Summer snowfall on the Greenland Ice Sheet : a study with the updated regional climate model RACMO 2 . 3

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Introduction
Since the mid-1990's, atmospheric and oceanic warming in the Arctic has led to accelerated Greenland ice sheet (GrIS) mass loss (Enderlin and Howat, 2013;Fettweis et al., 2013;Wouters et al., 2013).Combined observational and model studies show that increased meltwater runoff and solid ice discharge through the acceleration of marine-terminating outlet glaciers (Hanna et al., 2009;Nick et al., 2009;Fettweis et al., 2011;Rignot et al., 2011) account for ∼ 60 and ∼ 40 % of the recent GrIS mass loss, respectively (Rignot et al., 2008;Van den Broeke et al., 2009;Enderlin and Howat, Figures Back Close Full Since surface melt over the GrIS is mainly driven by the absorption of shortwave radiation (Van den Broeke et al., 2008), surface albedo is a primary factor governing ice sheet surface mass balance (SMB) (Bougamont et al., 2005;Tedesco et al., 2011;Fitzgerald et al., 2012) and surface energy balance (SEB) (Tedesco et al., 2008;Van Angelen et al., 2012).Ice albedo is mainly a function of impurity content, while snow albedo is sensitive to several snow physical properties, e.g. grain size, liquid water content and soot concentration.Satellite and in-situ observations have revealed a general decay of GrIS surface albedo in recent years (Box et al., 2012;Stroeve et al., 2013).In the ablation zone, this decrease is mainly caused by the prolonged exposure of dark, bare ice (Fettweis et al., 2011;Tedesco et al., 2011).In the accumulation zone, enhanced melt-driven snow metamorphism has also led to a surface darkening (Box et al., 2012).Here, the positive melt-albedo feedback is especially pronounced: once snow melts, snow grain size quickly increases, reducing the surface albedo, enhancing the absorption of solar radiation and hence reinforcing surface melting (Stroeve, 2001).
Summer snowfall events have the potential to interrupt this feedback, by covering dark ice and/or metamorphosed snow with a highly reflective fresh snow layer.Greuell and Oerlemans (1986) showed that significant summer snowfall events (> 5 mm w.e.) on an Alpine glacier caused a major reduction in ablation during the following days, subsequently leading to a long-term positive SMB anomaly.They estimated this positive SMB response to be two to three times larger than the mass of deposited solid precipitation.Fettweis et al. (2005) analysed two heavy snowfall events in southeast Greenland at the end of July 1991, using MAR (Modèle Atmosphérique Régional) and AVHRR satellite imagery.These events temporarily raised surface albedo, delaying the appearance of darker bare ice.Based on data from automatic weather stations (AWS), van den Broeke et al. (2011) showed that even minor summer snowfall events (< 5 mm w.e.) can considerably reduce surface melting.Therefore, an accurate representation of (summer) snowfall events, incoming shortwave radiation and surface processes is essential to model the SMB of the GrIS (Fettweis et al., 2005;Van Angelen et al., 2012).This requires a high-resolution model, Introduction

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Full to resolve the narrow ablation zone, and the use of an explicit atmospheric and surface snow/ice physics.Here, we use the polar version of the regional atmospheric climate model RACMO2.3, at 11 km horizontal resolution, which is coupled to a multilayer snow model with prognostic albedo formulation.We compare the simulated GrIS SMB and SEB with the previous model version ( RACMO2.1, Van Angelen et al., 2012) and with stake and AWS measurements along the K-transect in west Greenland, with special reference to the representation of summer snowfalls.In Sect.2, the physics upgrades in RACMO2.3 and the measurements along the K-transect are briefly described.The impact of upgraded physics on GrIS SMB through the snow-albedo feedback is discussed in Sect.3. Section 4 evaluates model output using K-transect data, after which conclusions are drawn in Sect. 5.

