ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-9-557-2017A new phase in the production of quality-controlled sea level dataQuartlyGraham D.gqu@pml.ac.ukhttps://orcid.org/0000-0001-9132-9511LegeaisJean-FrançoisAblainMichaëlZawadzkiLionelFernandesM. Joanahttps://orcid.org/0000-0002-0946-0092RudenkoSergeihttps://orcid.org/0000-0001-5149-3827CarrèreLorenGarcíaPablo NiloCipolliniPaolohttps://orcid.org/0000-0002-3682-5675AndersenOle B.https://orcid.org/0000-0002-6685-3415PoissonJean-ChristopheMbajon NjicheSabrinaCazenaveAnnyBenvenisteJérômePlymouth Marine Laboratory, Plymouth, PL1 3DH, UKCLS, 31520 Ramonville-Saint-Agne, FranceFaculdade de Ciências, Universidade do Porto, 4169-007, Porto,
PortugalCentro Interdisciplinar de Investigação Marinha e Ambiental
(CIIMAR), 4450-208 Matosinhos, PortugalDeutsches Geodätisches Forschungsinstitut, Technische
Universität München, 80333 Munich, GermanyHelmholtz Centre Potsdam GFZ German Research Centre for
Geosciences,
Telegrafenberg 14473 Potsdam, GermanyisardSAT, 08042 Barcelona, SpainNational Oceanography Centre, Southampton, SO14 3ZH, UKDTU Space, 2800 Kongens Lyngby, DenmarkCGI, Leatherhead, KT22 7LP, UKLEGOS, 31400 Toulouse, FranceISSI, 3912 Bern, SwitzerlandESA/ESRIN, 00044 Frascati, ItalyGraham D. Quartly (gqu@pml.ac.uk)14August20179255757227March201713April20173July201714July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://essd.copernicus.org/articles/9/557/2017/essd-9-557-2017.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/9/557/2017/essd-9-557-2017.pdf
Sea level is an essential climate variable (ECV) that has a direct effect on many
people through inundations of coastal areas, and it is also a clear indicator
of climate changes due to external forcing factors and internal climate
variability. Regional patterns of sea level change inform us on ocean
circulation variations in response to natural climate modes such as El
Niño and the Pacific Decadal Oscillation, and anthropogenic forcing.
Comparing numerical climate models to a consistent set of observations
enables us to assess the performance of these models and help us to
understand and predict these phenomena, and thereby alleviate some of the
environmental conditions associated with them. All such studies rely on the
existence of long-term consistent high-accuracy datasets of sea level. The
Climate Change Initiative (CCI) of the European Space Agency was established
in 2010 to provide improved time series of some ECVs, including sea level,
with the purpose of providing such data openly to all to enable the widest
possible utilisation of such data. Now in its second phase, the Sea Level CCI
project (SL_cci) merges data from nine different altimeter missions in a
clear, consistent and well-documented manner, selecting the most appropriate
satellite orbits and geophysical corrections in order to further reduce the
error budget. This paper summarises the corrections required, the provenance
of corrections and the evaluation of options that have been adopted for the
recently released v2.0 dataset
(https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612). This information
enables scientists and other users to clearly understand which corrections
have been applied and their effects on the sea level dataset. The overall
result of these changes is that the rate of rise of global mean sea level
(GMSL) still equates to ∼ 3.2 mm yr-1 during 1992–2015, but
there is now greater confidence in this result as the errors associated with
several of the corrections have been reduced. Compared with v1.1 of the
SL_cci dataset, the new rate of change is 0.2 mm yr-1 less during
1993 to 2001 and 0.2 mm yr-1 higher during 2002 to 2014. Application
of new correction models brought a reduction of altimeter crossover variances
for most corrections.
Introduction
Sea level is widely recognised as an essential climate variable (ECV) that
has a significant impact on mankind. An accelerated rise in global mean sea
level (GMSL) shows the integrated effect of increased ocean heat content and
the enhanced melting of glaciers and ice sheets. Many major conurbations are
sited on the coast and vulnerable to long-term sea level rise. This is also
critical for low-lying islands (such as the Maldives) and highly populated
river deltas (such as the Brahmaputra in Bangladesh) where continued sea
level rise threatens the lives of many.
The issue of sea level rise thus has aspects that are global, regional and
local. Global mean sea level rise is related to increased forcing within the
global climate through increased ocean warming and land ice loss. Satellite
altimetry also reveals significant regional variability, with some regions
experiencing greater rates of sea level rise. The sea level in a region also
responds to ocean circulation changes associated with various modes of
climatic variability, which may temporally ameliorate or exacerbate the
effects of global change; such regional climatic oscillations need to be
better measured, modelled and understood. An accurate robust record of
regional changes can help to provide the “fingerprint” to distinguish
between different models of the Earth's response to enhanced climate forcing
(Hasselmann, 1997). At the coast what is important to the population is the
combined effects of large-scale climate variations, local changes in waves
and currents, and vertical land motion. In many regions the ground is
subsiding in response to increased sediment load in deltas or ground water
depletion near megacities. Also, the land masses are still undergoing a
delayed response to the removal of their burden from the last ice age (a
phenomenon known as “glacial isostatic adjustment”). Together these effects
and sea level rise amplify the vulnerability of coastal regions, producing
major societal impacts. Finally, sea level variations need to be precisely
monitored at the mesoscale (50–200 km) as the variability associated with
eddies and current fluctuations provides many of the mechanisms for
transporting and mixing water masses, with attendant effects on primary
productivity.
The European Space Agency (ESA) set up the Climate Change Initiative (CCI) in
2010 to develop consistent long-term datasets of many of the recognised
essential climate variables (ECVs), with one using satellite altimetry to
provide sea level data over most of the open ocean, with the aim of
addressing part of the aforementioned wide range of scientific and societal
needs. The initial (v1.0) dataset spanned 1993–2010 (Ablain et al., 2015,
2016); the second phase of the CCI (2014–2016) has not only extended the
data duration (up to end of 2015) but also revisited many aspects of the
data processing and corrections to improve the quality of the dataset for
global, regional and mesoscale applications. This paper details the
processing options selected for the production of the v2.0 dataset.
The whole dataset is based on the concept of altimetry, i.e. that a satellite
flying in a near-polar orbit measures the ocean surface topography by
recording the time taken for radar pulses emitted by the satellite to reflect
off the surface and be recorded on the satellite. There are many technical
details to the measurement of this distance to within a few centimetres from
a satellite ∼ 720–1350 km above the Earth's surface, which are
described in Chelton et al. (1989), Fu and Cazenave (2001) and Escudier et
al. (2017). Range is then computed by multiplying half the time delay by the speed
of light in vacuo, and then applying corrections for the components
of the return path where speed is slightly less – these are the dry
tropospheric correction (DTC), wet tropospheric correction (WTC), and the
ionospheric correction (Iono). Subtracting this altimetric range from a
well-modelled orbit height then gives a value for the sea surface height
relative to some reference surface. To give a measure that is useful for
oceanographic applications, the value needs to be adjusted for the effect of
changes in atmospheric conditions (dynamic atmosphere correction, DAC) and
tides. Finally there is an empirical correction, sea state bias (SSB),
accounting for various effects related to the wind and wave conditions. Thus
the required oceanographic parameter, the sea level anomaly (SLA), is defined
as
(a) Gantt chart of the available altimetry missions. (Full names of satellites and other commonly used abbreviations are given in
Appendix A.)
* The spacecraft TOPEX/Poseidon had two separate altimeters, with the
experimental Poseidon instrument on for ∼ 10 % of the time, during
which TOPEX did not operate. The “reference missions” all commenced in the
same orbit, with 66∘ orbit inclination and a repeat period of
9.92 days; subsequent phases of those missions were then in a 9.92-day
interleaved orbit (pink outline) or a long-repeat (geodetic) orbit (black
outline). The missions highlighted in orange were principally in another
common orbit (98.5∘ inclination and 35-day repeat), except for
geodetic phases (black outline) and short periods in a 3-day repeat (ERS-1).
The other complementary missions are GFO (72∘ inclination, 17.05-day)
and CryoSat-2 (88∘ inclination, geodetic orbit). The periods
indicated by white bars with red outlines are not used in the production of
CCI v2.0 product. (b) Annual amount of independent altimeter data
used in the production of the v2.0 dataset. (Note, for example, that during
the 6-month “tandem” phases between successive “reference missions” the
contribution of one of the pair to the sea level record is
redundant.)
