1. Yang Zhang, Xin-Yu Wen, and Ying Pan North Carolina State University, Raleigh, NC 27695 Prakash Karamchandani and Christian Seigneur Atmospheric and Environmental Research, Inc., San Ramon, CA 94583 David G. Streets and Qiang Zhang Argonne National Laboratory, Argonne, IL 60439 William C. Skamarock National Center for Atmospheric Research, Boulder, CO 80307 the 7 th Annual CMAS Conference, October 6-8, 2008, Chapel Hill, NC Development and Evaluation of Global-Through-Urban WRF/Chem: Gas-Phase Mechanism, Gas-Aerosol Coupling, and Aerosol-Cloud Interactions

2. Presentation Outline Background and Motivation Model Development Highlights Incorporation of CB05 and Its Global Extension (CB05_GE) Coupling of CB05/CB05_GE with MADRID Incorporation of An Aerosol Activation/CCN Module Development of Global Through Urban Nesting Capabilities Summary and Potential Applications

3. Modeling Climate Change (CC)-Air Quality (AQ) Interactions: Background and Motivation General Circulation Model Global Air Quality Model Mesoscale Climate Model Mesoscale Air Quality Model Neighborhood Scale Human Health Effect Biogenic emissions Anthro. emissions Population/Economic Growth Energy and Land Use Economy/ Policy Analysis Climate Policy Analysis Air Quality Management Global Warming Mitigation Offline models Online models Unified models Downscale: BCs/ICs Fine to Coarse Grid Feedbacks Downscale: BCs/ICs Fine to Coarse Grid Feedbacks Large scaleWeather Radiation,CCN, Cloud MesoscaleWeather Radiation,CCN, Cloud

4. Development of Global-through-Urban Weather Research and Forecasting Model with Chemistry (GU-WRF/Chem) Key Model Development –Compile an adequate global emission inventory –Link global WRF with chemistry/aerosol modules in mesoscale WRF/Chem –Develop appropriate model treatments for upper troposphere and stratosphere –Incorporate CB05/CB05_GE into GU-WRF/Chem –Couple CB05/CB05_GE with aerosol modules and aqueous chemistry –Improve SOA and incorporate a more accurate aerosol activation module –Nest from global to urban domains with mass conservation/consistency Globalize WRF/Chem Improve science Apply/Evaluate the model Quantify CC-AQ feedbacks Overall Approach Objectives –Develop a unified GU-WRF/Chem for integrated modeling at all scales –Apply GU-WRF/Chem to replicate and examine feedbacks and to reduce uncertainties in climate-chemistry modeling at regional and global scales

5. Incorporation of CB05 and CB05_GE into GU-WRF/Chem Box Model Test A Total of 120 New Reactions in CB05_GE –5 stratospheric reactions (O 2, N 2 O, O 1 D) –78 reactions for 25 halogen species (48 for 14 Cl and 30 for 11 Br species) –4 mercury reactions (Hg(0) and Hg(II)) –13 heterogeneous reactions on aerosol/cloud and 20 reactions on PSCs –H 2 O, CH 4, O 2 and H 2 are treated as chemically-reactive species Arctic Upper Troposphere O3O3 Hg(II)Hg(0)

6. Cl July Monthly Mean Mixing Ratios of New Species from GU-WRF/Chem-CB05GE (D01 and D03) CLONO 2 N2ON2O Hg(0)

7. ALD2O3O3 Absolute Changes in July Monthly Mean Mixing Ratios of ALD2 and O 3 (CB05_GE - CB05) 0.015 km 25 km -1.8% to 0.04% -41.7% to -5% -0.3% to 0.6% -9.0% to 0.0%

8. CB05-MADRID Observed vs. Simulated Column Predictions in July 2001 (GU-WRF/Chem-CB05-MADRID) Observation MOPITT CO TOMS/ SBUV TOR MODIS AOD

9. Abdul_Razzak & GhanFountoukis and Nenes CCN (S=0.02%) Europe Asia N. America Africa CCN (S=1%) CCN (S=0.1%) Simulated Surface CCN Using Different Aerosol Activation Modules in July 2001 Correlation CCN6

10. Abdul_Razzak & GhanNenes and Seinfeld Observed vs. Simulated Column CCN and Total Cloud Fractions Column CCN (S=1%) (1 x 10 9 cm -2 )Total Cloud Fraction Obs. MeanSim. MeanCorrelationNMB%Obs. MeanSim. MeanCorrelationNMB% AR-G0.240.260.356.190.690.300.22-56.92 F-N0.24 0.32-0.010.690.280.69-59.16 MODIS Column CCN (S=1%) Total Cloud Fraction

