by Vassilios D. Vervatis, Pierre De Mey-Frémaux, Nadia Ayoub, John Karagiorgos, Stefano Ciavatta, Robert J.W. Brewin, Sarantis Sofianos
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 âÂÂarray modesâ 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 physicalâÂÂbiogeochemical 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 physicalâÂÂbiogeochemical
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 âÂÂarray modesâ 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.}
}