Implementing Gaussian Probability Density Function cloud fraction scheme in WRF much reduces the wet bias over the Tibetan Plateau in high-resolution simulations

Abstract

A noticeable wet bias persists over the Tibetan Plateau (TP) during summer in both global and regional climate models, despite numerous advancements and ongoing efforts to lower it. This study investigates the performance of the Gaussian Probability Density Function (GPDF) cloud fraction scheme in the Weather Research and Forecasting (WRF) model over the TP during July and August 2018. The evaluation reveals that the GPDF scheme mitigates the wet bias over the TP in simulations at two resolutions (0.1° and 0.05°), with a significant reduction in the bias. This scheme also reduces the overestimation of downward surface shortwave radiation, indicating an improvement in cloud simulations. We propose that the GPDF scheme alleviates the wet bias through both local moisture process and dynamic process. Specifically, an increase in cloud water/ice content leads to a reduction in surface net radiation and subsequent decrease in surface sensible heat flux and evapotranspiration. This diminished surface heating lessens the thermal effect of the TP, causing a weakened monsoon circulation and decreased moisture flux convergence over the TP. Both the decreases in local evapotranspiration and remote moisture flux convergence contribute to the alleviation of the wet bias, and the latter plays a dominant role, contributing to approximately 70% of the precipitation decrease.