B. Medley1, J. T. M. Lenaerts2, M.
Dattler1,3, E. Keenan2, and N.
Wever2
1Cryospheric Sciences Laboratory, NASA Goddard Space
Flight Center, Greenbelt, MD, USA.
2Department of Atmospheric and Oceanic Sciences,
University of Colorado Boulder, Boulder, CO, USA.
3Department of Atmospheric and Oceanic Science,
University of Maryland College Park, College Park, MD, USA.
Corresponding author: Brooke Medley
(brooke.c.medley@nasa.gov)
Key Points:
- We predict spatial deviations in net accumulation through synthesis of
topographic and atmospheric characteristics on a 1-km grid
- Kilometer-scale deviations in net accumulation typically range between
-41 and 33% and are as large as -172% of the MERRA-2 mean
- We link spatial deviations in net accumulation to seasonal
satellite-derived height changes from ICESat-2.
Abstract
Sub-grid-scale processes occurring at or near the surface of an ice
sheet have a potentially large impact on local and integrated net
accumulation of snow via redistribution and sublimation. Given
observational complexity, they are either ignored or parameterized over
large-length scales. Here, we train random forest models to predict 1-km
variability in net accumulation over the Antarctic Ice Sheet using
atmospheric variables and topographic characteristics as predictors.
Observations of net snow accumulation from both in situ and
airborne radar data provide the input observable targets needed to train
the random forest models. We find that kilometer-scale processes modify
local net accumulation by as much as 172% of the atmospheric model
mean. The correlation in space between the predicted net accumulation
variability and satellite-derived surface-height change indicates that
kilometer-scale processes operate differently through time, driven
largely by the seasonal anomalies in snow accumulation.
1 Introduction
Large-scale snowfall events deposit a substantial amount of freshwater
over the Antarctic Ice Sheet (AIS), acting in opposition to present-day
sea-level rise. Approximately 7 mm of global sea-level equivalent falls
annually in the form of snow over the entire ice sheet (Mottram et al.,
2021); any short-to-long-term deviations in time and space from this
mean will directly impact the temporal evolution mass balance of the AIS
and its individual glacial drainage systems (Rignot et al., 2019; Smith
et al., 2020). State-of-the-art atmospheric models do not agree,
however, on the total magnitude of annual snow accumulation (Mottram et
al., 2021), ranging by more 500 Gt yr-1, a value which
largely overshadows a reconciled AIS total mass balance of -109 Gt
yr-1 (Shepherd et al., 2018). This lack of constraint
yields arguably the largest source of uncertainty in estimates of AIS
mass balance and its contribution to global sea level (Rignot et al.,
2019; Shepherd et al., 2018; Smith et al., 2020). We aim to constrain
the magnitude of net snow accumulation over the AIS at fine spatial
resolution within a global atmospheric model using airborne and
ground-based measurements.
While snowfall events over the ice sheet are synoptic, blowing snow
processes occurring prior to or after deposition at the surface impart
local-scale variability as snow is redistributed or preferentially
sublimated (Lenaerts et al., 2019). At present, global atmospheric
models are not capable of accounting for these small-scale impacts
(Gelaro et al., 2017), and only a small handful of regional climate
models simulate these processes albeit at much coarser scales than they
actually occur (Amory et al., 2021; Van Wessem et al., 2018). A lack of
observed accumulation rates at the scale necessary to measure these
local processes challenged development of both physics-based and
empirical models. Recently, ground-based (Das et al., 2013; Spikes et
al., 2004) and airborne (Dattler et al., 2019; Medley et al., 2013)
radar observations of the ice sheet’s near-surface internal stratigraphy
have revealed the small-scale variability in snow accumulation at fine
along-track resolution and over large swaths of the ice sheet. Here, we
built on prior work (Das et al., 2013; Dattler et al., 2019; Scambos et
al., 2012; Studinger et al., 2020) investigating small-scale variability
in snow accumulation and regions of net scour, including their
relationship to local topography and wind characteristics, to predict
kilometer-scale net snow accumulation over the entire ice sheet.
