Journal of Glaciology, Vol. 58, No. 209, 2012 doi: 10.3189/2012JoG11J201

529

Snow density along the route traversed by the Japanese–Swedish
Antarctic Expedition 2007/08
Shin SUGIYAMA,1 Hiroyuki ENOMOTO,2 Shuji FUJITA,2 Kotaro FUKUI,3
Fumio NAKAZAWA,2 Per HOLMLUND,4 Sylviane SURDYK2
1

Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan
E-mail: sugishin@lowtem.hokudai.ac.jp
2
National Institute of Polar Research, Tachikawa, Japan
3
Tateyama Caldera Sabo Museum, Toyama, Japan
4
Department of Physical Geography and Quaternary Geology, Stockholm University, Stockholm, Sweden
ABSTRACT. During the Japanese–Swedish Antarctic traverse expedition of 2007/08, we measured thesurface snow density at 46 locations along the 2800 km long route from Syowa station to Wasa stationin East Antarctica. The mean snow density for the upper 1 (or 0.5) m layer varied from 333 to
439 kg m–3 over a region spanning an elevation range of 365–3800 m a.s.l. The density variations wereassociated with the elevation of the sampling sites; the density decreased as the elevation increased,moving from the coastal region inland. However, the density was relatively insensitive to the change inelevation along the ridge on the Antarctic plateau between Dome F and Kohnen stations. Because
surface wind is weak in this region, irrespective of elevation, the wind speed was suggested to play a
key role in the near-surface densification. The results of multiple regression performed on the density
using meteorological variables were significantly improved by the inclusion of wind speed as apredictor. The regression analysis yielded a linear dependence between the density and the windspeed, with a coefficient of 13.5 kg m–3 (m s–1)–1. This relationship is nearly three times stronger than avalue previously computed from a dataset available in Antarctica. Our data indicate that the windspeed is more important to estimates of the surface snow density in Antarctica than has beenpreviously assumed.

1. INTRODUCTION
Knowledge of the near-surface snow density is essential forthe study of surface processes in the Antarctic ice sheet. For
instance, the snow density is required to calculate thesurface mass balance from stake measurements (e.g.
Hubbard and Glasser, 2005; Takahashi and Kameda, 2007)
and to interpret satellite/airborne altimetry (e.g. Zwally andLi, 2002; Helsen and others, 2008). The density is also
important for various types of satellite data analyses because
it is directly related to the dielectric permittivity of snow (e.g.
Tiuri and others, 1984), which controls the emission andreflection of electromagnetic waves (e.g. Grody, 2008;
Lacroix and others, 2009). Further, snow densificationaccompanies changes in the size, structure and bonding ofsnow particles, properties which are important for finding
climatic signals in deep ice cores.
With these factors as motivation, surface snow density
and the densification process have been studied by numerous observational (field and satellite), theoretical andnumerical methods. The snow density has been measured
at many locations in Antarctica (e.g. Cameron and others,
1968; Endo and Fujiwara, 1973; Braaten, 1997; Oerter andothers, 1999; Eisen and others, 2008; Kameda and others,2008). Near-surface densification processes have beenstudied by measuring the vertical density profiles of firncores and shallow pits (e.g. Alley, 1988; Craven and Allison,
1998; Van den Broeke and others, 1999; Fujita and others,
2009; Vihma and others, 2011). These field data have beenused to develop and calibrate numerous snow densification
models (e.g. Herron and Langway, 1980; Kameda andothers, 1994; Spencer and others, 2001).

Since the direct determination of snow density fromsatellite data remains difficult, firn-core analyses and pit
measurements are the only means to obtain reliable data.
However, it is difficult to cover a large area using point
measurements, and the measurement sites are not uniformly distributed throughout Antarctica. To overcome
these problems, densification models have been coupledwith climate and meteorological models to compute thedensity distribution over the entire ice sheet. Density maps
obtained using these models have contributed to ourunderstanding of the age of gases contained in ice cores(Kaspers and others, 2004), firn thickness (Van den Broeke,2008) and ice-sheet elevation change (Helsen and others,2008). Such an approach requires an accurate densification model calibrated with field data, and the modeled
density map needs to be validated through comparison
with field observations.
Despite the need for reliable near-surface snow density
data, previously reported field measurements are insufficient
to cover the large variability in meteorological and geometrical conditions in Antarctica. Data are particularly
scarce for the interior of the ice sheet. To contribute to ourunderstanding of the snow density distribution and densification processes in Antarctica, we carried out shallow pit
measurements during the Japanese–Swedish traverse expedition in the 2007/08 austral summer. In this paper, we
report upper 1 (or 0.5) m density profiles collected at
46 survey sites, from coastal to inland plateau areas in East
Antarctica. The influence of meteorological variables onnear-surface densification is discussed in light of the density
variation measured along the 2800 km long route.

530

Sugiyama and others: Snow density along the JASE traverse routeFig. 1. Map of the study region, along with the route traversed by the Japanese–Swedish Antarctic Expedition in 2007/08. Open circles
denote the locations of snow-pit measurements. Red circles denote measurement sites referred to in the text. The contours represent surface
elevation at intervals of 200 m, based on Bamber and others (2009).

