Projections of extreme storm surge levels along Europe

The Delft3D-Flow module of the open source model Delft3D (Deltares 2014) has been applied to estimate the propagation of the SSL due to the combined effect of the wind and the atmospheric pressure gradient. The model has been used successfully in similar applications in the past (Sembiring et al. 2015). The Delft3D-Flow module set-up that has been adopted solves the 2D non-linear shallow water equations on a staggered Arakawa C-grid, according to an implicit finite difference approximation on a vertical σ—coordinate system.

A hindcast run spanning from 01/01/1979 to 01/06/2014 was forced by hindcast atmospheric pressure and wind fields obtained from the ERA-Interim database (Dee et al. 2011) and the resulting storm surge values were validated against water level time series available from the JRC Sea Level Database (http://webcritech.jrc.ec.europa.eu/SeaLevelsDb). The temporal resolution of the measurements is typically in the order of few hours and the temporal extent of the validation dataset varies among stations (Fig. 1), covering the period from 2008 and on; a period characterized by an increased marine storm activity including high impact events (e.g. Bertin et al. 2014; Breilh et al. 2013; Met Office and Centre for Ecology and Hydrology 2014; Vousdoukas et al. 2012). After identifying and excluding gaps or periods with rogue data, a tidal harmonic analysis was applied with the widely used t-tide package (Pawlowicz et al. 2002) in order to obtain the residual storm surge water levels η_s. The latter were then compared directly with the model output and evaluated in terms of the root mean square error (RMSE):

$$RMSE = \sqrt {\frac{{\sum\nolimits_{i}^{n} {\left( {\eta_{{s,{\text{measured}}}}^{i} - \eta_{{s,{\text{model}}}}^{i} } \right)^{2} } }}{n}}$$

(1)

where n is the number of measurements in the storm surge time series at a given location. The relative RMSE error (%RMSE) was also estimated in order to take into account spatial variations in the range of the SSL:

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$$\% RMSE = \frac{{\sqrt {\frac{{\sum\limits_{i}^{n} {\left( {\eta_{{s,{\text{measured}}}}^{i} - \eta_{{s,{\text{model}}}}^{i} } \right)^{2} } }}{n}} }}{{\hbox{max} \left( {\eta_{{s,{\text{measured}}}}^{{}} } \right)}} \times 100$$

(2)

Agreement in terms of the probability density function of the values was assessed after applying the Kolmogorov–Smirnov test (K–S), considering a 5 % significance level. Given that the study focuses on the extreme SSL, we evaluated the monthly maxima SSL of the model output and the tidal gauge measurements with the K–S test rather than considering the complete measured and simulated time series.

The period 1970–2000 was considered as baseline period, while 2010–2040 and 2070–2100 were considered as the short and long term future scenarios for RCP4.5 and RCP8.5, respectively. The two time slices will be mentioned as 2040 and 2100 hereinafter for reasons of brevity (e.g. RCP8.5₂₀₄₀).

The model was forced by the 6-h output of 8 climate models available at the CMIP5 database, namely the ACCESS1-0, ACCESS1-3, (CSIRO-BOM Australia), CSIRO-Mk3.6.0 (CSIRO-QCCCE, Australia), EC-EARTH (EC-EARTH consortium), GFDL-ESM2M (NOAA Geophysical Fluid Dynamics Laboratory USA), HadGEM2-CC (Met Office Hadley Centre UK), and MPI-ESM-LR, MPI-ESM-MR (Max-Planck-Institut für Meteorologie Germany). The specific models were selected as previous studies have shown (Perez et al. 2014) that they have good skill to reproduce the synoptic climatologies and the inter-annual variations across Europe.

For better analysis of the model performance and the storm surge scenarios, the European coastal zone was divided into 10 regions on the grounds of the geographical and physical setting: Black Sea, East, Central and West Mediterranean, South- and North-North Atlantic, Bay of Biscay, as well as North, Baltic and Norwegian Sea (Fig. 2).

