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Ocean Science An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 2
Ocean Sci., 8, 121–142, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ocean Sci., 8, 121–142, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Mar 2012

Research article | 06 Mar 2012

Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations

J.-M. Brankart1, C.-E. Testut2, D. Béal1, M. Doron1, C. Fontana1, M. Meinvielle1, P. Brasseur1, and J. Verron1 J.-M. Brankart et al.
  • 1LEGI/CNRS, UMR5519, Grenoble, France
  • 2Mercator-Océan, Toulouse, France

Abstract. The objective of this paper is to investigate if the description of ocean uncertainties can be significantly improved by applying a local anamorphic transformation to each model variable, and by making the assumption of joint Gaussianity for the transformed variables, rather than for the original variables. For that purpose, it is first argued that a significant improvement can already be obtained by deriving the local transformations from a simple histogram description of the marginal distributions. Two distinctive advantages of this solution for large size applications are the conciseness and the numerical efficiency of the description. Second, various oceanographic examples are used to evaluate the effect of the resulting piecewise linear local anamorphic transformations on the spatial correlation structure. These examples include (i) stochastic ensemble descriptions of the effect of atmospheric uncertainties on the ocean mixed layer, and of wind uncertainties or parameter uncertainties on the ecosystem, and (ii) non-stochastic ensemble descriptions of forecast uncertainties in current sea ice and ecosystem pre-operational developments. The results indicate that (i) the transformation is accurate enough to faithfully preserve the correlation structure if the joint distribution is already close to Gaussian, and (ii) the transformation has the general tendency of increasing the correlation radius as soon as the spatial dependence between random variables becomes nonlinear, with the important consequence of reducing the number of degrees of freedom in the uncertainties, and thus increasing the benefit that can be expected from a given observation network.

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