 |
| Titel |
Characterization of mixing errors in a coupled physical biogeochemical model of the North Atlantic: implications for nonlinear estimation using Gaussian anamorphosis |
| VerfasserIn |
D. Béal, P. Brasseur, J.-M. Brankart, Y. Ourmières, J. Verron |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1812-0784
|
| Digitales Dokument |
URL |
| Erschienen |
In: Ocean Science ; 6, no. 1 ; Nr. 6, no. 1 (2010-02-17), S.247-262 |
| Datensatznummer |
250003357
|
| Publikation (Nr.) |
 copernicus.org/os-6-247-2010.pdf |
|
|
|
|
|
| Zusammenfassung |
In biogeochemical models coupled to ocean circulation models, vertical mixing
is an important physical process which governs the nutrient supply and the
plankton residence in the euphotic layer.
However, vertical mixing is often poorly represented in numerical simulations
because of approximate parameterizations
of sub-grid scale turbulence, wind forcing errors and other mis-represented
processes such as restratification by mesoscale eddies.
Getting a sufficient knowledge of the nature and structure
of these errors is necessary to
implement appropriate data assimilation methods and to evaluate
if they can be controlled by a given observation system.
In this paper, Monte Carlo simulations are conducted to study mixing errors
induced by approximate wind forcings in a three-dimensional coupled
physical-biogeochemical model of the North Atlantic with a 1/4°
horizontal resolution.
An ensemble forecast involving 200 members is
performed during the 1998 spring bloom, by prescribing perturbations of the
wind forcing to generate mixing errors.
The biogeochemical response is shown
to be rather complex because of nonlinearities
and threshold effects in the coupled model.
The response of the surface phytoplankton depends on the region
of interest and is particularly sensitive to the local stratification.
In addition, the statistical relationships computed between the various
physical and biogeochemical variables reflect the signature of the
non-Gaussian behaviour of the system.
It is shown that significant information on the ecosystem can be retrieved
from observations of chlorophyll concentration or sea surface temperature
if a simple nonlinear change of variables (anamorphosis) is performed
by mapping separately and locally the ensemble percentiles of the distributions
of each state variable on the Gaussian percentiles.
The results of idealized observational updates
(performed with perfect observations and neglecting horizontal correlations)
indicate that the implementation
of this anamorphosis method into sequential
assimilation schemes can substantially improve
the accuracy of the estimation
with respect to classical computations based on the Gaussian
assumption. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|