estimates of future mobile source emissions to assumptions about economic
31 growth, demographic change, technological innovation, and behavioral responses.
32
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1 D.1.4. Regional Development, Population Trend, and Technology Change Impacts on
2 Future Air Pollution Emissions
3 Recognizing the importance of the location and design of new development for creating
4 accurate long-term (50+ years) emissions projections, this RFA focused on methods to project
5 changes in a wide range of key drivers and policy variables. Examples of such changes include
6 transportation infrastructure investments, regional development patterns (e.g., sprawl, Smart
7 Growth), structural and spatial shifts in the organization of production and delivery of services,
8 transportation modal choices (and other lifestyle factors), air quality and climate policies, and
9 population movements, in addition to technological change. More specifically, the spatial and
10 temporal distribution of transportation activities and emissions are key concerns. Because
11 regional development patterns (e.g., housing, roads, commercial development, mass transit
12 systems) vary across the country, both the amount and spatial distribution of air pollution
13 emissions from mobile sources are likely to be affected. Eight proposals (Table D-5) were
14 funded under this RFA.
15
16 D.l.4.1. University of Wisconsin-Madison
17 This study is testing the hypothesis that "smart growth" land use strategies can
18 significantly improve regional air quality throughout the upper midwestern U.S. over the next 25
19 to 50 years. To investigate this question, a fully integrated land use, vehicle travel, and air
20 quality modeling framework is being developed to (1) estimate vehicle trips and miles of travel
21 (VMT) as a function of changes in population density, employment rates, income, and vehicle
22 ownership, (2) estimate mobile source emissions as a function of changing land use patterns (as
23 reflected in VMT), hybrid vehicle technology dissemination, and regional climate, (3) model
24 regional Os and PM concentrations as a function of regional land use, hybrid technology, and
25 energy production scenarios, and (4) account for the effects of continental and global scale
26 pollutant transport on Os and PM chemistry for the target years 2005, 2025, and 2050.
27
28 D.l.4.2. Georgia Institute of Technology
29 Rather than trying to predict how emissions will change in the future and what impact
30 they will have on future air quality, this project is using an inverse approach to identify the
31 desirable distributions of emissions in 50 years. That is, a desirable air quality state is defined
32 and then the emissions and activity profiles required to achieve this state are derived. The
33 project uses the rapidly growing north Georgia area, including Atlanta, to demonstrate the
34 method. Since the Global Program is focused on longer time scales (i.e., 50+ years), emissions
35 and the activities, processes, and infrastructure associated with them can be considered to be
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1
2
Table D-5. 2004 STAR grant recipients: regional development, population
trend, and technology change impacts on future air pollution emissions
Institution
University of
Wi sconsin-Madi son
Georgia Institute of
Technology
University of
California - Davis
Johns Hopkins
University
State University of
New York at Buffalo
University of North
Carolina at Chapel
Hill
University of
Washington- S eattl e
University of Texas at
Austin
Title
Modeling the Effects of Land Use and Technology Change on Future
Air Quality in the Upper Midwestern United States
Air Quality, Emissions, Growth, and Change: A Method to Prescribe a
Desirable Future
Regional Development, Population Trend, and Technology Change
Impacts on Future Air Pollution Emissions in the San Joaquin Valley
Methodology for Assessing the Effects of Technological and Economic
Changes on the Location, Timing and Ambient Air Quality Impacts of
Power Sector Emissions
A Long Term Integrated Framework Linking Urban Development,
Demographic Trends and Technology Changes to Stationary and
Mobile Source Emissions
Advanced Modeling System for Assessing Long-Term Regional
Development Patterns, Travel Behavior, Emissions, and Air Quality
Integrating Land Use, Transportation, and Air Quality Modeling
Predicting the Relative Impacts of Urban Development Policies and
On-Road Vehicle Technologies on Air Quality in the United States:
Modeling and Analysis of a Case Study in Austin, Texas
4
5
6
7
8
9
10
11
12
13
14
15
16
pliant (i.e., adaptable). With the required emission and activity profiles, the types and amounts
of land use modifications, technology advancements, and other changes that will be required to
transform or morph the present emissions scenario into the future desired emissions scenario are
being identified.
D.l.4.3. University of California - Davis
Future progress towards the abatement of air pollution in cities throughout the U.S. is
uncertain because population expansion and current socioeconomic trends affect pollutant
emissions. Further, there is an incomplete understanding of how these factors will combine to
influence air quality at the urban and regional scale. The objective of this project is to combine
land use forecasting models, water constraint models, travel demand models, emissions models,
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1 and a source-oriented air quality model into a modeling system with feedback loops to predict
2 future emissions and associated air quality impacts. The modeling system is being used to assess
3 the sensitivity of emissions inventories to future policy scenarios in the areas of land use policies,
4 transportation investments, technological innovations, air quality regulations, and agricultural
5 practices in the San Joaquin Valley in the year 2030.
6 D.l.4.4. Johns Hopkins University
1 The amounts, locations, and timing of power sector emissions are sensitive to economic
8 and technological assumptions. The purpose of this project is to develop and demonstrate a
9 methodology for creating geographically and temporally disaggregated emissions scenarios for
10 the electric power sector on a multidecadal time-scale for use with air quality models. This
11 project focuses on power generation for three reasons. First, this sector represents a large share
12 of SOx, NOx, mercury, and CC>2 emissions in the U.S. Moreover, future shares are highly
13 uncertain, depending upon technology change, fuel mix, electric load growth, regulation of the
14 electricity sector, and the evolution of environmental policy. Second, alternative scenarios
15 concerning these key drivers can make huge differences in total emissions and their spatial and
16 temporal distribution. Finally, emissions and associated ambient air concentrations are sensitive
17 to the growth and distribution of electricity demands, which in turn are strongly linked to
18 temperature and other climatic variables that may change significantly over the next few
19 decades.
20
21 D.l.4.5. State University of New York at Buffalo
22 The goal of this project is the development of a tool capable of producing long-term (25-
23 to 50-year) projections of stationary- and mobile-source emissions in a metropolitan area.
24 Currently, emission models are used mostly for short time horizons, taking, as given, projections
25 of local economic activity and population change. Instead of simply extrapolating these local
26 trends, this effort models the fundamental behavioral relationships among individuals and firms
27 and links these underlying economic relationships to secular national and international trends in
28 population, economic development, and technological changes relevant to emissions. Among
29 the specific demographic trends to be examined are the aging of the population, reductions in
30 household size, and international immigration. In addition, the possibility of the continued
31 deindustrialization of U.S. manufacturing and its impact on a metropolitan area with
32 considerable manufacturing will be investigated, using the Chicago metropolitan statistical area
33 as a case study. New technologies likely to impact emissions, such as electric vehicles and
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1 hydrogen fuel-cell vehicles, as well as electricity/energy production with a higher renewable fuel
2 mix under higher sustained energy prices also will be investigated.
O
4 D.l.4.6. University of North Carolina at Chapel Hill
5 Through simulation modeling of land use, transportation, emissions, and air quality, this
6 research project rigorously tests the hypothesis that alternative development patterns,
7 implemented regionally over a planning horizon of 50 years, can substantially influence the
8 quantity and location of emissions from on- and off-road mobile sources and thus affect ozone
9 and PM levels. The development patterns of interest include the type of development and its
10 location (e.g., transit oriented development, dense mixed use development, development
11 supportive of non-motorized transportation modes for non-work trips, neo-traditional suburbs,
12 new urban core development, and redevelopment). A case study will be developed, using recent
13 data for Charlotte (NC), Mecklenburg County, and the multi-county Metrolina region.
14
15 D.l.4.7. University of Washington-Seattle
16 The objective of this research is to develop an integrated, Open Source software platform
17 that integrates land use, activity-based travel, and network assignment, and tightly couples this
18 integrated system to current and emerging emissions modeling software (e.g., Mobile6 and its
19 successor, Motor Vehicle Emission Simulator or MOVES). By improving existing models to
20 better reflect and integrate lifestyle, economic production, and public policy factors that drive
21 vehicle miles traveled, this platform will provide a new capacity for integrated land use,
22 transportation, and air quality modeling to support air quality planning in metropolitan areas
23 throughout the U.S. The UW-Seattle group is testing this integrated system in the Puget Sound
24 region, working collaboratively with the Puget Sound Regional Council. They use this
25 integrated model to assess the relative influence of transportation infrastructure, pricing, land use
26 policies—including smart growth, and demographic and economic trends, on VMT and
27 emissions over a 30-year horizon.
28
29 D.l.4.8. University of Texas at Austin
30 The objective of this research is to develop and use an integrated transportation-land use
31 model (ITLUM) to investigate the impacts of regional development scenarios and trade policies
32 on the magnitude and spatial distribution of emissions of Os precursors. ITLUM-based forecasts
33 are being compared with four pre-determined metropolitan development scenarios: (1) low-
34 density, segregated-use development based on extensive highway provision, (2) concentrated,
35 contiguous regional growth within 1-mile of transportation corridors, (3) concentrated growth in
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1 existing and new communities with distinct boundaries, and (4) high-density development and
2 balanced-use zoning. The resulting air quality impacts and predicted human exposures are being
3 evaluated. In addition, ITLUM emission forecasts are compared to those based on the U.S.
4 EPA's post-Clean Air Act Amendment emission scenario projections. The research team is also
5 evaluating whether changes in land use and dry deposition patterns have at least as significant an
6 impact on future air quality as changes in on-road vehicle emission control technologies.
7
8 D.1.5. Fire, Climate, and Air Quality
9 While some attention has been given to the influence of fires on air quality and to the
10 consequences of climate change for wildfires, the focus of this research solicitation was on the
11 integration of the complex interactions of fire, climate, and air quality. In order to produce
12 plausible future emission inventories from fires, critical information must include estimates of
13 location, time, frequency, and fuel characteristics. Due to the inherent uncertainties in predicting
14 the future, this RFA emphasized using a range of scenarios in order to demonstrate which forces
15 and linkages are most important, rather than attempting to develop an exact forecast of the
16 future. Three proposals (Table D-6) were funded under this RFA.
17
18 Table D-6. 2004 STAR grant recipients: fire, climate and air quality
19
Institution
Georgia Institute of
Technology
Harvard University
University of North
Carolina at Chapel
Hill
Title
Interaction of Ecosystems, Fires, Air Quality and Climate Change in
the Southeast
Investigation of the Effects of Changing Climate on Fires and the
Consequences for U.S. Air Quality, Using a Hierarchy of Chemistry
and Climate Models
Investigation of the Interactions between Climate Change, Biomass,
Forest Fires, and Air Quality with an Integrated Modeling Approach
20
21
22 D.l.5.1. Georgia Institute of Technology
23 Large amounts of biomass are burned in the Southeast, and fire emissions have been
24 found to significantly affect air quality in the region. It is expected that the effects of fire
25 emissions will change significantly as a result of climate and land-use changes. The objectives
26 of this research are to (1) integrate process-based ecosystem, fire emissions, air quality, and
27 regional climate models to systematically understand the complex interaction of these
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1 components in the Southeast, (2) evaluate the integrated modeling system with state fire statistics
2 and ground and satellite observations and understand better the effects of fire emissions on air
3 quality in the Southeast, (3) calculate the sensitivities of the modeling system to major inputs and
4 use these sensitivities to quantify uncertainties in the system results, and (4) assess the impact of
5 regional climate and land use changes and fire management on ecosystems and fire emissions
6 and the consequent effects on air quality in the Southeast.
7
8 D.I.5.2. Harvard University
9 Existing studies show that fires in North America can have a significant effect on
10 visibility and air quality in the U.S. on an episodic basis. The Harvard group is exploring the
11 relationships between climate and frequency and intensity of forest fires in North America.
