Validation of a limited area model over Dome C,Antarctic Plateau, during winter

Hubert Gallee Æ Irina V. Gorodetskaya

Received: 7 July 2008 / Accepted: 6 November 2008! Springer-Verlag 2008

Abstract The limited area model MAR (Modele Atmo-spherique Regional) is validated over the Antarctic Plateau

for the period 2004–2006, focussing on Dome C during the

cold season. MAR simulations are made by initializing themodel once and by forcing it through its lateral and top

boundaries by the ECMWF operational analyses. Model

outputs compare favourably with observations from auto-matic weather station (AWS), radiometers and atmospheric

soundings. MAR is able to simulate the succession of cold

and warm events which occur at Dome C during winter.Larger longwave downwelling fluxes (LWD) are respon-

sible for higher surface air temperatures and weaker

surface inversions during winter. Warm events are bettersimulated when the small Antarctic precipitating snow

particles are taken into account in radiative transfer com-

putations. MAR stratosphere cools during the cold season,with the coldest temperatures occurring in conjunction with

warm events at the surface. The decrease of saturation

specific humidity associated with these coldest tempera-tures is responsible for the formation of polar stratospheric

clouds (PSCs) especially in August-September. PSCs thencontribute to the surface warming by increasing the surface

downwelling longwave flux.

Keywords Antarctic Plateau ! Dome C !Regional climate model ! Cloud radiative properties

1 Introduction

The aim of this paper is to validate the Modele Atmo-

spherique Regional (MAR) over the East Antarctic Plateauduring winter. A comparison of MAR outputs will be made

with meteorological observations from Dome C, a new

permanent station over a dome of East Antarctica. Detailedmeteorological observations made at Dome C have already

been reported (Argentini et al. 2005). Several reasons may

explain the need of such observations. Among them onemaycite (1) the need to better know the very stable boundary layer

and (2) the need to infer optical properties of the atmosphere

at Dome C, since that site is a good candidate for thedevelopment of astronomical observations (Aristidi et al.

2005).

Winter is the season when meteorological conditions arethe most extreme over the East Antarctic Plateau. The simu-

lation of such situations may contain errors affecting model

climatology and the subsequent interpretation of the linkbetween climate over the East Antarctic Plateau and global

climate. At least the parameterizations of two processes arestill debated: turbulence under very stable conditions and

cloud formation (Parish and Bromwich 2002). First, the low

troposphere over the East Antarctic Plateau is extremelystable during winter, with a time mean surface temperature

inversion as large as 25"C (Connolley 1996). A good esti-

mation of the surface inversion is important when retrievingclimate information from ice cores (Masson-Delmotte et al.

2008; Fujita and Abe 2006; Helsen et al. 2005). Second,

clouds may be very thin over Antarctica. Antarctic meteo-rological models are sensitive to the parameterizations of

cloud microphysical processes and in particular to their

impact on the radiative transfer through the atmosphere(Lubin et al. 1998; Guo et al. 2003; Hines et al. 2004). Since

the cloud cover may affect the surface energy balance by

H. Gallee (&) ! I. V. GorodetskayaLaboratoire de Glaciologie et de Geophysique del’Environnement, CNRS, 54, rue Moliere, BP. 96,38402 St Martin d’Heres Cedex, Francee-mail: gallee@lgge.obs.ujf-grenoble.fr

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Clim Dyn

DOI 10.1007/s00382-008-0499-y

modulating the longwave downward radiation (LWD), it is

important to know themodel sensitivity to the representationof cloud radiative properties. Finally properties of precipi-

tating snow are important when retrieving the climatic signal

from the ice cores.Since Dome C is a permanent station situated on the East

Antarctic Plateau one of the goals of this paper is to identify a

possible need of new observations and improvements ofmodel parameterizations. Systematic errors in the model

outputs will be analyzed and possible shortcomings in theparameterizations will be identified. Since the parameteri-

zations used in this study are not definitive the results

discussed here must be considered as preliminary.The rest of the paper is divided in 5 parts: a short

description of already available observations is given in the

second part. Then the model is described with a particularattention to the parameterizations of clouds. The period

2004–2006 is simulated in part 4. Indeed more observa-

tions were made at Dome C since 2004: AWS observations(temperature, wind speed and direction), surface radiative

fluxes, atmospheric soundings. Model sensitivity to the

parameterization of cloud radiative properties is deter-mined in part 5. The last part is reserved for a discussion

and some conclusions.

