The Northeast Atlantic possesses an energetic and variable wind and wave
climate which has a large potential for renewable energy extraction; for
example along the western seaboards off Ireland. The role of surface winds in
the generation of ocean waves means that global atmospheric circulation
patterns and wave climate characteristics are inherently connected. In
quantifying how the wave and wind climate of this region may change towards
the end of the century due to climate change, it is useful to investigate the
influence of large scale atmospheric oscillations using indices such as the
North Atlantic Oscillation (NAO), the East Atlantic pattern (EA) and the
Scandinavian pattern (SCAND). In this study a statistical analysis of these
teleconnections was carried out using an ensemble of EC-Earth global climate
simulations run under the RCP4.5 and RCP8.5 forcing scenarios, where EC-Earth
is a European-developed atmosphere ocean sea-ice coupled climate model. In
addition, EC-Earth model fields were used to drive the WAVEWATCH III wave
model over the North Atlantic basin to create the highest resolution wave
projection dataset currently available for Ireland. Using this dataset we
analysed the correlations between teleconnections and significant wave
heights (Hs) with a particular focus on extreme ocean states using a range
of statistical methods. The strongest, statistically significant correlations
exist between the 95th percentile of significant wave height and the NAO.
Correlations between extreme Hs and the EA and SCAND are weaker and not
statistically significant over parts of the North Atlantic. When the NAO is
in its positive phase (NAO+) and the EA and SCAND are in a negative phase
(EA-, SCAND-) the strongest effects are seen on 20-year return levels of
extreme ocean waves. Under RCP8.5 there are large areas around Ireland where
the 20-year return level of Hs increases by the end of the century,
despite an overall decreasing trend in mean wind speeds and hence mean Hs.
Introduction
The Northeast Atlantic has an energetic, variable wind and wave climate with
a significant potential for renewable energy applications
().
Global atmospheric circulation patterns and wave climate characteristics are
inherently connected through the role of surface winds in the generation of
ocean waves. Several previous studies have shown strong correlations between
the wave climate of the North Atlantic Ocean and atmospheric teleconnection
patterns such as the North Atlantic Oscillation (NAO) and the East Atlantic
teleconnection pattern (EA) (for example, ). The Scandinavian teleconnection pattern (SCAND) and East
Atlantic Western Russian (EA/WR) pattern are other modes of Northern
Hemisphere atmospheric variability, and along with the EA have a weaker, but
nevertheless significant, influence on the North Atlantic than the NAO
. In particular, the EA and SCAND have been found to
have an impact on the relationship between the NAO and European precipitation
patterns , and wind energy resources
in winter, by modulating the location and relative
intensity of the NAO centres of action.
A strong link between low-frequency modes of atmospheric variability and mean
significant wave height (Hs), wave period and peak direction of the waves
in Irish coastal waters was identified by . The
influence of the NAO on extreme sea states in the Northeast Atlantic Ocean
was investigated by explaining how this may change in the
future using an ensemble of WAVEWATCH III simulations driven
by output from the Coupled Model Intercomparison Project 5
(CMIP5) climate simulations carried out using the
EC-Earth global climate model.
The study focused on the NAO, which is the leading mode of atmospheric variability in the North Atlantic region and is manifested as a meridional dipole in mean sea-level pressure (MSLP), with centres of action over Iceland and the
Azores
.
Variations in the amplitude and phase of the NAO are linked to changes in the
intensity and frequency of storms and blocking
patterns . A positive NAO phase is associated with a
stronger pressure gradient over the North Atlantic, stronger westerly winds
and larger waves. On the other hand, a negative NAO phase is associated with
a weaker pressure gradient, slacker winds and smaller waves.
showed that the 95th percentile of Hs is strongly positively correlated to
the NAO, where the station-based interpretation of NAO was employed.
Projections of Hs extremes were found to be location dependent; under the
influence of positive NAO, the return levels of Hs may increase in the
future despite the overall decreasing trend in the projections of Hs.