12 Using linear stepwise regression, the best predictors for area burned for different ecosystems,
13 including temperature, relative humidity, wind speed, precipitation, and components of the Fire
14 Weather Index (FWI) system, or of the Fire Weather Danger Rating System (NFSRS) are being
15 determined. The group is using area burned prediction schemes in simulations with the
16 NASA/GISS general circulation model (GCM) to derive estimates of area burned for 2000-2050.
17 Plume heights from fires in North America are related to areas of fires in a study of the effect of
18 present day fires on ozone and PM using the global aerosol-chemistry model, GEOS-CHEM, and
19 CMAQ. Future climate predicted using a general circulation model and relationships between
20 fire and climate is being used to predict future fires in the U.S.. Using global and regional scale
21 chemistry-aerosol transport models, this group is assessing the role of future wild fires on air
22 quality.
23
24 D. 1.5.3. University of North Carolina at Chapel Hill
25 Forest fires not only change landscapes and destroy property but also emit trace gases and
26 aerosols (e.g., CO, methane, NOx, and black carbon) that affect regional and global air quality.
27 These impacts can be felt over long distances because of the long-range transport of these
28 pollutants both as primarily emitted species and as precursors for other pollutants formed in the
29 atmosphere through photochemical reactions. Recently, the increased frequency of large fires in
30 the U.S. has been thought to be associated with short-term changes in climate variables such as
31 precipitation and temperature that have exacerbated the conditions for fire occurrence. The
32 overall goal of research by the UNC-Chapel Hill team is to assess the impact of climate change
33 and variability on biomass and forest fires, evaluate the impact of evolving emissions from forest
34 fires on 63 and PM air quality, and determine the regional climate response to these changes in
35 the Southern U.S. using an integrated modeling approach.
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1
2 D.1.6. Consequences of Global Change for Air Quality
3 The focus of this RFA was on improving the understanding of linkages between climate,
4 atmospheric chemistry, and global air quality and the ability to assess future states of the
5 atmosphere by coupling local- and regional-scale air quality models with global-scale climate
6 and chemistry models. Predictions of future air quality that rely on global climate simulations
7 will require consideration of how larger scale climatic parameters and processes are transferred
8 to regional models. In addition, accurate prediction of precipitation events is a key challenge to
9 modeling air pollution episodes. Successful predictions of future air quality also require a good
10 understanding of both current and future emissions of pollutants and their precursors. Due to the
11 inherent uncertainties in predicting the future, the RFA emphasized that an explicit treatment of
12 uncertainty, such as using multiple scenarios was desirable. Moreover, the focus of the research
13 should be on exploring a range of scenarios to demonstrate which forces and linkages are most
14 important, rather than an exact forecast of the future. Ten projects (Table D-7) were funded
15 under this RFA.
16
17 D.l.6.1. University of California - Davis
18 Ozone and PM standards designed to protect public health are routinely violated in
19 California's South Coast Air Basin surrounding Los Angeles and the San Joaquin Valley (SJV)
20 in central California. The project by the UC-Davis team aims to quantitatively assess the
21 consequences of global change on California air quality by (1) measuring emissions from mobile
22 sources powered by alternative fuels as a function of temperature and humidity, (2) creating a
23 source-oriented PM module for the Weather Research & Forecasting (WRF) model to quantify
24 feedback between air quality and regional meteorology, and (3) calculating California air quality
25 in 2030 for a range of Os and PM2.5 pollution events. GCM simulations of future climate are
26 being dynamically downscaled to the regional scale using the Weather Research and Forecasting
27 (WRF) meteorological model. A source-oriented PM module is being integrated into WRF to
28 study the interactions between pollution and local meteorology. The new model will be used to
29 compare current air pollution episodes in California with those that are expected to occur in the
30 year 2030. Multiple episodes (-30) will be studied in current and future periods to understand
31 the distribution of possible events.
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1
2
Table D-7. 2006 STAR grant recipients: consequences of global change for
air quality
Institution
University of
California - Davis
University of Illinois
at Urbana
University of
Wisconsin - Madison
Desert Research
Institute
Stanford University
University of
Michigan
North Carolina State
University
Harvard University
Carnegie Mellon
University
Washington State
University
Title
Impact of Global Change on Urban Air Quality via Changes in Mobile
Source Emissions, Background Concentrations, and Regional Scale
Meteorological Feedbacks
Impacts of Global Climate and Emissions Changes on U.S. Air Quality
(Ozone, Parti culate Matter, Mercury) and Projection Uncertainty
Sensitivity of Heterogeneous Atmospheric Mercury Processes to
Climate Change
Effects of Global Change on the Atmospheric Mercury Burden and
Mercury Sequestration Through Changes in Ecosystem Carbon Pools
Effects of Future Emissions and a Changed Climate on Urban Air
Quality
Global and Regional-Scale Models for Ozone, Aerosols and Mercury:
Investigation of Present and Future Conditions
Study the Impact of Global Change on Air Quality Using the Global-
Through-Urban Weather Research and Forecast Model with Chemistry
Global Change and Air Pollution (GCAP) Phase 2: Implications for
U.S. Air Quality and Mercury Deposition of Multiple Climate and
Global Emission Scenarios for 2000-2050
Changes in Climate, Pollutant Emissions, and U.S. Air Quality: An
Integrating Modeling Study
Ensemble Analyses of the Impact and Uncertainties of Global Change
on Regional Air Quality in the U.S.
4
5
6
7
8
9
10
11
12
13
D. 1.6.2. University of Illinois at Urbana
The objective of this study by UICU is to quantify and understand the impacts and
uncertainties of global climate and emission changes, from the present to 2050 and 2100, on U.S.
air quality, focusing on Os, PM, and mercury. State-of-the-art, well-established ensemble
modeling systems that couple a global climate-chemical transport component with a mesoscale
regional climate-air quality component is being applied over North America. Both components
incorporate multiple alternative models representing the likely range of climate sensitivity and
chemistry response under plausible emissions scenarios to rigorously assess uncertainty. These
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1 systems are used to quantify the individual and combined impacts of global climate and
2 emissions changes on U.S. air quality. Sensitivity experiments refine understanding of
3 relationships with major contributing source regions and types and uncertainties associated with
4 key conclusions.
5
6 D. 1.6.3. University of Wisconsin - Madison
1 The goal of the proposed research is to quantify the impact of climate change on key
8 atmospheric processes that control the fate of mercury in transport from emissions to deposition.
9 Researchers at UW-Madison build on existing scientific understanding of atmospheric mercury
10 processes by examining the incremental impact of climate change variables on heterogeneous
11 atmospheric mercury oxidation and depositional processes. Specifically, an integrated laboratory
12 and modeling approach is used to quantify (1) the sensitivity of dry deposition of elemental
13 mercury, reactive gaseous mercury, and particulate mercury to temperature, humidity, Os, NOX,
14 and sunlight intensity and (2) the sensitivity of atmospheric mercury oxidation and reduction
15 reaction in fog and cloud water to temperature, sunlight intensity, and the composition of these
16 atmospheric waters. In addition, the oxidation of elemental mercury in the presence of the
17 complex atmospheric reactions that produce photochemical smog and secondary organic aerosols
18 are being investigated. Finally, the group uses a regional chemical transport model to explore
19 the sensitivity of mercury deposition to temperature, precipitation, and atmospheric circulation
20 patterns associated with climate change.
21
22 D.l.6.4. Desert Research Institute
23 Terrestrial carbon pools play an important role in uptake, deposition, sequestration, and
24 emission of atmospheric mercury. Biomass and soil carbon pools are highly sensitive to climate
25 and land use changes with potentially serious consequences for the fate of an estimated 50,000
26 Mg of atmospheric mercury associated within carbon pools. The objective of the research by the
27 Desert Research Institute is to assess how global change over the next 100 years affects mercury
28 cycling processes—atmospheric mercury uptake, sequestration, and emission—associated with
29 vegetation and soil carbon pools. Effects of global change on plant-derived atmospheric mercury
30 inputs to ecosystems via changes in plant productivity, plant senescence, and litterfall are being
31 assessed. In addition, global change impacts on plant, litter, and soil carbon pools and the
32 resulting effects on sequestered mercury within these pools and feedback on the future
33 atmospheric mercury burden are investigated. This effort involves several components including
34 a systematic collection of data on mercury in vegetation and soil carbon pools in terrestrial
35 ecosystems, field and laboratory experimental studies, and modeling.
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1 D.l.6.5. Stanford University
2 This research study examines the effects of changes in emissions on climate and the
3 resulting feedback of climate on air quality in Los Angeles, the Central Valley, and Atlanta
4 during the next 50 years. In addition to applying A1B and Bl IPCC-SRES emission factors to
5 the 2005 U.S. National Emission Inventory to develop future air pollutant scenarios, this project
6 investigates the effects on emissions due to implementing a future fleet of ethanol-gasoline (85%
7 ethanol-15% gasoline), plug-in gasoline-electric hybrids, and wind-electrolysis-hydrogen-fuel-
8 cell vehicles. Of interest is determining whether such vehicles will increase or decrease Os and
9 PAN in different parts of the U.S. and how global warming may affect their emissions. Finally,
10 the researchers are considering the contribution of Asian emissions to U.S. pollution. It has been
11 suggested that higher future emissions from Asia will increase urban air quality problems in
12 California and the west, and this research is intended to provide useful information on this issue.
13
14 D.l.6.6. University of Michigan
15 This research proj ect investigates the impact of future climate and emissions of air
16 quality in the U.S. with a focus on Os and mercury. The University of Michigan uses linked gas-
17 phase and aqueous photochemistry models and a new approach for representing the interaction
18 between aerosols and tropospheric chemistry. Importantly, the meteorology derived from linked
19 global circulation and chemistry/transport models includes event-specific aerosol impacts on
20 climate. Model correlations of Os with temperature are being used as a basis for evaluating
21 accuracy of the predicted response to climate. Other species correlations (Os-CO, Os-NOy, Os-
22 PAN) are also investigated as indicators for the effect of global emissions on air quality. For
23 mercury, the project aims to identify the relative impact of local emissions and global transport
24 in two regions where mercury has caused environmental damage (the Great Lakes and Florida).
25 EPA field measurements in those regions will be used to evaluate model accuracy. A series of
26 species correlations will be investigated as possible measurement-based evidence for the impact
27 of local versus global emissions. Finally, correlations between reactive mercury and Os are
28 being investigated to determine whether Os formation also affects mercury.
29
30 D.l.6.7. North Carolina State University
31 An overarching goal of the proposed research is to develop a community global-through-
32 urban model framework that fully couples meteorology and chemistry and contains state-of-the-
33 science treatments for Os, PM2.5, and Hg in both troposphere and stratosphere at all scales.
34 Application of this unified model with consistent physics in a two-way nesting mode allows the
35 researchers to examine the two-way feedbacks between climate changes and air quality and
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1 determine their importance in quantifying the impact of global changes on air quality.
2 Sensitivity simulations with respect to inputs, configurations, resolutions, and physics help
3 quantify the uncertainties in these model parameters. In addition, a unified model will improve
4 our scientific understanding of the interactions among multiple pollutants and multiple processes
5 (e.g., transport, chemistry, radiation, removal). Results from this project aim to inform policy
6 makers about current and future integrated emission control strategies for multiple pollutants in a
7 changing world.
