Summertime Evolution of Snow Specific Surface Area Close to The

Home , Plateau Station

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , and 3 , S. Morin 3 X class copernicus.cls. E T A , M. Lafaysse 3 1 , M. Dumont 1,2 , L. Arnaud 1,2 , G. Picard 1,2 1,2,a

Univ. Grenoble Alpes, LGGE (UMR5183),CNRS, 38041 LGGE Grenoble, France (UMR5183), 38041 Grenoble,Météo-France France – CNRS, CNRM – GAME UMR 3589, Centre d’Étudesnow de at: la ESCER Neige, Centre, Department of Earth and Atmospheric Sciences, Université du Québec à Correspondence to: Q. Libois ([email protected]) 2 3 Grenoble, France a Montréal, 201 Av. du Président-Kennedy, Montreal, QC H3C3P8, Canada 1 E. Lefebvre

Q. Libois Antarctic Plateau surface area close to the surface on the Summertime evolution of snow speciﬁc Date: 16 November 2015

with version 2015/04/24 7.83 Copernicus papers of the L Manuscript prepared for The Cryosphere Discuss. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2 onto the surface during snowfall and blowing snow events. 1

−

kg 2

might be overestimated in the model. of metamorphism and precipitation on surface SSA, but that snow compaction by the wind ulations with the observations. This suggests that Crocus reproduces well the contributions we found that disabling strong wind events in the model is sufficient to reconciliate the sim- in the model of the precipitation on metamorphism, with only 6 % enhancement. However surface SSA. A simulation with disabled summer precipitation confirms the weak influence simulated when compared to 14 years of microwave satellite data sensititve to the near reanalysis. On the contrary, the inter-annual variability of summer SSA decrease is poorly daily to seasonal time scales, except for few cases when snowfalls are not captured by the tals with SSA up to 100 m model was forced by ERA-Interim reanalysis. It successfully matches the observations at characterised by significant daily to weekly variations, due to the deposition of small crys- end, some Crocus parameterizations have been adapted to Dome C conditions, and the sorption of solar radiation in the snowpack during spring and summer. Surface SSA is also inter-annual variability of summer SSA decrease and summer precipitation amount. To this to February in the topmost 10 cm, in response to the increase of air temperature and ab- simulate SSA, with the intent to further investigate the previously found correlation between these factors. The results show an average two-to-three-fold SSA decrease from October To complement these field observations, the detailed snowpack model Crocus is used to is analysed with respect to meteorological conditions to assess the respective roles of contributions remain unclear. Here, a comprehensive set of SSA observations at Dome C strong wind events are known to drive SSA variations, usually in opposite ways, their relative the surface energy budget of the ice sheet. While snow metamorphism, precipitation and complex variations at daily to seasonal scales which affect the surface albedo and in turn On the Antarctic Plateau, snow specific surface area (SSA) close to the surface shows Abstract

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20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , C ◦ 50 − , Frezzotti et al., 2004), so 2 − kg m 3 and snow metamorphism barely operates (Town et al., 2008). -folding depth, especially in the near-infrared (NIR, e.g. Domine C ◦ e 50 − -folding depth (Libois et al., 2013) and the effective thermal conductivity of the e

The Plateau is characterized by very low temperatures (annual average around

metamorphism, which leads to an overall decrease of snow SSA (e.g. Picard et al., 2012), On the contrary, at the end of spring, the increase of snow temperature causes significant remain well below et al., 2011) and low annual accumulation (less thanother 30 environments (e.g. Alps, tundra, . . . ). During the polar night, temperatures usually Augustin et al., 2004), low precipitable waterthat vapour the physical content processes (less controlling snow than characteristics 1 are mm, substantially different Tremblin from surface snow evolution on the Antarctic Plateau. for climate studies. This highlights the need to identify the main processes which drive snowpack gives rise to feedbacks (e.g. Albert et al., 2004) and is therefore of great interest The resulting dependence between snow physical properties and the energy budget of the events such as snowfall and wind events (Kuhn et al., 1977; Champollion et al., 2013). 1983) and densification (Gallée et al., 2001). The surface is also affected by meteorological in response to internal thermodynamical processes such as snow metamorphism (Colbeck, et al., 2005; Lacroix et al., 2009; Champollion et al., 2013). Snow properties evolve over time recent studies pointed out that it is subject to large and rapid variations (e.g. Bindschadler time, especially for the calibration of satellite radiometers (e.g. Loeb, 1997; Six et al., 2004), of the Antarctic Plateau has often been considered homogeneous in space and stable in snowpack (Sturm et al., 1997; Calonne et al., 2011), among others. Although the surface the light Warren, 1982; Gardner and Sharp, 2010; Carmagnola et al., 2013). Snow density controls et al., 2006), and thus controls the amount of solar radiation absorbed by the surface (e.g. snow albedo and light the ice–air interface per unit mass of snow, hereafter referred as SSA) strongly affects (Van As et al., 2005; Brun et al., 2011). Snow specific surface area (the surface area of The surface energy budget of the Antarctic Plateau depends on snow physical properties 1 Introduction

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | -folding depth increases. This positive feed- e 4 -folding depth adds up to the snow albedo feedback (e.g. Flanner and e

Hitherto, this summertime SSA decrease has been generally deduced from albedo mea-

properties. Nevertheless, correctly simulating SSA evolution remains crucial to better un- (Dang et al., 1997; Groot Zwaaftink et al., 2013), rather than on temporal evolution of snow surface temperatures (e.g. Brun et al., 2011; Fréville et al., 2014), or on punctual profiles properties on the Antarctic Plateau focus on the evolution of the snowpack internal and an drier areas. In addition, the few studies dedicated to the simulation of snow physical Marbouty, 1980; Guyomarc’h et al., 1998), and do not necessarily perform well in colder characteristics are indeed often based on observations made in alpine environments (e.g. semi-empirical parameterizations for e.g. snow metamorphism, compaction and fresh snow fully adequate to polar environments (Dang et al., 1997; Groot Zwaaftink et al., 2013). Their with a more detailed snowpack model such as Crocus. In fact, such models are usually not their hypothesis, but these features of the seasonal cycle of SSA have never been simulated above mentioned positive feedbacks. They used a simple snow evolution model to support correlation with summer amount of precipitation, and hypothesized a strong inhibition of the ability of summer metamorphism is poorly understood. Picard et al. (2012) showed a strong decrease has seldom been measured directly in the field. In addition, the inter-annual vari- nation conditions, cloudiness and surface roughness (Wang and Zender, 2011), but such surements (Jin et al., 2008; Kuipers Munneke et al., 2008), which also depend on illumi- optical properties at the surface. back involving the Zender, 2006; Box et al., 2012), making summer metamorphism very sensitive to snow consequence, SSA generally decreases and temperature gradients, which in turn enhances metamorphism close to the surface. As a feedback: when solar energy is absorbed deeper, it warms up the snowpack and increases tween snow optical properties and SSA accelerates snow metamorphism through a positive (van den Broeke, 2004). Picard et al. (2012) have shown that this interdependence be- and Zender, 2011), which significantly alters the surface energy budget of the snowpack (Gow, 1965). As a result, albedo decreases by several percents (Jin et al., 2008; Wang densification (Fujita et al., 2009) and other morphological changes of the surface snow

