Dynamic changes of marine-terminating outlet glaciers are projected to be
responsible for about half of future ice loss from the Greenland Ice Sheet.
However, we lack a unified, process-based understanding that can explain the
observed dynamic changes of all outlet glaciers. Many glaciers undergo
seasonal dynamic thickness changes, and classifying the patterns of seasonal
thickness change can improve our understanding of the processes that drive
glacier behavior. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2)
provides space-based, seasonally repeating altimetry measurements of the ice
sheets, allowing us to quantify near-termini seasonal dynamic thickness
patterns of 37 outlet glaciers around the Greenland Ice Sheet. We classify
the glaciers into seven common patterns of seasonal thickness change over a
2-year period from 2019 to 2020. We find small groupings of neighboring
glaciers with similar patterns of seasonal thickness change, but, within larger
sectors of the ice sheet, patterns of seasonal thickness change are mostly
heterogeneous. Future studies can build upon our results by extending these
time series, comparing seasonal dynamic thickness changes with external
forcings, such as ocean temperature and meltwater runoff, and with other
dynamic variables such as seasonal glacier velocity and terminus position
changes.
Introduction
Understanding the complex nature of Earth's ice sheets is of critical
importance, as they have undergone dynamic changes in recent decades (Church
et al., 2013; Oppenheimer et al., 2019). Greenland Ice Sheet (GrIS)
marine-terminating outlet glaciers, which drive dynamic ice mass change, are
projected to account for 50±20 % of the total mass loss over the
21st century (Choi et al., 2021). While multi-year and decadal changes
of ice sheet discharge via outlet glaciers have been studied before
(Mouginot et al., 2019), patterns of seasonal thickness change have not yet
been studied for a representative sample of GrIS outlet glaciers. Outlet
glaciers exhibit seasonal fluctuations in velocity with distinct patterns
(Moon et al., 2014; Vijay et al., 2019, 2021), but the lack of
measurements of seasonal thickness change contributes to a lack of
understanding of what processes control glacier dynamics on seasonal timescales. Seasonal thickness changes of outlet glaciers are driven by both
external forcings (e.g., precipitation, evaporation, runoff, and terminus melt)
and internal glacier dynamics (e.g., subglacial and englacial hydrology and
terminus calving), and classifying their patterns of seasonal thickness
change is the first step towards a more holistic understanding of the
processes that control them. Prior work has used satellite altimetry to
study seasonal surface elevation changes of the ice sheet (e.g., Johannessen
et al., 2005; McMillian et al., 2016; Sutterley et al., 2018; Gray et al.,
2019). Here, we focus on measuring dynamic ice sheet thickness changes near
the termini of 37 GrIS outlet glaciers at seasonal resolution using the
ATL06 (ATLAS/ICESat-2 L3A Land Ice Height; Advanced Topographic Laser Altimeter System) land-ice along-track altimetry dataset from the Ice, Cloud and land
Elevation Satellite-2 (ICESat-2; Markus et al., 2017; Neumann et al., 2019).
Large-scale observational studies such as this allow for smaller, less well-studied glaciers to be observed at the same time as more well-studied
glaciers and comparisons to be drawn into how these lesser-known glaciers
compare with the seasonal thinning of larger glaciers, which is critical for
better understanding the drivers of dynamic change in a changing climate
across all outlet glaciers. We use each glacier's temporal pattern of
seasonal dynamic thickness changes to group glaciers into seven distinct
patterns over 2019 and 2020. We use the spatial distribution of glacier
patterns to investigate whether they can be attributed to atmospheric
forcing, with the hypothesis that glaciers exhibit similar seasonal patterns
within regions on the order of several hundreds of kilometers, commensurate
with mesoscale atmospheric-circulation patterns. Given that we present just
1 to 2 years of data, our results are not intended to definitively
characterize these glaciers but, rather, to present a method for quantifying
seasonal dynamic thickness changes and to highlight the heterogeneity
exhibited by these glaciers over the study time period. We discuss ways in
which future work could build on our results in Sect. 4.
