OSOcean ScienceOSOcean Sci.1812-0792Copernicus GmbHGöttingen, Germany10.5194/os-11-67-2015Improved sea level record over the satellite altimetry era (1993–2010) from
the Climate Change Initiative projectAblainM.mablain@cls.frCazenaveA.LarnicolG.BalmasedaM.CipolliniP.FaugèreY.FernandesM. J.https://orcid.org/0000-0002-0946-0092HenryO.JohannessenJ. A.KnudsenP.AndersenO.https://orcid.org/0000-0002-6685-3415LegeaisJ.MeyssignacB.https://orcid.org/0000-0001-6325-9843PicotN.RocaM.RudenkoS.https://orcid.org/0000-0001-5149-3827ScharffenbergM. G.https://orcid.org/0000-0001-5948-9849StammerD.TimmsG.BenvenisteJ.Collecte Localisation Satellite (CLS), Ramonville Saint-Agne, FranceLaboratoire d'Etudes en Géophysique et Océanographie Spatiales
(LEGOS), Toulouse, FranceNansen Environmental and Remote Sensing Center (NERSC), Bergen, NorwayUniversity of Hamburg, Hamburg, GermanyCGI, London, UKTechnical University of Denmark (DTU), Lyngby, DenmarkNational Oceanography Centre (NOC), Southampton, UKisardSAT, Barcelona, Catalonia, SpainHelmholtz Centre Potsdam GFZ German Research Centre for Geosciences,
Telegrafenberg 14473 Potsdam, GermanyFaculdade de Ciências, Universidade do Porto, 4169-007 Porto,
PortugalEuropean Centre for Medium-Range Weather Forecasts (ECMWF), Reading,
UKCentre National d'Etudes Spatiales (CNES), Toulouse, FranceEuropean Space Agency (ESA), ESRIN, Frascati, ItalyCentro Interdisciplinar de Investigação Marinha e Ambiental
(CIIMAR/CIMAR), Universidade do Porto, 4050-123 Porto, PortugalM. Ablain (mablain@cls.fr)13January201511167827July201421August201425November201427November2014This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.ocean-sci.net/11/67/2015/os-11-67-2015.htmlThe full text article is available as a PDF file from https://www.ocean-sci.net/11/67/2015/os-11-67-2015.pdf
Sea level is one of the 50 Essential Climate Variables (ECVs) listed by the
Global Climate Observing System (GCOS) in climate change monitoring. In the
past two decades, sea level has been routinely measured from space using
satellite altimetry techniques. In order to address a number of important
scientific questions such as “Is sea level rise accelerating?”, “Can we
close the sea level budget?”, “What are the causes of the regional and
interannual variability?”, “Can we already detect the anthropogenic forcing
signature and separate it from the internal/natural climate variability?”,
and “What are the coastal impacts of sea level rise?”, the accuracy of
altimetry-based sea level records at global and regional scales needs to be
significantly improved. For example, the global mean and regional sea level
trend uncertainty should become better than 0.3 and 0.5 mm year-1,
respectively (currently 0.6 and 1–2 mm year-1). Similarly, interannual
global mean sea level variations (currently uncertain to 2–3 mm) need to be
monitored with better accuracy. In this paper, we present various data improvements achieved within the European Space Agency (ESA) Climate
Change Initiative (ESA CCI) project on “Sea Level” during its first phase
(2010–2013), using multi-mission satellite altimetry data over the 1993–2010
time span. In a first step, using a new processing system with dedicated
algorithms and adapted data processing strategies, an improved set of sea
level products has been produced. The main improvements include: reduction
of orbit errors and wet/dry atmospheric correction errors, reduction of
instrumental drifts and bias, intercalibration biases, intercalibration
between missions and combination of the different sea level data sets, and
an improvement of the reference mean sea surface. We also present
preliminary independent validations of the SL_cci products,
based on tide gauges comparison and a sea level budget closure approach, as
well as comparisons with ocean reanalyses and climate model outputs.
Introduction
Global warming as a result of anthropogenic greenhouse gas emissions
has already shown several visible consequences, among them the increase of
the earth's mean air temperature and ocean heat content, melting of
glaciers, and loss of ice masses from glaciers and the Greenland and
Antarctica ice sheets. Ocean warming and land ice melting in turn are
causing sea level to rise, with potentially negative impacts in many
low-lying regions of the world. The precise measurement of sea level changes
as well as its different components, at global and regional scales, is an
important issue for a number of reasons. It provides information on how the
climate system and its components respond to global warming and on
the relative contributions of anthropogenic forcing and natural/internal
climate variability. This also allows validation of the climate models
developed for projecting future changes, as the models are supposed to
correctly reproduce present-day and recent-past changes. The Global Climate
Observing System (GCOS) has recently defined a set of 50 climate variables
(called Essential Climate Variables – ECVs) that need to be precisely
monitored on the long term in order to improve our understanding of the
climate system, its functioning and its response to anthropogenic forcing,
as well as to provide constraints for climate modelling (GCOS, 2011). In
2010, the European Space Agency (ESA) developed a new programme, the Climate
Change Initiative (CCI), dedicated to reprocessing a set of 13 ECVs
currently observed from space; among them, the satellite altimetry-based sea
level ECV. The objective of the CCI sea level project (called
SL_cci below) was to produce a consistent and precise sea
level record covering the past two decades, based on the reprocessing of all
satellite altimetry data available from all missions (including the
ERS-1&2 and Envisat missions, in addition to the TOPEX/Poseidon,
Jason-1&2 and Geosat Follow-on (GFO) missions). During the first phase
of the project, which lasted 3 years from 2011 to 2013, satellite altimetry
data from seven altimeter satellites were reprocessed by the
SL_cci consortium. Improved satellite orbits have been
computed for all satellites except TOPEX/Poseidon and GFO using up-to-date
force models and an improved reference frame realization. Updated
geophysical corrections adapted to each satellite mission have been
implemented after being evaluated and selected. Other improvements concern
the reduction of instrumental drifts and biases (in particular for the
Envisat mission), a new calculation of the mean sea surface used as
reference, the method used for geographical averaging of sea surface height
data, and the reduction of systematic bias between missions. The main
SL_cci products computed during phase 1 consist of: (1) a
global mean sea level (GMSL) time series at monthly intervals between January 1993 and December 2010, and (2) a global gridded sea level time series
(resolution 0.25∘×0.25∘) at the same time interval.
