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Controllability of mixing errors in a coupled physical biogeochemical model of the North Atlantic: a nonlinear study using anamorphosis
1LEGI/CNRS, Université de Grenoble, CNRS, BP 53X, 38041 Grenoble, France
2LSEET, Université du Sud Toulon Var, 83957 La Garde Cedex, France
Abstract. 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, 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 error sources is necessary to implement appropriate data assimilation methods and to evaluate their controllability 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 realistic wind perturbations to generate mixing errors. It is shown that the biogeochemical response can be rather complex because of nonlinearities and threshold effects in the coupled model. In particular, the response of the surface phytoplankton depends on the region of interest and is particularly sensitive to the local stratification. We examine the robustness of the statistical relationships computed between the various physical and biogeochemical variables, and we show that significant information on the ecosystem can be obtained from observations of chlorophyll concentration or sea surface temperature. In order to improve the analysis step of sequential assimilation schemes, we propose to perform a simple nonlinear change of variables that operates separately on each state variable, by mapping their ensemble percentiles on the Gaussian percentiles. It is shown that this method is able to substantially reduce the estimation error with respect to the linear estimates computed by the Kalman filter.
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Citation: Béal, D., Brasseur, P., Brankart, J.-M., Ourmières, Y., and Verron, J.: Controllability of mixing errors in a coupled physical biogeochemical model of the North Atlantic: a nonlinear study using anamorphosis, Ocean Sci. Discuss., 6, 1289-1332, 2009. Bibtex EndNote Reference Manager
