Climate Impacts on Air Quality Response to Controls: Not Such an Uncertain Future K.J. Liao, E. Tagaris, K. Manomaiphiboon, A. G. Russell, School of Civil & Environmental Engineering Georgia Institute of Technology J.-H. Woo, S. He, P. Amar Northeast States for Coordinated Air Use Management (NESCAUM) C. Wang Massachusetts Institute of Technology 6 th CMAS conference Oct. 1, 2007 Introduction Impacts of future climate change on ozone and PM 2.5 have been investigated for different regions and future emission scenarios (e.g., [Hogrefe, et al., 2004; Murazaki and Hess, 2006; Prather, et al., 2003; Tagaris, et al., 2007]). Prather et al. (2003) predict that the tropospheric ozone levels in 2100 based on six SRES scenarios. Hogrefe et al. (2004) predict averaged summertime daily maximum 8-hour O 3 concentrations in the 2050s based on IPCC A2 scenario. Murazaki and Hess (2006) suggest an increase of up to 12 additional days in the Northeast of U.S. assuming that future precursor emissions remain at 1990 levels and GHG emissions follow A1 scenario. Objective Investigate: - Impact of uncertainties inherent in climate change forecasts on regional air quality predictions over the continental U.S. using multiple climate futures. - Robustness of effectiveness of currently planned control strategies for reducing ground-level ozone and PM 2.5 concentrations. 21 st -Century Climate (IPCC) Source: IPCC (2001), Climate Change 2001: The Scientific Basis Uncertainties are Considered for: (MITs IGSM) Anthropogenic emissions of greenhouse gases Anthropogenic emissions of short-lived climate-relevant air pollutants Oceanic heat uptake Specific aerosol forcing Source: Webster et al., 2003, 2002 Uncertainty Simulation Our studies suggested that T and Abs. Hum. had major impacts Perturbations: -- 3-dimensional temperature -- 3-dimensional absolute humidity Levels of perturbation: th percentile (High- extreme) th percentile (Base: rerun*) th percentile (Low-extreme) *For consistency, the 50 th percentile is rerun as the fields are changed since the IGSM monthly average distribution is not identical to the GISS-MM5 Expansion of IGSM into the 3 rd Dimension Reynolds Decomposition Write a 3D time-dependent variable a using Reynolds Decomposition (m = monthly mean specifically): y: latitude, z: altitude, x: longitude m: monthly (averaged) values t: MM5 temporal resolution of every 6-hr where denotes the longitude-averaged term of a (also called the steady component), and is the fluctuating term and Improved Conversion of Temperature Based on a Remapping of Coordinate Index Original MM5 = steady + fluctuating terms New Temperature: combined new monthly (from IGSM) & fluctuating term (MM5) Original IGSM Modeling approach EI SMOKE MM5 CMAQ-DDM MCIP GISS GCM MM5 IGSM GCM IntermediateMeteorology Meteorological data derived based on climatic change runs using MITs Integrated Global System Model (IGSM) for future years Perturbation and Remapping of Temperature and Humidity Air Quality Simulation Domain 147 x 111 grid cells 36-km by 36-km grid size 9 vertical layers U.S. regions: West (ws) Plains (pl) Midwest (mw) Northeast (ne) Southeast (se) Also investigating Mexico and Canada Mexico Canada Summary of Uncertainty Simulations ScenarioPerturbationsSources High-Extreme Scenario 99.5 % percentile of 3-D temperature and absolute humidity IGSM and GISS Base Scenario 50.0 % percentile of 3-D temperature and absolute humidity IGSM ~IPCC A1B scenario Low-Extreme Scenario 0.5 % percentile of 3-D temperature and absolute humidity IGSM and GISS Annualized and summer-averaged T and Q in 2001 and 2050 with the three uncertainty scenarios Emission Inventory Projection Accurate projection of emissions key to comparing relative impacts on future air quality and control strategy effectiveness Working with NESCAUM vital Step 1. Use latest projection data available for the near future - Use EPA CAIR Modeling EI (Point/Area/Nonroad, from 2001 to 