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Controllability of Mixing Errors in a Coupled Physical Biogeochemical Model of the North Atlantic: a Nonlinear Study Using Anamorphosis : Volume 6, Issue 2 (30/06/2009)

By Béal, D.

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Book Id: WPLBN0004021046
Format Type: PDF Article :
File Size: Pages 44
Reproduction Date: 2015

Title: Controllability of Mixing Errors in a Coupled Physical Biogeochemical Model of the North Atlantic: a Nonlinear Study Using Anamorphosis : Volume 6, Issue 2 (30/06/2009)  
Author: Béal, D.
Volume: Vol. 6, Issue 2
Language: English
Subject: Science, Ocean, Science
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2009
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Brankart, J., Ourmières, Y., Verron, J., Béal, D., & Brasseur, P. (2009). Controllability of Mixing Errors in a Coupled Physical Biogeochemical Model of the North Atlantic: a Nonlinear Study Using Anamorphosis : Volume 6, Issue 2 (30/06/2009). Retrieved from http://worldlibrary.net/


Description
Description: LEGI/CNRS, Université de Grenoble, CNRS, BP 53X, 38041 Grenoble, France. 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, 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 error sources is necessary to implement appropriate data assimilation methods and to evaluate their controllability 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 realistic wind perturbations to generate mixing errors. It is shown that the biogeochemical response can be rather complex because of nonlinearities and threshold effects in the coupled model. In particular, the response of the surface phytoplankton depends on the region of interest and is particularly sensitive to the local stratification. We examine the robustness of the statistical relationships computed between the various physical and biogeochemical variables, and we show that significant information on the ecosystem can be obtained from observations of chlorophyll concentration or sea surface temperature. In order to improve the analysis step of sequential assimilation schemes, we propose to perform a simple nonlinear change of variables that operates separately on each state variable, by mapping their ensemble percentiles on the Gaussian percentiles. It is shown that this method is able to substantially reduce the estimation error with respect to the linear estimates computed by the Kalman filter.


Summary
Controllability of mixing errors in a coupled physical biogeochemical model of the North Atlantic: a nonlinear study using anamorphosis

Excerpt
Barnier, B., Madec, G., Penduff, T., Molines, J. M., Tréguier, A. M., Beckmann, A., Biastoch, A., Boning, C., Dengg, J., Gulev, S., Le Sommer, J., Rémy, E., Talandier, C., Theetten, S., Maltrud, M., and Mc Lean, J.: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy permitting resolution, Ocean Dynam., 56(5–6), 543–567, 2006.; Berline, L., Brankart, J.-M., Brasseur, P., Ourmières, Y., and Verron, J.: Improving the physics of a coupled physical-biogeochemical model of the North Atlantic through data assimilation: impact on the ecosystem, J. Mar. Syst., 64(1–4), 153–172, 2007.; Bertino, L., Evensen, G., and Wackernagel, H.: Sequential Data Assimilation Techniques in Oceanography, International Statistical Reviews, 71, 223–241, 2003.; European Centre for Medium-Range Weather Forecasts, 2002. The ERA-40 Archive.; Blanke, B. and Delecluse, P.: Variability of the tropical Atlantic ocean simulated by a general circulation model with two different mixed layer physics, J. Phys. Oceanogr., 23, 1363–1388, 1993.; Brasseur, P., Gruber, N., Barciela, R., Brander, K., Doron, M., El Moussaoui, A., Hobday, A., Huret, M., Kremeur, A.-S., Lehodey, P., Moulin, C., Murtugudde, R., Senina, I., Svendsen, E., and Matear, R.: Integrating biogeochemistry and ecology into ocean data assimilation systems, Oceanography, in press, 2009.; Conkright, M. E., Antonov, J. I., Baranova, O., Boyer, T. P., Garcia, H. E., Gelfeld, R., Johnson, D., Locarnini, R. A., Murphy, P. P., O'Brien, T. D., Smolyar, I., and Stephens, C.: NOAA Atlas NESDIS 42, WORLD OCEAN DATABASE 2001 Volume 1: Introduction, US Gov. Printing Office, Wash., D.C., 160 pp., 2002.; Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99(C5), 10143–10182, 1994.; The DRAKKAR Group: Eddy-permitting Ocean Circulation Hindcasts of past decades, CLIVAR Exchanges 42, 12, 8–10, 2007.; Gruber, N., Doney, S. C., Emerson, S. R., Gilbert, D., Kobayashi, T., Körtzinger, A., Johnson, G. C., Johnson, K. S., Riser, S. C., and Ulloa, O.: The Argo-Oxygen program: a white paper to promote the addition of oxygen sensors to the international Argo float program, http://www-argo.ucsd.edu/o2_white_paper_web.pdf, 2007.; Lauvernet, C., Brankart, J.-M., Castruccio, F., Broquet, G., Brasseur, P., and Verron, J.: A truncated Gaussian filter for data assimilation with inequality constraints: Application to the hydrostatic stability in ocean models, Ocean Modell., 27, 1–17, doi:10.1016/j.ocemod.2008.10.007, 2009.; Levitus, S., Antonov, J. I., Boyer, T. P., and Stephens, C.: World Ocean Database 1998, National Oceanographic Data Center, Silver Spring, MD, 2001..; Lévy, M., Gavart, M., Mémery, L., Caniaux, G., and Paci, A.: A four-dimensional mesoscale map of the spring bloom in the northeast Atlantic (POMME experiment): Results of a prognostic model, J. Geophys. Res., 110, C07S21, doi:10.1029/2004JC002588, 2005a..; Lévy, M., Krémeur, A. S., and Mémery, L.: Description of the LOBSTER biogeochemical model implemented in the OPA system, Internal report IPSL/LOCEAN, 2005b.; Losa, S. N., Kivman, G. A., Schröter, J., and Wenzel, M.: Sequential weak constraint parameter estimation in an ecosystem model, J. Mar. Syst., 43, 31–49, 2003.; Madec, G., Delecluse, P., Imbard, M., and Lévy, C.: OPA 8.1 Ocean General Circulation Model reference manual, Note du Pole de modélisation, Institut Pierre-Simon Laplace (IPSL), France, No. 11, 91 pp., 1998.; Ourmières, Y., Brasseur, P., Lévy, M., Brankart, J.-M., and Verron, J.: On the key role of nutrient data to constrain a coupled physical-biogeochemical assimilative model of the North Atlantic Ocean, J. Mar. Syst., 75, 100–115, doi:10.1016/j.jmarsys.2008.08.003, 2009.; Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T.: Numerical Recipes i

 

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