Enhancing the predictability limits of ENSO with physics-guided deep echo state networks The Xiamen Symposium on Marine Environmental Sciences XMAS 2027

ID：1630 View Protection：ATTENDEE Updated Time：2026-09-02 16:56:12 Hits：3 Oral Presentation

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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.

Institute of Atmospheric Physics, Chinese Academy of Sciences

Jun Meng Institute of Atmospheric Physics, Chinese Academy of Sciences

The Xiamen Symposium on Marine Environmental Sciences (XMAS 2027)