ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-10-1551-2018Global sea-level budget 1993–presentGlobal sea-level budget 1993–presentWCRP Global Sea Level Budget GroupA full list of authors and their affiliations appears at the end of the paper.Anny Cazenave (anny.cazenave@legos.obs-mip.fr)28August20181031551159013April201815May201831July20181August2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/10/1551/2018/essd-10-1551-2018.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/10/1551/2018/essd-10-1551-2018.pdf
Global mean sea level is an integral of changes occurring in the climate
system in response to unforced climate variability as well as natural and
anthropogenic forcing factors. Its temporal evolution allows
changes (e.g., acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions,
such as the unsurveyed deep ocean or the still uncertain land water
component. In the context of the World Climate Research Programme Grand
Challenge entitled “Regional Sea Level and Coastal Impacts”, an
international effort involving the sea-level community worldwide has been
recently initiated with the objective of assessing the various datasets
used to estimate components of the sea-level budget during the altimetry era
(1993 to present). These datasets are based on the combination of a broad
range of space-based and in situ observations, model estimates, and
algorithms. Evaluating their quality, quantifying uncertainties and
identifying sources of discrepancies between component estimates is
extremely useful for various applications in climate research. This effort
involves several tens of scientists from about 50 research
teams/institutions worldwide
(www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented
in this paper are a synthesis of the first assessment performed during
2017–2018. We present estimates of the altimetry-based global mean sea level
(average rate of 3.1 ± 0.3 mm yr-1 and acceleration of
0.1 mm yr-2 over 1993–present), as well as of the different components of the sea-level budget
(http://doi.org/10.17882/54854, last access: 22 August 2018). We further
examine closure of the sea-level budget, comparing the observed global mean
sea level with the sum of components. Ocean thermal expansion, glaciers,
Greenland and Antarctica contribute 42 %, 21 %, 15 % and 8 % to
the global mean sea level over the 1993–present period. We also study the sea-level
budget over 2005–present, using GRACE-based ocean mass estimates instead of
the sum of individual mass components. Our results demonstrate that the global
mean sea level can be closed to within 0.3 mm yr-1 (1σ). Substantial
uncertainty remains for the land water storage component, as shown when
examining individual mass contributions to sea level.
Introduction
Global warming has already several visible consequences, in particular
an increase in the Earth's mean surface temperature and ocean heat content
(Rhein et al., 2013; IPCC, 2013), melting of sea ice, loss of mass
of glaciers (Gardner et al., 2013), and ice mass loss from the Greenland and
Antarctica ice sheets (Rignot et al., 2011a; Shepherd et al., 2012). On
average over the last 50 years, about 93 % of heat excess accumulated in
the climate system because of greenhouse gas emissions has been stored in
the ocean, and the remaining 7 % has been warming the atmosphere and
continents, and melting sea and land ice (von Schuckmann et al., 2016).
Because of ocean warming and land ice mass loss, sea level rises. Since the
end of the last deglaciation about 3000 years ago, sea level remained nearly
constant (e.g., Lambeck, 2002; Lambeck et al., 2010; Kemp et al., 2011).
However, direct observations from in situ tide gauges available since the
mid-to-late 19th century show that the 20th century global mean
sea level has started to rise again at a rate of 1.2 to 1.9 mm yr-1
(Church and White, 2011; Jevrejeva et al.,
2014; Hay et al., 2015;
Dangendorf et al., 2017). Since the early 1990s sea-level rise
(SLR) is measured
by high-precision altimeter satellites and the rate has increased to
∼3 mm yr-1 on average (Legeais et al., 2018; Nerem et al., 2018).
Accurate assessment of present-day global mean sea-level variations and its
components (ocean thermal expansion, ice sheet mass loss, glaciers mass
change, changes in land water storage, etc.) is important for many reasons.
The global mean sea level is an integral of changes occurring in the Earth's
climate system in response to unforced climate variability as well as
natural and anthropogenic forcing factors, e.g., net contribution of ocean
warming, land ice mass loss and changes in water storage in continental
river basins. Temporal changes in the components are directly reflected in
the global mean sea-level curve. If accurate enough, study of the sea-level
budget provides constraints on missing or poorly known contributions, e.g.,
the deep ocean undersampled by current observing systems, or still
uncertain changes in water storage on land due to human activities
(e.g., groundwater depletion in aquifers). Global mean sea level corrected for
ocean mass change in principle allows one to independently estimate temporal
changes in total ocean heat content, from which the Earth's energy imbalance
can be deduced (von Schuckmann et al., 2016). The sea level and/or ocean
mass budget approach can also be used to constrain models of glacial
isostatic adjustment (GIA). The GIA phenomenon has a significant impact on the
interpretation of GRACE-based space gravimetry data over the oceans (for
ocean mass change) and over Antarctica (for ice sheet mass balance).
However, there is still no complete consensus on best estimates, a result of
uncertainties in deglaciation models and mantle viscosity structure.
Finally, observed changes in the global mean sea level and its components
are fundamental for validating climate models used for projections.
In the context of the Grand Challenge entitled “Regional Sea Level and
Coastal Impacts” of the World Climate Research Programme (WCRP), an
international effort involving the sea-level community worldwide has been
recently initiated with the objective of assessing the sea-level budget
during the altimetry era (1993 to present). To estimate the different
components of the sea-level budget, different datasets are used. These are
based on the combination of a broad range of space-based and in situ
observations. Evaluating their quality, quantifying their uncertainties and
identifying the sources of discrepancies between component estimates,
including the altimetry-based sea-level time series, are extremely useful
for various applications in climate research.
Several previous studies have addressed the sea-level budget over different
time spans and using different datasets. For example, Munk (2002) found
that the 20th century sea-level rise could not be closed with the data
available at that time and showed that if the missing contribution were due
to polar ice melt, this would be in conflict with external astronomical
constraints. The enigma has been resolved in two ways. Firstly, an improved
theory of rotational stability of the Earth (Mitrovica et al., 2006)
effectively removed the constraints proposed by Munk (2002) and allows a
polar ice sheet contribution to 20th century sea-level rise of as much
as ∼ 1.1 mm yr-1, with about 0.8 mm yr-1 beginning in the
20th century. In addition, more recent studies by Gregory et al. (2013)
and Slangen et al. (2017), combining observations with model estimates,
showed that it was possible to effectively close the
20th century sea-level budget within uncertainties, particularly over the altimetry era (e.g., Cazenave et al., 2009; Leuliette
and Willis, 2011; Church and White, 2011; Llovel et al., 2014; Chambers et al., 2017; Dieng et
al., 2017; X. Chen et al., 2017; Nerem et al., 2018). Assessments of the
published literature have also been performed in past IPCC
(Intergovernmental Panel on Climate Change) reports (e.g., Church et al.,
2013). Building on these previous works, here we intend to provide a
collective update of the global mean sea-level budget, involving the many
groups worldwide interested in present-day sea-level rise and its
components. We focus on observations rather than model-based estimates and
consider the high-precision altimetry era starting in 1993. This era includes the
period since the mid-2000s in which new observing systems, like the Argo float
project (Roemmich et al., 2012) and the GRACE space gravimetry mission
(Tapley et al., 2004a, b), provide improved datasets of high value for such
a study. Only the global mean budget is considered here. Regional budget
will be the focus of a future assessment.
Section 2 describes for each component of the sea-level budget equation the
different datasets used to estimate the corresponding contribution to sea
level, discusses associated errors and provides trend estimates for the two
periods. Section 3 addresses the mass and sea-level budgets over the study
periods. A discussion is provided in Sect. 4, followed by a conclusion.
Methods and data
In this section, we briefly present the global mean sea-level budget (Sect. 2.1) and then provide, for each term of the budget equation, an
assessment of the most up-to-date published results. Multiple organizations
and research groups routinely generate the basic measurements as well as the
derived datasets and products used to study the sea-level budget.
Sections 2.2 to 2.7 summarize the measurements and methodologies used to
derive observed sea level, as well as steric and mass components. In most
cases, we focus on observations but in some instances (e.g., for GIA
corrections applied to the data), model-based estimates are the only
available information.
Sea-level budget equation
Global mean sea level (GMSL) change as a function of time t is usually
expressed by the sea-level budget equation:
GMSL(t)=GMSL(t)steric+GMSL(t)ocean mass,
where GMSL(t)steric refers to the contributions of ocean thermal
expansion and salinity to sea-level change, and GMSL(t)oceanmass
refers to the change in mass of the oceans. Due to water conservation in the
climate system, the ocean mass term (also noted as M(t)ocean) can
further be expressed as follows:
M(t)ocean+M(t)glaciers+M(t)Greenland+M(t)Antarctica+M(t)TWS+M(t)WV+M(t)Snow+uncertainty=0,
where M(t)glaciers, M(t)Greenland, M(t)Antarctica,
M(t)TWS, M(t)WV and M(t)Snow represent temporal changes in
mass of glaciers, Greenland and Antarctica ice sheets, terrestrial water
storage (TWS), atmospheric water vapor (WV), and snow mass changes. The
uncertainty is a result of uncertainties in all of the estimates. For the
altimetry era, many studies have investigated closure of the sea-level
budget and potentially missing mass terms, for example, permafrost melting.
From Eq. (2), we deduce the following:
GMSL(t)ocean mass=-[M(t)glaciers+M(t)Greenland+M(t)Antarctica+M(t)TWS+M(t)WV+M(t)Snow+missing mass terms]
In the next subsections, we successively discuss the different terms of the
budget (Eqs. 1 and 2) and how they are estimated from observations. We
do not consider the atmospheric water vapor and snow components, assumed to
be small. Two periods are considered: (1) 1993–present (i.e., the entire
altimetry era) and (2) 2005–present (i.e., the period covered by both Argo
and GRACE).
Altimetry-based global mean sea level over 1993–present
The launch of the TOPEX/Poseidon (T/P) altimeter satellite in 1992 led to a
new paradigm for measuring sea level from space, providing for the first
time precise and globally distributed sea-level measurements at 10-day
intervals. At the time of the launch of T/P, the measurements were not
expected to have sufficient accuracy for measuring GMSL changes. However, as
the radial orbit error decreased from ∼10 cm at launch to
∼ 1 cm presently, and other instrumental and geophysical
corrections applied to altimetry system improved (e.g., Stammer and
Cazenave, 2018), several groups regularly provided an altimetry-based GMSL
time series (e.g., Nerem et al., 2010; Church et al., 2011; Ablain et al.,
2015; Legeais et al., 2018). The initial T/P GMSL time series was extended
with the launch of Jason-1 (2001), Jason-2 (2008) and Jason-3 (2016). By
design, each of these missions has an overlap period with the previous one
in order to intercompare the sea-level measurements and estimate instrument
biases (e.g., Nerem et al., 2010; Ablain et al., 2015). This has allowed the
construction of an uninterrupted GMSL time series that is currently 25 years long.
Global mean sea-level datasets
Six groups (AVISO/CNES, SL_cci/ESA, University of Colorado,
CSIRO, NASA/GSFC, NOAA) provide altimetry-based GMSL time series. All of
them use 1 Hz altimetry measurements derived from T/P, Jason-1, Jason-2 and
Jason-3 as reference missions. These missions provide the most accurate
long-term stability at global and regional scales (Ablain et al., 2009,
2017a), and are all on the same historical T/P ground track. This allows
computation of a long-term record of the GMSL from 1993 to present. In
addition, complementary missions (ERS-1, ERS-2, Envisat, Geosat Follow-on,
CryoSat-2, SARAL/AltiKa and Sentinel-3A) provide increased spatial
resolution and coverage of high-latitude ocean areas, pole-ward of
66∘ N–S latitude (e.g., the European Space Agency/ESA Climate
Change Initiative/CCI sea-level dataset; Legeais et al., 2018).
The above groups adopt different approaches when processing satellite
altimetry data. The most important differences concern the geophysical
corrections needed to account for various physical phenomena such as
atmospheric propagation delays, sea state bias, ocean tides, and the ocean
response to atmospheric wind and pressure forcing. Other differences come
from data editing, methods to spatially average individual measurements
during orbital cycles and links between successive missions (Masters et al.,
2012; Henry et al., 2014).
Overall, the quality of the different GMSL time series is similar. Long-term
trends agree well to within 6 % of the signal, approximately
0.2 mm yr-1
(see Fig. 1) within the GMSL trend uncertainty range (∼0.3 mm yr-1;
see next section). The largest differences are observed at
interannual timescales and during the first years (before 1999; see below).
Here we use an ensemble mean GMSL based on averaging all individual GMSL
time series.
Evolution of GMSL time series from six different groups'
(AVISO/CNES, SL_cci/ESA, University of Colorado, CSIRO, NASA/GSFC,
NOAA) products. Annual signals are removed and 6-month smoothing applied. All GMSL
time series are centered in 1993 with zero mean. A GIA correction of
-0.3 mm yr-1 has been subtracted from each dataset.
Global mean sea-level uncertainties and TOPEX-A drift
Based on an assessment of all sources or uncertainties affecting satellite
altimetry (Ablain et al., 2017a), the GMSL trend uncertainty (90 %
confidence interval) is estimated as 0.3 to 0.4 mm yr-1 over the
whole altimetry era (1993–2017). The main contribution to the uncertainty is
the wet tropospheric correction with a drift uncertainty in the range of
0.2–0.3 mm yr-1 (Legeais et al., 2018) over a 10-year period. To a lesser
extent, the orbit error (Couhert et al., 2015; Escudier et al.,
2018) and the
altimeter parameters' (range, σ0
and significant wave height – SWH)
instability (Ablain et al., 2012) also contribute to the GMSL trend
uncertainty, at the level of 0.1 mm yr-1. Furthermore, imperfect links between
successive altimetry missions lead to another trend uncertainty of about
0.15 mm yr-1 over the 1993–2017 period (Zawadzki and Ablain, 2016).
Uncertainties are higher during the first decade (1993–2002), when T/P
measurements display larger errors at climatic scales. For instance, the
orbit solutions are much more uncertain due to gravity field solutions
calculated without GRACE data. Furthermore, the switch from TOPEX-A to
TOPEX-B in February 1999 (with no overlap between the two instrumental
observations) leads to an error of ∼3 mm in the GMSL time
series (Escudier et al., 2018).
However, the most significant error that affects the first 6 years
(January 1993 to February 1999) of the T/P GMSL measurements is due to an
instrumental drift of the TOPEX-A altimeter, not included in the formal
uncertainty estimates discussed above. This effect on the GMSL time series
was recently highlighted via comparisons with tide gauges (Valladeau et al.,
2012; Watson et al., 2015;
X. Chen et al., 2017; Ablain et al., 2017b), via a sea-level budget approach (i.e., comparison with the sum of mass and steric
components; Dieng et al., 2017) and by comparing with Poseidon-1
measurements (Lionel Zawadsky, personal communication, 2017). In a recent study, Beckley
et al. (2017) asserted that the corresponding error on the 1993–1998 GMSL
resulted from incorrect onboard calibration parameters.
All approaches conclude that during the period January 1993 to
February 1999, the altimetry-based GMSL was overestimated. TOPEX-A drift
correction was estimated to be close to 1.5 mm yr-1 (in terms of sea-level trend)
with an uncertainty of ±0.5 to ±1.0 mm yr-1 (Watson et al., 2015;
X. Chen et al., 2017; Dieng et al., 2017). Beckley et al. (2017) proposed to not
apply the suspect onboard calibration correction on TOPEX-A measurements.
The impact of this approach is similar to the TOPEX-A drift correction
estimated by Dieng et al. (2017) and Ablain et al. (2017b). In the latter
study, accurate comparison between TOPEX-A-based GMSL and tide gauge
measurements leads to a drift correction of about -1.0 mm yr-1 between
January 1993 and July 1995, and +3.0 mm yr-1 between August 1995 and February 1999,
with an uncertainty of 1.0 mm yr-1 (with a 68 % confidence level,
see Table 1).
