More than 29 years of altimeter data have been recently
reprocessed by the multi-satellite Data Unification and Altimeter
Combination System (DUACS) and made available under the name of DT2021 through the Copernicus Marine Service (CMEMS) and the Copernicus
Climate Change Service (C3S). New standards have been applied and various
geophysical correction parameters have been updated compared to the previous
release in order to improve the product quality.
This paper describes the assessment of this new release through the
comparison of both the all satellites and the two satellites product with external in situ tide gauge
measurements in the coastal areas of the European seas for a time period
from 1 January 1993 to 31 May 2020. The aim is to quantify the
improvements on the previous DT2018 processing version for the retrieval of
sea level in the coastal zone.
The results confirmed that the CMEMS product in the new DT2021 processing
version better solves the signal in the coastal band. The all satellites dataset showed a
reduction of 3 % in errors when compared with tide gauges and of 5 % in
the variance of the differences between the datasets compared to DT2018
reprocessing. Moreover, the all satellites dataset provided more accurate sea level
measurements when making a comparison with tide gauges with respect to the climatic two satellites
dataset due to the better performance of the former for the assessment of
higher than climatic frequency signals. By contrast, the two satellite dataset is the
most suitable product for the assessment of long-term sea level sea surface height (SSH) trends
in the coastal zone due to its larger stability to the detriment of the all satellites
dataset.
Introduction
On December 2021, more than 29 years of Level 3 (L3) and Level 4 (L4)
altimetry products were reprocessed, released and made freely available for
users as the Delayed Time (DT) DT2021 version (CMEMS-SL-QUID, 2022; PUGS, 2021;
Faugère et al., 2022) of the multi-satellite Data Unification and
Altimeter Combination System (DUACS) products by the European Copernicus
Program (http://marine.copernicus.eu/, last access: 15 July 2022) substituting the former
DT2018 product version (Taburet et al., 2019), which is no longer
available in the Copernicus Catalogue. Currently, two types of altimetric L4
gridded products generated by the DUACS production system are available: the
so-called all satellites global and regional (European seas) gridded products disseminated
via the Copernicus Marine Service (CMEMS) project (CMEMS-SL-QUID, 2022) and
the two satellites global gridded products distributed via the Copernicus Climate Change
Service (C3S) project (PUGS, 2021). Currently, the two satellites products are also
distributed via the CMEMS project. The all satellites products are dedicated to the
retrieval of mesoscale signals on a global or regional scale, whereas the
two satellites ones are dedicated to monitoring the long-term evolution of sea level, thus
being suitable for use in climate applications (Taburet et al., 2019).
The Level 2 altimeter standards used to compute sea level anomaly (SLA) in
the CMEMS and C3S products are identical (CMEMS-SL-QUID, 2022), but the
reference used to compute SLAs differs: CMEMS products use a mean profile of
sea surface heights along the theoretical track of the satellites with a
repetitive orbit, whilst C3S products use a mean sea surface (MSS) for all
missions. In the latest release, new up-to-date standards have been applied
and various geophysical correction parameters have been updated compared to
the previous DT2018 version (Table A1 in Appendix A). This provides both an
improved accuracy of SLA and lower regional sea level biases.
The updates are as follows:
A new internal tide correction that allows the prediction of the
two main tidal constituents of both diurnal and semidiurnal tidal
frequencies has been applied. The solution proposed by Zaron (2019) is used
(HRET 8.1 version). This correction reduces the coherent signal
characteristic of internal tide and provides a more precise reconstruction
of mesoscale eddies. The use of the internal tide correction induces a
reduction in internal tide signature on along-track data improving the
precision of the resulting L4 gridded product (CMEMS-SL-QUID, 2022).
A new MSS for non-repetitive missions and recent missions consisting in a
hybrid gridded MSS field made up of three different gridded MSS models is
used. The SIO MSS model (Sandwell et al., 2017) is used in the open ocean,
the CNES_CLS-2015 model (Pujol et al., 2018) is used in coastal
areas (distance to the coast less than 20 km), and the DTU15 model (Andersen et
al., 2016) is used in the Arctic region (latitude larger than 80∘ N). This hybrid solution contributes to reducing the SLA errors at short
wavelengths. A new mean profile (precise MSS along the altimeter tracks) is
used for historical repetitive missions (CMEMS products). New mean profiles
were estimated along the historical repetitive tracks of
TOPEX/Poseidon–Jason, the TOPEX/Poseidon–Jason-interleaved phase,
ERS–Envisat–AltiKa, Sentinel-3A and GFO consistent with the different
standards used in the DT2021 version. This improves the SLA signal at long
wavelengths.
A new mean dynamic topography for the global (Mulet et al., 2021) and
the Mediterranean and Black seas is applied.
An improved long-wavelength error (LWE) correction, delivered in the L3
product, has been computed as the final step of multi-mission
cross-calibration processing. Progress with respect to the previous version
has been made by first estimating the LWE correction with higher frequency
along the different tracks (100 km instead of 500 km used previously) and then
by improving the interpolation methodology (optimal interpolation instead of
spline used previously) to retrieve the correction on each along-track
position. This method is expected to remove local SLA residual biases between
neighbouring altimeter tracks.
And finally, the DT2021 product version includes an upgraded mapping
parameterization that contributes to improving the mesoscale signal visible on
L4 products. The spatial and temporal correlation scales are
optimized improving the reconstruction of the mesoscale signal, a more
precise definition of the observation's errors computed with regard to the
new altimeter standards is provided, and finally, a more precise estimation
and correction of LWE in the mapping process is applied removing local SLA
residual biases. A complete description of the different evolutions
implemented in the DUACS DT2021 product version can be found in
CMEMS-SL-QUID (2022).
The validation (quality check) of altimetry products is a key step in the
data processing pipe to assess and characterize the errors associated with
the altimetry measurements. This issue is crucial in the coastal zone, where
traditional altimetry has been often unable to produce meaningful signals
of sea level change due to the typically shallower water, bathymetric
gradients and shoreline shapes, among others (Vignudelli et al., 2019;
Sánchez-Román et al., 2020). Actually, global and regional products
from DT2021 and DT2018 reprocessings are not optimized for the coastal band
promoting larger errors in the retrieval of sea level with regard to the
open ocean.
Nevertheless, the monitoring of sea level changes in coastal areas is an
important societal issue (Pujol et al., 2023). Thus, most of the efforts of
the international community in the recent past have been focused on the
research and development of techniques for coastal altimetry, with
substantial support from space agencies such as the European Space Agency
(ESA), the Centre National d'Études Spatiales (CNES) and other research
institutions (Cipollini et al., 2017). Efforts of the coastal altimetry
community are aimed at extending the capabilities of current altimeters
closer to the coastal zone. This includes the application of improved
geophysical corrections, data recovery strategies near the coast using new
editing criteria and high-frequency along-track sampling associated with
updated quality control procedures (Vignudelli et al., 2019). As a result,
regional altimeter products such as PISTACH (Mercier et al., 2010), X-TRACK
(Roblou et al., 2011; Birol et al., 2017), X-TRACK-ALES (Birol et al., 2021)
and ESA EO4SIBS (Grégoire, 2021) focused on the coastal zone have been
developed over the last few years (Pujol et al., 2023). These products are
disseminated to both the international scientific community and society
through regular specific coastal altimetry workshops.
