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Recent Contributions of Glaciers and Ice Caps to Sea Level Rise



Glaciers and ice caps (GICs) are important contributors to present-day global mean sea level rise. Most previous global mass balance estimates for GICs rely on extrapolation of sparse mass balance measurements representing only a small fraction of the GIC area, leaving their overall contribution to sea level rise unclear. Here we show that GICs, excluding the Greenland and Antarctic peripheral GICs, lost mass at a rate of 148 ± 30 Gt yr(-1) from January 2003 to December 2010, contributing 0.41 ± 0.08 mm yr(-1) to sea level rise. Our results are based on a global, simultaneous inversion of monthly GRACE-derived satellite gravity fields, from which we calculate the mass change over all ice-covered regions greater in area than 100 km(2). The GIC rate for 2003-2010 is about 30 per cent smaller than the previous mass balance estimate that most closely matches our study period. The high mountains of Asia, in particular, show a mass loss of only 4 ± 20 Gt yr(-1) for 2003-2010, compared with 47-55 Gt yr(-1) in previously published estimates. For completeness, we also estimate that the Greenland and Antarctic ice sheets, including their peripheral GICs, contributed 1.06 ± 0.19 mm yr(-1) to sea level rise over the same time period. The total contribution to sea level rise from all ice-covered regions is thus 1.48 ± 0.26 mm (-1), which agrees well with independent estimates of sea level rise originating from land ice loss and other terrestrial sources.
LETTER doi:10.1038/nature10847
Recent contributions of glaciers and ice caps to sea
level rise
Thomas Jacob
{, John Wahr
, W. Tad Pfeffer
& Sean Swenson
Glaciers and ice caps (GICs) are important contributors to present-
day global mean sea level rise
. Most previous global mass balance
estimates for GICs rely on extrapolation of sparse mass balance
representing only a small fraction of the GIC
area, leaving their overall contribution to sea level rise unclear.
Here we show that GICs, excluding the Greenland and Antarctic
peripheral GICs, lost mass at a rate of 148 630 Gt yr
January 2003 to December 2010, contributing 0.41 60.08 mm yr
to sea level rise. Our results are based on a global, simultaneous
inversion of monthly GRACE-derived satellite gravity fields, from
which we calculate the mass change over all ice-covered regions
greater in area than 100 km
. The GIC rate for 2003–2010 is about
30 per cent smaller than the previous mass balance estimate that
most closely matches our study period
. The high mountains of
Asia, in particular, show a mass loss of only 4 620 Gt yr
2003–2010, compared with 47–55 Gt yr
in previously published
. For completeness, we also estimate that the Greenland
and Antarctic ice sheets, including their peripheral GICs, con-
tributed 1.06 60.19 mm yr
to sea level rise over the same time
period. The total contribution to sea level rise from all ice-covered
regions is thus 1.4860.26 mm yr
, which agrees well with inde-
pendent estimates of sea level rise originating from landiceloss and
other terrestrial sources
Interpolation of sparse mass balance measurements on selected
glaciers is usually used to estimate global GIC mass balance
Models are also used
, but these depend on the quality of input
climate data and include simplified glacial processes. Excluding
Greenland and Antarctic peripheral GICs (PGICs), GICs have
variously been reported to have contributed 0.43–0.51 mm yr
sea level rise (SLR) during 1961–2004
during 2001–2005
and 0.95 mm yr
The Gravity Recovery and Climate Experiment (GRACE) satellite
has provided monthly, global gravity field solutions since
2002, allowing users to calculate mass variations at the Earth’s sur-
. GRACE has been used to monitor the mass balance of selected
GIC regions
that show large ice mass loss, as well as of Antarctica
and Greenland
Here we present a GRACE solution that details individual mass
balance results for every region of Earth with large ice-covered areas.
The main focus of this paper is on GICs, excluding Antarctic and
Greenland PGICs. For completeness, however, we also include results
for the Antarctic and Greenland ice sheets with their PGICs. GRACE
does not have the resolution to separate the Greenland and Antarctic
ice sheets from their PGICs. All results are computed for the same 8-yr
time period (2003–2010).
To determine losses of individual GIC regions, we cover each region
with one or more ‘mascons’ (small, arbitrarily defined regions of
Earth) and fit mass values for each mascon (ref. 16 and Supplemen-
tary Information) to the GRACE gravity fields, after correcting for
hydrology and for glacial isostatic adjustment (GIA) computed using
the ICE-5G deglaciation model. We use 94 monthly GRACE solutions
from the University of Texas Center for Space Research, spanning
January 2003 to December 2010. The GIA corrections do not include
the effects of post-Little Ice Age (LIA) isostatic rebound, which we
separately evaluate and remove. All above contributions and their
effects on the GRACE solutions are discussed in Supplementary
Figure 1 shows mascons for all ice-covered regions, constructed
from the Digital Chart of the World
and the Circum-Arctic Map
of Permafrost and Ground-Ice Conditions
. Each ice-covered region
is chosen as a single mascon, or as the union of several non-overlapping
mascons. We group 175 mascons into 20 regions. Geographically iso-
lated regions with glacierized areas less than 100 km
in area are
excluded. Because GRACE detects total mass change, its results for
an ice-covered region are independent of the glacierized surface area
(Supplementary Information).