This paper intends to provide a global overview of the main results
obtained in the frame of the SL_cci project. We firstly
describe the validation protocol (Sect. 2) that has been applied to
evaluate and select the algorithms and corrections used (Sect. 3) to
generate the SL_cci products (described in Sect. 4). Then,
Sects. 5 and 6 are focused on the assessment and the error
characterization.
Definition of a formal validation protocol
The altimetry data processing system used to compute sea level (or the sea
surface height, SSH) integrates a number of components: the altimeter range
measurement (Range), the satellite orbit height (Orbit), and the instrumental
and geophysical corrections. The estimation of these components needs
additional information coming from different domains as orbitography (a
force model) for the precise orbit determination, geodesy (geoid, mean sea
surface, global isostatic adjustment (GIA), etc.), atmosphere (pressure,
wind, dry and wet troposphere, etc.), and ocean (ocean tides, sea state,
etc.). This information may be eventually linked together either directly or
indirectly. Because of these complex interactions, sea level estimates
(i.e. SSH = Orbit Range ∑i=0NCorrectioni)
are provided with different standards. In practice, an optimized sea level
calculation requires a large number of algorithms and corrections that need
to be rigorously validated and regularly updated.
In the framework of the SL_cci project, we developed a new
formal validation protocol which allowed us to evaluate the impact of new
altimeter corrections or standards on a sea level record of climate quality,
i.e. precise enough for climate studies. It consists in comparing the new
altimeter corrections with corrections designed as a reference through their
impact on the sea level calculation. This was done using a common set of
validation diagnoses defined in such a way that they fulfil the sea level
accuracy and precision requirements. The validation diagnoses are
distributed into three distinct families allowing the assessment of altimetry
data with complementary objectives:
the “global internal analyses” with the aim of checking the internal consistency of a
specific mission-related altimetry system by analysing the computed sea level, its instrumental
parameters (from altimeter and radiometer) and associated geophysical corrections,
the “global multi-mission comparisons” allowing evaluation of the coherence between two
different altimetry systems through comparison of SSH data,
the “altimetry in situ data comparison” dedicated to the computation of the sea level
differences between altimeter data and in situ sea level measurements, e.g. from tide gauges or
Argo-based steric sea level data (Valladeau et al., 2012); this 3rd approach allows for
the detection of potential drifts or jumps in the long-term sea level time series.
For each family, several validation diagnoses have been defined using
elementary statistical approaches (e.g. mean, standard deviation, linear
regression) and data representation (e.g. global mean time series, maps,
histograms, periodograms). Other tests based on altimeter correction
differences, sea surface height differences at satellite track crossovers,
sea level anomalies, etc. were also performed. The list of all the diagnoses
and their specification is described in detail in the Product Validation
Plan (PVP) report of the SL_cci project for all
referenced SL_cci reports available on the SL_cci website).
Definition of the temporal and spatial scales (left panel) and
the indicator value table (right panel) allowing the impact
characterization in sea level of new SL_cci corrections in comparison with
corrections defined as reference (AVISO-2010).
The analyses of these diagnoses were performed for different spatial (global
mean and regional sea level, mesoscale) and temporal scales
(Fig. 1, left panel): long-term > 10 years; interannual, 2–5 years; and periodic signals (annual, semi-annual)
scales. These spatio-temporal scales were chosen according to the sea level
user requirements document (SL_cci User Requirements
Document, 2014) presented below. This formal validation
protocol allows us to determine, for each spatial and temporal scale, the
level of impact (i.e. low or strong) of the new altimetry corrections on the
sea level calculation (Fig. 1, right panel). For
instance, if a new altimetry correction causes a GMSL trend > 0.15 mm year-1 (over a period > 10 years), we consider that the
impact is strong, whereas if the trend effect is in the range 0.05–0.15 mm year-1, it is assumed low, and negligible below 0.05 mm year-1.
Our goal is also to check whether the new altimeter corrections improved or
degraded the sea level estimates for each timescale. Most of the time, it
was possible to clearly detect either improvement or degradation
(illustrated Fig. 1, left panel, with the symbols
“+” and “-” meaning improvement and degradation). For example, increased
consistency between GMSL trends derived from two different altimetry
missions or from in situ measurements demonstrates that the
accuracy/precision of sea level data has been improved. In only a few cases were
the diagnoses inconclusive. This occurred when errors of altimetry
missions were of the same order of magnitude or correlated (e.g. same error
for the regional mean sea level trends). In these rare cases, thorough
investigations could be conducted through a “case by case” approach. When no
obvious conclusion could be reached, the sea level differences due to the
new correction were then allocated to the altimetry error budget (see
Sect. 6).
Thanks to this formal validation protocol, the impact of all altimeter
corrections could be described through a homogeneous approach and is
therefore comparable between corrections. The table presented in
Fig. 1 (left panel) allows us to provide easily
and quickly relevant information about the impact of each correction on the
sea level products.
