ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-691-2017Atmospheric moisture supersaturation in the near-surface atmosphere at
Dome C, Antarctic PlateauGenthonChristophechristophe.genthon@univ-grenoble-alpes.frhttps://orcid.org/0000-0002-3678-4447PiardLucVignonEtiennehttps://orcid.org/0000-0003-3801-9367MadeleineJean-BaptisteCasadoMathieuGalléeHubertUniv. Grenoble Alpes, CNRS, IRD, IGE, 38000, Grenoble, FranceSorbonne Universités, UPMC Univ. Paris 06, UMR 8539, Laboratoire de
Météorologie Dynamique (IPSL), 75005, Paris, FranceCNRS, UMR 8539, Laboratoire de Météorologie Dynamique (IPSL), 75005, Paris, FranceLSCE-IPSL, CEA-CNRS-UVSQ-U. Paris-Saclay, Gif-sur-Yvette, FranceChristophe Genthon (christophe.genthon@univ-grenoble-alpes.fr)13January201717169170424July201618August20167December201618December2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/691/2017/acp-17-691-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/691/2017/acp-17-691-2017.pdf
Supersaturation often occurs at the top of the troposphere where cirrus
clouds form, but is comparatively unusual near the surface where the air is
generally warmer and laden with liquid and/or ice condensation
nuclei. One exception is the surface of the high Antarctic
Plateau. One year of atmospheric moisture measurement at the surface of
Dome C on the East Antarctic Plateau is presented. The measurements are
obtained using commercial hygrometry sensors modified to allow air sampling
without affecting the moisture content, even in the case of supersaturation.
Supersaturation is found to be very frequent. Common unadapted hygrometry
sensors generally fail to report supersaturation, and most reports of
atmospheric moisture on the Antarctic Plateau are thus likely biased low. The
measurements are compared with results from two models implementing cold
microphysics parameterizations: the European Center for Medium-range Weather
Forecasts through its operational analyses, and the Model Atmosphérique
Régional. As in the observations, supersaturation is frequent in the
models but the statistical distribution differs both between models and
observations and between the two models, leaving much room for model
improvement. This is unlikely to strongly affect estimations of surface
sublimation because supersaturation is more frequent as temperature is lower,
and moisture quantities and thus water fluxes are small anyway. Ignoring
supersaturation may be a more serious issue when considering water isotopes,
a tracer of phase change and temperature, largely used to reconstruct past
climates and environments from ice cores. Because observations are easier in
the surface atmosphere, longer and more continuous in situ observation series
of atmospheric supersaturation can be obtained than higher in the atmosphere
to test parameterizations of cold microphysics, such as those used in the
formation of high-altitude cirrus clouds in meteorological and climate
models.
Introduction
Ice supersaturation is
frequently found in the upper troposphere (Spichtinger
et al., 2003), and specific cloud microphysics parameterizations are developed
to represent this process in meteorological and climate models. These models
need to be validated against observations to reproduce cirrus and other
clouds including contrails, which develop at altitudes where supersaturation
occurs (e.g., Rädel and Shine, 2010). Radiosondes provide snapshot
information but obtaining in situ observation time series to comprehensively
calibrate and validate such parameterizations is a challenge because it
requires flying and operating instruments on high-altitude aircrafts or
balloons. Sampling supersaturated air parcels without affecting the air
moisture content is also a challenge, as the excess moisture with respect to
saturation tends to condense on any surfaces, including those of the sampling
device and the sensor itself. There are thus not many in situ observations
available to characterize and quantify natural supersaturations and their
evolution in time, and evaluate and validate microphysics parameterizations
in such conditions.
While they are frequent at high altitude, ice supersaturations do not
generally occur in the surface atmosphere, where operating instruments is
obviously much easier. Atmospheric conditions close to those occurring at the
tropopause are, however, found at the surface of the Antarctic ice sheet both
in terms of temperature and humidity levels. Because of the distance from the
nearest coasts and the high elevation, the Antarctic Plateau is also
particularly secluded from sources of aerosols. This is the most likely place
on Earth to observe frequent and large ice supersaturation in the near-surface atmosphere. For instance, Schwerdtfeger (1970) reports on
observations of relative humidity with respect to ice exceeding 120 % at
Vostok station in the heart of Antarctica.
The possibility of surface atmospheric supersaturation on the Antarctic
Plateau raises a potential issue: that of the relative contribution of the
different terms of the surface mass balance of the Antarctic ice sheet. The
terms are precipitation (positive for the surface) and evaporation/sublimation (negative or positive), and
possibly blowing snow (positive or negative as blown snow redeposits, but
generally negative because of enhanced snow evaporation, e.g., Barral et al.,
2014). Melting and runoff do not occur on the Antarctic Plateau and can be
excluded. The net surface mass balance, observed using glaciological methods,
is very small on the Antarctic Plateau. It is typically a few centimeters water equivalent per year
(Arthern et al., 2006): the Antarctic Plateau is one of the
driest places on Earth. This is because it is so cold, and the laws of
thermodynamics indicate that the various terms of the surface mass balance
are bound to be correspondingly small. Because they are so
small, and because of a harsh environment, the direct determination of
precipitation and evaporation/sublimation on
the Antarctic Plateau is not conclusive. Their relative contribution to the
surface mass balance of the Antarctic Plateau is still poorly quantified,
using indirect approaches (Frezzotti et al., 2004). In most places on
continents, precipitation largely dominates. This is not necessarily the case
on the Antarctic Plateau. In particular, if atmospheric supersaturation
occurs near the surface, then moisture concentration is likely larger in the
surface atmosphere than at the snow surface and the turbulent moisture flux
is thus directed towards the surface (surface condensation). Unlike most
other regions of the Earth, this turbulent flux could contribute positively
to the surface water budget and thus, here, on the surface mass balance.
Another potential issue with ice supersaturation on the Antarctic Plateau is
that of the impact on the water isotopic composition of snow. Supersaturation
leads to kinetic fractionation of the stable isotopic composition of water
when it condenses. Since the 1980's (Jouzel et al., 1987), the longest ice
core records of past climate and environment are obtained from drilling
operations on the Antarctic Plateau. Past atmospheric temperatures are
deduced from the variations of the concentration of stable water isotopes
along the core. Variations in supersaturation levels may impact kinetic
fractionation and thus on this reconstruction. Supersaturations thus involve
not only meteorological (clouds, precipitation, surface evaporation/sublimation) but also climate and paleoclimate reconstruction issues. It is therefore
important to measure and assess supersaturations on the Antarctic plateau.
