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Surface Mass Balance and Runoff Modeling Using HIRHAM4 RCM at (Søndre Strømfjord), West , 1950–2080

SEBASTIAN H. MERNILD Climate, Ocean and Sea Ice Modeling Group, Computational Physics and Methods, Los Alamos National Laboratory, Los Alamos, New Mexico

GLEN E. LISTON Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

CHRISTOPHER A. HIEMSTRA Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, and Cold Regions Research and Engineering Laboratory, Fairbanks, Alaska

JENS H. CHRISTENSEN AND MARTIN STENDEL Danish Climate Centre, Danish Meteorological Institute, Copenhagen, , and Greenland Climate Research Centre, , Greenland

BENT HASHOLT Department of Geography and Geology, University of Copenhagen, Copenhagen, Denmark

(Manuscript received 10 December 2009, in final form 6 October 2010)

ABSTRACT

A regional atmospheric model, the HIRHAM4 regional climate model (RCM) using boundary conditions from the ECHAM5 atmosphere–ocean general circulation model (AOGCM), was downscaled to a 500-m gridcell increment using SnowModel to simulate 131 yr (1950–2080) of hydrologic cycle evolution in west Greenland’s Kangerlussuaq drainage. Projected changes in the Greenland Ice Sheet (GrIS) surface mass balance (SMB) and runoff are relevant for potential hydropower production and prediction of ecosystem changes in sensitive Kangerlussuaq Fjord systems. Mean annual surface air temperatures and precipitation in the Kangerlussuaq area were simulated to increase by 3.48C and 95 mm water equivalent (w.eq.), re- spectively, between 1950 and 2080. The local Kangerlussuaq warming was less than the average warming of 4.88C simulated for the entire GrIS. The Kangerlussuaq SMB loss increased by an average of 0.3 km3 because of a 0.4 km3 rise in precipitation, 0.1 km3 rise in evaporation and sublimation, and 0.6 km3 gain in runoff (1950–2080). By 2080, the spring runoff season begins approximately three weeks earlier. The average modeled SMB and runoff is approximately 20.1 and 1.2 km3 yr21, respectively, indicating that ;10% of the Kangerlussuaq runoff is explained by the GrIS SMB net loss. The cumulative net volume loss (1950–2080) from SMB was 15.9 km3, and runoff was 151.2 km3 w.eq. This runoff volume is expected to have important hydrodynamic and ecological impacts on the stratified salinity in the Kangerlussuaq Fjord and on the transport of freshwater to the ocean.

1. Introduction Corresponding author address: Dr. Sebastian H. Mernild, Cli- The Greenland Ice Sheet (GrIS) is the largest mass of mate, Ocean and Sea Ice Modeling Group, Computational Physics and Methods (CCS-2), Los Alamos National Laboratory, Mail land-based ice in the Northern Hemisphere. Net mass Stop B296, Los Alamos, NM 87545. balance from the GrIS has an important influence on E-mail: [email protected] global sea level rise (e.g., Bindoff et al. 2007; Lemke

