JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D23108, doi:10.1029/2012JD018047, 2012

Simulated Siberian snow cover response to observed
Arctic sea ice loss, 1979–2008
Debjani Ghatak,1 Clara Deser,2 Allan Frei,3,4 Gavin Gong,5 Adam Phillips,2David A. Robinson,6 and Julienne Stroeve7
Received 7 May 2012; revised 2 October 2012; accepted 11 October 2012; published 7 December 2012.

[1] The loss of Arctic sea ice has wide-ranging impacts, some of which are readily
apparent and some of which remain obscure. For example, recent observational studies
suggest that terrestrial snow cover may be affected by decreasing sea ice. Here, we examine
a possible causal link between Arctic sea ice and Siberian snow cover during the past
3 decades using a suite of experiments with the National Center for Atmospheric ResearchCommunity Atmospheric Model version 3. The experiments were designed to isolate
the influence of surface conditions within the Arctic Ocean from other forcing agents suchas low-latitude sea surface temperatures and direct radiative effects of increasing
greenhouse gases. Only those experiments that include the observed evolution of Arctic sea
ice and sea surface temperatures result in increased snow depth over Siberia, while thosethat maintain climatological values for Arctic Ocean conditions result in no snow signal
over Siberia. In the former, Siberian precipitation and air temperature both increase,but because surface air temperatures remain below freezing during most months, thesnowpack thickens over this region. These results suggest that Arctic Ocean surface forcing
is necessary and sufficient to induce a Siberian snow signal, and that other forcingsin combination can modulate the strength and geographic extent of the response.
Citation: Ghatak, D., C. Deser, A. Frei, G. Gong, A. Phillips, D. A. Robinson, and J. Stroeve (2012), Simulated Siberian snow
cover response to observed Arctic sea ice loss, 1979–2008, J. Geophys. Res., 117, D23108, doi:10.1029/2012JD018047.

1. Introduction[2] Changes in the Northern Hemisphere high-latitudeenvironment, including a rapid decline in summer Arctic sea
ice cover, are under scrutiny for their impact on global climate [Hassol, 2004; Stroeve et al., 2005, 2007; Comiso et al.,2008]. For example, one recent study examines changes inprecipitation, evapotranspiration, and river discharge over
the terrestrial pan-Arctic region through the analyses ofobservations and coupled GCM simulations [Rawlins et al.,2010]. They found positive trends in precipitation, evapotranspiration, and river discharge both in observations and in
1
Institute of Marine and Coastal Sciences, Rutgers University,
New Brunswick, New Jersey, USA.
2
Global and Climate Dynamics, National Center for Atmospheric Research,
Boulder, Colorado, USA.
3
Department of Geography, Hunter College, New York, New York, USA.
4
Program in Earth and Environmental Sciences, City University of
New York, New York, New York, USA.
5
Department of Earth and Environmental Engineering, Columbia
University, New York, New York, USA.
6
Department of Geography, Rutgers University, Piscataway, New Jersey,
USA.
7
National Snow and Ice Data Center, University of Colorado Boulder,
Boulder, Colorado, USA.

Corresponding author: D. Ghatak, Institute of Marine and Coastal
Sciences, Rutgers University, 71 Dudley Rd., New Brunswick, NJ 08901,
USA. (ghatak@marine.rutgers.edu)
This paper is not subject to U.S. copyright.
Published in 2012 by the American Geophysical Union.