However, as already mentioned, measuring atmospheric supersaturation is a
challenge because sampling a supersaturated air mass can affect its moisture
content. Schwerdtfeger (1970) expresses concerns about the reliability of
reports of supersaturation at Vostok station. On the other hand, many reports
of relative humidity with respect to ice (RHi) on the Antarctic Plateau
reach but seem to be capped at 100 % (King and Andersen, 1999). Genthon
et al. (2013) compare RHi observed at Dome C on the Antarctic Plateau,
using conventional solid-state sensors, with results from the ECMWF (European
Center for Medium-range Weather forecasts) meteorological analyses and from
the MAR (Modèle Atmosphérique Régional) meteorological model. In
both models, cold microphysics parameterizations are used which, depending on
local conditions, allow for supersaturations (Sect. 4). More often than not,
when ∼ 100 % RHi is observed at Dome C with conventional
instruments (not adapted to sample supersaturation), both models produce
significant supersaturation, occasionally reaching more than 150 %
(Genthon et al., 2013). The cold microphysics parameterizations differ in the
two models (see Sect. 4), and other aspects such as the vertical resolution
also differ. If both models produce significant supersaturations, they do not
quantitatively agree as to the amplitude of the supersaturations.
To verify such model results, to decide between and to improve the models,
using direct is situ measurements, instruments must be designed and/or
adapted so as to bring the air mass to the moisture sensor without affecting
its moisture content. This can be done by warming the air above its
condensation temperature before ushering it to the sensor. Here, after the
present general introduction (Sect. 1), Sect. 2 presents two instruments which
are adapted from commercial sensors to perform in very cold conditions and to
enable the measurement of atmospheric supersaturation at Dome C. The
measurement site and deployment are also described in Sect. 2, and previous
atmospheric humidity reports from this site are revisited. In Sect. 3,
results from the conventional instruments are compared with the reports by
the two adapted instruments and shown to fail. A 1-year climatology of
atmospheric moisture at Dome C from the adapted instruments is presented,
first for summer when both adapted instruments work well but not the
unadapted one, then for a full year when instrumental limits occur in the
coldest and driest periods and are discussed. The impact of the
supersaturations on the turbulent exchange at the surface is calculated and
shown to be minor. In Sect. 4, simulation results from recent versions of the
two atmospheric models discussed in Genthon et al. (2013) are shown to agree
with the observation of frequent occurrences of supersaturation at all times
in the year including in summer. It is also shown that details of the
climatology and the statistics of occurrence of supersaturation differ
between the models and the observations and between the two models. Section 5
discusses the results, issues related to limited ability of models to
properly account for supersaturation (including potential consequences for
the record of isotopic signals in the ice), and finally concludes the paper.
Topographic map of Antarctica, showing the location of Dome C
(red C). Elevation (color scale) is in meters.
Measurement site, instruments, and observation methods
Dome C (Fig. 1) is one of the main topographic domes on the East Antarctic
Plateau. Since 2005, the summit of the dome (75∘06′ S,
123∘20′ E, 3233 m a.s.l.) has hosted a permanently manned station,
Concordia, jointly operated by the French and Italian polar institutes (IPEV
and PNRA). One of the first Antarctic Meteorological Research Center
automatic weather stations (AMRC AWS, https://amrc.ssec.wisc.edu/) deployed in
Antarctica, back in the 1980s, was at Dome C. When the actual location of the
summit of the dome was later more accurately determined using satellite and
aircraft radar altimeters in the 1990s, the AWS was moved about 50 km to its
present position. This induced a 30 m rise and correspondingly slight mean
surface pressure change but otherwise little impacted on the series
consistency because the local environment is very homogeneous. The AWS
provides one of the longest quasi-continuous meteorological reporting on the
high Antarctic Plateau. The station measures pressure, temperature, and wind,
but not moisture. Additional meteorological reports are available since the
construction of Concordia station, including another AWS closer to the
station and a daily radiosonde. Both the new station and the radiosondes
report atmospheric humidity using solid-state film capacitive sensors
(Kämpfer et al., 2013). In early 2008, a system to vertically profile the
lower part of the atmosphere was deployed along a ∼ 45 m high tower.
Temperature, wind, and moisture are measured, the latter again using solid-state film capacitive sensors. This profiling system is fully described in
Genthon et al. (2010, 2011, 2013).
From the tower measurements, Genthon et al. (2013) evaluate and compare
2 contrasting years, 2009 and 2010, respectively the warmest and coldest in a
10-year period. They report measuring humidity up to ∼ 100 % with
respect to ice but also observing frequent frost deposition, a hint that
supersaturation occurs but is missed by standard hygrometers without an
adaption. Occurrences of supersaturation are further supported by a
comparison with models that implement cold microphysics parameterizations:
the models often simulate supersaturation when the hygrometers hit the
100 % RHi ceiling. That raw solid-state hygrometers cannot measure
supersaturation is understandable: a supersaturated air mass will deposit its
excess moisture on any hard surface that serves as a condensation surface.
The hygrometer body itself will condense the excess moisture before it can be
measured. One way to overcome this problem is to aspirate and warm the air
above its thermodynamic saturation temperature at the intake.
There are several techniques to measure atmospheric moisture (Kämpfer,
2013). The traditional wet bulb thermometer is not very practical,
particularly when measuring well below freezing temperature. The dew-point
hygrometer provides a direct physical measure of the saturation temperature.
This is done by progressively cooling a surface from ambient temperature
until atmospheric moisture condensation is detected on the
surface. The cooled surface
is generally a mirror and condensation is optically detected when the
reflection of a light beam is observed to be diffused and diffracted. The
device also works below freezing temperature but should then be referred to
as a frost-point hygrometer (King and Anderson, 1999). Dew and frost-point
hygrometers are accurate but bulky, complex, and expensive. They require
significant amounts of energy, and they have moving parts because the mirror
must be periodically cleaned. They are thus comparatively prone to
disfunction and failures, and they cannot be used in remote unattended places
or in radiosondes. On the other hand, they mechanically aspirate air to the
sensing mirror, and if the aspiration intake is heated significantly above
ambient temperature (such as in King and Anderson, 1999), the measured air is
sampled without affecting its moisture content, even if supersaturated. Some
commercial instruments ensure this such as the Meteolabor VTP6 Thygan
described below.