Different metrics are used to assess the quality of altimetry data. They
mainly consist in the analysis of the SLA field at different steps of the
processing, checks of the consistency of the SLA along the tracks of different
altimeters and between gridded and along-track products, and comparisons
with external in situ measurements (CMEMS-SL-QUID, 2022). In situ and
altimetric observations are complementary and are often assumed to observe
the same signals (Wöppelmann and Marcos, 2016). In coastal areas, tide
gauge measurements are commonly used. In Taburet et al. (2019), DUACS DT2018
L4 global gridded products were assessed in the coastal areas through a
comparison with monthly tide gauge measurements from the Permanent Service
for Mean Sea Level (PSMSL) Network (PSMSL, 2016). These authors reported a
global reduction of 0.6 % in variance with respect to the previous
processing (DUACS DT2014 dataset). Pascual et al. (2006, 2009) investigated
the consistency between previous versions of the altimeter L4 gridded
products and tide gauge data from the PSMSL repository in the coastal zone
reporting mean square differences between the two datasets ranging between
30 % and 90 % on the European coasts. More recently,
Sánchez-Román et al. (2020) assessed the quality of DUACS L3 products
in the coastal band of the European seas through comparison with independent
tide gauge measurements. These authors reported a mean root mean square
(rms) difference between both datasets lower than 7 cm for the whole region,
with mean values ranging around less than 4 cm in the Mediterranean Basin
and around 10 cm for the North-West Shelf (NWS) area (see Fig. 2 in
Sánchez-Román et al., 2020, for the location of this region). The
quality of the DUACS DT2021 product version has been also assessed through
the comparison with monthly tide gauge measurements from the Global Sea
Level Observing System (GLOSS)/Climate Variability and Predictability
(CLIVAR) network. CMEMS-SL-QUID (2022) reports improved results when using
the latest reprocessing with a reduction in variance of the differences
between altimetry and tide gauges ranging between 0.2 % and more than
5 % of the tide gauge signal on the European coasts, with respect to the
previous product version.
Location of the 213 tide gauges of the global product in the
Copernicus Catalogue along the European coasts and the western Mediterranean
Sea used for a comparison with altimetry data after applying the selection
criteria described in the text. Colours indicate the mean square differences
between the tide gauge and altimetry sea level (DT2021 all satellites series). Units are
the percentage of the tide gauge variance.
This paper focuses on improvements in the latest DUACS
DT2021 reprocessing in the retrieval of sea level in the coastal band of the European seas with respect
to the previously available DT2018 products. To do that, an intercomparison of L4 global altimetry gridded
products and in situ tide gauges located along the European coasts from the
Copernicus Catalogue is conducted. The performance of the DT2021 processing
all satellites and two satellites versions on the sea level retrieval is also assessed. The paper is
organized as follows: the SLA data used, the tide gauge dataset and the
method for comparing altimeter and in situ measurements are detailed in
Sect. 2. Section 3 describes the performance of the DT2021 processing
product version in the retrieval of sea level in the coastal band. Also, the
improvements over the previous DT2018 processing product version are assessed. Finally, the discussion and main conclusions are included in
Sect. 4.
Materials and methodsSea level anomaly data
The DUACS reprocessed L4 global satellite SLA maps used in this study
correspond to both the DT2021 (CMEMS-SL-QUID, 2022; PUGS, 2021;
Faugère et al., 2022) and the DT2018 product (Taburet et al., 2019)
versions. SLA gridded products cover the global ocean with a spatial and
temporal resolution of 0.25∘ and 1 d, respectively.
Two different SLA datasets for each one of the DUACS product versions are
considered: the all satellites L4 global gridded product disseminated via the CMEMS and
the two satellites L4 global gridded product distributed via the C3S and CMEMS. The first
one is computed with a satellite constellation including all the available
altimeters at a given time (ranging from two to seven over the period considered
in this study; see, e.g., Fig. 1 in International Altimetry Team, 2021; Morrow et
al., 2023). As a consequence, the errors are not constant in time since they
depend on the number of satellites used. This product focuses on the
mesoscale mapping capacity of the altimeter data together with the stability
of the overall dataset. The two satellites SLA dataset is obtained by merging a steady
number of altimeters (two) in the satellite constellation. Two satellites are
the minimum requirement to retrieve mesoscale signals in delayed time
conditions (Pascual et al., 2006; Dibarboure et al., 2011). This fact also
promotes nearly consistent errors during the whole time period (some
variation in the error can occur related to changes in the two satellites constellation).
This product focuses on the stability of the global mean sea level (MSL),
even if this implies a potential reduction in the spatial sampling of the
ocean. The reader is referred to Fig. 1 in Sánchez-Román et al. (2020) for more information about the DUACS procedure flowchart applied to
the altimetry data and also to the processing of the tide gauge data used for the comparison with altimetry (next section). The time period investigated common
to both DT2021 and DT2018 reprocessings runs from 1 January 1993 to 31 May 2020 due to the presently availability of DUACS DT2018 products. A complete
description of the SLA datasets can be found in CMEMS-SL-QUID (2022).
Tide gauge observations
The sea level records used for a comparison with satellite altimetry were
extracted from the Copernicus Catalogue (http://www.marineinsitu.eu, last access: 3 June 2022).
The tide gauge stations located in the European seas' domain were initially
considered for this study. Following the methodology described in
Sánchez-Román et al. (2020), the quality flags of the tide gauge
records were checked in order to remove observations with no quality check,
potentially bad data and changes in the vertical reference of the tide
gauge. Also, observations with values larger than 3 times the standard
deviation of the time series were rejected as they might not be
representative of ocean sea level changes but local features (e.g. river
discharge, Laíz et al., 2013). The final dataset consists of 213 tide
gauge stations (Fig. 1) with time series exhibiting between 90 % and
100 % of valid data. The stations and their information are listed in
Table B1 in Appendix B.
Before they can be compared with altimeter data, tide gauge measurements
have to be processed to remove oceanographic signals whose temporal periods
are not resolved by altimetry, thus avoiding important aliasing errors
(Vignudelli et al., 2019). We applied the methodology described in
Sánchez-Román et al. (2020). In the following we summarize the
corrections applied to the tide gauge records.
Correction of oceanic tidal effects by filtering tidal components (mainly diurnal and semidiurnal tidal constituents). The u-tide software (Codiga,
2011) is used. The annual and semiannual frequencies, mainly driven by
steric effect, are kept in the tidal residuals since they are included in
the altimetry data.
Removal of the atmospherically induced sea level caused by the action of atmospheric pressure and wind (Dorandeu and Le Traon, 1999; Carrère and Lyard, 2003). The same dynamic atmospheric correction (DAC) as for altimetry
is applied for the sake of consistency. The 6-hourly fields of this
correction, available from the Archiving, Validation and Interpretation of
Satellite Oceanographic Data (AVISO) website, are used. For each tide gauge
site, the nearest grid point was selected and used to remove the
atmospherically induced sea level from observations, previously converted
into 6-hourly records (Marcos et al., 2015).
Correction of vertical movements associated with glacial isostatic adjustment (GIA). GIA was regarded as the only source of vertical land
motions. Its effects were removed from the sea surface height (SSH) records, previously averaged
into daily data, by using the Peltier mantle viscosity model (VM2) (Peltier,
1998, 2004).
Method for comparing altimeter and in situ tide gauge records
The comparison method of altimetry with tide gauges consisted of collocating
both datasets in time and space. As a first step, a 15 d low-pass LOESS
filter was applied to altimetry and tide gauge time series to remove the
high frequencies that cannot be resolved by the altimetric data (Pascual et
al., 2009; Ballarotta et al., 2019; Sánchez-Román et al., 2020).
Then, the correlations between each tide gauge record and SLA time series
corresponding to grid points within a radius of 1∘ around the tide
gauge site were computed and the most correlated altimetry point was chosen.