Mass balance rates for each region are shown in Table 1 (see
Supplementary Information for details on the computation of the rates
and uncertainties). We note that Table 1 includes a few positive rates,
but none are significantly different from zero. We also performed an
inversion with GRACE fields from the GFZ German Research Centre
for Geosciences and obtained results that agreed with those from the
Center for Space Research (Table 1) to within 5% for each region.
The results in Table 1 are ingeneral agreement with previous GRACE
studies for the large mass loss regions of the Canadian Arctic
, as well as for the Greenland and Antarctic ice sheets with
Department of Physics and Cooperative Institute for Environmental Studies, University of Colorado at Boulder, Boulder, Colorado 80309, USA.
Institute of Arctic and Alpine Research, University of
Colorado at Boulder, Boulder, Colorado 80309, USA.
Department of Civil, Environmental, and Architectural Engineering,University of Colorado at Boulder, Boulder, Colorado 80309, USA.
National Center
for Atmospheric Research, Boulder, Colorado 80305, USA. {Present address: Bureau de Recherches Ge
´ologiques et Minie
`res, Orle
´ans 45060, France.
12 13
15 19
Figure 1
Mascons for the ice-covered regions considered here. Each
coloured region represents a single mascon. Numbers correspond to regions
shown in Table 1. Regions containing more than one mascon are outlined with
a dashed line.
00 MONTH 2012 | VOL 000 | NATURE | 1
their PGICs
. Our results for Alaska also show considerable mass loss,
although our mass loss rate is smaller than some previously published
GRACE-derived rates that used shorter and earlier GRACE dataspans
(Supplementary Information). The global GIC mass balance, exclud-
ing Greenland and Antarctic PGICs, is 2148 630 Gt yr
, con-
tributing 0.41 60.08 mm yr
to SLR.
Mass balance time series for all GIC regions are shown in Fig. 2. The
seasonal and interannual variabilities evident in these time series have
contributions from ice and snow variability on the glaciers, as well as
from imperfectly modelled hydrological signals in adjacent regions
and from random GRACE observational errors. Interannual variability
can affect rates determined over short time intervals. Figure 2 and
Supplementary Table 2 show that there was considerable interannual
variability during 2003–2010 for some of the regions, especially High
Mountain Asia (HMA). The HMA results in Supplementary Table 2
show that this variability induces large swings in the trend solutions
when it is fitted to subsetsof the entire time period. These results suggest
that care should be taken in extending the 2003–2010 results presented
in this paper to longer time periods.
For comparison with studies in which PGICs are included with
GICs, we upscale our GIC-alone rate to obtain a GIC rate that includes
PGIC, based on ref. 3 (Supplementary Information). The result is that
GICs including PGICs lost mass at a rate of 229 682 Gt yr
(0.63 60.23 mm yr
SLR), and that the combined ice sheets without
their PGICs lost mass at 303 6100 Gt yr
(0.84 60.28 mm yr
SLR). Although no other study encompasses the same time span,
published non-GRACE estimates for GICs plus PGICs are larger:
0.98 60.19 mm yr
over 2001–2004
,1.4160.20 mm yr
and 0.765 mm yr
(no uncertainty given) over 2006–
. These differences could be due to the small number of mass
balance measurements those estimates must rely on, combined with
uncertain regional glacier extents. In addition, there are indications
from more recent non-GRACE measurements that the GIC mass loss
rate decreased markedly beginning in 2005
Our results for HMA disagree significantly with previous studies.
A recent GRACE-based study
over 2002–2009 yields significantly
Table 1
Inverted 2003–2010 mass balance rates
Region Rate (Gt yr
1. Iceland 211 62
2. Svalbard 2362
3. Franz Josef Land 0 62
4. Novaya Zemlya 2462
5. Severnaya Zemlya 2162
6. Siberia and Kamchatka 2 610
7. Altai 3 66
8. High Mountain Asia 24620
8a. Tianshan 2566
8b. Pamirs and Kunlun Shan 2165
8c. Himalaya and Karakoram 2566
8d. Tibet and Qilian Shan 7 67
9. Caucasus 1 63
10. Alps 2263
11. Scandinavia 3 65
12. Alaska 246 67
13. Northwest America excl. Alaska 5 68
14. Baffin Island 233 65
15. Ellesmere, Axel Heiberg and Devon Islands 234 66
16. South America excl. Patagonia 26612
17. Patagonia 223 69
18. New Zealand 2 63
19. Greenland ice sheet 1PGICs 2222 69
20. Antarctica ice sheet 1PGICs 2165 672
Total 2536 693
GICs excl. Greenland and Antarctica PGICs 2148 630
Antarctica 1Greenland ice sheet and PGICs 2384 671
Total contribut ion to SLR 1.48 60.26 mm yr
SLR due to GICs excl. Greenland and Antarctica PGICs 0.41 60.08 mm yr
SLR due to Antarctica 1Greenland ice shee t and PGICs 1.06 60.19 mm yr
Uncertainties are given at the 95% (2s) confidence level.