The most commonly used calculation of the NAO index uses the station-based
definition which involves the difference between MSLP anomalies in the Icelandic Low and Azores High action
regions . This definition was
used in and applied to the EC-Earth gridded MSLP fields
(using the nearest grid point to the location of interest). Disadvantages of
this method are that it is fixed in space and shows low signal-to-noise
ratios.
An alternative method for deriving the NAO index involves calculating the
principal component (PC) time series of the leading empirical orthogonal
function (EOF) of gridded MSLP or 500 hPa geopotential height fields spanning
an area bounded by 20–90∘ N and 80∘ W–40∘ E. Note that this method is
particularly sensitive to the spatial domain and time period used. The PC of
the second leading EOF is usually the East Atlantic (EA) pattern which has a
centre of action in the Atlantic Ocean west of Ireland; the third leading
mode is usually the Scandinavian (SCAND) pattern. However, these modes often
account for approximately the same percentage of atmospheric variability and
thus the second and third EOFs often interchangeably correspond to either the
EA or SCAND and are identifiable using a plot of the particular 2-D EOF.
The study presented in this paper extends the analysis of .
Here we include the three most dominant modes of northern hemisphere
atmospheric variability – the NAO, the EA and the SCAND and employ the EOF
analysis method in their calculation. The analysis was carried out for the
following North Atlantic area 20–90∘ N 80∘ W–40∘ E using a 3-member ensemble
of historical and RCP4.5/8.5 projection EC-Earth data for the months of
December to March. It is important to note that our ensemble size is small.
This was due to the computational demands required to run the very high
resolution wave simulations.
The EA was first described by . It is defined by a
centre of positive 500 hPa height anomalies around the subtropical North
Atlantic. It is known to play a role, with the NAO, in determining the
latitude and extent of the jet stream, and therefore, the main Atlantic storm
track . In its negative phase (negative MSLP
anomalies in the mid Atlantic) the EA is known to contribute to northwest
swells in the Bay of Biscay .
The SCAND was defined by as the Eurasia-1 pattern. It is
characterised by high pressure anomalies over the Scandinavian Peninsula and
a more diffuse centre of opposite sign over Greenland. It corresponds to the
Scandinavian blocking regime identified in anticyclonic set-ups, and is
associated with colder than average winter temperatures and higher
occurrences of easterly winds over Western Europe .
This pattern is known to be negatively correlated with wind speeds and
significant wave heights during at least the extended winter
months .
The paper is organised as follows: Sect. provides details
about the EC-Earth and WAVEWATCH III models used in this study. The
atmospheric teleconnections are described in more detail in
Sect. . In Sect. the results of the
various statistical tests are presented and discussed. Conclusions on the
findings of this study are presented in Sect. .
Models
The work presented in this paper used data from
CMIP5 simulations carried out using version 2.3 of
the EC-Earth global climate model to drive the WAVEWATCH III
wave model . Details on each of these models are provided
in but a summary is repeated here for completeness.
EC-Earth climate simulations
The EC-Earth global climate model used for the CMIP5 climate simulations
consists of an atmosphere-land surface module coupled to an ocean-sea ice
module . The atmospheric component of the model was based on
the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated
Forecasting System (horizontal resolution of 1.125∘ or approximately
125 km and 62 vertical layers up to 5 hPa). The Nucleus for European
Modelling of the Ocean (NEMO) version 2 was used for the oceanic component
with an average horizontal resolution of 1∘
(approximately 110 km) and 42 vertical levels. The sea-ice component was the
Louvain-la-Neuve Sea Ice Model (LIM) version 2
. The Ocean Atmosphere Sea Ice Soil coupler
(OASIS) version 3 was used to couple the
atmosphere-land surface module with the ocean-sea ice module.
The EC-Earth CMIP5 climate simulations span the period 1850 to 2100. The
years 1850 to 2009 are classified as the historical period and included
observed greenhouse gases and aerosol concentrations such as black carbon and
volcanic eruptions. The future period span from 2006 to 2100 under both
RCP4.5 and RCP8.5 CMIP5 climate forcing scenarios. 3 of the 14 EC-Earth CMIP5
ensemble members were generated by Met Éireann and available for use in
generating the high resolution wave dataset for Ireland. The EC-Earth
ensemble does not have a large spread in terms of annual mean wind speeds and
the three Met Éireann ensemble members encapsulate the range of
interannual variability.
