In:
Biogeosciences, Copernicus GmbH, Vol. 19, No. 21 ( 2022-11-07), p. 5079-5106
Abstract:
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 the 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 the 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 retrieves parameter values perfectly regardless
of data coverage. As differences between the model and the system underlying
the target field increase, the choice of the misfit function can greatly impact
optimisation results, while limitation of data coverage is in most cases of
subordinate significance. In cases where optimisation to full or limited
data coverage produces relatively distinct model behaviours, we find that
applying a misfit metric that compensates for differences in data coverage
between ocean basins considerably improves agreement between optimisation
results obtained with the two data situations.
Type of Medium:
Online Resource
ISSN:
1726-4189
DOI:
10.5194/bg-19-5079-2022
DOI:
10.5194/bg-19-5079-2022-supplement
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2022
detail.hit.zdb_id:
2158181-2
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