Climate Impacts on Air Quality Response to Controls: Not Such an Uncertain Future

1. 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 6th CMAS conference Oct. 1, 2007

2. Introduction • Impacts of future climate change on ozone and PM2.5 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) predictaveraged summertime daily maximum 8-hour O3 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.

3. 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 PM2.5 concentrations.

4. 21st-Century Climate (IPCC) Source: IPCC (2001), Climate Change 2001: The Scientific Basis

5. Uncertainties are Considered for: (MIT’s 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

6. Uncertainty Simulation • Our studies suggested that T and Abs. Hum. had major impacts • Perturbations: -- 3-dimensional temperature -- 3-dimensional absolute humidity • Levels of perturbation: -- 99.5th percentile (High-extreme) -- 50th percentile (Base: “rerun”*) -- 0.5th percentile (Low-extreme) *For consistency, the 50th percentile is rerun as the fields are changed since the IGSM monthly average distribution is not identical to the GISS-MM5

7. and Expansion of IGSM into the 3rd Dimension 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

8. Improved Conversion of Temperature Based on a Remapping of Coordinate Index New Temperature: combined new monthly (from IGSM) & fluctuating term (MM5) Original IGSM Original MM5 = steady + fluctuating terms

9. EI IGSM GCM Intermediate Meteorology GISS GCM MM5 MM5 MCIP SMOKE CMAQ-DDM Modeling approach Meteorological data derived based on climatic change runs using MIT’s Integrated Global System Model (IGSM) for future years Perturbation and Remapping of Temperature and Humidity

10. 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 Canada Mexico

11. Summary of Uncertainty Simulations

12. Annualized and summer-averaged T and Q in 2001 and 2050 with the three uncertainty scenarios

13. 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 2050 • - Use SMOKE/Mobile6 for Mobile source control Woo et. al, 2007

14. Regional Emissions Year 2001 Year 2020 Year 2050 Present and future years NOx emissions by state and by source types

15. Emission Changes

16. 10 ppbV Uncertainties in Summertime (JJA) 4th Highest Daily Maximum 8-hr Average O3* (High-extreme)-Base 2001-2050 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)

17. 10 ppbV Uncertainties in Annualized* PM2.5 * Average of one month from each season: Jan., Apr., Jul. and Oct. NAAQS: annual PM2.5 - 35 μg m-3 Summertime 4th MDA8hr O3 (High-extreme)-Base 2001-2050 Base-(Low-extreme) • Annualized PM2.5 is slightly sensitive to the extreme climate scenarios (-1.0 μg m-3 - +1.5 μg m-3) • Precipitationhas more significant effects on PM2.5 levels

18. Spatial Distribution of Difference in Precipitation

19. Summertime (JJA) 4th Highest Daily Maximum 8-hr Average O3 Levels and Sensitivities - Uncertainty in climate forecasts has limited impacts on O3 levels and their sensitivities - Decreases in anthropogenic NOx emissions are still to be the most effective to reduce ground-level ozone concentrations

20. Annualized PM2.5 Levels and Sensitivities - Uncertainty in climate forecasts has limited impacts on PM2.5 levels and their sensitivities - Decreases in anthropogenic SO2 and NOx emissions are still to be the most effective to reduce ground-level PM2.5 concentrations

21. How do uncertainties in climate change, impact the ozone and PM2.5 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.

22. Conclusions • Differences in ozone and PM2.5 levels between the extreme and base scenarios are predicted to be relatively small compared with pollutant reductions between 2001 and 2050. • Ozone and PM2.5 formation in 2050 is expected to be most sensitive to NOx and SO2 emissions, respectively, with little change between the scenarios tested. • The results imply that planned control strategies for reducing regional ozone and PM2.5 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.

23. Acknowledgements • U.S. EPA STAR grant No. RD83096001, RD82897602 and RD83107601 • 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

24. Annualized and summer-averaged emissions of NOx, SO2 and NH3 Base Low-extreme High-extreme

25. Annualized and summer-averaged total VOC emissions in 2001 and 2050 with the three uncertainty scenarios.

26. RPO 2018 Activity data (On-road mobile) Use EPA 2020 CAIR-case inventory Update cross-references SMOKE/M6-ready activity data for 2050 Emission Inventory Projection

27. Expansion into the 3rd Dimension (cont’d) • Steps: • Using MM5 proxy data to derive a’ and ā for given months; build index relations between them; • Replace ā with IGSM result; • Convert the new ā back to a using a’ to derive needed 3D field. • Use as boundary conditions to re-run MM5 • Note that in order to derive ā 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 ā was based on MM5-weighted IGSM http://www.nature.com/news/2004/040913/images/climate.jpg

28. Annual Average Zonal Temperatures and Their Difference from the IGSM