Data and methods
We used three data sources within this study: (1) the ATLAS/ICESat-2 L3A
Land Ice Height, Version 3 (ATL06) data product, acquired by the Advanced
Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2
observatory, which provides geolocated measurements of land-ice surface
heights (Smith et al., 2019); (2) the Making Earth System Data Records for Use
in Research Environments (MEaSUREs) glacier termini dataset of annual
Greenland outlet glacier locations from synthetic-aperture radar (SAR)
mosaics and Landsat 8 Operational Land Imager (OLI) imagery, version 1 (Joughin et al., 2015, 2017), from
which we use outlet glacier locations and identifier (ID) numbers; and (3) the Arctic digital elevation model mosaic (ArcticDEM; Porter et al., 2018), a
digital surface elevation model of the GrIS that we used as a reference
height dataset to remove along- and across-track surface slopes from the
ATL06 measurements.
ATL06 provides measurements of ice sheet surface elevation at an along-track
spatial resolution of 20 m, which allows for ample spatial sampling of the
fast-flowing, dynamic portions of GrIS outlet glaciers (Smith et al., 2020).
We use elevation data (h_li) retrieved from all six ATLAS
ground tracks to achieve the highest density of data available. ICESat-2 has
a repeat cycle of 91 d, allowing for sufficient temporal sampling to
measure seasonal changes of glaciers, although we do not receive data from
every satellite pass due to cloud interference. We filter out poor-quality
ATL06 height data using the ATL06 quality summary flag (atl06_quality_summary), keeping only data for which the flag is set
to zero.
The MEaSUREs glacier termini dataset contains locations for 238 glaciers
across the GrIS, as well as an ID number (Joughin et al., 2015, 2017). We selected
65 glaciers from the MEaSUREs dataset due to their spatial distribution
across several GrIS regions and a range of average ice velocities between 68 and 8141 m yr-1 (Rignot and Mouginot, 2012). The 65 glaciers chosen for
this study also correspond to the glaciers for which a dense record of
terminus positions has been generated by the Calving Front Machine (CALFIN;
Cheng et al., 2021). The CALFIN dataset is currently the only pan-Greenland dataset
of seasonal terminus positions. Although we do not use this dataset in this
study, due to the fact that currently available CALFIN data do not extend
past mid-2019, our selection of glaciers will enable comparisons of seasonal
thickness change with seasonal terminus position in future studies. We
define glacier seasons by 3-month periods of winter (December–January–February),
spring (March–April–May), summer (June–July–August), and autumn (September–October–November). We
removed glaciers that do not contain a full year (four seasons) of ICESat-2
data from either 2019 or 2020, reducing the number of glaciers categorized
to 42 (listed in the Supplement).
To collect ATL06 measurements representative of near-terminus glacier
thickness change, we created a 2 km × 2 km bounding box for each glacier,
centered on each glacier's location in the MEaSUREs dataset, within which we
aggregated ATL06 data. We manually adjusted the MEaSUREs glacier locations
slightly to ensure that between one and three ICESat-2 repeat ground tracks
intersect each box, but we kept each bounding box within 10 km of the
terminus for each glacier. The 4 km2 bounding box was chosen as an
arbitrary size; however it was kept to this size, as a larger box may include
data off the main fast-flowing section of the outlet glacier.
The ArcticDEM mosaic represents the mean ice sheet surface elevation between
ca. 2015 and 2016 (Porter et al., 2018). The DEM has a 32 m
spatial resolution and is used as the reference ice sheet surface elevation
to account for the surface slope of the glaciers. Because the repeating
passes of ICESat-2 do not exactly survey the same location on the surface of
the ice sheet (particularly in the first 9 months of the ICESat-2 mission),
ATL06 measurements from season to season are affected by both the vertical
component of surface elevation change and differences in surface
elevation due to surface slope. To account for this, we sampled the
ArcticDEM at each ATL06 measurement and subtracted the ArcticDEM elevation
from each ATL06 surface elevation measurement. This effectively changes the
datum of the ATL06 measurements to that of the ArcticDEM, thereby accounting for the
surface slope of the ice sheet within our bounding boxes, leaving just the
vertical component of surface elevation differences.