DOI: 10.1175/2010JCLI3560.1

Ó 2011 American Meteorological Society Unauthenticated | Downloaded 10/03/21 08:23 PM UTC 610 JOURNAL OF CLIMATE VOLUME 24 et al. 2007; Box et al. 2009), ocean salinity and density, In this study we simulated Greenland’s climate vari- and thermohaline circulation (e.g., Symon et al. 2005; ability and change from 1950 through 2080. From 2000 Rahmstorf et al. 2005; Nielsen et al. 2010). The GrIS forward, the analysis is based on the Intergovernmental plays an essential role in the Arctic hydrological cycle, Panel on Climate Change (IPCC) A1B climate scenario not only because of its extent, elevation, and reflectivity and the resulting hydrological impacts on the Kanger- (albedo), but also because of the reservoir of freshwater lussuaq drainage area. Emphasis was placed on projec- stored as ice. Importantly, the GrIS is an indicator of tions of precipitation, SMB, and freshwater runoff to the ongoing climate changes, and impacts have already been ocean. The A1B scenario was used in the high-resolution observed for the entire ice sheet (e.g., Steffen et al. 2008; (25-km horizontal gridcell increment) regional climate Ettema et al. 2009; van den Broeke et al. 2009) and model (RCM) HIRHAM4 (Stendel et al. 2008) using at subcatchment scales, for example, at Kangerlussuaq boundary conditions from the ECHAM5 atmosphere– (Søndre Strømfjord) (Mernild and Hasholt 2009). In ocean general circulation model (AOGCM) (;200-km particular, rates of both GrIS mass loss [which is influ- gridcell resolution). Output from the RCM was used enced by net surface mass balance (SMB), calving, and to further downscale and force a well-tested, state-of- basal melting] and surface runoff increased as temper- the-art snow and ice evolution modeling system, Snow- atures rose. During summer, temperature in Greenland Model (e.g., Liston and Elder 2006a,b; Mernild et al. coastal areas increased by 1.78C from 1991 to 2006 2006; Mernild and Liston 2010), applied to the Kan- (Comiso 2003, 2006; Hanna et al. 2008). gerlussuaq region (500-m gridcell resolution). Such an There are significant uncertainties in modeling Green- approach has gained considerable interest in many land ice sheet dynamics (e.g., Parizek and Alley 2004; other scientific disciplines, often referred to as statistical Alley et al. 2007; Nick et al. 2009), partly related to in- downscaling (see, e.g., J. H. Christensen et al. 2007b; van sufficient knowledge of basal conditions at the ice bed der Linden and Mitchell 2009). Before being used as and ice–ocean interface. In contrast, Greenland’s SMB meteorological forcing for SnowModel, the RCM out- and runoff are better understood and documented as put data were calibrated and tested using in situ meteo- part of numerical model simulations (e.g., Box et al. rological observations. RCM SnowModel runoff output 2006; Fettweis 2007; Mernild et al. 2009), even though was tested further by comparison with coincident high- few high-resolution freshwater runoff observations at resolution Kangerlussuaq runoff observations and sim- the GrIS periphery are available for model verification. ulations. A time series of river discharge from the Kangerlussuaq We performed the Kangerlussuaq drainage area sim- drainage area (Mernild and Hasholt 2009) has been re- ulations for the 131-yr period 1950–2080 with the fol- corded since spring 2007. This dataset is important for lowing objectives: 1) to illustrate and assess the trends of quantifying changes in runoff from the GrIS, since the the HIRHAM4 RCM meteorological driving data; 2) mass loss from Kangerlussuaq is by surface ablation to quantify and analyze the water balance components and subsequent runoff, and not from calving. By pro- important to water resources (large-scale hydropower viding information about the onset, duration, variability, development and the fjord ecosystem), precipitation, and intensity of GrIS melting and runoff, these ob- SMB, and runoff; 3) to estimate the cumulative volume servations can be used to verify model simulations of of SMB and runoff; and 4) to compare the runoff from past and present conditions and to assess future pre- Kangerlussuaq with simulations for the entire GrIS to dictions. investigate local differences and to test the applicabil- Broad hydropower development plans, based on run- ity of the downscaling procedure. off from the western GrIS, have been proposed to boost Compared with the overall GrIS HIRHAM4 study Greenland’s industrial development. Since hydropower by Mernild et al. (2010a), this Kangerlussuaq study is depends on a stable water supply, it is of vital interest different in several ways: 1) it specifically addresses to predict trends in runoff at local and regional scales. detailed local-scale hydrological impacts, precipitation Part of this investigation aims at projecting runoff trends trends, SMB, runoff, and specific runoff for the Kanger- in the Kangerlussuaq region. A major part of the runoff lussuaq catchment considering the IPCC A1B climate from the GrIS is transferred to the ocean via fjord sys- projection; 2) it compares hourly and daily-resolution runoff tems. Fjords modify the timing of outflow to the open calibration routines; 3) it uses local meteorological station ocean and act as independent ecosystems, which are data to define lapse rates used to downscale HIRHAM4 sensitive to changes in hydrographic conditions. Infor- simulations; 4) it uses a 500-m gridcell increment digital mation about the freshwater supply to Kangerlussuaq elevation model [DEM; instead of a 5-km increment as Fjord could be used as boundary conditions in the hy- used in Mernild et al. (2010a)]; and 5) it performs a com- drodynamic and ecological modeling of the fjord. parison between the Kangerlussuaq-simulated catchment

