GEOPHYSICAL RESEARCH LETTERS, VOL. 38, L20505, doi:10.1029/2011GL049216, 2011
Large‐scale surveys of snow depth on Arctic sea ice
from Operation IceBridge
Nathan T. Kurtz1,2 and Sinead L. Farrell2,3
Received 5 August 2011; revised 26 September 2011; accepted 26 September 2011; published 27 October 2011.
[1] We show the first results of a large‐scale survey of snow
depth on Arctic sea ice from NASA’s Operation IceBridge
snow radar system for the 2009 season and compare the data
to climatological snow depth values established over the
1954–1991 time period. For multiyear ice, the mean radar
derived snow depth is 33.1 cm and the corresponding mean
climatological snow depth is 33.4 cm. The small mean
difference suggests consistency between contemporary estimates of snow depth with the historical climatology for the
multiyear ice region of the Arctic. A 16.5 cm mean difference
(climatology minus radar) is observed for first year ice areas
suggesting that the increasingly seasonal sea ice cover of the
Arctic Ocean has led to an overall loss of snow as the region
has transitioned away from a dominantly multiyear ice cover.
Citation: Kurtz, N. T., and S. L. Farrell (2011), Large‐scale surveys
of snow depth on Arctic sea ice from Operation IceBridge, Geophys.
Res. Lett., 38, L20505, doi:10.1029/2011GL049216.
1. Introduction
[2] Knowledge of snow depth on sea ice is critical for
understanding the precipitation, heat, and radiation budgets
of the polar regions. The snow cover on sea ice has a high
surface albedo which limits the amount of shortwave heating
during the spring season when the shortwave flux is high and
melt ponds have not yet formed [e.g., Lindsay, 1998]. The
low thermal conductivity of snow makes it an effective
insulator thereby impacting the growth and decay of the
underlying sea ice layer, as well as reducing the transfer of
heat between the ocean and atmosphere [Maykut, 1978; Kurtz
et al., 2011]. Furthermore, knowledge of snow loading (snow
depth and density) on sea ice is required for retrievals of
sea ice thickness from airborne and spaceborne altimeters
[e.g., Kurtz et al., 2009; Kwok and Cunningham, 2008; Giles
et al., 2008]. For these reasons, the determination of snow
depth using the University of Kansas’ frequency modulated
continuous‐wave (FMCW) snow radar is one of the sea ice
mission objectives for NASA’s ongoing Operation IceBridge
airborne campaign. The IceBridge mission has flown more
than twenty trans‐oceanic surveys over Arctic sea ice since
its inception in 2009.
[3] To date, climatological [Warren et al., 1999], observational [e.g., Markus and Cavalieri, 1998; Gerland and
1
School of Computer, Math, and Natural Sciences, Morgan State
University, Baltimore, Maryland, USA.
2
Hydrospheric and Biospheric Sciences Laboratory, NASA Goddard
Space Flight Center, Greenbelt, Maryland, USA.
3
Earth System Science Interdisciplinary Center, University of
Maryland, College Park, Maryland, USA.
Copyright 2011 by the American Geophysical Union.
0094‐8276/11/2011GL049216
Haas, 2011], and model approaches [e.g., Kwok and
Cunningham, 2008] have all been used to investigate the
snow cover on sea ice. These snow data sets have been
combined with satellite altimetry data to determine sea ice
thickness in the Arctic, but errors in the snow depth data are
presently not well constrained. The climatology of snow
depth on sea ice developed by Warren et al. [1999] (hereafter
referred to as W99) was derived from in‐situ data gathered
over multiyear ice during the 1954–91 time period. Due to
the large observed loss of multiyear ice in recent years, the
climatology may no longer provide an accurate representation of current snow conditions. Despite these potential differences, the W99 climatology has been combined with data
from past and current satellite altimetry missions for the
retrieval of sea ice thickness [e.g., Laxon et al., 2003; Giles
et al., 2008; Kwok and Cunningham, 2008; Kurtz et al.,
2009]. Model approaches suffer from uncertainties in the
magnitude of precipitation [Serreze and Hurst, 2000], difficulties in estimating snow loss to leads [Déry and Tremblay,
2004; Leonard and Maksym, 2011], compaction and densification issues, and many other factors. Observational
approaches to measuring snow depth using passive microwave data have been developed [e.g., Markus and Cavalieri,
1998], but uncertainties due to surface roughness variations remain [Stroeve et al., 2006] and the methodology is
unsuitable for the retrieval of snow depth over multiyear ice
in the Arctic [Comiso et al., 2003]. The IceBridge snow
radar has been designed specifically for the determination of
snow depth from an airborne platform [Panzer et al., 2010].
