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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2020-137
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2020-137
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 24 Apr 2020

Submitted as: research article | 24 Apr 2020

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This preprint is currently under review for the journal BG.

Assessing the value of BGC Argo profiles versus ocean colour observations for biogeochemical model optimization in the Gulf of Mexico

Bin Wang1, Katja Fennel1, Liuqian Yu1,2, and Christopher Gordon1 Bin Wang et al.
  • 1Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada
  • 2Department of Mathematics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong

Abstract. Biogeochemical ocean models are useful tools subject to uncertainties arising from simplifications, inaccurate parameterization of processes, and poorly known model parameters. Parameter optimization is a standard method for addressing the latter but typically cannot constrain all biogeochemical parameters because of insufficient observations. Here we assess the trade-offs between satellite observations of ocean colour and biogeochemical (BGC) Argo profiles, and the benefits of combining both observation types, for optimizing biogeochemical parameters in a model of the Gulf of Mexico. A suite of optimization experiments is carried out using different combinations of satellite chlorophyll and profile measurements of chlorophyll, phytoplankton biomass, and particulate organic carbon (POC) from autonomous floats. As parameter optimization in 3D models is computationally expensive, we optimize the parameters in a 1D model version, and then perform 3D simulations using these parameters. We show first that the use of parameters optimized in 1D improves the skill of the 3D model. Parameters that are only optimized with respect to surface chlorophyll cannot reproduce subsurface distributions of biological fields. Adding profiles of chlorophyll in the parameter optimization yields significant improvements for surface and subsurface chlorophyll but does not accurately capture subsurface phytoplankton and POC distributions because the parameter for the maximum ratio of chlorophyll to phytoplankton carbon is not well constrained in that case. Using all available observations leads to significant improvements of both observed (chlorophyll, phytoplankton, and POC) and unobserved variables (e.g. primary production). Our results highlight the significant benefits of BGC Argo measurements for biogeochemical parameter optimization and model calibration.

Bin Wang et al.

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Latest update: 03 Jun 2020
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Short summary
We assess trade-offs between different types of biological observations, specifically satellite ocean colour and BGC Argo profiles, and the benefits of combining both, for optimizing a biogeochemical model of the Gulf of Mexico. Using all available observations leads to significant improvements in observed and unobserved variables (incl. primary production & C export). Our results highlight the significant benefits of BGC Argo measurements for biogeochemical model optimization and validation.
We assess trade-offs between different types of biological observations, specifically satellite...
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