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Data from 184 tidal gauge stations were processed in order to identify data gaps or to mask periods with low-quality data, resulting in a set of 110 stations with periods of valid ground-truth residual water level data coinciding with the simulation period. Following the initial filtering of the tide gauge records, the region with the largest number of acceptable quality tidal gauge records was the North Atlantic (36), followed by Central Mediterranean (21), the North Sea (14) and East/West Mediterranean (9); while only one station was found in the West Iberia and 2 in the Black Sea (Table 3). It is also important to underline that the temporal availability (Fig. 1) and data quality was poorer along the Black Sea and East Mediterranean. Overall, the model showed to reproduce satisfactory the measurements (Fig. 3) and spatial variations in the model performance were observed (Fig. 4), with RMSE values ranging from 0.06 to 0.29 m, while  %RMSE varied between 10 and 29 % (Table 3).

Table 3

Overview of model performance along the 10 defined European regions: number of tidal gauge stations, percentage of stations with the same distribution of monthly maxima according to the Kolmogorov–Smirnov test, as well as mean, maximum and minimum values of the RMS error in m and as a percentage of the SSL range

Region name	No stations	KS %	RMSE mean	RMSE min	RMSE max	%RMSE mean	%RMSE min	%RMSE max
Black Sea	2	100	0.14	0.12	0.16	25	22	28
East Mediterranean	9	96	0.11	0.06	0.29	24	13	29
Central Mediterranean	21	82	0.14	0.09	0.28	18	16	20
West Mediterranean	9	95	0.10	0.09	0.13	19	15	21
West Iberia	1	98	0.10	0.10	0.10	17	17	17
Bay of Biscay	7	100	0.10	0.07	0.12	16	15	17
North Atlantic	36	97	0.12	0.08	0.23	14	10	20
North Sea	14	100	0.17	0.11	0.22	16	12	19
Baltic Sea	7	100	0.15	0.09	0.21	14	11	18
Norwegian Sea	4	100	0.09	0.07	0.10	13	11	15

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Most of the Mediterranean, the Atlantic coast and the Norwegian Sea were characterized by absolute RMSE values below 0.1 m, while RMSE > 0.15 m were observed along the North Adriatic and the North Sea (Fig. 4a). The latter high RMSE values appeared to be related to the higher η_s range, as implied by the relatively low %RMSE values in the same areas (Fig. 4b). The highest %RMSE values were observed along the Aegean Sea (%RMSE > 0.2), while overall model performance was poorer along the Black and Mediterranean Sea (mean %RMSE ranging from 18 to 25 %). On the contrary, the lowest  %RMSE was observed in the Norwegian Sea (mean  %RMSE = 13 %, see Table 3).

Q–Q plots indicate that the model was capable of reproducing the probability distribution function of the measured SSL (Fig. 5), which was the crucial skill given the extreme value statistical analysis to follow. The model overestimated the lower values at some locations in S. Europe (e.g. Fig. 5a–f), while the opposite was observed in the North, Baltic and Norwegian Sea (e.g. Fig. 5g–j); however the lower values are of minor interest for the scope of the present study. On the other hand, the extremes were underestimated at some stations (see Fig. 5a–c, e, f), a possible artifact of the coarse atmospheric forcing and numerical grid. However, the model’s capacity to simulate SSL is considered satisfactory given that the scope of the study is to project trends under climate change scenarios and not to provide accurate operational forecasts. This is also confirmed by the results of the Kolmogorov–Smirnov test (Table 3), which showed agreement in the monthly maxima distributions for almost all validation points along North Europe, and at least for 80 % of the points along South Europe.

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The monthly maxima SSL values from the ERA-INTERIM forced reanalysis run were compared with the values obtained from the baseline period runs forced by the 8-member ensemble and the estimated average  %RMSE values per model varied from 28.1 % (EC-EARTH; see also Table 4) to 33.6 % (GFDL-ESM2M) around a mean of 30.4 %, before applying the Bias correction. As expected, the coarser CMIP5 models resulted in lower SSL values with mean normalized Bias around −12 % and the biggest deviations observed for ACCESS1-3, CSIRO-Mk3-6-0, and HadGEM2-CC (Table 4). After applying the BIAS correction and repeating the extreme value analysis, the agreement with the reanalysis values improved substantially to a mean %RMSE of 10.8 % (Table 4), with Kolmogorov–Smirnov test results indicating the same probability distributions between the Bias corrected CMIP5 and ERA-INTERIM runs. Overall, the monthly maxima values were similar among models and the biggest deviations from the reanalysis were found along the North and Baltic Sea (Fig. 6). The reported accuracy is considered satisfactory given that %RMSE was estimated from monthly maxima and the CMIP5 models are reproducing climate in a stochastic manner and thus the time domains are approximately matching with the reanalysis.