2 Observations

Dome C is a site on the East Antarctic Plateau where a

permanent station allows performing detailed meteorolo-

gical observations during the Antarctic winter. The firstwintering occurred in 2005. Observations from AWS

(Automatic Weather Station) Dome C (Stearns and

Weidner 1990) are used here with observations from sur-face radiometers (shortwave and longwave, G. Lanconelli

2007, personal communication) and aerological soundings

made by the IPEV-PNRA (Institut Paul Emile Victor—Programma Nazionale Ricerche in Antartide) ’Concordia’

Cooperative Programme (Tomasi et al. 2006). Temperature

and wind data are available for the entire period (2004–2006) while radiation measurements—during 2006 and

soundings—during 2005. The AWS is located at 75.12"S,123.37"E (3250 m above sea level) and measures airtemperature and wind speed at 3 m above ground level

(a.g.l.) (Aristidi et al. 2005). Instantaneous values at

10 min frequency are provided by the Antarctic Meteoro-logical Research Center in Wisconsin (http://uwamrc.

ssec.wisc.edu). For comparison with the model the data

were subsampled at 6-h frequency, which has filtered outmost of the extreme value outliers. Soundings were not

assimilated in the European Centre for medium-range

weather forecast (ECMWF) operational analyses in 2005(A. Pellegrini personal communication).

3 MAR

Modele atmospherique regional (MAR) has been developed

for Polar Regions. It is a primitive equations hydrostatic

model. Atmospheric dynamics is fully described in Galleeand Schayes (1994). The turbulence scheme is based on the

E-e model of Bintanja (2000) and on the Monin-Obukhov

similarity theory (MOS) respectively outside and inside thelowest model layer, assumed to be the surface boundary

layer (SBL). A dependence of the Prandtl number on the

Richardson number has been included in order to take intoaccount the less efficient turbulent transport of heat under

very stable conditions (Sukoriansky et al. 2005). The first

model level is assumed to be the SBL and is situatedroughly 8 m a.g.l. at Dome C. The radiation transfer scheme

is that of Morcrette et al. (2001) and includes the RRTM

longwave radiation transfer model of Mlawer et al. (1997).It is also used by ECMWF in European Re-Analyses ERA-

40 (Uppala et al. 2005). The cloud microphysical scheme is

described in Gallee (1995) and is improved in Gallee et al.(2001, 2005). It includes 6 prognostic equations for specific

humidity, cloud droplet concentration, cloud ice crystals

(concentration and number), concentration of precipitatingsnow particles and rain drops. Ice microphysical processes

are included based on the work of Lin et al. (1983). The

Fletcher (1962) equation for ice nuclei concentration isreplaced with the more realistic parameterization of Meyers

et al. (1992). The conversion from cloud ice crystals toprecipitating snow and the prognostic equation for the ice

crystal number are based on Levkov et al. (1992). Cloud

radiative properties are computed from the concentration ofcloud droplets and cloud ice crystals qi (Ebert and Curry

1992). Here the concentration of snow particles q* is also

included in the computation of cloud radiative properties inorder to take into account their small effective radius re* inthe Antarctic interior (Walden et al. 2003; Ellison et al.

