Preliminary studies on forecasting the sea surface temperature in the Indian Ocean and South China Sea

【摘要】：The Sea Surface Temperature(SST) in the Indian Ocean and South China Sea affects the regional climate over the Asian continent mostly by a modulation of the monsoon system.And it is still a difficult issue and a need to provide a priori indication of the expected seasonal variability over the Indian Ocean and South China Sea.It is widely recognized that the warm and cold events of the SST in these two areas are strongly linked to those of the equatorial eastern Pacific.In this study,a statistical prediction model has been developed to forecast the monthly SST in the Indian Ocean,and South China Sea.It is a linear regression model based on a lagged relationship between the SST over the Indian Ocean and South China Sea and the NINO3.4 SST Index.The advanced approach to the statistical modeling has been adopted from Kug et al.(2004),in which the model predictors are obtained from the predicted results provided by a large size ensemble ENSO forecast system with coupled data assimilation(Leefs_CDA).The tropical Pacific SST has been operationally forecasted by the Leefs_CDA,which has a high predictive skill of up to a 24-month lead time for the equatorial eastern Pacific SST.As a result,the forecast skill of the present statistical model is better than that of persistence prediction.