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Fig. 6. Reconstruction of surges based on 20CRv2 winds and 
SLP. The 10-yr moving averages of the 99.9th percentiles of ob 
served surges ± standard deviations (black line with gray shaded 
area; the standard deviation has been computed as a measure of 
variability over each 10-yr window) and their reconstructions based 
on 20CRv2 (light red: individual ensemble members; dark red: 
ensemble mean; both normalized to a common period from 1950 to 
2011: i.e., the mean has been removed) are presented. Differences 
between both are shown in blue. Annual efficiency criteria between 
observed and reconstructed daily surges are presented in black. 
The red shaded area marks the period for which significant dif 
ferences between observations and 20CRv2 are detected. The 
different shades demonstrate the gradual increase of inconsistencies 
before the 1910s. 
for the year 1883), exceeding the mean of the calibra 
tion period by approximately 150%. As shown before 
with the stationary correlation of observed surge levels 
with the NSCI since 1850 (Fig. 5b), the deviations of 
observed surge levels with those predicted through 
20CRv2 are unlikely to be caused by the observational 
record (the NSCI and the storm surge record are mea 
sures independently). 
A similar picture is retrieved by comparing the 10-yr 
moving averages of the 99.9th percentile time series of 
observed and statistically reconstructed surges (Fig. 6). 
Over the past 100 yr, the reconstruction fits well to the 
observations. The model predicts the known decline in 
storminess in the mid-twentieth century, the rapid in 
crease until the mid-1990s, and the downturn afterward. 
Nevertheless, in the early 1910s, the prediction starts to 
decrease in a manner not visible in the observations 
(for both the ensemble mean as well as the ensemble 
spread). This decrease finally results in significant posi 
tive long-term trends over the entire reanalysis period 
from 1871 to 2010 if 20CRv2 is taken as predictor (note 
that a similar behavior was also observed for the 95th 
percentile; Fig. SI of the supplementary material). 
Related to this, Bronnimann et al. (2012) demon 
strated that the ensemble mean appears to be biased 
toward lower wind speeds during earlier decades. They 
recommended the use of single ensemble members 
rather than the ensemble mean when investigating long 
term changes. To examine whether the results from 
Fig. 6 are influenced by such biases, we additionally 
evaluated long-term changes in each ensemble member 
separately. First, we calculated the differences between 
the percentile time series from each 20CRv2 ensemble 
member prediction and the observed time series. Then, 
in a second step, we computed linear trends for each of 
the residual time series. The results show that for each 
percentile all ensemble members point to significant 
positive long-term changes, which are further signifi 
cantly different from the observations (Fig. 7a). Addi 
tionally, we found that the residual trends are generally 
increasing with the order of the percentiles: that is, highest 
deviations are found within the highest percentiles. 
To determine the exact timing from which the 20CRv2 
generated surges start to deviate significantly from the 
observations, we further computed linear trends for 
the residual time series over 30-yr moving windows. The 
results are shown in Figs. 7b,c for the 99th and 99.9th 
percentiles, respectively. While the trend estimates scat 
ter around zero back to approximately 1910, before that 
time statistically significant differences are found for the 
ensemble mean as well as each individual ensemble 
member. While we can confirm the bias of the ensemble 
mean reported by Bronnimann et al. (2012) (Fig. 7b), our 
results also illustrate that using individual members can 
not improve the results significantly (when assessing the 
long-term behavior of storm surges in the German Bight). 
The reanalysis is significantly biased toward a lower oc 
currence of extreme values in the period prior to 1910 in 
both the ensemble mean as well as all members (in this 
region). 
The decreasing coherence between reanalysis forcing 
and observed surges is generally in line with increasing 
uncertainties in the reanalysis because of fewer assimi 
lated observational data in the earlier periods (Compo 
et al. 2011; Krueger et al. 2013b). The results therefore 
partly confirm the inconsistencies between storm