In the 2019/2020 austral summer, the surface melt duration and
extent on the northern George VI Ice Shelf (GVIIS) was exceptional compared
to the 31 previous summers of distinctly lower melt. This finding is based
on analysis of near-continuous 41-year satellite microwave radiometer and
scatterometer data, which are sensitive to meltwater on the ice shelf
surface and in the near-surface snow. Using optical satellite imagery from
Landsat 8 (2013 to 2020) and Sentinel-2 (2017 to 2020), record volumes of
surface meltwater ponding were also observed on the northern GVIIS in
2019/2020, with 23 % of the surface area covered by 0.62 km3 of ponded meltwater on 19 January. These exceptional melt and
surface ponding conditions in 2019/2020 were driven by sustained air
temperatures ≥0∘C for anomalously long periods (55 to 90 h)
from late November onwards, which limited meltwater refreezing.
The sustained warm periods were likely driven by warm, low-speed (≤7.5 m s-1) northwesterly and northeasterly winds and not by foehn wind
conditions, which were only present for 9 h total in the 2019/2020 melt
season. Increased surface ponding on ice shelves may threaten their
stability through increased potential for hydrofracture initiation; a risk
that may increase due to firn air content depletion in response to
near-surface melting.
Introduction
Since the 1950s, the Antarctic Peninsula (AP) (Fig. 1a) has experienced
faster increases in ocean and atmospheric warming than the rest of the
Antarctic Ice Sheet (Siegert et al., 2019; Smith et al., 2020; Trusel et al.,
2015). The rate of mass loss from the AP has tripled since the 1990s, with
an average of 24 Gt yr-1 from 1979 to 2017 and an acceleration of 16 Gt yr-1 per decade (Rignot et al., 2019). Mass loss is currently
focused at marine margins, where the mass balance is controlled by complex
interactions between the ice, ocean, atmosphere, and inland bed conditions
(Scambos et al., 2000; Bell et al., 2018; Shepherd et al., 2018; Tuckett et
al., 2019; Smith et al., 2020). An important part of this system are the ice
shelves, which have a total area of ∼ 120 000 km2 around
the AP (Siegert et al., 2019) and act to buttress the inland grounded ice
flowing into the ocean (Scambos et al., 2004; De Rydt et al., 2015; Fürst
et al., 2016; Gudmundsson et al., 2019).
Ice shelf surface melting, which results in surface lowering and (if
sustained) thinning (Paolo et al., 2015), is connected to ice shelf
stability as follows. In warm summers, meltwater produced at the ice shelf
surface is stored in the perennial snowpack (“firn”). Refreezing of this
meltwater releases latent heat into the firn, causing additional melting,
firn saturation, and firn air content depletion; eventually facilitating
meltwater ponding on the ice shelf surface (Holland et al., 2011; Kuipers
Munneke et al., 2014). Extensive surface ponding (Kingslake et al., 2017;
Arthur et al., 2020a; Dell et al., 2020) may threaten ice shelf stability
due to stress variations associated with overall meltwater movement, ponding,
and drainage (Scambos et al., 2000, 2003; MacAyeal et al., 2003; Banwell and
MacAyeal, 2015; Banwell, 2017; Banwell et al., 2019). These processes may
initiate meltwater-induced vertical fracturing (“hydrofracturing”) (Van der
Veen, 2007; Dunmire et al., 2020; Lai et al., 2020), especially if the ice
shelf is already damaged with a high density of crevasses (Lhermitte et al.,
2020). The near-complete collapse of the Larsen B Ice Shelf in 2002 is
arguably the most famous break-up event due to its rapidity and extent (e.g.
Scambos et al., 2003) and may have been driven by the drainage of
∼ 3000 lakes (Banwell et al., 2013; Robel and Banwell, 2019;
Leeson et al., 2020). However, surface melting has also been implicated in
the large-scale collapse events of the Prince Gustav and Larsen A ice
shelves over just a few days in late January 1995 (Rott et al., 1996; Doake
et al., 1998; Scambos et al., 2003; Glasser et al., 2011) and in other,
smaller-scale collapses of the Wilkins, Larsen B, George VI, and Larsen A
ice shelves (Scambos et al., 2003, 2009; Cook and Vaughan, 2010).
Occurrences of extreme melt seasons can lead to substantial changes that may
potentially impact the mass balance of the AP and consequently global sea
level rise. In the austral summer of 2019/2020, widespread surface meltwater
ponding was observed on ice shelves, low-elevation outlet glaciers, and
ice-capped islands of the AP (Fig. 1a). Out of all AP ice shelves, the most
extensive area of surface meltwater ponding in 2019/2020 was observed on the
northern George VI Ice Shelf (GVIIS), which is the focus of this study (Fig. 1b).
However, as Fig. 1a shows, in 2019/2020 surface meltwater ponding was also
prevalent on the northwestern Larsen C (Bevan et al., 2020), the eastern
Wilkins (also visible in the bottom-left corner of Fig. 1b), and the northern
and northwestern Bach ice shelves. This extensive surface ponding across the
AP was accompanied by a record-high (as of yet unverified) instantaneous
surface air temperature of 18.4 ∘C, recorded by an automatic
weather station (AWS) at Esperanza on the northern tip of the AP on 6
February 2020
(https://public.wmo.int/en/media/news/new-record-antarctic-continent-reported,
last access: 8 January 2021).
(a) Mosaic of cloud-free Moderate Resolution Imaging
Spectroradiometer (MODIS) images over the AP from 19 January to 7 February
2020. The MODIS mosaic is sea ice masked and the ice shelves are delineated
with grey lines using the U.S. National Ice Center Operational Antarctic Ice
Front and Coastline Data Set 2017–2020 (Readinger, 2021). Ice shelves are
labelled with white text; those with * have lost >50 % of
their original area since the 1950s (Cook and Vaughan, 2010). The red
outline shows the study's area of interest (AOI) over the northern GVIIS. The
orange box depicts the area shown in (b). (b) A mosaic of optical images
over the northern GVIIS AOI. All images are Sentinel-2 tiles dated 19 January 2020, apart from the two darker tiles (top right and lower right,
outside of the AOI), which are Landsat 8 image tiles from 17 and 19 January 2020. The study AOI is delineated by the red outline, and the yellow star
shows the location of the Fossil Bluff AWS.
Study site
GVIIS is located in the southwestern AP between Alexander Island and Palmer
Land (Fig. 1). With an area of ∼ 23 500 km2 (Rignot et
al., 2013), it is the second largest remaining ice shelf on the AP after the
Larsen C. GVIIS has two ice fronts, separated by ∼450 km
along its centreline: a northern ice front that calves into Marguerite Bay,
and a southern ice front that terminates into the Ronne Entrance (Holt et
al., 2013). GVIIS is structurally complex, with distinct flow units
originating in Palmer Land flowing across to, and impinging against,
Alexander Island (Reynolds and Hambrey, 1988; Hambrey et al., 2015; Davies
et al., 2017), resulting in a dominantly compressive flow regime (LaBarbera
and MacAyeal, 2011). The ice shelf decelerates as it flows westwards across
the sound, with ice velocities on the northern GVIIS varying from
∼ 400 m yr-1 near the grounding line to ∼ 30 m yr-1 near Alexander Island (Bishop and Walton 1981). This complex
flow regime controls ice shelf thickness, which varies from ∼ 100 m at both ice fronts to ∼ 600 m in the centre (Smith et
al., 2007; Davies et al., 2017).
