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

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.