Improving the treatment of subgrid cloud variability in warm rain simulation in CESM2

Abstract:

Representing subgrid variability of cloud properties has always been a challenge in global climate models (GCMs). In microphysical schemes, the effects of subgrid cloud variability on warm rain process rates are usually accounted for by scaling process rates calculated based on mean cloud properties by an enhancement factor (EF) that is derived from subgrid variance of cloud water. In our study, we find that the EF derived from Cloud Layers Unified by Binormals (CLUBB) in Community Earth System Model Version 2 (CESM2) is strongly overestimated, which leads to the strong overestimation in the autoconversion rate. We adopt an EF formula that is based on empirical fitting of MODIS observations and show that the model shows a more reasonable relationship between autoconversion rate, cloud liquid water content (LWC) and droplet number concentration (CDNC) in warm rain simulation. The liquid cloud fraction, LWC and CDNC show obvious increase for marine stratocumulus. The susceptibility of the cloud water to aerosol is in better agreement with the observation. These results indicate the importance of representing the subgrid cloud variability in the simulation of cloud properties and aerosol-cloud interaction in climate models.