AbstractA three-dimensional variational data assimilation (3DVAR) method is implemented in a coupled physical–biogeochemical (CPB) model in the Baltic Sea. This study carries out a 10-yr assimilation experiment with satellite sea surface temperature (SST) and observed in situ temperature (T) and salinity (S) profiles. The impact of the assimilation is assessed with the focus on how the biogeochemical model responds to the improved hydrodynamics. The assimilation of temperature and salinity data yields considerable improvements in the physical model. On a basin scale, the mean bias of SST, T, S, and mixed layer depth (MLD) is decreased by 0.18°C (57%), 0.31°C (49%), 0.34 psu (43%), and 1.8 m (43%), respectively. More importantly, the biogeochemical simulation is improved in response to the physical data assimilation. Compared with in situ observations, the mean biases of chlorophyll a (Chl), dissolved inorganic nitrogen (DIN) and phosphorus (DIP) are decreased by 0.09 mg m−3 (15.5%), 0.19 mmol m−3 (9%), and 0.15 mmol m−3 (23%). Physical data assimilation also improves the simulated variability of Chl, DIN, and DIP and their correlations with observation. Compared with satellite observations, the mean bias of surface chlorophyll is reduced by 0.10–0.32 mg m−3 especially in the Skagerrak–Kattegat area and Bornholm basin. The decrease of total Chl change is caused by different mechanisms for winter and summer. While the deepened mixed layer acts as a dilution factor in winter, strengthened stratification agrees well with the decrease of chlorophyll in summer. In the vertical, relatively large changes of DIN and DIP occur below 60 m, which corresponds to the mean permanent halocline depth (~60–80 m) of the Baltic Sea.
Sensitivity analysis with respect to observations in variational data assimilation for parameter estimation
