TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-12-577-2018Changes in glacier dynamics in the northern Antarctic Peninsula since 1985SeehausThorstenthorsten.seehaus@fau.dehttps://orcid.org/0000-0001-5055-8959CookAlison J.https://orcid.org/0000-0003-0374-9167SilvaAline B.https://orcid.org/0000-0002-4026-4591BraunMatthiasInstitute of Geography, Friedrich-Alexander-University Erlangen-Nuremberg,
Wetterkreuz 15, 91058 Erlangen, GermanyDepartment of Geography, Durham University, South Road, Durham DH1 3LE, UKLaboratório de Monitoramento da Criosfera, Universidade Federal
do Rio Grande, Av. Itália, km 8, 96203-900, Rio Grande, BrazilThorsten Seehaus (thorsten.seehaus@fau.de)20February201812257759428March201713January201829December20177April2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/12/577/2018/tc-12-577-2018.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/12/577/2018/tc-12-577-2018.pdf
The climatic conditions along the northern Antarctic Peninsula
have shown significant changes within the last 50 years. Here we present
a comprehensive analysis of temporally and spatially detailed observations of
the changes in ice dynamics along both the east and west coastlines of the
northern Antarctic Peninsula. Temporal evolutions of glacier area
(1985–2015) and ice surface velocity (1992–2014) are derived from a broad
multi-mission remote sensing database for 74 glacier basins on the northern
Antarctic Peninsula (< 65∘ S along the west coast and north of
the Seal Nunataks on the east coast). A recession of the glaciers by
238.81 km2 is found for the period 1985–2015, of which the
glaciers affected by ice shelf disintegration showed the largest retreat by
208.59 km2. Glaciers on the east coast north of the former Prince
Gustav Ice Shelf extent in 1986 receded by only 21.07 km2
(1985–2015) and decelerated by about 58 % on average (1992–2014).
A dramatic acceleration after ice shelf disintegration with a subsequent
deceleration is observed at most former ice shelf tributaries on the east
coast, combined with a significant frontal retreat. In 2014, the flow speed
of the former ice shelf tributaries was 26 % higher than before 1996.
Along the west coast the average flow speeds of the glaciers increased by
41 %. However, the glaciers on the western Antarctic Peninsula revealed
a strong spatial variability of the changes in ice dynamics. By applying
a hierarchical cluster analysis, we show that this is associated with the
geometric parameters of the individual glacier basins (hypsometric indexes,
maximum surface elevation of the basin, flux gate to catchment size ratio).
The heterogeneous spatial pattern of ice dynamic evolutions at the northern
Antarctic Peninsula shows that temporally and spatially detailed observations
as well as further monitoring are necessary to fully understand glacier
change in regions with such strong topographic and climatic variances.
(a) Location of study site on the Antarctic Peninsula and
on the Antarctic continent (inset). (b) Separation of study site in
three sectors and retreat states of Prince Gustav and Larsen A ice shelves. Red
lines: profiles at glacier front for velocity measurements. Map base, Landsat
LIMA Mosaic USGS, NASA, BAS, NSF, coastlines (ice shelf extent) and catchment
delineations are from SCAR Antarctic Digital Database 6.0.
Introduction
During the last century, the northern Antarctic Peninsula (AP) and its
outlying islands have undergone significant warming (Turner et al., 2005),
leading to substantial glaciological changes. Skvarca et al. (1998) reported
a significant increase in surface air temperatures at the northeastern AP in
the period 1960–1997 and correlated it with the recession of the Larsen and
Prince Gustav ice shelves (Fig. 1) and the observed retreat of tidewater
glaciers on James Ross Island in the period 1975–1995 (Skvarca et al.,
1995). However, a recent cooling trend on the AP was revealed by Oliva
et al. (2017) and Turner et al. (2016) since the late 1990s. Shepherd
et al. (2012) compiled a comprehensive glacier mass balance database of
the polar ice sheets. The authors estimated a mass loss on the whole AP
(< 73∘ S) of -36 ± 10 Gta-1 for the period
2005–2010, which corresponds to 35 % of the total mass loss of
Antarctica. A projection of sea level rise contribution by the AP ice sheet
amounts to 7–16 mm sea level equivalent by 2100 and
10–25 mm by 2200 (Barrand et al., 2013a). However, along the western
AP and on the higher-elevation areas an increase in snow accumulation in the
late 20th century was derived from ice cores – e.g., at Palmer Land
(73.59∘ S, 70.36∘ W; Thomas et al., 2008), Detroit Plateau
(64.08∘ S, 59.68∘ W; Potocki et al., 2011) and at Bruce Plateau
(66.03∘ S, 64.07∘ W; Goodwind, 2013) – and climate models
(e.g., Dee et al., 2011), whereas Van Wessem et al. (2016) obtained
insignificant trends in precipitation.
Numerous ice shelves along the AP have retreated widely (e.g., Müller,
Wilkins, Wordie) or disintegrated in recent decades (e.g., Larsen A in 1995,
Larsen B in 2002) (Braun and Humbert, 2009; Cook and Vaughan, 2010; Doake and
Vaughan, 1991; Rack et al., 1998; Rack and Rott, 2003; Wendt et al., 2010).
As a consequence of the reduced buttressing, former tributary glaciers showed
increased ice discharge and frontal retreat (e.g., De Angelis and Skvarca,
2003; Rack and Rott, 2004; Rignot et al., 2004; Seehaus et al., 2015; Wendt
et al., 2010). For the northern AP (< 66∘ S), a mass loss rate of
-24.9 ± 7.8 Gta-1 was reported by Scambos et al. (2014)
for the period 2003–2008, indicating that major ice mass depletion happened
at the northern AP, especially along the eastern side, where numerous glaciers
have been affected by ice shelf collapses. Seehaus et al. (2015, 2016)
quantified the ice loss of former ice shelf tributaries. Mass loss rates of
-2.14 ± 0.21 Gta-1 (1995–2014) and
-1.16 ± 0.16 Gta-1 (1993–2014) were found at
Dinsmoor–Bombardier–Edgeworth glacier system and Sjögren Inlet, respectively. Glaciers that were not terminating in an ice shelf
also showed considerable changes. Cook et al. (2005, 2014) have analyzed the
variations of tidewater glacier fronts since the 1940s. The authors reported
that 90 % of the observed glaciers retreated, which they partly
attributed to atmospheric warming. A more recent study revealed a mid-ocean
warming along the southwestern coast of the AP, forcing the glacier retreat
in this region (Cook et al., 2016). Pritchard and Vaughan (2007) observed an
acceleration of ice flow by ∼ 12 % along the west coast of the AP
(1995–2005) and linked it to frontal retreat and dynamic thinning of the
tidewater glaciers. Observations by Kunz et al. (2012) support this
supposition. They analyzed surface elevation changes of 12 glaciers on the
western AP based on stereoscopic digital elevation models (DEMs) over the
period 1947–2010. Frontal surface lowering was found at all glaciers.
