15 May 2014 
DANGENDORF ET AL. 
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Table 2. Pearson correlation coefficients between winter 
(ONDJFM) surge percentiles and winter (ONDJFM) SLP indices. 
Significant correlations (f test) are marked in boldface. 
Surge percentiles 
Indices 
95th 
98th 
99th 
99.9th 
NAO 
0.45 
0.41 
0.40 
0.37 
NSCI 
0.66 
0.57 
0.51 
0.44 
The largest contribution to the observed variability in 
the storm surge record can be found on time scales up to 
a few decades. From a variety of studies, it is well known 
that especially during the winter season a considerable 
fraction of sea level variability can be explained by the 
NAO (e.g., Yan et al. 2004; Dangendorf et al. 2012). It is 
also obvious that this relationship does not only exist for 
mean but also for extreme sea levels (Woodworth et al. 
2007; Dangendorf et al. 2013a). We therefore examined 
the relationship for the winter season by comparing the 
updated station-based NAO index from Jones et al. 
(1997) to the four upper percentiles of storm surges 
(Table 2). In all cases, the comparison exhibits a weak 
but significant correlation (r = 0.37-0.45) between the 
time series, being slightly lower for the highest percen 
tiles. This relationship is not stationary over time; it 
shows considerable fluctuations over the entire period 
(Figs. 5b,d). In agreement to earlier studies between the 
NAO and MSL over the Northern European shelf 
(Jevrejeva et al. 2005) the correlations are high during 
the mid-nineteenth century, decreasing to insignificant 
values until the 1960s and then returning back to par 
ticular high values at the end of the twentieth century up 
to the present. This suggests that (i) other factors besides 
the NAO play an important role for the variability of 
surges as found earlier also for storminess (Matulla et al. 
2008), (ii) the statistical relationship stagnates in times 
of low large-scale atmospheric variability (i.e., bathy 
metric effects on the surge generation become more 
influential), and/or (iii) the influence of the NAO on 
surges depends on the position of the NAO centers of 
action (Kolker and Hameed 2007). 
To further examine the mechanisms behind this var 
iability we computed the cross correlations between 
daily surges in Cuxhaven and daily pressure fields from 
the 20CRv2 (Compo et al. 2011) over the larger geo 
graphic area from 60°W to 40°E and from 20° to 80°N. To 
keep the results unbiased by the increasing uncertainties 
of reanalysis data in the early decades (Krueger et al. 
2013a,b), we evaluated the data over the period from 
1950 to 2010. The correlation analysis suggests a dipole 
like pattern between surges and SLP with significant 
negative correlations over Scandinavia and positive cor 
relations over Iberian Peninsula (Fig. 5a). This pattern is 
also known from MSL time series (Dangendorf et al. 
2013b; Dangendorf et al. 2014) in that region and repre 
sents the mean weather situation triggering strong storm 
surges (Heyen et al. 1996). Composite plots (not shown) 
suggest an increased westerly flow if surges deviate pos 
itively from the mean, while the opposite is true for par 
ticular negative surges. The dipole-like pattern generally 
shows similarities to the NAO, but it has a more regional 
character with a more robust link to the local climate of 
the German Bight that is also able to reproduce surges in 
response to serial clustering of extratropical cyclones, 
such as in January 2007 (Fig. 1; Pinto et al. 2013). 
For taking this regionally more relevant large-scale 
feature of atmospheric variability into account, we de 
fine an additional index that is referred to as northern 
Scandinavia-central Iberia index (NSCI). The index is 
computed in a similar manner as the station-based NAO 
index (Jones et al. 1997) by using homogenized daily 
SLP records of Stockholm and Madrid since 1850 
(EMULATE; Ansell et al. 2006). Both stations are lo 
cated in the closest vicinity to the centers of the corre 
lation pattern of surges at Cuxhaven with the pressure 
fields (Fig. 5a). 
As shown in Table 2, the correlations between the 
winter half-year NSCI and high storm surge percentiles 
exceed those of the NAO. More importantly, the link of 
surges to the NSCI is temporally more stationary than to 
the NAO. This is indicated by the fact that the running 
30-yr correlations with the NSCI remain significant and 
relatively stable over the entire investigation period 
(Fig. 5d). As a locally important circulation index like 
the NSCI can be defined for any location, the main ad 
vantage of such an index relates to the temporal long 
term robustness of the link between a local variable and 
the dominating large-scale atmospheric variability. The 
robust link (in terms stationary running correlations) of 
high surges at Cuxhaven to the NSCI over time back to 
1850 therefore suggests the homogeneity of the surge 
record, since both parameters (surge levels and SLP) are 
measured completely independently. Note that the high 
correlation between both can be taken as an indepen 
dent measure of homogeneity in terms of low-frequency 
variability and long-term trends (e.g., Arns and Jensen 
2010; Gouriou et al. 2013), while a partial disagreement 
could be explained either by inhomogeneities or 
changes in wind circulation (e.g., direction). Such pe 
riods of disagreement are, however, not detectable in 
the presented time series. 
b. Differences between observations 
and reanalysis data 
Since we have shown that the storm surge record has 
a stationary link to the NSCI back to 1850 and is also