Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 15
Articles | Volume 18, issue 15
https://doi.org/10.5194/gmd-18-4789-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-18-4789-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 15
Development and technical paper
 | 
04 Aug 2025
Development and technical paper |  | 04 Aug 2025

Correction of sea surface biases in the NEMO ocean general circulation model using neural networks

Andrea Storto, Sergey Frolov, Laura Slivinski, and Chunxue Yang

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Cited articles

Agarwal, N., Small, R. J., Bryan, F. O., Grooms, I., and Pegion, P. J.: Impact of stochastic ocean density corrections on air-sea flux variability, Geophys. Res. Lett., 50, e2023GL104248, https://doi.org/10.1029/2023GL104248, 2023. 
Balmaseda, M. A., Dee, D., Vidard, A., and Anderson, D. L. T.: A multivariate treatment of bias for sequential data assimilation: Application to the tropical oceans, Q. J. Roy. Meteor. Soc., 133, 167–179, https://doi.org/10.1002/qj.12, 2007. 
Barnier, B., Madec, G., Penduff, T., Molines, J.-M., Treguier, A.-M., Le Sommer, J., Beckmann, A., Biastoch, A., Böning, C., Dengg, J., Derval, C., Durand, E., Gulev, S., Remy, E., Talandier, C., Theetten, S., Maltrud, M., McClean, J., and De Cuevas, B.: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution, Ocean Dynam., 56, 543–567, https://doi.org/10.1007/s10236-006-0082-1, 2006. 
Bonavita, M. and Laloyaux, P.: Machine learning for model error inference and correction, J. Adv. Model. Earth Sy., 12, e2020MS002232, https://doi.org/10.1029/2020MS002232, 2020. 
Brodeau, L., Barnier, B., Treguier, A.-M., Penduff, T., and Gulev, S.: An ERA40-based atmospheric forcing for global ocean circulation models, Ocean Model., 31, 88–104, https://doi.org/10.1016/j.ocemod.2009.10.005, 2010. 
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Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct air–sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.

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Nucleus for European Modelling of the Ocean - NEMO