Development, validation and selection of new altimeter corrections and algorithms
New corrections selected for the sea level calculation for the
SL_cci project. The unfilled boxes indicate that the AVISO standards
(release 2010) have been applied.
CorrectionsERS-1ERS-2EnvisatJason-1Jason-2T/PGFOOrbitReaper combined orbit GDR-D CNES (Rudenko et al., 2012) (Couhert et al., 2015) Instrumental correctionNew PTR correction (Garcia and Roca, 2010)Sea state biasV2.1 releaseGDR-D releaseWet troposphereGPD corrections (Fernandes et al., 2010, 2014) Dry troposphereERA-interim based (Carrere et al., 2014) Dynamical atmospherical correctionsERA-interim based (Carrere et al., 2014) Ocean tideGOT 4.8 (Ray et al., 2013) Mean sea surfaceDTU 2010 (Andersen et al., 2010)
In this section, we present applications of the formal validation protocol
described in Sect. 2. An important output of the SL_cci
project was the development of new altimetry corrections (mentioned in
Sect. 2) and algorithms (e.g. for merging data from different altimetry
missions). A total of 42 new corrections/algorithms were evaluated within
the project using the validation protocol described above. The reference
standards were those used for AVIS0 products (Dibarboure et al., 2011) at
the beginning of the SL_cci project. In order to select the
best corrections, a “selection meeting” in Toulouse in May 2012
gathered a team of international experts in satellite altimetry, not
involved in the SL_cci project. The new corrections were then
selected on the condition that they led to improvements in sea level
calculation. In the rare cases where the new processing did not improve the
results or, even worse, led to deterioration, a conservative approach was
applied and finally, the former corrections were unchanged.
Table 1 presents the new selected corrections for
each component and altimetry missions (for detailed information, see all the “SL_cci Validation Reports”). One
of the most dramatic improvements comes from the use of ERA-interim
reanalyses (from the European Centre for Medium-Range Weather Forecasts
– ECMWF; Dee et al., 2011) instead of operational ECMWF fields to calculate
the dry tropospheric and other dynamical atmospheric corrections. Applying
our validation protocol, we noted strong improvements at mesoscale and
regional spatial scales, over the first altimetry decade (1993–2003)
(Carrere et al., 2014; “SL_cci Validation reports,
Atmospheric corrections”, 2014). The GMSL error reduction
(Fig. 2, top) obtained from crossover analyses is
of the order of 2.5 cm on the early years of the altimetry era (1993–1995).
Then, the error decreases linearly until 2004, and remains stable close to
0 during recent years. The improvement observed in the first decade
(1993–2003) is stronger at high latitudes (6 cm) where the atmospheric
pressure and wind fields have strong high-frequency variability. Looking at
regional sea level trends (Fig. 2), significant
trend differences are observed (> 1 mm year-1) mainly in the South
Pacific Ocean below 50∘ S latitude.
Evolution of the sea level error reduction applying the new
dynamical atmospheric and dry troposphere corrections derived from
ERA-Interim reanalyses instead of operational ECMWF fields (top) and impact
on sea level regional trends (bottom).
Similarly, the model-based wet tropospheric correction was also strongly
improved (until 1 cm error reduction on the GMSL) before 2002 using
ERA-interim instead of ECMWF operational fields (Legeais et al., 2014).
While not as good as the wet troposphere corrections derived from the
on-board microwave radiometers (MWRs), the ERA-Interim wet tropospheric
correction allows us to better characterize the uncertainty of wet
troposphere content over the long term (Thao et al., 2014; Legeais et al.,
2014). However, this was not used in the sea level calculation, where the
radiometer-based corrections were preferred.
In parallel, the radiometer-based corrections have been improved using
combined estimates from valid on-board MWR values, global navigation
satellite systems (GNSS) measurements and ECMWF model (ERA Interim fields)
in areas where the MWR measurements are degraded due to, for example, land or ice
contamination or instrument malfunction (Fernandes et al., 2010, 2014). This
new correction, called GNSS-derived path delay (GPD), computed for all ESA
and reference missions, brings improvements mainly in coastal areas and in
the polar regions. In Fig. 3, the sea level error
reduction is plotted vs. the distance to the coast using the new GPD
corrections instead of the reference radiometer-based corrections. For
almost all missions, except Jason-2, which already benefits from an improved
coastal radiometer correction (Brown et al., 2009), there is a significant
SSH error reduction, close to 1 cm between 20 and 40–50 km from the coast.
Improvements have also been noticed in the open ocean, especially for TOPEX
data (Fernandes et al., 2014), where radiometer data gaps degrade the
interpolation process. Finally, the GPD corrections have been selected for
all altimeter missions because of the noted improvement in the sea level
calculation at short and long timescales, mainly in coastal and polar
regions.
Evolution of the error reduction vs. the coastal distance
applying the new GPD wet troposphere corrections instead of the reference
radiometer-based corrections used in AVISO-2010.
Orbit error is the main source of the error for the long-term sea level
evolution at oceanic basin scales (Couhert et al., 2015). Strong efforts
have been made within the SL_cci project to develop new orbit
solutions (Rudenko et al., 2014) and to compare them with external solutions
provided by other projects. The International Terrestrial Reference Frame
(ITRF) realization (Altamimi et al., 2011) and the earth gravity field model used
in the orbit computation are crucial as far as the quality of orbit
solutions is concerned. After analysing all orbit solutions for all the
missions, the REAPER combined orbit solutions (Rudenko et al., 2012) have
been selected for ERS-1 and ERS-2, with the new CNES GDR-D orbit solutions
(Couhert et al., 2015) being selected for the Jason-1, Jason-2 and Envisat
missions. Strong effects were observed on the regional sea level trend, in
the range of 1–2 mm year-1, with large patterns at hemispheric scale when using
static and time-variable earth gravity field models for orbit computation
(Fig. 4). Thanks to cross-comparisons between
altimetry missions (Ollivier et al., 2012) and with in situ measurements
(Valladeau et al., 2012), we have demonstrated that these new orbit
solutions dramatically improved the regional sea level trends. Furthermore,
this inter-comparison, using different orbit solutions, provided interesting
information on the orbit sensitivity to the choice of the earth gravity
field model (Rudenko et al., 2014).