2003 2004 2005 2006
2007 2008 2009 2010 2011
Mass (Gt)100 Gt
1. Iceland
2. Svalbard
3. Franz Joseph Land
4. Novaya Zemlya
5. Severnaya Zemlya
6. Siberia and Kamchatka
7. Altai
8. High Mountain Asia
9. Caucasus
10. Alps
11. Scandinavia
12. Alaska
13. North America excl. Alaska
14. Bafn Island
15. Ellesmere, Axel Heiberg and Devon Islands
16. South America excl. Patagonia
17. Patagonia
18. New Zealand
Figure 2
Mass change during 2003–2010 for all GIC regions shown in
Fig. 1 and Table 1. The black horizontal lines run through the averages of the
time series. The grey lines represent 13-month-window, low-pass-filtered
versions of the data. Time series are shifted for legibility. Modelled
contributions from GIA, LIA and hydrology have been removed.
2 | NATURE | VOL 000 | 00 MONTH 2012
larger mass loss for HMA than does ours; we explain why the resultof
ref. 5 may be flawed in Supplementary Information. Conventional
mass balance methods have been used to estimate a 2002–2006 rate
of 255 Gt yr
for this entire region
, with 229 Gt yr
over the
eastern Himalayas alone, by contrast with our HMA estimate, of
24620 Gt yr
(Table 1). We show results for the four subregions
of HMA (Fig. 3) in Table 1.
This difference prompts us to examine this region in more detail.
GRACE mass trends show considerable mass loss across the plains of
northern India, Pakistan and Bangladesh, centred south of the glaciers
and at low elevations (Fig. 3a, b). Some of the edges of this mass loss
region seem to extend over adjacent mountainous areas to the north,
but much of that, particularly above north-central India, is leakage of
the plains signal caused by the 350-km Gaussian smoothing function
used to generate the figure. The plains signal has previously been
identified as groundwater loss
. To minimize leakage in the HMA
GIC estimates, additional mascons are chosen to cover the plains
(Fig. 3a), the sum of which gives an average 2003–2010 water loss rate
of 35 Gt yr
. Our plains results are consistent with the results of refs
16 and 21, which span shorter time periods.
The lack of notable mass loss over glacierized regions is consistent
with our HMA mascon solutions that indicate relatively modest losses
(Table 1). We simulate what the ice loss rates predicted by ref. 2 would
look like in the GRACE results. We use those rates to construct
synthetic gravity fields and process them using the same methods
applied to the GRACE data, to generate the trend map shown in
Fig. 3c. It is apparent that an ice loss of this order would appear in
the GRACE map as a large mass loss signal centred over the eastern
Himalayas, far larger in amplitude and extent than the GRACE results
in that region (compare Fig. 3b with Fig. 3c).
It is reasonable to wonder whether a tectonic process could be
causing a positive signal in the glacierized region that offsets a large
negative glaciersignal in HMA. To see whatthis positive ratewould have
to look like, we remove the simulated gravity field (based on ref. 2) from
the GRACE data and show the resulting difference map in Fig. 3d. If the
ice loss estimate were correct, the tectonic process would be causing an
anomalous mass increase over the Himalayas of ,3cmyr
water thickness, equivalent to ,1cmyr
of uncompensated crustal
uplift. Although we cannot categorically rule out such a possibility, it
seems unlikely. Global Positioning System and levelling observations in
this region indicate long-term uplift rates as large as 0.5–0.7 cm yr
some places
. But it is highly probable that any broad-scale tectonic
uplift would be isostatically compensated by an increasing mass
deficiency at depth, with little net effect on gravity
and, consequently,
no significant contribution to the GRACE results. The effects of com-
pensation are evident in the static gravity field. Supplementary Fig. 4
−3 2.4 1.8 1.2 0.6 0 0.6 1.2 1.8 2.4 3
32.4 1.8 1.2 0.6 0 0.6 1.2 1.8 2.4 3
4,000 2,000 0 2,000 4,000 6,000
32.4 1.8 1.2 0.6 0 0.6 1.2 1.8 2.4 3
Elevation (m)
GRACE trend (cm w.e. yr
GRACE trend – s
nthetic trend (cm w.e.
Longitude east
Longitude east
Latitude northLatitude north
nthetic trend (cm w.e.
60˚ 70˚ 80˚ 90˚
60˚ 70˚ 90˚
60˚ 70˚ 80˚ 90˚
60˚ 70˚ 80˚ 90˚
Figure 3
HMA mass balance determination. a, Topographic map overlaid
with the HMA mascons (crosses) and India plain mascons (dots); the dashed
lines delimit the four HMA subregions (labelled as in Table 1). b, GRACE mass
rate corrected for hydrology and GIA and smoothed with a 350-km Gaussian
smoothing function, overlaid with the HMA mascons. w.e., water equivalent.
c, Synthetic GRACErates that would be caused by a total massloss of 55 Gt yr
over HMA mascons, with 29 Gt yr
over the eastern Himalayas, after ref. 2.
d, The difference between band c.