Glacier-wide surface lowering was observed by various author groups (e.g.,
Berthier et al., 2012; Rott et al., 2014; Scambos et al., 2014) at former ice
shelf tributaries along the northeastern AP. The collected observations
suggest that the ice masses on the AP are contributing to sea level rise and
show that glaciers' responses to climate change on the AP is not homogeneous
and that more detailed knowledge of various aspects on the glacier changes
is required. Previous studies often cover a specific period or area or
focus on one particular aspect of glacier change. By now, the availability of
remote sensing data, time series data and other datasets in this region
facilitates the comprehensive analysis of glacier change. Therefore, we study
the changes in glacier extent in combination with detailed investigations on
ice dynamics as well as other derived geometrical attributes of glaciers on
the northern AP (< 65∘ S along the west coast and north of the
Seal Nunataks on the east coast; Fig. 1b colored polygons) between 1985 and
2015. We analyze various multi-mission remote sensing datasets in order to
obtain methodologically consistent and temporally detailed time series of ice
dynamic changes of 74 glacier basins. The observations are individually
discussed for the subregions, considering the different atmospheric,
glaciological and oceanic conditions and changes.
Study site
The AP is the northern-most region of Antarctica and stretches from
63 to 75∘ S (Huber et al., 2017). It covers only 3 % of the entire
continent in area but receives 13 % of the total mass input (Van Lipzig
et al., 2002, 2004). The AP's mountain chain (typically 1500–2000 m
high) acts as an orographic barrier for the circumpolar westerly air streams,
leading to very high precipitation values on the west coast and on the
plateau region of up to 5000 mmw.e.a-1, as well as frequent
foehn-type wind occurrences on the east coast (Cape et al., 2015; Marshall
et al., 2006; Van Wessem et al., 2016). The foehn events are characterized by
strong winds and high air temperatures. Consequently, the climatic mass
balance (bclim) shows a strong gradient across the mountain chain
(Turner, 2002; Van Wessem et al., 2016). Aside from those that are ice shelf
tributaries, almost all glaciers on the AP are marine terminating, and the
majority of the glacier catchments extend up to the high-elevation plateau
regions (north to south: Laclavère, Louis Philippe, Detroit, Herbert,
Foster, Forbidden, Bruce, Avery, Hemimont, Dyer). Typically the AP plateau is
separated from the outlet glaciers by escarpments and ice falls. Glaciers on
the west coast drain into the Bellingshausen Sea and on the east coast into
the Weddell Sea. Since the 1980s, the ice shelves along the east coast have
substantially recessed and disintegrated (Larsen Inlet in 1987–1989, Prince
Gustav and Larsen A in 1995 and Larsen B in 2002) (Cook and Vaughan, 2010;
Rott et al., 1996; Skvarca et al., 1999), which Scambos et al. (2003)
attributed to higher summer air temperatures and surface melt. A more recent
study by Holland et al. (2015) discovered that significant thinning of the
Larsen C Ice Shelf is caused by basal melting and that ungrounding from an
ice rise and frontal recession could trigger its collapse. The northern AP
has a maritime climate and is the only region of Antarctica that frequently
experiences widespread surface melt (Barrand et al., 2013b; Rau and Braun,
2002).
Overview of SAR sensors and specifications used in this study.
PlatformSensorModeSARRepetitionTime intervalGround rangeTracking patchTracking stepMean uncertaintybandcycle (days)resolution (m)asizes (p×p)bsize (p×p)bof trackingresults (md-1)ERS-1/2SARIMC band35/18 December 19923048 × 2405 × 250.15 ± 0.102 April 201064 × 320RADARSAT 1SARSTC band2410 September 20003048 × 1925 × 200.11 ± 0.033 September 200664 × 256EnvisatASARIMC band355 December 20033032 × 1605 × 250.12 ± 0.0516 August 200964 × 320128 × 640ALOSPALSARFBSL band4618 May 20061064 × 19210 × 300.05 ± 0.0617 March 201196 × 192128 × 384TerraSAR-XSARSMX band1114 October 20083128 × 12825 × 250.06 ± 0.04TanDEM-X22 December 2014256 × 256512 × 512
a Nominal
resolution,
dependent on the incidence angle. b Intensity
tracking parameters are provided in pixels (p) in slant range geometry.
Our study site stretches approximately 330 km from the northern tip
of the AP mainland southwards to Drygalski Glacier on the east coast and
Grubb Glacier on the west coast (Fig. 1). This facilitates the analyses of
the temporal evolution (∼ 20 years) of the response of tributary
glaciers to ice shelf disintegration at the former Larsen A and Prince Gustav
ice shelves on the east coast, the investigation of glaciers north of the
former Prince Gustav Ice Shelf, where no information on change in ice flow is
currently available, and the comparison with temporal variations in ice
dynamics along the west coast at the same latitude. The study site covers
∼ 11 000 km2 (∼ 11 % of the whole AP including
islands; Cook et al., 2014; Huber et al., 2017) with elevations stretching
from sea level up to 2220 m. The glacier basin delineations are based
on the Antarctic Digital Database (ADD) 6.0 (Cook et al., 2014). Glacier names
are taken from the Global Land Ice Measurements from Space (GLIMS) project
database. The local GLIMS glacier IDs (e.g., TPE62, LAB2) are used for unnamed
glaciers and further missing glacier basin names are replaced with the ADD
6.0 glacier IDs. Neighboring basins with coalescing ice flow at the termini
are merged (many are already merged in ADD 6.0), as the delineation of
the individual glacier sections is not always possible and the width can vary
temporally (due to changes in mass flux of the individual glaciers). In these
cases, the names of the glaciers are also merged (e.g.,
Sikorsky–Breguet–Gregory, SBG; see Table 1 for abbreviations of glacier
names). Due to the sparse data coverage (fewer than three good-quality
velocity measurements), no time series analysis of the glaciers at the
northern tip of the AP or at some capes and peninsulas (e.g., Sobral
Peninsula, Cape Longing) is possible. Therefore, the northern-most analyzed
catchments are Broad Valley Glacier on the east coast and TPE8 Glacier on the
west coast, resulting in 74 studied glacier basins. Furthermore, the study
site is divided into three sectors, taking into account the different
climatic settings and drainage orientation as well as former ice shelf
extent: sector West, which contains glaciers on the west coast, draining into the
Bransfield and Gerlache straits; sector East, which contains glaciers on the east
coast, draining into the Prince Gustav Channel; and sector
East-Ice-Shelf, which contains glaciers on the east coast that were former
tributaries to the Larsen A, Larsen Inlet and Prince Gustav ice shelves.
Data and methods
A large number of various remote sensing datasets are analyzed in order to
obtain temporally and spatially detailed information on changes in ice
dynamics in the study area. Glacier area changes are derived from satellite
and aerial imagery. Repeat-pass synthetic aperture radar (SAR) satellite
acquisitions are used to compute surface velocity fields in order to obtain
information on changes in glacier flow speed. Auxiliary data from sources
such as a DEM and glacier inventory are included in the
further analyses and discussion of the results.
