The impact of aerosol-cloud interaction on mesoscale numerical weather prediction in winter over major polluted regions of China

Compared with climate models, the role of aerosol-cloud interaction (ACI) in mesoscale numerical weather prediction (NWP) models still needs to be better understood, especially in haze regions with relatively high aerosol concentration. Here, we perform two sensitivity experiments with and without ACI (ACI and NO-ACI) in the atmospheric chemistry model CMA_Meso/CUACE to investigate the impact of ACI on mesoscale NWP during the low-cloud period in winter 2016 over varying haze regions (severe polluted Jing-Jin-Ji (JJJ), polluted Yangtze River Delta (YRD), and weak polluted Pearl River Delta (PRD)) in China. The study results show that the real-time ACI improves underestimated cloud optical thickness (COT) and cloud water liquid path (CLWP) in haze regions, with the mean bias of simulated COT (CLWP) decreased by 27% (3%), 60% (14%), and 55% (3%) in JJJ, YRD, and PRD, respectively. The increased COT and CLWP lead to a decrease of 6.8, 21, and 13 W m^-2 in daytime surface downward shortwave radiation (SDSR) in JJJ, YRD, and PRD, helping to reduce the mean bias of daytime SDSR by 6%, 13%, and 9%. In addition, ACI mitigates the warm bias of temperature at 2 m and dry bias of relative humidity (RH) at 2 m to a certain extent in haze regions, particularly in YRD with the mean absolute bias improved by 13% and 6%. The simulated vertical structure of temperature and RH in the ACI experiment is more consistent with observations than in the NO-ACI experiment. Further investigations find that the ACI effects on mesoscale NWP strongly depend on COT and CLWP magnitude over varying haze regions. Higher COT and CLWP, hence more significant meteorology changes due to ACI, occur in YRD, followed by PRD and JJJ. This study demonstrates the importance and complexity of ACI in modifying mesoscale NWP over varying haze regions of China, which promotes the further understanding of ACI in operational NWP models and bridges the gap with climate models.