00 MONTH 2012 | VOL 000 | NATURE | 3
shows the free-air gravity field, computed using a 350-km Gaussian
smoothing function (used to generate Fig. 3) applied to the EGM96
mean global gravity field
. The topography leaves no apparent sig-
nature on the static gravity field at these scales, indicating near-perfect
For a solid-Earth process to affect GRACE significantly, it must be
largely isostatically uncompensated, which for these broad spatial
scales would require characteristic timescales of the same order or less
than the mantle’s viscoelastic relaxation times (several hundred to a
few thousand years). One possible such process might be the ongoing
viscoelastic response of the Earth to past glacial unloading. We have
investigated this effect, as well as possible contributions from erosion,
and find that neither is likely to be important (Supplementary
Another possible explanation for the lack of a large GRACE HMA
signal is that most of the glacier melt water might be sinking into the
ground before it has a chance to leave the glaciated region, thus causing
GRACE to show little net mass change. Some groundwater recharge
undoubtedly does occur, but it seems unlikely that such cancellation
would be this complete. Much of HMA, for example, is permafrost, so
local storage capacity is small (see the Circum-Arctic Map of
Permafrost and Ground-Ice Conditions;
ipa_browse.html). Therefore, although there would be surface melt,
the frozen ground would inhibit local recharge and there would be
little ability to store the melt water locally. How far the water might
have to travel before finding recharge pathways, we do not know. It is
true that some rivers originating in portions of HMA do not reach the
sea. Most notable are the Amu Darya and Syr Darya, which historically
feed the Aral Sea but have been diverted for irrigation. Any fraction of
that diverted water that ends up recharging aquifers will not directly
contribute to SLR. However, the irrigation areas lie well outside our
HMA mascons, and so even if there is notable recharge it is unlikely to
affect the HMA mascon solutions significantly.
Our emphasis here is on GICs; the Greenland and Antarctic ice
sheets have previously been well studied with GRACE
. But for com-
parison withnon-GRACE global estimates, we combine our GIC results
with our estimates for Greenland plus Antarctica to obtain a total SLR
contribution from all ice-covered regions of 1.48 60.26 mm yr
during 2003–2010. Within the uncertainties, this value compares
favourably with the estimate of 1.8 60.5 mm yr
for 2006 from
ref 4. However, there are regional differences between these and prior
results, which need further study and reconciliation.
SLR from the addition of new water can be determined from
GRACE alone as well as by subtracting Argo steric heights from
altimetric SLR measurements
. The most recent new-water SLR
estimate, comparing the two methods, is 1.3 60.6 mm yr
, which agrees with our total ice-covered SLR value to
within the uncertainties. The difference, 0.2 60.6 mm yr
, could rep-
resent an increase in land water storage outside ice-covered regions,
but we note that it is not significantly different from zero.
GRACE solutions consist of spherical harmonic (Stokes) coefficients and are used
to determine month-to-month variations in Earth’s mass distribution
. We use
monthly values of C
(the zonal, degree-2 spherical harmonic coefficient of the
geopotential) from satellite laser ranging
, and include degree-one terms
To determine mass variability for each mascon, we find the set of Stokes coeffi-
cients produced by a unit mass distributed uniformly across that mascon. We fit
these sets of Stokescoefficients, simultaneously, to theGRACE Stokes coefficients,
to obtain monthly mass values for each mascon. This method is similar to prev-
iously published mascon methods
, though here we fit to Stokes coefficients
rather than to raw satellite measurements and we do not impose smoothness
constraints. To determine the optimal shape and number of mascons in a region,
we construct a sensitivity kernel for severalpossible configurations, and choose the
configuration that optimizes that kernel andminimizes the GRACE trendresiduals
(Supplementary Fig. 1c).
The average of two land surface models is used to correct forhydrology,and the
model differences are usedto estimate uncertainties (Supplementary Information).
LIA loading corrections have been previously derived for Alaska
, and equal 7 and 9 Gt yr
, respectively. These numbersare subtracted
from our Alaska and Patagonia inversions. For other GIC regions, where LIA
characteristics are not well known, we estimate an upper bound for the correction
by constructing a GIA model that tends to maximize the positive LIA gravity
trend. Of all the additional GIC regions, only HMA has a predicted LIA correction
that reaches 1 Gt yr
. There, the model suggests we remove 5Gt yr
from our
inverted result. But because the LIA correction in this region is likely to be an
overestimate (Supplementary Information), our preferred result splits the differ-
ence (Supplementary Table 1), and we use that difference to augment the total
HMA uncertainty.
Received 28 July 2011; accepted 9 January 2012.
Published online 8 February 2012.
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Supplementary Information is linked to the online version of the paper at
Acknowledgements We thank Geruo A for providing the glacial isostatic adjustment
model, and G. Cogley, G. Kaser, I. Velicogna, T. Perron and M. Tamisiea for comments.
This work was partially supported by NASA grants NNX08AF02G and NNXI0AR66G,
and by NASA’s ‘Making EarthScience Data Records for Use in Research Environments
(MEaSUREs) Program’.
Author ContributionsT.J. and J.W. developedthe study and wrotethe paper. W.T.P. and
S.S. discussed,commented on and improved themanuscript. S.S. provided the CLM4
hydrology model output.
Author Information Reprints and permissions information is available at The authors declare no competing financial interests.
Readers are welcome to comment on the online version of this article at Correspondence and requests for materials should be
addressed to J.W. (
00 MONTH 2012 | VOL 000 | NATURE | 5
... If each mascon was covered by a uniformly distributed unit EWT (m j ), which can be decomposed into SH coefficients ∆c j lm and ∆s j lm (j = 1, 2, · · · , M) with Equations (3) and (4) by Xiang et al. [9] (except that Ω now represents the mascon region considered), according to Jacob et al. [34], the mass change (by EWT) can be computed for each mascon at any time with the spectral domain inverse (SEDI) [34,35] and the space domain inverse (SADI) [35]. ...
