Introduction
The ice masses of the Antarctic Peninsula (AP) potentially make
a large contribution to sea level rise (SLR) since a large amount of water is
stored in the ice and a high sensitivity to temperature increase has been
reported (Hock et al., 2009). However, the glaciers on the AP were not
separately taken into account for their individual sea level contribution in
the Fifth Assessment Report of the IPCC (Vaughan et al., 2013) because a
complete glacier inventory of the AP was not available at that time. As a
result, only the ice masses of the surrounding islands were considered from
the inventory compiled by Bliss et al. (2013). The freely available data sets
for the AP were incomplete and of a varied nature (see Fig. 1), ranging from
the World Glacier Inventory (WGI; WGMS and NSIDC, 2012), which provides
extended parameters for most of the glaciers on the AP from the second half
of the 20th century but without area information and only available as point
data, to the vector data sets (two-dimensional outlines) from the Global Land
Ice Measurements from Space (GLIMS; GLIMS and NSIDC, 2015) database and the
Randolph Glacier Inventory (RGI; Arendt et al., 2015), which were spatially
incomplete. Moreover, the spatial overlap of the WGI with the boundaries of
individual glaciers in the RGI was limited (Fig. 1) so that an automated
digital intersection (spatial join) for parameter transfer was not possible.
Conversely, for Graham Land, representing the part of the AP north of
70∘ S, several more specific data sets exist that could be combined
for a full and coherent glacier inventory: a detailed 100 m resolution
DEM was prepared by Cook et al. (2012);
glacier catchment outlines based on this DEM and the Landsat Image Mosaic of
Antarctica (LIMA; Bindschadler et al., 2008) were derived by Cook et
al. (2014); a recently updated data set of rock outcrops for all of
Antarctica is available from the Antarctic Digital Database (ADD;
http://www.add.scar.org/home/add7); a modeled raster data set of
bedrock topography is available from Huss and Farinotti (2014).
Landsat Image Mosaic of Antarctica (LIMA) overlaid by existing
GLIMS and RGI glacier outlines and WGI glacier point locations for Graham
Land on the AP. Inset map illustrating that the distribution of the WGI
points does not enable assignation of points to individual glacier outlines.
Here, we present the first comprehensive glacier inventory of the Antarctic
Peninsula north of 70∘ S (Graham Land) and describe methods used to
digitally combine the existing data sets. The final outline data set of the
AP is supplemented with several glacier-specific parameters, such as
topographic information and hypsometry, and thickness and volume information, as
well as the earlier classification of glacier front characteristics. With
these parameters we analyze similarities and differences with other glacierized
regions, as well as glacier-specific contributions to sea level and climate
sensitivities. For a clear handling by different modeling and remote sensing
communities, each glacier is assigned one of three connectivity levels to the
ice sheet (CL0 is no connection, CL1 is a weak connection and CL2 is a strong
connection) following the approach introduced by Rastner et al. (2012) to
separate the peripheral glaciers on Greenland from the ice sheet.
Study region
The AP extends northwards of the mainland from approximately 75∘ S
for more than 1500 km northeasterly to 63∘ S, and it is enclosed to
the west by the Bellingshausen Sea and to the east by the Weddell Sea of the
Southern Ocean. The part of the AP north of 70∘ S represents Graham
Land and its peripheral islands, for which the glacier inventory is created.
The South Shetland Islands are not regarded as being part of the AP and are
therefore not included in the present inventory. The central part of the mainland is
dominated by a narrow mountain chain with a mean height of 1500 m
(maximum 3172 m) and an average width of 70 km. The unique
topography, with an interior high-elevation plateau surrounded by steep
slopes and flat valley bottoms results in distinct glacier types. In general,
the highest regions are covered by ice caps, and much lower-lying valley
glaciers are either connected to them and heavily crevassed in the steep
regions, or they are entirely separated from them, uncovering several rock
outcrops.
The AP has a polar-to-subpolar maritime climate, but the climatic and
oceanographic regime varies across the AP, causing varying glacier dynamics
(Arigony-Neto et al., 2014). The often polythermal glaciers experience a
distinct melting period in austral summer, particularly the glaciers in the
northern part of the AP. The special topographic characteristics of the AP
make the flat, low-lying parts of its glaciers particularly vulnerable to
climate change: for example, a small increase in temperature might cause
large parts of their area to become ablation regions; most of them are
marine-terminating glaciers that also experience melt from surrounding ocean
waters (Cook et al., 2016), and many of them nourish ice shelves (Cook et
al., 2014) that currently buttress them but can quickly disappear (Rott et
al., 1996) causing rapid shrinkage of the related glaciers (Rott et
al., 1996; Hulbe et al., 2008).
Since the early 1950s, significant atmospheric warming trends (Turner et
al., 2009) and increasing ocean temperatures (Shepherd et al., 2003) have
been observed across the AP. As a consequence, ice shelves are collapsing and
glacier fronts are retreating (Pritchard and Vaughan, 2007; Davies et
al., 2012; Cook et al., 2014, 2016). Conversely, knowledge about the
mass balance of the glaciers of the AP is sparse (Rignot and Thomas, 2002),
although a few studies exist that indicate a general mass loss (Helm et
al., 2014; Kunz et al., 2012).
For the purpose of this study, the AP is additionally divided into four
sectors (NW, NE, SW and SE) to reveal differences between climatically
different regions of the AP. The division west–east is based on the main
topographic divide, and north–south is based on the 66∘ S latitude.
Data sets
This section gives a short description of the preexisting data sets covering
the AP (Graham Land) that are used for generating the glacier inventory.
Table 1 summarizes their key characteristics, presenting their content,
sources, access, references and application in this study. The following data
sets are used:
the digital elevation model (DEM) by Cook et al. (2012);
the glacier catchment outlines by Cook et al. (2014);
the rock outcrop data set of Antarctica by Burton-Johnson et al. (2016);
the bedrock elevation grid by Huss and Farinotti (2014);
the Antarctic ice-sheet drainage divides by Zwally et al. (2012) and
the Landsat Image Mosaic of Antarctica (LIMA) by Bindschadler et
al. (2008).
Data sets used for the generation of the glacier inventory and a
description of their properties.
