Preprints
https://doi.org/10.5194/bg-2022-105
https://doi.org/10.5194/bg-2022-105
 
03 Jun 2022
03 Jun 2022
Status: this preprint is currently under review for the journal BG.

Influence of GEOTRACES data distribution and misfit function choice on objective parameter retrieval in a marine zinc cycle model

Claudia Eisenring1, Sophy E. Oliver2,3, Samar Khatiwala2, and Gregory F. de Souza1 Claudia Eisenring et al.
  • 1Institute of Geochemistry and Petrology, ETH Zurich, Clausiusstrasse 25, Zurich, 8092, Switzerland
  • 2Department of Earth Sciences, University of Oxford, South Parks Road, Oxford, OX1 3AN, UK
  • 3National Oceanography Centre, Southampton, SO14 3ZH, UK

Abstract. Biogeochemical model behaviour for micronutrients is typically hard to constrain because of the sparsity of observational data, the difficulty of determining parameters in situ, and uncertainties in observations and models. Here, we assess the influence of data distribution, model uncertainty and misfit function on objective parameter optimisation in a model of the oceanic cycle of zinc (Zn), an essential micronutrient for marine phytoplankton with a long whole-ocean residence time. We aim to investigate whether observational constraints are sufficient for reconstruction of biogeochemical model behaviour, given that the Zn data coverage provided by the GEOTRACES Intermediate Data Product 2017 is sparse. Furthermore, we aim to assess how optimisation results are affected by the choice of misfit function and by confounding factors such as analytical uncertainty in the data or biases in the model related to either seasonal variability or the larger-scale circulation. The model framework applied herein combines a marine Zn cycling model with a state-of-the-art estimation of distribution algorithm (Covariance Matrix Adaption Evolution Strategy, CMA-ES) to optimise the model towards synthetic data in an ensemble of 26 optimisations. Provided with a target field that can be perfectly reproduced by the model, optimisation results in perfect parameter retrieval regardless of data coverage. As differences between the model and the system underlying the target field increase, the choice of misfit function can greatly impact optimisation results, while limitation of data coverage is in most cases of subordinate significance. In cases where relatively distinct model behaviours are determined with full and limited data coverage, we find considerable alignments between the two when applying a misfit metric that compensates for differences in data coverage between ocean basins.

Claudia Eisenring et al.

Status: open (until 27 Jul 2022)

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Claudia Eisenring et al.

Claudia Eisenring et al.

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Short summary
Given the sparsity of observational constraints on micronutrients such as zinc (Zn), we assess the sensitivities of a framework for objective parameter optimisation in an oceanic Zn cycling model. Our ensemble of optimisations towards synthetic data with varying kinds of uncertainty shows that deficiencies related to model complexity and the choice of misfit function generally have a greater impact on the retrieval of model Zn-uptake behaviour than does the limitation of data coverage.
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