U. Callies et al.: Surface drifters in the inner German Bight 
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www.ocean-sci.net/13/799/2017/ 
Ocean Sei., 13, 799-827, 2017 
(a) Mean currents (1958-2015) 
(b) First mode of variability (EOF) 
Bathymetry 
6‘ r 8' 9‘ 
Figure 2. (a) Mean currents in the inner German Bight, calculated running a 2-D version of model TRIM for the period January 2014- 
August 2015. (b) Leading mode of variability (first empirical orthogonal function (EOF); see von Storch and Zwiers, 1999) of daily 25 h 
mean currents obtained from a PCA restricted to data from the white box region in panel (a) (Callies et al., 2017). Vector densities in the two 
plots do not represent spatial resolution of the underlying model (1.6 km). Vectors in the right panel are scaled in such a way that the EOF 
represents an anomaly that would arise from the first principal component (PC i) assuming the (positive) value of 1 standard deviation. 
Baltic Sea, resolution in the German Bight is 1.6 km. The 
FES2004 tidal model (Lyard et al., 2006) is used to deter 
mine tidal signals at the lateral boundaries of the outer coarse 
grid. Hourly values of wind and sea level pressure are taken 
from COSMO-CLM hindcasts (Geyer, 2014), which resulted 
from a regionalization of global NCEP/NCAR Reanalysis-1 
data (Kistler et al., 2001) using a spectral nudging technique 
(von Storch et al., 2000). Similar to BSHcmod, wind stress 
was parametrized according to Smith and Banke (1975), 
a parametrization validated from gentle breeze to gale force 
winds. An evaluation of TRIM simulations on a 6.4 km grid 
(first of three refinements applied in the present study) can 
be found in a recent model intercomparison study regarding 
simulations for the whole North Sea (Patsch et al., 2017). 
2.2.3 Effects of winds and waves 
Simulated Eulerian currents can usually not fully repro 
duce observed currents. Additional wind effects may man 
ifest themselves in different ways. This study explores the 
strengths of windage effects and Stokes drift as alternative 
tuning parameters for optimizing simulated drift trajectories. 
Hourly fields of surface Stokes drift were simulated with 
the third-generation spectral wave model WAM (WAMDI- 
Group, 1988; Komen et al., 1996), extending an existing 
wind-wave hindcast for the years 1949-2014 (Groll and 
Weisse, 2017) and including surface Stokes drift as a new 
element of archived model output. Wave simulations were 
driven with the same COSMO-CLM hindcast also used for 
TRIM simulations. The wave model was used in a nested 
mode, with the finer spatial resolution of about 3x3 nau 
tical miles over the entire North Sea. Wave breaking and 
depth refraction were enabled. A more detailed description of 
the wave simulation and its validation is given by Groll and 
Weisse (2017). For the present study, no assumption about 
the vertical profile of Stokes drift (Breivik et al., 2016, for 
instance) was made. Instead, the empirical weighting factor 
a in Eq. (1) was used to translate surface Stokes drift ob 
tained from WAM into a value relevant for drifters that rep 
resent displacements in a surface layer of approximately 1 m 
depth. Choosing a — 0.5 resulted in a reasonable overall fit 
with observations (see below). 
Windage (or leeway) effects occur when drag resulting 
from part of a drifter being exposed to the wind is not fully 
compensated by a drogue attached to the drifter. Generally, 
the direct influence of winds on the drifter type used in this 
experiment is supposed to be small as long as the drogues 
attached are in a proper condition. However, specification of 
windage effects may also be needed when model currents 
used do not adequately represent the surface layer drifters 
are immersed in. An extra wind drift parametrized as 0.6 % 
of 10 m wind velocity was used in combination with archived