Projection of ENSO using observation-informed deep learning.

Journal: Nature communications
Published Date: Aug 19, 2025

Abstract

The El Niño-Southern Oscillation (ENSO) profoundly impacts global climate, but its sea surface temperature (SST) variability projected by climate models remains uncertain, with a substantial inter-model spread in 21st-century projections. Model-observation discrepancies in ENSO physics contribute to this uncertainty, necessitating observational constraints to refine projections. However, methods to achieve this constraint remain unclear. Here, we show that deep learning informed by the observed response of ENSO SST variability to tropical Pacific warming patterns reduces projection uncertainty by 54% under a high-emission scenario. Specifically, artificial neural networks (ANNs), trained on climate model simulations and observations, successfully capture the real-world ENSO response. Interpretability analyses reveal that replicating observed ENSO physics by ANNs is critical, identifying warming in the far-eastern and central equatorial Pacific as key to ENSO change. A model-as-truth approach further confirms the robustness of ANN-generated projections. By conditioning future ENSO SST variability projection on the ANN-inferred ENSO response to tropical Pacific warming, uncertainty is reduced from a range of 0.59 °C to 0.27 °C. Our results highlight the prospect of integrating machine learning with observations to reduce uncertainty in climate projections.

Authors

  • Yuchao Zhu
    Key Laboratory of Ocean Observation and Forecasting & Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China.
  • Rong-Hua Zhang
    State Key Laboratory of Climate System Prediction and Risk Management/School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China. rzhang@nuist.edu.cn.
  • Fan Wang
    Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.
  • Wenju Cai
    Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China.
  • Delei Li
    College of Chemistry and Chemical Engineering, Yunnan Normal University , Yunnan, Kunming, 650092, People's Republic of China.
  • Shoude Guan
    Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China.
  • Yuanlong Li
    GE Healthcare, Beijing, China.

Keywords

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