Impact of the new orbit solutions on the regional sea level trends
for ERS-2 (Reaper combined vs. DEOS DGM-E04 orbit solutions), Envisat,
Jason-1, and Jason-2 (CNES GDR-D vs. CNES GDR-C orbit solutions).
In addition to these major improvements, other corrections were also
selected, although their impact on the sea level estimate was lower. These
concern the ionospheric correction with the use of the NIC09 (New Ionosphere
Climatology) model for ERS-1 (Scharroo and Smith, 2010), the GOT4.8
(Geocentric Ocean Tide) ocean tide solution (Ray et al., 2013), and the DTU10
(Danish Technical University) mean sea surface (Andersen et al., 2010) for
all missions. In addition, we benefited from the reprocessing of
Envisat and Jason-2 level-2 products “GDR V2.1” (Ollivier et al., 2012)
and “GDR-D” (Philipps and Roinard, 2013). This allowed us to increase the data
coverage (mainly for Envisat) and to improve the sea-state bias corrections
along with instrumental bias and drift corrections. For the latter, the
impact is strong for Envisat since a global instrumental drift of about 2 mm year-1 was identified and corrected in the altimeter range (Thibaut et al.,
2010; Roca and Thibaut, 2009; Garcia and Roca, 2010). It is worth mentioning that
the SL_cci project contributed to correcting this anomaly, while
Envisat was designed not for climate studies but rather for mesoscale
variability.
The last new algorithm developed and selected aims at better combining the
different sea level time series from TOPEX, Jason-1, and Jason-2 at regional
scale. Thanks to the verification phase between these missions, systematic
geographical biases could be detected. These biases are mainly
latitude-dependent, with variations close to 0.5 cm between Jason-1 and
Jason-2, and 1 cm between TOPEX and Jason-1. Correcting these regional and
systematic sea level differences (see the SL_cci Validation
Report, Regional SSH bias corrections between altimetry missions, 2014), led
us to better combine these three altimetry missions and therefore
better estimate the long-term sea level evolution at regional scales. The
impact of these corrections on regional MSL trends plotted in
Fig. 5 from 1993 to 2010 is close to ±0.3 mm year-1, with large hemispheric dependence.
New CCI-based sea level records
MSL trend differences from 1993 to 2010 between sea level maps
without and with regional bias corrections for TOPEX/Jason-1 and
Jason-1/Jason-2.
Sea level products were generated using the new altimeter corrections
described in Sect. 3. The same procedure was adopted as for the SSALTO
DUACS (Segment Sol Multimission Altimetrie et Orbitographie, Data Unification
and Altimeter Combination System; Dibarboure et al., 2011). After
calculating the along-track sea level for each of the seven missions
(TOPEX/Poseidon, Jason-1, Jason-2, ERS-1, ERS-2, Envisat, and Geosat
Follow-on) over the [1993, 2010] period, the main steps consisted of:
combining all missions together, reducing the orbit and the long wavelength
errors, computing the gridded sea level anomalies using an objective
analysis approach (Ducet et al., 2000; Le Traon et al., 2003), and generating
mean sea level products (e.g. GMSL time series, gridded sea level time
series) dedicated for climate studies. The SL_cci
products are monthly grids time series with a spatial resolution of
0.25∘ using a rectangular projection. The GMSL time series
(also at monthly interval) is based on the geographical averaging over the
oceanic domain observed by the altimetry data (82∘ S to
82∘ N) of the gridded data. Additional products (called
indicators) are provided: GMSL trend, regional MSL trends, amplitudes
and phases of the main periodic signals (annual, semi-annual), etc.
Access to the SL_cci products can be obtained by sending an
email to: info-sealevel@esa-sealevel-cci.org. The Product User Guide (PUG, 2013) and
Product Specification Document (PSD, 2013) provide further details.
Comparisons between the SL_cci product and the AVISO-2010
products (Dibarboure et al., 2011) were performed by applying the formal
validation protocol described above (Sect. 2). Concerning the GMSL trend,
similar values were obtained for both time series: 3.2 mm year-1 over the
1993–2010 time span. At the interannual timescale (highlighted by
calculating the difference between the two GMSL time series
(Fig. 6, top panel), small differences in the
range 1-2 mm or lower are noticed, except for 1994 where a 4 mm jump is
observed. This jump is due to an anomalous value of the AVISO-2010 products
caused by an inadequate merging of the TOPEX data with the ERS-1 data of the
non-repetitive geodetic phase (Pujol et al., 2014). The most impressive
result is obtained by separating the ERS-1/ERS-2/Envisat and
TOPEX/Jason-1/Jason-2 global GMSL time series using alternately the old and
new altimeter corrections (Fig. 7): the trend
difference between the two time series is now close to 0.6 mm year-1 from 1993 to
2010 instead of about 1.5 mm year-1 previously. This improved consistency does
not have a direct impact on the GMSL trend, which depends only on the
TOPEX/Jason-1/Jason-2 missions. However, this provides increased confidence
in the long-term GMSL time series.
GMSL (top panel) and regional sea level (bottom panel) differences
between the SL_cci (release 1.1) and AVISO products (release 2010).
