On the Antarctic Plateau, snow specific surface area (SSA) close to
the surface shows complex variations at daily to seasonal scales which
affect the surface albedo and in turn the surface energy budget of the
ice sheet. While snow metamorphism, precipitation and strong wind
events are known to drive SSA variations, usually in opposite ways,
their relative contributions remain unclear. Here, a comprehensive set
of SSA observations at Dome C is analysed with respect to
meteorological conditions to assess the respective roles of these
factors. The results show an average 2-to-3-fold SSA decrease
from October to February in the topmost 10 cm in response to
the increase of air temperature and absorption of solar radiation in
the snowpack during spring and summer. Surface SSA is also
characterized by significant daily to weekly variations due to the
deposition of small crystals with SSA up to 100 m
The surface energy budget of the Antarctic Plateau depends on snow
physical properties
The Plateau is characterized by very low temperatures
Hitherto, this summertime SSA decrease has been generally deduced from
albedo measurements
The aim of this paper is to investigate the summertime evolution of
snow SSA at Dome C, as well as to further understand its variability, from
the daily to the interannual scale. To quantify this evolution, we
use three data sets. Firstly, a large number of in situ SSA
measurements were collected at Dome C during summer campaigns (Sect.
The temporal variations of snow SSA at Dome C were estimated from
in situ measurements and satellite data. Snow spectral albedo in
the visible and NIR range has been measured using a specifically
designed automatic instrument, from which surface SSA variations
were deduced over the summers
Using the dependence between snow albedo and SSA close to the
surface
Spectral albedo measurement at Dome C (picture taken on 12 January
2014, 11:00 LT). The vertical mast is approximately 2
The SSA retrieved with this algorithm roughly corresponds to the
SSA of the top 1–2
The SSA of surface snow was also manually measured during the
summer campaigns
Experimental setup for transect measurements of SSA using ASSSAP in
horizontal position. The distance between both vertical stakes is
1
From 23 November 2012 to 16 January 2013, 98 vertical profiles of
SSA were measured with ASSSAP from the surface to 10
The SSA time series from 1999 to present was estimated from
high-frequency microwave radiometers using the approach proposed in
Following the method of
This method is simple because using two observations it considers
only two unknowns, while the density and layer thickness are probably
variable and are known to affect microwave signal as well (even if
this effect is of second order compared to the SSA). As a result,
this SSA time series is not expected to be as accurate as the
spectrometry-based approach described in
Sect.
The temporal evolution of snow physical properties at Dome C was
computed with the detailed snowpack model Crocus
In Crocus the impact of drift SSA decrease is computed from the formulation F06 of snow metamorphism The vertical profiles of absorbed solar energy were computed
with the physically based radiative transfer model TARTES
Although in Crocus the roughness length for momentum is usually
10 times larger than that for heat transfer at the air–snow
interface, here both were fixed to
Crocus was forced by ERA-Interim atmospheric reanalysis for
The snowpack was first initialized with a depth of
Crocus simulations performed for this study.
To estimate the sensitivity of Crocus simulations to summer
precipitation and air temperature and to test the hypotheses
proposed by
Simulations and measurements show that SSA close to the surface
evolves at different timescales. The SSA of the top millimetres
is essentially driven by meteorological conditions such as
snowfall and drift events
For each 1
Figure
These observed SSA variations, corresponding roughly to the top
2 mm of the snowpack, were compared to the reference
Crocus simulation A. For this, the average SSA of the top
2 mm was computed from the simulated SSA profiles. The
simulated SSA vary in the same range as the measured ones
(Fig.
Beyond these rapid variations of surface SSA, mainly due to snow
deposition and transport, the spectral albedo measurements and the
vertical profiles show that SSA decreases all along the
summer. Figure
Variations of SSA close to the surface deduced from the spectral
albedo measurements, and average SSA of the top
The average SSA of the top 2
Evolution of SSA averaged over the top 10
The summertime decrease of SSA is confirmed by the series of
vertical profiles of SSA taken independently with ASSSAP during the
same two summers (Fig.
Comparison of SSA evolution deduced from AMSU brightness
temperatures at 89 and 150
In situ measurements of SSA down to 10
The SSA simulated with Crocus and that deduced from AMSU
observations (Fig.
