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Large uncertainties remain in the current and future contribution to sea level rise from Antarctica. Climate warming may increase snowfall in the continent’s interior1, 2, 3, but enhance glacier discharge at the coast where warmer air and ocean temperatures erode the buttressing ice shelves4, 5, 6, 7, 8, 9, 10, 11. Here, we use satellite interferometric synthetic-aperture radar observations from 1992 to 2006 covering 85% of Antarctica’s coastline to estimate the total mass flux into the ocean. We compare the mass fluxes from large drainage basin units with interior snow accumulation calculated from a regional atmospheric climate model for 1980 to 2004. In East Antarctica, small glacier losses in Wilkes Land and glacier gains at the mouths of the Filchner and Ross ice shelves combine to a near-zero loss of 4±61 Gt yr−1. In West Antarctica, widespread losses along the Bellingshausen and Amundsen seas increased the ice sheet loss by 59% in 10 years to reach 132±60 Gt yr−1 in 2006. In the Peninsula, losses increased by 140% to reach 60±46 Gt yr−1 in 2006. Losses are concentrated along narrow channels occupied by outlet glaciers and are caused by ongoing and past glacier acceleration. Changes in glacier flow therefore have a significant, if not dominant impact on ice sheet mass balance.
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© 2008 Nature Publishing Group
Recent Antarctic ice mass loss from
radar interferometry and regional
climate modelling
1University of California Irvine, Earth System Science, Irvine, California 92697, USA
2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA
3Centro de Estudios Cientificos, Arturo Prat 514, Valdivia, Chile
4University of Bristol, Bristol BS8 1SS, UK
5Institute for Marine and Atmospheric Research (IMAU), Utrecht University, 3584 CC Utrecht, The Netherlands
6University of Missouri-Columbia, Columbia, Missouri 65211, USA
7Royal Netherlands Meteorological Institute (KNMI), 3732 GK De Bilt, The Netherlands
Published online: 13 January 2008; doi:10.1038/ngeo102
Large uncertainties remain in the current and future
contribution to sea level rise from Antarctica. Climate warming
may increase snowfall in the continent’s interior1–3 , but enhance
glacier discharge at the coast where warmer air and ocean
temperatures erode the buttressing ice shelves4–11. Here, we use
satellite interferometric synthetic-aperture radar observations
from 1992 to 2006 covering 85% of Antarctica’s coastline to
estimate the total mass flux into the ocean. We compare the
mass fluxes from large drainage basin units with interior snow
accumulation calculated from a regional atmospheric climate
model for 1980 to 2004. In East Antarctica, small glacier losses in
Wilkes Land and glacier gains at the mouths of the Filchner and
Ross ice shelves combine to a near-zero loss of 4 ±61 Gt yr1. In
West Antarctica, widespread losses along the Bellingshausen and
Amundsen seas increased the ice sheet loss by 59% in 10 years to
reach 132 ±60 Gt yr1in 2006. In the Peninsula, losses increased
by 140% to reach 60±46 Gt yr1in 2006. Losses are concentrated
along narrow channels occupied by outlet glaciers and are caused
by ongoing and past glacier acceleration. Changes in glacier flow
therefore have a significant, if not dominant impact on ice sheet
mass balance.
The mass balance of Antarctica is determined from the
dierence between two competing processes of ice discharge into
the ocean by glaciers and ice streams and accumulation of snowfall
in the vast interior, which are two large numbers aected by
significant uncertainties2,12. Estimates of ice discharge have been
sporadic in nature owing to the limited availability of ice velocity
and thickness data at the grounding line of Antarctica, as well
as precise knowledge of the grounding-line positions. Similarly,
estimates of snowfall have been aected by uncertainties associated
with the interpolation of sparse in situ data of varying quality and
temporal coverage over the entire continent.
Here, we present a nearly complete map of surface velocities
along the periphery of Antarctica (Fig. 1) obtained from
interferometric synthetic-aperture radar (InSAR) data collected
between 1992 and 2006 by the European Earth Remote Sensing
(ERS-1 and 2), the Canadian Radarsat-1 and the Japanese Advanced
Land Observing satellites. Our map covers all major outlet glaciers,
ice streams and tributaries of importance for mass flux calculation,
with ice velocity ranging from 100 to 3,500 m yr1, at a precision
of 5 to 50 m yr1(see the Methods section). Short-time variations
in velocity, for example, due to ocean tides, are averaged out over
the 24 to 46 day repeat period of our measurements. Velocities
at the grounding line of fast-moving glaciers are assumed to be
depth independent, which introduces errors of much less than
1% (ref. 3).
Using double-dierence interferometry, we mapped glacier
grounding lines with a precision of 100 m all around Antarctica,
except for eight glaciers south of 81South where we used
the Moderate Resolution Imaging Spectroradiometer (MODIS)
mosaic13 with a precision of 1 km. Grounding-line thickness is
derived from surface elevation assuming ice to be in hydrostatic
equilibrium with sea water (see the Methods section). In selected
parts of West Antarctica, we have direct measurements of ice
thickness with a precision of 10 m instead (see Supplementary
Information, Table S1). For surface elevation, we use a new digital
elevation model (DEM) combining precise laser altimeter data
from the Ice Cloud and land Elevation Satellite from 2003–2004,
ERS-1/2 radar altimeter data from 1994 corrected for temporal
changes in between1,14 and the new GGM02 geoid15. Comparison
of the DEM with independent laser altimeter data at the grounding
line of West Antarctica indicates a vertical precision in elevation
of 0.15 ±4 m. Surface elevation above mean sea level is then
converted into solid-ice surface elevation after applying a firn depth
correction16. We estimate the random error in inferred thickness
to range from 80 to 120 m when accounting for uncertainties in
grounding-line position, surface elevation, firn depth correction
and geoid height. For verification, at the grounding line of Pine
Island Bay glaciers, our thickness values are within 14 ±60 m
of direct thickness measurements10 ranging from 420 to 1,460 m.
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200 km
+10 Gt yr–1
–10 Gt yr–1
Ross Sea
Weddell Sea
Amundsen Sea
Bellingshausen Sea
–10 010
0 0.01 0.1 1 2 3
East Antarctica
West Antarctica
–88 –90
km yr–1
Figure 1 Ice velocity of Antarctica colour coded on a logarithmic scale and overlaid on a MODIS mosaic13.Circles denote mass loss (red) or gain (blue) of large basins
in gigatonnes per year. Drainage basins are black lines extending from the grounding-line flux gates. Letters A–K0indicate large basins20. Ice velocities for Siple Coast ice
streams and Ronne Ice Shelf are from refs 22,23. See Supplementary Information for acronyms and the Methods section for velocity precision.