(a–c) The three wave model grids as described in . (a) The large
North Atlantic grid has a 0.75∘×0.75∘ resolution. (b) The grid for the Northeast
Atlantic has a 0.25∘×0.25∘ resolution. Right panel (c) The wave model unstructured
grid around Ireland has a resolution ranging from 15 km offshore to 1 km in the nearshore characterised by 4473 nodes.
WAVEWATCH III simulations
WAVEWATCH III is a third-generation “phase-averaged” model based on a
stochastic representation of the sea surface solving the wave-action balance
equation . The evolution of the wave energy spectrum
in the presence of currents and bathymetry is described through the
conservation of action density (advection and refraction), which is balanced
by source terms . The dissipation and source term
parameterisations formulated in were used in this study.
EC-Earth 10 m wind speeds and sea-ice fields were used to
drive the ensemble of nested regional wave projections over the North
Atlantic (see Fig. ). The outermost grid was a regular grid
of 0.75∘×0.75∘ resolution over the North Atlantic; the
second grid covered part of the Northeast Atlantic on a 0.25∘×0.25∘ regular grid and the innermost grid centred around Ireland was
unstructured with a resolution of approximately
15 km at the grid boundaries increasing to 1 km in the nearshore. Full
details regarding this set-up can be found in .
We needed to limit the simulations to the following 30-year blocks: 1980–2009
and 2070–2099 for each available EC-Earth ensemble member, because of the
computational weight of the high resolution nested simulations carried out
using the WAVEWATCH III model. The comparisons referred to in this paper are
between the future period 2070–2099 (for both RCP4.5 and RCP8.5) and the
historical/industrial period 1980–2009.
The following nomenclature is used throughout this paper when referring to
the ensemble members and the historical and future periods. Each ensemble
member consists of an historical simulation and 2 future simulations (RCP4.5
and RCP8.5) and are denoted meiX, me4X and me8X where X=1,2,3 denotes
the ensemble member, “i” refers to the historical or industrial period, 4 and 8
refer to RCP4.5 and RCP8.5 respectively and “me” denotes the fact that it is
a Met Éireann ensemble member. This is also summarised in
Table .
Table explaining the logic behind the experiment names used in this
paper. The last number in the name refers to the ensemble member. The third
letter identifies whether the data refer to the historical period (i) or were
generated under RCP4.5 (4) or RCP8.5 (8). Under RCP4.5 greenhouse gas
emissions are expected to peak around the year 2040 and decline after that.
Under RCP8.5 greenhouse gas emissions are expected to increase throughout the
21st century.
We considered the first three modes of MSLP variability for a Northern
Hemisphere region spanning 20–90∘ and 80∘ W–40∘ E
using the 3-member EC-Earth ensemble consisting of 3 historical periods
(mei1, mei2, mei3), 3 future periods under RCP4.5 (me41, me42, me43) and 3
future periods under RCP8.5 (me81, me82, me83). The months of December
through to March were used in the calculations and analysis. For comparison,
MSLP variability was also computed using the ERA-Interim reanalysis dataset.
We used Empirical Orthogonal Function (EOF) analysis to examine the
variability in the EC-Earth MSLP fields for the periods mentioned above. This
multivariate statistical technique is used in order to reduce the
dimensionality of a dataset containing numerous related variables and at the
same time retain as much variance as possible. It has been extensively
applied to spatio-temporal datasets and it outputs a set of spatial patterns
and associated time series, which typically account for a decreasing
proportion of variability of the original data. The spatial variance patterns
are orthogonal to each other and are termed EOFs. The one-dimensional
time-series of the EOFs are referred to as Principal Component (PC)
time-series. Here we used monthly mean gridded MSLP fields for December,
January, February and March for each year in the 30-year periods (i.e.
gridded files containing 120 months of MSLP data).