The feasibility of using an airborne radar for the retrieval of
snow depth was established during previous surveys from
ground and helicopter platforms [e.g., Kanagaratnam et al.,
2007; Galin et al., 2011].
[4] Initial comparisons with an in‐situ data set have shown
that the IceBridge snow radar is capable of accurate snow
depth retrieval [Farrell et al., 2011], providing a valuable tool
for altimetric sea ice thickness retrievals and future studies of
the Arctic snow pack. IceBridge snow depths will thus provide a new source for quantifying errors in the different
approaches used to estimate basin‐wide Arctic snow depth on
sea ice and for investigating the impact of such errors on sea
ice thickness retrievals from satellite altimetry data including
ICESat [Zwally et al., 2002], CryoSat‐2 [Wingham et al.,
2006], and ICESat‐2 [Abdalati et al., 2010]. The data will
also be useful for the development and validation of improved
methods to estimate snow depth on sea ice from existing
model and observational data.
[5] In this study, we use snow depth retrievals from the
snow radar system to quantify the large‐scale snow depth
properties of Arctic sea ice in April, 2009. A comparison with
the climatology of W99 is conducted to determine the utility
of climatological data for combination with current laser and
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radar altimeter data sets for ice thickness retrievals. In addition, the comparison with the historical climatology gives
insight into the extent to which recently observed shifts in the
Arctic sea ice regime towards younger and thinner sea ice
have impacted the snow cover. This comparison provides a
baseline for determining changes in the mean depth and
spatial variability of the snow cover within the IceBridge
survey area which have occurred since the 1954–1991 time
period.
2. Data Sets and Methods
[6] The IceBridge snow radar data used in this study
[Leuschen, 2009] consists of 4 flights between April 2nd and
April 25th, 2009. An IceBridge flight over the Fram Strait
was conducted on March 31st. However, our assessment of
this data revealed that differences in the radar operating
parameters may limit the quality of snow depth retrieved from
the algorithm presented in this study. We therefore do not use
data collected during this flight. The FMCW radar uses a
nominal sweep from 2–8 GHz with a usable bandwidth of
∼4.5 GHz. This results in a maximum range resolution of
∼3 cm and an estimated 5 cm snow depth retrieval resolution
based on the 3 dB width of returns observed over flat, specular targets. The radar has a pulse‐limited footprint size of
16 m at the nominal IceBridge flight altitude (460 m) and a
spatial sampling frequency of 1 m (see Panzer et al. [2010]
for full technical details on the radar design and operation).
To boost the signal‐to‐noise ratio, the data are averaged to
provide an along track resolution of 40 m for snow depth
retrieval. The snow depth is found by identifying the snow‐air
and snow‐ice interfaces in the time domain and multiplying
the time separation of the two interfaces by the speed of light
within the snow pack. The propagation velocity of light in
the snow pack was taken to be constant (2.34e8 m/s) based
on the W99 climatological mean snow density of 320 kg/m3
and the relation between snow density and dielectric constant
given by Tiuri et al. [1984]. For a typical snow depth of
30 cm, an uncertainty in snow density of 100 mkg3 (taken
from W99) leads to a maximum snow depth retrieval error
of 2 cm due to errors in the propagation velocity of light. The
algorithm used for the retrieval of snow depth in this study
follows from the technique described in detail by Farrell et al.
[2011]. The conditions for the minimum return power level,
Ps−a (in dB), for the snow‐air interface are:
Psa N þ xthresh N
ð1Þ
Psa N þ xthresh N
ð2Þ
where N is the mean noise level, sN, is the standard deviation of the noise level, xthresh = 2.3, and Psa is the mean
power in the 6 range bins that follow the estimated snow‐air
interface. These conditions were chosen to separate the
beginning of the return (at the snow surface) from the noise
level. For sea ice, the surface roughness is typically larger
than the Rayleigh criterion over the first two Fresnel zones
of the radar active area [Carsey, 1992]. Therefore, diffuse
rather than specular reflections generally dominate the
returns, but the manner in which the radar signal is backscattered from the snow‐air interface is quite variable over the
survey lines due to the snow property variations. To account
for these variations, the snow‐air interface was chosen to be
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the first local maxima in the radar signal beyond the minimum
power level. But if the return power reached a point greater
than Ps−a ≥ N + xmaxsN (where xmax = 2.8) above the noise
level before a local maxima was found then this point was set
to be an initial estimate for the snow‐air interface location.