2006). Since re* is roughly 3 times larger than the effectiveradius rei of Antarctic ice crystals, it is assumed that the ice

concentration used to force the radiative transfer scheme is

qi þ q#3 rather than qi. The inclusion of snow particles into

the cloud ice crystal concentration scaled using the particle

radius ratio contributes similarly to the cloud emissivity of

thin clouds, which are typical over Antarctic plateau (icewater path not exceeding 1 g m-2). This modification is

discussed in Sect. 5. MAR is coupled with a snow model

and an interactive blowing snow model (Gallee et al. 2001).Snow particles may be eroded from the snow pack by the

wind. The erosion threshold of snow particles depends on

their dendricity, sphericity and size. The strong negativefeedback of snow erosion by the wind on atmospheric tur-

bulence is taken into account (Gallee et al. 2001). Blowing

snow particles are included in the precipitating snow par-ticle concentration.

H. Gallee, I. V. Gorodetskaya: Validation of a limited area model over Dome C, Antarctic Plateau, during Winter

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4 Control experiment

4.1 Model Set-up

MAR is set-up over the whole Antarctic ice sheet for theperiod 2004–2006. The horizontal resolution is 80 km

and is probably sufficient for simulating Dome C cli-

mate. Indeed the area around Dome C is flat and theterrain is homogeneous. Model sentivity to a change of

the horizontal resolution from 80 to 20 km is not sig-

nificant at Dome C. MAR simulation is made byinitializing the model once on the 1st of January 2004

and by forcing it through its lateral boundaries by the

ECMWF operational analyses (Marbaix et al. 2003).Note that Dome C is situated roughly 2,000 km away

from the closest lateral boundary (Fig. 1). A Newtonian

relaxation is also included for wind and temperature inthe upper sponge of the model (sigma levels r = 0.0157,

0.0283, 0.0437, 0.0618, 0.0825, 0.1055 corresponding to

z = 29,558, 25,756, 23,000, 20,826, 19,034 and17,512 m at Dome C). The Newtonian relaxation factor

decreases downwards from the model top.

MAR is first validated by comparing model outputs withavailable observations. The behaviour of some relevant

variables (temperature, wind, LWD, specific humidity,

concentration of cloud particles–ice crystals, droplets–andsnow) is then discussed. In the last two sections we discuss

validation of MAR meteorological variables, its capability

to simulate the alternation of warm and cold regimes, and

sensitivity to representation of cloud radiative properties.

4.2 Validation

MAR temperature at Dome C is compared with AWS

observations on Fig. 2 and Table 1. Model temperature is

interpolated from lowest model level (8 m) to AWS level(3 m) by using MOS theory. Some agreement is found

between observation and simulation both for the timing andamplitude of temperature variations. Synoptic scale events

are slightly better simulated than shorter time scale events.

The correlation between simulated and observed tempera-tures is improved up to 0.66, 0.34 and 0.49 for 2004, 2005

and 2006 respectively when applying a 5-day running

mean to the data. MAR simulates the warm events over anatmospheric layer which is much thicker than the SBL.

Figure 3 contains a Hovmoller diagram of the temperature

vertical profile in the lowest 500 m (50 dam) during winter2006. The boundary layer is generally thinner than 30 m

(not shown) while the atmospheric layer affected by the

warming is much deeper. A look at levels higher than500 m indicates that the warmings occur over a much

deeper part of the atmosphere (not shown). This suggests a

synoptic scale influence. Note also the high variability intime of the temperature inversion strength at Dome C (blue

line), with a weaker inversion strength and a higher wind

speed (dark line) when the low troposphere warms.

Fig. 1 Antarctic topography inMAR for a grid spacing of80 km. C refers to Dome C(75"S, 123"E)

H. Gallee, I. V. Gorodetskaya: Validation of a limited area model over Dome C, Antarctic Plateau, during Winter

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The good representation by MAR of the warming 3-m

above the surface in conjunction with the simulation of awarming over a deep atmospheric column suggests that the

model is able to capture correctly the synoptic scale

meteorological forcing through its boundaries. In particularthe Newtonian relaxation towards the ECMWF analyses in

the upper sponge of the model could be able to better

capture the timing of the synoptic forcing. A sensitivity

simulation was also made using 60 levels, with an upperrelaxation zone including the layers located at 55,200,

42,770, 37,770, 34,550, 32,100 and 30,085 m at Dome C.

