Surface snow density is an important variable for the surface mass balance
and energy budget. It evolves according to meteorological conditions, in
particular, snowfall, wind, and temperature, but the physical processes
governing atmospheric influence on snow are not fully understood. A reason is
that no systematic observation is available on a continental scale. Here, we
use the passive microwave observations from AMSR-E satellite to retrieve the
surface snow density at Dome C on the East Antarctic Plateau. The retrieval
method is based on the difference of surface reflections between horizontally
and vertically polarized brightness temperatures at 37 GHz, highlighted by
the computation of the polarization ratio, which is related to surface snow
density. The relationship has been obtained with a microwave emission
radiative transfer model (DMRT-ML). The retrieved density, approximately
representative of the topmost 3 cm of the snowpack, compares well with in
situ measurements. The difference between mean in situ measurements and mean
retrieved density is 26.2 kg m
Snow density is an important variable
that relates snow thickness and mass. Close to the surface, knowledge of its
value is necessary to establish the surface mass balance from in situ
measurements using stakes, ultrasonic sensors, ground-penetrating radar, snow
pits or firn cores
Estimating the surface snow density in Antarctica remains difficult
The objective of this study is to develop and validate a new method of
determining the snow density near the surface from passive microwave
satellite observations. The study is performed at Dome C
(75
Section
Satellite remote sensing datasets, measurements of snow properties, and the microwave radiative transfer model are described in the three following sections.
Passive microwave satellite observations were acquired at 18.7 and 36.5 GHz
by the AMSR-E instrument on-board the Aqua satellite in dual-polarization
mode with an observation zenith angle of 54.8
A detailed analysis of the spatial and temporal variability of passive
microwave data close to Dome C is given by
Two complementary datasets of active microwave observations are used for
comparison with the retrieved snow density. The first dataset was acquired by
the nadir-looking Radar Altimeter 2 (RA-2) instrument on-board the ENVIronment
SATellite (ENVISAT) at 13.6 GHz (Ku band). The dataset contains the total
backscatter power from 12 March 2002 to 8 April 2012 based on along-track
data and the Ice-2 retracking algorithm
The second dataset of active microwave observations was acquired by the
SeaWinds instrument on-board the Quick SCATterometer (QuikSCAT) at 13.4 GHz
in dual-polarization mode. Zenith viewing angles are 46 and 54.1
Snow observations include the snow density near the surface and the vertical profiles of snow temperature, density, and specific surface area (SSA).
Three time series of snow density were measured at Dome C. The first set of measurements is from the CALVA programme (CALibration-VAlidation of climate models and satellite retrieval, Antarctic coast to Dome C). Surface snow density was measured every 3 to 5 days from 3 February 2010 to 4 October 2011 by using a cylinder cutter (10 cm length and diameter of 5 cm) inserted horizontally so that the cutter top grazes the surface. The value used here is the average of three measurements. The two other sets of measurements are from the project MAPME (Monitoring of Antarctic Plateau by means of Multi-Frequency Microwave Emission) funded by PNRA (Programma Nazionale di Ricerche in Antartide). The second dataset contains the snow density measured from 9 May 2008 to 4 October 2011 by using a cylinder cutter of 25 cm height and a diameter of 4.5 cm. Measurements were performed at 0.1 m depth every 15 days in a snow stake network composed of eight poles placed 10 m apart . The value used here is the average of eight measurements. The last dataset contains the snow density measured in snow pits, which were excavated every month from 18 December 2007 to 4 October 2011. Density values were collected every 10 cm in the 0–1 m depth range. A single measurement was collected at each depth of the snow pit.
The mean density value is 329, 344, and 321 kg m
For the first time, the spatial variability of surface snow density
measurements has been assessed with three series of 40 measurements (measurements
taken every 1, 10, and 100 m). The averages and standard deviations are,
respectively,
Snow temperature was recorded every hour from 1 December 2006 to
4 October 2011 and from the surface to 21 m depth, with 35 probes initially
installed every 0.1 m down to 0.6 m depth, every 0.2 m down to 2 m, every
0.5 m down to 5 m, and every 1 m down to 21 m. The probes are located
around 1 km west of the Concordia station. All probes
(100
The measured profiles of snow density, SSA, and DMRT-ML radius at
Dome C, Antarctica. DMRT-ML radius is derived from SSA measurements and is
the input of the model (Sect.
The snow density profile (Fig.
The snow core and snow pit density profiles below 0.3 m depth are close
together. Indeed, the mean and standard deviation of both profiles (0–0.5 m
deep) are about 340
The snow SSA (Fig.