model simulations, suggesting an intensification of the
freshwater cycle.
[3] Land-surface snow over the middle and high latitudes
has also been changing [Ye et al., 1998; Clark et al., 1999; Frei
and Robinson, 1999; Brown, 2000; Ye, 2001; Ye and Ellison,
2003; Ye et al., 2003; Hassol, 2004; Iijima et al., 2007;
Brown and Mote, 2009; Bulygina et al., 2009, 2011; Brown
et al., 2010; Fontana et al., 2010; Ghatak et al., 2010;
McCabe and Wolock, 2010]. The response of snow to climatic
forcing is complex and depends on the spatial and temporalscales of study. Biancamaria et al. [2011] showed a statisticallysignificant negative trend in snow volume ( 9.7  3.8 km3 yr 1,
p value = 0.02) over North America and a statistically insignificant positive trend in snow volume (11.3  9.3 km3 yr 1,
p value = 0.25) over Eurasia by analyzing data from the Special Sensor Microwave/Imager (SSM/I) between 1989 and2006 period. The loss of Northern Hemisphere spring snow
cover extent has accelerated during the last 40 years with a rateof decrease of 0.8 million km2 per decade [Brown andRobinson, 2011]. The diminished spring snow covered area
since 1970 is mainly due to the increased winter warming,
which has strengthened in recent years [McCabe and Wolock,2010; Brown and Robinson, 2011]. Pan-Arctic snow coverextents during May and June have also decreased, 14% and
46% respectively, over the 1967–2008 period due to earlier
snowmelt [Brown et al., 2010].
[4] A study based on 110 stations from the Former Soviet
Union (FSU) showed that the mean cold season snow depth

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Table 1. The Forcing Characteristics of the CAM3 Experiments
Radiative Forcings(Greenhouse Gases, Troposphericand Stratospheric Ozone,
Sulfate and Volcanic Aerosols,
Experiment Nameand Solar Insolation)
GHG + SST + ICE
SST + ICE
GHG
Tropical SST
ICE

time-varying
fixed at 1990 leveltime-varying
fixed at 1990 level
fixed at 1990 level

Extra Tropical Surface Forcings(SSTs and Sea Ice Poleward 20 )Tropical Surface
Forcings (SSTs 20 N–20 S) Time Domain

time-varying
time-varying
time-varying
time-varying
climatological seasonal cycle
climatological seasonal cycle
climatological seasonal cycle
time-varying
time-varying for sea ice and local SST (see text); climatological seasonal cycle
climatological seasonal cycle for SSTs elsewhere.

was affected by warming in only a few areas during thesecond half of 20th century [Fallot et al., 1997]. A more
recent study of 820 Russian stations with snow measurements between 1966 and 2007 revealed that snow cover
duration decreased over the northern regions of European
Russia, and increased over central Siberia (Yakutia) and theFar East [Bulygina et al., 2009].
[5] Warming due to increasing greenhouse gases leads tothe loss of seasonal snowpacks, which causes a threat to thewater supply of one-sixth of the world’s population [Barnett
et al., 2005]. General circulation model simulations suggestthat in response to increasing greenhouse gases, snow waterequivalent (SWE) will decrease over most of the midlatitudinal regions of the Northern Hemisphere, but will
increase over the Canadian Arctic and Siberia [Barry et al.,2007; Räisänen, 2008]. A recent study based on 11 highresolution regional model simulations collectively suggest
an expected loss of snow water equivalent over Northern
Europe during the 21st century; though there are some exceptions (e.g., the mountains of Northern Sweden) [Räisänen andEklund, 2011].
[6] While snow has generally decreased over Eurasia[Ye et al., 2003; Hassol, 2004; Ye et al., 2008] during the last
half of the 20th century, some areas have experienced moresnow [Bulygina et al., 2009, 2011; Ghatak et al., 2010].
Ghatak et al. [2010] examined the covariation between
observed changes in snow duration (1979–2007) and Arctic
sea ice loss, which suggest that snow over Siberia potentially
increases with Arctic sea ice loss. According to model, thissignal can be expected to strengthen over the next one to
3 decades [Ghatak et al., 2010]. This is consistent with themodeling study by Deser et al. [2010]. Liu et al. [2012]
showed a linkage between retreating Arctic sea ice andwinter snowfall over both the continents of North America
and Eurasia.
[7] Here, we investigate a possible causal linkage between
Siberian snow and sea ice using a suite of modelingexperiments, the objective of which is to isolate the impact
of changing sea ice from other forcings. Details of theexperiments are discussed in section 2, results are shown insection 3 and conclusions are discussed in section 4.