DEM
Glacier catchment
Rock outcrops
Bedrock elevation grid
Antarctic ice-sheet
outlines
drainage divides
Content
Elevation on a 100 m grid
Inventory of 1590 glacier
New rock outcrop
Bedrock data set
Drainage divides
of the AP
basins of the AP
data set
for the AP
of the Antarctic
(Graham Land, 63–70∘ S)
(Graham Land, 63–70∘ S)
for Antarctica
(Graham Land, 63–70∘ S)
ice sheet
on the mainland and
on a 100 m grid
surrounding islands
Sources
ASTER Global Digital
DEM of Cook et al. (2012),
Landsat 8 data
Simple ice-dynamic modeling
GLAS/ICESat 500 m laser
Elevation Model (GDEM)
LIMA (Bindschadler et
with a variety of available
altimetry DEM (DiMarzio, 2007)
al., 2008), grounding line
data sets (surface mass
Landsat Image Mosaic of Antarctica
based on the Antarctic
balance, point ice thickness
(LIMA; Bindschadler et al., 2008) and
Surface Accumulation and
and ice flow velocity)
the MODIS Mosaic of Antarctica
Ice Discharge (ASAID)
(Haran et al., 2005)
project data source
(Bindschadler et al., 2011)
Access
http://nsidc.org/data/
http://add.scar.org/
http://add.scar.org/
Available online from
http://icesat4.gsfc.nasa.gov/cryo
(available only with
the article's supplement
a limited number
(10.5194/tc-8-1261-2014-supplement)
of attributes)
Reference
Cook et al. (2012)
Cook et al. (2014)
Burton-Johnson et al. (2016)
Huss and Farinotti (2014)
Zwally et al. (2012)
Application
Calculation of
Initial data set for
Used to remove the
Calculation of the thickness
Separation of the glaciers
in this
(a) glacier-specific
the generation of
(ice-free) rock outcrops
grid combined with
form the ice sheet
study
topographic parameters
glacier outlines
from the glacier catchment
the DEM of
(min, max, mean, median
outlines to generate
Cook et al. (2012)
elevation, slope, aspect),
glacier outlines
(b) overall and glacier
specific hypsometry, and
(c) thickness grid combined
with the bedrock elevation grid
of Huss and Farinotti (2014)
Digital elevation model
Cook et al. (2012) generated a 100 m resolution DEM of the AP
(63–70∘ S), which is available from the National Snow and Ice Data
Center (NSIDC; http://nsidc.org/data/NSIDC-0516) in the WGS84
Stereographic South Pole projection. This DEM is an improvement of the ASTER
Global Digital Elevation Model (GDEM) product, which locally contained large
errors and artifacts (see Cook et al., 2012). The accuracy of the DEM is in
particular improved on gentle slopes of the high plateau region. However,
they removed small anomalies, which has resulted in small inherent gaps along
the coast, and some islands are missing (Cook et al., 2012). As a result, the
DEM does not entirely cover the study region (approximately 1 % of the
area is missing). This DEM has also been used by Cook et al. (2014) for the
generation of catchment outlines (see next section) and is used in this study
for the calculation of glacier-specific parameters (see Sect. 4.3) for the
glacierized areas it covers.
Catchment outlines
Glacier inventories, such as those available in GLIMS or the RGI, require
glaciers to be separated into individual entities (Paul et al., 2009). This
can be accomplished by intersecting drainage divides derived from watershed
analysis (e.g., Bolch et al., 2010; Kienholz et al., 2013) with outlines of
glacier extents derived from semiautomated mapping techniques (e.g., Paul et
al., 2002). Cook et al. (2014) automatically delineated glacier catchments of
the AP in ArcGIS from ESRI by applying hydrological tools to the DEM described
above (Fig. 2). They digitized the AP coastline and some islands in that data
set based on images acquired by Landsat 7 between 2000 and 2002 for the
LIMA (Bindschadler et al., 2008). Since the
DEM misses some islands around the AP, mainly in the central western region,
the drainage divide analysis is missing for these regions. Additionally, they
used grounding lines from the Antarctic Surface Accumulation and Ice
Discharge (ASAID) project data source (Bindschadler et al., 2011), modified
in places with features visible on the LIMA to divide glaciers from ice shelves.
Furthermore, the ice-velocity data set of Rignot et al. (2011) was considered
by Cook et al. (2014) to manually verify and adjust the lateral boundaries of
glaciers.
The resulting data set consists of 1590 glacier catchment outlines for the AP
with an area of 96 982 km2, covering the region between 63 and
70∘ S. Islands smaller than 0.5 km2 and ice shelves are
excluded. The data set provides a consistent time period of all basins and
includes several parameters for each basin, such as location, time stamp,
area, and a classification of glacier type, form and front. The definition of
the parameters and category numbers conform to the GLIMS classification
system provided by the GLIMS Classification Manual (Rau et al., 2005) and
based on the UNESCO (1970) guidelines as well as the Glossary of Glacier Mass
Balance (Cogley et al., 2011). However, topographic parameters such as
minimum, maximum, mean, and median elevation, or mean slope and aspect, are
missing.
This catchment outline data set is available from the Scientific Committee on
Antarctic Research (SCAR) ADD (http://add.scar.org/home/add7; ADD
Consortium, 2012), but it does not include any of the glacier-specific
attributes mentioned above aside from area and length. The data set with the complete information has not been published so far and has been generated and provided by A. Cook in the framework of this study in the WGS84 Stereographic South Pole projection.
Whereas the
catchment outlines provide a solid foundation for the generation of a glacier
inventory, rock outcrops are part of the glacierized area and need to be
removed (Raup and Khalsa, 2010).
Glacier catchment outlines of Cook et al. (2014) and the newest rock
outcrop data set from Burton-Johnson et al. (2016) overlaying the LIMA.
