OSOcean ScienceOSOcean Sci.1812-0792Copernicus PublicationsGöttingen, Germany10.5194/os-14-1491-2018Multi-decadal variability in seasonal mean sea level along the North Sea coastMulti-decadal seasonal sea-level variabilityFrederikseThomasthomas.frederikse@jpl.nasa.govhttps://orcid.org/0000-0002-5024-0163GerkemaTheoJet Propulsion Laboratory, California Institute of Technology, 4800
Oak Grove Drive, Pasadena, California, USANIOZ
Royal Netherlands Institute for Sea Research, Department of Estuarine and
Delta Systems (EDS), and Utrecht University, P.O. Box 140, 4400 AC Yerseke,
the NetherlandsThomas Frederikse (thomas.frederikse@jpl.nasa.gov)6December20181461491150130August20183September20187November201825November2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://os.copernicus.org/articles/14/1491/2018/os-14-1491-2018.htmlThe full text article is available as a PDF file from https://os.copernicus.org/articles/14/1491/2018/os-14-1491-2018.pdf
Seasonal deviations from annual-mean sea level in the North Sea
region show a large low-frequency component with substantial variability at
decadal and multi-decadal timescales. In this study, we quantify
low-frequency variability in seasonal deviations from annual-mean sea level
and look for drivers of this variability. The amplitude, as well as the
temporal evolution of this multi-decadal variability shows substantial
variations over the North Sea region, and this spatial pattern is similar to
the well-known pattern of the influence of winds and pressure changes on sea
level at higher frequencies. The largest low-frequency signals are found in
the German Bight and along the Norwegian coast. We find that the variability
is much stronger in winter and autumn than in other seasons and that this
winter and autumn variability is predominantly driven by wind and sea-level
pressure anomalies which are related to large-scale atmospheric patterns. For
the spring and summer seasons, this atmospheric forcing explains a smaller
fraction of the observed variability.
Large-scale atmospheric patterns have been derived from a principal component
analysis of sea-level pressure. The first principal component of sea-level
pressure over the North Atlantic Ocean, which is linked to the North Atlantic
Oscillation (NAO), explains the largest fraction of winter-mean variability
for most stations, while for some stations, the variability consists of a
combination of multiple principal components.
The low-frequency variability in season-mean sea level can manifest itself as
trends in short records of seasonal sea level. For multiple stations around
the North Sea, running-mean 40-year trends for autumn and winter sea level
often exceed the long-term trends in annual mean sea level, while for spring
and summer, the seasonal trends have a similar order of magnitude as the
annual-mean trends. Removing the variability explained by atmospheric
variability vastly reduces the seasonal trends, especially in winter and
autumn.
Introduction
Analyses of sea-level records, with respect to deducing trends and their
causes, as well as sea-level projections commonly focus on annual-mean values
e.g..
However, next to interannual sea-level variability, which is captured by
annual-mean sea level, season-mean sea level (e.g. winter mean or
summer mean) could have its own variability on top of the annual-mean
variability. In this study, we quantify this seasonal sea-level variability
in the North Sea region and look into its causes. It has already been
demonstrated that for the southwestern North Sea, different seasons show
distinct variability patterns:
demonstrated the
difference between annual-mean and seasonal variability from the Cuxhaven
tide-gauge record. In particular, variability in spring and summer (which
were broadly similar) diverged strongly from the autumn and winter seasons.
Hence, variability in annual-mean sea level is not necessarily representative of variability in seasonal sea level. In the case of Cuxhaven, the
disparities were almost entirely explained by local atmospheric forcing (wind
stress and atmospheric pressure).
While the common variability in and around the North Sea on decadal and
long-term timescales is mostly driven by the baroclinic response to remote
longshore wind stress
, local
atmospheric forcing is known to cause large and localised interannual
sea-level signals in the North Sea
.
Therefore, regional variations in seasonal sea-level variability are to be
expected. Zonal winds have been shown to affect sea level much more along the
Dutch and German coasts than along the British coast, where atmospheric
pressure is relatively more important
.
It is known that the sea-level response to local atmospheric pressure
variability along the British coast often deviates from the equilibrium
inverse barometer effect, which has its likely cause in factors that co-vary
with atmospheric pressure, such as wind-induced surges
. For the
German and Dutch coast, a large part of the observed sea-level variability in
the winter months can be explained by the North Atlantic Oscillation (NAO)
,
which mostly acts through local wind forcing .