Area changes
Changes in glacier area are derived by differencing glacier outlines from
various epochs. All observed glaciers are tidewater glaciers and only area
changes along the calving front were considered. Information on the positions
of the glacier fronts is taken from Cook et al. (2014) and is available
for the whole AP in ADD 6.0 (1945–2010). This coastal-change inventory
is based on manually digitized ice-front positions using imagery from various
satellites (e.g., Landsat, ERS) and aerial photo campaigns. This dataset is
updated (up to 2015) and gaps are filled by manual mapping of the ice-front
positions based on SAR and optical satellite images. Consistent with Cook
et al. (2014), the ice-front positions are assigned to 5-year intervals in
order to analyze temporal trends in glacier area changes in the period
1985–2015. Before 1985, only sparse information on ice-front positions for
the whole study site is available, and the coverage by SAR data for analyzing
glacier flow starts in 1992. Additionally, the analysis of the area changes
for the Larsen A and Prince Gustav ice shelf tributaries is limited to the
period 1995–2015, as the ice shelves disintegrated in 1995.
The uncertainties of the glacier change measurements strongly depend on the
specifications of the imagery used (e.g., spatial resolution, geodetic
accuracies) as well as the methods used. To each record in the coastal-change
inventory from ADD 6.0, a reliability rating is assigned according to
Ferrigno et al. (2006). The rating ranges from 1 to 5 (reliability within
60 m to 1 km) and takes into account errors due to manual
digitization and interpretation (see Ferrigno et al., 2006, for a detailed
description). This approach is also applied on the updated ice-front
positions. Nearly all mapped ice fronts in the area studied have a good
reliability rating of 1 (76 %) and 2 (21 %). Only a few glacier
fronts (3 %) have a rating of 3. No ice fronts with reliability ratings
of 4 and 5 are mapped in the study area.
Surface velocities
Surface velocity maps are derived from repeat-pass SAR acquisitions. SAR image time series of the satellite missions ERS-1/2,
Envisat, RADARSAT-1, ALOS, TerraSAR-X (TSX) and TanDEM-X (TDX)
are analyzed, covering the period 1992–2014. Specifications of the SAR
sensors are listed in Table 2. The large number of SAR images were provided by
the German Aerospace Center (DLR), the European Space Agency (ESA) and the
Alaska Satellite Facility (ASF). To obtain displacement fields for the
glaciers, the widely used and well-approved intensity offset tracking method
is applied on co-registered single look complex SAR image pairs (Strozzi
et al., 2002). In order to improve the co-registration of the image pairs, we
mask out fast-moving and unstable regions such as outlet glaciers and the sea
during the co-registration processes. Furthermore, single SAR image tiles
acquired during the same satellite flyover are concatenated in the
along-track direction. This helps to further improve the co-registration in
coastal regions (by including more stable areas in the co-registration
process) but also simplifies the analysis of the final results as no
mosaicking of the results is needed. Image pairs with low-quality
co-registration are filtered out. A moving window technique (step-size; see
Table 2) is used by the intensity offset tracking method to compute the
cross-correlation function of each image patch and to derive its azimuth and
slant range displacement. The resolution of the obtained displacement fields
depends on the combination of the step-size and the resolution of the images
in slant range geometry. A resolution of the velocity fields of
∼ 50 m for the high-resolution sensors TSX, TDX and
∼ 100 m for all other sensors was targeted. Less reliable
offset measurements are filtered out by means of the signal-to-noise ratio of
the normalized cross-correlation function. Moreover, we apply an additional
filter algorithm based on a comparison of the magnitude and alignment of the
displacement vector relative to its surrounding offset measurements. This
technique removes more than 90 % of incorrect measurements (Burgess
et al., 2012). Finally, the displacement fields are transferred from slant
range into ground range geometry, taking into account the contortion caused
by the topography (topographic effects on the local SAR incidence angle). The
results are then geocoded, orthorectified, resampled and converted into
velocity fields (with 100 m pixel spacing for all sensors) by means
of the time span between the SAR acquisitions. The mean date of the
consecutive SAR acquisitions is assigned to each velocity field. The ASTER
Global DEM of the Antarctic Peninsula (AP-DEM; Cook
et al., 2012) is used as elevation reference. It is currently the best
available DEM of the Antarctic Peninsula. It has a mean
elevation bias of -4 m (±25 m RMSE) from ICESat data
and horizontal accuracy better than 2 pixels. Since the accuracy varies
regionally, Huber et al. (2017) estimated the uncertainty to be
±50 m for the AP-DEM, based on their experiences with other DEMs.
Velocity data are analyzed close to the calving front (see further down), where
the slope of the glaciers at the AP is typically quite low. Thus, the impact
of the DEM accuracy on the velocity fields is insignificant (see Seehaus
et al., 2015, Supplement).
Depending on the displacement rate and resolution of the SAR sensor, the
tracking window size needs to be adapted (de Lange et al., 2007). For the
fast-flowing central glacier sections, larger window sizes are needed since
large displacements cannot be tracked by using small correlation patches.
Small tracking window sizes are suitable for the slow-moving lateral sections
of the outlet glaciers. Wide parts of large tracking patches cover the stable
area next to the glacier, which biases the tracking results towards lower
velocities. Consequently, we compute surface velocity fields of the same
image pairs for different correlation patch sizes in order to get the best
spatial coverage. Table 2 shows the different tracking window sizes for each
sensor. The results of each image pair are stacked by starting with the
results of smallest tracking window size and filling the gaps with the
results of the next biggest tracking window size.
The accuracy of the velocity measurements strongly depends on the
co-registration quality and the intensity offset tracking algorithm settings.
The mismatch of the co-registration σvC is quantified by
measuring the displacement on stable reference areas close to the coast line,
such as rock outcrops and nunataks. Based on the Bedmap2 (Fretwell et al.,
2013) and ADD 6.0 rock outcrop masks, reference areas are defined and the
median displacements magnitude of each velocity field is measured at these
areas. The uncertainty of the tracking process σvT is
estimated according to McNabb et al. (2012) and Seehaus et al. (2015)
depending on accuracy of the tracking algorithm C, image resolution
dx, oversampling factor z and time interval dt.
σvT=Cdxzdt.
The accuracy of the tracking algorithm is estimated to be 0.2 pixels and an
oversampling factor z=2 is applied to tracking patches in order to improve
the accuracy of the tracking process. Both independent error estimates are
quadratically summed to compute the uncertainties of the individual velocity
fields σv.
σv=σvT2+σvC2
Two approaches to measure and analyze the temporal changes in ice flow of the
studied glacier are evaluated (see also Sect. S1 in the Supplement).
Description of velocity change categories.
CategoryDescriptionRating∗PositiveGeneral increase2of flow speedPeakIncrease of flow speed with1subsequent decelerationStableVariability of measurements0< 0.25 md-1FluctuatingShort-term speed-ups and0deceleration, no clear trendTroughDecrease of flow speed with-1subsequent accelerationNegativeGeneral decrease-2of flow speed
∗ Ratings used for cluster analysis
Sect. .
