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the Creative Commons Attribution 4.0 License.
| 29 Jan 2019
Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979–2015) and identification of dominant processes
The Antarctic ice sheet mass balance is a major component of the sea levelbudget and results from the difference of two fluxes of a similar magnitude:ice flow discharging in the ocean and net snow accumulation on the ice sheetsurface, i.e. the surface mass balance (SMB). Separately modelling icedynamics and SMB is the only way to project future trends.In addition, mass balance studies frequently use regional climate models(RCMs) outputs as an alternative to observed fields because SMB observationsare particularly scarce on the ice sheet. Here we evaluate new simulations ofthe polar RCM MAR forced by three reanalyses, ERA-Interim, JRA-55, and MERRA-2,for the period 1979–2015, and we compare MAR results to the last outputs ofthe RCM RACMO2 forced by ERA-Interim. We show that MAR and RACMO2 performsimilarly well in simulating coast-to-plateau SMB gradients, and we find nosignificant differences in their simulated SMB when integrated over the icesheet or its major basins. More importantly, we outline and quantify missingor underestimated processes in both RCMs. Along stake transects, we show thatboth models accumulate too much snow on crests, and not enough snow invalleys, as a result of drifting snow transport fluxes not included in MARand probably underestimated in RACMO2 by a factor of 3. Our results tendto confirm that drifting snow transport and sublimation fluxes are muchlarger than previous model-based estimates and need to be better resolved andconstrained in climate models. Sublimation of precipitating particles inlow-level atmospheric layers is responsible for the significantly lowersnowfall rates in MAR than in RACMO2 in katabatic channels at the ice sheetmargins. Atmospheric sublimation in MAR represents 363 Gt yr−1 over the grounded ice sheet for the year 2015, which is 16 %of the simulated snowfall loaded at the ground. This estimate is consistentwith a recent study based on precipitation radar observations and is morethan twice as much as simulated in RACMO2 because of different timeresidence of precipitating particles in the atmosphere. The remaining spatialdifferences in snowfall between MAR and RACMO2 are attributed to differencesin advection of precipitation with snowfall particles being likely advected toofar inland in MAR.
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Mass loss from the Antarctic ice sheet (AIS) and therewith itscontribution to the sea level budget results from the difference of twofluxes of a similar magnitude: ice flow discharging in the ocean (D) and netsnow accumulation on the ice sheet surface, i.e. the surface mass balance(SMB). The total ice sheet mass balance (SMB minus D) can be assessed usingsatellite altimetry, gravimetry, or the input–output method(Shepherd et al., 2018), which all request SMB estimates.The input–output method, which consists in separately modelling ice dynamicsand SMB, is also the only way to project future trends.
SMB as used in this study is the sum of mass gains (mainlysnowfall accumulation and some riming), mass losses (mainly surface anddrifting snow sublimation, some liquid water run-off), and drifting snowtransport (defined as the horizontal advection of the drifting snow), whichcan lead to either mass gain or mass loss. Snowfall rates are 1 order ofmagnitude larger than all of the other SMB fluxes at the continental scale(Lenaerts et al., 2012b), with the largest amounts found along the ice sheetmargins due to cyclonic activity in the Southern Ocean and to the orographiclifting of relatively warm and moist air masses(van Wessem et al., 2014;Favier et al., 2017). Accumulation patterns are highlyvariable at the kilometre scale and from year to year(e.g. Agosta et al., 2012). Consequently, proper observations of SMBrequire a high spatial coverage (e.g. stake lines, accumulation radars plusice cores for layer dating and snow density) and a temporal sampling spanningseveral years(Eisen et al., 2008). Even if efforts have been made to fulfilthose requirements, ground-based observations are scarce and carry with themhigh logistical costs in this cold, windy, and remote environment.Interpolation techniques used to interpolate the scarce SMB observations(Vaughan et al., 1999;Arthern et al., 2006) encounter major caveats(Magand et al., 2008;Genthon et al., 2009;Picard et al., 2009).
This is why many AIS mass balance studies use output of regional climatemodels (RCMs) to estimate ice sheet SMB for the recent decades(e.g. Rignot et al., 2011;Gardner et al., 2018;Shepherd et al., 2018). In order toobtain a good agreement with observations, atmospheric models requireaccurate large-scale circulation patterns together with a properrepresentation of snow surface processes, clouds, and turbulent fluxes and arelatively high horizontal resolution to properly resolve the complex icesheet topography at the margins.
Here, we present new simulations of the RCM MAR, appliedfor the first time over the whole AIS, but already widely used for polarstudies, e.g. in Greenland(Fettweis et al., 2013,2017), Svalbard(Lang et al., 2015), Adélie Land(Antarctic coastalarea; Gallée et al., 2013;Amory et al., 2015), and Dome C(Antarcticplateau; Gallée et al., 2015). We compare MAR-simulated SMB with thestate-of-the-art RCM RACMO2(van Wessem et al., 2018). Weuse available SMB observational datasets to show that MAR and RACMO2 performsimilarly well in simulating the SMB spatial gradients. In addition, weidentify significant processes that still need to be included or improved inboth RCMs.