Compared to the southern GVIIS (∼ 72∘00′ to
∼ 77∘00′ S), the northern GVIIS (∼ 70∘30′ to ∼ 72∘00′ S) experiences higher surface
summer melt rates (<250 mm w.e. yr-1; Trusel et al., 2013;
Datta et al., 2018) and lower accumulation rates (< 200 kg m-2 yr-1; Bishop and Walton, 1981; Reynolds, 1981); the latter is
attributed to the presence of a precipitation shadow downwind of Alexander
Island (Bishop and Walton, 1981). As a result, winter snowfall on the
northern GVIIS rarely lasts through the summer (Holt et al., 2013), and
extensive areas of ponded surface water have been observed here since at
least the early 1940s (Wager, 1972; Reynolds, 1981). However, as these
surface lakes have generally been observed as refreezing at the end of each
austral summer, with only limited evidence of meltwater drainage into
ice-marginal moulins (Reynolds, 1981), minimal mass is lost through surface
melting. Instead, mass is mostly lost due to high basal melt rates of
<6 m yr-1 (Adusumilli et al., 2020), attributed to the warm
Circumpolar Deep Water current that extends under the entire length of the
GVIIS (Holland et al., 2010; Pritchard et al., 2012), though rates of basal
melting are greatest at the ice shelf's southern end (Adusumilli et al.,
2020; Smith et al., 2020). High basal melt rates have resulted in sustained
thinning rates of <2 m yr-1 for the southern GVIIS (Pritchard
et al., 2012), which together with frontal calving (Pearson and Rose, 1983;
Reynolds and Hambrey, 1988; Lucchitta and Rosanova, 1998) have contributed
to the ice shelf's negative net mass balance since at least 2003 (Rignot et
al., 2013; Paolo et al., 2015). As an example, Rignot et al. (2019) estimated
that the GVIIS lost 9 Gt of mass in 2017, compared to a balance flux of 70±4 Gt yr-1. Due to the strong buttressing forces that the GVIIS
provides relative to the large volume of grounded ice in Palmer Land, if
this ice shelf were to completely collapse, the resultant acceleration of
the inland glaciers would add <8 mm to global sea levels by 2100
and <22 mm by 2300 (Schannwell et al., 2018). In contrast,
Schannwell et al. (2018) calculate that sea level contributions resulting
from the collapse of the much larger Larsen C Ice Shelf would be relatively
low (<2.5 mm by 2100, <4.2 mm by 2300).
On the northern GVIIS, three types of surface lake patterns usually form
each summer. The principal pattern of lakes, which are generally the most
extensive in area, is aligned with the ice flow lines (Reynolds, 1981; Smith
et al., 2007), similar to the dominant pattern of lakes on the Amery Ice
Shelf (Hambrey and Dowdeswell, 1994). This set of lakes is intersected by a
second pattern of generally smaller, ribbon-type lakes, which lie parallel
to the prevailing wind (Reynolds, 1981), suggesting that wind processes
initiate the surface depressions that meltwater then fills. These first two
sets of lakes appear to remain in similar locations each year due to the ice
shelf's overall compressive flow; i.e. unlike the situation on most ice
shelves where lakes move with ice flow towards the shelf front (Banwell et
al., 2014; Langley et al., 2016; Arthur et al., 2020b). The third set of
lakes are the deepest and exist within pressure ridge complexes along the
western margin of the ice shelf, onto which ice shelf flow is directed
(Reynolds, 1981). These lakes are therefore en échelon (i.e. closely
spaced, sub-parallel) in shape and propagate along the ice shelf margin and
hence have been referred to as “travelling lakes” (LaBarbera and MacAyeal,
2011).
Unlike other AP ice shelves that have fully or partially disintegrated due
to high rates of surface and/or basal melting, the retreat of GVIIS thus far
has been relatively gradual, despite this ice shelf having the most
extensive meltwater ponding and the longest history of surface lakes of any
AP ice shelf (Smith et al., 2007). This is likely due to the GVIIS' unique
geographical setting, with its dominantly compressive flow regime, as
described above, enabling it to support a large volume of surface meltwater
(Alley et al., 2018; Lai et al., 2020).
In this study we focus on the northern area of the GVIIS only; defined as
our area of interest (AOI) (see Fig. 1b, location shown by the red outline)
with a total area of 7850 km2. This is the region where a high density
of surface lakes are often observed each melt season.
Data and methods
To quantify our understanding of surface melt over the northern GVIIS for
the austral summers from 1979/1980 to 2019/2020, we analyse large-scale melt
information from 25 km gridded passive microwave observations for both the
AP and the northern GVIIS. For the northern GVIIS, these data are
corroborated by smaller-scale (4.45 km) active microwave observations
available from 2007/2008 to 2019/2020. For austral summers from 2013/2014 to
2019/2020, we also calculate volumes of ponded meltwater on the northern
GVIIS from all available cloud-free optical images from the Landsat 8 (2013
to 2020) and Sentinel-2 (2017 to 2020) satellites. Both our
microwave-derived melt and optical image-derived surface ponding results are
evaluated alongside surface air temperature and wind data from the British
Antarctic Survey (BAS) Fossil Bluff AWS (1979 to 2020) on the northwestern
margin of the GVIIS (Fig. 1b, yellow star).
Large-scale microwave radiometer observations of melt
Microwave radiometer observations of melt, expressed as brightness
temperatures, depend primarily on the snow temperature profile and
emissivity (Zwally, 1977). When liquid water exists in the snow, there is a
significant increase in the absorption and therefore an increase in the
microwave emissivity, resulting in a higher brightness temperature.
Large-scale melt information over the AP, including the GVIIS, was derived
from microwave radiometer (i.e. passive) observations using the 1979 to 2020
near-daily 25 km melt product (version 2) of Picard et al. (2007) and Picard
and Fily (2006), distributed on a polar stereographic grid. This
melt or no-melt product, which has been used in several previous studies (e.g.
Magand et al., 2008; Brucker et al., 2010; Wille et al., 2019), is based on
the algorithm of Torinesi et al. (2003) that identifies the higher microwave
brightness temperatures corresponding to melt using the radiation observed
at 19 GHz in horizontal polarization. If the observed brightness temperature
on a given day exceeds an empirical threshold (defined by the mean and
variability of the brightness temperatures observed during the previous
winter season, when melt did not occur), the algorithm reports melt in the
25 km grid cell. Throughout this paper, we use the word “melt” when
referring to the presence of liquid meltwater (either in the near-surface
snow or on the surface) in the microwave data but note that we are not
referring to the process of active melting; information that is that specific cannot
be obtained from passive microwave data.