Unauthenticated | Downloaded 10/03/21 08:23 PM UTC 1FEBRUARY 2011 M E R N I L D E T A L . 611 runoff and the overall GrIS simulated runoff condi- 3. Models and methods tions. The current study performed runoff calibrations a. Model hierarchy for downscaling ECHAM5 GCM using hourly runoff observations (2007/08) and daily and HIRHAM4 RCM runoff simulations (1979–2008) from observed mete- orological input data and verified runoff observa- There are several kinds of uncertainties related tions from the Kangerlussuaq drainage area, while the to climate projections using simulations with coupled Mernild et al. (2010a) study employed calibrations of atmosphere–ocean GCMs. Apart from uncertainties in HIRHAM4-simulated meteorological data and melt ex- future greenhouse gas and aerosol emissions and their tent only. conversion to radiative forcings (not discussed here), there are uncertainties in global and, in particular, re- gional climate responses to these forcings. Furthermore, 2. Study area large regional-scale natural variability makes it difficult The Kangerlussuaq drainage area (6130 km2) is lo- to determine the contributions of anthropogenic forc- cated on the west coast of Greenland (678N, 508W; ing and natural variability. Further uncertainties result Fig. 1a). The catchment outlet—the Watson River from insufficient resolution of the GCM. outlet—is located 28 km downstream from the GrIS This implies that there is no single ‘‘best’’ model to use terminus, near the town of Kangerlussuaq (Søndre in an assessment of Greenland climate changes. How- Strømfjord) and at the innermost point of the Kanger- ever, one of the models identified by Walsh et al. (2008) lussuaq Fjord. This outlet is one of the best locations for exhibiting skill over the Arctic in general and Greenland observing GrIS runoff because of the well-defined, in particular is the ECHAM5–Max Planck Institute Ocean stable bedrock cross sections. For 2007 and 2008, the Model (MPI-OM1), documented in Marsland et al. (2003), observed accumulated runoff was 1.77 and 1.28 km3, Roeckner et al. (2003), and Jungclaus et al. (2006). This respectively (Fig. 2; Mernild and Hasholt 2009). The state-of-the-art GCM was used to force HIRHAM4 lower parts of the terrain (;12%; elevation below (J. H. Christensen et al. 1996; O. B. Christensen et al. ;430 m MSL) are dominated by bare bedrock (or 1998) for the Greenland domain. HIRHAM4 is based bedrock with a veneer of till), sparse vegetation cover, on the adiabatic part of the High-Resolution Limited- and braided river valleys with gravel and sand. The Area Model (HIRLAM) short-range weather predic- higher area (;88%; elevation above ;430 m MSL) is tion model (Ka¨lle´n 1996). In HIRHAM4, the physical covered by the GrIS. The mean (1990–2003) equilib- parameterization of HIRLAM has been replaced by rium line altitude (ELA; defined as the elevation where that of ECHAM5’s predecessor ECHAM4 (Roeckner the net mass balance is zero) in the region is ;1530 m et al. 1996), so that HIRHAM4 can be thought of as MSL (van de Wal et al. 2005). a high-resolution limited-area version of ECHAM4. The mean annual air temperature (MAAT) for the Stendel et al. (2008) ran the HIRHAM4 RCM for the catchment is 210.98C (1979–2008). Mean annual rela- period 1950–2080 using the IPCC scenario A1B using tive humidity is 64%, and mean annual wind speed is boundary conditions from the ECHAM5/MPI-OM1. For 5.3 m s21. The corrected mean total annual precipita- Arctic meteorological conditions, the performance of the tion (TAP) is 246 mm w.eq. yr21 (after Allerup et al. HIRHAM4 RCM (J. H. Christensen et al. 1996, 2001; 1998, 2000). Meteorological data are based on obser- Bjørge et al. 2000; Christensen and Christensen 2007) vations from several stations. Station Kangerlussuaq has been found to be state of the art (e.g., Christensen (hereafter station K) is a long-term standard synoptic and Kuhry 2000; Dethloff et al. 2002; Kiilsholm et al. World Meteorological Organization (WMO) meteoro- 2003). The RCM simulations were conducted over logical station operated by the Danish Meteorological Greenland and adjacent sea areas, including the Kan- Institute (DMI). The station is located at the airport gerlussuaq region in west Greenland. The A1B experi- within the town of Kangerlussuaq and is representa- ment, as described in the IPCC Fourth Assessment tive of proglacial conditions. Additional meteorologi- Report, began in year 2000, and the AOGCM used the cal data are available from stations S5, S6, and S9 final state from a detailed simulation of the twentieth (operated by Utrecht University), which are part of century as initial conditions in 2000 (e.g., Randall et al. the K transect located on the ice sheet and represen- 2007). The HIRHAM4 RCM A1B scenario was run on tative of GrIS conditions. Data from these three sta- a 25-km gridcell increment with 19 vertical levels [for tions were used (to define lapse rates) for downscaling a more detailed description, see Stendel et al. (2008) and of HIRHAM4 simulations and for defining transfer Mernild et al. (2010a)]. functions between the long-term station and the con- HIRHAM4 is nested using a standard procedure in ditions on the GrIS. RCM downscaling. A 10-point-wide relaxation or sponge