We use the ATL06 data within each bounding box, a surface mass balance
model, and a firn model to calculate each glacier's dynamic thickness change
from season to season. For each glacier, we calculate the surface elevation
change (dH) between ICESat-2 observations and the ArcticDEM. We then
calculated the seasonal dynamic dH as the mean of the dH values within each
bounding box for each year and season, and we subtracted the surface
elevation change due to changes in surface mass balance (SMB) and firn air
content changes using output from the Community Firn Model (CFM; Medley et
al., 2020), forced by Modern-Era Retrospective analysis for Research and
Applications, Version 2 (MERRA-2) climate reanalysis (Gelaro et al., 2017).
Over the 2-year timescale of our study, we assumed constant bed elevation,
and, thus, our surface elevation change measurements are equal to ice
thickness change. We removed the trend from each glacier's seasonal dynamic
dH, calculated over the entire duration of the available data to isolate the
seasonal fluctuations from the longer-term trend. We removed 5 of the 42 glaciers with measurements of seasonal dynamic dH larger than 50 m over one
season, assuming that these are errors (Joughin et al., 2020), leaving 37 glaciers for which we classified seasonal dynamic dH patterns.
To account for uncertainty in seasonal dynamic dH, we propagated error
through our calculations from each data source with the assumption of
random, uncorrelated error. We used the error estimates provided by ATL06 to
account for error on each height data point (h_sigma). We
conservatively assume 5 m of random error in the ArcticDEM elevations,
although the actual uncertainty in ArcticDEM elevations is likely less than
this value (Noh and Howat, 2015). We assume a 20 % uncertainty on the
thickness change due to SMB and firn components, estimated by the CFM.
Assuming uncorrelated and random errors in the ATL06 and ArcticDEM surface
elevation measurements, we used standard error propagation rules to
calculate the error on seasonal dynamic dH, which is σs.d.dH:
σs.d.dH=1n∑i=1nσh_li,i2+521/2+02×|dHCFM|,
where σh_li,i represents the error on each ATL06
surface elevation measurement (h_li_sigma), 5 m represents the error in each ArcticDEM surface elevation, n represents the
number of ATL06 elevations within the bounding box for a particular season,
and dHCFM is the absolute value of the magnitude of surface elevation
change due to changes in SMB and firn air content changes from CFM. We do
not account for uncertainty in the trend that is removed from each glacier's
seasonal dynamic dH because the trend is removed solely to present the
thickness changes more clearly in plots. Quantifying uncertainty in the
trend of dynamic thickness change could be done more thoroughly in future
studies, given that more ICESat-2 data will be collected over the coming
years. Additionally, keeping the trend in the seasonal dynamic dH has no
impact on our categorization of glacier behavior for all but five glaciers,
as we discuss in Sect. 4.
Using the time series of seasonal dynamic dH for each glacier, we manually
grouped glaciers into categories based on their seasonal patterns of
thickness change. Because seasonal dynamic dH had not been surveyed for a
representative set of GrIS outlet glaciers, we did not prescribe categories
prior to generating results. Instead, we based the categories on the timing
of observed seasonal dynamic thinning and thickening for our surveyed
glaciers. These classifications are based on the difference from one season
to the next, rather than at each point in time. Each year of data is
individually categorized; in other words, the classification for one glacier
in 2019 does not influence the classification of the same glacier in 2020.
Patterns of outlet glacier dynamic seasonal thickness change with
annual trend removed: (a) mid-year thinning, (b) mid-year thickening, (c) summer thinning with spring and autumn thickening, (d) spring and autumn
thinning with summer thickening, (e) sharp single-season thickening, (f) full-year thickening, and (g) no statistically significant change. Curly
brackets highlight (f) the full-year thickening pattern of Nansen Gletsjer in
2020 and (g) the extent of the error bars encompassing no seasonal change
for Hayes Gletsjer. Each value plotted is relative to the first value in
the time series, which is shifted to zero.