2. Experiments and Methodology[8] We use the National Center for Atmospheric Research(NCAR) Community Atmospheric Model version 3 (CAM3)
coupled to the Community Land Model (CLM), which are
the atmospheric and terrestrial components of the Community Climate System model version 3 (CCSM3), respectively.

1950–2008
1950–2008
1950–2008
1950–2009
1979–2009

These models are described in detail by Collins et al. [2006]
and references therein. The strengths and weaknesses of
CAM3 in simulating the mean state and interannual variability are reported by Hurrell et al. [2006].
[9] We analyze results from a suite of five CAM3/CLM
experiments, which have been specifically designed to isolate the impacts of Arctic Sea Surface Temperature (SST)and sea ice changes from other forcings (discussed below).
These experiments have been performed at T42 horizontal
resolution (approximately 2.8 of latitude and 2.8 of longitude). Each of the 5 experiments consists of five ensemble
members each of which begins from different atmosphericinitial conditions. We present results for the average of fiveensemble members.
[10] For these experiments we group boundary forcings
into two categories: radiative and surface. Radiative boundary
forcings include atmospheric chemical composition (greenhouse gases, tropospheric and stratospheric ozone, sulfate andvolcanic aerosols) and their associated radiative impacts aswell as solar insolation variations. Surface boundary forcings
include global SST and sea ice concentration variations. The
differences between the experiments are whether or not they
include the time-evolving observed values for these forcings.
When the time-varying radiative boundary forcings are notincluded, they are maintained at 1990 levels to approximate
conditions during the late 20th century. When the time-varying surface boundary forcings are not included, the meanseasonal cycle is repeated during each year of the experiment.
The Hurrell et al. [2006] data set has been used for the SST
and sea ice concentrations.
[11] Table 1 summarizes the forcings included in each of
the five experiments denoted as “GHG+SST+ICE,” “SST+ICE,”“ICE,” “GHG” and “Tropical SST.” The “GHG+SST+ICE”
experiment is forced by all observed, time-evolving boundary
conditions, including both radiative and surface. “SST+ICE”
is forced by global time-evolving surface boundary conditions and constant radiative forcing. “ICE” is forced by timevarying Arctic sea ice concentrations, with climatologicalvalues for the other surface forcing components with the
exception of SSTs within grid boxes partially covered by ice.
In these regions, we have incorporated the local, direct effectof ice loss on SST (e.g., when there is less ice, there is more
incident solar radiation at the sea surface which heats theocean mixed layer) by including time-varying SSTs in thosegrid boxes and months when the sea ice concentration is lessthan the climatological value by at least 10% (see Screen
et al. [2012b] for additional details). “GHG” is forced by
the observed evolution of radiative forcings [Meehl et al.,2006], with climatological values for the mean seasonal