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FIG. 1. (a) Kangerlussuaq region, west Greenland, including the Kangerlussuaq drainage area with to- pographic watershed (the watershed divide is estimated based on the surface topography in the software program RiverTools; Mernild and Hasholt 2009), simulation area, and area of interest (the area from where surface runoff occurs). (b) Location of the HIRHAM4 RCM–simulated meteorological grid points (simu- lation area), including meteorological tower stations and the hydrometric station at the catchment outlet. (c) Topography (100-m contour interval) within the simulation area.

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FIG. 2. Time series of 1979–2008 verified Kangerlussuaq HIRHAM4 RCM SnowModel– simulated runoff and long-term simulated runoff (based on observed meteorological data, verified against runoff observations; Mernild et al. 2010b), and short-term observed runoff (Mernild and Hasholt 2009). zone with a gradually decreasing relaxation of the prog- domain, without any time lag accounting for the distance nostic atmospheric fields toward the driving model is between the grid cell and the basin outlet. Meltwater used (Davies 1976). The fields influenced in the free retention and refreezing is captured in SnowModel. Not atmosphere are the horizontal wind components, tem- including retention/refreezing routines in SnowModel perature, and specific humidity vertically and horizon- would lead to an overestimation of runoff to the ocean, tally interpolated to the grid used in HIRHAM, along and a consequent overestimation of the global sea level with the horizontally interpolated surface pressure. These rise. In recent Greenland glacier studies, SnowModel fields are provided every 6 h in the sponge zone. In addi- was modified (Mernild et al. 2010b) to take into account tion, HIRHAM received information about sea surface variable snow albedo calculated after Douville et al. temperatures and ice fraction once per day, again hori- (1995) and Strack et al. (2004). The albedo decreases zontally interpolated to the model grid (O. B. Christensen gradually from 0.8 to a minimum of 0.5 as the snow ages et al. 1998). [for further information about SnowModel, see Liston et al. (2008) and the references contained therein]. A b. SnowModel description study by Greuell (2000) indicated that a ‘‘dark zone’’ of The GrIS surface water balance, with emphasis on meltwater accumulation partly overlies the ice-covered precipitation, SMB, and runoff, was simulated using surface (from approximately 950 to 1350 m MSL) in SnowModel (Liston and Elder 2006a), a spatially and the Kangerlussuaq catchment, affecting the albedo and temporally distributed meteorological and snowpack creating a positive feedback between albedo and melt. modeling system. SnowModel is composed of five sub- Since the impact from surface meltwater accumulation models: MicroMet defines the meteorological forcing is not included as a standard SnowModel routine, un- conditions (Liston and Elder 2006b); EnBal calculates certainties related to this phenomenon may occur [see the surface energy exchanges (Liston 1995; Liston et al. Eq. (1)]. 1999); SnowPack simulates mass and heat transfer pro- c. SnowModel input cesses due to, for example, retention and internal re- freezing (Liston and Hall 1995); SnowTran-3D is a Distributed point meteorological data, including air blowing-snow model that accounts for snow redistribu- temperature, relative humidity, wind speed and direction, tion (Liston and Sturm 1998, 2002; Liston et al. 2007); and precipitation, were obtained from the HIRHAM4 and SnowAssim is a snow-data assimilation model (Liston RCM model (1950–2080) based on the IPCC scenario and Hiemstra 2008). SnowAssim was not used in this A1B and downscaled by SnowModel for the Kangerlussuaq study. SnowModel simulates the melting of glacier ice drainage area. The MicroMet component of SnowModel after winter snow accumulation has ablated (Mernild calculates the other required atmospheric forcing data, et al. 2006). The simulated runoff is equal to the grid- such as incoming shortwave and longwave radiation, cell runoff at each time step, summed over the drainage based on A1B input data [for additional information