... If each mascon was covered by a uniformly distributed unit EWT (m j ), which can be decomposed into SH coefficients ∆c j lm and ∆s j lm (j = 1, 2, · · · , M) with Equations (3) and (4) by Xiang et al. [9] (except that Ω now represents the mascon region considered), according to Jacob et al. [34], the mass change (by EWT) can be computed for each mascon at any time with the spectral domain inverse (SEDI) [34,35] and the space domain inverse (SADI) [35]. ...
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The Tibetan Plateau (TP) has the largest number of high-altitude glaciers on Earth. As a source of major rivers in Asia, this region provides fresh water to more than one billion people. Any terrestrial water storage (TWS) changes there have major societal effects in large parts of the continent. Due to the recent acceleration in global warming, part of the water environment in TP has become drastically unbalanced, with an increased risk of water disasters. We quantified secular and monthly glacier-mass-balance and TWS changes in water basins from April 2002 to December 2021 through the Gravity Recovery and Climate Experiment and its Follow-on satellite mission (GRACE/GRACE-FO). Adequate data postprocessing with destriping filters and gap filling and two regularization methods implemented in the spectral and space domain were applied. The largest glacier-mass losses were found in the Nyainqentanglha Mountains and Eastern Himalayas, with rates of −4.92 ± 1.38 Gt a−1 and −4.34 ± 1.48 Gt a−1, respectively. The Tien Shan region showed strong losses in its eastern and central parts. Furthermore, we found small glacier-mass increases in the Karakoram and West Kunlun. Most of the glacier mass change can be explained by snowfall changes and, in some areas, by summer rainfall created by the Indian monsoon. Major water basins in the north and south of the TP exhibited partly significant negative TWS changes. In turn, the endorheic region and the Qaidam basin in the TP, as well as the near Three Rivers source region, showed distinctly positive TWS signals related to net precipitation increase. However, the Salween River source region and the Yarlung Zangbo River basin showed decreasing trends. We suggest that our new and improved TWS-change results can be used for the maintenance of water resources and the prevention of water disasters not only in the TP, but also in surrounding Asian countries. They may also help in global change studies.
... During the last two decades, the successful satellite gravity mission, for example, Gravity Recovery And Climate Experiment (GRACE) mission (Tapley et al., 2004(Tapley et al., , 2019, and its successor GRACE Follow On (GRACE-FO) (Flechtner et al., 2016;Landerer et al., 2020;Loomis et al., 2012), have primarily contributed to the understanding of the temporal variations of Earth's gravity field. GRACE and GRACE-FO have not only provided valuable data to refine the gravity field modeling in the Geodesy community but also contributed to the progress of other geosciences, for example, glaciology (e.g., Jacob et al., 2012;, hydrology (e.g., Rodell et al., 2018;Tregoning et al., 2009), seismology (e.g., Fuchs et al., 2016;Han et al., 2006), oceanography (e.g., Knudsen et al., 2011;Rignot et al., 2011), and so on. ...
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Over the past 20 years, the Gravity Recovery And Climate Experiment (GRACE), and its successor mission, GRACE‐Follow on (GRACE‐FO) have made significant contributions to time‐variable gravity field modeling. A Chinese low‐low satellite‐to‐satellite tracking gravimetry mission (i.e., Chinese future gravimetry mission) has been confirmed to be selected as the polar‐orbiting satellite gravimetry mission for China, because of the capability to collect gravity data globally. However, the analysis of potential contributions to geosciences from GRACE‐FO coupling with the Chinese future gravimetry mission is still limited. This study combines GRACE‐FO and Chinese future gravimetry mission as the Dual GRACE‐like Polar satellite Constellation (DGPC). By carefully choosing the initial orbit parameters of the Chinese future gravimetry mission with the Differential Evolution algorithm, the DGPC is expected to mitigate the temporal aliasing effects by improving the temporal resolution of time‐variable gravity solutions (i.e., 1‐day and 3‐day solutions). Regarding the spectral‐domain evaluation, zonal, tesseral, and sectorial coefficients estimated by the DGPC show approximately 6.01 ∼ 13.42% noise reductions compared with GRACE‐FO. Regarding the spatial‐domain evaluation, the DGPC can suppress noises of about 39.44% and 31.12% in annual amplitude and long‐term trend, respectively. On this basis, this paper analyzes the effectiveness of the DGPC in potential contributions to geosciences (e.g., hydrology, glaciology, and seismology). Specifically, the DGPC can improve accuracy by about 36.96%, 25.85%, and 33.16% with respect to GRACE‐FO for signals in the sub‐humid basin, signals of ice‐sheet mass balance over Greenland, and co‐seismic displacement of the fault zone, respectively. In general, the potential capability for high‐frequency signals recovery of the DGPC would facilitate contributions of satellite gravimetry to geosciences.