The threshold conditions restrict the return power from the
snow‐air interface such that it is less than or equal to the return
power we expect from a more specularly reflecting snow
surface, but still above the noise level. After determination of
the estimated snow‐air interface location, an initial estimate
for the snow‐ice interface was then taken to be the location of
the largest peak in the radar return. Due to the larger dielectric
contrast between ice and snow, the signal from the snow‐ice
interface is stronger and easier to identify than that from the
snow‐air interface. The final locations of the snow‐air and
snow‐ice interfaces were then chosen using a second iteration
of the previous method, but with adjustments to the values
of xthresh and xmax to account for variations in the radar
operating parameters which occurred during each flight (e.g.
aircraft altitude and radar transmit power). The average
power of the noise level, hNi, and initial estimate snow‐ice
interface power, hPs−ii, were determined for ∼4 km segments,
corresponding to the length of each radar echogram. The
values of xthresh and xmax were then multiplied by the scale
factor (hPs−ii − hNi)/13.0, where 13.0 is the average power
separation between the noise level and the snow‐ice interface as observed in the study of Farrell et al. [2011]. A
locally weighted robust linear regression was then applied
at a 40 m length scale to reduce the impact of outliers in the
final determination of the snow‐air and snow‐ice interface
locations.
[7] A comparison between in‐situ snow depth measurements and those retrieved from the airborne snow radar over
fast ice north of Greenland [Farrell et al., 2011] proved
the capability of the retrieval algorithm for deriving snow
depth over a variety of ice types and snow thicknesses. On
undeformed first year (FY) ice the mean snow depth measured in‐situ was 9.5 cm with a mode at 4.5 cm, while
mean snow depth retrieved from the radar was 9.6 cm with
a mode at 5.7 cm, demonstrating the ability of the algorithm
to accurately retrieve of snow depth over level ice with a
thin snow cover. Similarly, over multiyear (MY) ice the
mean difference between the in‐situ and radar measurements was also small and the overall mean difference for the
full 2 km survey was 0.8 cm [Farrell et al., 2011]. A comparison between coincident IceBridge laser altimetry and
aerial photography data revealed two surface types where the
retrieval algorithm could not accurately define the air‐snow
interface: 1) New or partially refrozen leads were found to
cause a characteristic series of multiple bands within the radar
signal rendering it unusable for interface detection. These
areas were flagged when three bands four standard deviations
above the noise level were present. The snow depth in these
regions was set to zero corresponding to the negligible snow
cover on new leads. 2) Data over the apexes of steep pressure ridges caused a loss in the return power such that the
returns from the snow‐air and snow‐ice interfaces became
indistinguishable from the noise level. Following Farrell
et al. [2011] we discard data where the maximum return
power is less than six standard deviations above the mean
noise level. Overall, 84% of the radar data had a sufficient
return energy for the retrieval of snow thickness and was
used in this study. Even though snow depth data directly over
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KURTZ AND FARRELL: ICEBRIDGE SNOW DEPTH ON SEA ICE
Figure 1. Map of climatological snow depths from W99
with the 2009 IceBridge snow radar results overlain. The
multiyear ice boundary is outlined in dark red. Data from the
Nares Strait is from Flight 4 only.
steep pressure ridges could not be found, comparisons with
the distribution and mean snow depth from the in‐situ and
radar survey study suggests that the deep snow cover typical
of snow drifts on the leeward side of ridges is largely captured
by the snow radar system.
3. Snow Depth Results
[8] In 2009, the IceBridge flight lines were dedicated to
surveying a variety of ice types and capturing the gradient
in ice thickness on basin scales. Flights on April 2, 21, and 25
were primarily over areas of thick MY Arctic sea ice in the
Canada Basin, while the flight on April 5 surveyed a mixture
of FY and MY ice areas. A map of snow depth derived along
IceBridge flight lines is presented in Figure 1. The 16 m
by 40 m resolution snow depth derived from the snow radar
data have been placed into the 12.5 km AMSR‐E grid for
comparison with the scale of current snow depth and gridded
altimetric (e.g., ICESat) sea ice thickness retrieval methods
and for ease of separating the FY and MY ice areas. The
distinction between FY and MY ice areas was identified
using passive microwave data from AMSR‐E [Cavalieri
et al., 2004] (updated daily), and is outlined in dark red in
Figure 1. Also shown in Figure 1 is the W99 climatological
snow depth for the month of April. Compared to the smooth
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gradient of the climatological snow depths, the observational
data indicate higher variability in snow depth on scales of
12.5 km and greater. This higher variability has been seen
in other observational studies as well [Gerland and Haas,
2011]. Therefore the combination of climatological snow
depth values with higher‐resolution altimeter data sets such
as ICESat or CryoSat‐2 may result in local ice thickness
errors due to snow depth inaccuracies. Overall, the IceBridge
radar data show similar gradients in the snow cover with
the lowest snow depth generally occurring towards the
Alaskan coast and the highest snow depth towards the
northern coasts of Greenland and the Canadian Archipelago.
