Very High Resolution Regional Climate Model Simulations Over Greenland: Identifying Added Value Philippe Lucas-Picher,1,2 Maria Wulff-Nielsen,3,4 Jens H

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D02108, doi:10.1029/2011JD016267, 2012

Very high resolution regional climate model simulations over Greenland: Identifying added value Philippe Lucas-Picher,1,2 Maria Wulff-Nielsen,3,4 Jens H. Christensen,3 Guðfinna Aðalgeirsdóttir,3 Ruth Mottram,3 and Sebastian B. Simonsen3,5 Received 18 May 2011; revised 18 November 2011; accepted 18 November 2011; published 25 January 2012.

[1] This study presents two simulations of the climate over Greenland with the regional climate model (RCM) HIRHAM5 at 0.05° and 0.25° resolution driven at the lateral boundaries by the ERA-Interim reanalysis for the period 1989–2009. These simulations are validated against observations from meteorological stations (Danish Meteorological Institute) at the coast and automatic weather stations on the ice sheet (Greenland Climate Network). Generally, the temperature and precipitation biases are small, indicating a realistic simulation of the climate over Greenland that is suitable to drive ice sheet models. However, the bias between the simulations and the few available observations does not reduce with higher resolution. This is partly explained by the lack of observations in regions where the higher resolution is expected to improve the simulated climate. The RCM simulations show that the temperature has increased the most in the northern part of Greenland and at lower elevations over the period 1989–2009. Higher resolution increases the relief variability in the model topography and causes the simulated precipitation to be larger on the coast and smaller over the main ice sheet compared to the lower-resolution simulation. The higher-resolution simulation likely represents the Greenlandic climate better, but the lack of observations makes it difficult to validate fully. The detailed temperature and precipitation fields that are generated with the higher resolution are recommended for producing adequate forcing fields for ice sheet models, particularly for their improved simulation of the processes occurring at the steep margins of the ice sheet. Citation: Lucas-Picher, P., M. Wulff-Nielsen, J. H. Christensen, G. Aðalgeirsdóttir, R. Mottram, and S. B. Simonsen (2012), Very high resolution regional climate model simulations over Greenland: Identifying added value, J. Geophys. Res., 117, D02108, doi:10.1029/2011JD016267.

1. Introduction Without robust and validated climate forcing, it is not possible to compute realistic estimates of the surface mass balance and [2] Remote sensing observations show that the Greenland surface evolution of the Greenland ice sheet. ice sheet is thinning and losing mass at an accelerating rate in [3] Coastal weather stations have been operated in Green- the recent years [Luthcke et al., 2006; Pritchard et al.,2009; land by the Danish Meteorological Institute (DMI) since the Velicogna, 2009]. In the literature, considerable discrepancies late 19th century [Cappelen, 2010]. However, the Greenland appear between the mass balance estimates of the Greenland ice sheet itself suffers from both poor spatial and temporal ice sheet and their inherited uncertainties [Dahl-Jensen et al., coverage of observations. Although, this situation has 2009, Table 2.3]. Even different estimates applying similar recently improved considerably with an increasing number of methodology and data sets show discrepancies [Sørensen weather stations on the ice sheet, from the GC-Net stations et al., 2011; Zwally et al., 2011], which may be partly [Steffen and Box, 2001], the K transect [van de Wal et al., explained by the limited knowledge of the Greenland climate. 2005] and the monitoring project PROMICE [Ahlstrøm et al., 2008]. (GC-Net data are available online at http:// 1Centre National de Recherches Météorologiques, Météo-France, cires.colorado.edu/science/groups/steffen/gcnet/.) There are Toulouse, France. still large regions without any weather data, especially in the 2Also at Danish Climate Centre, Danish Meteorological Institute, ablation zone where the Greenland ice sheet loses most mass. Copenhagen, Denmark. [4] Climate models are thus the only physically sound 3Danish Meteorological Institute, Copenhagen, Denmark. 4Also at Planetary Physics and Geophysics, Niels Bohr Institute, tools that are capable of filling the spatial gap between the University of Copenhagen, Copenhagen, Denmark. weather stations and to enable computation of the past and 5Also at Centre for Ice and Climate, Niels Bohr Institute, University of future evolution of the climate over Greenland. Global cli- Copenhagen, Copenhagen, Denmark. mate models (GCMs), with horizontal resolution of about 100–200 km [Randall et al., 2007], are too coarse to repre- Copyright 2012 by the American Geophysical Union. 0148-0227/12/2011JD016267 sent the detailed topography that controls the surface