In the first approach, an across glacier profile is defined (red lines in Fig. 1)
close to the terminus of each basin, considering the maximum retreat state of
the ice front in the observation period. The changes in the ice flow of the
individual glaciers are analyzed by measuring the surface velocities along
the profiles. In order to reduce the number of data gaps along the profile
due to pixel size data voids in the velocity fields, the velocity data are
extracted within a buffer zone of 200 m around the profiles. The
results are visually inspected in order to remove unreliable measurements,
based on the magnitude and direction of ice flow along the profiles. Datasets
with partial profile coverage, large data gaps or large-scale tracking errors
are rejected. The resulting profile coverage by velocity measurements is on
average 97 % and data coverage of more than 93 % is obtained for
90 % of all extracted profiles. To minimize the impact of potential
outliers (still remaining tracking errors), median velocities along the
profiles are calculated and their temporal developments are plotted for each
basin (Figs. S1–S74 in the Supplement).
In the second approach, the velocity values are picked at the location of maximum
ice thickness at the across glacier profiles (taken from the first approach).
Ice thickness is obtained from the ice thickness reconstruction of the AP by
Huss and Farinotti (2014). By means of visual inspection of the velocity
profiles obtained by the first approach, outliers in the measurements using
the second approach are manually filtered out and the resulting evolution of
the flow speeds of each glacier are plotted (Figs. S75–S148 in the
Supplement).
Hypsometric index (HI) and glacier basin category descriptions.
HI∗HypsometricNumber ofcategoriesglaciersHI <-1.5Very top heavy8-1.5 < HI < - 1.2Top heavy7-1.2 < HI < 1.2Equidimensional181.2 < HI < 1.5Bottom heavy13HI > 1.5Very bottom heavy28
∗ According to Jiskoot
et al. (2009).
The glaciers are manually classified in six categories according to the
temporal evolution of the ice flow speeds (see Table 3), since automatic
classification attempts did not achieve satisfying results. Only glaciers
with three or more observations and an observation period of more than
10 years are considered in the categorization, resulting in
74 categorized glacier basins (colored polygons in Fig. 1b). The GAMMA Remote
Sensing software is used for processing of the SAR data.
Catchment geometries and settings
Glacier velocities and area change measurements provide information on the
ice dynamics of the individual glaciers. To facilitate a better and
comprehensive interpretation of these observations, additional attributes
regarding the different geometries and settings of the glaciers are derived.
In addition to glacier attributes derived by Huber et al. (2017), we
calculated the hypsometric index (HI) and the ratio of the flux gate cross section
divided by the glacier catchment area.
Mass input strongly affects the dynamics of a glacier. The climatic mass
balance at the northern AP shows a strong spatial variability, with very high
accumulation rates along the west coast (3769 mmw.e.a-1 on
average in sector West, 1992–2014, RACMO2.3), significantly lower values
on the east coast (1119 mmw.e.a-1 on average in sector
East, 1992–2014, RACMO2.3) and an increase towards higher altitudes
along both coast lines (Turner, 2002; Van Wessem et al., 2016). Consequently,
the mass input depends on the basin orientation (east coast or west coast),
elevation range and the hypsometry. For each glacier basin an HI, defined by Jiskoot et al. (2009), is calculated by means of
surface elevations from the AP-DEM. Based on this index the glaciers are
grouped into the five categories according to Jiskoot et al. (2009), ranging
from “very top heavy” to “very bottom heavy” (Table 4). Moreover, the maximum
elevations of the individual glacier catchments are derived from the AP-DEM,
which represents the altitude range of the catchment, since all observed
glaciers are marine terminating.
Temporal evolution of surface velocity (red, using first measuring
approach) and area (blue) changes of selected glaciers in the study area for
each velocity change category (see Table 3).
In order to characterize the catchment shape, the ratios (FA) of the flux
gate cross sections divided by the glacier catchment areas are calculated.
The flux gates are defined along the profiles used for the glacier flow
analysis (Sect. ). Lower values of FA indicate a channelized
outflow (narrowing towards the glacier front), whereas higher FA ratios
imply a broadening of the glacier towards the calving front. Ice thickness at
the flux gates is taken from the AP Bedmap dataset from Huss and Farinotti
(2014).
Cluster analysis
The glaciers in sector West (Fig. 1, red shaded area) show
a heterogeneous spatial pattern of ice dynamics as compared to the other
sectors changes (Sects. and ). In order to analyze the
influence of the glacier geometries on the glaciological changes and to find
similarities, a cluster analysis is carried out in sector West. This is
a proven method to classify glaciers based on a set of variables (Lai and
Huang, 1989; Sagredo and Lowell, 2012). Variables of the glacier dynamics
used are the derived area changes (in percent) and velocity changes (ratings
of the categories, Table 3). Glaciers categorized as “stable” showed
a temporal variability in flow speeds of less than 0.25 md-1.
Therefore, we used the same rating for the velocity change categories
“stable” and “fluctuating” to perform the cluster analysis. The glacier
geometry parameters used are the HI, maximum surface
elevation hmax of the basin and the ratio of flux gate to catchment size, FA. The variables are standardized in the traditional way of
calculating their standard scores (also known as z scores or normal
scores). It is done by subtracting the variables mean value and dividing by
its SD (Miligan and Cooper, 1988). Afterwards a dissimilarity matrix is
calculated using the Euclidean distances between the observations (Deza and
Deza, 2009). A hierarchical cluster analysis (Kaufman and Rousseeuw, 1990) is
applied on the dissimilarities using Ward's minimum variance method (Ward,
1963). At the start, the most similar glaciers (samples) are grouped. The
resulting clusters are iteratively joined based on their similarities until
only one cluster is left, resulting in a dendrogram (see Sect. ).
The distances between the clusters are updated in each iteration step by
applying the Lance–Williams algorithms (Lance and Williams, 1967).
Categorizations of glaciers based on the temporal variations of area
changes (dots) and flow velocities (symbols). Colors of catchment delineation
indicate hypsometric categories according to Jiskoot et al. (2009).
Background: Landsat LIMA Mosaic USGS, NASA, BAS, NSF.
Summary of observed parameters for each sector and all glaciers.
N – number of studied glaciers lf –
Length of ice front A – glacier area in the respective period
(subscript)∗ dA – change in glacier area between 1985 and
2015∗ dt – mean time period of velocity
measurements vS – mean of earliest velocity measurements
(1992–1996) vE – mean of latest velocity measurements
(2010–2014) dv – mean velocity change nv
– sum of velocity measurements in the observation period (dt)
∗ Since 1995 for the former Larsen A and Prince Gustav ice shelf
tributaries (see Sect. ).
Total glacier area (gray bars) of the whole study site (a)
and of the individual sectors (b–d) in the period 1985–2015.
Changes in glacier area (blue points) are relative to the measurements in
time interval 1985–1990. Note the different scaling of the left y axes.
∗ In sector East-Ice-Shelf, area changes before 1995 are only
measured at Larsen Inlet tributaries (APPE glaciers).
Spatial distribution of glacier types along the west coast. Glaciers
are grouped based on a hierarchical cluster analysis (dots). In
Sect. the characteristics of the groups are discussed in detail.