Rock outcrops
The ADD website (www.add.scar.org) provides a detailed vector data set
of rock outcrop boundaries in the WGS84 Stereographic South Pole projection
that has recently been updated (see Burton-Johnson et al., 2016). A former
rock outcrop data set, which has already been used (by Bliss et
al. (2013) for instance) to create the inventory for the glaciers of the islands
surrounding Antarctica, originated from a digitization of outcrops from
different maps prepared in the 1990s at different scales and with variable
accuracy. As a result, the data set has some major georeferencing
inconsistencies, misclassifications and overestimations of the ice-free area
of Antarctica (Burton-Johnson et al., 2016). The recently improved data set
of exposed rock outcrops by Burton-Johnson et al. (2016) used here (Fig. 2),
overcomes these issues and has a much better accuracy. It is based on a new
automated method that identifies sunlit as well as shaded rock outcrops using
multispectral classification of Landsat 8 satellite imagery. They manually
removed incorrectly classified pixels (illuminated and shaded) such as snow,
clouds and liquid water. The new data set reveals that 0.18 % of the
total area of Antarctica is rock outcrops, which is approximately one-half
of previous estimates (Burton-Johnson et al., 2016).
Bedrock elevation grid
Huss and Farinotti (2014) derived a new bedrock elevation grid with 100 m
spatial resolution as well as the related ice thickness grid based on glacier
surface topography and simple ice-dynamic modeling. Compared to the Bedmap2
data set by Fretwell et al. (2013) with a resolution of 1 km, the new
version also captures the rugged subglacial topography in great detail. The
narrow and deep subglacial valleys that are often below sea level are more
accurately represented, allowing the modeling of even small-scale processes.
Their data set is available online from the article supplement
(10.5194/tc-8-1261-2014-supplement) on WGS84 Antarctic Polar
Stereographic projection. Their data set already excluded the rock outcrops
using the former version of the ADD (ADD Consortium, 2012). Since we have used
the updated version of the rock outcrops data set for creating the glacier
inventory, a new thickness grid is calculated (see Sect. 4.1).
Antarctic ice-sheet drainage divides
The Cryosphere Science Laboratory of NASA's Earth Sciences Divisions (Zwally
et al., 2012) provides an Antarctic ice-sheet drainage divide data set
developed by the Goddard Ice Altimetry Group from ICESat data based on the
GLAS/ICESat 500 m laser altimetry DEM (DiMarzio, 2007). They used other
sources, such as LIMA (Bindschadler et
al., 2008) and the MODIS Mosaic of Antarctica (Haran et al., 2013), as a
guide to refine the drainage divides. Ice-sheet drainage systems were
delineated to identify regions that are broadly homogeneous regarding surface slope
orientation relative to atmospheric advection and denoting the ice-sheet
areas feeding large ice shelves. The AP is assigned to four different basins
(drainage system ID numbers 24–27), with a relatively clear separation from the ice
sheet along 70∘ S latitude (see Sect. 4.2).
Methods
The data generation workflow is roughly divided into four steps:
(1) intersecting data sets, (2) defining connectivity levels, (3) calculating
glacier-specific attributes (topographic parameters), including ice thickness
and volume information, and (4) the calculation of the overall and
glacier-specific hypsometry. All calculations are performed with various
tools available in ESRI's ArcGIS version 10.2.2. All of the functionality is
also available in other geographic information system (GIS) software packages. The four main steps are
described in the following sections in more detail.
Intersecting data sets
When generating an inventory based on the semiautomated band ratio method
(Paul et al., 2009), rock outcrops are automatically excluded from the
glacier area. In this study the glacier catchment outlines are intersected
with the latest vector data set of rock outcrop boundaries from the ADD (see
Sect. 3.3). By removing the new rock outcrops from the catchment outlines of
Cook et al. (2014), a mask of individual glaciers is generated, assuming that
areas not identified since rock outcrops are ice covered. Apart from the rock
outcrops, the data set of Cook et al. (2014) is generally in agreement with
the procedures and GLIMS guidelines (Racoviteanu et al., 2009; Raup and
Khalsa, 2010) for deriving glacier information.
To include glacier-specific ice thickness and volume information, the bedrock
grid of Huss and Farinotti (2014) is subtracted from the DEM of Cook et
al. (2012) and combined with the new glacier outlines. A grid with ice volume
is then derived by multiplying the ice thickness grid with the cell area
(10 000 m2).
Defining connectivity levels
Rastner et al. (2012) suggested that peripheral glaciers on Greenland with a
strong dynamic connection to the Greenland Ice Sheet should be regarded as
part of the ice sheet and assigned the connectivity level 2 (CL2). This is
where glaciers have an extended connection to the ice sheet and the location
of their drainage divide on the DEM is uncertain due to the low-sloping
terrain. For the Antarctic ice-sheet drainage divides (see Sect. 3.5), basins
south of 70∘ S are strongly connected to the West Antarctic ice
sheet. Accordingly, they are assigned CL2 and are not included or further
considered in the inventory presented here. The assignment of CL1 (i.e., weak
connectivity to ice sheet) to the glaciers on the mainland and north of
70∘ S is performed automatically within the GIS following the
heritage rule introduced by Rastner et al. (2012), i.e., a glacier connected
to a glacier assigned CL1 will also receive the attribute CL1. With this
strategy, all glaciers on surrounding islands (i.e., those in the inventory
from Bliss et al., 2013) are assigned the value CL0. Large glaciers that are
theoretically separable but otherwise closely connected to the ice sheet
(e.g., Pine Island and Thwaites) have the value CL2.
Glacier-specific topographic parameters, ice thickness and
volume
All glacier-specific attributes (minimum; maximum; mean; and median
elevation; mean slope, aspect, and thickness; total ice volume; and ice volume
grounded below sea level) are calculated by combining the glacier outlines
with the DEM, the ice thickness and volume grids using the zonal statistics
tool in ArcGIS. This tool statistically summarizes the values of the
underlying raster data sets (e.g., DEM, ice thickness) within specific zones
with a unique ID (glacier outlines) and organizes the results into an attribute
table. The table is joined with the attribute table of the glacier outlines
data set based on a common and unique identifier in both tables (i.e., the
glacier ID). All calculations are performed using the WGS84 South Pole
Lambert Azimuthal Equal Area projection.