We used the Python eof library to calculate the EOFs and PCs using the
EC-Earth MSLP data where the EOFs are expressed as a covariance between the
PC time series and the MSLP anomalies at each grid point. The MSLP anomalies
were computed using the 1864–1963 base period of the relevant
EC-Earth historical ensemble member. The same base period as in
was used for consistency.
The first three EOF patterns for ERA-Interim (a) to (c), EC-Earth me43 (d) to (f) and EC-Earth
me82 (g) to (i).
Individual EOFs sometimes have a physical interpretation associated with
them. In terms of MSLP in the Atlantic region, the first EOF refers to the
NAO. The second and third EOFs refer to the EA or the SCAND patterns. In our
calculations the NAO accounted for 35 %–46 % of the MSLP variability, the EA
accounted for 15 %–20 % and the SCAND accounted for 10 %–15 %.
Sample EOF maps for the ERA-Interim dataset are shown in
Fig. a to c. me43 and me82 are shown in
Fig. d to f and g to i respectively. Note that the
EOF2 of me43 corresponds to the SCAND pattern and EOF3 corresponds to the EA
pattern and that the positive centre of the EA has a more northwesterly
position than in ERA-Interim or me82. For some of the ensemble members it was
more difficult to determine whether EOF2/3 corresponded to the EA or SCAND
pattern. Figures and in the Appendix show
the EA and SCAND patterns respectively for each historical, RCP4.5 and RCP8.5
ensemble member.
Analysis and results
We evaluated means and extremes of the EC-Earth 10 m wind speeds using the
ERA-Interim dataset, and the WAVEWATCH III outputs using buoy observations,
scatterometer data and a historical WAVEWATCH III simulation driven by
ERA-Interim fields. The biases are mostly within 10 %. The EC-Earth ensemble
of projections suggests decreases of up to 14 % in the 95th percentile of
10 m wind speed over the North Atlantic by the end of the century for winter
(DJF) under RCP8.5. In accordance, WAVEWATCH III suggests decreases in the
95th percentile of Hs of 5 %–10 % around Ireland by the end of the century.
Further details on the evaluation of the winds from EC-Earth and the winds
and waves from WAVEWATCH III are available in and
.
In the following subsections we analyse the relationships between the 95th
percentile of Hs and the PC time series associated with the NAO, EA and
SCAND teleconnection patterns for the historical period 1980–2009 and the
future period 2070–2099 under both RCP4.5 and RCP8.5 forcing scenarios.
Spearman correlations are presented in Sect. and 20-year
return levels of Hs for different combinations of the NAO, EA and SCAND
indices using extreme value theory are discussed in Sect. . 20 years return levels were used, following on from previous work by
. Other return periods can, of course, be calculated with the
fitted model parameters.
The Spearman correlation coefficient between the EA index and the
95th percentile of Hs for DJFM. (a–c) historical period (1980–2009) 3× ensemble members; (d–f) future period 2070–2099 under RCP4.5 and
similarly (g–h) is for 2070–2099 under RCP8.5. Correlations statistically
significant at the α<0.05 level are dotted.
Correlations between the NAO, EA and SCAND and the 95th percentile of Hs
As in , but with minor differences, the 95th percentile of
Hs is positively correlated with the PC time-series associated with the
NAO, with large areas west of Ireland where it exceeds +0.7.
Figures and show the Spearman
correlation coefficient between the PC time-series corresponding to the EA
and the SCAND respectively and the 95th percentile of Hs for the months of
December to March. In Figs. and the
results shown in (a)–(c) are for the historical period and show each of the 3
ensemble members, (d)–(f) are for the future period under RCP4.5 and
similarly (g)–(i) are for the future period under RCP8.5. Correlations which
are statistically significant at the 0.05 level are dotted.
