Assessment of a regional physical–biogeochemical stochastic ocean model. Part 1: Ensemble generation (bibtex)
by , , , , , , ,
Abstract:
In this article, Part 1 of a two-part series, we run and evaluate the skill of a regional physical–biogeochemical stochastic ocean model based on NEMO. The domain covers the Bay of Biscay at 1/36° 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.
Reference:
Assessment of a regional physical–biogeochemical stochastic ocean model. Part 1: Ensemble generation (Vassilios D. Vervatis, Pierre De Mey-Frémaux, Nadia Ayoub, John Karagiorgos, Malek Ghantous, Marios Kailas, Charles-Emmanuel Testut, Sarantis Sofianos), In Ocean Modelling, volume 160, 2021.
Bibtex Entry:
@Article{	  vervatis.ea_2021,
  title		= {Assessment of a regional physical–biogeochemical
		  stochastic ocean model. Part 1: Ensemble generation},
  journal	= {Ocean Modelling},
  volume	= {160},
  pages		= {101781},
  year		= {2021},
  issn		= {1463-5003},
  doi		= {https://doi.org/10.1016/j.ocemod.2021.101781},
  url		= {https://www.sciencedirect.com/science/article/pii/S1463500321000317},
  author	= {Vassilios D. Vervatis and Pierre {De Mey-Frémaux} and
		  Nadia Ayoub and John Karagiorgos and Malek Ghantous and
		  Marios Kailas and Charles-Emmanuel Testut and Sarantis
		  Sofianos},
  keywords	= {Ensemble modelling, Model uncertainties, Stochastic
		  physics–biogeochemistry, Ocean colour, Data
		  assimilation, Bay of Biscay},
  abstract	= {In this article, Part 1 of a two-part series, we run and
		  evaluate the skill of a regional
		  physical–biogeochemical stochastic ocean model based
		  on NEMO. The domain covers the Bay of Biscay at 1/36°
		  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.}
}
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