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Yang Zhang, Xin-Yu Wen, and Ying Pan North Carolina State University, Raleigh, NC 27695

Development and Evaluation of Global-Through-Urban WRF/Chem: Gas-Phase Mechanism, Gas-Aerosol Coupling, and Aerosol-Cloud Interactions. Yang Zhang, Xin-Yu Wen, and Ying Pan North Carolina State University, Raleigh, NC 27695 Prakash Karamchandani and Christian Seigneur

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Yang Zhang, Xin-Yu Wen, and Ying Pan North Carolina State University, Raleigh, NC 27695

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  1. Development and Evaluation of Global-Through-Urban WRF/Chem: Gas-Phase Mechanism, Gas-Aerosol Coupling, and Aerosol-Cloud Interactions 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 7th Annual CMAS Conference, October 6-8, 2008, Chapel Hill, NC

  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 Downscale: BCs/ICs General Circulation Model Mesoscale Climate Model Offline models Fine to Coarse Grid Feedbacks Online models Population/Economic Growth Energy and Land Use Unified models Large scale Weather Radiation, CCN, Cloud Radiation, CCN, Cloud Mesoscale Weather Biogenic emissions Anthro. emissions Global Air Quality Model Mesoscale Air Quality Model Neighborhood Scale Human Health Effect Downscale: BCs/ICs Fine to Coarse Grid Feedbacks Air Quality Management Global Warming Mitigation Climate Policy Analysis Economy/ Policy Analysis

  4. Development of Global-through-Urban Weather Research and Forecasting Model with Chemistry (GU-WRF/Chem) • Objectives • Develop a unified GU-WRF/Chemfor 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 • Overall Approach Globalize WRF/Chem Improve science Apply/Evaluate the model Quantify CC-AQ feedbacks • 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

  5. Incorporation of CB05 and CB05_GE into GU-WRF/Chem • A Total of 120 New Reactions in CB05_GE • 5 stratospheric reactions (O2, N2O, O1D) • 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 • H2O, CH4, O2 and H2 are treated as chemically-reactive species • Box Model Test O3 Hg(0) Hg(II) Arctic Upper Troposphere

  6. July Monthly Mean Mixing Ratios of New Species from GU-WRF/Chem-CB05GE (D01 and D03) Cl N2O CLONO2 Hg(0)

  7. Absolute Changes in July Monthly Mean Mixing Ratios of ALD2 and O3 (CB05_GE - CB05) ALD2 O3 0.015 km -1.8% to 0.04% -0.3% to 0.6% 25 km -41.7% to -5% -9.0% to 0.0%

  8. Observedvs.Simulated Column Predictions in July 2001 (GU-WRF/Chem-CB05-MADRID) Observation CB05-MADRID MOPITTCO TOMS/ SBUV TOR MODIS AOD

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

  10. Observed vs. Simulated Column CCN and Total Cloud Fractions MODIS Abdul_Razzak & Ghan Nenes and Seinfeld 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. • 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) D03 D02 D01 D01: Global D02: Trans-Pacific D03: CONUS

  12. July Monthly Mean Near-Surface O3Global vs. Trans-Pacific vs. CONUS D01, GU-WRF/Chem D03, GU-WRF/Chem Trans-Pacific Domain Monthly Mean O3 concentration ppmv D02, GU-WRF/Chem 108 × 108 km2, MM5/CMAQ

  13. July Monthly Mean Near-Surface PM2.5Global vs. Trans-Pacific vs. CONUS D01, GU-WRF/Chem D03, GU-WRF/Chem Trans-Pacific Domain Monthly Mean Pm2.5 aerosol dry mass ug m^-3 D02, GU-WRF/Chem 108 × 108 km2, MM5/CMAQ

  14. Evaluation of Near-Surface O3 and PM2.5 over CONUS Sim. vs. Obs. Overlay Max-8h O3 24h avg PM2.5 Statistics (NMB,%)

  15. July Monthly Mean Tropospheric O3 Residual (TOR)Obs. vs. Sim. Over Global and CONUS (D01 and D03) D03 D01 Obs Obs Sim 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 CO2 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 NO2 data; Hilary E. Snell,AER Inc., for processing MOPITT CO and GOME NO2; Jack Fishman and John K. Creilson,NASA Langley Research Center, for providing TOR data • Carey Jang, JonathanPleim, 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

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