TOPEX-A GMSL drift corrections proposed by different studies.
TOPEX-A drift correctionto be subtracted from the first 6 years (Jan 1993to Feb 1999) of the uncorrected GMSL recordWatson et al. (2015)1.5 ± 0.5 mm yr-1 over Jan 1993–Feb 1999X. Chen et al. (2017), Dieng et al. (2017)1.5 ± 0.5 mm yr-1 over Jan 1993–Feb 1999Beckley et al. (2017)No onboard calibration appliedAblain et al. (2017b)-1.0 ± 1.0 mm yr-1 over Jan 1993–Jul 1995+3.0±1.0 mm yr-1 over Aug 1995–Feb 1999Global mean sea-level variations
The ensemble mean GMSL rate after correcting for the TOPEX-A drift (for all
of the proposed corrections) amounts to 3.1 mm yr-1 over 1993–2017 (Fig. 2).
This corresponds to a mean sea-level rise of about 7.5 cm over the whole
altimetry period. More importantly, the GMSL curve shows a net acceleration,
estimated to be at 0.08 mm yr-2 (X. Chen et al., 2017; Dieng et al.,
2017) and 0.084 ± 0.025 mm yr-2 (Nerem et al., 2018) (note
Watson et al., 2015 found a smaller acceleration after correcting for the
instrumental bias over a shorter period up to the end of 2014.). GMSL trends
calculated over 10-year moving windows illustrate this acceleration (Fig. 3).
GMSL trends are close to 2.5 mm yr-1 over 1993–2002 and 3.0 mm yr-1 over
1996–2005. After a slightly smaller trend over 2002–2011, the 2008–2017
trend reaches 4.2 mm yr-1. Uncertainties (90 % confidence interval)
associated with these 10-year trends regularly decrease through time from
1.3 mm yr-1 over 1993–2002 (corresponding to T/P data) to 0.65 mm yr-1 for 2008–2017
(corresponding to Jason-2 and Jason-3 data).
Removing the trend from the GMSL time series highlights interannual
variations (not shown). Their magnitudes depend on the period (+3 mm in
1998–1999, -5 mm in 2011–2012 and +10 mm in 2015–2016) and are well
correlated in time with El Niño and La Niña events (Nerem et al.,
2010, 2018; Cazenave et al., 2014). However, substantial
differences (of 1–3 mm) exist between the six detrended GMSL time series.
This issue needs further investigation.
Evolution of ensemble mean GMSL time series (average of the six GMSL
products from AVISO/CNES, SL_cci/ESA, University of Colorado,
CSIRO, NASA/GSFC and NOAA). On the black, red and green curves, the TOPEX-A
drift correction is applied respectively based on Ablain et al. (2017b),
Watson et al. (2015) and Dieng et al. (2017), and Beckley et al. (2017). Annual
signal removed and 6-month smoothing applied; GIA correction also applied.
Uncertainties (90 % confidence interval) of correlated errors over a
1-year period are superimposed for each individual measurement (shaded
area).
For the sea-level budget assessment (Sect. 3), we will use the ensemble
mean GMSL time series corrected for the TOPEX-A drift using the Ablain et
al. (2017b) correction.
Comparison with tide gauges
Prior to 1992, global sea-level rise estimates relied on the tide gauge
measurements, and it is worth mentioning past attempts to produce global sea-level reconstructions utilizing these measurements (e.g., Gornitz et al.,
1982; Bartnett, 1984; Douglas, 1991, 1997, 2001). Here we focus on global sea-level reconstructions that overlap with satellite altimetry data over a
substantial common time span. Some of these reconstructions rely on tide
gauge data only (Jevrejeva et al., 2006, 2014; Merrifield et al., 2009; Wenzel
and Schroter, 2010; Ray and Douglas, 2011; Hamlington et al., 2011; Spada and
Galassi, 2012; Thompson and Merrifield, 2014; Dangendorf et al., 2017;
Frederikse et al., 2017). In addition, there are reconstructions that jointly
use satellite altimetry, tide gauge records (Church and White, 2006, 2011)
and reconstructions, which combine tide gauge records with ocean models
(Meyssignac et al., 2011) or physics-based and model-derived geometries of
the contributing processes (Hay et al., 2015).
Ensemble mean GMSL trends calculated over 10-year moving windows. On
the black, red and green curves, the TOPEX-A drift correction is applied
respectively based on Ablain et al. (2017b),
Watson et al. (2015) and Dieng et al. (2017), and Beckley et al. (2017).
Uncorrected GMSL trends are shown by the blue curve. The shaded area
represents trend uncertainty over 10-year periods (90 % confidence
interval).
For the period since 1993, with most of the world coastlines densely
sampled, the rates of sea-level rise from all tide-gauge-based
reconstructions and estimates from satellite altimetry agree within their
specific uncertainties, e.g., rates of 3.0±0.7 mm yr-1 (Hay et al. 2015), 2.8±0.5 mm yr-1
(Church and White, 2011; Rhein et al., 2013), 3.1±0.6 mm yr-1
(Jevrejeva et al., 2014), 3.1±1.4 mm yr-1 (Dangendorf
et al., 2017) and the estimate from satellite altimetry 3.2±0.4 mm yr-1
(Nerem et al., 2010; Rhein et al., 2013). However,
classical tide-gauge-based reconstructions still tend to overestimate the
interannual to decadal variability of global mean sea level (e.g., Calafat
et al., 2014; Dangendorf et al., 2015; Natarov et al., 2017) compared to
global mean sea level from satellite altimetry, due to limited and uneven
spatial sampling of the global ocean afforded by the tide gauge network. Sea-level rise being non uniform, spatial variability of sea-level measured at
tide gauges is evidenced by 2-D reconstruction methods. The most widely used
approach is the use of empirical orthogonal functions (EOFs) calibrated with
the satellite altimetry data (e.g., Church and White, 2006). Alternatively,
Choblet et al. (2014) implemented a Bayesian inference method based on a
Voronoi tessellation of the Earth's surface to reconstruct sea level during
the 20th century. Considerable uncertainties remain, however, in long-term assessments due to poorly sampled ocean basins such as the South
Atlantic, or regions which are significantly influenced by open-ocean
circulation (e.g., subtropical North Atlantic) (Frederikse et al., 2017).
Uncertainties involved in specifying vertical land motion corrections at
tide gauges also impact tide gauge reconstructions (Jevrejeva et al., 2014;
Wöppelmann and Marcos, 2016; Hamlington et al., 2016). Frederikse et
al. (2017) also recently demonstrated that both global mean sea level
reconstructed from tide gauges and the sum of steric and mass contributors
show a good agreement with altimetry estimates for the overlapping period
1993–2014.
Steric sea level
Steric sea-level variations result from temperature- (T) and salinity- (S)
related density changes in sea water associated with volume expansion and
contraction. These are referred to as thermosteric and halosteric
components. Despite clear detection of regional salinity changes and the
dominance of the salinity effect on density changes at high latitudes (Rhein
et al., 2013), the halosteric contribution to present-day global mean steric
sea-level rise is negligible, as the ocean's total salt content is
essentially constant over multidecadal timescales (Gregory and Lowe, 2000).
Hence, in this study, we essentially consider the thermosteric sea-level
component.
Averaged over the 20th century, ocean thermal expansion associated with
ocean warming has been the largest contribution to global mean sea-level
rise (Church et al., 2013). This remains true for the altimetry period
starting in the year 1993 (e.g., X. Chen et al., 2017; Dieng et al., 2017; Nerem
et al., 2018). But total land ice mass loss (from glaciers, Greenland and
Antarctica) during this period now dominates the sea-level budget (see Sect. 3).
Until the mid-2000s, the majority of ocean temperature data were
retrieved from shipboard measurements. These include vertical temperature
profiles along research cruise tracks from the surface sometimes all the way
down to the bottom layer (e.g., Purkey and Johnson, 2010) and upper-ocean
broad-scale measurements from ships of opportunity (Abraham et al., 2013).
These upper-ocean in situ temperature measurements, however, are limited to
the upper 700 m depth due to common use of expandable bathythermographs
(XBTs). Although the coverage has been improved through time, large regions
characterized by difficult meteorological conditions remained under-sampled,
in particular the southern hemispheric oceans and the Arctic area.
Thermosteric datasets
Over the altimetry era, several research groups have produced gridded time
series of temperature data for different depth levels, based on XBTs (with
additional data from mechanical bathythermographs – MBTs – and
conductivity–temperature–depth – CTD – devices and moorings) and Argo float
measurements. The temperature data have further been used to provide
thermosteric sea-level products. These differ because of different
strategies adopted for data editing, temporal and spatial data gaps filling,
mapping methods, baseline climatology, and instrument bias corrections (in
particular the time-to-depth correction for XBT data, Boyer et al., 2016).
The global ocean in situ observing system has been dramatically improved
through the implementation of the international Argo program of autonomous
floats, delivering a unique insight into the interior ocean from the surface
down to 2000 m depth of the ice-free global ocean (Roemmich et al., 2012;
Riser et al., 2016). More than 80 % of initially planned full deployment
of Argo float program was achieved during the year 2005, with quasi global
coverage of the ice-free ocean by the start of 2006. At present, more than
3800 floats provide systematic T and S data, with quasi (60∘ S–60∘ N
latitude) global coverage down to 2000 m depth. A full
overview on in situ ocean temperature measurements is given for example in
Abraham et al. (2013).
In this section, we consider a set of 11 direct (in situ) estimates,
publicly available over the entire altimetry era, to review global mean
thermosteric sea-level rise and, ultimately, to construct an ensemble mean
time series. These datasets are as follows:
CORA = Coriolis Ocean database for ReAnalysis, Copernicus Service, France
(marine.copernicus.eu/), product name: INSITU_GLO_ TS_OA_
REP_OBSERVATIONS_013_002_b;
CSIRO (RSOI) = Commonwealth Scientific and Industrial Research
Organisation/Reduced-Space Optimal Interpolation, Australia;
ACECRC/IMAS-UTAS = Antarctic Climate and Ecosystem Cooperative Research
Centre/Institute for Marine and Antarctic Studies-University of Tasmania,
Australia
(http://www.cmar.csiro.au/sealevel/thermal_expansion_ocean_heat_timeseries.html);
ICCES = International Center for Climate and Environment Sciences,
Institute of Atmospheric Physics, China (http://ddl.escience.cn/f/PKFR);
ICDC = Integrated Climate Data Center, University of Hamburg,
Germany;
IPRC = International Pacific Research Center, University of Hawaii, USA
(http://apdrc.soest.hawaii.edu/projects/Argo/data/gridded/On_standard_levels/index-1.html);
JAMSTEC = Japan Agency for Marine-Earth Science and Technology, Japan
(ftp://ftp2.jamstec.go.jp/pub/argo/MOAA_GPV/Glb_PRS/OI/);
MRI/JMA = Meteorological Research Institute/Japan Meteorological Agency,
Japan (https://climate.mri-jma.go.jp/~ishii/.wcrp/);
NCEI/NOAA = National Centers for Environmental Information/National
Oceanic and Atmospheric Administration, USA;
SIO = Scripps Institution of Oceanography, USA;
Deep–abyssal: https://cchdo.ucsd.edu/;
SIO = Scripps Institution of Oceanography, USA;
Deep–abyssal: https://cchdo.ucsd.edu/ (for the abyssal ocean).
Their characteristics are presented in Table 2.
Compilation of available in situ datasets from different
originators and/or contributors.The table indicates the time span covered by
the data, the depth of integration, as well as the temporal resolution and latitude coverage.
Product/institution PeriodDepth integration (m) Temporal resolution/latitudinal rangeReference0–700700–20000–2000≥20001CORA1993–2016YYY–Monthly 60∘ S– 60∘ Nhttp://marine.copernicus.eu/services-portfolio/access-to-products/2CSIRO (RSOI)2004–2017Y/E (0–300)Y/EY/E–Monthly 65∘ S– 65∘ NRoemmich et al. (2015), Wijffels et al. (2016)3CSIRO/ ACECRC/ IMAS-UTAS1970–2017Y/E (0–300)–––Yearly (3-year running mean) 65∘ S–65∘ NDomingues et al. (2008), Church et al. (2011)4ICCES1970–2016Y/E (0–300)Y/EY/E–Yearly 89∘ S– 89∘ NCheng et al. (2017)5ICDC1993–2016Y (1993)–Y (2005)–MonthlyGouretski and Koltermann (2007)6IPRC2005–2016––Y–Monthlyhttp://apdrc.soest.hawaii.edu/projects/argo (last access: 22 August 2018)7JAMSTEC2005–2016––Y–MonthlyHosoda et al. (2008)8MRI/JMA1970–2016 (rel. to 1961–1990 averages)Y/E (0–300)Y/EY/E–Yearly 89∘ S– 89∘ NIshii et al. (2009, 2017)9NCEI/NOAA1970–2016Y/EY/EY/E–Yearly 89∘ S– 89∘ NLevitus et al. (2012)10SIO2005–2016––Y–MonthlyRoemmich and Gilson (2009)11SIO (Deep–abyssal)1990–2010 (as of Jan 2018)–––Y/ELinear trend 89∘ S–89∘ N, as an aggregation of 32 deep ocean basinsPurkey and Johnson (2010)
Left panels: annual mean global mean thermosteric anomaly
time series since 1970, from various research groups (color) and for three depth integrations:
0–700 m (top), 700–2000 m (middle) and below 2000 m (bottom). Vertical dashed lines are plotted
along 1993 and 2005. For comparison, all time series were offset arbitrarily. Right
panels: respective linearly detrended time series for 1993–2015. Black bold dashed line is the
ensemble mean and gray shadow bar the ensemble spread (1 standard deviation). Units are millimeters.
Individual estimates
All in situ estimates compiled in this study show a steady rise in global
mean thermosteric sea level, independent of depth integration and
decadal or multidecadal periods (Figs. 4 and 5, left panels). As the
deep–abyssal ocean estimate only illustrates the updated version of the
linear trend from Purkey and Johnson (2010) for 1990–2010 extrapolated to
2016, it does not have any variability superimposed.
Interannual to decadal variability during the altimeter era (since 1993) is
similar for both 0–700 and 700–2000 m, with larger amplitude in the upper
ocean (Figs. 4 and 5, right panels). For the 0–700 m, there is an apparent
change in amplitude before and after the Argo era (since 2005), mostly due to a
maximum (2–4 mm) around 2001–2004, except for one estimate. Higher amplitude
and larger spread in variability between estimates before the Argo era is a
symptom of the much sparser in situ coverage of the global ocean.
Interannual variability over the Argo era (Figs. 4 and 5, right panels) is
mainly modulated by El Niño–Southern Oscillation (ENSO) phases in the
upper 500 m of the ocean, particularly for the Pacific, the largest ocean basin
(Roemmich and Gilson, 2011; Roemmich et al., 2016; Johnson and Birnbaum, 2017).
In terms of depth contribution, on average, the upper 300 m explains the
same percentage (almost 70 %) of the 0–700 m linear rate over both
altimetry and Argo eras, but the contribution from the 0–700 to 0–2000 m
varies: about 75 % for 1993–2016 and 65 % for 2005–2016. Thus, the
700–2000 m contribution increases by 10 % during the Argo decade, when
the number of observations within 700–2000 m has significantly increased.
Left panel: annual mean global mean thermosteric anomaly
time series since 2004, from various research groups (color) in the upper 2000 m.