2. METHOD
2.1. Study site
From November 2007 to January 2008, Japanese andSwedish research teams carried out a traverse expeditionin East Antarctica, between the Japanese inland base S16(30 km from Syowa station; 69.038 S, 40.058 E; 589 m a.s.l.)and the Swedish Wasa station (73.058 S, 13.378 W; 292 ma.s.l.) (Fig. 1; Table 1) (Holmlund and Fujita, 2009). The two
teams departed from their home stations in snow vehicles
and met at a point 1400 km from each of the starting points(75.898 S, 25.838 E; 3661 m a.s.l.). They exchanged some
expedition members and scientific instruments before they
began their return trips. Accordingly, various research
activities could be carried out along the entire stretch from
S16 to Wasa (Sugiyama and others, 2010; Fujita and others,2011).
During the expedition, we measured the density, grain
size and structure of snow in the near-surface layer at
46 locations along the route (to 1 m depth at 35 locations
and to 0.5 m at 11 locations) (Fig. 1). The first survey was
made at Z8 (180 km inland from S16) on 17 November
2007. The snow survey was repeated along the rest of the
expedition route (covering >2650 km), with measurements
taken at intervals of approximately 20–100 km until the final
measurement was completed at AWS5 near Wasa station, on

Table 1. Sites along the expedition route referred to in the text
Site

S16
Z8
MD228
Dome F
91B
C107
KohnenAWS5

Lat.

69.038 S
70.088 S
72.798 S
77.328 S
76.058 S
74.978 S
75.008 S
73.118 S

Long.

40.058 E
43.248 E
43.528 E
39.708 E
22.748 E
12.898 E
0.018 E13.178 W

Elevation

Distance from S16

m

km

589
1991
2960
3800
3595
3424
2890
365

0
180
495
1006
1497
1831
2202
2823

28 January 2008. The elevation of the measurement sites
increased from 1991 m (Z8) to 3800 m (Dome F), thengradually decreased to 2500 m at the point 290 km west of
Kohnen station. From this point, the route descended steeply
from the Antarctic plateau to the coastal region. Annual
snow layer thicknesses near the surface have previously
been measured at 50–400 mm for the section between Z8
and Dome F (Furukawa and others, 1996), 140–250 mm in
the vicinity of Kohnen (Oerter and others, 1999) and
550  110 mm for the coastal region near Wasa (Kärkäsand others, 2005). The uppermost 1 m of snow cover
therefore consists of several annual layers, in some cases
more than ten.

2.2. Measurements
Snow measurements were performed on a side-wall of asnow pit excavated at each survey site. We measured thedensity by sampling a 30 mm thick snow block using a boxtype stainless density cutter (30 mm  60 mm  56 mm,
Climate Engineering Co.). A digital bench scale (CS200,
OHAUS Co.) with a resolution of 0.1 g was used to weighthe snow blocks. This error corresponds to <1% of measured
weight. The accuracy for density measurements performed
with similar devices and procedures has been reported as
4% (Conger and McClung, 2009). The sampling was
performed from the surface to 1 (or 0.5) m depth, at intervalsof 30 mm.
Stratigraphical information was recorded following theguidelines given by Colbeck and others (1990). We
identified layers after a visual inspection of the snow
structure and grain size. The snow structure was classified
into four types: fresh snow; compacted snow (Fig. 2a);faceted crystals or depth hoar (we refer to both types ofstructure as depth hoar in the remainder of this paper)(Fig. 2b); and a crust layer (Fig. 2c). The compacted snow
layers include those compacted under overburden stress aswell as wind compaction. The crust layers are less thanseveral millimeters thick well-bonded snow, formed probably at the surface due to radiation or wind. The grain size
was measured using a magnifier on a plastic plate ruled inmillimeters. Several snow samples were taken from eachstratigraphic layer, to allow precise measurement of theSugiyama and others: Snow density along the JASE traverse route531

average grain size. The permittivity of snow was alsomeasured using a snow fork (a parallel-wire transmissionline resonator), whose results are presented elsewhere(Sugiyama and others, 2010).

3. RESULTS
Figure 3 shows vertical profiles of snow density, grain size
and the four structures obtained at six selected locations as
typical examples of the data. At MD228 (2960 m a.s.l.;
495 km from S16), on the slope ascending from S16 to
Dome F, the snow was relatively dense (mean density
425 kg m–3), fine-grained (mean grain size 0.33 mm) anddominated by a compacted snow structure (Fig. 3a). At themeasurement sites located inland (Dome F, 91B, C107 andKohnen), the snow was characterized by depth-hoar
structures consisting of medium (0.5–1.0 mm) to coarse(1.0–2.0 mm) grains (Fig. 3b–e). The grain size increased
with depth in the pits. Several compacted snow layers were
scattered in the snowpack, forming relatively dense and finegrained layers. At C107, for example, there were threecompacted snow layers at depth ranges of 0.17–0.35, 0.40–
0.42 and 0.81–0.96 m from the surface. All three layers
were dense and fine-grained (Fig. 3d). Some of these were
exceptionally hard and dense, with tightly bound fine snow
grains (0.2–0.5 mm) (Fig. 2a). Crust layers several millimeters
thick were frequently observed in this region (Fig. 2c). In the
coastal region near Wasa station (AWS5), the snow properties were similar to those at MD228 in terms of high density(419 kg m–3) and domination by compacted snow (Fig. 3f).
Figure 4 displays vertical profiles of snow density, grain
size and stratification obtained over the traverse route. The
plot shows an inhomogeneous distribution of snow properties in the horizontal and vertical directions. In general, thedensity decreased from the coastal region towards Dome F,
and increased from Kohnen to Wasa (Fig. 4a). However, agiven density profile obtained at one site was not necessarily
similar to those obtained at nearby sites. For example, very
high-density layers (>500 kg m–3) were observed in a few
inland profiles (1000–2000 km from S16), but vertical
positions of the layers do not correspond. Thus, it was very
difficult to define a characteristic high- or low-density layerthat could be interpreted as an isochrone.
The grain size generally increased with distance from thesurface (Fig. 4b). Grains were <0.5 mm near the surface, butranged from 1.0 to 3.0 mm at depths of 1 m. Inland snow
was more dominated by depth-hoar structures, while
compacted snow structure accounted for a larger proportion
near the coast (Fig. 4c). As observed in the density data,neighboring stratifications did not correspond with eachother. The vertical positions of compacted snow layers andcrust layers were not continuously distributed in thehorizontal direction.
The mean density variations along the route showedinteresting features when compared with the surface elevation (Fig. 5a and b). On the route ascending from Z8 to DomeF, the mean density was relatively constant until around
MD228, with values in the range 403–439 kg m–3. After
MD228, we observed a marked decrease in mean density as
we approached Dome F. This density decrease at 3000 ma.s.l. has been reported in previous measurements performed
along the same route (Endo and Fujiwara, 1973; Yamada and
Watanabe, 1978; Shiraiwa and others, 1996). The decreasein density is not consistent with the steadily increasing