SLA=Orbit-(Range+DTC+WTC+Iono)-DAC-Tides-SSB-MSS,
where the mean sea surface (MSS) is the sum of the geoid (the geopotential
surface indicating the level that would be recorded for a motionless ocean)
and the mean dynamic topography (MDT), which corresponds to the topographic
variations associated with the mean circulation of the ocean. Values for
these corrections are supplied in the geophysical data records (GDRs) provided
by the space and meteorological agencies; however, there is a need to review
whether new ones are more accurate, and also to establish a consistent
selection across all missions used.
Gridded altimeter products combine information from two sets of altimeters – the
“reference missions” (TOPEX/Poseidon, Jason-1, Jason-2 etc.) in a high-altitude (∼ 1336 km) orbit, with a 9.92-day repeat cycle, and the
“complementary missions”, which are in a lower orbit, several of which
(ERS-1, ERS-2, Envisat) have been in a 35-day repeat orbit. In progressing
from the Sea Level CCI (SL_cci) v1.1 product to the v2.0 product, the
length of the dataset has been extended and two new sources of altimeter data
have been included (SARAL/AltiKa and CryoSat-2; see Fig. 1), and all the
corrections have been reappraised to ensure that they are the most
appropriate for establishing a consistent and stable long-term record for use
at global, regional and mesoscale. Note that the SL_cci Algorithms
Theoretical Basis Document (Ablain et al., 2016) provides the details on all
algorithms used to compute the 1 Hz along-track measurements. This paper
deals with each of these correction terms, documenting the selections made
and their justification; subsequent papers will exploit the SL_cci v2.0
data to improve our understanding of present-day sea level variations at
global and regional scales, and their causes.
The assessment of new corrections has been carried out by a formal
validation protocol using a common set of diagnoses defined to fulfil the
sea level accuracy and precision requirements, as defined by the Global
Climate Observing System (GCOS, 2011). This protocol consists of comparing
new altimeter corrections with previous ones through their impact on the sea
level calculation. The validation diagnoses are distributed into three
distinct families allowing the assessment of altimetry data with
complementary objectives.
“Global internal analyses”, which check 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,
“Global multi-mission comparisons”, which evaluate the coherence
between two different altimetry systems through comparison of SLA data,
“Altimetry comparison with in situ data”, which computes differences
between altimeter SLA data and those from in situ sea level
measurements, e.g. tide gauges or Argo-based steric sea level data (Legeais
et al., 2016a); this third approach allows for the detection of potential
drifts or jumps in the long-term sea level time series.
Orbits and range
Orbital height and altimeter range are the two large terms that are
differenced in the calculation of SLA. The former term refers to the height
of the satellite above the reference ellipsoid, whilst the range is the
measurement from the radar altimeter to the ocean surface. The orbit is not
measured everywhere but rather calculated from a sophisticated numerical theory of
satellite motion using a well-defined reference frame and taking into account
various forces acting on a satellite, such as gravitational fields of the
Earth, Moon, Sun and major planets of the Solar System; drag in the Earth's
atmosphere; and radiation from the Sun and the Earth. The orbit computation
for the various altimetry satellites uses a variety of data – precise
satellite laser ranging from ground stations, GNSS locations from navigation
satellites that are in a much higher orbit, and radio-positioning information
from DORIS and PRARE – although not all sources are available for every
satellite. The calculation of altimeter range includes waveform retracking
(i.e. fitting a model to the shape of the radar echo) and compensation for an
altimeter bias specific to the instrument (Ablain et al., 2017; Escudier et
al., 2017).
Modelled orbits
As the orbital height of the satellites needs to be known to centimetric
accuracy (i.e. one part in 108), the Earth's gravity field requires a
detailed representation usually expressed in spherical harmonic coefficients,
typically to degree and order 90–120 for satellites at altitudes between 700
and 1400 km. Terrestrial gravimeters and geodetic satellites, such as
LAGEOS, and more recently the space gravimetry mission GRACE (Tapley et al.,
2004) revealed that the Earth's gravity field changes with time. Detailed
analysis of the observations of satellites in low Earth orbit, in particular,
from the missions designed to observe the Earth's gravity field, such as
CHAMP (2000–2010), GRACE (2002–present) and GOCE (2009–2013) has
significantly improved knowledge about the Earth's static and time-variable
gravity. Time variations in the gravity field include the mass redistribution
within and between the Earth's atmosphere, hydrosphere, ocean and cryosphere,
on a variety of timescales, from subseasonal to multidecadal. Ollivier et
al. (2012) and Rudenko et al. (2014) showed that ignoring a time-variable
(secular) part of the geopotential causes up to 3 mm yr-1 east–west
errors in the regional sea level trends. Additionally, ignoring non-tidal
high-frequency atmospheric and oceanic mass variations can lead to errors of
up to 7 mm in sea level and up to 0.25 mm yr-1 in the regional trend
(Rudenko et al., 2016). Achieving precise orbits also requires an accurate
model of the spacecraft itself in order to understand the drag terms from a
very tenuous atmosphere, the effects of solar radiation pressure and
relativistic effects.
New VER11 orbit solutions of ERS-1, ERS-2, Envisat, TOPEX/Poseidon, Jason-1
and Jason-2 have been generated at GFZ (Rudenko et al., 2017). Additionally,
a new orbit version (POE-E) has been computed at CNES for Jason-1, Jason-2,
AltiKa and CryoSat-2, and finally, a new orbit version (GSFC std1504) has
been derived at GSFC for TOPEX/Poseidon, Jason-1 and Jason-2 (Lemoine et al.,
2017). All these orbit solutions have been derived in the extended ITRF2008
reference frame (Altamimi et al., 2011) by using SLRF2008 (Pavlis,
2009), DPOD2008 (Willis et al., 2016) and IGS08 (Rebischung et al., 2012)
station solutions and are based on the GDR-E orbit standards (Dumont et al.,
2017) or similar standards. The main differences of these standards with
respect to the previous GDR-D (Dumont et al., 2017) orbit standards consist
of (i) using a more refined Earth time-variable gravity field model
EIGEN-GRGS.RL03-v2.MEAN-FIELD including time-variable geopotential terms up
to degree and order 80 (instead of 50 in the previous standards),
(ii) increased expansion of the atmospheric gravity model (from degree and
order 20 to 70), (iii) modelling of tidal and non-tidal geocentre variations,
(iv) improved modelling of non-gravitational forces for some satellites,
(v) improvements in the troposphere correction model for DORIS observations,
and (vi) using Earth orientation parameters consistent with the ITRF2008
reference frame.
A validation of these new orbit solutions has been performed with respect to
those selected for the SL_cci v1.0 product (Table 1 of Ablain et al., 2015).
The main criteria for the selection are a reduction of the SLA crossover
variance differences and minimum absolute difference of the mean sea level
computed using ascending and descending passes. As a result of this
validation, the following orbit solutions have been selected: GFZ VER11
orbits for ERS-1, ERS-2 and Envisat; CNES POE-E orbits for Jason-1, Jason-2,
AltiKa and CryoSat-2; and GSFC std1504 orbit for TOPEX/Poseidon. Consequently,
using the GSFC std1504 orbit for TOPEX/Poseidon instead of the GSFC std1204
orbit (used for the SL_cci v1.0 product) reduces the mean of sea surface
height (SSH) crossovers from 0.34 to 0.24 cm. The standard deviation of
these crossovers shows an improvement from 4.99 to 4.96 cm for Jason-1, from
4.91 to 4.87 cm for Jason-2, and, from 5.55 to 5.51 cm for Cryosat-2, when
using the CNES POE-E orbit instead of the CNES POE-D orbit. Since no new
orbit solution has become available for GFO, the same (GSFC std08; Lemoine et
al., 2006) orbit was used for the generation of the SL_cci v2.0 product,
as for its predecessor. Couhert et al. (2015) showed that using Jason-1/2
orbits derived with SLR and DORIS measurements may cause up to
0.3 mm yr-1 decadal and 1 mm yr-1 interannual regional errors
when employing ITRF2005 reference frame instead of ITRF2008 one for orbit
computations. Since no DORIS data were used to derive GFO GSFC std08 orbit,
the impact of using this orbit on the regional sea level may be larger, when
using just one mission. However, since regional sea level is derived in the
SL_cci v2.0 product using data from nine altimetry missions over the time
span 1993–2015, the impact of using GFO orbit derived in ITRF2005, while the
orbits of the other eight missions are in the ITRF2008, is rather small. There is
no impact of the GFO orbit on the global mean sea level (GMSL), since GFO is
not included in the reference missions used to derive that in the SL_cci
v2.0 product.