A vertical dashed line is plotted along 2005. For comparison, all time series were
offset arbitrarily. Right panel: respective linearly detrended time series for
2005–2015. Black bold dashed line is the ensemble mean and gray shadow bar the
ensemble spread (1 standard deviation). Units are millimeters.
Ensemble mean thermosteric sea level
Given that the global mean thermosteric sea-level anomaly estimates compiled
for this study are not necessarily referenced to the same baseline
climatology, they cannot be directly averaged together to create an ensemble
mean. To circumvent this limitation, we created an ensemble mean in three
steps, as explained below.
Firstly, we detrended the individual time series by removing a linear trend
for 1993–2016 and averaged together to obtain an “ensemble mean variability
time series”. Secondly, we averaged together the corresponding linear
trends of the individual estimates to obtain an “ensemble mean linear
rate”. Thirdly, we combined this “ensemble mean linear rate” with the
“ensemble mean variability time series” to obtain the final ensemble mean
time series. We applied the same steps for the Argo era (2005–2016).
To maximize the number of individual estimates used in the final full-depth
ensemble mean time series, the three steps above were actually divided into
depth integrations and then summed. For the Argo era, we summed 0–2000 m
(nine estimates) and ≥2000 m (one estimate). For the altimetry era, we summed
0–700 m (six estimates), 700–2000 (four estimates) and ≥2000 m (one estimate),
although there is no statistical difference if the calculation was only
based on the sum of 0–2000 m (4 estimates) and ≥2000 m (1 estimate).
There is also no statistical difference between the full-depth ensemble mean
time series created for the Altimeter and Argo eras during their overlapping
years (since 2005).
Figure 6 shows the full-depth ensemble mean time series over 1993–2015 and
2005–2015. It reveals a global mean thermosteric sea-level rise of about 30 mm
over 1993–2016 (24 years) or about 18 mm over 2005–2016 (12 years), with
a record high in 2015. These thermosteric changes are equivalent to a linear
rate of 1.32 ± 0.4 and 1.31 ± 0.4 mm yr-1 respectively.
Ensemble mean time series for global mean thermosteric anomaly,
for three depth integrations (a) and for 0–2000 m and full depth (b).
In the bottom panel, dashed lines are for the 1993–2015 period whereas solid
lines are for 2005–2015. Error bars represent the ensemble spread (standard
deviation). Units are millimeters.
Figure 7 shows thermosteric sea-level trends for each of the datasets used
over the 1993–2015 (a) and 2005–2015 (b) time spans and
different depth ranges (including full depth), as well as associated
ensemble mean trends. The full depth ensemble mean trend amounts to 1.3±0.4 mm yr-1 over 2005–2015. It is similar to the 1993–2015 ensemble mean
trend, suggesting negligible acceleration of the thermosteric component over
the altimetry era.
Glaciers
Glaciers have strongly contributed to sea-level rise during the 20th
century – around 40 % – and will continue to be an important part of the
projected sea-level change during the 21st century – around 30 %
(Kaser et al., 2006; Church et al., 2013; Gardner et al., 2013; Marzeion et
al., 2014; Zemp et al., 2015; Huss and Hock, 2015). Because glaciers are
time-integrated dynamic systems, a response lag of at least 10 years to a
few hundred years is observed between changes in climate forcing and glacier
shape, mainly depending on glacier length and slope (Johannesson et al.,
1989; Bahr et al., 1998). Today, glaciers are globally (a notable exception
is the Karakoram–Kunlun Shan region, e.g., Brun et al., 2017) in a strong
disequilibrium with the current climate and are losing mass, due
essentially to the global warming in the second half of the 20th
century (Marzeion et al., 2018).
Global glacier mass changes are derived from in situ measurements of glacier
mass changes or glacier length changes. Remote sensing methods measure
elevation changes over entire glaciers based on differencing digital
elevation models (DEMs) from satellite imagery between two epochs (or at
points from repeat altimetry), surface flow velocities for determination of
mass fluxes and glacier mass changes from space-based gravimetry. Mass
balance modeling driven by climate observations is also used (Marzeion et
al., 2017, provide a review of these different methods).
Linear rates of global mean thermosteric sea level
for depth integrations (x axis), individual estimates and ensemble means,
over 1993–2015 (a) and 2005–2015 (b). Ensemble mean rates with a black
circle were used in the estimation of the time series described in Sect. 2.3.4.
Error bars are standard deviation due to spread of the estimates except
for ≥2000 m. Units are millimeters per year.
Glacier contribution to sea level is primarily the result of their surface
mass balance and dynamic adjustment, plus iceberg discharge and frontal
ablation (below sea level) in the case of marine-terminating glaciers. The
sum of worldwide glacier mass balances does not correspond to the
total glacier contribution to sea-level change for the following reasons:
Glacier ice below sea level does not contribute to sea-level change, apart
from a small lowering when replacing ice with seawater of a higher density.
Total volume of glacier ice below sea level is estimated to be 10–60 mm
sea-level equivalent (SLE, Huss and Farinotti, 2012; Haeberli and Linsbauer,
2013; Huss and Hock, 2015).
There is incomplete transfer of melting ice from glaciers to the ocean: meltwater
stored in lakes or wetlands, meltwater intercepted by natural processes and
human activities (e.g., drainage to lakes and aquifers in endorheic basins,
impoundment in reservoirs, agriculture use of freshwater, Loriaux and
Casassa, 2013; Käab et al., 2015).
Despite considerable progress in observing methods and spatial coverage
(Marzeion et al., 2017), estimating glacier contribution to sea-level change
remains challenging due to the following reasons:
The number of regularly observed glaciers (in the field) remains very low
(0.25 % of the 200 000 glaciers of the world have at least one observation
and only 37 glaciers have multidecade-long observations, Zemp et al., 2015).
Uncertainty of the total glacier ice mass remains high
(Fig. 8, Grinsted, 2013; Pfeffer et al., 2014;
Farinotti et al., 2017; Frey et al.
2014).
Uncertainties in glacier inventories and DEMs are not negligible. Sources
of uncertainties include debris-covered glaciers, disappearance of small
glaciers, positional uncertainties, wrongly mapped seasonal snow, rock
glaciers, voids and artifacts in DEMs (Paul et al., 2004; Bahr and
Radić, 2012).
Uncertainties of satellite retrieval algorithms from space-based
gravimetry and regional DEM differencing are still high, especially for
global estimates (Gardner et al., 2013; Marzeion et al., 2017; Chambers et
al., 2017).
Uncertainties of global glacier modeling (e.g., initial conditions, model
assumptions and simplifications, local climate conditions; Marzeion et al.,
2012).
Knowledge about some processes governing mass balance (e.g., wind
redistribution and metamorphism, sublimation, refreezing, basal melting) and
dynamic processes (e.g., basal hydrology, fracking, surging) remains limited
(Farinotti et al., 2017).
An annual assessment of glacier contribution to sea-level change is
difficult to perform from ground-based or space-based observations apart from
space-based gravimetry, due to the sparse and irregular observation of
glaciers, and the difficulty of accurately assessing the annual mass balance
variability. Global annual averages are highly uncertain because of the
sparse coverage, but successive annual balances are uncorrelated and
therefore averages over several years are known with greater confidence.
Glacier datasets
The following datasets are considered, with a focus on the trends of annual
mass changes:
update of Gardner et al. (2013) (Reager et al., 2016), from satellite
gravimetry and altimetry, and glaciological records, called G16;
update of Marzeion et al. (2012) (Marzeion et al., 2017), from global
glacier modeling and mass balance observations, called M17;
update of Cogley (2009) (Marzeion et al., 2017), from geodetic and direct
mass-balance measurements, called C17;
update of Leclercq et al. (2011) (Marzeion et al., 2017), from glacier
length changes, called L17;
average of GRACE-based estimates of Marzeion et al. (2017), from spatial
gravimetry measurements, called M17-G.
In general it is not possible to align measurements of glacier mass balance
with the calendar. Most in situ measurements are for glaciological years
that extend between successive annual minima of the glacier mass at the end
of the summer melt season. Geodetic measurements have start and end dates
several years apart and are distributed irregularly through the calendar
year; some are corrected to align with annual mass minima but most are not.
Consequently, measurements discussed here for 1993–2016 (the altimetry era)
and 2005–2016 (the GRACE and Argo era) are offset by up to a few months from
the nominal calendar years.
Peripheral glaciers around the Greenland and Antarctic ice sheets are not
treated in detail in this section (see Sects. 2.5 and 2.6 for mass-change
estimates that combine the peripheral glaciers with the Greenland ice sheet
and Antarctic ice sheet respectively). This is primarily because of the lack
of observations (especially ground-based measurements) and also because of
the high spatial variability of mass balance in those regions, and the
slightly different climate (e.g., precipitation regime) and processes (e.g.,
refreezing). In the past, these regions have often been neglected. However,
Radić and Hock (2010) estimated the total ice mass of peripheral
glaciers around Greenland and Antarctica as 191±70 mm SLE, with an
actual contribution to sea-level rise of around 0.23±0.04 mm yr-1
(Radić and Hock, 2011). Gardner et al. (2013) found a contribution from
Greenland and Antarctic peripheral glaciers equal to 0.12±0.05 mm yr-1.
Note that some new or updated datasets for peripheral glaciers surrounding
polar ice sheets are under development and will hopefully be available in
coming years in order to incorporate Greenland and Antarctic peripheral
glaciers in the estimates of global glacier mass changes.
Methods
No globally complete observational dataset exists for glacier mass changes
(except GRACE estimates; see below). Any calculation of the global glacier
contribution to sea-level change has to rely on spatial interpolation or
extrapolation or both, or to consider limited knowledge of responses to
climate change (due to the heterogeneous spatial distribution of glaciers
around the world). Consequently, most observational methods to derive
glacier sea-level contribution must extend local observations (in situ or
satellite) to a larger region. Thanks to the recent global glacier outline
inventory (Randolph Glacier Inventory – RGI – first release in 2012) as
well as global climate observations, glacier modeling can now also be used
to estimate the contribution of glaciers to sea level (Marzeion et al.,
2012; Huss and Hock, 2015; Maussion et al., 2018). Still, those
global modeling methods need to globalize local observations and glacier
processes which require fundamental assumptions and simplifications. Only
GRACE-based gravimetric estimates are global but they suffer from large
uncertainties in retrieval algorithms (signal leakage from hydrology, GIA
correction) and coarse spatial resolution, not resolving smaller glaciated
mountain ranges or those peripheral to the Greenland ice sheet.
The DEM differencing method is not yet global, but regional, and can hopefully
in the near future be applied globally. This method needs also to convert
elevation changes to mass changes (using assumptions on snow and ice
densities). In contrast, very detailed glacier surface mass balance and
glacier dynamic models are today far from being applicable globally, mainly
due to the lack of crucial observations (e.g., meteorological data, glacier
surface velocity and thickness) and of computational power for the more
demanding theoretical models. However, somewhat simplified approaches are
currently being developed to make the best use of the steadily increasing datasets.
Modeling-based estimates suffer also from the large spread in estimates of
the actual global glacier ice mass (Fig. 8). The mean value is 469±146 mm SLE,
with recent studies converging towards a range of values between
400 and 500 mm SLE global glacier ice mass. But as mentioned above, a part
of this ice mass will not contribute to sea level.
Evolution of global glacier ice mass estimates from different studies
published over the past 2 decades, based on different observations and methods.
The red marks correspond to IPCC reports. We clearly see the most recent publications
lead to less scattered results. Note that Antarctica and Greenland peripheral
glaciers are taken into account in this figure.
Results (trends)
Table 3 presents most recent estimates of trends in global glacier mass
balances.
Glacier contribution to sea level; all data are in millimeters per year of SLE.
a The time
period of G16 is 2002–2014. b The time period of C17 is 2003–2009.
c The time period of L17 is 2003–2009. d The time period
of M17-G is 2002/2005–2013/2015 because this value is an average of different estimates.
The ensemble mean contribution of glaciers to sea-level rise for the time
period 1993–2016 is 0.65 ± 0.051 mm yr-1 SLE and 0.74 ± 0.18 mm yr-1
for the time period 2005–2016 (uncertainties are averaged). Different
studies refer to different time periods. However, because of the probable
low variability of global annual glacier changes, compared to other
components of the sea-level budget, averaging trends for slightly different
time periods is appropriate.
The main source of uncertainty is that the vast majority of glaciers are
unmeasured, which makes interpolation or extrapolation necessary, whether
for in situ or satellite measurements, as well as for glacier modeling. Other main
contributions to uncertainty in the ensemble mean stem from methodological
differences, such as the downscaling of atmospheric forcing required for
glacier modeling, the separation of glacier mass change to other mass change
in the spatial gravimetry signal and the derivation of observational
estimates of mass change from different raw measurements (e.g., length and
volume changes, mass balance measurements, and geodetic methods), all with
their specific uncertainties.
Greenland
Ice sheets are the largest potential source of future sea-level rise
and represent the largest uncertainty in projections of future sea level.
Almost all land ice (∼99.5 %) is locked in the ice sheets,
with a volume in sea-level equivalent (SLE) terms of 7.4 m for Greenland and
58.3 m for Antarctica. It has been estimated that approximately 25 % to
30 % of the total land ice contribution to sea-level rise over the last
decade came from the Greenland ice sheet (e.g., Dieng et al., 2017; Box and
Colgan, 2017).
There are three main methods that can be used to estimate the mass balance
of the Greenland ice sheet: (1) measurement of changes in elevation of the
ice surface over time (dh/ dt) either from imagery or altimetry; (2) the mass
budget or input–output method (IOM), which involves estimating the difference
between the surface mass balance and ice discharge; and (3) consideration of
the redistribution of mass via gravity anomaly measurements, which only
became viable with the launch of GRACE in 2002. Uncertainties due to the GIA
correction are small in Greenland compared to Antarctica: on the order of
±20 Gt yr-1 mass equivalent (Khan et al., 2016). Prior
to 2003, mass trends are reliant on IOM and altimetry. Both techniques have
limited sampling in time and/or space for parts of the satellite era
(1992–2002) and errors for this earlier period are, therefore, higher
(van den Broeke et al., 2016; Hurkmans et al., 2014).
The consistency between the three methods mentioned above was demonstrated
for Greenland by Sasgen et al. (2012) for the period 2003–2009. Ice-sheet-wide estimates showed excellent agreement although there was less
consistency at a basin scale. We have, therefore, high confidence and
relatively low uncertainties in the mass rates for the Greenland ice sheet
in the satellite era (see also Bamber et al., 2018).
Datasets considered for the assessment
This assessment of sea-level budget contribution from the Greenland ice
sheet considers the datasets shown in Table 4.
Datasets considered in the Greenland mass balance assessment, as well as covered time span and type of observations.
ReferenceTime periodMethodUpdate from Barletta et al. (2013)2003–2016GRACEGroh and Horwath (2016)2003–2015GRACEUpdate from Luthcke et al. (2013)2003–2015GRACEUpdate from Sasgen et al. (2012)2003–2016GRACEUpdate from Schrama et al. (2014)2003–2016GRACEUpdate from van den Broeke et al. (2016)1993–2016Input–outputmethod (IOM)Wiese et al. (2016a, b)2003–2016GRACEUpdate from Wouters et al. (2008)2003–2016GRACEMethods and analyses
All but one of these datasets are based on GRACE data and therefore provide
annual time series from ∼2002 onwards. The one exception uses
IOM (van Den Broeke et al., 2016) to give an annual
mass time series for a longer time period (1993 onwards).
Notwithstanding this, each group has chosen their own approach to estimate
mass balance from GRACE observations. As the aim of this global sea-level
budget assessment is to compile existing results (rather than undertake new
analyses), we have not imposed a specific methodology. Instead, we asked for
the contributed datasets to reflect each group's `best estimate' of annual
trends for Greenland using the method(s) they have published.