Solid-ice fluxes are then calculated combining vector ice velocity
and ice thickness, with a precision that is glacier dependent and
ranges from 2 to 15% (see the Supplementary Information). The
end points of the selected flux gates define the extent of the glacier
drainage basins determined from the DEM. Individual drainage
basins are grouped into large units labelled A to K0.
Snowfall accumulation is from the RACMO2/ANT regional
atmospheric climate model, at 55 km resolution, averaged for
1980–2004 (refs 17–19). Lateral forcings are taken from European
Center for Medium-Range Weather Forecasting reanalyses
(ERA-40) for the period 1980–2002, supplemented with European
Center for Medium-Range Weather Forecasting operational
analyses after August 2002. Comparisons with 1,900 independent
field data show excellent agreement (R =0.82) with the model18 .
The model predicts higher coastal precipitation and wetter
conditions in West Antarctica and the western Peninsula17 than
older maps obtained by interpolating limited field data using
meteorological variables20 or satellite passive microwave data21 .
Few reliable in situ coastal accumulation data exist for comparison,
but in the high-accumulation sector of the Getz Ice Shelf (basin
F0G), the model predicts precipitation levels consistent with a
2,030 mm yr1record at Russkaya station (74460S, 136520W)
for 1981–1989. Older maps yield accumulation levels 3 times lower,
which imply a local mass balance 20 times more negative and high
rates of glacier thinning that are not observed2. The RACMO2/ANT
accumulation values yield comparable losses for Pine Island and
Thwaites glaciers, which is consistent with the similarity of their
thinning rates2; other maps yield twice more thinning for Thwaites.
Finally, the model does not mix data from dierent time periods
and fully incorporates temporal changes in snowfall between 1980
and 2004. A statistical analysis of absolute errors (see the Methods
section) yields an uncertainty in accumulation varying from 10%
in dry, large basins to 30% in wet, small coastal basins.
Ice flux and snowfall are compared for each glacier, for large
basins A–K0, and for the Peninsula, East and West Antarctica. To
include non-surveyed areas, we apply a scaling factor on the mass
fluxes of each large basin A–K0based on the percentage surveyed
area versus total area to cover 100% of Antarctica (Table 1). In East
Antarctica, we obtain a near-zero mass balance of 4±61 Gt yr1.
The J00K Filchner22 and E0E Ross sectors are gaining mass, but this
is compensated by the mass loss in Wilkes Land (basin CE) from
the Philippi, Denman, Totten, Moscow University Ice Shelf, Cook
Ice Shelf and David glaciers. Interestingly, all of these glaciers are
marine based, that is, grounded well below sea level2, and therefore
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Table 1 Mass balance of Antarctica in gigatonnes (1012 kg) per year by sector for the year 2000. Area: area surveyed. Input: snow accumulation ±σof surveyed area.
Outflow: grounding-line ice flux ±σof surveyed area. Net: mass balance calculated as Input minus Outflow, ±σ. Net+: mass balance scaled on the basis of total area
(Area+) versus area surveyed (Area), except for basin II00 where we use refs 8,9. Input+: total snowfall in Area+ ±σ. Mass losses for 1996 and 2006 differ from those in
the year 2000 in basins GH and II00 (see Table 2).
Sector Area Input Outflow Net Net+Area+Input+
(103km2) (Gt yr1) (Gt yr1) (Gt yr1) (Gt yr1) (103km2) (Gt yr1)
J00K Filchner 1,698 93 ±8 75±4 18±9 19 ±10 1,780 100±9
KK0Riiser Larsen 218 42 ±8 45±43±93±11 246 50±10
K0A Jutulstraumen 159 26 ±7 28±21±81±9 178 32±9
AA0Queen Maud Land 615 60 ±9 60±7 0±11 0 ±12 622 62±9
A0B Enderby Land 354 39 ±5 40±21±51±9 645 115±14
BC Lambert 1,197 73 ±10 77±44±11 4±12 1,332 87±12
CC0Philippi, Denman 434 81 ±13 87±77±15 11±24 702 137 ±22
C0D Totten, Frost 1,053 198 ±37 207±13 8±39 9±43 1,162 261±49
DD0Cook, Mertz, Ninnis 563 92 ±14 94±62±16 2±19 691 136 ±21
D0E Victoria Land 267 20 ±1 22±32±43±6 450 62±4
EE0TransAntarctic 1,441 61±10 49±4 11±11 13 ±13 1,639 89 ±15
East Antarctica 2000 7,998 786±48 785 ±20 1 ±52 4±61 9,447 1,131±69
E0F0Siple Coast 751 110 ±7 80±2 31±7 34 ±8 845 130±8
F0G Getz, Hull, Land 119 108 ±28 128±18 19±33 23 ±39 140 128±33
GH Pine Is., Thwaites 393 177±25 237±461 ±26 64 ±27 417 196±28
HH0Ferrigno, Abbot 55 51 ±16 86±10 35 ±19 49 ±27 78 71 ±22
JJ00 Ronne 933 142±11 145±74±13 4±14 1,028 165 ±13
West Antarctica 2000 2,251 588 ±49 676±22 88 ±54 106 ±60 2,508 690±57
H0I English Coast 92 71 ±21 78±77±23 7±24 98 77 ±23
II00 Graham Land 13 15 ±5 20±35±615±8 78 125±46
I00J East Palmer Land 11 8±4 9 ±21±46±18 52 32 ±14
Antarctic Peninsula 2000 116 94 ±21 107±813±23 28 ±45 228 234±53
Antarctica 2000 10,365 1,469±87 1,568±31 100±78 138 ±92 12,183 2,055 ±122
Table 2 Mass balance in gigatonnes (1012 kg) per year for 1996 and 2006 of basins
II00 and G H, West Antarctica, the Peninsula and the entire Antarctic ice sheet.
Sector Outflow Net Net+
(Gt yr1) (Gt yr1) (Gt yr1)
GH Pine Is. Thwaites 1996 215±339±25 41 ±27
GH Pine Is. Thwaites 2006 261±485±26 90 ±27
West Antarctica 1996 654±22 66 ±53 83 ±59
West Antarctica 2006 700±23 112 ±54 132 ±60
II00 Graham Land 1996 20±35±612±7
II00 Graham Land 2006 49±334±647 ±9
Peninsula 1996 107 ±813±23 25±45
Peninsula 2006 136 ±10 42±24 60±46
Antarctica 1996 1,546 ±30 78±78 112 ±91
Antarctica 2006 1,621 ±32 153±78 196 ±92
more prone to instabilities. In West Antarctica, the well-known
mass gain of the E0F0Siple Coast basin23 is small compared with
the combined mass loss from the F0G, GH and HH0basins, which
include the entire Amundsen and Bellingshausen sea coasts, and
not just Pine Island Bay. The mass loss inferred from F0H0is much
larger than in a previous survey12 that did not include many high-
loss, small glaciers in the GF0and HH0basins and ongoing glacier
acceleration in basin GH. Overall, the West Antarctic ice sheet lost
106±60 Gt yr1in the year 2000.