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D23108Figure 1. Climatology (1979–2007) of autumn (October–November) fractional snow cover (%) from(a) the “GHG + SST + ICE” CAM3 experiment and (b) observations.
cycle of SSTs and sea ice. “Tropical SST” is forced by theobserved evolution of SSTs in the tropics (20 N–20 S), withclimatological values elsewhere. More details of theseexperiments are provided by Deser and Phillips [2009] andalso can be found online at http://www.cesm.ucar.edu/working_groups/Climate/experiments/ccsm3.0/.
[12] The temporal domain of our analyses is restricted to
October through March, 1979/1980–2007/2008, which coincides with the period of regular satellite observations of sea icefrom passive microwave observations. This helps us to understand how the historic simulations can be compared with theobserved variability. The spatial domain of our analysis is limited to the Arctic Ocean and Eurasia north of 45 N. We present
results at bimonthly time scale, i.e., October–November (ON),
December–January (DJ) and February–March (FM).
[13] The variables analyzed include monthly mean snow
depth (‘SNOWDP’ in meters), lowest model level water
vapor mixing ratio (‘QBOT’ in gm/kg), surface air temperature (‘SAT’ in  C), precipitation (‘PRECIP’ in mm/d) andsnowfall (‘SNF’ in mm/d). Linear regression analysis has
been employed between the September sea ice extent timeseries (SIE) (available from NSIDC’s Sea Ice Index, http://nsidc.org/data/seaice_index) and each of the variables.
Because the SIE has a negative trend, the resulting regression coefficients are displayed with reversed sign so that
positive values represent increases in the regressed variable
as SIE decreases. Similar results are obtained with linear
trend analysis (not shown). We assess the 5% statisticalsignificance of the regression coefficients following a Student’s t test [Helsel and Hirsch, 1992]. Although we present
regression coefficients at each grid cell, we discuss only
those features that show significant values over large geographical areas.
[14] As context for the regression analysis, Figure 1 shows
climatological October–November snow cover conditions
from the “GHG + SST + ICE” model experiment and fromobservations. Snow cover extent (fraction of land covered bysnow) is shown for the model as well as from observations[Armstrong and Brodzik, 2005]. EASE grid (Equal Area
Scalable Earth Grid) snow extent data [Armstrong and
Brodzik, 2005] based on NOAA-NESDIS snow charts has
been obtained from the National Snow and Ice Data Center(NSIDC). Please note that Figures 1a and 1b have different
spatial resolution. A visual comparison indicates that the

spatial patterns match well, with larger values over highlatitudes and over mountainous regions (compare Figures 1aand 1b).

3. Results[15] Regression analyses between September SIE andSNOWDP in the following ON, DJ and FM show that loss of
September SIE is associated with an increase in SNOWDP
over central and eastern Siberia (over the northern Yenisey
and Lena river basins) in the “GHG + SST + ICE,”“SST + ICE” and “ICE” experiments (Figure 2). Significant
increases in SNOWDP are observed during DJ and FM in
the “GHG + SST + ICE” and “SST + ICE” experiments andduring ON, DJ and FM in “ICE” experiment. In the two
experiments without time-varying Arctic surface boundary
forcings (“GHG” and “Tropical SST”), no significant signal
in SNOWDP emerges over this region (not shown). Resultsfrom individual ensemble members (not shown) exhibit
similar Siberian snow depth signals, though the geographicalextent and statistical significance of this association varyslightly among them.
[16] Another noticeable pattern is found over western
Eurasia, where retreating September SIE is followed bysignificantly decreasing SNOWDP during October throughMarch over the region extending from Scandinavia to the Ob
river basin in both “GHG + SST + ICE” and “SST + ICE”
experiments (Figure 2). This signal is not apparent in the“ICE” experiment. The “GHG” and “Tropical SST” experiments show no coherent and consistent signal except overcentral Russia in “Tropical SST” (not shown).
[17] To understand the snow depth signals, we examine
regression maps between September SIE and subsequent
SAT (Figure 3), QBOT (Figure 4), PRECIP (Figure 5) andSNF (Figure 6). Reduced September SIE is followed bystatistically significant warming over the Pacific sector of
the Arctic Ocean and over the adjacent Eurasian landmass
including Siberia in all three experiments, mainly during ON(Figures 3a–3c). The strongest response is found over the
East Siberian, Chukchi and Laptev Seas which coincides
with the area of recent observed summer ice loss. In the“ICE” experiment, this signal does not persist into DJ and
FM. In other experiments, the ON signal is more extensive
than “ICE” experiment, and persists in some form through3 of 10