Results
We find that, over 2019 and 2020, the 37 surveyed glaciers can be
categorized into seven seasonal patterns: no statistically significant change, mid-year thinning, mid-year thickening, winter-to-spring
and summer-to-autumn thinning with spring-to-summer thickening,
spring-to-summer thinning with winter-to-spring and summer-to-autumn
thickening, sharp single-season thickening, and full-year thickening (Fig. 1). Glaciers were classified as “no statistically significant change” if
uncertainties of seasonal dynamic dH were larger than the amplitude of seasonal
change across all seasons within a given year. Sharp single-season
thickening includes glaciers that undergo a lone season of significant
(>3 times the change between any other seasons and >3 times the uncertainties for that glacier) thickening (either spring or
summer) followed immediately by a similar sharp decline in thickness. Rink
Isbræ is the best example of this, undergoing 6–10 m of change during this
spike (Fig. 1e). Mid-year thickening refers to glaciers exhibiting two
consecutive seasons of winter-to-spring and spring-to-summer thickening
before summer-to-autumn thinning. Conversely, glaciers with mid-year thinning
exhibit winter-to-spring and spring-to-summer thinning with summer-to-autumn thickening. Each glacier's detrended dynamic thickness
change, alongside the seasonal trend of SMB and total dH change, is plotted
in the Supplement (Figs. S1 through S34). Although we have
removed the trend to better illustrate seasonal dynamic dH for each glacier,
we note that keeping the trend in the data alters our classifications for
just five of the surveyed glaciers: Alanngorliup Sermia (Fig. S1),
Kangerlussuup Sermia (Fig. S9), Kakivfaat Sermiat (Fig. S13), Cornell
Gletsjer (Fig. S17), and Nansen Gletsjer (Fig. S22). Without the trend
removed from the dynamic dH, there is a thinning trend in 2019 for
Kangerlussuup Sermia (Fig. S9) and Kakivfaat Sermiat (Fig. S13), across both
years for Cornell Gletsjer (Fig. S17), and in 2020 for Nansen Gletsjer (Fig. S22). Alanngorliup Sermia (Fig. S1) exhibits a slight overall thickening.
These glaciers exhibit strong 1-to-2-year trends, and although, for
example, there is little seasonal change over 2019 for Kangerlussuup Sermia
in their detrended seasonal dynamic dH values, the glacier is actually thinning
overall across throughout the year without the annual trend removed. What this
does highlight is that for all other glaciers, their seasonal dynamic
thickness changes during at least one season are larger in magnitude than
changes due to the 1- or 2-year trend, and, thus, our classification is not
sensitive to the removal of the trend. That being said, in general, care
must be taken when interpreting seasonal changes with a trend removed that
have been estimated from just 1 or 2 years of data.
Locations, patterns of seasonal dynamic thickness change, and average
ice speeds of 37 GrIS outlet glaciers. Glaciers with different patterns in
2019 and 2020 are depicted on the ice sheet map with their 2019 pattern
coloration, while both 2019 and 2020 patterns are shown in their yearly pattern
in the left-side table.
We find that the 37 surveyed GrIS outlet glaciers are well distributed
across the seven patterns. Figure 2 shows glacier classifications for both
2019 and 2020 in the table but displays the classification from the earliest
available year on the map. With each year individually categorized, there
are 51 total seasonal cycles observed between 2019 (30) and 2020 (21). Of
these seasonal cycles, there are 15 seasonal cycles that exhibit spring-to-summer
thickening with winter-to-spring and summer-to-autumn thinning, 13 seasonal
cycles that experience mid-year thinning, 9 seasonal cycles within the
pattern of spring-to-summer thinning and winter-to-spring and summer-to-autumn thickening, 7 seasonal cycles with mid-year thickening, 2 seasonal cycles with
sharp single-season thickening, 1 seasonal cycle exhibiting full-year
thickening, and 4 seasonal cycles with a pattern of no statistically significant change. Of the 14 glaciers for which we have 2 years of data, we find
that most glaciers exhibit patterns of seasonal thickness change that differ
from year to year. Two glaciers exhibit repeating patterns: Ussing Braer N
(Fig. S31) and Alison Glacier (Fig. S35). However, the remaining glaciers,
for which ICESat-2 can so far provide two annual cycles worth of data,
exhibit changing patterns between 2019 and 2020.