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . To complement these automatic 2014 – 2013 5 and , and snow samples from the surface, both mea- 2013 10 cm – based on Picard et al. (2012) (Sect. 2.2). Thirdly, the de- 2012 2014 – 2000

The aim of this paper is to investigate the summertime evolution of snow SSA at Dome C,

deduced over the summers using a speciﬁcally designed automatic instrument, from which surface SSA variations were and satellite data. Snow spectral albedo in the visible and NIR range has been measured The temporal variations of snow SSA at Dome C were estimated from in situ measurements 2 Materials and methods measurements and to explore the variations deeper in the snowpack, supplementary SSA estimated, which also helps identifying the current potential and limits of Crocus (Sect. 4). lar, the sensitivity of simulated SSA to changes in precipitation, wind and temperature are summer metamorphism is eventually investigated in more details with Crocus. In particu- The observations and simulations are compared in Sect. 3. The inter-annual variability of bois et al., 2014a), and forced by ERA-Interim near-surface reanalysis (Dee et al., 2011). For this, it was adapted to Dome C conditions by changing some parameterizations (Li- 1989, 1992; Vionnet et al., 2012) was used to simulate snow SSA at Dome C (Sect. 2.3). tailed snowpack model SURFEX/ISBA-Crocus (hereinafter referred as Crocus, Brun et al.,

range over the period tres was estimated from remote sensing observations of the snowpack in the microwave the surface during daylight periods. Secondly, the evolution of SSA in the topmost centime- measurements of snow spectral albedo were used to estimate the evolution of SSA close to proﬁler ASSSAP (a light version of POSSSUM, Arnaud et al., 2011). In addition, automatic sured manually during the summer campaigns 2012–2013 and 2013–2014 using the SSA proﬁles between the surface and were collected at Dome C during summer campaigns (Sect. 2.1). These included vertical this evolution, we use three datasets. Firstly, a large number of in situ SSA measurements and to further understand its variability, from the daily to the inter-annual scale. To quantify (Krinner et al., 2006). derstand the sensitivity of this region to future changes in precipitation and air temperature

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | min ), in the . l . s . . One head ◦ m a E, 3230 ◦ 3 . depth. Eventually, to study 123 cm S, ◦ 1 . 75 6 resolution). Albedo was recorded every 12 E). The instrument was deployed on 10 December 3 nm ﬁeld of view. The ﬁbers are sequentially connected to ( ◦ ◦ 180 30244 . 1100 nm – 123 Downwelling hemispherical irradiance measurements are strongly West of Concordia station ( S, 300 ◦ m 09960 . 75

affected by the quality ofsolar the zenith angles cosine (SZA) response typical of ofDespite the the our light Antarctic effort Plateau collector, to (e.g. especially van buildthe at den highly ideal high Broeke, diffusing cosine 2004). collectors, response small need to remaining be deviations corrected. from To this end, the angular response of Albedo correction.

at approximately 600 surements in the range Domine et al., 2006), variations of the latter were estimated from spectral albedo mea- Using the dependence between snow albedo and SSA close to the surface (Warren, 1982; 2.1.1 SSA estimation from spectral albedo measurements 2.1 Field observations temperature were used. the inter-annual variations of surface SSA, satellite measurements of microwave brightness SSA measurements, as well as vertical proﬁles down to 10 measurements were taken manually with the instrument ASSSAP.These comprised surface signed cosine collectors having a of snow SSA from these measurements. Its steps are computed as follows: consists of one upward- and one downward-looking ﬁber optics mounted with specially de- calibration of the raw measurements. We developed an algorithm to estimate the time series data from the visible-NIR head were used in the present study. Each measurement head tral albedo is obtained by computing the ratio of upwelling to downwelling irradiance after is optimized for measurements in the UV and visible, the other for visible and NIR. Only the an Ocean Optics® Maya Pro spectrophotometer through an optical multiplexer. The spec- checked at installation with an electronic inclinometer and was better than 0.5 heads looking to the surface and to the sky (Fig. 1). The horizontality of the heads was 2012 and has been running almost continuously since then. It has 2 similar measurement “clean area” (

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , but increases be- ◦ difference). % in the visible range at high 0 . 1 for angles below 70 % 7 , indicating insufﬁcient correction. 05 . 1 To minimize the effect described above, we consider only . The procedure lasted less than 30 s and was performed ◦ due to the dome geometry of our collectors that capture a signiﬁcant amount ◦

our collectors was determined in theand laboratory, reflected and fluxes used from to the measured estimatethe ones the (Grenfell true perfect et incident cosine al., response 1994). The is deviation less from than 4 yond 80 of light at grazingquires angles knowledge (e.g. of Bernhard the et1994). al., direct Since 1997). vs. this In diffuse informationratio addition, is parts was the supposed not of to correction available depend the re- from onlyclear-sky incident on measurements, and SZA, flux the cloudy and (Grenfell was direct/diffuse conditions. thusspheric et It treated radiative al., was in transfer the calculated model same SBDART atclear-sky way (Ricchiazzi for all conditions et wavelengths al., at with 1998) Dome the forassumed C. typical atmo- isotropic. Contrary summer Although to the incident upward-to radiation, and have reflected downward-looking the fibers radiation same are is ferent. angular assumed To response, account the transmittances forcollectors of this were both consecutively effect, set optical both in linesvertically the lines are the downward-looking were dif- fibers position, by of inter-calibrated. simply 180 on For flipping a this, clear-sky the day, soboth two that spectra the was upward used fluxthese to could precautions, rescale be albedo all assumed values the constant. sometimes albedos The exceed ratio analysed of in this study. Despite all Daily computation of albedo. SZA and occasionally reach up to albedo at noon. For this,averaged the every 5 measurements day. taken This betweensolar means 11:30 azimuth and that angle 12:30 albedo LT throughout are choice the measurements is summer, also are made but because taken not preferentiallate at orientation necessarily of into constant constant surface relief an SZA. is azimuthal This knownthat to dependence trans- should of be albedo avoided here. (Wanglocal We and nevertheless hour Zender, tried and to 2011), azimuth) use anthe which constant collectors, artefact minimizes SZA and errors (i.e. the due variable results to were imperfect very similar cosine (less response than of 15 2.

