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 and Lu Zhou
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Yipeng Chen: Ocean University of China
Yishuai Jin: Ocean University of China
Zhengyu Liu: The Ohio State University
Xingchen Shen: Chinese Academy of Sciences
Xianyao Chen: Ocean University of China
Xiaopei Lin: Ocean University of China
Rong-Hua Zhang: Nanjing University of Information Science and Technology
Jing-Jia Luo: Nanjing University of Information Science and Technology
Wenjun Zhang: Nanjing University of Information Science and Technology
Wansuo Duan: Chinese Academy of Sciences
Fei Zheng: Chinese Academy of Sciences
Michael J. McPhaden: National Oceanic and Atmospheric Administration/Pacific Marine Environmental Laboratory
Lu Zhou: Nanjing University of Information Science and Technology

Nature Communications, 2025, vol. 16, issue 1, 1-8

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-59173-8

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