No significant sensitivity was found to that modification,indicating coherence between the numerical solution of

MAR and the forcing data.

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Fig. 2 Comparison betweensimulated (solid line) andobserved (dashed line)temperature 3 m above thesurface at Dome C duringwinter for 2004 (upper), 2005(middle) and 2006 (lowerpanel), sampled at 6-h time step

Table 1 Time average of observations (OBS), bias, correlation and RMSE of MAR temperature and wind speed at Dome C

Temperature (K) OBS mean MAR bias MAR RMSE Corr. Wind (m/s) OBS mean MAR bias MAR RMSE Corr.

2004 212.1 ?0.1 8.6 0.62 3.17 0.64 2.8 0.37

2005 214.0 -0.6 10.5 0.33 3.60 0.65 2.8 0.35

2006 209.6 -0.7 8.5 0.43 3.05 0.87 3.6 0.38

All correlations are significant at 95% level

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Looking at the cold events it is found that the amplitude

of the temperature minima is well simulated. Since cold

events occur for clear sky situations it may be deduced thatthe surface energy budget is well simulated in this case.

The surface energy budget during polar night is forced by

LWD fluxes, sensible heat fluxes and heat accumulation inthe snow pack. It will be shown later that the LWD fluxes

are no more underestimated in MAR, in contrast with that

was obtained when using in MAR the Morcrette (1984)parameterization of the radiation transfer (not shown).

MAR wind speed at Dome C is compared with AWS

observations on Fig. 4 and Table 1. The simulated windspeed is interpolated to AWS level (3 m) by using MOS

theory. As for temperature some agreement is found

between observation and simulation both for the timing andamplitude of wind speed variations. Again synoptic scale

events are slightly better simulated than shorter time scale

events. The correlation is now improved up to 0.54, 0.42and 0.49 for 2004, 2005 and 2006 respectively when

applying a 5-day running mean to the data. However the

time average of the wind speed is overestimated (Table 1).It is possible that this overestimation is due to a slight

destabilisation of the boundary layer through the long-term

overestimation of the LWD. Note that higher wind speedsare generally observed/simulated in conjunction with

higher observed/simulated temperatures.

Simulation of vertical profiles in temperature andhumidity is crucial for longwave radiative fluxes and thus

for correct representation of the warm and cold regimes at

Dome C. Vertical profiles of the simulated bias of tem-perature, specific humidity, wind speed and direction are

shown for MAR and ECMWF operational analyses in

Fig. 5, for winter 2005. The biases were computed relativeto the vertical profiles obtained from the radiosonde mea-

surements at Dome C station (Aristidi et al. 2005; Tomasi

et al. 2006). The winter profiles are averaged during June–August. This period has been chosen since the soundings

made at Dome C were not assimilitated in ECMWF model.

The agreement is generally good beyond 100 hPa above thesurface. The wind speed bias is smaller in MAR than in

ECMWF operational analyses. MAR temperature bias is

significant in the first 100 hPa above the surface. It may be

partly explained by a temperature difference between thesounder and the air outside when the sounder is launched

