Biogeosciences
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Articles | Volume 22, issue 19
Articles | Volume 22, issue 19
https://doi.org/10.5194/bg-22-5349-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-5349-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 19
Research article
 | 
08 Oct 2025
Research article |  | 08 Oct 2025

Optimization of the World Ocean Model of Biogeochemistry and Trophic dynamics (WOMBAT) using surrogate machine learning methods

Pearse J. Buchanan, P. Jyoteeshkumar Reddy, Richard J. Matear, Matthew A. Chamberlain, Tyler Rohr, Dougal Squire, and Elizabeth H. Shadwick

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Short summary
We calibrate a new version of the World Ocean Model of Biogeochemistry and Trophic dynamics (WOMBAT-lite) using a surrogate machine learning approach. A Gaussian process surrogate trained on 512 simulations emulated tens of thousands, enabling global sensitivity analysis and Bayesian optimization of 26 parameters. We constrain 13 key parameters, improving fit to 8 datasets (chlorophyll a, air–sea CO₂ fluxes, nutrient limitation), and provide an optimal set for community use.
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