Ocean Dynamics
Ö Springer
resulting positive and negative transport values along a
transect are summarized yielding the total inflow and
outflow. The net transport is given by summing up in
flow and outflow. The transport data of all contributing
models are displayed in charts and vertical profiles on
the NOOS and BOOS websites (www.noos.ee/index.
php?id=151, www.boos.org/index.php?id=24, accessed
24 October 2014).
Based on data from this ongoing project, a MME of
vertically integrated and surface water transport is devel
oped to provide information about model uncertainty.
Daily data across the defined transects are provided by
up to six models for NOOS transects and by up to four
models for BOOS transects (see Fig. 7 for transect loca
tions and numbering). The ensemble mean and standard
deviation of the model data are calculated and displayed
on daily maps. An additional statistical parameter, the
coefficient of variation (CV), helps to compare the dis
persion between the data (i.e., Brown (1998)). The CV is
the ratio of the standard deviation (T std ) to the absolute
ensemble mean of transports (T mea „)\
CV = J std , with T mean = and
| mean \ 72
Tstd = \J—j- (Tj-TmeanY
A low CV index means low variability between the
models. If the standard deviation is larger than the mean
transport, the CV index is higher than 1. For this study, the
CV index is subdivided into three categories: category 1
(CV<1), category 2 (1<CV<3), and category 3 (CV>3),
where a CV above 3 is often associated with high variability
or even outliers (Brown 1998).
2.3 Spatio-temporal statistics
For the statistical evaluations, only complete data sets
were included, thus only those days and regions where
all model data are available. The amount of complete
data sets varies with region and parameter and is also
due to the late inclusion of some forecasts. Accordingly,
the study period varies between the parameters: For SST
and SSS, the period is 01.01.2014-31.12.2014, SSC are
evaluated for the time period 01.05.2014-31.05.2015,
and TRA is studied for the period 01.04.2013-
31.05.2015.
2.3.1 Comparison of sea surface temperature forecasts
to satellite observations
Sea surface temperature of the MME mean, the MME me
dian (MME products), and the individual forecasts are
compared to remote sensing (satellite) data. It should be
mentioned that satellite SST measures skin temperature,
while the SST used for the MME is the 5-m mean of the
upper model layers. Due to the diverse performance of
satellite observations, several products are selected for
the comparison: For the North Sea, the daily level 3
MyOcean SST nighttime satellite data is used, which is
from the mono sensor AVHRR. For the Baltic Sea, the
comparison is carried out by using the daily level 3
MyOcean SST nighttime satellite product, which is provid
ed by various sensors: AATSR, AVHRR, AVHRR GAC,
SEVIRI, GOES Imager, MODIS, and TMI. It has to be
noted that satellite data is affected by cloud cover. In com
parison to the Baltic Sea, less satellite data are used for the
North Sea, where the satellite products are from mono
sensor.
Due to the limitation of the spatial coverage of SST
satellite data in the North Sea and the Baltic Sea, the
comparison is carried out on a monthly basis. The SST
01-h forecast is selected for comparison, since it is closest
to the nighttime satellite data. Satellite data at 0 h UTC
are interpolated to the reference grids of the MME prod
ucts. The bias between the individual SST 01-h forecast
and the satellite data (forecast-satellite data) is averaged
over each month at each grid point. In addition the root-
mean-square deviation (RMSD) of each forecast is calcu
lated for each month at each grid point. Moreover, the
number of days with available satellite data is divided
by the length of the month giving the available satellite
data (%) for each grid cell. It has to be noted that only
grid points are taken into account, where the satellite data
are available for more than 7 days per month. The month
ly mean values for bias, RMSD, and available satellite
data are further spatially averaged. Annual means of bias
and RMSD are compared respectively. The comparison is
done for the time period January-December 2014 using
the MATLAB package CalVal-toolbox (Lagemaa et al.
2013; Jandt et al. 2014). Results are presented in
Sect. 4.1.
2.3.2 Seasonal changes of sea surface salinity
For SSS, the differences among the individual forecasts
are evaluated for the time period January-December
2014. Therefore, the temporal mean of the MME mean
and the standard deviation between the forecasts is cal
culated at each grid point. In addition, the daily spatial
mean for each region is calculated for each forecast
and the MME products. The ensemble spread,
expressed as the ensemble standard deviation, is taken
into account for the comparison. Results are presented
in Sect. 4.2.