Looking at the regional sea level trend differences
(Fig. 6, bottom panel), large geographically
correlated structures are observed. Their amplitude is in the ±2 mm year-1 range. They primarily result from the new orbit solutions (hemispheric
effects), the new ERA-interim atmospheric fields (at high latitudes), the
new wet tropospheric correction, and the geographical biases arising when
linking altimetry missions together. Comparing with in situ measurements
(tide gauges and Argo-based steric sea level) indicates a better consistency
at the regional scale with the new SL_cci data (see
SL_cci Product Validation Internal Report – PVIR, 2014). It
is more difficult to detect any improvement at short spatial scales, because
either the spatial or temporal sampling of in situ measurements is not good
enough or because the error generated by the collocation method between the
in situ and altimetry data is larger than the target signal (Couhert et al.,
2015). We also examined the periodic (annual and semi-annual) sea level
signals. We found differences in the order of 5 mm on average for the
amplitude of the annual signal. In some regions (the tropics), the
differences can reach 1 cm. While we think that the new seasonal signal is
improved compared to the AVISO-2010 products, it is not possible to
demonstrate this through any independent validation diagnoses. Indeed,
comparisons with the in situ measurements are not accurate enough to observe
such signals.
Validation of the temporal and spatial variations of global sea level
The SL_cci products delivered at the end of Phase 1 are
currently under validation and evaluation. Two different approaches have
been developed:
assessment of the accuracy of the SL_cci products through their use in ocean reanalyses and earth system
models;
assessment of the global sea level budget.
In approach (1), the accuracy of the SL_cci data is evaluated
by quantifying the model performances and robustness (compared to the use of
a reference sea level data set, e.g. AVISO standard data) in
representing a number of physical processes (e.g. the sea level drop
associated with the 2011 La Niña, the Indonesian through flow, changes
in the Arctic circulation, effects of monsoon on sea level, regional sea
level fingerprint due to wind stress, steric sea level trend patterns).
Approach (2) consists of comparing the SL_cci GMSL and
variability to (i) other GMSLs, and (ii) the sum of the climatic and
non-climatic components estimated independently (changes in thermal
expansion, glacier and ice sheet mass balance, and land water storage).
Assessment based on numerical ocean models
Ocean model simulations are an effective way of translating wind and heat
fluxes information into sea level variations, thus providing independent
verification of their contribution to sea level. Sea level from ocean-only
simulations at different resolutions (1∘, 0.25∘) has been contrasted with along-track data and with
gridded (filtered and merged) sea level maps from AVISO (Dibarboure et al.,
2011) and SL_cci. The statistics of the comparison
(correlation, rms error, differences in trends) were similar when using
AVISO and SL_cci data. Differences between models and any
observed estimations were much larger than the differences between
observational products. The spatial patterns of these differences were
suggestive of model error. For instance, small-scale sea level variability
is much larger in observed products than in models, which is consistent with
insufficient resolution in the models. In contrast, the low-frequency and
large-scale variability is more obvious and better resolved in models. The large-scale patterns of interannual variability and trends are consistent between
models and observations, but differences exist associated with the precise
location of strong current systems, which models struggle to capture. This
information is in itself interesting, and suggests that a large part of the
sea level variability is of a dynamic nature, associated with changes in the
wind-driven circulation. Both AVISO and SL_cci were useful to
detect improvements in ocean model simulations due to the increased
resolution.
In the Arctic Ocean the SL_cci reprocessed data reveal some
distinct features of the elevated trend in sea level rise, notably in the
Beaufort Sea, in the Norwegian Sea, in the Sub-Polar gyre, and in the northeast Atlantic south of the Iceland–Faroe ridge. The Beaufort Sea rise of
about 6.5–7 mm year-1 has also been reported by Morrison et al. (2011) and
Laxon et al. (2012), while the elevated feature of around 6–7 mm year-1, as
detected in the SL_cci field in the Lofoten Basin of the
Norwegian Sea, compares rather well with the trend recovered from in situ
hydrographic observations.
GMSL time series separating ERS-1/ERS-2/Envisat and
TOPEX/Jason-1/Jason-2 altimeter missions using alternatively the old
(AVISO-2010 standards) on left and new altimeter correction (SL_cci) on
right.
A first look at the three general circulation models (GCMs), NorESM
(Norwegian Earth System Model), Hadley, and IPSL (Institut Pierre-Simon
Laplace), reveals large individual differences in the trend of sea level
change, regarding the overall trend as well as in its regional
characteristic changes. The contributions to these simulated changes include
the regional variability of the steric and the mass components, while there
is no account of the GIA. In comparison to the SL_cci sea
level change the NorESM simulations (1∘ resolution) yield the best
agreement in the Sub-Polar gyre, in the northeast Atlantic Ocean south
of the Iceland-Faroe ridge, in the Lofoten basin of the Norwegian Sea and in
the Beaufort Gyre. This inter-comparison of the SL_cci trends
with the trends derived from the three GCMs can therefore provide evidence
for how realistic the model simulations are with respect to the regional
variability of the water masses (steric height contribution) and
variability, spreading, and accumulation of freshwater discharges from
melting ice sheets and glaciers (mass changes).
In summary, as was to be expected from the beginning, even ocean-only
simulations are not able to identify the incremental improvement of
SL_cci vs. its predecessor. Nevertheless, this validation
exercise has shown that the SL_cci is a robust data set for
ocean and climate models validation, and can discern verification metrics.
Assessment based on ocean data assimilation
Data assimilation methods can be very effective methods to test the quality
of the input data. This approach was used here to evaluate the
SL_cci products, either by direct assimilation of the product
as an ocean synthesis (active mode) or by simple comparison with a reference
state (passive mode), obtained by a forced ocean model combined with in situ
observations, and even other sea level observations. In this way, the ocean
synthesis, containing information from both the model forced with realistic
atmospheric state and observations, should have less error than an
ocean model simulation alone. The passive comparison can be done
a posteriori (by comparing ocean reanalyses with SL_cci), or
during the assimilation process, by contrasting, at the appropriate location
and time, the along track altimeter data with the estimate given
by an ocean model that assimilates in situ temperature and salinity.