(Mahesh et al. 1997). Also note a possible dry bias in the

Vaisala RS90 radiosonde humidity profiles over Antarctica(Rowe et al. 2008). Nevertheless MAR is too warm and too

moist in particular in the layer below 600 hPa. An analysis

of individual events shows that MAR sometimes simulatesa warm contribution to the bias while sometimes it simu-

lates a cold contribution. Warm contributions to the bias

are the largest above 20 m a.g.l. Vertical extent of the coldcontributions to the bias is smaller. This is why a negative

bias is simulated near the surface while a positive bias is

simulated between 642 and 600 hPa. Each individualcontribution to the bias is generally simulated over a layer

extending well above 600 hPa. It is possible that because

the timing of cold and warm events is not fully simulatedby the model a small delay during a large temperature

variation may generate a strong impact. Finally it may be

argued that the impact of each individual contribution ismaximum at the surface through LWD heating. The strong

response of the surface rapidly weakens upwards due to the

weakening of turbulent processes under stable conditions.Integrating the LWD over all individual events it is found

that the contribution of the warm/moist bias situated below

600 hPa is masked by the contribution of the cold biasoccurring in the two lowest atmospheric layer of the model.

The aim is now to analyze themechanisms responsible for

warming events at Dome C. Observed and simulated LWDare compared on Fig. 6. May–September 2006 averages

amount respectively to 84 and 61 W/m2 in MAR and in the

observations.MAR overestimates LWDbut simulates ratherfairly well its variability in time and in amplitude. The

overestimation of LWD could be partly explained by the

overestimation of the simulated precipitable water vapour(PWV) and temperature in the layer between the surface and

300 hPa (see Fig. 5, panels (a) and (b)). Due to the inclusion

of snow particles into the radiative scheme, some overesti-mation of LWD also occurs during some relatively large

Fig. 3 Temperature profile in the lowest 50 dam (1 dam = 10 m) atDome C during winter 2006. Wind speed averaged over that layer(dark line, in m/s) and surface temperature inversion strength (blue

line, in K) are also plotted. Surface inversion strength is thetemperature difference between the surface and the warmest levelbelow 13 km

H. Gallee, I. V. Gorodetskaya: Validation of a limited area model over Dome C, Antarctic Plateau, during Winter

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snowfall events simulated byMARwhen observations show

small LWD values, as for example in mid June.The simulation of LWD and near surface temperature is

compared on Fig. 7. The strong correlation (0.88) is

explained by the determining influence of LWD on thesurface energy budget at Dome C during winter. The sen-

sible heat flux (SH) is much weaker than LWD (May–

September 2006 average amounting to 16 and 84 W/m2

respectively). SH is better correlated with the wind speed

(correlation amounting to 0.86) than with the near surface

temperature (correlation amounting to 0.34). Note that SHcould be overestimated at Dome C by the model since the

wind speed is overestimated.

As the variability in time of LWD is mainly due to thatof water vapour and cloudiness, a similar comparison is

made between LWD and (1) PWV (Fig. 8) and (2) cloud

optical thickness (Fig. 9). Again the correlation with LWD

is significant, amounting to 0.81 for PWV (Fig. 8) and to

0.77 for cloud optical thickness (Fig. 9). Note that cloudi-ness depends on PWV, explaining partly the coherence

between both correlations. Cloudiness may also depend on

temperature in the stratosphere (see the followingparagraph).

The vertical distribution of cloud particles (ice crystals

and droplets) and snow particles over Dome C for 2006 isshown on Fig. 10. In MAR, during the entire year cloud

droplet concentration dominates over the cloud ice crystals

in the troposphere over Dome C. Liquid layers in lowtropospheric clouds have been occasionally observed at

Dome C (M. Del Guasta 2008, personal communication).

Note that such clouds have been also observed over SouthPole during summer (Walden et al. 2005). Although MAR

probably overestimates the frequency of liquid occurrence

in tropospheric clouds, routine measurements of cloud

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Fig. 4 Comparison betweensimulated (solid line) andobserved (dashed line) windspeed 3 m above the surface atDome C during winter for 2004(upper), 2005 (middle) and 2006(lower panel), sampled at 6-htime step

H. Gallee, I. V. Gorodetskaya: Validation of a limited area model over Dome C, Antarctic Plateau, during Winter

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(a) (b)

(d)(c)

Fig. 5 Vertical profiles of simulated bias at Dome C of (a) temperature (K), (b) specific humidity (g/kg), (c) wind speed (m/s) and (d) winddirection (") in MAR (red) and ECMWF operational analyses (green) compared to radiosonde measurements