Since the bedrock and hence also thickness data sets are based, inter alia,
on the DEM of Cook et al. (2012), they are not universally spatially
congruent with the glacier outlines (i.e., the boundary limits differ between
the outlines and the other data sets). Of the 1589 glacier outlines, the
thickness and volume values could not be calculated for 50 glaciers of the
inventory. Accordingly, the topographic parameters, thickness and volume
values of the glaciers on the islands that are not completely covered by the
ice thickness and bedrock data set do not represent values for complete
glaciers. In addition, two glaciers are insufficiently covered by the
100m×100m pixel of the DEM. Hence these glaciers
are not or insufficiently covered by the bedrock data set of Huss and
Farinotti (2014). Hence, 1541 glaciers have topographic information and 1539
glaciers have thickness, volume and sea level equivalent (SLE) information,
of which some only have partial ice thickness and volume information.
To estimate the volume grounded below sea level for each glacier, a grid
representing the distribution of the volume grounded below sea level is
calculated by extracting the areas of the bedrock grid with negative values
(areas below sea level).
(a) Glacier outlines. Inset map showing Romulus Glacier
referred to in Table 2 and (b) exemplifying the glacier-specific
hypsometry.
The SLE of the ice volume is calculated by assuming a mean ice density of
900 kgm-3 (not taking into account firn-air content) and
dividing it by the ocean surface area (3.625×108 km2;
Cogley, 2012), assuming all ice volume contributes to sea level if melted.
This is not the case for the grounded ice below sea level, which has a
negative (lowering) effect since this volume will be replaced by water with a
higher density (Cogley et al., 2011). This effect has been considered in a
second step in the SLE estimations presented. Other effects, such as the
isostatic effect, the cooling and dilution effect on ocean waters by floating
ice (Jenkins and Holland, 2007), are not taken into account here.
Glacier parameters in the attribute table of the inventory of the
AP.
Name
Item
Glacier example
Description
Name
Name
Romulus Glacier
String, partially available
Satellite image date
SI_DATE
19.02.2001
Date of the satellite image used for digitizing
Year
SI_YEAR
2001
Year the outline is representing
Satellite image type
SI_TYPE
Landsat 7
Instrument name, e.g., Landsat 7
Satellite image ID
SI_ID
LE7220108000105050
Original ID of image
Coordinates
Lat, long
-68.391218, -66.82767
Decimal degree
Primary classification
Class
6 (mountain glacier)
See Cook et al. (2014)
Form
Form
2 (compound basin)
See Cook et al. (2014)
Front
Front
4 (calving)
See Cook et al. (2014)
Confidence
Confidence
1 Confident about all (class, form
See Cook et al. (2014)
and front) classification types
Mainland/island
Mainl_Isl
1 (situated on mainland)
See Cook et al. (2014)
Area
Area
68.9 km2
km2
Connectivity level
CL
1 (weak connection)
See Sect. 4.2
Sector
Sector
SW
NW, NE, SW or SE
Minimum elevation
min_elev
4.6 m a.s.l.
m a.s.l.
Maximum elevation
max_elevation
1610.6 m a.s.l.
m a.s.l.
Mean elevation
mean_elev
466.5 m a.s.l.
m a.s.l.
Median elevation
med_elev
425.6 m a.s.l.
m a.s.l.
Mean aspect in degree
mean_asp_d
222∘
∘
Mean aspect nominal
mean_aspect
SW
Eight cardinal directions
Aspect sector
asp_sector
6
Clockwise numbering of the eight cardinal directions
Mean slope
mean_slope
13∘
∘
Total volume
tot_vol
13.4 km3
km3
Volume below sea level
vol_below
8.0 km3
km3
Mean thickness
mean_thick
191.4 m
m
Glacier hypsometry
The distribution of the glacierized area with elevation (hypsometry) is
calculated (a) for the entire AP in 100 m elevation bins, (b) for the four
subregions also in 100 m bins and (c) for each individual glacier using
50 m elevation bins. The calculation is based on the DEM of Cook et
al. (2012) that is converted to 100 m bins using the “reclassify” tool and
the “extract by mask” tool for the respective subregions. Additionally,
the hypsometry of the catchment outlines is calculated to determine the
effect of removing rock outcrops from the hypsometry. For further comparisons
we also calculated the hypsometry of the marine and ice-shelf-terminating
glaciers and the hypsometry of the bedrock.
Results
Size distribution
The glacier inventory for the AP ranges from 63–70∘ S to
55–70∘ W and consists of 1589 glaciers covering an area of
95 273 km2 (Fig. 3a) without rock outcrops, ice shelves and islands
< 0.5 km2. Hence, compared to the preexisting data set of Cook et
al. (2014), this data set covers a smaller area and one glacier less due to
the intersection with the rock outcrop data set (we removed one glacier since
all of its area was rock outcrop). The rock outcrops cover an area of
1709.4 km2. The 619 glaciers located on islands (CL0) cover an area
of 14 299 km2, representing 15 % of the total glacierized area.
The remaining 970 glaciers are located on the mainland (CL1), covering
80 974 km2 and hence 85 % of the total area. Since the DEM is
spatially not perfectly congruent with the glacier outlines, of the total
1589 glacier outlines, 48 outlines do not have any elevation information. As a
result, the calculations including the DEM, the bedrock or the thickness data set
are only applied to 1541 glaciers, of which some only have partial elevation
information. In Table 2 all parameters of the attribute table are listed,
including the corresponding values of an example glacier (for location see
inset map in Fig. 3a). The hypsometry of each individual glacier, as
exemplified in Fig. 3b, is stored and available separately in a csv file.
Several parameters, such as primary classification, glacier form and front,
and metadata about the satellite image, have been determined and provided by
Cook et al. (2014), as defined for the GLIMS inventory. Others (i.e.,
connectivity levels, topographic parameters, ice thickness and volume) are
the result of the calculations described in Sect. 4. The inventory is
available for download from the GLIMS website:
http://www.glims.org/maps/glims (10.7265/N5V98602).
Regarding the connectivity levels, all glaciers on islands surrounding the AP
are assigned CL0 (no connection) and the glaciers on the mainland are all
assigned CL1 (weak connection). Even the glaciers at the very northern part
of the AP have CL1 due to the applied topological heritage rule (a glacier
connected to a glacier assigned CL1 also receives CL1). Since the glaciers
further south are connected to the ice sheet, they are assigned CL2 (strong
connection), are regarded as part of the ice sheet and hence are not included
in the present data set.
