WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions

X. Feng; H. Lin; T.-M. Fu; T.-M. Fu; M. P. Sulprizio; J. Zhuang; D. J. Jacob; H. Tian; Y. Ma; L. Zhang; X. Wang; Q. Chen; Z. Han

doi:10.5194/gmd-14-3741-2021

Geoscientific Model Development (Jun 2021)

WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions

X. Feng,
H. Lin,
T.-M. Fu,
T.-M. Fu,
M. P. Sulprizio,
J. Zhuang,
D. J. Jacob,
H. Tian,
Y. Ma,
L. Zhang,
X. Wang,
Q. Chen,
Z. Han

Affiliations

X. Feng: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
H. Lin: John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
T.-M. Fu: State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
T.-M. Fu: Shenzhen Institute of Sustainable Development, Southern University of Science and Technology, Shenzhen, Guangdong, China
M. P. Sulprizio: John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
J. Zhuang: John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
D. J. Jacob: John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
H. Tian: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Y. Ma: National Meteorological Information Center, China Meteorological Administration, Beijing, China
L. Zhang: Shanghai Central Meteorological Observatory, Shanghai, China
X. Wang: Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Q. Chen: State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
Z. Han: Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

DOI: https://doi.org/10.5194/gmd-14-3741-2021
Journal volume & issue: Vol. 14
pp. 3741 – 3768

Abstract

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We present the WRF-GC model v2.0, an online two-way coupling of the Weather Research and Forecasting (WRF) meteorological model (v3.9.1.1) and the GEOS-Chem model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol–radiation interaction (ARI) and aerosol–cloud interaction (ACI) based on bulk aerosol mass and composition, as well as the capability to nest multiple domains for high-resolution simulations. WRF-GC v2.0 is the first implementation of the GEOS-Chem model in an open-source dynamic model with chemical feedbacks to meteorology. In WRF-GC, meteorological and chemical calculations are performed on the exact same 3-D grid system; grid-scale advection of meteorological variables and chemical species uses the same transport scheme and time steps to ensure mass conservation. Prescribed size distributions are applied to the aerosol types simulated by GEOS-Chem to diagnose aerosol optical properties and activated cloud droplet numbers; the results are passed to the WRF model for radiative and cloud microphysics calculations. WRF-GC is computationally efficient and scalable to massively parallel architectures. We use WRF-GC v2.0 to conduct sensitivity simulations with different combinations of ARI and ACI over China during January 2015 and July 2016. Our sensitivity simulations show that including ARI and ACI improves the model's performance in simulating regional meteorology and air quality. WRF-GC generally reproduces the magnitudes and spatial variability of observed aerosol and cloud properties and surface meteorological variables over East Asia during January 2015 and July 2016, although WRF-GC consistently shows a low bias against observed aerosol optical depths over China. WRF-GC simulations including both ARI and ACI reproduce the observed surface concentrations of PM2.5 in January 2015 (normalized mean bias of −9.3 %, spatial correlation r of 0.77) and afternoon ozone in July 2016 (normalized mean bias of 25.6 %, spatial correlation r of 0.56) over eastern China. WRF-GC v2.0 is open source and freely available from http://wrf.geos-chem.org (last access: 20 June 2021).

Published in Geoscientific Model Development

ISSN: 1991-959X (Print); 1991-9603 (Online)
Publisher: Copernicus Publications
Country of publisher: Germany
LCC subjects: Science: Geology
Website: https://www.geoscientific-model-development.net/

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