Cloud Radiative Feedback to the Large‐Scale Atmospheric Circulation Greatly Reduces Monsoon‐Season Wet Bias Over the Tibetan Plateau in Climate Modeling
Jiarui Liu, Kun Yang, Dingchi Zhao, Peili Wu, Jiamin Wang, Xu Zhou, Yanluan Lin, Hui Lü, Yaozhi Jiang, Jiancheng Shi
Abstract
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.