Figure 4a portrays the percentages per size class in terms of number and
area. The mean area (60.0 km2) is considerably higher than the
median area (8.2 km2), reflecting the areal dominance of a few
larger glaciers. Most of the glaciers can be found in the size classes 4–6
(1.0–50 km2). These glaciers account for 77 % of the total
number but only for 14 % of the total area. The glaciers larger than
100 km2 cover the majority of the area (77 %) yet comprise only
11 % of the total number. With an area of 7018 km2, Seller
Glacier is the largest, accounting for 7 % of the total area and being
twice as large as the second largest glacier (Mercator Ice Piedmont,
3499 km2).
(a) Percentage
of glacier count and area per size class (only upper boundary of each size
class is given on the x axis) and (b) percentage of glacier count
and area per aspect sector.
(a) Mean and median elevation vs. area and
(b) minimum and maximum elevation vs. area of the 1541 glaciers,
including elevation information.
Topographic parameters
Figure 4b shows the distribution of glacier number and area as a percentage of
the total for each aspect sector of the AP. The distribution is rather
balanced and does not reveal any trends. Somewhat fewer glaciers and areas
have aspects from south to southeast. The large value in area of the
southwestern sector derives from the contribution of the largest glacier of
the region (Seller Glacier).
Figure 5a and b present a scatter plot of area against mean and median and
area against minimum and maximum elevation, revealing that mean, median and
maximum elevation increase towards larger glaciers. Three glaciers have a
maximum elevation above 3100 m a.s.l., being 300 m or more
higher than all other glaciers. The highest elevation is in southern Graham
Land with 3172 m. Many glaciers have a minimum elevation of (close
to) zero m a.s.l. since most of them are marine terminating. The
average mean elevation of the 1541 glaciers with elevation information is
409 m, and their median elevation is 317 m a.s.l. The
spatial distribution of median elevation reveals an increase from the coast
and islands (0–500 m a.s.l.) to the interior of the AP (up to about
1800 m a.s.l.). This can be seen in Fig. S1 in the Supplement.
Mean glacier elevation vs. mean glacier aspect of 1541 glaciers. The
top and bottom of the boxes indicate the 25th and 75th percentiles,
respectively. The whiskers extend to 1.5 times the height of the box and to
the minimum values.
When mean aspect is plotted against mean elevation (Fig. 6) there are also no
significant trends. However, the highest mean elevation values are lower in
the southeastern sector. The scatter plot of mean slope against area
(Fig. S2) reveals the common dependence on glacier size, where mean slope
decreases towards larger glaciers. Additionally, the scatter is smaller the
larger the glacier, indicating that small glaciers exhibit a larger range of
slope inclination.
Scatter plot of the 1541 glaciers involving thickness information.
(a) Mean thickness vs. area and (b) mean thickness vs. mean
slope.
The mean thickness of all 1539 glaciers involving thickness information is
130 m. The Eureka glacier, located in the south, has the largest mean
thickness of all CL0 and CL1 glaciers with 851 m. The dependence of
mean thickness on area and slope (indicating that the steeper or smaller the
glacier, the thinner the ice) (Fig. 7a, b) is not surprising because ice
thickness is modeled based on surface topography (Huss and Farinotti, 2012,
2014). However, low-sloping glaciers reveal a large range of mean thickness
values. The large but low-sloping glaciers of the high plateau and those in
the very south towards the Antarctic ice sheet form a cluster of glaciers
with higher mean thicknesses. The many small glaciers along the coast are
mostly thin. The mean thicknesses per sector and per mean aspect (Fig. S3) do
not reveal any significant spatial patterns.
The total ice volume of the AP is 34 590 km3. Since the volume is
calculated based on the thickness data set, the volume distribution is
basically a reflection of the thickness distribution. Table 3 lists the total
volume per sector, revealing that most of the ice volume can be found in the
southwestern and southeastern sectors (38.6 and 32 % of the total). This is
not surprising because these two sectors make up 63 % of the total glacierized
area. Regarding the glacier volume per glacier area for individual glaciers,
the highest values are found for the large glaciers at the very south of the
AP, adjacent to the ice masses regarded as being a part of the Antarctic ice
sheet.
Glacier number, area, volume, volume grounded below sea level, the
corresponding percentages and SLE per sector. For the estimation of SLE, see
Sect. 4.3.
Sector
Count
Count with
Area
Count
Area
Volume
Volume
Volume< 0
Volume< 0
SLE
volume info
[km2]
[%]
[%]
[km3]
[%]
[km3]
[%]
[mm]
NW
704
679
17 218
44
18
4026
12
1093
27
7
NE
246
237
18 278
15
19
6133
18
2939
48
7
SW
378
362
31 130
24
33
13 365
39
4849
36
20
SE
261
261
28 647
17
30
11 065
32
2890
26
20
Total
1589
1539
95 273
100
100
34 590
100
11 771
100
54
Numerous, partly very pronounced, valleys lie below sea level, especially in
the northeastern sector (the bedrock lying below sea level is visualized in
Fig. S4). In total, approximately one-third of the total grounded ice volume
is below sea level (Table 3), which has a negative effect on SLR (sea level
lowering). About 50 % of the volume of the northeastern sector is
grounded below sea level (Table 3). Although the negative effect on SLR is
very small, this effect can now be better considered for future sea level
estimations. Based on the results presented here, this results in a total
estimated SLE of 54 mm (Table 3).
As mentioned before, the nominal glacier parameters primary classifications,
glacier form and front, have been determined by and described in Cook et
al. (2014). They further illustrate the number of glaciers within each
classification and frontal type, which is therefore not repeated here.
Glacier hypsometry of the total area covered by the DEM.
(a) Total areal distribution excluding rock outcrops, areal
distribution for marine-terminating and ice-shelf-nourishing glaciers and
areal distribution of the underlying bedrock. (b) Areal distribution
of the glacier cover per sector.
