The Cryosphere, 16, 3907–3932, 2022
https://doi.org/10.5194/tc-16-3907-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Seasonal land-ice-flow variability in the Antarctic Peninsula
Karla Boxall1, Frazer D. W. Christie1, Ian C. Willis1, Jan Wuite2, and Thomas Nagler2
1Scott Polar Research Institute, University of Cambridge, Cambridge, UK
2ENVEO IT GmbH, Innsbruck, Austria
Correspondence: Karla Boxall (kb621@cam.ac.uk)
Received: 4 March 2022 – Discussion started: 14 March 2022
Revised: 20 August 2022 – Accepted: 26 August 2022 – Published: 6 October 2022
Abstract. Recent satellite-remote sensing studies have doc-
umented the multi-decadal acceleration of the Antarctic
Ice Sheet in response to rapid rates of ice-sheet retreat
and thinning. Unlike the Greenland Ice Sheet, where his-
torical, high-temporal-resolution satellite and in situ ob-
servations have revealed distinct changes in land-ice flow
within intra-annual timescales, observations of similar sea-
sonal signals are limited in Antarctica. Here, we use high-
spatial- and high-temporal-resolution Copernicus Sentinel-
1A/B synthetic aperture radar observations acquired between
2014 and 2020 to provide the first evidence for seasonal
flow variability of the land ice feeding George VI Ice Shelf
(GVIIS), Antarctic Peninsula. Our observations reveal a dis-
tinct austral summertime (December–February) speed-up of
∼ 0.06± 0.005 m d−1 (∼ 22± 1.8 m yr−1) at, and immedi-
ately inland of, the grounding line of the glaciers nourish-
ing the ice shelf, which constitutes a mean acceleration of
∼ 15 % relative to baseline (time-series-averaged) rates of
flow. These findings are corroborated by independent, opti-
cally derived velocity observations obtained from Landsat
8 imagery. Both surface and oceanic forcing mechanisms
are outlined as potential controls on this seasonality. Ulti-
mately, our findings imply that similar surface and/or ocean
forcing mechanisms may be driving seasonal accelerations
at the grounding lines of other vulnerable outlet glaciers
around Antarctica. Assessing the degree of seasonal ice-flow
variability at such locations is important for quantifying ac-
curately Antarctica’s future contribution to global sea-level
rise.
1 Introduction
Three decades of routine Earth observation have revealed the
progressive decay of the Antarctic Ice Sheet, evinced by ac-
celerated rates of ice thinning, retreat, and flow (Gardner et
al., 2018; Konrad et al., 2018; The IMBIE Team, 2018; Rig-
not et al., 2019). This phenomenon has been ascribed to an
array of atmospheric and oceanic forcing mechanisms im-
pinging upon the continent (Rignot et al., 2004; Thoma et al.,
2008; Cook and Vaughan, 2010; Joughin et al., 2012a; Steig
et al., 2012; Dutrieux et al., 2014; Paolo et al., 2018), from
which resulting land-ice losses are estimated to have totalled
an average of ∼ 109± 59 Gt yr−1 between 1992 and 2017
(The IMBIE Team, 2018). Alongside satellite-altimetry- and
gravimetry-based assessments of ice-mass change, this trend
has partly been constrained by satellite-derived velocity mea-
surements acquired sporadically throughout the year (Rig-
not et al., 2011a; Mouginot et al., 2012), under the implicit
(and unverified) assumption that no discernible intra-annual
(i.e. seasonal or shorter) variability in ice flow exists (Greene
et al., 2018). In terms of intra-annual ice-flow variability,
an overall dearth of systematic, high-temporal-resolution ob-
servations has also limited the ability to examine for such
changes across Antarctica; this is in contrast to mid-latitude
and Arctic ice masses, where the timing and large magnitude
of seasonal ice-flow variability are now well observed (Iken
et al., 1983; Hooke et al., 1989; Zwally et al., 2002; Moon
et al., 2014; Kraaijenbrink et al., 2016; King et al., 2018).
Within the context of recent ice-sheet modelling and mass
balance exercises (The IMBIE Team, 2018; Seroussi et al.,
2020; Edwards et al., 2021), knowledge of any intra-annual
variations in ice flow is critical for elucidating the processes
controlling Antarctica’s evolution in a changing climate.
Published by Copernicus Publications on behalf of the European Geosciences Union.
3908 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
In this study, we find evidence of seasonal ice-flow vari-
ability across the glaciers feeding the George VI Ice Shelf,
Antarctic Peninsula. We use 6/12 d repeat-pass Copernicus
Sentinel-1A/B synthetic aperture radar imagery to observe
this variability, together with independent, 16 d repeat-pass
observations acquired by the Landsat 8 Operational Land Im-
ager. We then evaluate the potential mechanisms responsible
for driving the observed seasonal ice-flow signals upstream
of George VI Ice Shelf.
2 George VI Ice Shelf
In this study, we investigate seasonal ice-flow variability
across 21 glaciers feeding the glaciologically compressive
George VI Ice Shelf (GVIIS) (Fig. 1). After Larsen C Ice
Shelf, GVIIS is the second largest of the remaining ice
shelves fringing the Antarctic Peninsula (Holt et al., 2013),
and it has an areal extent of ∼ 23500 km2. Ice-shelf flow bi-
furcates and advects towards both its northern (Marguerite
Bay) and southern (Ronne Entrance) ice fronts at an av-
erage rate of 0.7 m d−1 (255 m yr−1), with flow averaging
0.08 m d−1 (30 m yr−1) and 1.1 m d−1 (400 m yr−1) along
its Alexander Island and Palmer Land margins, respectively
(Fig. 1). The thickness of the ice shelf ranges between ap-
proximately 100 and 600 m (Morlighem et al., 2020).
Elsewhere in the Antarctic Peninsula, historical satellite
observations have documented the abrupt, climate-driven
disintegration of several ice shelves and the consequent dy-
namic acceleration of upstream glacial ice (Rott et al., 1996;
Rack and Rott, 2004; Rignot et al., 2004; Scambos et al.,
2004); this acceleration has increased Antarctica’s net contri-
bution to global sea level (Scambos et al., 2004; The IMBIE
Team, 2018). These events have been attributed primarily to
the surface-warming-induced presence of supraglacial melt-
water lakes (Dirscherl et al., 2021; Dell et al., 2022), which
are surmised to have instigated a process of rapid ice-shelf
hydrofracture and collapse such as that observed most re-
cently at Wilkins Ice Shelf (Fig. 1; Scambos et al., 2000,
2009; Banwell et al., 2013; Leeson et al., 2020). These phe-
nomena have, in turn, been linked to an Antarctic-Peninsula-
wide increase in surface temperatures over most of the ob-
servational record (Vaughan et al., 2003). At GVIIS, in-
tense supraglacial meltwater presence has been observed
over the ice shelf since routine satellite observations began
(Kingslake et al., 2017; Bell et al., 2018; Banwell et al.,
2021), and melt season duration is one of the longest on
the continent. This has led to the identification of GVIIS as
a potential site for future ice-shelf disintegration (Holt and
Glasser, 2022); recent modelling studies suggest that resul-
tant land-ice losses associated with such an event would con-
tribute an ∼ 8 mm rise in global sea level by 2100 (Schan-
nwell et al., 2018).
In addition to the effects of supraglacial meltwater, ocean-
driven basal melting has had, and will likely continue to have,
an important role in the evolution of GVIIS and its upstream
glaciers (Pritchard et al., 2009; Holt et al., 2013; Paolo et
al., 2015; Naughten et al., 2018). This is due to the flooding
of relatively warm (∼ 1–2 ◦C), high-salinity (∼ 34.7 PSU)
circumpolar deep water (CDW) beneath and offshore from
GVIIS’ sub-ice-shelf cavity (Jenkins et al., 2010; Jenkins
and Jacobs, 2008). Similar to the high rates of ice-shelf
melting observed across the Amundsen and western Belling-
shausen sectors in the past (Pritchard et al., 2012; Rignot et
al., 2013), this CDW presence has driven basal melting of
up to 7 m yr−1 at GVIIS over the satellite era (Paolo et al.,
2015; Adusumilli et al., 2018) and has been implicated as
the primary mechanism responsible for the multi-decadal ac-
celeration of GVIIS’ fastest-flowing feeder glaciers (Hogg et
al., 2017; Gardner et al., 2018; Winter et al., 2020).
3 Data and methods
We use high-spatial- and high-temporal-resolution, all-
weather, day/night imaging, Copernicus Sentinel-1A/B syn-
thetic aperture radar (SAR) observations as our primary data
source to both survey the seaward extent of the outlet glaciers
feeding GVIIS and examine for seasonal variability in their
flow. The methods used for these purposes are detailed be-
low.
3.1 Grounding line delineation
Grounding lines are sensitive indictors of climate change
and represent the boundary between seaward-flowing, ter-
restrial ice and adjoining, floating ice shelves. In parts of
West Antarctica – including the Amundsen and western
Bellingshausen sectors especially – grounding lines have re-
treated pervasively in response to ocean forcing over the
past ∼ 50 years (Park et al., 2013; Rignot et al., 2014;
Christie et al., 2016, 2018; Konrad et al., 2018). In com-
parison to the detailed knowledge of grounding-line mi-
gration within these sectors, however, there have been no
high-resolution, spatially complete grounding line surveys at
GVIIS since the mid-1990s (Rignot et al., 2016). Accurate
and updated knowledge of GVIIS’ grounding line is there-
fore of paramount importance for distinguishing precisely
between grounded and floating ice.