The 1979 to 2020 brightness temperature time series was acquired by five
successive sensors. The Scanning Multichannel Microwave Radiometer (SMMR),
on the Nimbus 7 satellite launched in late October 1978, collected data at
18 GHz (while the sensor operated every other day, daily averaged brightness
temperatures were used as input). Starting in 1987, the series of Special
Sensor Microwave Imager (SSMI) sensors on the Department of Defense
Meteorological Satellite Program (DMSP) platforms F8, F11, F13, and F17
collected data at 19 GHz. It is worth noting that there was a significant data gap between
3 December 1987 and 14 January 1988, and therefore we do not include any
data from this melt season in our analysis. Although the melt data are
provided with a spatial resolution of 25 km, the radiometers' 3 dB fields of
view at 19 GHz are far larger (e.g. 69 km × 43 km for SSMI). Grid cells
with surface elevations >1700 m a.s.l. were masked out so that
melt over the ice shelves was predominantly analysed and so that large
topographic features (i.e. mountain peaks) in the radiometer's field of view
were avoided.
Based on radiative transfer simulations, radiometer brightness temperatures
at 19 GHz are typically sensitive to melt down to a snow depth of
∼2 m (Picard et al., 2007; Leduc-Leballeur et al., 2020). Wet
snow has a very high emissivity compared to dry snow, but a flat surface of
liquid water has a low emissivity as well (Zwally, 1977). Therefore, at the
transition from dry to wet snow, brightness temperatures increase quickly
(i.e. indicating the presence of meltwater), but if melt intensifies,
resulting in the formation of surface lakes, the brightness temperatures
decrease. This effect has been observed over sea ice when melt ponds are
extensive (e.g. Kern et al., 2020). Cautious interpretation of the “melt
day” maps is therefore required, particularly if surface ponding represents
a large proportion of a grid cell's total area.
For austral melt seasons from 1979/1980 to 2019/2020 (apart from 1987/1988
due to its missing data) and for each 25 km grid cell, we calculated the
daily time series of microwave-radiometer-derived melt or no melt and the
cumulative melt days each melt season (defined as 1 November to 31 March
inclusive). This was done for both the whole AP (i.e. extent of Fig. 1a) and
for the northern GVIIS AOI (Fig. 1b, red outline).
Small-scale microwave scatterometer observations of melt
For the northern GVIIS, we also derive smaller-scale melt information from
an enhanced resolution C-band (5.225 GHz) VV polarization radar backscatter
image time series collected by EUMETSAT's Advanced SCATterometer (ASCAT),
aboard the tandem polar-orbiting satellites MetOp-A and MetOp-B. The
4.45 km enhanced product is obtained by applying the Scatterometer Image
Reconstruction (SIR) algorithm with filtering (Lindsley and Long, 2016),
which is used to improve the spatial resolution of irregularly and
oversampled data (Early and Long, 2001). The effective spatial resolution
was estimated at ∼ 12–15 km, three-fold finer than the
effective resolution of the SMMR/SSMI-based product (∼ 50 km).
For each day and for each grid cell, melt is assumed to be present when the
ASCAT signal is lower than the winter mean signal minus 3 dB, as proposed by
Ashcraft and Long (2006) using a melt model and QuikSCAT Ku-band (13.4 GHz)
observations. Where snow and firn layers are completely frozen, the C-band
penetration depth is on the order of metres to tens of metres, but where
snow and firn layers have a high volumetric fractions of meltwater, the
penetration depth is likely to be up to tens of centimetres only (Weber Hoen
and Zebker, 2000). As the penetration depth at 5 GHz in dry snow and firn is
larger than at 19 GHz, ASCAT C-band radar is likely to be more sensitive to
melt at depth than microwave radiometers at 19 GHz.
For austral melt seasons from 2007 to 2020, we calculate
scatterometer-derived cumulative melt days for each 4.45 km grid cell over
our study AOI.
Landsat 8 and Sentinel-2 derived meltwater areas and volumes
To calculate the time series of areal extents, depths, and therefore total
volumes of surface meltwater lakes on the northern GVIIS for the seven
austral summers from 2013/2014 to 2019/2020, we applied the threshold-based
algorithm developed by Moussavi et al. (2020) to selected multispectral
imagery (see below) from Landsat 8 (30 m resolution, since 2013) and
Sentinel-2 (10 m resolution, since 2017). Technical specifications for
Landsat 8's Operational Land Imager data are available online from NASA
(https://landsat.gsfc.nasa.gov/operational-land-imager-oli/, last access: 9 February 2021), and technical
specifications for Sentinel-2's MultiSpectral Instrument are available
online from the ESA
(https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi, last access: 9 February 2021).
Analysis of pre-2013 optical imagery could have been undertaken by tuning
Moussavi et al.'s (2020) threshold-based algorithm for the Landsat 7 Enhanced
Thematic Mapper Plus (ETM+) sensor. However, significant data are missing
since May 2003 due to the failure of the scan line corrector (SLC) on ETM+
causing SLC-off gaps. Therefore, lake volumes derived from this sensor would
not be easily comparable to Landsat 8 and Sentinel-2 and therefore would
not necessarily extend the time series.
Moussavi et al.'s (2020) method, developed in parallel for Landsat 8 and
Sentinel-2, combines separate threshold-based algorithms to detect (1) lakes, (2) rocks, and (3) clouds. Optimal thresholds for each band and band
combination (e.g. Normalized Difference Water Index (NDWI), Normalized
Difference Snow Index (NDSI), and others) were determined by creating a
training dataset based on selected Landsat 8 and Sentinel-2 images, which
represented spectral properties of several classes (e.g. lakes, slush, snow,
clouds, rocks, cloud shadows). Most notably, to classify liquid-water-covered pixels, the NDWI is used (Pope et al., 2016; Bell et al.,
2017) with NDWI thresholds of >0.19 and >0.18 for
Landsat 8 and Sentinel-2, respectively. Subsequently, to calculate the water
depths of those pixels determined to be water-covered, Moussavi et al. (2020) apply a physically based algorithm that has more commonly been
applied in Greenland (Sneed and Hamilton, 2007; Banwell et al., 2014; Pope
et al., 2016; Williamson et al., 2018) and more recently in Antarctica (Bell
et al., 2017; Dell et al., 2020; Arthur et al., 2020b). This algorithm
calculates lake water depth using the rate that sunlight passing through a
water column is attenuated with depth, lake-bottom albedo, and optically
deep water reflectance (Philpot, 1989). This approach makes a number of
assumptions, including that (1) the lake bottom has a homogenous albedo, (2) there is little to no particulate matter in the water column to alter its
optical properties, and (3) there is minimal wind-induced surface roughness
(Sneed and Hamilton, 2007).