... Variations in the TWS in endorheic basins are not entirely dependent on sparse precipitation, with glacial melting and alpine precipitation being the major sources of water recharge Li et al., 2018;Yao et al., 2018;Zhu et al., 2021). Even though glaciers are melting at an accelerated rate due to climate change (Aizen et al., 2007;Khan and Holko, 2009;Jacob et al., 2012;Unger-Shayesteh et al., 2013;Brun et al., 2017), the current rate of glacial melting is not sufficiently high to offset the land surface water depletion caused by excessive dissipation which raises concerns about the future availability of water resources (Rodell et al., 2018). ...
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The terrestrial water storage anomaly (TWSA) is an important parameter for assessing the land water budget, and it interacts well with terrestrial ecosystems via complex hydrological processes. Recently, the decline in central Asian terrestrial water storage (TWS) has threatened the health of local ecosystems. Therefore, it is of great significance to adopt an efficient approach to explore and identify the nonlinear relationship between two important indicators, i.e., the TWSA and normalized difference vegetation index (NDVI) in the arid central Asian endorheic basins. In this study, we analysed the long-term trends of the TWSA and NDVI, and identified the lag month (1 month) as the optimal moving window of the nonlinear Granger causality test embedded in random forest to detect the nonlinear NDVI change response of NDVI changes in vegetation to the TWSA from 2003 to 2015. There are decreasing trends in TWSAs over approximately 81.7% of the study area and the NDVI generally decreased resulting in approximately 36% vegetation browning in the study area. The nonlinear Granger uni-directional causes of the TWSA were responsible for 97.9% of the NDVI variation in the study area considering the optimal response time for moving windows. The causes of vegetation browning in the central Asian Aral Sea basin and vegetation greening in the basins of Northwest China could be mostly explained by the changes in TWS. Our findings contribute to understanding the nonlinear causal linkages between vegetation and the TWSA in endorheic basins, and these findings provide insights for obtaining terrestrial water consumption patterns and water resource management under the joint influence of climate change and human activities.
... Logistic and economic aspects play a crucial role in the selection of research areas, so data collection to document glacier changes (including field measurements) focuses mainly on the more accessible western coasts of the Spitsbergen island (Hagen and Liestøl, 1990). The use of traditional research methods, e.g. in situ stake mass balance measurements, is costly and time-consuming, even if the research programme is reduced to a minimum, so changes in the geometry of Svalbard glaciers are often inferred from satellite data and aerial photographs (Jacob et al., 2012;Nuth et al., 2013;Martín-Moreno et al., 2017;Girod et al., 2018;Geyman et al., 2022). The use of remote sensing has a number of advantages in glacier research, the most important of which is that the data do not require a large team in the field and can be used to quickly generate precise results. ...
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Archival maps are an important source of information on the state of glaciers in polar zones and are very often basic research data for analysing changes in glacier mass, extent, and geometry. However, basing a quantitative analysis on archival maps requires that they be standardised and precisely matched against modern-day cartographic materials. This can be achieved effectively using techniques and tools from the field of geographic information systems (i.e. GIS). The objective of this research was to accurately register archival topographic maps of the area surrounding the Hornsund fjord (southern Spitsbergen) published by the Polish Academy of Sciences and to evaluate their potential for use in studying changes in the geometry of glaciers in the northwestern part of the Sørkapp Land peninsula in the following periods. The area occupied by the investigated glaciers in the northwestern Sørkapp Land decreased in the years 1961-2010 by 45.6 km 2 , i.e. by slightly over 16 %. The rate of glacier area change varied over time and amounted to 0.85 km 2 yr −1 in the period 1961-1990 and sped up to 1.05 km 2 yr −1 after 1990. This process was accompanied by glacier surface lowering by about 90-100 m for the largest land-terminating glaciers on the peninsula and by up to more than 120 m for tidewater glaciers (above the line marking their 1984 extents). The dataset is now available from the Zenodo web portal: (Dudek and Pętlicki, 2021).
... In fact, global climate change has affected the melting of glaciers and ice caps, which could cause sea levels to rise by more than 2 m. From 2003 to 2010, ice and ice sheet mass decreased by about 148 ± 30 Gt yr -1 as continental glacier melt increased and this contributed about 0.41 ± 0.08 mm yr -1 to sea level rise [16]. During the period from 1992 to 2018, the average sea-level rise was about 10.8 mm due to the melting of Antarctic glaciers [17] To date, as greenhouse gas emissions increase, the melting of glaciers and ice sheets caused by climate change has become the main factor of sea-level rise, contributing about 50% [18,19]. ...
Coastal areas are subjected to both natural and man-made actions, leading to a deterioration of coastal structures. Climate change has had a heavy impact on these areas in recent years. An important consequence of these actions is sea level rise. This phenomenon is the most important cause of coastal erosion, a serious problem with ecological, economic, and human health consequences. The countermeasures to contrast this phenomenon and the degradation of the entire coastal system, are represented by engineering interventions. These basically consist of approaches for adaptation to sea level rise, namely protection, retreat, and accommodation. Variations and site adaptation of these actions can involve procedures of no intervention; advancement; protection; retreat; accommodation; and ecosystem-based adaptation. While these procedures have provided coastal benefits and protection, in the long run, they may cause further coastal disruption and further aggravate the situation. Such interventions, therefore, require an accurate assessment of the advantages and disadvantages. However, it is certainly necessary to proceed with actions aimed at mitigating climate change, respecting the rules in a sustainable way.