In the Antarctic Peninsula, the H0I and I00J basins of Palmer
Land are near balance, despite a reported increase in snowfall17,
but basin II00 of Graham Land is out of balance. On the east coast,
the Larsen A and B glaciers experienced an abrupt acceleration
(300% on average) in 2002, which increased their mass loss from
3±1 in 1996 and 2000, to 31 ±9 Gt yr1in 2006 (ref. 11). Farther
south, airborne laser altimetry data suggest that the Larsen C
glaciers are close to balance6. But on the west coast, the glaciers
have experienced widespread ice-front retreat, enhanced melt
and continuous speed up9. We have no thickness data for these
glaciers, and there is no floating section. A 12% speed up in
10 years, enhanced melt and a net accumulation of 42 ±14 Gt yr1
suggest a loss of 7 ±4 Gt yr1in 1996, 10 ±5 Gt yr1in 2000 and
13 ±7 Gt yr1in 2006. The combined loss for the Peninsula then
becomes 25 ±45 Gt yr1in 1996, increasing by 140% in 2006 to
60±46 Gt yr1(Table 2).
Changes in surface elevation in basin F0H0for 1995–2005
(Fig. 2) reveal broad-scale, centimetre-level variations in snowfall
in the interior (wetter conditions in H0H, and drier in F0E0(ref. 17)),
but pronounced, metre-scale thinning concentrated in narrow
channels occupied by outlet glaciers and extending in the flow
direction across the entire coastal range. The strong, widespread
correlation between ice thinning and ice velocity (>50 m yr1), for
example, on the Berg, Ferrigno, Venable, Pine Island, Thwaites,
Smith and Getz glaciers, indicates that thinning is caused by the
velocity of glaciers being well above that required to maintain
mass balance, that is, ice stretches longitudinally, which causes
it to thin vertically. In basin GH, we find that Pine Island
Glacier accelerated 34% in 1996–2006, Smith 75%, Pope 20%,
Haynes 27% and Thwaites is widening11. The mass flux from
basin GH thereby increased 21% since 1996 and the mass loss
doubled from 41 ±27 Gt yr1in 1996 to 64 ±27 Gt yr1in 2000
and 90 ±27 Gt yr1in 2006 (Table 2). This is the largest loss in
Antarctica. In contrast, we detect no glacier acceleration in basins
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cm yr–1
50 m yr–1
200 km
West Antarctica
–50 – 20 –10 –3 3 10 20 50
–100 – 30 –15 –6 0 6 15 30 100
Figure 2 Changes in surface elevation (centimetres per year) for basin F0H0.
Values were derived from ERS-2 and Envisat radar altimeter data for 1995–2005
and colour coded from red (thinning) to blue (thickening), overlaid on 50 myr1
velocity contours in green. Areas of fast flow in F0H0correspond to areas of
concentrated thinning, in contrast with other glaciers.
HH0and GF0in 1992–2006, which implies that these glaciers
must have accelerated out of equilibrium well before 1992 and
maintained high speeds since then. This is exactly what happened
to Fleming Glacier following the demise of Wordie Ice Shelf8.
Similarly, in basin CE00 in East Antarctica, ice thinning is detected
along the trenches of the David, Cook, Frost, Moscow University,
Totten and Denman glaciers2, yet we find no change in speed of
these glaciers between 1996 and 2006, and not even a change in
speed since 1957 on Denman Glacier11. Glacier acceleration out of
equilibrium must have preceded our period of observation there
as well, and the glaciers must have been steadily out of balance for
many decades, not just the recent past.
Glaciers that flow into large ice shelves (basins JK, F0E, BC)
are near balance or thickening. This is consistent with their
stabilization by buttressing and their distance to ocean heat sources
associated with the Antarctic Circumpolar Current22. Mass losses
in the Amundsen Sea and the northern Peninsula are caused by
ongoing acceleration, not by a change in snowfall because snowfall
increased in 1980–2004, especially in the Peninsula17. Fast flow is
explained by the ungrounding of glaciers owing to the thinning
or collapse of their buttressing ice shelves6or to a reduction in
backforce resistance at the ice front as glacier fronts thin because of
warmer air or warmer ocean temperatures4,9. In the Amundsen Sea
and the western Peninsula, ice-shelf melting is fuelled by intrusions
of warm circumpolar deep water (CDW) onto the continental
shelf down deep troughs carved into the sea floor during past ice
ages24,25. In East Antarctica, there is no report of CDW intrusion in
Wilkes Land. A southward migration of the Antarctic Circumpolar
Current26 caused by an increasingly positive southern annular
mode may have, in favourable conditions24 , entrained overspills of
CDW onto the continental shelf and trigger glacier acceleration, but
this hypothesis cannot be confirmed at present.
Our results provide a nearly complete assessment of the spatial
pattern in mass flux and mass change along the coast of Antarctica,
glacier by glacier, with lower error bounds than in previous
incomplete surveys, and a delineation of areas of changes versus
areas of near stability. Over the time period of our survey, the
ice sheet as a whole was certainly losing mass, and the mass
loss increased by 75% in 10 years. Most of the mass loss is
from Pine Island Bay sector of West Antarctica and the northern
tip of the Peninsula where it is driven by ongoing, pronounced
glacier acceleration. In East Antarctica, the loss is near zero, but
the thinning of its potentially unstable marine sectors calls for
attention. In contrast to major increases in ice discharge, snowfall
integrated over Antarctica did not change in 1980–2004 (ref. 27)
and even slightly increased in areas of large loss17. We conclude that
the Antarctic ice sheet mass budget is more complex than indicated
by the temporal evolution of its surface mass balance. Changes in
glacier dynamics are significant and may in fact dominate the ice
sheet mass budget.
Ice thickness, H, is deduced from surface elevation above mean sea level with
reference to the GGM02 geoid15,h, as H=(h1H)ρsea /(ρsea ρice ), where
the density of sea water, ρsea =1,028 kg m3(at 34 p.s.u. salinity, 1 km depth),
the density of solid ice, ρice =917 kg m3and 1His the firn depth correction.