8
9 D.l.6.8. Harvard University
10 The proposed research by Harvard University builds on previous work that resulted in the
11 construction of powerful and versatile machinery for investigating the effects of climate change
12 on air quality and mercury deposition. This global-regional model capability will be used to
13 address three critical issues over the 2000-2050 time horizon. First, the potential range of global
14 change impacts on air quality is being assessed through consideration of an ensemble of
15 scenarios for greenhouse gas and air pollutant emissions. Second, a series of sensitivity
16 simulations is being carried out to investigate the effects of global climate and emission changes
17 on intercontinental transport of pollution to the U.S. Finally, taking advantage of the capability
18 for dynamic coupling of mercury between atmospheric, oceanic, and terrestrial reservoirs, the
19 Harvard group examines mercury deposition to ecosystems, including how climate change might
20 perturb the cycling of mercury between the atmosphere and surface reservoir. This set of
21 projects will provide important information to policymakers as they consider issues such as co-
22 benefits of greenhouse gas reductions, long-range transport of air pollutants, mercury deposition,
23 and the effects of global change on regional air quality.
24
25 D.l.6.9. Carnegie Mellon University
26 Future changes in climate, biogenic emissions, and long-range transport of pollution may
27 provide additional challenges to air quality management in the U.S. The goal of the CMU study
28 is to quantify the expected magnitude and range of these impacts on ozone, PM25, PM25.i0, and
29 ultrafme PM concentrations, visibility, mercury, and acid deposition. This project builds on
30 previous work by CMU that resulted in a coupled global-regional climate and air pollution
31 modeling system. The system is being extended to incorporate and account for climate-sensitive
32 emissions (e.g., biogenic, ammonia, evaporative emissions, etc.), recent developments in
33 understanding of the formation and partitioning of secondary organic aerosol, and the volatility
34 of primary organic aerosol components, mercury atmospheric chemistry and deposition, and
35 ultrafme aerosol size-composition distribution. The researchers also are using a new approach
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1 for screening and selecting climate scenarios for regional air quality simulations: an ensemble of
2 approximately 30 years of future climate is screened to select both "representative" years but
3 also more extreme (colder-warmer, wetter-drier, clearer-cloudier) years for uncertainty analysis.
4 Finally, the CMU group is conducting a range of sensitivity simulations and alternative future
5 scenarios to explore uncertainties associated with future emissions. The ultimate goal of this
6 research is to provide insights and tools to inform air quality management decisions about the
7 impacts of global climate and emission changes on U.S. air quality.
8
9 D.I.6.10. Washington State University
10 This proposal builds on current research by WSU on the effects of global change on
11 continental and regional air quality to include quantitative estimates of uncertainties. An
12 ensemble modeling approach is being used to develop a quantitative measure of the uncertainty
13 in the WSU modeling framework in comparison to current 1990-1999 observations and to
14 project these uncertainties into the future. Bayesian analyses of the coupled global-regional
15 model configurations for a base climate period (1990-1999) are conducted to produce weighted
16 ensemble members based upon their skill in representing observed climate and air quality. Using
17 this analysis, the number of ensemble members for future climate runs will be reduced to those
18 that provide significant skill to the overall composite. In addition, the WSU group is
19 quantitatively addressing the uncertainties that accompany projections of future emissions,
20 including changes in landcover, urbanization, biogenic emissions, and fire emissions.
21 Combining the reduced ensemble set with a range of potential emission scenarios in a factorial
22 design encompasses a number of model/emission scenarios so that quantitative estimates of the
23 air quality impact and uncertainties associated with both modeling errors and emission scenarios
24 are obtained.
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1 APPENDIX E
2 MODELING APPROACH FOR INTRAMURAL PROJECT ON CLIMATE IMPACTS
3 ON REGIONAL AIR QUALITY
4
5 As described in the main body of this report, a number of modeling studies have been
6 developed to support this assessment of potential impacts of climate on air quality. In addition to
7 the extramural projects supported through the National Center for Environmental Research
8 (NCER) (see Appendix 4), an intramural modeling study referred to as the Climate Impacts on
9 Regional Air Quality (CIRAQ) project was initiated in 2002. CIRAQ is organized into two
10 phases: Phase I, where the focus is the impact of future climate on air quality if anthropogenic
11 emission sources remained at current levels and Phase II, where the focus is the impact on air
12 quality both from future climate and future emission scenarios for ozone (63) and aerosol-related
13 emissions.
14 The CIRAQ project was separated into Phase I and Phase II to distinguish the influence
15 of future climate scenarios separately from changes in emissions that effect O3 and PM2.5. In the
16 December 2000 workshop (see Chapter 1 of this report), this approach was discussed, and it was
17 agreed upon as necessary for teasing out these different influences. The schedule for Phase I was
18 organized to contribute results to this 2007 interim report, and Phase II will be completed for the
19 2010 final report on climate impacts on national air quality. The future emission scenarios that
20 will be used for Phase II of CIRAQ have been under development in the EPA Office of Research
21 and Development (see Appendix 6) while Phase I has been underway.
22 Another decision made in the design of CIRAQ was that multiple years of simulation
23 were needed to insure that interannual variability would not be misinterpreted as climate change
24 in the comparison of current to future simulations. Interannual variability in meteorological
25 conditions such as temperature and precipitation can have a strong effect on air quality, yet it is
26 driven by periodic patterns such as the El Nino-Southern Oscillation (ENSO) or North American
27 Oscillation (NAO) cycles. These ENSO and NAO cycles are part of natural climate variability
28 and not related directly to climate warming from greenhouse gases. The schedule for this project
29 was used to determine the maximum number of years that could be simulated for the project.
30 Specifically, 10 years of meteorology and 5 years of air quality were modeled each for the
31 current and future periods.
32 To study potential impacts of future climate on air quality, models are needed to simulate
33 hypothetical future scenarios. These models must include processes that are involved in global -
34 scale climate as well as processes that are involved in regional-scale air quality. A dynamical
35 downscaling approach (Leung et al., 2003) was taken that links global scale climate and
36 chemistry models with regional scale meteorology and air quality models. In this way, global
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1 and hemispheric influences on climate and long-range transport of pollutants can be incorporated
2 into the regional predictions. Below, a description of each of these global and regional modeling
3 components is described along with some background on why each of the models or options was
4 chosen.
5 For the global scale models used in this study, the CIRAQ project coordinated with
6 several extramural projects supported by NCER grants. The global climate model (GCM) is
7 derived from the Goddard Institute for Space Studies (GISS) II' model, as described by Mickley
8 et al. (2004). A benefit from this model is that it also includes a tropospheric Os chemistry
9 model (Mickley et al., 1999) that could provide chemical boundary conditions for 63 and 63
10 precursors to the regional scale air quality model. Consistency between the global climate and
11 chemistry was another criterion for the CIRAQ modeling simulations, so that the climate and
12 chemical boundary conditions for the regional model would be consistent. The GCM has a
13 horizontal resolution of 4° latitude and 5° longitude and nine vertical layers in a sigma coordinate
14 system extending from the surface to 10 mb. The global climate simulation covers the period
15 1950-2055, with greenhouse gas concentrations updated annually using observations for 1950-
16 2000 (Hansen et al., 2002) and the A1B scenario from the IPCC for 2000-2055 (IPCC, 2000).
17 The GISS IF GCM's radiation scheme assumes present-day climatological values for 63 and
18 aerosol concentrations, i.e., without any feedbacks due to future concentration changes. These
19 GCM simulations were developed by Dr. Loretta Mickley at Harvard University, and the GCM
20 simulation is described in Mickley et al. (2004). Support was initially provided by the intramural
21 CIRAQ project for these simulations, and Dr. Mickley is also a co-investigator on the NCER-
22 funded grant at Harvard (PI: Dr. Daniel Jacob), where a new version of the GISS GCM is now
23 being coupled with their GEOS-Chem global chemistry model.
24 The regionally downscaled climate simulations for the CIRAQ project were developed by
25 Dr. Ruby Leung, who is a leading expert in dynamical downscaling from Pacific Northwest
26 National Laboratory. A regional climate model (RCM) based on the Penn State/National Center
27 for Atmospheric Research (NCAR) Mesocale Model (MM5) (Grell et al., 1994) was used to
28 downscale the GCM output for 1990-2003 and 2045-2055. Climate fields, at a temporal
29 resolution of 6 hours, from Dr. Mickley's GISS IF simulations were used as lateral boundary
30 conditions for these RCM simulations, and the same CO2-equivalent concentrations were used
31 within the RCM domain as used in the GCM simulations. Dr. Leung's RCM simulations were
32 designed with a two-way nested configuration with 108 km and 36 km horizontal resolution for
33 the outer and inner domains, respectively and 23 vertical layers (Leung and Gustafson, 2005).
34 Unlike standard MM5 simulations for air quality modeling, no assimilation of observational data
35 was used for the current RCM simulations. The primary reasons for this were to evaluate the
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1 RCM simulations under current climate to establish the model performance and to insure
2 consistency between the current and future simulations. MM5 options used included the Grell
3 cumulus parameterization scheme with shallow convection, Reisnerl mixed phase cloud
4 microphysics, the Medium Range Forecast Model (MRF) planetary-boundary-layer scheme, the
5 NOAH land-surface model, and the Rapid Radiative Transfer model (RRTM). Further
6 discussion of the physics parameterizations used is provided in Leung et al., (2003). In general,
7 choices were made to preserve the large-scale dynamical features of the GCM simulation rather
8 than attempt to match present observed climatological patterns. This requirement distinguishes
9 this approach from that taken by Liang et al. (2006) and Hogrefe et al. (2004), where MM5
10 options were chosen that evaluated best against observational data within the domain.
11 Dr. Leung provided the RCM hourly outputs to EPA via external hard drives and
12 automated quality assurance steps were taken to check for corrupt or missing data files. This
13 was critical since a total of 4 terrabytes of data were transferred. Leung and Gustafson (2005)
14 provides a comparison of these current and future RCM simulations, where temperature
15 increases over the continental U.S. were consistent with those predicted in the GISS IF
16 simulation at a coarser scale. Leung and Gustafson (2005) identify a difference in the ventilation
17 where the future RCM simulations show increases in ventilation while the GISS IF shows
18 increased stagnation that could lead to increased or longer pollution episodes (Mickley et al.,
19 2004). The fact that the ventilation is different between the global and the regional models is
20 substantial since stagnation is a large driver for pollution events. Once the RCM simulation
21 results were archived at EPA, an extensive evaluation of the RCM results during the current time
22 period was conducted.
23 Evaluation of the RCM shows that the RCM-derived climate across the western U.S. was
24 generally well simulated for all seasons. The western U.S. weather patterns, temperature, and
25 precipitation from the RCM were similar to the North American Regional Reanalysis (NARR)
26 during all seasons, particularly in the summer. Many of the primary weather patterns that occur
27 over the eastern U.S. were well simulated by the RCM during the winter. However, a main
28 component of the warm season weather patterns over the eastern U.S., the subtropical Bermuda
29 high pressure system off the southeast U.S. coast, was not well simulated by the RCM. This
30 could have been influenced by the Bermuda High being further east in the GCM than typically
31 observed or because of the coarse resolution of the GCM. More detailed information about the
32 RCM evaluation can be found in Gilliam and Cooler (2007) and Cooler et al. (2007).
33 For the CIRAQ project, the regional-scale air quality simulations were conducted using
34 the CMAQ model version 4.5 (Byun and Schere, 2006) for the two 5-year periods "1999-2003"
35 and "2048-2052." These years are placed in quotes to emphasize that the simulations are
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1 climatological representations of present and future air quality under the A1B scenario and are
2 not intended to represent or predict the actual day-to-day variations in pollutant concentrations
3 for either the present or future modeling periods. The 5 years of simulation for the current and
4 the future time periods was the maximum number of years that could be completed on the
5 schedule for this 2007 report and provides the best current estimate of interannual variability
6 when estimating air quality changes with future climate. The current years "1999-2003" were
7 selected because they were prior to the NOx SIP Call that was implemented in May 2004 (U.S.
8 EPA, 2005) and because it represents the most recent emission inventory estimates of 2001.
9 Results do suggest that it was important to consider interannual variability as well as an extended
10 summer season for Os, where the Leung and Gustafson (2005) RCM simulation scenario
11 suggests an extension of the Os season into the fall.