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Fig. 6 Comparison betweensimulated (solid line) andobserved (dashed line) surfacedownward longwave radiationat Dome C, for winter 2006.Bias and RMSE amountrespectively to 13.9 and 27.4 W/m2. Correlation is 0.40(significant at 95% level)

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Fig. 7 Comparison betweensimulated air temperature (solidline) for the lowest model level(roughly 8 m a.g.l.) and surfacelongwave downward radiation(dashed line) at Dome C forwinter 2006

H. Gallee, I. V. Gorodetskaya: Validation of a limited area model over Dome C, Antarctic Plateau, during Winter

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properties are needed to make any conclusions. From Juneuntil October very high (stratospheric) clouds sometimes

form with ice crystals. This is partly due to the important

radiational cooling of the stratosphere during polar nightwhich decreases saturation specific humidity. Short term

variations of stratospheric clouds concentration are corre-

lated with the short term variations of the temperature ratherthan by those of specific humidity (not shown). These short

term stratospheric cooling occur in conjunction with warm

events in the lower part of the atmosphere. Since MAR doesnot include chemical processes, stratospheric clouds inMAR

only form with water vapour and could be classified as Polar

stratospheric clouds of type II (PSC II). Snow flakes con-centration may increase downwards, in agreement with the

observed fact that PSCs may occur in conjunction with

clouds in the troposphere (Spinhirne et al. 2005;Wang et al.2008). Note that snow particles generated inMARPSCsmay

fall down to the surface, but their contribution to the surface

mass balance (SMB) is small (see panel (b) of Fig. 10). Themost important part of the SMB at Dome C is rather due to a

few snow fall events occurring the 2 or 3 first kilometres

above ground level. Note that 2006 SMB simulated byMARis 22 mm and could be underestimated. Indeed long term

SMB at Dome C is roughly 30 mmw.e. (Urbini et al. 2008).

The comparison between panels (a) and (b) of Fig. 10reveals that snow particles occupy a deeper atmospheric

column than cloud ice crystals. The small size of snow

particles during the Antarctic winter observed by Waldenet al. (2003) suggests that their contribution to the radiative

properties of the atmosphere is not negligible. In order toinfer the respective influence of cloud particles and snow

flakes on cloud radiative properties sensitivity experiments

are discussed in the following section.

5 Model sensitivity to cloud radiative properties

Sensitivity of LWD to the presence of hydrometeors in the

atmosphere is assessed first by performing off-line simu-lations of the longwave radiative transfer, using data from

the control experiment. For example LWD standard devi-

ations from time average amount respectively to 29.7 or10.1 W/m2 for May–September 2005 (22.1 or 10.3 W/m2

for May–September 2006) when hydrometeors are inclu-

ded in or removed from the radiative computations.The contribution of ice crystals to LWD is not signifi-

cant except when a large amount of PSCs form (e.g., in

July–September 2006, see Fig. 10). In that case the con-tribution to LWD of ice crystals is roughly similar to that of

snow particles. The impact of PSCs on LWD has been

assessed by removing from radiative computationshydrometeors situated above 10 km altitude. The LWD

becomes smaller during winter in this case, with a differ-

ence reaching 6.3 W/m2 for the August-September 2006average (14.9 W/m2 for August–September 2005). The

difference increases during the winter indicating an

increasing influence of PSCs on SBL temperature vari-ability when stratosphere cools.