Table 4

Results from the baseline runs validation using the 8-member climate model ensemble vs the ERA-INTERIM forced reanalysis: normalized RMSE (%RMSE), Normalized Bias (NBI) before Bias correction and  %RMSE after Bias correction (%RMSE-BC)

Model	%RMSE	NBI (%)	%RMSE-BC
ACCESS1-0	28.3	−14.7	10.2
ACCESS1-3	28.7	−17.1	9.8
CSIRO-Mk3-6-0	28.7	−16.0	11.4
EC-EARTH	28.1	−14.3	10.4
GFDL-ESM2 M	33.6	−7.3	11.0
HadGEM2-CC	28.2	−14.8	10.5
MPI-ESM-LR	33.9	0.4	11.5
MPI-ESM-MR	33.9	0.4	11.4

All values were estimated considering the monthly maxima

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The estimated extreme SSL obtained by the 8-member model ensemble appeared to follow similar spatial patterns among the different scenarios and return periods (Fig. 7); with SSL values along the North Sea increasing eastwards, and being substantially higher than the ones along the rest of Europe. Another area characterized by higher SSL values was the UK coastline of the Irish Sea, followed by marginal areas of the Baltic Sea (e.g. Kattegat, Gulf of Finland and the North Gulf of Bothnia; see Fig. 7) and the Norwegian Sea. Anticipated extreme SSL appeared to be η_s < 2 m for most of South Europe with higher values observed along parts of the North Adriatic and of the North Black Sea.

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The spatial variations of the relative changes in extreme SSL for all the studied RCPs are shown in Fig. 7, while an overview of the general tendencies per European region is provided by the mean frequency curves (Fig. 8). The reader should note that hereinafter changes which are shown and discussed concern only the ones for which strong model agreement has been found among the 8-member ensemble (CV < 1).

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An increasing tendency was shown for the Black Sea, especially under RCP8.5 (Fig. 8a), with the current 100-year event expected to occur every 90.2 and 85 years under RCP8.5₂₀₄₀ and RCP8.5₂₁₀₀, respectively. While the projected increase of the extreme η_s is estimated especially at the east part of the Black Sea, the overall values are small and the changes are not significant in terms of absolute values (Fig. 7; see also “Appendix”).

Minor changes are projected for the extreme SSL projections at the Mediterranean Sea, with the most prominent increase projected along the West Mediterranean under RCP8.5₂₁₀₀ indicating a 29 year reduction in the return period of the present 100-year event (Fig. 8d). Projected increase in SSL was also found along the East Mediterranean; indicatively the present 100-year event was projected to occur every 75, 95.2 and 95.3 years under RCP8.5₂₀₄₀, RCP4.5₂₁₀₀ and RCP8.5₂₁₀₀, respectively (Fig. 8b). Along the Central and West Mediterranean the frequency of the present-day 100-year event was shown decrease in 2040 and to decrease towards the end of the century (Fig. 8c,d).

Projected SSL change along the South Atlantic coast of Europe and the Bay of Biscay was small (Figs. 7, 8e, f), as changes became more prominent at latitudes above 50°N. The present 100-year event at the South Atlantic coast was projected to occur every 143.5 and 130.8 years under RCP4.5₂₁₀₀ and RCP8.5₂₁₀₀, respectively (Fig. 8e), while a mild increase was projected for the Bay of Biscay for most scenarios (Fig. 8f). An increase was projected under all the studied RCPs along the Atlantic coast of North Europe (Figs. 7, 8g), with the present 100-year event projected to occur every T_r = 82.4, 89.5, 89.2 and 83.8 under RCP4.5₂₀₄₀, RCP8.5₂₀₄₀, RCP4.5₂₁₀₀, and RCP8.5₂₁₀₀, respectively (Fig. 8g); even though the projected frequency of the 10-year event remained relatively stable for all RCPs but RCP8.5₂₁₀₀.