12 CMAQ options used included the Statewide Air Pollution Research Center (SAPRC)
13 chemical mechanism (Carter, 2000), the Rosenbrock chemical solver (Sandu et al., 1997), and
14 the Regional Acid Deposition Model (RADM) cloud scheme. The domain, slightly smaller than
15 the innermost downscaled MM5 domain, encompassed the entire continental U.S., parts of
16 Canada and Mexico, and the surrounding oceans at a horizontal resolution of 36 km and with 14
17 vertical layers. The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system
18 (Houyoux et. al., 2000) version 2.2 was used to calculate the plume rise in preparing daily
19 emissions inputs consistent with the meteorology. Biogenic emissions were computed using the
20 Biogenic Emissions Inventory System (BEIS) (Pierce et al., 1998) version 3.13 and the
21 downscaled RCM outputs. Anthropogenic emissions were based on the U.S. Environmental
22 Protection Agency 2001 National Emission Inventory (NEI). NEI inventories typically are
23 developed incrementally for every third year, and 2001 was the most recent time period available
24 for this study.
25 Chemical boundary conditions for Os and Osprecursors were taken from monthly
26 averaged outputs of the tropospheric O3 chemistry module coupled to the GISS IF GCM. While
27 results included in this report focus on Os, the CMAQ simulations also included aerosol
28 predictions. Boundary conditions for the aerosols were provided by Dr. Peter Adams from
29 Carnegie Mellon University, who has an NCER grant for climate and air quality. Those
30 boundary conditions were also based on a GISS H'-driven chemistry model for aerosols;
31 however, the GISS IF GCM was driven by the IPCC A2 scenario (Racherla and Adams, 2006).
32 This difference should not be substantial at 2050 since the IPCC climate scenarios do not diverge
33 drastically until post-2050.
34 Results from the CMAQ simulations were evaluated against observed O3 data from the
35 Air Quality System (AQS) observational network. These evaluations were based on the
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1 comparisons of the observed and modeled distribution of 63 during the summer season because
2 these climatological simulations were not designed to replicate the actual series of changes in Os
3 on specific days. Results showed a substantial over-prediction bias in summertime Os that
4 appears to be influenced primarily by the SAPRC chemical mechanism. A secondary cause of
5 the Os bias is the meteorological prediction uncertainties from the RCM. Gilliam and Cooler
6 (2007) show that the RCM simulations used in the CIRAQ study, under-predicted precipitation
7 and had a positive bias in temperature in the areas of the Southeast and Midwest, where the over-
8 prediction biases were most evident. The full details from this analysis can be found in Nolte et
9 al. (2007). Since the CIRAQ study is focused on the change in 63 from current to future climate
10 scenarios, it is anticipated that this Os bias would exist in the current and future simulations and,
11 therefore, be cancelled out to some degree.
12 The SAPRC chemical mechanism was chosen for the CIRAQ CMAQ simulations
13 because it is considered a more detailed, up-to-date mechanism than CB4 (Gery et al., 1989) and
14 because the chemical groupings are more consistent with the chemical families in the Harvard
15 global chemistry model (Mickley et al., 2004). Based on the findings from this study, it would
16 be preferable to include CMAQ simulations using the new CB05 chemical mechanism now
17 available in the most recent release of CMAQ version 4.6 in the second phase of this CIRAQ
18 study. This could require development of current climate CMAQ CB05 simulations for
19 comparison as well; therefore, it would not be possible to follow the 5-year time series approach
20 used in Phase I of this study.
21 As described earlier, Phase I of this project was only intended to focus on the impacts of
22 future climate on air quality without including any future scenarios for the anthropogenic
23 emissions for 63 and PM2.5. In preparation for Phase II, it was, however, decided that a
24 simplified sensitivity test that adjusted the current anthropogenic emissions based on IPCC
25 scaling factors would be helpful. For the future simulation with emission changes, scaling
26 factors consistent with the A1B AIM scenario for the OECD90 region were applied for all
27 anthropogenic emission sectors, as shown in Table E-l. This control-case approach is
28 admittedly simplistic and is intended to be a minimal sensitivity test of the range of impacts that
29 could result from the A1B scenario.
30 Results from this sensitivity test with future emissions suggested that substantial
31 decreases in 63 would occur under the future A1B climate scenario and these A1B AIM
32 OECD90-based reductions in anthropogenic emissions. The modeling results had suggested a
33 2-5 ppb increase in 63 with future climate only (i.e., no change in current anthropogenic
34 emissions); therefore, the change in emissions is anticipated to have a much larger influence in
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1 the model predicted changes in 63. While that is not unexpected, the results highlight how
2 important the selection of future emission scenarios will be. If the IPCC A2 scenario had been
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1
2
Table E-l. Scaling factors for future emissions sensitivity test
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Species
NOx
SO2
VOCs
CO
A1B AIM 2050 Scaling
0
0
0
1
Factor (relative
52
37
79
.5
to 2000)
selected for this study, the conclusions would have been conversely different where future
emissions and future climate would results in dramatic increases in Os, as shown by Hogrefe et
al. (2004). While it is far more likely that NOx and SO2 emissions will be reduced in the future
rather than as the A2 scenario suggests, the exact amount of reduction is more uncertain the
further into the future these scenarios are developed. Therefore, the conclusion from this
analysis is that Phase II of the CIRAQ project needs to focus on multiple 2050 anthropogenic
emission scenarios to develop a plausible range of results.
This conclusion that a series of simulations is needed with different options or choices is
a common recommendation from the evaluation of the CIRAQ CMAQ results and the sensitivity
tests with A1B emission scaling factors described above. It leads to new challenges for Phase II
of CIRAQ since 5-year simulations are not feasible for multiple chemical mechanisms and future
emission scenarios. Interannual variability in the current series of simulations can be used to
help guide selection of shorter time periods that represent extreme and average years. This may
be the best approach for reducing the number of simulation years and increasing the range of
options and sensitivity tests.
For the 2007 interim report and current manuscripts developed for CIRAQ, the primary
focus has been on O3 rather than PM2.5. The current vs. future PM2.5 results are more uncertain
and complicated since PM is composed of multiple chemical species, the concentrations of
which are influenced by different emission sources. Preliminary analyses suggest that the
ventilation or stagnation in the future scenarios could have a substantial influence on the results,
and discrepancies between the global and regional simulations of future stagnation frequency
lend more uncertainty to the future PM2.5 change estimates. Further, several factors could
influence the future primary PM2 5 emissions that can be directly influenced by future climate
conditions, such as forest fires and windblown dust. Current emissions from these types of
sources are static and do not vary based on changes in climate. Another factor of uncertainty is
the influence of future air quality changes on the regional climate, where for example lower
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1 concentrations of sulfate aerosols could lead to more positive net radiative forcing. New
2 modeling tools are becoming available, such as WRF-CMAQ, which includes feedbacks from air
3 quality to regional climate. These new modeling tools are needed to better understand how
4 climate and air quality interact in future scenarios and increase our confidence in the future
5 scenarios for PM2.5.
6
7 E.I. REFERENCES
8 Byun, D; Schere, KL. (2006) Review of the governing equations, computational algorithms, and other components
9 of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl Mech Rev 59:51-77.
10 Carter, WPL. (2000) Implementation of the SAPRC-99 chemical mechanism into the Models-3 framework. Report
11 to the United States Environmental Protection Agency, January 29, 2000. Available online at
12 http://pah.cert.ucr.edu/ftp/pub/carter/pubs/s99mod3 .pdf.
13 Cooler, EJ; Gilliam, RC; Swall, J; et al. (2007) Evaluation of 700 mb steering level winds from downscaled global
14 climate model results. Part II: Comparison of reanalysis and regional climate model data. J Appl Meteorol Climatol:
15 in NOAA review.
16 Gery, MW; Whitten, GZ; Killus, JP; et al. (1989) A photochemical kinetics mechanism for urban and regional scale
17 computer modeling. J Geophys Res 94(D10):12,925-12,956.
18 Gilliam, RC; Cooler, EJ. (2007) Evaluation of the large-scale weather patterns, temperature, and precipitation of a
19 10-year GISS regional climate simulation, in review
20 Grell, G; Dudhia, J; Stauffer, DR. (1994) A description of the fifth generation Perm State/NCAR mesoscale model
21 (MM5). NCAR Tech Note, NCAR/TN-398+STR. NCAR (National Center for Atmospheric Research), Boulder,
22 CO. Available online at http://www.mmm.ucar.edu/mm5/documents/mm5-desc-doc.html.
23 Hansen, J; Sato, M; Nazarenko, L; et al. (2002). Climate forcings in Goddard Institute for Space Studies SI2000
24 simulations. J Geophys Res 107(D18):4347, doilO. 1029/2001JDOO1143.
25 Hogrefe, C; Lynn, B; Civerolo, K; et al. (2004) Simulating changes in regional air pollution over the eastern United
26 States due to changes in global and regional climate and emissions. J Geophys Res. 109:D22301,
27 doi: 10.1029/2004 JD004690.
28 Houyoux, M.R., J.M. Vukovich, C.J. Coats, Jr., N.W. Wheeler, and P.S. Kasibhatla, (2000). Emission inventory
29 development and processing for the seasonal model for regional air quality (SMRAQ) project. J.. Geophys. Res.,
30 Atmospheres, 105(D7): 9079-9090.
31
32 IPCC (Intergovernmental Panal on Climate Change). (2000) Special report on emissions scenarios. New York, NY:
33 Intergovernmental Panel on Climate Change. Available online at http://www.grida.no/climate/ipcc/emission/.
34 Leung, LR; Qian, Y; Bian, X. (2003) Hydroclimate of the western United States based on observations and regional
35 climate simulation of 1981-2000. Part LSeasonal statistics. J Climate 16(12):1892-1911.
36 Leung, LR; Gustafson, WI, Jr. (2005) Potential regional climate change and implications to U.S. air quality.
37 Geophys Res Lett 32(16):L16711,doi:10.1029/2005.
38 Liang X-Z; Pan, P; Zhu, J; et al. (2006) Regional climate model downscaling of the U.S. summer climate. J
39 Geophys Res 111(D10):D10108, doi:10.1029/2005JD006685.
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1 Mickley, LJ; Jacob, DJ; Field, BD; et al. (2004) Effects of future climate change on regional air pollution episodes
2 in the United States. Geophys Res Lett 3LL24103, doi:10.1029/2004GL021216.
3 Mickley, LJ; Murti, PP; Jacob, DJ; et al. (1999) Radiative forcing from tropospheric ozone calculated with a unified
4 chemistry-climate model. J Geophys Res 104(D23):30,153-30,172.
5 Nolte et al. (2007), submitted/under peer review.
6 Pierce, T; Geron, C; Bender, L; et al. (1998) Influence of increased isoprene emissions on regional ozone modeling.
7 JGeophysRes 103(D19):25,611-25,629.
8 Racherla, PN; Adams, PJ. (2006) Sensitivity of global tropospheric ozone and fine paniculate matter concentrations
9 to climate change. J Geophys Res 111(D24):D24103, doi!0.1029/2005JD006939.
