Toward long-range ENSO prediction with an explainable deep learning model
Abstract El Niño-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivaria...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-025-01159-w |
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| author | Qi Chen Yinghao Cui Guobin Hong Karumuri Ashok Yuchun Pu Xiaogu Zheng Xuanze Zhang Wei Zhong Peng Zhan Zhonglei Wang |
| author_facet | Qi Chen Yinghao Cui Guobin Hong Karumuri Ashok Yuchun Pu Xiaogu Zheng Xuanze Zhang Wei Zhong Peng Zhan Zhonglei Wang |
| author_sort | Qi Chen |
| collection | DOAJ |
| description | Abstract El Niño-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. By integrating multiple oceanic and atmospheric predictors, CTEFNet extends the effective forecast lead time to 20 months while mitigating the impact of the spring predictability barrier, outperforming both dynamical models and state-of-the-art deep learning approaches. Furthermore, CTEFNet offers physically meaningful and statistically significant insights through gradient-based sensitivity analysis, revealing the key precursor signals that govern ENSO dynamics, which align with well-established theories and reveal new insights about inter-basin interactions among the Pacific, Atlantic, and Indian Oceans. The CTEFNet’s superior predictive skill and interpretable sensitivity assessments underscore its potential for advancing climate prediction. Our findings highlight the importance of multivariate coupling in ENSO evolution and demonstrate the promise of deep learning in capturing complex climate dynamics with enhanced interpretability. |
| format | Article |
| id | doaj-art-38f29f6eaedd4832ae8324ee28e68b03 |
| institution | Kabale University |
| issn | 2397-3722 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Climate and Atmospheric Science |
| spelling | doaj-art-38f29f6eaedd4832ae8324ee28e68b032025-08-20T04:01:53ZengNature Portfolionpj Climate and Atmospheric Science2397-37222025-07-01811910.1038/s41612-025-01159-wToward long-range ENSO prediction with an explainable deep learning modelQi Chen0Yinghao Cui1Guobin Hong2Karumuri Ashok3Yuchun Pu4Xiaogu Zheng5Xuanze Zhang6Wei Zhong7Peng Zhan8Zhonglei Wang9Department of Ocean Science and Engineering, Southern University of Science and TechnologyDepartment of Statistics and Data Science, School of Economic, Xiamen UniversityMOE Key Laboratory of Econometrics, Xiamen UniversityCentre for Earth, Ocean and Atmospheric Sciences, University of HyderabadMeituanShanghai Zhangjiang Institute of MathematicsKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesDepartment of Statistics and Data Science, School of Economic, Xiamen UniversityDepartment of Ocean Science and Engineering, Southern University of Science and TechnologyDepartment of Statistics and Data Science, School of Economic, Xiamen UniversityAbstract El Niño-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. By integrating multiple oceanic and atmospheric predictors, CTEFNet extends the effective forecast lead time to 20 months while mitigating the impact of the spring predictability barrier, outperforming both dynamical models and state-of-the-art deep learning approaches. Furthermore, CTEFNet offers physically meaningful and statistically significant insights through gradient-based sensitivity analysis, revealing the key precursor signals that govern ENSO dynamics, which align with well-established theories and reveal new insights about inter-basin interactions among the Pacific, Atlantic, and Indian Oceans. The CTEFNet’s superior predictive skill and interpretable sensitivity assessments underscore its potential for advancing climate prediction. Our findings highlight the importance of multivariate coupling in ENSO evolution and demonstrate the promise of deep learning in capturing complex climate dynamics with enhanced interpretability.https://doi.org/10.1038/s41612-025-01159-w |
| spellingShingle | Qi Chen Yinghao Cui Guobin Hong Karumuri Ashok Yuchun Pu Xiaogu Zheng Xuanze Zhang Wei Zhong Peng Zhan Zhonglei Wang Toward long-range ENSO prediction with an explainable deep learning model npj Climate and Atmospheric Science |
| title | Toward long-range ENSO prediction with an explainable deep learning model |
| title_full | Toward long-range ENSO prediction with an explainable deep learning model |
| title_fullStr | Toward long-range ENSO prediction with an explainable deep learning model |
| title_full_unstemmed | Toward long-range ENSO prediction with an explainable deep learning model |
| title_short | Toward long-range ENSO prediction with an explainable deep learning model |
| title_sort | toward long range enso prediction with an explainable deep learning model |
| url | https://doi.org/10.1038/s41612-025-01159-w |
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