ENSO Forecasts With Spatiotemporal Fusion Transformer Network

The El Niño-Southern Oscillation (ENSO) is a global significant signal which exerts substantial climatic and socioeconomic impacts worldwide. However, the long-term prediction of ENSO persists as a challenge because of its diversity, irregularity, and asymmetry. Here, we develop a spatiotemporal fusion transformer network (STFTN), which designed a parallel encoder structure to effectively extract the spatiotemporal information from sea surface temperature anomaly and Niño3.4 index simultaneously, thereby enhancing the precision of Niño3.4 index forecasts. STFTN leverages the attention mechanism within its parallel encoder structure to extract global characteristics and establish remote dependencies on targets. With this structure, STFTN displays better prediction accuracy in different lead months. Furthermore, the activation map used in STFTN visualizes the contribution of the predictors to the output which helps to comprehend the factors contributing to ENSO events. The results highlight the potential of our model of ENSO forecasts and comprehension.