11. GU-WRF/Chem Configurations for Nested Simulations Period: 1-31 Jul., 2001 Vertical resolution: 27 layers (1000-50 mb) Emissions: CAM4, MOZART4, and RETRO Mechanisms: CBMZ/MOSAIC/CMU Aq. Chem. D03 D02 D01 D01: Global D02: Trans-Pacific D03: CONUS Domain: Global: 4 × 5˚, 45 (lat) × 72 (lon) Trans-Pacific: 0.8 × 1˚, 55 (lat) × 240 (lon) Continental U.S.: 0.27 × 0.33˚, 105 (lat) × 210 (lon)

12. D01, GU-WRF/Chem D02, GU-WRF/Chem D03, GU-WRF/Chem July Monthly Mean Near-Surface O 3 Global vs. Trans-Pacific vs. CONUS Trans-Pacific Domain Monthly Mean O3 concentrationppmv 108 × 108 km 2, MM5/CMAQ

13. July Monthly Mean Near-Surface PM 2.5 Global vs. Trans-Pacific vs. CONUS Trans-Pacific Domain Monthly Mean Pm2.5 aerosol dry massug m^-3 D01, GU-WRF/ChemD03, GU-WRF/Chem D02, GU-WRF/Chem108 × 108 km 2, MM5/CMAQ

14. Max-8h O 3 24h avg PM 2.5 Evaluation of Near-Surface O 3 and PM 2.5 over CONUS Sim. vs. Obs. Overlay Global (D01)Trans Pacific (D02)CONUS (D03) GU-WRF/Chem MM5/CMAQGU-WRF/Chem Mesoscale WRF/ChemMM5/CMAQ Max 1-hr O 3 CASTNET 23.8 19.4 -12.121.7 9.7 -8.7 Max 8-hr O 3 CASTNET 30.422.8 -4.2 23.2 14.5 -0.2 24-hr avg. PM 2.5 IMPROVE -49.1 STN -61.9 -49.5 -49.2 27.0 21.7 -53.6 -48.0 8.5 21.5 -30.2 -18.3 Column NO 2 80.982.0-28.381.847.113.4 Column O 3 -16.5-17.4-51.9-16.84.5-17.5 AOD -37.7-35.2-47.5-33.63.7-61.3 Statistics (NMB,%)

15. D01 D03 July Monthly Mean Tropospheric O 3 Residual (TOR) Obs. vs. Sim. Over Global and CONUS (D01 and D03) Obs Sim

16. Summary and Potential Applications of GU-WRF/Chem Summary GU-WRF/Chem provides consistent boundary conditions and physical/chemical mechanisms to initiate nested regional/urban simulations. Initial application demonstrates promising skills for surface, vertical, and column meteorological and chemical variables. Potential Applications and Extensions Impact of intercontinental transport on air quality management Asian pollution export and its impact on US air quality control Use of feedbacks to guide integrated emission control strategies for CC/AQ Isolate feedbacks of species and quantify air quality/health/climate benefits Impacts of global CC/AQ on human health and implications on control policies The effects of CO 2 and fuel-use on air pollution and associated mortality Impacts of global CC/AQ on water resources and ecosystems The effects of Hg in air and water quality Interactions among atmosphere, ocean, and land The roles of biogeochemical cycles in climate change and resource management

17. Acknowledgments Project sponsor: EPA STAR #R83337601 Mark Richardson, Caltech, for sharing global WRF Ken Schere, Golam Sarwar, and Shawn Roselle, U.S. EPA, for providing CB05 and CB05Cltx, Shaocai Yu, U.S. NOAA/EPA, for providing Fortran code for statistical calculation Athanasios Nenes, Georgia Tech, for providing aerosol activation code Andreas Richter, the University of Bremen, Germany, for providing GOME NO 2 data; Hilary E. Snell, AER Inc., for processing MOPITT CO and GOME NO 2 ; Jack Fishman and John K. Creilson, NASA Langley Research Center, for providing TOR data Carey Jang, Jonathan Pleim, and Sharon Phillips, U.S. EPA, for helpful discussions on coupled meteorology and air quality models Xiao-Ming Hu and Kai Wang, NCSU, for help in post-processing results