Only long-term monitoring stations with a lifetime of more than 3 years
were used in order to allow statistical significance. Statistical analyses
were performed using all available data pairs (altimetry–tide gauge). The
collocated altimeter and tide gauge measurements were analysed in terms of
the rms difference and variance of the time series. In addition, the
robustness of the results was investigated according to
Sánchez-Román et al. (2017, 2020) using a bootstrap method (Efron
and Tibshirani, 1986), which allows us to estimate quantities related to a
dataset by averaging estimates from multiple data samples. To do that, the
dataset is iteratively resampled with replacement. A total of 1000
iterations were used to ensure that meaningful statistics such as standard
deviation could be calculated on the sample of estimated values, thus
allowing us to assign measures of accuracy to sample estimates
(Sánchez-Román et al., 2020).
ResultsPerformance of DUACS DT2021 products in the retrieval of sea level in
the coastal band
This section presents the statistics of the comparisons performed between
the DUACS DT2021 all satellites and two satellites datasets and the tide gauge observations from the
Copernicus Catalogue in the coastal region of the European seas in terms of
errors (rms differences) and variance of the differences between the
datasets. According to Sánchez-Román et al. (2020), the
bootstrapping technique was applied to gain an estimation of the standard
errors in the differences between the datasets.
The mean value of the rms difference between the all satellites dataset and tide gauges is
4.11 cm, the variance of the differences (altimetry–tide gauge) is 17 cm2, and the mean distance between the location of the tide gauge and
the corresponding altimeter data with the highest correlation is 82 km
(Table 1). These values rise to 4.35 cm, 19 cm2 and 87 km,
respectively, when using the two satellites dataset. The tide gauge stations (213
stations) common to both datasets were used. Thus, the all satellites dataset reduces the
rms differences with tide gauges on the European coasts by 5 %, the
variance differences between the datasets by 10 %, and the mean distance
between the most correlated altimetry point and tide gauges by 6 %. Also,
the number of valid data pairs used to conduct the intercomparison enhanced
by 0.2 % when using the all satellites dataset. This is due to the larger number of
satellite missions used to generate this dataset that provides lower errors
in the optimal interpolation procedure compared to the two satellites dataset.
Intercomparison of DUACS DT2021 satellite altimetry (ALT) and tide
gauge (TG) data from the European coasts in terms of the rms differences
(cm) and variance (cm2) of the differences between the datasets. The
number of tide gauge stations used in the comparison, the mean distance
between tide gauges and the most correlated gridded altimetry points, and
the number of total data pairs (altimetry–tide gauge) used in the
computation are displayed. The common tide gauge stations for the all satellites and two satellites
datasets were used. Values in parentheses show the uncertainties (error
bars) computed for the rms differences and variance from the bootstrap
method using 1.000 iterations. Finally, the improvement (%) of the all satellites
dataset in comparison with tide gauges in terms of lower rms differences,
lower variance of the differences (altimetry–tide gauge), and lower mean
distance between the most correlated altimetry point and tide gauges with
respect to the two satellites dataset is also displayed.
Figure 1 shows the consistency between the DUACS DT2021 all satellites dataset and the tide
gauge data computed from Eq. (1) in Sánchez-Román et al. (2020).
Consistency is expressed as the mean square differences between both
datasets, computed as the variance of the differences (altimetry–tide
gauge), in terms of percentage of the tide gauge variance. Overall, mean
square differences lower than 5 % are observed in the central and eastern
parts of the Baltic Sea, emphasizing the precision of the corrections
applied to the altimeter data in the basin, whereas they reach values
between 20 % and 30 % for stations located in the connection region with
the North Atlantic Ocean. The mean square differences are between 20 % and
50 % for most of the stations located along the Atlantic shore; this
includes the Strait of Gibraltar area. Such a large error could be related to
imprecisions in the correction applied (i.e. ocean tide and DAC) to the
altimeter data (Pascual et al., 2008; Laíz et al., 2016;
Sánchez-Román et al., 2020) and also to both the larger
spatiotemporal variability observed in this region (figure not shown) and
to a larger non-tidal variance with respect to that found in the Baltic Sea
(Von Schuckmann et al., 2018). Finally, the Mediterranean and Norwegian seas
show mean square differences ranging between 15 % and 30 %, except for
the Balearic Islands (western Mediterranean) and the south-western part of
Norway where values between 5 % and 15 % are obtained. The consistency
between the DUACS DT2021 two satellites dataset and tide gauges (figure not shown)
presents quite a similar spatial pattern and results. These outcomes improve
the ones reported in Sánchez-Román et al. (2020) from the
intercomparison conducted between the Sentinel-3A L3 along-track DUACS DT2018
dataset and tide gauge measurements in the region computed over a period of
2.5 years.
Spatial distribution of the differences (DT2021 minus DT2018) for
the mean square differences between the tide gauge and altimetry sea level.
Units are the percentage of the tidal variance. The SLA all satellites dataset has been
used. Blue colours denote an improvement in the DUACS DT2021 reprocessing, whilst red colours indicate its degradation with respect to the DT2018
reprocessing.
Improvement in DT2021 over DT2018 reprocessing<italic>All satellites</italic> SLA dataset
This section focuses on the statistics of the comparisons performed between
the DUACS DT2021 and DT2018 reprocessing all satellites datasets and the tide gauge
observations. The mean value of the rms difference between the DT2018
processing dataset and tide gauges is 4.22 cm, the variance of the
differences (altimetry–tide gauge) is 18 cm2, and the mean distance
between the location of the tide gauge and the corresponding altimeter data
with the highest correlation is 88 km (Table 2).
The same as Table 1 but for the intercomparison using the DUACS
DT2018 reprocessing. The improvements (%) of the DUACS DT2021
reprocessing all satellites and two satellites SLA datasets with respect to the previous DT2018
reprocessing are also shown.
Overall, these values are larger than those reported in the previous section
for the comparison using the DT2021 processing dataset (see Table 1). As a
consequence, the DT2021 all satellites dataset reduces (i) the errors with tide gauges on
the European coasts by 3 %, (ii) the variance of the differences between
the datasets by 5 %, and (iii) the mean distance between the most
correlated altimetry point and tide gauges by 7 %. Also, the number of
valid data pairs used to conduct the intercomparison is enhanced by 0.1 %
when using the DT2021 processing all satellites dataset. This highlights the impact of the
new DUACS DT2021 reprocessing on the coastal areas that provides more valid
measurements, located closer to the tide gauge sites, compared to the DT2018
reprocessing.
The new standards and updated geophysical corrections applied to the DUACS
DT2021 reprocessing compared to the previous DT2018 version have a direct
impact on the observation of coastal ocean sea level in the gridded
products. To characterize this impact, the difference between DT2021 and
DT2018 consistency is shown in Fig. 2. The spatial distribution of the
differences in consistency shows an overall better performance of the DT2021
reprocessing (blue colours) at the connection region between the Baltic Sea
and the eastern North Atlantic Ocean and in most of the Atlantic shore,
where an improvement larger than 15 % is found for some tide gauge sites.
A degradation of the DT2021 reprocessing is observed in most of the stations
located in the western Mediterranean Sea and the southern coasts of Spain,
including the Strait of Gibraltar area, and also in some stations located on the coasts of France, England and Ireland. On the other hand, discrepancies
are hardly observed between the two reprocessings in the Baltic and
Norwegian seas.