In Sect. 2, we describe MAR and its specific set-upfor Antarctica, together with RACMO2, the forcing fields, observationaldatasets, and methods designed for model evaluation. InSect. 3, we show that both RCMs share common biases againstobserved SMB, resulting from drifting snow transport fluxes. Secondly, weanalyse SMB differences among models and show that many of thediscrepancies can be attributed to low-level sublimation of precipitation inkatabatic channels and to the difference in precipitation advection inland.Finally, in Sect. 4, we summarise ourmain findings and discuss further efforts to be achieved for a betterassessment of the AIS SMB.
2.1Regional modelling
2.1.1Regional atmospheric models
For the first time, the polar-oriented regional atmospheric model MAR isapplied for decades-long simulations over the whole AIS. MARatmospheric dynamics are based on the hydrostatic approximation of theprimitive equations, fully described inGallée and Schayes (1994). Prognosticequations are used to depict five water species: specific humidity, clouddroplets and ice crystals, raindrops, and snow particles(Gallée, 1995). Sublimation of airborne snow particles is a directcontribution to the heat and moisture budget of the atmospheric layer inwhich these particles are simulated. The radiative transfer through theatmosphere is parametrised as inMorcrette (2002), with snowparticles affecting the atmospheric optical depth(Gallée and Gorodetskaya, 2010). Theatmospheric component is coupled to the surface scheme SISVAT(soil icesnow vegetation atmosphere transfer; De Ridder and Gallée, 1998) dealing with theenergy and mass exchanges among surface, snow, and atmosphere. Thesnow-ice part of SISVAT is based on the snow model CROCUS(Brun et al., 1992). It is a one-dimensional multilayered energy balancemodel which simulates meltwater refreezing, snow metamorphism, and snowsurface albedo depending on snow properties. We used MAR version 3.6.4,simply called MAR hereafter. In this version the physical settings are thesame as in MAR version 3.5.2 used for Greenland(Fettweis et al., 2017),except for the adaptations detailed below.
Grid. Projection is the standard Antarctic polar stereographic method(EPSG:3031). The horizontal resolution is 35 km, an intermediateresolution that results from a computation time compromise in order to runthe model with multiple reanalyses and global climate model forcings over the20th and the 21st centuries. The vertical discretisation is composed of 23hybrid levels from∼2 m to∼17 000 m above the ground.
Boundaries. The topography is derived from the Bedmap2 surfaceelevation dataset(Fretwell et al., 2013). Because the Antarctic domain isabout 4 times larger than the Greenland domain, the circulation has to bemore strongly constrained. This is why we use a boundary relaxation oftemperature and wind in the upper atmosphere starting from 400 hPa(∼6000 m above the ground) to 50 hPa (upper level), as invan de Berg and Medley (2016), whereas relaxation starts from 200 hPa inFettweis et al. (2017).
Parameterisations.
- a.
The surface snow densityρs (kg m−3) is computed as a function of 10 mwind speed ws10 (m s−1) and surface temperatureTs (K):
with minimum–maximum values of 200–400 kg m−3. This parameterisation was defined so that the simulated density ofthe first 50 cm of snow fits observations collected over the AIS (see Fig. S1, with the snow density database detailed in Table S1 in the Supplement).
- b.
The aerodynamic roughness lengthz0 is computed as a function of the air temperature, as proposed inAmory et al. (2017).The parameterisation was tuned so thatz0 fit the observed seasonal variation between high (>1 mm) summer and lower (0.1 mm)winter values in coastal Adélie Land, for air temperatures above−20 ∘C. For lower temperatures,z0 is kept constant andset to 0.2 mm, in agreement with observedz0 values on the Antarctic plateau(e.g. Vignon et al., 2016);
- c.
As inFettweis et al. (2017), the MAR drifting snow scheme is not activated because this scheme was sensitive to parameterchoices(Amory et al., 2015). An updated version of the drifting snow scheme is currently being developed and evaluated for applicationat the scale of the whole ice sheet.
We compare MAR results over the AIS to the latest outputs of the regionalatmospheric model RACMO2 version 2.3p2(van Wessem et al., 2018), calledRACMO2 hereafter, using a horizontal resolution of 27 km, a verticalresolution of 40 atmospheric levels, and a topography based on the digitalelevation model fromBamber et al. (2009). This regional model is developedby the Royal Netherlands Meteorological Institute (KNMI) and hassubsequently been adapted for modelling the Antarctic climate and its SMB(van de Berg et al., 2006). It includes a drifting snow scheme(Lenaerts et al., 2012a), an albedo routine with prognostic snow grain size(Kuipers Munneke et al., 2011), and a multilayer snow model computing melt,percolation, refreezing, and run-off(Ettema et al., 2010).
MAR and RACMO2 models were developed independently. We will not detail herethe many physical parameterisation differences between both RCMs, but we willlater highlight some of them we show having a significant impact on themodelled SMB.
2.1.2Forcing reanalyses
Regional atmospheric models are forced by atmospheric fields at their lateralboundaries (pressure, wind, temperature, humidity), at the top of thetroposphere (temperature, wind), as well as by sea surface conditions (seaice concentration, sea surface temperature) every 6 h. Consequently,regional atmospheric models add details and physics to the forcing model inthe middle and lower troposphere and at the land or iced surface, whereaslarge-scale circulation patterns are driven by the forcing fields. We forcedMAR with three reanalyses over Antarctica in order to evaluate theuncertainty in the simulated surface climate arising from the uncertainty inthe assimilation systems: the European Centre for Medium-Range WeatherForecasts “Interim” re-analysis(hereafter ERA-Interim, resolution∼0.75∘, i.e.∼50 km at 70∘ S; Dee et al., 2011), the Modern-Era Retrospective Analysis forResearch and Applications version 2(hereafter MERRA-2, resolution∼0.5∘; Gelaro et al., 2017), and the Japanese 55-yearReanalysis from the Japan Meteorological Agency(hereafter JRA-55,resolution∼1.25∘; Kobayashi et al., 2015).