To recover the location of GVIIS’ modern-day ground-
ing line, we employed double-differential interferometric
SAR (DInSAR) processing techniques to all consecutive 6 d
repeat-pass Sentinel-1A/B Interferometric Wide (IW) single
look complex (SLC) images acquired during extended aus-
tral wintertime (May–October) 2020. Extended austral win-
tertime imagery was used to maximise phase coherence be-
tween successive image pairs which may be degraded due to
the attenuation of radar waves by summertime supraglacial
water presence (Sect. 2). Similar to earlier work (Park et al.,
2013; Rignot et al., 2014; Christie et al., 2016, 2022), we co-
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3909
Figure 1. Mean ice flow of George VI Ice Shelf (GVIIS) and its drainage basins derived from Sentinel-1A/B synthetic-aperture-radar-derived
observations acquired between 2019 and 2020. Where data coverage exists, the landward margins of GVIIS are delineated using the modern-
day (2018–2020) grounding line (black) and, elsewhere, its position as imaged in the 1990s (pink; Sect. 3.1). Drainage basin limits are from
Mouginot et al. (2017). Background DEM is the Reference Elevation Model of Antarctica (Howat et al., 2019). Numbered flowlines delimit
the centreline of the fastest-flowing outlet glaciers draining to GVIIS (named according to the UK Antarctic Place-names Committee), and
the 10 km2 dashed boxes located inland of the grounding line of these glaciers indicate the averaging regions used in the production of Figs. 4,
5, C1, and D1. Map projection: EPSG:3031. Inset map shows location. Arrow indicates the region of dominant CDW inflow as inferred from
in situ ocean observations (Jenkins and Jacobs, 2008; see Sect. 5.2 for further discussion).
registered each successive image pair and removed the topo-
graphical component of phase from all subsequently gener-
ated interferograms using the Reference Elevation Model of
Antarctica (REMA; Howat et al., 2019). Assuming ice creep
to be common between each SAR image, we then differenced
all successive interferograms to locate the limit of tidally in-
duced vertical ice-shelf flexure. This limit is represented as
the landward extent of a band of closely spaced fringes on
double-differenced interferograms (Rignot et al., 2011b) and
is an accurate proxy for the true grounding line which cannot
be recovered directly by satellite-based imaging techniques
(Fricker et al., 2009; Friedl et al., 2020). Finally, all interfer-
ograms were geocoded to EPSG:3031 (Antarctic Polar Stere-
ographic projection) using REMA.
To supplement our grounding line observations across re-
gions of poor phase coherence during extended wintertime
2020, we filled data gaps using recently generated (albeit spa-
tially discontinuous) Sentinel-1-derived grounding line infor-
mation from 2018 (Mohajerani et al., 2021a). Where this was
not possible, for example in areas of phase aliasing resulting
from very fast ice flow exceeding '1.6 m d−1, we used the
1994–1996 MEaSUREs grounding line dataset (Rignot et al.,
2016; pink grounding lines in Fig. 1).
3.2 Derivation of ice velocity
Land-ice velocities upstream of GVIIS were retrieved from
all successive Sentinel-1 IW SLC image pairs acquired be-
tween October 2014 and August 2020 using a combination
of coherent and incoherent offset tracking techniques as de-
scribed in Nagler et al. (2015, 2021) and Wuite et al. (2015).
These 12 d repeat-pass image pairs, which reduced to 6 d re-
peat following the launch of Sentinel-1B in April 2016, were
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3910 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
then used to produce monthly composites of mean ice flow
and associated grids of uncertainty (1σ ) and valid pixel count
(the number of non-NaN observations used in the production
of each monthly estimate). If during composite creation an
image pair crossed into the neighbouring month between the
reference and secondary images used to retrieve ice velocity,
then it was weighted accordingly (Nagler et al., 2015; Wuite
et al., 2015). Final grids of monthly ice velocity, uncertainty,
and pixel count were outputted with a posting of 200 m and,
like our double-difference interferograms, were geocoded
from radar coordinates to EPSG:3031 (Antarctic Polar Stere-
ographic) projection (Sect. 3.1). Using our updated GVIIS
grounding line product (Sect. 3.1 and Fig. 1), floating ice was
subsequently masked from all monthly velocity grids. Prox-
imal to the grounding line, where uncertainties associated
with offset tracking techniques are typically much smaller
relative to inland areas characterised by steep terrain and/or
slow-flowing ice (Mouginot et al., 2012), the mean per-pixel
standard error totals 0.005 m d−1 (1.8 m yr−1). This value is
comparable to that of other SAR-derived velocity products
(Rignot et al., 2017; Friedl et al., 2021) and was calculated
for each pixel according to
SE= σ√
n
, (1)
where SE denotes standard error, and σ and n denote the
standard deviation and valid pixel count, respectively.
We averaged velocities over monthly timescales to min-
imise contamination associated with ionospheric and tro-
pospheric delay between successive (6/12 d repeat pass)
Sentinel-1 image acquisitions (Rosen et al., 2000; Selley
et al., 2021). Moreover, across the Antarctic Peninsula,
Sentinel-1A/B acquisitions are currently acquired in de-
scending mode only (ESA, 2022), meaning that the velocity
data utilised in this study represent the relative displacement
of point targets from a single look angle only. Recent work
has shown that below sub-monthly resolution, these veloc-
ities can be subject to bias owing to radar penetration dif-
ferences associated with the freezing state of the snow–firn–
ice interface between image acquisitions (Rott et al., 2020).
This phenomenon can induce shifts in the radar line-of-sight
(LOS) distance to target by up to several metres (Joughin et
al., 2018; Rott et al., 2020), resulting in either an under- or
overestimation of velocities depending on the flow direction
of the ice relative to LOS (Rott et al., 2020). At present, re-
liable, sub-monthly estimates of the magnitude of this bias
over the western Antarctic Peninsula are difficult to model
owing to a lack of detailed in situ information on the tempo-
rally variable composition of the firn layer; for these reasons,
monthly composites were also utilised to dampen the effect
of this bias.
3.3 Detection of seasonal ice-flow variability
We examined for seasonal variations in ice flow using the
methodology summarised in Fig. 2. First, we examined for
spatial patterns of seasonal flow variability in our SAR-
derived velocity datasets. We then supplemented our find-
ings with intra-annual time-series analyses at and proximal
to the grounding line and corroborated observed patterns of
ice-flow variation using independent satellite-derived obser-
vations. The methods associated with these analyses are dis-
cussed in Sect. 3.3.1, 3.3.2, and 3.3.3, respectively.
3.3.1 Spatial patterns of flow
Following Fig. 2, for each year spanning 2014–2019, we
first constrained the month in which observed velocities were
greatest on a pixelwise basis throughout the entire domain,
vtime. This step was carried out to determine (a) if a spa-
tially coherent signal of ice-flow change was present up-
stream of GVIIS and (b) the season in which such a phe-
nomenon occurred. Next, for each year (considered here to
span December–November, i.e. four complete seasons), the
relative magnitude of any observed flow change was quanti-
fied in the form of a “seasonal anomaly”, vanomSAR . This met-
ric is defined as the difference in median velocity for the sea-
son (either December–February, DJF, March–May, MAM,
June–August, JJA, or September–November, SON) in which
vtime occurred and the median across all other seasons that
year. In the case of an austral summertime speed-up associ-
ated with a maximum velocity observation in either Decem-
ber, January, or February, for example, vanomSAR is given by
vanomSAR = ˜(v12,v1,v2)− ˜(v3, . . .,v11) , (2)
where vn denotes the velocity magnitude as observed during
month n (v1, January; . . . ; v12, December). Using this no-
tation, positive vanomSAR indicates a greater median velocity
during austral summertime compared with all other seasons.
In our calculation of vtime and vanomSAR above, pixels without
continuous monthly data coverage throughout the entire year
were culled from our analyses. Pixels where the velocity fell
within standard error bounds (Sect. 3.2) were also discarded.
Notably, the calculation of either metric was not possible for
2019/20 given the lack of velocity data beyond August 2020
(Sect. 3.2).
3.3.2 Flow variability near the grounding line of
GVIIS’ fast-flowing outlet glaciers
To supplement our spatially resolved observations discussed
above, we examined in greater detail the temporal varia-
tions in ice flow near the coast of the outlet glaciers shown
in Fig. 1. To do this, we calculated mean velocity and
mean standard error (Sect. 3.2) at monthly intervals within
a 10 km2 region located directly upstream of the ground-
ing line between 2014 and 2020. Glaciers without suffi-
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3911
Figure 2. Workflow detailing the establishment of spatial patterns of flow (Sect. 3.3.1), the assessment of flow variability near the grounding
line (Sect. 3.3.2), and the corroboration with optical satellite observations (Sect. 3.3.3).
cient modern-day grounding line coverage were excluded
from further analysis (Fig. 1; Sect. 3.1). Next, to accentu-
ate intra-annual trends, we removed long-term trends in ve-
locity over each 10 km2 region (Hogg et al., 2017; Gardner
et al., 2018) using a 13-month moving average. At flow-
lines where the removal of a 13-month moving average was
not possible, for example due to gaps in the time series, the
data were excluded from our subsequent calculation of de-
trended monthly means. Finally, we quantified the dominant
frequency of these trends using discrete Fourier transform
analysis. In signal processing, this technique decomposes a
time domain function into the frequency domain, from which
its most dominant frequency (i.e. number of cycles per unit
of time) and phase and amplitude characteristics can be iden-
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3912 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
tified (Bracewell, 1978). To enable direct quantitative com-
parison between outlet glaciers, we fitted a cosine wave op-
timised to the most dominant frequency observed across all
outlet glacier time series and extracted their associated phase
and amplitude, which in this case denote the approximate
timing and magnitude of the seasonal velocity signals, re-
spectively.
3.3.3 Seasonal flow variability: corroboration with
optically derived satellite observations
To supplement our SAR-based observations, we also calcu-
lated a similar seasonal metric, vanomOLI , to that described in
Sect. 3.3.1 using independent, spatially and temporally col-
located velocity information derived from Landsat 8 Oper-
ational Land Imager (OLI) imagery acquired between 2014
and 2019 (Fig. 2). While Landsat 8’s sun-synchronous or-
bit precludes any imaging outside extended austral summer-
time (September–April), derived velocity observations dur-
ing this time offer an important dataset with which to cor-
roborate any summertime ice-flow variability observed in the
SAR record. This is due to the passive, on-nadir imaging
characteristics associated with OLI which, unlike the SAR-
based calculation of vanomSAR , are insensitive to any viewing
geometry or snow/firn penetration-related biases (Sect. 3.2).
Any agreement between calculated values of vanomSAR and
vanomOLI would therefore underscore the utility of our SAR-
based methodology to detect extended summertime signals
with confidence. Such agreement would, by extension, also
imply a high degree of certainty in our ability to quantify
ice-flow variability during non-daylight seasons.
We calculated vanomOLI using Landsat-8-derived velocity
grids acquired from the Inter-mission Time Series of Land
Ice Velocity and Elevation (ITS_LIVE) data archive (Gard-
ner et al., 2019). These grids were obtained for each unique
Landsat granule over GVIIS and its environs, and they repre-
sent velocities constrained by feature tracking of all succes-
sive 16 d repeat-pass Landsat 8 image pairs acquired between
January 2014 and April 2019 (Gardner et al., 2018, 2019). In
total, 817 image pairs were utilised in this study which, com-
parable to our Sentinel-1-derived velocity grids, have a mean
standard error of < 0.034 m d−1 (12.5 m yr−1) (Gardner et
al., 2018, 2019).