Unauthenticated | Downloaded 10/03/21 08:23 PM UTC 614 JOURNAL OF CLIMATE VOLUME 24 about the radiation calculations, see Liston and Elder Mean monthly offsets between RCM-modeled output (2006b)]. Simulations were performed on a 1-day time and the observed meteorological data were added to the step. daily RCM meteorological parameters to correct each Greenland topographic data at 625-m resolution were variable (air temperature, relative humidity, wind speed, provided by Bamber et al. (2001) and the image-derived and corrected precipitation) for the 1950–2080 period, correction by Scambos and Haran (2002). This time- before being downscaled and processed by SnowModel. invariant DEM was interpolated to a 500-m gridcell For seasonal variations in monthly bias corrections, see increment covering a 540 km 3 520 km simulation do- Mernild et al. (2010a; Fig. 2). main (280 800 km2; Fig. 1a). The location of the GrIS An important result from RCM downscaling experi- terminus was confirmed or estimated by using aerial ments over Europe is that it is not easy or maybe not even photos, maps (1:250 000; from Geodetic Institute), and possible from a practical pointofviewtoattributethe satellite images (from Google Earth). source of model bias directly to the GCM or the RCM Each grid cell within the domains was assigned a U.S. (e.g., J. H. Christensen et al. 2007a, 2010). The role of the Geological Survey (USGS) Land Use/Land Cover Sys- RCM is to inform in a physically consistent way about tem class according to the North American Land Cover a plausible realization of finescale details given the driving Characteristics Database, version 2.0 [available online model.Thetwoareintimatelylinkedandsoisthebias. at the USGS Earth Resources Observation and Science Given the finer-scale information provided by the RCM, (EROS) Data Center’s Distributed Active Archive Center the corrections needed to interpret the information at Web site at http://edc2.usgs.gov/glcc/nadoc2_0.php#vers2]. a site level or on an even finer grid is, however, less User-defined constants for SnowModel were shown in problematic than going from the cause GCM scale directly Mernild et al. [2009; see also Liston and Sturm (1998) for to the refined grid. parameter definitions]. For downscaling to the 500-m gridcell increment, mean monthly lapse rates were defined based on observations d. HIRHAM4 RCM and SnowModel along a transect drawn between the K-transect meteoro- verification and uncertainty logical stations [for SnowModel downscaling procedures, Since HIRHAM4 RCM was running in a full climate see Liston and Elder (2006b)]. The K-transect lapse rates mode—that is, the driving GCM only knows about the (for air temperature) were nearly identical to lapse rates state of the atmosphere–ocean system from the external from Jakobshavn, west Greenland, and the GrIS in general drivers (sun, aerosols, and greenhouse gases), whether (Steffen and Box 2001; Mernild et al. 2010b). To assess the actually realized (1950–2000) or projected (2001–80)— performance of the adjusted RCM SnowModel–simulated we need to assess the simulation with the observed climate spatial-distributed meteorological data, the data were system. Before the daily HIRHAM4 RCM data were used tested against independent in situ meteorological ob- as meteorological input for SnowModel, Greenland RCM servations (data not used for calibration) spanning 1995– data were tested and bias corrected, producing a cali- 2005. Data were ranked, and the ranked numbers were brated dataset for the 1995–2005 period using available compared 1) to illustrate the ability of HIRHAM to cap- in situ daily meteorological data from 25 stations located ture the span of realized parameters for the period around Greenland [operated by Greenland Climate in concern and 2) to give a rough estimate about the Network (GC-Net), University of Colorado; and coastal calibration method. Validations of the simulated GrIS areas by DMI]. For a bias adjustment of the HIRHAM meteorological data (air temperature, relative humidity, results, a 10-yr period is relatively short; however, we and wind speed) indicated substantial correlation with have assessed the role of this short period by adding an in situ–observed meteorological data from different additional calibration period in which the model years meteorological stations at the GrIS—JAR1, Humboldt, were 1980–90 and observed years were 1995–2005 (see Saddle, and Summit at different elevations—and with Mernild et al. 2010a). The resulting dissimilarity in pre- in situ–observed precipitation from outside the GrIS— cipitation for the GrIS averaged 42 mm w.eq. (or ;7%) Station Nord, Danmarkshavn, Ittoqqortoormiit, and and the corresponding temperature difference was 1.58C Ikerasassuaq—at different latitudes. Modeled air tem- for 1980–90 with respect to the calibration period 1995– perature values account for 98%–99% of the variance 2005. Relative humidity and wind were both insignificantly in the observed 1995–2005 mean monthly dataset. The different (see Mernild et al. 2010a). Given inherent var- relative humidity, corrected precipitation, and wind speed iability in weather, a longer period for bias correction have the same or slightly fewer strong correlations, but should be used, but thorough examination of the overall results remain respectable for relative humidity (between performance of HIRHAM4 over Greenland was pro- 85% and 96%), wind speed (between 83% and 98%), and vided by Stendel et al. (2008). precipitation (between 89% and 98%) for representations