For H=1 km, a 4 m uncertainty in 1Hintroduces a 4% uncertainty in
thickness and flux. Earlier work assumed a constant firn depth correction. We
calculate 1Hfrom a firn densification model16 driven by surface density using
25-year-average air temperature, snow accumulation and wind speed from
RACMO2/ANT. 1Hvaries from 0 to 20 m. Its precision is 2–3m on the basis of
a comparison with firn core data at the critical densities of 550 and 880 kgm3.
Snow accumulation is the arithmetic average of the values given in refs 18,19.
We use 1,900 in situ independent observations, SMBo, to calculate absolute
errors. The error for the observations is modelled as Eo=5+0.15 SMBoin
kg m2yr1where the second term accounts for the uncertainty associated
with spatial variability. The error for the modelled values, SMBm, is modelled
as Em=9+0.10 SMBm+0.00033 SMB2
min kg m2yr1. This representation
reflects that the model is well calibrated with many good observations for low
and medium values, but that the relative and absolute errors increase for high
values where few reliable observations exist. The relative error is maximized at
30% for SMBm>557 kg m2yr1. Coecients for the modelling of Emwere
optimally chosen by examining the distribution of dierences, SMBmSMBo,
normalized by the total error margin, that is, the squared sum of Eoand Em.
With our selection of coecients, we obtain a normal distribution with σ=1,
which provides strong statistical support for the error analysis. To calculate
accumulation uncertainty at the basin scale, we also account for the spatial
autocorrelation of errors. The correlation length of (SMBmSMBo) varies
from 161 km below 2,000 m elevation to 300 km above 2,000m. Combining
these correlation lengths with the error modelling, we obtain total errors in
AK0basins (Table 1) ranging from 10% in large, dry basins to 30% in wet
and smaller coastal basins. These errors represent our most likely estimate of
absolute errors, not the 95% confidence interval. Previous attempts at defining
accumulation errors only addressed interpolation errors.
Ice velocity is measured with speckle tracking on Radarsat-1 24 day, Japanese
Advanced Land Observing PALSAR 46day (basin GF0) and ERS-1 9 day (basin
H0I) repeats, and interferometrically using ascending/descending ERS-1/2
tandem pairs (basin HG) with an ERS-1/2 precision of 2–5 m yr1; and with
a combination of interferometric phase and speckle tracking (basin D0C0),
with a precision of 20–50 m yr1. Systematic errors are negligible compared
with random errors because we use stagnant areas for calibration and combine
multiple tracks with dierent look directions. The unknown positive bias
between surface and vertically integrated velocity is much less than 1%.
nature geoscience VOL 1 FEBRUARY 2008 109
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Systematic errors in thickness are less than 1%, whereas random errors range
from 10 to 120 m (see Supplementary Information, Table S1). The percentage
error in mass flux is calculated as the sum of the percentage error in velocity and
the percentage error in thickness. This is appropriate for plug flow or U-shaped
velocity profiles, which is the case for most large Antarctic glaciers. For glaciers
that approach a V-shaped velocity profile, our errors may be underestimated
by a factor of 2. Errors in Table 1 and Supplementary Information, Table S1 are
only random errors. Systematic errors are not known but small, so that actual
errors may be slightly higher. Decadal changes in velocity were only available
for glaciers mentioned in the text, ref. 11 or Table 2, and were assumed to be
zero elsewhere.
Received 29 August 2007; accepted 27 November 2007; published 13 January 2008.
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We thank R. Arthern for discussions. This work was carried out at Caltech’s Jet Propulsion Laboratory,
the University of California Irvine and the University of Missouri, Columbia, under a contract with
NASA’s Cryospheric Science Program. J.L.B. was supported by NERC grant NE/E004032/1. SAR data
were provided by the European Space Agency VECTRA project, the Canadian Space Agency, the
Japanese Space Agency, and the Alaska Satellite Facility. ERS-2 radar altimeter data were provided by
Correspondence and requests for materials should be addressed to E.R.
Supplementary Information accompanies this paper on
Author contributions
All authors discussed the results and commented on the manuscript. E.R. led the remote sensing
analysis, development of the paper and integration of the results, J.L.B. provided a digital elevation
model and analysed its accuracy, M.R.B., W.J.B. and E.M. contributed calculations of snow
accumulation, firn depth correction and associated errors and C.D. and Y.L. analysed elevation changes
from satellite radar altimeter data.
Reprints and permission information is available online at
110 nature geoscience VOL 1 FEBRUARY 2008
... Pine Island Glacier is one of the fastest glaciers in the world and also a rapidly thinning and melting glacier, which is responsible for about 20 % of Antarctica Ice Sheet's mass loss (Thomas et al., 2011;Favier et al., 2014;Nilsson et al., 2022). The glacier has thinned at an increasing rate over the past 40 years with the grounding line retreated by 10s of kilometres (Rignot et al., 2008;Favier et al., 2014). ...
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The NASA MEaSUREs Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project seeks to accelerate understanding of critical glaciers and ice sheet processes by providing researchers with global, low-latency, comprehensive and state of the art records of surface velocities and elevations as observed from space. Here we describe the image-pair ice velocity product and processing methodology for ESA Sentinel-1 radar data. We demonstrate improvements to the core processing algorithm for dense offset tracking, “autoRIFT”, that provide finer resolution (120 m instead of the previous 240 m used for version 1) and higher accuracy (20 % to 50 % improvement) data products with significantly enhanced computational efficiency (>2 orders of magnitude) when compared to earlier versions and the state of the art “dense ampcor” routine in the JPL ISCE software. In particular, the disparity filter is upgraded for handling finer grid resolution with overlapping search chip sizes, and the oversampling ratio in the subpixel cross-correlation estimation is adaptively determined for Sentinel-1 data by matching the precision of the measured displacement based on the search chip size used. A novel calibration is applied to the data to correct for Sentinel-1A/B subswath and full-swath dependent geolocation biases caused by systematic issues with the instruments. Sentinel-1 C-band images are affected by variations in the total electron content of the ionosphere that results in large velocity errors in the azimuth (along-track) direction. To reduce these effects, slant range (line of sight or LOS) velocities are used and accompanied by LOS parameters that support map coordinate (x/y) velocity inversion from ascending and descending slant range offset measurements, as derived from two image pairs. After the proposed correction of ionosphere errors, the uncertainties in velocities are reduced by 9 %–61 %. We further validate the ITS_LIVE Version 2 Sentinel-1 image-pair products, with 6-year time series composed of thousands of epochs, over three typical test sites covering the globe: the Jakobshavn Isbræ Glacier of Greenland, Pine Island Glacier of the Antarctic, and Malaspina Glacier of Alaska. By comparing with other similar products (PROMICE, FAU, and MEaSUREs Annual Antarctic Ice Velocity Map products), as well as other ITS_LIVE version 2 products from Landsat-8 and Sentinel-2 data, we find an overall variation between products around 100 m yr−1 over fast-flowing glacier outlets, where both mean velocity and variation are on the order of km yr−1, and increases up to 300–500 m yr−1 (3 %–6 %) for the fastest Jakobshavn Isbræ Glacier. The velocity magnitude uncertainty of the ITS_LIVE Sentinel-1 products is calculated to be uniformly distributed around 60 m yr−1 for the three test regions investigated. The described product and methods comprise the MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: version 2 (DOI:, Lei et al., 2022).