Greenland contains glaciers and ice caps (GIC) around the margins of the main ice
sheet, often referred to as peripheral GIC (PGIC), which are a significant
proportion of the total mass imbalance (circa 15–20 %) (Bolch et al.,
2013). Some studies consider the mass balance of the ice sheets and the PGIC
separately but there has been, in general, no consistency in the treatment
of PGIC and many studies do not specify if they are included or excluded
from the total. The GRACE satellites have an approximate spatial resolution
of 300 km and the large number of studies that use GRACE, by default,
include all land ice within the domain of interest. For this reason, the
results below for Greenland mass trends all include PGIC.
From these datasets, for each year from 1993 to 2015 (and 2016 where
available), we have calculated an average change in mass (calculated as the
weighted mean based on the stated error value for each year) and an error
term. Prior to 2003, the results are based on just one dataset
(van den Broeke et al., 2016).
Results
Greenland annual mass change from 1993 to 2016.
The medium blue region shows the range of estimates from the datasets
listed in Table 1. The lighter blue region shows the range of estimates
when stated errors are included, to provide upper and lower bounds. The
dark blue line shows the mean mass trend.
Annual time series of Greenland mass change (GT yr-1, negative values
mean decreasing mass). The Δ mass is calculated as the weighted
mean based on the stated error value for each year. The error for each year
is calculated as the mean of all stated 1σ errors divided by sqrt(N)
where N is the number of datasets available for that year, assuming that the
errors are uncorrelated. The standard deviation (σ) is also
given to illustrate the level of agreement between datasets for each year
when multiple datasets are available (2003 onwards).
There is generally a good level of agreement between the datasets (Fig. 9),
and taken together they provide an average estimate of
171 Gt yr-1 of ice
mass loss (or sea-level budget contribution) from Greenland for the period
1993 to 2016, increasing to 272 Gt yr-1 for the period 2005 to 2016 (Table 5).
All the datasets illustrate the previously documented accelerating mass loss
up to 2012 (Rignot et al., 2011a; Velicogna, 2009) . In 2012, the ice sheet
experienced exceptional surface melting reaching as far as the summit
(Nghiem et al., 2012) and a record mass loss, since at least 1958, of
over 400 Gt (van Den Broeke et al., 2016). The
following years, however, show a reduced loss (not more than 270 Gt in any
year). Inclusion of the years since 2012 in the 2005–2016 trend estimate
reduces the overall rate of mass loss acceleration and its statistical
significance. There is greater divergence in the GRACE time series for 2016.
We associate this with the degradation of the satellites as they came
towards the end of their mission. For 2005–2012, it might be inferred that
there is a secular trend towards greater mass loss and from 2010 to 2012 the
value is relatively constant. Interannual variability in mass balance of
the ice sheet is driven, primarily, by the surface mass balance (i.e., atmospheric
weather) and it is apparent that the magnitude of this year-to-year variability can be large: it exceeded 360 Gt (or 1 mm sea-level
equivalent) between 2012 and 2013. Caution is required, therefore, in
extrapolating trends from a short record such as this.
Antarctica
The annual turnover of mass of Antarctica is about 2200 Gt yr-1 (over 6 mm yr-1
of SLE), 5 times larger than in Greenland (Wessem et al., 2017). In
contrast to Greenland, ice and snow melt have a negligible influence on
Antarctica's mass balance, which is therefore completely controlled by the
balance between snowfall accumulation in the drainage basins and ice
discharge along the periphery. The continent is also 7 times larger than
Greenland, which makes satellite techniques absolutely essential to survey
the continent. Interannual variations in accumulation are large in
Antarctica, showing decadal to multidecadal variability, so that many years
of data are required to extract trends, and missions limited to only a few
years may produce misleading results (e.g., Rignot et al., 2011a, b).
As in Greenland, the estimation of the mass balance has employed a variety
of techniques, including (1) the gravity method with GRACE since April 2002
until the end of the mission in late 2016; (2) the IOM method using a series
of Landsat and synthetic-aperture radar (SAR) satellites for measuring ice
motion along the periphery (Rignot et al., 2011a, b), ice thickness from
airborne depth radar sounders such as Operation IceBridge (Leuschen,
2014a), and reconstructions of surface mass balance using regional
atmospheric climate models constrained by re-analysis data (RACMO, MAR and
others); and (3) a radar or laser altimetry method which mixes various satellite
altimeters and correct ice elevation changes with density changes from firm
models. The largest uncertainty in the GRACE estimate in Antarctica is the
GIA, which is larger than in Greenland, and a large fraction of the observed
signal. The IOM method compares two large numbers with large uncertainties
to estimate the mass balance as the difference. In order to detect an
imbalance at the 10 % level, surface mass balance and ice discharge need
to be estimated with a precision typically of 5 to 7 %. The altimetry
method is limited to areas of shallow slope; hence, it is difficult to use in
the Antarctic Peninsula and in the deep interior of the Antarctic continent
due to unknown variations of the penetration depth of the signal in
snow and firn. The only method that expresses the partitioning of the mass
balance between surface processes and dynamic processes is the IOM method
(e.g., Rignot et al., 2011a). The gravity method is an integrand method which
does not suffer from the limitations of surface mass balance models but is limited in spatial
resolution (e.g., Velicogna et al., 2014). The altimetry method provides
independent evidence of changes in ice dynamics, e.g., by revealing rapid ice
thinning along the ice streams and glaciers revealed by ice motion maps, as
opposed to large-scale variations reflecting a variability in surface mass
balance (McMillan et al., 2014).
All these techniques have improved in quality over time and have accumulated
a decade to several decades of observations, so that we are now able to
assess the mass balance of the Antarctic continent using methods with
reasonably low uncertainties and multiple lines of evidence as the methods
are largely independent, which increases confidence in the results (see
recent publication by the IMBIE Team, 2018). There is broad agreement in the
mass loss from the Antarctic Peninsula and West Antarctica; most residual
uncertainties are associated with East Antarctica as the signal is
relatively small compared to the uncertainties, although most estimates tend
to indicate a low contribution to sea level (e.g., Shepherd et al., 2012).
Datasets considered for the assessment
This assessment considers the datasets shown in Table 6.
Datasets considered in this assessment of the Antarctica mass
change, and associated trends for the 2005–2015 and 1993–2015 expressed
in millimeters per year of SLE. Positive values mean positive contribution to sea level
(i.e., sea-level rise).
2005–20151993–2015trend (mm yr-1)trend (mm yr-1)ReferenceMethodSLESLEUpdate from Martín-Español et al. (2016)Joint inversion GRACE–altimetry–GPS0.43±0.07–Update from Forsberg et al. (2017)Joint inversion GRACE–CryoSat0.31±0.02–Update from Groh and Horwath (2016)GRACE0.32±0.11–Update from Luthcke et al. (2013)GRACE0.36±0.06–Update from Sasgen et al. (2013)GRACE0.47±0.07–Update from Velicogna et al. (2014)GRACE0.33±0.08–Update from Wiese et al. (2016b)GRACE0.39±0.02–Update from Wouters et al. (2013)GRACE0.41±0.05–Update from Rignot et al. (2011b)Input–output method (IOM)0.46±0.050.25±0.1Update from Schrama et al. (2014); version 1GRACE ICE6G GIA model0.47±0.03Update from Schrama et al. (2014); version 2GRACE Updated GIA models0.33±0.03
In Table 6, the negative trend estimate by Zwally et al. (2016) is not
added. It is worth noting that including it would only slightly reduce the
ensemble mean trend.
Methods and analyses
The datasets used in this assessment are Antarctica mass balance time series
generated using different approaches. Two estimates are a joint inversion of
GRACE, altimetry and GPS data (Martín-Español et al., 2016) as well as GRACE
and CryoSat data (Forsberg et al., 2017). Two methods are mascon solutions
obtained from the GRACE intersatellite range-rate measurements over
equal-area spherical caps covering the Earth's surface (Luthcke et al.,
2013; Wiese et al., 2016b), three estimates use the GRACE spherical harmonics
solutions (Velicogna et al., 2014; Wiese et al., 2016b; Wouters et al., 2013)
and one uses gridded GRACE products (Sasgen et al., 2013).
All GRACE time series were provided as monthly time series (except for the
one using the Martín-Español et al., 2016, method, which was
provided as annual estimates). In addition, different groups use different
GIA corrections, therefore the spread of the trend solutions also represents
the error associated with the GIA correction which, in Antarctica, is the
largest source of uncertainty. Sasgen et al. (2013) used their own GIA
solution (Sasgen et al., 2017), as did Martín-Español et al. (2016);
Luthcke et al. (2013), Velicogna et al. (2014), and Groh and
Horwath (2016) used IJ05-R2 (Ivins et al., 2013). Wouter et al. (2013) used
Whitehouse et al. (2012), and Wiese et al. (2016b) used A et al. (2013). In
addition, Groh and Horwath (2016) did not include the peripheral glaciers
and ice caps, while all other estimates do.
Table 6 shows the Antarctic contribution to sea level during 2005–2015 from
the different GRACE solutions, and for the input and output method.
There is a single IOM-based dataset that provides trends for the period
1993–2015 (update of Rignot et al., 2011a). For the period 2005–2015, we
calculated the annual sea-level contribution from Antarctica using GRACE and
IOM estimates (Table 7).
As we are interested in evaluating the long-term trend and interannual
variability of the Antarctic contribution to sea level, for each GRACE
dataset available in monthly time series, we first removed the annual and
subannual components of the signal by applying a 13-month averaging filter
and we then used the smoothed time series to calculate annual mass
change. Figure 10 shows the annual sea-level contribution from Antarctica
calculated from the GRACE-derived estimates and for the input–output method.
The GRACE mean annual estimates are calculated as the mean of the annual
contributions from the different groups, and the associated error calculated
as the sum of the spread of the annual estimates and the mean annual error.
Results
Antarctic annual sea-level contribution during 2005 to 2015.
The black squares are the mean annual sea level calculated using the GRACE
datasets listed in Table 6. The darker blue band shows the range of estimates
from the datasets. The light blue band accounts for the error in the different
GRACE estimates. The brown squares are the annual sea-level contribution
calculated using the input–output method (updated from Rignot et al., 2011a); the light brown band is the associated error.
Annual sea-level contribution from Antarctica during 2005–2015
from GRACE and input–output method (IOM) calculated as described above and expressed
in millimeters per year of SLE. Also shown is the mean of the estimate from the two
methods;
associated errors are the mean of the two estimated errors. Positive values mean
positive contribution to sea level (i.e., sea-level rise).
There is generally broad agreement between the GRACE datasets (Fig. 10),
as most of the differences between GRACE estimates are caused by differences
in the GIA correction. We find a reasonable agreement between GRACE and the
IOM estimates although the IOM estimates indicate higher losses. Taken
together, these estimates yield an average of 0.42 mm yr-1 sea-level budget
contribution from Antarctica for the period 2005 to 2015 (Table 7) and
0.25 mm yr-1 sea level for the time period 1993–2005, where the latter value is
based on IOM only.
All the datasets illustrate the previously documented accelerating mass loss
of Antarctica (Rignot et al., 2011a, b; Velicogna, 2009). In 2005–2010, the ice
sheet experienced ice mass loss driven by an increase in mass loss in the
Amundsen Sea sector of West Antarctica (Mouginot et al., 2014). The
following years showed a reduced increase in mass loss, as colder ocean
conditions prevailed in the Amundsen Sea embayment sector of West Antarctica
in 2012–2013 which reduced the melting of the ice shelves in front of the
glaciers (Dutrieux et al., 2014). Divergence in the GRACE time series is
observed after 2015 due to the degradation of the satellites towards the end
of the mission.
The large interannual variability in mass balance in 2005–2015,
characteristic of Antarctica, nearly masks out the trend in mass loss, which
is more apparent in the longer time series than in short time series. The
longer record highlights the pronounced decadal variability in ice sheet
mass balance in Antarctica, demonstrating the need for multidecadal time
series in Antarctica, which have been obtained only by IOM and altimetry.
The interannual variability in mass balance is driven almost entirely by
surface mass balance processes. The mass loss of Antarctica, about
200 Gt yr-1 in recent years, is only about 10 % of its annual turnover of mass (2200 Gt yr-1),
in contrast with Greenland where the mass loss has been growing
rapidly to nearly 100 % of the annual turnover of mass. This comparison
illustrates the challenge of detecting mass balance changes in Antarctica,
but at the same time, that satellite techniques and their interpretation
have made tremendous progress over the last 10 years, producing realistic
and consistent estimates of the mass using a number of independent methods
(Bamber et al., 2018; the IMBIE Team, 2018).
Terrestrial water storage
Human transformations of the Earth's surface have impacted the terrestrial
water balance, including continental patterns of river flow and water
exchange between land, atmosphere and ocean, ultimately affecting global sea
level. For instance, massive impoundment of water in man-made reservoirs has
reduced the direct outflow of water to the sea through rivers, while
groundwater abstractions, wetland and lake storage losses, deforestation, and
other land use changes have caused changes to the terrestrial water balance,
including changing evapotranspiration over land, leading to net changes in
land–ocean exchanges (Chao et al., 2008; Wada et al., 2012a, b; Konikow,
2011; Church et al., 2013; Döll et al., 2014a, b). Overall, the combined
effects of direct anthropogenic processes have reduced land water storage,
increasing the rate of sea-level rise by 0.3–0.5 mm yr-1 during recent
decades (Church et al., 2013; Gregory et al., 2013; Wada et al., 2016).
Additionally, recent work has shown that climate-driven changes in water
stores can perturb the rate of sea-level change over interannual to decadal
timescales, making global land mass budget closure sensitive to varying
observational periods (Cazenave et al., 2014; Dieng et al., 2015a; Reager et
al., 2016; Rietbroek et al., 2016). Here we discuss each of the major
component contributions from land, with a summary in Table 8, and estimate
the net terrestrial water storage contribution to sea level.
Direct anthropogenic changes in terrestrial water storageWater impoundment behind dams
Wada et al. (2016) built on work by Chao et al. (2008) to combine multiple
global reservoir storage datasets in pursuit of a quality-controlled global
reservoir database. The result is a list of 48 064 reservoirs that have a
combined total capacity of 7968 km3. The time history of growth of the
total global reservoir capacity reflects the history of the human activity
in dam building. Applying assumptions from Chao et al. (2008), Wada et al. (2016)
estimated that humans have impounded a total of 10 416 km3 of
water behind dams, accounting for a cumulative 29 mm drop in global mean sea
level. From 1950 to 2000 when global dam-building activity was at its
highest, impoundment contributed to the average rate of sea-level change at
-0.51 mm yr-1. This was an important process in comparison to other natural
and anthropogenic sources of sea-level change over the past century, but has
now largely slowed due to a global decrease in dam-building activity.
Global groundwater depletion
Groundwater currently represents the largest secular trend component to the
land water storage budget. The rate of groundwater depletion (GWD) and its
contribution to sea level has been subject to debate (Gregory et al., 2013;
Taylor et al., 2013). In the IPCC AR4 (Solomon et al., 2007), the
contribution of nonfrozen terrestrial waters (including GWD) to sea-level
variation was not considered due to its perceived uncertainty (Wada et al., 2016).
Observations from GRACE opened a path to monitor total water storage changes,
including groundwater in data-scarce regions (Strassberg et al., 2007;
Rodell et al., 2009; Tiwari et al., 2009; Jacob et al., 2012; Shamsudduha et
al., 2012; Voss et al., 2013). Some studies have also applied global
hydrological models in combination with the GRACE data (see Wada et al.,
2016, for a review).