The regional atmospheric model RACMO2 is forced by ERA-Interim. We focus ourstudy to the period 1979–2015, as reanalyses are known to be unreliablebefore 1979, when satellite sounding data started to be assimilated(Bromwich et al., 2007).
2.2Observations
2.2.1SMB observations and sectors of strong SMB gradients
We use SMB observations of the GLACIOCLIM-SAMBA datasetdetailed inFavier et al. (2013) and updated byWang et al. (2016). Thisdataset is an update of the one assembled byVaughan et al. (1999) followingthe quality-control methodology defined byMagand et al. (2007). It includes3043 reliable SMB values averaged over more than 3 years. We add accumulationestimates fromMedley et al. (2014), retrieved over the Amundsen Sea coast(Marie Byrd Land) with an airborne-radar method combined with ice-coreglaciochemical analysis.
The first-order feature of the Antarctic SMB is a strong coastal–inlandgradient, with mean values ranging from typically greater than 500 kg m−2 yr−1 atthe ice sheet margins to about 30 kg m−2 yr−1 in the dry interior plateau(Fig. 1a; see also, e.g. Wang et al., 2016). As observationsonly cover 5 % of MAR grid cells over the ice sheet, we divide that sparseobservation dataset into 10 sectors detailed in Table 1 and shownin Fig. 2. Six of them are stake transects with a stake every∼1.5 km, which have been proven very valuable for evaluatingmodelled SMB(Agosta et al., 2012;Favier et al., 2013;Wang et al., 2016). The four othersectors are composed of more scattered observations covering large elevationranges (Victoria Land, Dronning Maud Land, and Ross Ice Shelf–Marie ByrdLand).
Table 1Sectors extracted from the GLACIOCLIM-SAMBA database.
1 Medley et al. (2014).2 Clausen et al. (1979).3 Venteris and Whillans (1998).4 Vaughan et al. (1999).5 Magand et al. (2007).6 Frezzotti et al. (2004).7 Frezzotti et al. (2007).8 Pettré et al. (1986).9 Agosta et al. (2012).10 Verfaillie et al. (2012).11 Goodwin (1988).12 Ding et al. (2011).13 Wang et al. (2016).14 Higham and Craven (1997).15 Wang et al. (2015).16 GLACIOCLIM-BELARE.17 Picciotto et al. (1968).18 Mosley-Thompson et al. (1995).19 Mosley-Thompson et al. (1999).20 Anschütz et al. (2011).
2.2.2Model–observation comparison method
RACMO2 outputs are bilinearly interpolated to the35×35 km MARgrid. For each SMB observation, we consider the four surrounding MAR grid cells,from which we eliminate ocean grid cells. We also eliminate surrounding gridcells with an elevation difference with the observation greater than 200 m (missing elevation of observation is set to Bedmap2 elevation at1 km resolution). Finally, we bilinearly interpolate model values ofthe remaining grid cells at the observation location (see schematic inFig. S2).
As we restrict our modelling study to the 1979–2015 period, we only considerobservations beginning after 1950. For observations beginning after 1979, wetime-average model outputs for the same period as the observation. We keepobservations beginning before 1979 only if they cover more than 8 years,and in this case we compare the observed value with the modelled valuetime-averaged for 1979–2015.
In a last step, we average-out the kilometre-scale variability in theobserved SMB(Agosta et al., 2012) by binning point values onto grid cells.For each grid cell containing multiple observations, we average allobservations contained in the grid cell weighted by the time span ofobservations, and in the same way we weight-average the modelled valuesinterpolated to observation locations. This way, we obtain consistentobserved and modelled averaged values on grid cells.
We discard 66 observations beginning before 1979 and spanning less than 8years. We also discard 12 observations for which the four surrounding gridcells fall in ocean and seven observations located at specific topographicfeatures for which none of the four surrounding grid cell has an elevationdifference of less than 200 m with respect to the actual location. Afterthis, we retain 559 model–observation comparisons.
Figure 1MAR SMB for the period 1979–2015:(a) mean annual SMB, with coloureddots showing the observed SMB values (shared colour scale);(b) standarddeviation of annual SMB;(c) standard deviation divided by mean annual SMB;(d) difference between MAR and observed SMB on MAR grid cells, following themethodology detailed in Sect. 2.2.2.Magenta dots in panels (b) and(c) show the location of SMB observations. Solidgrey lines are contours of surface height every 1000 m a.s.l. Latitude circles are−60,−70, and−80∘ S, and longitude lines are from 145∘ W to145∘ E by steps of 45∘.
