Projection of ENSO using observation-informed deep learning
Abstract The El Niño-Southern Oscillation (ENSO) profoundly impacts global climate, but its sea surface temperature (SST) variability projected by climate models remains uncertain, with a substantial inter-model spread in 21st-century projections. Model-observation discrepancies in ENSO physics cont...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-63157-z |
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| author | Yuchao Zhu Rong-Hua Zhang Fan Wang Wenju Cai Delei Li Shoude Guan Yuanlong Li |
| author_facet | Yuchao Zhu Rong-Hua Zhang Fan Wang Wenju Cai Delei Li Shoude Guan Yuanlong Li |
| author_sort | Yuchao Zhu |
| collection | DOAJ |
| description | Abstract The El Niño-Southern Oscillation (ENSO) profoundly impacts global climate, but its sea surface temperature (SST) variability projected by climate models remains uncertain, with a substantial inter-model spread in 21st-century projections. Model-observation discrepancies in ENSO physics contribute to this uncertainty, necessitating observational constraints to refine projections. However, methods to achieve this constraint remain unclear. Here, we show that deep learning informed by the observed response of ENSO SST variability to tropical Pacific warming patterns reduces projection uncertainty by 54% under a high-emission scenario. Specifically, artificial neural networks (ANNs), trained on climate model simulations and observations, successfully capture the real-world ENSO response. Interpretability analyses reveal that replicating observed ENSO physics by ANNs is critical, identifying warming in the far-eastern and central equatorial Pacific as key to ENSO change. A model-as-truth approach further confirms the robustness of ANN-generated projections. By conditioning future ENSO SST variability projection on the ANN-inferred ENSO response to tropical Pacific warming, uncertainty is reduced from a range of 0.59 °C to 0.27 °C. Our results highlight the prospect of integrating machine learning with observations to reduce uncertainty in climate projections. |
| format | Article |
| id | doaj-art-8b8e5da0636c4075865a42b28a2c0e86 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-8b8e5da0636c4075865a42b28a2c0e862025-08-24T11:37:25ZengNature PortfolioNature Communications2041-17232025-08-0116111210.1038/s41467-025-63157-zProjection of ENSO using observation-informed deep learningYuchao Zhu0Rong-Hua Zhang1Fan Wang2Wenju Cai3Delei Li4Shoude Guan5Yuanlong Li6Key Laboratory of Ocean Observation and Forecasting & Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of SciencesState Key Laboratory of Climate System Prediction and Risk Management/School of Marine Sciences, Nanjing University of Information Science and TechnologyKey Laboratory of Ocean Observation and Forecasting & Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of SciencesFrontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Physical Oceanography, Ocean University of ChinaLaoshan LaboratoryFrontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Key Laboratory of Physical Oceanography, Ocean University of ChinaKey Laboratory of Ocean Observation and Forecasting & Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of SciencesAbstract The El Niño-Southern Oscillation (ENSO) profoundly impacts global climate, but its sea surface temperature (SST) variability projected by climate models remains uncertain, with a substantial inter-model spread in 21st-century projections. Model-observation discrepancies in ENSO physics contribute to this uncertainty, necessitating observational constraints to refine projections. However, methods to achieve this constraint remain unclear. Here, we show that deep learning informed by the observed response of ENSO SST variability to tropical Pacific warming patterns reduces projection uncertainty by 54% under a high-emission scenario. Specifically, artificial neural networks (ANNs), trained on climate model simulations and observations, successfully capture the real-world ENSO response. Interpretability analyses reveal that replicating observed ENSO physics by ANNs is critical, identifying warming in the far-eastern and central equatorial Pacific as key to ENSO change. A model-as-truth approach further confirms the robustness of ANN-generated projections. By conditioning future ENSO SST variability projection on the ANN-inferred ENSO response to tropical Pacific warming, uncertainty is reduced from a range of 0.59 °C to 0.27 °C. Our results highlight the prospect of integrating machine learning with observations to reduce uncertainty in climate projections.https://doi.org/10.1038/s41467-025-63157-z |
| spellingShingle | Yuchao Zhu Rong-Hua Zhang Fan Wang Wenju Cai Delei Li Shoude Guan Yuanlong Li Projection of ENSO using observation-informed deep learning Nature Communications |
| title | Projection of ENSO using observation-informed deep learning |
| title_full | Projection of ENSO using observation-informed deep learning |
| title_fullStr | Projection of ENSO using observation-informed deep learning |
| title_full_unstemmed | Projection of ENSO using observation-informed deep learning |
| title_short | Projection of ENSO using observation-informed deep learning |
| title_sort | projection of enso using observation informed deep learning |
| url | https://doi.org/10.1038/s41467-025-63157-z |
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