10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | = (1) (2) (3) ice ρ 1050 nm – are related 700 dir λ α and diff λ α is optimized along with SSA, A is the wavelength-dependent ab- after Libois et al., 2014b), λ is meant to compensate for all the , γ 86 . A ! , ) i g ) = 0 , and − g describe snow single scattering properties C ◦ λ g (1 SSA 8 ( and Bγ dir λ 2 6 and SSA α . ! is actually given by: ice ) B ρ g diff = 1 λ α 3 r − B s − λ ) (1 1 µ Bγ The SSA of surface snow is then estimated from the daily 2 SSA ) + is the SZA, ice (1 + 2 ρ θ 3 7 , 12 SSA ) ( θ s − 4 diff λ

− α is the bulk density of ice at 0 gives the proportion of diffuse light.

3 diff λ = cos( r − diff λ h µ r ) = exp A µ = exp ( = dir diff λ λ λ sorption coefficient of ice, takenminimizing from the Warren root and mean Brandtthe square (2008). theoretical SSA deviation albedo, accounting is between for retrieved the theance by direct spectra calculated and with of diffuse SBDART. The components daily comparison of is albedo solar computed irradi- and in the range albedo spectral dependence. To this end,uniform. the In snowpack this is case, assumed the semi-infinite diffuse and and direct spectral albedos α where and are assumed constant917 kg ( m Daily SSA estimation. α to SSA using the analytical formulation of Negi et al. (2011): where the impact of lightand absorbing impurities the is sensitivity minor of (Warren snow andcertainties albedo Wiscombe, in to 1980) the snow albedo SSA spectra, is a high. scaling To coefficient account for remaining un- so that the function to optimize α factors affecting in anot wavelength-independent way explicitly albedo corrected measurements, bysomehow that similar the were to previous estimating SSA processinget from steps. al., albedo 2008). ratios This at inversion different wavelengths method (Zege is where

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15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . nm 3% 2 cm − (Fig. 2), 1 at 1050 nm 5 mm using the algorithm , gives an estimation % +3% to long horizontal rail ﬁxed approx- 2% − m obtained during the retrieval. Over the A 9 , surface SSA was measured using ASSSAP in This procedure is applied every day independently from 1, that is A 2013 . Out of this period, we consider that albedo measure- – . ◦ % -folding depth ranges from approximately 2012 e . In 2014 – . The deviation of depending on snow characteristics (Libois et al., 2013). A rigorous for- 2013 = 1 nm A above the surface and measures the snow reﬂectance at 1310 and cm

at 700 2013 of sky conditions. It is repeatedat every noon year remains from lower 18 than Octoberments 67 to are 27 February, not when accurate SZA enough to retrieve SSA (e.g. Wang and Zender, 2011). SSA evolution through the summer.

–

4 cm 4. The SSA retrieved with this algorithm roughly corresponds to the SSA of the top

from which the surface SSA is estimated with an accuracy of 10–15 the SSA retrieval is better than 25 range of interest and the SSA values encountered at Dome C, the estimated accuracy of of the albedo measurementThe accuracy. Hence corresponding accuracy we on assume SSA that estimation is the then albedo derived accuracy from is Eq. 1. For the spectral horizontal position. For this,imately ASSSAP slides 5 along a 1 described in Arnaud et al. (2011). Every two days from 22 November 2012 to 16 January 2012 The SSA of surface snow was also manually measured during the summer campaigns 2.1.2 Surface SSA

approach based on the analysis of the coefﬁcient article and will be addressed in future work. Here the accuracy is estimated usinghave a yielded global Taking into account all these factors and their inter-correlation is beyond the scopeperiod of of this observations, it varied in the range 0.98 – 1.03, while ideal measurements would the potential tilt of the sensor, the validity of the semi-inﬁnite snowpack assumption, etc. snow anisotropy (Carmagnola et al., 2013), the shadowing of the surface by the instrument, eral factors including the uncertainty on the collector calibration procedure, the effect of ward estimation of the accuracy of the algorithm would require a thorough analysis of sev- to of the snowpack since the light

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | m S, ◦ situated 10379 . m ) is smaller 75 2 mm E) and the second ∼ ( ◦ between each other. nm m 30224 . 123 S, E). During this summer campaign, ◦ resolution (e.g. Carmagnola et al., ◦ cm 09971 . 34093 . 75 -folding depth at 1310 123 e 10 S, depth at 1 ◦ long horizontal transects of SSA were thus mea- m cm . As light 10374 . further East compared to the previous year ( 75 , the SSA measured with ASSSAP are not directly compa- m days nm , amounting to 32 proﬁles taken in the same area where surface over 64 days 2 South East of the station, just besides the surface SSA measurements. 1000 m m E), and amounted to a total of 630 snow samples taken randomly in an area of ◦

South East of the station (

34484 m

with ASSSAP from the surface to 10 From 23 November 2012 to 16 January 2013, 98 vertical profiles of SSA were measured 2.1.3 Profiles of SSA 2013 (except from 3 to 6 January), 1 surface. sured without disturbing the snow surface, at 2 fixed locations distant of about 5 rable to the albedo-derived estimates given that the snow is rarely homogeneous near the 2014). The measurements were performed at two different sites. The first one was 600

than in the range 700–1050 500 West of the main building of Concordia station (

measurements were taken 100 taken from the surface were measured almost every day using ASSSAP sampler. These to fill the sampler. From 27 November 2013 to 29 January 2014, the SSA of snow samples approximately places in undisturbed snow areas with a minimum distance of 5 sampler. For this, freshly deposited particles were collected on a metallic plate and gathered day at the first site, the other day at the second one. All these profiles were taken at different the SSA of precipitation particles was also measured at several occasions using ASSSAP 123 one was 500 Every day during this period (except from 3 to 6 January), two profiles were measured, one

snow samples were taken. with ASSSAP every 2 From 25 November 2013 to 25 January 2014, one vertical proﬁle of SSA was measured

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20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . ) for 8 . r 2 % yields 3 ice ρ − ( / kg m approximately. = 3 cm ) instead of 320 according to the mean surface 3 . Using observations at 150 and match the satellite observations. 3 − − GHz GHz kg m kg m thick and the bottom one is semi-inﬁnite. average air temperature taken from ERA- ), which gives a broad estimate of 40 cm required by the DMRT theory, an empirical % day r 11 period from 1999 to present, the SSA in both layers is (respectively 350 3 − day (respectively -40 kg m %

makes it possible to estimate the averaged SSA over the top 7

GHz This method is simple because using 2 observations it considers only 2 unknowns, while Following the method of Picard et al. (2012), the DMRT-ML forward microwave emission