Figure 2Modelled vs. observed SMB for sectors and transects as detailed inTable 1. RACMO2 outputs are bilinearly interpolated to the MARgrid. SMB values are first averaged on MAR grid cells(Sect. 2.2.2) then along a chosen griddirection (Fig. S2) or by elevation bins. Distance along the transect starts atthe coast. Uncertainty of observed SMB (grey shaded area) is the standarddeviation of observations contained in each grid cell (sub-grid variability),estimated as a function of the mean observed SMB (see Fig. S3). Despite SMBvalues corresponding to grid cell averages, we display one marker for eachobservation, with thex axis corresponding to the observation locationalong the transect or elevation. For observed SMB plots, markers with white facesare for bins containing fewer than 10 observations and black faces for binscontaining more than 10 observations. Magenta bands mark grid cells in whichmore than 15 % of precipitation sublimates in the katabatic layers accordingtoGrazioli et al. (2017). The map shows the main Antarctic basins:Antarctic Peninsula in purple, West Antarctic ice sheet in green, and EastAntarctic ice sheet in orange. Ice shelves are mapped in blue and groundedislands in red, and the blue line shows the location of the grounding line.
3.1Evaluation of the modelled SMB
The large spatial Antarctic SMB gradients, shown in Fig. 1a asmodelled by MAR forced by ERA-Interim for the period 1979–2015, coincide witha strong interannual variability (Fig. 1b), expressed by a standarddeviation of∼22 % of the mean SMB on average over the ice sheet(Fig. 1c). MAR SMB shows no systematic spatial bias(Fig. 1d), with a mean bias of 6 kg m−2 yr−1 (4 % of the mean observed SMB), as well as a very strongcorrelation with the observed SMB (R2=0.83,p value <0.01, computed on thelogarithm of SMB values, as SMB distributions are log-normal). RACMO2 showssimilar performance (mean bias of−3 kg m−2 yr−1,R2=0.86, computed on the logarithm of SMB as well).
The model–observation comparison by sectors (Fig. 2) reveals a goodrepresentation of the coast-to-plateau SMB gradients by both RCMs. MAR andRACMO2 are in good agreement despite MAR not including drifting snowprocesses whereas RACMO2 does, except in Ross–Marie Byrd Land and inVictoria Land where MAR simulates larger SMB than RACMO2. Another noticeableresult is that MAR forced by ERA-Interim, JRA-55, and MERRA-2 gives very similarresults for the SMB spatial pattern, not only at the observation locations(Fig. 2) but also at the ice sheet scale (comparisons of MAR SMBfor different forcing reanalyses are shown in Fig. S4, with colour map scales10 times smaller than in Fig. S5 in which MAR is compared to RACMO2). This is whywe focus on MAR forced by ERA-Interim in the following.
We find no significant differences in the SMB simulated by MAR and RACMO2when integrated over the ice sheet or its major basins (Table 2).SMB is driven by snowfall amounts, which are more than 10 times larger thanother SMB components. Snow sublimation in RACMO2 is the sum of sublimation atthe surface of the snowpack and of drifting snow sublimation and isapproximately 50 % larger than in MAR, which only includes surface snowsublimation. However, surface snow sublimation alone is almost 2 timeslarger in MAR than in RACMO2 (Table 2 and spatial patterns shown inFig. S6), which we investigate in the next section. Modelled surface melt isless than half of the sublimation amount; however liquid water almostentirely refreezes into the snowpack in both models (maps of MAR- and RACMO2-modelled melt amounts are shown in Fig. S7). Temporal variability in the SMBand its components is fully driven in both RCMs by the forcing reanalyses andare therefore strongly correlated with each other (time series shown inFig. S8). We do not elaborate on the SMB temporal variability here as thisaspect will be further detailed in a forthcoming study.
Table 2Antarctic integrated SMB on average for 1979–2015± 1 standarddeviation of annual values, in gigatonnes per year. Antarctic ice sheet(AIS) and basin geometry are based on Rignot basins(Shepherd et al., 2018),shown in Fig. 2. RACMO2 is bilinearly interpolated on the MAR grid andthe same mask is applied to both models, with area given for this mask. SMBis computed as follows: MAR SMB= snowfall+ rainfall− surface snowsublimation− run-off; RACMO2 SMB= snowfall+ rainfall− surface snowsublimation− drifting snow sublimation− drifting snow transport− run-off.
3.2Drifting snow transport features
Figure 3Annual mean 10 m wind speed, curvature of elevation, and modelled SMB minusobserved SMB for the four long transects:(a) Low Dome–Wilkes Land,(b) Zhongshan–Dome A,(c) Mawson–Lambert Glacier, and(d) Syowa–Dome F. Blue lines and colour shading are for MAR (ERA-Interim)outputs and red lines are for RACMO2 (ERA-Interim) outputs. Values are computed as in Fig. 2.
Figure 4(a) Difference in SMB by grid cell (ΔSMB) betweenMAR (ERA-Interim) and observations for four transects (Law Dome–Wilkes Land,Zhongshan–Dome A, Mawson–Lambert Glacier, and Syowa–Dome F) vs. surfacecurvature on MAR grid. Curvature is shifted by± 1 grid cell according tothe maximum correlation withΔSMB (Fig. S10). Linear regressionthrough the origin is plotted with a pink dashed line. We excluded theregression of two outliers (dots with black outlines) and seven data for whichMAR annual 10 m wind speed is lower than 7 ms−1(squares with black outlines).(b) Estimate of mean annual drifting snowtransport based on a scaling of the curvature: drifting snow transport(kg m−2 yr−1)=α (106 kg m−1 yr−1)× curvature(10−6 m−1), withα=0 kg m−1 yr−1 forwind speed lower than 5 m s−1,α=3700 106 kg m−1 yr−1 for wind speed greater than9 m s−1, andα linearly increasing as a function of wind speedin between, around the 7 m s−1 wind speed threshold. Windspeed is the annual mean of 10 m wind speed modelled by MAR (ERA-Interim).Coloured dots show the difference between MAR SMB and observed SMB with thesame colour scale.(c) Mean annual drifting snow transport flux in RACMO2 onaverage for 1979–2015 (kg m−2 yr−1). Coloured dotsshow the difference between MAR SMB and observed SMB with the same colourscale.