The North Sea was projected to experience increased storm surge activity (Fig. 8h), especially towards the end of the century, i.e. the present 100-year event was projected to occur every 80.2 and 81.3 years under RCP4.5₂₁₀₀ and RCP8.5₂₁₀₀, respectively. The relative change in extreme SSL was shown to increase eastwards, as most of the UK east coast showed small decrease or no change (Fig. 7). Strong projected increase in frequency was also observed for the Baltic Sea, for all scenarios apart from RCP4.5₂₀₄₀; with the present day 100-year event projected to take place every 44, 72, and 51 years under RCP8.5₂₀₄₀, RCP4.5₂₁₀₀, and RCP8.5₂₁₀₀, respectively (Fig. 8i). Finally an increase in storm surge intensity was projected for the Norwegian Sea for all RCPs, with the present day 100-year event projected to take place every 79.4, 51, 63.5, and 47.7 years under RCP4.5₂₀₄₀,RCP8.5₂₀₄₀, RCP4.5₂₁₀₀, and RCP8.5₂₁₀₀, respectively (Fig. 8j).

Monthly maxima were grouped into seasons and were averaged in order to give insight to the seasonal variations of the extreme SSL (Fig. 9); as expected, the spatial variations were similar to the ones of the extreme values. For all scenarios the season with the highest SSL was winter, followed by autumn; while summer was typically the season characterized by the lowest SSL extremes. The standard deviation σ_SSL was used as a proxy of inter-annual variability and its spatial variations followed the ones of the actual values; e.g. areas like the North and the Baltic Sea where the higher SSL occur, were also characterized by high standard deviation values (Fig. 9).

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The projected relative changes of the mean seasonal SSL maxima were grouped and averaged for each studied region along with the corresponding inter-annual variability, standard deviation σ_SSL (Fig. 10). Overall the seasonal SSL values appeared intensified towards the end of the century, which was also relevant for the inter-annual variability. The Baltic Sea was the area with the highest increase in the seasonal maxima %Δη_s, with an increasing tendency for all seasons especially towards the end of the century, and under RCP8.5 (Fig. 10d). The second highest increase was shown along the North Sea, and especially towards the end of the century, with the projected increase exceeding 2 % in most cases. The general trend for the Norwegian Sea shows a decrease in the inter-annual variability, driven by decreasing or stable winter and spring SSL values and increase summer and autumn ones, especially towards the end of the century.

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On the contrary, for both RCPs and more significantly for the last period of the century, the seasonal mean maximum SSL at the European Atlantic coast appeared to decrease; a pattern that was more obvious along the Bay of Biscay (up to 3 %); while the projected changes in the remaining areas were very limited. The projected decrease of the seasonal maxima does not appear to be of similar intensity on a year-long basis, and as a result the inter-annual variability is projected to increase along all the areas found south of 50°N, except from the East Mediterranean.

Previous studies have shown that the uncertainties introduced by the ocean models are small compared to the ones related to the accuracy and resolution of the atmospheric forcing (e.g. Jordà et al. 2012). Moreover, in the case of SSL projections in view of climate change additional uncertainty is introduced by: (A) the future socio-economic development and policy actions (manifested as RCPs); (B) knowledge gaps in understanding and predicting the climate system, expressed as differences among climate models; (C) the skill of the hydrodynamic model to reproduce short duration/high energy, extreme events over large spatial domains (Calafat et al. 2014; Conte and Lionello 2013), especially given the limitations in the spatial resolution of both the meteorological forcing and ocean model, which are inevitable due to the spatial and temporal extent of the projections; and (D) the extreme value statistical analysis, affected by the selected frequency analysis approach and distribution shape, as well as the return period curve fitting (Hamdi et al. 2014). All the above points are discussed in the following paragraphs, apart from point A which is considered beyond the scope of the present study.

Climate model uncertainties (point B) were reduced to the greatest possible extent by (1) ensuring the skill of the circulation model to reproduce extreme SSL values along Europe through validation against tidal gauge data (Sect. 3.1); (2) selecting the CMIP5 climate models which according to Perez et al. (2014) are ranked with high skill in reproducing the synoptic climatologies and inter-annual variations along Europe; (3) using the validated SSL reanalysis, forced by more detailed atmospheric forcing, to apply BIAS correction on the SSL values generated from the atmospheric forcing of each climate model, in order to further ensure that the validity of the SSL projections; and (4) using a 8-member climate model ensemble and considering the ensemble mean only when model agreement is acceptable through a threshold in the coefficient of variation.