All Landsat 8 and Sentinel-2 images acquired from 1 November to 31 March
each austral summer with a solar angle of >15∘, with ≥0.45 km2 water-covered pixels (equivalent to 500 Landsat pixels or
4500 Sentinel-2 pixels; Moussavi et al., 2020) and which overlapped our
study's AOI (Fig. 1b, red outline) were analysed using the methods
described above. Once the images had been analysed, all tiles with the same
date were mosaicked together and then clipped to a mask of our AOI in the
Geographic Information System package, QGIS v3.2. In total, for Landsat 8
we analysed mosaicked images for 191 dates from 6 December 2013 to 12 March 2020, and for Sentinel-2 we analysed mosaicked images for 14 dates from 3 January 2017 to 19 January 2020. Of those images, nine Landsat 8 and
Sentinel-2 image mosaics had the same dates, and thus we merged those. First, we
resampled the Sentinel-2 data (10 m resolution) to the resolution of
Landsat (30 m). Second, we kept overlapping water-covered pixels in
preference to dry pixels and kept the largest depths of the
overlapping water-covered pixels. This resulted in a time series of 196
mosaicked images from 6 December 2013 to 12 March 2020 (Table S1 in the Supplement). Errors and
uncertainties associated with lake area and depth retrieval methods for each
sensor are thoroughly discussed in Moussavi et al. (2016, 2020), Pope et al. (2016), Williamson et al. (2018), and Fricker et al. (2020).
Due to temporally varying satellite paths and/or cloud cover, only 11 out of
the 196 mosaicked images covered the entirety of our AOI (Table S1 in the Supplement).
Therefore, to be able to compare areas and volumes of surface meltwater on
dates with incomplete AOI coverage, we first created a mask of all pixels
that were wet on at least 1 of the 196 dates analysed from 2013 to 2020
(Williamson et al., 2018), hereafter called a “maximum wetted area mask”.
Second, we created a “maximum volume mask” by assigning all wet pixels in
the maximum wetted area mask a depth equal to the maximum water depth
observed out of all 196 images. Finally, for each image mosaic with ≥10 % cloud-free coverage of our AOI (113 image dates), we normalized their
total area and total volume of meltwater to our entire AOI using the
following approaches. For each mosaicked image, we calculated the total
observed meltwater area as a fraction of the total wetted area mask for the
equivalent area. This fraction was then multiplied by the total area of the
maximum wetted area mask over the whole AOI. To normalize the meltwater
volume to the AOI, we did the same but instead used the maximum volume
mask.
Local weather station data
We analyse the only available local AWS data in order to investigate the
possible atmospheric driver(s) of the exceptional melt event over the
northern GVIIS in 2019/2020. Near-surface (2 m) temperature, relative
humidity, wind direction, and wind speed data are available from the BAS
Fossil Bluff AWS (Fig. 1b, yellow star, location: -71.329∘ S, -68.267∘ W, 66 m a.s.l.), at 12 h intervals from 1979 to 1999 and at 5 or 10 min
intervals from 2000 to 2020. However, significant data gaps are present
between 2000 and early 2007.
First, we compare the 2019/2020 daily mean air temperatures with the daily
mean temperatures from 1979 to 2020 (using 12 h data at noon and midnight
local time), i.e. the complete time period for which we also have microwave
radiometer data. Second, for 2007 to 2020, which is when AWS data are
available at a higher frequency and data gaps are minimal (6 months in the
total record were missing values, but these were <15 % of the
expected total for each of those months), we use the 10 min data to
calculate the length of time (in hours) when surface air temperatures are
continuously ≥0∘C during each melt season. Although the air
temperature measured at a height of 2 m by the AWS will vary slightly from
that at the ice surface, for the purposes of this study, we assume these
temperatures to be equivalent (Kuipers Munneke et al., 2012). We also
consider the occurrence of foehn winds, which are warm, dry winds often
produced on the leeward side of mountains (Cape et al., 2015) and commonly
occur on the AP (Luckman et al., 2014; Elvidge et al., 2016). Over GVIIS,
the steep topography that generates foehn flow is provided by Alexander
Island. We analyse foehn wind occurrence using a modified version of a
metric previously used over the Larsen C Ice Shelf (Wiesenekker et al.,
2018; Datta et al., 2019), whereby a “foehn condition” is considered to
initiate when air temperatures increase by ≥1∘C, wind speed
increases by ≥ 1.5 m s-1, and relative humidity decreases by ≥5 %, all relative to the previous time step. We use a wind speed threshold
of 1.5 m s-1 instead of the higher threshold of 3.5 m s-1 used by
Datta et al. (2019) for the Cabinet Inlet AWS to account for lower foehn
wind speeds over the northern GVIIS, which result from the lower mean
elevation of the mountains on Alexander Island compared to those on the AP
west of Cabinet Inlet (van Wessem et al., 2015). This foehn condition is
assumed to remain until the conditions (with respect to the period preceding
the foehn condition) are no longer met. Finally, we also examine differences
in atmospheric regimes (temperature, wind direction and speed) within each
wind direction class (northeasterly, northwesterly, southeasterly, and
southwesterly).
We note that it is beyond the scope of this study for us to identify
specific mesoscale drivers of this exceptional melt event, especially as
2019/2020 is not a record melt season for the AP as a whole (see Sect. 4.1), and regional climate models frequently struggle to resolve localized
surface melt in regions with highly variable topography (Van Wessem et al.,
2015; Barrand et al., 2013), such as is the case for the GVIIS and its surrounding higher terrain.
ResultsMicrowave-radiometer-derived melt observations over the Antarctic
Peninsula
For the AP, cumulative melt days in the 2019/2020 austral melt season are
highest in the southwestern areas of the AP (including the Wilkins and
George VI ice shelves), in addition to the northern area of the Larsen C Ice
Shelf (Fig. 2b). In comparison, cumulative melt days in 2019/2020 are
relatively low over the southern areas of the Larsen C. The
spatially averaged cumulative melt days in the 2019/2020 melt season over
the entire AP amount to 47 d (Fig. 2b), which is 53 % higher (Fig. 2c) than
the spatially averaged climatology from 1979/1980 to 2019/2020 (31 d;
Fig. 2a). However, of these 41 melt seasons, the 1992/1993 melt season has
the most spatially averaged cumulative melt days over the AP (62 d;
Fig. S1 in the Supplement). During the 1992/1993 season, although cumulative melt days over
the northern GVIIS were only slightly higher than the 1979/1980 to 2019/2020
mean (Fig. S1d), the cumulative melt days on the Larsen C Ice Shelf were
particularly high, with a maximum of 117 cumulative melt days in the
southern area of this ice shelf (Fig. S1c). This finding is contrary to the
results of Bevan et al. (2020), who report that Larsen C experienced a
41-year record-high melt year in 2019/2020. Bevan et al.'s (2020) results are
based on microwave radiometer (SMMR/SSMI) data for melt seasons from
1979/1980 until 2016/2017, followed by microwave scatterometer (ASCAT) data
from 2017/2018 to 2019/2020. In contrast, we use SMMR/SSMI data over the AP
for the full 1979 to 2020 period to preserve consistency. As we explain in
Sect. 3.2, ASCAT C-band radar is likely to be more sensitive to melt at
depth than microwave radiometers, thus resulting in Bevan et al.'s (2020)
higher calculated melt over Larsen C in the 2019/2020 season when combining
data sources into one time series.