The NAO contains a strong multi-decadal component, which results in
multi-decadal winter-mean sea-level variability in this region
. However, the NAO does not explain all
winter-mean atmospheric variability, and new atmospheric proxies have been
proposed: use a proxy based on
shifted centres of action, while use the combination of teleconnection patterns, including the NAO, the East
Atlantic Pattern (EAP), and Scandinavia Pattern (SCAN), to explain a larger
factor of the local wind and pressure forcing along the east coast of the
North Atlantic Ocean. These indices characterise the prevailing large-scale
patterns in the atmosphere, but on a regional level, they translate into
regional wind- and sea-level pressure changes that induce local sea-level
variations.
Seasonal deviations from annual-mean sea level contain strong multi-decadal
components, which manifest themselves as possible trends and accelerations in
records that span multiple decades . For
several stations along the Dutch coast,
also suggested that this
variability causes differences in trends estimated over 100-year-long
tide-gauge records.
The purpose of this paper is twofold: first, we want to quantify the
multi-decadal variability in season-mean sea level for the North Sea region,
and, secondly, we want to investigate which fraction of the seasonal variability is
caused by local and large-scale atmospheric forcing. As an explanatory factor
for local forcing, we will look into winds and atmospheric pressure. Those
local forcing agents do contain a signal that is linked to large-scale
atmospheric oscillation patterns. We will investigate whether changes in
these large-scale atmospheric patterns are responsible for the multi-decadal
variability in seasonal sea level.
Data and methods
For this study, we use monthly-mean sea-level observations from 33 tide-gauge
stations around the North Sea, the Norwegian coast, and the English Channel.
The data have been obtained from the Permanent Service for Mean Sea Level (PSMSL) database
. We only use
stations that are not flagged for possible problems and for which the data
are provided in a revised local reference (RLR) to avoid stations with unstable
datums. For Trondheim and Aberdeen, two individual tide-gauge records have
been merged into a single record by adjusting both records to the mean over
the period where both records overlap. Figure shows a map
with all the tide-gauge stations used in this study and the periods over
which the stations have data.
Locations of the tide-gauge stations and the availability of data at
each station over the period 1890–2014. The numbers in
panel (a) correspond to the numbers in
panel (b).
We limit our tide-gauge data to the period 1890–2014 to avoid the inclusion
of the sea-level jump that is apparent in many Dutch tide-gauge stations
around 1885. From that year, monthly mean sea level is based on mean sea-level readings rather than mean tide level readings, which could result in a
jump in the monthly data . According
to the PSMSL documentation, a correction has been applied to avoid this jump,
but the jump is not apparent in some neighbouring stations and, as such,
suspect. We only consider years for which at least 10 months of data is available. Starting from the monthly tide-gauge data, we compute seasonal
sea-level anomalies as follows. First, we remove the annual-mean sea level
from the monthly data. To ensure that each season consists of consecutive
months, each year runs from December of the year before until November. Then,
gaps of 2 months and shorter are linearly interpolated. The resulting
monthly time series is then separated into seasonal deviations for four
seasons: winter (December, January, February – DJF), spring (March, April,
May – MAM), summer (June, July, August – JJA), and autumn (September, October,
November – SON). The monthly sea-level data are averaged over each season, which
results in four sea-level anomalies per year – one for each season. The
resulting sea-level time series are thus seasonal deviations from annual mean
sea level. Annual sea level can be affected by a large deviation in a
particular season (e.g. a winter with very high sea level results in a higher
annual mean), which in that case will result in an anomaly with an opposite
sign during the other seasons. We have followed this approach instead of
removing the linear trend or the low-frequency component from the tide-gauge
data because in this region, the aforementioned baroclinic response to
longshore wind forcing causes a large interannual variability signal, which
would leak into the seasonal anomalies if only the linear trend or
low-frequency variability instead of the annual mean is removed.
To obtain continuous records of wind stress and sea-level pressure anomalies,
we use monthly-mean output from the NOAA 20th-century reanalysis project
version V2C . To obtain seasonal
deviations from the annual-mean values, we follow the same procedure as with
the tide-gauge data: we remove the annual-mean and group the monthly
anomalies into season-average anomalies. We compute wind stress from the
10 m wind speeds using the following relation
τu=ρairCDuu2+v2,τv=ρairCDvu2+v2,
where ρair is the density of air, u and v the zonal and
meridional 10 m wind velocity, and CD the drag coefficient,
which is parameterised following :
CD=0.8+0.065u2+v2.