Regarding point C, recent efforts to simulate extreme storm surge events along large domains, such as the Mediterranean Sea, have shown that models often underestimate the extremes and in particular the ones related to short duration/high energy events (Calafat et al. 2014; Conte and Lionello 2013); something that could also apply for some locations at the present study. The latter might be related to processes which take place in finer temporal and spatial scales than the ones presently considered, where the quality of the output is directly affected by the resolution of both the meteorological (Cavaleri and Bertotti 2004) and ocean model (Cid et al. 2014). For example, it has been shown that along shallow areas storm surge is practically wind driven and detailed representation of the wind field is important; for that reason many regional/local scale studies apply a downscaling of the wind/pressure input using a finer atmospheric model (see Table 1 and references therein); which was not feasible in the present case due to the size of the computational domain and the related computational cost. Overall the approach followed appears to be valid since model validation showed that the model could reproduce satisfactorily the measured SSL, and the RMSE errors were at similar levels with previous efforts (Cid et al. 2014). This is also in agreement to previous studies which have demonstrated that global driven simulations are capable of predicting changes in extreme SSL without previous downscaling (Howard et al. 2010).

Water level variations are important for SSL, as the depth modulates the bottom friction, and thus the circulation patterns (Arns et al. 2015); while tidal currents interact with the wind-driven circulation applying an additional non-linear effect on the extreme η_s levels (Bernier and Thompson 2007; Zijl et al. 2013). As also shown by the validation results, the errors related to omitting the tidal circulation in the simulations are low and well below the uncertainty introduced by other components such as the atmospheric forcing. Moreover, tidal contributions on total water levels have their own probability density functions and thus they would affect also the SSL ones, therefore it was decided to run storm surge simulations without a tidal sea level signal; since the increased complexity and computational times from considering the tidal component in the simulations outweigh the potential gains in data quality. The same applies to the benefits of considering projections of relative sea level rise (RSLR) instead of running all simulations at the present day sea levels, since there is consensus in previous studies that to a first order approximation changes in mean sea level and SSL can be added linearly (Howard et al. 2010; Lowe and Gregory 2005; Sterl et al. 2009; Weisse et al. 2012).

Finally, an important factor of uncertainty in generating SSL projections is the extreme value statistical analysis, mainly related to (1) the selected frequency analysis approach; (2) the selected distribution shape; and (3) the fact that sometimes the return period considered can be even 2 orders of magnitude higher than the duration of the analyzed time series. The latter is a common limitation in similar studies and it has be to highlighted that while projected SSL changes for higher return periods >100 years carry an increased level of uncertainty and therefore should be considered without caution; despite compensation efforts compensated by the use of the model ensemble, and the coefficient of variation as proxy of model agreement. The frequency analysis approach along with the distribution shape have been shown to affect the projected SSL values (Hamdi et al. 2014), and there is certain variety in the approach selected in previous similar studies; i.e. r-largest (Marcos et al. 2011), GEV (Gaslikova et al. 2013) and non-stationary methods (Méndez et al. 2007; Serafin and Ruggiero 2014).

Regarding this study, several extreme value statistical approaches were tested and the extreme SSL values estimated for the baseline run were compared with values available in the literature; and the present implementation of the GPD was chosen as the most reliable approach. Overall, the effect of all the discussed potential limitations on data quality becomes even less critical since the present study focuses on assessing relative changes in extreme SSL, rather than presenting accurate η_s predictions or operational forecasts. Still, it is important to remind the reader and potential user of the dataset that the relative accuracy of the absolute SSL values should be considered in the 10–20 % range. Moreover, the subtraction between baseline and scenario values cancels out most possible shortcomings in the SSL simulations.