In Fig. areas to the north of Ireland tend to mostly show a
positive correlation between the PC corresponding to the EA and 95th
percentile of Hs while around Ireland and to the south of the country,
correlations are negative. This is consistent with the fact that EA in its
negative phase is associated with a centre of low pressure in the North
Atlantic west of Ireland and hence larger waves. Correlations of both sign
increase further away from Ireland. Correlations are mostly statistically not
significant around the north coast of Ireland.
Again in Fig. the statistically significant correlations at
the 95 % confidence limit are dotted. Correlations are mostly negative
around Ireland and positive further north. This is consistent with the
behaviour of the SCAND index. In its positive phase there is an area of high
pressure extending from Scandinavia towards Europe with an area of low
pressure around Greenland. Note that the SCAND pattern for mei2 and me82 (see
Fig. ) shows the area of low pressure around Greenland
extending further south into the Atlantic than in the other ensemble members.
The influence of this on the correlations between the PC time-series and the
95th percentile of Hs can be clearly seen in
Fig. b and h where areas of positive correlation extend much
further south.
The NAO, EA and SCAND teleconnections vs 20-year return levels of Hs
The Generalised Extreme Value (GEV) distribution may be used to calculate the
extremes of a dataset . The maxima of blocks of data (for
example monthly or annual) may be modelled with the distribution function
given by
G(z)=exp-1+ξz-μσ-1/ξ
The three parameters in the distribution are the shape -∞<ξ<∞, the location -∞<μ<∞ and the scale σ>0.
Having fitted the parameters to a given dataset, the distribution function
above in Eq. () may be inverted to yield N-year return levels; that
is, the value that has a 1/N probability of being exceeded in a given year,
given by
zN=μ-σξ1-[-log(1-/N)]-ξ
In this work we applied the GEV to the DJFM monthly maxima of the
Hs data described above. The model was fitted with maximum
likelihood (ML) inference using the ismev package in R
(http://CRAN.R-project.org/package=ismev, last access: 18 March 2019).
The parameters in the GEV are often allowed to be non-stationary. For
instance, linear or harmonic dependence in time may be included to model
long-term trends or seasonality in extremes; see, for example,
, , . In
, the GEV model was applied to the present dataset with
station-based NAO used as a covariate for the location and scale parameters.
In this present work, we allowed the location parameter to depend on the
three PCs: μ(t)=μ0+μ1×PC1(t)+μ2×PC2(t)+μ3×PC3(t). The shape and scale parameters were
kept constant, as was the case in . With such a model, we
see from Eq. () that any overall increase (decrease) in μ will result
in higher (lower) return levels of extremes.
The Spearman correlation coefficient between the SCAND index and the
95th percentile of Hs for DJFM. (a–c) historical period (1980–2009) 3× ensemble members; (d–f) future period 2070–2099 under RCP4.5 and
similarly (g–h) is for 2070–2099 under RCP8.5. Correlations statistically
significant at the α<0.05 level are dotted.
In all ensemble members for both historical and future scenarios, the ML
estimate of μ1, relating to the NAO (PC1), was found to be non-negative
throughout the domain. Thus, a positive phase of the NAO may contribute
to an increase in extremes of Hs. This effect of the NAO was
discussed in detail in , and in particular how this is expected
to vary under the two future scenarios. Here we focus on the EA and SCAND. In
Figs. and in the Appendix we show the ML
estimates of μ2 and μ3, respectively. Each ensemble member is shown
for the historical and two future periods. Note the similarities, as
expected, between the correlation maps in Fig. and the
spatial distribution of μ2 in Fig. and similarly
between Figs. and for the SCAND and
the distribution of μ3.
20-year return levels of Hs where the effects of the NAO, EA and SCAND are
isolated by setting each of the remaining two indices to zero. The ensemble mean is shown for the historical simulations.
The remainder of this section is dedicated to the influence of the NAO, EA
and SCAND indices on 20-year return levels of Hs. Varying the NAO from
-2
to 0 to +2, while keeping the EA and SCAND in a neutral state of zero, shows
a clear increase in the 20-year return levels of Hs when the NAO is in its
positive phase. This is shown in Fig. a–c where the
ensemble means of the 3 members for the historical period are shown.