In the 1970s, Vaisala Oy (Finland) developed a very different, very compact
humidity sensor, the Humicap thin-film capacitive
sensor
. The dielectric properties and capacitance of a polymer film
vary with the relative humidity of the ambient air. Although the physical
processes for dependence have been described (e.g., Anderson, 1994), the
relationship between capacitance and atmospheric moisture is an empirical
one. The sensor needs calibration and a small but significant uncertainty
affects the measurement. The uncertainty increases as temperature decreases.
On the other hand, the Humicap is convenient, very compact, comparatively
inexpensive, robust, its use can be automated, and it can be deployed even in remote
places and on radiosondes. It is thus currently widely used for such
purposes. Thin-film capacitive sensors are used in all automatic weather
stations in Antarctica that report moisture as well as on the 45 m profiling
system at Dome C mentioned above, in the latter case bundled in Vaisala
HMP155 thermometers–hygrometers (thermohygrometers) (Genthon et al.,
2013). According to the manufacturer, the uncertainty is
±1.4 + 0.032 of the reading in percentage in the -60 to
-40 ∘C temperature range. It is smaller at warmer temperature and
may be expected to be larger below -60 ∘C. However, then, the
absolute moisture content of the atmosphere is smaller and absolute
measurement errors are correspondingly smaller.
To tentatively confirm and quantify supersaturations at Dome C, both
frost-point and thin-film capacitive hygrometers were deployed at a height of
3 m and adapted as necessary to operate in the general Dome C conditions and
to sample the air without altering its moisture content, even when above
saturation. In both systems, the hygrometer-aspirated intake is heated so
that the temperature of the sampled air parcel is raised above condensation
level and condensation is avoided. The frost-point hygrometer is a Meteolabor
VTP6 Thygan chilled mirror instrument. It was selected because it is
factory-designed to perform in cold temperatures and correspondingly low
specific humidities. According to the manufacturer the lowest measurable
frost-point temperature is -65 ∘C. The fact that the air is heated
at the intake (see below) does not improve the temperature range of the
instrument as the actual limitation is due to the ability to cool the mirror
to the condensation temperature. A -65 ∘C temperature limit is not
quite low enough to consistently operate at Dome C, where the surface
atmospheric temperature can occasionally drop below -80 ∘C. In
addition, the sensor was found to begin and increasingly fail below
-55 ∘C rather than -65 ∘C. However, data from the
vertical profiling system show that from 2009 to 2015, the air temperature
∼ 3 m above the snow surface was warmer than -55 ∘C more
than 50 % of the time, and almost consistently (more than 99.5 % of
the time) warmer than -55 ∘C during the local summer
(December–January–February). Assuming near saturation, the instrument can
nominally operate for a large fraction of the time at Dome C. For our
application, the frost-point hygrometer (denoted as FP from now on) is hosted in a
heated box so that the electronics and mechanics are not affected by the
extreme cold temperatures in winter. By factory design, the outside air is
aspirated inside the instrument through an intake protected by a heated hood
which prevents frost deposition. This design is not modified, the intake and
heated hood being simply made to protrude out of the heated box, to sample
the outside air. This is the only part of the instrument outside the heated
box and, because it is itself heated, loss of moisture along the way to the
mirror is consistently prevented. Visual inspection confirms that even when
frost deposition occurs on other instruments on the tower, no frost
deposition is observed in the vicinity of the instrument intake. Each
measurement cycle lasts 10 min: heating and defrosting the mirror from the
previous measurement, cleaning, then cooling until frost point is reached.
The sensor thus reports measurements of frost-point temperature, and
conversion to relative humidity, on a 10′ time-step basis. The manufacturer
claims a very high accuracy: 0.1 % expressed in term of relative
humidity. Dew and frost-point hygrometers are indeed often used to calibrate
other types of hygrometers. Here the FP is used as a reference against which
other sensors may be adjusted and are evaluated, at least down to
temperatures where the FP performs well.
Schematic drawing of the modified (HMPmod) hygrometer. The air is
aspirated by the fan (1) and heated through an inlet (2). The temperature and
the moisture content of the heated air (3) is measured by the HMP155 (4). The
ambient air temperature (5) is measured by a separate PT100 (6) located in
the unheated aspirated inlet shaded from sun radiation (7).
For the other type of hygrometer used here, the manufacturer (Vaisala Oy)
guaranties its HMP155 sensor down to -80 ∘C for the measurement of
temperature, but only to -60 ∘C for the measurement of moisture.
However, the main issue with colder temperatures for this instrument is that
the time response increases. Yet, unlike in a radiosonde for which the
environment quickly varies during ascent, variations are comparatively slow
for fixed instruments and the operational limit is actually much below
-60 ∘C (Genthon et al., 2013). In addition, to avoid frost
deposition and preserve the air moisture content, for our application, the
instrument aspirates the air through an inlet consistently heated
∼ 5 ∘C above the ambient temperature (Fig. 2). The ambient
temperature itself is measured by a separate PT100 platinum resistance
thermometer in an unheated derivation of the system. A comparison with the
frost-point hygrometer shows that this simple and low-cost innovative design
succeeds in measuring even highly supersaturated air. In addition, the fact
that the air reaching the hygrometer sensor is 5 ∘C above ambient
temperature correspondingly extends the actual nominal temperature range of
the instrument with respect to ambient temperature. The sensor reports
relative humidity. According to manufacturer, the accuracy in the low
temperature range (-60 – -40 ∘C) is ±1.4 % of the
reading. Accuracy improves at warmer temperature, and may conversely be
expected to deteriorate for even colder temperatures. The temperature range
-40 to -60 ∘C is typical at Dome C although temperatures as cold
as -80 ∘C and as warm as -15 ∘C may be encountered.
Note that in accordance with meteorological conventions, all sensors report
relative humidity with respect to liquid water rather than ice even when the
air temperature is below 0 ∘C. Goff–Gratch formulas (Goff and
Gratch, 1945) are commonly used in meteorology to convert between RH with
respect to liquid, water vapor partial pressure, and RHi: they are also
used here. Differences up to 20 % have sometimes been reported with
alternate formulas in extremely cold temperature ranges. However, because the
formula are used here to converted form RH with respect to liquid water to
partial pressure then to RHi, the inaccuracies partially compensate.
