Subgrid-scale aerosol–cloud interaction in the atmospheric chemistry model CMA_Meso5.1/CUACE and its impacts on mesoscale meteorology prediction

Aerosol–cloud interaction (ACI) significantly influences global and regional weather and is a critical focus in numerical weather prediction (NWP), but subgrid-scale ACI effects are often overlooked. Here, a subgrid-scale ACI mechanism is implemented by explicitly treating cloud microphysics in the KFeta convective scheme with real-time size-resolved hygroscopic aerosol activation and introducing subgrid-scale cloud radiation feedback in an atmospheric chemistry model, CMA_Meso5.1/CUACE. With a focus on summer over central and eastern China, the performance evaluation shows that this developed model with subgrid-scale cloud microphysics and radiation feedback refines cloud representation, even in some grid-scale unsaturated areas, and subsequently leads to attenuated surface downward shortwave radiation (∼ 18.5 W m−2) that is more realistic. The increased cloud radiative forcing results in lower temperature (∼ 0.35 °C) and higher relative humidity (∼ 2.5 %) at 2 m, with regional mean bias (MB) decreasing by ∼ 40 % and ∼ 18.1 %. Temperature vertical structure and relative humidity below ∼ 900 hPa are improved accordingly due to cooling and humidifying. The underestimated precipitation is enhanced, especially at the grid scale, thus reducing regional MB by ∼ 34.4 % (∼ 1.1 mm). The performance differences between various subregions are related to convective conditions and model local errors. Additionally, compared to simulations with anthropogenic emissions turned off, subgrid-scale actual aerosol inhibits cumulative precipitation during a typical heavy rainfall event by ∼ 4.6 mm, aligning it with observations, associated with lower autoconversion at the subgrid scale and less available water vapor for grid-scale condensation, suggesting competition between subgrid- and grid-scale cloud. This study contributes to the understanding of the impact of subgrid-scale ACI on NWP.