SSA differences up to +20

Assuming a density of 300 described in Sect. 2.1.1. The most critical assumption is probably that of constant density. SSA time-series is not expected to be as accurate as the spectrometry-based approach signal as well (even if this effect is of second order compared to the SSA). As a result, this the density and layer thickness are probably variable and are known to affect microwave (AMSU) constellation that is able to operate up to 150 on the surface snow, we used observations from the Advanced Microwave Sounding Unit which allows to retrieve SSA year-round even during the polar night. To obtain information ing in the microwave domain is the independence to weather conditions and illumination, radiometers using the approach proposed in Picard et al. (2012). The advantage of observ- The SSA time-series from 1999 to present was estimated from high-frequency microwave 2.2 Satellite observations optimized so that the model predictions at 150 and 89 estimated. For this, for each 10 idealized two-layer snowpack. The top layer is 7 density reported by Libois et al. (2014a). The SSA in both layers are the unknowns to be model (Picard et al., 2013) is used to compute the microwave brightness temperature of an (Surdyk, 2002; Picard et al., 2009) and present a lower interest for this study. 89 Lower frequencies are more sensitive to snow properties deeper down in the snowpack The temperature of both layers isInterim set to and the the 10 density is assumed constant at 320 To relate the SSAscaling to coefﬁcient is the used grain according to size Brucker metric et al. (2010), such that SSA

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . 1 − kg 2 100 m of data it gives a good indication of years and fresh snow SSA is ﬁxed at 3 12 − 170 kg m . In case of higher SSA as encountered at Dome C, this formulation 1 − kg 2 m

leads to SSA decreasetory during to wind observations in driftparameterization Antarctica events. (Kuhn predicts Since et a such al., decrease decrease 1977;unchanged. of Grenfell is SSA, et contradic- the al., 1994), latter when is the actually forced to remain In Crocus the impactchanges of in snow drift dendricity (Vionnetterms and et sphericity of al., (Brun SSA 2012) et usingthan al., was Eq. 65 1992). originally 5 It given of was in reformulated Carmagnola terms in et of al. (2014), which is valid for SSA less

–

modifications were made: The fresh snow density isAll fixed these at adaptations are detailed in Libois et al. (2014a). For the present study, a few more set at 2, 3, 5, 5 and 10 mm, to ensure that surface processes are accurately represented. rate of metamorphism. In particular, the optimal thicknesses of the 5 topmost layers were acteristics of fresh snow, the compaction of snow by the wind during drift events and the prevailing at Dome C, essentially to handle the very low amount of precipitation, the char- (Carmagnola et al., 2014). Crocus was adapted to the specific meteorological conditions relevant for this study are snow SSA, snow density, snow temperature and snow sphericity and thickness of numerical snow layers evolve with time. The snow prognostic properties dimensional multi-layer snowpack in response to meteorological conditions. The number tailed snowpack model Crocus (Vionnet et al., 2012), which simulates the evolution of a one- The temporal evolution of snow physical properties at Dome C was computed with the de- 2.3.1 Reference simulation (A) the accuracy of the method. Nevertheless, with 14 2.3 Crocus simulations the inter-annual variability of the seasonal variations of SSA.

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , as , where C 1 mm ◦ 50 − . TARTES sim- spectral resolu- 86 . nm air temperature and = 0 g 2 m wind speed, and downward and 6 . 10 m = 1 B and the absorption enhancement parameter 13 g was assumed, in agreement with observations (Warren et al., 1 − 3 ng g . According to Libois et al. (2014b), we chose

The vertical profilesbased of radiative absorbed transfer model solar TARTES (Libois energy et were al., 2013) computed at with 10 the physically- the parameterization is nodependence more of valid, snow we metamorphism implemented baseduration a on pressure. Clausius–Clapeyron scaling This law of latter for choice the vapor hassnow sat- temperature little metamorphism impact is because at anyway negligible. such low temperatures tion rather than with the originalcus semi-empirical (Brun parameterization et implemented al., in 1992).used Cro- Indeed, to TARTES has compute been the fully vertical implementedbased profile in on of Crocus energy and density absorption is and ofshape, SSA a through multi-layered profiles. the snowpack TARTES asymmetry alsoB factor accounts explicitly for snow grain ulates the impact of lightcarbon equal absorbing to impurities in snow. Here a constant load of black Brun et al. (2011)between did simulated in and observed a surface previous temperatures study at Dome because C. this choice produced the best fit Although in Crocus thethan that roughness for length heat for transfer at momentum the is air–snow usually interface, here 10 both times were fixed larger to 2006). SSA decrease is computedmagnola from et the al., formulationmodel 2014) F06 of of which snow SSA is metamorphism decreaseworking based (Car- rate in on the proposed mid-latitude a by context,over fit Flanner the a fit period of and in of Zender the Carmagnola 14but (2006). et days, semi-empirical as extend al. Because microphysical in (2014) the of Oleson was period et computed ing al. to from (2010). the 100 Here low we days temperatures use prevailing to the at account same Dome approach for C. Moreover, the below slower metamorphism result-

– – – Crocus was forced by ERA-Interim atmospheric reanalysis for speciﬁc humidity, surface pressure, precipitation amount,

10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | air tempera- m using observations of density 12 m 14 to ensure that simulated annual snow accumula- 5 . 1 throughout the year to mimic the average warming predicted by the K of the snowpack were consistently initialized. Then, Crocus was run from 2000 2 m

The snowpack was ﬁrst initialized with a depth of

ture is increased by 3 borgh et al., 2013). To estimate the strength of the positive feedback between snow albedo CMIP5 ensemble on the Antarctic Plateau by 2100 for the RCP4.5 scenario (van Olden- disabled throughout the summer (November–February). In simulation C, 2 ulations were performed and are summarized in Table 1. In simulation B, precipitation is and inter-annual variability of summer metamorphism at Dome C, 4 additional Crocus sim- ature, and to test the hypotheses proposed by Picard et al. (2012) to explain the intensity To estimate the sensitivity of Crocus simulations to summer precipitation and air temper- 2.3.2 Supplementary simulations (B, C, D, E) here since the analysis focuses priority on the end of the period. simulation A that is analysed in the following. The discontinuity in the spinup is not critical the top to 2014 and the full state of the snowpack was recorded every 12 h, yielding the reference times consecutively on the period 2000–2010, which ensured that snow characteristics in phism barely operates in winter. and SSA at Dome C (Picard et al., 2014). It comprised 25 layers. Crocus was then run 3 winter (Fréville et al., 2014), but this is not critical for our study because snow metamor- tion (http://amrc.ssec.wis.edu/aws). It does show a positive bias of about 2 K during the bias during the summer from 2000 to 2013 compared to Dome C II automatic weather sta- porting the consistency of wind data. As for air temperature, it does not show any signiﬁcant precipitation rate was multiplied by at Dome C could satisfactorily be predicted from ERA-Interim wind time series, further sup- snow surface temperature on the Antarctic Plateau. As detailed in Libois et al. (2014a), Dome C (Genthon et al., 2013).Libois et al. (2014a) also pointed that drift events observed radiative ﬂuxes. ERA-Interim data were already used by Fréville et al. (2014) to simulate good agreement with measurements performed on the 40 m high instrumented tower at tion matches observations at Dome C. On the contrary, ERA-Interim wind was found in