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D23108Figure 2. Regression coefficients (m/106 km2) of bimonthly SNOWDP upon September SIE during
1979–2008 for the “GHG + SST + ICE,” “SST + ICE” and “ICE” experiments in (a–c) October–November;(d–f) December–January; and (g–i) February–March. Regression coefficients are plotted with reversed sign
to denote conditions associated with ice loss. Regression coefficients significant at the 5% confidence levelare indicated by the white contours and shading.
March (Figure 3). Neither of the experiments that exclude
time-varying Arctic surface boundary forcing exhibit significant Siberian warming (not shown).
[18] These analyses indicate that September Arctic sea ice
loss and associated local SST increases cause ON warmingof surface air temperatures both over the Arctic Ocean andover adjacent land surfaces, consistent with previous observational studies [Serreze et al., 2009; Screen and Simmonds,
2010] and modeling experiments [Deser et al., 2010; Kumar
et al., 2010; Screen et al., 2012b]. Our results indicate thatwhile SIE alone can cause such a response, other forcings intandem with SIE can result in a spatial pattern similar towhat has been observed [e.g., see Serreze et al., 2009,
Figure 3b; Screen et al., 2012a].
[19] Response of QBOT to the loss of September Arctic
SIE during ON, DJ and FM is similar to the responses of
SAT (compare Figures 3 and 4). Figures 4a–4c confirm an
increase in moisture over the Arctic Ocean and over Siberia
mainly during ON in all three experiments, which is intuitivefrom significant warming as seen in Figures 3a–3c over thesame region. The increased QBOT in the atmosphere provides a mechanism for increased precipitation and snowfallin this region associated with changing sea ice conditions.
[20] Therefore, we investigate whether this model showsthe expected precipitation changes. In fact, a decrease in
September SIE is associated with a significant increase inprecipitation over the Arctic Ocean and adjacent Siberia
during ON in the experiments with time-varying sea ice
forcing (Figures 5a–5c). The experiments without timevarying sea ice forcing show no such signal (not shown).
There is some persistence of the significantly increased
precipitation signal into DJ in the “GHG + SST + ICE” and“SST + ICE” experiments, but not in the “ICE” experiment.
[21] There are other regions with significant precipitation
responses that are unique to each of the five experiments.
For example, in “GHG + SST + ICE,” precipitation increases over the western North Pacific Ocean and over eastern
Eurasia during DJ and FM associated with retreating summer SIE (Figures 5d and 5g). In “SST + ICE,” we find a FM
precipitation signal that is mainly centered over the Arctic

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D23108Figure 3. As in Figure 2 but for SAT ( C/106 km2).
Ocean and over the Eurasian landmasses (Figure 5h).
Regression plots for the Tropical SST experiment (not shown)show an increase in precipitation over the North Pacific and adecrease over part of Eurasia during DJ following summer SIE
loss. A more detailed discussion of these changes is beyond
the scope of this manuscript.
[22] Regression patterns of snowfall are consistent withthe precipitation patterns (compare Figures 5 and 6). The
experiments with time-varying sea ice forcing show anincrease in snowfall associated with decreasing sea ice over
the Arctic Ocean and over adjacent Siberia during ON(Figures 6a–6c). Furthermore, a decrease in snowfall is foundover western Eurasia during ON and DJ in “GHG+SST+ICE”
experiment and during ON in “SST+ICE” experiment.
[23] Regional time series of SAT and PRECIP over
Siberia (60 N–75 N, 95 E–135 E) are shown in Figure 7.
This region coincides with the area where a loss of September SIE corresponds to an increase in SNOWDP (recall
Figure 2). Temperature trends over Siberia appear, at firstglance, to be related to radiative and SST forcing, but not to
sea ice forcing: significant temperature trends are found onlyin those experiments where ice forcing is complemented byother forcings (Table 2). However, this may be due to thefact that, while the other forcings have trends during theentire period of this analysis, the sea ice forcing becomesstrong only after 2000. Figure 7 shows that during the more
recent period, Siberian temperature and precipitation bothincrease in concert with decreasing sea ice extent. In experiments without time-varying sea ice forcing, no significant
trend is found with the exception of “GHG” during DJ(Table 2).
[24] In the experiments with time-varying sea ice forcing,
precipitation trends over Siberia are significant and positive
during ON (Table 2). In those without sea ice forcing, nosignificant precipitation trends are found over Siberia during
this season. There is no indication that sea ice alone is related
to precipitation variations in December through March.
[25] As a result of these temperature and precipitation
signals, we find statistically significant positive trends inSiberian snow depth in “GHG+SST+ICE” during DJ andFM; in “SST+ICE” during FM; and in “ICE” during ON(Table 2). Despite the positive temperature trends over

