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Paramétrisations stochastiques de processus biogéochimiques non résolus dans un modèle couplé NEMO/PISCES de l'Atlantique Nord : Applications pour l'assimilation de données de la couleur de l'océan

Abstract : In spite of recent advances, biogeochemical models are still unable to represent the full complexity of marine ecosystems.Since mathematical formulations are still based on empirical laws involving many parameters, it is now well established that the uncertainties inherent to the biogeochemical complexity strongly impact the model response.Improving model representation therefore requires to properly describe model uncertainties and their consequences.Moreover, in the context of ocean color data assimilation, one of the major issue rely on our ability to characterize the model uncertainty (or equivalently the model error) in order to maximize the efficiency of the assimilation system.This is exactly the purpose of this PhD which investigates the potential of using random processes to simulate some biogeochemical uncertaintiesof the 1/4° coupled physical–biogeochemical NEMO/PISCES model of the North Atlantic ocean.Starting from a deterministic simulation performed with the original PISCES formulation, we propose a genericmethod based on AR(1) random processes to generate perturbations with temporal and spatial correlations.These perturbations are introduced into the model formulations to simulate 2 classes of uncertainties: theuncertainties on biogeochemical parameters and the uncertainties induced by unresolved scales in the presenceof non-linear processes. Using these stochastic parameterizations, a probabilistic version of PISCES is designedand a 60-member ensemble simulation is performed.The implications of this probabilistic approach is assessed using the information of the probability distributions given of this ensemble simulationThe relevance and the impacts of the stochastic parameterizations are assessed from a comparison with SeaWIFS satellite data.In particular, it is shown that the ensemble simulation is able to produce a better estimate of the surface chlorophyll concentration than the first guess deterministic simulation.Using SeaWIFS ocean color data observations, the statistical consistency (reliability) of this prior ensemble is demonstrated using rank histograms.Finally, the relevance of our approach in the prospect of ocean color data assimilation is demonstrated by considering a 3D optimal analysis of the ensemble (one updateat one time step) performed from the statistic errors of the stochastic ensemble simulation previously stated.During this experiment, the high resolution SeaWIFS ocean color data are assimilated using a Ensemble Transform Kalman Filter (ETKF) analysis scheme and the non gaussian behaviour and non linear relationshipbetween variables are taken into account using anamorphic transformations.More specifically, we show that the analysis of SeaWIFS data improves the representation and the ensemble statistics of chlorophyll concentrations.
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https://tel.archives-ouvertes.fr/tel-01661414
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Submitted on : Monday, December 11, 2017 - 11:30:58 PM
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Florent Garnier. Paramétrisations stochastiques de processus biogéochimiques non résolus dans un modèle couplé NEMO/PISCES de l'Atlantique Nord : Applications pour l'assimilation de données de la couleur de l'océan. Océanographie. Université Grenoble Alpes, 2016. Français. ⟨NNT : 2016GREAU044⟩. ⟨tel-01661414⟩

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