Significant Land Contributions to Interannual Predictability of East Asian Summer Monsoon Rainfall

Abstract

Abstract Marked by large interannual variability, East Asian summer monsoon (EASM) rainfall has profound socio‐economic impacts through its dominant influence on floods and droughts. Improving predictions of the interannual variations of EASM rainfall has important implications for over 20% of the world's population. While coupled modeling systems have demonstrated some prediction skill related to the El Niño Southern Oscillation with remote influence on EASM rainfall, the impact of soil moisture has heretofore not been systematically investigated. Using a weakly coupled data assimilation (WCDA) system to constrain the soil moisture and soil temperature in a coupled climate model with a global land data assimilation product, this study demonstrates significant improvements in simulating the interannual variations of EASM rainfall, capturing the notable shift to a “wetter‐South‐drier‐North” rainfall pattern in China in the early 1990s. Hindcast simulations initialized with the well‐balanced states from a coupled simulation with WCDA also show significant multi‐year rainfall prediction skill over East China and Tibetan Plateau. Improvements in predicting the EASM rainfall are attributed to the strong land‐atmosphere coupling in large areas over China, which allows improved predictions of soil moisture to influence precipitation through soil moisture‐precipitation feedback, and the effects of land anomalies on the EASM circulation. This study highlights the significant contribution of land to the interannual predictability of EASM rainfall, with a great potential to advance skillful interannual predictions of benefit to the large populations influenced by the annual whiplash of the summer monsoon rain.

Keywords

• data assimilation
• land‐atmosphere interactions
• land memory
• monsoon rainfall
• predictability