Deep learning for skillful long-lead ENSO forecasts - Patil, Kalpesh Ravindra; Doi, Takeshi; Jayanthi, Venkata Ratnam; Behera, Swadhin (Frontiers in Climate, 2023)
El Niño-Southern Oscillation (ENSO) is one of the fundamental drivers of the Earth’s climate variability; thus, its skillful prediction at least a few months to years ahead is of utmost importance to the society. Using both dynamical and statistical methods, several studies reported skillful ENSO predictions at various lead times. Predictions with long lead times, on the other hand, remain difficult. In this study we propose a convolutional neural network (CNN) based statistical ENSO prediction system with heterogeneous CNN parameters for each season with a modified loss function to predict ENSO at least 18-24 months ahead. The developed prediction system indicates the CNN model to be highly skillful in predicting ENSO at long-lead times of 18-24 months with high skills in predicting extreme ENSO events compared to the Scale Interaction Experiment-Frontier ver. 2 (SINTEX-F2) dynamical system and several other statistical prediction systems. The analysis indicates the CNN model to overcome the spring barrier, the major hindrance to dynamical prediction systems, in predicting ENSO at long-lead times. The improvement in the prediction skill can partly be attributed to the heterogeneous parameters of seasonal CNN models used in this study and also to the use of a modified loss function in the CNN model. In this study, we also attempted to identify various precursors to ENSO events using CNN heatmap analysis.