<italic>Two satellites</italic> SLA dataset
We present here the statistics of the intercomparison between the climatic
(two satellites) DT2021 and DT2018 processing and tide gauges. The mean
value (Table 2) of the rms difference between the DT2018 processing dataset
and tide gauges is 4.41 cm, the variance of the differences (altimetry–tide
gauge) is 19 cm2, and the mean distance between the location of the
tide gauge and the corresponding altimeter data with the highest correlation
is 90 km. If these results are compared with those reported above for the
comparison using the DT2021 processing dataset (Table 1), it can be observed
that the latter only improves the previous DT2018 reprocessing in terms of
the errors with tide gauges that are reduced by 1 % and the mean
distance between the most correlated altimetry point and tide gauges,
reduced by 3 %, whereas the variance of the differences between the
datasets and the number of valid data pairs used to conduct the
intercomparison are quite similar. Such improvements are around 60 % lower
than those reported for the all satellites datasets. This fact is reflected in the spatial
distribution of the differences between DT2021 and DT2018 consistency with
tide gauges (figure not shown). A better performance of the DT2021
reprocessing is obtained at the connection region between the Baltic Sea and
the eastern North Atlantic Ocean and in parts of the Atlantic shore (coasts
of United Kingdom and France). There is a degradation of the DT2021
reprocessing in most of the stations located in the western Mediterranean
Sea and the southern coasts of Spain; and in some stations located on the coasts of France, England and Ireland. Also, negligible discrepancies
between the two reprocessings are found in the Baltic Sea. This spatial
pattern is quite similar to that obtained for the all satellites dataset described above.
However, a degradation of the DT2021 reprocessing is observed in most of the
stations located in both the NWS region (southern coasts of the North Sea)
and the Norwegian Sea. This is a novelty with respect to the previous
computation emphasizing the overall poorer improvements in the DUACS DT2021
two satellites dataset over the previous reprocessing.
Performance of DT2021 reprocessing in monitoring the long-term evolution
of sea level
The computation described above has been conducted by using all available
information from the tide gauge dataset, thus including time series of
different length from a few years to less than 3 decades (Table B1 of Appendix B). To assess the performance of the DUACS DT2021 processing
version in monitoring the long-term evolution of sea level in the coastal
zone of the European seas the analyses described above were repeated for a
specific time period spanning 20 years: from 1 January 2000 to 31 December 2019. This time period has been chosen because of the largest number of
available altimeter missions used to generate the all satellites SLA maps. Tide gauge time
series with valid data within such a time interval were considered, allowing of the intercomparison altimetry–tide gauges for long-term time
series with the same length. Moreover, only tide gauge time series with at
least 99 % of valid data were used in order to allow the analysis of
linear trends. This reduced the original tide gauge dataset to a subset of
27 stations (Tables B1, B2 of Appendix B) mainly located in the northern
half of the Baltic Sea (70 % of stations) with sparse stations distributed
along the coasts of France and Spain (Fig. 3). This analysis has also been
conducted for the DUACS DT2018 reprocessing for comparison purposes.
Spatial distribution of linear trends (mm yr-1) for
altimetry (a) and tide gauges (b) computed from monthly
averaged data for the 20-year time period from 1 January 2000 to 31
December 2019. The all satellites dataset from the DUACS DT2021 reprocessing has been
used.
Linear trends based on monthly observations at each tide gauge site (Fig. 3
and Table B2 of Appendix B) computed from the DUACS DT2021 all satellites dataset (Fig. 3a) show a homogeneous spatial pattern with overall values varying from
2.30 to 4.10 mm yr-1 in the Baltic and Mediterranean seas and between
2.30 and 3.30 mm yr-1 in the sparse stations located along the
North Atlantic European shore, except for the station of SaintMalo that
presents a linear trend of 1.26 mm yr-1. Linear trends computed from
tide gauges (Fig. 3b) exhibit a more heterogeneous spatial pattern with
values ranging between less than 1 mm yr-1 for some stations
located in the Baltic Sea and 5.06 mm yr-1 for the station of
Barcelona (western Mediterranean Sea). However, most of the tide gauge
stations present trend values ranging from 1.30 to 3 mm yr-1. These
results provide further evidence, if needed, of the European seas coastal
sea level rise, including the westernmost part of the Mediterranean Sea. The
differences in trends between the two datasets vary, in absolute values,
between near 0 mm yr-1 (Brest station, Atlantic French coast) to
close to 2.60 mm yr-1 found in the station of Spikarna (Baltic
Sea).
Linear trends computed from the DUACS DT2021 two satellites dataset (figure not shown) exhibit quite a similar spatial pattern with values ranging from 2.60 to 3.80 mm yr-1 in the Baltic and Mediterranean seas and between 2.40 and
3.40 mm yr-1 along the North Atlantic European coasts. However,
some discrepancies between the two datasets are observed. These differences,
computed as all satellites minus the two satellites datasets are displayed in Fig. 4. Overall larger linear
trends (up to 1 mm yr-1) were obtained for the all satellites dataset in the
northernmost and central Baltic Sea as well as in the stations located in
the Mediterranean Sea, whilst lower values of the same magnitude are mainly
observed at the entrance of the Baltic Sea and in most of the stations
located along the North Atlantic European shore.
Spatial distribution of the differences (all satellites minus two satellites dataset) for the
linear trends (mm yr-1) from altimetry computed from monthly averaged
data for the 20-year time period from 1 January 2000 to 31 December 2019. The DUACS DT2021 processing version has been used. Blue (red) colours
denote lower (larger) trends for the all satellites dataset.
On the other hand, linear trends computed from the DT2018 reprocessing
(figures not shown) exhibit quite a similar spatial pattern to that
reported for the DT2021 processing version with overall values ranging from
2.20 (2.40) to 4.35 (3.60) mm yr-1 in the Baltic and Mediterranean
seas and between 2.40 (2.10) and 3.05 (2.85) mm yr-1 along the
North Atlantic European coasts for the all satellites (two satellites) dataset. Thus, hardly any
differences in range are observed between the all satellites dataset from the two
reprocessings, whereas these differences increase for the two satellites dataset, with a
lower variability observed for the DT2018 reprocessing. This fact has an
impact on the spatial distribution of the differences between the two
processing versions (Fig. 5).
Spatial distribution of the differences (DT2021 minus DT2018
reprocessing) for linear trends (mm yr-1) for altimetry computed
from the all satellites dataset (a) and the two satellites dataset (b). Monthly
averaged data for the 20-year time period from 1 January 2000 to 31 December 2019 have been used. Blue (red) colours denote lower (larger) trends
for the DT2021 reprocessing.
For the all satellites dataset (Fig. 5a), two different spatial patterns
were observed with lower trends for the DT2021 reprocessing in the Baltic
Sea basin and most of the stations located along the North Atlantic European
coasts, whereas larger values are obtained for the tide gauge stations
located in the western Mediterranean Sea and some sparse stations at the
entrance of the Baltic Sea. By contrast, the spatial distribution of the
differences between the two reprocessings for the two satellites dataset (Fig. 5b) depicts a homogeneous spatial pattern with overall larger trends for
the DT2021 reprocessing except for the tide gauge station of Barseback
located in the connection region between the Baltic Sea and the eastern
North Atlantic Ocean (Table B2 in the Appendix B). Figure 5 also reveals the
differences between the two reprocessings and for the two datasets, when
making a comparison with linear trends from tide gauges: the two satellites dataset from the DT2021
processing version presents larger differences with tide gauges with respect
to the DT2018 reprocessing in the whole domain, whilst this is only observed
for sparse stations along the North Atlantic shore and the stations located
in the Mediterranean Sea for the all satellites dataset. Thus, closer results were
obtained from the DT2021 all satellites product with respect to the former DT2018
processing version in most of the Baltic Sea region and the stations located
along the North Atlantic European coast.
