Ocean Dynamics 
4) Springer 
to be critical when the MME mean is close to zero. Neverthe 
less, it gives information about the variability between the 
products which appears to be low in most parts of the study 
area. Transports are calculated using residual currents while 
the SSC, characterized by high uncertainties, include tides. 
This consolidates the assumption that the boundary condi 
tions, and thus tidal constituents, play an important role in 
the forecast uncertainties. Further comparisons should be per 
formed using residual currents to evaluate the causes for high 
deviation between forecasts. 
Spatio-temporal statistics have been calculated yielding in 
formation about possible seasonal deviation patterns or region 
al differences between the forecasts, including information 
about forecast deviations from the MME or from observations. 
The region with high forecast uncertainty for SSS and SSC was 
found to be the highly dynamic Transition Area and the Nor 
wegian Coastal Current, where large differences in PVD dis 
placements occur (Fig. 18). This pattern is not frilly reflected in 
the transport data since CV statistics and correlations have 
comparatively good results at most transects in this area. As 
mentioned above, high uncertainties in SSC, as reflected in the 
PVDs, might therefore be due to differences in boundary con 
ditions and tidal constituents of the models, while transports are 
calculated from residual currents. The major cause for high 
standard deviation in SSC and SSS in this area are difficulties 
in simulating the frontal structures and movements of the low- 
salinity water of the Baltic outflow. The vertical coordinate 
systems and turbulence schemes of the individual models are 
different causing varying distributions of density and mixed 
layer depths, both having a strong effect on the surface param 
eters. In addition, there are two models which cover only the 
North Sea where the eastern boundary is located in the Katte 
gat. This might complicate a correct simulation in this area. 
Fligh uncertainties in SSS between the individual forecasts, 
simulating the salt plume in the Baltic outflow area, are 
displayed in Fig. 16. Regions close to river mouths are also 
prone to high forecast uncertainty for SSS due to different data 
sets for river runoff used by the forecasting models. 
Regarding forecast inter-comparisons, no forecast could be 
revealed which deviates most from the others in the whole 
study area for all parameters. The amount of deviation of each 
forecast for SSC is dependent on the area. In region III, the 
Norwegian Coastal Current, METNOROMS has the highest 
PVD displacements at most transects, while in the Baltic Sea, 
DMIHBM has the highest values at most transects in regions 
V and VI. This is also displayed in the spatio-temporal statistic 
of TRA where DMI HBM exhibits higher deviation from the 
median at most transects in regions I—III, V, and VI. Transects, 
where products with opposed or extremely differing transport 
pattern are included in the MME, are clearly detectable in the 
mean correlation and the deviation from the median (i.e., re 
gion VII). As the number of ensemble members in the MME 
of TRA is relatively low at most transects, products with a 
strongly differing pattern also have a strong impact on the 
MME (Figs. 23 and 24). The same effect is displayed in the 
spatial mean of SSS in the North Sea, where METNO ROMS 
has clearly higher values than the other forecast throughout 
the whole year. On those days, when this forecast is missing, 
the MME mean varies sharply and the standard deviation 
drops significantly. In the Baltic Sea, FCOO GETM has the 
highest deviation in SSS from the MME mean although the 
spread between the forecasts varies little during the whole 
analysis period. 
A comparison of SST forecast to satellite observations 
showed that the biases and RMSD of the MME mean, the 
MME median, and METUKFOAM are lowest in the North 
Sea compared to the other forecasts. In addition, a distinct 
seasonal pattern has been detected, characterized by high 
spread between the forecasts during summer. Although the 
availability of satellite data varies strongly between the 
months, there seems to be no clear link between the monthly 
mean RMSD and the availability of satellite observations. In 
order to frilly understand the mechanisms influencing the fore 
cast uncertainties in SST including seasonal features, the at 
mospheric forcing of each forecast needs to be taken into 
account. Regarding the annual mean bias, the lowest errors 
and thus values close to zero appear for METUK FOAM and 
METNO ROMS, which both imply data assimilation. These 
results show that the ensemble process can improve the accu 
racy of the forecasts. Similar seasonal patterns with higher 
values for RMSD and bias and larger spread between the 
forecasts during summer can be detected in the Baltic Sea. 
The forecasts of the models applying data assimilation, 
SMHI HIROMB NS03 and SMHI HIROMB BSOl, also 
exhibit comparatively low errors. 
This study has demonstrated that the MME is a useful tool 
to evaluate the spread, based on uncertainty measures, be 
tween individual forecasts for different parameters. The com 
parison of SST forecast to satellite observations showed that 
the combined MME products provided better results than 
most individual forecasts. However, the low number of 
MME members (i.e., 3 to 4 for TRA), and thus a non 
representative spread, seems to have an impact on the results. 
Thus, a large number of ensemble members are quite impor 
tant for a qualitatively promising MME. In this study, both 
mean and median have been taken into account for evaluation. 
As no weighting is applied on the individual forecasts, the 
resulting MME mean is prone to outliers, especially in a 
low-member MME. In contrast, the MME median is less im 
pacted by outliers. Therefore, the spatio-temporal statistics for 
TRA have been calculated using the median, due to the low 
number of products. 
In this study, it was not intended to undertake validation 
for each forecast or to provide the best overall estimate with 
the MME for all parameters. The latter simply cannot be 
realized due to the lack of reliable in situ data with