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Fig. 8 Comparison betweensimulated precipitable watervapour (PWV, solid line) andsurface downward longwaveradiation (dashed line) at DomeC for winter 2006

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H. Gallee, I. V. Gorodetskaya: Validation of a limited area model over Dome C, Antarctic Plateau, during Winter

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In order to take into account the relatively tiny size of

Antarctic snow particles their concentration is taken intoaccount in the computation of cloud radiative properties

(see Sect. 3). Here that assumption is tested by performing

a simulation in which that contribution has been switchedoff. Simulated temperatures for both the control and the

sensitivity experiments are compared to those of the stan-

dard in Fig. 11.Surface temperatures are underestimated in the sensi-

tivity experiment. May–September average is 205.2 K

while it is 209.5 K in the observations and 208.9 K in thecontrol (Table 2). Note in particular that the amplitude of

the simulated temperature is underestimated during warm

events in the sensitivity test. The same behaviour is foundin the simulated LWD, see Fig. 12). Similar underestima-

tions are found for winter 2004 and 2005 (Table 2). In

contrast a smaller sensitivity is found in the time average ofthe simulated wind speed, although the slightly larger

simulated wind speed in the control simulation was

expected. Indeed the larger influence of clouds on theradiative transfer in the control is responsible for a larger

LWD, a larger heating of the surface, a less strong vertical

stability of the low atmosphere, more turbulence and asubsequent stronger transfer of momentum downwards in

the SBL. Thus the sensitivity test shows that inclusion of

snow particles in the computation of cloud radiativeproperties improves the simulation of temperature during

the warm events. On the other hand the simulation of the

wind speed is degraded due to the long-term overestima-tion of the LWD.

6 Discussion and conclusions

MAR has been validated over Dome C, the new permanentstation situated over the East Antarctic Plateau. The model

(a)

(b)

Fig. 10 Vertical distribution of simulated hydrometeors at Dome C for 2006: (a) cloud ice crystals and droplets, (b) snow particles. Snowaccumulation is also plotted (cyan line)

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Fig. 11 Comparison betweenobserved (thin dash) andsimulated temperature 3 mabove the surface at Dome C.Solid line: control experiment;thick dashed line: sensitivityexperiment (no contribution ofthe snow particles to the cloudradiative properties)

H. Gallee, I. V. Gorodetskaya: Validation of a limited area model over Dome C, Antarctic Plateau, during Winter

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behaves fairly well at the AWS level. In particular it

simulates accurately the amplitude and to some extent thetiming of cold and warm events in the troposphere during

winter. Wind speed is larger during warm tropospheric

events, as in the observations. Temperature and wind speedvariations associated with these events are also simulated

over a tropospheric column much thicker than the SBL.

The main forcing of the surface energy budget at Dome Cduring winter is the LWD. Precipitable water vapour,

cloudiness and precipitating snow particles increase during

warm events. Polar Stratospheric Clouds (PSCs) are alsosimulated during warm events and may influence LWD

significantly.

The short term variations of PSCs are due to additionalstratospheric cooling associated with the troposphere

warming. MAR PSCs are responsible for the precipitation

of snow particles over a large thickness of the atmosphere.These snow particles may reach the surface but their con-

tribution to the surface mass balance at Dome C is small.

Nevertheless an overestimation of the vertical extension ofPSCs is possible and could be due to an overestimation of

the snow sedimentation velocity. Indeed snow sedimenta-

tion velocity is parameterized in the model from thecharacteristics of heavier mid-latitude snow particles.

MAR vertical extension of PSCs should be validated in the

future. Observed Antarctic snow particles are small duringwinter and their observed effective radius is larger than that

of ice crystals by roughly a factor 3 only. This is why their

contribution to cloud radiative properties has been takeninto account in MAR. Model sensitivity to that contribution

is important. In particular the simulation of warm events is

significantly degraded when switching it off. As chemicalprocesses are not represented in MAR, only water PSCs are

allowed to form, so that the PSCs concentration could be

underestimated. Thus the possibility exists that the inclu-sion of snow particles in the computation of cloud radiative

properties could partly compensate an underestimation of

PSCs concentration. On the other hand it could be arguedthat overestimation of the snow sedimentation rate depletes

hydrometeors originating from the low troposphere but

does not deplete those originating from the stratosphere. Asubsequent underestimation of optical thickness and

emissivity should result for low clouds, but not for PSCs.