Fig. 2. Photographs of characteristic snow structures: (a) very hardcompacted snow, (b) depth hoar and (c) crust layers indicated by
the arrows.

surface elevation along this section. The mean density was
nearly constant from Dome F to Kohnen, with values ranging
from 333 to 375 kg m–3, whereas the elevation drops from3800 to 2890 m. The overall mean density between Dome F
and Kohnen was 351 kg m–3, 17% lower than the meandensity between Z8 and MD228 (421 kg m–3). The density
increased from Kohnen towards the margin of the Antarctic
plateau, and exceeded 400 kg m–3 after the steep descentfrom the plateau to the coastal region. Similar density
variations have previously been reported in this region (Vanden Broeke and others, 1999).
The changes in grain size and snow structure in the vicinityof Dome F corresponded with the density variations. The
grain size increased from MD228 to Dome F, then remained
fairly steady between Dome F and Kohnen (Fig. 5c). Thischange can be seen by comparing the regression lines
obtained for the two sections. Similarly, the proportion ofcompacted snow layers decreased from MD228 to Dome F,
but was relatively constant between Dome F and Kohnen(Fig. 5d). In general, the compacted snow-layer proportion
varied in a manner similar to the density over the entire
traverse route. It was also observed that the number of crustlayers increased at around Dome F and remained large until
the route descended towards Wasa (Fig. 5e).

4. DISCUSSION
4.1. Spatial heterogeneity
Within each of the snow pits, individual density measurements showed large variations (Fig. 5b). These signals in the

532

Sugiyama and others: Snow density along the JASE traverse routeFig. 3. Density (red line), grain size (blue line) and stratigraphy measured at (a) MD228, (b) Dome F, (c) 91B, (d) C107, (e) Kohnen and(f) AWS5 (see Fig. 1 and Table 1 for locations). The snow structures are classified as faceted crystals and depth hoar (magenta), compacted
snow (cyan) or a crust layer (red line).

vertical density profiles are often useful to study seasonal/
annual variations and events in meteorological conditions.
However, the standard deviation of density measurements ateach site ranged from 17 to 62 kg m–3, with no significant
trend along the expedition route (Fig. 5e). The frequency andlocations of the high-density layers were not consistentamong nearby survey sites. These observations suggest
spatial heterogeneity in the depositional processes, on arelatively small scale.
On the scale of 1–10 m, the density profile is influenced
by non-uniformly distributed snow surface features such as
sastrugi and small-scale dunes (e.g. Watanabe, 1978;
Frezzotti and others, 2005). These features are created bywind-driven deposition and erosion. Even when the layering
is uniform over a 10 m scale, it is often difficult to crosscorrelate layers observed at locations separated by 100 m toseveral hundred meters (Sturm and Benson, 2004). Depositional conditions are strongly affected by the wind,which is controlled by the ice-sheet surface topography on
scales greater than several kilometers (e.g. Furukawa andothers, 1996; Frezzotti and others, 2007). Since spatial
scales of these variabilities are smaller than the intervals of
our pits, it is not straightforward to use the vertical density
profiles to understand temporal changes in meteorologicaland depositional conditions. In the remainder of the paper,
we focus our attention on the vertically integrated meandensity, and discuss its variations along the route and therole of meteorological conditions in this variable.

4.2. Elevation/temperature effectChanges in the upper 1 (or 0.5) m mean density wereassociated with changes in the surface elevation along theroute (Fig. 5a and b). The mean density decreased as theelevation increased from MD228 to Dome F, but this valuechanged only a little between Dome F and Kohnen. This isbecause our route after Dome F followed a ridge with

Sugiyama and others: Snow density along the JASE traverse route533

Fig. 4. Vertical profiles of (a) snow density, (b) grain size and (c) stratigraphy measured at 46 locations along the expedition route.

relatively constant elevation on the Antarctic plateau(Fig. 1). The density increased again when the route
descended from Kohnen to Wasa. Accordingly, we obtainan inverse relationship between the mean density andelevation (Fig. 6a). A likely interpretation of this relationshipconcerns the surface temperature, which is known to play a
key role in snow densification in polar regions (e.g. Herronand Langway, 1980; Kameda and others, 1994; Arthern andWingham, 1998; Li and Zwally, 2002). Kaspers and others(2004) compiled near-surface snow density data from
Antarctica, and found that surface temperature was themeteorological variable that most influenced the density.
We obtain essentially the same plot when the density isplotted against the 10 m depth snow temperature (Fig. 6b).
The temperatures used here are based on the 10 m depth
snow temperature measured at 25 locations during our
expedition, and the annual mean air temperatures measured
at Dome F (Takahashi and others, 2004) and Kohnen (Vanden Broeke and others, 2004). We calculated the temperature at density measurement sites by assuming a piecewise
linear relationship between the temperature and surface
elevation. That is, the relationships were determined
separately for the three sections S16–Dome F, Dome F–
Kohnen and Kohnen–Wasa. Also shown in Figure 6a and b
are near-surface snow densities previously reported for thelocations in Antarctica indicated in Figure 7 (Kaspers andothers, 2004, and references therein).
The effect of air temperature on the surface snow density
is reasonable, but the relationship between these variables