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D23108Figure 4. As in Figure 2 but for QBOT (gm/kg/106 km2).
Siberia, monthly average temperatures remain below 10 C(Figure 7a), resulting in atmospheric conditions that remainfavorable for precipitation falling as snow (Figure 6), andfor the persistence of snow on the ground. This explains why
the trend in SNOWDP becomes stronger during FM in“GHG+SST+ICE” and “SST+ICE,” as is observed in
Figure 2 as well. Synergistic effects between SST and sea ice
trends may result in an extension of the duration of the
SNOWDP signal into late winter in these experiments
compared to the “ICE” experiment, but further investigationis needed to substantiate this claim. In the “GHG” and“Tropical SST” experiments, we find no significant trend in
Siberian SNOWDP in any season (Table 2).

4. Conclusions and Discussion[26] We have presented results from a suite of five CAM3/
CLM experiments designed to isolate the impact of changes
in sea ice and SSTs in the Arctic Ocean and marginal seas on
Siberian snow cover. The experiments include two groupsof forcings—radiative and surface boundary—in different
combinations. Snow depth is found to increase over central
and eastern Siberia following summer sea ice loss only inthose experiments which include time-varying Arctic sea ice
and SSTs (e.g., “GHG+SST+ICE,” “SST+ICE” and “ICE”):
the other experiments show no such signal. This leads us toconclude that, according to the CAM3/CLM model, Arctic
surface boundary forcing plays a major role in generating
this Siberian snow increase. These results are consistent withthe observational findings of Ghatak et al. [2010] whichshow an empirical connection between summer Arctic sea
ice loss and autumn/early winter snow cover duration over
Siberia.
[27] Statistically significant warming is found over theArctic Ocean as well as over Siberia. This suggests a positive feedback in which the loss of summer sea ice results in
atmospheric surface level warming over the Arctic Ocean
and Eurasia. These results are consistent with the recent
warming observed over the Arctic primarily during autumn[Serreze et al., 2009; Screen and Simmonds, 2010]. This
suite of experiments suggests that the effect of sea ice variations on temperature, water vapor content, precipitationand snowfall over the Arctic Ocean and Siberia is mostextensive during ON. Stroeve et al. [2011] suggest more
frequent and intensified cyclones over the Atlantic sector of
the Arctic during years of low sea ice; this leads to an

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D23108Figure 5. As in Figure 2 but for PRECIP (mm d 1/106 km2).
increase in autumn cyclone associated precipitation, whichis also apparent over Eurasia. Screen and Simmonds[2012] also showed an increase in Arctic precipitation in
September–October–November.
[28] Increased snow cover over Siberia in these threeexperiments is caused by increased surface air temperatureand precipitation during ON. The temperature connection
may seem counterintuitive, but in a scenario with significant
warming, when average monthly temperatures remain below
freezing during the snow season, as they do in this region,
conditions are favorable for increased snowfall and more
persistent snow on ground. Atmospheric warming over theseregions increases the availability of moisture, facilitating
increased snow fall and a deeper snowpack over Siberia.
[29] In the experiment with only Arctic Ocean surface
forcing, the snow signal over Siberia is apparent. In the twoother experiments that include Arctic Ocean surface forcingsplus other surface and radiative forcings, the signal is moregeographically extensive during winter. In the experimentswith no Arctic Ocean surface forcing, no Siberian snow
signal is apparent. Thus, Arctic Ocean surface forcing isnecessary and sufficient to produce a Siberian snow signal,
but other surface and radiative boundary forcings can modulate the magnitude and geographic extent of the signal.