Model and data
2.1 The regional climate model RACMO2 The Regional Atmospheric Climate Model (RACMO2) is developed and maintained at the Royal Netherlands Meteorological Institute (KNMI) (Van Meijgaard et al., 2008).
RACMO2 adopts the atmospheric physics module from the European Centre for Medium-range Weather Forecasts Integrated Forecast System (ECMWF-IFS) and the dynamical core of the High Resolution Limited Area Model (HIRLAM) (Undèn et al., 2002).The polar version of RACMO2 was developed by the Institute for Marine and Atmospheric research, Utrecht University (UU/IMAU) to specifically represent the SMB evolution over the ice sheets of Greenland, Antarctica and other glaciated regions.
To that end, the atmosphere model has been interactively coupled to a multilayer snow model that simulates meltwater percolation, refreezing and runoff (Ettema et al., 2010).
It includes an albedo scheme with prognostic snow grain size (Kuipers Munneke et al., 2011) and a drifting snow routine that simulates interactions between drifting snow, the ice sheet surface and the lower atmosphere (Lenaerts et al., 2012).Introduction

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RACMO2.3 update
The RACMO2 physics package has recently been updated from cycle CY23r4 used in RACMO2.1 (White, 2001) to cycle CY33r1 in the current RACMO2.3version (ECMWF-IFS, 2008).These updates include major changes in the description of cloud microphysics, surface and boundary layer turbulence, and radiation transport (Van Wessem et al., 2014).The updated physics package includes an Eddy-Diffusivity Mass Flux scheme (EDMF) (Siebesma et al., 2007), representing turbulence and shallow convection in the atmospheric boundary layer.The surface flux computation is based on Monin-Obukhov similarity theory (Beljaars et al., 2004).The new radiation scheme McRad (Morcrette et al., 2008), based on the Monte Carlo Independent Column Approximation (McICA) (Barker et al., 2008), computes the shortwave and longwave radiation transmission through clouds.In addition, the interaction between shortwave or longwave radiation with multi-layered clouds has been improved by revising the cloud optical properties (ECMWF-IFS, 2008).
The new cloud scheme includes an ice supersaturation parameterisation, which prolongs the vapour phase at low temperatures (Tompkins et al., 2007).The autoconversion coefficient, controlling the conversion rate of water-vapour into precipitation in convective clouds, has been defined individually for liquid and ice water clouds, following Sundqvist (1978).Moreover, under marginally freezing conditions, precipitation occurs exclusively as snowfall even though the precipitating clouds are mixed phase.In the previous model version, similar atmospheric conditions could also have resulted in a mix of liquid and solid precipitation for temperatures above −7 • C. The update results in improved relative contributions of rainfall and snowfall to the total precipitation flux (Lin et al., 1983).The auto-conversion coefficient now remains constant for liquid water clouds whereas it decreases with temperature for ice clouds.Furthermore, the ice and liquid water auto-conversion coefficients have been increased in CY33r1 to reduce the overestimated updraft condensation simulated in previous cycles (ECMWF-IFS, 2008;Van Wessem et al., 2014).Other minor adjustments have been applied to the physics Introduction

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Full package and the dynamical core but these are not relevant for this study.A complete overview of all updates is provided by ECMWF-IFS (2008) and Van Meijgaard et al. (2012).

RACMO2 simulations set-up
In RACMO2.3,identical domain and resolutions (∼ 11 km, 40 vertical layers) were used as in the previous RACMO2.1 simulation (Van Angelen et al., 2013).The integration domain includes the GrIS, the Canadian Arctic Archipelago, Iceland and Svalbard.At the lateral atmospheric boundaries, RACMO2.3 is forced at 6 hourly time interval by reanalysis data of ERA-40 (Uppala et al., 2005) for the period 1958-1978 and ERA-Interim (Stark et al., 2007;Dee et al., 2011) for the period 1979-2013.Sea surface temperature and sea ice cover are prescribed from the same reanalysis data.Since RAMO2.1 has been forced by ERA-Interim data only for the period 1990-2012 and by ERA-40 prior to that, we compare model results for the overlapping period .This period coincides with long-term SMB and AWS measurements performed along the K-transect in west Greenland, which are therefore used for model evaluation (see Sect. 2.4).
In both RACMO2 versions, Moderate Resolution Imaging Spectroradiometer (MODIS) albedo products (Stroeve et al., 2005) are used to prescribe a background ice albedo, which is assumed to vary in space but to be constant in time.MODIS products were retrieved from satellite observations at 0.05 • spatial and 8 day temporal resolutions.The background ice albedo field (Fig. 1) is based on 2001-2010 MODIS values, and ranges from 0.3 to 0.55.