Discussion and conclusions
More than 29 years of DUACS Level 3 and Level 4 altimeter data have been
recently reprocessed and delivered under the name of the DT2021 processing
version through the Copernicus Marine Service and the Copernicus Climate
Change Service. The all satellites SLA products include all the available altimeter
missions (ranging from two to seven over the period considered in this study),
which makes the errors not constant in time since they depend on the number
of satellites used. Maps from the all satellites products provide the most accurate sea
level estimation with the best spatial and temporal sampling of the ocean at
all times. The two satellites SLA dataset is obtained by merging a steady number of
altimeters (two) in the satellite constellation. This promotes consistent
errors during the whole time period. Maps that include only two satellites
are used to compute the most homogeneous and stable sea level record over
time and space. Thus, two satellites products are dedicated to monitoring long-term sea
level evolution for climate applications and analysing ocean climate
indicators such as global and regional MSL evolution (Taburet et al., 2019).
The new standards applied to the DT2021 version and the update of various
geophysical correction parameters compared to the previous release improved
the all satellites product quality having a direct impact on the observation of coastal
ocean sea level in the gridded products. To achieve independent comparisons,
SLA from altimetry in the coastal zone of the European seas was examined
through comparison with in situ tide gauge measurements. Compared to the
previous DT2018 version, an improvement in the all satellites dataset was obtained, with a
reduction of 3 % in errors when compared with tide gauges and of 5 % in
the variance of the differences between the datasets. The mean distance
between the most correlated altimetry point and tide gauges reduced by
7 %. Also, the number of valid data pairs used to conduct the
intercomparison enhanced by 0.1 % when using the DT2021 processing. This
highlights the impact of the new DUACS DT2021 version on the coastal areas
that provides more valid measurements and located closer to the tide gauge
sites, compared to DT2018 reprocessing. On the other hand, almost no
improvement in the DT2021 two satellites dataset over the previous reprocessing was found
when using all available information from the tide gauge dataset (time
series of different length) in the computation: errors with tide gauges were
reduced by 1 %, and the mean distance between the most correlated
altimetry point and tide gauges was reduced by 3 %. The variance of the
differences between the datasets and the number of valid data pairs used to
conduct the intercomparison were quite similar among the DT2021 and DT2018
processing versions. These improvements were around 60 % lower than those
reported for the all satellites datasets. This fact could be explained by differences in
the mapping parameters used for the two products: DT2021 mapping parameters
(i.e. spatial and temporal correlation scales, a priori errors on the
measurements) are evolved in CMEMS products (CMEMS QUID, 2022) with the
objective of better retrieving mesoscale signals, whilst no evolution of the
mapping parameter was implemented in the C3S DT2021 product (PUGS, 2021).
The quality assessment of DUACS DT2021 reprocessing revealed a better
performance of the all satellites products in the retrieval of SSH in the coastal zone
with respect to the two satellites products for the time period investigated (27 years). A reduction of 5 % in errors with tide gauges and 10 % in
variance difference between altimetry and tide gauges was obtained when
using the all satellites dataset with respect to the two satellites product. This is because despite the
larger stability of the two satellites dataset, this product is optimized for climatic
signal when analysing low-frequency signals (SSH trends). Thus, it performs less well for higher-frequency signals. In this context (analysis of high-frequency signals), the results reported here show that the all satellites dataset should
be considered for the analysis of long time series of SSH in the coastal
zone of the European seas including all frequency signals. This can be
clearly seen in Fig. 6 showing the differences (computed as all satellites minus two satellites
datasets) for consistency between altimetry and tide gauges.
Spatial distribution of the differences (all satellites minus two satellites dataset) for the
mean square differences between the tide gauge and altimetry sea level.
Units are the percentage of the tidal variance. The DUACS DT2021 processing
version has been used. Blue (red) colours denote an improvement
(degradation) of the all satellites dataset.
An overall better performance (blue colours) of the all satellites product with respect to
the two satellites one was observed in the whole domain except in the Baltic Sea and the
westernmost part of the Norwegian Sea, where similar results are obtained.
The improvement is larger along most of the Atlantic shore, namely at the
connection region between the Baltic Sea and the eastern North Atlantic
Ocean, the NWS region, and the northern Norwegian Sea, with a reduction in
the variance difference between the two datasets larger than 15 %. The
Mediterranean Sea and the Strait of Gibraltar area show closer values
between the two products with an improvement lower than 5 %. These
improvements could be explained by the better sampling of the high-frequency
signal in the coastal zone in the all satellites dataset due to the large number of altimeters
available to generate the SLA maps compared to the two satellites maps. Improved mapping
parameters for mesoscale (and thus high-frequency) processes could also
contribute. The observed degradation of the all satellites product with respect to the
two satellites one at some tide gauge sites could be due to high-frequency local features
badly captured by the all satellites product that translate into larger errors when
making a comparison with tide gauges.
Linear trends based on monthly observations at each tide gauge site were
computed to assess whether the DUACS DT2021 release can be representative of
the local sea level along the European coasts and western Mediterranean Sea.
To do that, sea level linear trends for the period 2000–2019 were computed
from both the all satellites and two satellites datasets. The analysis was repeated for the DT2018
reprocessing to have a term of comparison. A homogeneous spatial pattern
with overall values ranging from 2.30 (2.40) to 4.10 (3.80) mm yr-1 was obtained for the all satellites (two satellites) dataset from the DT2021 reprocessing. This
promotes a mean trend for the whole domain of 3.14 (3.13) mm yr-1.
These trends differ slightly from those computed from the tide gauge subset
covering the 20-year time period that show values ranging between less than
1 and 5.06 mm yr-1; the mean trend for the whole domain is 1.96 mm yr-1.
Thus, trends computed from DT2021 products are on average around 1.2 mm yr-1 larger than those obtained from tide gauges. Similar
overestimations in altimetry mean trends were reported by Agha-Karimi et al. (2021) in the Baltic Sea for datasets covering the time period
between 1993 and 2020. These discrepancies could be attributed to the
heterogeneous distribution of both datasets and also the crustal land uplift
due to postglacial rebound resulting from the last glacial age affecting the
Baltic Basin, where most of the tide gauge stations are located. This
translates into conventional altimetry measurements not being accurate enough
in the coastal zone. On the other hand, when using the former DUACS DT2018
processing version, slightly larger discrepancies with tide gauges were
obtained for the all satellites dataset, with a mean trend of 3.18 mm yr-1, whilst
the two satellites product showed closer values to tide gauges with a mean linear trend of
2.85 mm yr-1.
Overall, linear trend differences (altimetry–tide gauge) for the DT2021
reprocessing varying, in absolute value, from 0.16 to 2.57 mm yr-1,
with an average of 1.43 mm yr-1 were obtained for the all satellites dataset. They
varied from 0.03 to 2.65 mm yr-1, with an average of 1.40 mm yr-1 for the two satellites dataset. These discrepancies are lower than 1.5 mm yr-1 on average and corroborate the agreement and complementarity
of the two techniques to measure sea level variability in the coastal zone.
They also emphasize a better performance of the C3S DT2021 dataset in the
estimation of sea level linear trends in the coastal zone. This was also
corroborated by the computation conducted for the DT2018 reprocessing: lower
differences between tide gauge and altimetry trends computed from the two satellites
dataset were obtained. Figure 7 displays the spatial distribution of the
differences in trend computed as altimetry minus tide gauges for the two satellites
dataset from the DT2021 reprocessing. An overall overestimation of trends
from altimetry in the whole domain was obtained. By contrast, three tide
gauge sites (Bilbao on the Atlantic Spanish coast, Pori on the eastern side
of the Baltic Sea and Barcelona in the western Mediterranean Sea; Table B2
in Appendix B) showed a long-term sea level linear trend that was 0.58, 0.70 and 1.81 mm yr-1 larger, respectively, than that found for the closest
altimetry point with the largest correlation. The differences in trend could
be attributed to the aforementioned factors rendering altimetry measurements not accurate enough in the coastal zone. In any case, the linear
trends for the tide gauge of Barcelona described above are of the same order
of magnitude than those reported by Taibi and Haddad (2019) computed for the
time period from 1993 to 2015 (linear trend of 2.74 mm yr-1 for altimetry; 6.73 mm yr-1 for the tide gauge; trend difference
of 3.99 mm yr-1), thus supporting the results obtained here.