Ratio of the rms differences RMS_AVISO and RMS_SL_cci between the
GECCO model and the satellite time series of ERS-1, ERS-2, and ENVISAT in
percent improvement.
In a first step, sea surface height fields available from the GECCO2
assimilation approach (Köhl, 2014) were compared to the AVISO products
as well as to the SL_cci product. Of these two,
the AVISO product was used to constrain the model, but not the
SL_cci product. The comparison was performed to investigate
whether the new SL_cci product is closer to the GECCO2 ocean
reanalysis product, which is constrained by most of the available global
data sets, than the previous AVISO data set, a test that would highlight a
better consistency of the new SSH data with ocean dynamics and other ECV
information. The comparisons have been performed separately for the ERS
(ERS-1, ERS-2 and ENVISAT) and the TOPEX/Poseidon satellite-series
(TOPEX/Poseidon, Jason-1 and Jason-2). Figure 8
shows the ratio (RMS_AVISO/RMS_SL_cci) of the rms differences between the GECCO model and
the satellite time series of ERS-1, ERS-2 and ENVISAT for AVISO
(RMS_AVISO) and SL_cci (RMS_SL_cci) in percent improvement at model resolution. Red
indicates improvements of the SL_cci compared to the AVISO
data set and blue degradation. Remarkable are the improvements in the North
Atlantic, in the Indian Ocean through flow and in many parts of the ocean.
The regions where SL_cci shows less skill compared to AVISO
are the ones where the GECCO2 solution has adapted very well to AVISO and at
the same time where the standard deviation of the data sets are very small, indicating a
small signal to noise ratio in these regions. Therefore, the model might
have adapted to the not as good AVISO data and thus gives less skill in
comparison to the improved SL_cci data set. The improved
regions (red colours) cover 62.8 % of the ocean area that had valid data
for the comparison, leaving 37.2 % of the ocean area that has degraded
(blue colours). Further, when averaging the ratio of RMS_AVISO/RMS_SL_cci globally, weighted by the
area of each grid point, a global mean improvement of 0.91 % can be seen
from the analysis on the model grid. This could demonstrate that the
SL_cci has been improved in many regions.
Differences (m) in the sea level time evolution (12 month running
mean) with respect to the AVISO product of SL_cci (red) and ORAS4 (blue) . Left:
eastern equatorial Pacific
(5∘ N–5∘ S, 130–90∘ W). Right: southern Indian
Ocean (30–70∘ S, 20–150∘ E). The differences in trends
between SL_cci and AVISO are confirmed by ORAS4. In the eastern Pacific,
both ORAS4 and SL_cci have a stronger El Niño–Southern Oscillation signature than AVISO.
GMSL based on multi-mission satellite altimetry data processed by
different groups (including SL_cci project). Left/right panel: with/without
the global mean trend.
Both AVISO and SL_cci sea levels have also been compared with
the sea level from the ORAS4 ocean reanalyses (Balmaseda et al., 2013),
which assimilate in situ temperature, salinity, and AVISO data along a track
altimeter. Time series of standard area-averaged climate indices have been
used to gain insight on the differences between the AVISO and
SL_cci products. Figure 9 shows a
time series of the 12 month running mean sea level anomaly differences. In the
eastern Pacific (5∘ N–5∘ S, 130–90∘ W, left panel) both ORAS4 and
SL_cci show a positive offset with respect to AVISO data
after 2005 (from 2005 onwards the ocean state in ORAS4 is relatively well
constrained by Argo). In addition, SL_cci and ORAS4 data
consistently show stronger local maxima associated with El Niño, 1997. The
precursor of this El Niño is visible in the western Pacific slightly
earlier, and it is also more pronounced in SL_cci and ORAS4
than in AVISO (not shown). The right panel of Fig. 9 shows the equivalent time series for the southern Indian Ocean
(30–70∘ S, 20–150∘ E), where
both ORAS4 and SL_cci consistently show a negative tendency
with respect to AVISO, suggesting that AVISO overestimates the sea level
rise in this area. The differences in trends between SL_cci
and AVISO shown in these time series are similar to those shown in Fig. 6
(bottom). The variability of the ORAS4 reanalysis agrees better with the
SL_cci product than with AVISO.
Comparison of the SL_cci GMSL time series with other GMSL products
We constructed a GMSL time series by geographically averaging the
SL_cci gridded data between 66∘ S and 66∘ N. A simple cosine of latitude weighting was applied to the data. As no
glacial isostatic adjustment (GIA) correction was applied to the gridded
data, we added the usual +0.3 mm year-1 GIA trend from the SL_cci GMSL (as usually done by other processing groups). We further compared
the SL_cci GMSL with altimetry-based GMSL time series
computed by different processing groups (AVISO, University of Colorado
(CU), NOAA (National Oceanic and Atmospheric Administration), GSFC (Goddard
Space Flight Center), and CSIRO (Australia's Commonwealth Scientific and
Industrial Research Organisation). The results are shown in
Fig. 10 (left panel). In terms of trends, all
curves are very similar to each other and trend differences
(< 0.2 mm year-1) are fully covered by the formal error on the trend
computation. However, it is interesting to note that all sea level curves
differ significantly (by several mm) over an interannual timescale. This is
illustrated in Fig. 10 (right panel), and is
particularly noticeable during the TOPEX/Poseidon period (1993–2001), with a
significant big departure of the CSIRO GMSL from other curves. The detrended
SL_cci GMSL is in general close to the AVISO GMSL, although
slight differences are noticed at the end of the study period.