was not simple in our data. It is clear from Figure 6a and bthat the forms of the density–elevation/temperature
relationships differ along the sections ascending from
MD228 to Dome F and descending from Dome F to Wasa.
As described in Section 3, the density was relatively
insensitive to elevation change from Dome F to Kohnen.
This segment of the path generates the hysteresis loop in
the density–elevation/temperature plots, and implies thatthe density variation was significantly influenced by additional variables. For instance, the accumulation rate is as
important as the temperature in several densification
theories and empirical models (e.g. Herron and Langway,
1980; Kameda and others, 1994; Arthern and Wingham,
1998; Li and Zwally, 2002). Thus, we compared ourdensity data with accumulation rates reported along theroute. The accumulation data were obtained using stakemeasurements for the section between S16 and Dome F(Motoyama and others, 2008), ice and snow radar surveys
between Dome F and Kohnen (Fujita and others, 2011) andfirn-core studies between Kohnen and Wasa (Rotschky andothers, 2007). According to these data, the accumulation
rate decreased from S16 to Dome F, and gradually
increased towards Kohnen (Fig. 5f). A scatter plot shows
good correlation between the density and accumulation
rate measured from Z8 to Dome F. Nevertheless, thedensity was rather insensitive to the accumulation rateincrease from Dome F to Kohnen (Fig. 6c). Thus, theaccumulation rate does not adequately explain the density
variation along this section.

534

Sugiyama and others: Snow density along the JASE traverse routeFig. 5. (a) Surface elevation (blue) and slope (red); (b) snow density; (c) grain size; (d) proportion of fresh snow (yellow), depth hoar (cyan)
and compacted snow (magenta); (e) standard deviation of density (blue) and frequency of crust layers; and (f) snow accumulation rate (blue)and 10 m depth snow temperature (red) along the expedition route. In (b), individual measurements and mean values are denoted by black
dots and red circles respectively. The red lines in (b–d) are linear regressions of the mean density, grain size and the proportion of compacted
snow, performed for the sections Z8–MD228, MD228–Dome F and Dome F–Kohnen. The surface slope in (a) was determined using thedigital elevation model by Bamber and others (2009).

4.3. Wind-speed effectAnother meteorological variable relevant to surficial snow
densification is wind speed. The wind-speed pattern over
the Antarctic ice sheet is strongly affected by topography, inparticular the surface slope (Parish and Bromwich, 1987).

Along the expedition route, the surface slope progressively
decreased from S16 to Dome F, and was relatively flat
thereafter (Fig. 5a). The surface slope and density showedsimilar variations between MD228 and Kohnen (Fig. 5a andb), suggesting that the wind influenced the densification. In

Sugiyama and others: Snow density along the JASE traverse route535

Fig. 7. Locations of the snow density measurements in this study(red cross) and previously reported data compiled by Kaspers andothers (2004) (blue markers). The sources of the previous data are
Kaspers and others (2004) (); Sturges and others (2001) (); Butler
and others (1999) (?); Trudinger and others (1997) (); Bender andothers (1994) (&); Gow (1968) (4); Herron and Langway (1980) ();Van den Broeke and others (1999) (); and Cameron and others(1968) (+).

Fig. 6. Scatter plots of the density vs (a) surface elevation, (b) 10 m
depth snow temperature and (c) accumulation rate. The dataobtained in the sections Z8–Dome F and Dome F–Wasa are
denoted by red and blue circles respectively. Field data previously
reported in Antarctica (Kaspers and others, 2004 and references
therein) are indicated by the grey markers in Figure 7.

Figure 8a, the expedition route is superimposed on a 10 m
annual mean wind-speed map generated by a climate
model. This map was constructed from the 20 year (1989–2009) averaged output of a regional atmospheric climate
model with a horizontal resolution of 27 km (Lenaerts andVan den Broeke, 2012). The computed wind patterngenerally represents a topographically controlled katabatic
wind system, but also includes the effects of sporadic strong
wind events. The first half of the section between S16 andDome F (the windiest region along our expedition route)
was characterized as a strong katabatic wind zone. The
wind speed was >8 m s–1 from the first measurement site Z8(180 km from S16) to 128 km inland of MD228 (623 kmfrom S16). The wind speed rapidly decreased inland,
reaching a minimum value near Dome F (5.0 m s–1). These
relatively weak wind conditions extended along the ridge
towards Kohnen. The measured density correlated well withthe simulated wind speed, with a correlation coefficientr = 0.79 (Fig. 8b).
It has been recognized that the wind, as well as temperature, plays a critical role in snow densification in polar
regions. Snow grains near the surface are rounded by thewind. This process enhances grain settling and packing, thedominant mechanisms in the initial stage of dry snow
densification (e.g. Anderson and Benson, 1963; Herron andLangway, 1980). Strong wind conditions cause moisturetransfer within a snowpack, which facilitates wind slab
formation over a depth-hoar layer (Benson, 1967). Moreover,snow density is affected by features such as sastrugi, dunes
and glazed surfaces, all of which are formed through erosionand deposition processes driven by strong katabatic winds(e.g. Watanabe, 1978). The effects of the wind were also
observed in the grain size and snow structure. The grain size
increased and the fraction of compacted snow decreased
from MD228 to Dome F (Fig. 5c and d). These variations canbe explained by the degree of wind compaction, which was
expected to decrease towards Dome F.
The influence of wind on surface snow density has been
pointed out in previous studies in Antarctica. Based on firncore analyses at widely spread sampling sites in Antarctica,
Craven and Allison (1998) concluded that a 5 m s–1 windspeed increase and a 108C temperature increase haveequivalent effects on the density. Relying on the relationships
between snow density and meteorological variables derived
by Craven and Allison (1998), Van den Broeke and others(1999) reconstructed a wind-speed pattern from firn density
profiles in Dronning Maud Land. According to their analysis,
the density at 1 m depth should increase by 8 kg m–3 for eachwind-speed increase of 1 m s–1. This ratio is approximately
half the value observed in this study (16.0 kg m–3 for every
1 m s–1) (Fig. 8b). In addition to the studies just cited, rapid
density increases during sporadic strong wind events havebeen reported in the interior of the ice sheet (Lacroix andothers, 2009; Birnbaum and others, 2010).
At the four measurement sites located within 220 km of
Wasa station, the density data deviated from the linear

