EGU23-3321, updated on 10 Jan 2024
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using machine learning to improve dynamical predictions in a coupled model

Zikang He1,2, Julien Brajard2, Yiguo Wang2, Xidong Wang1,3, and Zheqi Shen1
Zikang He et al.
  • 1Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, China
  • 2Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
  • 3Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

Dynamical models used in climate prediction often have systematic errors that can bias the predictions. In this study, we utilized machine learning to address this issue. Machine learning was applied to learn the error corrected by data assimilation and thus build a data-driven model to emulate the dynamical model error. A hybrid model was constructed by combining the dynamical and data-driven models. We tested the hybrid model using synthetic observations generated by a simplified high-resolution coupled ocean-atmosphere model (MAOOAM, De Cruz et al., 2016) and compared its performance to that of a low-resolution version of the same model used as a standalone dynamical model.

To evaluate the forecast skill of the hybrid model, we produced ensemble predictions based on initial conditions determined through data assimilation. The results show that the hybrid model significantly improves the forecast skill for both atmospheric and oceanic variables compared to the dynamical model alone. To explore what affects short-term forecast skills and long-term forecast skills, we built two other hybrid models by correcting errors either only atmospheric or only oceanic variables. For short-term atmospheric forecasts, the results show that correcting only oceanic errors has no effect on atmosphere variables forecasts but correcting only atmospheric variables shows similar forecast skill to correcting both atmospheric and oceanic errors. For the long-term forecast of oceanic variables, correcting the oceanic error can improve the forecast skill, but correcting both atmospheric and oceanic errors can obtain the best forecast skill. The results indicate that for the long-term forecast of oceanic variables, bias correction of both oceanic and atmospheric components can have a significant effect.

How to cite: He, Z., Brajard, J., Wang, Y., Wang, X., and Shen, Z.: Using machine learning to improve dynamical predictions in a coupled model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3321,, 2023.