Earlier estimates of GWD contribution to sea level range from 0.075 to 0.30 mm yr-1
(Sahagian et al., 1994; Gornitz, 1995, 2001;
Foster and Loucks, 2006). More recently, Wada et al. (2012b), using
hydrological modeling, estimated that the contribution of GWD to global sea
level increased from 0.035 (±0.009) to 0.57 (±0.09) mm yr-1
during the 20th century and projected that it would further increase to
0.82 (±0.13) mm yr-1 by 2050. Döll et al. (2014b) used
hydrological modeling, well observations and GRACE satellite gravity
anomalies to estimate a 2000–2009 global GWD of 113 km3 yr-1 (0.314 mm yr-1
SLE). This value represents the impact of human groundwater
withdrawals only and does not consider the effect of climate variability on
groundwater storage. A study by Konikow (2011) estimated global GWD to be
145 (±39) km3 yr-1 (0.41±0.1 mm yr-1 SLE) during 1991–2008
based on measurements of changes in groundwater storage from in situ
observations, calibrated groundwater modeling, GRACE satellite data and
extrapolation to unobserved aquifers.
An assumption of most existing global estimates of GWD impacts on sea-level
change is that nearly 100 % of the GWD ends up in the ocean. However,
groundwater pumping can also perturb regional climate due to land–atmosphere
interactions (Lo and Famiglietti, 2013). A recent study by Wada et al. (2016)
used a coupled land–atmosphere model simulation to track the fate of
water pumped from underground and found it more likely that ∼80 % of the GWD ends up in the ocean over the long term, while 20 %
re-infiltrates and remains in land storage. They estimated an updated
contribution of GWD to global sea-level rise ranging from 0.02
(±0.004) mm yr-1 in 1900 to 0.27 (±0.04) mm yr-1 in 2000 (Fig. 11). This
indicates that previous studies had likely overestimated the cumulative
contribution of GWD to global SLR during the 20th century and early
21st century by 5–10 mm.
Land cover and land-use change
Humans have altered a large part of the land surface,
replacing about 40 % of natural
vegetation by anthropogenic land cover such as crop fields or pasture. Such
land cover change can affect terrestrial hydrology by changing the
infiltration-to-runoff ratio and can impact subsurface water dynamics by
modifying recharge and increasing groundwater storage (Scanlon et al.,
2007). The combined effects of anthropogenic land cover changes on land
water storage can be quite complex. Using a combined hydrological and water
resource model, Bosmans et al. (2017) estimated that land cover change
between 1850 and 2000 has contributed to a discharge increase of
1058 km3 yr-1, on the same order of magnitude as the effect of human water
use. These recent model results suggest that land-use change is an important
topic for further investigation in the future. So far, this contribution
remains highly uncertain.
Deforestation and afforestation
At present, large losses in tropical forests and moderate gains in
temperate-boreal forests result in a net reduction of global forest cover
(FAO, 2015; Keenan et al., 2015; MacDicken, 2015; Sloan and Sayer,
2015). Net
deforestation releases carbon and water stored in both biotic tissues and
soil, which leads to sea-level rise through three primary processes:
deforestation-induced runoff increases (Gornitz et al., 1997), carbon
loss-related decay and plant storage loss, and complex climate feedbacks
(Butt et al., 2011; Chagnon and Bras, 2005; Nobre et al., 2009; Shukla et
al., 1990; Spracklen et al., 2012). Due to these three causes, and if
uncertainties from the land–atmospheric coupling are excluded, a summary by
Wada et al. (2016) suggests that the current net global deforestation leads
to an upper-bound contribution of ∼0.035 mm yr-1 SLE.
Wetland degradation
Wetland degradation contributes to sea level primarily through (i) direct
water drainage or removal from standing inundation, soil moisture and plant
storage, and (ii) water release from vegetation decay and peat combustion.
Wada et al. (2016) consider a recent wetland loss rate of 0.565 % yr-1
since 1990 (Davidson, 2014) and a present global wetland area of
371 mha averaged from three databases: Matthews natural wetlands (Matthews
and Fung, 1987), ISLSCP (Darras, 1999), and DISCover (Belward et al., 1999;
Lovel and Belward, 1997). They assume a uniform 1 m depth of water in
wetlands (Milly et al., 2010), to estimate a contribution of recent global
wetland drainage to sea level of 0.067 mm yr-1. Wada et al. (2016) apply a
wetland area and loss rate as used for assessing wetland water drainage, to
determine that the annual reduction of wetland carbon stock since 1990, if
completely emitted, releases water equivalent to 0.003–0.007 mm yr-1 SLE.
Integrating the impacts of wetland drainage, oxidation and peat combustion,
Wada et al. (2016) suggest that the recent global wetland degradation
results in an upper bound of 0.074 mm yr-1 SLE.
Lake storage changes
Lakes store the greatest mass of liquid water on the terrestrial surface
(Oki and Kanae, 2006), yet, because of their “dynamic” nature (Sheng et
al., 2016; Wang et al., 2012), their overall contribution to sea level
remains uncertain. In the past century, perhaps the greatest contributor in
global lake storage was the Caspian Sea (Milly et al., 2010), where the
water level exhibits substantial oscillations attributed to meteorological,
geological, and anthropogenic factors (Ozyavas et al., 2010; Chen et al.,
2017a). Assuming the lake level variation kept pace with groundwater changes
(Sahagian et al., 1994), the overall contribution of the Caspian Sea,
including both surface and groundwater storage variations through 2014, has
been about 0.03 mm yr-1 SLE since 1900, 0.075 (±0.002) mm yr-1 since
1995 and 0.109 (±0.004) mm yr-1 since 2002. Additionally, between 1960
and 1990, the water storage in the Aral Sea Basin declined at a striking
rate of 64 km3 yr-1, equivalent to 0.18 mm yr-1 SLE (Sahagian, 2000;
Sahagian et al., 1994; Vörösmarty and Sahagian, 2000) due mostly to
upstream water diversion for irrigation (Perera, 1993), which was modeled by
Pokhrel et al. (2012) to be ∼500 km3 during 1951–2000,
equivalent to 0.03 mm yr-1 SLE. Dramatic decline in the Aral Sea continued in recent decades, with an annual rate of 6.043 (±0.082) km3 yr-1
measured from 2002 to 2014 (Schwatke et al., 2015).
Assuming that groundwater drainage has kept pace with lake level reduction
(Sahagian et al., 1994), the Aral Sea has contributed 0.0358
(±0.0003) mm yr-1 to the recent sea-level rise.
Water cycle variability
Natural changes in the interannual to decadal cycling of water can have a
large effect on the apparent rate of sea-level change over decadal and
shorter time periods (Milly et al., 2003; Lettenmaier and Milly, 2009;
Llovel et al., 2010). For instance, ENSO-driven modulations of the global
water cycle can be important in decadal-scale sea-level budgets and can mask
underlying secular trends in sea level (Fasullo et al., 2013; Cazenave et al., 2014; Nerem et al.,
2018).
Sea-level variability due to climate-driven hydrology represents a
super-imposed variability on the secular rates of global mean sea-level
rise. While this term can be large and is important in the interpretation of
the sea-level record, it is arguably the most difficult term in the land
water budget to quantify.
Time series of the estimated annual contribution
of terrestrial water storage change to global sea level over the period
1900–2014 (rates in millimeters per year of SLE) (modified from Wada et al., 2016).
An example of trends in land water storage from GRACE
observations, April 2002 to November 2014. Glaciers and ice sheets are
excluded. Shown are the global map (gigatons per year), zonal trends and
full time series of land water storage (in mm yr-1 SLE). Following methods
detailed in Reager et al. (2016), GRACE shows a total gain in land water
storage during the 2002–2014 period, corresponding to a sea-level trend of
-0.33±0.16 mm yr-1 SLE (modified from Reager et al., 2016).
These trends include all human-driven and climate-driven processes in
Table 1 and can be used to close the land water budget over the study period.
Net terrestrial water storageGRACE-based estimates
Measurements of non-ice-sheet continental land mass from GRACE satellite
gravity have been presented in several recent studies (Jensen et al., 2013,
Rietbroek et al., 2016; Reager et al., 2016; Scanlon et al., 2018) and can
be used to constrain a global land mass budget. Note that these “top-down”
estimates contain both climate-driven and direct anthropogenic driven
effects, which makes them most useful in assessing the total impact of land
water storage changes and closing the budget of all contributing terms.
GRACE observations, when averaged over the whole land domain following
Reager et al. (2016), indicate a total TWS change (including glaciers) over
the 2002–2014 study period of approximately +0.32±0.13 mm yr-1 SLE
(i.e., ocean gaining mass). Global mountain glaciers have been estimated to
lose mass at a rate of 0.65 ± 0.09 mm yr-1 (e.g., Gardner et al.,
2013; Reager et al., 2016) during that period, such that a mass balance
indicates that global glacier-free land gained water at a rate of -0.33±0.16 mm yr-1 SLE (i.e., ocean losing mass; Fig. 12). A roughly
similar estimate was found from GRACE using glacier-free river basins
globally (-0.21±0.09 mm yr-1) (Scanlon et al., 2018). Thus, the
GRACE-based net TWS estimates suggest a negative sea-level contribution from
land over the GRACE period (Table 8). However, mass change estimates from
GRACE incorporate uncertainty from all potential error sources that arise
in processing and postprocessing of the data, including from the GIA model,
and from the geocenter and mean pole corrections.
Estimates based on global hydrological models
Global land water storage can also be estimated from global hydrological
models (GHMs) and global land surface models. These compute water, or water
and energy balances, at the Earth's surface, yielding time variations of water
storage in response to prescribed atmospheric data (temperature, humidity
and wind) and the incident water and energy fluxes from the atmosphere
(precipitation and radiation). Meteorological forcing is usually based on
atmospheric model reanalysis. Model uncertainties result from several
factors. Recent work has underlined the large differences among different
state-of-the-art precipitation datasets (Beck et al., 2017), with large
impacts on model results at seasonal (Schellekens et al., 2017) and longer
timescales (Felfelani et al., 2017). Another source of uncertainty is the
treatment of subsurface storage in soils and aquifers, as well as dynamic
changes in storage capacity due to representation of frozen soils and
permafrost, the complex effects of dynamic vegetation, atmospheric vapor
pressure deficit estimation and an insufficiently deep soil column. A recent
study by Scanlon et al. (2018) compared water storage trends from five
global land surface models and two global hydrological models to GRACE
storage trends and found that models estimated the opposite trend in net
land water storage to GRACE over the 2002–2014 period. These authors
attributed this discrepancy to model deficiencies, in particular soil depth
limitations. These combined error sources are responsible for a range of
storage trends across models of approximately 0.5±0.2 mm yr-1 SLE. In
terms of global land average, model differences can cause up to
∼0.4 mm yr-1 SLE uncertainty.
Estimates of TWS components due to human intervention and net TWS based on hydrological models and GRACE.
2002–2014/15(mm yr-1) SLE(positive values meanEstimate terrestrial water storage contribution to sea level sea-level rise)Human contributions by component Groundwater depletionWada et al. (2016)0.30 (±0.1)Reservoir impoundmentWada et al. (2017)-0.24 (±0.02)Deforestation (after 2010)Wada et al. (2017)0.035Wetland loss (after 1990)Wada et al. (2017)0.074Endorheic basin storage lossCaspian SeaWada et al. (2017)0.109 (±0.004)Aral SeaWada et al. (2017)0.036 (±0.0003)Aggregated human intervention (sum of above)Scanlon et al. (2018)0.15 to 0.24Hydrological model-based estimates WGHM model (natural variability plus human intervention) 0.15±0.14Döll et al. (2017) ISBA-TRIP model (natural variability only; Decharme et al., 2016) 0.23±0.10+ human intervention from Wada et al. (2016) (from Dieng et al., 2017) GRACE-based estimates of total land water storage (not including glaciers) -0.20 to -0.33(Reager et al., 2016; Rietbroek et al., 2016; Scanlon et al., 2018) (±0.09–0.16)Synthesis
Based on the different approaches to estimate the net land water storage
contribution, we estimate that the corresponding sea-level rate ranges from
-0.33 to 0.23 mm yr-1 during the period of 2002–2014/15 due to water storage
changes (Table 8). According to GRACE, the net TWS change (i.e., not
including glaciers) over the period 2002–2014 shows a negative contribution
to sea level of -0.33 and -0.21 mm yr-1 by Reager et al. (2016) and
Scanlon et al. (2018) respectively. Such a negative signal is not currently
reproduced by hydrological models which estimate slightly positive trends
over the same period (see Table 8). It is to be noted, however, that looking
at trends only over periods on the order of a decade may not be appropriate
due the strong interannual variability of TWS at basin and global scales.
For example, Fig. 5 from Scanlon et al. (2018) (see also Fig. S9 from
their Supplement), which compares GRACE TWS and model estimates
over large river basins over 2002–2014, clearly shows that the discrepancies
between GRACE and models occur at the end of the record for the majority of
basins. This is particularly striking for the Amazon basin (the largest
contributor to TWS), for which GRACE and models agree reasonably well until
2011, and then depart significantly, with GRACE TWS showing a strongly
positive trend since then, unlike the models. Such a divergence at the end of
the record is also noticed for several other large basins (see Scanlon et
al., 2018, Fig. S9). No clear explanation can be provided yet, even though
one may question the quality of the meteorological forcing used by
hydrological models for the recent years. But this calls for some caution
when comparing GRACE and other models on the basis of trends only because of the
dominant interannual variability of the TWS component. Much more work is
needed to understand differences among models, and between models and GRACE.
Of all components entering in the sea-level budget, the TWS contribution
currently appears to be the most uncertain one.
Glacial isostatic adjustment
The Earth's dynamic response to the waxing and waning of the
late-Pleistocene ice sheets is still causing isostatic disequilibrium in
various regions of the world. The accompanying slow process of GIA is
responsible for regional and global fluctuations in relative and absolute
sea level, 3-D crustal deformations, and changes in the Earth's gravity field
(for a review, see Spada, 2017). To isolate the contribution of current
climate change, geodetic observations must be corrected for the effects of
GIA (King et al., 2010). These are obtained by solving the “sea-level
equation” (Farrell and Clark, 1976; Mitrovica and Milne, 2003). The sea level
can be expressed as S=N-U, where S is the rate of change in sea level relative
to the solid Earth, N is the geocentric rate of sea-level change, and U is the
vertical rate of displacement of the solid Earth. The sea-level equation
accounts for solid Earth deformational, gravitational and rotational effects
on sea level, which are sensitive to the Earth's mechanical properties and
to the melting chronology of continental ice. Forward GIA modeling, based on
the solution of the sea-level equation, provides predictions of unique
spatial patterns (or fingerprints; see Plag and Juettner, 2001) of relative and geocentric
sea-level change (e.g., Milne et al., 2009; Kopp et al., 2015). During recent decades, the two fundamental components of GIA modeling have been
progressively constrained from the observed history of relative sea level
during the Holocene (see, e.g., Lambeck and Chappell, 2001; Peltier, 2004). In the
context of climate change, the importance of GIA has been recognized since the
mid-1980s, when the awareness of global sea-level rise stimulated the
evaluation of the isostatic contribution to tide gauge observations (see
Table 1 in Spada and Galassi, 2012). Subsequently, GIA models have been
applied to the study of the pattern of sea-level change from satellite
altimetry (Tamisiea, 2011), and since 2002 to the study of the gravity field
variations from GRACE. Our primary goal here is to analyze GIA model outputs
that have been used to infer global mean sea-level change and ice sheet
volume change from geodetic datasets during the altimetry era. These outputs
are the sea-level variations detected by satellite altimetry across oceanic
regions (n), the ocean mass change (w) and the modern ice sheets mass balance
from GRACE. We also discuss the GIA correction that needs to be applied to
GRACE-based land water storage changes. The GIA correction applied to tide-gauge-based sea-level observations at the coastlines is not discussed
here. Since GIA evolves on timescales of millennia (e.g.,
Turcotte and Schubert, 2012), the rate of change of all the isostatic
signals can be considered constant on the timescale of interest.