A potential concern with the heated inlet approach is that, if there are
airborne ice particles, they may be aspirated and evaporated in the heated
section, leading to a spuriously elevated water vapor ratio in the sampled
air, translating into supersaturation when RHi is recalculated against
the ambient air temperature. Airborne ice particles may result from either
blowing snow or occurrence of a cloud. A signature of the bias would thus be
that supersaturation magnitude and occurrence increase with wind speed and/or
with downwelling infrared radiation. The reason is that blowing snow occurs
if the wind is strong enough to erode and lift snow from the surface, while
even a very light cloud in such a cold and dry atmosphere induces a
significant increase the IR emissivity and thus of downward IR (Gallée
and Gorodetskaya, 2000; Town et al., 2007). In the observations to be
presented next, the correlations are actually negative. RHi is
consistently at or below 100 % for wind speed above 8 m s-1
(Fig. 3), which is a typical speed at which blowing snow can be
triggered (Libois et
al., 2014). Stronger winds are generally associated with air masses
originated from the coast and thus comparatively laden with aerosols
preventing supersaturation. Supersaturations sharply increase rather than
decrease in frequency and amplitude with downwelling IR below ∼ 130
W.m-2 characteristic of a clear sky (not shown). These results are fair
signals that airborne ice particles are not likely to bias the measurements
presented here. Caution with the reliability of the most extreme, and less
frequent, supersaturation events may nonetheless by recommendable.
Scatter plot of observed RHi between 60 and 160 % (from
HMPmod) versus 10 m wind speed. All available half-hourly data in 2015 are
plotted.
The two adapted instruments are deployed side by side ∼ 3 m above the
snow surface on the ∼ 45 m tower. At the same level, hosted in an
aspirated but unheated radiation shield (see Fig. 1 of Genthon et al., 2011),
an unmodified HMP155 allows for comparison with a traditional design – and
to exhibit biases of the latter. From now on, the original and modified
HMP155 will be referred to as “HMP” and “HMPmod”, respectively. Table 1
lists the instruments and adaptations. The various instruments performed over
the duration of 2015 except for limited periods due to data-logging failures
or servicing in summer. The results are presented and analyzed in the next
section and compared with models in Sect. 4.
List of hygrometers and adaptations. See text for details.
Short nameInstrument/sensorHousingHMPVaisala HMP155 thermohygrometer/Aspirated radiation shieldthin-film polymer hygrometerHMPmodModified Vaisala HMP155 thermohygrometer/Aspirated radiation shieldthin-film polymer hygrometer+ heated intake (Fig. 2)FPMeteolabor Thygan VPT6 mirrorHeated enclosure, heated intakefrost-point hygrometerObservation data and resultsSummer
Mean December–January–February diurnal cycle of: (a) water vapor
partial pressure from FP instrument; (b) difference with respect to
FP of water vapor partial pressure from original (HMP, black) and modified
(HMPmod, red) thin-film polymer sensors; (c) RHi from the three
instruments; (d) 3 m air temperature.
Figure 4 displays the mean diurnal cycle of atmospheric moisture and
temperature in January, February, and December 2015 according to the various
instruments. During this period, the FP is consistently running within its
nominal manufacturer-stated temperature range and can serve as a moisture
measurement reference for the other instruments. The sun never completely
sets at this time of the year, however its changing elevation above the
horizon induces a strong temperature cycle near the surface (Fig. 4d).
Here, “night” refers to the local hours during which sun elevation is lower
at Dome C and sets at lower latitudes, broadly the coldest half of the day.
Figure 4a shows the mean cycle of partial pressure of water vapor from FP.
The numbers are low due to the cold temperature: the water partial pressure
ranges on average between ∼ 15 Pa in the early morning and slightly
over 35 Pa in the early afternoon. This cycle demonstrates that surface
evaporation occurs during the day, followed by deposition at night, resulting
in surface (3 m) atmospheric moisture diurnally changing by a factor of more
than 2. Figure 4b shows small differences and consistent agreement between
the HMPmod and FP instruments. Note here that HMPmod is slightly calibrated
for moisture reports against the FP instrument for agreement in the early
afternoon at the warmest part of the day. This calibration does not exceed
manufacturer stated accuracy for HMP155 (Sect. 2). The calibration proves
robust and valid at all times during the day in this period. Results from
(unmodified) HMP significantly depart from those of the FP, and thus HMPmod
instruments: the agreement is good in the afternoon only, but quite poor the
rest of the day and at night. Figure 4c displays the calculated RHi for
the three instruments, using the independent moisture measurements by each
instrument, but all finally reported to one same atmospheric temperature,
that of the (unmodified) HMP. This is likely the most accurate estimation of
temperature, i.e., the least-likely affected by radiation and other biases
because it is unheated and most efficiently ventilated (Genthon et al.,
2011). Temperature differences of as much as 2 ∘C are occasionally
observed with the other instruments in low wind conditions.
RHi differs markedly between the unmodified HMP and the two other
instruments. The latter two both report RHi significantly exceeding
100 % while the unmodified instrument hardly reaches saturation. All
instruments agree well in the early afternoon at the warmest time of the day
but HMP disagrees at night. The FP and HMPmod instruments consistently agree
with each other, including when reporting averaged summer supersaturations
reaching 120 % at night, confirming the high levels of supersaturation
hinted by Genthon et al. (2013) from models.
Regression of anomaly to saturation vapor pressure from HMP
(a) and HMPmod (b) instruments against FP.
Figure 5 displays correlation plots of moisture reports from the unmodified
(HMP) and modified (HMPmod) thin-film capacitive sensors with respect to FP
in summer. The direct correlations between water vapor pressures would be
very high because humidity is largely controlled by temperature. Plotting
deviations to the saturation vapor pressure, rather than the vapor pressure
itself, removes much of the temperature codependence effect and concentrates
on the relative ability of the instruments to correctly measure moisture. The
correlation between the regular HMP and FP is good below saturation but is
obviously very poor above since the HMP fails to capture supersaturations.
The correlation between HMPmod and FP reports is very high, above 0.97. The
regression constant (the intercept) is 0.1 but the standard error on the
constant is larger than 0.1. The linear regression is thus not statistically
different from a 1/1 regression.
Annual variations and statistics
A strong diurnal cycle dominates the variability of atmospheric moisture in
summer. The partial pressure is maximum in the early afternoon while RHi
peaks near local midnight (Fig. 4) when it occasionally reaches more than
150 % (not shown). As the diurnal cycle variability progressively
vanishes and is replaced by synoptic variability in the colder months,
RHi occasionally reaches values above 200 %. Figure 6 displays the
distributions of observed RHi with the various instruments, both
limiting the range of RHi between 50 and 150 % (more than 99 %
of all HMPmod reports) and extending the range to 200 %. A logarithmic
RHi scale is used in the latter case because with the linear scale the
highest RHi values would almost merge with the axis and hardly be
visible.