Microwave-radiometer-derived maps of surface and near-surface melt
days over the AP: (a) the climatology (i.e. mean cumulative melt days per
season) from 1979/1980 to 2019/2020 (excluding 1987/1988 due to missing
data), (b) cumulative melt days in 2019/2020, and (c) the 2019/2020 melt season
anomaly (i.e. b minus a). Melt days are counted within the period 1 November to 31 March (inclusive) each austral summer. The location of
the study AOI is shown by a red outline in (a) and (b) and as a green
outline in (c). The black outline of the AP is from the MODIS Mosaic of
Antarctica (Haran et al., 2014).
Microwave-derived melt observations over the northern GVIIS
Over the northern GVIIS, microwave-radiometer-derived spatially averaged
cumulative melt days over the study AOI (12 grid cells, total area = 7556 km2) in the 2019/2020 austral melt season amount to 101 d (Fig. 3b and d),
which is higher than for any other melt season since the record began in
1979/1980 and is 53 % higher (Fig. 3c) than the spatially averaged
climatology (66 melt days) from 1979/1980 to 2019/2020 (Figs. 3 and S2).
However, as 41 d of microwave radiometer data for the 1987/1988 season
are missing, we only conclude that 2019/2020 was the most significant melt
season over 32 years (Fig. 3d). This result is supported by the analysis of
scatterometer-derived melt data from ASCAT, which show that the number of
spatially averaged cumulative melt days over the study AOI in the 2019/2020
austral melt season was 117 d, which is 70 % higher than the spatially averaged
climatology (69 melt days) from 2007/2008 to 2019/2020 (Fig. 4). The
microwave-radiometer-derived data suggests that 1989/1990 has the second
highest number of spatially averaged cumulative melt days (93) over the study AOI
(Figs. 3d and S2).
Microwave-radiometer-derived cumulative melt days over the
northern GVIIS AOI (see Fig. 1b for location, red outline) from 1 November
to 31 March. (a–c) Maps of surface and near-surface melt days per 25 km grid
cell. The relative location and shape of the study's AOI is shown by the red
outline. White cells are out of the AOI. (a) Mean cumulative melt days for
each 25 km grid cell for austral summers from 1979/1980 to 2019/2020, apart
from 1987/1988 due to data unavailability. (b) Cumulative melt days per grid
cell in the 2019/2020 austral summer. (c) Anomaly of the 2019/2020 melt
season (i.e. b minus a). (d) Light blue bars represent
spatially averaged (i.e. over the 12 grid cells in the AOI) cumulative melt
days for each austral summer from 1979/1980 to 2019/2020 (apart from
1987/1988). The x axis dates indicate the second year of each austral
summer, e.g. 2020 corresponds to the 2019/2020 season. Black error bars
show ± 1 standard deviation from the spatially averaged cumulative
melt days. Dark blue bars show cumulative days when the melt extent is
100 % of the AOI for each summer from 1979/1980 to 2019/2020. For melt
seasons with missing data, the total number of missing data days is
indicated by the black number above the corresponding bar.
Using the microwave radiometer data to consider the cumulative days of
melting occurring over 100 % of the AOI each season, 2019/2020 also sees
the highest such number of days (93; see Fig. 3d, dark blue bars), and 1989/1990
sees the second highest number of days (85). These values can be compared to
a mean value of 53 cumulative melt days over 100 % of the AOI from
1979/1980 to 2019/2020. Note that for each season, we do not specifically
consider the mean areal extent of melting as this variable is found to be
almost directly proportional (r2=0.9973) to the spatially averaged
cumulative melt days (Fig. S3).
In terms of intra-annual patterns in percentage melt area over the northern
GVIIS in 2019/2020, the microwave radiometer data shows that 100 % of the
AOI area experiences melting every day from 24 November 2019 to 22 February 2020 (Fig. 5c). After 22 February, the area of melting drops to 0 % of the
AOI over just 3 d, which is consistent with a drop in the mean daily
air temperature (Fig. 5a). For a few weeks after 25 February, the area of
melting fluctuates significantly, consistent with the air temperature
fluctuating around 0 ∘C. On 6 and 7 March 2020, 100 % of the AOI
is observed to melt again. From 16 March 2020, no additional melting is
observed. Compared to the mean melt area over the two time periods shown in
Fig. 5c (i.e. 1979/1980 to 2019/2020 and 2013/2014 to 2019/2020), the
observed melt area in 2019/2020 covers 100 % of the AOI for a
significantly longer continuous period (91 d) relative to any other year
in this record.
Since addressing uncertainties associated with microwave data products of
binary melt–no melt information is challenging, this study uses two distinct
microwave remote sensing techniques and algorithms to build further
confidence in our conclusion. Moreover, our analysis of the sensitivity of
the microwave radiometer (Fig. S4) and scatterometer (Fig. S5) melt
detection algorithms to decreasing or increasing their threshold values shows
that the 2019/2020 melt season remains exceptional and that it is a 32-year record.
(a–c) Active microwave-derived (i.e. ASCAT) cumulative melt
days over the northern GVIIS AOI (see Fig. 1b for location, red outline),
relative to the passive microwave time series (Fig. 3a–c). (a) Mean
cumulative melt days for austral summers from 2007/2008 to 2019/2020. (b) Cumulative melt days in the 2019/2020 austral summer. (c) Anomaly of the
2019/2020 melt season (i.e. b minus a). (d) Red bars represent microwave
scatterometer-derived, spatially averaged (i.e. over the AOI) cumulative melt
days for austral summers from 1979/1980 to 2019/2020, with red error bars
showing ± 1 standard deviation from the mean. Blue bars show microwave-radiometer-derived, spatially averaged cumulative days from 1979/1980 to
2019/2020, with blue error bars showing ± 1 standard deviation from
the mean. The x axis dates indicate the second year of each austral summer,
e.g. 2020 = 2019/2020.
(a) Surface (2 m) air temperature data from the Fossil Bluff AWS
(location in Fig. 1b, yellow star). The daily mean air temperature for the
2019/2020 melt season is shown by the red line, daily mean temperatures
for the seven melt seasons from 2013/2014 to 2019/2020 are shown by the blue
line, ± 1 standard deviation from that blue line is shown by the areas
of blue shading, and the daily mean temperature from 1979 to 2020 (using
12 h data) is shown by the green line. The horizontal dashed black line
depicts 0 ∘C. (b) Calculated volumes of surface meltwater ponding in the
GVIIS AOI from 2013/2014 to 2019/2020 derived from Landsat 8 and Sentinel-2
optical imagery. Data from mosaicked images are only plotted if the image
includes >10 % of the study's AOI (Fig. 1, red outline) that
is cloud free; data from mosaicked images on 113 images total are shown. On
dates when imagery does not cover 100 % of the AOI, observed meltwater
volumes are normalized to the AOI (see Sect. 3.3 for further details and
Fig. S7 for a plot of all the observed meltwater volumes). (c) Microwave-radiometer-derived near-surface melt extent over the GVIIS AOI (Fig. 1b, red
outline) as a % of the total area (7556 km2). Daily areas of melting
for 2019/2020 are shown by the red line, the daily mean area of melting from
2013/2014 to 2019/2020 is shown by the dark blue line, and the daily mean
area of melting from 1979/1980 to 2019/2020 (excluding 1987/1988) is shown
by the light blue line.