Local fluctuations of the observed SMB around the smooth modelled SMBgradients are apparent along the four stake transects covering more than 500 km: Law Dome–Wilkes Land, Zhongshan–Dome A, Mawson–Lambert Glacier,and Syowa–Dome F. We related these fluctuations to drifting snow transport.Indeed, the snow eroded from the snowpack is loaded into the atmosphere,where it can sublimate and be transported by the wind. Katabatic windsblowing on the surface of the ice sheet result from the downslope gravityflow of cold, dense air. As a consequence, the surface wind divergence, whichdrives the snowdrift mass transport, is strongly related to the curvature ofthe topography, and both have similar spatial patterns (shown in Fig. S9).This is because slopes becoming steeper (crests, positive curvature) willlead to wind speed acceleration (positive wind divergence), and thus to driftingsnow export (mass loss), whereas slopes becoming more gentle (valleys,negative curvature) will lead to wind speed deceleration (negative winddivergence), and thus to drifting snow deposit (mass gain).
To test our hypothesis, we computed the mean curvature of the MAR35 km×35 km elevation field. In Fig. 3, we notice thatboth RCMs commonly exhibit an excess of accumulation on crests and a deficitof accumulation in valleys, in the range of±40 kg m−2 yr−1. To quantify this curvature effect, we correlate MAR SMB bias(ΔSMB) with the curvature. For each transect, we apply a constantshift of± one grid cell to the curvature in order to find the maximumcorrelation withΔSMB. For three out of the four transects, we findonly one shift for which the correlation is significant, and for the remainingtransect (Syowa–Dome F) we find no significant correlation (Fig. S10). Thesign and the amplitude of those shifts are in line with curvature being usedas a proxy for wind divergence, as they are consistent with the Coriolis winddeflection westward of the topography gradient (detailed in Fig. S11). Afterapplying those shifts, we find that the difference between modelled andobserved SMB (kg m−2 yr−1) is scaled toapproximately3700 ± 1100 (106 kg m−1 yr−1) times the curvature(10−6 m−1), with asignificant relationship (R2=0.41, Fig. 4a), when the meanannual 10 m wind speed (ws10) is greater than 7 m s−1. For lower wind speed (ws10 <7 m s−1), we no longer observe any relationship between model bias inSMB and curvature (horizontally aligned squares in Fig. 4a). Thisis consistent with the drifting snow transport process, which requires thewind speed to reach threshold values for the erosion to be initiated(Amory et al., 2015).
Hence, a large part of the discrepancies between modelled and observed SMBareexplained by surface curvature when wind speed is sufficiently high, which werelate to the unresolved drifting snow transport in MAR. We are able to catchthe drifting snow transport signal because drifting snow sublimation isnegligible for the four studied transects, as they are located at highelevation, above 2000 m above sea level (a.s.l.), where the coldatmosphere has a low capacity to be loaded with moisture (see detailed analysisin Fig. S12). The moisture holding capacity of the atmospheric boundary layeris an upper bound for drifting snow sublimation and quickly tends to zerowhen the mean air temperature decreases below−30∘C, which is thecase along most of the transects, whereas the amplitude of observed SMBfluctuations around the smooth SMB gradient is independent of the temperature(Fig. S13).
Consequently, we propose that drifting snow transport fluxes (dstr) notresolved by MAR can be estimated as a scaling of curvature depending on windspeed: ds(Fig. 4b). The scaling factorα(ws10) depends on windthresholds to simulate the transition between no drifting snow transport forlow wind speed (α=0 for ws10<5 m s−1) anddrifting snow transport scaled to curvature for high wind speed(α=3700 106 kg m−1 yr−1 forws10>9 m s−1), with a linearly increasing scalingfactor between5 and9 m s−1 for a smooth transitionaround the7 m s−1 wind threshold defined above. Thatestimate of drifting snow transport fluxes shows little sensitivity to thechoice of the wind thresholds and of the scaling factor (see fluxes summedover the ice sheet for different thresholds and scaling factors in Table S2).The spatial pattern of drifting snow transport we obtain is comparable to theone simulated by RACMO2 (Fig. 4c), except that it gives fluxes morethan 3 times larger than in RACMO2 (Table S2, and note the differentcolour map scales between Fig. 4b and c). The driftingsnow transport estimate consists in a redistribution of mass with negligiblenet mass loss over the AIS (total AIS mass gain of∼75 Gt yr−1 and total AIS massloss of∼80 Gt yr−1; see Table S2).
Our drifting snow transport estimate gives a good constraint for driftingsnow fluxes above 2000 m a.s.l., where low temperatures inducenegligible atmospheric sublimation. As drifting snow transport isproportional to the amount of snow in suspension in the atmosphere,quantifying this flux also enables us to constrain the amount of snow erodedfrom the snowpack to the atmosphere, which drives drifting snow sublimationfluxes at lower elevation. This is of importance as drifting snow sublimationis a much larger mass sink than drifting snow transport over the whole icesheet(Palm et al., 2017;Lenaerts et al., 2012a) but is still poorly constrainedbecause observations are very scarce below 2000 m a.s.l. where itoccurs.
