Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models

Kern, Skyler; McGuinn, Mary E.; Smith, Katherine M.; Pinardi, Nadia; Niemeyer, Kyle E.; Lovenduski, Nicole S.; Hamlington, Peter E.

doi:https://doi.org/10.5194/gmd-17-621-2024

Articles | Volume 17, issue 2

https://doi.org/10.5194/gmd-17-621-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-17-621-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 2

Development and technical paper

|

26 Jan 2024

Development and technical paper |

| 26 Jan 2024

Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models

Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington

Data sets

BFM17 Optimization Data and Plotting Scripts (Version 2) S. Kern et al. https://doi.org/10.5281/zenodo.10049012

Model code and software

BFM17-56 K. M. Smith et al. https://doi.org/10.5281/zenodo.3839984

BFM17-Opt: Coupling BFM17+POM1D to DAKOTA for Optimization (Updated Work Flow) S. Kern et al. https://doi.org/10.5281/zenodo.10049146

skylerjk/BFM17-SA-SinglePert: One-at-a-Time Sensitivity Analysis Code for BFM17 S. Kern et al. https://doi.org/10.5281/zenodo.7786746

Short summary

Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.