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.
Highlights
Reanalysis combining state-of-the-art models and assimilation methods with quality controlled observations has helped enormously to generate homogeneous historical data
A 3DVAR scheme is used to construct a retrospective analysis of temperature, salinity, and sea level in the Baltic Sea from 1990 to 2009
The goal of this reanalysis is two-fold: first, the performance of the 3DVAR scheme can be assessed in a multi-decadal integration and provide more experience for future operational applications; second, the reanalysis can provide a uniformly gridded dataset for studies such as model intercomparisons, physical processes, climate variability and other purposes in the Baltic Sea
Summary
Reanalysis combining state-of-the-art models and assimilation methods with quality controlled observations has helped enormously to generate homogeneous historical data. Fu et al (2011) attempted an Ensemble Optimal Interpolation (EnOI) to assimilate temperature and salinity profiles in two-way nested model Major objectives of these studies are as follows: first, validating the assimilation schemes; second, enhancing the understanding of the ocean state in the Baltic Sea; and third, examining the role of adjusting model parameters in the assimilation of coastal/shelf seas. The goals are twofold: first, to explore and assess the impact of data assimilation on rectifying the model’s deficiencies such as the poor simulation of saline water intrusion in the Baltic Proper region; second, to construct a long homogeneous analysis of sea level, temperature and salinity of the Baltic Sea. A three-dimensional variational (3DVAR) approach is adopted in which the numerical model provides the first guess of the ocean state at each update time and is modified by inserting corrections into the initial condition on an regular basis. The historical dataset comprises most of the mea- servation will be discarded if the magnitude of innovation is surements collected from the Baltic Sea region for the past larger than 3.0 ◦C or 2.5 psu
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