We are only focused on dry snow processes (snowfall, sublimation,
erosion, deposition) that yield net snow accumulation, whereas the
surface mass balance (SMB) also accounts for mass loss via runoff. We
define net accumulation as the of snow that accumulates at the surface
after accounting for all the dry snow processes, and in this work, we
allow net accumulation less than zero. Dry snow processes account for
almost the entirety of ice-sheet SMB; runoff is relevant in only small
number of areas.
The 1980–2017 mean annual snow accumulation (± 1 standard deviation)
derived from NASA’s Modern-Era Retrospective analysis for Research and
Applications, Version 2 (MERRA-2; Gelaro et al., 2017) over the AIS
totals to 2568 ± 147 Gt yr-1, with 2037 ± 125 Gt
yr-1 over grounded ice and 531 ± 34 Gt
yr-1 over floating ice without accounting for erosion
and deposition. This global model is coarsely resolved and does not
include physical processes that occur over ice sheets over short length
scales, such as blowing snow. Regional climate models (RCMs) (Agosta et
al., 2019; Lenaerts et al., 2012; Van Wessem et al., 2018) have
accounted for these processes with varying degrees of complexity;
however, while some of the parameterizations hold for transport over
smaller length scales (Amory et al., 2021), the model outputs are
resolved at the 10s of km scales. For instance, results from a 5 km RCM
run over West Antarctica did not show significant improvement in SMB
representation against the same RCM run at 27 km (Lenaerts et al.,
2018). Another study (Das et al., 2013) used thresholding of wind and
topographic regimes to determine regions of net wind scour (i.e., SMB
< 0) which yielded an estimated loss of snow mass input due to
wind erosion between 11 and 36.5 Gt yr-1. The latter
study does not provide context for the total impact of snow
redistribution because net snow deposition was not considered, providing
only one side of the balance equation. Here, we built a static map of
net accumulation variability over the grounded and floating portions of
the AIS at 1-km resolution.
2 Data
2.1 ICESat-2 Surface Height and Height Change
Launched in 2018, NASA’s next generation Ice, Cloud, and land Elevation
satellite (ICESat-2) is a photon-counting laser altimeter designed to
provide precise, repeatable measurements of ice-surface height change
every 91 days, globally to latitudes not exceeding 88° in magnitude
(Markus et al., 2017). Here, we use the ICESat-2 L3A Land Ice Height,
Version 2 (ATL06; Smith et al. (2019)) collected during the first three
91-day cycles (October 14, 2018–June 26, 2019). Because ICESat-2 was
not pointing at its designed repeat tracks during the first two cycles,
data collected during the first ~180 days provide
additional height measurements, which improved spatial coverage. More
details regarding building a DEM using ICESat-2 data are in Section S2.
To investigate spatial patterns of height change, we also use the
ICESat-2 L3B Slope-Corrected Land Ice Height Time Series, Version 4
(ATL11; Smith et al. (2021)) spanning cycles 3–11 (March 29, 2019–June
23, 2021). The ATL11 dataset provides along-track height that is
slope-corrected onto a reference pair track for each cycle beginning
with cycle 3 when ICESat-2 began pointing at its designed reference
ground tracks. We eliminate less robust surface heights by using heights
that have a quality summary flag set to zero.
2.2 Atmospheric
We use several atmospheric variables from NASA’s Modern-Era
Retrospective analysis for Research and Applications, Version 2
(MERRA-2; Gelaro et al., 2017), including hourly 10-m winds, and
snowfall in addition to monthly evaporation, humidity, and surface
temperature from January 1, 1980 to December 31, 2019 (GMAO, 2015a,
2015b, 2015c, 2015d). MERRA-2 data are provided globally at 0.5°
latitude by 0.625° longitude resolution.