Corresponding results under RCP4.5 and RCP8.5 are shown in
Fig. in the Appendix. The effect of the NAO is clear
and robust throughout the datasets; both for the historical period and the
RCP scenario simulations, return levels increase significantly when the NAO
is positive and are lower when the NAO is negative or zero. This coincides
well with the known effects of the NAO in enhancing the westerlies in the
North Atlantic and positioning of the jet stream and therefore the storm
track, towards the west coast of Ireland.
In comparison to the NAO, the influence of the EA and SCAND indices on Hs
over our domain is much smaller (Fig. ).
Figures and in the Appendix show
the corresponding EA and SCAND 20-year return level plots under RCP4.5 and
RCP8.5 forcings. When the EA index becomes positive, the 20-year return
levels of Hs decrease in the south of the study domain in particular and
the higher wave heights seem to be “pushed further north”. The decrease
in wave heights south of Ireland is consistent with the centre of positive
MSLP anomalies that characterises the EA pattern. The negative effect of a
positive-phase EA on 20-year return levels of Hs is also observed in the
RCP simulations.
Ensemble mean 20-year return levels of Hs where the NAO is set to +2 and
both the EA and SCAND are -2. A positive phase NAO and negative phase EA and SCAND is associated with the highest waves off Ireland.
Figure g to i shows the isolated effect of the SCAND
pattern on 20-year return levels of Hs by keeping the NAO and the EA
indices set to values of 0. The effect is more difficult to see in the
ensemble mean of the historical simulations but is clear in the RCP4.5/8.5
SCAND plots in Fig. in the Appendix. As mentioned
earlier, the SCAND pattern in mei2 has the area of low pressure over
Greenland extending further south than in the other 2 ensemble members and
makes the effect of the SCAND pattern on wave heights more difficult to see.
As the SCAND index goes from a positive to a negative phase, 20-year return
levels of Hs decrease in the southeast of the domain but seem to increase
in the north, for the historical period as well as for the RCPs. The SCAND is
associated with easterly winds from a blocking anticyclonic set-up over
Scandinavia and is known to divert the Atlantic jet stream east of its
climatological position . The physical effects we see
here are consistent with the known climatic impacts of the SCAND pattern.
Ensemble means of the 20-year return levels of Hs are shown in
Fig. for the historical period and future periods
under RCP4.5 and RCP8.5 where the NAO index is +2 and the EA and SCAND
indices are set to -2. Of the three teleconnections, the NAO has the
strongest influence on extreme waves in the northeast Atlantic, followed by
the EA with the SCAND having the smallest influence.
Figure c clearly shows that under RCP8.5 in
particular the 20-year return levels of Hs may increase off the west coast
of Ireland despite a prediction of an overall decreasing trend in mean wind
speeds and thus waves.
Conclusions
We analysed principal component time-series associated with the NAO, EA and
SCAND teleconnection patterns computed using an ensemble of global EC-Earth
climate projections and the influence of these patterns on regional wave
projections over the North Atlantic. The influence of the NAO on extreme
waves is greater than that of the EA and SCAND teleconnection patterns. We
found that the 20-year return levels of Hs are largest when the NAO is in
a strong positive phase (e.g. +2) and the EA and SCAND are in strong negative
phases (e.g. -2). During the positive phase of the NAO the pressure
gradient over the North Atlantic increases due to strengthening of the
Icelandic Low and Azores High. Stronger westerly winds, associated with the
increased pressure gradient, also generate larger waves. The East Atlantic
pattern, in its negative phase, has a centre of negative MSLP anomalies over
the eastern North Atlantic, roughly between the two centres of the NAO. This
is also associated with stronger winds and therefore larger waves. The
negative phase of the SCAND pattern has a negative pole of MSLP anomalies
centered over Scandinavia. When the NAO is in a positive phase, the SCAND
pattern enhances the westerly winds over the Atlantic which in turn has a
positive impact on wave heights off the west coast of Ireland. This is why
the +, -, - patterns for the NAO, EA and SCAND respectively are assoicated with
the largest significant wave heights as found in this study.
showed that local increases in extreme waves are possible in the future under
RCP8.5. The results presented here are consistent with this but also include
the effects of the EA and SCAND whose centres of action modulate the influence of the NAO on Hs.