Spatial distribution of the differences in linear trends (mm yr-1) between the altimetry and tide gauge sea level computed for the
20-year time period from 1 January 2000 to 31 December 2019. The
two satellites dataset from the DUACS DT2021 reprocessing has been used. Blue (red)
colours denote a larger (lower) altimetry linear trend.
The intercomparison conducted here between L4 gridded products from the new
DUACS DT2021 release and the DT2018 version previously available on the one hand and tide
gauges on the other hand have demonstrated the better performance of the new DT2021 version in
the retrieval of sea level in the coastal zone of the European seas.
Furthermore, the all satellites dataset provided more accurate sea level measurements when
making a comparison with tide gauges with respect to the climatic two satellites dataset due to the
better performance of the former for the assessment of higher than climatic
frequency signals. Conversely, when analysing linear trends from
20-year-long time series the two satellite dataset was the most suitable product for the
assessment of long-term sea level SSH trends in the coastal zone due to its
larger stability to the detriment of the CMEMS all satellites dataset.
SLA and derived geostrophic velocities from altimeter data have been widely
compared with in situ multiplatform measurements by the coastal altimetry
community in order to both validate altimetry measurements and demonstrate
their capabilities to monitor sea level and surface currents in the coastal
zone. Heslop et al. (2017) provided the first multiplatform evaluation
evolving data from the Sentinel-3A altimeter in the Balearic Sea (western
Mediterranean Sea). Their outcomes demonstrated the capacity of this
satellite mission to retrieve fine-scale oceanographic features of around 20 km of diameter showing differences between along-track absolute dynamic
topography (ADT) from altimetry and glider-derived dynamic height (DH) data
along the satellite track of 1.23 cm. In the same region, Aulicino et al. (2018) compared along-track ADT data from the SARAL/AltiKa mission with
glider-derived DH along two satellites tracks. They found a very similar
spatial pattern with differences ranging between 1.10 and 2.90 cm. Pascual
et al. (2015) also conducted an assessment of SARAL/AltiKa data in the
coastal band through the comparison of along-track surface-derived
geostrophic velocities with surface velocities from a coastal high-frequency
(HF) radar system installed in the Ibiza Channel (Balearic Sea). These
authors found that the velocities derived from altimetry solved the general
mesoscale features in the region with rms differences with the in situ
measurements of 13 cm s-1.
The new Surface Water and Ocean Topography (SWOT) satellite mission,
launched in December 2022, is considered to be the next major breakthrough
in satellite ocean observation (Morrow et al., 2023). The SWOT mission aims
to provide SSH measurements in two dimensions along a wide-swath altimeter
track with an expected effective resolution down to wavelengths of 15–30 km
(Barceló-Llull et al., 2021). Thus, SWOT observations will fill the gap
in our knowledge of the 15–150 km 2D SSH dynamics (Morrow et al., 2019)
allowing, in some regions, the observation of the full range of mesoscale
features. The assessment of their impact on the large-scale ocean
circulation and climate system will be one of the major challenges for the
next decade.
Altimeter standard used in DT2021 release. Changes with the DT2018
reprocessing are highlighted in bold format.
TPJason 1Jason 2Jason 3ERS-1ERS-2ENVISATSARALSentinel 3ASentinel 3BGeosat FOCryosat 2HY 2AHY-2BOrbitGSFC STD18POE-EPOE-F Reaper POE-EPOE-FPOE-F GSFCPOE-FPOE-DPOE-FIonosphericFiltered dual-frequency Filtered dual-frequencyFiltered dual-frequencyReaper NIC09 modelGIMFiltered from L2;GIM (Iijima et al., 1999)Filtered from L2 GIM (Iijima et al., 1999) GIM (Iijima et al., 1999)altimeter range measurements altimeter range (Ablainaltimeter range measurements(Scharroo and Smith, 2010;(Iijima et al., 1999)cycle > 65: GIM(Ablain and Legeais, 2010); and Legeais, 2010)(Ablain and Legeais, 2010)Iijima et al., 1999) correctedfrom 8 mm biasDORIS on Poseidon from SSB C-bandSea state biasNon-parametric2D non-parametricNon-parametricNon-parametricBM3 (Gaspar andNon-parametric2D non-parametricNon-parametricNon-parametric(Tran, 2012) Non-parametric2D non-parametricNon-para2metricFrom L2 product(Tran et al., 2010) on TOPEX;(Tran, 2015)(Tran et al., 2012)(Tran et al., 2012)Ogor, 1994)(Mertz et al., 2005)(Tran, 2017)(Tran, 2019)(Tran, 2010)(baseline C) (Tran, 2018)(Tran, 2012)BM4 on PoseidonBaseline CWet troposphereGPD+ (FernandesJMR (GDRE)AMR radiometer GPD+ (Fernandes et al., 2015) MWR radiometerneuronal networkMWR 3 radiometer GFO radiometer andGPD+ (FernandesECMWF modelECMWF modelet al., 2015)radiometerreprocessedfive entries V4ECMWF modeland Lázaro, 2016)Dry troposphereERA (1 h) model based Mean dynamic topographyMDT CNES_CLS_2018 (Mulet et al., 2021) merged with regional MDT CMEMS_2020 Mediterranean and Black Sea Ocean tideFES 2014 B (Carrere et al., 2016) Pole tide(Desai et al., 2015); mean pole location 2017 (Ries and Desai, 2017) Solid tideElastic response to tidal potential (Cartwright and Tayler, 1971; Cartwright and Edden, 1973) Internal tide(Zaron, 2019) (HRETv8.1 tidal frequencies: M2, K1, S2, O1) Mean dynamicMDT CNES_CLS_2018 (Mulet et al., 2021) merged with regional MDT CMEMS_2020 topographyand Black Sea (Jousset and Mulet, 2020; Jousset et al., 2022) DynamicalTUGO high frequencies forced TUGO HF forced with analysedMOG2D HF forced with analysedTUGO high frequencies forced TUGO HF force d with analysedMOG2D high frequencies forced TUGO high frequencies forcedTUGO high frequencies forced MOG2D high frequencies forcedatmosphericwith analysed ERA 5 pressure ERA 5 pressure and wind field;ECMWF pressure and windwith analysed ERA 5 pressure ERA 5 pressure and wind field;with analysed ECMWF pressure with analysed ERA 5 pressurewith analysed ER A 5 pressure with analysed ECMWF pressurecorrectionand wind field plus inverse after February 2016 MOG2D HF(Carrère and Lyard, 2003)and wind field plus inverse after February 2016 MOG2and wind field (Carrère an d wind field plus inverseand wind field;after and wind field (Carrère andbarometer low frequencies forced with analysed ECMWF pressure(operational version 3.2.0)barometer low frequencies D HF forced wit h analysedand Lyard, 2003) (operational barometer low frequenciesFebruary 2016 MO G2D high Lyard, 2003) (operationaland wind fieldplus inverse barometer LFECMWF pressure and wind fieldversion 3.2.0) plus inverse frequencies forced with analysed version 3.2.0) plus inverseinverse barometer LF+ inverse barometer LFbarometer low frequencies ECMWF pressure and wind field barometer low frequencies+ inverse barometer low frequencies MeanMean profileMean profile for repetitive Mean profileMean profile for repetitiveMean profileMean profile for repetitive orbit Mean profileHybrid MSS (SIO,Hybrid MSS (SIO, CNES/CLS15, DTU15) seaorbit phases; hybrid MSS orbit phases; hybrid MSSphases; composite MSS CNES/CLS15, DTU15)surface(SIO, CNES/CLS15, DTU15) for (SIO, CNES/CLS1 5, DTU15) for(SIO, CNES/CLS15, DTU15) for geodetic/ geodetic/LOR phase geodetic phasedrifting phase
List of the 213 tide gauge records with their location and time
period analysed. Bold stations indicate the tide gauge sites from the subset
covering the 20-year period from January 2000 to December 2019
listed in Table B2.