2.3 Snow Accumulation
2.3.1 AntSMB Database
Snow accumulation measurements over the large scales of interest to this
work are few. We use a comprehensive collection of Antarctic SMB
measurements derived from various sources and methods including ground
penetrating radar (GPR), stakes, snow pits and ice cores referred to as
the AntSMB dataset (Wang et al., 2021). Specifically, we use the
multi-year averaged SMB observations that exceed a 3-year span, the
majority of which are from GPR analysis. Given that the data set
contains SMB, there might be some observations where runoff occurs and
that are not equivalent to our dry snow net accumulation; however,
without a straightforward way to differentiate these sites as well as
the relatively small impact of runoff over the AIS, we use all points
that meet the time-span requirement. Thus, we assume net accumulation
equals SMB, equivalent to assuming no runoff occurs.
We also derive snow accumulation (Section 4.2.1) from additional snow
radar data collected October 25, 2019 (Leuschen, 2014) that was released
subsequent to the development of the Dattler et al. (2019) dataset. We
replicate the methodology from Dattler et al. (2019).
2.3.1 Supplemental GPR Measurements
We also use additional GPR data not included in the AntSMB database that
were presented by Medley et al. (2014), which cover the Pine Island and
Thwaites Glacier catchments. They represent the 1985–2009 mean
accumulation rate and are provided at 500-m along-track spacing.
3 Prediction of Kilometer-Scale
Variability in Snow Accumulation
We built a 1-km static map of predicted spatial deviations in net
accumulation from the background large-scale MERRA-2 annual mean using
airborne and ground-based observations of accumulation and a series of
topographic and atmospheric predictors. We next outline the various
predictors, and then explain the random forest method implemented for
prediction.
3.1 Predictors
In all, we used 11 predictors that described the topographic and
climatic characteristics as well as their interactions over the AIS.
Topographic predictors were based on the DEM described in Section S2 and
include height, slope, aspect, curvature, and a 20-km high-pass filter
of the surface height (Figures S3-S7). Because the outer ICESat-2 beam
pairs are separated by ~6 km, prior to determination of
the topographic characteristics, we applied a 6-km low-pass filter to
the DEM to minimize any tracking artifacts. Climatic predictors were
built from MERRA-2 mean annual variables and include 10-meter wind
speed, 10-meter wind direction, air temperature, specific humidity, and
total precipitation-minus-evaporation (P−E ; Figures S8-S12). TheP−E is the MERRA-2 net accumulation. Finally, we use the mean
slope in the mean wind direction (Figure S13), the dot product of the
wind and slope vectors, as described in the Section S3.
3.2 Training Data
The AntSMB and Medley et al. (2014) data were modified to represent the
relative deviation in snow accumulation from the large-scale MERRA-2
mean annual P−E (i.e., the percent deviation from MERRA-2), which
we hereinafter refer to as the small-scale variability, SSV :
\begin{equation}
\begin{matrix}\text{SSV}=\ \frac{Ob\text{servation}-\text{MERRA}2}{\left|\text{MERRA}2\right|}\times 100\ .\#\left(1\right)\\
\end{matrix}\nonumber \\
\end{equation}Because most observations are from GPR analysis, we generate two
subsets: GPR and traditional, the latter includes stake, core, and snow
pit measurements. Each set was then gridded onto the same 1-km grid as
the DEM by averaging points that fall within the same grid cell (Figure
S2). As done with the DEM, we applied a 6-km low-pass filter.
The fact that this work used measurements from a large compilation of
observations from a variety of techniques, means that there was not
consistency in the temporal reference window across all observations.
Thus, observations represented anywhere from a minimum of 3 years to
over one thousand years, some of which overlapped with the MERRA-2 time
window and some of which did not, introducing additional uncertainty.
While not ideal, we used all observations from all time windows to
maximize the number of observations across as many conditions as
possible; however, the bias introduced from a non-coincident model and
observation time window could have been propagated throughout our
results.