Larger errors were found above 0.3 m depth using POSSSUM than when using
ASSSAP. Thus, we decided to use SSA measurements from ASSSAP for the first
0.3 m and POSSSUM observations below. SSA profiles from both instruments
overlap between 0.3 and 0.5 m depth and show very close values. Averaged SSA
values from ASSSAP are 13.2 and 13.9 m
The snow SSA is not directly used in DMRT-ML model. The snow parameter that
characterizes the snow grain size in the electromagnetic model
(
The snow microwave emission model DMRT-ML
We use the model in a non-sticky grain configuration, i.e. grains which do
not form aggregates, and with a unique optical radius of snow grains, i.e. no
grain size distribution. Snow crystal aggregates are not considered in this
study because the
The atmosphere attenuates the microwave emissions emerging from the surface
and itself emits microwaves due to its own temperature
Some elements of the theoretical background of microwave satellite remote sensing are described in the next two sections.
We use the brightness temperature polarization ratio (PR) at 19 and 37 GHz
to increase the sensitivity of passive microwave observations to surface
properties
In order to understand the polarization ratio evolution, two cases are
explored: (1) snow properties that vary with depth but are constant in time and
(2) snow properties that also vary over time. For case (1), the PR evolution is
only due to changes in the snow density near the surface. For case (2), PR
evolution is also influenced by changes in the snow density stratification.
Snow evolution is mainly influenced by atmospheric conditions. The surface is
first affected and then atmospheric influence diffuses deeper into the
snowpack. This process is slow on the Antarctica Plateau
The signal returned by the snowpack is a complex combination of surface and
volume scattering
Because the incidence angles of the SeaWinds instrument are close to the
Brewster angle for the air–snow interface, the surface reflection at
vertical polarization is weakly influenced by the near-surface snow density.
Consequently, the evolution of the radar backscatter coefficient at vertical
polarization is mostly dependent on surface roughness changes. Therefore,
considering the independence of volume scattering from the polarization
The different steps of the method of retrieving the surface snow density are
described in this section. Surface density variations are deduced from the PR
evolution. However, to correctly simulate PR evolution, we first need to
simulate the mean state of the PRs.
In the first step, we follow the forward modelling approach of In the second step, we simulate the polarization ratio variations
due to changes in the properties of a 0.03 m snow layer on top of the snowpack theoretically (as in
The third step corresponds to the retrieval algorithm itself. We estimate
the time series of surface snow density by minimizing the deviations between
the modelled and observed polarization ratio at 37 GHz.
Some studies have reported high vertical snow stratification around
Dome C
The results of the different steps to retrieve the surface snow density are presented in the next three sections. The fourth section is dedicated to the comparison and validation of the retrieved density, and the last section examines the different sources of uncertainties.
TOA horizontally and vertically polarized brightness temperatures at 19
and 37 GHz – written
Time series of modelled (lines) and observed (dots) brightness
temperatures, at 19 and 37 GHz and at vertical and horizontal polarizations
–
The time series of observed and modelled brightness temperatures are shown in
Fig.
Errors between modelled and observed brightness temperatures (K) for the calibration and validation periods.
The polarization ratio evolution, calculated from the simulated brightness
temperatures, does not reproduce the observed variations, and the 5-year
average of modelled PR
Time series of modelled (lines) and observed (dots) polarization
ratios at 19 and 37 GHz (PR
The poor simulation of the mean PR
Errors between modelled and observed polarization ratios for the calibration and validation periods.
In contrast to the long-term average, the seasonal and faster variations in the polarization ratio at 37 GHz are not reproduced. We explain this by the fact that the evolution of polarization ratio is mainly governed by variations in the snow density close to the surface, whereas we have considered the snow density profile constant over time in our simulation here.
In order to represent the snow evolution close to the surface and thus to
simulate PR
PR
The results clearly show that only the density of the first layer can
significantly change the polarization ratio. For comparison, large variations
in SSA from 10 to 100 m
Surface snow density
Time series of the snow density near the surface
The time series of
The annual cycles have a mean amplitude of about 30 kg m
A pluri-annual decrease trend of
Figure
Time series of the snow density near the surface at Dome C,
Antarctica, retrieved from AMSR-E passive microwave observations and measured
in the field (three different datasets) from 18 June 2002 to
4 October 2011
The four time series of surface snow density show a pluri-annual decreasing
trend. The linear trends during the common period (February 2010 to
October 2011) for the four time series are of the same order of magnitude,
between
The QuikSCAT 7-year time series of the residual backscatter at vertical and
horizontal polarization and the radar polarization ratio are shown in
Fig.
Time series of the residual vertical and horizontal backscattering coefficients and the radar polarization ratio from QuikSCAT at Dome C, Antarctica, from 18 June 2002 to 23 November 2009. Grey lines are the original data and red, blue, and black dots are the 5-day moving averages. Note the different vertical axis scales for horizontal polarization (H-pol) and vertical polarization (V-pol)
The ENVISAT–RA-2 time series of the residual backscatter at 13.6 GHz is
presented in Fig.
Time series of the backscattering coefficient from ENVISAT–RA-2 at Dome C, Antarctica, from 12 March 2002 to 8 April 2012.