This gradient is consistent with contemporary snow precipitation and ice circulation patterns from model data [e.g.,
Kwok and Cunningham, 2008] and ice type/age differences.
[9] Flight 3 shows an anomalous decrease in snow depth
towards the western portion of the flight track that was not
observed in the overlapping data set from Flight 1. Possible
reasons for these anomalous snow depth values include
dynamic redistribution of the snow pack or sea ice during the
3 week period between flights, or thermodynamic processes.
The AMSR‐E sea ice mask delineating FY and MY ice types
did not shift significantly during the period (not shown)
suggesting the flight survey was entirely over MY ice. To
further investigate thermodynamic causes we calculated
surface temperatures for each flight line using the thermodynamic sea ice model of Kurtz et al. [2011] forced with
ECMWF meteorological data. The mean surface temperature reached −4 °C in the region where the anomalously low
snow depths occurred. In contrast, surface temperatures for
all previous days and flights were typically less than −10 °C.
Evidence of variations in radar penetration depth due to
surface temperature effects have been observed in previous
studies [e.g., Giles and Hvidegaard, 2006; Willatt et al.,
2011]. Barber et al. [1995] found that surface radiative
forcing led to phase changes in the snow surface which
enabled free water to percolate through the snow pack for
temperatures greater than −5 °C, this free water greatly
changed the dielectric properties of the snow pack. We
speculate that wet layers such as this changed the dielectric
properties of the snow pack causing errors in the snow depths
retrieved at the western end of Flight 3. We therefore discarded data where surface temperatures were greater than
−5 °C (i.e. westward of ∼235 W longitude for Flight 3) in
the subsequent analysis.
[10] The distributions of the individual snow radar measurements (at 16 m by 40 m resolution) are shown in Figure 2
and demonstrate variability in the small‐scale snow depth.
Figure 2. Distributions of all 40 m resolution snow depth data for each flight. The distributions for first year ice are in blue,
while those for multiyear ice are in red.
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Table 1. Snow Depth Comparisons for the 12.5 km Gridded Dataa
Snow Depth
MY Radar
MY Radar
(Excluding Leads)
MY W99
Number of
MY Grid Cells
FY Radar
FY Radar
(Excluding Leads)
FY W99
Number of
FY Grid Cells
Flight 1 (2 Apr 2009)
Flight 2 (5 Apr 2009)
Flight 3 (21 Apr 2009)
Flight 4 (25 Apr 2009)
All flights
33.2 (6.9)
27.7 (7.1)
38.7 (8.6)
35.4 (8.6)
32.7
33.6 (6.9)
28.0 (7.2)
39.0 (6.7)
35.8 (8.6)
33.1
34.3 (2.4)
36.9 (0.7)
34.4 (0.5)
38.1 (0.6)
33.4
178
105
46
63
392
23.0 (4.9)
17.1 (3.8)
‐
14.5 (6.7)
16.6
23.1 (4.9)
17.3 (3.8)
‐
19.7 (5.3)
18.1
29.7 (0.1)
34.3 (0.6)
‐
36.3 (1.0)
34.6
6
118
0
45
169
a
Standard deviations are in parentheses.
The three cross‐basin surveys (Flights 1–3) showed a broad
distribution of snow depths with standard deviations of
15–19 cm for both the FY and MY ice regions (Figure 2). As
expected, modal snow depth over FY ice was less than the
modal snow depth over MY ice. Figure 1 shows a clear
transition between the FY and MY snow depth covers for
Flight 3 in particular. Overall, snow depth on sea ice in the
central Arctic was observed to be highly variable, ranging
from near zero (over refrozen leads) to 60 cm over thick
MY ice.
