1. 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

2. Largest uncertainty (IPCC, 2007):
indirect aerosol forcing

5. 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

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

9. 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

10. 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

11. 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)

12. 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

13. 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

14. Contacts:
Brian K. Eder

15. 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.