Observational data
For from the ice margin through the ablation zone and into the lower accumulation zone of the west Greenland ice sheet along ∼ 67 • N, covering the elevation interval between 400 and 1850 m a.s.l.(Fig. 1).Since 1990, annual stake measurements have been performed at eight sites along the transect: S4, S5, SHR, S6, S7, S8, S9 and S10 (Van de Wal et al., 2005Wal et al., , 2012)).Since August 2003, three AWS with capability to close the SEB have been operated at sites S5 (∼ 500 m a.s.l.), S6 (∼ 1000 m a.s.l.) and S9 (∼ 1500 m a.s.l.) ( Van den Broeke et al., 2008, 2009;van den Broeke et al., 2011).Stations S5 and S6 are located in the ablation zone at about 5 and 40 km from the ice sheet margin, while station S9 is located close to the equilibrium line at approximately 90 km from the ice sheet margin.Since 2011, an AWS is also operated in the accumulation zone at S10 (∼ 1850 m), about 140 km from the ice sheet margin.At the AWS sites, SEB components are computed using a SEB model that uses as input hourly mean observations of wind, temperature, humidity and radiation components (van den Broeke et al., 2011).ents across the main topographical divide.The negative SMB change becomes gradually more pronounced towards the southern and southeastern ice sheet, while the positive anomalies peak in the east.This large-scale pattern can be attributed to changes in the general circulation over the GrIS, as developed in Sect.3.2.Secondly, superimposed on this large-scale pattern, Fig. 2b shows pronounced positive SMB changes that are spatially restricted to the ablation and lower accumulation zones of the south-western and north-eastern ice sheet.These regional changes can be ascribed to enhanced summer snowfall in RACMO2.3,following the revised rainfall to snowfall partitioning.These changes are discussed in detail in Sect.3.3.

Large-scale precipitation changes
The average mid-tropospheric circulation at 500 hPa is directed southwest-northeast over Greenland (Fig. 3a), resulting in a large-scale precipitation gradient in the same direction.In addition, the proximity of the polar front causes eastwards depressions propagation in south Greenland which advect moist air towards the GrIS, further leading to a pronounced topographically forced precipitation maximum in southeast Greenland.
Compared to RACMO2.1,RACMO2.3 simulates a 0.1 to 0.3 • C cooling in the upper troposphere (above 500 hPa, not shown).The cooling is caused by reduced upperair condensation, attributed to the introduction of ice supersaturation in the updated physics, leading to increased precipitation in northeast Greenland.Moreover, a lowering of the 500 hPa geopotential height is modelled over the ice sheet with a minimum situated over coastal southeast Greenland (Fig. 3b).The resulting cyclonic circulation anomaly results in stronger onshore flow and increased precipitation in the northeastern GrIS and a decrease in precipitation in the north-western ice sheet, at the lee side of the main divide.In south Greenland, RACMO2.3 simulates decreased precipitation overall; this is related to enhanced north-westerly advection of colder and drier air masses on the western side of the divide, reinforced offshore katabatic circulation and subsequently weakened onshore flow to the east (Noël et al., 2014).Introduction

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Full The large-scale circulation anomaly also reduces evaporation over the north Atlantic Ocean, by up to 200 mm w.e. per year (not shown).Moreover, because condensation in the updated scheme is enhanced for moderately cold conditions (< 10 • C), precipitation over the ocean is enhanced, further limiting precipitation in coastal southeast Greenland.Precipitation differences locally reach 25 %, and integrated over the GrIS the average 1990-2012 precipitation is reduced by 6 %, from 729 Gt yr −1 in RACMO2.1 to 687 Gt yr −1 in RACMO2.3.Note that the erratic box-like pattern in Fig. 3b results from an error in the meridional momentum advection scheme in RACMO2.1, which is solved in the current formulation.

