Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
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Published:2024-01-26
Issue:2
Volume:17
Page:621-649
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Kern SkylerORCID, McGuinn Mary E.ORCID, Smith Katherine M.ORCID, Pinardi NadiaORCID, Niemeyer Kyle E.ORCID, Lovenduski Nicole S.ORCID, Hamlington Peter E.ORCID
Abstract
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 high-dimensional (in parameter space) BGC ocean models. 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 (in space) 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, sometimes referred to as cost functions, which quantify the disagreement between model and observational data. We further examine 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.
Funder
National Science Foundation
Publisher
Copernicus GmbH
Reference47 articles.
1. 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 2. Athias, V., Mazzega, P., and Jeandel, C.: Selecting a global optimization method to estimate the oceanic particle cycling rate constants, J. Mar. Res., 58, 675–707, 2000. a 3. Bagniewski, W., Fennel, K., Perry, M. J., and D'Asaro, E. A.: Optimizing models of the North Atlantic spring bloom using physical, chemical and bio-optical observations from a Lagrangian float, Biogeosciences, 8, 1291–1307, https://doi.org/10.5194/bg-8-1291-2011, 2011. a 4. Bianchi, D., Zavatarelli, M., Pinardi, N., Capozzi, R., Capotondi, L., Corselli, C., and Masina, S.: Simulations of ecosystem response during the sapropel S1 deposition event, Palaeogeogr. Palaeocl., 235, 265–287, https://doi.org/10.1016/J.PALAEO.2005.09.032, 2006. a 5. Blumberg, A. F. and Mellor, G. L.: A description of a three-dimensional coastal ocean circulation model, Costal and Estuarine Science, vol. 4, American Geophysical Union, 1987. a
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