The Prediction of the Tibetan Plateau Thermal Condition with Machine Learning and Shapley Additive Explanation

Abstract

The thermal condition over the Tibetan Plateau (TP) plays a vital role in the South Asian high (SAH) and the Asian summer monsoon (ASM); however, its prediction skill is still low. Here, two machine learning models are employed to address this problem. Expert knowledge and distance correlation are used to select the predictors from observational datasets. Both linear and nonlinear relationships are considered between the predictors and predictands. The predictors are utilized for training the machine learning models. The prediction skills of the machine learning models are higher than those of two state-of-the-art dynamic operational models and can explain 67% of the variance in the observations. Moreover, the SHapley Additive exPlanation method results indicate that the important predictors are mainly from the Southern Hemisphere, Eurasia, and western Pacific, and most show nonlinear relationships with the predictands. Our results can be applied to find potential climate teleconnections and improve the prediction of other climate signals.