Hypsometry
Figure 8a and b depict the glacier hypsometry (area–altitude distribution)
for (a) the entire AP and for (b) each sector, revealing a bimodal shape of
the hypsometry. Figure 8a additionally displays the hypsometry only for
marine-terminating and ice-shelf-nourishing glaciers, as well as the
hypsometry of the underlying bedrock. Exclusion of the rock outcrops, with a
total area of 1709.4 km2, does not change the general shape of the
hypsometry. However, it slightly reduces the glacierized areas below
1500 m a.s.l., with a maximum areal reduction at 200–600 m
and 1000–1200 m a.s.l. The hypsometry for marine-terminating and
ice-shelf-nourishing glaciers confirms that most of the glacierized area is
covered by these types. Additionally, these types extend over the
entire elevation range. Accordingly, the bimodal shape of the curve does not
arise from different glacier (types) at lower and higher elevations. Rather,
it is determined by and reflects the topography of the AP: the low-sloping
and low-lying coast regions covered by valley glaciers account for the
maximum of the glacierized area between approximately
200 and 500 m a.s.l. The glacierized plateau region accounts for a
secondary maximum at about 1500–1900 m a.s.l. The steep valley
walls connecting the plateau with the coastal region result in the minimum at
about 800–1400 m a.s.l. In addition, the hypsometry reveals that
approximately 6000 km2 of the 93 767 km2 of glacierized area
covered by the DEM is found in the lowest elevation band (0–100 m).
These areas are in direct or in close contact with water or ice shelves
The hypsometry per AP sector (Fig. 8b; all excluding rock outcrops) reveals
that in the two northern sectors both maxima of the hypsometric curve are
less than those of the two southern sectors. The elevations of the maxima
are about the same for NW, NE and SW, whereas both maxima of the SE sector
are somewhat lower. The glacier cover per sector reflects the bedrock
topography of each sector. The bedrock of the northern sectors has less area
in the high plateau regions, and therefore most of the glacierized areas are
at lower elevations. The southern sectors have a more dominant plateau region
favoring more glacierized areas at higher elevations compared to the northern
sectors. However, the northeastern sector has the largest fraction of
glacierized area in the lowest 100 m and is therefore in direct or in
close water or ice shelf contact.
Discussion
Source data
This study has presented a complete and now publicly available glacier
inventory for the Antarctic Peninsula north of 70∘ S that has been
compiled from the best and most recent preexisting data sets, complemented
with information for individual glaciers that was not available before
(topographic parameters, hypsography and ice thickness). To allow
traceability of source data, we have not altered or corrected the available
data sets despite some obvious shortcomings. For example, the DEM by Cook et
al. (2012) does not cover all glaciers and covers several only partly, but we have
not attempted to fill these missing regions with other source data (e.g., the
ASTER GDEM). Consequently, the sample of glaciers with complete attribute
information (1539) is reduced compared to the number of all glaciers in the
study region (1589). The same applies for glaciers with a modeled
ice thickness distribution. For 48 of these glaciers, the DEM information was
incomplete and ice thickness was accordingly not modeled by Huss and
Farinotti (2014). Similarly, for rock outcrops, although the reported
accuracy is only 85±8 % and we could identify wrongly classified
rock outcrops in comparison to LIMA, we used them as they are. This helps to
also be consistent with other studies that will use the same data sets for
their purposes. For the same reasons (consistency, traceability), we have also
not corrected basin outlines or drainage divides using flow velocity fields
derived from satellite sensors because this was also already been done by
Cook et al. (2014) for the catchment outlines. Alterations here would also
impact the already-existing detailed classification of glacier fronts and
we think it is better not to change this at this stage. Overall, results are
as good as the source data used and their errors or incompleteness fully
propagate into the products we have created here. However, we do not expect
any major changes in the glacier characteristics or our overall conclusions
with such corrections being implemented. Conversely, addressing the
shortcomings and improving the related data sets is certainly an issue to be
considered for future work.
Comparison with other regions
In comparison with other recently compiled glacier inventories in regions of
similar environmental conditions (mountainous coastal regions with maritime
climate), such as Alaska (Kienholz et al., 2015), Greenland (Rastner et
al., 2012) and Svalbard (Nuth et al., 2013), the AP has the largest
glacierized area (95 273 km2), closely followed by Greenland
(89 720 km2), Alaska (86 723 km2) and with some distance
Svalbard (33 775 km2). The AP also has the largest absolute,
although only the second largest relative, area covered by marine-terminating
glaciers, which are expected to react very sensitively to small changes in
climate and associated ocean temperature changes. The glacier number and area
distributions in the corresponding studies of Alaska, Greenland, Svalbard and
the AP reveal that a few larger glaciers contribute the most to the area in all
regions. This dominance is also reflected in a median area, which is
considerably smaller than the mean area. However, in Alaska, Greenland and
Svalbard the number of small glaciers is distinctively higher, with maximum
counts between 0.25 and 1 km2. The glaciers of the AP do not exhibit
this pattern, which confirms findings by Pfeffer et al. (2014) for glaciers
in the RGI Antarctic and subantarctic regions. Only the glaciers on Svalbard
have a favored northern aspect (Nuth et al., 2013), which is interpreted as
evidence for the importance of solar radiation incidence for glacier
distribution in this region (Evans and Cox, 2010).
The bimodal hypsometric curve for the glaciers on the AP
(Fig. 8) is very important compared to the parabolic shape of the three other regions that have
increasing area percentages towards their mid-elevation. Hence, the AP has
most of its glacierized area at lower elevations (around 200–500 m),
with a secondary peak at higher elevations (around 1500–1900 m).
Since the hypsometry of a glacier is an indicator of its climatic sensitivity
(Jiskoot et al., 2009), this comparison reveals that the future evolution of
AP glaciers cannot be modeled with the same simplified approaches as
glaciers in other regions (Raper et al., 2000) and that volume
loss for a small rise in the equilibrium line altitude (ELA) might indeed be
high (Hock et al., 2009). The aspect preference with poleward tendencies of
glacier distribution that is common in other mountain ranges (Evans, 2006,
2007; Evans and Cox, 2005, 2010) could not be found for the AP because the entire
AP is glacierized and most glaciers are marine terminating.