... ATM data can then be compared with ARGON DEM after registration by using ICESat-2 data to detect elevation changes. Along Graham Land's elevation profile AA' (see Fig. 7 [56][57][58], snow accumulation may have increased, which could compensate for surface melting [59]. Each point in Fig. 7(a) shows the mean surface mass balance (SMB) from 1979 to 2008 using the RACMO 2.3p2 simulation results with a 5.5km spacing [60]. ...
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The Antarctic Peninsula has undergone dramatic changes in recent decades, including ice-shelf melting, disintegration, and retreat of the grounding line. The Larsen B ice shelf is of particular concern due to the unprecedented ice-shelf collapse in 2002. Since few observations on the Antarctic Peninsula were available before the 1970s, long-term investigation of the surface elevation change in the Larsen B region could not be pursued. In 1995 the United States administration declassified a collection of archived intelligence satellite photographs from the 1960s to the 1970s, including analogue satellite images from the ARGON program covering parts of the Larsen B region. We chose overlapping ARGON photos captured in the Larsen B region in 1963. These photos were all subjected to a tailored photogrammetric stereo-matching process, which overcomes those specific challenges related to the use of historical satellite images, such as poor image quality, low resolution, and a lack of high-precision validation data. We discovered that between 1963 and 2001, the surface elevations of the main tributary glaciers in the Larsen B embayment have undergone little change before the ice shelf collapse from 1963 to 2001 by comparing the reconstructed ARGON-derived DEM (1963) and ASTER-derived DEM (2001). In addition, the results demonstrated that the hierarchical image matching method can be modified and applied to reconstruct a historical Antarctic DEM using satellite images acquired ∼60 years ago through an innovative and rigorous ground control point selection procedure that guarantees no changes occurred at these points over the period. The new ARGON-derived DEM derived from ARGON (1963) can be used to build a long-term spatiotemporal record of observations for extended analyses of ice-surface dynamics and mass balance in the Larsen B region.
... There has been a mass loss from the Antarctic Peninsula (AP) ice sheet over recent decades [1]. The main contribution comes from basal melt on the western side of the peninsula at the Thwaites and Pine Island glaciers in the Amundsen Sea Embayment. ...
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In this study, we used the stable water isotope record (δ18O) from an ice core drilled in Palmer Land, southern Antarctic Peninsula (AP). Utilizing δ18O we identified two climate regimes during the satellite era. During the 1979–1998 positive interdecadal Pacific oscillation (IPO) phase, a low-pressure system north of the Weddell Sea drove southeasterly winds that are associated with an increase in warm air mass intrusion onto the Larsen shelves, which melted and a decreased sea ice concentration in the Weddell Sea/increase in the Bellingshausen Sea. This climate setting is associated with anomaly low δ18O values (compared with the latter IPO period). There is significantly more melt along the northern AP ice shelf margins and on the Larsen D and southern Larsen C during the 1979–1998 IPO positive phase. The IPO positive climatic setting was coincidental with the Larsen A ice shelf collapse. In contrast, during the IPO negative phase (1999–2011), northerly winds caused a reduction in sea ice in the Bellingshausen Sea/Drake Passage region. Moreover, a Southern Ocean north of the Weddell Sea high-pressure system caused low-latitude warm humid air over the tip and east of the AP, a setting that is associated with increased northern AP snowfall, a high δ18O anomaly, and less prone to Larsen ice shelf melt.
... On the contrary, substantial buttressing is provided by the larger Ross and Ronne ice shelves in the Ross and Weddell sea sectors, respectively (Figure 2.3b,c). (Bamber and Rignot, 2002;Christianson et al., 2016;Groh et al., 2014;Han et al., 2016;Joughin et al., 2010Joughin et al., , 2009Joughin et al., , 2003Milillo et al., 2017;Mouginot et al., 2014;Rabus et al., 2003;Rignot, 2008Rignot, , 2006Rignot et al., 2008Rignot et al., , 2002Shen et al., 2018;Thomas et al., 2004). Holding ~0.5% of the Antarctic ice mass , the API is by far the smallest Antarctic ice sheet. ...
