Long-term prediction of ENSO with physics-guided Deep Echo State Networks
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915737602359296 |
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| author | Zhang, Zejing Meng, Jun Qiu, Zhongpu Duan, Wansuo Gao, Jian Yan, Zixiang Xiao, Jinghua Chen, Xiaosong Cai, Wenju Kurths, Jürgen Havlin, Shlomo Fan, Jingfang |
| author_facet | Zhang, Zejing Meng, Jun Qiu, Zhongpu Duan, Wansuo Gao, Jian Yan, Zixiang Xiao, Jinghua Chen, Xiaosong Cai, Wenju Kurths, Jürgen Havlin, Shlomo Fan, Jingfang |
| contents | 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 Niño3.4 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12251 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Long-term prediction of ENSO with physics-guided Deep Echo State Networks Zhang, Zejing Meng, Jun Qiu, Zhongpu Duan, Wansuo Gao, Jian Yan, Zixiang Xiao, Jinghua Chen, Xiaosong Cai, Wenju Kurths, Jürgen Havlin, Shlomo Fan, Jingfang Atmospheric and Oceanic Physics Chaotic Dynamics 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 Niño3.4 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. |
| title | Long-term prediction of ENSO with physics-guided Deep Echo State Networks |
| topic | Atmospheric and Oceanic Physics Chaotic Dynamics |
| url | https://arxiv.org/abs/2601.12251 |