Stochastic parameterizations of unresolved biogeochemical processes in a coupled NEMO/PISCES model of the north Atlantic

• Main Author: Garnier, Florent
• Other Authors: Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 )-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 ), Université Grenoble Alpes, Pierre Brasseur, Emmanuel Cosme
• Format: Doctoral or Postdoctoral Thesis
• Language: French
• Published: HAL CCSD 2016
• Subjects: Data assimilation; Ocean colour; Stochastic; Sangoma; My Ocean; Ensemble simulation; Assimilation de données; Données couleur de l'eau; Stochastique; Simulation d'ensemble; [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography; North Atlantic
• Online Access: https://theses.hal.science/tel-01661414; https://theses.hal.science/tel-01661414/document; https://theses.hal.science/tel-01661414/file/GARNIER_2016_diffusion.pdf

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 ...