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With accelerating global climate change, the Antarctic Ice Sheet is exposed to increasing ice dynamic change. During 1992 and 2017, Antarctica contributed ~7.6 mm to global sea-level-rise mainly due to ocean thermal forcing along West Antarctica and atmospheric warming along the Antarctic Peninsula (API). Together, these processes caused the progressive retreat of glaciers and ice shelves and weakened their efficient buttressing force causing widespread ice flow accelerations. Holding ~91% of the global ice mass and 57.3 m of sea-level-equivalent, the Antarctic Ice Sheet is by far the largest potential contributor to future sea-level-rise. Despite the improved understanding of Antarctic ice dynamics, the future of Antarctica remains difficult to predict with its contribution to global sea-level-rise representing the largest uncertainty in current projections. Given that recent studies point towards atmospheric warming and melt intensification to become a dominant driver for future Antarctic ice mass loss, the monitoring of supraglacial lakes and their impacts on ice dynamics is of utmost importance. In this regard, recent progress in Earth Observation provides an abundance of high-resolution optical and Synthetic Aperture Radar (SAR) satellite data at unprecedented spatial and temporal coverage and greatly supports the monitoring of the Antarctic continent where ground-based mapping efforts are difficult to perform. As an automated mapping technique for supraglacial lake extent delineation in optical and SAR satellite imagery as well as a pan-Antarctic inventory of Antarctic supraglacial lakes at high spatial and temporal resolution is entirely missing, this thesis aims to advance the understanding of Antarctic surface hydrology through exploitation of spaceborne remote sensing. In particular, a detailed literature review on spaceborne remote sensing of Antarctic supraglacial lakes identified several research gaps including the lack of (1) an automated mapping technique for optical or SAR satellite data that is transferable in space and time, (2) high-resolution supraglacial lake extent mappings at intra-annual and inter-annual temporal resolution and (3) large-scale mapping efforts across the entire Antarctic continent. In addition, past method developments were found to be restricted to purely visual, manual or semi-automated mapping techniques hindering their application to multi-temporal satellite imagery at large-scale. In this context, the development of automated mapping techniques was mainly limited by sensor-specific characteristics including the similar appearance of supraglacial lakes and other ice sheet surface features in optical or SAR data, the varying temporal signature of supraglacial lakes throughout the year as well as effects such as speckle noise and wind roughening in SAR data or cloud coverage in optical data. To overcome these limitations, this thesis exploits methods from artificial intelligence and big data processing for development of an automated processing chain for supraglacial lake extent delineation in Sentinel-1 SAR and optical Sentinel-2 satellite imagery. The combination of both sensor types enabled to capture both surface and subsurface lakes as well as to acquire data during cloud cover or wind roughening of lakes. For Sentinel-1, a deep convolutional neural network based on residual U-Net was trained on the basis of 21,200 labeled Sentinel-1 SAR image patches covering 13 Antarctic regions. Similarly, optical Sentinel-2 data were collected over 14 Antarctic regions and used for training of a Random Forest classifier. Optical and SAR classification products were combined through decision-level fusion at bi-weekly temporal scale and unprecedented 10 m spatial resolution. Finally, the method was implemented as part of DLR’s High-Performance Computing infrastructure allowing for an automated processing of large amounts of data including all required pre- and postprocessing steps. The results of an accuracy assessment over independent test scenes highlighted the functionality of the classifiers returning accuracies of 93% and 95% for supraglacial lakes in Sentinel-1 and Sentinel-2 satellite imagery, respectively. Exploiting the full archive of Sentinel-1 and Sentinel-2, the developed framework for the first time enabled the monitoring of seasonal characteristics of Antarctic supraglacial lakes over six major ice shelves in 2015-2021. In particular, the results for API ice shelves revealed low lake coverage during 2015-2018 and particularly high lake coverage during the 2019-2020 and 2020-2021 melting seasons. On the contrary, East Antarctic ice shelves were characterized by high lake coverage during 2016-2019 and extremely low lake coverage during the 2020-2021 melting season. Over all six investigated ice shelves, the development of drainage systems was revealed highlighting an increased risk for ice shelf instability. Through statistical correlation analysis with climate data at varying time lags as well as annual data on Southern Hemisphere atmospheric modes, environmental drivers for meltwater ponding were revealed. In addition, the influence of the local glaciological setting was investigated through computation of annual recurrence times of lakes. Over both ice sheet regions, the complex interplay between local, regional and large-scale environmental drivers was found to control supraglacial lake formation despite local to regional discrepancies, as revealed through pixel-based correlation analysis. Local control factors included the ice surface topography, the ice shelf geometry, the presence of low-albedo features as well as a reduced firn air content and were found to exert strong control on lake distribution. On the other hand, regional controls on lake evolution were revealed to be the amount of incoming solar radiation, air temperature and wind occurrence. While foehn winds were found to dictate lake evolution over the API, katabatic winds influenced lake ponding in East Antarctica. Furthermore, the regional near-surface climate was shown to be driven by large-scale atmospheric modes and teleconnections with the tropics. Overall, the results highlight that similar driving factors control supraglacial lake formation on the API and EAIS pointing towards their transferability to other Antarctic regions.
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Ice shelves surrounding the Antarctic perimeter moderate ice discharge towards the ocean through buttressing. Ice-shelf evolution and integrity depend on the local surface accumulation, basal melting and on the spatially variable ice-shelf viscosity. These components of ice-shelf mass balance are often poorly constrained by observations and introduce uncertainties in ice-sheet projections. Isochronal radar stratigraphy is an observational archive for the atmospheric, oceanographic and ice-flow history of ice shelves. Here, we predict the stratigraphy of locally accumulated ice on ice shelves with a kinematic forward model for a given atmospheric and oceanographic scenario. This delineates the boundary between local meteoric ice (LMI) and continental meteoric ice (CMI). A large LMI to CMI ratio hereby marks ice shelves whose buttressing strength is more sensitive to changes in atmospheric precipitation patterns. A mismatch between the steady-state predictions of the kinematic forward model and observations from radar can highlight inconsistencies in the atmospheric and oceanographic input data or be an indicator for a transient ice-shelf history not accounted for in the model. We discuss pitfalls in numerical diffusion when calculating the age field and validate the kinematic model with the full Stokes ice-flow model Elmer/Ice. The Roi Baudouin Ice Shelf (East Antarctica) serves as a test case for this approach. There, we find a significant east–west gradient in the LMI / CMI ratio. The steady-state predictions concur with observations on larger spatial scales (>10 km), but deviations on smaller scales are significant, e.g., because local surface accumulation patterns near the grounding zone are underestimated in Antarctic-wide estimates. Future studies can use these mismatches to optimize the input data or to pinpoint transient signatures in the ice-shelf history using the ever growing archive of radar observations of internal ice stratigraphy.
Recent studies indicate that - due to climate change - the Earth is undergoing rapid changes in all cryospheric components, including polar sea ice shrinkage, mountain glacier recession, thawing permafrost, and diminishing snow cover. This book provides a comprehensive summary of all components of the Earth's cryosphere, reviewing their history, physical and chemical characteristics, geographical distributions, and projected future states. This new edition has been completely updated throughout, and provides state-of-the-art data from GlobSnow-2 CRYOSAT, ICESAT, and GRACE. It includes a comprehensive summary of cryospheric changes in land ice, permafrost, freshwater ice, sea ice, and ice sheets. It discusses the models developed to understand cryosphere processes and predict future changes, including those based on remote sensing, field campaigns, and long-term ground observations. Boasting an extensive bibliography, over 120 figures, and end-of-chapter review questions, it is an ideal resource for students and researchers of the cryosphere.
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Ice shelves buttress ice streams and glaciers, slowing the rate at which they flow into the ocean. When this buttressing is reduced, either through increased melt or calving, the increased discharge of grounded ice upstream contributes to sea level rise. The thickness, strength, and stability of ice shelves can be influenced by channels in the ice base. Here, we focus on a subglacially sourced basal channel which is observed to have melted up to 50% of the ice shelf thickness. The channel extends 6 km upstream of the previously estimated grounding line of the stagnant Kamb Ice Stream. Using a combination of ground–based observations and remote sensing, we find that the channel is growing upstream over time. Over–snow radar surveying images the shape of the channel, constrains a steep inception, and shows that not all of the basal shape is manifest at the surface. Modern surface lowering at the upstream head of the channel is interpreted as a region of focused melt where a subglacial outlet meets the ocean cavity. We estimate this basal melt to be at least 35 m/a in a narrow (200 m × 1.5 km) zone. Downstream from the melt region, repeat phase sensitive radar observations reveal accretion contributing to the growth of a ledge on the true–right side of the channel. We conclude that the channel is likely formed by a retreating subglacial outlet which enhances basal melt episodically.
