Combined dynamical-deep learning ENSO forecasts

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.

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Publication Details

Journal
Nature Communications
Published
2025-04-24
DOI
https://doi.org/10.1038/s41467-025-59173-8
Citations
6
Primary Topic
Atmospheric and Environmental Gas Dynamics
Type
article
Field-Weighted Citation Impact
6.63

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article

Combined dynamical-deep learning ENSO forecasts

Jing‐Jia Luo, Xingchen Shen, Yishuai Jin, Fei Zheng et al.
6 citations
Nature Communications
Atmospheric and Environmental Gas Dynamics
6.63
article

Combined dynamical-deep learning ENSO forecasts

Jing‐Jia Luo, Xingchen Shen, Yishuai Jin, Fei Zheng, Yonglin Chen, Wansuo Duan, Xianyao Chen, Michael J. McPhaden, Lu Zhou, Xiaopei Lin, Wenjun Zhang, Zhengyu Liu, Rong‐Hua Zhang
article en
6 citations

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.

Nature CommunicationsVol. 16(1)
National Oceanic and Atmospheric Administration (US), Chinese Academy of Sciences (CN), Nanjing University of Information Science and Technology (CN), NOAA Pacific Marine Environmental Laboratory (US), Institute of Software (CN), Sanya University (CN), Institute of Atmospheric Physics (CN), Laoshan Laboratory, The Ohio State University (US), Ocean University of China (CN)
National Science Foundation, National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, National Natural Science Foundation of China-Yunnan Joint Fund, National Key Research and Development Program of China
Openalex Percentile: Top 3%
Atmospheric and Environmental Gas Dynamics
6.63
DOIOpen Access PDFOpenAlex
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