Individual glacier catchment colors indicate relative area change in the period
1985–2015. Colored polygon outlines indicate boundaries of the three sectors.
Background: Landsat LIMA Mosaic USGS, NASA, BAS, NSF.
ResultsArea changes
Area changes relative to the measurements in the epoch 1985–1989 (1995–2000
for the former Larsen A and Prince Gustav ice shelf tributaries; see
Sect. ) of the observed glaciers are plotted in Figs. S1–S74 in
the Supplement. The glaciers are classified in three groups based on the
latest area change measurements, which are illustrated in Fig. 2: retreat
(Fig. 2a–c and f), which is the loss of glacier area by frontal retreat; stable
(Fig. 2e), in which there are no significant area changes (within the error bars); advance
(Fig. 2d), in which there is a gain of glacier area by frontal advance. In Fig. 3 the spatial
distribution of the area change classification is illustrated. All glaciers
along the east coast, including the former ice shelf tributaries, retreated,
whereas along the west coast numerous glaciers show stable ice-front
positions and some glaciers even advanced. In total, 238.81 km2 of
glacier area was lost in the survey area in the period 1985–2015, which
corresponds to a relative loss of 2.2 %. All sectors show glacier area
loss (Table 5), of which the area loss by 5.7 % (208.59 km2)
in sector East-Ice-Shelf clearly dominates. The glaciers in sectors
West and East recessed by 0.2 % (9.14 km2) and
1.4 % (21.07 km2), respectively. The temporal trends of total
glacier area and area loss of all observed glaciers and of each sector are
presented in Fig. 4. Catchment areas and changes between 1985 and 2015 of the
individual basins are listed in Table S1 in the Supplement and relative
changes are illustrated in Fig. 5.
Surface velocities
A total of 282 stacked and filtered velocity fields are derived from the SAR
acquisitions covering the period from 25 December 1992 until 16 December
2014. Figures S157–S160 in the Supplement show exemplary velocity fields of
the studied area obtained for ERS, Envisat, ALOS and TSX–TDX data. The
average total uncertainty of the velocity fields amounts to
0.08 ± 0.07 md-1 and the values for each SAR sensor are
provided in Table 2. In Table S3 in the Supplement the error estimates of
each velocity field are listed. The mean sample count to estimate the
co-registration quality is 11 717 and the average mismatch amounts to
0.07 md-1. The error caused by the tracking algorithm strongly
varies depending on the source of the SAR data (sensor). A mean value of
0.05 md-1 is found. ERS image pairs with time intervals of 1
day have very large estimated tracking uncertainties, biased by the very
short temporal baselines. Therefore, only the errors caused by the mismatch
of the co-registration are considered in the total error computations of the
seven ERS tracking results with 1-day temporal baselines.
All measured velocity profiles of the 74 observed glaciers are visually
inspected and in total 2256 profile measurements (first approach) and
2736 point measurements (second approach) passed the quality check. The
shortest observation period is 14.83 years at DBC31 Glacier, the
average number of velocity measurements per glacier is 30.5 and 37.0 and the
average observation period is 19.25 years
(σ= 2.06 years) and 19.21 years
(σ= 1.96 years) for the first and second measuring
approach, respectively. Figure 2 shows by example the temporal evolution of
the ice flow (using the first approach) for each velocity change category
(see Table 3) and Figs. S149–S156 in the Supplement show surface velocity
profiles across the terminus for the same glaciers as well as for the small
glacier catchments DGC14 and TPE61. For small and narrow glaciers, the
capturing of the flow velocity gradients in the margins is still limited
mainly by the sensor resolution, even applying different tracking window
sizes (see Sect. ).
The temporal evolution of the surface velocities at the termini of each
glacier are depicted in the Supplement (Figs. S1–S74 for the first approach,
Figs. S75–148 for the second approach) and the related categories are listed
in Tables S1 and S2 in the Supplement.
For both velocity measuring approaches and each glacier, the flow velocities
in the first vS and last year vE of the observation period as well
as the absolute and relative change dv is presented in Tables S1 and S2 in
the Supplement. The mean values of vS, vE and dv of all analyzed
glaciers and for each sector are listed in Table 5. On average the ice flow
in the whole studied area increased by 0.061 md-1 (13 %) and
0.071 md-1 (7 %) for the first and second approach,
respectively, but the average changes of the individual sectors are more
pronounced. Along the west coast an average acceleration by 41 %
(0.177 md-1) and 44 % (0.369 md-1) occurred and
the former ice shelf tributaries on the east coast accelerated by 26 %
(0.118 md-1) and 41 % (0.312 md-1) for both
approaches, respectively. In sector East the glaciers decelerated
resulting in a mean velocity change of -58 %
(-0.423 md-1) and -69 % (-1.272 md-1) for
the first and second approach, respectively. The presented average flow speed
change values are based on the observed changes of all glaciers in the
respective sector (Table S1 in the Supplement), ignoring the different size
of the individual glaciers.
Detailed results and differences of both approaches to measure the glacier
velocities are presented and discussed Sect. S1. Based on this discussion, we
decided to favor the first approach and its results are used for the
subsequent analysis.
The spatial distribution of the categories is illustrated in Fig. 3. At
nearly all glaciers in sector East-Ice-Shelf a peak in ice velocities is
observed. In sector East, most glaciers showed a decrease in flow
velocities in the observation period. The glaciers on the west coast show
a more irregular distribution than along the east coast, but a local
clustering of accelerating glaciers can be observed at Wilhelmina Bay. In
order to analyze the quality of obtained velocity change signal, the ratio of
the maximum measured velocity difference (maximum velocity minus minimum
velocity) divided by the average error of the velocity measurements is
calculated for each glacier. An average signal-to-noise ratio of 14.6 is
found. At three glaciers (DGC14, DGC22 and Orel) a signal-to-noise ratio of
less than 2 is observed. These glaciers are characterized as stable,
which justifies the low signal-to-noise ratio.
Catchment geometries and settings
The spatial distribution of HI and categories of the glacier
basins is presented in Fig. 3 and the values are listed in Table S1 in the
Supplement. The HI values range between -4.6 and 9.1 (mean: 0.88,
σ: 2.10). No clear spatial distribution pattern can be identified,
reflecting the heterogeneous topography of the AP. The maximum elevation of
the catchments and the FA factors are also listed in Table S1 in the
Supplement.
Dendrogram of hierarchical cluster analysis of glaciers in sector
West. The glaciers are assorted in four groups (red rectangles). See also
Sect. .
Box plots of cluster analysis input variables (sector West) for
each group. Whiskers extend to the most extreme data points. (b) The
symbols used for the velocity change categories (see Table 3) are the same as
in Fig. 3. (d) The pictograms illustrate the catchment shape (see
Sect. ).
Cluster analysis
The resulting dendrogram of the hierarchical cluster analysis is plotted in
Fig. 6. Four groups are distinguished. The box plots of each input variable
are generated based on this grouping and are shown in Fig. 7. The
characteristics of the groups are discussed in Sect. .