Optical image-derived meltwater areas and volumes over the northern GVIIS
From 2013 to 2020, when we have Landsat 8 and/or Sentinel-2 optical imagery
available, the day with the maximum observed area (1.2×109 m2)
and volume (6.2×108 m3) of ponded surface meltwater on the
northern GVIIS is 19 January 2020 (Figs. 1b, 5b, S6 and S7, Table S1), when
23 % of the AOI is covered in ponded water. On this date, it is fortuitous
that the whole of our AOI is visible in a mosaic of cloud-free Sentinel-2
image scenes (Fig. 1b; background image) and is also fully visible in a
mosaic of Landsat 8 images acquired on 17 and 19 January (not shown).
Calculated areas and depths of meltwater lakes on 19 January 2020 over the
entire AOI are shown in Fig. S6. The mean depth of all water-covered pixels
on this date is 0.52 m, and the maximum depth is 3.9 m. Unlike on other
dates with much cloudier imagery, normalization of meltwater areas and
volumes to the AOI on 19 January 2020 is not required (see Sect. 3.3 for
method details, Fig. S7 for plots of both the observed and normalized
meltwater areas and volumes for the 2013/2014 to 2019/2020 melt seasons, and
Table S1 for details of all optical imagery analysed).
In all the seven melt seasons analysed with optical imagery, ponded surface
meltwater volumes do not peak until January or February (Fig. 5b). However,
in 2019/2020, meltwater volumes start to increase rapidly in late
December/early January, which is earlier than in any other season, and
corresponds with above-average air temperatures in late December 2020 (Fig. 5a, also see Sect. 4.4 for analysis of local weather conditions). In
2019/2020, volumes of meltwater ponding are highest in early January and
then again in early February; corresponding with periods when mean daily air
temperatures are ≥0∘C for extended periods (Fig. 5a). There is a
notable decrease in surface meltwater ponding volume in mid to late January 2020 during a period of substantially colder air temperatures (i.e.
<0∘C, Fig. 5a) that likely resulted in widespread refreezing
of surface meltwater (Fig. 5b).
The second largest melt season in terms of meltwater ponding is 2017/2018,
with a peak in total meltwater area (4.6×108 m2) and volume (2.5×108 m3) on 29 January 2018 (Figs. 5b and S7). However, these
two values are less than half of the respective values measured on 19 January 2020. Aside from 2019/2020 and 2017/2018 (i.e. the melt seasons with
the greatest and second greatest volumes of surface ponding, respectively),
the other five melt seasons have relatively low volumes of ponded meltwater.
Near-surface atmospheric conditions
Analysis of the mean daily surface air temperatures (derived from 12 h
values) from the Fossil Bluff AWS from 1979 to 2020 indicates that 2019/2020
is anomalously warm over five multi-day periods starting in late November
(Figs. 5a and S8). During these periods, mean daily air temperatures are ≥ 0 ∘C for sustained time periods of up to a week. The total number of positive
degree days for the 2019/2020 melt season (1 November to 31 March inclusive)
is 40, compared to 19±14 d (mean ± 1 standard
deviation) from 1979/1980 to 2019/2020.
We also analyse the high-resolution (10 min) AWS data from 2007 to 2020
to identify periods of sustained high temperatures, when it is possible that
no refreezing at all occurred during the diurnal cycles, potentially
enhancing the surface melt–albedo feedback effect. We find that the longest
continuous period when air temperatures are ≥0∘C in 2019/2020 is
90 h in early February (Fig. 6, cyan line, and Fig. 7, label C). The
longest five such time periods in 2019/2020 are labelled A to E in Fig. 7,
and it is notable that two pairs of periods, A and B and C and D, are only
separated by a matter of hours. The mean length of these five longest
periods when temperatures ≥0∘C in 2019/2020 is 61 h, which is
longer than for any other season in the 2007 to 2020 high-resolution AWS
record (Fig. 6, black line). We also note that the temperature during these
five periods is often more than 1 standard deviation greater than the
multi-year daily mean (Fig. 5a). Considering all recorded temperature data
in each melt season from 2007 to 2020, a higher percentage (33 %) of
2019/2020 has air temperatures ≥0∘C compared to any prior season
(Fig. 6, red line).
We also examine the potential role of foehn winds on driving melt in
2019/2020. Foehn conditions (as described in Sect. 3.4) are only present
for about 9 h over the entire 2019/2020 season (Fig. 6, blue line), and
occur in early and late summer (Fig. 7b, blue circles) when winds are
typically stronger. We also note that the total time during each season when
foehn conditions are calculated from AWS data has been relatively low since
the 2007/2008 and 2008/2009 melt seasons, which each had a total of about 72 h of foehn flow (Fig. 6). Therefore, foehn conditions do not appear to
be dominant in driving melt in the 2019/2020 season.
Analysis of sustained warm (≥0∘C) air temperature (T)
periods and foehn wind occurrence for the 2007/2008 to 2019/2020 melt
seasons. The cyan line shows the maximum number of consecutive hours in each
melt season when T≥0∘C. The black line shows the mean length
(hours) of the five longest periods when T≥0∘C for each season,
with the grey shading indicating ±1 standard deviation from that mean.
The red line shows the proportion of each season (1 November to 31 March)
when T is ≥0∘C. The blue line shows the total number of hours
spent in a foehn condition (see Sect. 3.4 for definition) each season. The
x axis dates indicate the second year of each austral summer, e.g. 2020
corresponds to the 2019/2020 season.
Finally, we analyse the potential role of warm air advection, resulting in
sensible heat transport, on the high melt in 2019/2020. Considering wind
direction alongside air temperature for melt seasons from 2007 to 2020, the
climatology indicates that northwesterly winds dominate flow at all
temperatures (Fig. 8a) but are more dominant when temperatures are ≥
0 ∘C (Fig. 8b) and are even more so when we limit analysis to
just the five longest periods of sustained temperatures ≥0∘C in each season (Fig. 8c). However, in the 2019/2020 season, northwesterly
winds are less dominant (33 % vs. 39 %; Fig. 8a), especially when only
temperatures ≥0∘C are considered (39 % vs. 47 %; Fig. 8b), and are further limited when only the five longest periods of sustained
temperatures ≥0∘C (Fig. 7; periods A–E) are considered
(37 % vs. 59 %; Fig. 8c). Instead, the proportion of wind coming from
the northeast is higher in 2019/2020 compared to the climatology (26 % vs.
24 %; Fig. 8a), particularly when temperatures are ≥0∘C (32 %
vs. 24 %, Fig. 8b).
The 2007 to 2020 climatology shows that (as expected) northwesterly winds
typically include a higher proportion of warmer, faster winds, than other
wind directions (Fig. S9a), whereas northeasterly winds are typically
lower speed overall and are generally colder (Fig. S9b). However, in the
2019/2020 melt season, we show that both northwesterly and northeasterly
winds are warmer at lower wind speeds. Therefore, having eliminated foehn
flow as a significant driver for surface melt in this season, we suggest
that the increased advection of warm air from both the northwest and
northeast contributed to the sustained warm air temperatures we observe.
