A singular evolutive extended Kalman filter to assimilate ocean color data in a coupled physical–biochemical model of the North Atlantic ocean

Abstract Within the European DIADEM project, a data assimilation system for coupled ocean circulation and marine ecosystem models has been implemented for the North Atlantic and the Nordic Seas. One objective of this project is to demonstrate the relevance of sophisticated methods to assimilate satellite data such as altimetry, surface temperature and ocean color, into realistic ocean models. In this paper, the singular evolutive extended Kalman (SEEK) filter, which is an advanced assimilation scheme where three-dimensional, multivariate error statistics are taken into account, is used to assimilate ocean color data into the biological component of the coupled system. The marine ecosystem model, derived from the FDM model [J. Mar. Res. 48 (1990) 591], includes 11 nitrogen and carbon compartments and describes the synthesis of organic matter in the euphotic zone, its consumption by animals of upper trophic levels, and the recycling of detritic material in the deep ocean. The circulation model coupled to the ecosystem is the Miami isopycnic coordinate ocean model (MICOM), which covers the Atlantic and the Arctic Oceans with an enhanced resolution in the North Atlantic basin. The model is forced with realistic ECMWF ocean/atmosphere fluxes, which permits to resolve the seasonal variability of the circulation and mixed layer properties. In the twin assimimation experiments reported here, the predictions of the coupled model are corrected every 10 days using pseudo-measurements of surface phytoplankton as a substitute to chlorophyll concentrations measured from space. The diagnostics of these experiments indicate that the assimilation is feasible with a reduced-order Kalman filter of small rank (of order 10) as long as a sufficiently good identification of the error structure is available. In addition, the control of non-observed quantities such as zooplankton and nitrate concentrations is made possible, owing to the multivariate nature of the analysis scheme. However, a too severe truncation of the error sub-space downgrades the propagation of surface information below the mixed layer. The reduction of the actual state vector to the surface layers is therefore investigated to improve the estimation process in the perspective of sea-viewing wide field-of-view sensor (SeaWiFS) data assimilation experiments.

[1]  Jacques Verron,et al.  A singular evolutive extended Kalman filter for data assimilation in oceanography , 1998 .

[2]  Jacques Verron,et al.  An extended Kalman filter to assimilate satellite altimeter data into a nonlinear numerical model of the tropical Pacific Ocean: Method and validation , 1999 .

[3]  M. Fasham,et al.  Modelling the Marine Biota , 1993 .

[4]  Rainer Bleck,et al.  Salinity-driven Thermocline Transients in a Wind- and Thermohaline-forced Isopycnic Coordinate Model of the North Atlantic , 1992 .

[5]  Isabelle Dadou,et al.  Assimilation of surface data in a one-dimensional physical-biogeochemical model of the surface ocean. 2. Adjusting a simple trophic model to chlorophyll, temperature, nitrate, and pCO2 data , 1996 .

[6]  H. Ducklow,et al.  A nitrogen-based model of plankton dynamics in the oceanic mixed layer , 1990 .

[7]  Richard J. Matear,et al.  Parameter optimization and analysis of ecosystem models using simulated annealing: a case study at Station P , 1995 .

[8]  Philippe Gaspar,et al.  A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: Tests at Station Papa and long-term upper ocean study site , 1990 .

[9]  G. Hurtt,et al.  A pelagic ecosystem model calibrated with BATS data , 1996 .

[10]  P. Malanotte‐Rizzoli,et al.  An approximate Kaiman filter for ocean data assimilation: An example with an idealized Gulf Stream model , 1995 .

[11]  Alexey Kaplan,et al.  Mapping tropical Pacific sea level : Data assimilation via a reduced state space Kalman filter , 1996 .

[12]  G. Evans,et al.  The Use of Optimization Techniques to Model Marine Ecosystem Dynamics at the JGOFS Station at 47 degrees N 20 degrees W [and Discussion] , 1995 .

[13]  Geir Evensen,et al.  Coordinate Transformation on a Sphere Using Conformal Mapping , 1999 .

[14]  Rainer Bleck,et al.  Wind‐driven spin‐up in eddy‐resolving ocean models formulated in isopycnic and isobaric coordinates , 1986 .

[15]  N. Mahowald,et al.  Inverse methods in global biogeochemical cycles , 2000 .

[16]  J. Toggweiler,et al.  A seasonal three‐dimensional ecosystem model of nitrogen cycling in the North Atlantic Euphotic Zone , 1993 .

[17]  S. Gorshkov,et al.  World ocean atlas , 1976 .

[18]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[19]  Andreas Oschlies,et al.  Eddy-induced enhancement of primary production in a model of the North Atlantic Ocean , 1998, Nature.

[20]  Howard P. Hanson,et al.  Mixed Layer-Thermocline Interaction in a Three-Dimensional Isopycnic Coordinate Model , 1989 .

[21]  Pierre Brasseur,et al.  Assimilation of altimetric data in the mid-latitude oceans using the Singular Evolutive Extended Kalman filter with an eddy-resolving, primitive equation model , 1999 .

[22]  H. Drange,et al.  A 3-dimensional isopycnic coordinate model of the seasonal cycling of carbon and nitrogen in the Atlantic Ocean , 1996 .

[23]  A. Oschlies,et al.  Sensitivity of ecosystem parameters to simulated satellite ocean color data using a coupled physical-biological model of the North Atlantic , 1999 .

[24]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[25]  Jens Schröter,et al.  Testing a marine ecosystem model: Sensitivity analysis and parameter optimization , 2001 .

[26]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[27]  G. Evensen,et al.  An ensemble Kalman smoother for nonlinear dynamics , 2000 .

[28]  Robert A. Armstrong,et al.  Monitoring Ocean Productivity by Assimilating Satellite Chlorophyll into Ecosystem Models , 1995 .

[29]  G. Evensen,et al.  A weak constraint inverse for a zero-dimensional marine ecosystem model , 2001 .

[30]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[31]  Rainer Bleck,et al.  A wind-driven isopycnic coordinate model of the north and equatorial Atlantic Ocean 2. The Atlantic Basin Experiments , 1990 .

[32]  Thomas M. Powell,et al.  Ecological Time Series , 1994, Springer US.