The SL_cci v1.1 product used the REAPER combined orbit for ERS-1 and ERS-2
(Rudenko et al., 2012), whilst GFZ VER11 orbit was used for the new (v2.0)
product detailed here. The differences in the regional sea level trends
computed using these two different orbits reach ±2.0 mm yr-1
(Fig. 2). A switch from CNES POE-D orbit to POE-E orbit for Jason-1 caused
changes in the SLA trend of up to ±1.5 mm yr-1 (Fig. 3). The broad
dipole pattern corresponds to errors in the modelling of geocentre motion,
whilst individual tracks are prominent where changes to the gravity field
have a more local effect.
Difference in sea level trends for ERS-1 data (October 1992 to
June 1996) computed using GFZ VER11 orbit and the REAPER combined orbit
(which was used in an earlier CCI sea level product; Ablain et al., 2015).
Precise determination of the altimeter range
A waveform, i.e. the full radar echo recorded on board the altimeter,
corresponds to the radar return from a disc a few kilometres across on the
sea surface. Provided the surface is homogeneous, the shape of the waveform
will conform to the Brown model (Brown, 1977; Hayne, 1980). In such
circumstances, the position of the waveform (and thus the range) may be very
accurately extracted; these values are stored in the GDR provided by the
space agencies. In general, the sea level CCI project has not attempted to
perform its own retracking of all the different missions but has assessed the
quality of those available. In particular, the v2.0 product makes use of the
latest ERS-1 and ERS-2 reprocessings from the REAPER project (Brockley et
al., 2017), and incorporates the new GDR (version E) for Jason-1, which
includes improved estimates of internal errors. The TOPEX waveform data show
a sawtooth effect plus various data spikes associated with specific waveform
bins (Hayne et al., 1994) and some of the waveform bins are averaged in pairs
or groups of four, making the variability statistics complicated (Quartly et
al., 2001). There has also been a degradation of the point target response of
the “side A” instrument, heading to significant changes in wave height
(Queffeulou, 2004), signal amplitude (Quartly, 2000) and derived range
(Chambers et al., 2003). No new product for that mission was available in
time for the reprocessed SL_cci v2.0 product, although Dieng et al. (2017)
have recently suggested that a new correction for that period would yield a
slightly smaller rate of sea level rise (see also Watson et al., 2015; Chen
et al., 2017).
Change in SLA trend for Jason-1 sea level upon a switch from CNES
orbit POE-D to POE-E.
As part of the Level 1b processing, corrections are applied to the range for
changes in the point target response (PTR) in response to ageing of the
instrument, and also any drift in the ultra-stable oscillator (USO) that
controls the on-board timing of pulses. Within the early years of the
SL_cci project it had been found that Envisat's PTR waveform needed to be
reversed in the Level 1b processing at Ku band (García and Roca, 2010);
this change caused a notable impact on range, leading to better agreement of
the long-term trends between Envisat and the reference missions. During the
second phase, the S-band signal (used to compute the ionospheric correction)
was assessed, but no change was made because there was no discernible
benefit.
During the first phase of the SL_cci project, the coastal zone and the
Arctic had been recognised as two areas requiring special effort because the
waveforms were not “Brown-like” due to inhomogeneities within the full
instrument footprint. Waveforms in coastal regions may contain early
contributions from land or “bright target” responses from glassy seas in
sheltered regions (Gómez-Enrí et al., 2010; Cipollini et al., 2017).
The SL_cci project has been assessing two methodologies to overcome such
anomalous waveforms: including a Gaussian peak within the shape model (Halimi
et al., 2013) or focussing the shape-fitting mainly on the leading edge
(Passaro et al., 2014). In the Arctic, the inhomogeneities are due to a mix
of ice floes and thin leads (gaps within the ice exposing very calm waters).
Poisson et al. (2017) have developed a processing scheme for classifying the
data according to reflecting surface and retracking the waveforms from leads
using an extended Brown model. So far, only data from the Envisat and
SARAL/AltiKa missions have been processed, which has led to the production of
a promising Arctic sea level product now available for the users. However,
both the coastal and Arctic work are part of ongoing research, and additional
efforts are required so that these retracked data could be included in a
future SL_cci product.
Corrections to atmospheric propagation
The main atmospheric retardation of the radar signal, the dry tropospheric
correction (DTC), is simply due to the mass of neutral dry air that it
propagates through, and that can be retrieved from atmospheric pressure at
sea level. As that cannot be measured from space, what is required is a good
atmospheric model that incorporates measurements, i.e. a reanalysis product.
The wet tropospheric component (WTC), representing the extra delay from
atmospheric water vapour and liquid water, can also be extracted from an
assimilating model, but the scales of temporal and spatial variations of the
water vapour are usually not adequately resolved by global reanalyses, so
some direct measurements of water vapour and liquid water are beneficial.
Most altimetric satellites carry a nadir-viewing microwave radiometer (MWR)
to record relevant emissions for WTC retrieval; however, CryoSat-2 has no such
package, as its focus is on polar latitudes, where the WTC may largely be
neglected. However, microwave radiometers are not reliable in the coastal zone
due to their large footprint (typically 20–40 km) and global atmospheric
models lack the resolution to incorporate coastal processes. An alternative
data source is provided by shore-based GNSS stations, as the WTC derived from
their L-band measurements is also valid at Ku and Ka band, since the
troposphere is a non-dispersive medium at these frequencies.
The ionospheric delay is a retardation of the passage of radio waves by free
electrons, which get accelerated. Such an effect predominantly occurs on the
Sun-facing side of the Earth, and is strongest in two bands near the
tropics. It is proportional to the columnar total electron content (TEC)
divided by the square of the radar frequency. The TOPEX, Jason and Envisat
spacecraft were designed with dual-frequency altimeters specifically to
allow an estimation of the pertinent ionospheric correction from the
difference in range delay recorded at the two frequencies. This was because
the early ionospheric models were not deemed to be accurate enough to
support the high precision required from the reference missions, and indeed
the measuring and modelling of the ionospheric correction is a topic that
still needs further development. However, there have been marked
improvements in the ionospheric models in the past decade. Since AltiKa
operates at Ka band, the size of this correction is only one-seventh of that
for the other instruments (which operate at Ku) and so operation at multiple
frequencies was not justified.
Dry tropospheric correction
Dry tropospheric corrections (DTC) were calculated (Ablain et al., 2016)
according to three different numerical models: ECMWF operational, ERA-Interim and
JRA-55. Analysis of the sea level variance at crossovers and investigation of
trends were performed for sea level data computed with each correction (ASM,
2015b). Although the operational version of the ECMWF model has the highest
spatial resolution for recent years, giving it a superior performance to the
others, it is not consistent for the whole 20+ year period; thus, the
atmospheric model reanalyses are better suited for the present climate
purpose. The ERA-Interim correction led to a smaller variance of crossover
differences than when using the JRA-55 model, especially at southern
latitudes, where the pressure variability is higher, which indicates a better
performance for the ERA-Interim model. Thus, considering the long-period
reanalyses for climate purposes, the ERA-Interim corrections were the ones
adopted for SL_cci v2.0 for all altimeter instruments.
Difference in variance at TOPEX/Poseidon crossovers for SLA
calculated with different WTC. Orange compares GPD+ with ERA-Interim and
purple with the composite WTC. Negative values indicate an improvement (i.e.
reduction) in crossovers for GPD+.
Wet tropospheric correction
The University of Porto has developed a robust method for determining the WTC
by data combination through space–time objective analysis of various data
types: valid measurements from the on-board MWR (whenever available) and
third-party observations from GNSS and scanning imaging MWR. The latest
version of these corrections, designated GNSS-derived Path Delay Plus
(GPD+; see Fernandes and Lázaro, 2016), includes improved calibration
of all radiometers on altimetric satellites by comparing them with the known
stable performance of the SSMI and SSMIS. In addition to the calibration with
respect to SSMI and SSMIS, the original GPD solution (Fernandes et al., 2015)
has been augmented by adding new datasets (from scanning imaging radiometers)
and improved selection criteria for selecting valid MWR observations. The
GPD+ correction is implemented in SL_cci v2.0 for all missions except
GFO, although similar corrections have subsequently become available for this
satellite (Fernandes and Lázaro, 2016). In SL_cci v2.0, the WTC for
GFO is calculated from its MWR for observations located > 50 km from the
coast, and from the ECMWF operational model for data between 10 and 50 km
from coast. There were problems with the radiometer during GFO cycles
135–137, 166, 181, 189 and after 201; in such cases ECMWF values were used
for all observations.
