Improving Seasonal Prediction of East Asian Summer Rainfall Using NESM3.0: Preliminary Results

Atmosphere

Bin Wang¹  Juan Li¹  Young-Min Yang¹ 

[1] Key Laboratory of Meteorological Disaster of Ministry of Education and Earth System Modeling Center, Nanjing University of Information Science and Technology, Nanjing 21044, China;

关键词: East Asian summer monsoon;    seasonal prediction;    dynamic prediction;    summer rainfall prediction;    NESM3.0;    ENSO teleconnection;   

DOI  :  10.3390/atmos9120487

来源: DOAJ

【 摘 要 】

It has been an outstanding challenge for global climate models to simulate and predict East Asia summer monsoon (EASM) rainfall. This study evaluated the dynamical hindcast skills with the newly developed Nanjing University of Information Science and Technology Earth System Model version 3.0 (NESM3.0). To improve the poor prediction of an earlier version of NESM3.0, we modified convective parameterization schemes to suppress excessive deep convection and enhance insufficient shallow and stratiform clouds. The new version of NESM3.0 with modified parameterizations (MOD hereafter) yields improved rainfall prediction in the northern and southern China but not over the Yangtze River Valley. The improved prediction is primarily attributed to the improvements in the predicted climatological summer mean rainfall and circulations, Nino 3.4 SST anomaly, and the rainfall anomalies associated with the development and decay of El Nino events. However, the MOD still has biases in the predicted leading mode of interannual variability of precipitation. The leading mode captures the dry (wet) anomalies over the South China Sea (northern East Asia) but misplaces precipitation anomalies over the Yangtze River Valley. The model can capture the interannual variation of the circulation indices very well. The results here suggest that, over East Asia land regions, the skillful rainfall prediction relies on not only model’s capability in predicting better summer mean and ENSO teleconnection with EASM, but also accurate prediction of the leading modes of interannual variability.

【 授权许可】

Unknown