Observed distributions of RHi in 2015 for cases of RHi
between 50 and 150 % with linear vertical RHi scale (a) and
between 50 and 200 % and with logarithm vertical RHi
scale (b).
Although measurement uncertainties and uncertainties on conversions from
relative humidity with respect to liquid to RHi allow some occurrences
above 100 %, as expected, the reports from HMP peak near and hardly
exceed the 100 % ceiling. More than 50 % of all reports between
50 and 150 % are above 100 % for HMPmod and FP, with
similarities of distribution for the two instruments. There are differences
between observations by even the two modified hygrometers though. In the
50–150 % range, there are more quantitative differences between the
HMPmod and FP below 100 % than above. Both the HMP155 and frost-point
hygrometer lose accuracy and sensitivity as the temperature is colder and/or
water vapor partial pressure is lower. Below -55 ∘C, FP
occasionally, and more and more frequently as temperature gets colder,
reports unrealistically low moisture content. A limit with the colder
temperatures for this instrument is reported in Sect. 2. Figure 7 displays
the regressions of water vapor partial pressure differences with saturation,
separately for partial pressure ranging between 2 and 5, 5 and 10, 10 and 20,
and exceeding 20 Pa. The correlation deteriorates, and the regression line
increasingly deviates from 1 to 1, as the moisture content decreases.
Obviously, the smallest moisture partial pressures occur when the temperature
is coldest. The instruments show their limits during the coldest periods of
the winter. Figure 8 displays the annual cycle of monthly-averaged
temperature and RHi. HMP displays weak seasonal variability of RHi
compared to the other instruments. On the other hand, FP displays extreme
seasonal variability with values reaching below 30 % (beyond the plot
scale in Fig. 8) in winter. Such unrealistically low values, at odds with the
other instruments, reflect instrument limitation with very low moisture
content. Limiting the analysis to cases of partial pressure of moisture above
2 Pa (dashed curves in Fig. 8) excludes significant portions of the coldest
parts of the winter records. This is reflected by monthly winter temperatures
more than 20 ∘C warmer (Fig. 8a). The fact that HMPmod reports are
strongly increased suggests that this sensor also does not perform well at
very low moisture levels. When restricting to above 2 Pa, both HMPmod and FP
show strong seasonal variability with monthly mean RHi reaching
120 % for HMPmod and exceeding 130 % for FP. In both cases, the
maximum monthly supersaturation is reached in early winter (April) and
remains above 100 % all year long, except in October for HMPmod when it
is slightly below. Figure 9, same as Fig. 6 but for partial pressure of
moisture above 2 Pa only, confirms that in the surface atmosphere of Dome C,
supersaturation is the norm rather than an exception.
Regressions of partial pressure difference with saturation from
HMPmod against FP, depending on partial pressure range as indicated on the
upper left corner of each plot. The black line is the first bisector, the red
line shows the linear regression.
Seasonal variability of monthly-mean temperature Tm(a) and RHi(b) for all reports (solid lines) and
reports with moisture partial pressure ph above 2 Pa only (dashed lines).
With all reports, the curve for FP reaches below 30 %, well beyond the
plot scale (green solid line).
Impact on surface sublimation calculations
There are very few direct estimates of surface evaporation on the Antarctic Plateau. This is firstly because eddy correlation techniques use delicate
high-frequency sampling instruments such as sonic anemometers, which are hard
to operate and maintain at the required level of performance in the extreme
environment of the Antarctic Plateau. Moreover, due to the very low
temperature, the water vapor content is very small and moisture sensors are
neither fast nor sensitive enough for measurement in such conditions. For
instance, Van As et al. (2005) report that eddy correlation measurements of
latent heat flux were unsuccessful even in the summer at Kohnen station in
Antarctica, ∼ 3000 m above sea level. The authors thus resigned
themselves to using bulk methods, a widely employed approach in Antarctica
(Stearns and Weidner, 1993). However, bulk methods are equally affected by
measurement biases such as underestimation of water vapor content due to
failure to measure supersaturation. The magnitude of the error can be
estimated at Dome C by comparing bulk calculations using HMP and HMPmod water
vapor reports.
Same as Fig. 6 but for moisture partial pressure above 2 Pa only.
The water vapor flux E from the snow surface (subscript “s”) to
the atmosphere is calculated using bulk-transfer formulas:
E=ρCQU(z)[qs-q(z)],
where ρ is the air density, U(z) and q(z) are the wind
speed and the specific humidity, respectively, at
the height z in the atmospheric surface layer, and
qs is the specific humidity at the surface, assuming
saturation with respect to ice at the snow surface temperature. Here the wind
speed and specific humidity are measured at z=∼3 m above the
surface, and the snow surface temperature is obtained from measurement of the
upwelling infrared radiation (Vignon et al., 2016) considering a snow
emissivity of 0.99 (Brun et al., 2011). CQ is a bulk transfer
coefficient which is written as follows:
CQ=κ2[ln(z/z0)-ψm(z/L)]-1[ln(z/z0q)-ψq(z/L)]-1,
where κ is the Von Kármán's constant, z0 and z0q the
roughness lengths for momentum and water vapor respectively, and ψm
and ψq are the corresponding surface-layer similarity stability
functions. Stability functions depend solely on the dimensionless height
z/L, where L is the Monin–Obukhov length (Vignon et al., 2016;
Stull, 1990). The same four function schemes taken for stable conditions in
Vignon et al. (2016) are tested here, and the functions from
Hogström (1996) are selected for unstable conditions because they provide
reasonable results for momentum and heat fluxes at Dome C (Vignon et al.,
2016). L and thus CQ are calculated with an iterative
resolution of the Monin–Obukhov equations system. The value of z0 is
the mean value reported by Vignon et al. (2016) for Dome C (0.56 mm). The
value of z0q is difficult to estimate at Dome C because the very low
vapor content of the atmosphere induces high uncertainties and because the
scarcity of near-neutral conditions prevents an independent selection of a
scheme for the stability functions. Two different approaches are used. By
default, z0q=z0 as in King et al. (2001), and in a second case,
z0q is calculated with Andreas (1987) theoretical formula, which at
Dome C yields z0q values lower than z0 by approximately one order
of magnitude. Uncertainties on flux calculations are estimated from the
variance of results obtained with the different choices of stability
functions and roughness length.
