A Deep Learning-Based Long-Term ENSO Forecasting Model: 3D-STransformer

Abstract

The El Niño-Southern Oscillation (ENSO) significantly impacts global climate variability, causing extreme events like droughts, floods, and heatwaves. Accurate prediction of ENSO is critical for managing agriculture, water resources, disaster prevention, and economic planning. Despite advances in understanding ENSO's mechanisms and developing prediction models, forecasting its timing, intensity, and duration precisely continues to be a significant obstacle because of the nonlinear and complex characteristics of the phenomenon. In this study, we introduce a 3D-STransformer model, with the aim of improving the accuracy and reliability of long-term prediction by integrating multiple local and remote factors affecting ENSO dynamics, such as wind stress and upper-ocean temperature at different depths. In addition, the model employs a multi-head spatiotemporal attention mechanism to capture long-range dependencies and complex interactions across time and space. We pre-train the proposed model on the CMIP6 data set, then perform the transfer learning on the SODA data set, and finally validate the model on the GODA data set. The model employs an end-to-end, multistep rolling prediction strategy. It takes 12 months of input data and produces forecasts for the following 20 months. The experiment demonstrates that the model has superior performance, maintaining a high correlation in the Niño3.4 SST anomaly predictions up to 20 months ahead.