536

Sugiyama and others: Snow density along the JASE traverse routeFig. 8. (a) Locations of snow-pit measurements superimposed on a contour map showing 10 m wind-speed distributions. The wind-speed
map represents the 20 year (1989–2009) averaged output of a regional atmospheric climate model (Lenaerts and Van den Broeke, 2012).
(b) Scatter plot of the density vs the wind speed. Markers are as in Figures 6 and 7.

relationship with wind speed (Fig. 8b). These sites werelocated at low elevation, near the coast (Fig. 5a and b). A
plausible reason for their difference could be that temperatures and accumulation rates are higher near the coast.
Another possible interpretation is that sporadic wind eventsoccur more frequently in coastal regions, enhancing thedensification. Such short-term events would affect theannual mean wind speed only a little.

4.4. Parameterization
Kaspers and others (2004) proposed the following parameterization of surface snow density 0 with respect tometeorological variables:0 ¼  þ T þ A þ "W ,

ð1Þwhere T is the annual mean surface temperature, A is theaccumulation rate and W is the annual mean wind speed.
They calibrated the parameters , ,  and " by fitting theformula to density and meteorological datasets previously
obtained at the locations indicated by blue markers in
Figure 7 (Kaspers and others, 2004, and references therein).
The data used for their calibration included snow density,
10 m depth snow temperature and accumulation ratemeasured in the field, as well as the 10 m wind speed
provided by a regional climate model. Despite the simplicityof this formula, the densities computed using Eqn (1) showedreasonable agreement with the measured values.

We calibrated Eqn (1) using our own snow densities and
10 m depth snow temperatures (this study; Van den Broeke
and others, 2004), accumulation rates (Rotschky and others,
2007; Motoyama and others, 2008; Fujita and others, 2011)
and annual mean wind speed (Lenaerts and Van den
Broeke, 2012), as described earlier in this section andshown in Figures 5f and 8a. Calibration parameters
obtained by the least-squares method are listed in Table 2,
along with correlation coefficients between the measured
and modeled densities. The densities estimated using ourown calibration of Eqn (1) correlate well with the field data,
as indicated by the blue circles in Figure 9a (correlation
coefficient r = 0.91, p < 10–6 and root-mean-square error
 = 13.1 kg m–3). The linear coefficient of wind speed was = 13.5 kg m–3 (m s–1)–1, nearly three times greater than thevalue (4.77 kg m–3 (m s–1)–1) reported by Kaspers and others(2004). The regression analysis yielded a very small
temperature coefficient ( = 0.543 kg m–3 8C–1), indicatingthat the contribution of temperature to the density variation
was small for the temperature range considered in our
analysis (T = 328C;  = 17.4 kg m–3). The importance of
the wind speed was confirmed by a second regression
analysis performed omitting the wind-speed term (Table 2).
The densities estimated by this model (two variables)
deviated more from the measurements (r = 0.68;  = 23.0 kgm–3) than the densities determined using Eqn (1) (threevariables) (Fig. 9a). Figure 10 shows the performance of bothSugiyama and others: Snow density along the JASE traverse route537

Fig. 10. Snow density measured along the traverse route () andcomputed with a linear regression model. The regression coefficientsin Eqn (1) were optimized for the data obtained in this study (bluesolid line) and for those including previously reported data (bluedashed line). The red lines (solid and dashed) were obtained byomitting the wind-speed term from Eqn (1).

Fig. 9. (a) Scatter plot of the densities measured in this study andthose estimated from meteorological variables. (b) The same plot,
but using a dataset including the densities reported by Kaspers andothers (2004). The circles were estimated from the 10 m depth snow
temperature, accumulation rate and wind speed. The crosses show
estimates from the 10 m depth snow temperatures and accumulation rates only.