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . The 1 − kg 2 90 m , the average sur- to 25 2013 – 2012 . To isolate the impact of precipitation 1 − kg to compensate the fact that averaged wind 2 3 15 − 100 m was computed. For that, the measured transect was 270 kg m to 75 m . 0 – 170

25 . 0

intervals over which the median SSA value was taken. These medians wind speed forcing was taken constant – equal to its mean annual value – cm m long horizontal transect taken with ASSSAP in m

features are the same for both sites, with SSA ranging from about which the temporal evolution of SSA at the two locations was deduced (Fig. 3a). The main were then used to compute the average value and standard deviation for the transect, from dust events, as expected because such events bring at the surface snow particles char- indicated. It highlights that most rapid SSA increases followed precipitation and diamond face SSA in thefirst range divided in 1 periods when precipitation or diamond dust were visually observed at Dome C are also For each 1 3.1 Seasonal variations of SSA in the uppermost 2 mm from one year to another. The following sections address these two time-scales. variations. This decrease extends from late October to early February and is highly variable at the daily scale. Deeper, a seasonal decreasing trend is superimposed to these rapid such as snowfall and drift events (e.g. Grenfell et al., 1994), leading to rapid variations from the nominal value of scales. The SSA of the top millimetres is essentially driven by meteorological conditions throughout the simulation. In this simulation, the density for fresh snow was also increased Simulations and measurements show that SSA close to the surface evolves at different time 3 Results ulation E the 10 snow density and thus decreases snow metamorphism (Flanner and Zender, 2006), in sim- that simulated vertical profiles of density remained consistent with the observations. on summer metamorphism inter-annual variability from that of snow drift which increases speed is not sufficient to increase snow density through snow drift. This choice ensured ing the SSA remains constant, equal to and snow metamorphism, in simulation D the snow optical properties are calculated assum-

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , slightly . Again, the . More rapid 2013 1 1 – − − . SSA generally 1 kg kg − 2 2 2012 kg 2 85 m 30 m – to 35 120 m 90 to 90 16 . The SSA of individual samples was in the range 2014 – despite the absence of precipitation. 2013 1 for SSA to drop from approximately − December 2013). Snow drift were regularly observed during these kg 28 2 – , probably because metamorphism is better captured when an individual 15 10 days and the daily median SSA was in the range 60 m 1 2014 − – kg 2 2013

185 m – Figure 3b shows the time series of surface SSA obtained from the snow samples mea- These observed SSA variations, corresponding roughly to the top 2 mm of the snowpack,

between precipitation events is also correctly simulated for the summer 2013 and 25–27 January 2014). The rate of decrease of SSA due to metamorphism in SSA increases, except when precipitation is not predicted by ERA-Interim (e.g. 1 January 28 range as the measured ones (Fig. 3). In addition, Crocus reproduces relatively well the rapid the SSA around 2 mm was computed from the simulated SSA profiles. The simulated SSA vary in the same sured during the summer From 12 to 16 January, hoar crystals covered most of the surface and somehow maintained were compared to the reference Crocus simulation A. For this, the average SSA of the top the surface (Domine et al., 2009; Gallet et al., 2013). spatial variability of surface SSA, mainly resulting from snow drift (Libois et al., 2014a). et al. (2013). The formation of such crystals may thus contribute to increase snow SSA at periods. The large standard deviation of measurements taken the same day highlights the the formation of hoar crystals at the surface as reported by Gow (1965) and Champollion slight continuous increase in SSA observed from 10 to 16 January 2013 is concomitant with precipitation (e.g. low SSA. Erosion rather than snow metamorphism thus explains such rapid changes. The with SSA decrease. Significant variations also occurred during periods without observed decreases were observed, like ona 14 thin December layer 2012, of after soft a and strong high SSA wind snow event and blew let away apparent a hard windpacked old snow having largest SSA increases occurred after precipitation events. These were followed by periods less for and it took about decreased after fresh snow deposition due to metamorphism (e.g. Taillandier et al., 2007), measurements of precipitation particles that ranged from acterized by high SSA as pointed out by Walden et al. (2003) and confirmed by our SSA

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10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 − , but kg 2 2013 – 30 m of the snow- to 2012 cm 80 . The model also cap- 2014 and because the time series begins on and a root mean square difference 2013 1 − 2013 – kg 2 2012 17 2 m . 1 , as well as the large increase resulting from the was computed from the SSA proﬁles simulated by , the SSA clearly decreased from 2014 cm – 2014 – 2013 exponential decay (e.g. Mary et al., 2013) rather than a linear 2013 cm . The effect of soft snow removal by the wind as well as the formation over the two summers, the simulated values are in very good agreement 1 2014 − – kg

2 2013

1 m

. 8 The average SSA of the top 2

also computed using a 2 by an exponential to a ﬁrst approximation. To account for this effect, the simulated SSA was trieved from albedo measurements is the result of a convolution of the actual SSA proﬁle snowpack contribute more to the albedo than the snow below. As a result, the SSA re- Since solar irradiance decreases exponentially with depth, the uppermost millimetres of the tures the rapid variations of SSA due to precipitation, as already mentioned in Sect. 3.1. large amounts of fresh snow deposited in February crease of SSA observed in of with the observations (Fig. 4). Moreover, Crocus successfully simulates the seasonal de- 11 February 2014). surface SSA at the end of summer, resulting from a large snowfall (25 February 2013 and to snowfall are visible. Both years are characterized by a large and sudden increase of 10 December (date of deployment of the instrument), so that only the rapid variations due Crocus. With a mean negative bias of

measurements, which corresponds approximately to the SSA of the top 2 all along the summer. Figure 4 shows the time series of SSA deduced from spectral albedo port, the spectral albedo measurements and the vertical proﬁles show that SSA decreases Beyond these rapid variations of surface SSA, mainly due to snow deposition and trans- 3.2 Seasonal variations of SSA in the uppermost 2 and 10 cm between model and observations when these processes were observed. not in of surface hoar are currently not simulated by Crocus, which explains the discrepancies pack. During the summer snow location is followed throughout the summer, which was the case in

This seasonal trend is not fully observed in from late October to late January, with most of the decrease occurring before December.

