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Presentation transcript:

Atmospheric Modeling and Analysis Division, Grid-scale indirect radiative forcing of climate due to aerosols over the northern hemisphere simulated by the integrated WRF-CMAQ model: Preliminary results Shaocai Yu*, Kiran Alapaty, Jonathan Pleim, Rohit Mathur, David Wong, and Jia Xing Atmospheric Modeling and Analysis Division, National Exposure Research Lab, U.S. EPA, RTP, NC 27711 *now ORAU at Atmospheric Modeling Branch, Army Research Lab, WSMR, NM 88002 Approved for Public Release; Distribution Unlimited

Largest uncertainty (IPCC, 2007): indirect aerosol forcing

Model Description (Configuration) Calculation of indirect aerosol forcing in WRF-CMAQ (Yu et al., 2013) Aerosols: number, size, chemical composition Coupled WRF-CMAQ aerosol simulation Sulfate, BC, OC, dust CAM ice nucleation scheme (Liu et al. 2007) Aerosol activation scheme (Abdul-Razzak and Ghan, 2000, 2002) Updraft velocity, ice water content (WRF), temperature Updraft velocity, liquid water content (WRF) Cloud microphysics (Morrison): cloud vapor and water, rain, ice, snow, graupel CCN, Cloud droplet number Ice number Conc., IN Radiative transfer model: RRTMg: re(2-60) mm Cloud effective radius (re), COD Ice effective radius (rie), IOD Met fields (WRF) The 1st and 2nd IAF Glaciation IAF

Model domain 108 km domain over the northern hemisphere Simulation period: August of 2006

Results (SWCF) (preliminary results) Monthly Daily August 1, 2006 CERES Obs WRF (only) with subgrid cloud-radiation effect (Alapaty et al. 2012) WRF (only) WRF-CMAQ

Results (SWCF) (preliminary results) Monthly Daily August 2, 2006 CERES Obs WRF (only) with subgrid cloud-radiation effect (Alapaty et al. 2012) WRF (only) WRF-CMAQ

Results (SWCF) (preliminary results) CERES Obs Monthly Mean for August, 2006 WRF-CMAQ significantly improves relative to WRF WRF (only) with subgrid cloud-radiation effect (Alapaty et al. 2012) WRF (only) WRF-CMAQ (Aug 1-3 mean)

Results (Shortwave cloud forcing) Comparison of Monthly means SWCF (August) over the continental U.S. (Yu et al., 2013) Land 12-km simulations with both indirect and direct aerosol forcing (WRF-CMAQ) are the best with very good correlation coefficients 12-km runs still underestimate SWCF over land Land Ocean Obs (CERES) Corr NMB (%) CAM WRF-CMAQ 0.96 -18.18 0.90 1.21 WRF (only) 0.50 -5.01 -0.55 53.86 RRTMG -27.44 0.93 -18.91 0.72 -30.45 -0.48 14.90 Ocean

Results Possible use of NH simulation results for Army Research Lab’s (ARL) globally relocatable limited-area convective-scale WRF FDDA nowcasting project ARL is developing Weather Running Estimate-Nowcast (WRE-N) (Dumais et al., 2013) Based on WRF-ARW model Observation nudging-based 4-D data assimilation (FDDA) methodology WRF-CMAQ NH simulations can provide ARL NH WRE-N nowcasting for specific locations and regions: Initial conditions and boundary conditions Aerosol fields

Contacts: Brian K. Eder email: eder@hpcc.epa.gov www.arl.noaa.gov/ www.epa.gov/asmdnerl

Coupler Two-way coupled WRF-CMAQ modeling System (Interaction and feedback) Meteorological Model WRF modeling System: x=12 km, 4km 34 layers Land-Surface: PX LSM PBL: ACM2 Cloud Physics: Morrison Cumulus: Kain-Fritsch, not for 4km Shortwave: RRTMg, or CAM Longwave: RRTMg, Chemical Transport Model CMAQ Modeling System: Photochemistry: CB05 59 organic and inorganic species, 156 chemical reactions Aerosol module: AE6 3 lognormal modes, organic and inorganic Emission: SMOKE In-line emission for biogenic species AQPREP Prepares virtual CMAQ compatible input met. files Coupler CMAQ-mixactivate: cloud drop, ice number conc. Direct forcing: Aerosol size, composition, conc.