Finally the overestimation of PWV below 300 hPa duringwarm events could suggest that the formation rate of tro-

pospheric clouds is underestimated by the model. A

detailed comparison between observation and simulation of(low) tropospheric clouds will help in clarifying that point.

MAR is too warm between 640 and 600 hPa and too

cold below 640 hPa. This temperature bias is the sum ofwarm and cold contributions, probably because the model

does not fully reproduce the timing of cold and warm

events and the associated strong variations of temperatureand humidity. Positive contributions to the bias are the

strongest in the bulk of the boundary layer and dominate

there for the period which was considered (June–August2005). In contrast the negative contribution to the bias is

the strongest just near the surface.

A consequence of the strong variability in time ofatmospheric conditions at Dome C during winter is the

strong variability of the simulated temperature inversion,

which ranges from 0"C up to 40"C. More precisely varia-tions of the inversion strength Ta-Ts, with Ta the inversiontemperature and Ts the surface temperature, are negatively

correlated with those of the atmospheric temperatures. Thetime average of Ta-Ts(21.7"C), is not representative of any

Table 2 May - september averages of temperatures at Dome C at AWS - level. OBS refers to Dome C II AWS observations and CRP to ‘‘CloudRadiative Properties’’

Temperature 2004 (K) 2005 (K) 2006 (K) Wind speed 2004 (m/s) 2005 (m/s) 2006 (m/s)

OBS 212.1 214.0 209.6 3.17 3.60 3.05

CRPðqi þ q#3 Þ 212.2 213.4 208.9 3.81 4.25 3.92

CRP(qi) 206.4 207.5 205.2 3.48 3.84 3.43

5 10 15 20 25 30 5 10 15 20 25 30 5 10 15 20 25 30 5 10 15 20 25 30 5 10 15 20 25 3040

60

80

100

120

140

160

180

200

Dow

nwel

ling

LW fl

ux, W

m −

2

MAY JUN JUL AUG SEP

OBSMAR controlMAR sensitivity

Fig. 12 Comparison betweensimulated (solid line: controlexperiment; thick dashed line:sensitivity experiment in whichsnow particles do not contributeto cloud radiative properties)and observed (thin dashed line)surface downward longwaveradiation at Dome C

H. Gallee, I. V. Gorodetskaya: Validation of a limited area model over Dome C, Antarctic Plateau, during Winter

123

typical situation, which makes difficult the interpretation of

the link between the ice core record and the surface tem-perature, at least at short climatic time scales. Such

behaviour was also found for drilling site Kohnen in Queen

Maud Land (Helsen et al. 2005). It is noted in particularthat precipitation events occur mainly when the tempera-

ture inversion is less strong than its time average. This may

be explained by an increase of surface warming by LWDdue to the occurrence of clouds.

This paper has shown the high sensitivity of the Antarcticclimate to cloud radiative properties. This high sensitivity

could be partly explained by the fact that Antarctic clouds are

rather thin, so that taking into account an additional smallamount of hydrometeors has a large effect on cloud optical

thickness and emissivity. Of course observations of

hydrometeors above the Antarctic plateau (Walden et al.2003, 2005; Ellison et al. 2006) are scarce and new obser-

vations are needed to base more firmly our assumption.

Accurate representation of cloud microphysics is essentialfor simulating surface radiative budget and temperature

variability on synoptic time scale over the Antarctic Plateau.

Acknowledgments We thank Steve Colwell for the quality check ofthe AWS data. I. Gorodetskaya was supported by Agence Nationalede la Recherche (France) grant OTP 232 333. Computation wererealised with IDRIS computing resources. The IPEV-PNRA ‘Con-cordia’ Cooperative Programme—Routine MeteorologicalObservations is acknowledged for providing atmospheric soundingsdata. French LEFE/IDAO project Charmant is acknowledged forproviding support for publishing the present paper.

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