For a speed-up during the austral summertime (DJF), for
example, vanomOLI was calculated using
vanomOLI = ˜
(
v201412,1,2, . . .,v201912,1,2
)
− ˜(v20143,4,9,10,11, . . ., v20193,4,9,10,11) , (3)
where v201412,1,2 denotes the median velocity magnitude as
observed during December, January, and February 2014, re-
spectively, v201912,1,2 is the same but for 2019 (i.e. the end
of the observational record), and v20193,4,9,10,11 is the veloc-
ity magnitude spanning all non-summertime daylight months
for 2019. Similar to Eq. (2), positive vanomOLI would indi-
cate a greater median velocity observed during austral sum-
mertime than all other (non-summertime) daylight months.
Unlike in our derivation of vanomSAR , we calculated the me-
dian of all summer months over the entire 2014–2019 pe-
riod to maximise spatial coverage at the expense of temporal
resolution. This was due to the preponderance of clouds in
the Landsat record and resulting lack of velocity coverage at
monthly resolution. Prior to any further analysis, values of
vanomOLI falling within standard error (< 0.034 m d
−1) were
removed.
To corroborate our SAR-derived metric (Sect. 3.3.1), we
differenced vanomOLI from a second measure of vanomSAR
whose temporal limits were clipped to match the daylight
months imaged by Landsat 8 OLI (September–April; Fig. 2).
Like vanomOLI , this second metric, vanomSAR_day , was calculated
using Eq. (3), and all values lying within SAR-derived stan-
dard error bounds (±0.005 m d−1) were discarded during cal-
culation. Upon differencing vanomSAR_day and vanomOLI , we fil-
tered the resulting grid, vanomdiff , to remove pixels containing
unrealistically high values. Visual examination of the raw im-
age data associated with these pixels (not shown) reveal such
values to be associated with regions of frequent cloud cover
in the Landsat 8 OLI record, which result in more poorly re-
fined velocity estimates with a high standard error. For this
purpose, we discarded all instances of vanomdiff with a com-
bined error surpassing 0.034 m d−1 (12.5 m yr−1), calculated
from the mean standard errors associated with our optically
and SAR-derived datasets summed in quadrature. Finally, we
culled all pixels located within 10 km of the ice divide (where
signal-to-noise is often poor using offset tracking techniques;
Mouginot et al., 2012), as well as across regions of complex
topography (> 15◦ slope) and near-stagnant (< 0.013 m d−1)
flow as defined by, respectively, REMA DEM (Howat et al.,
2019) and an independent velocity dataset (Rignot et al.,
2017) (Fig. 2). All remaining valid pixels were then used to
produce a heatmap emphasising the spatial coherence of sea-
sonal ice-flow speed-up observed in both our optically and
SAR-derived velocity records.
4 Results
4.1 GVIIS’ grounding line
Our new 2018–2020 grounding line location compilation
(Sect. 3.1) provides an important update on GVIIS’ ge-
ometry, whose only other publicly available interferometric
SAR (InSAR)-based records were derived from observations
acquired in the mid-1990s (Rignot et al., 2016). In total,
our compilation provides revised grounding line information
across 76 % of GVIIS’ coastal margin (Fig. 1). From the mid-
1990s onwards, however, we detect no significant change in
grounding line location, with the position of the mid-1990s
grounding line falling firmly within the range of tidally in-
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3913
duced grounding line locations observed by Sentinel-1A/B
(1–5 km depending on the glacier; Fig. A1). In the following
sections, all results presented are derived from observations
located inland of the high-tide (i.e. most landward) 2018–
2020 grounding line location.
4.2 Seasonal ice-flow variability
4.2.1 Spatially resolved patterns of flow
Figures 3 and B1–B4 show clear seasonal ice-flow variabil-
ity along the entire GVIIS coastal margin, with the great-
est magnitude of seasonality clustered tightly at or proxi-
mal to the deep-bedded grounding lines of the fastest-flowing
outlet glaciers (Fig. 1). Along both the Alexander Island
and Palmer Land coasts, our calculation of vtime (i.e. the
month of maximum velocity; Sect. 3.3.1) reveals the ubiq-
uitous occurrence of ice-flow speed-up during austral sum-
mertime months (DJF; Figs. 3a and B1–B4a). This phe-
nomenon is mirrored in our calculation of vanomSAR (Figs. 3b
and B1–B4b), which provides a quantitative measure of the
observed ice-flow speed-up. For 33 % of the outlet glaciers
numbered in Fig. 1, observed summertime velocities are
≥ 0.1 m d−1 (36.5 m yr−1) faster than the median velocity of
all non-summertime seasons, and summertime velocity in-
creases near the grounding line of all (100 %) glaciers ex-
ceed error bounds (0.005 m d−1 (1.8 m yr−1)). Notably, no
coherent seasonal signal exists beyond ∼ 10–20 km of the
grounding line, where vtime is randomly distributed across
all 12 months, and vanomSAR shows no associated clustering.
4.2.2 Temporal flow variability near the grounding line
Near the modern-day grounding line, spatially averaged
observations of both raw (Fig. 4) and detrended (Fig. 5)
monthly ice flow between 2014 and 2020 are consistent
with the seasonal signals discussed above (Figs. 3 and B1–
B4). There, 94 % of the outlet glaciers underwent an average
summertime (DJF) speed-up of 0.06 m d−1 (min 0.02 m d−1;
max 0.15 m d−1; 1σ = 0.037 m d−1) or 21.9 m yr−1 (Figs. 5
and C1). On average, this corresponds to a ∼ 15 % sum-
mertime increase in ice flow relative to baseline (time-
series-averaged) rates along GVIIS’ grounding line. Fur-
thermore, our observations show that 88 % of the glaciers
underwent velocity minima (blue in Fig. 5) during non-
summertime months, and the error bounds associated with
all velocity minima fell firmly outside those corresponding
to velocity maxima (red in Fig. 5). Of the velocity pro-
files shown in Fig. 5, the only glaciers opposing this gen-
eral pattern were those corresponding to Flowlines 8 (Goode-
nough Glacier 1) and 10 (unnamed), which exhibit a bimodal
and non-summertime speed-up signal, respectively. The lat-
ter likely arises from poor temporal data coverage, while the
double peak associated with Flowline 8 is attributed to an
anomalous wintertime speed-up in 2016 which dominated
the mean velocity signal of this glacier.
Spatially, our results are also suggestive of an apparent
regional contrast in the timing of summertime speed-up,
whereby the glaciers nourishing GVIIS from Alexander Is-
land appear to have accelerated in early-to-mid summer-
time (December–January) compared to those draining from
Palmer Land (mid-to-late summertime/autumn (January–
April)) (Fig. 6). This finding is consistent with the ap-
proximate timing of peak velocity as determined from dis-
crete Fourier transform analysis (Appendix D), which fur-
ther reveals that most (88 %) outlet glaciers have a dom-
inant intra-annual signal characterised by a cosine wave
with a frequency of 1 (implying one complete seasonal
cycle per 12-month period; Fig. D1). Within this trend,
Figs. 4 and 5 also present evidence of the ability of se-
lect GVIIS’ outlet glaciers to influence each other across
the ice shelf, whereby the earlier acceleration of Alexander
Island’s glaciers initially arrest those flowing from Palmer
Land, delaying the onset of their acceleration until the late
summertime (compare, for example, the timing of peak ve-
locity at the geographically opposite Flowlines 21 and 3,
and Flowlines 20 and 4–5). Upon late-summertime acceler-
ation, Palmer Land’s glaciers then arrest the flow of those
on Alexander Island in a similar manner. Continued moni-
toring of this phenomenon beyond the current Sentinel-1A/B
observational record may shed additional light upon the im-
portance of this mechanism (or otherwise) for controlling the
seasonal signal exhibited at these outlet glaciers.
4.2.3 SAR vs. optical observations of flow
Figure 7 reveals strong agreement between our optically
and SAR-derived seasonal observations (i.e. vanomSAR_day and
vanomOLI ; Sect. 3.3.3), whereby summertime speed-ups are
observed in both datasets within ∼ 10–20 km of the ground-
ing line. There, coincident Sentinel-1- and Landsat-8-derived
speed-up observations are tightly clustered and confined to
the lateral dimensions of the fast-flowing outlet glaciers (con-
tours in Fig. 7). Upon examination of the velocity field
(Fig. 1) in conjunction with the phenomena observed in
Fig. 7, we infer the clustered regions of agreement farther in-
land (∼ 40–150+ km from the grounding line) to be falsely
identified regions of speed-up falling close to combined sen-
sor error limits (Sect. 3.3.3). There, clustering resides mostly
over areas of near-stagnant flow unlikely to have experienced
significant seasonal variability (∼ 10 m yr−1). In the few in-
stances where such phenomena are located over regions of
faster flow, the spatial distribution of clustering is not bound
to the dimensions of the tributary glaciers shown in Figs. 1
and 7 and so is assumed also not to represent a true geophys-
ical signal.
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3914 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
Figure 3. Spatially resolved patterns of seasonal ice flow as observed between December 2018 and November 2019. Panel (a) shows the
month of maximum velocity (vtime) and (b) the calculated velocity anomaly (vanomSAR ). In (b), positive velocity anomalies (blue) indicate
summertime speed-up and negative (red) greater velocity during non-summertime months. Dashed boxes denote the location of the inset
boxes (i–iii). See also Figs. B1–B4.
5 Discussion
Our observations present unambiguous evidence for seasonal
ice-flow variability on the grounded ice draining to GVIIS.
Proximal to the grounding line, glacier velocity increased
during summertime months (December–February) by 0.06±
0.005 m d−1 on average, constituting a ∼ 15 % speed-up rel-
ative to baseline (time-series-averaged) velocities. In the fol-
lowing sections, we evaluate the potential surface and ocean
forcing mechanisms driving this phenomenon, which are im-
portant, ultimately, for better constraining the future timing
and evolution of the Antarctic Ice Sheet’s decay.
5.1 Surface forcing
The role of surface-sourced meltwater in stimulating sea-
sonal accelerations in land-ice flow is well established on
valley glaciers and the Greenland Ice Sheet (Iken et al.,
1983; Hooke et al., 1989; Zwally et al., 2002; Moon et al.,
2014; Kraaijenbrink et al., 2016). At both locations, early
summertime surface water inputs to a subglacial drainage
system drive near-instantaneous reductions in effective pres-
sure, increased basal sliding, and the acceleration of the en-
tire ice column (Iken, 1981; Schoof, 2010). Enabled pri-
marily through the mass drainage of supraglacial meltwater
via surface-visible “moulins” (which are themselves formed
through the sustained meltwater-driven erosion of the ice
column over one or more summers), such velocity accel-
erations are typically short-lived as the subglacial hydro-
logical drainage system becomes more efficient through
time, thereby increasing effective pressure and arresting flow
(Bartholomew et al., 2010). Near the surface of the Green-
land Ice Sheet, the drainage of perennial, summer meltwater-
fuelled “firn aquifers” may also deliver large quantities of
meltwater to the bed via crevasses and/or other englacial
pathways (Harper et al., 2012; Koenig et al., 2014), instigat-
ing similar, transient accelerations in ice velocity (Schoof,
2010).
