Cloud Radiative Feedback to the Large-Scale Atmospheric Circulation Greatly Reduces Monsoon-Season Wet Bias Over the Tibetan Plateau in Climate Modeling

Abstract

Over-estimation of summer precipitation over the Tibetan Plateau (TP) is a well-known and persistent problem in most climate models. This study demonstrates the impact of a Gaussian Probability Density Function cloud fraction scheme on rainfall simulations using the Weather Research and Forecasting model. It is found that this scheme in both 0.1° and 0.05° resolutions significantly reduces the wet bias through both local feedbacks and large-scale dynamic process. Specifically, increased cloud water/ice content with this scheme reduces surface shortwave radiation, and consequently surface heat fluxes and evapotranspiration. This, in turn, dampens the large-scale thermal effect of the TP and weakens the exaggerated monsoon circulation and low-level moisture convergence. It is this large-scale dynamic process that contributes the most (∼70%) to the wet bias reduction. Although this paper presents a modeling study, it highlights the cloud radiative feedback to the large-scale dynamics and precipitation over the TP.