3.3 Random Forest Method
Using the random forest (RF) regression algorithm we predictedSSV over the entire AIS using 11 predictors (Section 3.1) and 2
training datasets (Section 3.2). The GPR (n = 27,316) gridded
data were randomly sampled into 80% training and 20% testing
partitions. We reserved an entire stake transect (n = 581) from
the traditional data set to act as an independent model evaluation, and
the remaining 2,535 traditional gridded values were split 80/20. Thus, a
total of 23,881 observations were used for RF training. The testing
partition was not used to build the RF model but rather for performance
evaluation. Specifically, we employed bootstrap aggregation method
(i.e., bagging) and an interaction-based predictor-selection technique
for all RF experiments to increase detection of predictor interactions
(Loh, 2002). The ensemble bagging technique builds decision trees each
generated from a random sample with replacement of the training dataset,
diversifying the individual trees. The training data were weighted by
the mean distance to all other observations, giving higher weights to
those with more distant neighbors; this scheme minimizes the impact of
GPR oversampling regions like West Antarctica.
Using two RF parameter scenarios (optimized and standard practice;
Section S4), we built two final RF of 200 decision trees for SSVprediction using our ICESat-2 DEM. The standard deviation amongst the
individual trees provided an assessment of the spread in the prediction
at the cell-by-cell basis, each of which were combined with the RMSE of
the testing set (8.7–9.0% depending on RF model; Figure S14; Table S2)
through root sum of squares to generate uncertainty, which is typically
lower the closer the proximity to training observations. To investigate
the impact of the choice of DEM, we employed the same exercise outlined
above using the REMA DEM resampled to the same 1-km grid as our ICESat-2
DEM. Two CryoSat-2 DEMs were not used (see Sections S1.2 and S2.1).
Thus, we built 4 SSV models.
4 Results
4.1 Small-Scale Variability Predictions
The SSV map provides insight into both the kilometer-scale
variability as well as the large-scale biases in MERRA-2 accumulation.
Typical SSV (Figure 1a) range between -40.8% to +32.5% (lower
and upper 5%), whereas absolute deviations in SSV (Figure 1b)
span -63.2 to +64.2 mm w.e. yr-1. Similarly, theSSV uncertainties (Figure 1c) range between 14.9% and 59.7%,
whereas the absolute uncertainties (Figure 1d) span 5.3 to 201.4 mm w.e.
yr-1. The uncertainties are larger in locations that
are further from observations (Figure 1c). The RF models are strongly
correlated with each other (all combinations r2> 0.94). When integrated in space, they predict between a
reduction of 23.3 Gt yr-1 and an increase of 3.3 Gt
yr-1 in the MERRA-2 net accumulation (Table S2).
Predictions on ice shelves suggest a more positive accumulation (+12.1
to +22.4 Gt yr-1). All indicate small to moderate
reductions in the MERRA-2 accumulation over grounded ice (-18.8 to -35.4
Gt yr-1).
Uncertainty calculations for the integrated values account for
correlated errors within a 20-km radius, a value chosen to correspond
with the 20-km high-pass filtered surface heights used as a predictor.
Because no model outperformed the others (Table S2), we present the most
likely representation of SSV as the mean of all four predictions;
we conservatively combine their cell-by-cell uncertainties through the
root sum of squares. This approach yields integrated SSV for
floating and grounded ice of +17.3 ± 11.7 Gt yr-1 and
-25.0 ± 16.4 Gt yr-1, combining to -7.7 ± 20.1 Gt
yr-1. Hereinafter, all results presented are in
reference to this scenario. We note that the signal-to-noise ratio is
>1 for only 11% of the ice sheet, indicating the
uncertainty outweighs the signal (Figure S15). When comparing the RF
model and its uncertainty bounds with the independent stake transect,
however, we find the uncertainties are predominantly inflated (Figure
S16). Specifically, the RMSE between the observed and modeledSSV s over the independent transect is 23% (Table S2), but the
mean RF uncertainty amounts to 31%.