GIA correction to altimetry-based sea level
Unlike tide gauges, altimeters directly sample the sea surface in a
geocentric reference frame. Nevertheless, GIA contributes significantly to
the rates of absolute sea-level change observed over the “altimetry era”,
which require a correction Ngia that is obtained by solving the
SLE (e.g., Spada, 2017). As discussed in detail by Tamisiea (2011),
Ngia is sensitive to the assumed rheological profile of the Earth and
to the history of continental glacial ice sheets. The variance of Ngia
over the surface of the oceans is much reduced, being primarily determined
by the change in the Earth's gravity potential, apart from a spatially
uniform shift. As discussed by Spada and Galassi (2016), the GIA
contribution Ngia is strongly affected by variations in the
centrifugal potential associated with Earth's rotation, whose fingerprint is
dominated by a spherical harmonic contribution of degree l=2 and order
m=±1. Since Ngia has a smooth spatial pattern, the global
GIA correction to altimetry data can be obtained by simply subtracting its
average n=<Ngia> over the ocean sampled by the
altimetry missions. The computation of the GIA contribution Ngia
has been the subject of various investigations, based on different GIA
models. The estimate by Peltier (2001) of n=-0.30 mm yr-1 is based on the
ICE-4G (VM2) GIA model. Such a value has been adopted in the majority of
studies estimating the GMSL rise from altimetry. Since n appears to be small
compared to the global mean sea-level rise from altimetry (∼3 mm yr-1),
a more precise evaluation has not been of concern until recently.
However, it is important to notice that n is of comparable magnitude as the
GMSL trend uncertainty, currently estimated to be ∼0.3 mm yr-1
(see Sect. 2.2). In Table 9a, we summarize the values of n according to
works in the literature where various GIA model models and averaging methods
have been employed. Based on values in Table 9a for which a standard
deviation is available, the average of n (weighted by the inverse of
associated errors), assumed to represent the best estimate, is n=(-0.29±0.02) mm yr-1, where the uncertainty corresponds to 2σ.
GIA correction to GRACE-based ocean mass
GRACE observations of present-day gravity variations are sensitive to GIA,
due to the sheer amount of rock material that is transported by GIA
throughout the mantle and the resulting changes in surface topography,
especially over the formerly glaciated areas. The continuous change in the
gravity field results in a nearly linear signal in GRACE observations. Since
the gravity field is determined by global mass redistribution, GIA models
used to correct GRACE data need to be global as well, especially when the
region of interest is represented by all ocean areas. To date, the only
global ice reconstruction publicly available is provided by the University
of Toronto. Their latest product, named ICE-6G, has been published and
distributed in 2015 (Peltier et al., 2015); note that the ice history has
been simultaneously constrained with a specific Earth model, named VM5a.
During the early period of the GRACE mission, the available Toronto model
was ICE-5G (VM2) (Peltier, 2004). However, different groups have
independently computed GIA model solutions based on the Toronto ice history
reconstruction, by using different implementations of GIA codes and somehow
different Earth models. The most widely used model is the one by Paulson et
al. (2007), later updated by A et al. (2013). Both studies use a
deglaciation history based on ICE-5G, but differ for the viscosity profile
of the mantle: A et al. (2013) use a 3-D compressible Earth with VM2 viscosity
profile and a PREM-based elastic structure used by Peltier (2004), whereas
Paulson et al. (2007) use an incompressible Earth with self-gravitation, and
a Maxwell 1-D multilayer mantle. Over most of the oceans, the GIA signature
is much smaller than over the continents. However, once integrated over the
global ocean, the signal w due to GIA is about -1 mm yr-1 of equivalent sea-level change (Chambers et al., 2010), which is of the same order of
magnitude as the total ocean mass change induced by increased ice melt
(Leuliette and Willis, 2011). The main uncertainty in the GIA contribution
to ocean mass change estimates, apart from the general uncertainty in ice
history and Earth mechanical properties, originates from the importance of
changes in the orientation of the Earth's rotation axis (Chambers et al.,
2010; Tamisiea, 2011). Different choices in implementing the so-called
“rotational feedback” lead to significant changes in the resulting GIA
contribution to GRACE estimates. The issue of properly accounting for
rotational effects has not been settled yet (Mitrovica et al., 2005; Peltier
and Luthcke, 2009; Mitrovica and Wahr, 2011; Martinec and Hagedoorn, 2014).
Table 9b summarizes the values of the mass-rate GIA contribution w according
to the literature, where various models and averaging methods are employed.
The weighted average of the values in Table 9b, for which an assessment of the
standard deviation is available, is w=(-1.44±0.36) mm yr-1 (the
uncertainty is 2σ), which we assume represents the preferred estimate.
GIA correction to GRACE-based terrestrial water
storage
As discussed in the previous section, the GIA correction to apply to GRACE
over land is significant, especially in regions formerly covered by the ice
sheets (Canada and Scandinavia). Over Canada, GIA models significantly
differ. This is illustrated in Fig. 13, which shows difference between two
models of GIA correction to GRACE over land, the A et al. (2013) and Peltier
et al. (2009) models. We see that over the majority of the land areas,
differences are small, except over northern Canada, in particular around the
Hudson Bay, where differences larger than ± 20 mm yr-1 SLE are noticed.
This may affect GRACE-based TWS estimates over Canadian river basins.
Difference map between two models of GIA correction to
GRACE over land: A et al. (2013) versus Peltier et al. (2015). Units in millimeters per year of SLE.
When averaged over the whole land surface as done in some studies to
estimate the combined effect of land water storage and glacier melting from
GRACE (e.g., Reager et al., 2016; see Sect. 2.7), the GIA correction
ranges from ∼0.5 to 0.7 mm yr-1 (in mm yr-1 SLE). Values for
different GIA models are given in Table 9c.
GIA correction to GRACE-based ice sheet mass balance
The GRACE gravity field observations allow the determination of mass
balances of ice sheets and large glacier systems with inaccuracy similar or
superior to the input–output method or satellite laser and radar altimetry
(Shepherd et al., 2012). However, GRACE ice-mass balances rely on
successfully separating and removing the apparent mass change related to
GIA. While the GIA correction is small compared to the mass balance for
the Greenland ice sheet (ca. <10 %), its magnitude and uncertainty in
Antarctica is on the order of the ice-mass balance itself (e.g.,
Martín-Español et al., 2016). Particularly for today's glaciated
areas, GIA remains poorly resolved due to the sparse data constraining the
models, leading to large uncertainties in the climate history, the geometry
and retreat chronology of the ice sheet, as well as the Earth structure. The
consequences are ambiguous GIA predictions, despite fitting the same
observational data. There are two principal approaches towards resolving GIA
underneath the ice sheets. Empirical estimates can be derived that make use of
the different sensitivities of satellite observations to ice-mass changes
and GIA (e.g., Riva et al., 2009, 2010; Wu et al., 2010). Alternatively, GIA can be
modeled numerically by forcing an Earth model with a fixed ice retreat
scenario (e.g., Peltier, 2009; Whitehouse et al., 2012) or with output from a
thermodynamic ice sheet model (Gomez et al., 2013; Konrad et al., 2015).
Values of GIA-induced apparent mass change for Greenland and Antarctica as
listed in the literature should be applied with caution (Table 9d) when
applying them to GRACE mass balances. Each of these estimates may rely on a
different GRACE postprocessing strategy and may differ in the approach used
for solving the gravimetric inverse problem (mascon analysis,
forward-modeling, averaging kernels). Of particular concern is the modeling
and filtering of the pole tide correction caused by the rotational
variations related to GIA, affecting coefficients of harmonic degree
l=2 and order m=±1. As mentioned above, agreement on the modeling of
the rotational feedback has not been reached within the GIA community.
Furthermore, the pole tide correction applied during the determination
gravity-field solutions differs between the GRACE processing centers and may
not be consistent with the GIA correction listed. This inconsistency may
introduce a significant bias in the ice-mass balance estimates (e.g., Sasgen
et al., 2013, Supplement). Wahr et al. (2015) presented
recommendations on how to treat the pole tides in GRACE analysis. However, a
systematic intercomparison of the GIA predictions in terms of their
low-degree coefficients and their consistency with the GRACE processing
standards still need to be done.
Estimated contributions of GIA to the rate of absolute sea-level change observed by altimetry (a), to the rate of mass change observed by
GRACE over the global oceans (b), to the rate of mass change observed by GRACE
over land (c), and to Greenland and Antarctic ice sheets (c), during the altimetry
era. The GIA corrections are expressed in millimeters per year SLE except over Greenland
and Antarctica where values are given in gigatons per year (ice mass equivalent). Most of the
GIA contributions are expressed as a value ± 1 standard deviation; a few
others are given in terms of a plausible range, and for some the uncertainties are not specified.
(a) GIA correction to absolute sea level measured by altimetry ReferenceGIA (mm yr-1 SLE)NotesPeltier (2009) (Table 3)-0.30±0.02-0.29±0.03-0.28±0.02Average of three groups of four values obtained by variants of the analysis procedure, using ICE-5G(VM2), over a global ocean, in the range of latitudes 66∘ S to 66∘ N and 60∘ S to 60∘ N, respectively.Tamisiea (2011) (Fig. 2)-0.15 to -0.45 -0.20 to -0.50Simple average over the oceans for a range of estimates obtained varying the Earth model parameters, over a global ocean and between latitudes 66∘ S and 66∘ N.Huang (2013) (Table 3.6)-0.26±0.07-0.27±0.08Average from an ensemble of 14 GIA models over a global ocean and between latitude from 66∘ S to 66∘ N.Spada (2017) (Table 1)-0.32±0.08Based on four runs of the sea-level equation solver SELEN (Spada and Stocchi, 2007) using model ICE-5G(VM2), with different assumptions in solving the SLE.(b) GIA contribution to GRACE mass rate of change over the oceans ReferenceGIA (mm yr-1 SLE)NotesPeltier (2009) (Table 3)-1.60±0.30Average of values from 12 corrections for variants of the analysis procedure, using ICE-5G (VM2).Chambers et al. (2010) (Table 1)-1.45±0.35Average over the oceans for a range of estimates produced by varying the Earth models.Tamisiea (2011) (Figs. 3 and 4)-0.5 to -1.9 -0.9 to -1.5Ocean average of a range of estimates varying the Earth model, and based on a restricted set, respectively.Huang (2013) (Table 3.7)-1.31±0.40-1.26±0.43Average from an ensemble of 14 GIA models over a global ocean and between latitude from 66∘ S to 66∘ N, respectively.(c) GIA contribution to GRACE-based terrestrial water storage change ReferenceGIA correction (mm yr-1 SLE) without Greenland, Antarctica, Iceland, Svalbard, Hudson Bay and the Black SeaA et al. (2013)0.63Peltier ICE5G0.68Peltier ICE6G_rc0.71ANU_ICE6G0.53
Continued.
(d) GIA contribution to GRACE mass rate of the ice sheets ReferenceGreenland GIA (Gt yr-1)NotesSimpson et al. (2009)r-3±12mThermodynamic sheet/solid Earth model, 1-D (uncoupled); constrained by geomorphology; inversion results in Sutterley et al. (2014).Peltier (2009) (ICE-5G)b-4dIce load reconstruction/solid Earth model, 1-D (ICE-5G/similar to VM2); Greenland component of ICE-5G (13 Gt yr-1) + Laurentide component of ICE-5G (-17 Gt yr-1); inversion results in Khan et al. (2016), Discussion.Khan et al. (2016) (GGG-1D)a15±10fIce load reconstruction/solid Earth model, 1-D (uncoupled); constrained with geomorphology and GPS; Greenland component (+32 Gt yr-1) + Laurentide component of ICE-5G (-17 Gt yr-1); inversion results in Khan et al. (2016), Discussion.Fleming and Lambeck (2004)a (Green1)3dIce load reconstruction/solid Earth model, 1-D (uncoupled); constrained with geomorphology; Greenland component (+20 Gt yr-1) + Laurentide component of ICE-5G (-17 Gt yr-1); inversion in Sasgen et al. (2012, Supplement).Wu et al. (2010)b-69±19mJoint inversion estimate based on GPS, satellite laser ranging, and very long baseline interferometry, and bottom pressure from ocean model output; inversion results in Sutterley et al. (2014).ReferenceAntarctica GIA (Gt yr-1)NotesWhitehouse et al. (2012) (W12a)a60fThermodynamic sheet/solid Earth model, 1-D (uncoupled); constrained by geomorphology; inversion results in Shepherd et al. (2012), Supplement (Fig. S8).Ivins et al. (2013) (IJ05_R2)a40–65fIce load reconstruction/solid Earth model, 1-D; constrained by geomorphology and GPS uplift rates; Ivins et al. (2013); inversion results in Shepherd et al. (2012), Supplement (Fig. S8).Peltier (2009) (ICE-5G)b140–180fIce load reconstruction/solid Earth model ICE-5G(VM2); constrained by geomorphology; inversion results in Shepherd et al. (2012), Supplement (Fig. S8).Argus et al. (2014) (ICE-6G)b107fIce load reconstruction/solid Earth model ICE-6G(VM5a); constrained by geomorphology and GPS; theory recently corrected by Purcell et al. (2016); inversion results in Argus et al. (2014), conclusion 7.8.Sasgen et al. (2017) (REGINA)a55±22fJoint inversion estimate based on GRACE, altimetry, GPS and viscoelastic response functions; lateral heterogeneous Earth model parameters; inversion results in Sasgen et al. (2017), Table 1.Gunter et al. (2014) (G14)aca. 64±40a (multimodel uncert.)Joint inversion estimate based on GRACE, altimetry, GPS and regional climate model output; conversion of uplift to mass using average rock density; inversion results in, Gunter et al. (2014) Table 1.Martín-Español et al. (2016) (RATES)a55±845±7*Joint inversion estimate based on GRACE, altimetry, GPS and regional climate model output; inversion results in Sasgen et al. (2017), * is improved for GIA of smaller spatial scales; inversion results in Martin-Español et al. (2016), Fig. 6.
a Regional model. b Global model. c Mascon
inversion.
d Forward modeling inversion. e Averaging kernel
inversion.
f Inversion method not specified.
The GRACE-based ocean mass, Antarctica mass and terrestrial water storage
changes are very model dependent. As these GIA corrections cannot be
assessed from independent information, they represent a large source of
uncertainties to the sea-level budget components based on GRACE.
Ocean mass change from GRACE
Since 2002, GRACE satellite gravimetry has provided a revolutionary means
for measuring global mass change and redistribution at monthly intervals
with unprecedented accuracy, and offered the opportunity to directly
estimate ocean mass change due to water exchange between the ocean and other
components of the Earth (e.g., ice sheets, mountain glaciers, terrestrial
water). GRACE time-variable gravity data have been successfully applied in a
series of studies of ice mass balance of polar ice sheets (e.g., Velicogna
and Wahr, 2006; Luthcke et al., 2006) and mountain glaciers (e.g., Tamisiea
et al., 2005; J. Chen et al., 2007) and their contributions to global sea-level
change. GRACE data can also be used to directly study long-term oceanic mass
change or nonsteric sea-level change (e.g., Willis et al., 2008; Leuliette
and Miller, 2009; Cazenave et al., 2009), and provide a unique opportunity to
study interannual or long-term TWS change and its potential impacts on sea-level change (Richey et al., 2015; Reager et al., 2016).