The GPD+ correction allows the recovery of a significant number of
altimeter measurements, ensuring the continuity and consistency of the
correction in the transition region between the open ocean and coastal zone, and also at high latitudes. Figure 4 illustrates the improved performance of the GPD+
correction over that from ERA-Interim and the composite correction present in
the AVISO products.
Ionospheric correction
Within the SLOOP project (Faugere et al., 2010), there has been considerable
effort to develop an improved ionospheric correction using an iterative
filtering scheme applied to the dual-frequency altimeter missions (TOPEX,
Jason-1, Jason-2 and Envisat). This has been independently evaluated by a
round-robin comparison with previous ionospheric corrections, and it was
found that the SLOOP set of corrections led to an improvement in the
recovery of mesoscale signals and increased data gain (due to less flagging
of suspect data).
For the missions that do not have a second frequency (including Envisat
after the loss of S-band data), a model is required. The one used in
SL_cci v2.0 is GIM (Iijima et al., 1999), which is based on
measurements from GPS satellites. However, prior to 1998 there were
relatively few GPS data, so for ERS-1 and ERS-2 we use an interpretation
based on the NIC09 climatology (Scharroo and Smith, 2010) modified by
contemporaneous TOPEX records of global mean TEC. The corrections for
Poseidon are based on the measurements from the DORIS system on board its
satellite.
Corrections for sea state bias
Sea state bias (SSB) is a correction term encompassing three different
effects: electro-magnetic (EM) bias, skewness and tracker bias. A wave field
is not usually uniformly covered with identical reflecting facets – the
surface tends to be smoother in the troughs of waves than at the crests, so
there will be a proportionately stronger response from the lower-lying
facets. This effect, the EM bias, will depend upon the radar frequency. Most
altimetric retrackers are designed to locate the mid-power point of the
leading edge of the waveform; this equates to the median height of reflecting
surfaces, rather than the mean. Thus a second effect, the skewness, relates
to the difference between the heights of mean and median surfaces, which is a
property of the ocean, independent of the radar frequency used for the
sensing. The third effect relates to the algorithms used to find the range –
this effect will vary with each retracker implemented, but should be the same
for identical instruments. However, there are always slight differences
between sister instruments, e.g. ERS-1 and ERS-2, so the overall sea state
bias model is usually determined independently for each
altimeter plus retracker. In practice, all three of these effects scale
roughly with wave height, so the overall sea state bias is expressed as a
multiplier of wave height that is a weakly varying function of sea state
conditions.
Change in crossover differences between processing TOPEX/Poseidon
mission with IB calculated using JRA-55 or ERA-Interim. Positive values
indicate greater variance with corrections from JRA-55.
Although the first two components of SSB should be the same for all Ku-band
observing systems, a separate total SSB solution has to be derived for each
individual altimeter. For each dataset, minimisation procedures are used to
express SSB in terms of wave height and wind speed, leading to the least
variance at crossovers. Many of these solutions remain as defined at the end
of their respective missions, i.e. once all available data have been
analysed. However, as these are optimisations based on observational data,
improvements to the orbits or a change in the modelled PTR or the retracker
applied could necessitate a revision to the SSB model.
Early solutions for SSB expressed the SSB coefficient in terms of two key
parameters: wave height and wind speed. Those parametric forms are still used
for ERS-1 (Gaspar and Ogor, 1994) and Poseidon (Gaspar et al., 1996). A
non-parametric form, offering a better fit to the data, can be achieved for
later missions for which there are greater volumes of more precise data. The
non-parametric models adopted within SL_cci v2 are for ERS-2 (Mertz et
al., 2005), TOPEX (Tran et al., 2010), Jason-1 & 2 (Tran et al., 2012),
Envisat (Tran, 2015), GFO (N. Tran and S. Labroue, personal communication, 2009) and AltiKa (from the PEACHI
project). The Cryosat-2 data used in this product are solely those in low-resolution mode; at the time that algorithm selection was completed, the most
appropriate choice was that derived from Jason-1 GDR-C products, although
ones based on CryoSat-2 data have subsequently become available. The changes
from the previous product, slcci_v1.1, are the use of the Tran et
al. (2012) for the Jason instruments and Tran (2015) for Envisat to replace
the versions on their GDRs.
Corrections for short-term atmospheric and oceanographic phenomena
Our concern within the sea level CCI project is to provide the best dataset
for observing climate scale variations in sea level and changes associated
with geostrophic currents. The temporal sampling by altimeters is
insufficient to resolve all timescales, so high-frequency ocean variability
is aliased to longer timescales, thus polluting climate estimations if not
adequately corrected. Thus, short-term effects have to be removed using
accurate physical ocean models, which are expected to be independent of
satellite missions.
Atmospheric pressure correction
Early altimeter processing included an “inverse barometer effect” (IB; see
Fu and Pihos, 1994) whereby the sea surface was deemed to be depressed by
1 cm for each increase in atmospheric pressure by 1 mbar, with this computed
effect being removed from the data to give the sea level expected in the
absence of atmospheric effects. Instead a dynamic atmospheric correction
(DAC) was introduced, based on a barotropic global ocean model forced by
instantaneous atmospheric pressure and winds fields, and taking into account
the ocean dynamic response to atmospheric forcing at high frequencies
(Carrère and Lyard, 2003) and keeping the IB for low frequencies.
Several atmospheric models have been used to compute the IB and the DAC
corrections (ECMWF, ERA-Interim, NCEP, JRA-55) in order to find the
atmospheric reanalysis most suitable for the present climate analysis. The
comparison of input weather models is another exercise of finding which
correction (here, IB and DAC), when applied to altimeter measurements, leads
to the greatest consistency between ascending and descending passes and thus
reduces the altimeter crossover variance. Figure 5 shows that, for
TOPEX/Poseidon data, the sea level anomalies calculated using the JRA-55
model produces greater crossover differences than the SLA using ERA-Interim,
with much greater variance in the high southern latitudes, where the
variability in the atmospheric forcing is strong. Moreover, using a DAC forced
by ERA-Interim significantly reduced the crossover variance compared with the
operational DAC forced by ECMWF analysis (Carrère et al., 2016). Based on
such crossover variance analysis, ERA-Interim is the preferred model to force
the DAC for all missions (ASM, 2015b).
Change in crossover differences between processing Envisat mission
with FES2014 tides or GOT4.10. Negative values indicate reduced variance
with FES2014.
Tides
There are five separate phenomena linked under the label “tides”: ocean
tide, ocean loading tide, solid Earth tide, pole tide and internal tides.
The ocean tide is usually by far the largest, but all aspects need to be
included in order to discern correctly regional variations and long-term
trends. An ocean tide model will include many harmonics (not just M2 and S2)
and may be an empirical fit to altimetric sea level data or produced by a
high-resolution fluid flow model or a combination of both. Early in the
altimetry era there could be as many as 12 independent models to be assessed
(Andersen et al., 1995), with, more recently, Stammer et al. (2014)
evaluating seven data-constrained models. However, there are presently two
main families of solutions to be compared, termed GOT (Ray, 2013) and FES
(Carrère et al., 2012; Lyard et al., 2017). The GOT4.10 solution is
mostly based on Jason data, excluding those from TOPEX/Poseidon because of poorly understood effects occurring at the S2 alias period (59 days).
Figure 6 shows that the variance for Envisat data is reduced with the FES2014
model, especially in the Arctic. This model is also very effective in
reducing the 59-day signal noted with some of the reference missions
(Zawadzki et al., 2017).
The second aspect is the loading tide, which corresponds to the flexing of
the Earth in response to the weight of water lying on it. For this we adopt
the solution of Ray (2013), which, at the time of algorithm selection,
was the only one consistent with the FES2014 ocean tide. The third aspect is
the Earth tide, i.e. the changes in the Earth's topography due to the changing
gravitational attraction of the moon and sun – here the long established
solutions by Cartwright and Tayler (1971), modified by Cartwright and
Edden (1973), continue to be applied.