4.2 Comparison with ICESat-2 Height Change
4.2.1 Case Study with Coincident Snow Radar
Over long timescales and an unchanging climate, the amount of snow that
falls and accumulates is balanced by firn compaction and the loss of
firn via conversion to ice suggesting that ice-surface-height does not
evolve because of snowfall processes; however, at sub annual scales,
episodic and seasonal evolution of precipitation and temperature have a
large impact on surface-height changes. Thus, if our static SSVmodel is stable in time, then we should observe height changes that
resemble the variability in snow accumulation. To investigate the
importance of this variability on our interpretation of
ice-surface-height evolution, we analyze the relationship between
ICESat-2 observed changes with our SSV model, Operation IceBridge
(OIB) snow radar data, and MERRA-2 climate.
In 2019, OIB underflew ICESat-2 ground tracks over coastal Wilkes and
Victoria Land, which provides us the ability to directly compare OIB
snow radar, our SSV models, and ICESat-2 height change. We
analyze a 100-km segment from October 25, 2019 that follows a trajectory
near-perpendicular to the coast (Figure 2; Figure S1b). The ICESat-2
height change along this ground track between May 2, 2020 and August 1,
2020 shows an overall increase with significant small-scale variations
along track (Figure 2a). We next compare the ICESat-2 data with
coincident OIB snow radar data by following the same procedure as
outlined by Dattler et al. (2019) to produce net accumulation by
tracking a single radar horizon through space. We show the resulting
radar-derived accumulation, the SSV models, and the MERRA-2 meanP−E interpolated to each radar measurement in Figure 2b, and the
snow radar echogram and tracked layer in Figure 2c. As with the Dattler
et al. (2019) dataset, our radar-derived accumulations were calculated
in a way that matches them with the large-scale MERRA-2 mean. That
assumption does not impact our assessment of the SSV in the snow
accumulation.
Based on this exercise, we confirm that our models are capable of
predicting SSV in snow accumulation and that there are not
substantial differences between the RF models. We note that these snow
radar data were collected in 2019 and are not part of the GPR
accumulation dataset compiled by Dattler et al. (2019), which used data
collected up through 2017. Thus, the comparison here is independent of
our RF model development. We also confirm that the RF models
underestimate the total magnitude of the larger deviations.
Nevertheless, we observe significant correlation between the
radar-derived accumulations, RF models of SSV , and ICESat-2
height change variability.
4.3 Ice-Sheet-Wide Height Change and Snow Accumulation Variability
ICESat-2 ATL11 provides along-track, slope-corrected heights spanning
nine 91-day cycles, providing seasonal height change over a two-year
period. For each reference pair track, we calculate cycle-by-cycle
height change (i.e., height change over a 91-day interval) and apply a
6-km moving mean to match the same filter applied to our ICESat-2 DEM
(Section S2). For each reference pair track, we find the mean MERRA-2P−E anomaly over the exact time epoch for each cycle and for that
specific track. This step provides the temporal accumulation anomaly
along each reference pair track over the same time and space as the
ICESat-2 ATL11 data. We also interpolate our static RF SSV models
onto the ATL11 heights. To investigate the relationship between observed
height change and our predicted SSV , we correlate the ATL11
height changes with the mean predicted SSV along 50-km segments
for each cycle pair. This relationship, as well as the temporal
accumulation anomaly over each cycle pair, is summarized in Figure 3. We
find that the sign and magnitude of the correspondence between spatial
variations in snow accumulation and observed height changes varies by
season.
5 Discussion
We use a combination of snow accumulation derived from GPR, as well as
other traditional observational constraints, with topographic and
atmospheric characteristics derived from ICESat-2 surface height data
and MERRA-2 to predict net accumulation on a 1-km grid. Neither
selection of the RF model parameters nor choice of DEM largely impacted
the results, suggesting that we used a robust choice of predictors.
Comparison of performance statistics on the testing and training
datasets suggest some RF model overfitting given the increased
performance of the training dataset; however, the models remain
performant at a level similar to the statistics for the testing and
transect subsets in unobserved regions, and the uncertainties at those
locations reflect the reduced performance. Not all predictors, however,
were equally important. We found that MSWD was by far the most
influential predictor followed by wind speed, P-E , and wind
direction in order.