Summer snowfall events: the snow-albedo-melt feedback
The enhanced rainfall-to-snowfall conversion in RACMO2.3favours solid precipitation at the expense of liquid precipitation, especially at higher air temperatures.In winter this has no major impact on the rainfall/snowfall ratio because the air temperature remains mostly below the solid precipitation threshold.In summer (JJA), however, RACMO2.3predicts locally enhanced snowfall (10-40 mm w.e.), notably in southwest, northeast and northwest Greenland (Fig. 4a).These regional changes are accompanied by an equivalent decrease in rainfall (Fig. 4b), so we conclude that they result from the updated precipitation scheme.The negative (resp.positive) summer snowfall changes in central and southeast (resp.east) Greenland are not compensated by an opposite and equivalent rainfall change; here, precipitation changes are caused by the circulation change discussed in Sect.3.2.
The regions experiencing increased summer snowfall coincide with positive changes in JJA surface albedo (Fig. 4c).The impact of summer snowfall on albedo is largest in the ablation zone, where the amount of absorbed shortwave radiation is reduced by a factor of ∼ 3 when dark bare ice (albedo ∼ 0.3-0.55) is covered by fresh snow (albedo ∼ 0.85).As a consequence, meltwater runoff, which in RACMO2 is assumed to occur instantaneously over bare ice, is also substantially reduced (Fig. 4d).Note that this reduction in runoff (40-160 mm w.e.) in magnitude significantly exceeds the snow-Introduction

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Full fall anomaly in Fig. 4a (5-30 mm w.e.), stressing the importance of the snow-albedomelt feedback mechanism, in line with previously published results for valley glaciers (Greuell and Oerlemans, 1986).The pronounced runoff reductions are mirrored in the map of SMB change (Fig. 2b).
4 Evaluation using observational data: a case study for west Greenland

SEB evaluation along the K-transect
In this section, we compare modelled and observed monthly mean SEB components (2004SEB components ( -2012) ) along the K-transect, conveniently situated in a region of west Greenland where significant SMB changes occur (Fig. 2b).The SEB is given by: where: SW d and SW u are the downward and upward shortwave radiations (W m −2 ), LW d and LW u are the downward and upward longwave radiations (W m −2 ), SHF and LHF are the sensible and latent turbulent heat fluxes (W m −2 ), G is the ground heat flux (W m −2 ), and SW n and LW n are the net short/longwave radiations (W m −2 ).
SEB data from the AWS at S6 are not used because of gaps in the time series at this location.Figure 5 and Tables 1 to 3 show observed and modelled monthly mean SEB components, surface albedo, melt energy and the differences for the period 2004-2012 (S5 and S9) and 2010-2012 (S10).For station S9, a distinction is made between the sub-periods 2004-2008 and 2009-2012; this is deemed relevant because of the significantly warmer summer conditions near the surface and in the upper atmosphere during the latter period.Figure 5a, c, e and g shows that there is qualitative agreement between the modelled and observed seasonal cycle of the SEB in the ablation, equilibrium and accumulation zones.However, important biases remain, as discussed below.Introduction

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Ablation zone (S5)
At station S5, Table 1 shows that both RACMO2 versions significantly overestimate SW d and underestimate LW d , even more so in RACMO2.3, which is indicative of underestimated cloud optical thickness.In combination with underestimated ice albedo (Fig. 5b) this leads to significantly overestimated net shortwave radiation (SW n ) in summer (Fig. 5a).On the other hand, RACMO2 underestimates the large summertime SHF values at S5, although this is improved in RACMO2.3(Table 1).The reason is that station S5 has a complex topography: neither the summertime advection of warm tundra air over the glacier tongue that protrudes onto the tundra, leading to underestimated air temperature (Table 1), nor the high surface roughness at the marginal glaciers (Smeets and van den Broeke, 2008) is well described at 11 km resolution.This leads to underestimated surface to air gradients of temperature and wind, and hence too small SHF values.This does not affect LHF, which remains close to zero at S5.The net effect on melt energy is a negative bias (Fig. 5b) that has become smaller in RACMO2.3(from 18 to 13 %), owing to significant error compensation.

Around the equilibrium line (S9)
At S9, RACMO2.3 reduces the bias in most SEB components (Table 2).The 2 m temperature bias has almost vanished, which has improved the representation of SHF.Despite a notable improvement of winter LW n (not shown), LW d remains underestimated (Table 2).Average biases in SW d , SW u and SW n are greatly reduced in RACMO2.3.In RACMO2.1, the average melt bias was small at S9 (Table 2), but this was the result of overestimated melt in the period 2004-2008, and underestimated melt in 2009-2012 (Fig. 5d and f).For the period 2004-2008, enhanced summer snowfall has increased surface albedo in RACMO2.3 (Fig. 6a), which leads to an overall improved representation and a clearly reduced melt bias (Fig. 5d).In contrast, summer snowfall has not considerably changed at station S9 for the period 2009-2012 (Fig. 6b) and biases in albedo and melt energy have persisted (Fig. 5d and f).The explanation is that sum-Introduction

