Effective forecast of Northeast Pacific sea surface temperature based on a complementary ensemble empirical mode decomposition–support vector machine method

Abstract

The sea surface temperature (SST) has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST. Here, the authors combine the complementary ensemble empirical mode decomposition (CEEMD) and support vector machine (SVM) methods to predict SST. Extensive tests from several different aspects are presented to validate the effectiveness of the CEEMD-SVM method. The results suggest that the new method works well in forecasting Northeast Pacific SST at a 12-month lead time, with an average absolute error of approximately 0.3 °C and a correlation coefficient of 0.85. Moreover, no spring predictability barrier is observed in our experiments.

Keywords

• Sea surface temperature
• complementary ensemble empirical mode decomposition
• support vector machine
• prediction