Next, there is the “pole tide”, a term describing the small long-period
oscillations associated with the movement of the Earth's rotational axis. The
recent advance by Desai et al. (2015) takes into account self-gravitation,
loading, conservation of mass, and geocentre motion. Moreover, this new model
includes a bias and a drift, which means that the new computed pole tide does
not include the effects of the Earth's displacement response to that mean
pole drift. Removing the long-term mean pole drift has a significant impact
on the regional MSL trend estimation; this impact has been validated by
comparisons with an Argo database over the time span of the Envisat mission
(ASM, 2015a; Legeais et al., 2017). Thus the recent model of Desai et
al. (2015) is the one implemented in SL_cci v2.0. At present, there is no
satisfactory model of the internal tides, so there is no correction for the
effect of this phenomenon.
Comparison of time series of global mean sea level (seasonal signal
removed). The v1.1 dataset had been updated until the end of 2014 and has a
mean trend of 3.18 mm yr-1; v2.0, described in this paper, now
extends to end of 2015 and has a trend of 3.21 mm yr-1 over the same
period as v1.1.
Reference surfaces
For some applications, it is useful to estimate sea level anomalies with
respect to the mean sea surface (MSS), which is the sum of the geoid and mean
dynamic topography (see Eq. 1). Frequent updates of the MSS are provided as
new data become available, in particular from CryoSat-2 at high latitudes,
and from the “end of life” geodetic phases of recent missions.
The SL_cci v2.0 is referenced to the DTU15 MSS (ASM, 2015e), and
corresponds to a mean over the period 1993–2012. The DTU datasets provide a
complete global coverage (including the high-latitude Arctic). This version
is an improvement on earlier versions (DTU10 & DTU13, see Andersen et al., 2015) in that it makes use
of 4 years of CryoSat-2 data but gives less weighting to data from IceSat
(whose large errors gave an unrealistic stripiness to derived MSS fields).
Thus the major improvements within DTU15 are the increased data coverage in
the high latitudes (both Arctic and Antarctic) and the Mediterranean, and the
finer scales resolved due to the use of shorter correlation scales in the
interpolation. The inter-annual content of the reprocessed v2.0 product will
change compared with the previous version due to the evolution of the
reference period (1993–2008 for DTU10 in the SL_cci v1.1 product). This
will affect assimilating models since these systems are sensitive to the
reference period used.
CryoSat-2 has contributed significantly in the band 82–88∘ N not
sampled by the other radar altimeters and, due to its long repeat orbit,
provides finer longitudinal resolution than the ERS-1, ERS-2 and Envisat
instruments for latitudes south of 82∘ N. (The DTU15 MSS no longer
utilises data from the geodetic phases of ERS-1 and Geosat, as those
measurements were noisy; consequently less spatial filtering is required
leading to a higher resolution product.) The delay-Doppler mode of CryoSat-2
makes its measurements more resilient to stray reflections from nearby land;
thus CryoSat-2 data have led to marked improvements in the MSS in many
coastal areas, particularly those around the Mediterranean and the Bay of
Fundy.
Editing and gridding
The production of the SL_cci v2.0 product uses the same procedures as for
the previous version v1 (Ablain et al., 2015). An overview of the different
processing steps to produce the Sea Level CCI products can be found in Ablain
and Legeais (2014). In brief, these are to acquire and pre-process data,
perform input checks and quality control (data are discarded if flagged for
rain, land or ice), inter-calibrate and unify the multi-satellite
measurements, and generate along-track and gridded merged products.
In addition to the reduction of the global and regional biases between two
successive altimeter missions (thanks to the calibration phase during which
both satellites observe the same ocean), the unification also involves a
further orbit error reduction. This is first carried out for the “reference
missions” (TOPEX/Poseidon, Jason-1 and Jason-2) by minimising the crossover
differences between ascending and descending tracks. These missions have all
been in the same 9.92-day orbital cycle and have a high altitude (1336 km),
making their trajectories less sensitive to higher-order terms of the Earth's
gravity field and to the drag effects. Then the “complementary missions”
are adjusted to minimise crossovers with data from the reference missions (Le
Traon and Ogor, 1998). Thus, the reference missions are used to ensure the
stability of the ECV. The global MSL estimation and large-scale changes rely
on these reference missions. The complementary missions (adjusted on the
reference missions) contribute to increase the spatial resolution of the
grids and to increase their accuracy. This adjustment towards the orbits of
the reference missions also overcomes a spurious SLA drift during Envisat's
first year of operation.
Finally, output checks and quality control are performed and the
multi-satellite along-track data are mapped to generate gridded sea level
products. The sensitivity of the gridded products to the mapping algorithms
is described in detail in Pujol (2012). Different mapping methods were
tested in order to assess their ability to accurately reproduce climate
signals. This evaluation has been carried out separating the different
temporal and spatial scales related to climate applications. A monthly
optimal interpolation is applied (including additional weighted information
from part of the previous and following months) to produce maps of sea level
on a 0.25∘ grid for the middle of each month. Note that this approach
differs from the one used in the production of the DUACS dataset (Pujol et
al., 2016) (daily optimal interpolation with different parameters) as the
SL_cci approach has been designed to better answer the needs of climate
users.
Summary of the data sources and the corrections applied to each
altimeter instrument.
TOPEXPoseidonJason-1Jason-2ERS-1ERS-2EnvisatAltiKaGFOCryoSat-2OrbitGSFC CNES GFZv11 CNESGSFCCNESstd1504 POE-E (Rudenko et al., 2017) POE-Estd08POE-EData sourceRGDRMLE-3GDR-E REAPER GDRGDRon-boardGDR(Retracker)(least squares)(MLE-4) (Ocean-1)(Ocean-3)α-β(SAMOSA 2.5.0)Dry trop.ERA-Interim Wet trop.GPD+MWR/ECMWFGPD+IonoSLOOPDORISSLOOP NIC09NIC09/GIMSLOOP/GIMGIM SSBTran etGaspar etTran et Gaspar andMertz etTranPEACHIN. Tran and S. Labroue (personalTran etal. (2010)al. (1996)al. (2012) Ogor (1994)al. (2005)(2015)communication, 2009)al. (2012)DACERA-Interim Ocean tideFES2014 Loading tideGOT4v8AC Earth tideCartwright–Tayler–Edden Pole tideDesai et al. (2015) MSSDTU MSS 2015
GDR is the geophysical data record, which is the standard product providing
altimeter data, with some recommended corrections.
The gridded monthly files of sea level anomaly at
0.25∘ resolution
(https://doi.org/10.5270/esa-sea_level_cci-MSLA-1993_2015-v_2.0-201612; Legeais et al.,
2016b) are freely available (upon email application to
info-sealevel@esa-sealevel-cci.org). The Sea Level CCI website
(http://www.esa-sealevel-cci.org/products) also contains derived
products suitable for some climate studies:
Global Mean Sea Level temporal evolution
(https://doi.org/10.5270/esa-sea_level_cci-IND_MSL_MERGED-1993_2015-v_2.0-201612).
Regional Mean Sea Level trend
(https://doi.org/10.5270/esa-sea_level_cci-IND_MSLTR_MERGED-1993_2015-v_2.0-201612).
Amplitude and Phase of annual cycle
(https://doi.org/10.5270/esa-sea_level_cci-IND_MSLAMPH_MERGED-1993_2015-v_2.0-201612).
Conclusions
During phase 2 of the ESA Sea Level CCI project, the consortium has
reappraised all the corrections to be used in the production of the v2.0
dataset. In some cases, e.g. Earth tide, there has been no change in the
recommended correction; in others, such as the pole tide, a new model has
become available that is readily endorsed since it significantly improves the
accuracy. For many other terms, there was a choice of two or three
corrections: the project evaluated these through a variety of techniques
including minimisation of mono-mission crossovers, comparison between
different altimeter missions, and validation with in situ data. This paper
has documented the choices made (Table 1).
