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.
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.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
04 Aug 2025
Development and technical paper |
| 04 Aug 2025
| 04 Aug 2025
Correction of sea surface biases in the NEMO ocean general circulation model using neural networks
National Research Council of Italy (CNR), Institute of Marine Sciences (ISMAR), Rome, Italy
National Research Center for High Performance Computing, Big Data and Quantum Computing (ICSC), Italy
Sergey Frolov
National Oceanic and Atmospheric Administration (NOAA), Physical Sciences Laboratory (PSL), Boulder, CO, USA
Laura Slivinski
National Oceanic and Atmospheric Administration (NOAA), Physical Sciences Laboratory (PSL), Boulder, CO, USA
Chunxue Yang
National Research Council of Italy (CNR), Institute of Marine Sciences (ISMAR), Rome, Italy
National Research Center for High Performance Computing, Big Data and Quantum Computing (ICSC), Italy
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Short summary
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.
Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical...
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