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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-31
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-31
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model evaluation paper 28 Feb 2019

Submitted as: model evaluation paper | 28 Feb 2019

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This discussion paper is a preprint. A revision of the manuscript is under review for the journal Geoscientific Model Development (GMD).

Physical-biogeochemical regional ocean model uncertainties stemming from stochastic parameterizations and potential impact on data assimilation

Vassilios D. Vervatis1, Pierre De Mey-Frémaux2, Nadia Ayoub2, Sarantis Sofianos1, Charles-Emmanuel Testut3, Marios Kailas1, John Karagiorgos1, and Malek Ghantous2 Vassilios D. Vervatis et al.
  • 1University of Athens, Faculty of Physics, Athens, Greece
  • 2LEGOS/CNRS, Toulouse, France
  • 3Mercator Océan, Ramonville St. Agne, France.

Abstract. We generate ocean biogeochemical model ensembles including several kinds of stochastic parameterizations. The NEMO stochastic modules are complemented by integrating a subroutine to calculate variable anisotropic spatial scales, which are of particular importance in high-resolution coastal configurations. The domain covers the Bay of Biscay at 1/36° resolution, as a case study for open-ocean and coastal shelf dynamics. At first, we identify uncertainties from assumptions subject to erroneous atmospheric forcing, ocean model improper parameterizations and ecosystem state uncertainties. The error regimes are found to be mainly driven by the wind forcing, with the rest of the perturbed tendencies locally augmenting the ensemble spread. Biogeochemical uncertainties arise from inborn ecosystem model errors and from errors in the physical state. Model errors in physics are found to have larger impact on chlorophyll spread than those of the ecosystem. In a second step, the ensembles undergo verification with respect to observations, focusing on upper-ocean properties. We investigate the statistical consistency of prior model errors and observation estimates, in view of joint uncertainty vicinities, associated with both sources of information. OSTIA-SST L4 distribution appears to be compatible with ensembles perturbing physics, since vicinities overlap, enabling data assimilation. The most consistent configuration for SLA along-track L3 data is in the Abyssal plain, where the spread is increased due to mesoscale eddy decorrelation. The largest statistical SLA biases are observed in coastal regions, sometimes to the point that vicinities become disjoint. Missing error processes in relation to SLA hint at the presence of high-frequency error sources currently unaccounted for, potentially leading to ill-posed assimilation problems. Ecosystem model-data samples with respect to Ocean Colour L4 appear to be compatible with each other only at times, with data assimilation being marginally well-posed. In a third step, we illustrate the potential influence of those uncertainties on data assimilation impact exercise, by means of multivariate representers and EnKF-type incremental analysis for a few members. Corrections on physical properties are associated with large-scale biases between model and data, with diverse characteristics in the open-ocean and the shelves. The increments are often characteristic of the underlying mesoscale features, chlorophyll included due to the vertical velocity field. Small scale local corrections are visible over the shelves. Chlorophyll information seems to have a very measurable potential impact on physical variables.

Vassilios D. Vervatis et al.
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Vassilios D. Vervatis et al.
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Physical-biogeochemical regional ocean model uncertainties stemming from stochastic parameterizations and potential impact on data assimilation V. D. Vervatis, P. De Mey-Frémaux, N. Ayoub, S. Sofianos, C.-E. Testut, M. Kailas, J. Karagiorgos, and M. Ghantous https://doi.org/10.5281/zenodo.2556529

Vassilios D. Vervatis et al.
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Short summary
Our contributions were specifically targeted at the generation of ensembles, in particular (but not solely) for high-resolution ocean configurations including regional and coastal physics and biogeochemistry. The most important paradigm of this work was to adopt a balanced approach building ocean biogeochemical model ensembles and testing their relevance against observational networks monitoring upper-ocean properties, in the sense of nonzero joint probabilities.
Our contributions were specifically targeted at the generation of ensembles, in particular (but...
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