Annual march of the monthly vapor flux at the surface
according to HMP (red) and HMPmod( green), the black line showing 0 (upper
plot), and cumulated difference (HMPmod–HMP, lower plot).
Figure 10 shows the monthly seasonal water flux calculated by the bulk method for 2015 using either HMP data or
HMPmod reports, and also shows the cumulated difference between the two calculations. The flux is positive
during the summer months indicating sublimation of snow, while during winter
months the flux is negative, indicating condensation to the surface. Such
seasonality is in agreement with that reported by King et al. (2001) at
Halley station, which is situated in coastal Antarctica but at a latitude
similar to that of Dome C. The positive summer values reflect the
predominance of snow sublimation during the summer diurnal cycle (Genthon et
al., 2013) because, in summer, the surface–atmosphere exchanges are larger
during convective activity in the afternoon than in the night hours when the
boundary layer becomes stable (King et al., 2006; Vignon et al., 2016).
Integrated over the full year 2015, the net water vapor flux is
0.2763 cm w.e. using HMPmod data and 0.2863 cm w.e. using HMP data. These
numbers can vary by as much as ±100 % with the different choices of
stability functions and roughness length values. They are very small anyway
compared to the total surface water budget, given that the mean annual
accumulation is about 2.5 cm w.e. (Genthon et al., 2015). However, a mean
positive evaporation agrees with Stearns and Weidner (1993) who, for other
regions of Antarctica, conclude that the annual-mean net sublimation exceeds
the annual-mean net deposition. In fact, Fig. 10 shows very little difference
between calculations made with HMP and HMPmod data: the impact of
supersaturation on the water heat flux is thus very small. This is because
supersaturations predominantly occur when the wind speed and thus turbulence
are weaker (Fig. 3), and when specific humidity is low (Fig. 11) and thus
turbulent flux are weak (Eq. 1). A possible contribution of blowing or
drifting snow sublimation (King et al., 2001; Frezzotti et al., 2004; Barral
et al., 2014) is not taken into account in the calculations here.
According to HMPmod, relative humidity versus water vapor partial
pressure, saturation shown by the red line (upper plot), and probability
distribution function of RHi above 105 % with respect to water vapor
partial pressure (lower plot).
Meteorological models and cold microphysics parameterizations
The introduction (Sect. 1) refers to the results of Genthon et al. (2013)
showing that two models with cold microphysics parameterizations for water
condensation predict significant supersaturation at Dome C. Observations are
now available to verify these results and the general models' ability to
simulate the characteristics of supersaturation at Dome C. Model results from
both the MAR and ECMWF are again evaluated here, although these are results
from “up to date” model versions as of 2015, which differ somewhat from
those in Genthon et al. (2013).
Meteorological models and microphysics highlights
MAR is a limited area coupled atmosphere–surface model. Atmospheric
dynamics are based on the hydrostatic approximation of the primitive
equations (Gallée and Schayes, 1994). The vertical coordinate is the
normalized pressure. Near the surface where observations are made,
parameterization of turbulence in the surface boundary layer is based on the
Monin–Obukhov similarity theory and turbulence above the surface boundary
layer is parameterized using the K-ε model, consisting of
two equations for turbulent kinetic energy and its dissipation. The prognostic
equation of dissipation allows one to relate the mixing length to local
sources of turbulence and not only to the surface. The
K-ε model used here has been adapted to neutral and stable
conditions by Duynkerke (1988). The influence of changes in water phases on
the turbulence is included following Duynkerke and Dreidonks (1987). The
relationship between the turbulent diffusion coefficient for momentum and
scalars (Prandtl number) is dependent on the Richardson number following
Sukoriansky et al. (2005).
Mean December–January–February diurnal cycle of observed (FP and
HMPmod) and analyzed (ECMWF, 2 m and first model level at ∼ 8 m) water
vapor partial pressure (a), relative humidity with respect to ice
(b) and temperature (c). The reference temperature is that
from the unmodified HMP (brown curve in c).
Prognostic equations are used to describe five water species (Gallée,
1995): specific humidity, cloud droplets and ice crystals, rain drops and
snow particles. A sixth equation is added describing the number of ice
crystals. Cloud microphysical parameterizations are based on Kessler (1969),
Lin et al. (1983), and Levkov et al. (1992). In particular, cloud droplets
are assumed to freeze at temperatures below 238.15 kK while contact-freezing
nucleation, deposition and condensation freezing nucleation of ice crystals
follow the formulation of Meyers et al. (1992) improved by Prenni et
al. (2007). Surface processes in MAR are modeled using the soil ice
vegetation atmosphere scheme (SISVAT). For the present experiment, MAR is set
up over the region of Dome C with a horizontal resolution of 20 km over a
41 × 41 grid. Lateral forcing is taken from ERA-Interim (Dee et al.,
2001). There are 15 model levels in the vertical between the surface and
32 m, where temperature and moisture are explicit prognostic variables of
the primitive equations and parameterizations.
Numerical weather forecasts are produced by meteorological models
initialized with meteorological analyses. Meteorological analyses are the
result of optimally combining (assimilating) meteorological observation from
various sources (surface, radiosounding, satellites, etc.) with(in) a
meteorological model. Unlike observations, which are scattered in time and
space, meteorological analyses have the full time and space coverage and
resolution of the model. The ECMWF produces global meteorological analyses
to initialize its forecasts: these are the near real-time operational
analyses. Like other weather services, the ECMWF has also produced
reanalyses, retrospective analyses for purposes other than real time
operational weather forecasts. The ERA-Interim data used as lateral forcing
for MAR (see above) are reanalyses produced by ECMWF. Reanalyses are more
consistent in time than operational analyses because they use the same
meteorological model and assimilation package while these are constantly
changed towards improvement and finer resolution in the operational
analyses. Some changes occurred in the ECMWF operational system in the
course of 2015 but such major aspects as horizontal and vertical resolution
were not affected. Because the vertical resolution is significantly finer
near the surface in the operational analyses than in the reanalyses, we
elect to use here the operational analyses to compare with the observations.
The ECMWF model (versions CY40R1 and CY41R1 for the year 2015) is part of the
ECMWF IFS (Integrated Forecasting System). The ECMWF provides a full
description online
. It is a spectral general circulation
model based on the hydrostatic primitive equations. Parameterization in the
surface boundary layer is again based on the Monin–Obukov similarity theory
while turbulent coefficients in the unstable mixed layer above are computed
using the eddy-diffusivity mass flux (EDMF) approach (Kohler et al., 2011).