In Antarctica, the clear velocity signals we observe inland
of GVIIS’ grounding line (Figs. 4 and 5) emulate closely the
summertime accelerations observed in valley glaciers and the
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3915
Figure 4. SAR-derived observations of ice flow for each month between October 2014 and August 2020 (black line) and the 13-month
moving mean ice flow (red line). Observations are averaged across the 10 km2 dashed boxes shown in Fig. 1. Translucent red shading
denotes the timing of austral summertime (December–February, inclusive). Note that the y-axis limits vary between panels.
Greenland Ice Sheet, implying that they may be driven by
similar surface meltwater-related processes. The relatively
short-lived (∼ 1–2 months duration) velocity maxima fol-
lowed by sharp deceleration trends at most glaciers lend cre-
dence to this interpretation (Figs. 4, 5, and C1), with the lat-
ter resembling a “Greenland-style” late- to post-summertime
switch towards more efficient subglacial drainage. A current
lack of in situ observations, however, makes this hypothesis
difficult to verify. Nonetheless, we note further that for 75 %
of the outlet glaciers nourishing GVIIS, the greatest instances
of ice-flow speed-up occurred during the austral summertime
of either 2016/17 or 2019/20 (Fig. C1), which correspond to
years characterised by exceptional surface melting on the ice
shelf (Banwell et al., 2021).
Despite the seemingly close correspondence between sur-
face meltwater forcing and the seasonal signals that we ob-
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3916 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
Figure 5. Detrended SAR-derived observations of mean monthly ice flow between 2014 and 2020 (black) and associated mean monthly
standard error (grey shading). Observations are averaged across the 10 km2 dashed boxes shown in Fig. 1. Blue and red error bounds
denote velocity minima and maxima, respectively. Translucent red shading denotes the timing of austral summertime (December–February,
inclusive). Note that the y-axis limits vary between panels.
serve at GVIIS’ glaciers, satellite observations show that
supraglacial meltwater presence and persistence are limited
inland of Antarctica’s grounding zone (Dirscherl et al., 2021;
Johnson et al., 2022), and no obvious regional contrasts
in melt exist near the grounding line of Alexander Island
and Palmer Land (Trusel et al., 2013; Bell et al., 2018). At
GVIIS, routine satellite observations have also revealed mi-
nor trends of decreasing meltwater presence over most of the
21st century (Johnson et al., 2022), which is consistent with
a previously documented, pervasive cooling of the Antarctic
Peninsula from the late 1990s onwards (Turner et al., 2016;
Adusumilli et al., 2018). Inland, we expect that melt rates
will have similarly decreased but at a greater rate given the
lapse rate associated with the Antarctic Peninsula’s moun-
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3917
Figure 6. Month of maximum velocity obtained from the detrended
SAR-derived observations shown in Fig. 5. Inset map shows the
location of GVIIS’ drainage basins.
tainous terrain. Together with the variable thicknesses of
the glaciers spanning GVIIS’ perimeter (Morlighem et al.,
2020), which would presumably require differing amounts
of meltwater flux to form surface-to-bed moulins and en-
hance basal sliding, these findings suggest that rapid sur-
face meltwater drainage events alone are unlikely to ex-
plain the region-wide, year-by-year, seasonal speed-up sig-
nals we observe near the grounding line. Summertime co-
herence in both the interferograms used to locate the posi-
tion of the grounding line in 2018 (Sect. 3.1; Mohajerani et
al., 2021a) and our offset-tracking-derived velocity estimates
(Sect. 3.2), alongside a previously documented absence of
any such rapid meltwater drainage events in the Antarctic
Peninsula (Rott et al., 2020), further supports this assertion.
At depth (i.e. beyond that detectable by C-band SAR
sensors, whose radiation penetrates typically a few metres
into the firn column), Greenland-style firn-aquifer-related
drainage events could also be involved in driving the seasonal
velocity signals we observe. Previous in situ campaigns have
confirmed the presence of such aquifers on Wilkins Ice Shelf
to the north (Fig. 1), and these have been implicated as po-
tential drivers of Wilkins’ past disintegration events (Mont-
gomery et al., 2020). We note, however, that the formation
and persistence of firn aquifers requires high levels of sur-
face melt and accumulation (Harper et al., 2012; Koenig et
al., 2014; Montgomery et al., 2020): phenomena which may
not be prevalent inland of GVIIS during most of the Sentinel-
1 era given the pervasive cooling of the Antarctic Peninsula
over approximately the past two decades noted above (Turner
et al., 2016). Notwithstanding this cooling, long-term model
outputs suggest that the spatial distribution of firn aquifers
in the Antarctic Peninsula is limited largely to the north-
ern reaches of Wilkins Ice Shelf, and that no aquifers re-
side inland of GVIIS (van Wessem et al., 2021) – patterns
which follow closely the thermal limit of ice-sheet viability
over this region of Antarctica (Cook and Vaughan, 2010).
While we acknowledge that the model estimates of, for ex-
ample, van Wessem et al. (2021) may underestimate firn
aquifer presence, the rapidly steepening topography inland
of GVIIS’ grounding line (which often exceeds 1◦ of slope;
Howat et al., 2019; Fig. 1) is presumably also unconducive to
their formation and persistence compared with the relatively
flat Wilkins Ice Shelf.
5.2 Ocean forcing
The seasonal velocity signals we observe at and proximal to
GVIIS’ grounding line (Figs. 3, 4, 5, and C1) may also be
diagnostic of seasonal fluctuations in ocean forcing. As dis-
cussed in Sect. 2, this interpretation is supported firstly by in
situ oceanographic observations revealing the widespread in-
flow and flooding of relatively warm circumpolar deep water
(CDW) to GVIIS’ cavity, which is sourced from the conti-
nental shelf via a net northwards throughflow from Ronne
Entrance (Jenkins and Jacobs, 2008). There, the strongest
inflows of CDW have been observed to occur underneath
its northern margin proximal to Alexander Island (red arrow
in Fig. 1; following Jenkins and Jacobs, 2008), which may,
by extension, explain the observed, earlier onset of summer-
time speed-up at and proximal to the grounding line along
that stretch of coastline relative to Palmer Land (Fig. 6).
On the basis of these earlier observations, we further ex-
pect that enhanced CDW upwelling in the cavity, enabled
by the buoyancy-driven advection of ice-shelf meltwater en-
trained within the northerly throughflow and deflected to-
wards Alexander Island due to Coriolis forcing, may have
also maximised this contrasting regional melting effect. This
hypothesis is consistent with in situ and modelling-based es-
timates of GVIIS’ sub-shelf circulation (Jenkins and Jacobs,
2008; Holland et al., 2010) and, more broadly, with inferred
patterns of melting observed recently along the Coriolis-
favoured flank of Dotson Ice Shelf (Gourmelen et al., 2017)
and Getz Ice Shelf (Alley et al., 2016).
It is important to note, however, that the findings of Jenk-
ins and Jacobs (2008) do not present any evidence for sea-
sonality in CDW presence and/or depth owing to the lim-
ited timeframe in which these in situ observations were col-
lected (less than 2 d worth of continuous measurements in
March 1994). Nonetheless, recent research has revealed two
possible mechanisms through which sea-ice conditions off-
shore from GVIIS may control CDW influx to and draft
within its sub-shelf cavity. First, modelling experiments, em-
ulating closely the observational records of Jenkins and Ja-
cobs (2008), have suggested a process of wintertime sea-ice
growth, brine rejection, and resulting convection throughout
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3918 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
Figure 7. Heatmap indicating the spatial coherence of seasonal ice-flow variability observed by both SAR and optical imaging techniques.
Warmer colours denote a higher density (per km2) of pixels having undergone speed-up of comparable magnitude (±10 m yr−1) during
austral summertime, as observed in both our SAR-derived (vanomSAR_day ) and optically derived (vanomOLI ) records (Sect. 3.3.3). Contours
are derived from our SAR-derived, time-series-averaged velocity observations (Sect. 3.2). Background DEM is derived from the Reference
Elevation Model of Antarctica (Howat et al., 2019). Dashed boxes denote the location of the inset boxes (i–iv). Inset map shows the location
of GVIIS’ drainage basins. Red crosses represent examples of areas of clustering not representative of a true geophysical signal.
the mixed layer that leads to a thickening of the underlying
CDW (Holland et al., 2010; Petty et al., 2014). These pro-
cesses drive a seasonal cycle in melt rate at GVIIS’ ground-
ing line (Holland et al., 2010), which is greatest around mid-
to-late wintertime and would precede the resulting summer-
time accelerations in ice flow we observe by ∼ 3–5 months.
Several ice-sheet modelling studies have suggested a lag
time of several weeks to months from the onset of ocean-
induced melt to surface ice acceleration at the grounding line
(Vieli and Nick, 2011; Joughin et al., 2012b), suggesting that
this timescale may be plausible. Second, in situ observations
from the neighbouring Amundsen Sea Sector have revealed
that sea-ice growth and associated brine rejection can alter-
natively result in a destratification of the water column and
thus restrict CDW inflow during austral wintertime (Webber
et al., 2017). This mechanism would, by implication, facili-
tate enhanced summertime melt at the grounding line more
in-phase with the accelerations in ice flow we observe.
Ultimately, a dearth of oceanographic observations in the
Bellingshausen Sea hinders our ability to ascertain which
mechanism is the dominant control on CDW influx to GVIIS’
sub-shelf cavity, justifying the future collection of detailed
oceanographic data in this region. Such data would also yield
high-resolution (and potentially more representative) insights
into the nature of oceanic circulation beneath GVIIS. Indeed,
we estimate that an ∼ 8–16 cm s−1 northwards throughflow
of CDW would be required over the course of ∼ 1–2 months
to induce the relatively narrow summertime speed-up win-
dows observed along the entirety of GVIIS’ 420 km long cav-
ity (Figs. 4, 5, and C1), assuming laminar and linear flow.
These rates are up to almost an order of magnitude greater
than the observationally constrained estimates reported in
Jenkins and Jacobs (2008; ∼ 2.5 cm s−1), although the lat-
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3919
ter, which were collected over ∼ 30 h, may not necessarily
be representative of seasonally averaged rates of flow. Else-
where in Antarctica, longer-term in situ observations have re-
vealed much greater rates of sub-ice-shelf CDW circulation
(∼ 8–20 cm s−1; Jacobs et al., 2013; Jenkins et al., 2018),
suggesting that similar speeds may in fact be plausible un-
derneath GVIIS.