D02108 1of16 D02108 LUCAS-PICHER ET AL.: RCM SIMULATIONS FOR GREENLAND D02108 forcing, such as the steep slopes of the ice sheet and the [7] The European Union Framework-7 project ice2sea description of the fjords necessary to simulate the mesoscale (see http://www.ice2sea.eu) that started in 2009 has the main climate in the coastal regions. For the last 20 years (see the goal to estimate the future contribution of continental ice to review in the work of Giorgi [2006]), regional climate sea level rise. It is the first coordinated effort to project long- models (RCMs) have been used to efficiently increase the term ice sheet surface mass balance changes with coupled resolution of climate simulations to the scale of a few tens of ice sheet and regional climate models designed for this kilometers. At this higher resolution, the RCMs describe purpose. Moreover, the Greenland climate research center, better the steep topography in the ablation zone and the established in 2009, has a research project focusing on cli- effects of orographically enhanced precipitation generated mate system simulations over Greenland (see http://www. on the upstream slopes of the mountains, with less precipi- natur.gl/en/climate-research-centre/research-projects/climate- tation on the lee side [e.g., Dahl-Jensen et al., 2009]. The simulations). In this project, the feedback processes within improved description of these effects is important to accu- the RCM will be improved with a more complete description rately compute the accumulation and the surface mass bal- of fjords, lakes and open seas with a target spatial resolution ance, especially when the climate model is coupled with, or of 1–2 km. otherwise used to drive, an ice sheet model. The RCM [8] The work presented here is a first step toward the employs a limited area domain that is driven at the bound- achievement of the goals of these two projects leading aries by large-scale atmospheric fields from a GCM or by toward a Greenland model system suitable for ice sheet and reanalysis data. RCMs can therefore be considered as smart permafrost studies. It consists of a robust validation of a new physically consistent interpolators because they are dynam- multidecadal (1989–2009) regional climate model simula- ically downscaling the large-scale atmospheric fields pro- tion. This simulation has the novelty of being computed at vided at their boundaries. an unprecedented horizontal spatial resolution of 0.05° [5] A number of RCMs have been used to simulate the (5.55 km) with the most up-to-date Danish regional cli- recent past climate over Greenland. In their validation of the mate model (HIRHAM5) driven at its boundaries by the climate simulated with the HIRHAM4 RCM, Box and Rinke latest ECMWF reanalysis (ERA-Interim). Following model [2003] recognized the importance of using an accurate validation, the climate of Greenland is described for different description of the topography of the Greenland ice sheet in regions and elevations. Then, the added value of the higher order to reduce the biases in the climate simulation. Fettweis resolution is assessed by comparing the results with a lower- [2007] computed the surface mass balance of the Greenland resolution (0.25°) simulation. The analysis focuses on 2 m ice sheet by using the regional climate model MAR for the air temperature and precipitation, which are the most period 1979–2006, but without validating the RCM climate important variables for the ice sheet models to compute the output against observations. Later, Ettema et al. [2009] surface mass balance and the dynamics controlling the computed higher surface mass balance than MAR over the extent of the Greenland ice sheet. ice sheet with RACMO2 during the period 1958–2007. [9] Section 2 describes the experimental setup where the According to Ettema et al. [2009], the higher surface mass HIRHAM5 RCM, the climate simulations and the observa- balance with RACMO2 is likely to be resulting from the tions are introduced. In section 3, the RCM simulated 2 m higher precipitation and melt computed with a higher spatial temperature and precipitation are compared to observations. resolution of 11 km compared to the 25 km resolution that Section 4 describes the simulated climate for different MAR used. The higher resolution facilitates capturing snow regions and elevations. The added value of the high-resolu- accumulation peaks that coarser RCMs miss because of tion RCM simulation is assessed in section 5. Finally, dis- poorer representation of the topography [Ettema et al., cussions and conclusions are presented in section 6. 2009]. [6] In another study, Ettema et al. [2010] demonstrated 2. Experimental Setup that RACMO2 can simulate the present-day near-surface characteristics of Greenland by doing a systematic compar- 2.1. Regional Climate Model HIRHAM5 ison of the model output against observations from coastal [10] The climate model used in this study is the Danish and ice sheet weather stations. Burgess et al. [2010] used the regional climate model (RCM) HIRHAM5 [Christensen RCM Polar MM5 to derive an accumulation field over et al., 2006], which is a hydrostatic RCM developed at the Greenland for the period 1958–2007 using a large number of Danish Meteorological Institute. It is based on the HIR- ice cores and the DMI coastal weather station measurements LAM7 dynamics [Eerola, 2006] and the ECHAM5 physics as input. They highlighted the paucity of the in situ data in [Roeckner et al., 2003] using the Tiedtke [1989] mass flux the southeastern part of Greenland in particular, which leads convection scheme, with modification after Nordeng [1994], to an uncertain correction of the simulated accumulation in and the Sundqvist [1978] microphysics. The land surface this region. An analysis of the precipitation and temperature scheme is unmodified from that used in the ECHAM5 model output of HIRHAM4 showed that the RCM simulated pre- [Roeckner et al., 2003], which employs the rainfall-runoff cipitation is smaller than the one observed over the main ice scheme described in the work of Dümenil and Todini [1992]. sheet and larger on the coast [Stendel et al., 2007; At the lateral boundaries of the model domain, a relaxation Aðalgeirsdóttir et al., 2009]. Forcing ice sheet models with scheme according to Davies [1976] is applied with a buffer this RCM output results in a thinner than observed ice sheet zone of ten grid cells. with a reduced extent toward the north and west coasts, [11] As in ECHAM5, the land surface scheme used in this while the simulated ice sheet is in good agreement with study does not include snow processes, including sublima- observations in the south. tion and snowmelt over land based ice (glaciers and ice

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Figure 1. Topography (in meters) and land-sea-glacier mask for (a) ERA-Interim interpolated on a 0.75° grid and the HIRHAM5 simulations at a resolution of (b) 0.25° and (c) 0.05°. The elevation is indicated with the color scale and with red contours from 1000 to 3000 m. The black horizontal lines indicate the location of the cross sections shown in Figure 2. (d) More detailed view of southwestern Greenland at 0.05° resolution as indicated by the black box in Figure 1c. sheets), although these are included where glaciers are not [2003]. Computing the surface mass balance is not the present. Instead, a snow layer of 10 m water equivalent is main purpose of this study and simplifications in the surface prescribed on all glacier surfaces. The energy and moisture scheme are likely to reduce the accuracy of such calcula- flux interactions at and below the surface are determined by tions. However, a comparison of the estimated SMB at the this thick snowpack. The albedo of the ice sheet is a linear two different resolutions can be used to further assess the function of surface temperature with a minimum albedo of added value of high-resolution runs, since the input fields to 0.6 at the melting point and a maximum albedo of 0.8 at calculate SMB are both likely to be affected by resolution. surface temperatures of 5°C and lower [Roesch et al., Also, it is important to understand how these effects are 2001; Roeckner et al., 2003]. Because most of the Green- summed together. land ice sheet is snow covered year round, we assess the [13] Snow processes are important for calculating the effect of this approximation on air temperature and radiative surface mass balance of glaciers. Most ice sheet modeling and turbulent fluxes to be small. The main bias is limited in studies use dedicated schemes to calculate the surface mass time and space to the ablation zone at low elevations around balance and these schemes usually include sophisticated the margins during the melt season. snow processes, often driven by output from atmospheric [12] Precipitation and evaporation are simulated by the regional climate models. Recent model development of model and it is possible to compute the surface mass balance HIRHAM5 includes implementing an interactive surface (SMB) offline by combining these with a separate melt scheme in which the surface mass budget over glaciers and model. We use a linear relationship identified by Ohmura ice sheets is explicitly computed. The new scheme will take et al. [1996] and also applied in a study by Kiilsholm et al. into account snowmelt, sublimation, retention and refreezing