Comparison of the SL_cci GMSL with steric and ocean mass components (sea level closure budget); interannual time scale
GMSL change is a combination of ocean mass and steric (thermal expansion)
changes. We compared the GMSL computed from the SL_cci
gridded product with the sum of steric and mass components over the Argo and
GRACE (Gravity Recovery and Climate Experiment) operating period (since
∼ 2005). Argo-based steric data used for this comparison are
based on those processed by Karina von Schuckmann (von Schuckmann and Le
Traon, 2011). Ocean mass has been estimated using the RL05 data from the
GRACE project (average of the three products: CSR, JPL and GFZ; Chambers and Bonin, 2012). The GRACE and steric data have been averaged over the
66∘ S and 66∘ N domain. Figure 11 compares three GMSL products (AVISO, CU, and SL_cci) with
the sum of steric and mass contributions over 2005–2010. Error bars of the
sum “steric plus mass” time series are not shown for clarity. They are
estimated to within ±2 mm for individual monthly values. The mean trend
over the study period (2005–2010) has been removed. The three GMSLs present
similar variations and show reasonably good agreement with the sum of the
components. Although small differences exist, the best agreement is found
for the SL_cci GMSL. Correlation coefficients between the sum
“steric plus mass” component and GMSL time series have also been computed.
The highest correlation (of 0.65) is found with the SL_cci
GMSL.
Sum of steric and ocean mass components based on Argo and Grace data
(see Sect. 5.4) (green curve) over the January 2005–December 2010 time
period and different GMSL products (left panels). Right panel: difference
between the GMSL products and sum of components.
The results presented above are first attempts to validate the
SL_cci products. We find some differences in terms of both
global mean and regional variability with the standard products. Preliminary
comparisons with the sum of the climate contributions (the sea level budget
closure approach) suggest that the CCI product fits better the sum of
the climatic components. However, this result is not robust considering the
large uncertainties affecting the steric and mass components. Further work
is needed on that matter, using different steric and ocean mass products
with assessed uncertainties. For instance, the steric height from ocean
reanalyses can also be used for global sea level budget closure (Balmaseda
et al., 2013). This will be a topic for the CCI phase 2 activities.
Error budget of sea level
Although improvements were made, the SL_cci products still
contain errors at different timescales. In order to inform users
about these errors, we have established an error budget dedicated to the
main spatio-temporal scales, i.e. global and regional, long-term (5–10 years or more), interannual (< 5 years), and seasonal (see
Table 2)). For each of these, an error was
determined and compared to the sea level Climate User requirements (GCOS,
2011) which have been updated in the framework of the Sea Level CCI project
(Sea Level CCI User Requirement Document – URD, 2013).
Error budget of SL_cci products for the main climate scales.
Spatial scalesTemporal scalesAltimetry errorsUser requirementsGlobal MSLLong-term evolution (> 10 years)< 0.5 mm year-10.3 mm year-1Interannual signals (< 5 years)< 2 mm over 1 year0.5 mm over 1 yearAnnual signals< 1 mmNot definedRegional MSLLong-term evolution (> 10 years)< 3 mm year-11 mm year-1Annual signals< 1 cmNot defined
Regarding the GMSL trend, an uncertainty of 0.5 mm year-1 was estimated over the
whole altimetry era (1993–2010). This uncertainty is reduced by 0.1 mm year-1
compared to the previous data based on AVISO-2010 standards over 1993–2008
(Ablain et al., 2009). While small, this reduction is mainly due to a 2-year
longer record as well as to the homogenization of the altimetry corrections
between all the missions. The main source of the error remains the
radiometer wet tropospheric correction with a drift uncertainty in the range
of 0.2–0.3 mm year-1 (Legeais et al., 2014). To a lesser extent, the orbit
error (Couhert et al., 2015) and the altimeter parameters (range, sigma-0,
SWH) instabilities (Ablain et al., 2012) also add uncertainty, of
the order of 0.1 mm year-1. Notice that for these two corrections, the
uncertainties are higher in the first altimetry decade (1993–2002) where
TOPEX/Poseidon, ERS-1 and ERS-2 measurements display stronger errors (Ablain
et al., 2013). Furthermore, imperfect links between TOPEX-A and TOPEX-B
(February 1999), TOPEX-B and Jason-1 (April 2003), and Jason-1 and Jason-2
(October 2008) lead to the errors of 2 mm, 1 mm and 0.5 mm respectively
(Ablain et al., 2009). They cause a GMSL trend error of about 0.15 mm year-1
over the 1993–2010 period. Although the SL_cci project work
has led to significant improvements, the remaining uncertainty of 0.5 mm year-1
on the GMSL trend remains 0.2 mm year-1 higher than the GCOS requirements (of
0.3 mm year-1; see GCOS, 2011).
All sources of errors described above have also had an impact at the
interannual timescale (< 5 years). Recent studies (Henry et al.,
2013) have shown that the methodology applied to calculate sea level is
particularly sensitive for the interannual scales (Henry et al., 2014). We
estimated that the methodology uncertainty is on average ∼ 2 mm over a 1-year period. Although improvements have been made, this
level of error is still 1.5 mm higher than the GCOS requirement of (0.5 mm).
This may have consequences on the sea level closure budget studies at the
interannual timescale. For the annual signal, the amplitude error was
estimated to be < 1 mm. Knowing that the annual amplitude of the
GMSL is of the order of 9 mm, we can consider this error to be low. Notice that
no requirement has yet been defined by GCOS for the periodic signals (at
global and regional scales).