GRACE time-variable gravity data can be used to quantify ocean mass change
from three different main approaches. One is through measuring ice mass
balance of polar ice sheets and mountain glaciers and variations of TWS, and
their contributions to the GMSL (e.g., Velicogna and Wahr, 2006; Schrama et
al., 2014). The second approach is to directly quantify ocean mass change
using ocean basin mask (kernel) (e.g., A and Chambers, 2008; Llovel et al.,
2010; Johnson and Chambers, 2013). In the ocean basin kernel approach,
coastal ocean areas within certain distance (e.g., 300 or 500 km) from the
coast are excluded, in order to minimize contaminations from mass change
signal over the land (e.g., glacial mass loss and TWS change). The third
approach solves mass changes on land and over ocean at the same time via
forward modeling (e.g., Chen et al., 2013; Yi et al., 2015). The forward
modeling is a global inversion to reconstruct the “true” mass change
magnitudes over land and ocean with geographical constraint of locations of
the mass change signals, and can help effectively reduce leakage between
land and ocean (Chen et al., 2013).
Estimates of ocean mass changes from GRACE are subject to a number of major
error sources. These include (1) leakage errors from the larger signals
over ice sheets and land hydrology due to GRACE's low spatial resolution (of
at least a few to several hundred kilometers) and the need for coastal masking, (2) spatial
filtering of GRACE data to reduce spatial noise, (3) errors and
biases in geophysical model corrections (e.g., GIA, atmospheric mass) that
need to be removed from GRACE observations to isolate oceanic mass change
and/or polar ice sheets and mountain glaciers mass balance, and (4) residual
measurement errors in GRACE gravity measurements, especially those
associated with GRACE low-degree gravity changes. In addition, how to deal
with the absent degree-1 terms, i.e., geocenter motion in GRACE gravity
fields, is expected to affect estimates of GRACE-based oceanic mass rates and
ice mass balances.
Recently published (since 2013) estimates of GRACE-based
ocean mass rates (GIA corrected). Most of the listed studies use either the
A13 (A et al., 2013) or Paulson07 (Paulson et al., 2007) GIA model.
Ocean massData sourcesTime periodtrends (mm yr-1)Chen et al. (2013) (A13 GIA)Jan 2005–Dec 20111.80±0.47Johnson and Chambers (2013) (A13 GIA)Jan 2003–Dec 20121.80±0.15Purkey et al. (2014) (A13 GIA)Jan 2003–Jan 20131.53±0.36Dieng et al. (2015a) (Paulson07 GIA)Jan 2005–Dec 20121.87±0.11Dieng et al. (2015b) (Paulson07 GIA)Jan 2005–Dec 20132.04±0.08Yi et al. (2015) (A13 GIA)Jan 2005–Jul 20142.03±0.25Rietbroek et al. (2016)Apr 2002–Jun 20141.08±0.30Chambers et al. (2017)2005–20152.11±0.36
With a different treatment of the GRACE land–ocean signal leakage effect
through global forward modeling, Chen et al. (2013) estimated ocean mass
rates using GRACE RL05 time-variable gravity solutions over the period
2005–2011. They demonstrated that the ocean mass change contributes up to 1.80±0.47 mm yr-1 (over the same period), which is significantly larger
than previous estimates over about the same period. Yi et al. (2015) further
confirmed that correct calibration of GRACE data and appropriate treatment
of GRACE leakage bias are critical to improve the accuracy of GRACE-estimated ocean mass rates. Table 10 summarizes different estimates of GRACE
ocean mass rates. The uncertainty estimates of the listed studies (Table 10)
are computed from different methods, with different considerations of error
sources into the error budget, and represent different confidence levels.
As demonstrated in Chen et al. (2013), different treatments of just the
degree-2 spherical harmonics of the GRACE gravity solution alone can lead to
substantial differences in GRACE-estimated ocean mass rates (ranging from
1.71 to 2.17 mm yr-1). Similar estimates from GRACE gravity solutions from
different data processing centers can also be different. In the meantime,
long-term degree-1 spherical harmonics variation, representing long-term
geocenter motion and neglected in some of the previous studies (due to the
lack of accurate observations) are also expected to have a non-negligible
effect on GRACE-derived ocean mass rates (Chen et al., 2013). Different
methods for computing ocean mass change using GRACE data may also lead to
different estimates (Chen et al., 2013; Johnson and Chambers, 2013; Jensen
et al., 2013).
To help better understand the potential and uncertainty of GRACE satellite
gravimetry in quantification of the ocean mass rate, Table 11 provides a
comparison of GRACE-estimated ocean mass rates over the period January 2005
to December 2016 based on different GRACE data products and different data
processing methods, including the CSR, GFZ and JPL GRACE RL05 spherical
harmonic solutions (i.e., the so-called GSM solutions), as well as CSR, JPL and
GSFC mascon solutions (the available GSFC mascons only cover the period up
to July 2016). The three GRACE GSM results (CSR, GFZ and JPL) are updates
from Johnson and Chambers (2013), with degree-2 zonal term replaced by
satellite laser ranging results (Cheng and Ries, 2012), geocenter motion
from Swenson et al. (2008), GIA model from A et al. (2013), an averaging
kernel with a land mask that extends out 300 km, and no destriping or
smoothing, as described in Johnson and Chambers (2013). An update of GRACE
ocean mass rate from Chen et al. (2013) is also included for comparisons,
which is based on the CSR GSM solutions using forward modeling (a global
inversion approach), with similar treatments of the degree-2 zonal term,
geocenter motion and GIA effects.
The JPL mascon ocean mass rate is computed from all mascon grids over the
ocean, and the GSFC mascon ocean mass rate is computed from all ocean
mascons, with the Mediterranean, Black and Red seas excluded. A coastline
resolution improvement (CRI) filter is already applied in the JPL mascons to
reduce leakage (Wiese et al., 2016b), and in both the GSFC and JPL mascon
solutions, the ocean and land are separately defined
(Luthcke et al., 2013;
Watkins et al., 2015). For the CSR mascon results, an averaging kernel with
a land mask that extends out 200 km is applied to reduced leakage (Chen et
al., 2017b). Similar treatments or corrections of degree-2 zonal term,
geocenter motion and GIA effects are also applied in the three mascon
solutions. When solving GRACE mascon solutions, the GRACE GAD fields
(representing ocean bottom pressure changes, or combined atmospheric and
oceanic mass changes) have been added back to the mascon solutions. To
correctly quantify ocean mass change using GRACE mascon solutions, the means
of the GAD fields over the oceans, which represents mean atmospheric mass
changes over the ocean (as ocean mass is conserved in the GAD fields) need
to be removed from GRACE mascon solutions. The removal of GAD average over
the ocean in GRACE mascon solutions has very minor or negligible effect (of
∼0.02 mm yr-1) on ocean mass rate estimates, but is important
for studying GMSL change at seasonal timescales.
Over the 12-year period (2005–2016), the three GRACE GSM solutions show
pretty consistent estimates of ocean mass rate, in the range of 2.3 to
2.5 mm yr-1. Greater differences are noticed for the mascon solutions. The GSFC
mascons show the largest rate of 2.61 mm yr-1. The CSR and JPL mascon solutions
show relatively smaller ocean mass rates of 1.76 and 2.02 mm yr-1,
respectively, over the studied period. Based on the same CSR GSM solutions,
the forward modeling and basin kernel estimates agree reasonably well (2.52
vs. 2.44 mm yr-1). In addition to the degree-2 zonal term, geocenter motion,
and GIA correction, the degree-2, order-1 spherical harmonics of the current
GRACE RL05 solutions are affected by the definition of the reference mean
pole in GRACE pole tide correction (Wahr et al., 2015). This mean pole
correction, excluded in all estimates listed in Table 11 (for fair
comparison), is estimated to contribute ∼-0.11 mm yr-1 to
GMSL. How to reduce errors from the different sources plays a critical role
in estimating ocean mass change from GRACE time-variable gravity data.
Ocean mass trends (in mm yr-1) estimated from GRACE
for the period January 2005–December 2016 (the GSFC mascon solutions cover
up to July 2016). The uncertainty is based on 2 times the sigma of least-squares fitting.
Ocean massData sourcestrend (mm yr-1)GSM CSR forward modeling (update from Chen et al., 2013)2.52±0.17GSM CSR (update from Johnson and Chambers, 2013)2.44±0.15GSM GFZ (update from Johnson and Chambers, 2013)2.30±0.15GSM JPL (update from Johnson and Chambers, 2013)2.48±0.16Mascon CSR (200 km)1.76±0.16Mascon JPL2.02±0.16Mascon GSFC (update from Luthcke et al., 2013)2.61±0.16Ensemble mean2.3±0.19
GRACE satellite gravimetry has brought a completely new era for studying
global ocean mass change. Owing to the extended record of GRACE gravity
measurements (now over 15 years), improved understanding of GRACE gravity
data and methods for addressing GRACE limitations (e.g., leakage and
low-degree spherical harmonics), and improved knowledge of background
geophysical signals (e.g., GIA), GRACE-derived ocean mass rates from
different studies in recent years show clearly increased consistency
(Table 11). Most of the results agree well with independent observations from
satellite altimeter and Argo floats, although the uncertainty ranges are
still large. The GRACE Follow-On (FO) mission was launched in May 2018.
The GRACE and GRACE-FO together are expected to provide at least over 2
(or even 3) decades of time-variable gravity measurements. Continuous
improvements of GRACE data quality (in future releases) and background
geophysical models are also expected, which will help improve the accuracy
GRACE observed ocean mass change.
For the sea-level budget assessment over the GRACE period,
we use the
ensemble mean.
Sea-level budget results
In Sect. 2, we have presented the different terms of the sea-level budget
equation, mostly based on published estimates (and in some cases, from their
updates). We now use them to examine the closure of the sea-level budget.
For all terms, we only consider ensemble mean values.
Entire altimetry era (1993–present)Trend estimates over 1993–present
Because it is now clear that the GMSL and some components are accelerating
(e.g., Nerem et al., 2018), we propose to characterize the long-term
variations of the time series by both a trend and an acceleration. We start
by looking at trends. Table 12 gathers the trends estimated in Sect. 2. The
end year is not always the same for all components (see Sect. 2). Thus the
word “present” means either 2015 or 2016 depending on the component. As no
trend estimate is available for the entire altimetry era for the terrestrial
water storage contribution, we do not consider this component. The residual
trend (GMSL minus sum of components trend) may then provide some constraint
on the TWS contribution.
Trend estimates for individual components of the sea-level
budget, sum of components and GMSL minus sum of components over 1993–present.
Uncertainties of the sum of components and residuals represent rooted mean
squares of components errors, assuming that errors are
independent.
Trends (mm yr-1)Component1993–present1.GMSL (TOPEX-A drift corrected)3.07±0.372. Thermosteric sea level (full depth)1.3±0.43. Glaciers0.65±0.154. Greenland0.48±0.105. Antarctica0.25±0.106. TWS/7. Sum of components2.7±0.23(without TWS →2.+3.+4.+5.)8. GMSL minus sum of components0.37±0.3(without TWS)
Results presented in Table 12 are discussed in detail in Sect. 4.
Acceleration
The GMSL acceleration estimated in Sect. 2.2 using Ablain et al.'s (2017b)
TOPEX-A drift correction amounts to 0.10 mm yr-2 for the 1993–2017
time span. This value is in good agreement with the Nerem et al. (2018) estimate
(of 0.084 ± 0.025 mm yr-2) over nearly the same period, after
removal of the interannual variability of the GMSL. In Nerem et al. (2018),
acceleration of individual components are also estimated as well as
acceleration of the sum of components. The latter agrees well with the GMSL
acceleration. Here we do not estimate the acceleration of the component
ensemble means because time series are not always available. We leave this
for a future assessment.
GRACE and Argo period (2005–present)Sea-level budget using GRACE-based ocean mass
If we consider the ensemble mean trends for the GMSL, thermosteric and ocean
mass components given in Sects. 2.2, 2.3 and 2.9 over 2005–present, we
find agreement (within error bars) between the observed GMSL (3.5 ± 0.2 mm yr-1)
and the sum of Argo-based thermosteric plus GRACE-based ocean mass
(3.6 ± 0.4 mm yr-1) (see Table 13). The residual (GMSL minus sum of
components) trend amounts to -0.1 mm yr-1. Thus in terms of trends, the sea-level budget appears closed over this time span within quoted uncertainties.
Trend estimates over 2005–present from estimates of individual contributions
Table 13 gathers trends of individual components of the sea-level budget
over 2005–present, as well as the trend of the sum of components and residuals (GMSL minus sum
of components). As for the longer period, ensemble mean values are
considered for each component.
Trend estimates for individual components of the sea-level budget, sum of components and GMSL minus sum of components
over 2005–present.
Trend (mm yr-1)Component2005–present1. GMSL3.5±0.22. Thermosteric sea level (full depth)1.3±0.43. Glaciers0.74±0.14. Greenland0.76±0.15. Antarctica0.42±0.16. TWS from GRACE (mean of Reager et al., 2016 and Scanlon et al., 2018)-0.27±0.157. Sum of components (2.+3.+4.+5.+6.)2.95±0.218. Sum of components (thermosteric full depth + GRACE-based ocean mass)3.6±0.49. GMSL minus sum of components (including GRACE-based TWS → 2.+3.+4.+5.+6.)0.55±0.310.GMSL minus sum of components (without GRACE-based TWS → 2.+3.+4.+5.)0.28±0.211. GMSL minus sum of components (thermosteric full depth + GRACE-based ocean mass)-0.1±0.3
As for Table 12, the results presented in Table 13 are discussed in detail
in Sect. 4.
Year-to-year budget over 2005–present using GRACE-based ocean
mass
We now examine the year-to-year sea-level and mass budgets. Table 14
provides annual mean values for the ensemble mean GMSL, GRACE-based ocean
mass and Argo-based thermosteric component. The components are expressed as
anomalies and their reference is arbitrary. So to compare with the GMSL, a
constant offset for all years was applied to the thermosteric and ocean mass
annual means. The reference year (where all values are set to zero) is 2003.
Annual mean values for the ensemble mean GMSL and sum of
components (GRACE-based ocean mass and Argo-based thermosteric, full depth).
Constant offset applied to the sum of components. The reference year (where
all values are set to zero) is 2003.
EnsembleSum ofGMSLmean GMSLcomponentsminus sum ofYear(mm)(mm)components (mm)20057.008.78-0.78200610.2510.78-0.53200710.5111.35-0.85200815.3315.070.25200918.7818.88-0.10201020.6420.530.11201120.9121.38-0.48201231.1029.331.77201333.4033.87-0.47201436.6536.220.43201546.3445.690.65
Figure 14 shows the sea-level budget over 2005–2015 in terms of an annual bar
chart using values given in Table 14. It compares for years 2005 to 2016 the
annual mean GMSL (blue bars) and annual mean sum of thermosteric and
GRACE-based ocean mass (red bars). Annual residuals are also shown (green
bars). These are either positive or negative depending on the years. The trend
of these annual residuals is estimated to be 0.135 mm yr-1.
In Fig. 15 is also shown the annual sea-level budget over 2005–2015 but
now using the individual components for the mass terms. As we have no annual
estimates for TWS, we ignore it, so that the total mass includes only
glaciers, Greenland and Antarctica. The annual residuals thus include the
TWS component in addition to the missing contributions (e.g., deep ocean
warming). For years 2006 to 2011, the residuals are negative, an indication
of a negative TWS to sea level as suggested by GRACE results (Reager et al.,
2016; Scanlon et al., 2018). But as of 2012, the residuals become positive
and on average over 2005–2015, the residual trend amounts to +0.28 mm yr-1, a
value larger than when using GRACE ocean mass.