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2.2. Domain and the RCM Simulations [14] The HIRHAM5 model is used to simulate the climate over Greenland at two horizontal resolutions, 0.05° (5.55 km) and 0.25° (27.75 km). The two domains are of similar size with 31 vertical levels and use a rotated map projection of 402 602 and 92 122 grid cells to reduce grid cell dis- tortion at higher latitudes. The topography of the Greenland ice sheet is determined from the database of Bamber et al. [2001], interpolated on the HIRHAM5 grid. The two domains and the land-sea-glacier mask are shown in Figure 1. The HIRHAM5 domain sizes and limits were chosen such that the whole of Greenland and Iceland are included when the relaxation zone has been removed. This explains why the 0.25° resolution domain is larger than the one at 0.05°. The corresponding fields from the ERA-Interim reanalysis, interpolated on a 0.75° rotated grid, are also shown in Figure 1a. With increasing resolution, there is a better description of the topography and the land-sea contrast around Greenland. In Figure 2. Cross section of Greenland and its ice sheet Figure 1d, a small part of the southwestern coast of Greenland showing the topography of the HIRHAM5 simulations at at 0.05° resolution is shown in greater detail. This shows that 0.05° resolution (red line) and 0.25° resolution (blue line). the fjord systems, in this case near the capital Nuuk, are now The black line shows the topography used by the ERA- almost resolved in the land-sea mask. This is not the case for Interim reanalysis interpolated on a 0.75° resolution grid. the lower-resolution representations. This is the main reason The location of the cross section is indicated on Figure 1 for our attempt to use the very high resolution. by the black lines. [15] Figure 2 shows a cross section of Greenland for the two HIRHAM5 resolutions and the ERA-Interim reanalysis in the snowpack. This will allow an online coupling between data set interpolated on a 0.75° grid. The incremental rise in the RCM and an ice sheet model (R. Mottram et al., Surface the topography between two grid cells within the ablation – mass balance of the Greenland ice sheet 1989 2009 using zone is 300 to 500 m at 0.25° and even larger (800 m) at the Regional Climate Model HIRHAM5, manuscript in 0.75°. Such a steep increase is potentially in conflict with the preparation, 2012). formulation of the vertical structure of the climate model and

Table 1. List of Stations Used for the Validation of the Simulations Station Name Source Latitude (ºN) Longitude (ºW) Altitude (m) Time Period Crawford Point 1 GC-Net 69.87 46.98 2022 1996–2009 Crawford Point 2 GC-Net 69.90 46.85 1990 1997–2000 DYE-2 GC-Net 66.47 46.27 2099 1996–2009 GITS GC-Net 77.13 61.03 1869 1996–2007 Humboldt GC-Net 78.52 56.82 1995 1996–2008 JAR 1 GC-Net 69.48 49.70 932 1996–2009 JAR 2 GC-Net 69.40 50.08 507 1999–2008 JAR 3 GC-Net 69.38 50.30 283 2001–2004 NASA-E GC-Net 75.00 29.98 2614 1997–2008 NASA-SE GC-Net 66.47 42.48 2373 1998–2007 NASA-U GC-Net 73.83 49.50 2334 1996–2008 NGRIP GC-Net 75.08 42.32 2941 1997–2005 Petermann GC-Net 80.68 60.28 37 2002–2006 Saddle GC-Net 69.98 44.50 2901 1997–2009 Swiss Camp GC-Net 69.55 49.32 1176 1996–2006 South Dome GC-Net 63.13 44.82 2901 1997–2008 Summit GC-Net 72.57 38.50 3199 1996–2009 Tunu-N GC-Net 78.00 33.98 2052 1996–2008 Aasiaat (4220) DMI 68.70 52.75 43 1989–1999 Danmarkshavn (4320) DMI 76.77 18.66 11 1989–2009 Illoqqortoormiut (4339) DMI 70.48 21.95 65 1989–2009 Ilulissat (4221) DMI 69.22 51.05 29 1989–2009 Kangerlussuaq (4231) DMI 67.02 50.70 50 1989–1999 Narsarsuaq (4270) DMI 61.17 45.42 34 1989–2009 Nuuk (4250) DMI 64.17 51.75 80 1989–2009 Pituffik (4202) DMI 76.53 68.75 77 1989–1999 P.C. Sund (4390) DMI 60.05 43.17 88 1989–1999 Qaqortoq (4272) DMI 60.43 46.05 32 1989–1999 Station Nord (4310) DMI 81.60 16.65 36 1989–1999 Tasiilaq (4360) DMI 65.60 37.63 50 1989–2009 Upernavik (4211) DMI 72.78 56.17 126 1989–2009

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surface pressure are transmitted to the RCM every 6 h for each of the 31 atmospheric levels. A climate simulation at 6 km is challenging the hydrostatic assumption. The numerical weather prediction forecast system HIRLAM, using the same dynamical core as HIRHAM5, is used for operational weather forecast at a few kilometers (3–6 km) over Greenland and Denmark with satisfactory results (see http://www.dmi.dk/eng/index/research_and_development/dmi- hirlam-2009.htm). A comparison with a new nonhydrostatic model planned to be operational within a few years indicates that the hydrostatic assumption is not a critical limitation for these simulations. We have therefore confidence in using the model at this high resolution. 2.3. Weather Station Data Sets [17] The two HIRHAM5 simulations are validated against two observational data sets. The first comes from weather stations located around the coast of Greenland, maintained by the Danish Meteorological Institute (DMI) [Cappelen et al., 2001; Cappelen, 2010] and measuring the 2 m temperature and precipitation among other variables. The measured precipitation can be affected by many factors such as

Figure 3. Average 2 m (a, b, c) summer (JJA) and (d, e, f) winter (DJF) temperature (in degrees Celsius) for the period 1989–2009. ERA-Interim reanalysis interpolated on a 0.75° grid (Figures 3a and 3d). HIRHAM5 simulation at 0.25° resolution (Figures 3b and 3e). HIRHAM5 simulation at 0.05° resolution (Figures 3c and 3f). introduces systematic surface temperature errors [e.g., Dahl- Jensen et al., 2009]. The incremental rise is considerably smaller in the 0.05° resolution simulation and does not introduce inconsistencies. The high resolution of the RCM may therefore contribute to accurate simulation of the temperature and precipitation gradients that are required for a realistic description of the surface mass balance in the ablation zone. Moreover, Figure 2 shows that the models at lower resolutions than 0.05° do not depict the fjords on the east coast of Greenland. [16] For each resolution (0.05° and 0.25°), a continuous simulation was realized for the period 1989–2009 using the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data at T255 (0.7°) Figure 4. Average (a, b, c) summer (JJA) and (d, e, f) win- horizontal resolution [Dee et al., 2011] as lateral boundary ter (DJF) precipitation (in millimeters per day) for the period conditions. The sea surface temperature and the sea ice dis- 1989–2009. ERA-Interim reanalysis interpolated on a 0.75° tribution from the ERA-Interim reanalysis are prescribed grid (Figures 4a and 4d). HIRHAM5 simulation at 0.25° res- daily in the model. The horizontal wind components, the olution (Figures 4b and 4e). HIRHAM5 simulation at 0.05° atmospheric temperature, the specific humidity and the resolution (Figures 4c and 4f).