Drifting snow sublimation included in RACMO2 and not in MAR moistens thesurface atmospheric layers, consequently reducing the sublimation at thesurface of the snowpack. This might explain the stronger surface snowsublimation in MAR than in RACMO2 (Table 2 and Fig. S6). However,drifting snow sublimation is a potentially larger mass sink than surface snowsublimation, as drifting snow particles are continuously ventilated and fullyexposed to the ambient air. Consequently, by accounting for drifting snow inMAR we expect that the drifting snow sublimation mass sink could be enhancedat the expense of surface snow sublimation at the ice sheet margins.
3.3Sublimation of precipitation in low-level atmosphere
Figure 5The four maps show mass fluxes inkg m−2 yr−1 for the year 2015.(a) Difference in SMB between MAR andRACMO2. Blue lines delimitate areas where the SMB difference is 30 % greaterthan MAR SMB, with solid lines when MAR is greater than RACMO2 and dashedlines when MAR is lower than RACMO2.(b) Same as(a) but for the snowfallamounts at the ground.(c) Same as(a) but for the sublimation ofprecipitation in the atmospheric layers.(d) Same as(a) but for the maximumsnowfall amount (equal to ground snowfall plus atmospheric sublimation).Locations of transects A1–A2 and B1–B2 extracted in Fig. 6 areshown in panels (b) and (d).
As described above, MAR and RACMO2 RCMs forced withERA-Interim simulate similar spatial patterns for SMB compared toobservations (Fig. 2). However, at the ice sheet scale, MAR andRACMO2 SMB show regional discrepancies (shown in Fig. 5a for 2015,and similar to the 1979–2015 mean shown in Fig. S5a), which are primarilythe result of differences in simulated snowfall rates (Figs. 5b and S5b). We notice that areas where MAR snowfall is much lower than RACMO2snowfall (Fig. 5b, blue dashed lines) coincide almost exactly withthe pattern of precipitation that is able to sublimate in the low-levelatmosphere according toGrazioli et al. (2017). In that study, the amountof atmospheric sublimation is quantified for the year 2015 using atmosphericmodelling constrained with precipitation radar observations. Atmosphericsublimation happens because the katabatic surface air flux, moving fromhighly elevated inland plateau toward sea level, is subject to adiabaticcompression when it moves downslope. This compression induces an increase inair temperature, which reduces relative humidity and drives sublimation ratesin the lower troposphere (∼ first 1000 m above the ground), enhanced inthe katabatic channels at the ice sheet margins.
To deepen this analysis, we re-ran MAR for the year 2015 in order to save thefull atmosphere snowfall fields. From the daily 3-D snowfall amounts, wederived the atmospheric sublimation amount from the difference between themaximum snowfall and the ground snowfall in each atmospheric column, as inGrazioli et al. (2017). The same was done for RACMO2. We find that theatmospheric sublimation simulated by MAR (363 Gt for the year 2015 over thegrounded ice sheet) is higher than estimated inGrazioli et al. (2017)(299 Gt after interpolation on the same mask) and much higher than simulated byRACMO2 (128 Gt, Fig. 5c). A major difference between MAR and RACMO2is the advection of precipitation in the atmosphere: in MAR, precipitatingparticles are explicitly advected through the atmospheric layers until theyreach the surface, while in RACMO2, precipitation is added to the surfacewithout horizontal advection, and is able to interact with the atmosphereonly in a single time step (6 min in this simulation). Consequently,atmospheric sublimation is likely to be underestimated in RACMO2.
We conclude, in agreement withGrazioli et al. (2017), that atmosphericsublimation is a major mass sink at the ice sheet margins in MAR, as for theyear 2015 it represents 16 % of the snowfall loaded on the grounded icesheet(12 % in Grazioli et al., 2017) and 26 % for areas below 1000 m a.s.l.(17 % in Grazioli et al., 2017).
It is noticeable that very few SMB observations are available in areas whereGrazioli et al. (2017) identify low-level sublimation, marked by magentabands in Fig. 2. Except for Ross–Marie Byrd Land, the only otherareas where low-level sublimation is greater than 15 % of the totalprecipitation as defined byGrazioli et al. (2017) are close to Dumontd'Urville (coastal Adélie Land) and to Syowa (coastal Dronning Maud Land). Inthose areas the SMB amount is indeed larger in RACMO2 than in MAR and inobservations. Both RCMs overestimate SMB around 2000 m a.s.l. inDronning Maud Land and in Ross–Marie Byrd Land (Fig. 2), whichcould indicate katabatic channels not being resolved enough by the topography ofthe models.
3.4Precipitation formation and advection
Figure 6MAR- and RACMO2-simulated fields for the year 2015, extracted with abilinear interpolation for (left) transect A1–A1 and (right) transect B1–B2(locations shown in Fig. 5b and d). Each panelshows MAR fields (blue lines) and RACMO2 fields (red lines) for(a) surfaceheight, in metres above sea level;(b) maximum snowfall amounts, equal to groundsnowfall plus atmospheric sublimation, inkg m−2 yr−1; and(c) snowfall amounts at the ground,inkg m−2 yr−1. In (b) and (c), the thick black line is for thedifference in snowfall between MAR and RACMO2 (MAR-RACMO2), with green-filledareas when MAR snowfall is larger than RACMO2 snowfall, and brown-filledareas when MAR snowfall is lower than RACMO2 snowfall (same convention as inFig. 5); the dotted lines are for the atmospheric sublimationmodelled by MAR (blue) and by RACMO2 (red), negative when it induces adecrease in precipitation; light coloured bands show crests (light blue,curvature of MAR topography greater than0.005 10−6 m−1)and valleys (light yellow, curvature of MAR topography lower than m−1).The thick black arrows show the main 800 hPa wind direction during cyclonic activity.
