A 20-year reanalysis experiment in the Baltic Sea using three-dimensional variational (3DVAR) method

Abstract

Abstract.A 20-year retrospective reanalysis of the ocean state in the Baltic Sea is constructed by assimilating available historical temperature and salinity profiles into an operational numerical model with three-dimensional variational (3DVAR) method. To determine the accuracy of the reanalysis, the authors present a series of comparisons to independent observations on a monthly mean basis.In the reanalysis, temperature (T) and salinity (S) fit better with independent measurements than the free run at different depths. Overall, the mean biases of temperature and salinity for the 20 year period are reduced by 0.32 • C and 0.34 psu, respectively. Similarly, the mean root mean square error (RMSE) is decreased by 0.35 • C for temperature and 0.3 psu for salinity compared to the free run. The modeled sea surface temperature, which is mainly controlled by the weather forcing, shows the least improvements due to sparse in situ observations. Deep layers, on the other hand, witness significant and stable model error improvements. In particular, the salinity related to saline water intrusions into the Baltic Proper is largely improved in the reanalysis. The major inflow events such as in 1993 and 2003 are captured more accurately as the model salinity in the bottom layer is increased by 2-3 psu. Compared to independent sea level at 14 tide gauge stations, the correlation between model and observation is increased by 2 %-5 %, while the RMSE is generally reduced by 10 cm. It is found that the reduction of RMSE comes mainly from the reduction of mean bias. In addition, the changes in density induced by the assimilation of T/S contribute little to the barotropic transport in the shallow Danish Transition zone.The mixed layer depth exhibits strong seasonal variations in the Baltic Sea. The basin-averaged value is about 10 m in summer and 30 m in winter. By comparison, the assimilation induces a change of 20 m to the mixed layer depth in deep waters and wintertime, whereas small changes of about 2 m occur in summer and shallow waters. It is related to the strong heating in summer and the dominant role of the surface forcing in shallow water, which largely offset the effect of the assimilation.

MOMBA 1.1 – a high-resolution Baltic Sea configuration of GFDL's Modular Ocean Model

“…The underestimation of deep salinities is an infamous feature of z-level models which do not apply data assimilation techniques (e.g. Fu et al, 2012). However, the especially bad performance of MOMBA came as a surprise because of the model's the high and competitive spacial resolution and its -as suggested in the following sea surface height analysisrealistic exchange of water through the Danish Straits.…”

Nutrient transports in the Baltic Sea – results from a 30-year physical–biogeochemical reanalysis

“…Fu (2016) analyzed the response of a coupled physical-biogeochemical model to the improved hydrodynamics in the Baltic Sea. Recently, several data assimilation studies have focused on the historical reanalysis of salinity and temperature in the Baltic Sea (e.g., Fu et al, 2012;Liu et al, 2013Liu et al, , 2014. Reanalysis has helped enormously in making the historical record of observed ocean parameters more homogeneous and useful for many purposes.…”

Improving the multiannual, high-resolution modelling of biogeochemical cycles in the Baltic Sea by using data assimilation

“…Assimilation of sea surface temperatures estimated from satellite data (NOAA) is used, for example, in the Baltic operational circulation model presented by Losa et al (2012Losa et al ( , 2014 and recently several data assimilation studies have focused on the historical reanalysis of salinity and temperature in the Baltic Sea (e.g. Fu et al, 2012;Fu, 2013;Liu et al, 2013). As a next step in the development of historical, marine reanalyses, the present paper focuses on the assimilation also of nutrients and oxygen in the Baltic Sea following Liu et al (2013).…”