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ensemble mean. The source, position, elevation and period considered for each station is described in Table 1.

3. Validation With Observations 3.1. Spatial Distribution of Temperature and Precipitation [18] Figures 3 and 4 give a general impression of the climate simulated by the RCM HIRHAM5 over Greenland, showing the average 2 m temperature and precipitation, for summer (JJA) and winter (DJF), at 0.05° and 0.25° resolution, respectively, during the period 1989–2009. The same variables from the ERA-Interim reanalysis, interpolated to a 0.75° grid, are also shown. The temperature from ERA- Interim is derived directly from the assimilated measurements, while the precipitation is simulated with the inte- grated forecast system (IFS) model. Figure 3 shows that for the same season, the large-scale spatial distribution of the 2 m temperature is similar from one resolution to another. HIRHAM5 is therefore simulating the climate realistically by being forced at the lateral boundaries and by the sea surface temperature from the ERA-Interim reanalysis. As the resolution increases, more details in the spatial pattern can be observed, especially near the coast where the topography and the land-sea mask are complex and sensitive to the model resolution. The precipitation at the southeast coast of Greenland during winter is more intense in the fjords and has a higher spatial variability at 0.05° resolution (Figure 4f) than at 0.25° (Figure 4e). A more detailed analysis of the added value of the higher-resolution simulation is presented in section 5. 3.2. Comparison of Simulated 2 m Temperature With Data From Stations on the Coast and Ice Sheet [19] Figure 5 presents an overview of the comparison of Figure 5. Temperature bias (in degrees Celsius) between the 2 m winter (DJF) and summer (JJA) temperatures after the HIRHAM5 simulations at 0.05° (top color cells) and matching the time periods between the RCM HIRHAM5 0.25° (bottom color cells) resolution and observations from simulations and the available data from the coastal DMI DMI (blue) and GC-Net (red) stations for the period 1989– stations and the GC-Net ice sheet stations. The temperature 2009. The left column of color cells is for winter (DJF), and of the closest land grid cell is used to compare to the weather the right column of color cells is for summer (JJA). The sta- 1 stations and a lapse rate correction of 6°C km is applied to tions JAR1, JAR2, JAR3, Swiss Camp, Crawford Point 1 take into account any elevation difference between the (CP1), and Crawford Point 2 (CP2) are not shown in their HIRHAM5 cells and the weather stations. exact locations but are presented next to each other for clarity. [20] In general, the biases between the HIRHAM5 simulations and the observations are between 2 and +2°C dur- the wind speed and the type of precipitation [Allerup et al., ing the summer season (JJA). In winter (DJF), there is a 1997, 2000; Yang et al., 1999]. Therefore, the observed warm bias compared to the GC-Net stations on the ice sheet precipitation used in this study has been corrected with and a cold bias on the southwest coast compared to the DMI respect to evaporation, wetting losses and wind forcing such stations. Figure 6 shows this opposite winter temperature as drifting snow [Wulff, 2010]. The second data set comes bias more clearly with a scatter diagram of the 2 m tem- from a network of 19 automatic weather stations (AWS) peratures simulated at 0.05° and 0.25° resolution against the distributed on the Greenland ice sheet [Steffen and Box, observations. The correlation between the observed and 2001]. The AWS also measure other weather parameters, simulated values is good in summer with a small warm bias. but in this study, only the temperature will be considered. In winter, the correlation is also good but the simulated Each AWS has four temperature sensors at different heights values are up to 5°C too warm for the coldest stations on the (1 and 2 m). In order to have the most consistent data set and ice sheet and up to 5°C too cold for the warmest stations on to avoid data gaps, a mean of the four temperature sensors the coast. There is only a small difference in the biases at for each weather station was computed for the available 0.05° and 0.25°. For spring (MAM) and fall (SON) (not period. A careful inspection of the time series temperature of shown), the biases are similar to winter with an overesti- the four sensors for all the AWS was done in order to mation of temperature on the ice sheet and an underestima- remove the temperature of sensors far from the four sensor tion on the coast.

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Figure 6. Simulated 2 m average temperature on (left) 0.05° and (right) 0.25° resolution against the average observed temperature for the period 1989–2009. Winter (DJF) temperature is shown in blue, and summer (JJA) temperature is shown in red. The observations from the DMI stations are shown with squares and from the GC-Net stations with crosses. The slopes and the correlations of the best linear fits are indicated on the graph.