... Cluster (III) articles consider the physical basis for sea-level change including gain or loss of ice sheets and glaciers 54,55 , thermal expansion and variations in global water storage 1 , significance of atmosphere-ocean models and data requirements (e.g. from tidal gauges and altimetry satellites) 56 , and studies about sea-level trajectories 57,58 . Cluster (IV) articles reflect on the value of coastal ecosystems (e.g. ...
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As sea-level rise (SLR) accelerates due to climate change, its multidisciplinary field of science has similarly expanded, from 41 articles published in 1990 to 1475 articles published in 2021, and nearly 15,000 articles published in the Web of Science over this 32-year period. Here, big-data bibliometric techniques are adopted to systematically analyse this large literature set. Four main research clusters (themes) emerge: (I) geological dimensions and sea-level indicators, (II) impacts, risks, and adaptation, (III) physical components of sea-level change, and (IV) coastal ecosystems and habitats, with 16 associated sub-themes. This analysis provides insights into the evolution of research agendas, the challenges and opportunities for future assessments (e.g. next IPCC reports), and growing focus on adaptation. For example, the relative importance of sub-themes evolves consistently with a relative decline in pure science analysis towards solution-focused topics associated with SLR risks such as high-end rises, declining ecosystem services, flood hazards, and coastal erosion/squeeze.
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The variations in the lake water storage in the Tianshan region are an important indicator of climate change and play a key role in understanding the hydrological mass balance. Based on altimetry and satellite gravity, we investigated the spatiotemporal characteristics of the lake water storage changes during 2002–2022, and examined the contributions and proportions of all of the hydrological components to the mass balance. The results indicate that the total water storage of the lake complex showed an increasing rate (0.73±0.10 Gt/a). We found two abrupt wet periods in 2010 and 2016 (the regional total mass increased by 65.73 Gt and 67.35 Gt, respectively), which were reflected not only by the lake water storage but also by the soil moisture, snow water, and even GNSS displacement fields. Compared with their contributions to the mass (22% and 14%), the variations in lake area were remarkably slight (0.01% and 0.014%). Among the hydrological components, the soil moisture played a dominant role, and the contribution of the snow accumulation changes was also considerable. The mass anomalies were closely related to the precipitation caused by the increase of water vapor content, which was further associated with the occurrence of ENSO events (r=0.55, p<0.01). The results revealed that the long-term trend of the GNSS vertical displacements exhibited a better stability after the load correction was applied, which could reflect the long-term ground deformation more accurately. This study contributes to our understanding of the complex hydrological and tectonic processes in the Tianshan region.
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Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional understanding of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent products such as the land-ocean mass budget, atmospheric and terrestrial water budget in 341 large river basins, and streamflow measurements at 10,168 gauges. The results show that our proposed GTWS-MLrec performs overall as well as or is more reliable than previous TWS datasets. Moreover, our reconstructions successfully reproduce the consequences of climate variability, such as strong El Niño events. GTWS-MLrec dataset consists of three reconstructions based on JPL, CSR and GSFC mascons, three detrended and de-seasonalized reconstructions, and six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a wide range of geoscience applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management. GTWS-MLrec is available on Zenodo through (Yin et al., 2023c).
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The NASA Goddard Space Flight Center, the National Imagery and Mapping Agency (NIMA; formerly the Defense Mapping Agency or DMA) and The Ohio State University have collaborated to produce EGM96, an improved degree 360 spherical harmonic model representing the Earth’s gravitational potential. This model was developed using: (1) satellite tracking data from more than 20 satellites, including new data from GPS and TDRSS, as well as altimeter data from TOPEX, GEOSAT and ERS-1. (2) 30’ x 30’ terrestrial gravity data from NIMA’s comprehensive archives, including new measurements from areas such as the former Soviet Union, South America, Africa, Greenland, and elsewhere. (3) 30’ x 30’ gravity anomalies derived from the GEOSAT Geodetic Mission altimeter data, as well as altimeter derived anomalies derived from ERS-1 by KMS (Kort and Matrikelstyrelsen, Denmark) in regions outside the GEOSAT coverage. The high degree solutions were developed using two different model estimation techniques: quadrature, and block diagonal. The final model is a composite solution consisting a combination solution to degree 70, a block diagonal solution to degree 359, and the quadrature model at degree 360. This new model will be used to define an undulation model that will be the basis for an update of the WGS-84 geoid. In addition, the model will contribute to oceanographic studies by improving the modeling of the ocean geoid and to geodetic positioning using the Global Positioning System (GPS).
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1] Northern India and its surroundings, home to roughly 600 million people, is probably the most heavily irrigated region in the world. Temporal changes in Earth's gravity field in this region as recorded by the GRACE satellite mission, reveal a steady, large-scale mass loss that we attribute to excessive extraction of groundwater. Combining the GRACE data with hydrological models to remove natural variability, we conclude the region lost groundwater at a rate of 54 ± 9 km 3 /yr between April, 2002 (the start of the GRACE mission) and June, 2008. This is probably the largest rate of groundwater loss in any comparable-sized region on Earth. Its likely contribution to sea level rise is roughly equivalent to that from melting Alaskan glaciers. This trend, if sustained, will lead to a major water crisis in this region when this non-renewable resource is exhausted.