Differences between MAR and RACMO2 snowfall fields are strongly reduced whenconsidering the maximum snowfall amounts (before sublimation in the low-levelatmosphere) rather than the ground snowfall amounts (Fig. 5b and d). However, MAR snowfall rates generally exceed thosesimulated by RACMO2, by more than 30 % on the lee side of the West AIS(Marie Byrd Land toward Ross ice shelf), on the lee side of theTransantarctic Mountains (Victoria Land), and close to crests at the ice sheetmargins. MAR maximum snowfall rates are lower than simulated by RACMO2windward of topographic barriers and in valleys at the ice sheet margins.This spatial pattern looks similar to the one obtained in RACMO2 whendelaying the conversion of cloud ice–water into snow–rain(Fig. 3aof van Wessem et al., 2018). This change led to both ice and water clouds lastinglonger in the atmosphere before precipitating and therefore being advectedfurther towards the ice sheet interior(van Wessem et al., 2018).
For a more in-depth analysis, we extract MAR and RACMO2 snowfall rates on twotransects at the ice sheet margins (Fig. 6), following the mainwind direction during cyclonic activities (locations shown inFig. 5b and d). On these transects the observeddifference in maximum snowfall between MAR and RACMO2 is largely explained bya phase difference in the snowfall peaks windward of the topographicbarriers, with MAR peaking closer to the crests than RACMO2(Fig. 6b). This induces a wave-like pattern of precipitationdifference strongly related to the shape of the topography, with largersnowfall amounts in MAR than in RACMO2 just windward of crests and lowersnowfall amounts in MAR than in RACMO2 around windward valleys. At theground, lower snowfall in MAR than in RACMO2 in valleys is amplified bylow-level atmospheric sublimation, which peaks in katabatic channels(Fig. 6c).
Observations do not enable us to definitively discriminate one model against theother, but we observe a general tendency for MAR to overestimate accumulationon Ross–Marie Byrd Land and close to ice sheet summits (Dome C, Dome A, DomeF; see Figs. 1d and 2). Close to summits the wind islow, so a missing drifting snow transport process is an unlikely explanation for apositive bias in SMB modelled by MAR (Fig. 4b). Over the Greenlandice sheet, MAR tends to overestimate ice cores based on accumulation inland(Fettweis et al., 2017) while RACMO2 underestimates it(Noël et al., 2018).
We conclude that the differences in MAR and RACMO2 snowfall patterns are verylikely related to differences in the advection of precipitation inland, whichmay arise from (i) the different advection of precipitating particles to theground described in Sect. 3.3, (ii) different timing ofprecipitation formation (cloud–precipitation conversion thresholds), and/or(iii) different dynamical response to the topographic forcing, caused by eitherdifferent dynamical cores or the different resolutions (the 27 km resolution in RACMO2 better resolves the ice sheet topography thanthe 35 km resolution in MAR).
In our study, we evaluate new estimates of the Antarctic SMB obtained withthe polar RCM MAR ran for the first time for decades-longsimulations at the scale of the whole AIS. We use modelsettings comparable to previous MAR simulations over Greenland(Fettweis et al., 2017) but with a specific upper atmosphere relaxation andnew surface snow density and roughness length parameterisations. We presentsimulations of MAR forced by ERA-Interim, JRA-55, and MERRA-2 for the satelliteera (1979–2015) in which we can rely on reanalyses products. Remarkably, MARforced by those three reanalyses gives similar spatial and temporal SMBpatterns. We also compare MAR with the latest simulations of the RCM RACMO2forced by ERA-Interim(van Wessem et al., 2018). We find no significantdifferences between MAR and RACMO2 SMB when integrated on the AIS and itsmajor basins (Table 2).
As the dominant feature of the Antarctic SMB is its strong coast-to-plateaugradient, we extract stake transects and sectors with large elevation rangesfrom the GLACIOCLIM-SAMBA SMB observational dataset. We show that both RCMsshow similar performances when compared to observations, with a goodrepresentation of the SMB gradient (Fig. 2). But more importantly,we outline and quantify missing or underestimated processes in both RCMs.
Along stake transects, we relate 100 km scale fluctuations ofobservations around the smooth modelled SMB pattern to the shape of the icesheet captured on the35 km×35 km MAR grid. Both RCMs accumulatetoo much snow on crests, and not enough snow in valleys, as a result ofdrifting snow transport fluxes not included in MAR and probablyunderestimated in RACMO2 by a factor of 3 (Fig. 4). In theRACMO2.3p2 version used here, the modified drifting snow routine inducedalmost halved drifting snow transport and sublimation fluxes compared to theprevious RACMO2.3p1 version(Lenaerts and van den Broeke, 2012). In a recent studycombining satellite observation of drifting snow events and reanalysisproducts,Palm et al. (2017) estimate the drifting snow sublimation to beabout∼393 Gt yr−1 over the AIS, vs.181 Gt yr−1 in RACMO2.3p1 and 102 Gt yr−1in RACMO2.3p2(van Wessem et al., 2018). Consequently, observationalconstraints from our study and fromPalm et al. (2017) both tend to confirmthat drifting snow transport and sublimation fluxes are likely much largerthan previous model-based estimates and need to be (better) resolved andconstrained in climate models.