[21] Several model specifications can contribute to the that represent weather conditions at a very local scale (of a winter warm temperature bias. The vertical resolution of the few meters), have to be considered. The local-scale mea- model is too low (at standard atmosphere the lowest three surements are sensitive to the immediate surrounding factors model levels are at 33 m, 106 m, 189 m) to resolve the and are thus not directly comparable to the weather condi- boundary layer processes that cause strong temperature tions simulated by a climate model on a grid cell of many inversion as well as the katabatic winds, which prevail in km2. This may partly explain why the simulations do not winter over the ice sheet. The temperature bias can also be have smaller biases at 0.05° than at 0.25° in Figure 5 (see related to biases in the incoming longwave radiation (likely section 5). due to errors in the cloud cover) during the dark and 3.3. Comparison of Simulated Precipitation With Data cloudless winter conditions and errors in the turbulent From Stations on the Coast and on the Ice Sheet exchange near the surface, that is poorly resolved in the model. [24] As with temperature, the validation of simulated [22] The cold bias of the HIRHAM5 simulations on the precipitation is limited owing to the fact that there are only a southwest coast, compared to the DMI coastal stations, is few stations that measure precipitation. These stations are probably partly related to the sea ice distribution that is located on the coast [Cappelen, 2010] and the measured prescribed by the ERA-Interim reanalysis data set in the precipitation is affected by a number of factors. In most Labrador Sea and the Davis Strait. The sea ice distribution cases, the measured precipitation does not reflect the plays a critical role for the atmospheric conditions, as the “actual” precipitation. To obtain an estimate of the “actual” surface fluxes are dependent on whether the sea surface is precipitation, the measurements have to be corrected with ice covered or not. Kauker et al. [2010] reported an over- respect to evaporation, wetting losses, wind speed and the estimation of the sea ice extent in the ERA-Interim reanal- type of precipitation (snow, rain). Further details on the ysis data set, which could explain the cold bias in the method to correct the precipitation are described in the work HIRHAM5 simulation. The model does not include frac- of Wulff [2010]. tional land or sea points but does allow fractional sea ice [25] Accumulation values measured from ice cores cover. The model even at 0.05 degree resolution tends to be [Andersen et al., 2006; Banta and McConnel, 2007; Bales dominated by land points close to the coast, while many et al., 2009] can be used to extend the validation of the coastal observational sites are mainly influenced by nearby simulated precipitation to the whole ice sheet. Ice core oceanic conditions and therefore tend to be much warmer derived accumulation includes deposition, sublimation, melt than just a few tens of kilometers further inland. and snow transport by the wind. All the ice cores are taken [23] Some care should be taken when comparing a climate high on the ice sheet where the mean temperatures are below variable from a grid cell, representing the mean conditions freezing and melting is therefore negligible. Transport of over an area of many km2 (6 6or28 28 km2 in our snow by wind is difficult to estimate, but as the ice cores are case), with a point measurement from a weather station. located in areas with no obstacles, it is assumed that this Climate models represent the full climate system, on the transport is evenly distributed. Sublimation and evaporation basis of physical principles valid on large scales, and employ is estimated from the model. To make the precipitation parameterization to solve sub grid cell processes. Therefore, measurements on the coast and the simulated precipitation to validate a climate simulation, it is common to consider comparable to the accumulation measurements on the ice many grid cells over a region like a watershed or a climatic sheet, the simulated evaporation is subtracted from both the zone. In our case, high-quality gridded observations are not observations and the simulated precipitation. available and therefore measurements from weather stations, [26] Figure 7 shows the relative difference (r) (see equation (1)) between the simulated accumulation (Asim), for

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Figure 8 shows a scatter diagram of the simulated against the observed accumulations. There is a similar correlation for the comparison of the accumulation from the ice cores data and the one estimated with the 0.05° and 0.25° resolution simulations. However, the correlation of the accumulation at the DMI weather stations and the one estimated by the simulations is lower at 0.05° than at 0.25°. This could be related to the high variability in precipitation between 0.05° grid cells. [27] Figure 9 shows a comparison of land-sea-glacier mask, topography and precipitation at the two resolutions for the region surrounding Narsarsuaq in southwest Greenland. The increase in resolution improves the description of the fjords and the topography at the coast. The more detailed description of the topography depicted in the 0.05° run has a strong influence on the precipitation pattern (Figure 9f). The 0.05° grid cells contained within one 0.25° grid cell closest to Narsarsuaq show a large variability of precipitation. Figure 10 shows the average 1989–2009 monthly mean precipitation for the 0.05° and 0.25° grid cell closest to the Narsarsuaq station and the seven 0.05° neighboring land grid cells. The simulated precipitation can double or even triple fromonegridcelltoanother.Figure10alsoshowstheuncor- rected and corrected precipitation measurements at the DMI station Narsarsuaq (4270). The correction factor for the precipitation is close to two in the winter months. The simulated precipitation at the closest 0.05° grid cell is considerably higher than the corrected measured values. However, the comparison is better at a few of the neighboring grid cells. The height of the closest and neighboring 0.05° grid cells varies from 124 to 622 m. This is far from the elevation of the station at 34 m. The neighboring 0.05° cell with the lowest elevation is not the one, which shows the closest time series to the observations. The large cell-to-cell variability amply demonstrates why validation is difficult for the precipitation on the coast of Greenland. The precipitation field in the interior of Greenland has a smaller cell-to-cell variability than on the coast. Therefore, more confidence should be put on the precipitation validation over the ice sheet. Figure 7. Relative difference (r; see equation (1)) between the HIRHAM5 simulated accumulation at 0.05° (small cir- 4. Description of the Simulated Climate cle) and 0.25° (large circle) resolution and the observed accumulation from ice core measurements and the DMI Over Greenland weather stations. [28] The model validation presented above confirms that the RCM HIRHAM5 is doing a fair job simulating the 2 m temperature and precipitation over Greenland. These simu- each of the closest land grid cell to the stations, and the lations can therefore be used to describe the climate over observed accumulation (A ) from both the ice cores obs Greenland, which is to a large extent unknown due the small [Andersen et al., 2006; Banta and McConnell, 2007; Bales amount of in situ observations. To examine the regional et al., 2009] and the accumulation computed for the DMI variability, four regions are defined on the basis of the sur- weather stations. face topography of the Greenland ice sheet [Bamber et al., A A 2001]. Ice sheet drainage basins were determined using the r ¼ sim obs ð1Þ Aobs method described by Schwanghart and Kuhn [2010] and the location of the major outlet glaciers identified [Hardy and The relative difference is generally closer to 0 for the 0.05° Bamber, 2000]. The resulting nine drainage basins were simulation than for the 0.25° simulation, especially for the later joined together according to the similarities in their high elevations in the center of Greenland, suggesting the climate to form the final four drainage basis shown in higher-resolution simulation is capturing the precipitation Figure 11a. and evaporation better than the lower. On the southwest [29] Region 1 covers the north of Greenland with cold coast, both simulations underestimate the accumulation (<30°C) and dry (<0.5 mm d 1) winters as shown in while the other regions show both over and underestimates. Figures 11b and 11c. Region 2 on the southeast coast has a

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Figure 8. Simulated average accumulation at (a) 0.05° and (b) 0.25° resolution against the average observed accumulation for the period 1989–2009. The observations from ice cores are shown in red, and observations from the DMI stations are shown in blue. The slopes and the correlations for the best linear fits are indicated on the graph. milder climate with wet conditions, especially in the winter. the west coast, is cold and dry with wetter conditions in the Region 3 located on the southern tip of Greenland presents summer and autumn. Similar analysis for the whole of the warmest conditions with an annual mean 2 m tempera- Greenland including land points not covered with ice, as ture around 10°C and wet conditions (>4 mm d1 annu- well as the surrounding sea is shown in Figure 11. ally), with higher precipitation in the winter. Region 4, on

Figure 9. Comparison of (a and d) land-sea-glacier mask (sea in blue, land in green, glacier in white); (b and e) topography (in meters); and (c and f) the 1989–2009 winter (DJF) precipitation (millimeters per day) simulated with HIRHAM5 at a resolution of 0.25° (Figures 9a–9c) and 0.05° (Figures 9d–9f). The closest 0.05° and 0.25° grid cells to the DMI station Narsarsuaq are shown with a red and black squares, respectively.

