Articles | Volume 17, issue 2
https://doi.org/10.5194/gmd-17-621-2024
https://doi.org/10.5194/gmd-17-621-2024
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

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Cited articles

Adams, B. M., Eldred, M. S., Geraci, G., Hooper, R. W., Jakeman, J. D., Maupin, K. A., Monschke, J. A., Rushdi, A. A., Stephens, J. A., Swiler, L. P., Wildey, T. M., Bohnhoff, W. J., Dalbey, K. R., Ebeida, M. S., Eddy, J. P., Hough, P. D., Khalil, M., Kenneth, T. H., Ridway, E. M., Vigil, D. M., and Winokur, J. G.: Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.10 User’s Manual, Tech. Rep. SAND2014-4633, Sandia National Laboratory, https://doi.org/10.2172/1177077, 2019. a
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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.
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