Finally, we note that beyond relatively local-scale ocean
forcing, modelling experiments suggest that CDW influx
to GVIIS’ cavity is relatively insensitive to far-field intra-
annual-to-decadal-scale atmosphere–ocean variability (Hol-
land et al., 2010). This lies in contrast to the atmosphere–
ocean processes controlling CDW transmission to the
Amundsen Sea and wider Bellingshausen Sea coastal mar-
gins (i.e. wind-driven Ekman transport; Steig et al., 2012;
Dutrieux et al., 2014; Christie et al., 2018; Jenkins et al.,
2018; Paolo et al., 2018), further implicating relatively local-
scale, sea-ice-induced oceanographic modification as a key
potential control on the seasonal ice velocity signals we ob-
serve at GVIIS.
6 Summary and implications
We provide the first evidence for seasonal flow variability
of land ice draining to George VI Ice Shelf (GVIIS). Using
monthly Sentinel-1 SAR-based velocity information derived
from high-frequency (6/12 d) repeat-pass observations, we
detect a ∼ 15 % mean austral summertime speed-up of the
outlet glaciers feeding GVIIS at, and immediately inland of,
the grounding line. This seasonal variability is corroborated
by independent, optically derived observations of ice flow.
Both surface and oceanic forcing mechanisms are evaluated
as potential controls on this seasonality, although insufficient
observational evidence currently exists with which to ver-
ify the relative importance of each. Elucidating the precise
surface and/or ocean mechanisms governing GVIIS’ outlet
glacier flow variability is therefore a critical area for future
research.
Ultimately, our findings imply that other glaciers in
Antarctica may be susceptible to – and/or currently under-
going – similar ice flow seasonality including at the highly
vulnerable and rapidly retreating Pine Island and Thwaites
glaciers. We further expect that such behaviour would not
necessarily be captured in, for example, input–output method
mass balance calculations, leading to potentially misesti-
mated rates of ice-sheet mass loss. Accurately ascertaining
the nature of seasonal ice, ocean, and/or atmospheric interac-
tions at such locations is important, therefore, for better un-
derstanding, modelling, and ultimately refining projections
of the rate at which future Antarctic ice losses will contribute
to global sea-level rise.
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3920 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
Appendix A
Figure A1. Landward (high-tide) and seaward (low-tide) limits of the modern-day (2018–2020) grounding line (blue) and the position of
the grounding line in the mid-1990s (red). Background hillshade is derived from the Reference Elevation Model of Antarctica (Howat et al.,
2019). Dashed boxes denote the location of the inset boxes (i–iv).
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3921
Appendix B
Figure B1. Same as Fig. 3 but showing spatially resolved patterns of seasonal ice flow as observed between December 2014 and Novem-
ber 2015. Panels (a) shows the month of maximum velocity (vtime) and (b) the calculated velocity anomaly (vanomSAR ). In (b), positive
velocity anomalies (blue) indicate summertime speed-up and negative (red) greater velocity during non-summertime months. Dashed boxes
denote the location of the inset boxes (i–iii). Note that insets ii and iii contain no data for the period December 2014–November 2015. See
also Figs. B2–B4.
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3922 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
Figure B2. Same as Fig. 3 but showing spatially resolved patterns of seasonal ice flow as observed between December 2015 and Novem-
ber 2016. Panel (a) shows the month of maximum velocity (vtime) and (b) the calculated velocity anomaly (vanomSAR ). In (b), positive
velocity anomalies (blue) indicate summertime speed-up and negative (red) greater velocity during non-summertime months. Dashed boxes
denote the location of the inset boxes (i–iii). See also Figs. B1, B3–B4.
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3923
Figure B3. Same as Fig. 3 but showing spatially resolved patterns of seasonal ice flow as observed between December 2016 and Novem-
ber 2017. Panel (a) shows the month of maximum velocity (vtime) and (b) the calculated velocity anomaly (vanomSAR ). In (b), positive
velocity anomalies (blue) indicate summertime speed-up and negative (red) greater velocity during non-summertime months. Dashed boxes
denote the location of the inset boxes (i–iii). See also Figs. B1–B2 and B4.
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3924 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
Figure B4. Same as Fig. 3 but showing spatially resolved patterns of seasonal ice flow as observed between December 2017 and Novem-
ber 2018. Panel (a) shows the month of maximum velocity (vtime) and (b) the calculated velocity anomaly (vanomSAR ). In (b), positive
velocity anomalies (blue) indicate summertime speed-up and negative (red) greater velocity during non-summertime months. Dashed boxes
denote the location of the inset boxes (i–iii). See also Figs. B1–B3.
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3925
Appendix C
Figure C1. Detrended SAR-derived observations of mean monthly ice flow for each year between 2014 and 2020 (coloured lines) and the
mean monthly ice flow calculated over the entire time series (black line; same as Fig. 5). Observations are averaged across the 10 km2 dashed
boxes shown in Fig. 1. Translucent red shading denotes the timing of austral summertime (December–February, inclusive). Note that the
y axis limits vary between panels.
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3926 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
Appendix D
For most of the outlet glaciers numbered in Figs. 4, 5, C1, and
D1, the modelled timing of peak velocity (Table D1) does not
align precisely with the month of maximum velocity estab-
lished from observations (Figs. 4, 5, and 6). To model peak
velocity timing, a cosine wave, optimised to the most domi-
nant frequency observed across all outlet glacier time series
(1 month), is fitted to each time series (Fig. D1, left-hand
panels). The modelled results are subsequently calculated
from the phase shift of each fitted cosine wave (Table D1); as
such, the modelled timing of peak velocity should be consid-
ered a broad-brush indicator of the approximate timing only.
The most dominant frequency present in the original func-
tion, however, is derived directly from the discrete Fourier
transform (Fig. D1, right-hand panels) and hence presents
meaningful information on the seasonality of each time se-
ries.
Table D1. Phase and amplitude characteristics of the fitted cosine function optimised to the dominant frequency determined by discrete
Fourier transform analysis (Fig. D1, orange). Flowlines 2–10 drain Palmer Land, while flowlines 15–21 drain Alexander Island.
Flowline Amplitude Phase Max occurs Modelled timing of Season of Dominant
(A) (m d−1) (φ) (rad) (1 Oct + t (months)) peak velocity∗ peak velocity∗ frequency
2 0.012 −2.35 1 Oct + 4.48 15 Feb Late summer 1
3 0.009 −2.70 1 Oct + 5.16 5 Mar Late extended summer 1
4 0.011 −2.13 1 Oct + 4.07 3 Feb Late summer 1
5 0.017 −2.58 1 Oct + 4.93 28 Feb Late summer 1
6 0.010 −2.12 1 Oct + 4.04 2 Feb Late summer 3
7 0.019 −1.63 1 Oct + 3.10 4 Jan Midsummer 1
8 0.009 −2.97 1 Oct + 5.67 21 Mar Late extended summer 3
9 0.050 −2.42 1 Oct + 4.63 19 Feb Late summer 1
10 0.032 −2.97 1 Oct + 5.68 21 Mar Late extended summer 1
15 0.016 −1.49 1 Oct + 2.85 26 Dec Early summer 1
16 0.017 −1.69 1 Oct + 3.23 7 Jan Midsummer 1
17 0.016 −1.91 1 Oct + 3.66 20 Jan Midsummer 1
18 0.018 −1.51 1 Oct + 2.88 27 Dec Early summer 1
19 0.005 −1.48 1 Oct + 2.82 25 Dec Early summer 1
20 0.005 −0.88 1 Oct + 1.68 21 Nov Early extended summer 1
21 0.006 −0.43 1 Oct + 0.82 25 Oct Early extended summer 1
∗ Note: the modelled timing of peak velocity is derived from the phase value associated with each of the numbered outlet glaciers.
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3927
Figure D1. Discrete Fourier transform outputs. Left-hand panels indicate the time domain where time 0 denotes 1 October. Panels show
mean monthly ice flow of each outlet glacier (blue; same as Fig. 5) with fitted cosine functions (orange) optimised to the most dominant
frequency observed across all outlet glacier time series. Right-hand panels indicate the frequency domain, whereby the greatest amplitude
associated with each glacier time series denotes the most dominant frequency. In the context of the present study, a dominant frequency of 1
denotes one complete seasonal cycle per year. Annotated values indicate the phase associated with each frequency component.
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3928 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
Data availability. All grounding line and veloc-
ity datasets presented in this study are avail-
able at https://doi.org/10.17863/CAM.82248 and
https://doi.org/10.17863/CAM.82252, respectively (Boxall
et al., 2022a, b). Copernicus Sentinel-1A/B data used in
this study are available from the European Space Agency at
https://scihub.copernicus.eu/ (last access: September 2022); Land-
sat 8 ITS_LIVE velocity data (Gardner et al., 2019) are available
from https://its-live.jpl.nasa.gov/ (last access: September 2022);
annual MEaSUREs Antarctic ice velocity maps (Rignot et al.,
2017) are available from the National Snow and Ice Data Center
(NSIDC) at https://nsidc.org/data/NSIDC-0720/versions/1 (last
access: September 2022); REMA DEM (Howat et al., 2019) is
publicly available at https://www.pgc.umn.edu/data/rema/ (last
access: September 2022); and the 2018 grounding line position is
available at https://doi.org/10.7280/D1VD6G (Mohajerani et al.,
2021b).
Author contributions. KB designed the study under the supervision
of FDWC and ICW. JW and TN processed the monthly composite
SAR-derived velocity grids. KB performed all analyses. KB wrote
the manuscript under the guidance of FDWC, with contributions
from all co-authors.
Competing interests. The contact author has declared that none of
the authors has any competing interests.
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Acknowledgements. This research was undertaken while Karla
Boxall was in receipt of a United Kingdom Natural Environment
Research Council PhD studentship awarded through the University
of Cambridge C-CLEAR Doctoral Training Partnership. Thomas
Nagler and Jan Wuite acknowledge support from the European
Space Agency through the Antarctic Ice Sheet Climate Change Ini-
tiative (CCI) programme. The authors thank Gareth Rees for his
initial discussions regarding the discrete Fourier transform analy-
ses presented in this article. The authors also wish to thank the
editor, Etienne Berthier, and the two reviewers, Ted Scambos and
Thorsten Seehaus, for their constructive comments which improved
the manuscript.