Station nameLongLatPeriod analysedStation nameLongLatPeriod analysed(mm/yyyy)(mm/yyyy)1Bagenkop10.6854.7511/2006–05/202052Ratan20.9063.9901/1993–05/20202Bandholm11.4854.8301/2014–05/202053Ringhals12.1157.2501/1993–05/20203Barhoeft13.0354.4401/2011–05/202054Rodby11.3554.6501/2005–05/20204Barseback12.955.7601/1993–05/202055Rodvig12.3755.2501/1993–05/20205Bogense10.0855.5701/2014–05/202056Rohukula23.4258.9012/2009–12/20196Dragor12.6855.6007/2011–05/202057Roskilde12.0855.6512/2011–05/20207Drogden12.7155.5401/1993–05/202058Rostock12.1554.0801/2011–05/20208Eckernfoerde9.8454.4701/2011–05/202059Simrishamn14.3655.5601/1993–05/20209Faaborg10.2555.1001/2014–05/202060SjaellandsOdde11.3755.9701/1993–05/202010Forsmark18.2160.4101/1993–05/202061Skagen10.5957.7204/1993–09/201811Fredericia9.7555.5701/2005–05/202062Skagsudde19.0163.1910/1993–05/202012Furuogrund21.2364.9201/1993–05/202063Skanor12.8355.4201/1993–07/201813Gedser11.9354.5703/1993–05/202064Smogen11.2258.3501/1993–05/202014GoteborgAgnesberg12.0157.7901/2013–05/202065Sonderborg9.7854.9201/2014–05/202015GoteborgEriksberg11.9157.7001/2013–05/202066Spikarna17.5362.3601/1993–05/202016GoteborgLarjeholm12.0157.7701/2013–05/202067Stenungsund11.8358.0901/1993–05/202017GoteborgTingstadstunneln11.9957.7201/2013–05/202068Stockholm18.0859.3201/1993–05/202018GoteborgTorshamnen11.7957.6801/1993–05/202069Stralsund13.1054.3201/2011–05/202019Greifswald13.4554.0901/2011–05/202070Tallinn24.7659.4411/2005–05/202020Grena10.9356.4101/1993–05/202071TimmendorfPoel11.3853.9901/2011–05/202021Hanko22.9859.8201/1993–05/202072Travemuende10.8753.9601/2005–05/202022Heiligenhafen11.0154.3701/2011–05/202073Uddevalla11.8958.3512/2010–05/202023Holbaek11.7255.7212/2011–05/202074Ueckermuende14.0753.7501/2011–05/202024Hov10.2755.9212/2011–05/202075Vedbaek12.5755.8512/2011–05/202025Juelsminde10.0255.7212/1996–05/202076Viken12.5856.1401/1993–05/202026Kalix23.1065.7001/1993–05/202077Virtsu23.5158.5812/2009–05/202027Kalkgrund9.8954.8201/2011–05/202078Visby18.2857.6401/1993–05/202028Kalvehave12.1755.0001/2014–05/202079Wismar11.4653.9001/2011–05/202029Kappeln9.9454.6601/2011–05/202080Wolgast13.7754.0401/2011–05/202030Karrebaeksminde11.6555.1801/2014–05/202081BrestTG-4.5048.3801/1993–05/202031Kelnase25.0159.6402/2017–05/202082CherbourgTG-1.6449.6501/1993–05/202032KielHoltenau10.1654.3701/2005–05/202083ConcarneauTG-3.9147.8706/1999–05/202033KielLTG10.2754.5001/2011–05/202084LaRochelleTG-1.2346.1510/1995–05/202034Koege12.2055.4501/2012–05/202085LeConquetTG-4.7848.3601/1993–05/202035Koserow14,0054.0611/2005–11/201986LeHavreTG0.1149.4801/1993–05/202036Kristineberg111.4558.2504/2012–05/202087MarseilleTG5.3543.2810/1998–05/202037Kungsholmsfort15.5956.1101/1993–05/202088MonacoTG7.4243.7304/1999–05/202038Kungsvik11.1359.0001/1993–05/202089NiceTG7.2943.7003/1998–05/202039LandsortNorra17.8658.7710/2004–05/202090RoscoffTG-3.9748.7201/1993–05/202040Langballigau9.6554.8201/2011–05/202091SaintGildasTG-2.2547.1402/1993–06/201741Leppneeme24.8759.5502/2017–05/202092SaintMaloTG-2.0348.6408/1993–04/202042Luebeck10.7053.8901/2011–05/202093ToulonTG5.9143.1201/1993–05/202043Marviken16.8458.5501/1993–09/201994Aberdeen-2.0857.1501/1993–05/202044Munalaiu24.1258.2302/2016–05/202095AlcudiaTG3.1439.8309/2009–05/202045Neustadt10.8154.1001/2011–05/202096AlgecirasTG-5.4036.1807/2009–05/202046OlandsNorraUdde17.1057.3701/1993–05/202097AlmeriaTG-2.4836.8301/2006–05/202047Onsala11.9257.3906/2015–05/202098Aranmore-8.5054.9905/2008–05/202048Oskarshamn16.4857.2801/1993–05/202099ArklowHarbur-6.1552.7908/2003–05/202049Paldiski24.0859.3310/2006–05/2020100Ballycotton-8.0051.8310/2010–05/202050Pori21.4661.5901/1993–05/2020101Ballyglass-9.8954.2505/2008–04/202051Porvoo25.6360.2108/2014–05/2020102Bangor-5.6754.6711/1994–05/2020
Continued.
