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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Oct 2025
Research article |
| 08 Oct 2025
| 08 Oct 2025
Optimization of the World Ocean Model of Biogeochemistry and Trophic dynamics (WOMBAT) using surrogate machine learning methods
Pearse J. Buchanan
CORRESPONDING AUTHOR
CSIRO Environment, Hobart, TAS, Australia
Australian Antarctic Program Partnership, Hobart, TAS, Australia
P. Jyoteeshkumar Reddy
CSIRO Environment, Hobart, TAS, Australia
Richard J. Matear
CSIRO Environment, Hobart, TAS, Australia
Matthew A. Chamberlain
CSIRO Environment, Hobart, TAS, Australia
Tyler Rohr
Australian Antarctic Program Partnership, Hobart, TAS, Australia
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia
Dougal Squire
CSIRO Environment, Hobart, TAS, Australia
ACCESS-NRI, Australian National University, Canberra, 2601, Australia
Elizabeth H. Shadwick
CSIRO Environment, Hobart, TAS, Australia
Australian Antarctic Program Partnership, Hobart, TAS, Australia
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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.
We calibrate a new version of the World Ocean Model of Biogeochemistry and Trophic dynamics...
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