The v2.0 dataset was released in December 2016, with details provided at
http://www.esa-sealevel-cci.org/products. This will provide a
consistent unbiased estimate of sea level spanning 1993–2015, which should
greatly enhance the potential for climatic studies of sea level. The
SL_cci ECV v2.0 products and their validation results are described in
Legeais et al. (2017). In terms of the GMSL, the change from v1.1 to v2.0
products has led to changes of the order of 0.1 mm that persist for many months to
years, but has not led to a significantly different long-term trend
(∼ 3.2 mm yr-1; see Fig. 7). The changes that have had the most
impact on derived trends are those for orbits and for wet tropospheric
correction. Improvements to the Earth's time-variable gravity field model
have led to major changes in the regional mean sea level trends
(> 0.5 mm yr-1; ASM, 2015c). Through its revision of the
calibration of the MWR on altimetric satellites, the GPD+ solution has a
significant impact on the trend of GMSL during the first and second decades
of continuous altimetry: -0.2 mm yr-1 during 1993–2001 and
+0.2 mm yr-1 during 2002–2014 (ASM, 2015d).
Abbreviations used
This appendix provides details of the abbreviations not expanded in the main text,
because doing so would adversely affect the readability.
CHAMPCHAllenging Minisatellite PayloadCNESCentre National d'Etudes SpatialesDORISDoppler Orbitography and Radiopositioning Integrated by SatelliteDPODDORIS terrestrial reference frame for precise orbit determinationDTUDanish Technical UniversityECMWFEuropean Centre for Medium-Range Weather ForecastsEnvisatEnvironmental SatelliteERAECMWF ReanalysisERSEuropean Remote-sensing SatelliteGFOGEOSAT Follow-On (satellite)GNSSGlobal Navigation Satellite SystemGOCEGravity field and steady-state Ocean Circulation ExplorerGPSGlobal positioning by satelliteGRACEGravity Recovery and Climate ExperimentGSFCGoddard Space Flight CenterIGSInternational GNSS ServiceITRFInternational Terrestrial Reference FrameJRAJapanese Meteorological Agency ReanalysisLAGEOSLaser Geodynamics SatelliteNCEPNational Centers for Environmental PredictionPOEprecise orbit ephemerisPRAREPrecise Range And Range-Rate EquipmentREAPERREprocessing of Altimeter Products for ERSSARALSatellite with ARgos and ALtiKaSLOOPa Step forward aLtimetry Open Ocean ProductsSLRsatellite laser rangingSLRFsatellite laser ranging frameSSMISpecial Sensor Microwave ImagerSSMISSpecial Sensor Microwave Imager/SounderTOPEXOcean Topography ExperimentVER11version 11
Phase 2 of the Sea Level CCI project was managed by JFL, who oversaw the
evaluation and selection of corrections. The initial draft of the paper was
written by GQ. All other authors contributed through their derivation and
evaluation of the suite of possible corrections, provision of figures and
revision of the text.
The authors declare that they have no conflict of
interest.
Acknowledgements
The authors acknowledge the support of ESA in the frame of the Sea Level CCI
project, launched and co-ordinated by technical officer Jérôme
Benveniste. Edited by: Robert
Key Reviewed by: Nicolas Picot and one anonymous referee
ReferencesAblain, M. and Legeais, J.-F.: Detailed Processing Model for Sea-Level CCI
system, Ref. CLS-DOS-NT-13-248, nomenclature SLCCI-DPM-33, Issue 1.1,
available at: http://www.esa-sealevel-cci.org/webfm_send/239 (last access: 10 August 2017),
2014.Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P.,
Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen,
P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko,
S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.:
Improved sea level record over the satellite altimetry era (1993–2010) from
the Climate Change Initiative project, Ocean Sci., 11, 67–82,
10.5194/os-11-67-2015, 2015.Ablain, M., Zawadzki, L., and Legeais, J.-F.: ATBD v3.3: SL_cci Algorithm
Theoretical Basis Document, Ref. CLS-SLCCI-16-0008, Nomenclature
SLCCI-ATBDv1-016, Issue 3.3, available at:
http://www.esa-sealevel-cci.org/webfm_send/538 (last access: 10 August 2017), 2016.Ablain, M., Legeais, J.-F., Prandi, P., Marcos, M., Fenoglio-Marc, L., Dieng,
H.-B., Benveniste, J., and Cazenave, A.: Satellite altimetry-based sea level
at global and regional scales, Surv Geophys., 38, 7–31,
10.1007/s10712-016-9389-8, 2017.Altamimi, Z., Collilieux, X., and Metivier, L.: ITRF2008: An improved
solution of the International Terrestrial Reference Frame, J. Geodesy, 85,
457–473, 10.1007/s00190-011-0444-4, 2011.Andersen, O. B., Woodworth, P. L., and Flather, R. A.: Intercomparison of
recent ocean tide models, J. Geophys. Res., 100, 25261–25282,
10.1029/95JC02642, 1995.Andersen, O. B., Knudsen, P., and Stenseng, L.: The DTU13 MSS (mean sea
surface) and MDT (mean dynamic topography) from 20 years of satellite
altimetry, International Association of Geodesy Symposia, Springer, 111–121,
10.1007/1345_2015_182, 2015.
ASM: Sl_cci phase 2 Algorithm Selection Meeting. Tides Selection,
Toulouse, 26 November 2015, available at: http://www.esa-sealevel-cci.org/webfm_send/389
(last access: 10 August 2017), 2015a.ASM: Sl_cci phase 2 Algorithm Selection Meeting. Atmospheric Corrections
Selection, Toulouse, 26 November 2015, available at:
http://www.esa-sealevel-cci.org/webfm_send/384 (last access: 10 August 2017), 2015b.ASM: Sl_cci phase 2 Algorithm Selection Meeting. Selection of new Orbit
Solutions, Toulouse, 26 November 2015, available at:
http://www.esa-sealevel-cci.org/webfm_send/382 (last access: 10 August 2017), 2015c.ASM: Sl_cci phase 2 Algorithm Selection Meeting. Wet Troposphere
correction Selection, Toulouse, 26 November 2015, available at:
http://www.esa-sealevel-cci.org/webfm_send/385 (last access: 10 August 2017), 2015d.ASM: Sl_cci phase 2 Algorithm Selection Meeting. MSS Model Selection,
Toulouse, 26 November 2015, available at: http://www.esa-sealevel-cci.org/webfm_send/390 (last access: 10 August 2017), 2015e.Brockley, D., Baker, S., Féménias, P., Martinez, B., Massmann, F.-H.,
Otten, M., Paul, F., Picard, B., Prandi, P., Roca, M., Rudenko, S., Scharroo,
R., and Visser, P.: REAPER: Reprocessing 12 years of ERS-1 and ERS-2
altimeter and microwave radiometer data, IEEE T. Geosci. Remote. Sens.,
10.1109/TGRS.2017.2709343, in press, 2017.Brown, G.: The average impulse response of a rough surface and its
applications, IEEE T. Antennas Propag., 25, 67–74,
10.1109/JOE.1977.1145328, 1977.Carrère, L. and Lyard, F.: Modeling the barotropic response of the global
ocean to atmospheric wind and pressure forcing – comparisons with
observations, Geophys. Res. Lett., 30, 1275, 10.1029/2002GL016473, 2003.
Carrère, L., Lyard, F., Cancet, M., Guillot, A., and Roblou, L.: FES2012:
A new global tidal model taking advantage of nearly twenty years of
altimetry, Proceedings of the 20 Years of Progress in Radar Altimetry
Symposium (Venice, Italy), 1–20, 2012.Carrère, L., Faugère, Y., and Ablain, M.: Major improvement of
altimetry sea level estimations using pressure-derived corrections based on
ERA-Interim atmospheric reanalysis, Ocean Sci., 12, 825–842,
10.5194/os-12-825-2016, 2016.Cartwright, D. E. and Edden, A. C.: Corrected tables of tidal harmonics,
Geophys. J. Internat., 33, 253–264, 10.1111/j.1365-246X.1973.tb03420.x,
1973.Cartwright, D. E. and Tayler, R. J.: New computations of the tide-generating
potential, Geophys. J. Internat., 23, 45–73,
10.1111/j.1365-246X.1971.tb01803.x, 1971.Chambers, D. P., Hayes, S. A., Ries, J. C., and Urban, T. J.: New TOPEX sea
state bias models and their effect on global mean sea level, J. Geophys.