Discussion
Most of the observed glaciers (62 %) retreated and only 8 % advanced
in the study period. These findings are comparable to the results of Cook
et al. (2005, 2014, 2016). Only glaciers along the west coast showed stable
or advancing calving fronts and all glaciers on the east coast receded since
1985. This heterogeneous area change pattern was also observed by Davies
et al. (2012) on western Trinity Peninsula. Most significant retreat occurred
in sector East-Ice-Shelf. In the period 1985–1995, the Larsen Inlet
tributaries (APPE glaciers) lost 45.0 km2 of ice. After the
disintegration of Prince Gustav and Larsen A Ice Shelf, the tributaries
rapidly retreated in the period 1995–2005. The recession slowed down in the
latest observation interval (2005–2010). This trend is comparable to
detailed observations by Seehaus et al. (2015, 2016) at individual glaciers
(DBE glaciers and Sjögren Inlet glaciers). In sector East the highest
area-loss is found in the earliest observation interval (1985–1990). Davies
et al. (2012) also reported higher retreat rates for most of the glaciers in
this sector in the period 1988–2001 than in the period 2001–2009. However,
another significant recession is also found in sector East after 1995
(Fig. 4). Davies et al. (2012) and Hulbe et al. (2004) supposed that the
disintegration of a nearby ice shelf affects the local climate. The air
temperatures would rise due to the presence of more ice-free water in
summers. Thus, the higher retreat rates in sector East after 1995 could
be indirectly caused by the disintegration of Prince Gustav and Larsen A Ice
Shelf in Sector East-Ice-Shelf. At Base Marambio, ∼ 100 km
east of this sector, approximately 2 ∘C higher mean annual air
temperatures were recorded in the period 1996–2005 as compared to the period
1986–1995 (Oliva et al., 2017). Unfortunately, no temperature data recorded
within sector East are available covering this period that could be used
to validate this hypothesis.
The average changes of flow velocities at each sector also vary strongly
(Table 5) in the observation period 1992–2014. On the west coast an increase
of 41 % is found, whereas in sector East the glaciers slowed down by
approximately 58 % and at the ice shelf tributaries the ice flow
increased on average by 26 %. Pritchard and Vaughan (2007) reported an
increase in mean flow rate of 7.8 % in frame 4923 (the central and much
of the northern part of sector West) and 15.2 % in frame 4941 (the
southern part of sector West) for the period 1992–2005 (frame numbers
correspond to European Space Agency convention for identifying ERS coverage).
This spatial trend corresponds to our observations, since most of the
glaciers which accelerated are located at the southern end of sector
West. However, for the same observation period we derived a mean increase
in flow velocity by 18.9 % in sector West, which is an approximately
1.6 times higher acceleration. Pritchard and Vaughan (2007) estimated the
mean velocity change by measuring the flow speed at profiles along the flow
direction of the glacier, whereas we measured the velocity across glacier
profiles at the terminus. If a tidewater glacier speeds up due to the
destabilization of its front, the highest acceleration is found at the
terminus (see Seehaus et al., 2015, Fig. 3). Consequently, the different
profile locations explain the deviations between both studies.
In the following section the observed changes in the individual sectors are
discussed in more detail.
East
The glaciers north of the former Prince Gustav Ice Shelf show a general
deceleration. Eyrie, Russell East, TPE130, TPE31, TPE32, TPE34 and 2731
glaciers experienced a rapid decrease and, except 2731 Glacier,
a subsequent stabilization or even gentle acceleration of flow velocities
(Figs. S2, S6, S7 and S9–S12 in the Supplement). A significant retreat
followed by a stabilization or slight re-advance of the calving front
position is also observed at these glaciers. According to Benn and
Evans (1998), a small retreat of a glacier with an overdeepening behind its
grounding line (i.e., where the bed slopes away from the ice front) can result
in a rapid recession into the deepening fjord. The increased calving and
retreat of the ice front cause stronger up-glacier driving stress, higher
flow speed as well as glacier thinning and steepening (Meier and Post, 1987;
Veen, 2002). The glacier front stabilizes when the grounding line reaches
shallower bathymetry and ice flow also starts to slowdown. A delay between
the front stabilization and slowdown can be caused by thinning and steepening
of the glacier. Additionally, the accelerated ice flow can surpass the
retreat rates and cause short-term glacier advances in the period of high
flow speeds (e.g., Eyrie, Russel East, TPE130 and TPE32 glaciers; Figs. S6,
S7, S9 and S11 in the Supplement) (Meier and Post, 1987). This process can be
initiated by climatic forcing (Benn and Evans, 1998). Significant higher
surface air temperature at the northeastern AP and a cooling trend in the
21st century was reported by Oliva et al. (2017) and Turner et al. (2016)
(see Sect. ). Hence, we assume that the initial recessions of the
glaciers in sector East were forced by the warming observed by Oliva
et al. (2017) and Skvarca et al. (1998) since the 1970s. Therefore, this
initial frontal destabilization and retreat led to high flow speeds at the
beginning of our ice dynamics time series (earliest velocity measurements
from 1992) and the subsequently observed frontal stabilization (after 1985)
caused the deceleration of the ice flow. The fjord geometry significantly
affects the dynamics of the terminus of a tidewater glacier (Benn and Evans,
1998; Van der Veen, 2002). The tongues of Aitkenhead and 2707 glaciers
are split into two branches by nunataks, resulting in rather complex fjord
geometries. A retreat from pinning points (e.g., fjord narrowing) causes
further rapid recession and higher flow speeds until the ice front reaches
a new stable position as observed at 2707 and Aitkenhead Glacier
(Figs. S1 and S3 in the Supplement). At TPE10 Glacier (Figs. S8 and S82 in
the Supplement) a “peaked” flow velocity evolution is observed as at
Aitkenhead Glacier (Figs. S3 and S77 in the Supplement). No nunatak is
present at the terminus, but small rock outcrops, indicating a shallow
bedrock bump, are identified north of the center of the ice front by visual
inspection of optical satellite imagery. Most probably, this shallow bedrock
acts as a pinning point and prevents further retreat. The front of Broad
Valley Glacier (Fig. S4 in the Supplement) is located in a widening fjord.
This geometry makes the glacier less vulnerable to frontal changes (Benn and
Evans, 1998). Therefore, no significant changes in flow velocities are
observed as a consequence of the frontal recession and re-advance.
Diplock and Victory glaciers (Figs. S5 and S13 in the Supplement) show
a decrease of flow speed during retreat (1995–2010) followed by an
acceleration combined with frontal advance (2010–2015). Surge-type glaciers
(tidewater as well as land terminating) found in various regions worldwide
show similar behavior (Meier and Post, 1969; Sevestre and Benn, 2015). They
are characterized by episodically rapid down-wasting, resulting in a frontal
acceleration and strong advance. Regarding tidewater glaciers the advance can
be strongly compensated by increased calving rates in deepwater in front of
the glacier. It is therefore possible that these glaciers may have
experienced a surge cycle in our observation period; however, a longer time
series analysis is necessary to prove this hypothesis.