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Modern geodetic techniques, such as precise Global Positioning System (GPS) measurements and high resolution space gravity mapping, make it possible to measure the present-day rate of viscoelastic gravitational Earth response to present and past glacier mass change. Patagonia is a rapidly evolving glacial environment. Over the past decade, the local rate of surface stress unloading may be the largest anywhere on the planet. We compute the present-day land uplift rate that could be observed using bedrock GPS measurements. The Little Ice Age (LIA) of the past half millennium generates large vertical rates since the underlying mantle material is likely to have anomalously low viscosity owing to late-Neogene ridge subduction.
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[1] Analysis of satellite laser ranging (SLR) data indicates that the Earth's dynamic oblateness (J2) has undergone significant variations during the past 28 years. The dominant signatures in the observed variations in J2 are (1) a secular decrease with a rate of approximately −2.75 × 10−11 yr−1, (2) seasonal annual variations with a mean amplitude of 2.9 × 10−10, (3) significant interannual variations with timescales of 4–6 years, and (4) a variation with period of ∼21 years and an amplitude of ∼1.4 × 10−10 with minimum in December 1988. Two large interannual variations are related to the strong El Niño-Southern Oscillation events during the periods of 1986–1991 and 1996–2002, and it appears that another interannual cycle may have started in late 2002. The superposition of the decadal variation on the interannual signal makes the J2 fluctuation appear to be anomalously large during the 1996–2002 period. Contemporary models of the mass redistributions in the atmosphere, ocean, and surface water can explain a major part of the 4- to 6-year fluctuations. However, the cause of the decadal variation remains unknown.
Analysis of Satellite Laser Ranging (SLR) data from multiple satellites over 26 years from 1976 to 2002 indicate that the Earth?|s dynamic oblateness, as represented by the J2 coefficient, undergoes both seasonal and long-term variations. The dominant signatures in the observed variations in J2 are (1) a secular decrease with a rate of -2.75x1.0e-11/year, which is mostly due to postglacial rebound, (2) seasonal variations, which have a mean amplitude of 2.9x1.0e-10, associated with the Earth?|s annual mass redistribution and (3) significant interannual variations with time scales of 4-6 years. Two large interannual variations in J2 can be related to the strongest El Ni?o-Southern Oscillation (ENSO) during the periods of 1986-1991 and 1996-2002. The ENSO related large fluctuations cannot be explained by current models since the interannual mass redistributions within the atmosphere, ocean and continental hydrology contain significant uncertainties. The observed fluctuations with 4-6 year time scale indicate the impact of the ENSO phenomenon on the large-scale global mass redistributions within Earth?|s system components.
The GRACE mission is designed to monitor mass flux on the Earth's surface at one month and high spatial resolution through the estimation of monthly gravity fields. Although this approach has been largely successful, information at submonthly time scales can be lost or even aliased through the estimation of static monthly parameters. Through an analysis of the GRACE data residuals, we show that the fundamental temporal and spatial resolution of the GRACE data is 10 days and 400 km. We present an approach similar in concept to altimetric methods that recovers submonthly mass flux at a high spatial resolution. Using 4° × 4° blocks at 10-day intervals, we estimate the mass of surplus or deficit water over a 52° × 60° grid centered on the Amazon basin for July 2003. We demonstrate that the recovered signals are coherent and correlate well with the expected hydrological signal.
Airy isostasy has been observed in many active orogenic regions including Himalayas/Tibetan plateau and Tian Shan. To better understand the temporal evolution of mountain building, we investigate how topography at the surface and crust-mantle boundary (i.e., Moho) that evolves in response to crustal shortening depends on mechanical properties of continental lithosphere. Our dynamic models reveal that if the effective viscosity of continental lithosphere is less than 100 times of the viscosity of the underlying mantle, orogenic belts and their corresponding roots at Moho can grow simultaneously with the Airy isostasy being approximately maintained. This result is consistent with the relatively small viscosity inferred for continental lithosphere of actively deforming central Asia regions including Tibetan plateau.
In this study, we estimate a time series of geocenter anomalies from a combination of data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission and the output from ocean models. A matrix equation is derived relating total geocenter variations to the GRACE coefficients of degrees two and higher and to the oceanic component of the degree one coefficients. We estimate the oceanic component from two state-of-the-art ocean models. Results are compared to independent estimates of geocenter derived from other satellite data, such as satellite laser ranging and GPS. Finally, we compute degree one coefficients that are consistent with the processing applied to the GRACE Level-2 gravity field coefficients. The estimated degree one coefficients can be used to improve estimates of mass variability from GRACE, which alone cannot provide them directly.
Spirit leveling data from the Nepal Himalaya between 1977 and 1990 indicate localized uplift ar 2-3 mm/yr in the Lesser Himalaya with spatial wavelengths of 25-35 km and at 4-6 mm/yr in the Greater Himalaya with a wavelength of approximately 40 km. Leveling data with significantly sparser spatial sampling in southern Tibet between 1959 and 1981 suggest that the Himalayan divide may be rising at a rate of 7.5 +/- 5.6 mm/yr relative to central Tibet. We use two-dimensional dislocation modeling methods to examine a number of stuctural models that yield vertical velocity fields similar to those observed.