Abstract
A basic feature of El Niño is an abnormal increase in the surface temperature of the equatorial Pacific Ocean, which can throw ocean–atmosphere interactions out of balance, resulting in heavy rainfall and severe storms. This climate anomaly causes different levels of impacts worldwide, such as causing droughts in some regions and excessive rainfall in others. Therefore, it is important to determine the formation of El Niño by predicting the changes in the sea surface temperature (SST) in the equatorial Pacific Ocean. In this paper, we propose a multi-element fusion network model based on convolutional long short-term memory (ConvLSTM) and an attention mechanism to predict the SST and analyze the effects of different elemental inputs on the model’s prediction performance using the prediction results. The experimental results show that using the sea surface wind (SSW) and sea level anomaly (SLA) as multi-element inputs to predict the SST overcame the shortcomings of the single-element forecast model, and the prediction accuracy of the two-element fusion model was better than that of the three-element fusion model. In the two-element fusion model, using the SSW as an input predicted the SST with a lower prediction error than using the SLA as an input and had better prediction performance compared with other benchmark models. For predicting the SST in the equatorial Pacific Ocean, the monthly average root mean square error (RMSE) was mainly concentrated in the range of 0.4–0.8 °C, and the regions with a larger error dispersion were located in the spatial range of 5° S–5° N and 130° W–90° W, and the monthly average regional RMSE was mainly concentrated in the range of 0.5–1 °C. Finally, we also validated the prediction performance of the model for the SST in El Niño and La Niña years, and the prediction results of the model in La Niña years were better than those in El Niño years.