At the regional scale, the regional trend uncertainty is of the order of 2–3 mm year-1. Although the orbit error has been significantly reduced for this
spatial scale, it remains the main source of the error (in the range of 1–2 mm year-1; Couhert et al., 2015) with large spatial patterns at hemispheric
scale. The earth gravity field model errors explain an important part of
these uncertainties (Rudenko et al., 2014). Furthermore, errors are higher
in the first decade (1993–2002), where the earth gravity field models are less
accurate due to the unavailability of the GRACE data before 2002. Additional errors are still observed;
for example, for the radiometer-based wet tropospheric correction in tropical
areas, other atmospheric corrections in high latitudes, and high-frequency
corrections in coastal areas. The combined errors give rise to an
uncertainty of 0.5–1.5 mm year-1. Finally, the 2–3 mm year-1 uncertainty on regional
sea level trends remains a significant error compared to the 1 mm year-1 GCOS
requirement, even if this project has led to a 0.5 to 1.5 mm year-1 reduction
(Fig. 6).
Conclusions and perspectives
Several groups (AVISO, University of Colorado, CSIRO, JPL (Jet Propulsion
Laboratory), etc.) are currently processing satellite altimetry data to
provide sea level products to user for climate applications. Within the
SL_cci project, we have continued to improve the
multi-mission sea level products over the altimetry era (1993–2010) through
the development and computation of new corrections listed in
Table 1. As far as possible, we have homogenized
these corrections between all the missions in order to reduce the sources of
discrepancies. Thanks to our formal validation protocol, we have been able
to select the best corrections and algorithms applied in the sea level
calculation. We have produced new sea level products and additional
indicators over the 1993–2010 period. The SL_cci products
exhibit improvements of different levels of importance for climate studies. Some of
them are substantial, for instance for the estimation of the regional sea
level trends, with an error reduction of 0.5–1.5 mm year-1 with large correlated
spatial patterns. In parallel, the uncertainties of altimetry sea level have
been better characterized and the sea level user requirements refined for
climate applications.
The validation exercise has demonstrated that the existence of an additional
good-quality sea level record has value in itself. Firstly, it clearly shows
that the AVISO and SL_cci altimeter-derived sea level gridded
products are robust (small uncertainty compared with the model error) and
able to identify model improvements. Therefore they are a suitable data set
to define metrics in the validation of ocean and climate models.
SL_cci can be treated as an independent data set for
verification. It has been used in the recent inter-comparison of ocean
reanalyses ORAIP (Balmaseda et al., 2014; Hernandez et al., 2014).
Preliminary results show that the SL_cci is closer to the
ensemble mean of ocean reanalyses (a robust estimator) than its predecessor
AVISO, and suggest that some ocean reanalyses that assimilate AVISO may
over-fit the altimeter data. Model outputs using ocean assimilation
techniques also provide independent sea level estimations that can be used
to validate the SL_cci. Results obtained in the frame of the
SL_cci project show that the low-frequency variability and
trends of SL_cci agree better with ocean data assimilation
estimators than with AVISO, especially in the Southern Ocean, the eastern
Pacific, and coastal areas.
However, while a lot of improvements have been made, the user requirements
are not yet reached. Remaining uncertainties are still 0.2 and 1–2 mm year-1 higher than the GCOS requirements for the GMSL trend and regional
trends respectively. Similarly, the sea level error over a 1-year period is
about 2 mm on average instead of the required 0.5 mm. Therefore it is still
necessary to continue to improve the sea level time series to better
understand key scientific issues, as raised in the abstract. Several ways
of making improvements have already been identified and will be implemented during
phase 2 of SL_cci project (January 2014 to December 2016).
For example, we plan to extend the sea level time series beyond 2010 using
the same sea level corrections. By the end of year 2014, the current
CCI_SL release will be extended until 2013 (included). And
each subsequent year, we will extend the time series by 1 year. Additional
improvements will be implemented; in particular, new orbit solutions, use of
new atmospheric reanalyses based on the ERA-Clim project (Dee, 2014), new
ocean tides, new radiometer-based wet troposphere corrections with improved
long-term stability, etc. Furthermore, several level-2 altimetry data
reprocessing activities are already planned by space agencies (CNES, NASA,
ESA) for Jason-1, TOPEX/Poseidon, Envisat, and ERS missions, allowing us to
benefit from homogenized data for both instrumental parameters and
geophysical corrections. In addition, we intend to account for new altimeter
missions already in orbit (CryoSat-2, SARAL/Altika) or to be launched in the
near future (Jason-3, Sentinel-3). They are all relevant to extending the sea
level time series with the same level of accuracy, and to improving coastal
and high-latitude areas, which are of great interest for climate studies.
Dedicated analyses will be performed in the Arctic region in order to
improve sea level estimates near or under sea ice where no data are
currently available. In parallel, we will continue to refine the user
requirements, further developing the link with users and space agencies.
This will include a quantification of the requirements for accuracy and
long-term stability for climate-quality observations of sea level in the
coastal zone, a key area for climate change. We also would like to refine
the budget error with the new measurements and the new corrections. Lastly,
to continuously answer user needs, we will produce
by the end of 2016 a new, improved sea level time series covering the
1993–2015 period.
Acknowledgements
This work was performed in the framework of the ESA CCI program supported by
ESA. It was also made possible thanks to the support of CNES for several
years in altimetry data processing; in particular, thanks to the use of
SSALTO/DUACS system developed in the framework of the SALP project (Service
d'Altimétrie et de Localisation Précise). We would also like to
thank all contributors to this project who have participated actively in the
SL_cci project, with special recognition to S. Dinardo and
B. M. Lucas in support of ESA, for their diligent reviewing of all the
documents and data sets produced by the SL_cci
team.Edited by: A. Schiller
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