Improving the Treatment of Subgrid Cloud Variability in Warm Rain Simulation in CESM2. (26th September 2022) - Wang, Hao; Wang, Minghuai; Zhang, Zhibo; Larson, Vincent E.; Griffin, Brian M.; Guo, Zhun; Zhu, Yannian; Rosenfeld, Daniel; Cao, Yang; Bai, Heming
Abstract: Representing subgrid variability of cloud properties has always been a challenge in global climate models (GCMs). In many cloud microphysics schemes, the warm rain non‐linear process rates calculated based on grid‐mean cloud properties are usually scaled by an enhancement factor (EF) to account for the effects of subgrid cloud variability. In our study, we find that the EF derived from Cloud Layers Unified by Binormals in Community Atmosphere Model version 6 (CAM6) is severely overestimated in most of the cloudy oceanic areas, which leads to strong overestimation of the autoconversion rate. We improve the EF in warm rain simulation by developing a new formula for in‐cloud subgrid cloud water variance. With the updated subgrid cloud water variance and EF treatment, the liquid cloud fraction (LCF) and cloud optical thickness (COT) increases noticeably for marine stratocumulus, and the shortwave cloud forcing (SWCF) matches better with observations. The updated formula improves the relationship between autoconversion rate and cloud droplet number concentration, and it decreases the sensitivity of autoconversion rate to aerosols. The sensitivity of liquid water path to aerosols decreases noticeably and is in better agreement with that in MODIS. Although the sensitivity of COT is similar to that in MODIS, CAM6 underestimates the sensitivity of grid‐mean SWCF to aerosols because of the underestimation in the sensitivities of LCF and in‐cloud SWCF. Our results indicate the importance of representing reasonable subgrid cloud variability in the simulation of cloud properties and aerosol‐cloud interaction in GCMs. Plain Language Summary: Restricted by the coarse horizontal resolution, the global climate model needs to scale the warm rain non‐linear process rates with an enhancement factor to account for the effects of subgrid variability of cloud water. However, this variability is severely overestimated in most of the cloudy oceanic areas in Community Atmosphere Model version 6, which leads to a strong overestimation of the precipitation. We develop a new formula for calculating the subgrid variability of cloud water, which greatly improves the magnitude of the variability and contributes to better simulation of cloud properties and shortwave cloud forcing (SWCF) using satellite observation as a benchmark. The sensitivity of cloud liquid water path to aerosols decreases noticeably with the new formula. The model underestimates the sensitivity of SWCF to aerosols due to the underestimation in the sensitivities of liquid cloud fraction and cloud albedo. Our results indicate the importance of representing reasonable subgrid cloud variability in the simulation of cloud properties and aerosol‐cloud interaction in the climate model. Key Points: A new formula for the in‐cloud subgrid cloud water variance is developed to improve the enhancement factor of autoconversion rate The simulated sensitivity of cloud liquid water path to aerosols decreases obviously with the updated formula The simulated sensitivity of grid‐mean shortwave cloud forcing to aerosols is underestimated compared to MODIS observation