Assessment of a regional physical–biogeochemical stochastic ocean model. Part 2: Empirical consistency (bibtex)
by , , , , , ,
Abstract:
In this Part 2 article of a two-part series, observations based on satellite missions were used to evaluate the empirical consistency of model ensembles generated via stochastic modelling of ocean physics and biogeochemistry. A high-resolution Bay of Biscay configuration was used as a case study to explore the model error subspace in both the open and coastal ocean. In Part 1 of this work, three experiments were carried out to generate model ensembles by perturbing only physics, only biogeochemistry, and both of them simultaneously. In Part 2 of this work, empirical consistency was checked, first by means of rank histograms projecting the data onto the model ensemble classes, and second, by pattern-selective consistency criteria in the space of “array modes” defined as eigenvectors of the representer matrix. Rank histograms showed large dependency on geographical region and on season for sea surface temperature (SST), sea-level anomaly (SLA), and phytoplankton functional types (PFT), shifting from consistent model-data configurations to large biases because of model ensemble underspread. Consistency for SST array modes was found to be verified at large, small and coastal scales soon after the ensemble spin-up. Array modes for the along-track sea-level showed useful consistent information at large scales and at the mesoscale; for the gridded SLA was verified only at large scale. Array modes showed that biogeochemical model uncertainties generated by stochastic physics, were effectively detected by PFT measurements at large scales, as well as at mesoscale and small-scale. By contrast, perturbing only biogeochemistry, with an identical physical forcing across the ensemble, limits the potential of PFT measurements at detecting and possibly correcting small-scale biogeochemical model errors. When an ensemble was found to be inconsistent with observations along a particular direction (here, an array mode), a plausible reason is that other error processes must have been active in the model, in addition to the ones at work across the ensemble.
Reference:
Assessment of a regional physical–biogeochemical stochastic ocean model. Part 2: Empirical consistency (Vassilios D. Vervatis, Pierre De Mey-Frémaux, Nadia Ayoub, John Karagiorgos, Stefano Ciavatta, Robert J.W. Brewin, Sarantis Sofianos), In Ocean Modelling, volume 160, 2021.
Bibtex Entry:
@Article{	  vervatis.ea_2021_1,
  title		= {Assessment of a regional physical–biogeochemical
		  stochastic ocean model. Part 2: Empirical consistency},
  journal	= {Ocean Modelling},
  volume	= {160},
  pages		= {101770},
  year		= {2021},
  issn		= {1463-5003},
  doi		= {https://doi.org/10.1016/j.ocemod.2021.101770},
  url		= {https://www.sciencedirect.com/science/article/pii/S1463500321000202},
  author	= {Vassilios D. Vervatis and Pierre {De Mey-Frémaux} and
		  Nadia Ayoub and John Karagiorgos and Stefano Ciavatta and
		  Robert J.W. Brewin and Sarantis Sofianos},
  keywords	= {Stochastic modelling, Ensembles, Phytoplankton functional
		  types, Prior error covariances, Array modes, Bay of
		  Biscay},
  abstract	= {In this Part 2 article of a two-part series, observations
		  based on satellite missions were used to evaluate the
		  empirical consistency of model ensembles generated via
		  stochastic modelling of ocean physics and biogeochemistry.
		  A high-resolution Bay of Biscay configuration was used as a
		  case study to explore the model error subspace in both the
		  open and coastal ocean. In Part 1 of this work, three
		  experiments were carried out to generate model ensembles by
		  perturbing only physics, only biogeochemistry, and both of
		  them simultaneously. In Part 2 of this work, empirical
		  consistency was checked, first by means of rank histograms
		  projecting the data onto the model ensemble classes, and
		  second, by pattern-selective consistency criteria in the
		  space of “array modes” defined as eigenvectors of
		  the representer matrix. Rank histograms showed large
		  dependency on geographical region and on season for sea
		  surface temperature (SST), sea-level anomaly (SLA), and
		  phytoplankton functional types (PFT), shifting from
		  consistent model-data configurations to large biases
		  because of model ensemble underspread. Consistency for SST
		  array modes was found to be verified at large, small and
		  coastal scales soon after the ensemble spin-up. Array modes
		  for the along-track sea-level showed useful consistent
		  information at large scales and at the mesoscale; for the
		  gridded SLA was verified only at large scale. Array modes
		  showed that biogeochemical model uncertainties generated by
		  stochastic physics, were effectively detected by PFT
		  measurements at large scales, as well as at mesoscale and
		  small-scale. By contrast, perturbing only biogeochemistry,
		  with an identical physical forcing across the ensemble,
		  limits the potential of PFT measurements at detecting and
		  possibly correcting small-scale biogeochemical model
		  errors. When an ensemble was found to be inconsistent with
		  observations along a particular direction (here, an array
		  mode), a plausible reason is that other error processes
		  must have been active in the model, in addition to the ones
		  at work across the ensemble.}
}
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