Our new SSV predictions over the entire AIS suggest an
insignificant reduction of 7.7 ± 20.1 Gt yr-1, which
means there is no significant difference from the integrated MERRA-2
large-scale mean; however, our map shows substantial deviations at the
regional to local scales that are indicative of increased net
accumulation over the ice shelves (+17.3 ± 11.7 Gt
yr-1 ) and decreased net accumulation over the
grounded ice sheet (-25.0 ± 16.4 Gt yr-1). Thus, we
find that while MERRA-2 provides realistic estimates of integrated
accumulation, locally it fails to capture the local-to-regional
deviations, which is unsurprising as the global model cannot resolve
finer-scale topography.
5.1 Snow accumulation variability and height change
At seasonal time scales, variations in surface height fluctuate in
response to strong positive or negative snowfall anomalies in time,
albeit in a different fashion. Over the entire AIS, an integrated
positive anomaly (Figure 3: red/orange) typically occurs in winter, when
the SSV model is positively correlated with observed height
changes. Locations that receive higher net accumulation than its
immediate vicinity experience larger height increases, which have not
yet been modulated by their enhanced compaction rates, which operate on
slower timescales. The opposite is true in the summer when the ice sheet
typically experiences negative accumulation anomalies (Figure 3:
green/brown): locations that receive anomalously higher net accumulation
than its immediate vicinity experience larger height decreases. Even
though a region might not receive any accumulation, densification
processes are more rapid where the long-term net accumulation is larger;
thus, under anomalously low accumulation conditions, we observe the
spatial variations in compaction rates that are generated from the
spatial variations in the long-term net accumulation.
The signal when integrated over the entire ice sheet is less obvious in
spring and summer. We hypothesize that while during spring (Figure 3:
purple/pink) there are typically large negative anomalies in
accumulation, the firn column remains cold coming out of winter, which
reduces compaction rates and thus the correspondence between theSSV and ATL11 height changes. We expect the opposite as well:
during the fall (Figure 3: blue/yellow), the firn column is warmer
leading to more compaction, which counterbalances the typical positive
snow accumulation anomalies, although the signal is weaker.
These results indicate that substantial deviations in ATL11 height
changes along-track exist in response to kilometer-scale variations in
the net accumulation and that the sign of the height change anomaly
likely reflects the sign of the temporal accumulation anomaly over the
cycle-pair epoch. Thus, kilometer-scale variability observed in ATL11
derived height change reflect surface processes and should not be
considered instrument noise but rather highlight precision and data
product capability. Thus, any studies interested in change over short
length scales will need to strongly consider the impact of surface
processes on the interpretation of the observed spatiotemporal height
changes.
5.2 Limitations
While we have provided a product of AIS net snow accumulation that is
largely capable of reproducing its spatial variability, several
limitations remain that if addressed could improve the methodology. In
the generation of the DEM, we chose to remove any ATL06 surface heights
that had an RMS error larger than 0.1 m, which likely excluded too much
data in steeply sloping regions. This limitation could be overcome using
an RMS threshold as a function of slope. Because the technique used to
derive the OIB snow accumulation is tied to the MERRA-2 large-scale mean
(Dattler et al., 2019), we only use MERRA-2 atmospheric data as
predictors. Given that the RF models predict accumulation variability
due to small-scale topographic deviations as well as to large-scale
biases in MERRA-2, we cannot disentangle the two from one another,
making it is difficult to attribute their individual contributions.
Other limitations stem from the predictor training data used. While the
topographic data are well resolved at 1-km resolution, the atmospheric
data only resolves variables at several 10s of km; thus, atmospheric
downscaling could lead to improved predictions. The set of predictors
used might also be incomplete. Our analysis suggests that height change
from ICESat-2 is also strongly related to the SSV in snow
accumulation, and it could provide more constraint in the future at the
ice-sheet-wide scale. Similarly, the RF model relies on training data
spanning several different atmospheric and topographic regimes, however,
most of the GPR observations are from the Antarctic Peninsula and West
Antarctica. The traditional dataset fills in much of the missing areas
in East Antarctica, but much of the data are representative of a single
point, which might not be representative of the 1 km-by-1 km region in
which it falls.