Although there are spatial clusters of glaciers with similar patterns of seasonal
thickness change, there is heterogeneity within the regions that
contain multiple surveyed glaciers (Fig. 2). We use the 2019
classifications, for all glaciers with data in 2019, to compare glaciers per
region because we have more glaciers classified in that year (30 glaciers)
than in 2020. In the northwest, six glaciers exhibit a mid-year thinning pattern, five
glaciers exhibit spring-to-summer thinning with winter-to-spring and
summer-to-autumn thickening, two exhibit spring-to-summer thickening with
winter-to-spring and summer-to-autumn thinning, two exhibit mid-year thickening,
one glacier exhibits sharp single-season thickening, and two exhibit no
statistically significant change. In the central-western region, three glaciers exhibit
spring-to-summer thinning with winter-to-spring and summer-to-autumn
thickening, three glaciers exhibit mid-year thinning, two glaciers exhibit
mid-year thickening, one glacier exhibits sharp single-season thickening, and
one glacier exhibits no statistically significant change. Within the southeast, six
glaciers exhibit spring-to-summer thickening with winter-to-spring and
summer-to-autumn thinning and one glacier exhibits a mid-year thinning pattern.
In the north, the single surveyed glacier, Petermann Gletsjer, exhibits
spring-to-summer thickening with winter-to-spring and summer-to-autumn
thinning in 2019 but switches to mid-year thickening in 2020. Small
clusters of neighboring glaciers with similar patterns can be seen in the northwest
with some form of mid-year or summer thinning (glacier IDs 31, 32, 34, and
35) and the central-western region (glacier IDs 5, 7, 8, and 9), and the southeast presents the most
homogeneity, with six glaciers exhibiting the same pattern (glacier IDs 147,
148, 153, 158, 169, and 173), but there is no one pattern that is
representative of all glaciers within each region.
Discussion and conclusions
Enabled by 91 d repeat measurements from ICESat-2, we have developed the
first classification of GrIS outlet glacier patterns of seasonal dynamic thickness
change for a representative sample of glaciers from around the ice
sheet. We have chosen to use the ATL06 data product and to account for
along- and across-track surface slopes using the ArcticDEM as a reference
elevation dataset. This method allowed us to aggregate surface elevation
data within customized bounding boxes, representative of each glacier's
behavior. Higher-level data products, such as ATL11 and ATL15, will provide
estimates of surface elevation change through time, and we believe it will be
worthwhile for future work to compare our results against the higher-level
ICESat-2 products, both to build confidence in our results and as a check on
the data products themselves.
Our results reveal little regional coherency in patterns of seasonal dynamic thickness
change, outside of the southeastern region, indicating that mesoscale
atmospheric-circulation patterns are not the likely driver of differences in
patterns among glaciers. While we do find small clusters of similar
patterns, we do not observe similar patterns across the larger northwestern or
central-western ice sheet regions. If atmospheric forcing (or errors in our
model for the atmospheric forcing) were the primary driver of seasonal
dynamic thickness changes, we would expect to see coherent patterns of
seasonal changes across each region. However, we do not find this to be the
case, indicating that other factors that differ from glacier to glacier
within each region are causing the differences in observed patterns. This
finding is consistent with seasonal glacier velocity changes, which also
exhibit spatial heterogeneity (Moon et al., 2014; Vijay et al., 2019, 2021). Ocean forcing may be responsible for the differences in patterns of
seasonal dynamic thickness change because heat transport from the
continental shelf to the termini of outlet glaciers is modulated by fjord
geometry, which is heterogeneous among glaciers (Carroll et al., 2017). Each
glacier's unique geometry, including both fjord geometry and subglacial bed
topography, which have been shown to govern observed differences in terminus
retreat (Catania et al., 2018), and the multi-annual upstream diffusion of
thinning (Felikson et al., 2021), may also be responsible for the observed
heterogeneity in seasonal thickness changes. Additionally, glacier geometry
may influence each glacier's dynamic seasonal response by modulating the
effects of changes in driving stress and surface melt, driven by atmospheric
forcing.