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Full mer atmospheric temperatures in 2009-2012 were too high for the new precipitation scheme to enhance snowfall.The bias in surface albedo between models and observations is ascribed to a too high prescribed bare ice albedo of about 0.55 (Fig. 1).In fact, both RACMO2 versions fail to reproduce the recent decline in summer ice albedo at S9, which is transforming from an accumulation towards an ablation site.In recent warm summers, the exposed bare ice surface at S9 showed albedo values of about 0.43 and 0.45 in 2010 and 2012 respectively, while RACMO2 is restricted to 0.55 since no ice albedo could be derived from MODIS imagery at this location (Fig. 5f).

Accumulation zone (S10)
At S10, biases in shortwave fluxes are greatly reduced but again the negative LW d bias persists (Table 3).In winter this is mainly compensated by an overestimated SHF, but not so in summer (Fig. 5g).In June and July, the representation of albedo has improved, but in August albedo is now overestimated.SW n remains somewhat too large, however, since LW n is underestimated, the errors in melt energy are less than 10 W m −2 (Fig. 5h).The lower accumulation zone responds similarly to station S9 during 2004-2008 but with a reduced surface albedo sensitivity to summer snowfall, because snow metamorphism is slower in this colder area and snow wetting occurs less frequently.
The generally improved representation of surface snow albedo is attributed to enhanced summer snowfall in RACMO2.3(see Sect. 3.3), thickening the melting snow cover and allowing a partial recovery of the snow layer over bare ice areas in summer.As a result, snowmelt decreases, further delaying snow cover disappearance and maintaining the surface albedo high until summer snowfall events cease (Fig. 6a).The summer surface albedo increase is further reinforced by a drop in cloud cover.This process reduces LW d , also decreasing snowmelt at station S9 (Fig. 5d).Introduction

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Temporal SMB variability
Table 4 compares time series of modelled and measured annual SMB values (1990-2012) collected at 7 stake sites, ranging from station S5 in the lower ablation zone to station S10 in the accumulation zone.Figure 7 shows these time series for RACMO2.3 at four sites.The lowermost stake S4 (∼ 400 m a.s.l.) is excluded from the analysis because it is not well resolved by the model ice sheet mask.At all sites except S10, the agreement improves in RACMO2.3,expressed as lower biases and a higher percentage of variance explained (r 2 , Table 4).At S10, SMB inter-annual variability is not well captured, but it must be stated that stake SMB measurements have limited accuracy in the percolation zone due to uncertainties in the snow density and subsurface refreezing.

Spatial SMB variability
Table 4 and Fig. 8 compare modelled and observed K-transect average SMB  as deduced from annual stake measurements.Modelled SMB values are determined by minimizing both the horizontal distance and the elevation differences between RACMO2.3 grid-cells and K-transect locations.Figure 8 also shows the prescribed MODIS background albedo (green dots, scale on right axis).The covariance of ice albedo with modelled SMB once more underlines the importance of ice albedo for the ablation zone SMB (Van Angelen et al., 2012).Again it must be noted that the stake sites are not necessarily representative for a larger region, e.g. for the area of a model grid cell (∼ 120 km 2 ).
In the lower ablation zone, between 500 to 800 m a.s.l., RACMO2.3simulates lower (more negative) SMB values than RACMO2.1, which better matches observations (Fig. 8).This improvement can be ascribed to a smaller bias in melt energy (Table 1) and hence a more realistic runoff.Solving the persistent overestimation of Introduction

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Full SMB between 500 and 800 m a.s.l. will require a better representation of SHF which, in combination to SW d and LW d , is a primary factor governing melt rate in the lower ablation zone.For elevations between 800 m and the equilibrium line at about 1500 m a.s.l., RACMO2.3simulates higher SMB values compared to RACMO2.1, resulting mainly from reduced runoff following enhanced summer snowfall through the snow-albedo-melt feedback.The absence of rapid SMB fluctuations in the model between 1400 m a.s.l. and the equilibrium line is clearly related to the fixed upper threshold (0.55) of bare ice albedo prescribed in RACMO2 (Van Angelen et al., 2012).In the accumulation zone (above 1500 m), enhanced snowfall and less runoff have significantly improved the agreement with the K-transect stake observations.
An alternative way to assess model performance is to quantify SMB gradients, here determined by simple least square fitting of a linear function.This yields 3.15 ± 0.22 mm w.e.yr −1 m −1 for the observations and 2.73 ± 0.09 and 2.91 ± 0.07 mm w.e.yr −1 m −1 for RACMO2.1 and RACMO2.3,respectively; in the updated model, the deviation from the observed gradient has thus decreased from 0.42 to 0.24 mm w.e.yr −1 m −1 , a 43 % improvement of the SMB gradient representation.

