Spatial–Temporal Variability of the Calculated Characteristics of the Ocean in the Arctic Zone of Russia by Using the NEMO Model with Altimetry Data Assimilation

Konstantin Belyaev,Andrey Kuleshov,Ilya Smirnov

doi:10.3390/jmse8100753

Abstract

The spatial–temporal variability of the calculated characteristics of the ocean in the Arctic zone of Russia is studied. In this study, the known hydrodynamic model of the ocean Nucleus for European Modelling of the Ocean (NEMO) is used with assimilation of observation data on the sea surface height taken from the Archiving, Validating and Interpolation Satellite Observation (AVISO) archive. We use the Generalized Kalman filter (GKF) method, developed earlier by the authors of this study, in conjunction with the method of decomposition of symmetric matrices into empirical orthogonal functions (EOF, Karhunen–Loeve decomposition). The investigations are focused mostly on the northern seas of Russia. The main characteristics of the ocean, such as the current velocity, sea surface height, and sea surface temperature are calculated with data assimilation (DA) and without DA (the control calculation). The calculation results are analyzed and their spatial–temporal variability over a time period of 14 days is studied. It is shown that the main spatial variability of characteristics after DA is in good agreement with the localization of currents in the North Atlantic and in the Arctic zone of Russia. The contribution of each of the eigenvectors and eigenvalues of the covariation matrix to the spatial–temporal variability of the calculated characteristics is shown by using the EOF analysis.

Highlights

In this study, we continue a cycle of investigations on development and application of data assimilation (DA) methods in the models of ocean dynamics [1,2,3]
The ocean dynamics in the Arctic zone is simulated with the help of the known hydrodynamic model Nucleus for European Modelling of the Ocean (NEMO) developed in the Institute Pierre Simon Laplace in Paris and described in several papers with application of the DA method developed earlier by the authors of this study, namely, the Generalized Kalman filter (GKF) [1,2,3]
The GKF method is the generalization of the known data assimilation method—the Ensemble Optimal Interpolation (EnOI) method, and has some advantages over this method, which is shown, in particular, in [2]

Summary

Introduction

We continue a cycle of investigations on development and application of data assimilation (DA) methods in the models of ocean dynamics [1,2,3]. In meteorology and physical oceanography, the observation data are assimilated into numerical models with the purpose of correcting the results of simulations in the process of calculations to enhance the quality of calculations of the atmosphere and ocean dynamics, weather forecast, predictions of climate changes, as well as to improve the models themselves and the ways of setting the initial and boundary conditions [10,11]. An important direction in physical oceanography is the project whose aim is to provide the analysis and prognosis of the state of the ocean in its particular regions For this purpose, different mathematical models of the ocean dynamics are used with observation data assimilation and application of different DA methods.

Configuration of the General Circulation Model and Data Assimilation Method

Experiments on Data Assimilation and Their Analysis

Findings

July 2013