They are determined above the mixed layer and in stable conditions using a
first-order closure based on the wind shear, a mixing length and the local
Richardson number.
Mean December–January–February diurnal cycle of observed (FP and
HMPmod for moisture, HMP for temperature) and MAR-simulated
RHi(a) and air temperature (b), the brown curve being
the observed as in Fig. 12.
The cloud microphysics scheme is described in Forbes et al. (2011).
Prognostic equations are used for cloud liquid, cloud ice, rain, and snow
water contents. The scheme allows supercooled liquid water to exist at
temperatures warmer than the homogeneous nucleation threshold of 235.15 K.
At temperatures colder than this, water droplets are assumed to freeze
instantaneously. For temperatures below the homogeneous freezing temperature,
the scheme also assumes that ice nucleation initiates when local RHi
reached a threshold (Karcher and Lohmann, 2002).
At the surface, the snowpack is treated taking into account its thermal
insulation properties and a representation of density (Dutra et al., 2010).
The vertical resolution in the atmosphere near the surface is not as fine as
in the MAR model. The mean elevation of the first prognostic model level at
Dome C in summer is 8.2 m, significantly higher than the observation level.
Variables are also calculated at the meteorological standard 2 m level by
interpolation between the first level and the surface using gradient equations
of the surface layer. Vignon et al. (2016) show that the surface layer where
gradient interpolation relationships are valid is often much shallower than
8 m in stable conditions at Dome C. The 2 m interpolated values probably
encompass biases due to the interpolation formula and may have to be
considered carefully. However, the elevations of the 2 m and first-level data
bracket that of the observations, allowing a more detailed comparison in a
region where vertical gradients can be steep.
Model data comparison
Figure 12 compares the observed diurnal cycles of temperature and moisture
with the ECMWF analyses at the first model level and at the standard 2 m
level. A similar comparison is shown with MAR at the closest model level in Fig. 13. There are only four analysis steps per day, so ECMWF data are shown as
dots in Fig. 12 when the observations (48 data per day) and MAR results (240
per day) are shown as continuous curves in Figs. 12 and 13.
The ECMWF analyses overestimate nighttime temperature and consequently
underestimate the amplitude of the diurnal cycle. The amplitude of the cycle
of moisture partial pressure is also underestimated but not as badly as could
be expected considering a non-linear relation between temperature and
saturation humidity. The model thus agrees with a large diurnal change in
magnitude and sign of the surface turbulent flux of moisture. The surface
atmosphere is expectedly moister, and the vertical gradient and turbulent
flux directed upward (surface sublimation) in the early afternoon. It is
downward (deposition) and much weaker at night. Because of the temperature
errors, RHi is less than observed at night, yet it is significantly
larger than 100 %. The analyses reproduce supersaturation at night and
minimum RHi in the early afternoon. MAR also produces large
supersaturations, which are actually larger than the observations in summer
(Fig. 13a). However, the model is significantly and consistently too warm
(Fig. 13b), which was not the case in the model version used in Genthon et
al. (2013). Supersaturations are nonetheless a robust feature of this model.
ECMWF-analyzed (a) and MAR-modeled
(b) distributions of RHi in 2015 for cases of RHi between
50 and 150 %. The fact that a different time sampling in the models (this
figure) and the observations (Fig. 6) does not affect the comparison was
verified.
Figure 14 displays the distribution functions of ECMWF-analyzed and MAR-modeled RHi. This is to be compared with Fig. 6a for the observations.
The 2 models are successful at reproducing very frequent occurrences of
supersaturation, however their distributions differ both with the
observations and with each other. The MAR model is much more often
supersaturated than the observations report, and also than the ECMWF
analyses. RHi in the MAR model exceeds 200 % much more often than
both the observation and the ECMWF analyses (not illustrated in Fig. 14 for
scale reasons as discussed in Sect. 3.2/Fig. 6), raising particular concerns
about the treatment of the cold microphysics in this model. Differences
between models and between one or the other model and the observations are
beyond observation uncertainties. Further analyses of these differences,
comparing the respective cold physics parameterizations and tracking possible
contributions of temperature biases, is beyond the scope of the present
study. However, this result illustrates that because long series of
consistent in situ observations are feasible at Dome C, not only short term
chronology but also the statistics of supersaturation can be quantified and
used to exhibit differences in behavior of models and parameterizations of
natural atmospheric supersaturation.
Discussion and conclusions
Major ice supersaturations are observed in the surface atmosphere of Dome C
on the Antarctic Plateau in atmospheric temperature and moisture conditions
that are similar to those of the upper troposphere. To our knowledge it is
the first time such strong supersaturations (up to 200 %) are observed in
the natural surface atmosphere of the Earth. The presence of high ice
supersaturations suggests very low concentrations of ice nuclei (see King and
Anderson, 1999). More instruments on the tower of Dome C are being considered
to detect fogs and monitor their properties, including supercooled water fogs
(see, e.g., Anderson, 1993). This would help understanding the microphysical
conditions under which these ice supersaturations occur and improve
microphysics schemes used in models. Atmospheric supersaturations are
frequent in the high troposphere where cirrus clouds form (Spichtinger et
al., 2003). On the other hand, atmospheric supersaturation is an infrequent
situation in the surface atmosphere because of the high concentration of
aerosols and relatively mild temperatures which are both favorable to liquid
and solid cloud formation. In this respect, the surface atmosphere of the
high Antarctic Plateau is a relative exception. Because of the high albedo of
snow, high latitude, and high elevation, the temperature and humidity are
close to that of the high troposphere elsewhere even in summer. Long-distance
transport to such a remote area is insufficient to import significant amounts
of cloud and ice condensation nuclei even from the closest sources at the
oceans, thus the possibility of strong and frequent supersaturation.