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[30] The mean annual temperature and precipitation simulated with HIRHAM5 at 0.05° are shown in Figures 12a and 12b, respectively. Region 1 in the north shows the strongest warming trend with a mean annual increase of 0.11°C yr1 (significant above the 99% significance level according to a two-tailed t test). The other regions show lower positive trends for the temperature, also significant above 99%. The precipitation trends are weak and not significant (above 99%) according to a two-tailed t test. The trends of the 0.05° simulation are not statistically different from those of the 0.25° simulation and do not reflect any added value. The mean temperature and the warming trend determined by HIRHAM5 for the ERA-Interim period over Greenland are consistent with those reported by other model studies including Fettweis [2007], Box et al. [2009], and Figure 10. Simulated mean monthly precipitation from Ettema [2010]. HIRHAM5 at 0.05° resolution for the period 1989–2009 at [31] In Figure 13, the analysis of the mean annual tem- the closest grid cell to the DMI station Narsarsuaq (station perature and precipitation is extended to examine whether 4270; thick black line) and its seven neighboring land grid different elevation bands of Greenland have evolved differ- cells (gray lines). The corresponding values for the closest ently during the period 1989–2009. The results indicate that grid cell of HIRHAM5 at 0.25° are indicated in green. The the lower elevations (0–1000 m) have warmed the most uncorrected and corrected observed precipitation measure- during the period 1989–2009, with a linear trend of 0.13°C 1 ments at Narsarsuaq are shown in red and blue, respectively. yr . The temperature trends decrease toward the higher elevations. All trends for the temperature are significant above 99% according to a two-tailed t test. For the precipitation, the trends are weak and not significant. Moreover, it is interesting to notice the strong temporal correlation

Figure 11. (a) Division of the Greenland ice sheet into four drainage regions. (b) Mean monthly 2 m temperature (in degrees Celsius) and (c) the mean monthly precipitation (in millimeters per day) for the four regions (all Greenland and all ocean cells are shown in Figure 11a) averaged over the period 1989–2009 for the 0.05° HIRHAM5 simulation. The numbers in the graphs show the annual mean temperature and precipitation for each region.

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Figure 12. Mean annual (a) 2 m temperature (in degrees Celsius) and (b) precipitation (in millimeters per day) for the period 1989–2009 for the four drainage regions (all Greenland and all ocean cells are shown in Figure 11a) for the 0.05° HIRHAM5 simulation. The numbers in the graphs show the 21 year average and the trend for this period. between the 2 m temperature and the precipitation in the different heights. With higher resolution, the elevation of Figures 13a and 13b. A warm year has generally stronger the small mountains on the coast of Greenland is generally precipitation than a cold year. higher than at lower resolution. Consequently, the 0.05° simulation is mainly colder and wetter on the coast of Greenland 5. Impact of Higher Resolution: Assessment compared to the 0.25° simulation. The increase in resolution of Added Value allows a better description of the topography, which increases the orographically enhanced precipitation on the coast. The [32] The comparison of the model output at the two reso- increase in precipitation on the coast of Greenland at 0.05° lutions with observations in section 3 shows that the precip- compared to 0.25° resolution simulation dries out the atmo- itation and temperature biases do not decrease significantly sphere and may explain the lower cloud cover at 0.05° gen- with increased resolution. However, the validation is not erating less precipitation. The colder conditions over the main comprehensive owing to the few available observations, their ice sheet at 0.05° are mainly linked to the reduced downward short duration and the inherent measurement errors, espe- longwave radiation at the surface associated to the lower cloud cially the precipitation measurements, which have to be cover for the higher-resolution simulation. corrected for various factors. For a more robust comparison [34] This effect is shown more clearly in Figure 15, which between the two simulations and to assess the added value of shows a cross section of the 1989–2009 2 m temperature and the higher resolution, the two HIRHAM5 simulations are precipitation for the summer months June, July and August compared directly to one another. (JJA). Figure 15b shows that the maximum precipitation on [33] Figure 14 shows the difference of the elevation, the – the west coast of Greenland is located closer to the ice sheet 1989 2009 mean annual 2 m temperature, precipitation and edge at 0.05° than at 0.25°. This leads to drier and colder cloud cover between the 0.05° and 0.25° simulations. Here, no conditions at higher elevations for the simulation at higher correction was done on the temperature to take into account resolution. Figure 15 also shows the difference between the

Figure 13. Mean annual (a) 2 m temperature (in degrees Celsius) and (b) precipitation (in millimeters per day) for the period 1989–2009 for different height bins of 500 m for the 0.05° HIRHAM5 simulation over Greenland. The numbers in the graphs show the 21 year average and the trend for this period.

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Figure 14. Difference over Greenland between the HIRHAM5 simulations at 0.05° and 0.25° for (a) elevation (meters), (b) 1989–2009 mean 2 m temperature (in degrees Celsius), (c) 1989–2009 mean precipitation (in millimeters per day) and (d) 1989–2009 mean cloud fraction [0,1]. The inner black contour indicates the limit of the ice sheet at 0.25°. two simulations on the east side of Greenland where the method, although the complete physical understanding of topography is sensitive to the resolution (see also Figure 2). the relationship behind it is not fully accounted for. With a [35] Further to this, we show in Figure 15c the surface resolved topographical gradient near the ice edge, the abla- mass balance calculated offline using annual snow fall and tion zone is well constrained. Note for example that within a evaporation, plus summer temperature output from the two few tens of kilometers from the ice sheet margin, the altitude runs. To estimate ablation, we use an empirical relationship jump between neighboring grid points is on the order of a [Ohmura et al., 1996; Kiilsholm et al., 2003] between the hundred meters even at 0.05° resolution. This influences the annual loss of mass and the seasonal mean air temperature SMB quite substantially as both the local ablation and pre- above 2°C for June, July and August. A summer mean cipitation is strongly altitude dependent. As a result, the temperature of 2°C is the minimum temperature where local SMB on the western part of the ice sheet in this cross mass loss due to ablation can be expected and a linear rela- section is reduced in the higher-resolution experiment com- tion for the trend has been calculated on basis of observa- pared to the coarser one with a similar pattern on the east tions by Ohmura et al. [1996] and Wild and Ohmura [2000]. coast, likely reflecting the precipitation pattern shown in Refreezing at the surface is taken into account by this Figure 15b. Despite comparable performance between the