10 Sandu, A, Verwer, JG; Blom, JG. (1997) Benchmarking stiff ODE solvers for atmospheric chemistry problems II:
11 Rosenbrock solvers. Atmos Environ 31(20):3459-3472.
12 U.S. EPA (Environmental Protection Agency). (2005) Evaluating ozone control programs in the Eastern United
13 States: focus on the NOXbudget trading program, 2004. Office of Air and Radiation, Washington, DC:
14 EPA/454/K-05/001. Available online at http://www.epa.gov/airtrends/2005/ozonenbp.pdf.
15
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1 APPENDIX F
2 USING MARKAL TO GENERATE EMISSIONS GROWTH PROJECTIONS FOR THE
3 EPA GCRP AIR QUALITY ASSESSMENT
4
5 F.I. INTRODUCTION
6 F.I.I. Background
7 The U. S. EPA contributes to the U. S. Climate Change Science Program (CCSP) by
8 working to develop an understanding of the potential environmental impacts of anticipated future
9 global changes, including population growth and migration, economic growth, land use change,
10 technology change, climate change, and government actions and policies. As a central
11 component of EPA's contribution to the CCSP, the EPA Office of Research and Development's
12 Global Change Air Quality Assessment is building upon traditional EPA expertise by examining
13 the connection between these global changes and air quality.
14 Air pollutants of particular concern are tropospheric ozone (63) and fine particulate
15 matter (PM 2.5). These pollutants, which are components of urban smog, contribute to human
16 respiratory problems, damage ecosystems, and reduce visibility, among other impacts. They are
17 formed through atmospheric reactions of precursor emissions. Precursors to 63 include nitrogen
18 oxides (NOx) and volatile organic compounds (VOCs). In most areas of the U.S., VOCs from
19 vegetation are in sufficient concentration that NOx is the limiting chemical species in Os
20 formation. The predominant source of NOx emissions is the combustion of fossil fuels. Fine
21 PM formation can involve many chemical species, but sulfur oxides (SOx), NOx, elemental and
22 organic carbon, and ammonia are common precursors. Coal and diesel combustion are sources
23 of SOx, and carbonaceous PM is most often a product of incomplete combustion.
24 Human health concerns have led to ambient air quality standards being implemented for
25 Os and particulates. Many areas of the country are not currently in attainment with these
26 standards, however, leading to recent air quality legislation, including the NOx SIP Call (U.S.
27 EPA, 2006a), Clean Air Interstate Rule (CAIR) (U.S. EPA, 2005), Heavy Duty Highway Diesel
28 Rule (U.S. EPA, 2007a), and Nonroad Diesel Rule (U.S. EPA, 2004). These regulations are
29 expected to bring most urban areas of the U.S. into attainment by 2015.
30 The ability of these programs to maintain air quality further into the future is less certain.
31 The U.S. population is projected to continue to grow through 2050 as is the U.S. economy.
32 These factors potentially yield increases in emissions from additional demands for energy and
33 transportation services, among others. Further, climate change projections predict generally
34 warmer temperatures, exacerbating pollution by increasing summer energy demands for cooling
35 and by increasing the photochemical reaction rates that produce tropospheric Os. Countering
36 these factors, economic and policy drivers will likely result in technology change, including
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
energy efficiency improvements and reduced pollutant emissions rates. Characterizing the
relative and combined impact of these factors, thus, is an important step in anticipating future-
year air quality and in identifying whether additional technologies or policy measures will be
necessary to protect human health and the environment. This characterization is one of the
primary products of the Global Change Air Quality Assessment, with contributing work being
carried out both through intramural and extramural research activities.
F.1.2. Conceptual Framework
The intramural modeling activities of the Global Change Air Quality Assessment are
aimed at evaluating the individual and combined impacts of climate and emissions changes on
air quality in 2050. The work described in the main body of the 2007 Interim Assessment Report
has largely focused on characterizing climate change impacts. Air quality modeling was carried
out with year 2000 emissions for two cases of meteorology thought to be representative of the
years 2000 and 2050, respectively. The 2012 Final Assessment Report will augment the 2007
analysis by evaluating the 2050 meteorological case with projected emissions for 2050.
The relationship between the modeling runs is illustrated in Figure F-l. This
experimental design is anticipated to allow the meteorological and emissions signals on air
quality to be evaluated individually and together.
20
21
22
23
Emissions
2000
2050
Meteorological Scenario
2000
2007 Interim
Assessment
Report
Meteoroloj
^i
^jfjtJ ^^^^
^"V*
2050
2007 Interim
Assessment
Report
jical Signal
201 2 Final
Assessment
^ Report
ra
c
c
o
Ul
Ul
'E
^^w * 'm
9/ ^w T
Figure F-l. Experimental design of the global change
air quality assessment.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 One of the major challenges in carrying out this experimental design is the generation of
2 a realistic and representative emissions inventory for 2050. A step in creating such an inventory
3 was to develop a conceptual model that outlines the various system components and linkages that
4 influence future year emissions and air quality. Figure F-2 is a graphical depiction of this
5 conceptual model.
7
8
9
10
11
12
13
14
15
16
17
18
Scenario
Assumptions
National-level population &
economic growth
Technology availability and cost
Air quality and GHG policies
Population
Growth &
Migration
Economic
Growth
Land-Use
Change
Regional
climate
ch
Emissions
growth rinirts
Emissions
Characterization
Regional
Meteorology
Emissions
inventory
r
Voncentr.itiods
\
Global
Meteorology
& Chemistry
Background
pollutant
concentrations
concentrations •* ~^r
PaiiutanT* ~~ ^ ~"~ \
Pollutant
concentrations
Ambient
air quality
Impacts
Economic
impacts
Figure F-2. Conceptual framework outlining the influence of global change
factors on air quality.
At this high level, the overall system is driven by various global- and national-scale
assumptions. Assumed global greenhouse gas emissions drive global circulation patterns,
meteorology, and chemistry. These, in turn, affect regional meteorology, temperature-sensitive
anthropogenic and biogenic emissions, and pollutant transport and chemistry. Assumptions also
drive technology change, economic growth, population growth and migration, and land-use
change which are, themselves, interrelated. These factors have great implications on the quantity
and location of pollutant emissions and, thus, on air quality. The blue text and lines represent
This document is a draft for review purposes only and does not constitute Agency policy.
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1
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feedbacks that may be important. For example, pollutants such as aerosols and black carbon
have radiative forcings that can affect regional climate. Similarly, health and environmental
impacts may lead to better or worse economic conditions and changes in mortality rates, thereby
affecting some of the drivers for emissions growth.
In Figure F-3, the box encompassing technology change, economic growth, population
growth and migration, and land-use change is examined in more detail.
GDP trajectory
Climate-related
! changes in amenities ',
Climate-related
Economic impacts
Climate-related
changes in demands
and technology
properties
commercial
energy demands
Population growth
trajectory
Population Population
• SJZB& geographic
•^ distribution
Migration
Industrial i
commercial
land growth
Residential
energy demands
Land-Use
Change
Technology
Change
Emissions
growth-
Meteorological
changes
Economic
growth for
lion-energy
sectors
'Land use
projections
for use as
surrogates
Emissions
Characterization
Figure F-3. Conceptual model detail on the factors affecting future-year
emissions growth.
This figure indicates the relationship between economic growth and population changes.
Economic growth is a function of the cost of labor while population migration is affected by the
availability of jobs. Both economic growth and population changes drive energy demands and
This document is a draft for review purposes only and does not constitute Agency policy.
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1 may indirectly influence changes in the technologies. Population growth and migration have an
2 effect on land use, including the transformation of rural, agricultural, and forest land to
3 accommodate housing. These changes, in turn, affect the quantity, nature, and geographic
4 distribution of both biogenic and anthropogenic emissions. Climate change has the potential to
5 impact processes represented within many of these components. For example, climate changes
6 can change the attractiveness of living in various areas, the ability to use land for agricultural and
7 recreational purposes, and demands for energy services such as heating and cooling. The
8 feedbacks indicated in Figure F-2, including the effects on the economy and population resulting
9 from air quality impacts, are not included in Figure F-3.
10
11 F.1.3. Intramural Emissions Modeling Effort for the 2010 Assessment Report
12 Developing emissions projections for the 2010 assessment report involves realizing the
13 conceptual model illustrated in Figures F-2 and F-3 with a modeling methodology. The level of
14 available resources necessitates the leveraging of existing expertise, models, and tools. For
15 example, within its ongoing regulatory and research air quality modeling applications, EPA uses
16 the Sparse Matrix Operator Kernel Emissions (SMOKE) processing model, the MM5 regional -
17 scale meteorological model, and the CMAQ air quality model. Given future-year projections of
18 meteorology and emissions, these models readily can be applied to evaluate a 2050 emissions
19 scenario.
20 Generating a 2050 emissions inventory for input into SMOKE is not straightforward,
21 however. EPA typically uses the Integrated Planning Model, or IPM, to model fuel use and
22 emissions from the electricity production sector. IPM has been applied to model past and present
23 emissions, as well as to project emissions to a near-term future year, such as 2007, 2015, or
24 2020. IPM was not developed with the goal of producing emissions projections to 2050.
25 Similarly, EPA's current methods for generating near-term emissions projections for mobile,
26 residential, commercial, industrial, and biogenic emissions, among others, have a limited ability
27 to account for many types of changes that are expected over a nearly 50-year time period. These
28 include changes such as the introduction of new technologies (e.g., advanced nuclear power, coal
29 gasification with carbon capture and sequestration, plug-in gasoline-electric hybrids, and
30 hydrogen fuel cell vehicles), growth and redistribution of population and industries, expansion of
31 urban and suburban areas, and changes in heating and cooling demands related to population
32 shifts and climate change. Accounting for these factors requires the development of a new
33 emissions projection methodology.
34 To this end, an emissions projection methodology is being developed that includes the
35 EMPAX economic model, which is a state-level computational general equilibrium model of the
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1
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U.S.; the ICLUS (Integrated Climate and Land Use Scenario) system, a modeling system that
links a population growth and migration model with a land use change model; and, MARKAL
(MARKet ALlocation), an energy system model that projects the penetration of technologies and
their associated emissions. These models and their data linkages are shown in Figure F-4.
GDP trajectory
EMPAX
Economic
Model
Industrial and
commercial
energy demands
Population growth
trajectory
ICLUS
Population Growth & Migration,
Land-Use Change
Residential
energy
demands
MARKAL
Energy
System
Model
Meteorological
changes
Economic
growth -
non-energy
sectors
Emissions
growth -
energy
system
Land use
projections
for use as
surrogates
SMOKE
Emissions
Processing Model
Figure F-4. Models and linkages for developing emissions growth factors.
In Figure F-4, the "Jobs," "Labor," and "Energy Use" linkages are deemphasized to
indicate that these linkages may or may not be included in the 2012 Assessment Report,
This document is a draft for review purposes only and does not constitute Agency policy.
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1 depending on available time and resources. The feasibility of including the additional climate-
2 related linkages and feedbacks shown in Figures F-2 and F-3 is being evaluated.
3 The focus of this appendix is to describe the MARKAL energy-modeling component.
4 Results from a MARKAL run illustrate the types of outputs that the model produces. The
5 appendix concludes with a description of the process by which the MARKAL results are
6 converted to emissions growth factors for use in SMOKE.
7
8 F.2. ENERGY SYSTEM MODELING
9 F.2.1. The MARKAL Energy System Model
10 In modeling the role of technology change on future-year emissions, the focus here is on
11 the U.S. energy system. The energy system includes the fuels and technologies that extend from
12 the import or extraction of fuel resources, to the conversion of these resources to useful forms, to
13 their use in meeting energy service demands. The energy system is selected for special
14 consideration because of the large amount of pollutant emissions that it produces. For example,
15 current demands for transportation and electricity are met largely through the combustion of
16 fossil fuels. Based on an analysis of the EPA's 2001 National Emissions Inventory, combustion
17 in the U.S. is estimated to contribute approximately 95% of anthropogenic emissions of nitrogen
18 oxides (NOx) and carbon monoxide (CO), 89% of sulfur oxides (SOx), and 87% of mercury.
19 Figure F-5 provides a simplified depiction of an energy system in which fossil fuels
20 dominate. Major air pollutant emissions from each component of the system are shown.