Unauthenticated | Downloaded 10/03/21 08:23 PM UTC 1FEBRUARY 2011 M E R N I L D E T A L . 615 of the GrIS meteorological processes [for additional in- elevation were not incorporated in the model routines. formation see Mernild et al. (2010a)]. Changes in supraglacial, englacial, and subglacial stor- RCM SnowModel–simulated Kangerlussuaq runoff ages, the internal GrIS runoff drainage system, and sub- was compared both with hourly 2007/08 observed Kan- glacial geothermal melting were not taken into account, gerlussuaq runoff and daily long-term simulated runoff even though all these processes can influence naturally (1979–2008; Mernild et al. 2010c; see Fig. 2). RCM occurring runoff. Neither does SnowModel simulate tran- SnowModel–simulated runoff was initially overestimated spiration from the (proglacial landscape), but this by 80% (2007) and 10% (2008) according to hourly run- was likely to be insignificant because of the dry conditions off observations (Mernild and Hasholt 2009). This over- during summer. estimation was related to uncertainties associated with Based on the uncertainties in the modeled SMB and unrepresented or poorly represented model processes, runoff from previous Greenland SnowModel simulations including englacial and subglacial runoff flow (probably and statistical analysis, along with uncertainties in ob- also from nearby catchments) affected by seasonal changes served runoff (for verification), the maximum estimated in the internal drainage system and spatial changes in uncertainty ranged between 10% and 25%. We assumed basal topography. The internal drainage system and basal that the present Kangerlussuaq runoff and SMB study topography are likely the main flow pattern determi- included an almost similar maximum uncertainty (e.g., nants for glacial runoff of the Kangerlussuaq drainage Mernild et al. 2006, 2008; Mernild and Hasholt 2009). area; more than ;70% of the area’s runoff originated However, uncertainties are related to the A1B scenario, from the GrIS. especially in the latter half of the twenty-first century. The observed hourly runoff values for the entire 2007 and 2008 runoff seasons were used for validation (n 5 5088, where n is the number of observations), representing the 4. Results and discussion total span in Kangerlussuaq seasonal runoff from almost a. Kangerlussuaq climate model trends, 1950–2080 no runoff at the beginning/end of the runoff seasons to peak values during midsummer and during jo¨kulhlaups Figure 3 shows the HIRHAM4 RCM bias-corrected (glacier bursts) of ;540 m3 s21. The use of robust detailed meteorological anomalies (air temperature, relative hu- runoff observations spanning the yearly variability in runoff midity, wind speed, and precipitation) for the 1950–2080 supports the use of the 2007 and 2008 observations for Kangerlussuaq drainage area. For each parameter the validation [for additional information about observations, trend over Kangerlussuaq was illustrated and compared see Mernild and Hasholt (2009)]. Further, the RCM with trends for the entire GrIS (GrIS HIRHAM4 RCM SnowModel–simulated runoff was compared with long- IPCC A1B simulations; Mernild et al. 2010a). From 1950 term daily Kangerlussuaq runoff simulations (1979–2008; to 2080, there were significant increases (p , 0.01, where n 5 10 958) (Mernild et al. 2010c) to cover the interannual/ p is the level of significance) in simulated air temperature interdecadal trend in runoff. Long-term runoff simulations and precipitation on both local (Kangerlussuaq) and re- were modeled based on observed meteorological input gional (GrIS) scales. The average change in Kangerlus- data representative of GrIS conditions (K transect) and of suaq temperature was 3.48C (significant; 97.5% quantile) proglacial conditions (station K), and verified against the compared to 4.88C (significant; 97.5% quantile) for the 2007 and 2008 observed runoff (Mernild et al. 2010c), nor- entire GrIS. This was probably mainly due to the projected malizing the RCM SnowModel runoff to have the same impact of the changing sea ice extent in the Arctic Ocean mean value as the long-term simulated runoff of 1.02 km3 and . Mean annual precipitation increased w.eq. yr21 (1979–2008; Fig. 2), before upscaling to 1950– significantly by 95 mm w.eq. for the Kangerlussuaq drain- 2080. Using model results to verify model simulations is age area [97.5% quantile; linear regression (when nothing commonly done when other options are not available, (e.g., is mentioned, linear regression is used)], compared to only Fettweis 2007). 80 mm w.eq. for the entire GrIS (significant; 97.5% quan- For this Kangerlussuaq study, only a one-way nesting tile; Figs. 3a and 3d). Relative humidity increased 0.2% between HIRHAM4 (the atmosphere) and SnowModel on average (insignificant) for Kangerlussuaq and 1.2% (the surface) was used, where SnowModel was driven (significant; 97.5% quantile) for the entire GrIS (Fig. 3b). with HIRHAM4 meteorological conditions. SnowModel Average wind speed increased 0.2 m s21 (significant; 97.5% is a surface model producing first-order effects of climate quantile) for Kangerlussuaq, while it decreased by change based on the time-invariant DEM. SnowModel ,0.1 m s21 (insignificant) for the entire GrIS. possessed uncertainties because of processes not repre- The Kangerlussuaq annual climate description was di- sented by the modeling system, just like other available vided into the four seasons: winter (December–February), models. For example, changes in the GrIS extent and spring (March–May), summer (June–August), and autumn

