@article {VERVATIS2021101781,
	title = {Assessment of a regional physical{\textendash}biogeochemical stochastic ocean model. Part 1: Ensemble generation},
	journal = {Ocean Modelling},
	volume = {160},
	year = {2021},
	pages = {101781},
	abstract = {In this article, Part 1 of a two-part series, we run and evaluate the skill of a regional physical{\textendash}biogeochemical stochastic ocean model based on NEMO. The domain covers the Bay of Biscay at 1/36{\textdegree} resolution, as a case study for open-ocean and coastal shelf dynamics. We generate model ensembles based on assumptions about errors in the atmospheric forcing, the ocean model parameterizations and in the sources and sinks of the biogeochemical variables. The resulting errors are found to be mainly driven by the wind forcing uncertainties, with the rest of the perturbed forcing and parameters locally influencing the ensemble spread. Biogeochemical uncertainties arise from intrinsic ecosystem model errors and from errors in the physical state. Uncertainties in physical forcing and parameterization are found to have a larger impact on chlorophyll spread than uncertainties in ecosystem sources and sinks. The ensembles undergo quantitative verification with respect to observations, focusing on upper-ocean properties. Despite a tendency for ensembles to be generally under-dispersive, they appear to be reasonably consistent with respect to sea surface temperature data. The largest statistical sea-level biases are observed in coastal regions. These biases hint at the presence of high-frequency error sources currently unaccounted for, and suggest that the ensemble-based uncertainties are unfit to model error covariances for assimilation. Model ensembles for chlorophyll appear to be consistent with ocean colour data only at times. The stochastic model is qualitatively evaluated by analysing its ability at generating consistent multivariate incremental model corrections. Corrections to physical properties are associated with large-scale biases between model and data, with diverse characteristics in the open-ocean and the shelves. Mesoscale features imprint their signature on temperature and sea-level corrections, as well as on chlorophyll corrections due to the vertical velocities associated with vortices. Small scale local corrections are visible over the shelves. Chlorophyll information has measurable impact on physical variables.},
	keywords = {Bay of Biscay, data assimilation, Ensemble modelling, Model uncertainties, Ocean colour, Stochastic physics{\textendash}biogeochemistry},
	issn = {1463-5003},
	doi = {https://doi.org/10.1016/j.ocemod.2021.101781},
	url = {https://www.sciencedirect.com/science/article/pii/S1463500321000317},
	author = {Vervatis, Vassilios D. and De Mey-Fr{\'e}maux, Pierre and Ayoub, Nadia and Karagiorgos, John and Ghantous, Malek and Kailas, Marios and Testut, Charles-Emmanuel and Sofianos, Sarantis}
}
