Preprints
https://doi.org/10.5194/acp-2022-405
https://doi.org/10.5194/acp-2022-405
 
05 Jul 2022
05 Jul 2022
Status: this preprint is currently under review for the journal ACP.

Aerosol cloud interaction in the atmospheric chemistry model GRAPES_Meso5.1/CUACE and its impacts on mesoscale numerical weather prediction under haze pollution conditions in Jing-Jin-Ji in China

Wenjie Zhang1,2, Hong Wang1, Xiaoye Zhang1, Liping Huang3, Yue Peng1, Zhaodong Liu1, Xiao Zhang4, and Huizheng Che1 Wenjie Zhang et al.
  • 1State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, China
  • 2Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, China
  • 3Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, China
  • 4Department of Atmospheric Sciences, Yunnan University, Kunming, China

Abstract. The representation of aerosol-cloud interaction (ACI) and its impacts in the current climate or weather model remains a challenge, especially for the severely polluted region with high aerosol concentration, which is even more important and worthy of study. Here, ACI is first implemented in the atmospheric chemistry model GRAPES_Meso5.1/CUACE by allowing for real-time aerosol activation in the Thompson cloud microphysics scheme. Two experiments are conducted focusing on a haze pollution case with coexisted high aerosol and stratus cloud over the Jing-Jin-Ji region in China to investigate the impact of the ACI on the mesoscale numerical weather prediction (NWP). Study results show that the ACI increases cloud droplets number concentration, water mixing ratio, liquid water path (CLWP), and optical thickness (COT), as a result, improving the underestimated CLWP and COT (reducing the mean bias by 21 % and 37 %, respectively) over a certain subarea by the model without ACI. Cooling in temperature at daytime below 950 hPa occurs due to ACI, which can reduce the mean bias of 2 m temperature at daytime by up to 14 % (~0.6 °C) in the subarea with the greatest change in CLWP and COT. The 24 h cumulative precipitation in this subarea corresponding to moderate rainfall events increases with reduced the mean bias by 18 %, depending on the enhanced melting of the snow by more cloud droplets. In other areas or periods with a slight change in CLWP and COT, the impact of the ACI on NWP is not significant, suggesting the inhomogeneity of the ACI. This study demonstrates the critical role of the ACI in the current NWP model over the severely polluted region and the complexity of the ACI effect.

Wenjie Zhang et al.

Status: open (until 16 Sep 2022)

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  • RC1: 'Comment on acp-2022-405', Anonymous Referee #1, 07 Jul 2022 reply

Wenjie Zhang et al.

Wenjie Zhang et al.

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Short summary
Aerosol-cloud interaction (ACI) is implemented in the atmospheric chemistry system GRAPES_Meso5.1/CUACE. The ACI can improve the simulated cloud, temperature, and precipitation under haze pollution conditions in severe haze polluted Jing-Jin-Ji region in China. This paper demonstrates the critical role of the ACI in the current numerical weather prediction system and the complexities of the ACI effect in haze pollution episodes.
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