Abstract
Abstract. A realistic circulation model of the North Atlantic ocean at 0.25° resolution (NATL025 NEMO configuration) has been adapted to explicitly simulate model uncertainties. This is achieved by introducing stochastic perturbations in the equation of state to represent the effect of unresolved scales on the model dynamics. The main motivation for this work is to develop ensemble data assimilation methods, assimilating altimetric data from past missions Jason-1 and Envisat. The assimilation experiment is designed to provide a description of the uncertainty associated with the Gulf Stream circulation for years 2005/2006, focusing on frontal regions which are predominantly affected by unresolved dynamical scales. An ensemble based on such stochastic perturbations is first produced and evaluated using along-track altimetry observations. Then each ensemble member is updated by a square root algorithm based on the SEEK (singular evolutive extended Kalman) filter (Brasseur and Verron, 2006). These three elements – stochastic parameterization, ensemble simulation and 4-D observation operator – are then used together to perform a 4-D analysis of along-track altimetry over 10-day windows. Finally, the results of this experiment are objectively evaluated using the standard probabilistic approach developed for meteorological applications (Toth et al., 2003; Candille et al., 2007). The results show that the free ensemble – before starting the assimilation process – correctly reproduces the statistical variability over the Gulf Stream area: the system is then pretty reliable but not informative (null probabilistic resolution). Updating the free ensemble with altimetric data leads to a better reliability with an information gain of around 30% (for 10-day forecasts of the SSH variable). Diagnoses on fully independent data (i.e. data that are not assimilated, like temperature and salinity profiles) provide more contrasted results when the free and updated ensembles are compared.
Highlights
One of the challenges in ocean data assimilation is to faithfully describe the uncertainty of the ocean state estimates using observations and models
The results show that the free ensemble – before starting the assimilation process – correctly reproduces the statistical variability over the Gulf Stream area: the system is pretty reliable but not informative
The effects of the unresolved scales on the ocean circulation are simulated, and a stochastic perturbations ensemble is produced with the stochastic NATL025formulation
Summary
One of the challenges in ocean data assimilation is to faithfully describe the uncertainty of the ocean state estimates using observations and models. Ensemble methods are designed to describe the evolution of the probability density function (pdf) of the ocean and provide a useful way to represent the uncertainties associated with complex systems These uncertainties mainly come from the unresolved scales by the model, and from the interactions between the model and the external forcings (e.g. the atmospheric forcing). The probabilistic approach allows objective comparisons between the model (in A) and the observations (in A ∪ B) by providing sufficient conditions to invalidate the model This approach considers the model as a weak constraint to data assimilation problems by including the explicit description of model uncertainties. At this stage, first objective comparisons are performed between the model and the observations, and the uncertainty is quantified.
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