We parameterise the effects of seasonal local wind stress and sea-level
pressure on sea level as a linear model:
ηobs=α0+α1p′(t)+α2τu(t)+α3τv(t)+ϵ(t).
In this model, ηobs is the observed seasonal sea-level
deviation, t is the time of the observation, p′(t) is the local sea-level
pressure anomaly, τu(t) the zonal wind stress, τv(t) the
meridional wind stress, and ϵ(t) the residual. We obtained the
pressure anomalies and wind stress values by taking the model value with the
highest correlation coefficient within a 500 km radius around each
tide-gauge station. We solve this system using ordinary least squares, which
gives us the regression parameters [α0…α3]. We test for
the significance of each regressor using a t-test statistic and only
include regressors for which the accompanying 95 % confidence interval does
not cross zero. Since the individual regressors could be mutually correlated,
we apply a stepwise regression scheme. We start with the regressor that has
the highest (absolute) correlation with the seasonal sea-level deviations and subsequently add the extra regressors, ranked by their correlation. We
only consider regressors for which the adjusted R2 statistics increases
after inclusion of that regressor. The adjusted R2 statistic determines
whether the extra regressor results in a significantly higher fraction of
explained variance and is defined as
R^2=1-1-R2n-1n-p-1,
with n the number of available season years, and p the number of included
regressors. R2 is the fraction of explained variance, which is defined as
R2=1-var(ηobs-ηexp)var(ηobs),
with var() the variance operator and ηexp the
sea-level deviations explained by the regression models.
Standard deviation of the seasonal sea-level anomalies for each
season after applying a 10-year low-pass filter at all tide-gauge
locations.
To obtain a link between coastal sea level and large-scale atmospheric
patterns over the North Atlantic Ocean, we have computed the three leading
principal components of the sea-level pressure field from the 20th-century reanalysis following the procedure described in
. The monthly gridded sea-level pressure
field between 80∘ W–50∘ E and 30–80∘ N is
selected, the linear trend, the annual cycle and the semi-annual cycle are
removed, and from the resulting field, the three leading empirical orthogonal
functions and associated principal components have been computed. We have
chosen the method from over selecting
the original indices to obtain a coherent set of large-scale atmospheric
variability from a single data source. Note that removing the semi-annual and
annual cycle does remove the mean cycles themselves but not the
low-frequency variability around the mean cycles, which is the quantity we
are interested in.
From the monthly principal components, we compute seasonal anomalies by first
removing the annual mean from the monthly data, and are subsequently average the
monthly data into seasonal anomalies. As with the effects of local winds and
pressure changes, we compute the effect of large-scale atmospheric
variability on local sea level using a linear regression model
ηobs=β0+β1PC1(t)+β2PC2(t)+β3PC3(t)+ϵ(t),
in which PCn(t) is the nth principal component. The regression
coefficients, [β0…β3] are estimated using ordinary least
squares using the same stepwise regression approach as used for the local
wind and pressure model.
To obtain information on low-frequency variability, we use a second-order
Butterworth low-pass filter with a cutoff period of 10 years. Note that the
regression models are applied to unfiltered data and that the filters have
been applied as a post-processing step. To estimate trends in seasonal
sea-level deviations, we use the Hector software
, which computes the trend and the 1σ
confidence intervals under the assumption that the residuals can be described
by a first-order autoregressive (AR1) process.
Results
The first objective of this paper is to quantify low-frequency seasonal
variability for each season, which is shown in
Fig. . This figure depicts the standard deviation
of the low-pass-filtered seasonal sea-level time series, which is a measure
of the typical amplitude of the multi-decadal variability in seasonal sea
level.
The figure shows that the low-frequency seasonal variability in winter and
autumn is generally larger than in summer and spring for most stations, with
the winter-mean variability being the largest. The amplitude of low-frequency
winter and autumn variability shows a clear regional pattern: high
variability can be found in the German Bight, the Skagerrak between Norway
and Denmark, as well as along the Norwegian coast towards the north, while
for the southern North Sea, Brest, Newlyn, and the British coast, this
variability is smaller. Although the low-frequency variability in spring and
summer is substantially smaller than in winter, the spatial patterns for each
season show some similarities: also in spring and summer, the variability is
highest for the stations surrounding the German Bight. The southeastern North
Sea is a hotspot for low-frequency variability in the region, but the
differences between seasonal and annual sea-level variability found by
for the Cuxhaven
station are not a purely local phenomenon. This low-frequency variability can
be interpreted as trends when short records are used. To quantify typical
trends that could emerge from low-frequency variability in season-mean sea
level, we have computed 40-year running mean trends in seasonal sea level
deviations for four stations. The resulting trends are depicted in
Fig. .