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20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 – − kg 2013 2 simula- 29 m 2013 than in to – 37 2013 2012 – was computed daily larger in 2012 than in cm 2012 45% , and from ). Likewise, since the choice of was 2013 % – st 1 2012 in is successfully simulated, even at low tem- 1 depth are only available for the summers − . Hence the simulated seasonal trend remains kg 18 cm 2014 2 – to November cm st 1 28 m 2013 to changes, except for the simulated SSA spikes following 45 % ), which highlights the ability of Crocus to simulate the annual cycle 70 . = 0 decreased from r cm

, which is reproduced by Crocus (Fig. 5). More precisely, the main difference between . It resulted in less than 5

The summertime decrease of SSA is conﬁrmed by the series of vertical proﬁles of SSA The SSA simulated with Crocus and that deduced from AMSU observations (Fig. 6) are

cm particular, the rapid decrease at the end of spring, as well as the slower rate of increase of surface SSA over more than a decade. The SSA values are also in the same range. In well correlated (

snowpack brightness temperatures (Sect. 2.2). 2013. The SSA decrease observed in peratures although Crocus was developed for alpine conditions. In the for the simulated snowpack. It was compared to the SSA estimated from the measured from 2000 to 2014 (Fig. 6). For this, the average SSA of the top 7 time series of simulated SSA was compared to the SSA estimated from AMSU observations 2012–2013 and 2013–2014. To further understand summer metamorphism at Dome C, the

precipitation accumulated from March In situ measurements of SSA down to 10 both summers is the initial value of SSA. This can be explained by the fact that ERA-Interim 3.3 Inter-annual variability of SSA in the uppermost 10 cm ERA-Interim is able to simulate the summer variations of SSA close to the surface. two independent sets of measurements nevertheless demonstrate that Crocus forced by in 2013–2014. The summer decrease was2014 thus more signiﬁcant in tion Crocus underestimates the rapid SSA decrease measured from mid-December. These in the top 10 taken independently with ASSSAP during the same 2 summers (Fig. 5). The average SSA consistent with the observations however near-surface SSA is deﬁned.

4 2 cm is to some extent arbitrary, theprecipitation average that was were also more computed marked over for 1 the topmost 1 and average. This resulted in slightly higher SSA (less than 5

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10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 19 is much larger in the model than suggested by 2008 – , as noted from in situ measurements (Fig. 5). 2007 2014 – and 2013 2002 according to Libois et al., 2014a) mainly reﬂect the properties of winter – 6 cm 2001 than in ∼ 2013 –

2012

SSA by mid February. The time of initiation of summer metamorphism in Crocus simulation creases, snow metamorphism starts, conducting to an approximate three-fold decrease of precipitation. In late October, as solar radiation becomes stronger and air temperature in-

this period ( As a consequence, at the end of winter, snow properties in the layer accumulated during phism is very limited during this period due to the prevailing extremely low temperatures. generally depend on weather conditions, but whose SSA is invariably high. Snow metamor- mid October, snowfalls deposit onto the surface fresh snow whose detailed characteristics During the winter period at Dome C, which extends approximately from late February to 4.1 Metamorphism, snowfall and wind driven SSA variations between model and observations. remote sensing observations of snow SSA, even though some discrepancies remained general, a satisfactory agreement was obtained with regards to in situ measurements and in order to identify the physical processes responsible for summertime SSA variations. In used to simulate the temporal variability of snow SSA close to the surface at Dome C, A version of Crocus adapted to the meteorological conditions of the Antarctic Plateau was 4 Discussion

in AMSU observations nevertheless conﬁrm that summer metamorphism was more intense 2007 and 2010 while AMSU data suggest that this increase was much less than usual. satellite observations. Moreover, the simulated SSA increased almost as usual in winters the summers of SSA variations is occasionnaly very different. For instance, the SSA decrease during correspond to strong precipitation events (e.g. 2002, 2004, 2011). In contrast, the amplitude around 15 February and already observed in Fig. 4 are generally well reproduced and in winter, are similar in the simulation and observations. The rapid increases occurring

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | % lower 12% ) as input of 1 − kg 2 m lower in simulation B than in year-long, show a % K 20 at the end of summer simulated by Crocus, that is used to cm air temperature was increased by 3 m

The results of simulation B, where summer precipitation was inhibited, imply that snow

to another in Crocus than in the observations (Fig. 7). relatively weak. This probably explains why the SSA decrease is less variable from one year the sensitivity of simulated summer SSA decrease to air temperature and precipitation is over the XXIst century. This also highlights the primordial role of feedback loops. Overall, rect effect of atmospheric warming on dry snow metamorphism is likely to remain moderate SSA at the end of summer with respect to the reference simulation. This shows that the di-

in less intense summer metamorphism (Fig. 7), with the SSA at the end of summer 15 snow SSA variations and albedo, all other things being equal (simulation D). This resulted portance of this feedback,the we radiative run transfer Crocus calculations with performed a with constant TARTES SSA to (100 deactivate the link between by inhibiting the positive feedback as proposed in Picard et al. (2012). To quantify the im- simulation C, where 2 posited during snowfall increases the overall SSA, but also reduces snow metamorphism erties evolution is essential to correctly simulate SSA evolution at Dome C. The results of a quantity that is likely to be erroneous in Crocus simulations. High SSA snow actually de- that using a ﬁne representation of snow optical properties which accounts for snow prop- because the latter is highly dependent on the SSA value at the end of the previous winter, Crocus but with similar optical scheme, as noticed by Picard et al. (2012). This suggests simulation A. This metamorphism indicator is chosen instead of using the decrease in SSA, higher than in simulation A, which is consistent with simulations using a simpler model than age SSA for thequantify topmost the 7 intensity of metamorphism, is on average only 6 (Fig. 7), probably less than proposed by Picard et al. (2012). Indeed, the minimum aver- metamorphism only weakly depends on the total amount of precipitation during summer amplitude of summertime metamorphism in Crocus. and model. Supplementary simulations were thus performed to investigate what drives the Conversely, the amplitude of the SSA decrease is more contrasted between observations is very consistent with the observations, as well as the date when minimum SSA is reached.

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20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , which is 1m × 1cm 21 (Schwerdtfeger, 1976) and probe deeper into the snowpack. 6m

at Dome C, even though larger-scale spatial variability exists (e.g. Picard et al., 2014).