East-Ice-Shelf
In sector East-Ice-Shelf the tributary glaciers in the Larsen A
embayment (2558, Arron Icefall, DBE, Drygalski, LAB2, LAB32, TPE61 and
TPE62; Figs. S14, S17, S19–S22, S25 and S26 in the Supplement) and
Sjögren Inlet (Boydell, Sjögren and TPE114; Figs. S18, S23 and S24 in
the Supplement) lost the downstream Larsen A and Prince Gustav ice shelves in
1995. Nearly all glaciers showed a rapid and significant acceleration after
ice shelf breakup and a subsequent slowdown. A gentle peak in flow speeds
is obtained at LAB32 and TPE114 glaciers. They are classified as stable
because the variations are below the threshold of 0.25 md-1,
according to the categorization in Table 3. Dramatic speed-up with subsequent
deceleration of former ice shelf tributaries was reported by various authors:
e.g., in this sector by Seehaus et al. (2015, 2016) at DBE and
Sjögren Inlet glaciers and further south at Larsen B embayment by Rott
et al. (2011) and Wuite et al. (2015). The velocities reported by Rott
et al. (2014) at Sjögren, Pyke, Edgeworth and Drygalski glaciers are
generally higher than our findings. The authors measured the velocities at
locations near the center of the glacier fronts, where the ice flow
velocities are typically highest, whereas we measured the median velocities
at cross profiles close to the glacier fronts (Seehaus et al., 2015). The
different approaches result in different absolute values (see also Sect. S1
in the Supplement), but comparable temporal developments in glacier flow
speeds are observed by both author groups. For example, Rott et al. (2015)
presented surface velocity measured along a central flow line of Drygalski
Glacier. Figure S149 shows our surface velocity measurements across the
terminus of Drygalski Glacier and Fig. S94 in the Supplement velocity
measurements at the maximum ice thickness across the terminus profile. Both
studies show comparable values (e.g., in 1995: this study
∼ 2.7 md-1, Rott et al. (2015)
∼ 2.8 md-1; in 2009: this study
∼ 5.5 md-1, Rott et al. (2015)
∼ 6.0 md-1) at the terminus.
Highest peak values of 6.3 md-1 are found at TPE61 Glacier in
November 1995 and January 1996. Most glaciers (Arron Icefall, Drygalski,
LAB2, TPE61, TPE62) strongly decelerated after the initial acceleration and
show almost constant flow speeds in recent years, indicating that the
glaciers adjusted to the new boundary conditions, albeit significant higher
flow speeds (compared to pre-ice-shelf-collapse conditions) can be observed
at the central sections of the terminus (see Sect. S1 and Fig. S149 in the
Supplement). At 2558, Boydell, DBE and Sjögren glaciers the
deceleration is ongoing and Boydell and DBE glaciers still show increased
flow speeds at the glacier fronts. We suppose that these tributary glaciers
show a prolonged response to ice shelf disintegration, caused by local
settings (e.g., bedrock topography or fjord geometry), and are still adjusting
to the new boundary conditions, as suggested by Seehaus et al. (2015, 2016).
In the 1980s, Prince Gustav Ice Shelf gradually retreated (see Fig. 1) and
2668 Glacier (Fig. S15 in the Supplement) has not been buttressed by the
ice shelf since the early 1990s. A deceleration is found in the period
2005–2010. Hence, this glacier may also have experienced a speed-up in the
early 1990s due to the recession of Prince Gustav Ice Shelf in the 1980s.
However, the earliest velocity measurement at 2668 Glacier is only
available from February 1996.
The ice shelf in Larsen Inlet disintegrated in 1987–1988 and earliest
velocity measurements are obtained in 1993. As for 2668 Glacier no
sufficient cloud-free coverage by Landsat imagery is available which
facilitates the computation of surface velocities for the 1980s. The ice flow
speeds at APPE glaciers (Fig. S16 in the Supplement) are nearly stable with
short-term variations in the order of 0.2–0.5 md-1 between 1993
and 2014. Rott et al. (2014) also found nearly constant flow velocities at
Pyke Glacier (part of the APPE basin, Table 1). The authors suggest that the
ice flow of APPE glaciers was not strongly disturbed by the ice shelf removal
due to the steep glacier surfaces and shallow seabed topography at the
glacier fronts (Pudsey et al., 2001).
West
The glacier geometries differ strongly along the west coast. In the southern
part of sector West the shoreline is more ragged and islands are near the
coast. An impact of the islands on the climatic conditions at the AP
mainland's coastline (e.g., orographic barrier) is not obvious (visual
inspection of RACMO2.3 5.5 km grid cell model results; Van Wessem
et al., 2016). However, the climatic conditions on the AP show strong spatial
and temporal variability (see Sects. and ). These factors
cause the heterogeneous spatial pattern of area and flow speed changes in
sector West as compared to the eastern sectors.
Kunz et al. (2012) observed thinning at the glacier termini along the western
AP, by analyzing airborne and spaceborne stereo imagery in the period
1947–2010. Two of the 12 studied glaciers are located within our study
area: Leonardo Glacier (1968–2010) and Rozier Glacier (1968–2010). An
acceleration and terminus retreat can be caused by frontal thinning as shown
by Benn et al. (2007). However, Benn et al. (2007) also point out that
changes in ice thickness do not necessarily affect the ice flow and that
calving front positions and ice dynamics are strongly dependent on the fjord
and glacier geometries, derived from modeling results which have higher
uncertainties especially for smaller basins.
The large number of glaciers in this sector is analyzed by means of
a hierarchical cluster analysis (Sect. ) and assorted into four
groups based on the resulting dendrogram (Fig. 6). Box plots of the individual
input variables of each group are shown in Fig. 7. The correlation between
the observed ice dynamics and the glacier geometries of each group are
discussed in the following sections (see also Fig. 7).
Group 1 (14 glaciers)
Most glaciers experienced acceleration in the period 1992–2014. The majority
of the glacier basins are “very top heavy” or “top heavy” (median
HI =-1.8), stretching from sea level up to 1892 m on
average. The bclim increases toward higher altitudes (Van Wessem
et al., 2016) and highest values are found in the zone between 1000 and
1700 ma.s.l. Consequently these glaciers receive high mass input in
their large high altitude accumulation areas. The accumulation is known to
have significantly increased on the AP by 20 % since 1850 (Thomas et al.,
2008). Pritchard and Vaughan (2007) reported that only a small fraction of
the acceleration can be attributed to glacier thickening due to increased
mass input. Up-glacier thickening combined with frontal thinning (reported by
Kunz et al., 2012) leads to a steepening of the glacier and an increase in
driving stress, resulting in faster ice flow (Meier and Post, 1987) as
observed in this study. Moreover, a thinning of the terminus reduces the
effective basal stress of a tidewater glacier and facilitates faster ice flow
(Pritchard and Vaughan, 2007). The flux gate cross sections to catchment size
ratios are relatively small, indicating narrowing catchments towards the ice
front. The channelized increased ice flow almost compensates for the
increased calving rates (due to frontal thinning), resulting in an average
recession of the glaciers by only 0.2 % in the period 1985–2015. The
high flow speeds may outweigh the calving and lead to ice-front advances as
measured at Krebs and TPE46 glaciers. The glacier termini of this group are
typically located in narrow fjords (Fig. 5) and are clustered in Charcot,
Charlotte and Andvord bays.
