Combined dynamical-deep learning ENSO forecasts - Yipeng Chen; Yishuai Jin; Zhengyu Liu; Xingchen Shen; Xianyao Chen; Xiaopei Lin; Rong-Hua Zhang; Jing-Jia Luo; Wenjun Zhang; Wansuo Duan; Fei Zheng; Michael J. McPhaden; et al. (Nature Communications)
Abstract Improving the prediction skill of El Niño-Southern Oscillation (ENSO) is of critical importance for society. Over the past half-century, significant improvements have been made in ENSO prediction. Recent studies have shown that deep learning (DL) models can substantially improve the prediction skill of ENSO compared to individual dynamical models. However, effectively integrating the strengths of both DL and dynamical models to further improve ENSO prediction skill remains a critical topic for in-depth investigations. Here, we show that these DL forecasts, including those using the Convolutional Neural Networks and 3D-Geoformer, offer comparable ENSO forecast skill to dynamical forecasts that are based on the dynamic-model mean. More importantly, we introduce a combined dynamical-DL forecast, an approach that integrates DL forecasts with dynamical model forecasts. Two distinct combined dynamical-DL strategies are proposed, both of which significantly outperform individual DL or dynamical forecasts. Our findings suggest the skill of ENSO prediction can be further improved for a range of lead times, with potentially far-reaching implications for climate forecasting.