Articles | Volume 32, issue 4
https://doi.org/10.5194/npg-32-397-2025
https://doi.org/10.5194/npg-32-397-2025
Research article
 | 
20 Oct 2025
Research article |  | 20 Oct 2025

Improving dynamical climate predictions with machine learning: insights from a twin experiment framework

Zikang He, Julien Brajard, Yiguo Wang, Xidong Wang, and Zheqi Shen

Data sets

Improve dynamical climate prediction with machine learning Movie SI Zikang He https://doi.org/10.5281/zenodo.11887170

Model code and software

Code for "mproving dynamical climate predictions with machine learning: insights from a twin experiment framework code" Zikang He https://doi.org/10.5281/zenodo.7725687

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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.
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