Financial support. This research has been supported by the Nat-
ural Environment Research Council (to Karla Boxall, grant
no. NE/S007164/1, and to Ian C. Willis, grant no. NE/T006234/1)
and the Prince Albert II of Monaco Foundation (to Frazer
D. W. Christie, grant no. 3095).
Review statement. This paper was edited by Etienne Berthier and
reviewed by Ted Scambos and Thorsten Seehaus.
References
Adusumilli, S., Fricker, H. A., Siegfried, M. R., Padman, L., Paolo,
F. S., and Ligtenberg, S. R. M.: Variable Basal Melt Rates
of Antarctic Peninsula Ice Shelves, 1994–2016, Geophys. Res.
Lett., 45, 4086–4095, https://doi.org/10.1002/2017GL076652,
2018.
Alley, K. E., Scambos, T. A., Siegfried, M. R., and Fricker,
H. A.: Impacts of warm water on Antarctic ice shelf stabil-
ity through basal channel formation, Nat. Geosci., 9, 290–293,
https://doi.org/10.1038/ngeo2675, 2016.
Banwell, A. F., MacAyeal, D. R., and Sergienko, O. V.: Breakup
of the Larsen B Ice Shelf triggered by chain reaction drainage
of supraglacial lakes, Geophys. Res. Lett., 40, 5872–5876,
https://doi.org/10.1002/2013GL057694, 2013.
Banwell, A. F., Datta, R. T., Dell, R. L., Moussavi, M., Brucker,
L., Picard, G., Shuman, C. A., and Stevens, L. A.: The 32-year
record-high surface melt in 2019/2020 on the northern George
VI Ice Shelf, Antarctic Peninsula, The Cryosphere, 15, 909–925,
https://doi.org/10.5194/tc-15-909-2021, 2021.
Bartholomew, I., Nienow, P., Mair, D., Hubbard, A., King, M. A.,
and Sole, A.: Seasonal evolution of subglacial drainage and ac-
celeration in a Greenland outlet glacier, Nat. Geosci., 3, 408–411,
https://doi.org/10.1038/ngeo863, 2010.
Bell, R. E., Banwell, A. F., Trusel, L. D., and Kingslake,
J.: Antarctic surface hydrology and impacts on ice-
sheet mass balance, Nat. Clim. Change, 8, 1044–1052,
https://doi.org/10.1038/s41558-018-0326-3, 2018.
Boxall, K., Christie, F. D. W., Willis, I. C., Wuite, J., and
Nagler, T.: West Antarctic Peninsula grounding line loca-
tion datasets supporting “Seasonal land-ice-flow variability
in the Antarctic Peninsula”, Cambridge Apollo [data set]
https://doi.org/10.17863/CAM.82248, 2022a.
Boxall, K., Christie, F. D. W., Willis, I. C., Wuite, J., and
Nagler, T.: West Antarctic Peninsula seasonal ice veloc-
ity products supporting “Seasonal land-ice-flow variability
in the Antarctic Peninsula”, Cambridge Apollo [data set]
https://doi.org/10.17863/CAM.82252, 2022b.
Bracewell, R.: The Fourier transform and its applications, McGraw-
Hill, Inc., New York, 1978.
Christie, F. D. W., Bingham, R. G., Gourmelen, N., Tett,
S. F. B., and Muto, A.: Four-decade record of perva-
sive grounding line retreat along the Bellingshausen mar-
gin of West Antarctica, Geophys. Res. Lett., 43, 5741–5749,
https://doi.org/10.1002/2016GL068972, 2016.
Christie, F. D. W., Bingham, R. G., Gourmelen, N., Steig, E. J.,
Bisset, R. R., Pritchard, H. D., Snow, K., and Tett, S. F. B.:
Glacier change along West Antarctica’s Marie Byrd Land Sec-
tor and links to inter-decadal atmosphere–ocean variability, The
Cryosphere, 12, 2461–2479, https://doi.org/10.5194/tc-12-2461-
2018, 2018.
Christie, F. D. W., Benham, T. J., Batchelor, C. L., Rack, W.,
Montelli, A., and Dowdeswell, J. A.: Antarctic ice-shelf ad-
vance driven by anomalous atmospheric and sea-ice circulation,
Nat. Geosci., 15, 356–362, https://doi.org/10.1038/s41561-022-
00938-x, 2022.
Cook, A. J. and Vaughan, D. G.: Overview of areal changes of the
ice shelves on the Antarctic Peninsula over the past 50 years,
The Cryosphere, 4, 77–98, https://doi.org/10.5194/tc-4-77-2010,
2010.
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3929
Dell, R. L., Banwell, A. F., Willis, I. C., Arnold, N. S., Hal-
berstadt, A. R. W., Chudley, T. R., and Pritchard, H. D.: Su-
pervised classification of slush and ponded water on Antarctic
ice shelves using Landsat 8 imagery, J. Glaciol., 68, 401–414,
https://doi.org/10.1017/jog.2021.114, 2022.
Dirscherl, M., Dietz, A. J., Kneisel, C., and Kuenzer, C.: A novel
method for automated supraglacial lake mapping in Antarctica
using Sentinel-1 SAR imagery and deep learning, Remote Sens-
ing, 13, 197, https://doi.org/10.3390/rs13020197, 2021.
Dutrieux, P., Rydt, J. D., Jenkins, A., Holland, P. R., Ha,
H. K., Lee, S. H., Steig, E. J., Ding, Q., Abrahamsen, E.
P., and Schröder, M.: Strong Sensitivity of Pine Island Ice-
Shelf Melting to Climatic Variability, Science, 343, 174–178,
https://doi.org/10.1126/science.1244341, 2014.
Edwards, T. L., Nowicki, S., Marzeion, B., Hock, R., Goelzer,
H., Seroussi, H., Jourdain, N. C., Slater, D. A., Turner, F. E.,
Smith, C. J., McKenna, C. M., Simon, E., Abe-Ouchi, A., Gre-
gory, J. M., Larour, E., Lipscomb, W. H., Payne, A. J., Shep-
herd, A., Agosta, C., Alexander, P., Albrecht, T., Anderson,
B., Asay-Davis, X., Aschwanden, A., Barthel, A., Bliss, A.,
Calov, R., Chambers, C., Champollion, N., Choi, Y., Cullather,
R., Cuzzone, J., Dumas, C., Felikson, D., Fettweis, X., Fujita,
K., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Greve,
R., Hattermann, T., Hoffman, M. J., Humbert, A., Huss, M.,
Huybrechts, P., Immerzeel, W., Kleiner, T., Kraaijenbrink, P.,
Le Clec’h, S., Lee, V., Leguy, G. R., Little, C. M., Lowry, D.
P., Malles, J.-H., Martin, D. F., Maussion, F., Morlighem, M.,
O’Neill, J. F., Nias, I., Pattyn, F., Pelle, T., Price, S. F., Quiquet,
A., Radic´, V., Reese, R., Rounce, D. R., Rückamp, M., Sakai,
A., Shafer, C., Schlegel, N.-J., Shannon, S., Smith, R. S., Stra-
neo, F., Sun, S., Tarasov, L., Trusel, L. D., Van Breedam, J., van
de Wal, R., van den Broeke, M., Winkelmann, R., Zekollari, H.,
Zhao, C., Zhang, T., and Zwinger, T.: Projected land ice contri-
butions to twenty-first-century sea level rise, Nature, 593, 74–82,
https://doi.org/10.1038/s41586-021-03302-y, 2021.
ESA: User Guides – Sentinel-1 SAR – Sentinel Online – Sen-
tinel Online, https://sentinel.esa.int/web/sentinel/user-guides/
sentinel-1-sar, last access: January 2022.
Fricker, H. A., Coleman, R., Padman, L., Scambos, T. A.,
Bohlander, J., and Brunt, K. M.: Mapping the ground-
ing zone of the Amery Ice Shelf, East Antarctica using
InSAR, MODIS and ICESat, Antarct. Sci., 21, 515–532,
https://doi.org/10.1017/S095410200999023X, 2009.
Friedl, P., Weiser, F., Fluhrer, A., and Braun, M. H.:
Remote sensing of glacier and ice sheet ground-
ing lines: A review, Earth-Sci. Rev., 201, 102948,
https://doi.org/10.1016/j.earscirev.2019.102948, 2020.
Friedl, P., Seehaus, T., and Braun, M.: Global time series
and temporal mosaics of glacier surface velocities derived
from Sentinel-1 data, Earth Syst. Sci. Data, 13, 4653–4675,
https://doi.org/10.5194/essd-13-4653-2021, 2021.
Gardner, A. S., Moholdt, G., Scambos, T., Fahnstock, M.,
Ligtenberg, S., van den Broeke, M., and Nilsson, J.: In-
creased West Antarctic and unchanged East Antarctic ice dis-
charge over the last 7 years, The Cryosphere, 12, 521–547,
https://doi.org/10.5194/tc-12-521-2018, 2018.
Gardner, A. S., Fahnestock, M. A., and Scambos, T. A.: MEaSUREs
ITS_LIVE Landsat Image-Pair Glacier and Ice Sheet Surface Ve-
locities: Version 1, National Snow and Ice Data Center [data set],
https://doi.org/10.5067/IMR9D3PEI28U, 2019.
Gourmelen, N., Goldberg, D. N., Snow, K., Henley, S. F., Bing-
ham, R. G., Kimura, S., Hogg, A. E., Shepherd, A., Mouginot,
J., Lenaerts, J. T. M., Ligtenberg, S. R. M., and van de Berg,
W. J.: Channelized Melting Drives Thinning Under a Rapidly
Melting Antarctic Ice Shelf, Geophys. Res. Lett., 44, 9796–9804,
https://doi.org/10.1002/2017GL074929, 2017.
Greene, C. A., Young, D. A., Gwyther, D. E., Galton-Fenzi, B. K.,
and Blankenship, D. D.: Seasonal dynamics of Totten Ice Shelf
controlled by sea ice buttressing, The Cryosphere, 12, 2869–
2882, https://doi.org/10.5194/tc-12-2869-2018, 2018.
Harper, J., Humphrey, N., Pfeffer, W. T., Brown, J., and Fet-
tweis, X.: Greenland ice-sheet contribution to sea-level rise
buffered by meltwater storage in firn, Nature, 491, 240–243,
https://doi.org/10.1038/nature11566, 2012.
Hogg, A. E., Shepherd, A., Cornford, S. L., Briggs, K. H., Gourme-
len, N., Graham, J. A., Joughin, I., Mouginot, J., Nagler, T.,
Payne, A. J., Rignot, E., and Wuite, J.: Increased ice flow in West-
ern Palmer Land linked to ocean melting, Geophys. Res. Lett.,
44, 4159–4167, https://doi.org/10.1002/2016GL072110, 2017.