Res., 108, 3305, 10.1029/2003JC001839, 2003.Chelton, D. B., Walsh, E. J., and MacArthur, J. L.: Pulse compression and sea
level tracking in satellite altimetry, J. Atmos. Ocean. Tech., 6, 407–438,
10.1175/1520-0426(1989)006<0407:PCASLT>2.0.CO;2, 1989.Chen, X., Zhang, X., Church, J. A., Watson, C. S., King, M. A., Monselesan,
D., Legresy, B., and Harig, C.: The increasing rate of global mean sea-level
rise during 1993–2014, Nat. Clim. Change, 7, 492–495,
10.1038/nclimate3325, 2017.Cipollini, P., Calafat, F. M., Jevrejeva, S., Melet, A., and Prandi, P.:
Monitoring sea level in the coastal zone with satellite altimetry and tide
gauges, Surv. Geophys., 38, 35–59, 10.1007/s10712-016-9392-0, 2017.Couhert, A., Cerri, L., Legeais, J. F., Ablain, M., Zelensky, N. P., Haines,
B. J., Lemoine, F. G., Bertiger, W. I., Desai, S. D., and Otten, M.: Towards
the 1 mm/y stability of the radial orbit error at regional scales, Adv.
Space Res., 55, 2–23, 10.1016/j.asr.2014.06.041, 2015.Desai, S., Wahr, J., and Beckley, B.: Revisiting the pole tide for and from
satellite altimetry, J. Geodesy, 89, 1233–1243,
10.1007/s00190-015-0848-7, 2015.Dieng, H. B., Cazenave, A., Meyssignac, B., and Ablain, M.: New estimate of
the current rate of sea level rise from a sea level budget approach, Geophys.
Res. Lett., 44, 3744–3751, 10.1002/2017GL073308, 2017.Dumont, J. P., Rosmorduc, V., Carrère, L., Picot, N., Bronner, E.,
Couhert, A., Desai, S., Bonekamp, H., Scharroo, R., and Leuliette, E.:
OSTM/Jason-2 Products Handbook, rev. 11, available at:
https://www.aviso.altimetry.fr/fileadmin/documents/data/tools/hdbk_j2.pdf (last access: 10 August 2017),
2017.
Escudier, P., Ablain, M., Amarouche, L., Carrère, L., Couhert, A.,
Dibarboure, G., Dorandeu, J., Dubois, P., Mallet, A., Mercier, F., Picard,
B., Richard, J., Steunou, N., Thibaut, P., Rio, M.-H., and Tran, N.:
Satellite radar altimetry: principle, accuracy & precision, in: Satellite
Altimetry Over Oceans and Land Surfaces, edited by: Stammer, D. and Cazenave,
A., CRC Press, in press, 2017.Faugere, Y., Rio, M.-H., Labroue, S., Rosmorduc, V,. Thibaut, P., Amarouche, L.,
Obligis, E., Ablain, M., Legeais, J.-F., Carrere, L., Schaeffer, P., Tran, N.,
Pujol, I., Dufau, C., Dibarboure, G., Lux, M., Bronner, E., and Picot, N.: The SLOOP project:
Preparing the next generation of altimetry products for open ocean, OSTST
meeting 2010, Lisbon, Portugal, available at:
http://www.aviso.altimetry.fr/fileadmin/documents/OSTST/2010/Faugere_SLOOP.pdf (last access: 10 August 2017), 2010.Fernandes, M. J., Lázaro, C., Ablain, M., and Pires, N.: Improved wet
path delays for all ESA and reference altimetric missions, Remote Sens.
Environ., 169, 50–74, 10.1016/j.rse.2015.07.023, 2015.Fernandes, M. J. and Lázaro, C.: GPD+ wet tropospheric corrections for
CryoSat-2 and GFO altimetry missions, Remote Sens., 8, 851,
10.3390/rs8100851, 2016.
Fu, L.-L. and Cazenave, A. (Eds.): Satellite altimetry and Earth sciences: A
handbook of techniques and applications, Academic Press, San Diego, 463 pp.,
2001.Fu, L.-L. and Pihos, G.: Determining the response of sea level to atmospheric
pressure using TOPEX/POSEIDON data, J. Geophys. Res., 99, 24633–24642,
10.1029/94JC01647, 1994.
García, P. and Roca, M.: On-board PTR processing analysis: MSL drift
differences, Technical Note, ISARD_ESA_L1B_ESL_CCN_PRO_064,
isardSAT under ESA Contract (RA-2 L1b ESL), 2010.
Gaspar, P., Ogor, F., and Escoubes, C.: Nouvelles calibration et analyse du
biais d'etat de mer des altimètres TOPEX et POSEIDON, Technical note
96/018 of CNES Contract 95/1523, 1996.
Gaspar, P. and Ogor, F.: Estimation and analysis of the sea state bias of the
ERS-1 altimeter, Rapport technique, Report of task B1-B2 of IFREMER Contract
no. 94/2.426 016/C.84, 1994.GCOS: Systematic observation requirements for satellite-based data products
for climate (2011 update) – supplemental details to the satellite-based
component of the “Implementation plan for the global observing system for
climate in support of the UNFCCC (2010 update)”, GCOS-154 (WMO), available
at: https://library.wmo.int/opac/doc_num.php?explnum_id=3710 (last access: 10 August 2017),
2011.Gómez-Enrí, J., Vignudelli, S., Quartly, G. D., Gommenginger, C. P.,
Cipollini, P., Challenor, P. G., and Benveniste, J.: Modeling Envisat RA-2
waveforms in the coastal zone: Case-study of calm water contamination, IEEE
Geosci. Remote Sens. Lett., 7, 474–478, 10.1109/LGRS.2009.2039193, 2010.Halimi, A., Mailhes, C., Tourneret, J.-Y., Thibaut, P., and Boy, F.:
Parameter estimation for peaky altimetric waveforms, IEEE T. Geosci. Remote
Sens., 51, 1568–1577, 10.1109/TGRS.2012.2205697, 2013.Hasselmann, K.: Multi-pattern fingerprint method for detection and
attribution of climate change, Clim Dynam., 13, 601–611,
10.1007/s003820050185, 1997.Hayne, G. S.: Radar altimeter mean return waveforms from
near-normal-incidence ocean surface scattering, IEEE T. Antennas Propag., 28,
687–692, 10.1109/TAP.1980.1142398, 1980.Hayne, G. S., Hancock, D. W., Purdy, C. L., and Callahan, P. S.: The
corrections for significant wave height and attitude effects in the TOPEX
radar altimeter, J. Geophys. Res., 99, 24941–24955, 10.1029/94JC01777,
1994.Iijima, B. A., Harris, I. L., Ho, C. M., Lindqwister, U. J., Mannucci, A. J.,
Pi, X., Reyes, M. J., Sparks, L. C., and Wilson, B. D.: Automated daily
process for global ionospheric total electron content maps and satellite
ocean altimeter ionospheric calibration based on Global Positioning System
data, J. Atmos. Sol.-Terr. Phys., 61, 1205–1218,
10.1016/S1364-6826(99)00067-X, 1999.Legeais, J.-F., Prandi, P., and Guinehut, S.: Analyses of altimetry errors
using Argo and GRACE data, Ocean Sci., 12, 647–662,
10.5194/os-12-647-2016, 2016a.Legeais, J.-F., et al.: Sea Level CCI Phase 2. Time series of gridded Sea Level Anomalies
(SLA), 10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612, 2016b.
Legeais, J.-F., Cazenave, A., Ablain, M., Zawadzki, L., Zuo, H., Johannessen,
J. A., Scharffenberg, M. G., Fenoglio-Marc, L., Fernandes, M. J., Andersen,
O., Rudenko, S., Cipollini, P., Quartly, G., Passaro, M., and Benveniste, J.:
An accurate and homogeneous altimeter sea level record: The reprocessed ESA
Essential Climate Variable, Nature Scientific Data, in preparation,
2017.Lemoine, F. G., Zelensky, N. P., Chinn, D. S., Beckley, B. D., and
Lillibridge, J. L.: Towards the GEOSAT Follow-On precise orbit determination
goals of high accuracy and near-real-time processing, AIAA/AAS Astrodynamics
Specialist Conference and Exhibit, 21–24 August 2006, Keystone, Colorado,
available at: https://arc.aiaa.org/doi/pdfplus/10.2514/6.2006-6402 (last access: 10 August 2017),
2006.Lemoine, F. G., Zelensky, N. P., Chinn, D. S., Beckley, B. D., Rowlands, D.
D., and Pavlis, D. E.: A new time series of orbits (std1504) for
TOPEX/Poseidon. Jason-1, and Jason-2 (OSTM), Ocean Surface Topography Science
Team Meeting, Reston, available at:
http://meetings.aviso.altimetry.fr/fileadmin/user_upload/tx_ausyclsseminar/files/OSTST2015/POD-02-Lemoine.pdf_01.pdf,
last access: 18 May 2017.Le Traon, P.-Y. and Ogor, F.: ERS-1/2 orbit improvement using TOPEX/POSEIDON:
The 2 cm challenge, J. Geophys. Res., 103, 8045–8057,
10.1029/97JC01917, 1998.
