Application of 3-D ensemble variational data assimilation to a Baltic Sea reanalysis 1989–2013

Abstract

A 3-D ensemble variational (3DEnVar) data assimilation method has been implemented and tested for oceanographic data assimilation of sea surface temperature (SST), sea surface salinity (SSS), sea ice concentration (SIC), and salinity and temperature profiles. To damp spurious long-range correlations in the ensemble statistics, horizontal and vertical localisation was implemented using empirical orthogonal functions. The results show that the 3DEnVar method is indeed possible to use in oceanographic data assimilation. So far, only a seasonally dependent ensemble has been used, based on historical model simulations. Near-surface experiments showed that the ensemble statistics gave inhomogeneous and anisotropic horizontal structure functions, and assimilation of real SST and SIC fields gave smooth, realistic increment fields. The implementation was multivariate, and results showed that the cross-correlations between variables work in an intuitive way, for example, decreasing SST where SIC was increased and vice versa. The profile data assimilation also gave good results. The results from a 25-year reanalysis showed that the vertical salinity and temperature structure were significantly improved, compared to both dependent and independent data.

• data assimilation
• physical oceanography
• Baltic Sea
• reanalysis

6. Acknowledgements

Financial support from the Swedish–Finnish Winter Navigation Board, project VARICE (contract TRAFI/12261/ 02.03.01/2011) and the EU-funded projects MyOcean2 (contract FP7-SPACE-20011-1) and Copernicus (contract no 2015/S 009-011266) are gratefully acknowledged. The in situ T/S data were extracted from the ICES oceanographic data base (http://www.ices.dk/) and is gratefully acknowledged. We also thank three anonymous reviewers for constructive criticism.