Characterization of mixing errors in a coupled physical biogeochemical model of the North Atlantic: implications for nonlinear estimation using Gaussian anamorphosis

Abstract

Abstract. In biogeochemical models coupled to ocean circulation models, vertical mixing is an important physical process which governs the nutrient supply and the plankton residence in the euphotic layer. However, vertical mixing is often poorly represented in numerical simulations because of approximate parameterizations of sub-grid scale turbulence, wind forcing errors and other mis-represented processes such as restratification by mesoscale eddies. Getting a sufficient knowledge of the nature and structure of these errors is necessary to implement appropriate data assimilation methods and to evaluate if they can be controlled by a given observation system.In this paper, Monte Carlo simulations are conducted to study mixing errors induced by approximate wind forcings in a three-dimensional coupled physical-biogeochemical model of the North Atlantic with a 1/4 • horizontal resolution. An ensemble forecast involving 200 members is performed during the 1998 spring bloom, by prescribing perturbations of the wind forcing to generate mixing errors. The biogeochemical response is shown to be rather complex because of nonlinearities and threshold effects in the coupled model. The response of the surface phytoplankton depends on the region of interest and is particularly sensitive to the local stratification. In addition, the statistical relationships computed between the various physical and biogeochemical variables reflect the signature of the non-Gaussian behaviour of the system. It is shown that significant information on the ecosystem can be retrieved from observations of chlorophyll concentration or sea surface temperature if a simple nonlinear change of variables (anamorphosis) is performed by mapping separately Correspondence to: P. Brasseur (pierre.brasseur@hmg.inpg.fr) and locally the ensemble percentiles of the distributions of each state variable on the Gaussian percentiles. The results of idealized observational updates (performed with perfect observations and neglecting horizontal correlations) indicate that the implementation of this anamorphosis method into sequential assimilation schemes can substantially improve the accuracy of the estimation with respect to classical computations based on the Gaussian assumption.

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

“…Studies have demonstrated the sensitivity of biogeochemical variables to errors in bottom-up forcings such as wind stress and vertical mixing (e.g. Evans, 1988;Friedrichs et al, 2006;Béal et al, 2010;Sinha et al, 2010) and top-down forcings such as fishing (e.g. Heath, 2012).…”

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

“…The CPBM described here above has been considered as a good benchmark in a number of earlier studies, such as Ourmières et al (2009), Béal et al (2010, Doron et al (2011) andFontana et al (2012), making its choice fully consistent with our present goal of parameter uncertainty reduction. These papers addressed different aspects of data assimilation relatively to the CPBM: assimilation of physical quantities and of nutrients; sensitivity study to the physical forcing and the corresponding model response; stochastic estimation of biogeochemical parameters and data assimilation of ocean colour satellite observations to update the multivariate model state.…”

“…The idea of anamorphosis was first applied to data assimilation problems by Bertino et al (2003). Subsequent applications of anamorphosis have been proposed by Simon and Bertino (2009), Simon and Bertino (2012) or Béal et al (2010). The present implementation is essentially based on the scheme developed by Béal et al (2010).…”

“…Subsequent applications of anamorphosis have been proposed by Simon and Bertino (2009), Simon and Bertino (2012) or Béal et al (2010). The present implementation is essentially based on the scheme developed by Béal et al (2010). Doron et al (2011) proposed a method based on univariate transformations of the model variables and uncertain parameters performed independently at each grid point.…”

Stochastic Inverse Methods to Identify non-Gaussian Model Parameters in Heterogeneous Aquifers

“…Similar applications can be found in other disciplines; for instance, in reservoir modeling, transformation from non-Gaussian distribution to Gaussian distribution is applied to the state variables, such as saturation, by Gu and Oliver (2006) and, in ocean ecosystem modeling, a similar transformation is applied to chlorophyll-a concentration by Simon and Bertino (2009). Other transformation algorithms can be found in the literature such as in Béal et al (2010) and Bocquet et al (2010). In contrast with all of these applications, the method proposed in this paper focuses on transforming not only the non-Gaussian distributed state variables but, most importantly, the non-Gaussian distributed model parameters, i.e., the hydraulic conductivities, which are commonly assumed to follow a log-normal distribution.…”