6 Conclusions
While atmospheric models generally agree on the synoptic-scale
signatures of snow accumulation over the AIS (Mottram et al., 2021),
they at present either do not account for drifting snow processes or do
so at a coarse scale. Shallow radar studies have revealed significant
deviations in the snow accumulation at sub-grid-cell scales (Medley et
al., 2013; Richardson et al., 1997; Spikes et al., 2004), which suggest
that atmospheric model evaluations against sparse point measurements of
snow accumulation are likely flawed. The predictions generated for this
study will hopefully provide new context for model evaluations by
eliminating some of the scale ambiguity in model-observation
comparisons. The resulting spatial anomalies in the net accumulation are
manifested in satellite-derived measurements of surface height changes,
which also adds uncertainty to interpretation especially when
considering seasonal timescales. Additional measurements of the
small-scale variations in snow accumulation as well as more targeted
studies bringing together satellite altimetric height changes and firn
densification models at the local scale would prove more edifying in
untangling the full response of the surface to these various processes.
Acknowledgments
The work was supported by the NASA Studies with ICESat and CryoSat-2 and
Interdisciplinary Research in Earth Sciences programs.
Open Science
The ICESat-2 data used in this study are available in Smith et al.
(2019) and Smith et al. (2021). The MERRA-2 data used are in GMAO
(2015a, 2015b, 2015c, 2015d). The AntSMB data are available at
https://doi.org/10.11888/Glacio.tpdc.271148. The IceBridge snow radar
data are available in Leuschen et al. (2014), and the IceBridge Airborne
Topographic Mapper data are available in Krabill et al. (2014). The
Reference Elevation Model of Antarctica is available in Howat et al.
(2019), and the CryoSat-2 DEMs are available in Helm et al. (2014a,
2014b) and Slater et al. (2018). The data created in this study, as well
as the Medley et al. (2014) radar-derived snow accumulation data, are
available temporarily during review via this link:
https://nasagov.box.com/s/qyxo2k9dabdll3jdqbe5n51px7mzpaj3. Once
accepted, the data will be made available in an open access repository.
Figure 1. Predicted small-scale variability (SSV) from the
large-scale mean MERRA-2 accumulation and associated uncertainty. The
relative (a) and absolute (b) predicted SSV show heterogenous patterns
of deposition/erosion as well as larger-scale model biases. The
uncertainty in both relative (c) and absolute (d) predictions are the
largest for the coastal slopes of the East Antarctic Ice Sheet.
Figure 2. Comparison of ICESat-2 ATL11 height change with the
random forest models of small-scale variability (SSV) and radar-derived
snow accumulation. (a) The wintertime change in height (May 2,
2020–August 1, 2020) over a 100-km ICESat-2 ground track posted at 60 m
(grey) and with a 1-km moving average applied (black). (b) Snow
accumulation relative to the large-scale mean from MERRA-2 (green), the
four random forest SSV models (pink/purple) named by the DEM used and
whether the model used optimized (O) or standard (S) practice
parameters, and coincident OIB snow radar-derived snow accumulation. (c)
OIB snow radar echogram collected October 25, 2019 that is coincident in
space with the ICESat-2 ATL11 reference pair track 2. The layer traced
in dashed orange provided the basis of the radar-derived snow
accumulation represented by an orange line in (b). This snow radar
transect is mapped in Figure S1b.
Figure 3. Comparison of 50-km along-track (a) correlations
between the mean random forest net accumulation model and ICESat-2 ATL11
height change for 8 cycle pairs and (b) temporal snow accumulation
anomalies over the entire AIS. The results are presented as histograms
of either the correlation coefficient or the magnitude of the temporal
anomaly in snow accumulation over 50-km ATL11 segments and are color
coded by cycle pair. The median of each distribution is displayed as a
dotted vertical line. (c) The median correlation coefficient values from
(a) plotted in time referenced to the cycle pair and its associated
season. (d) the same as (c) but a time series of the median accumulation
anomaly. Colors in both (c) and (d) match those from (a) and (b).
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