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FIG. 3. HIRHAM4 RCM–calibrated anomaly time series and average changes for the Kangerlussuaq drainage area (local perspective) and the GrIS (regional perspective; results based on Mernild et al. 2010a), and for the winter (December–February), spring (March–May), summer (June–August), and autumn (September–November) seasons at Kangerlussuaq from 1950 through 2080. (a),(e) Air temperature time series on regional and local scales, (b),(f) relative humidity, (c),(g) wind speed, and (d),(h) precipitation. For all parameters the zero line and r2 are included for the trend line. The average changes are calculated based on linear regression.

(September–November) (Figs. 3e–h). The greatest sea- in accordance with observed Greenland trends from the sonal changes in predicted air temperature of 4.68C (sig- 1970s through the 1990s (Box 2002). The lowest seasonal nificant; 97.5% quantile) and 6.98C (significant; 97.5% changes of 1.58C (significant; 97.5% quantile) and 0.88C quantile) occurred during winter and spring, respectively, (insignificant) occurred during summer and autumn,

Unauthenticated | Downloaded 10/03/21 08:23 PM UTC 1FEBRUARY 2011 M E R N I L D E T A L . 617 respectively (Fig. 3e), and both were below the MAAT basis in Table 1 for the period 1950–59 through 2070–80. average change of 3.48C. For Kangerlussuaq the trend in The interannual variability in precipitation and ablation mean annual summer temperature for the simulation results in considerable SMB fluctuations (Fig. 4a), indi- period (1950–2080) generally followed the overall trend cating that SMB fluctuations were largely tied to changes in summer temperature for GrIS, even though the GrIS in precipitation (accumulation) (r2 5 0.47; p , 0.01) mean summer temperature (22.18C) was significantly rather than runoff (ablation) (r2 5 0.35; p , 0.01). This colder by 21.88C (97.5% quantile) compared to tem- was unexpected, but there was no reason to believe that peratures for Kangerlussuaq (20.38C). The predicted the model setup and the calibration procedures could be decadal-average trend in MAAT (1950–2080) was posi- the reason for SMB to be more dependent on precipita- tive for both Kangerlussuaq and the whole GrIS. Kan- tion fluctuations than on runoff fluctuations, since simu- gerlussuaq temperatures increased from 213.3 (60.5)8C lations were forced toward runoff observations. and GrIS increased from 214.8 (60.4)8C (1950–59) to Fluctuation patterns illustrated in Fig. 4a (1950–2080) almost equal average decadal values of 210.2 (60.6)8C show the SMB is largest (most positive) near the be- and 210.1 (60.5)8C, respectively, for 2070–80 (Table 1). ginning of the simulation period, with subsequent loss as In 2070–80 the average decadal difference in predicted temperatures and runoff increased. For the period 1950– MAAT between Kangerlussuaq and GrIS was almost 2080, precipitation rose ;0.4 km3 (r 2 5 0.53; p , 0.01), gone, indicating no differences in MAAT conditions be- evaporation and sublimation increased ;0.1 km3 (r2 5 tween local (Kangerlussuaq) and regional scales (GrIS). 