Although the impact of precipitation seems moderate in Crocus simulations at the sea-

radius of approximately ations of surface SSA. Conversely, the spectral albedo measurements cover an area with the snowpack. This might explain the discrepancies with Crocus simulation for the rapid vari- not sufficient to capture this spatial heterogeneity, especially in the topmost millimetres of spectral albedo measurements (Fig. 4). This probably explains the success of Crocus to simulate the SSA variations derived from 2cm Hence they are more likely to be representative of the average snow SSA in the topmost transects of SSA used in this study are representative of approximately observations and simulations (Groot Zwaaftink et al., 2013). In particular, the horizontal geneity is difficult to simulate and makes complicated the comparison between punctual the large standard deviation of surface SSA measurements in Fig. 3b. This spatial hetero- older snow at other locations through erosion (Libois et al., 2014a), which is illustrated by the surface because it can accumulate fresh snow at some locations and make apparent bert et al., 2004). Snow drift also generates spatial variability of snow properties close to snow and controls density and SSA in the topmost centimetres (Gallée et al., 2001; Al- cipitation, surface snow at Dome C is also largely shaped by snow drift, which redistributes et al., 2010), which probably participates to these differences as well. Indeed, besides pre- Plateau sometimes conduct to poorly simulated air temperature and wind profiles (Genthon More generally, shortcomings in parameterizing the surface boundary layer on the Antarctic Dome C (Fig. 3), which explains most differences between observations and simulations. et al., 2011). In practice, ERA-Interim reanalysis sometimes misses precipitation events at very precisely, a quantity that is difficult to obtain from reanalyses in Antarctica (Bromwich To simulate the evolution of the snowpack at Dome C, it is thus critical to know precipitation SSA evolution, the surface layer mostly reflects day-to-day variations of weather conditions. top 2 mm, consistently with field observations. While the deeper layers show a seasonal sonal scale, snowfall occurrence and amount drive Crocus-simulated SSA variations in the

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). As for fresh snow SSA 50 km ∼ ), based on our observations of summer 1 − kg 22 2 m . This highlights the need to extend the existing 1 − kg 2 m

The differences between simulated and observed SSA at the end of winter (Fig. 6) can Despite a few deviations from the observations, Crocus captured well the variations of

SSA of diamond dust up to 300 higher, though, as suggested by the observations of Walden et al. (2003) who measured precipitation at Dome C. In winter, due to colder conditions, the SSA of fresh snow might be in Crocus, it was assumed constant (100 parameterizations of fresh snow properties developed in the Alps to polar regions.

result from the large horizontal scale of the reanalysis ( ERA-Interim reanalysis, probably stronger than the actual event. This overestimation might simulated in January 2007 (Fig. 6) results from a strong deposition event forecasted by fresh snow characteristics in Crocus. For instance, the large and sudden increase of SSA be attributed either to inaccuracies in ERA-Interim precipitation or to the simple treatment of

and on the rate of SSA decrease during summer, which are driven by different processes. apparent intensity of the metamorphism depends both on the SSA value at the end of winter, while it proved efﬁcient to simulate the seasonal variations, is more puzzling. Actually, the The fact that Crocus poorly simulates the inter-annual variability of SSA summer decrease, 4.2 Inter-annual variability of summer metamorphism from the combined effects of precipitation, snow drift, and metamorphism. pattern of SSA over the Antarctic continent (Scambos et al., 2007), which probably results over the Antarctic Plateau, and puts Crocus as an appropriate tool to investigate the spatial in complex conditions as encountered at Dome C. This is promising for larger scale studies snow on the Antarctic Plateau, although this parameterization cannot be strictly assessed the metamorphism parameterization of Flanner and Zender (2006) is appropriate to study as already pointed out by Brun et al. (2011) and Fréville et al. (2014). It also proves that that Crocus successfully resolves the energy budget of the snowpack close to the surface, morphism strongly depends on the temperature proﬁle close to the surface, this suggests SSA in response to meteorological conditions and metamorphism at Dome C. Since meta-

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ), in of the 81 . cm = 0 r ) over this summer period cm

As to the summer decrease in SSA, Picard et al. (2012) found a strong correlation be-

forcing is adequate. In particular, the intensity of the summer metamorphism and the date These variations of SSA are successfully simulated by Crocus, provided the meteorological continuous increase in winter due to accumulation of precipitation crystals with high SSA. snowpack, characterised by a two-to-three-fold decrease of SSA in summer and a slower, drift. They also conﬁrm the existence of a seasonal cycle of SSA in the topmost 10 tions show rapid variations of SSA close to the surface, mainly due to precipitation and snow to identify the processes controlling SSA evolution on the Antarctic Plateau. The observa- compared to in situ and satellite derived measurements of snow SSA at Dome C, in order Crocus simulations suited to the meteorological conditions of the Antarctic Plateau were 5 Conclusions

need to further understand the impact of drift events on surface snow density. wind a major driver of snow metamorphism in the reference simulation, and highlights the to an over-sensitivity of snow metamophism in Crocus which may be incorrect. This makes to an inappropriate parameterization of snow compaction and SSA increase by the wind, or Zender, 2006). The apparent deficiency of the reference simulation can be attributed either increases density through compaction, which decreases metamorphism rate (Flanner and may be too strong in simulation A. In Crocus, snow drift increases surface SSA, but also agreement with AMSU observations. This suggests that the impact of wind on snow SSA 15 January). Figure 8 shows the minimum SSA (topmost 7 itation predicted by ERA-Interim (in their study, summer refers to the period 1 December– tween AMSU estimated metamorphism amplitude and the total amount of summer precip- present paper. On the contrary, for simulation E the correlation is significant ( et al. (2012) and to the observed influence of snowfall on the rapid variations of SSA in the reference simulation, there is no significant correlation, which is contradictory to Picard in terms of accumulatedwhere precipitation wind for remains the weak reference throughout simulation the A year and and for drift simulation events E, are thus inhibited. For the

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15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 24 Q. Libois, G. Picard, L. Arnaud and E. Lefebvre participated to in situ measure- LGGE is part of LabEx OSUG@2020 (ANR10 LABX56). This study was sup-