Uncertainties
Impacts on outlines and meta-information
A wide range of interconnected uncertainties impact the glacier outlines and
the associated meta-information. Since we have taken data sets from the
literature as they are, we restrict the analysis of uncertainties to the
information reported in the related studies (see Sect. 5.7.2) and add here a
more generalized description of the respective impacts. Glacier outlines are composed of (A) the outlines from LIMA, (B) the drainage
divides from Cook et al. (2014) and (C) the rock outcrop data set by
Burton-Johnson et al. (2016). Key factors influencing their accuracy are
related to (A1) grounding line position (only for glaciers merging with ice
shelves), (A2) accuracy of the digitizing, (B1) the accuracy of the DEM from
Cook et al. (2012), (C1) correct mapping (yes or no) and (C2) positional
accuracy of the rock outcrops. Whereas the impact of (A2) and (C2) on the
derived glacier areas is small since deviations are generally normally
distributed (i.e., they only impact precision), impacts of (A1) and (B1) on
glacier area can be large. However, for (B1) the impact is mostly on the size
class distribution of the glaciers since a shift of an internal drainage
divide does not change the total area. Factor (C1) might have a larger impact
on smaller glaciers (i.e., a missed rock outcrop can increase glacier area by
5 % or more), but for most of the larger glaciers the area overestimation
will be less than 1 or 2 %. Therefore, the largest impact on glacier area
comes from source (A1), albeit only for a subsample (264) of glaciers merging
with ice shelves.
The accuracy of the meta-information provided with each glacier (topographic
parameters, ice thickness) depends on (D) the DEM used to calculate them,
(E) the bedrock data set by Huss and Farinotti (2012) and (F) the glacier
outlines that provide the perimeter for the calculation. For these sources we
can identify the following impacts: (D1) a glacier is not or only partly
covered by DEM information, (D2) the parameter is more or less impacted by
DEM accuracy, (E1) there is direct propagation of DEM accuracy (slope) into
the ice thickness calculation, (E2) there is missing consideration of rock
outcrops (C) in (E), and (F1) changes of the meta-information due to errors
in the extent. For the latter (F1), one can expect under- or overestimation
of mean slope in case of a grounding line being too extensive (resulting in
more area with small slopes) or rock outcrops in steep terrain having been
missed (resulting in more area with steep slopes). Positional uncertainty
will impact all topographic parameters, but very likely not systematically
(i.e., not resulting in a bias) since terrain differences should average out.
Uncertainty source (E1) is highly variable and discussed by Huss and
Farinotti (2012), and (E2) causes inconsistencies among the derived glacier
volumes, but overall differences are likely small since they are not
systematic. Glaciers not covered by DEM cells (D1) have simply no data, but
for those partly covered, the existing DEM cells have been used for the
calculation. Depending on the coverage, results might still be useful, but in
general it might be better to also set them to no data to avoid
misinterpretation. Finally, the impact of (D2) will vary with the parameter.
These parameters calculated from individual
cells (e.g., minimum or maximum elevation) will be more strongly influenced
by DEM errors or artifacts than those based on aggregate numbers such as mean
or median elevation (Frey and Paul, 2012). A quantitative assessment of the
related impacts can only be performed once a better DEM is available for the
region. So far, the manually corrected DEM from Cook et al. (2012) is likely
the best data set available.
Input data uncertainties
The uncertainties for the input data sets used are given as follows. The
positional accuracy of the grounding line varies strongly with the nature of
the boundary and is given as ±502 m for the outlet glacier
boundaries merging with ice shelves (Bindschadler et al., 2011). For the
outline positions of other glaciers, an uncertainty of ±2 pixel
(30 m) is assumed. The classification of rock outcrops is based on an
automated but manually checked classification. The mean value for correct
pixel identification is given as 85±8 % (Burton-Johnson et
al., 2016). The application of the DEM causes uncertainties in drainage
divides and topographic parameters. According to Cook et al. (2012), the
accuracy of the DEM is < 200 m horizontally and about
±25 m vertically, but it varies regionally. Large shifts of the outlines in
flat terrain are thus possible, causing the highly variable impacts on
glacier area as described before. The impact of DEM uncertainties on
topographic parameters is higher for smaller glaciers and those depending on
single-cell values. Given our experiences with other DEMs, we estimate the
uncertainty to be ±50 m for all elevations and ±5∘
for mean slope and aspect. For the regionally varying uncertainty of
thickness and the corresponding uncertainty in volume and SLE, we refer to the
detailed estimates of Huss and Farinotti (2012).
Further comments on the input data sets
The DEM of Cook et al. (2012) currently provides the highest resolution and
quality for the area of the AP. However, the DEM only covers
93 250 km2 and hence 98.4 % of the total glacierized area. In
consequence, the calculation of the topographic parameters (mean, median,
minimum and maximum elevation, slope, aspect) was not possible for 48 glaciers,
representing an area of 1044 km2, or 3 % (1 %) of the total
number (area). For example, the region of Renaud and Biscoe islands at the
midwestern coast of the AP does not have any elevation information. As some
glaciers are only partially covered by the DEM, their parameters are likely
based on a nonrepresentative part of the glacier. Moreover, glacier
hypsometry is calculated based on the DEM and represents only the area covered
by the DEM. Since the ice thickness and bedrock data set of Huss and Farinotti
(2014) is also based on the DEM of Cook et al. (2012), mean thickness and
volume could not be calculated for 50 glaciers. We suggest adding the now
missing topographic information as soon as these glaciers are covered by a
DEM of appropriate quality (e.g., the forthcoming TanDEM-X DEM). This new DEM
might also then be used to recalculate drainage divides, grounding lines,
glacier extents and ice thickness distribution considering the improved rock
outcrop data set. This would also help to overcome the current
inconsistencies among the applied data sets.
The new rock outcrop data set already has a higher accuracy and is more
consistent than the former data set provided by the ADD. However, as
mentioned above, some areas are still misclassified and an in-depth check and
correction for the glaciers on the AP would help further improve the new
inventory. Conversely, manual correction of these errors for the
entire region would remove the traceability to the source data sets and we
decided to maintain it for this first version.
To divide the glaciers from ice shelves, Cook et al. (2014) used the
grounding line based on the ASAID project data source (Bindschadler et
al., 2011), modified in places with features visible in the LIMA. Because the
definition of the location of the grounding line significantly influences the
extent of a glacier flowing into an ice shelf, grounding line positions
obtained from new and forthcoming techniques will also alter glacier extent.
However, this is then more a matter of definition rather than uncertainty.