A version of Crocus adapted to the meteorological conditions of the Antarctic Plateau was used to simulate the temporal variability of snow SSA close to the surface at Dome C, in order to identify the physical processes responsible for summertime SSA variations. In general, a satisfactory agreement was obtained with regards to in situ measurements and remote sensing observations of snow SSA, even though some discrepancies remained between model and observations.
During the winter period at Dome C, defined here as the period extending
from late February to mid-October when metamorphism is insignificant, snowfalls deposit onto the
surface fresh snow whose detailed characteristics generally depend
on weather conditions, but whose SSA is invariably high. Snow
metamorphism is very limited during this period due to the
prevailing extremely low temperatures. As a consequence, at the end
of winter, snow properties in the layer accumulated during this
period
Minimum SSA (top 7
The results of simulation B, where summer precipitation was
inhibited, imply that snow metamorphism only weakly depends on the
total amount of precipitation during summer (Fig.
Minimum SSA of the topmost 7
Although the impact of precipitation seems moderate in Crocus
simulations at the seasonal scale, snowfall occurrence and amount
drive Crocus-simulated SSA variations in the top 2 mm, consistently with field observations.
While the deeper layers show a seasonal SSA evolution, the surface layer mostly reflects
day-to-day variations of weather conditions. To simulate the evolution of the snowpack
at Dome C, it is thus critical to know precipitation very
precisely, a quantity that is difficult to obtain from reanalyses
in Antarctica
Despite a few deviations from the observations, Crocus captured
well the variations of SSA in response to meteorological conditions
and metamorphism at Dome C. Since metamorphism strongly depends on
the temperature profile close to the surface, this suggests that
Crocus successfully resolves the energy budget of the snowpack
close to the surface, as already pointed out by
The fact that Crocus poorly simulates the interannual variability of SSA summer decrease, while it proved efficient to simulate the seasonal variations, is more puzzling. Actually, the apparent intensity of the metamorphism depends both on the SSA value at the end of winter and on the rate of SSA decrease during summer, which are driven by different processes.
The differences between simulated and observed SSA at the end of
winter (Fig.
As to the summer decrease in SSA,
Crocus simulations suited to the meteorological conditions of the
Antarctic Plateau were compared to in situ and satellite-derived
measurements of snow SSA at Dome C in order to identify the
processes controlling SSA evolution on the Antarctic Plateau. The
observations show rapid variations of SSA close to the surface,
mainly due to precipitation and snow drift. They also confirm the
existence of a seasonal cycle of SSA in the topmost 10
The data used in this study are available upon request from the authors (ghislain.picard@ujf-grenoble.fr). The Crocus simulations were performed with SURFEX v7.3, adapted to Antarctic conditions. The version of Crocus incorporating the specific developments described in this article has not yet been officially released but is available upon request to crocus@meteo.fr.
Q. Libois, G. Picard, L. Arnaud and E. Lefebvre participated to in situ measurements at Dome C. Q. Libois developed the Antarctic parameterizations of Crocus and performed the corresponding simulations. G. Picard performed the DMRT-ML simulations. Q. Libois and G. Picard analysed satellite and field data. M. Dumont contributed to the parameterization of incident solar radiation in TARTES. M. Lafaysse implemented the model TARTES in Crocus. S. Morin helped with Crocus simulations and implemented the Antarctic parameterizations in the code. Q. Libois and G. Picard prepared the manuscript with contributions from M. Dumont, M. Lafaysse and S. Morin.
We are grateful to the anonymous reviewers and the editor for their valuable comments. LGGE is part of LabEx OSUG@2020 (ANR10 LABX56). This study was supported by the ANR program 1-JS56-005-01 MONISNOW. The authors acknowledge the French Polar Institute (IPEV) for the financial and logistic support at Concordia station in Antarctica through the CALVA-Neige and NIVO programs. We thank Eric Brun for insightful discussions about Crocus parameterizations. We are grateful to Arnaud Mialon for helping with the measurements at Dome C. The editor thanks the anonymous reviewers for their work. Edited by: M. Schneebeli