21 While much of the energy system in the U.S. may be dominated by fossil fuels,
22 renewables and advanced technologies may play an increasing role in the future. These include
23 advanced nuclear reactors, wind and solar power, biomass and coal gasification, combined cycle
24 natural gas systems, hydrogen fuel cell vehicles and plug-in hybrids. An alternative energy
25 future, emphasizing renewable and advanced technologies is depicted in Figure F-6. The extent
26 to which the future energy system evolves toward this or other alternatives will have important
27 implications on future pollutant emissions and air quality.
28 For the 2012 assessment, the MARKAL model is being used to identify and evaluate the
29 pollutant emissions associated with alternative future realizations of the U.S. energy system. The
30 MARKAL model was developed in the late 1970s at Brookhaven National Lab in response to the
31 oil crisis of the mid-1970s. In 1978, the International Energy Agency adopted MARKAL and
32 created the Energy Technology and Systems Analysis Programme (ETSAP) to oversee its
33 ongoing development (ETSAP, 2006). In addition, the U.S. Department of Energy's Energy
34 Information Administration (EIA) made MARKAL the basis for the System for the Analysis of
35 Global Energy Markets (SAGE) model (U.S. DOE, 2003a). SAGE is used to produce EIA's
This document is a draft for review purposes only and does not constitute Agency policy.
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2
3
4
5
6
7
8
9
10
11
12
Extraction/Import Conversion
__.rV°C' CH., Hg
End-Use
Imported —
Domestic-
Imported
Domestic
Natural Gas
LNOx,
raT5"
, SOx, Hg, CO.
Electricity Generation :
NOx, HjO, VOC, CO,
CO., fine PM, CH4
^ N20, VOC, C02,
HAPs
CH,
, C02, PM,
Uranium
Agriculture '--•; w.isle &
Figure F-5. A simplified depiction of an energy system, in which energy
demands are largely met by fossil fuel resources and conventional nuclear
technologies.
Extraction/Import
Conversion
_T VOC, CH4
End-Use
VOC,
, CH4,Hg
, CO,, PM,
Renewable
Resources
: Waste
S1 Wastewater
Agriculture
Clean Energy
Figure F-6. A depiction of an alternative future energy system that has an
increased emphasis on renewables and advanced technologies.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 Annual International Energy Outlook (U.S. DOE, 2006). Altogether, MARKAL and its variants
2 are used in approximately 40 countries around the world.
3 MARKAL, a data-driven, optimization model, includes a representation of the structure
4 of an energy system. Data must be provided to characterize the specific energy system being
5 modeled. This typically involves developing a representation of the current energy system, as
6 well as projections of resource supplies, energy demands, and technology characteristics over a
7 modeling horizon of 20 to 50 years. Depending on the application, the input database may scale
8 from representing a single sector (e.g., transportation or electricity generation) to representing all
9 energy-related sectors in the economy (e.g., transportation, residential, commercial, industrial,
10 agricultural, and electricity production). Further, the database can define a single region, such as
11 the continental U.S., or it can represent the system at a finer resolution, such as at the regional- or
12 state-level, explicitly modeling resource supplies, energy demands, and technology
13 characteristics within regions, as well as the trade of electricity and fuels among modeled
14 regions.
15 Given a mathematical representation of the system as input, MARKAL uses linear or
16 mixed-integer linear programming solution techniques to calculate the least cost technology
17 pathway for meeting demands. Outputs of the model include a projection of the technological
18 mix at intervals into the future, estimates of total system cost, energy demand (by type and
19 quantity), and estimates of criteria pollutant and greenhouse gas emissions. If multiple sectors of
20 the energy system are represented, then MARKAL can be used to identify cross-sector
21 dynamics. For example, the introduction of a large number of vehicles powered by compressed
22 natural gas would drive demand for natural gas. This would impact the competition for natural
23 gas and could potentially influence the adoption of technologies within the electricity generation,
24 residential, and commercial sectors. MARKAL can provide insight into these interactions.
25 MARKAL features a number of options that can be useful in tailoring it toward particular
26 investigations. For example, MARKAL can be configured to account for demand elasticities and
27 endogenous technological learning as well as to represent consumer hesitancy to adopt new
28 technologies through hurdle rates. While MARKAL, by default, is configured to examine all
29 steps of the modeling time horizon simultaneously (e.g., the model has perfect foresight in
30 identifying the least cost technology pathway), it also can be applied in a myopic manner in
31 which it examines the technological choices to be made for each subsequent time step
32 independently. Further, when using this latter approach, a market share algorithm can be applied
33 in which the market penetration of alternative technologies is a function of their relative marginal
34 costs. An additional MARKAL option is an implementation of a methodology called Modeling
35 to Generate Alternatives, or MGA (Brill et al., 1990). The MGA algorithm allows MARKAL to
This document is a draft for review purposes only and does not constitute Agency policy.
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1 generate a set of alternative technology pathways to achieve the modeled objectives and
2 constraints. These alternatives are constrained to be within a small cost increment of the least
3 cost pathway. The individual solutions may be of interest while the similarity or difference
4 among the alternatives provides an indication in the flexibility available in meeting future energy
5 demands.
6 MARKAL also accommodates the consideration of uncertainties in future technology
7 characteristics, energy service demands, and policies. The stochastic optimization option allows
8 alternative states of the world in the future to be specified. For example, these states may differ
9 by characteristics such as oil supply, availability of advanced nuclear power, or viability of coal
10 capture and sequestration technologies. MARKAL then identifies the optimal short-term, least
11 cost technology pathway that is robust given the myriad of potential futures that were
12 represented.
13 The EPA has integrated MARKAL into a modeling framework that supports Monte Carlo
14 simulation, as well as parametric and global sensitivity analyses. This framework allows
15 sensitivities of the energy system to input assumptions and uncertainties in model outputs to be
16 examined. Results provide powerful insight into the dynamics of the energy system that are
17 difficult to examine using deterministic approaches alone. For example, a single deterministic
18 optimization run may suggest that a technology is not economically competitive and thus will not
19 penetrate the market. Global sensitivity analysis, however, can be used to identify the conditions
20 under which that technology is competitive and the technologies with which it competes.
21 To apply MARKAL to the 2012 assessment, EPA is developing a regionalized U.S. EPA
22 MARKAL database, referred to as EPA9R. The database represents the energy demands and
23 technologies in the major sectors in the U.S. energy system, including the commercial, industrial,
24 residential, transportation, and electricity generation sectors. These data are represented at a
25 regional-level, with the nine modeled regions being analogous to the nine U.S. Census Bureau
26 census divisions, shown in Figure F-7. Alaska and Hawaii are included in the Pacific region in
27 the EPA9R database.
28 The EPA9R database extends from 2000 to 2050 in 5-year increments. In the process of
29 developing a 9-region database, EPA first developed a one-region, national-scale database
30 referred to as EPANMD (U.S. EPA, 2006b). EPANMD was released to the public in 2006, and
31 several groups are now using the model. EPANMD, which has recently extended to 2050, is
32 used for the MARKAL run described in this appendix. The final 2012 Assessment Report and
33 related modeling are expected to make use of EPA9R, allowing regional energy supply and
34 demands to be considered.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 The primary source of data for populating EPANMD has been the U.S. Department of
2 Energy's 2005 Annual Energy Outlook (AEO) report (U.S. DOE, 2003b). Versions of
Pacific
Contiguous
New England
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Mountain
East North Central "T JME
Middle Atlantic
South Atlantic
Pacific
Nonconti
Wast South Central
Figure F-7. Census divisions represented in the EPA9R MARKAL database.
Source: Energy Information Administration, Office of Coal, Nuclear, Electric and
Alternate Fuels.
EPANMD have been updated to reflect AEO 2006 and are currently being updated to
incorporate data from AEO 2007. Data for many of the technologies not represented in the AEO
were derived from other widely recognized authoritative sources (e.g., the Electric Power
Research Institute's Technical Assessment Guide [EPRI, 2003]) while the data characterizing
light duty transportation options were obtained from the U.S. EPA's Office of Transportation
and Air Quality (U.S. DOE, 2002). Most pollutant emissions factors used within the model were
derived from the EPA's Air Quality and Emissions Trends Report (U.S. EPA, 2006c) and AP 42
listings (U.S. EPA, 2007b).
In 2004, EPA used an earlier version of EPANMD to produce a report that demonstrated
the use of MARKAL in carrying out scenario-based analyses of the transportation sector. In
2006, a companion piece focusing on electricity generation was completed. The 2006 analysis
also demonstrated how parametric and global sensitivity analysis techniques could be used to
This document is a draft for review purposes only and does not constitute Agency policy.
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1 identify how the model responds to changes to various inputs. This, in turn, provides useful
2 information in understanding model dynamics and in identifying the key inputs that drive outputs
3 of interest (e.g., high levels of emissions).
4 Compared to the 2004 and 2006 reports, results presented in this appendix to the 2007
5 Interim Assessment Report reflect updated technology, demand, and resource supply data.
6 Further, the electric sector has been calibrated to represent the electricity generation mix and
7 emissions control technologies through 2020 that were included in EPA's 2006 analysis of the
8 national ambient air quality standards for particulate matter. Major regulatory drivers included
9 in that analysis were CAIR and the on- and off-road diesel regulations. As with the previous
10 work, the database represents the U.S. as a single region.
11
12 F.3. APPLICATION
13 F.3.1. Scenario Analysis
14 MARKAL is a useful tool for carrying out scenario analyses of the energy system.
15 Scenarios are internally consistent depictions of how the future may unfold, given assumptions
16 about economic, social, political, and technological developments, as well as consumer
17 preferences (Schwartz, 1996). Scenarios explore plausible futures by using a model or models to
18 generate an outcome (or set of alternative outcomes) consistent with a set of motivating
19 assumptions, sometimes called a "storyline." It is important to stress that a scenario is not a
20 prediction but instead represents one realization of the wide-ranging potential futures.
21 Scenario analysis, involving the evaluation of a small number of such scenarios, aims to
22 examine how changes in model parameters (inputs) affect outputs across sets of related
23 storylines, rather than focusing on the results from a particular scenario. No attempt is made to
24 consider every possible future. These comparative analyses alternately look forward ("What-
25 if?") to examine how competing sets of input assumptions drive technology adoption and
26 emissions, and backward ("How-could?") to identify the energy technology pathways available
27 to meet some future environmental or technological goal. Scenarios, therefore, facilitate
28 assessment of the consequences of varying assumptions, the range of possible futures, and trade-
29 offs and branch points that govern choices among these futures. Results from a selected set of
30 scenarios will serve as input to the ORD 2012 Air Quality Assessment Report.
31
32 F.3.2. Illustrative Application
33 To demonstrate the use of MARKAL and the types of outputs that can be generated from
34 a scenario, a reference case storyline was identified and evaluated. While the storyline is called a
35 reference case, it represents only one of many possible futures. The reference case was
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19
calibrated such that sectoral energy demands and fuel use through 2020 approximate projections
in the U.S. Department of Energy's 2006 Annual Energy Outlook (AEO). Electric sector
emissions through 2020 were constrained to approximate the EPA's 2006 PM NAAQS analysis,
and thus included CAIR, the Heavy Duty Highway Diesel Rule, and the Nonroad Diesel Rule.
F.3.3. Reference Case
Outputs from MARKAL include technology penetrations for meeting various energy
service demands as well as the fuel use, emissions, and costs associated with individual
technologies, sectors, and the entire system. Results from the reference case are provided
graphically in Figures F-8 through F-14.
Figure F-8 characterizes the system-wide primary energy use. Units are in petajoules (PJ
=1015 J). The oil and petroleum category includes both imported crude oil and imported
petroleum products such as gasoline. "Other" is primarily natural gas liquids. The "renewables"
category includes biomass, wind, solar, hydropower, geothermal, and landfill gas combustion.
