Stochastic estimation of biogeochemical parameters from Globcolour ocean color satellite data in a North Atlantic 3D ocean coupled physical-biogeochemical model

• Published in: Journal of Marine Systems
• Main Authors: Doron, Maéva, Brasseur, Pierre, Brankart, Jean-Michel, Loza, Svetlana, Melet, Angélique
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: ELSEVIER SCIENCE BV 2013
• Subjects: North Atlantic
• Online Access: https://epic.awi.de/id/eprint/32553/; https://epic.awi.de/id/eprint/32553/1/Doron_et_al_sub_JMS_revised_v3.pdf; https://doi.org/10.1016/j.jmarsys.2013.02.007; https://hdl.handle.net/10013/epic.41152; https://hdl.handle.net/10013/epic.41152.d001

Biogeochemical parameters remain a major source of uncertainty in coupled physical-biogeochemical models of the ocean. In a previous study (Doron et al., 2011), a stochastic estimation method was developed to estimate a subset of biogeochemical model parameters from surface phytoplankton observations. The concept was tested in the context of idealized twin experiments performed with a 1/4° resolution model of the North Atlantic ocean. The method was based on ensemble simulations describing the model response to parameter uncertainty. The statistical estimation process relies on nonlinear transformations of the estimated space to cope with the non-Gaussian behaviour of the resulting joint probability distribution of the model state variables and parameters. In the present study, the same method is applied to real ocean colour observations, as delivered by the sensors SeaWiFS, MERIS and MODIS embarked on the satellites OrbView-2, Envisat and Aqua respectively. The main outcome of the present experiments is a set of regionalised biogeochemical parameters. The benefit is quantitatively assessed with an objective norm of the misfits, which automatically adapts to the different ecological regions. The chlorophyll concentration simulated by the model with this set of optimally derived parameters is closer to the observations than the reference simulation using uniform values of the parameters. In addition, the interannual and seasonal robustness of the estimated parameters is tested by repeating the same analysis using ocean colour observations from several months and several years. The results show the overall consistency of the ensemble of estimated parameters, which are also compared to the results of an independent study.