Preliminary Studies on Predicting the Tropical Indian Ocean Sea Surface Temperature through Combined Statistical Methods and Dynamic ENSO Prediction

Abstract

The sea surface temperature (SST) in the Indian Ocean affects the regional climate over the Asian continent mostly through a modulation of the monsoon system. It is still difficult to provide an a priori indication of the seasonal variability over the Indian Ocean. It is widely recognized that the warm and cold events of SST over the tropical Indian Ocean are strongly linked to those of the equatorial eastern Pacific. In this study, a statistical prediction model has been developed to predict the monthly SST over the tropical Indian Ocean. This model is a linear regression model based on the lag relationship between the SST over the tropical Indian Ocean and the Niño3.4 (5°S–5°N, 170°W–120°W) SST Index. The predictor (i.e., Niño3.4 SST Index) has been operationally predicted by a large size ensemble El Niño and the Southern Oscillation (ENSO) forecast system with coupled data assimilation (Leefs_CDA), which achieves a high predictive skill of up to a 24-month lead time for the equatorial eastern Pacific SST. As a result, the prediction skill of the present statistical model over the tropical Indian Ocean is better than that of persistence prediction for January 1982 through December 2009.

Key Words:

• Indian Ocean
• SST
• ENSO prediction
• statistical method
• dynamical prediction