Spatial-Temporal Variability of the Model Characteristics in the Southern Atlantic

Abstract

The present article is aimed at studying spatial-temporal variability of some model characteristics, particularly, the sea level data in the Southern Atlantic. Methods and Results. The eigenvector decomposition method (called the Karhunen-Loeve decomposition) has been used as a main research technique. Variability of the eigenvectors and eigenvalues of the corresponding covariance matrices, and their distribution in time and space are represented. Application of the method to the problem of assimilating the observation data is shown, and physical sense of such assimilation is analyzed. The ocean hydrodynamics model developed in the Institute of Numerical Mathematics, Russian Academy of Sciences, was applied. The problem of dynamical-stochastic and hybrid assimilation of the sea level data is formulated. Spatial-temporal variability of the model sea level and the one observed in the Southern Atlantic were compared. The variability difference and similarity are analyzed. Conclusions. The correlation structure between the observed and model ocean level fields is considered. This can permit to assimilate the observational data using the obtained weight matrices. Such studies of the sought characteristics' correlation structures of surface temperature, currents, joint covariance etc. will make it possible to understand exactly how the observed values correct model calculations and to carry out observations in the manner most convenient for data assimilation. Climatic behavior of the structure of eigenvectors and eigenvalues is shown. The represented technique permits to model and to forecast the hydrodynamic processes in the Southern Atlantic in more details.