0.28; p , 0.01), and runoff swelled ;0.6 km3 (r2 5 0.75; From 1950 to 2080 the seasonal modeled changes in p , 0.01), leading to an enhanced average SMB loss of relative humidity all varied within ,2%, indicating no ;0.3 km3 (r2 5 0.24; p , 0.01)(Table1;Fig.4a).The significant difference, even though the winter humidity average trend in Kangerlussuaq precipitation, runoff, and decreased an average of 0.9% (Fig. 3f). There was no sig- SMB compared well with the overall past and present nificant seasonal difference for wind speed; the maximum (1958–2008) GrIS precipitation, runoff, SMB trends (see seasonal change in wind speed was 0.3 m s21 (1950–2080; Ettema et al. 2009) and the future (2010–2100) SMB Fig. 3g). For precipitation there was an average annual trends identified by Fettweis et al. (2008). The study by increase of 95 mm w.eq. (significant; 97.5% quantile) at Fettweis et al. (2008) projected enhanced average SMB Kangerlussuaq, with a minimum seasonal increase of loss during the twenty-first century based on values of 16 mm w.eq. during winter (insignificant) and a maximum 24 AOGCMs using projections of temperature and pre- increase of 29 mm w.eq. during spring (insignificant). The cipitation anomalies from AOGCMs performed for the largest projected changes in both precipitation and tem- IPCC Fourth Assessment Report for 2010–2100. perature occurred in spring. The SMB and runoff tendencies mentioned above in- dicate that, over the simulation period, the water supply will likely be stable for potential hydropower production b. The climate impact at Kangerlussuaq in the Kangerlussuaq drainage area. The increased runoff The balance between net accumulation (due to snow was manifested in a longer runoff season, most pro- accumulation and redistribution) during winter and net nounced in spring because of the seasonal warming of ablation (evaporation, sublimation, and runoff) during 6.98C. The average modeled first day of runoff shifted summer can be described by the water balance equation. from late May (1950–59) to early May (2070–80). In au- The yearly water balance equation for the GrIS can be tumn the change in number of runoff days was negligible. described as follows: During the simulation period, the decadal-average SMB was 20.1 (60.6) km3 yr21, while runoff was 1.2 P À (E 1 SU) À R 6DS 5 0 6 h, (1) (60.4) km3 yr21. The average annual runoff from Kan- where P is precipitation input from snow and rain (and gerlussuaq accounted for only approximately 0.3% of possible condensation), E is evaporation, SU is surface the average entire GrIS runoff of 442 km3 yr21 for the and blowing-snow sublimation, R is runoff, DS is change period 1950–80 (Table 1). Kangerlussuaq runoff ac- in glacier ice storage and snowpack storage (DS is also counted for a small amount of the overall average GrIS referred to as the SMB), and h is the water balance runoff. Yet, the specific runoff (runoff per unit drainage discrepancy (error). Mass gain (accumulation) is calcu- area per time; L s21 km22) showed that the 13.0 (64.6) lated as positive, and mass loss (ablation) is considered Ls21 km22 Kangerlussuaq runoff was ;70% greater negative in the water balance equation. than the GrIS runoff of 7.6 (62.3) L s21 km22 (Table 1). The RCM SnowModel–simulated precipitation, SMB, The higher specific runo