Given that, according to the authors’ knowledge, there are no previous studies on projected SSL values at European scale, the obtained results will be discussed against findings from existing regional studies. For the case of the Baltic Sea, earlier studies in historical storm surge trends have reported no statistically significant increasing trend (Baerens and Hupfer 1999; Menéndez and Woodworth 2010; Suursaar et al. 2015), while previous studies report a projected increase under SRES scenarios (Debernard and Røed 2008; Gräwe and Burchard 2012; Meier 2006; Weisse et al. 2009; Woth et al. 2006); in agreement with the present findings. The area shows some of the highest increases in projected extreme SSL in Europe and the seasonal values (Fig. 10) indicate increased storm surge activity during all the seasons of the year, but especially during spring and summer.

At the same time while some studies on SLR trends along the Baltic Sea have indicated a decreasing trend, also shown in the dataset produced by Pardaens et al. (2011); Johansson et al. (2014) concluded that the past negative trend in mean sea level in the Gulf of Finland will not continue in the future, because an accelerated global average SLR will offset the land uplift. Therefore, the presently projected increase in SSL could balance a potential decrease in future sea levels, resulting in comparable levels of coastal hazard in the future. However, in the case of positive RSLR supported by several studies (Johansson et al. 2014; Meier 2006) the Baltic Sea will experience even higher pressure by extreme coastal events, in agreement to results from Gräwe and Burchard (2012) for the West Baltic Sea.

Given that there are limited, if any, sources of information on storm surge projections along the Norwegian Sea, the present projections can be compared mostly with observations based on historical data. The results obtained project small or no increase in SSL, and when there is it is mostly restricted to the summer and autumn values for most scenarios (Fig. 10). This is partially contradicting with the findings of Menéndez and Woodworth (2010) who found a statistically significant increase for the historical data for both the total water level and the SSL. Overall the Norwegian coastline appears to be at low-risk in terms of coastal inundation as it is characterized by (1) a steep topography, providing a natural protection against increased water levels; and (2) sophisticated coastal protection schemes for low-lying areas of high socio-economic value; e.g. port facilities.

The North Sea is an area subject to some of the highest SSL in Europe (Fig. 7), with the projections indicating a future increase in the extremes, especially along the eastern part. The latter is in agreement with previous projections based on SRES scenarios (Debernard and Røed 2008; Woth et al. 2006), which also indicated an increase in strong westerly winds (Gaslikova et al. 2013), but also of the inter-annual variability (Dangendorf et al. 2014b). Furthermore, previous studies report results relatively similar to the present patterns along the south east coast of UK, i.e. small, or no projected storm surge change (Debernard and Røed 2008; Gaslikova et al. 2013; Howard et al. 2014; Lowe et al. 2009; Woth et al. 2006); as well as along the Dutch coast (Howard et al. 2014; Sterl et al. 2009).

The storm surge projections showed an increase along the Atlantic coast of the UK and Ireland, which was related to a consistent increase of the winter extremes, as well as of the inter-annual variability among the scenarios studied (Fig. 10); in line with previous studies predicting that the projected RSLR in the area will be combined with an increase in wind speeds and consequently in extreme SSL (Brown et al. 2010, 2012; Debernard and Røed 2008; Lowe et al. 2009). Lowe et al. (2009) found the highest projected increase in η_s values along the Bristol Channel and the Severn Estuary, which was also shown by the present dataset for some scenarios.

The Atlantic coast of France, Spain and Portugal is exposed to very energetic waves generated along the North Atlantic (Pérez et al. 2014), which constitute the dominant coastal hazard component (Almeida et al. 2012; Ciavola et al. 2011); with the latter potentially justifying the limited number of previous efforts to generate regional storm surge projections. The present findings indicate relatively stable or even decreasing η_s levels along most of the Bay of Biscay, being in agreement with the results of Marcos et al. (2012), who also reported a decrease towards the end of the century for both A1B and A2 SRES scenarios. The above are also in agreement with the observations of Menéndez and Woodworth (2010) on historical data.

The Mediterranean Sea has been studied extensively in terms of projected storm surge dynamics and there is consensus among studies based on SRES scenarios for no changes, or even a decrease in the frequency and intensity of extreme events (Androulidakis et al. 2015; Conte and Lionello 2013; Jordà et al. 2012; Marcos et al. 2011). This comes in agreement with the reported historical trends (Menéndez and Woodworth 2010), as well as with the present findings, projecting changes mostly in the ±5 % band, either positive or negative.