Station nameLongLatPeriod analysedStation nameLongLatPeriod analysed103BarcelonaTG2.1641.3401/1993–05/2020162AlteWeserTG8.1353.8601/2014–05/2020104Barmouth-4.0352.7201/1993–05/2020163AndenesTG16.1369.3301/2014–05/2020105BilbaoTG-3.0543.3601/1993–05/2020164AWGTG5.9453.4906/2015–05/2020106BonanzaTG-6.3436.8001/1993–05/2020165BergenTG5.3260.4001/2007–05/2020107Bournemouth-1.8750.7106/1996–05/2020166BodoeTG14.3967.2901/2007–05/2020108CarbonerasTG-1.9036.9707/2013–05/2020167BorkumTG6.7553.5601/2014–05/2020109Castletownbere-9.9051.6512/2006–05/2020168Brouwershavensegat8TG3.6251.7708/2014–12/2019110CorunaTG-8.3943.3601/1993–05/2020169CadzandTG3.3851.3808/2014–12/2019111Dundalk-6.3954.0104/2008–01/2013170DenHelderTG4.7552.9701/2014–12/2019112Felixstowe1.3551.9701/1993–01/2011171EemshavenTG6.8453.4608/2014–05/2020113Fenit-9.8652.2701/2007–05/2020172EuroplatformTG3.2852.0001/2014–12/2019114Ferrol2TG-8.2543.4801/2007–05/2020173F3platformTG4.7254.8508/2014–12/2019115FerrolTG-8.3343.4601/2007–05/2020174HammerfestTG23.6870.6601/2014–05/2020116Fishguard-4.9852.0201/1993–05/2020175HanstholmTG8.6057.1201/2015–05/2020117FormenteraTG1.4238.7309/2009–05/2020176HarstadTG16.5568.8001/2014–05/2020118GandiaTG-0.1538.9907/2007–05/2020177HavnebyTG8.5755.0901/2015–05/2020119GijonTG-5.7043.5607/1995–05/2020178HelgeroaTG9.8659,0001/2007–05/2020120Hinkley-3.1351.2201/1993–05/2020179HelgolandTG7.8954.1801/2014–05/2020121Holyhead-4.6253.3202/2005–05/2020180HirtshalsTG9.9757.6001/2015–05/2020122Howth-6.0753.3910/2006–11/2019181HoekVanHollandTG4.1251.9801/2014–12/2019123HuelvaTG-6.8337.1309/1996–05/2020182HoernumTG8.3054.7601/2014–05/2020124IbizaTG1.4538.9101/2003–05/2020183HonningsvaagTG25.9770.9801/2007–05/2020125Ilfracombre-4.1251.2201/1993–05/2020184HuibertgatTG6.4053.5706/2014–12/2019126Kinlochbervie-5.0558.4601/1993–05/2020185IJmondstroompaalTG4.5252.4608/2014–05/2020127LangosteiraTG-8.5343.3501/2014–05/2020186K141TG3.6353.2706/2015–05/2020128Leith-3.1855.9901/1993–05/2020187KabelvaagTG14.4868.2101/2007–05/2020129Llandudno-3.8253.3105/2014–05/2020188KristiansundTG7.7363.1101/2007–05/2020130Lowestoft1.7552.4701/1993–05/2020189L91TG4.8753.5706/2015–05/2020131MahonTG4.2739.8910/2009–05/2020190LauwersoogTG6.2053.4106/2015–12/2019132MalagaTG-4.4236.7101/1993–05/2020191LichteilandGoeree1TG3.6751.9301/2015–05/2020133MarinTG-8.6942.4101/2010–05/2020192ListTG8.4455.0201/2014–09/2018134MelillaTG-2.9235.2910/2007–05/2020193MaloyTG5.1161.9301/2007–05/2020135Milford-5.0551.7201/1993–05/2020194MandoTG8.5855.2801/2015–05/2020136Millport-4.9055.7501/1993–05/2020195NieuwpoortTG2.7351.1508/2014–05/2020137MotrilTG-3.5236.7201/2005–05/2020196NorderneyTG7.1653.7001/2014–05/2020138Newhaven0.0750.7801/1993–05/2020197NorthCormorantTG1.1661.3408/2014–05/2020139Newlyn-5.5350.1001/1993–09/2018198OostendeTG2.9351.2308/2014–05/2020140NorthShields-1.4355.0001/1993–05/2020199OscarsborgTG10.6059.6801/2007–05/2020141PalmadeMallorcaTG2.6439.5609/2009–05/2020200RorvikTG11.2364.8601/2007–05/2020142Plymouth-4.1950.3701/1993–05/2020201StavangerTG5.7358.9701/2014–05/2020143PortEllen-6.1955.6301/1993–02/2011202ThyboronKystTG8.2156.7101/2015–05/2020144Portpatrick-5.1254.8401/1993–05/2020203TorsmindeKystTG8.1256.3701/2015–05/2020145Portrush-6.6755.2007/1995–05/2020204TregdeTG7.5558.0101/2007–05/2020146Portsmouth-1.1150.8001/1993–05/2020205TromsoeTG18.9669.6501/2007–05/2020147RingaskiddyNMCI-8.3051.8401/2012–05/2020206VardoeTG31.1070.3701/2014–05/2020148RossaveelPier-9.5653.2709/2020–05/2020207VikerTG10.9559.0401/2007–05/2020149SaguntoTG-0.2139.6307/2006–05/2020208VlakteVdRaanTG3.2451.5008/2014–05/2020150SantanderTG-3.7943.4601/1993–05/2020209VlielandHavenTG5.0953.3008/2014–05/2020151StHelier-2.1249.1801/1993–05/2020210WangeroogeTG7.9353.8101/2014–05/2020152Stornoway-6.3858.2201/1993–05/2020211WestkapelleTG3.4451.5208/2014–05/2020153TarifaTG-5.6036.0107/2009–05/2020212WilhelmshavenTG8.1553.5101/2014–05/2020154TarragonaTG1.2141.0805/2011–05/2020213ZeebruggeTG3.2051.3508/2014–05/2020155Tobermory-6.0656.6203/1993–05/2020156ValenciaTG-0.3339.4601/1993–05/2020157VigoTG-8.7342.2401/1993–05/2020158Weymouth-2.4550.6101/1993–05/2020159Wick-3.0858.4301/1993–05/2020160ANDRATX2.3939.5506/2011–05/2020161AalesundTG6.1562.4701/2007–05/2020
Tide gauge stations from the subset covering the 20-year period from January 2000 to December 2019 located in the Baltic and
Mediterranean seas and along the North Atlantic European shore. The
location of the tide gauge sites, the linear trend (mm yr-1) computed
from the DUACS DT2021 and DT2018 reprocessing all satellites and two satellites most correlated
altimeter grid point to tide gauges, the tide gauges and the mean trend
value are displayed.
Altimetry datasets are available from the Copernicus Marine Service web
portal (https://resources.marine.copernicus.eu/products/, last
access: 15 July 2022, EU Copernicus Marine Service Product, 2023; Copernicus Climate Change Service, Climate Data Store, 2023). Tide gauge measurements are available from the
Copernicus Marine INS-TAC data repository web portal (http://www.marineinsitu.eu, last access: 3 June 2022, EU Copernicus Marine Service Product, 2022). Tide gauge data are
provided by the following regional in situ data production centres: Puertos
del Estado (Spain) for the Iberia–Biscay–Ireland region, HCMR (Greece) for
the Mediterranean Sea, IMR (Norway) for the Arctic, SMHI (Sweden) for the
Baltic Sea, BSH (Germany) for the North-West Shelf region and Coriolis
(France) for the global ocean. The ancillary data used to obtain the dynamic
atmospheric correction applied to the altimetry grid point closest to the
tide gauge locations are available from the AVISO web page: https://www.aviso.altimetry.fr/en/ (last access: 16 May 2022, LEGOS/CNRS/CLS, 1992).
Author contributions
Conceptualization: ASR, MIP, AP and YF; altimetry data processing: MIP; tide gauge data processing: ASR;
statistical analysis: ASR and AP;
paper writing: ASR, with input from all
co-authors. All authors have read and agreed to the published version of the
paper.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Oceanography at coastal scales: modelling, coupling, observations, and applications”. It is not associated with a conference.
Acknowledgements
This study has been conducted in the frame of the Copernicus Marine Service
SL-TAC and In Situ TAC (INS-TAC) projects. The Copernicus Marine Service,
led by Mercator Ocean, is based on a distributed model of service
production, relying on the expertise of a wide network of participating
European organizations involved in operational oceanography. We acknowledge
the regional in situ data production centres responsible for the collection
and distribution of the tide gauge data used in this study: Puertos del
Estado (Spain) for the Iberia-Biscay-Ireland region, HCMR (Greece) for the
Mediterranean Sea, IMR (Norway) for the Arctic, SMHI (Sweden) for the Baltic
Sea, BSH (Germany) for the North-West Shelf region and Coriolis (France) for
the global ocean. This work represents a contribution to the CSIC
Interdisciplinary Thematic Platform (PTI) Teledetección (PTI-TELEDETECT),
and it was carried out within the framework of the activities of the Spanish
Government through the “Maria de Maeztu Centre of Excellence” accreditation
to IMEDEA (CSIC-UIB) (CEX2021-001198).
Review statement
This paper was edited by Manuel Espino Infantes and reviewed by two anonymous referees.
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