Holland, P. R., Jenkins A., and Holland D. M.: Ice and ocean pro-
cesses in the Bellingshausen Sea, Antarctica, J. Geophys. Res.,
115, C05020, https://doi.org/10.1029/2008JC005219, 2010,
Holt, T. and Glasser, N. F.: Changes in area, flow speed and structure
of southwest Antarctic Peninsula ice shelves in the 21st century,
J. Glaciol., 1–19, https://doi.org/10.1017/jog.2022.7, 2022.
Holt, T. O., Glasser, N. F., Quincey, D. J., and Siegfried, M. R.:
Speedup and fracturing of George VI Ice Shelf, Antarctic Penin-
sula, The Cryosphere, 7, 797–816, https://doi.org/10.5194/tc-7-
797-2013, 2013.
Hooke, R. L., Calla, P., Holmlund, P., Nilsson, M., and
Stroeven, A.: A 3 year record of seasonal variations in sur-
face velocity, Storglaciären, Sweden, J. Glaciol., 35, 235–247,
https://doi.org/10.3189/S0022143000004561, 1989.
Howat, I. M., Porter, C., Smith, B. E., Noh, M.-J., and Morin, P.:
The Reference Elevation Model of Antarctica, The Cryosphere,
13, 665–674, https://doi.org/10.5194/tc-13-665-2019, 2019.
Iken, A.: The effect of the subglacial water pressure on the sliding
velocity of a glacier in an idealized numerical model, J. Glaciol.,
27, 407–421, https://doi.org/10.3189/S0022143000011448,
1981.
Iken, A., Röthlisberger, H., Flotron, A., and Haeberli, W.: The up-
lift of Unteraargletscher at the beginning of the melt season – a
consequence of water storage at the bed?, J. Glaciol., 29, 28–47,
https://doi.org/10.3189/S0022143000005128, 1983.
Jacobs, S., Giulivi, C., Dutrieux, P., Rignot, E., Nitsche,
F., and Mouginot, J. Getz Ice Shelf melting response to
changes in ocean forcing, J. Geophys. Res.,118, 4152–4168,
https://doi.org/10.1002/jgrc.20298, 2013.
Jenkins, A. and Jacobs, S.: Circulation and melting beneath
George VI Ice Shelf, Antarctica, J. Geophys. Res., 113, C04013,
https://doi.org/10.1029/2007JC004449, 2008.
Jenkins, A., Dutrieux, P., Jacobs, S. S., McPhail, S. D., Perrett, J.
R., Webb, A. T., and White, D.: Observations beneath Pine Island
Glacier in West Antarctica and implications for its retreat, Nat.
Geosci., 3, 468–472, https://doi.org/10.1038/ngeo890, 2010.
Jenkins, A., Shoosmith, D., Dutrieux, P., Jacobs, S., Kim, T.
W., Lee, S. H., Ha, H. K., and Stammerjohn, S.: West
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3930 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
Antarctic Ice Sheet retreat in the Amundsen Sea driven
by decadal oceanic variability, Nat. Geosci., 11, 733–738,
https://doi.org/10.1038/s41561-018-0207-4, 2018.
Johnson, A., Hock, R., and Fahnestock, M.: Spatial variability and
regional trends of Antarctic ice shelf surface melt duration over
1979–2020 derived from passive microwave data, J. Glaciol., 68,
533–546, https://doi.org/10.1017/jog.2021.112, 2022.
Joughin, I., Alley, R. B., and Holland, D. M.: Ice-Sheet
Response to Oceanic Forcing, Science, 338, 1172–1176,
https://doi.org/10.1126/science.1226481, 2012a.
Joughin, I., Smith, B. E., Howat, I. M., Floricioiu, D., Alley, R. B.,
Truffer, M., and Fahnestock, M. Seasonal to decadal scale vari-
ations in the surface velocity of Jakobshavn Isbrae, Greenland:
Observation and model-based analysis, J. Geophys. Res., 117,
F02030, https://doi.org/10.1029/2011JF002110, 2012b.
Joughin, I., Smith, B. E., and Howat, I.: Greenland Ice Map-
ping Project: ice flow velocity variation at sub-monthly
to decadal timescales, The Cryosphere, 12, 2211–2227,
https://doi.org/10.5194/tc-12-2211-2018, 2018.
King, M. D., Howat, I. M., Jeong, S., Noh, M. J., Wouters, B., Noël,
B., and van den Broeke, M. R.: Seasonal to decadal variability in
ice discharge from the Greenland Ice Sheet, The Cryosphere, 12,
3813–3825, https://doi.org/10.5194/tc-12-3813-2018, 2018.
Kingslake, J., Ely, J. C., Das, I., and Bell, R. E.: Widespread move-
ment of meltwater onto and across Antarctic ice shelves, Nature,
544, 349–352, https://doi.org/10.1038/nature22049, 2017.
Koenig, L. S., Miège, C., Forster, R. R., and Brucker, L.: Ini-
tial in situ measurements of perennial meltwater storage in
the Greenland firn aquifer, Geophys. Res. Lett., 41, 81–85,
https://doi.org/10.1002/2013GL058083, 2014.
Konrad, H., Shepherd, A., Gilbert, L., Hogg, A. E., McMil-
lan, M., Muir, A., and Slater, T.: Net retreat of Antarc-
tic glacier grounding lines, Nat. Geosci., 11, 258–262,
https://doi.org/10.1038/s41561-018-0082-z, 2018.
Kraaijenbrink, P., Meijer, S. W., Shea, J. M., Pellicciotti, F., De
Jong, S. M., and Immerzeel, W. W.: Seasonal surface veloci-
ties of a Himalayan glacier derived by automated correlation of
unmanned aerial vehicle imagery, Ann. Glaciol., 57, 103–113,
https://doi.org/10.3189/2016AoG71A072, 2016.
Leeson, A. A., Forster, E., Rice, A., Gourmelen, N., and Van
Wessem, J. M.: Evolution of supraglacial lakes on the Larsen B
ice shelf in the decades before it collapsed, Geophys. Res. Lett.,
47, e2019GL085591, https://doi.org/10.1029/2019GL085591,
2020.
Mohajerani, Y., Jeong, S., Scheuchl, B., Velicogna, I., Rig-
not, E., and Milillo, P.: Automatic delineation of glacier
grounding lines in differential interferometric synthetic-aperture
radar data using deep learning, Sci. Rep.-UK 11, 4992,
https://doi.org/10.1038/s41598-021-84309-3, 2021a.
Mohajerani, Y., Jeong, S., Scheuchl, B., Velicogna, I., Rig-
not, E., and Milillo, P.: Automatic delineation of glacier
grounding lines in differential interferometric synthetic-
aperture radar data using deep learning, Dryad [data set],
https://doi.org/10.7280/D1VD6G, 2021b.
Moon, T., Joughin, I., Smith, B., Van Den Broeke, M. R., Van De
Berg, W. J., Noël, B., and Usher, M.: Distinct patterns of seasonal
Greenland glacier velocity, Geophys. Res. Lett., 41, 7209–7216,
https://doi.org/10.1002/2014GL061836, 2014.
Montgomery, L., Miège, C., Miller, J., Scambos, T.A., Wallin, B.,
Miller, O., Solomon, D. K., Forster, R., and Koenig, L.: Hydro-
logic properties of a highly permeable firn aquifer in the Wilkins
Ice Shelf, Antarctica, Geophys. Res. Lett., 47, e2020GL089552,
https://doi.org/10.1029/2020GL089552, 2020.
Morlighem, M., Rignot, E., Binder, T., Blankenship, D., Drews, R.,
Eagles, G., Eisen, O., Ferraccioli, F., Forsberg, R., Fretwell, P.,
Goel, V., Greenbaum, J. S., Gudmundsson, H., Guo, J., Helm,
V., Hofstede, C., Howat, I., Humbert, A., Jokat, W., Karlsson,
N. B., Lee, W. S., Matsuoka, K., Millan, R., Mouginot, J.,
Paden, J., Pattyn, F., Roberts, J., Rosier, S., Ruppel, A., Seroussi,
H., Smith, E. C., Steinhage, D., Sun, B., van den Broeke, M.
R., van Ommen, T. D., van Wessem, M., and Young, D. A.:
Deep glacial troughs and stabilizing ridges unveiled beneath the
margins of the Antarctic ice sheet, Nat. Geosci., 13, 132–137,
https://doi.org/10.1038/s41561-019-0510-8, 2020.
Mouginot, J., Scheuchl, B., and Rignot, E.: Mapping of Ice Mo-
tion in Antarctica Using Synthetic-Aperture Radar Data, Remote
Sens., 4, 2753–2767, https://doi.org/10.3390/rs4092753, 2012.
Mouginot, J., Scheuchl, B., and Rignot, E.: MEaSURES
Antarctic Boundaries for IPY 2007–2009 from Satel-
lite Radar, Version 2, NASA National Snow and Ice
Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/AXE4121732AD, 2017.
Nagler, T., Rott, H., Hetzenecker, M., Wuite, J., and Potin,
P.: The Sentinel-1 Mission: New Opportunities for
Ice Sheet Observations, Remote Sens., 7, 9371–9389,
https://doi.org/10.3390/rs70709371, 2015.
Nagler, T., Wuite, J., Libert, L., Hetzenecker, M., Keuris,
L., and Rott, H.: Continuous Monitoring of Ice Motion
and Discharge of Antarctic and Greenland Ice Sheets and
Outlet Glaciers by Sentinel-1 A amp; B, in: 2021 IEEE
International Geoscience and Remote Sensing Symposium
IGARSS, presented at the 2021 IEEE International Geo-
science and Remote Sensing Symposium IGARSS, 1061–1064,
https://doi.org/10.1109/IGARSS47720.2021.9553514, 2021.
Naughten, K. A., Meissner, K. J., Galton-Fenzi, B. K., England,
M. H., Timmermann, R., and Hellmer, H. H.: Future Projec-
tions of Antarctic Ice Shelf Melting Based on CMIP5 Scenarios,
J. Climate, 31, 5243–5261, https://doi.org/10.1175/JCLI-D-17-
0854.1, 2018.
Paolo, F. S., Fricker, H. A., and Padman, L.: Volume loss from
Antarctic ice shelves is accelerating, Science, 348, 327–331,
https://doi.org/10.1126/science.aaa0940, 2015.
Paolo, F. S., Padman, L., Fricker, H. A., Adusumilli, S., Howard,
S., and Siegfried, M. R.: Response of Pacific-sector Antarctic ice
shelves to the El Niño/Southern Oscillation, Nat. Geosci., 11,
121–126, https://doi.org/10.1038/s41561-017-0033-0, 2018.