regression analyses. The density variations from the inland
plateau to the coastal regions are better reproduced by themodel that includes the wind speed. Without the windspeed, the deviation from the observed data increases
progressively from Dome F towards Wasa.
The major influence of the wind speed on the surface
density was reported by Endo and Fujiwara (1973) in datacollected during their traverse expedition from Syowa to theSouth Pole in 1968/69. The mean density from the surface to
2 m depth correlated well with the surface slope along theroute. Using a relationship between the slope and theannual mean wind speed, they derived a linear dependence of the density on the wind speed, with a value of
14 kg m–3 (m s–1)–1. Their coefficient agrees very well withthe value obtained in this study. They also found that thedensity slightly increased as the temperature decreased,within regions where the mean wind speed was less than
10–11 m s–1 and the temperature ranged from –60 to –488C.
Endo and Fujiwara (1973) argued that snowflakes are
smaller in colder air conditions, which makes the initialsnow density after deposition greater. Such an effect shouldbe particularly visible inland, where temperatures and windspeeds are relatively low. A negative dependence of thedensity on temperature has also been reported in seasonal
snow cover in the North American Arctic and Greenland(Bilello, 1967). The small temperature coefficient obtainedin our work is consistent with these studies.
We performed additional regression analyses including
the density and meteorological data compiled by Kaspers
and others (2004). However, the wind speeds in Kaspers’
original dataset were replaced with recent modeling results(Lenaerts and Van den Broeke, 2012). The parameters
determined for a combined dataset (46 sites from this study
and 49 sites compiled by Kaspers and others, 2004) (Fig. 7)
are listed in Table 2. The density is more accurately
estimated when we include the wind speed as a predictor,
as shown in Figure 9b (r = 0.75 and  = 20.7 kg m–3 for thethree-variable model, while r = 0.50 and  = 27.1 kg m–3 forthe two-variable model). Overall, the performance of theregression model worsened with the inclusion of Kaspers
and others’ (1994) data (Fig. 10), which were collected froma wide area of Antarctica. It is more difficult to reproduce
the clear change in the density from the coastal regions to
the inland area. This result implies that the relative importance of the three predictor variables differs significantly in

Table 2. Fitting equations, correlation coefficients (r), p-values and root-mean-square errors () obtained by multiple regression analysis of
the density ( in kg m–3), 10 m depth snow temperature (T in 8C), accumulation rate (A in mm a–1) and wind speed (W in m s–1), performed
with a dataset of size n
Equation

r

p


kg m

 = 305 + 0.629T + 0.150A + 13.5W
 = 515 + 2.93T – 0.106A
 = 284 + 0.170T + 0.0962 A + 13.8 W
 = 392 + 0.555T + 0.0851 A

0.91
0.72
0.75
0.50

<10–6
<10–6
<10–6
<10–6

n

Data source

46
46
95
95

This study
This study
This study and Kaspers and others (2004)
This study and Kaspers and others (2004)

–3

13.0
21.8
20.7
27.0

538

these regions, or that additional conditions with an impact
on the density are coming into play. Thus, care should betaken when a linear regression model is applied to an
extended region influenced by a broader range of meteorological and topographical conditions.

5. CONCLUSION
Density, structure and grain size were measured in the nearsurface snow layer at 46 locations along the route traversedin the Japanese–Swedish Antarctic Expedition 2007/08. The
route spanned two coastal stations in East Antarctica, Syowa
and Wasa. Its total length was 2800 km, and its altitudinalrange was 365–3800 m a.s.l. The mean density for the upper
1 (or 0.5) m layer varied from 333 to 439 kg m–3. In general,
the snow had a lower density at higher elevations and ininland locations; this variation was related to meteorologicalvariations between the coast and inland areas.
The elevation/temperature dependence of the density inthe section between Dome F and Kohnen stations was
significantly different from that in the section from thecoastal region to Dome F. Although the density variation
was fairly small from Dome F to Kohnen, the elevation
dropped significantly from 3800 to 2890 m a.s.l. The
traverse route between these two inland stations followed
a ridge on the Antarctic plateau, where the wind speed was
expected to be smaller than over other sections of theroute. Our density data showed a strong, positive correlation with wind-speed distributions computed using a
regional climate model (Lenaerts and Van den Broeke,2012), suggesting the importance of wind in the nearsurface densification in this region. The grain size increased
and the fraction of compacted snow decreased as we
ascended from the coastal region to Dome F, also
supporting the important effect of wind on snow metamorphism. Multiple regression analysis yielded a linear
dependence of the density on the wind speed. The
coefficient of this relationship is 13.5 kg m–3 (m s–1)–1,
nearly three times greater than a previously reported value(Kaspers and others, 2004). The strong influence of thewind on the density is consistent with field measurements
by Endo and Fujiwara (1973) during their traverse expedition to the South Pole. We conclude that wind-driven
densification is a more important process than has
previously been assumed in Antarctica.

ACKNOWLEDGEMENTS
We thank the members of the Japanese–Swedish Antarctic
Expedition 2007/08 for help in the field. The field activity
was part of the 48th and 49th Japanese Antarctic Research
Expedition, and was supported by the National Institute ofPolar Research, Tokyo, and the Swedish Polar Research
Secretariat. The wind-speed data, computed using a regional
climate model, were kindly provided by J.T.M. Lenaerts and
M.R. van den Broeke; we gratefully acknowledge theircontribution. The surface elevation and 10 m snow temperature data were provided by the US National Snow and Ice
Data Center. We thank two anonymous reviewers forcomments on the manuscript, and the scientific editor,
P. Bartelt. This study was funded by the Japanese Ministry of
Education, Science, Sports and Culture, Grant-in-Aid for
Scientific Research (A), 20241007, 2008–2010.