[11] The snow depth climatology of W99 was developed
using observations collected on MY ice only and is likely not
applicable over FY ice areas since the FY ice areas may form
after the heavy snowfall periods which typically occur in
September and October. A comparison between the 12.5 km
gridded FY and MY ice snow depths with W99 is shown in
Table 1 for each flight line. Two values are shown: 1) the
mean snow depth calculated for all data points i.e the relevant
value for combination with satellite laser altimetry data,
and 2) the mean snow depth excluding leads i.e. the value
which is relevant for comparison with the W99 data set and
for combination with satellite radar altimetry data. W99
estimate the upper bound of the interannual variability of
snow depth for April to be 6.1 cm providing an estimate on
the expected error in the climatological snow depths shown
here. Using the mean of all available 12.5 km grid cells shown
in Table 1, we find that the climatological snow depths of
W99 are only 0.3 cm higher than those from the IceBridge
radar data set implying consistency between the large‐scale
climatological mean snow depths and contemporary observations. However, on a flight‐by‐flight basis for the MY ice
areas some of the results compare quite well while others do
not. Flight 1 covered a large area of the MY ice cover with
only a 0.7 cm difference between W99 and the observations
(Table 1). In contrast, Flight 2 covered both FY and MY ice
but showed a much more significant difference of 8.9 cm
in the MY snow depth. Nonetheless, the results over MY
ice show that over a very large scale (several hundred to
thousands of km) the snow depth climatology continues to
provide an accurate estimate of the average snow depth.
[12] For FY ice areas, the climatological snow depth values
compare poorly with the mean climatological value being
16.5 cm (191%) higher than the snow radar observations
(Table 1). This is not unexpected, but illustrates that snow
depths from climatologies such as W99 do not accurately
represent snow depth on FY sea ice. While Flights 2 and 4
show large differences (>16 cm) between the climatology
and snow radar, Flight 1 showed less significant differences
of <7 cm. There is, however, little data in the FY ice area
of Flight 1 (comprising 4% of the FY ice observations), but
their proximity to the MY ice areas may mean that the ice
formed relatively early in the growing season allowing the
FY floes to accumulate more snow.
4. Discussion
[13] Recently observed changes in the Arctic sea ice regime
raise two questions which can be addressed with the current
results, and future IceBridge surveys: 1) How has the snow
cover changed in response to the changing MY sea ice cover
in the Arctic? 2) Have changing precipitation patterns [e.g.,
Serreze et al., 2000] in recent years acted to change the spatial
distribution and mean depth of the Arctic snow cover? The
present study provides an answer to the first question, in that
the observed April 2009 snow depths for FY ice areas were
∼52% of the 1954–1991 climatological snow depths. Snow
accumulates primarily in the early part of the fall, so as the ice
cover shifts to a more seasonal ice pack with later freeze up
dates [Markus et al., 2009] less snow now collects on Arctic
sea ice, precipitation which may have previously fallen onto
the sea ice is now lost to the ocean. However, determining the
magnitude of the precipitation loss and additional fresh water
input to the ocean requires more surveys over a larger expanse
of FY ice. This study also provides a baseline data set for
answering the second question, but subsequent IceBridge
surveys over the next several years will be needed to answer
this question with more certainty. W99 found a trend of
decreasing mean April snow depth of 0.1 cm/yr, representing
a ∼4 cm loss in mean snow depth between the middle of the
climatology period (1972) and the present time. We observe
only a 0.3 cm difference between our surveyed snow depths
and those from the climatology indicating that the mean
precipitation (over the fall, winter, and spring time periods)
and mean snow depth over MY ice may be similar to the
1954–1991 period. However, the maximum April interannual
variability in snow depth was estimated to be 6.1 cm by W99,
so more data over a longer time period is required to determine the long‐term trend in the Arctic MY ice snow cover.
5. Conclusion
[14] The IceBridge surveys over Arctic sea ice have and
will continue to provide much useful new information on
the distribution of snow on sea ice. The snow depth surveys
presented in this study reveal a snow cover that is more
variable on regional scales than the W99 climatology,
although on synoptic scales we observe a gradient similar
to the W99 climatology. Thus, we find that the climatological
snow depth is therefore useful for altimetric sea ice thickness retrievals on synoptic scales over MY ice, but may
introduce errors when used for regional‐scale studies. Additionally, due to the recent loss of MY ice across large areas of
the Arctic basin the sea ice cover is becoming increasingly
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seasonal. Therefore, it may no longer be valid to use the W99
climatology on basin scales, where we have shown large
differences with the observed snow depth over FY ice.
Overall, an analysis of future IceBridge surveys will enable us
to estimate the spatial and interannual variabilities of the
April snow pack, determine trends during the observation
period, and compare results to the historical climatology. The
comparison of snow radar data from more recent IceBridge
campaigns, including those conducted in 2010 and 2011, and
the analysis of interannnual variability of the Arctic snow
pack will be the subject of a follow‐on study.
[ 15] Acknowledgments. This work was supported by the NASA
Cryospheric Sciences Program under grant NNX10AV07G and the
NOAA/STAR Ocean Remote Sensing Program. The authors would like to
thank the editor and reviewers for their helpful suggestions on improving
the manuscript.
[16] The Editor thanks Jan Lieser and an anonymous reviewer for their
assistance in evaluating this paper.
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