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Figure 15. Cross sections of the 1989–2009 summer (JJA) (a) 2 m temperature (in degrees Celsius), (b) precipitation (in millimeters per day), and (c) offline estimate of the surface mass balance (SMB) (in millimeters per year). The simulated values at 0.05° and 0.25° are shown in red and blue, respectively. The topography of Greenland at 0.05° is shown with a black line. The location of the cross section is indicated on Figure 1.

Figure 16. Elevation dependency of the HIRHAM5 simulated (a) 2 m temperature (in degrees Celsius) and (b) precipitation (in millimeters per day) at 0.05° (red) and 0.25° (blue) using 200 m bins over Green- land. The same fields for the ERA-Interim reanalysis are presented in green. The numbers in the graphs correspond to the average over all the levels.

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at 0.1° (757 1012 kg) and 0.15° (729 1012 kg). The conclusion of Ettema et al. [2009], that more mass accumu- lates on the Greenland ice sheet with higher resolution, is not supported by the comparison of the 0.25° and 0.05° resolution simulations with HIRHAM5. In the HIRHAM5 model, it appears that the orographically enhanced precipitation reduces the amount of moisture over the main ice sheet and thereby reduces the total amount of precipitation for the higher-resolution simulation.

6. Discussions and Conclusions

[38] A robust validation of two 1989–2009 HIRHAM5 simulations (0.05° and 0.25° resolution) over Greenland using the ERA-Interim reanalysis as lateral boundary conditions is presented. The model output is compared with Figure 17. Total precipitation summed (GT per year) over observed 2 m temperature and precipitation from the DMI all Greenland (thin lines) and over the Greenland ice sheet meteorological stations on the coast and the GC-Net auto- (thick lines) for the two HIRHAM5 simulations for the matic weather stations on the ice sheet. The simulated 2 m period 1989–2009 (0.05° resolution in red; 0.25° in blue); temperature is in good agreement with observations over ERA-Interim is shown in green. The numbers in the graph Greenland in summer, while in winter the temperature is indicate the 21 year mean total precipitation for HIRHAM5 lower than observed in the southwest and higher at higher at 0.05° and 0.25° and ERA-Interim over the ice sheet and elevations of the ice sheet. The increase in resolution from over all Greenland. 0.25° to 0.05° does not reduce the temperature bias for the weather stations on the coast and on the ice sheet. This is in agreement with the study of Mass et al. [2002], who found two experiments (0.05 and 0.25° resolution) when validated no significant improvement of the objectively scored accu- against station data, we therefore assess that the higher res- racy of the forecasts as the grid spacing decrease to less than olution gives the best estimate of the true SMB. 10–15 km. A high-quality 2 m gridded temperature field [36] The impact of the resolution over the whole ice based on observations over Greenland would be necessary to sheet is shown in Figure 16 where the elevation dependency assess whether a higher-resolution model improves the of the 2 m temperature and the precipitation for the two HIRHAM5 simulations and ERA-Interim reanalysis is pre- temperature simulation. The accumulation bias is reduced with higher resolution at the higher elevation on the ice sheet sented. Figure 16b shows that below 1200 m, the simulation where the precipitation field is homogeneous. On the coast at 0.05° is generating more precipitation than the simulation of Greenland, the increase in resolution increases the spatial at 0.25°. This is probably due to the steeper slopes at 0.05° variability of the topography, which has a strong impact on resolution that increase the precipitation amount due to the the simulated precipitation. Therefore, it is not feasible to orographic enhancement. Above 1200 m, the opposite is make a fair comparison to the point measurements when the observed. The 0.25° simulation has more precipitation than spatial variability in the model output is so large. the 0.05° simulation. This is mainly caused by wetter and [39] An analysis of the simulated climate over Greenland warmer atmospheric conditions in the 0.25° resolution sim- for the period 1989–2009 shows that the largest warming ulation compared to the simulation at 0.05°, which loses most of the atmospheric moisture at the ice sheet edges as dis- trend is in the north of Greenland where the climate is dry and cold. Moreover, the warming trend is larger at lower cussed. As indicated in Figure 16b, it is interesting to note elevations than at higher elevations. No significant trends for that the mean precipitation over Greenland is larger at 0.25° (1.42 mm d1) than at 0.05° (1.37 mm d1). This may be the precipitation were found for the different regions or height intervals during the period 1989–2009. Direct com- associated with the fact that more precipitation is falling over parison of the HIRHAM5 simulations at 0.05° and 0.25° the ocean rather than on the land in the 0.05° resolution sim- resolution shows that the 0.05° simulation is significantly ulation owing to the larger topographic gradients. On the ice wetter close to the coast and drier at high elevations than the sheet, the drier conditions and the higher elevation of the 0.25° simulation. Also, the 0.05° simulation has less pre- 0.05° simulation also lead to lower temperatures (Figure 16a). cipitation than the 0.25° simulation in the ablation zone [37] The total amount of precipitation simulated with where the highest relief is found. This could have important HIRHAM5 at 0.05° and 0.25° resolutions over the whole of consequences when the climate outputs are given to an ice Greenland, including land points not covered with ice, is sheet model. almost the same for both (3% difference; see Figure 17). However, the total amount of precipitation simulated over [40] This study shows the usefulness of the high-resolution regional climate model simulation for ice sheet studies the ice sheet only is 11% larger at 0.25° than at 0.05°. More as the high spatial variability is not captured in the low- precipitation is simulated with the 0.05° resolution model at resolution driving model. With the higher-resolution simu- the coast of Greenland owing to the steeper slopes. The total lation, the description of the climate fields over Greenland amount of precipitation over the Greenland ice sheet in the 12 appears to be more physically plausible owing to the addi- year 1992 computed with HIRHAM5 at 0.05° (753 10 kg) 12 tional details in the precipitation and temperature patterns and 0.25° (856 10 kg) is in close agreement with that simulated at the margins of the ice sheet where most of the computed by Ettema et al. [2009] with the RCM RACMO2

