Deep Learning Reveals ENSO's Footprint on the Indian Ocean Dipole: Insights From the Eastern Pacific (American) Coast
文献类型：期刊论文
| 作者 | Wang, Haoyu1,2,3,4; Wang, Jing1,2,3,4; Li, Xiaofeng1,2,3,4 |
| 刊名 | GEOPHYSICAL RESEARCH LETTERS
 |
| 出版日期 | 2025-10-16 |
| 卷号 | 52期号:20页码:12 |
| ISSN号 | 0094-8276 |
| DOI | 10.1029/2025GL118949 |
| 通讯作者 | Li, Xiaofeng(lixf@qdio.ac.cn) |
| 英文摘要 | The Indian Ocean Dipole (IOD) significantly influences global climate and ecosystem dynamics, yet accurate forecasting remains challenging due to its complex nature. Here, we present an interpretable deep-learning framework, STPNet, that achieves the-state-of-the-art 8-month forecasting of fall IOD events by leveraging sea surface temperature anomalies (SSTA) and sea surface height anomalies (SSHA) from CMIP6 data. Through STPNet's interpretability features and targeted sensitivity experiments, we not only confirmed previously known IOD precursors but also identified a novel precursor along the eastern Pacific (American) Coast. Subsequent lagged regression analysis revealed this precursor's connection to ENSO-mediated coupling between extratropical and subtropical Pacific SST patterns, completing the global map of IOD precursors. Our findings substantially advance the understanding of IOD mechanisms and provide a robust framework for operational forecasting, with direct implications for global climate adaptation strategies. |
| WOS关键词 | INDONESIAN SEAS ; SHORT RAINS ; MODE ; PREDICTABILITY ; VARIABILITY ; DYNAMICS ; EVENTS |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001596324700001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/203635]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Li, Xiaofeng |
| 作者单位 | 1.Qingdao Key Lab Artificial Intelligence Oceanog, Qingdao, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 3.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Qingdao, Peoples R China 4.Univ Chinese Acad Sci, Coll Marine Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Haoyu,Wang, Jing,Li, Xiaofeng. Deep Learning Reveals ENSO's Footprint on the Indian Ocean Dipole: Insights From the Eastern Pacific (American) Coast[J]. GEOPHYSICAL RESEARCH LETTERS,2025,52(20):12. |
| APA | Wang, Haoyu,Wang, Jing,&Li, Xiaofeng.(2025).Deep Learning Reveals ENSO's Footprint on the Indian Ocean Dipole: Insights From the Eastern Pacific (American) Coast.GEOPHYSICAL RESEARCH LETTERS,52(20),12. |
| MLA | Wang, Haoyu,et al."Deep Learning Reveals ENSO's Footprint on the Indian Ocean Dipole: Insights From the Eastern Pacific (American) Coast".GEOPHYSICAL RESEARCH LETTERS 52.20(2025):12. |
入库方式： OAI收割
来源：海洋研究所
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