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Toward long-range ENSO prediction with an explainable deep learning model

文献类型：期刊论文

作者Chen, Qi5,6; Cui, Yinghao5; Hong, Guobin4; Ashok, Karumuri7; Pu, Yuchun8; Zheng, Xiaogu1,9; Zhang, Xuanze2; Zhong, Wei3,5; Zhan, Peng6; Wang, Zhonglei3,5
刊名NPJ CLIMATE AND ATMOSPHERIC SCIENCE
出版日期2025-07-09
卷号8期号:1页码:259
ISSN号2397-3722
DOI10.1038/s41612-025-01159-w
产权排序8
文献子类Article
英文摘要El Ni & ntilde;o-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.
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WOS关键词GENERAL-CIRCULATION MODEL ; SEA-SURFACE TEMPERATURE ; BARRIER LAYER ; EL-NINO ; ATMOSPHERIC TELECONNECTIONS ; CLIMATE ; ATLANTIC ; TRANSFORMER ; SENSITIVITY ; FORECASTS
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001525949200002
出版者NATURE PORTFOLIO
源URL[http://ir.igsnrr.ac.cn/handle/311030/215321]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Zhan, Peng; Wang, Zhonglei
作者单位1.Int Global Change Inst, Hamilton, New Zealand;
2.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
3.Xiamen Univ, Wang Yanan Inst Studies Econ, Xiamen 361005, Peoples R China
4.Xiamen Univ, MOE Key Lab Econometr, Xiamen 361005, Peoples R China;
5.Xiamen Univ, Sch Econ, Dept Stat & Data Sci, Xiamen 361005, Peoples R China;
6.Southern Univ Sci & Technol, Dept Ocean Sci & Engn, Shenzhen 518055, Peoples R China;
7.Univ Hyderabad, Ctr Earth Ocean & Atmospher Sci, Hyderabad, India;
8.Meituan, Beijing, Peoples R China;
9.Shanghai Zhangjiang Inst Math, Shanghai 201203, Peoples R China;
推荐引用方式
GB/T 7714		 Chen, Qi,Cui, Yinghao,Hong, Guobin,et al. Toward long-range ENSO prediction with an explainable deep learning model[J]. NPJ CLIMATE AND ATMOSPHERIC SCIENCE,2025,8(1):259.
APA Chen, Qi.,Cui, Yinghao.,Hong, Guobin.,Ashok, Karumuri.,Pu, Yuchun.,...&Wang, Zhonglei.(2025).Toward long-range ENSO prediction with an explainable deep learning model.NPJ CLIMATE AND ATMOSPHERIC SCIENCE,8(1),259.
MLA Chen, Qi,et al."Toward long-range ENSO prediction with an explainable deep learning model".NPJ CLIMATE AND ATMOSPHERIC SCIENCE 8.1(2025):259.

入库方式： OAI收割

来源：地理科学与资源研究所

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