Sugiyama and others: Snow density along the JASE traverse route

REFERENCES
Alley RB (1988) Concerning the deposition and diagenesis of strata
in polar firn. J. Glaciol., 34(118), 283–290
Anderson DL and Benson CS (1963) The densification anddiagenesis of snow. In Kingery WD ed. Ice and snow: properties,
processes, and applications. MIT Press, Cambridge, MA,
391–411
Arthern RJ and Wingham DJ (1998) The natural fluctuations of firndensification and their effect on the geodetic determination ofice sheet mass balance. Climatic Change, 40(4), 605–624
Bamber JL, Gomez-Dans JL and Griggs JA (2009) Antarctic 1 km
Digital Elevation Model (DEM) from combined ERS-1 radar and
ICESat laser satellite altimetry. National Snow and Ice Data
Center, Boulder, CO. Digital media: http://nsidc.org/data/docs/
daac/nsidc0422_antarctic_1km_dem/index.html
Bender M and 6 others (1994) Climatic correlations between
Greenland and Antarctica during the past 100,000 years.
Nature, 373(6513), 393–394.
Benson CS (1967) Polar regions snow cover. In Oura H ed. Physics
of snow and ice. Institute of Low Temperature Science,
Hokkaido University, Sapporo, 1039–1063
Bilello MA (1967) Relationships between climate and regionalvariations in snow-cover density in North America. In Oura H
ed. Physics of snow and ice. Institute of Low Temperature
Science, Hokkaido University, Sapporo, 1015–1028
Birnbaum G and 14 others (2010) Strong-wind events and theirinfluence on the formation of snow dunes: observations from
Kohnen station, Dronning Maud Land, Antarctica. J. Glaciol.,56(199), 891–902 (doi: 10.3189/002214310794457272)
Braaten DA (1997) Detailed assessment of snow accumulation in
katabatic wind areas of the Ross Ice Shelf, Antarctica.
J. Geophys. Res., 102(D25), 30 047–30 058
Butler JH and 8 others (1999) A record of atmospheric halocarbons
during the twentieth century from polar firn air. Nature,399(6738), 749–755
Cameron RL, Picciotto E, Kane HS and Gliozzi J (1968) Glaciologyof the Queen Maud Land traverse, 1964–1965 South Pole – poleof relative inaccessibility. Inst. Polar Stud. Rep. 23
Colbeck SC and 7 others (1990) The international classification forseasonal snow on the ground. International Commission on
Snow and Ice, International Association of Scientific Hydrology,
Wallingford, Oxon
Conger SM and McClung DM (2009) Comparison of density cuttersfor snow profile observations. J. Glaciol., 55(189), 163–169 (doi:10.3189/002214309788609038)
Craven M and Allison I (1998) Firnification and the effectsof wind-packing on Antarctic snow. Ann. Glaciol., 27,
239–245
Eisen O and 15 others (2008) Ground-based measurements ofspatial and temporal variability of snow accumulation in East
Antarctica. Rev. Geophys., 46(RG2), RG2001 (doi: 10.1029/
2006RG000218)
Endo Y and Fujiwara K (1973) Characteristics of the snow cover in
East Antarctica along the route of the JARE South Pole traverseand factors controlling such characteristics. JARE Sci. Rep. 7,
1–27
Frezzotti M and 13 others (2005) Spatial and temporalvariability of snow accumulation in East Antarctica fromtraverse data. J. Glaciol., 51(172), 113–124 (doi: 10.3189/
172756505781829502)
Frezzotti M, Urbini S, Proposito M, Scarchilli C and Gandolfi S(2007) Spatial and temporal variability of surface mass balancenear Talos Dome, East Antarctica. J. Geophys. Res., 112(F2),F02032 (doi: 10.1029/2006JF000638)
Fujita S, Okuyama J, Hori A and Hondoh T (2009) Metamorphismof stratified firn at Dome Fuji, Antarctica: a mechanism for local
insolation modulation of gas transport conditions during bubble
close off. J. Geophys. Res., 114(F3), F03023 (doi: 10.1029/
2008JF001143)

Sugiyama and others: Snow density along the JASE traverse route

Fujita S and 25 others (2011) Spatial and temporal variability ofsnow accumulation rate on East Antarctic divide between ice
Dome Fuji and EPICA DML. Cryosphere, 5(4), 1057–1081 (doi:10.5194/tc-5-1057-2011)
Furukawa T, Kamiyama K and Maeno H (1996) Snow surface
features along the traverse route from the coast to Dome Fuji
Station, Queen Maud Land, Antarctica. Proc. NIPR Symp. Polar
Meteorol. Glaciol., 10, 13–24
Gow AJ (1968) Deep core studies of the accumulation anddensification of snow at Byrd Station and Little America V.
CRREL Res. Rep. 197
Grody N (2008) Relationship between snow parameters andmicrowave satellite measurements: theory compared with Advanced Microwave Sounding Unit observations from 23 to 150
GHz. J. Geophys. Res., 113(D22), D22108 (doi: 10.1029/
2007JD009685)
Helsen MM and 7 others (2008) Elevation changes in Antarctica
mainly determined by accumulation variability. Science,
320(5883), 1626–1629 (doi: 10.1126/science.1153894)
Herron MM and Langway CC, Jr (1980) Firn densification: anempirical model. J. Glaciol., 25(93), 373–385
Holmlund P and Fujita S (2009) Japanese–Swedish Antarctic
Expedition, JASE. In Thorén A ed. Swedish Polar Secretariat
Year Book 2008. Swedish Polar Research Secretariat, Stockholm,
18–21
Hubbard B and Glasser N (2005) Field techniques in glaciology andglacial geomorphology. Wiley, New York
Kameda T, Shoji H, Kawada K, Watanabe O and Clausen HB (1994)
An empirical relation between overburden pressure and firndensity. Ann. Glaciol., 20, 87–94
Kameda T, Motoyama H, Fujita S and Takahashi S (2008) Temporaland spatial variability of surface mass balance at Dome Fuji, East
Antarctica, by the stake method from 1995 to 2006. J. Glaciol.,54(184), 107–116 (doi: 10.3189/002214308784409062)
Kärkäs E, Martma T and Sonninen E (2005) Physical properties andstratigraphy of surface snow in western Dronning Maud Land,
Antarctica. Polar Res., 24(1–2), 55–67 (doi: 10.1111/j.17518369.2005.tb00140.x)
Kaspers KA, Van de Wal RSW, Van den Broeke MR, Schwander J,
Van Lipzig NPM and Brenninkmeijer CAM (2004) Modelcalculations of the age of firn air across the Antarctic continent.
Atmos. Chem. Phys., 4(5), 1365–1380 (doi: 10.5194/acp-41365-2004)
Lacroix P, Legrésy B, Rémy F, Blarel F, Picard G and Brucker L(2009) Rapid change of snow surface properties at Vostok, East
Antarctica, revealed by altimetry and radiometry. Remote Sens.
Environ., 113(12), 2633–2641 (doi: 10.1016/j.rse.2009.07.019)
Lenaerts JTM and Van den Broeke MR (2012) Modeliing drift snowin Antarctica with a regional climate model. 2. Results.
J. Geophys. Res., 117(D5), D05109 (doi: 10.1029/2010
JD015419)
Li J and Zwally HJ (2002) Modeled seasonal variations offirn density induced by steady-state surface air-temperature
cycle. Ann. Glaciol., 34, 299–302 (doi: 10.3189/
172756402781817707)
Motoyama H, Furukawa T and Nishio F (2008) Study of ice flow
observations in Shirase drainage basin and around Dome Fujiarea, East Antarctica, by differential GPS method. Antarct. Rec.,52, 216–231 [In Japanese with English summary]
Oerter H, Graf W, Wilhelms F, Minikin A and Miller H (1999)
Accumulation studies on Amundsenisen, Dronning Maud Land,by means of tritium, dielectric profiling and stable-isotope
measurements: first results from the 1995–96 and 1996–97

