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Enhancing the predictability limits of ENSO with physics-guided deep echo state networks
编号：1630 访问权限：仅限参会人 更新：2026-09-02 16:56:12 浏览：0次 口头报告

报告开始：暂无开始时间（Asia/Shanghai）

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摘要
The El Niño–Southern Oscillation (ENSO) is a dominant mode of interannual climate variability, yet the mechanisms limiting its
long-lead predictability remain unclear. Here we develop a physics-guided Deep Echo State Network (DESN) that operates
on physically interpretable climate modes selected from the extended recharge oscillator (XRO) framework. DESN achieves
skillful Niño 3.4 predictions up to 16–20 months ahead with minimal computational cost. Mechanistic experiments show that
extended predictability arises from nonlinear coupling between warm water volume and inter-basin climate modes. Error-growth
analysis further indicates a finite ENSO predictability horizon of approximately 30 months. These results demonstrate that
physics-guided reservoir computing provides an efficient and interpretable framework for diagnosing and predicting ENSO at
long lead times.
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报告人
Jun Meng
Institute of Atmospheric Physics, Chinese Academy of Sciences

稿件作者
Jun Meng Institute of Atmospheric Physics, Chinese Academy of Sciences
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重要日期

  • 会议日期

    01月12日

    2027

    至

    01月15日

    2027

  • 07月21日 2026

    初稿截稿日期

  • 01月15日 2027

    注册截止日期

主办单位
State Key Laboratory of Marine Environmental Science, Xiamen University (MEL)
Department of Earth Sciences, National Natural Science Foundation of China (NSFC)
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