Lyard, F., Carrère, L., Cancet, M., Guillot, A., and Picot, N.: FES2014,
a new finite elements tidal model for global ocean, Ocean Dynam., in
preparation, 2017.Mertz, F., Mercier, F., Labroue, S., Tran, N., and Dorandeu, J.: ERS-2 OPR
data quality assessment; Long-term monitoring – particular investigation,
CLS.DOS.NT-06.001, available at: http://www.aviso.altimetry.fr/
fileadmin/documents/calval/validation_report/E2/annual_report_e2_2005.pdf (last access: 10 August 2017),
2005.Ollivier, A., Faugere, Y., Picot, N., Ablain, M., Féménias, P., and
Benveniste, J.: Envisat ocean altimeter becoming relevant for mean sea level
trend studies, Mar. Geodesy, 35, 118–136, 10.1080/01490419.2012.721632,
2012.Pavlis, E. C.: SLRF2008: The ILRS reference frame for SLR POD contributed to
ITRF2008, Ocean Surface Topography Science Team 2009, Seattle, Washington,
available at:
http://www.aviso.oceanobs.com/fileadmin/documents/OSTST/2009/poster/Pavlis_2.pdf (last access: 10 August 2017),
2009.Passaro, M., Cipollini, P., Vignudelli, S., Quartly, G. D., and Snaith, H.
M.: ALES: A multi-mission adaptive subwaveform retracker for coastal and open
ocean altimetry, Remote Sens. Environ., 145, 173–189,
10.1016/j.rse.2014.02.008, 2014.
Poisson, J.-C., Quartly, G. D., Kurekin, A., Thibaut, P., Hoang, D., and
Nencioli, F.: Development of an ENVISAT altimetry processor ensuring sea level continuity between open ocean and Arctic leads, IEEE T. Geosci. Remote Sens., submitted, 2017.Pujol I.: WP2500 mapping methods. CLS-DOS-NT-11-229, SALP-NT-MA-EA-22007-CLS,
Issue 1.1, SL_cci validation report, available at:
www.esa-sealevel-cci.org/webfm_send/184 (last access: 10 August 2017), 2012.Pujol, M.-I., Faugère, Y., Taburet, G., Dupuy, S., Pelloquin, C., Ablain,
M., and Picot, N.: DUACS DT2014: the new multi-mission altimeter data set
reprocessed over 20 years, Ocean Sci., 12, 1067–1090,
10.5194/os-12-1067-2016, 2016.Quartly, G. D.: Monitoring and cross-calibration of altimeter σ0
through dual-frequency backscatter measurements, J. Atmos. Ocean. Tech., 17,
1252–1258, 10.1175/1520-0426(2000)017<1252:MACCOA>2.0.CO;2, 2000.Quartly, G. D., Srokosz, M. A., and McMillan, A. C.: Analyzing altimeter
artifacts: Statistical properties of ocean waveforms, J. Atmos. Ocean. Tech.,
18, 2074–2091, 10.1175/1520-0426(2001)018<2074:AAASPO>2.0.CO;2, 2001.Queffeulou, P.: Long-term variation of wave height measurements from
altimeters, Mar. Geodesy, 27, 495–510, 10.1080/01490410490883478, 2004.Ray, R. D.: Precise comparisons of bottom-pressure and altimetric ocean
tides, J. Geophys. Res., 118, 4570–4584, 10.1002/jgrc.20336, 2013.Rebischung, P., Griffiths, J., Ray, J., Schmid, R. Collilieux, X., and
Garayt, B.: IGS08: the IGS realization of ITRF2008, GPS Solut., 16, 483,
10.1007/s10291-011-0248-2, 2012.Rudenko, S., Otten, M., Visser, P., Scharroo, R., Schöne, T., and
Esselborn, S.: New improved orbit solutions for the ERS-1 and ERS-2
satellites, Adv. Space Res., 49, 1229–1244, 10.1016/j.asr.2012.01.021,
2012.Rudenko, S., Dettmering, D., Esselborn, S., Schöne, T., Förste, Ch.,
Lemoine, J.-M., Ablain, M., Alexandre, D., and Neumayer, K.-H.: Influence of
time variable geopotential models on precise orbits of altimetry satellites,
global and regional mean sea level trends, Adv. Space Res., 54, 92–118,
10.1016/j.asr.2014.03.010, 2014.Rudenko, S., Dettmering, D., Esselborn, S., Fagiolini, E., and Schöne,
T.: Impact of atmospheric and oceanic de-aliasing Level-1B (AOD1B) products
on precise orbits of altimetry satellites and altimetry results, Geophys. J.
Int., 204, 1695–1702, 10.1093/gji/ggv545, 2016.Rudenko, S., Neumayer, K.-H., Dettmering, D., Esselborn, S., Schöne, T.,
and Raimondo, J.-C.: Improvements in precise orbits of altimetry satellites
and their impact on mean sea level monitoring, IEEE T. Geosci. Remote Sens.,
55, 3382–3395, 10.1109/TGRS.2017.2670061, 2017.Scharroo, R. and Smith, W. H. F.: A global positioning system–based
climatology for the total electron content in the ionosphere, J. Geophys.
Res., 115, A10318, 10.1029/2009JA014719, 2010.Stammer, D., Ray, R. D., Andersen, O. B., Arbic, B. K., Bosch, W.,
Carrère, L., Cheng, Y., Chinn, D. S., Dushaw, B. D., Egbert, G. D.,
Erofeeva, S. Y., Fok, H. S., Green, J. A. M., Griffiths, S., King, M. A.,
Lapin, V., Lemoine, F. G., Luthcke, S. B., Lyard, F., Morison, J., Muller,
M., Padman, L., Richman, J. G., Shriver, J. F., Shum, C. K., Taguchi, E., and
Yi, Y.: Accuracy assessment of global barotropic ocean tide models, Rev.
Geophys., 52, 243–282, 10.1002/2014RG000450, 2014.Tapley, B., Bettadpur, S., Watkins, M., and Reigber, C.: The gravity recovery
and climate experiment: Mission overview and early results, Geophys. Res.
Lett., 31, L09607, 10.1029/2004GL019920, 2004.
Tran, N.: Envisat Phase-F: Sea State Bias, Technical Report
CLS-DOS-NT-15-031, ESA Contract, ENVISAT RA-2 and MWR ESL and prototypes
maintenance support (level 1b and level 2), 2015.Tran, N., Labroue, S., Philipps, S., Bronner, E., and Picot, N.: Overview and
update of the sea state bias corrections for the Jason-2, Jason-1 and TOPEX
Missions, Mar. Geodesy, 33, 348–362, 10.1080/01490419.2010.487788, 2010.Tran, N., Philipps, S., Poisson, J.-C., Urien, S., Bronner, E., and Picot,
N.: Impact of GDR-D standards on SSB corrections, Aviso, OSTST, available at:
http://www.aviso.altimetry.fr/fileadmin/documents/OSTST/2012/oral/02_friday_28/01_instr_processing_I/01_IP1_Tran.pdf (last access: 10 August 2017), 2012.
Watson, C. S., White, N. J., Church, J. A., King, M. A., Burgette, R. J., and
Legresy, B.: Unabated global mean sea-level rise over the satellite altimeter
era, Nat. Clim. Change, 5, 565–568, 10.1038/NCLIMATE2635, 2015.Willis, P., Zelensky, N. P., Ries, J., Soudarin, L., Cerri, L., Moreaux, G.,
Lemoine, F. G., Otten, M., Argus, D. F., and Heflin, M. B.: DPOD2008, a
DORIS-oriented terrestrial reference frame for precise orbit determination,
IAG Symposia series, 143, 175–181, 10.1007/1345_2015_125, 2016.
Zawadzki, L., Ablain, M., Carrère, L., Ray, R. D., Zelensky, N. P.,
Lyard, F., Guillot, A., and Picot, N.: Reduction of the 59-day error signal
in the Mean Sea Level derived from TOPEX/Poseidon, Jason-1 and Jason-2 data
with the latest FES and GOT ocean tide models, IEEE T. Geosci. Remote Sens., submitted, 2017.