Group 2 (19 glaciers)
Glaciers of group 2 are spread all over sector West, with a local
clustering in Wilhelmina Bay. Group 2 shows similar hmax and FA
characteristics to group 1. Area changes are also quite small (-0.1 %).
Most of the glaciers experienced acceleration or show a “peaked” evolution
of the flow velocities. In contrast to group 1 the catchments are in general
“bottom heavy” and some are even “very bottom heavy”. We assume that the
constraints are similar to group 1 (increasing bclim, frontal
thinning and steepening). However, the additional mass accumulation in the
upper areas is smaller due to the bottom-heavy glacier geometries.
Consequently, the imbalance due to the frontal thinning and up-glacier mass
gain is less pronounced as in group 1 and numerous glaciers (“peak” type)
started to decelerate after the speed-up, indicating that these glaciers are
adjusting to the new boundary conditions.
Group 3 (13 glaciers)
These basins typically show a bottom-heavy hypsometry and smaller
elevation ranges (on average up to 1103 ma.s.l.). Thus,
bclim is relatively low. The smaller mean ice thickness at the
termini (161 m, compared to 211 m of all glaciers) of group 3
implies less interaction with the ocean, leading to a small average frontal
retreat of ∼ 0.1 %. The low frontal ablation does not significantly
affect the ice flow, probably due to the flat glacier topography and the low
mass input. Consequently, the flow speed is in general stable or even
slightly decreases in the observation period. Glaciers of group 3 usually
face the open ocean and do not terminate in narrow fjords (especially in the
northern part, Trinity Peninsula).
Group 4 (3 glaciers)
All basins in this group have a very bottom-heavy hypsometry and an
elevation range comparable to group 3 glaciers. The FA factors are in
general higher than in group 3, implying that outflow of the catchments is
less channelized and the glacier fronts are long compared to the catchment
sizes. Therefore, the largest relative area changes, on average -5.1 %,
are found at glaciers in group 4. However, the absolute frontal retreat is
small and does not significantly affect the glacier flow. Note that group 4
consists of only three samples, limiting the significance.
Conclusions
Our analysis expands on previous work (Pritchard and Vaughan, 2007) on ice
dynamic changes along the west coast of AP between TPE8 and Bagshawe–Grubb
glaciers, in regard to both temporal coverage and analysis methods. It also
spatially extends previous work on changes in ice dynamics along the east
coast between Eyrie Bay and the Seal Nunataks. The spatially and temporally
detailed analysis of changes in ice flow speeds (1992–2014) and ice-front
positions (1985–2015) reveals varying temporal evolution in glacier dynamics
along the northern AP. The results are in general in line with findings of
the previous studies; however, along the west coast a more accelerated
glacier flow is determined and on the eastern side temporal evolution of ice
dynamics of 21 glaciers is observed for the first time. A large variety of
temporal variations in glacier dynamics were observed in our studied area and
attributed to different forcing and boundary conditions.
On the east side all glacier fronts retreated in the study period (relative
to 1985, relative to 1995 for former Larsen A and Prince Gustav ice shelf
tributaries; see also Sect. ), with highest retreat rates observed
at former tributaries of the Prince Gustav, Larsen Inlet and Larsen A ice
shelves. Moreover, nearly all the glaciers affected by ice shelf
disintegration showed similar temporal evolutions of ice velocities. The
glaciers reacted with a strong acceleration to ice shelf breakup followed by
a deceleration, indicating that the glaciers adjusted or are still adjusting
to the new boundary conditions. Glaciers on the east coast north of the
former Prince Gustav Ice Shelf showed in general a significant deceleration
and a reduction in frontal ablation. Based on the observed warming trend
since the 1960s and the subsequent cooling since the mid-2000s in the
northern AP, we suggest that the initial recession and speed-up of the
glaciers took place before the start of our observation and that the glaciers
are now close to a new equilibrium.
The average flow speed of the glaciers along the west coast of the Antarctic
Peninsula significantly increased in the observation period but the total
frontal change is negligible. No general evolution in ice dynamics of the
glaciers at the west coast is obvious. However, correlations between the
changes in ice dynamics and the glacier geometries of the individual
catchments are obtained by applying a hierarchical cluster analysis. Thus,
the geometry of the individual glacier basin strongly affects the reaction of
the glacier to external forcing.
We conclude that for regions with such a strong spatial variation in
topographic and climatic parameters as the AP, it is impossible to derive
a regional trend in glacier change by simply analyzing individual glaciers in
this region. Therefore further detailed observation of the glaciological
changes along the AP is needed. Upcoming sensors hopefully facilitate the
region-wide measurement of recent surface elevation, since current estimates
have got only partial coverage or have got some issues due to the complex
topography of the AP. Moreover, future activities should link remote-sensing-derived ice dynamics and glacier extent with ocean parameters and ocean
models, as well as regional climate models and ice dynamic models, in order
to provide a better quantification of mass changes and physical processes
leading to the observed changes.
:Surface velocity fields and terminus profiles are
available upon request. Please contact Thorsten Seehaus for this purpose
(thorsten.seehaus@fau.de). Various glacier outline data are available at the
Antarctic Digital Database (https://www.add.scar.org/) and upon request
from Alison Cook (alison.cook@durham.ac.uk) and Aline B. Silva
(linebsilvaa@gmail.com). The ASTER Global DEM of the Antarctic Peninsula is
available at NSIDC doi:10.5060/D47P8W9D. The Antarctic Peninsula bed rock map
is available at doi:10.5194/tc-8-1261-2014.
The Supplement related to this article is available online at https://doi.org/10.5194/tc-12-577-2018-supplement.
TS designed the study, processed the SAR data, performed
the data analysis and led the writing of the manuscript, in which he received
support from all authors. AC and AS compiled and provided glacier front
position data sets. MB initiated the project and coordinated the research.
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the Deutsche Forschungsgemeinschaft (DFG) in the
framework of the priority programme “Antarctic Research with comparative
investigations in Arctic ice areas” by a grant to Matthias Braun (BR
2105/9-1). Matthias Braun and Thorsten Seehaus would like to thank the HGF
Alliance “Remote Sensing of Earth System Dynamics” (HA-310) and
Marie-Curie-Network International Research Staff Exchange Scheme IMCONet (EU
FP7-PEOPLE-2012-IRSES) for additional support. Access to satellite data was
kindly provided by various space agencies, e.g., under ESA AO 4032, DLR
TerraSAR-X Background Mission Antarctic Peninsula and Ice Shelves, TSX AO
LAN0013, TanDEM-X Mission TDX AO XTI_GLAC0264, ASF, GLIMS as well as NASA
and USGS. Edited by: Olaf Eisen
Reviewed by: two anonymous referees
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