Conclusions
An updated physics package has been implemented in the regional climate model RACMO2.3.Among other changes, the rainfall-to-snowfall conversion has been revised and an ice supersaturation parameterization included, to favour solid over liquid precipitation in summer and reduce the overestimated coastal cloud cover and precipitation simulated in previous versions, respectively (Van de Berg et al., 2006).The subsequent increase in modelled summer snowfall has generally improved the representation of surface energy balance (SEB) and surface mass balance (SMB) along the K-transect in west Greenland.For SEB, these improvements are more pronounced in the lower accumulation zone, where summer temperatures are generally below zero.
Close to the equilibrium line, SMB is especially sensitive to snowfall-induced fluctua-Introduction

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Full tions in surface albedo.The increase in summer snowfall enhances surface reflectivity, improving the modelled surface albedo in summer as well as SMB representation.However, in recent warm years (e.g.2010 and 2012) rainfall prevailed even in the new formulation, and no improvement was obtained.At station S5 in the lower ablation zone, summer albedo in RACMO2 is mainly determined by the prescribed MODIS ice albedo, due to near-continuous bare ice exposure.The updated physics in RACMO2.3 have considerably improved the modelled SMB gradient along the K-transect when compared to ablation stake measurements, reducing the SMB gradient bias by 43 %.Two remaining problems require particular attention in future model updates.Current RCMs still struggle to model the correct cloud cover and cloud type (ice/water) over the GrIS (Box et al., 2012).For instance, both RACMO2 and MAR models underestimate summer LW d and overestimate SW d due to an underestimated cloud optical thickness (Ettema et al., 2010;Fettweis et al., 2011).In fact, the inclusion of ice supersaturation in RACMO2.3might aggravate this problem over the ablation zone, because this change delays cloud formation to higher ice sheet elevations, as was also seen in simulations of Antarctic climate (Van Wessem et al., 2014).Another improvement that is simpler to implement is the background ice albedo, that is currently too low at the ice sheet margin.However, at this point, it is also important to realize that point AWS (SEB) and stake (SMB) measurements may not be representative for a wider area, especially for a spatially heterogeneous variable such as surface albedo.Sub-grid albedo variability may therefore be an important future topic of study.To assess the quality of the simulated SMB in the ablation zone elsewhere in Greenland, an evaluation of RACMO2.3 performance against a much larger dataset of ablation measurements, covering all sectors of the Greenland ice sheet, is currently being conducted.

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Full  Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | model evaluation, we use long-term measurements from the K-transect, operated by the Institute for Marine and Atmospheric Research of Utrecht University in the Netherlands (UU/IMAU).The K-transect runs for a distance of approximately 140 km Discussion Paper | Discussion Paper | Discussion Paper |

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Figure2shows (a) RACMO2.3averageSMB (1990SMB ( -2012) )  and (b) the difference in SMB between RACMO2.3 and RACMO2.1.Both model versions simulate a qualitatively realistic SMB field, with a narrow ablation zone fringing the ice sheet (Fig.2).The ablation zone is widest (∼ 100-150 km) in the southwest and northeast, but too narrow in the southeast to be resolved at a resolution of 11 km; in this part of the ice sheet, a steep topography and high precipitation rates induce a large SMB gradients, resulting in an ablation zone of only a few km wide.The SMB fields from RACMO2.1 and RACMO2.3 are qualitatively similar, but two patterns of change can be discerned (Fig.2b).First, a large-scale pattern with decreased/increased SMB in the west/east results in enhanced longitudinal SMB gradi- Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Fettweis, X., Tedesco, M., van den Broeke, M., and Ettema, J.: Melting trends over the Green- Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Table 3 .
Same as Table 1 but for station S10 (67 • 00 N, 47• 01 W, 1850 m a.s.l.) in the accumulation zone.SEB components include monthly mean data for the period 2004-2012.