Deploying machine learning components coupled to Earth  System Models with OASIS3-MCT (v6) and Eophis (v1.1)

Barge, Alexis; Le Sommer, Julien; Storto, Andrea; Valcke, Sophie

doi:10.5194/gmd-19-10345-2026

Articles | Volume 19, issue 21

https://doi.org/10.5194/gmd-19-10345-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Nucleus for European Modelling of the Ocean - NEMO

https://doi.org/10.5194/gmd-19-10345-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 21

Development and technical paper

|

30 Oct 2026

Development and technical paper |

| 30 Oct 2026

Deploying machine learning components coupled to Earth System Models with OASIS3-MCT (v6) and Eophis (v1.1)

Alexis Barge, Julien Le Sommer, Andrea Storto, and Sophie Valcke

Model code and software

eophis_v1.1.0 Alexis Barge and Julien Le Sommer https://doi.org/10.5281/zenodo.17736834

OASIS3-MCT_6.0 Sophie Valcke et al. https://doi.org/10.5281/zenodo.18020824

morays-community/NEMO-eORCA025_MLE Alexis Barge et al. https://doi.org/10.5281/zenodo.18244386

morays-community/NEMO-eORCA025_Subgrid_Density Alexis Barge and Julien Le Sommer https://doi.org/10.5281/zenodo.18244389

morays-community/NEMO-ORCA1_AirSeaHeat Alexis Barge et al. https://doi.org/10.5281/zenodo.18744705

morays-container Alexis Barge https://doi.org/10.5281/zenodo.23696121

Short summary

Scientists use programs, called Earth System Models, to study and predict climate. These models are based on physical theories but can be completed with AI tools. However, combining these tools with traditional models is difficult due to their different nature. Our research introduces a new method that connects these AI tools with existing climate models. We tested this method by integrating it with an ocean model. This work should help scientists explore new ways of making climate predictions.