Articles | Volume 32, issue 4
https://doi.org/10.5194/npg-32-397-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-32-397-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Oct 2025
Research article |
| 20 Oct 2025
| 20 Oct 2025
Improving dynamical climate predictions with machine learning: insights from a twin experiment framework
Zikang He
Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, 210098, China
College of Oceanography, Hohai University, Nanjing, 210098, China
Nansen Environmental and Remote Sensing Center, Bergen, 5007, Norway
Julien Brajard
Nansen Environmental and Remote Sensing Center, Bergen, 5007, Norway
Yiguo Wang
Nansen Environmental and Remote Sensing Center, Bergen, 5007, Norway
Xidong Wang
CORRESPONDING AUTHOR
Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, 210098, China
College of Oceanography, Hohai University, Nanjing, 210098, China
Zheqi Shen
Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, 210098, China
College of Oceanography, Hohai University, Nanjing, 210098, China
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Short summary
Climate prediction is challenging due to systematic errors in traditional climate models. We addressed this by training a machine learning model to correct these errors and then integrating it with the traditional climate model to form an AI-physics hybrid model. Our study demonstrates that the hybrid model outperforms the original climate model on both short-term and long-term predictions of the atmosphere and ocean.
Climate prediction is challenging due to systematic errors in traditional climate models. We...