The model indicates increases in demand for each fuel category although coal use levels,
to some extent, after 2035 as new electricity demands are met by other fuels.
70000
60000
50000
of 40000
30000 -
20000
10000
ij
•Oil & Petroleum
•Coal
•Natural Gas
Nuclear
•Renewables
Other
2000
2010
2040
2050
2020 2030
Year
Figure F-8. System-wide energy inputs. All values are net, accounting for
exports.
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1
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19
Figure F-9 depicts the amount of electricity generated via different types of fuels.
Natural gas, coal, and nuclear power are the three major fuels used to meet increasing electricity
demands.
14000
12000
10000 -
8000 -
£
6 6000 -
4000 -
2000
•Pulverized Coal
'Natural Gas
•Nuclear
•Hydro power
•Oil
•Renewables
2000
2010
2020
2030
2040
2050
Year
Figure F-9. Electricity generation by type of fuel. A breakdown of the
renewables category is provided in Figure F-10.
Figure F-10 provides a detailed look at the amount of electricity produced by different
types of renewables. Hydropower has the largest penetration of the renewable options.
Constraints on hydropower resources limit its use, however. The increase in hydropower from
2000 to 2005 is an artifact of optimization as the model attempts to make maximum use of
existing resources. Wind and geothermal capacities appear to increase substantially while
electricity from solar power is limited until the later years in the modeling horizon.
Figure F-l 1 shows the mix of vehicle technologies in the light-duty fleet. In this
scenario, fuel price pressures lead to the adoption of vehicles with advanced internal combustion
engines (ICEs) that achieve higher efficiencies than conventional and diesel ICEs. Hybrid
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gasoline-electric vehicles achieve some degree of penetration, but this does not exceed 7% over
the modeling period.
Figure F-12 shows the fractional change in NOx, SC>2, and CC>2 emissions over the
modeling horizon, compared to 2000. System-wide, NOx emissions decline by approximately
40% from 2000 through 2050. Sulfur dioxide emissions follow a less-discernable trend and are
slightly
800
700 -
•Hydro
-Geothermal
-Landfill Gas
Biomass
Wind
Solar
2000
2010
2020
2030
2040
2050
Year
Figure F-10. Use of renewables in electricity production.
This document is a draft for review purposes only and does not constitute Agency policy.
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2
O
2000
•Conventional ICE
•Advanced ICE
•Hybrids
•Diesel ICE
Ethanol ICE
2010
2040
2050
2020 2030
Year
Figure F-ll. Technology penetration into the light duty vehicle fleet.
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2
3
4
5
6
7
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9
10
11
12
13
14
a*
I
o
o
o
CM
0.2 -
0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year
Figure F-12. Reference case scenario emissions relative to 2000.
higher at the end of the modeling horizon. CC>2 emissions increase steadily as energy demands
and the related combustion of fossil fuels increase across sectors.
Figure F-13 provides a more detailed look at NOx emissions. The overall decrease in
NOx is driven by large reductions in electricity production resulting from CAIR and by
reductions in transportation sector, much of which is attributable to the EPA's on-road and off-
road diesel rules.
Figure F-14 characterizes the reference-case scenario's use of domestic and imported
energy. Exports are also shown. An increasing fraction of energy inputs is imported over the
time horizon.
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25000
20000 -
15000 -
5
=
O
10000 -
5000 -
0
2000
• Total NOx
•Transportation
Electricity Production
•Industrial
Commercial
•Residential
2010
2040
2
3
2020 2030
Year
Figure F-13. System-wide and sectoral NOx emissions.
2050
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100000
90000 -
80000 -
70000 -
~ 60000
D-
50000 -
40000 -
30000 -
20000 -
10000 -
0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
2 Year
3 Figure F-14. Domestic fossil fuel utilization, imports, and exports.
4
5
6 F.3.4. Discussion
7 The results presented in this section represent one realization of the future and are highly
8 dependent on assumptions regarding future technology characteristics, the costs of obtaining
9 fuels, and other factors. Many of these factors are uncertain. One approach for incorporating
10 consideration of uncertainty is to conduct a scenario analysis where widely ranging scenarios
11 encompass a range of futures considered. The use of parametric and global sensitivity analysis
12 techniques is also important in characterizing the model's response to changes in inputs. These
13 techniques allow important model interactions to be identified, including those that may not be
14 anticipated. Scenario and sensitivity analysis will be incorporated into the 2012 Final
15 Assessment Report.
16 While MARKAL results provide insight into future energy technology pathways, they are
17 nonetheless based on a model, and all models have limitations. For example, MARKAL is a
18 mixed integer linear programming model as opposed to a nonlinear programming model. As a
19 result, objectives and constraints in the model must be represented as linear functions. Many
20 real-world characteristics of the energy system are nonlinear, such as resource supply curves, so
This document is a draft for review purposes only and does not constitute Agency policy.
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1 detail is lost in a linear representation. Linearity has an advantage in modeling, however, since
2 linear and mixed integer linear programming models are typically much easier to solve than
3 nonlinear models. Another caveat is that MARKAL is an optimization model and not a
4 simulation model. Optimization models identify the least cost approach to achieve a desired
5 objective. MARKAL solutions thus represent those of a rational social planner acting to
6 minimize costs. Typically in MARKAL, this decision-maker has perfect foresight over the time
7 horizon, although MARKAL's myopic solution mode can be used to limit this foresight to each
8 model time period. In contrast, simulation models are designed to represent more complex and
9 realistic human behaviors.
10
11 F.4. GENERATION OF INPUTS TO THE AIR QUALITY ASSESSMENT
12 The SMOKE emissions processing model generates a future-year inventory by applying
13 technology- or industry-specific multiplicative factors to sources within a base-year inventory.
14 Emissions sources are classified by codes. Source Classification Codes (SCCs) are eight- and
15 10-digit codes that represent point and non-point sources emissions technologies, respectively.
16 Source Identification Codes (SICs) are four-digit codes that represent the type of industry.
17 Growth factors for SCCs or SICs are included in a growth and control file that is input into
18 SMOKE.
19 MARKAL does not produce SCC or SIC growth factors as output, and the aggregation of
20 technologies within MARKAL is different than that in the emissions inventory. Thus,
21 MARKAL outputs must be post-processed to derive emissions growth factors. Post-processing
22 to develop SCC growth factors involves the following steps:
23 Step 1 Emissions of NOx, sulfur, and PM by year and technology are extracted from a
24 MARKAL output file
25 Step 2 A "crosswalk" that links MARKAL technologies to SCCs is used to assign
26 MARKAL emissions to SCC categories.
27 Step 3 For each SCC, the change in emissions between the base year (e.g., 2000) and the
28 future year (e.g., 2050) is calculated and a multiplicative factor is determined
29 Step 4 Emissions growth factors by pollutant are output to the growth and control factor
30 file.
31
32 This process provides emissions growth factors for NOx, SOx, and PM from energy
33 system technologies. The proportion of emissions of VOCs from the energy system is low.
34 Thus, emissions growth factors for these sources will be generated outside of MARKAL, and
35 likely will be linked to population or economic growth estimates.
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1 Emissions growth factors can be applied within SMOKE across the entire inventory or at
2 the state or county level. The EPA9R database produces results at a census division level. Thus,
3 the emissions growth factors for a MARKAL region will be applied to all states within that
4 region.
5 Compared to the current state-of-the-art approach for projecting emissions, the
6 methodology outlined here is differentiated by
7 • representation of current and expected technology characteristics (e.g., cost, efficiency,
8 and emissions controls) for major energy-system source categories explicitly;
9 • estimation of technology change endogenously;
10 • portrayal of increased energy service demands, resource limitations, and current and
11 anticipated emissions and air quality policies;
12 • identification of cross-sector implications of changes in the demands for various fuels.
13
14 F.5. REFERENCES
15 EPRI (Electric Power Research Institute). (2003) Technology assessment guide: advanced technologies. Palo Alto,
16 CA: Electric Power Research Institute; Product ID: 1004976.
17 ETSAP (Energy Technology Systems Analysis Program). (2006) Energy technology systems analysis program
18 (ETSAP) [September 2006]. Available online at http://www.etsap.org/index.asp.
19 Brill, E.D, Jr., Flach, J.M., Hopkins, L.D., Ranjithan, S. (1990). MGA: A decision support system for complex,
20 incompletely defined problems. IEEE Transactions on Systems, Man and Cybernetics 20(4): 745-757.
21 Schwartz, P. (1996). The art of the long view: planning for the future in an uncertain world. New York, NY:
22 Doubleday.
23 U.S. DOE (Department of Energy). (2003a) Model documentation report: system for the analysis of global energy
24 markets (SAGE) - Volume 1: model documentation. Energy Information Administration, U.S. Department of
25 Energy, Washington, DC; DOE/EIA-M072(2003)/1. Available online at
26 http://tonto.eia. doe.gov/FTPROOT/modeldoc/m072(2003) 1 .pdf.
27 U.S. DOE (Department of Energy). (2003b) National energy modeling system: an overview 2003. Energy
28 Information Administration, U.S. Department of Energy, Washington, DC; DOE/EIA-0581(2003). Available online
29 at http://www.eia.doe.gov/oiaf/aeo/overview/index.html.
30 U.S. DOE (Department of Energy). (2002) Program analysis methodology: quality metrics 2003, final report. Office
31 of Transportation Technology, U.S. Department of Energy. Available online at
32 http://wwwl.eere.energy.gov/ba/pdfs/facts quality metrics 2003.pdf.
33 U.S. DOE (Department of Energy). (2006) International energy outlook 2006. Energy Information Administration,
34 U.S. Department of Energy, Washington, DC; DOE/EIA-0484(2006). Available online at
3 5 http://www.fypower.org/pdf/EIA_IntlEnergyOutlook(2006).pdf.
36 U.S. EPA (Environmental Protection Agency). (2004) Clean air nonroad diesel - tier 4 final rule [September 2006].
3 7 Office of Transportation and Air Quality, Washington, DC. Available online at http://www.epa.gov/nonroad-
38 diesel/2004fr.htm.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 U.S. EPA (Environmental Protection Agency). (2005) Clean air interstate rule [September 2006]. Office of Aire and
2 Radiation, Washington, DC. Available online at http://www.epa.gov/cair/.
3 U.S. EPA (Environmental Protection Agency). (2006a) Finding of significant contribution and rulemakings for
4 certain states in the ozone transport assessment group region for the purposes of reducing regional transport of ozone
5 ("NOx SIP Call") [September 2006]. Technology Transfer Network: oxone implemention. Office of Transportaion
6 and Air Quality. Available online at http://www.epa.gov/ttn/naaqs/ozone/rto/sip/index.html.
7 U.S. EPA (Environmental Protection Agency). (2006b) EPA U.S. national MARKAL database: database
8 documentation. U.S. Environmental Protection Agency, Washington, DC; EPA/600/R-06/057. EIMS ID: 150883.
9 U.S. EPA (Environmental Protection Agency). (2006c) Air trends reports [September 2006]. Office of Air and
10 Radiation, Washington, DC. Available online at http://www.epa.gov/air/airtrends/reports.html.
11 U.S. EPA (Environmental Protection Agency). (2007a) Clean diesel trucks, buses, and fuel: heavy-duty engine and
12 vehicle standards and highway diesel fuel sulfur control requirements (the "2007 heavy-duty highway rule")
13 [September 2006]. Office of Transportation and Air Quality, Washington, DC. Available online at
14 http://www.epa.gov/otaq/highway-diesel/regs/2007-heavy-duty-highway.htm.
15 U.S. EPA (Environmental Protection Agency). (2007b) AP 42, fifth edition: compilation of air pollutant emission
16 factors [September 2006]. Technology Transfer Network. Available online at http://www.epa.gov/ttn/chief/ap42/.