The running of CMIP6 climate simulations is currently in progress and we plan
to repeat the analysis by carrying out new multi-model global climate
simulations and downscaled simulations. It is also worth noting that the
second and third EOF patterns vary quite a lot. Having a larger ensemble size
would be of benefit and would make the results more robust. In addition, we
used two 30-year periods 1980–2009 and 2070–2099. The analysis would also
benefit from using rolling 30-year periods. In addition we are currently
doing a separate analysis of the wave energy flux and wave period.
Although climate projections suggest that Hs and wave energy flux will
decrease on average in the Northeast Atlantic Ocean by the end of the
century, extremes will still occur and may be enhanced depending on the phase
of large-scale patterns such as the NAO, EA and SCAND.
Data availability
The datasets
have been archived at Met Éireann. There is currently no publicly
available method for data access so the Met Éireann should be contacted
for dataset access.
EA and SCAND Patterns
EOF patterns (2 or 3) corresponding to the EA teleconnection for the
EC-Earth historical and future periods under RCP4.5 and RCP8.5.
EOF patterns (2 or 3) corresponding to the SCAND teleconnection for
the EC-Earth historical and future periods under RCP4.5 and RCP8.5.
Maximum likelihood estimate of the μ2 and μ3 parameters
Maximum likelihood estimate of the μ2 parameter. The three
ensemble members are shown for the historical period (a) to (c), future
period under RCP4.5 (d) to (f), and future period under RCP8.5 (g) to (i).
Maximum likelihood estimate of the μ3 parameter. The three
ensemble members are shown for the historical period (a) to (c), future
period under RCP4.5 (d) to (f), and future period under RCP8.5 (g) to (i).
20-year return levels of Hs for varying values of the NAO, EA and SCAND indices
20-year return levels of Hs. The NAO index varies from 3 values
of the NAO index while the EA and SCAND are held in their neutral state
(zero). The top row shows the ensemble mean for the future period under
RCP4.5 and the bottom row is similar but shows RCP8.5 results.
20-year return levels of Hs. The EA index varies from 3 values of
the NAO index while the NAO and SCAND are held in their neutral state (zero).
The top row shows the ensemble mean for the future period under RCP4.5 and
the bottom row is similar but shows RCP8.5 results.
20-year return levels of Hs. The SCAND index varies from 3 values
of the NAO index while the NAO and EA are held in their neutral state (zero).
The top row shows the ensemble mean for the future period under RCP4.5 and
the bottom row is similar but shows RCP8.5 results.
Author contributions
EG ran the EC-Earth global climate simulations, analysed the wind outputs and computed the
PC time-series using EC-Earth data; SG ran the WAVEWATCH III simulations using EC-Earth boundary
conditions, JJ analysed the wave outputs and correlations between significant wave height and the PC
time-series; CC did a statistical analysis of extreme waves and the PC time-series using a Generalised
Extreme Value distribution. EG, CC and LZ analysed the outputs of the statistical
tests. EG and CC prepared the manuscript and received contributions from JJ, LZ, FD and SG.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “18th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2018”.
It is a result of the EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2018,
Budapest, Hungary, 3–7 September 2018.
Acknowledgements
The authors wish to acknowledge Roxana Tiron who helped to run the wave
simulations and the EC-Earth community for useful discussions on principal
component analysis outputs. The numerical simulations were performed on the
Fionn cluster at the Irish Centre for High-end Computing (ICHEC) and at the
Swiss National Computing Centre under the PRACE-2IP project (FP7 RI-283493)
“Nearshore wave climate analysis of the west coast of Ireland”.
The authors would like to thank the reviewers for their useful comments and
feedback.
Review statement
This paper was edited by Sandro Carniel and reviewed by Angela Pomaro and one anonymous referee.
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