The 2019/2020 wind and air temperature and data (10 min) from the
Fossil Bluff AWS. (a) Wind roses for corresponding periods of sustained air
temperatures (A–E) indicated in (b). (b) Air temperature record for 2019/2020
with the five longest periods of temperatures ≥0∘C shown in red.
The red numbers below these time periods indicate the total number of hours
when the temperature is continuously ≥0∘C. It is notable that
only 9 h separates periods A and B and only 14 h separates C and D.
The six blue circles indicate periods when we calculate foehn conditions to
be present (see Sect. 3.4 for methods).
Percentage (%) of wind each season (1 November to 31 March) at
Fossil Bluff AWS that is northeasterly (NE), northwesterly (NW),
southeasterly (SE), and southwesterly (SW), with the interannual (2007/2008 to
2019/2020) mean shown in blue and the 2019/2020 values shown in red. (a) Wind direction proportions using all recorded air temperatures. (b) Wind
direction proportions only when recorded temperatures are ≥0∘C.
(c) Wind direction proportions only during the five longest periods of T≥0∘C (A to E, Fig. 7b) for all melt seasons (blue) and for
2019/2020 (red).
DiscussionComparison of the optical image and microwave-derived melt data over the northern GVIIS, 2013 to 2020
Microwave melt data are binary (i.e. the algorithm indicates there is either
melt or no melt); thus, these data do not measure the intensity of the
melting nor the volume of meltwater present. Additionally, while the
optical data are used to detect the presence of surface meltwater, the
microwave radiometer data can contain melt information through a snow depth
of <2 m, depending on the presence of surface lakes and/or wetness
of the subsurface snow and firn (see Sect. 3.1 for more detail). Therefore, we
cannot directly compare the microwave-derived melt data with the optical
image-derived ponding data. However, together these data provide
information on the time between melt onset and surface ponding over the
northern GVIIS, and likewise the disappearance of surface ponding and melt
termination at the end of the season.
For the time period from 2013/2014 to 2019/2020, when we have three
independent datasets, 2019/2020 was anomalous for the following reasons.
Optical imagery indicates this season had the largest volumes of observed
surface meltwater ponding (Fig. 5b), microwave radiometer- and
scatterometer-derived data show that it also had the most spatially
extensive melt (i.e. 100 % of the AOI) for the greatest number of days
(Fig. 5c), as well as the highest number of cumulative melt days (Figs. 3d,
4d and S2).
In the 2019/2020 season, the microwave radiometer data first indicate the
presence of surface and near-surface melt on 22 November, which extends to over
100 % of the AOI by 24 November (Fig. 5c). However, surface meltwater
ponding is not observed in the (non-continuous, both due to acquisition
coverage and cloud coverage) optical imagery until mid-December (Fig. 5b).
This offset in the timing of the observations is likely due to the fact that although
sustained positive air temperatures in late November 2020 increased surface
and near-surface melt rates, it takes time for surface ponds to develop in
the early melt season, and this will only happen once suitable surface and
firn and ice conditions are present. However, once surface ponds have developed,
this offset in the timing between warm temperatures and ponding is much less
apparent. For example, sustained warm temperatures in early January (Fig. 7b, periods A and B) and early February (Fig. 7b, periods C and D) coincide
with periods when surface meltwater volumes derived from optical imagery are
relatively high (Fig. 5b). Towards the end of the melt season, although
there are no cloud-free Landsat 8 or Sentinel-2 images available after
mid-February 2020 (Fig. 5b), our visual analysis of Terra and Aqua MODIS
optical imagery suggests that open-water lakes remain until at least 25 February, with some lakes potentially remaining until mid to late March.
Meanwhile, the microwave-radiometer-derived melt drops to zero by 25 February but then fluctuates until mid-March (Fig. 5c); perhaps indicative
of a melt–refreeze process.
Near-surface and surface melting over the northern GVIIS, 1979 to 2020
From 1979/1980 to 2019/2020 (excluding the 1987/1988 season), the microwave
radiometer data show that 2019/2020 was the largest melt season over the
northern GVIIS in terms of the most spatially extensive melt (i.e. 100 %
of the AOI) for the greatest number days (Fig. 5c), and the greatest number
of cumulative melt days (Figs. 3 and S2); results that are corroborated by
our scatterometer-derived melt data from 2007 to 2020 (Fig. 4). As mentioned
in Sect. 3.2, scatterometer-derived cumulative melt days (117 d) are
likely higher than those derived from the radiometer data (101 d; Fig. 4d) because C-band radiation has a larger penetration depth and thus likely
detect melt at greater depths (Weber Hoen and Zebker, 2000).
The microwave radiometer data suggest a slightly negative trend in
cumulative melt days and areal melt extent (Figs. 2, 3d and S2) from the mid-1990s until ∼ 2015/2016, which is consistent with negative
near-surface air temperature trends over the AP until 2016, likely relating
to oscillations in the Southern Annular Mode (SAM) (Picard et al., 2007;
Turner et al., 2016). This temperature trend is in contrast to the years
prior to the mid to late 1990s, when trends over the AP from available research
station AWSs had generally been positive since the 1950s (Turner et al., 2005)
(though this is not apparent in our microwave-radiometer-derived melt data).
Local climatic controls of the 2019/2020 melt event
Our air temperature analysis using both daily means (from 1979; Fig. S8),
and higher temporal resolution (10 min) data (from 2007; Fig. 7b) shows
anomalously long time periods when air temperatures were continuously ≥0∘C in 2019/2020. Using the 10 min data, the longest such period was
90 h in 2019/2020, suggesting that no refreezing occurred during that
time (Fig. 7b). Overall, 2019/2020 also had the highest proportion of an
entire season (33 %) when temperatures were ≥0∘C (Fig. 6). We
suggest that the sustained periods of warm temperatures, which started
unusually early in the melt season, both initiated and enhanced melting in
2019/2020. The presence of just a small quantity of surface meltwater early
in the melt season is especially important as this will have a
disproportionate effect on overall surface melt production due the
non-linear melt–albedo feedback process (Trusel et al., 2015).
Compared to the 2007 to 2020 AWS record, the 2019/2020 austral summer
experienced a lower proportion of northwesterly wind (Fig. 8), though these
winds are warmer at lower speeds (Fig. S9a). Instead, the proportion of
northeasterly wind was higher in 2019/2020 compared to the 2007 to 2020
climatology (Fig. 8), and these winds were also warmer at lower speeds (Fig. S9b). We therefore suggest that sensible heat transported by warm, lower-speed, northwesterly and northeasterly wind helped to drive melting in
2019/2020. We also note that a record high Indian Ocean Dipole (IOD) in the
early part of the 2019/2020 melt season is discussed in Bevan et al. (2020)
as a potential large-scale driver for warm, northerly surface winds on the
western AP. However, as the Fossil Bluff AWS does not measure radiation, we
cannot exclude the possibility that the high melt in the 2019/2020 was not
partially attributable to enhanced longwave radiation (potentially resulting
from cloud cover) and/or increased shortwave radiation (potentially
resulting from an absence of cloud cover).