The pluri-annual trend of ENVISAT–RA-2 observations of
We first present an assessment of the uncertainties and then discuss the
importance of several caveats that may affect the accuracy of
We use here the signal-to-noise ratio (SNR) to characterize the significance
of our results. SNR is the ratio between the mean of the observed data over
the standard deviation of the background noise. In our time series of surface
snow density, we assume that the standard deviation of quick variations to be
noise even though part of it may be a natural signal. That gives an upper
limit of the noise. We found a SNR of 5.9. This value is high enough to
conclude that a real signal emerges from the noise, and thus the negative
trend of surface snow density is significant at Dome C. Furthermore, the
spatial variability of surface snow density (41.6 kg m
Figure
Time series of the snow density near the surface
If satellite observations were performed over an isotropic surface, the
azimuth angle would have no effect on the measurements. This is not the case
over the Antarctic Plateau, as many studies demonstrated the effect of
azimuthal variation on satellite measurements
The roughness of the snow surface has a direct influence on passive and
active microwave observations
The radar backscatter is often reduced by an increase in the surface
roughness. The slopes of the large-scale topography around Dome C are small
enough, less than 1 m km
Concerning passive microwave observations, as discussed in
The snow below the top layer up to few metres depth influences the
polarization ratio through internal reflections. Changes in volume scattering
(due to snow grains), caused by the evolution of snow deeper into the
snowpack, certainly have a negligible direct effect on the retrieved density.
However, snow evolution can change the penetration depth of the microwave
emissions and consequently change the number of snow–snow interfaces caused
by abrupt changes in the snow density profile. Interface reflections are
nearly independent of the wavelength according to Fresnel coefficients. The
influence on
PR
The snow density near the surface at
Dome C on the East Antarctic Plateau has been estimated from passive
microwave observations over nearly 10 years. The surface snow density
retrieval method is based on the difference of Fresnel reflection
coefficients between horizontally and vertically polarized brightness
temperatures at the air–snow interface. The brightness temperatures were acquired by AMSR-E from 2002 to 2011. The DMRT-ML model has been used to
compute the polarization ratio (the ratio of horizontal on vertical
brightness temperature) at the TOA using in situ profiles of
snow parameters following
The main result of this study is the significant negative pluri-annual trend
of surface snow density of about
The retrieved time series of surface snow density has been compared to data
available in the literature and to in situ measurements. All data agreed
on the range of surface density values. Hoar crystals have a density
between 125 and 178 kg m
Thanks to the passive microwave observations that are all-weather, daily, and
available for more than 30 years, this method can potentially be applied to
the whole Antarctic Ice Sheet, after addressing the following issues:
(1) the inclusion of surface roughness, which can be more significant in other
regions than at Dome C
References describing the individual satellite datasets
are provided in the method section. DMRT-ML model code is available at
GP, MF, LA, and NC conceived the study. NC performed the simulation, collected and interpreted QuikSCAT data, carried out the data analysis, interpreted the results, and wrote the manuscript with the important help of GP. GP collected AMSR-E data and is the main developer of the DMRT-ML model. LA and EL measured the snow temperature profiles. LA performed the measurements devoted to the study of the surface snow density variability. NC, GP, LA, GM, and EL participated to the collection of all other in situ measurements. FR collected and interpreted ENVISAT data. All the co-authors discussed the results and participated in the writing of the manuscript.
The authors declare that they have no conflict of interest.
AMSR-E, RA-2 and QuikSCAT data were, respectively, obtained from the National Snow and Ice Data Center (NSIDC) at the University of Colorado, the Centre for Topographic studies of the Ocean and Hydrosphere (CTOH) supported by the European Space Agency (ESA) ENVISAT–RA-2 programme, and from the NASA-sponsored scatterometer climate record pathfinder (SCP) at Brigham Young University. We are very grateful to Ilhan Bourgeois, Sylvain Lafont, and Sébastien Aubin for making snow density measurements at Dome C during the polar winter. Field measurements around Concordia Station were made possible by the joint French–Italian Concordia Program, which established and currently runs the permanent Concordia station at Dome C. We warmly thank the French polar institute (Institut Paul Emile Victor, IPEV) and the Italian polar programme (Programma Nazionale di Ricerca in Antartide, PNRA). Finally, this work was supported by the Programme National de Télédétection Spatiale (PNTS), the IPEV programmes (IPEV–CALVA and IPEV–GLACIOLOGIE) and the project MAPME (Monitoring of Antarctic Plateau by means of Multi-Frequency Microwave Emission) funded by PNRA. We are grateful to the two reviewers and the editor, whose comments and discussion improved and clarified the manuscript. The article processing charges for this open-access publication were covered by the University of Bremen.
This paper was edited by Martin Schneebeli and reviewed by Zoe Courville and one anonymous referee.