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We investigate the atmospheric drivers of extreme precipitation over the Amundsen Sea Embayment (ASE) of West Antarctica (WA) using daily output from the RACMO2 model and reanalysis data (1979–2016). Overall, 93.7% of days with extreme precipitation at the two coastal stations of ASE are associated with the four dominant Empirical Orthogonal Function (EOF) modes of geopotential height anomalies (at 850 hPa) over WA. The second EOF mode, associated with a coupled pattern consisting of an Amundsen Sea Low and a blocking high to the east, is the main driver of extreme precipitation over ASE, linked to 44.75% of extreme precipitation days. This is followed by EOF‐3 (associated with El Niño Southern Oscillation/PSA‐1), EOF‐4 (likely associated with more frequent “atmospheric river” events), and EOF‐1 (i.e., Southern Annular mode) with a contribution of 22.16%, 21.1%, and 12%, respectively. Extreme precipitation linked to EOF‐2 and EOF‐4 is more intense (by ∼2 mm/day) than the rest.
The steric height (StH) and heat content (HC) are the integral variables most used as a measure of change of sea level at global scale. This study was carried out based on six data sets of average temperature and salinity profiles collected during the years 1939, 1962, 1974, 1984, 2003 and 2011 along the Ballenas Channel in the northern section of the Gulf of California. In order to eliminate high frequency thermal variability and evaluate its contribution in deep layers, StH and HC were calculated for the columns of 0–1000 m, 200–1000 m, 500–1000 m and 700–1000 m. We estimated StH rates of 1.0 ± 0.18 mm year⁻¹, 0.54 ± 0.14 mm year⁻¹ and 0.30 ± 0.11 mm year⁻¹ for the 200–1000 m, 500–1000 m and 700–1000 m columns, respectively. In particular, the contribution of the 700–1000 m column ranged from 15% in 1984 to 37% in 2011. We also found that the increase in HC in 72 years for the column of 200–1000 m was 1.6 x 10⁹ Jm⁻² representing an average net heating rate of 0.7 W m⁻². As a comparison, the average net heating rate in the Greenland Sea was 5.9 W m⁻² for 13 years. This result may indicate that the marginal seas located below mid-latitudes could be less susceptible to the effects of climate change.
The Shirase Glacier, one of the fastest-flowing outlet glaciers in East Antarctica, flows into a bay that is usually covered by landfast sea ice. Although the presence of landfast ice is considered to stabilize glacier tongue, quantitative assessments are insufficient. We investigated the spatiotemporal variations in the flow velocities of both the glacier tongue and the surrounding landfast ice using a correlation method with ALOS-2/PALSAR-2 images, before and after a landfast-ice collapse in April 2017. After the collapse, the glacier tongue flow accelerated by 8–15% (0.20–0.38 km a⁻¹). After reestablishment of landfast ice, the flow velocity of the landfast ice in contact with the glacier terminus is 20%–90% of the flow velocity of the glacier terminus The estimates of interactive changes in the motions of both glacier ice and sea ice can be used to quantify the buttress effect, which is a key factor affecting the mass budget of Antarctic outlet glaciers.
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The occurrence of Antarctic ablation areas in Dronning Maud Land, the Lambert Glacier Basin, Victoria Land, the Transantarctic Mountains and the Antarctic Peninsula is realistically predicted by the regional atmospheric climate model RACMO2/ANT, with snowdrift-related processes calculated offline. Antarctic ablation areas are characterized by a low solid precipitation flux in combination with strong sublimation, snowdrift erosion and/or melt. The strong interaction between atmospheric circulation and topography plays a decisive role in the precipitation distribution and hence that of ablation areas. Three types of Antarctic ablation areas can be distinguished, all occurring in dry regions: Type 1 is the erosion-driven ablation area, caused by 1-D and/or 2-D divergence in the katabatic wind field at high elevations (2000-3200 m asl). Type 2 is the sublimation-driven ablation area. This type occurs at lower elevations (
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Over the last half century, the Antarctic Peninsula (AP) has been among the most rapidly warming regions on Earth. This has led to increased summer snowmelt, loss of ice shelves, and retreat of 87% of marine and tidewater glacier fronts. Tidewater-glacier flow is sensitive to changes in basal water supply and to thinning of the terminus, and faster flow leads directly to sea level rise. The flow rates of most AP tidewater glaciers have never been measured, however, and hence their dynamic response to the recent changes is unknown. We present repeated flow rate measurements from over 300 glaciers on the AP west coast through nine summers from 1992 to 2005. We show that the flow rate increased by ~12% on average and that this trend is greater than the seasonal variability in flow rate. We attribute this widespread acceleration trend not to meltwater-enhanced lubrication or increased snowfall but to a dynamic response to frontal thinning. We estimate that as a result, the annual sea level contribution from this region has increased by 0.047 +/- 0.011 mm between 1993 and 2003. This contribution, together with previous studies that assessed increased runoff from the area and acceleration of glaciers resulting from the removal of ice shelves, implies a combined AP contribution of 0.16 +/- 0.06 mm yr-1. This is comparable to the contribution from Alaskan glaciers, and combined with estimated mass loss from West Antarctica, is probably large enough to outweigh mass gains in East Antarctica and to make the total Antarctic sea level contribution positive.
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Satellite radar interferometry data from 1995 to 2004, and airborne ice thickness data from 2002, reveal that the glaciers flowing into former Wordie Ice Shelf, West Antarctic Peninsula, discharge 6.8 +/- 0.3 km3/yr of ice, which is 84 +/- 30 percent larger than a snow accumulation of 3.7 +/- 0.8 km3/yr over a 6,300 km2 drainage basin. Airborne and ICESat laser altimetry elevation data reveal glacier thinning at rates up to 2 m/yr. Fifty km from its ice front, Fleming Glacier flows 50 percent faster than it did in 1974 prior to the main collapse of Wordie Ice Shelf. We conclude that the glaciers accelerated following ice shelf removal, and have been thinning and losing mass to the ocean over the last decade. This and other observations suggest that the mass loss from the northern part of the Peninsula is not negligible at present.
