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H.E.M. Meier and A. Höglund
4.2.2.5 Sea Level in the Kattegat
The situation is different for the sea level boundary conditions because the daily
variations affect the water exchange between the Kattegat and the western Baltic
Sea. Barotropic flows generated by the sea level difference along the Danish Straits
are important for the volume and salt exchange between the Kattegat and Baltic Sea.
Hence, daily sea level observations from a station close to the northern boundary,
e.g., the Swedish stations Smögen, Ringhals or Varberg, are prescribed. Although
these boundary conditions are very much simplified, they ensure a reasonable simulation of the water exchange variability on time scales from days during inflow
events to decades during high and low (stagnation) saline phases (Meier et al. 2003).
4.2.2.6 Atmospheric Forcing
In climate simulations the integration period is long compared to the overturning
time scale. Hence, the numerical integration of the primitive equations is a boundary rather than an initial value problem. Usually, empirical bulk formulae are used
to calculate the air–sea, air–ice and ice–ocean fluxes from atmospheric and oceanic
state variables. In the RCO model the surface fluxes are calculated from 2 m air
temperature, 2 m specific humidity, sea level pressure, 10 m wind, precipitation and
total cloudiness. The surface fluxes that need to be specified are sensible and latent
heat fluxes, incoming short- and long-wave radiation, precipitation minus evaporation and momentum fluxes as outlined in Sects. 4.2.1.3 and 4.2.1.4. Alternatively,
the surface fluxes from an atmospheric model might be used as forcing.
There are two alternatives of atmospheric forcing data sets for long-term simulations with high-resolution ocean models. Firstly, the Swedish Meteorological and
Hydrological Institute (SMHI) provided three-hourly gridded observations of sea
level pressure, 2 m air temperature, 2 m relative humidity, and total cloud cover
since 1980 (e.g., Kauker and Meier 2003). In addition, 12-hourly accumulated precipitation fields are available at 06 and 18 UTC. Geostrophic wind is calculated and
reduced to 10 m wind by using a varying factor for the geostrophic wind speed in
the range between 0.5 and 0.6, depending on the distance to the coast (Bumke and
Hasse 1989). In addition, a constant ageostrophic angle of 17 ◦ following (Bumke
and Hasse 1989) was used. Data from all available synoptic stations (about 700 to
800) covering the entire Baltic Sea drainage basin are interpolated with the help of
a 2D univariate optimum interpolation on a 1 ◦ × 1 ◦ regular horizontal grid with respective latitude and longitude ranges of 50 ◦ N to 72 ◦ N and 8 ◦ E to 40 ◦ E. These
atmospheric data are also available since 1970 but with only six-hourly temporal
resolution and calculated with fewer meteorological stations.
Some caution is necessary when applying these data as forcing for Baltic Sea
models. Firstly, the horizontal grid resolution is very coarse compared to the geographical dimensions of the Baltic Sea. Secondly, as most of the stations are located
on land (only a few of them are located on islands), the data set is obviously biased
H.E.M. Meier and A. Höglund
4.2.2.5 Sea Level in the Kattegat
The situation is different for the sea level boundary conditions because the daily
variations affect the water exchange between the Kattegat and the western Baltic
Sea. Barotropic flows generated by the sea level difference along the Danish Straits
are important for the volume and salt exchange between the Kattegat and Baltic Sea.
Hence, daily sea level observations from a station close to the northern boundary,
e.g., the Swedish stations Smögen, Ringhals or Varberg, are prescribed. Although
these boundary conditions are very much simplified, they ensure a reasonable simulation of the water exchange variability on time scales from days during inflow
events to decades during high and low (stagnation) saline phases (Meier et al. 2003).
4.2.2.6 Atmospheric Forcing
In climate simulations the integration period is long compared to the overturning
time scale. Hence, the numerical integration of the primitive equations is a boundary rather than an initial value problem. Usually, empirical bulk formulae are used
to calculate the air–sea, air–ice and ice–ocean fluxes from atmospheric and oceanic
state variables. In the RCO model the surface fluxes are calculated from 2 m air
temperature, 2 m specific humidity, sea level pressure, 10 m wind, precipitation and
total cloudiness. The surface fluxes that need to be specified are sensible and latent
heat fluxes, incoming short- and long-wave radiation, precipitation minus evaporation and momentum fluxes as outlined in Sects. 4.2.1.3 and 4.2.1.4. Alternatively,
the surface fluxes from an atmospheric model might be used as forcing.
There are two alternatives of atmospheric forcing data sets for long-term simulations with high-resolution ocean models. Firstly, the Swedish Meteorological and
Hydrological Institute (SMHI) provided three-hourly gridded observations of sea
level pressure, 2 m air temperature, 2 m relative humidity, and total cloud cover
since 1980 (e.g., Kauker and Meier 2003). In addition, 12-hourly accumulated precipitation fields are available at 06 and 18 UTC. Geostrophic wind is calculated and
reduced to 10 m wind by using a varying factor for the geostrophic wind speed in
the range between 0.5 and 0.6, depending on the distance to the coast (Bumke and
Hasse 1989). In addition, a constant ageostrophic angle of 17 ◦ following (Bumke
and Hasse 1989) was used. Data from all available synoptic stations (about 700 to
800) covering the entire Baltic Sea drainage basin are interpolated with the help of
a 2D univariate optimum interpolation on a 1 ◦ × 1 ◦ regular horizontal grid with respective latitude and longitude ranges of 50 ◦ N to 72 ◦ N and 8 ◦ E to 40 ◦ E. These
atmospheric data are also available since 1970 but with only six-hourly temporal
resolution and calculated with fewer meteorological stations.
Some caution is necessary when applying these data as forcing for Baltic Sea
models. Firstly, the horizontal grid resolution is very coarse compared to the geographical dimensions of the Baltic Sea. Secondly, as most of the stations are located
on land (only a few of them are located on islands), the data set is obviously biased