Conditions for surface supersaturation may be found elsewhere in the polar
regions. King and Anderson (1999) observed supersaturations of 150 % or
more, and a significant frequency of ice supersaturation of 120 % or
more, at the coastal Antarctic station Halley. Their climatological frequency
distribution of RHi (their Fig. 2) has similarities with Fig. 6a
here. This might seem surprising as one would expect to see higher
concentration of ice nuclei at a low-altitude coastal site than on the high
plateau. However, the number of active ice nuclei is a strong function of
temperature (DeMott et al., 2010). Thus it is possible that while aerosol
concentrations are higher at Halley than at Dome C, the concentration of
active ice nuclei is not so much higher because of the lower temperatures at
Dome C. For the same reason, supersaturations may be expected in the surface
atmosphere of other mildly isolated polar regions, but the high Antarctic Plateau is probably the most propitious place for the largest and the most
frequent cases of supersaturation, most similar to that in the upper
troposphere where cirrus clouds form. On the Antarctic Plateau itself, low-elevation clouds are a major issue for the local energy budget as even very
light clouds strongly affect the IR emissivity of the atmosphere (Gallée
and Gorodetskaya, 2000; Town et al., 2007): models that fail to reproduce
supersaturation will produce too much cloudiness and fail to account for the
surface energy budget.
Because they are compact, light-weight and comparatively low cost , both to
buy and to operate, solid-state hygrometers (thin-film capacitive sensors
such as Vaisala's Humicap) are widely used to report atmospheric moisture
from radiosondes or automatic weather stations. However, these sensors are
subject to icing in supersaturated environments (Rädel and Shine, 2010)
and require correction and/or adaptation. There are not many measurements of
atmospheric moisture in Antarctica, and most (including by the radiosondes)
are made using unadapted solid-state sensors. The atmospheric humidity of the
Antarctic atmosphere where supersaturation is frequent is likely often
underestimated from observations. Thus, the evaluation of meteorological and
climate models from these data may be biased. Observations at Dome C using
modified sensors to ensure that supersaturations can be sampled show that
models that implement parameterizations of cold cloud microphysics intended
to simulate cirrus clouds at high altitude qualitatively reproduce frequent
supersaturations but fail with respect to the statistics of supersaturation
events. Moreover, they fail differently, both models tested here producing
too much supersaturation but one model simulating much more frequent
occurrences of supersaturations than the other.
ECMWF and MAR supersaturation simulations are quite different for several
reasons. Water vapor concentration in the first model results from data
assimilation while it is fully free to respond to model equations and
parameterizations in the second. Parameterization of ice crystal nucleation
plays a particular role in the behavior of the supersaturation process. It is
based on theoretical developments in ECMWF and in this case the number of
crystals formed is rather insensitive to the aerosol physical properties. The
parameterization in MAR was developed using aircraft observations in the
Arctic. The results at Dome C probably show that parameterization tuning is
too narrow to properly account for the near-surface conditions at Dome C,
although temperature conditions probably play the most important role. Cloud
ice processes are still poorly understood and the parameterizations used here
must certainly be improved. A sensitivity test of the microphysical scheme in
the RACMO meteorological model to the inclusion of supersaturation
significantly improves the performance of this model over Antarctica (van
Wessem et al., 2014).
Estimations of the moisture budget of the Antarctic atmosphere may be
erroneous. Because it is comparatively undersampled by observation, studies
of the Antarctic atmosphere rely more than elsewhere on models and
meteorological analyses. However, only models with microphysics
parameterizations that account for supersaturation may, but not necessarily
do, correctly reproduce Antarctic atmospheric moisture. Consequences of
underestimating surface atmospheric moisture, whether in observations or
models not accounting for supersaturations, can include poor estimation of
precipitation, but it could also be that the surface turbulent moisture exchange
(evaporation or sublimation) is erroneous. Although the ground is made of
thousands of meters of snow and ice slowly accumulated through millions of
years, the Antarctic Plateau is one of the driest places on Earth. At Dome C,
only about ∼ 30 kg m-2 of water accumulates each year (Genthon
et al., 2015). Out of this, the relative contribution of precipitation and
evaporation is an open question. The direct measurement of both quantities is
an unsolved challenge. For the turbulent fluxes, bulk and profile method
parameterizations have their intrinsic limits because Monin–Obukov similarity
theory requires empirical correction functions which are not necessarily
well established in very stable conditions (Vignon et al., 2016). However,
even the best theory and best parameterization deployed based on this theory
will poorly apply if the observations are wrong. The consequences are limited
on the Antarctic Plateau though, because supersaturations are stronger and
more frequent as temperature is lower, and moisture content and thus
turbulent moisture flux smaller.
Finally, accurate measurements of supersaturation on the East Antarctic
Plateau are important to understanding the physical processes involved in the
water cycle in very dry conditions. In particular, it has important
consequences for the formation of the isotopic signal of the snow. While the
cumulated impact of water vapor exchange between the surface and the
atmosphere may be small and contributes only ∼ 10 % of the surface
mass balance, the asymmetry of the meteorological conditions (colder during
condensation than during sublimation) leads to differences in the
fractionation coefficients for the phase transition. As supersaturation
during snow accumulation induces additional fractionation (Jouzel and
Merlivat, 1984), we expect a significant impact of local supersaturation to
the water isotopic signal recorded in the snow (Casado et al., 2016).
Measurement of ice supersaturation as high as 200 % in this very dry
atmosphere invites some revision of our understanding of the physical
processes that control the water cycle in Antarctica. The deployment of more
hygrometers that can measure supersaturation on the ∼ 45 m
meteorological tower is underway and will give more insights into water vapor
fluxes. Comparisons to surface observations will also improve our
understanding of dry deposition and formation of hoarfrost, and possibly of
diamond dust. These results open new possibilities of using stations in
remote polar regions to study and understand phenomena normally occurring in
clouds at several kilometers of altitude.
Data availability
The meteorological dataset used in the present study has been obtained in the
framework of IPEV CALVA program and INSU/OSUG GLACIOCLIM observatory. Data
are made available on the CALVA web site
http://lgge.osug.fr/~genthon/calva/home.shtml or on request to the
authors.
Acknowledgements
Support for field measurements was provided by the French polar institute
IPEV through program CALVA (1013). Concordia station is jointly operated by
the IPEV and PNRA. INSU provided support through programs LEFE CLAPA and
DEPHY2. Support by OSUG through observatory program GLACIOCLIM is also
acknowledged. The BSRN upwelling infrared radiation data, which served to
calculate the snow surface temperature, were kindly provided by Christian
Lanconelli, CNR ISAC. The research leading to these results has received
funding from the European Research Council under the European Union's Seventh
Framework Programme (FP7/2007-2013)/ERC grant agreement no. (306045). JBM
also thanks UPMC university for financial assistance. We thank John King and
two anonymous reviewers for their careful evaluation and thoughtful comments
and suggestions on the initial (ACPD) version of the paper. Edited
by: E. Jensen Reviewed by: J. C. King and two anonymous referees
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