14 of 16 D02108 LUCAS-PICHER ET AL.: RCM SIMULATIONS FOR GREENLAND D02108 ablation occurs. A good description of the climate of the Banta, J. R., and J. R. McConnell (2007), Annual accumulation over recent ablation zone is critical when calculating surface mass bal- centuries at four sites in central Greenland, J. Geophys. Res., 112, D10114, doi:10.1029/2006JD007887. ance for the full ice sheet and when coupling climate models Box, J. E., and A. Rinke (2003), Evaluation of Greenland ice sheet surface to ice sheet models in order to simulate realistically the climate in the HIRHAM regional climate model using automatic weather current and future responses of the Greenland ice sheet to station data, J. Clim., 16, 1302–1319, doi:10.1175/1520-0442-16.9.1302. climate change. Box, J. E., L. Yang, D. H. Bromwich, and L.-S. Bai (2009), Greenland Ice Sheet surface air temperature variability: 1840–2007, J. Clim., 22, [41] A number of improvements are currently planned, 4029–4049, doi:10.1175/2009JCLI2816.1. including adding the computation of the surface mass bal- Burgess, E. W., R. R. Forster, J. E. Box, E. Mosley-Thompson, D. H. ance within the RCM for which more realistic snow pro- Bromwich, R. C. Bales, and L. C. Smith (2010), A spatially calibrated model of annual accumulation rate on the Greenland Ice Sheet (1958– cesses are required. Along with such improvements, a better 2007), J. Geophys. Res., 115, F02004, doi:10.1029/2009JF001293. treatment of the surface processes is planned, which will Cappelen, J. (Ed.) (2010), DMI monthly climate data collection, 1768–2009: improve the radiative balance on the ice sheet surface, by Denmark, the Faroe Islands and Greenland, Tech. Rep. 10-05, Dan. Meteorol. Inst., Copenhagen. [Available at http://www.dmi.dk/dmi/tr10- including a correct snowmelt scheme, an improved albedo 05.pdf.] scheme and allowing the melting of glacier ice (R. Mottram Cappelen, J., B. V. Jørgensen, E. V. Laursen, L. S. Stannius, and R. S. et al., manuscript in preparation, 2012). Thomsen (2001), The observed climate of Greenland, 1958–1999, with climatological standard normals, 1961–1990, Tech. Rep. 00-18, Dan. [42] This study provides baseline high-resolution simula- Meteorol. Inst., Copenhagen. [Available at http://www.dmi.dk/dmi/tr00- tions of the recent climate of Greenland. The small biases in 18.pdf.] the simulations show that HIRHAM5 generates a realistic Christensen, O. B., M. Drews, J. H. Christensen, K. Dethloff, K. Ketelsen, climate over Greenland, which is suitable to drive ice sheet I. Hebestadt, and A. Rinke (2006), The HIRHAM regional climate model, version 5, Tech. Rep. 06-17, Dan. Meteorol. Inst., Copenhagen. models. These results illustrate that the dynamical down- [Available at http://www.dmi.dk/dmi/tr06-17.pdf.] scaling of reanalysis is necessary to correctly characterize Dahl-Jensen, D., et al. (2009), The Greenland Ice Sheet in a changing the climate of Greenland at the regional scale. Furthermore, climate: Snow, Water, Ice and Permafrost in the Arctic (SWIPA), report, these results underline the sensitivity to the model resolu- 115 pp., Arctic Monit. and Assess. Programme, Oslo. [Available at http:// www.amap.no/swipa/press2009/GRISindex.html.] tion. Additionally, this study highlights both the difficulties Davies, H. C. (1976), A lateral boundary formulation for multi-level predic- that the lack of observations pose for validation of RCMs tion models, Q. J. R. Meteorol. Soc., 102, 405–418. and the advantages of using an RCM in areas where obser- Dee, D. P., et al. (2011), The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., vations are sparse in order to create a regional climatology. 137, 553–597, doi:10.1002/qj.828. RCMs allow us to make projections of future climate on a Dümenil, L., and E. Todini (1992), A rainfall–runoff scheme for use in the regional scale, but also to study the immediate causes and Hamburg climate model, in Advances in Theoretical Hydrology, Eur. Geophys. Ser. Hydrol. Sci., vol. 1, edited by J. P. O’Kane, pp. 129–157, effects of those climate changes. The output of those simu- Elsevier, New York. lations will be used to drive ice sheet models participating in Eerola, K. (2006), About the performance of HIRLAM version 7.0, the project ice2sea. The response of the ice sheet models to HIRLAM Newsl., 51,93–102. the climate forcing from the RCM will give additional Ettema, J. (2010), The present day climate of Greenland: A study with a regional climate model, PhD thesis, Univ. of Utrecht, Utrecht, information on the accuracy of the climate simulated by the Netherlands. RCMs. Ettema, J., M. R. van den Broeke, E. van Meijgaard, W. J. van de Berg, J. L. Bamber, J. E. Box, and R. C. Bales (2009), Higher surface mass balance [43] Acknowledgments. We acknowledge the ice2sea project funded of the Greenland ice sheet revealed by high-resolution climate modeling, by the European Commission’s 7th Framework Programme through grant Geophys. Res. Lett., 36, L12501, doi:10.1029/2009GL038110. 226375. This is ice2sea manuscript 034. In addition, this study was partially Ettema, J., M. R. van den Broeke, E. van Meijgaard, W. J. van de Berg, J. E. supported by the Greenland Climate Research Center (project 6504). Box, and K. Steffen (2010), Climate of the Greenland ice sheet using a high-resolution climate model—Part 1: Evaluation, Cryosphere, 4, 511–527, doi:10.5194/tc-4-511-2010. References Fettweis, X. (2007), Reconstruction of the 1979–2006 Greenland ice sheet surface mass balance using the regional climate model MAR, Ahlstrøm, A. P., et al. (2008), A new programme for monitoring the mass – loss of the Greenland ice sheet, Geol. Surv. Den. Greenl. Bull., 15,61–64. Cryosphere, 1,2140, doi:10.5194/tc-1-21-2007. Giorgi, F. 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