Assignment of connectivity levels
In the south, the assignment of connectivity levels corresponds with the
Antarctic ice-sheet drainage divides from the Cryosphere Science Laboratory
of NASA's Earth Sciences Divisions (Zwally et al., 2012), which has assigned
all unconnected glaciers (on islands) a CL0 and all glaciers on the AP CL1,
following the suggestion by Rastner et al. (2012) for peripheral glaciers on
Greenland. It is certainly the simplest possibility for such an assignment,
but we think it is nevertheless sensible and fulfils its purpose. Consistency
with earlier applications (e.g., all glaciers in the inventory by Bliss et
al., 2013 have CL0) and transparency of the method are further benefits. It
also allows the glacier and ice-sheet measuring and modeling communities to
perform their work with their respective methods and determine, for example,
past or future mass loss and/or sea level contributions independently. This
would allow a cross check of methods for individual glaciers that are not
resolved (such as results from gravimetry and glacier models) and possibly
also explain remaining differences between methods (Shepherd et al., 2012;
Briggs et al., 2017). The problem of double counting the contributions can
also be avoided.
Specific characteristics of the AP glaciers
The ELA for a balanced budget (ELA0) of land-terminating glaciers can be
well approximated from topographic indices such as the mean, median or
midpoint elevation (e.g., Braithwaite and Raper, 2009). The ELA is also a
good proxy for precipitation (Ohmura, 1992; Oerlemans, 2005) and useful for
modeling the effect of rising temperatures on future glacier extent (Zemp et
al., 2006, 2007; Paul et al., 2007; Cogley et al., 2011;). However, since the
glaciers of the AP are mainly marine-terminating glaciers (Vaughan et
al., 2013), the lower limits of these glaciers are predefined and the ELA
variability is largely determined by the variability of the topography (i.e.,
its maximum elevation). The increasing median elevation towards the interior
(Fig. S1) does not result from decreasing precipitation towards the interior
but is a consequence of glacier hypsometry and depends on whether a glacier
reaches sea level or not.
The bimodal shape of the hypsometry, revealing that half of the glacierized areas
are situated below 800 m a.s.l., as well as the high areal
fraction of marine-terminating glaciers, indicates a high sensitivity of the
AP glaciers to rising air and water temperatures (Hock et al., 2009). Due to
their special hypsometry, their sensitivity is likely higher than for
glaciers in Alaska, Greenland or Svalbard since these have a smaller fraction
of marine-terminating glaciers and a smaller share of area at very low
elevations.
The total ice volume and the volume below sea level are necessary for
accurate estimations of the sea level contribution. At 54 mm the AP's
glaciers have a higher contribution potential than the glaciers of Alaska
(45 mm), Central Asia (10 mm), the Greenland periphery
(38 mm), the Russian Arctic (31 mm) or Svalbard
(20 mm) (Huss and Hock, 2015). In total, global glaciers have a
potential SLR of approximately 374 mm (Huss and Hock, 2015) to
500 mm (Huss and Farinotti, 2012; Vaughan et al., 2013), which is
still significant for low-lying coastal regions (Paul, 2011; Marzeion and
Levermann, 2014). Compared to the Antarctic ice sheet, with a SLE of
58.3 m (Vaughan et al., 2013), the SLE of the AP seems negligible.
However, regarding the high sensitivity and much shorter response times of
these glaciers to climate change, they are expected to be major contributors
to SLR in the next decades (Hock et al., 2009). As the contribution of the
AP's glaciers has not yet been fully considered in most studies, the new
inventory can now be used to model their evolution explicitly with the
current best approaches (e.g., Huss and Hock, 2015).
The results presented here allow a rough approximation of the consequences of
ongoing climate change for the AP: with respect to the hypsometry, the lowest
800 m and hence 50 % of the glacierized area is prone to rising
ablation and mass loss, causing a sea level contribution of roughly 50 %
of the total AP SLE (27 mm). Regarding the glacier termini, about
30 % of the glacierized areas flow into the Larsen C ice shelf. Collapse
of this ice shelf (similar to Larsen A and B), which may happen soon due to a
growing rift (Jansen et al., 2015), would likely cause rapid dynamic thinning
of its tributary glaciers (e.g., Rott et al., 2011) due to debuttressing.
About 15 % of the SLE (9 mm) from the lowest 800 m is
attached to the Larsen C ice shelf.
Conclusions
The compilation of a glacier inventory of the AP
(63–70∘ S, Graham Land), consisting of glacier outlines accompanied
by glacier-specific parameters, was achieved by combining already existing
data sets with GIS techniques. The exclusion of rock outcrops by using the
latest corresponding data set of the ADD (Burton-Johnson et al., 2016) from
the glacier catchment outlines of Cook et al. (2014) resulted in 1589 glacier
outlines (excluding ice shelves and islands < 0.5 km2), covering
an area of 95 273 km2. Combining the outlines with the DEM of Cook
et al. (2012) enabled us to derive several topographic parameters for each
glacier. By applying the bedrock data set of Huss and Farinotti (2014),
volume and mean thickness information was calculated for each glacier.
Connectivity levels with the ice sheet were assigned to all glaciers
following Rastner et al. (2012) to facilitate observations and modeling by
different groups. We started with a simple and transparent rule: glaciers
south of 70∘ S (Palmer Land) are assigned CL2 and are regarded as
being part of the ice sheet, while all glaciers north of it and on the AP are
assigned CL1 and all glaciers on surrounding islands are assigned CL0. The
resulting inventory and its quality are largely influenced by the
availability and accessibility of accurate auxiliary data sets. For instance,
the DEM does only cover 98.4 % of the glacierized area. Hence, for 50
glaciers the topographic parameters, thickness and volume information are
missing. For other glaciers, the values are not representative for the entire
glacier because smaller parts have no DEM information. Future improved DEMs
might help completely cover these glaciers.
Since GLIMS now provides the complete glacier outlines data set of the AP
(see glims.org), a significant gap in the global glacier inventory has
been closed and a major contribution for forthcoming regional and global
glaciological investigations can be made. Furthermore, the new inventory
demonstrates the potential for improving knowledge about glacier
characteristics, sensitivities and similarities and differences to glaciers
in other regions. With the full inventory now freely available, approaches to
improving, extending and further investigating the glaciers of the AP are
strongly encouraged.