539

field seasons. Ann. Glaciol., 29, 1–9 (doi: 10.3189/
172756499781820914)
Parish TR and Bromwich DH (1987) The surface windfield over the
Antarctic ice sheets. Nature, 328(6125), 51–54
Rotschky G and 6 others (2007) A new surface accumulation map
for western Dronning Maud Land, Antarctica, from interpolationof point measurements. J. Glaciol., 53(182), 385–398 (doi:10.3189/002214307783258459)
Shiraiwa T, Shoji H, Saito T, Yokoyama K and Watanabe O (1996)
Structure and dielectric properties of surface snow along thetraverse route from coast to Dome Fuji Station, Queen Maud
Land, Antarctica. Proc. NIPR Symp. Polar Meteorol. Glaciol.,
10, 1–12
Spencer MK, Alley RB and Creyts TT (2001) Preliminary firndensification model with 38-site dataset. J. Glaciol., 47(159),
671–676 (doi: 10.3189/172756501781831765)
Sturges WT and 7 others (2001) Methyl bromide, other brominatedmethanes, and methyl iodide in polar firn air. J. Geophys. Res.,
106(D2), 1595–1606
Sturm M and Benson C (2004) Scales of spatial heterogeneity for
perennial and seasonal snow layers. Ann. Glaciol., 38, 253–260(doi: 10.3189/172756404781815112)
Sugiyama S, Enomoto H, Fujita S, Fukui K, Nakazawa F andHolmlund P (2010) Dielectric permittivity of snow measured
along the route traversed in the Japanese–Swedish Antarctic
Expedition 2007/08. Ann. Glaciol., 51(55), 9–15 (doi: 10.3189/
172756410791392745)
Takahashi S and Kameda T (2007) Snow density for measuring
surface mass balance using the stake method. J. Glaciol.,53(183), 677–680
Takahashi S, Kameda T, Enomoto H, Motoyama H and Watanabe O(2004) Automatic weather station (AWS) data collected by the33rd to 42nd Japanese Antarctic Research Expeditions during
1993–2001. JARE Data Rep. 276.
Tiuri MT, Sihvola AH, Nyfors EG and Hallikainen MT (1984) The
complex dielectric constant of snow at microwave frequencies.
IEEE J. Ocean. Eng., 9(5), 377–382
Trudinger C, Enting IG, Etheridge DM, Francey RJ, Levchenko VA
and Steele LP (1997) Modeling air movement and bubble
trapping in firn. J. Geophys. Res., 102(D6), 6747–6763
Van den Broeke M (2008) Depth and density of the Antarctic firnlayer. Arct. Antarct. Alp. Res., 40(2), 432–438
Van den Broeke MR and 6 others (1999) Climate variables along a
traverse line in Dronning Maud Land, East Antarctica.
J. Glaciol., 45(150), 295–302
Van den Broeke MR, Van As D, Reijmer CH and Van de Wal RSW(2004) The surface radiation balance in Antarctica as measured
with automatic weather stations. J. Geophys. Res., 109(D9),D09103 (doi: 10.1029/2003JD004394)
Vihma T, Mattila O-P, Pirazzini R and Johansson MM (2011) Spatial
and temporal variability in summer snow pack in Dronning
Maud Land, Antarctica. Cryosphere, 5(1), 187–201 (doi:10.5194/tc-5-187-2011)
Watanabe O (1978) Distribution of surface features of snow coverin Mizuho Plateau. Mem. Natl Inst. Polar Res., Special Issue 7,
154–181
Yamada T and Watanabe O (1978) Estimation of mass input in the
Shirase and the Soya drainage basins in Mizuho Plateau. Mem.
Natl Inst. Polar Res., Special Issue 7, 182–197
Zwally HJ and Li J (2002) Seasonal and interannual variations of firndensification and ice-sheet surface elevation at Greenlandsummit. J. Glaciol., 48(161), 199–207 (doi: 10.3189/
172756502781831403)

MS received 23 September 2011 and accepted in revised form 23 January 2012