Large scale atmospheric oscillations are known to have an influence on waves in the North Atlantic. 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 oscillations using indices such as the North Atlantic Oscillation (NAO: fluctuations
in the difference between the Icelandic low pressure system and the Azore high pressure system).
In this study a statistical analysis of the
station-based NAO index was carried out using an ensemble of EC-Earth global
climate simulations, where EC-Earth is a European-developed atmosphere ocean
sea-ice coupled climate model. The NAO index was compared to observations and
to projected changes in the index by the end of the century under the RCP4.5
and RCP8.5 forcing scenarios. In addition, an ensemble of EC-Earth driven
WAVEWATCH III wave model projections over the North Atlantic was analysed to
determine the correlations between the NAO and significant wave height
(
Means and extremes of
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 as discussed
in
The NAO index is sensitive to the method used in its definition. It is most
commonly defined as the difference between mean sea-level pressure (MSLP)
anomalies in the Icelandic Low and Azores High action regions. Stykkisholmur,
Reykavik or Akureyri (all in Iceland) are commonly used for the specific
locations in the Icelandic Low and Ponta Delgada (Azores), Lisbon (Portugal)
or Gibraltar for the Azores
High
It is important to point out that there is still considerable uncertainty in projected changes
in the frequency and intensity of extratropical cyclones over the North Atlantic
The paper is organised as follows: Sect.
Left panel: the three wave model grids as described in
The EC-Earth global climate model and WAVEWATCH III wave model were used to generate the atmospheric and wave datasets used in this study. A third datatset, the National Centre for Atmospheric Research (NCAR) NAO station-based time-series was also used. Details regarding the models and datasets are provided in this section.
The EC-Earth global climate model (version 2.3) used in this study consisted
of an atmosphere–land surface module coupled to an ocean–sea ice module
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 10 m wind speeds and sea-ice fields from an ensemble of EC-Earth global
climate projections
The EC-Earth historical simulations span from 1850 to 2009 and include observed
greenhouse gas and aerosol concentrations, including volcanic eruptions.
The future simulations ran from 2006 to 2100 where the RCP4.5 and RCP8.5 climate scenarios developed for CMIP5,
the Coupled Model Intercomparison Project 5
Each member consists of an historical simulation and 2 future simulations
(RCP4.5 and RCP8.5) and are denoted mei
The final dataset used in this study is the monthly observation station-based
NAO index by NCAR which was computed using MSLP data recorded in Reykjavik
(Iceland) and Ponta Delgada (Azores) and is based on
A full validation of means and extremes of EC-Earth surface winds and
WAVEWATCH III
The EC-Earth RCP4.5 and RCP8.5 projections show an average decrease in mean
10 m wind speeds over the North Atlantic Ocean for each season, greater
under RCP8.5 than RCP4.5
The annual (black), JJA (red, summer) and DJF (green, winter) change in the 10 m wind speed percentiles over the Atlantic Ocean for the future (2070–2099) versus the historical (1980–2099) period under RCP8.5.
Projected changes in the 95th percentile of
There are many ways to describe the temporal evolution of the NAO index. The
most popular and simplest method involves calculating a station-based NAO
index, as discussed in Sect.
Histograms of the distribution of monthly mean NAO index (using the months of
December, January, February and March; DJFM or winter hereafter and chosen
because winds are stronger and wave heights are larger during these months)
covering 30-year historical/future periods are shown in
Fig.
Histogram of the station-based index of NAO for the following cases: (left) observations (1980–2009), EC-Earth mei1 (1980–2009), EC-Earth me41 (2070–2099), EC-Earth me81 (2070–2099). (centre) shows the same for ensemble number 2 and (right) shows ensemble number 3. In the case of observation-based NAO the months of December to March are included. Similarly, December to March were included for the EC-Earth based calculations where monthly mean MSLP fields were applied.
The observed and modelled DFJM station-based NAO indices for the two 30-year
periods discussed above were used in the analysis presented in
Sects.
Figure
The Spearman correlation coefficient between the NAO index and the 95th
percentile of
In general, the correlation increases off the west coast of Ireland under
both RCP scenarios relative to the historical period, with the exception of
me81, which shows a reduction in correlation south of Ireland. The influence
of the NAO loses significance in southern parts of the model domain in each
of the ensemble members (historical and future periods), and is strongest to
the west and northwest, as can be seen in all panels in
Fig.
Ensemble means of the 20-year return levels of
In this section we examine the effect of the NAO on the most extreme winter
sea states, by fitting the Generalised Extreme Value (GEV) distribution to
the simulated
The GEV distribution function contains three parameters and is given by
While the theoretical basis for the use of the GEV assumes a stationary distribution,
it is common practice to allow some of the parameters to be non-constant. For example, long-term
trends in extremes may be studied by including a linear dependence in time
We adopt a similar, but more general, approach here and include the monthly
NAO index as a covariate in both the location and scale parameters. We now
have
The model outlined above was fitted to each of the three ensemble members in
the historical and future scenarios, for the domain covered by the second
grid. Return levels are now a function of the NAO index and may be plotted
for a given value. To investigate the influence of a positive NAO index on
extremes of
We first consider the left-hand column, that is, when the NAO index is zero.
We find mostly a decrease in return levels in the seas to the west of Ireland
for both future scenarios, when compared with the historical. This is
consistent with the general decreasing trend found in mean winter sea states
in
Much spatial variation can be seen under the climate change scenarios. Under RCP4.5 the NAO influence is strongest to the north-west of Scotland, whereas this occurs further south, closer to the north-west of Ireland, under RCP8.5. From the differences shown in the right-hand column, we find regions in the south of the domain where the NAO exerts little influence and even, particularly in the historical hindcast, where the effect is reversed; i.e. the increased NAO decreases the expected return levels.
Ensemble mean estimates of the 20-year return levels of
Next we focus on three specific locations, with coordinates
Again, we see clearly that the strength of this influence varies
geographically and also between the historical and future scenarios. At the
first location at the top of Fig.
We analysed time-series of the station-based NAO index computed using an
ensemble of global EC-Earth climate projections and the influence of this
index on regional wave projections over the North Atlantic. With the
exception of me4
The 95th percentile of
In order to examine extreme sea states, we fitted a non-stationary
Generalised Extreme Value distribution to the datasets. The resulting 20-year
return levels of
The work can be further expanded by generating a larger multimodel (both in
terms of the forcings and the wave model) ensemble and an ensemble of higher
resolution e.g. using CMIP6 simulation data. Recent studies, for example
by
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.
Emily Gleeson ran the EC-Earth global climate simulations, analysed the wind outputs and the NAO computed from EC-Earth data; Sarah Gallagher ran the WAVEWATCH III simulations using EC-Earth boundary conditions, analysed the wave outputs and correlations between significant wave height and the NAO; Colm Clancy did a statistical analysis of extreme waves and the NAO using a Generalised Extreme Value distribution. Emily Gleeson, Sarah Gallagher and Colm Clancy prepared the manuscript with contributions from Frédéric Dias.
The authors declare that they have no conflict of interest.
The authors are grateful to John O'Sullivan for helpful discussions on extreme value statistics. The authors also wish to acknowledge Roxana Tiron who helped to run the wave simulations. 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”. Thank you also to both reviewers for their useful comments which have helped to improve the paper. Edited by: S. Carniel Reviewed by: L. O'Brien and one anonymous referee