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

The representation of aerosol–cloud interaction (ACI) and its impacts in the current climate or weather model remains a challenge, especially for severely polluted regions 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 coexisting high aerosol and stratus cloud over the Jing–Jin–Ji region in China to investigate the impact of ACI on the mesoscale numerical weather prediction (NWP). Study results show that ACI increases cloud droplet 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. A cooling in temperature in the daytime below 950 hPa occurs due to ACI, which can reduce the mean bias of 2 m temperature in the daytime by up to 14% (∼ 0.6 ℃) 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, which can reduce 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 ACI on NWP is not signifificant, suggesting the inhomogeneity of ACI. This study demonstrates the critical role of ACI in the current NWP model over the severely polluted region and the complexity of the ACI effect.