Submitted as: development and technical paper
15 Jun 2023
Submitted as: development and technical paper |  | 15 Jun 2023
Status: a revised version of this preprint is currently under review for the journal GMD.

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

Abstract. Biogeochemical (BGC) models are widely used in ocean simulations for a range of applications, but typically include parameters that are determined based on a combination of empiricism and convention. Here, we describe and demonstrate an optimization-based parameter estimation method for ocean BGC models with large numbers of uncertain parameters. Our computationally efficient method combines the respective benefits of global and local optimization techniques and enables simultaneous parameter estimation at multiple ocean locations using multiple state variables. We demonstrate the method for a 17-state-variable BGC model with 51 uncertain parameters, where a one-dimensional physical model is used to represent vertical mixing. We perform a twin-simulation experiment to test the accuracy of the method in recovering known parameters. We then use the method to simultaneously match multi-variable observational data collected at sites in the subtropical North Atlantic and Pacific. We examine the effects of different objective functions, increasing levels of data sparsity, and the choice of state variables used during the optimization. We end with a discussion of how the method can be applied to other BGC models, ocean locations, and mixing representations.

Skyler Kern et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-107', Anonymous Referee #1, 24 Jul 2023
  • RC2: 'Comment on gmd-2023-107', Anonymous Referee #2, 25 Jul 2023
  • AC1: 'Response to Reviewer 1', Skyler Kern, 15 Sep 2023
  • AC2: 'Response to Reviewer 2', Skyler Kern, 15 Sep 2023

Skyler Kern et al.

Data sets

BFM17 Optimization Data and Plotting Scripts S. Kern, M. E. McGuinn, K. M. Smith, N. Pinardi, K. E. Niemeyer, N. S. Lovenduski, and P. E. Hamlington

Model code and software

BFM17-56 BFM17-56 K. M. Smith, S. Kern, P. E. Hamlington, M. Zavatarelli, N. Pinardi, E. F. Klee, and K. E. Niemeyer

BFM17-Opt: Coupling BFM17+POM1D to DAKOTA for Optimization S. Kern, M. E. McGuinn, K. M. Smith, N. Pinardi, K. E. Niemeyer, N. S. Lovenduski, and P. E. Hamlington

Skyler Kern et al.


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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.