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A detailed comparison of model-simulated and observed Antarctic surface mass balance (SMB) is presented, using output of a regional atmospheric climate model (RACMO2/ANT) for the period 1980 to 2004. All available SMB observations from Antarctica (N = 1900) are used for the comparison, except clearly erroneous observations and data which are in areas where dominant SMB patterns occur on scales smaller than the model resolution. A high correlation is found (r = 0.82), while the regression slope (1.2) indicates that the model slightly overemphasizes SMB gradients. Comparing the model SMB with the latest SMB compilation, a similarly high correlation is found (r = 0.79), but the regression slope is much too steep because model-simulated SMB agrees less with the compilation in data-sparse regions. Model-simulated SMB resembles the observed SMB as a function of elevation very well. This is used to calibrate model-simulated SMB to reassess the contemporary Antarctic SMB. Compared to the latest SMB compilation, calibrated model-simulated SMB is up to 1 m yr-1 higher in the coastal zones of East and West Antarctica, which are without exception in areas with few observations. As a result, the SMB integrated over the grounded ice sheet (171 +/- 3 mm yr-1) exceeds previous estimates by as much as 15%. Support or falsification of this model result can only be found in new SMB observations from high accumulation regions.
An isopleth map showing the spatial distribution of net mass accumulation at the surface on the Antarctic ice sheet, excluding Graham Land, the Larsen Ice Shelf and eastern Palmer Land, is produced based on field data from approximately 2000 sites. A database of accumulation values for 5365 gridpoint locations with 50 km spacing is interpolated from the isopleth map, giving a bulk accumulation of 2151 Gt a-1 and a mean of 159 kg m-2 a-1 for an area of 13.53 x 106 km2. Following the implementation of deflation and ablation adjustments applicable to sectors of the coastal zone the accumulation values are reduced to 2020 Gt a-1 and 149 kg m-2 a-1. The new accumulation distribution is compared with another recent distribution, which was based on essentially the same field data using different analysis and interpolation criteria. Differences between the distributions are assessed using residuals for the 50 km gridpoint locations and by comparing average accumulation values for 24 drainage systems. The assessment based on residuals indicates that the two distributions show patterns of accumulation that are coherent at the continental scale, a shared attribute underscored by a small mean residual value of 6 kg m-2a-1 (a difference of <4%). However the regional assessment based on average accumulation values for the drainage systems shows differences that are larger than the assessment error (≥22%) for six systems that collectively comprise approximately 4/10 of the ice-sheet area and 3/10 of the accumulation.
Volume changes of the Greenland and Antarctic ice sheets have the potential to sig- nificantly contribute to global sea-level changes in future warmer climates. The most crucial aspects are how climatic changes will affect the ice sheet's mass balance and how ice dynamics will react to the imposed environmental forcing. This is in addition to the longer-term background trend from adjustments as far back as the last glacial period. Here we focus on model predictions for the 20th and 21st centuries using 3- D thermomechanical ice sheet/ice shelf models driven by climate scenarios obtained from AOGCMs. We scaled high-resolution patterns from the ECHAM4 and HadAM3 time slice integrations with time series from a variety of lower-resolution AOGCM runs to obtain the spread of results for a similar emission scenario. Particular atten- tion is paid to the technique of pattern-scaling and on how GCM based predictions differ from older ice-sheet model results based on more parameterised mass-balance treatments. As a general result, it is found that the effect of increased precipitation on Antarctica clearly dominates over the effect of increased melting on Greenland for the entire range of predictions, implying that both polar ice sheets combined would con- tribute more negatively to sea-level in the 21st century than often thought. The results are very similar for both time-slice patterns driven by their underlying time evolution series, with most of the scatter in the results provided by the variability in the lower- resolution AOGCMs. These findings will be discussed in the broader framework of current-day model results and of the IPCC TAR sea-level predictions in particular.
Different parts of Antarctica receive different amounts of snowfall each year. In this paper we map the variations of the mean annual snow accumulation across the ice sheet. We also quantify the uncertainty in our estimates more objectively than has been possible for earlier maps. The new map is produced using observations from satellites and ground-based measurements. After a logarithmic transformation, these are combined using the geostatistical method of continuous-part universal kriging to give an estimate of the snow accumulation within each cell of a rectangular grid covering Antarctica. We also derive spatial averages over the major drainage systems of the ice sheet, along with their confidence intervals. We obtain a value of 143 $pm$ 4 kg m2 a1 for the average rate of snow accumulation upon the grounded ice sheet of Antarctica.
An isopleth map showing the spatial distribution of net mass accumulation at the surface on the Antarctic ice sheet, excluding Graham Land, the Larsen Ice Shelf and eastern Palmer Land, is produced based on field data from approximately 2000 sites. A database of accumulation values for 5365 gridpoint locations with 50 km spacing is interpolated from the isopleth map, giving a bulk accumulation of 2151 Gt a−1 and a mean of 159 kg m−2a−1 for an area of 13.53 × 106 km2. Following the implementation of deflation and ablation adjustments applicable to sectors of the coastal zone, the accumulation values are reduced to 2020 Gt a−1 and 149 kg m−2a−1. The new accumulation distribution is compared with another recent distribution, which was based on essentially the same field data using different analysis and interpolation criteria. Differences between the distributions are assessed using residuals for the 50 km gridpoint locations and by comparing average accumulation values for 24 drainage systems. The assessment based on residuals indicates that the two distributions show patterns of accumulation that are coherent at the continental scale, a shared attribute underscored by a small mean residual value of 6 kg m−2a−1 (a difference of <4%). However, the regional assessment based on average accumulation values for the drainage systems shows differences that are larger than the assessment error (≥22%) for six systems that collectively comprise approximately 4/10 of the ice-sheet area and 3/10 of the accumulation.
Pine Island Glacier, flowing into the Amundsen Sea from West Antarctica, thinned substantially during the 1990s, its grounding line receded by several km, and its velocity increased by >10% to values approaching 3 km a−1. Here, we use these observations, together with estimates of ice thickness and surface strain rates, to estimate the perturbation in forces resisting ice flow compatible with the observations. The analysis assumes that such perturbations are transmitted far upstream from where they originate, and that creep response to the perturbations can be described by equations similar to those that govern ice-shelf creep. It indicates that observed acceleration between 1996 and 2000 could have been caused by progressive ungrounding within the most seaward 25 km 'ice plain' of the grounded glacier. Earlier retreat and thinning of the glacier's floating ice shelf may have provided the conditions that initiated ungrounding of the ice plain. Our analysis indicates that continued ice-plain thinning at the current rate of about 2 m a−1 will result in a velocity increase by 1 km a−1 within the next 11 years as the ice plain becomes totally ungrounded.