Article ID Journal Published Year Pages File Type
4547942 Journal of Marine Systems 2016 14 Pages PDF
Abstract

•We propose a method to simulate some biogeochemical model uncertainties.•We performed a coupled NEMO/PISCES 60-member stochastic simulation.•We use probabilistic diagnostics to compare model and observations.•We present the benefits of this approach for data assimilation.

In spite of recent advances, biogeochemical models are still unable to represent the full complexity of natural ecosystems. Their formulations are mainly based on empirical laws involving many parameters. Improving biogeochemical models therefore requires to properly characterize model uncertainties and their consequences. Subsequently, this paper investigates the potential of using random processes to simulate some uncertainties of 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 generic method 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: the uncertainties on biogeochemical parameters and the uncertainties induced by unresolved scales in the presence of non-linear processes. Using these stochastic parameterizations, a probabilistic version of PISCES is designed and a 60-member ensemble simulation is performed.With respect to the simulation of chlorophyll, the relevance of the probabilistic configuration and the impacts of these stochastic parameterizations are assessed. In particular, it is shown that the ensemble simulation is in good agreement with the SeaWIFS ocean color data. Using these observations, the statistical consistency (reliability) of the ensemble is evaluated with rank histograms. Finally, the benefits expected from the probabilistic description of uncertainties (model error) are discussed in the context of future ocean color data assimilation.

Related Topics
Physical Sciences and Engineering Earth and Planetary Sciences Oceanography
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