[30] It should be noted that our conclusions are based
solely on CAM3/CLM simulations and thus may be modeldependent. For example, a recent study that performed
similar “ICE” experiments with the UK-Australian Unified
Model version 7.3 (UM7.3) finds considerably weaker fall/
winter warming over Siberia in UM7.3 compared to CAM3
as well as somewhat different precipitation responses [Screen
et al., 2012b].
[31] This study addresses one aspect of a feedback cyclethat has previously been identified in the high-latitude climate system. Other studies have established a remote teleconnection between autumn Siberian snow cover and thefollowing winter’s Northern Annular Mode atmosphericcirculation pattern [Gong et al., 2003; Fletcher et al., 2009;
Cohen et al., 2012]. In fact, a statistical model using Octobermean snow cover was able to generate a better forecast of thewinter air temperature over the extra-tropical Northern
Hemisphere than a dynamical forecast system [Cohen and
Fletcher, 2007]. Our study contributes to our understanding of the large-scale feedback system by explaining the
response of fall snow to the loss of summer sea ice loss. It is
hoped that additional examination of these model results willshed more light on the physical mechanisms responsible forthe relationships identified in this study.

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Figure 6. As in Figure 2 but for SNF (mm d 1/106 km2).

Figure 7. Time series of October–November (a) SAT and (b) PRECIP over Siberia (60 N–75 N and95 E–135 E) from “GHG+SST+ICE” (solid line) and “ICE” (dashed line) superimposed upon theobserved September SIE time series (dashed dotted line). The y axis scale on the left in Figure 7a indicates
SAT ( C) and in Figure 7b indicates PRECIP (mm/d). The y axis scale on the right of both indicates sea ice
extent (million sq km). Note that sea ice extent is plotted with reversed sign.
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Table 2. Linear Trend Coefficients for Seasonal Surface Air Temperature (SAT,  C/Decade), Precipitation (PRECIP, mm/Day/Decade),and Snow Depth (SNOWDP, cm/Decade) Over Siberia Between 1979 and 2008a
October–November

GHG+SST+ICE
SST+ICE
ICE
GHG
Tropical SST

December–January

February–MarchSAT

PRECIP

SNOWDPSAT

PRECIP

SNOWDPSAT

PRECIP

SNOWDP

1.09 (0.000)
0.80 (0.005)
0.34 (0.135)
0.16 (0.379)
0.25 (0.131)

0.07 (0.001)
0.05 (0.010)
0.04 (0.025)
0.000 (0.865)
0.01 (0.407)

0.29 (0.281)
0.05 (0.862)
0.70 (0.033)
0.25 (0.362)
0.13 (0.653)

0.73 (0.003)
0.54 (0.053)
0.04 (0.894)
0.51 (0.053)
0.17 (0.546)

0.02 (0.057)
0.01 (0.289)
0.01 (0.587)
0.03 (0.053)
0.02 (0.052)

0.96 (0.014)
0.71 (0.090)
0.69 (0.068)
0.03 (0.933)
0.22 (0.594)

0.04 (0.873)
0.98 (0.001)
0.41 (0.081)
0.11 (0.699)
0.16 (0.503)

5.50E-04 (0.971)
0.05 (0.007)
0.01 (0.620)
0.01 (0.545)
0.000 (0.919)

1.04 (0.017)
1.29 (0.009)
0.24 (0.548)
0.29 (0.385)
0.34 (0.461)

aSignificance levels are shown in parentheses; values in bold are significant at p < 0.05 (at least 5% confidence).

[32] Acknowledgments. This work has been supported by the NASA
Cryospheric Sciences Division (NNX08AQ70G), NASA MEaSUREsaward NNXO8AP34A, and NOAA grant NA08OAR4310678. Part of thefunding for Debjani Ghatak came from an Institute of Marine and Coastal
Sciences postdoctoral fellowship. James Miller is thanked for helpful discussions. NCAR is sponsored by the National Science Foundation. C. Desergratefully acknowledges support from the NSF Office of Polar Programs.
We thank the reviewers for their constructive comments, which improved
the quality of the manuscript.

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