Acknowledgements. G. Picard prepared the manuscript with contributions from M. Dumont, M. Lafaysse and S. Morin. Polar Institute (IPEV) for the financial and logistic support at Concordia station in Antarctica through Crocus simulations and implemented the Antarctic parameterizations in the code. Q. Liboisported and by the ANR program 1-JS56-005-01 MONISNOW. The authors acknowledge the French radiation in TARTES. M. Lafaysse implemented the model TARTES in Crocus. S. Morin helped with analysed satellite and field data. M. Dumont contributed to the parameterization of incident solar corresponding simulations. G. Picard performed the DMRT-ML simulations. Q. Libois and G. Picard ments at Dome C. Q. Libois developed the Antarctic parameterizations of Crocus and performed the Author contributions. request to [email protected]. velopments described in this article has not yet been officially released but is available upon v7.3, adapted to Antarctic conditions. The version of Crocus incorporating the specific de- ([email protected]). The Crocus simulations were performed with SURFEX The data used in this study are available upon request from the authors Data availability and the mixing of the topmost layers of the snowpack due to snow drift. simulating snow properties on the Antarctic Plateau, such as the formation of hoar crystals, processes not yet simulated by Crocus should also be regarded as potential progress for the characterisation of precipitation in this high and extremely dry region. Other physical are known to be difficult to predict by reanalysis, highlighting the need to further improve less, SSA is very dependent on the occurrence and intensity of precipitation events, which snow SSA and evolution at the scale of the Antarctic Plateau or whole continent. Neverthe- for which it was not originally developed, which is promising for extended studies of surface Crocus can capture the main features of snow metamorphism in the conditions of Dome C the effect of snow drift on snow SSA is too strong in the model. This study demonstrates that summer decrease in SSA is not well captured, probably because the parameterization of of its initiation agree well with the observations. However, the inter-annual variability of the

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15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ), 1 − m s K for calculations of optical properties 1 − kg 2 m 3 − 32 270 kg m air temperature increased by 3 m equal to the average ERA-Interim wind speed over the simulation period (5 fresh snow density is set to Crocus simulations performed for this study. E Same as A, wind speed in the forcing is constant, ABCD Reference Same as A, precipitation set Same to as 0 A, from 2 November to Same February as A, SSA kept constant at 100 Simulation Characteristics Table 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 33 high. m Spectral albedo measurement at Dome C (picture taken on 12 January 2014, 11:00 local time). The vertical mast is approximately 2 Figure 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . ASSSAP slides along the horizontal rail and m . For the measurements, the setup was covered ms 34 Experimental setup for transect measurements of SSA using ASSSAP in horizontal posi- records the SSA of theby surface beneath a every dark 10 tarpaulin to avoid the supersaturation of ASSSAP photodiodes. Figure 2. tion. The distance between both vertical stakes is 1 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | long

−

) m (kg Precipitations

− 2 ) m (kg Precipitations 2 8 7 6 5 4 3 2 1 0 20 15 10 05 00 ...... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 01/18 01/26 deduced from the 1 ASSSAP site 1 ASSSAP site 2 Crocus 01/11 ASSSAP samples Crocus 01/19 2013 – 01/04 01/12 2012 12/28 01/05 Evolution of surface SSA during the summer (b) 12/21 12/29 35 2013 – 2014 2012 – 2013 ), the shaded bands indicate the periods of observed b 12/22 12/14 and a 12/15 12/07 12/08 11/30 12/01 (b) (a) 11/23 0 0

60 40 20 80 40 20 80 60

120 100 100 − −

pcfi ufc ra(m area surface Specific ) kg pcfi ufc ra(m area surface Specific ) kg

1 2 1 2 Evolution of surface SSA during the summer deduced from the snow samples measured with ASSSAP, and SSA of the top 2 mm

2014 –

standard deviation is indicated by the shaded area. the top 2 mm simulated with Crocus. The points show the mean value ofthe each median transect value for and each the day. In ( horizontal transects taken with ASSSAP at 2 distinct locations2013 (ASSSAP 1 andsimulated 2), with Crocus. and The grey SSA circles of indicate single measurements and the white circles highlight shown (right y-axis, dark grey columns). snowfall or diamond dust at Dome C. The amount of precipitation predicted by ERA-Interim is also Figure 3. (a) Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | –

2012

− ) m (kg Precipitations 2 8 7 6 5 4 3 2 1 0 ...... 0 0 0 0 0 0 0 0 0 02/25 02/11 01/28 01/14 12/31 2013 – 2014 12/17 12/03 11/19 36 11/05 10/22 of the snowpack simulated with Crocus, for the summers 02/10 2 cm 01/27 01/13 2012 – 2013 . The shaded bands indicate the periods of observed snowfall at Dome C. The 12/30 Spectral albedo Crocus 2014 – 12/16

40 30 20 10 90 80 70 60 50

− pcﬁ ufc ra(m area surface Speciﬁc ) kg

2 1 Variations of SSA close to the surface deduced from the spectral albedo measurements,

2013

and

Figure 4. and average SSA of2013 the top The horizontal arrows highlight the periods of measurements shown in Fig. 3. amount of precipitation predicted by ERA-Interim is also shown (right y-axis, dark grey columns). Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 01/29 01/15 01/01 , and simulated with Crocus. The 2013 – 2014 12/18 2014 – , deduced from vertical proﬁles of SSA 12/04 cm 2013 11/20 and 37 2013 01/29 – ASSSAP Crocus 01/15 2012 01/01 2012 – 2013 12/18 12/04 11/20

10 50 40 30 20 70 60 −

pcﬁ ufc ra(m area surface Speciﬁc ) kg

1 2 Evolution of SSA averaged over the top 10 moving averages of the measurements. day Figure 5. 4 taken with ASSSAP forintegrated the value summers from each proﬁle is indicated by a circle. The continuous lines correspond to the Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2014 2013 2012 2011 2010 2009 2008 38 2007 ). cm 2006 2005 2004 2003 2002 AMSU Crocus 2001 0

80 60 40 20

120 100

−

pcﬁ ufc ra(m area surface Speciﬁc ) kg

2 1 Comparison of SSA evolution deduced from AMSU brightness temperatures at 89 and , and simulated with Crocus (top 7 GHz Figure 6. 150 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2014 1 − 2013 kg 2 2012 AMSU A - reference B - no summer precipitation C - T+3K D - SSA 100 m 2011 2010 2009 2008 39 2007 2006 2005 ) at the end of summer for AMSU estimations and Crocus simula- cm 2004 2003 2002

2001

25 20 35 30 45 40

− pcﬁ ufc ra(m area surface Speciﬁc ) kg

1 2 Minimum SSA (top 7 tions A, B, C and D, from 2001 to 2014. Figure 7. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 12 2001 ) 2 − 10 8 2011 2013 6 2003 2007 4 2010 2005 2002 2004 2012 2 2000 Accumulated precipitation (kg m 2008 2009 2006 Simulation E 0 12 40 2001 ) 2 of the snowpack simulated by Crocus for each summer − 10 cm 8 2011 2013 6 2003 2007 4 2010 2005 2002 2004 2012 2000 2 2008 Accumulated precipitation (kg m 2009 2006 Simulation A 0

24 34 32 30 28 26 36 − pcﬁ ufc ra(m area surface Speciﬁc ) kg

1 2 Minimum SSA of the topmost 7 (1 December–15 January), vs. accumulated precipitation during this period, for simulations A and E. Figure 8.