Although warm foehn winds are known to initiate periods of sustained melt
and/or produce firn densification due to near surface melt and refreezing
(Luckman et al., 2014), our analysis suggests that the 2019/2020 melt season
experienced limited foehn conditions (see Sect. 3.4) in the early (and
then late) melt season (Fig. 7b). This timing is predictable foehn flow
behaviour; e.g. over the Larsen C, foehn winds are strongest in winter,
when wind speeds are generally higher (Wiesenekker et al., 2018; Datta et
al., 2019). Our observation of minimal foehn wind conditions over the
northern GVIIS in 2019/2020 is consistent with our observation of an overall
decrease in the frequency of northwesterly winds (Fig. 8), which are
typically responsible for foehn flow. As we do not find 2019/2020 to be a
record melt season for the AP as a whole (see Sect. 4.1), we chose to
focus on identifying local climate drivers of this exceptional melt event
based on the observational record, rather than trying to establish
large-scale atmospheric drivers.
Conclusions
We have used microwave radiometer data from 1979 to 2020 and microwave
scatterometer data from 2007 to 2020 to show that the 2019/2020 austral
melt season on the northern GVIIS was exceptional in terms of both
cumulative melt days and areal extent compared to the previous 31 melt
seasons since 1988/1989 and possibly since the beginning of the record in
1979/1980. We also used multi-spectral satellite imagery from 2013 to 2020
to show that the observed surface meltwater ponding on the northern GVIIS in
2019/2020 was also exceptional in areal extent and estimated volume
since at least 2013/2014.
Our analysis, based on the local weather data from the Fossil Bluff AWS,
shows that sustained periods of warm (≥0∘C) temperatures from
early in the season (late November) likely contributed to the exceptional
2019/2020 melt event. These periods of sustained warm temperatures were
likely driven by sensible heat transported by warm northwesterly and
northeasterly low-speed winds. Consistent with our finding that the
proportion of northwesterly wind decreased in 2019/2020 compared to the 2007
to 2020 period, we only calculate a total of ∼9 h of
foehn conditions for this season, which occurred in early and late summer.
It is therefore notable that although the high melt event over the northern
GVIIS is 2019/2020 was caused by warmer than average air temperatures, such
local weather conditions were not foehn-driven.
Using Landsat 8 and Sentinel-2 satellite imagery, we observed the maximum
volume of meltwater ponding on the northern GVIIS (7850 km2) on 19 January 2020, when ∼ 23 % of this area was covered in
surface lakes with a mean depth of 0.5 m. In comparison, only 10 % of the
3200 km2 area of the Larsen B Ice Shelf that disintegrated in 2002 was
covered in surface ponds with a mean depth of 0.8 m (Banwell et al., 2014).
However, unlike the relatively unconstrained and therefore extensional ice
flow of the Larsen B Ice Shelf (e.g. MacAyeal et al., 2003; Scambos et al.,
2004), GVIIS has dominantly compressive flow, enabling the shelf to remain
relatively stable despite large volumes of surface water (Lai et al., 2020).
Despite this, our results show that some of the areas of dense surface
ponding near the eastern margin of the northern GVIIS coincide with areas
classified as vulnerable to hydrofracture by Lai et al. (2020, their Fig. 4), particularly if pre-existing surface crevasses are present. Though
individual years of exceptional high surface melt do work to decrease
ice shelf stability, further research is required to better constrain the
potential timing and style of a GVIIS collapse event due to the competing
controlling factors of surface melt, basal melt, and stress regime.
Code and data availability
The code used to calculate areas and volumes of surface meltwater is
available at https://github.com/mmoussavi/Lake_Detection_Satellite_Imagery/ (Moussavi, 2020a) and described in
detail in Moussavi et al. (2020). A comprehensive dataset of Antarctic lakes
from Landsat 8 imagery is available at
10.15784/601401 (Moussavi, 2020b). The passive microwave melt product is
available at http://pp.ige-grenoble.fr/pageperso/picardgh/melting/ (last access: 1 October 2020). The ASCAT enhanced resolution product is
available at https://www.scp.byu.edu/data/Ascat/SIR/msfa/Ant.html (last access: 1 October 2020). Temperature data are available from the BAS
Fossil Bluff AWS at 10 min intervals from 2006 to 2020
(https://legacy.bas.ac.uk/cgi-bin/metdb-form-2.pl?tabletouse=U_MET.FOSSIL_BLUFF_ARGOS&complex=1&idmask=.....&acct=u_met&pass=weather, last access: 1 October 2020), and at intervals ranging from 12 to 1 h from
1979 to 2006
(https://legacy.bas.ac.uk/cgi-bin/metdb-form-2.pl?tabletouse=U_MET.FOSSIL_BLUFF_SYNOP&complex=1&idmask=.....&acct=u_met&pass=weather, last access: 1 October 2020). The U.S. National Ice Center Operational Antarctic Ice Front and Coastline Data Set is available from https://usicecenter.gov/Resources/AntarcticShelf (last access: 17 February 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/tc-15-909-2021-supplement.
Author contributions
The authors are ordered by their relative levels of contribution. AFB
conceived the study, analysed the optical image-derived and
microwave-derived melt data, and drafted the manuscript. RTD performed the
local climate analysis. MM derived surface meltwater areas and volumes from
the optical satellite imagery. RLD further processed the optical
imagery-derived results. LB and GP processed the microwave radiometer and
scatterometer data to produce the melt data. CAS identified key Landsat 8,
Sentinel-2, and MODIS image dates for analysis. All authors discussed the
results and were involved in editing of the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank Steve Colwell at the British Antarctic Survey (BAS) for
help with the BAS Fossil Bluff AWS data acquisition. Doug MacAyeal, Ian Willis, Ted Scambos, and Julie Miller are all thanked for useful discussions,
and Julie Miller is also thanked for producing the MODIS mosaic in the
background of Fig. 1a.
Financial support
Alison F. Banwell received support from the U.S. National Science Foundation (NSF) under
award no. 1841607 to the University of Colorado Boulder and from a CIRES
Postdoctoral Visiting Fellowship. Rajashree Tri Datta was funded by the NASA ICESat-2
Project Science office. Rebecca L. Dell was funded by a Natural Environment Research
Council (NERC) Doctoral Training Partnership Studentship (CASE with the
British Antarctic Survey, grant no. NE/L002507/1). Mahsa Moussavi was funded by NSF GEO award
no. 1643715 to the University of Colorado Boulder. Ludovic Brucker and Christopher A. Shuman were funded by
the NASA Cryospheric Science Program. Ghislain Picard was funded by the European Space
Agency (ESA) project 4D Antarctica (ESRIN: 4000128611/19/I-DT). Laura A. Stevens received
support from the U.S. NSF under award no. 1841739 to Columbia University.
Review statement
This paper was edited by Stephen Howell and reviewed by three anonymous referees.
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