Refining the ATL06 data quality flag (atl06_quality_summary), with the goal of accepting additional
good-quality measurements that are currently flagged as poor-quality, would
benefit future studies of seasonal outlet glacier change by increasing the
data volume available. Because ICESat-2 has a repeat cycle of 91 d,
collecting good-quality data from each pass is critical to studies of the
seasonal thickness changes of outlet glaciers. The current set of parameters
used by the ATL06 quality summary flag may remove good-quality measurements
over rough topography, high surface slopes, or low-reflectivity surfaces
under clouds (Smith et al., 2021). In the course of our study, we found that
12 additional glaciers, of the subset of 65 glaciers we initially selected
from the MEaSUREs dataset, could be included in our results, had we ignored
the quality summary flag entirely. Of course, some of the measurements that
are removed by the quality summary flag are unusable, and we do not advocate
ignoring data quality checks entirely. However, we suggest that further
inspection of the parameters used for the quality summary flag to
potentially reduce the strictness by which data are eliminated may prove
useful and would allow additional glaciers to be considered in future
ICESat-2 data releases.
As ICESat-2 continues data collection, future work should build on our
2-year assessment of seasonal dynamic thickness changes by extending our
record and comparing it with other glacier variables and external forcings. The
MEaSUREs dataset identifies 239 total outlet glaciers around the ice sheet,
and, by adding more outlet glaciers and extending the record forward in
time, future studies can examine how consistent the patterns are from year
to year, identify new patterns not exhibited by the glaciers in our study,
and better identify glaciers that exhibit the same or different patterns
through time. With a longer and more comprehensive classification of
seasonal thickness changes, future work can focus on compiling a holistic
record of seasonal glacier dynamics by investigating thickness changes
together with terminus position and velocity changes. The subset of glaciers
that we have selected for study are ones that have a temporally rich dataset
of terminus position changes from the newly developed CALFIN automated deep
learning extraction method (Cheng et al., 2021) as well as from added
sources in the recent TermPicks (Goliber et al., 2021) dataset, which will
allow our results to be directly compared with seasonal terminus positions
once CALFIN data are extended into late 2019 and 2020. Finally, to advance
our understanding of the processes that drive seasonal glacier behavior,
future work should compare seasonal dynamic thickness changes with external
forcings such as seasonal ocean temperature changes and surface meltwater
runoff estimates. Our study provides the first classification of seasonal
dynamic thickness changes of outlet glaciers around the GrIS to complement
previous classifications of seasonal velocity change (Moon et al., 2014;
Vijay et al., 2019, 2021), bringing us one step closer to a
holistic understanding of seasonal glacier dynamics.
Code and data availability
The Supplement associated with
this brief communication contains the measurements of seasonal thickness change
presented in the paper, along with the surface mass balance component
of seasonal thickness change from the Community Firn Model and MERRA-2.
Additionally, a shapefile of locations of the glaciers surveyed is provided.
The supplement related to this article is available online at: https://doi.org/10.5194/tc-16-1341-2022-supplement.
Author contributions
CJT and DF conceptualized the experiment and goals.
CJT carried out the experiment, developed the code, and performed the
simulations. CJT prepared the manuscript with written review and editing
from DF and TN. TN performed project administration and funding
acquisition.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was performed through the NASA Goddard Space
Flight Center Internship Program, administered by the Goddard Space Flight
Center Office of Education and funded by the ICESat-2 Project Science
Office. Resources supporting this work were provided by the NASA High-End
Computing (HEC) Program through the NASA Center for Climate Simulation
(NCCS) at the Goddard Space Flight Center. We thank Brooke Medley for
providing output from the Community Firn Model.
Financial support
This research has been supported by the ICESat-2 Project Science
Office.
Review statement
This paper was edited by Bert Wouters and reviewed by two anonymous referees.
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