Running-mean trends in seasonal sea level deviations for four
stations, using a 40-year window. Trends are only shown for time windows with
at least 30 years of data. The shading denotes the 1σ confidence
interval. Note that these trends have been computed from the time series
without the low-pass filter applied. The numbers in the legend correspond to
the station numbers in Fig.
Fraction of 10-year low-pass-filtered seasonal variance (R2)
explained by the local (a) and large-scale model (b) for
each station.
For all seasons, the trends in seasonal sea level can reach values in the
same range of the secular trend in geocentric mean sea level, which is
typically in the order of 1–2 mmyear-1 for this region
, with the largest 40-year trends occurring
during autumn and winter. The confidence intervals for these 40-year trends
are in the order of 1 mmyear-1. For Oslo and Cuxhaven, the
seasonal trends sometimes reach values in the order of
4 mmyear-1, which is about twice the rate of the secular trend.
This variability in seasonal deviations could also be interpreted as an
acceleration: for example, the trend in winter sea-level deviations in Oslo
and Cuxhaven is generally higher in the last few decades than in the first
few decades, which could translate into a long-term positive acceleration in
the seasonal sea-level deviation time series. Again, note that these trends
are trends in seasonal deviations from annual-mean sea level, and they do not represent secular trends in sea level.
To determine whether local wind and pressure changes are responsible for this
variability, we compute the fraction of explained variance (R2) of the
local regression model (Eq. ) after applying a 10-year
low-pass filter to both the seasonal sea-level deviations and the solution to
the regression model. The results are depicted in the top row of
Fig. .
In autumn and winter, when the low-frequency variability is highest, the
local regression model explains a large fraction of the variability for most
stations. Especially in the winter (DJF) season, the model explains the
majority of the observed variability (R2>0.5) for most stations. In spring
and summer, generally a smaller part of the variability can be explained,
which suggests that the long-term seasonal variability, which is already much
smaller than the winter and autumn variability, is less predominantly driven
by wind and pressure changes. However, for some stations, such as Brest and
Newlyn, the local regression model explains the majority of the variability
for all seasons.
Local wind and pressure variability are affected by large-scale atmospheric
circulation patterns, such as the NAO and EAP. The next step is to
investigate whether the multi-decadal variability in seasonal sea level
deviations can be explained by the low-frequency variability in these
large-scale patterns. To this end, we use the three leading principal
components of surface pressure variability, as described in Sect. . These principal components and their associated empirical
orthogonal functions represent the major patterns of atmospheric variability
and are displayed in Fig. . As such, they share characteristics
with well-known atmospheric teleconnection patterns. The distinct north–south
pattern of the first principal component resembles the North Atlantic
Oscillation , while the second and
third principal components (PCs) are akin to the East Atlantic Pattern and the Scandinavia Pattern
respectively e.g.. It must be noted
that the principal components computed here are not fully interchangeable
with the commonly used original indices, which are generally computed using
different methods.
The first three empirical orthogonal functions (EOFs) and
accompanying principal components (PCs) of sea-level pressure above the North
Atlantic Ocean. Panel (a) shows the spatial patterns of each EOF. The
red arrows depict the associated geostrophic wind vectors.
Panel (b) shows the season-mean PCs (thin line) and the season-mean PCs after applying
a 10-year low-pass filter. The variance of the monthly-mean principal
components is scaled to 1, and the numbers in the top-left corner denote the
low-pass-filtered variance (bold) and the unfiltered variance (regular) for
each season. Multiplication of the EOF with the accompanying PC gives the
resulting sea-level pressure anomaly in pascal.
Time series of winter (DJF) sea level, the reconstructed sea level
from the regression model, and the residual at selected tide-gauge stations.
A 10-year low-pass filter has been applied to all time series.
The first EOF is associated with westerly winds over the whole North Sea
Basin, while the second EOF shows a more meridionally oriented wind effect,
albeit with a curvature over the North Sea. EOF3 has its centre of action
over southern Scandinavia, and hence, the wind strength associated with this
EOF shows a large north–south gradient over the North Sea, with stronger
winds in the south. All three patterns show both season-to-season variability
(thin lines in Fig. ) as well as variability on multi-decadal
scales (thick lines). For all three principal components, the variability in
winter is the largest, both for the seasonal and for the low-pass-filtered time
series. The difference is most pronounced for the first principal component,
which is a well-known feature of the NAO , but it is visible in all three PCs
for the seasonal and for the low-pass-filtered time series.