Park, J. W., Gourmelen, N., Shepherd, A., Kim, S. W.,
Vaughan, D. G., and Wingham, D. J.: Sustained retreat of
the Pine Island Glacier, Geophys. Res. Lett., 40, 2137–2142,
https://doi.org/10.1002/grl.50379, 2013.
Petty, A. A., Holland, P. R., and Feltham, D. L.: Sea ice and the
ocean mixed layer over the Antarctic shelf seas, The Cryosphere,
8, 761–783, https://doi.org/10.5194/tc-8-761-2014, 2014.
Pritchard, H. D., Arthern, R. J., Vaughan, D. G., and Edwards,
L. A.: Extensive dynamic thinning on the margins of the
Greenland and Antarctic ice sheets, Nature, 461, 971–975,
https://doi.org/10.1038/nature08471, 2009.
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022
K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula 3931
Pritchard, H. D., Ligtenberg, S. R. M., Fricker, H. A., Vaughan, D.
G., Van Den Broeke, M. R., and Padman, L.: Antarctic ice-sheet
loss driven by basal melting of ice shelves, Nature, 484, 502–505,
https://doi.org/10.1038/nature10968, 2012.
Rack, W. and Rott, H.: Pattern of retreat and disintegration of the
Larsen B ice shelf, Antarctic Peninsula, Ann. Glaciol., 39, 505–
510, https://doi.org/10.3189/172756404781814005, 2004.
Rignot, E., Mouginot, J., and Scheuchl, B.: MEaSUREs Antarc-
tic Grounding Line from Differential Satellite Radar In-
terferometry, Version 2, NASA National Snow and Ice
Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/IKBWW4RYHF1Q, 2016.
Rignot, E., Casassa, G., Gogineni, P., Krabill, W., Rivera, A. U.,
and Thomas, R.: Accelerated ice discharge from the Antarctic
Peninsula following the collapse of Larsen B ice shelf, Geophys.
Res. Lett., 31, 18, https://doi.org/10.1029/2004GL020697, 2004.
Rignot, E., Mouginot, J., and Scheuchl, B.: Ice flow
of the Antarctic Ice Sheet, Science, 333, 1427–1430,
https://doi.org/10.1126/science.1208336, 2011a.
Rignot, E., Mouginot, J., and Scheuchl, B.: Antarc-
tic grounding line mapping from differential satellite
radar interferometry, Geophys. Res. Lett., 38, L10504,
https://doi.org/10.1029/2011GL047109, 2011b.
Rignot, E., Jacobs, S., Mouginot, J., and Scheuchl, B.: Ice-
Shelf Melting Around Antarctica, Science, 341, 266–270,
https://doi.org/10.1126/science.1235798, 2013.
Rignot, E., Mouginot, J., Morlighem, M., Seroussi, H., and
Scheuchl, B.: Widespread, rapid grounding line retreat of Pine
Island, Thwaites, Smith, and Kohler glaciers, West Antarc-
tica, from 1992 to 2011, Geophys. Res. Lett., 41, 3502–3509,
https://doi.org/10.1002/2014GL060140, 2014.
Rignot, E., Mouginot, J., and Scheuchl, B.: MEaSUREs InSAR-
Based Antarctica Ice Velocity Map, Version 2, Boulder, Colorado
USA, NASA National Snow and Ice Data Center Distributed Ac-
tive Archive Center, https://doi.org/10.5067/D7GK8F5J8M8R,
2017.
Rignot, E., Mouginot, J., Scheuchl, B., Van Den Broeke,
M., Van Wessem, M. J., and Morlighem, M.: Four
decades of Antarctic Ice Sheet mass balance from
1979–2017, P. Natl. Acad. Sci. USA, 116, 1095–1103,
https://doi.org/10.1594/PANGAEA.896940, 2019.
Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Mad-
sen, S. N., Rodriguez, E., and Goldstein, R. M.: Syn-
thetic aperture radar interferometry, Proc. IEEE, 88, 333–382,
https://doi.org/10.1109/5.838084, 2000.
Rott, H., Skvarca, P., and Nagler, T.: Rapid collapse of north-
ern Larsen ice shelf, Antarctica, Science, 271, 788–792,
https://doi.org/10.1126/science.271.5250.788, 1996.
Rott, H., Wuite, J., De Rydt, J., Gudmundsson, G. H., Floricioiu,
D., and Rack, W.: Impact of marine processes on flow dynamics
of northern Antarctic Peninsula outlet glaciers, Nat. Commun.,
11, 1–3, https://doi.org/10.1038/s41467-020-16658-y, 2020.
Scambos, T. A., Hulbe, C., Fahnestock, M., and Bohlander,
J.: The link between climate warming and break-up of ice
shelves in the Antarctic Peninsula, J. Glaciol., 46, 516–530,
https://doi.org/10.3189/172756500781833043, 2000.
Scambos, T. A., Bohlander, J. A., Shuman, C. A., and Skvarca,
P.: Glacier acceleration and thinning after ice shelf collapse in
the Larsen B embayment, Antarctica, Geophys. Res. Lett., 31,
L18402, https://doi.org/10.1029/2004GL020670, 2004.
Scambos, T., Fricker, H. A., Liu, C. C., Bohlander, J., Fas-
took, J., Sargent, A., Massom, R., and Wu, A. M.: Ice
shelf disintegration by plate bending and hydro-fracture: Satel-
lite observations and model results of the 2008 Wilkins
ice shelf break-ups, Earth Planet. Sc. Lett., 280, 51–60,
https://doi.org/10.1016/j.epsl.2008.12.027, 2009.
Schannwell, C., Cornford, S., Pollard, D., and Barrand, N. E.: Dy-
namic response of Antarctic Peninsula Ice Sheet to potential col-
lapse of Larsen C and George VI ice shelves, The Cryosphere,
12, 2307–2326, https://doi.org/10.5194/tc-12-2307-2018, 2018.
Schoof, C.: Ice-sheet acceleration driven by melt supply variabil-
ity, Nature, 468, 803–806, https://doi.org/10.1038/nature09618,
2010.
Selley, H. L., Hogg, A. E., Cornford, S., Dutrieux, P., Shepherd,
A., Wuite, J., Floricioiu, D., Kusk, A., Nagler, T., Gilbert, L.,
and Slater, T.: Widespread increase in dynamic imbalance in the
Getz region of Antarctica from 1994 to 2018, Nat. Commun., 12,
1–10, https://doi.org/10.1038/s41467-021-21321-1, 2021.
Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W.
H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X.,
Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi,
B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve,
R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts,
P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry,
D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price,
S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Si-
mon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van
Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C.,
Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model
ensemble of the Antarctic ice sheet evolution over the 21st cen-
tury, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-
14-3033-2020, 2020.
Steig, E. J., Ding, Q., Battisti, D. S., and Jenkins, A.: Tropical forc-
ing of Circumpolar Deep Water Inflow and outlet glacier thin-
ning in the Amundsen Sea Embayment, West Antarctica, Ann.
Glaciol., 53, 19–28, https://doi.org/10.3189/2012AoG60A110,
2012.
The IMBIE team: Mass balance of the Antarctic Ice
Sheet from 1992 to 2017, Nature, 558, 219–222,
https://doi.org/10.1038/s41586-018-0179-y, 2018.
Thoma, M., Jenkins, A., Holland, D., and Jacobs, S.: Modelling
Circumpolar Deep Water intrusions on the Amundsen Sea con-
tinental shelf, Antarctica, Geophys. Res. Lett., 35, L18602,
https://doi.org/10.1029/2008GL034939, 2008.
Trusel, L. D., Frey, K. E., Das, S. B., Munneke, P. K., and van
den Broeke, M. R.: Satellite-based estimates of Antarctic sur-
face meltwater fluxes, Geophys. Res. Lett., 40, 6148–6153,
https://doi.org/10.1002/2013GL058138, 2013.
Turner, J., Lu, H., White, I., King, J. C., Phillips, T., Hosking,
J. S., Bracegirdle, T. J., Marshall, G. J., Mulvaney, R., and
Deb, P.: Absence of 21st century warming on Antarctic Penin-
sula consistent with natural variability, Nature, 535, 411–415,
https://doi.org/10.1038/nature18645, 2016.
van Wessem, J. M., Steger, C. R., Wever, N., and van den
Broeke, M. R.: An exploratory modelling study of perennial
firn aquifers in the Antarctic Peninsula for the period 1979–
https://doi.org/10.5194/tc-16-3907-2022 The Cryosphere, 16, 3907–3932, 2022
3932 K. Boxall et al.: Seasonal land-ice-flow variability in the Antarctic Peninsula
2016, The Cryosphere, 15, 695–714, https://doi.org/10.5194/tc-
15-695-2021, 2021.
Vaughan, D. G., Smith, A. M., Nath, P. C., and Meur,
E. L.: Acoustic impedance and basal shear stress beneath
four Antarctic ice streams, Ann. Glaciol., 36, 225–232,
https://doi.org/10.3189/172756403781816437, 2003.
Vieli, A. and Nick, F. M.: Understanding and Modelling
Rapid Dynamic Changes of Tidewater Outlet Glaciers:
Issues and Implications, Surv. Geophys., 32, 437–458,
https://doi.org/10.1007/s10712-011-9132-4, 2011.
Webber, B. G., Heywood, K. J., Stevens, D. P., Dutrieux, P., Abra-
hamsen, E. P., Jenkins, A., Jacobs, S. S., Ha, H. K., Lee, S.
H., and Kim, T. W.: Mechanisms driving variability in the
ocean forcing of Pine Island Glacier, Nat. Commun., 8, 1–8,
https://doi.org/10.1038/ncomms14507, 2017.
Winter, K., Hill, E. A., Gudmundsson, G. H., and Woodward, J.:
Subglacial topography and ice flux along the English Coast of
Palmer Land, Antarctic Peninsula, Earth Syst. Sci. Data, 12,
3453–3467, https://doi.org/10.5194/essd-12-3453-2020, 2020.
Wuite, J., Rott, H., Hetzenecker, M., Floricioiu, D., De Rydt,
J., Gudmundsson, G. H., Nagler, T., and Kern, M.: Evolu-
tion of surface velocities and ice discharge of Larsen B out-
let glaciers from 1995 to 2013, The Cryosphere, 9, 957–969,
https://doi.org/10.5194/tc-9-957-2015, 2015.
Zwally, H. J., Abdalati, W., Herring, T., Larson, K., Saba,
J., and Steffen, K.: Surface melt-induced acceleration
of Greenland ice-sheet flow, Science, 297, 218–222,
https://doi.org/10.1126/science.1072708, 2002.
The Cryosphere, 16, 3907–3932, 2022 https://doi.org/10.5194/tc-16-3907-2022