We also point out that MAR generally simulates larger SMB and snowfallamounts than RACMO2 inland, particularly on the lee side of theTransantarctic Mountains and on crests at the ice sheet margins, whereas MARsimulates lower snowfall than RACMO2 windward of mountain ranges andpromontories. Sublimation of precipitating particles in low-level atmosphericlayers is largely responsible for the significantly lower snowfall rates inMAR than in RACMO2 in valleys at the ice sheet margins. As precipitating snowparticles have larger time residence in the atmosphere in MAR than in RACMO2(Sect. 3.3), amounts of precipitation lost by sublimationin katabatic channels are more than twice as much in MAR as in RACMO2. Theremaining spatial differences in snowfall between MAR and RACMO2 areattributed to differences in advection of precipitation, with snowfall particlesbeing likely advected too far inland in MAR.
Atmospheric sublimation represents 429 Gt yr−1 in MAR overthe whole AIS (peninsula excluded) for the year 2015, 89 % of which is lostbelow 2000 m a.s.l. and 61 % below 1000 m a.s.l. This mightbe of importance for the mass balance of glacier drainage basins(SMBminus discharge; Rignot et al., 2008;Shepherd et al., 2018), as ice streams aretypically channel-shaped areas affected by low-level sublimation ofprecipitation. Consequently, we note the importance of saving precipitationfluxes in models at least 1300 m above the ground for comparison withCloudSat products, but ideally at all model levels below 1500 m abovethe ground to be able to compute sublimation of precipitation in thelow-level atmospheric layers. This will become a standard output inforthcoming MAR simulations.
We expect that accounting for drifting snow in MAR will lead to significantimprovements in describing the Antarctic SMB and surface climate, as it willenable (1) a quantification of the drifting snow sublimation mass sink, (2) amore realistic representation of relative humidity and temperature in theboundary layer, and (3) an explicit modelling of the drifting snow transportfrom crests to valleys. Exploring the impact of horizontal and vertical modelresolution on drifting snow estimates and on sublimation of precipitation inkatabatic channels will also be of importance as those processes are relatedto the shape of the ice sheet and to the advection of precipitation in theatmosphere. The accuracy of the topography has to be considered as well, asdigital elevation models are in constant improvement over the AIS(e.g. Slater et al., 2018) and should be regularly updated inclimate models.
Python scripts developed for this study as well as all required data are available athttps://doi.org/10.5281/zenodo.2548847(Agosta, 2019).The last version of MAR is freely distributed athttp://mar.cnrs.fr/ (last access: 24 January 2019).Monthly MARv3.6.4 outputs from this study are freely available athttps://doi.org/10.5281/zenodo.2547637 (Agosta and Fettweis, 2019), together with the associated MAR source code.The ECMWF reanalysis ERA-Interim 6-hourly outputs were downloaded fromhttps://doi.org/10.5065/D6CR5RD9 (ECMWF, 2009).The MERRA-2 reanalysis 6-hourly outputs were downloaded fromhttps://disc.gsfc.nasa.gov/datasets/ (GMAO, 2015).The JRA-55 reanalysis 6-hourly outputs were downloaded fromhttps://doi.org/10.5065/D6HH6H41 (Japan Meteorological Agency, 2013).
The supplement related to this article is available online at: https://doi.org/10.5194/tc-13-281-2019-supplement.
CA set-up the MAR model for Antarctica with several adaptations, performed model simulationsand analysed model outputs and observations. CA, AO, XF, and VF designed the study.CA, XF, HG, CA, and CK developed the MAR model and contributed tothe MAR set-up and output analyses. XF and HG are the main developers of the MAR model. MRvdB, JMvW, JTML, and WJvdB contributed to RACMO2 output analyses. Allauthors contributed to discussions in writing this paper.
The authors declare that they have no conflict ofinterests.
We thank Kenichi Matsuoka, Massimo Frezzotti, and the anonymousreviewer for their constructive and insightful comments, which led to a muchimproved paper. Cécile Agosta performed MAR simulations during her BelgianFund for Scientific Research (F.R.S.-FNRS) research fellowship. Computationalresources have been provided by the Consortium des Équipements de CalculIntensif (CÉCI), funded by the F.R.S.-FNRS under grant no. 2.5020.11. Weacknowledge Jacopo Grazioli for sharing the low-level sublimation product and hisexpertise on this dataset. We acknowledge Yetang Wang for sharing his updatedversion of the GLACIOCLIM-SAMBA dataset. We acknowledge Christophe Genthonfor fruitful discussions and suggestions. The authors acknowledge the supportfrom Agence Nationale de la Recherche Scientifique for the scientific traversesin Antarctica and the associated research on climate and surface mass balancemodelling, projects ANR-14-CE01-0001 (ASUMA) and ANR-16-CE01-0011 (EAIIST).Cécile Agosta acknowledges the support from Fondation Albert 2 de Monaco under the project Antarctic-Snow (2018–2020).
Edited by: Kenichi Matsuoka
Reviewed by: Massimo Frezzotti and one anonymous referee
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