The three PCs are used in the large-scale regression model (Eq. ), and the results are depicted in the bottom row of
Fig. . Like the local regression model, the large-scale
regression model also explains a large fraction of the multi-decadal
variability in winter and autumn sea level, while for spring and summer, the
explained fraction is generally smaller. Interestingly, for some stations in
winter, especially for the stations in Belgium and in the southern part of
the Netherlands, the large-scale model even explains more variability than
the local model. This difference may have its cause in the complex wind and
pressure patterns generated by the large-scale atmospheric patterns, which
may not be well-captured by the single-point wind and pressure time series
used in Eq. (). For some other stations, such as Brest
and Newlyn, the local model explains more variability than the large-scale
model, suggesting that not all variability is driven by large-scale patterns,
but local effects also play a role. The fact that both models explain a large
fraction of the variability in autumn and winter shows that the variability
is predominantly driven by wind and pressure changes that are linked to
large-scale atmospheric patterns.
Not only the amplitude of the variability (Fig. ) but also
the temporal pattern differs between stations in this region, which can be
seen in Fig. . This figure shows time series of
long-term winter-mean sea-level variability together with the results from
both regression models at 12 representative stations.
This figure again shows the major features of Figs.
and : high variability in the German Bight, low variability
along the British coast and the southern North Sea, and the ability of both
regression models to explain a large part of this variability. The pattern of
variability shows differences over the region: the stations in the German
Bight (Delfzijl, Cuxhaven, Esbjerg) and Oslo show a coherent variability
pattern, which differs from the patterns found in other locations. For
example, during the period 1985–2005, most stations along the eastern North
Sea coast show an above-average sea level, which is much less pronounced
along the British coast, and even corresponds to a drop in seasonal sea level
for the stations Brest and Newlyn. For all these stations, both regression
models explain these features, which shows that the differences between these
stations must be caused by a difference in wind and pressure forcing, and
consequently, the large-scale atmospheric patterns affect different stations
in a different way, and the variability at different stations may be
attributable to a different combination of influence from the large-scale
patterns.
To understand these differences between the forcing mechanisms between the
different stations, Fig. shows the fraction of
explained variance for each individual regressor and the full regression
model of low-pass-filtered DJF sea level both in the local and large-scale
regression models.
Total and explained variance of DJF sea level at each tide-gauge
station. The left bar shows the variance explained by each term in the local
model and the right bar the variance explained by the large-scale model. The
explained variance is computed using the regression coefficients from the
model with all accepted terms and not by only regressing each individual
term. Negative fractions of explained variance are not shown. The total and
explained variances were computed after applying a 10-year low-pass
filter. The numbers on the lower axis correspond to the station numbers in
Fig. .
Same as Fig. , but for the autumn (SON)
season.
This figure shows that the origins of the forcing differ substantially
throughout the region: while the stations from the German Bight towards the
southern North Sea are dominated by zonal wind stress, the more northern
stations are forced by a combination of zonal and meridional wind and
sea-level pressure. At the other side of the English Channel, Brest and
Newlyn are dominated by sea-level pressure effects. This is also the case for
Tórshavn, which can be explained by the fact that Tórshavn is an off-shelf
island, for which wind stress will not cause large storm surges due to the large
ocean depth and the absence of a large ocean boundary. Along the northern
Norwegian coast, which also shows a large variability signal, both zonal and
meridional wind as well as surface pressure variability explain a large
fraction of the variability.
Running-mean 40-year trends in seasonal sea level deviations for
four stations after removal of the local (a) and
large-scale (b) models. The top panels show trends in the original time series, and the
bottom row shows the trends Trends are only shown for time windows with at
least 30 years of data. Note that these trends have been computed from the
time series without the low-pass filter applied.
Despite the large regional variations in the local forcing agents, the first
principal component PC1, which is closely tied to the North Atlantic
Oscillation, explains the largest fraction of the variability for most
stations. For the stations in the southern North Sea, which are less affected
by the westerlies associated with this PC (see Fig. ), the
third PC explains a large part of the variability. The first PC is associated
with both zonal, meridional, and pressure changes along the Norwegian coast,
which explains the aforementioned impact of wind and pressure on the stations
in that region. The third PC is associated with a strong zonal geostrophic
wind component over the southern North Sea. The second PC explains a small
part of the variance, even though the signal does contain a considerable
decadal winter-mean signal. The only exceptions are Brest and Newlyn, where
PC2 affects the zonal wind.