The North Adriatic is a region which has been studied more thoroughly due to the highly vulnerable, and socio-economically important Venice area, with most previous projections reporting no statistically significant change, or even decrease (Mel et al. 2013; Troccoli et al. 2012), even though Lionello et al. (2012) report a projected increase in frequency of extreme events around Venice, under a B2 SRES scenario. The latter is in agreement with the present projections which indicate weakly increasing extreme η_s values for certain RCPs along parts of the North Adriatic, with the latter not always including the Venice area (Fig. 7).

The present dataset presents projections of changes in extreme SLL values during this century, as well as of their seasonal variations; while previous studies have provided evidence of variability also on intermediate time scales, e.g. controlled by the North Atlantic Oscillation (NAO) (Dangendorf et al. 2014b; Marcos et al. 2011). Gillett and Fyfe (2013) report no significant increase in the NAO under RCP4.5 projected by CMIP5 models, which is contradicting to the projected increase in SSL values; on the other hand could be justified by the inability of some climate models to correctly simulate the physical processes connected to the NAO (Davini and Cagnazzo 2014). Therefore, analyzing further the patterns and controls of SLL variability is a very interesting direction for further research.

The projections presented here only concern the storm surge (atmospheric) contribution to sea level, which is one of the main components of extreme water levels along the coast and therefore, they are complementary to other studies focusing on RSLR attributed to other causes, such as thermal expansion or ocean mass variations (Nicholls et al. 2014; Pardaens et al. 2011). The mean projected increase in extreme SSL along Europe in 2100 and for a moderate ice-sheet behavior scenario is around 46 and 67 cm for RCP4.5 and RCP 8.5, respectively (based on data available by Hinkel et al. 2014). Even though, some storm surge attenuation is likely to happen due to the increased sea levels (e.g. Arns et al. 2015), the projected increase in SSL reaches 30 cm at certain locations and especially for the high return period events.

For example, the mean contribution of the projected increase in the 100 year storm surge event to the projected increase in the corresponding TWL for the entire European coastline, under RCP8.5 and towards the end of the century was shown to be around 18 %; with the latter exceeding 30 % for 14 % of the studied coastal location. As a result the contribution of extreme SSL to anticipated increasing total water levels is not negligible and implies an additional stress to several coastal locations in Europe due the combined effect of the intensified extreme events and RSLR. At the same time, increasing extreme SSL values could even result in similar risk levels even under negative projections of RSLR; e,g. in areas with anticipated uplift (Johansson et al. 2014).

An additional coastal hazard component are the waves, which during extreme events result in an increase in water level and energy flux towards the coast, accelerating sediment transport processes and thus driving coastal erosion, dune breaching and inundation (Bertin et al. 2012; Ciavola et al. 2011; Roelvink et al. 2009). Wave heights can exceed 10 m along exposed coasts, such as the Atlantic ones, and in order to obtain a complete idea of future coastal hazards it is essential to include projections of extreme wave events; a work currently in progress. There is no doubt that waves are drivers of long term morphological changes on the coast which should not be underestimated, however the waves have the smallest temporal scales and coastal morphology tends to be in a state of dynamic equilibrium with wave-driven processes (Dean 1991; Yates et al. 2009). Therefore, the water level remains a very critical parameter for coastal erosion and impact, since even a small increase in SSL could enhance the consequences of extreme events, despite the fact that the SSL amplitude could be even an order of magnitude lower than the wave height (e.g. Vousdoukas et al. 2012).

Finally, significant increase of extreme η_s was projected for both RCP4.5 and RCP8.5 scenarios towards the end of the century and for both time slices under RCP8.5. The latter implies that even if moderate emission mitigation policies will be enacted, coastal adaptation and protection measures should appear as high priority in the European policy agenda, since for many regions the increase in total water levels from the combined effect of RSLR and SSL, by the end of the century, is projected to exceed 1 m with respect to the actual water levels. This increase can be above the limits of most present coastal protection measures (Burcharth et al. 2014; Hunter et al. 2013; Weisse et al. 2014). The indications are even stronger for the business-as-usual RCP8.5 scenario, under which most Northern Europe coasts were projected to experience a significant increase in storm surge extremes.