2020) - Mexico: Bravo - Canada: Environment Canada - Use RPO SIP Modeling EI (Mobile, from 2002 to 2018) Step 2. Get growth data for the distant future - Use IMAGE model (IPCC SRES, A1B) - From 2020 (2018 for mobile activity) to Use SMOKE/Mobile6 for Mobile source control Woo et. al, 2007 Regional Emissions Year 2001Year 2020 Year 2050 Present and future years NOx emissions by state and by source types Emission Changes Uncertainties in Summertime (JJA) 4 th Highest Daily Maximum 8-hr Average O 3 * 10 ppbV (High-extreme)-Base Base-(Low-extreme) * NAAQS: 80ppbv - Photochemical reactions - Higher VOC emissions in the domain - Increases in ground-level ozone levels in urban areas (VOC- sensitive areas) Summertime 4 th MDA8hr O 3 10 ppbV Uncertainties in Annualized* PM (High-extreme)-Base Base-(Low-extreme) * Average of one month from each season: Jan., Apr., Jul. and Oct. NAAQS: annual PM g m -3 - Annualized PM 2.5 is slightly sensitive to the extreme climate scenarios (-1.0 g m g m -3 ) - Precipitation has more significant effects on PM 2.5 levels Spatial Distribution of Difference in Precipitation Summertime (JJA) 4 th Highest Daily Maximum 8-hr Average O 3 Levels and Sensitivities - Uncertainty in climate forecasts has limited impacts on O 3 levels and their sensitivities - Decreases in anthropogenic NOx emissions are still to be the most effective to reduce ground-level ozone concentrations Annualized PM 2.5 Levels and Sensitivities - Uncertainty in climate forecasts has limited impacts on PM 2.5 levels and their sensitivities - Decreases in anthropogenic SO 2 and NOx emissions are still to be the most effective to reduce ground-level PM 2.5 concentrations How do uncertainties in climate change, impact the ozone and PM 2.5 concentrations and sensitivities? Results suggest that modeled control strategy effectiveness is not affected significantly, however, areas at or near the NAAQS in the future should be concerned more about the uncertainty of future climate change. Conclusions Differences in ozone and PM 2.5 levels between the extreme and base scenarios are predicted to be relatively small compared with pollutant reductions between 2001 and Ozone and PM 2.5 formation in 2050 is expected to be most sensitive to NO x and SO 2 emissions, respectively, with little change between the scenarios tested. The results imply that planned control strategies for reducing regional ozone and PM 2.5 levels will still be effective in the future. However, impacts of the extreme climate scenarios are predicted to somewhat offset the reductions in ozone concentrations, especially in some urban areas. Acknowledgements U.S. EPA STAR grant No. RD , RD and RD Eastern Tennessee State University Dr. L. Ruby Leung from Pacific Northwest National Laboratory for providing future meteorological data Dr. Loretta Mickley from Harvard University for the GISS simulation used by Dr. Leung and Marcus Sarofim for his help in collecting the needed IGSM data Annualized and summer-averaged emissions of NO x, SO 2 and NH 3 Base Low-extreme High-extreme Annualized and summer-averaged total VOC emissions in 2001 and 2050 with the three uncertainty scenarios. Emission Inventory Projection Update cross-references Use EPA 2020 CAIR-case inventory SMOKE/M6- ready activity data for 2050 RPO 2018 Activity data (On-road mobile) Expansion into the 3rd Dimension (contd) Steps: Using MM5 proxy data to derive a and for given months; build index relations between them; Replace with IGSM result; Convert the new back to a using a to derive needed 3D field. Use as boundary conditions to re-run MM5 Note that in order to derive of IGSM results: -The discrepancies in monthly and zonal means between MM5 and IGSM were defined and then minimized in conversion - Spatial resolution was corrected using interpolation of IGSM data - Latitudinal distribution of was based on MM5-weighted IGSM Annual Average Zonal Temperatures and Their Difference from the IGSM