For the autumn season, different factors affect the low-frequency
variability, as shown in Fig. .
In autumn, the variability, which is generally much smaller than in winter is
generally driven by the same drivers as the winter variability, but the local
wind and pressure variability is driven by a different combination of
large-scale patterns for some stations, especially along the German Bight,
where the seasonal variability is now mostly driven by the third PC instead
of the first PC.
The trends in seasonal sea-level deviations, depicted in
Fig. , are also to a large extent caused by the
atmospheric forcing. Figure shows the same trends in
seasonal deviation but after removing sea-level deviations explained by the
local and large-scale model.
For all seasons, the local and large-scale regression models explain a large
fraction of the running-mean trends. For winter and autumn, after applying the
regression models, the residual seasonal trends have the same order of
magnitude as typical secular mean sea level trends, and the trend differences
between the beginning and end of the considered periods have been
substantially reduced. However, not all low frequency has been explained by
the models, and the residual time series still contain trends and
accelerations.
Conclusions
In this paper, we have analysed the low-frequency variability in the seasonal
deviations from annual mean sea level in the North Sea region. Low-frequency
variability in winter-mean and autumn-mean sea level shows a
spatially varying pattern, with the highest values encountered along the
German Bight. This variability is largely forced by wind and pressure. The
wind generally plays a large role for locations that show large low-frequency
variability, and the variability is indeed weaker where wind plays a minor
role, e.g. the British North Sea coast. The low-frequency changes in local
wind and pressure are linked to large-scale atmospheric patterns, which
resemble the NAO, EAP, and Scandinavia Pattern. Hence, the low-frequency
variability in large-scale atmospheric patterns translates into low-frequency
winter-mean and autumn-mean sea-level variability. In spring and summer, the
low-frequency variability is smaller and can generally be explained only to a small extent by local and large-scale atmospheric forcing.
This seasonal sea-level variability is mostly caused by wind and pressure
changes. Therefore, extreme sea levels associated with storm surge events are
not superimposed onto this variability. In other words: a storm that occurs
during a “low phase” will not generate a lower surge level than when the same
storm occurs during a “high phase”. Because the sea-level response to local
wind and pressure changes in the North Sea is mostly barotropic in nature
e.g., the
typical sea-level adjustment timescale to wind and pressure changes will not
be longer than a few days .
However, the sea-level response to atmospheric forcing is mostly, but not
necessarily fully barotropic in the North Sea
,
and local and large-scale atmospheric changes do not explain all variability.
For example, processes like ocean circulation changes and low-frequency
variability in freshwater fluxes from rivers and locks
e.g. could drive
low-frequency sea-level deviations. As such, seasonal sea-level changes could
still play a role in variability in storm-surge heights.
Multi-decadal seasonal sea-level variability in the North Sea is of the same
order of magnitude as the long-term trend in mean sea level, and as a result,
multi-decadal trends in annual mean sea level are in general not
representative of the trends of the winter record in isolation. For
processes that rely on long-term sea level variability, for example coastal
sand suppletion, this difference needs to be taken into account. Furthermore,
seasonal wind and pressure effects will eventually influence annual-mean
sea-level variations. Since a large part of the variability can be explained
by a simple regression model, this correction should not pose a challenge.
The tide-gauge data were obtained from the Permanent
Service for Mean Sea Level (PSMSL, http://www.psmsl.org/, last access:
17 August 2017). The NOAA 20th Century
Reanalysis V2 data were downloaded from
https://www.esrl.noaa.gov/psd/data/20thC_Rean/ (last access:
18 February 2016).
TG conceived the idea for
the study. TF carried out the data analysis and created the figures. Both authors
contributed to the analysis of the results and wrote the article.
The authors declare that they have no conflict of
interest.
Acknowledgements
The principal
component analysis was conducted using eofs version 1.3
. All figures were produced with the
Generic Mapping Tools . Part of this
research (Thomas Frederikse) was carried out at the Jet Propulsion Laboratory, California
Institute of Technology, under a contract with the National Aeronautics and
Space Administration. Edited by: Markus Meier
Reviewed by: two anonymous referees
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