17
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1 APPENDIX G
2 CHARACTERIZING AND COMMUNICATING UNCERTAINTY: THE NOVEMBER
3 2006 WORKSHOP
4
5 G.I. INTRODUCTION
6 An effective scientific assessment process must explicitly address uncertainty to solidify
7 the credibility of the research effort underlying the assessment and to assure that the assessment
8 products fulfill the need of the intended users for accurate information. The U.S. EPA Global
9 Change Research Program (GCRP) Assessment of the Impacts of Global Change on Regional
10 U.S. Air Quality is, in part, a bounding exercise to determine whether or not the impacts of
11 climate and other drivers of change on air quality are significant enough that they must be folded
12 into planning and management. An analysis of the uncertainty in the assessment findings is
13 needed to determine if they are sufficient to answer to the questions originally posed.
14 However, complex, model-based environmental assessments, including climate change
15 impacts assessments, present unique challenges to characterizing and communicating scientific
16 uncertainty. Challenging elements include reliance on linked systems of detailed models at
17 multiple spatial and temporal scales, leading to the propagation of nonlinear model sensitivities
18 through the linked simulations; the presence of uncertainty about the state of our knowledge; the
19 characterization of this knowledge into a model; and the most appropriate values for the inputs
20 and empirical parameters within that model and the inherently multidisciplinary nature of the
21 problems considered, each discipline with its own norms for treating uncertainly. There is no
22 "best practice" guidance for handling uncertainty in this type of assessment.
23 EPA organized and conducted a workshop in November 2006 to solicit advice from
24 assembled experts about issues related to characterizing and communicating uncertainty in such
25 assessment in general, and the ongoing global change-air quality assessment in particular. The
26 goal was to begin identifying and developing principles and practices to apply in the current and
27 future assessments.
28 The rest of the report documents the workshop and summarizes preliminary findings that
29 emerged. Development of a comprehensive strategy for addressing uncertainty based on these
30 findings, as well as identifying and applying the formal uncertainty analysis techniques most
31 appropriate for the problem of global change impacts on air quality, are important future steps.
32
33 G.2. WORKSHOP GOALS, PARTICIPANTS, AND STRUCTURE
34 The "Workshop on Uncertainty in the U.S. EPA Assessment of the Impact of Global
35 Change on U.S. Air Quality" took place on November 1-2, 2006, at the Millennium Hotel in
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1 Durham, North Carolina, conducted by EPA's National Center for Environmental Assessment
2 (NCEA). The workshop goals were to provide
3 • A foundation for EPA to develop a strategy to properly track, quantify, and communicate
4 uncertainty in complex, model-based assessments, particularly concerning the impacts of
5 global change and
6 • Specific recommendations for how EPA can best track, quantify, and communicate
7 uncertainty in its current global change-air quality assessment.
8
9 EPA invited approximately 75 experts from academia and other government agencies,
10 covering various scientific disciplines and affiliations, to attend the workshop. These disciplines
11 included
12 • Regional climate modeling
13 • Global climate modeling
14 • Social sciences (e.g., urban and regional economists, energy economists, transportation
15 economists)
16 • Technology development (energy, transportation)
17 • Energy projections
18 • Vegetation modeling
19 • Global scenario development
20 • Emissions modeling
21 • Regional air quality modeling
22 • Global chemistry modeling.
23
24 In addition, key stakeholders from the EPA Office of Air Quality Planning and Standards
25 (OAQPS) and regional air quality planning and management entities were present and actively
26 involved in the discussions. Their perspective was crucial to help frame the context of the
27 uncertainty discussions and provide a focus on the questions most relevant for policy needs.
28 The two-day workshop began with an opening plenary session during which invited
29 speakers presented background information on EPA's GCRP, the global change-air quality
30 assessment, and discussions of general methods for evaluating uncertainty in complex model-
31 based systems. Speakers and their presentations during this plenary session were
32
33 • Welcome and Opening Remarks: Anne Grambsch, EPA ORD/NCEA
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1 • Decision Making in the Face of Uncertainty: Lydia Wegman, EPA Office of Air and
2 Radiation (OAR)/OAQPS
3 • Overview of EPA 's Global Change-Air Quality Program: Anne Grambsch, EPA
4 ORD/NCEA
5 • Research Summaries by EPA Science to Achieve Results (STAR) Grantees: Various
6 STAR Grantees
7 • Modeling Ozone Sensitivities to Future Climate: Alice Gilliland, EPA ORD/National
8 Exposure Research Laboratory (NERL)
9 • Evaluating Uncertainty in Linked Climate and Air Quality Modeling: Steve Hanna,
10 Hanna Consultants
11 • Responses to National Academy of Sciences (NAS) and Office of Management and
12 Budget (OMB) Recommendations and Guidelines for Probabilistic Uncertainty
13 Assessmentin OAQPS's Regulatory Analyses: Bryan Hubbell, EPA OAR/OAQPS.
14
15 Following the opening plenary, the participants split into three breakout groups that were
16 all given the same charge to provide EPA feedback on three topic areas: (1) tracking and
17 quantifying uncertainty in complex, model-based global change impacts assessments, (2)
18 effectively communicating uncertainty associated with these assessments, and (3) addressing
19 uncertainty specifically in EPA's global change-air quality assessment. Discussions on these
20 topics addressed issues raised in "key discussion questions" included in the workshop handouts
21 and "framing questions" that EPA distributed to participants prior to the workshop. While the
22 three breakout groups had the same general charge, which allowed for comparisons across the
23 groups to identify areas of common ground, the focus of the discussions during the workshop
24 varied across the breakout groups. The workshop concluded with a second plenary session
25 during which the workshop participants commented on materials developed by the breakout
26 groups.
27 The workshop was not designed to seek consensus on any topic or to prioritize the
28 participants' many suggestions. The ideas presented were viewed as a collection of ideas set
29 forth for EPA's further consideration and were not necessarily to be viewed as formal
30 recommendations.
31
32 G.3. PRELIMINARY FINDINGS
33 Complex, model-based environmental assessments, particularly assessments of climate
34 and global change impacts, offer substantial challenges. The challenges in global change
35 impacts assessments include the need to simulate interconnected global-scale human and natural
36 processes through to distant time horizons, the need to minimize the computational burden of
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1 these complex simulations by combining models with widely differing temporal and spatial
2 resolution, the complexities of the modeling tools required, and the multi-disciplinary nature of
3 the problems. Addressing the conceptual and linguistic differences, through dialogue at the
4 workshop, between the intellectual disciplines, and between the science and policy communities
5 involved in the assessment, is a key workshop outcome.
6
7 G.3.1. General Findings
8 A general finding of the workshop is that characterization and quantification of
9 uncertainty cannot be separated from the overall assessment process. A well-designed
10 assessment includes the following basic elements:
11 • A healthy, iterative, process between the scientists and the stakeholders (including
12 decision makers, policy planners, and resource managers). This process is a two-way
13 flow of information about needs and capabilities, including discussion of the level of
14 uncertainty in the assessment findings as they emerge. This dialogue is ongoing
15 throughout the assessment. In a fundamental sense, the process, not any particular
16 uncertainty analysis, is the product.
17 • A well-defined decision context that is informed by both the science and the stakeholder
18 imperatives. This context determines the variables and metrics upon which to focus, the
19 spatial and temporal resolutions at which they are needed, the scenarios of interest, and
20 the acceptable levels of uncertainty required (i.e., risk tolerance). Important
21 considerations in this aspect of the assessment process include differences in the criteria
22 and perspectives between stakeholders and scientists concerning the nature of reliable
23 knowledge. The discussion process must either reconcile or accommodate any such
24 differences. The type of information that is considered to be useful by the stakeholder
25 community may range from the direction of the effect, e.g., positive or negative with
26 respect to the current level, to orders of magnitude, to quantification of an effect at high
27 precision.
28 • A set of preliminary analyses (scoping analyses) to identify and prioritize the major and
29 minor uncertainties likely to be present when attempting to meet the needs of the
30 particular decision context. These analyses might include examinations of the existing
31 body of knowledge on the topic, elicitations of expert judgments, and sensitivity studies.
32 • A conceptual diagram of all components of the problem. The conceptual diagram is then
33 realized (to the extent possible) with the models that are available or that are developed
34 for the project. Comparing this realization to the original conceptual diagram yields
35 important insights into the compromises made in modeling that may yield uncertainty.
36
37 Based upon the requirements of the decision context and the findings from the
38 preliminary analyses, the extent and rigor of the uncertainty analysis needed can then be
39 determined, i.e., comprehensive and quantitative or back-of-envelope and qualitative. Resources
40 sufficient to carry out the required uncertainty analysis, which in the case of climate and air
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1 quality modeling includes sufficient computing power, must be factored into the cost of the
2 assessment.
3 G.3.2. Findings on Technical Issues Specific to the Global Change-Air Quality Modeling
4 Systems
5 • It is not appropriate to think of such systems as "prediction" tools. They are "scenario
6 analysis" tools, and must be used as such for decision support purposes.
7 • Formal, quantitative uncertainty analysis of the linked climate-air quality model system
8 that explores the whole parameter space is at this stage of technological development
9 extremely expensive and time-consuming. Efficiency in using available computational
10 resources is, therefore, paramount.
11 • As a subset of a full Monte Carlo analysis, an example approach to qualitative
12 uncertainly analysis, ensemble methods are likely the most appropriate for linked
13 climate-air quality modeling systems (Hanna et al., 2007).
14 • The role of the preliminary analyses, including tapping current scientific knowledge in
15 the literature, expert elicitation, and simplified sensitivity studies of different types, is
16 extremely important to intelligently guide more sophisticated, formal uncertainty
17 analyses to be performed if feasible (e.g., winnowing down the parameter space for
18 choosing the most relevant ensemble members).
19 • All analyses must include evaluating the predictive skill of model system versus
20 observations specifically for the air quality metrics of interest (as opposed to simply long-
21 term average climate variables, for example). The identified limits of the system's
22 predictive skill contribute to the uncertainty in the assessment projections.
23 • The temporal and spatial resolution of the analysis needs to be chosen in a manner that
24 maximizes the utility of the results to the client, e.g., a resolution that does not result in
25 uncertainties beyond the acceptable limit for the policy application. Again, these
26 quantities are identified via the iterative communication process discussed above.
27 • Effective coordination of research efforts across groups contributing the scientific
28 findings to the assessment is crucial, for example, in the consistency of scenarios and
29 modeling assumptions used to allow "apples to apples" comparisons.
30 • The role of reduced form models, simplified models, tailored policy planning tools, etc.,
31 is unclear. The workshop revealed a broad range of views with no consensus.
32
33 G.3.3. Findings on Communication Strategies
34 • Lead with what is known (i.e., more certain), then move to what is unknown (i.e., less
35 certain).
36 • Account for the different norms of communication between scientists (e.g., limitations,
37 caveats) and decision makers.
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1 • Use clear, unambiguous language to express likelihood and level of confidence. For
2 example, see the Intergovernmental Panel on Climate Change (IPCC) and U.S. Climate
3 Change Science Program (CCSP) practices.
4 • Establish the credibility of the findings by communicating the respect of the community
5 for the participating scientists and the extent of the peer-review process.
6 • Take advantage of creative visualization methods.
7
8 Finally, the workshop closed with a call for future meetings to focus on the specific
9 technical issues discussed above, with narrower questions and smaller groups of participants.
10
11 G.4. REFERENCES
12 Hanna, S; Weaver, CP; Hemming, B. (2007) A review and framework for evaluating uncertainties in the assessment
13 of the impacts of global climate change on U.S. air quality. J Air Waste Manage Assoc: to be submitted.
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