dinrichs et al. 
50° 
35° 
50° 
55° 
50° 
55° 
15° -10° 
4 5° 10° 15 
0 65‘ 
"50 
"20 
10 X 60° 
5 
2 
0) 
55° 
4 
100 65: 
50 
20 
10 = 60° 
LO° 1; 
Baltic and North Seas Climatology 
(B) 
’ 2 ° 9 
0 27 30° 
z 
530° 
15° 
1° 5° 10° 
15 
2 
55 
10° 
20° 
(D) 
vpAxN 
30° 
FIGURE 3 | Results of the quality control for water temperature [North Sea (A), Baltic Sea (B)] and salinity (North Sea) (C), (Baltic Sea} (D)]: spatial distribution 0! 
Jercentage of flagged profiles, box size 0.25° x 0.25° 
TABLE 2 | Results of the quality control for the North and the Baltic Sea and the 
WO Darameters temperature and salinity: summary 
North Sea Baltic Sea 
TEMPERATURE 
\lumber of profiles 
\lumber of observed depth levels 
% flagged depth levels 
% profiles (min one flagged depth level} 
SALINITY 
\lumber of profiles 657,568 365,111 
\lumber of observed depth levels 34,225,844 6,470,368 
% flagged depth levels 6.86 6.45 
% profiles (min. one flagged depth level 21.20 27.20 
Altogether, there are 105 different depth levels from 0 m to almost 
5,000 m depth; the maximum distance (between the last and 
second last depth levels) accounts for 99 m. 
The interpolation is performed following the procedure 
described by Reiniger and Ross (1968). 
The applied horizontal grid has an edge length of 0.25° both 
in meridional and in zonal direction. 
Correction of the Temporal Sampling Error 
The observational data are available in temporal irregular 
distribution, see also inset in Figure 1B. Simple temporal 
averages of those observations are biased by periods with a high 
data density. Therefore, the irregular temporal distribution of the 
observations has to be taken into account and the observations 
must be adjusted accordingly before averaging. With respect to 
rontiers in Barth Science | www.frontiersin.oru 
the annual mean, a single observation of parameter P, Pops, can 
be described as the sum of three terms: 
Pops = Pi + Pinterann + Pseas 
where Py denotes the long-term mean, Pipnterann Stands for the 
part corresponding to the interannual variability and finally, Psegs 
expresses the seasonally variable fraction. It is this last term 
in combination with the temporal irregular sampling of the 
observations that would lead to the above mentioned bias if a 
simple arithmetic mean was applied to the original observations 
in order to receive an annual mean. The solution for this problem 
is to eliminate the influence of the seasonal variability on the 
observations and subtract the last term Pseas from Pops» This 
allows the calculation of unbiased annual averages independent 
an the temporal distribution and number of observations. The 
adjustment is performed on the basis of a depth dependent long- 
term mean annual cycle that is derived from the observational 
data for each grid box separately. The long-term mean annual 
cycle is expressed as a polynomial (11th order) that is fitted to 
the observations in a certain area including the respective grid 
box b. This daily resolved fit for temperature (T) and salinity (S) 
observations is given in the following two equations. 
12 
Tr (d) = Sp Od? DD, d=11:3651 
i=1 
12 ; 
SP, (d) = > pi 6d? 7, d = [1:365] 
1 
pi (b), ps (b): polynomial coefficients for T and S 
for the corresponding fit in box b 
Jury 9019 | alııme 7.1 Article 15£