Finally, Fig. 16 presents the mass budget. It compares annual GRACE-based
ocean mass to the sum of the mass components, without TWS as in Fig. 15.
The residual trend over the 2005–2015 time span is 0.14 mm yr-1. It may
dominantly represent the TWS contribution. From one year to another
residuals can be either positive or negative, suggesting important
interannual variability in the TWS or even in the deep ocean.
Annual sea level (blue bars) and sum of thermal
expansion (full depth) and GRACE ocean mass component (red bars). Black vertical bars
are associated uncertainties. Annual residuals (green bars) are also shown.
Annual global mean sea level (blue bars) and sum
components without TWS (full depth thermal expansion + glaciers +
Greenland + Antarctica) (red bars). Black vertical bars are associated
uncertainties. Annual residuals (green bars) are also shown.
Discussion
The results presented in Sect. 2 for the components of the sea-level
budget are based on syntheses of the recently published literature. When
needed, the time series have been updated. In Sect. 3, we considered
ensemble means for each component to average out random errors of individual
estimates. We examined the closure or nonclosure of the sea-level budget using
these ensemble mean values, for two periods: 1993–present and 2005–present
(Argo and GRACE period). Because of the lack of observation-based TWS
estimates for the 1993–present time span, we compared the observed GMSL trend
to the sum of components excluding TWS. We found a positive residual trend
of 0.37±0.3 mm yr-1, supposed to include the TWS contribution, plus
other imperfectly known contributions (deep ocean warming) and data errors.
For the 2005–present time span, we considered both GRACE-based ocean mass
and the sum of individual mass components, allowing us to also look at the mass
budget. For TWS, as discussed in Sect. 2.7, GRACE provides a negative
trend contribution to sea level over the last decade (i.e., increase on
water storage on land) attributed to internal natural variability (Reager et
al., 2016), unlike hydrological models that lead to a small (possibly not
significantly different from zero) positive contribution to sea level over
the same period. Assuming that GRACE observations are perfect, such
discrepancies could be attributed to the inability of models to correctly
account for uncertainties in meteorological forcing and inadequate modeling
of soil storage capacity (see discussion in Sect. 2.7). However, when
looking at the sea-level budget over the GRACE time span and using the
GRACE-based TWS, we find a rather large positive residual trend
(>0.5 mm yr-1) that needs to be explained. Since GRACE-based ocean
mass is supposed to represent all mass terms, one may want to attribute this
residual trend to an additional contribution of the deep ocean to the
abyssal contribution already taken into account here, but possibly
underestimated because of incomplete monitoring by current observing
systems. If such a large positive contribution from the deep ocean (meaning
ocean warming) is real (which is unlikely, given the high implied heat
storage), this has to be confirmed by independent approaches, e.g., using
ocean reanalysis, and eventually model-based and top-of-the-atmosphere
estimates of the Earth energy imbalance.
Annual GRACE-based ocean mass (red bars) and sum
components without TWS (full depth thermal expansion + glaciers +
Greenland + Antarctica) (blue bars). Annual residuals (green bars) are also shown.
In addition to mean trends over the period, we also looked at the annual
budget for all years, starting in 2005. For most components, annual mean
values are provided during the Argo-GRACE era, except for the terrestrial
water storage component. However, the sea-level budget based on GRACE ocean
mass (plus ocean thermal expansion; Fig. 14) includes the TWS
contribution. As shown in Fig. 14, yearly residuals are small, suggesting
near closure of the sea-level budget. The residual trend amounts to
0.13 mm yr-1. It could be interpreted as an additional deep ocean contribution not
accounted by the SIO estimate (see Sect. 2.3). However, when looking at
Fig. 14, we note that yearly residuals are either positive or negative, an
indication of interannual variability that can hardly be explained by a deep
ocean contribution. The residual trend derived from the difference (GMSL
minus sum of components) (Table 13) amounts -0.1±0.3 mm yr-1, suggesting
a sea-level budget closed within 0.3 mm yr-1 over 2005–present, with no
substantial deep ocean contribution.
Figure 16 compares GRACE ocean mass to the sum of mass components (excluding
TWS, for the reasons mentioned above). In principle, this mass budget may
provide a constraint on the TWS contribution. The corresponding residual
trend amounts to 0.14 mm yr-1 over the GRACE period, a value that disagrees
with the above-quoted GRACE-based TWS estimates. However, it is worth noting
that the GRACE-based TWS trend is very dependent on the considered time span
because of the strong interannual variability; a recent study by Palanisamy
et al. (2018), based on 347 land river basins, found
GRACE-based TWS trend of zero over 2005–2015. Given the remaining data
uncertainties, any robust conclusion can hardly be reached so far. That
being said, more work is needed to clarify the sign discrepancy between
GRACE-based and model-based TWS estimates.
The data sets used in this study are freely available
at 10.17882/54854. We provide annual mean time series
(expressed in millimeters of equivalent sea level) between 2005 and 2015 for all
components of the sea level budget: (1) global mean sea level (GMSL, GMSL.txt
as data file) time series from multi-mission satellite altimetry; ensemble
mean of six different sea level products (AVISO/CNES, CSIRO, University of
Colorado, ESA SL_cci, NASA/GSFC, NOAA). (2) Global mean ocean thermal
expansion (Steric.txt as data file) time series: ensemble mean from 10
processing groups (CORA, CSIRO, ACECRC/IMAS-UTAS, ICCES, ICDC, IPRC, JAMSTEC,
MRI/JMA, NECI/NOAA, SIO). (3) Glacier contribution (Glaciers.txt) from 5
different products (update of Gardner et al., 2013, update of Marzeion et
al., 2012, update of Cogley, 2009, update of Leclercq et al., 2011 and
average of GRACE-based estimates of Marzeion et al., 2017). (4) Greenland
ice sheet contribution (GreenlandIcesheet.txt as data file): ensemble mean
from eight different products (Update from Barletta et al., 2013, Groh and
Horwath, 2016, Update from Luthcke et al., 2013, Update from Sasgen et al.,
2012, Update from Schrama et al., 2014, Update from van den
Broeke et al., 2016, Wiese et al., 2016b, Update from Wouters et al., 2008).
(5) Antarctica ice sheet contribution (AntarcticIcesheet.txt as data file):
ensemble mean from 11 different products (Updated Martin-Espagnol et al.,
2016, Updated Fosberg et al., 2017, Updated Groh and Horwath, 2016,
Updated Luthcke et al., 2013, Updated Sasgen et al., 2013, Updated
Velicogna et al., 2014, Updated Wiese et al., 2016, Updated from Wouters et
al., 2013, Updated Rignot et al., 2011, Update Schrama et al., 2014 version
1, Update Schrama et al., 2014 version 2). We also provide the GRACE-based
ocean mass time series that is an ensemble mean of seven different products (GSM
CSR Forward Modeling (update from Chen et al., 2013), GSM CSR (update from
Johnson and Chambers, 2013), GSM GFZ (update from Johnson and Chambers,
2013), GSM JPL (update from Johnson and Chambers, 2013), Mascon CSR
(200 km), Mascon JPL, Mascon GSFC (update from Luthcke et al.,
2013)).
Concluding remarks
As mentioned in the introduction, the global mean sea-level budget has been
the subject of numerous previous studies, including successive IPCC
assessments of the published literature. What is new in the effort presented
here is that it involves the international community currently studying
present-day sea level and its components. Moreover, it relies on a large
variety of datasets derived from different space-based and in situ observing
systems. The near closure of the sea-level budget, as reported here over the
GRACE and Argo era, suggests that no large systematic errors affect these
independent observing systems, including the satellite altimetry system.
Study of the sea-level budget allows improved understanding of the different
processes causing sea-level rise, such as ocean warming and land ice melt.
When accuracy increases, it will offer an integrative view of the response
of the Earth system to natural and anthropogenic forcing and internal
variability, and provide an independent constraint on the current Earth
energy imbalance. Validation of climate models against observations is
another important application of this kind of assessment (e.g., Slangen et
al., 2017).
However, important uncertainties still remain, which affect several terms of
the budget; for example the GIA correction applied to GRACE data over
Antarctica or the net land water storage contribution to sea level. The
latter results from a variety of factors but is dominated by groundwater
pumping and natural climate variability. Both terms are still uncertain and
accurately quantifying them remains a challenge.
Several ongoing international projects related to sea level should provide,
in the near future, improved estimates of the components of the sea-level
budget. This is the case, for example, of the ice sheet mass balance
intercomparison exercise (IMBIE, second assessment), a community effort
supported by NASA (National Aeronautics and Space Administration) and ESA,
dedicated to reconciling satellite measurements of ice sheet mass balance (The
IMBIE Team, 2018). This is also the case for the ongoing ESA Sea Level
Budget Closure project (Horwath et al., 2018) that uses a number of
space-based essential climate variables (ECVs) reprocessed during the last
few years in the context of the ESA Climate Change Initiative project. The
recently launched GRACE follow-on mission will lengthen the current mass
component time series, with hopefully increased precision and resolution.
Finally, the deep Argo project, still in an experimental phase, will provide
important information on the deep ocean heat content in the coming years.
Availability of this new dataset will provide new insights into the total
thermosteric component of the sea-level budget, allowing other
missing or poorly known contributions to be constrained from the evaluation of the budget.
The sea-level budget assessment discussed here essentially relies on trend
estimates. But annual budget estimates have been proposed for the first time
over the GRACE-Argo era. It is planned to provide updates of the global sea-level budget every year, as done for more than a decade for the global
carbon budget (Le Queré et al., 2018). In the next assessments, updates
of all components will be considered, accounting for improved evaluation of
the raw data, improved processing and corrections, use of ocean reanalysis,
etc. The need for additional information where gaps exist should also be
considered. As a closing remark, study of the sea-level budget in terms of
time series and not just trends, as done here, will be required.
This community assessment was initiated by AC
and BM as a contribution to the Grand Challenge “Regional Sea
Level and Coastal Impacts” of the World Climate Research Programme (WCRP).
The results presented in this paper were prepared by nine different teams
dedicated to the various terms of the sea-level budget (i.e., altimetry-based
sea level, tide gauges, thermal expansion, glaciers, Greenland, Antarctica,
terrestrial water storage, glacial isostatic adjustment, ocean mass from
GRACE). Thanks to the team leaders (in alphabetic order) MA,
JB, NC, JC, CD, SJ, JTR,
KvS, GS, IV and RvdW, who interacted
with their team members, collected all needed information, provided a
synthesized assessment of the literature and when needed, updated the
published results. The coordinators AC and BM collected those materials
and prepared a first draft of the paper, but all authors contributed to its
refinement and to the discussion of the results. Special thanks are addressed
to JB, EB, GC, JC, GJ (PMEL
Contribution Number 4776), BM, FP, RP and ES for
improving the successive versions of the paper,
and to HP for
providing all figures presented in Sect. 3.
The views, opinions and findings contained in this paper are those of the
authors and should not be construed as an official position, policy or
decision of the NOAA, US Government or other institutions.
The authors declare that they have no conflict of
interest.
Acknowledgements
We are grateful to the anonymous reviewer for his/her thorough comments that
helped to improve the paper.
Edited by: Giuseppe M. R. Manzella
Reviewed by: one anonymous referee
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Anny Cazenave (LEGOS, France, and ISSI, Switzerland), Benoit Meyssignac
(LEGOS, France),
Michael Ablain (CLS, France), Magdalena Balmaseda (ECMWF, UK), Jonathan Bamber (U. Bristol, UK), Valentina
Barletta (DTU-SPACE, Denmark), Brian Beckley (SGT Inc./NASA GSFC, USA),
Jérôme Benveniste (ESA/ESRIN, Italy), Etienne Berthier (LEGOS,
France), Alejandro Blazquez (LEGOS, France), Tim Boyer (NOAA, USA), Denise
Caceres (Goethe U., Germany), Don Chambers (U. South Florida, USA), Nicolas
Champollion (U. Bremen, Germany), Ben Chao (IES-AS, Taiwan), Jianli Chen (U.
Texas, USA), Lijing Cheng (IAP-CAS, China), John A. Church (U. New South
Wales, Australia), Stephen Chuter (U. Bristol, UK), J. Graham Cogley (Trent
U., Canada), Soenke Dangendorf (U. Siegen, Germany), Damien Desbruyères
(IFREMER, France), Petra Döll (Goethe U., Germany), Catia Domingues
(CSIRO, Australia), Ulrike Falk (U. Bremen, Germany), James Famiglietti
(JPL/Caltech, USA), Luciana Fenoglio-Marc (U. Bonn, Germany), Rene Forsberg
(DTU-SPACE, Denmark), Gaia Galassi (U. Urbino, Italy), Alex Gardner
(JPL/Caltech, USA), Andreas Groh (TU-Dresden, Germany), Benjamin Hamlington
(Old Dominion U., USA), Anna Hogg (U. Leeds, UK), Martin Horwath
(TU-Dresden, Germany), Vincent Humphrey (ETHZ, Switzerland), Laurent Husson
(U. Grenoble, France), Masayoshi Ishii (MRI-JMA, Japan), Adrian Jaeggi (U.
Bern, Switzerland), Svetlana Jevrejeva (NOC, UK), Gregory Johnson
(NOAA/PMEL, USA), Nicolas Kolodziejczyk (LOPS, France), Jürgen Kusche (U. Bonn, Germany), Kurt Lambeck (ANU,
Australia, and ISSI, Switzerland), Felix Landerer (JPL/Caltech, USA), Paul
Leclercq (UIO, Norway), Benoit Legresy (CSIRO, Australia), Eric Leuliette
(NOAA, USA), William Llovel (LEGOS, France), Laurent Longuevergne (U.
Rennes, France), Bryant D. Loomis (NASA GSFC, USA), Scott B. Luthcke (NASA
GSFC, USA), Marta Marcos (UIB, Spain), Ben Marzeion (U. Bremen, Germany),
Chris Merchant (U. Reading, UK), Mark Merrifield (UCSD, USA), Glenn Milne
(U. Ottawa, Canada), Gary Mitchum (U. South Florida, USA), Yara Mohajerani
(UCI, USA), Maeva Monier (Mercator-Ocean, France), Didier Monselesan (CSIRO, Australia),
Steve Nerem (U. Colorado,
USA), Hindumathi Palanisamy (LEGOS, France), Frank Paul (UZH, Switzerland),
Begoña Perez (Puertos del Estados, Spain), Christopher G. Piecuch
(WHOI, USA), Rui M. Ponte (AER inc., USA), Sarah G. Purkey (SIO/UCSD, USA),
John T. Reager (JPL/Caltech, USA), Roelof Rietbroek (U. Bonn, Germany), Eric
Rignot (UCI and JPL, USA), Riccardo Riva (TU Delft, The Netherlands), Dean
H. Roemmich (SIO/UCSD USA), Louise Sandberg Sørensen (DTU-SPACE,
Denmark), Ingo Sasgen (AWI, Germany), E.J.O. Schrama (TU Delft, The
Netherlands), Sonia I. Seneviratne (ETHZ, Switzerland), C.K. Shum (Ohio
State U., USA), Giorgio Spada (U. Urbino, Italy), Detlef Stammer (U.
Hamburg, Germany), Roderic van de Wal (U. Utrecht, The Netherlands),
Isabella Velicogna (UCI and JPL, USA), Karina von Schuckmann
(Mercator-Océan, France), Yoshihide Wada (U. Utrecht, The Netherlands),
Yiguo Wang (NERSC/BCCR, Norway), Christopher Watson (U. Tasmania,
Australia), David Wiese (JPL/Caltech, USA), Susan Wijffels (CSIRO,
Australia), Richard Westaway (U. Bristol, UK), Guy Woppelmann (U. La
Rochelle, France), Bert Wouters (U. Utrecht, The Netherlands).