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  • View in gallery

    (a) Kangerlussuaq region, west Greenland, including the Kangerlussuaq drainage area with topographic 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 (simulation 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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    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).

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    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.

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    Kangerlussuaq drainage area time series for 1950–2080 of (a) precipitation, runoff, and changes in storage (SMB); (b) cumulative runoff and SMB volume; and (c) specific runoff. Here, specific runoff is shown together with values from GrIS based on Mernild et al. (2010a).

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    Simulated mean annual values for 1950–59 (the decade with the coldest MAAT) and 2070–80 (warmest MAAT) of (a) SWE depth, (b) runoff, and (c) SMB for the area of interest (from where runoff occurred) in the Kangerlussuaq drainage area.

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

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  • 1 Climate, Ocean and Sea Ice Modeling Group, Computational Physics and Methods, Los Alamos National Laboratory, Los Alamos, New Mexico
  • 2 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado
  • 3 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, and Cold Regions Research and Engineering Laboratory, Fairbanks, Alaska
  • 4 Danish Climate Centre, Danish Meteorological Institute, Copenhagen, Denmark, and Greenland Climate Research Centre, Nuuk, Greenland
  • 5 Department of Geography and Geology, University of Copenhagen, Copenhagen, Denmark
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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 3.4°C and 95 mm water equivalent (w.eq.), respectively, between 1950 and 2080. The local Kangerlussuaq warming was less than the average warming of 4.8°C 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 −0.1 and 1.2 km3 yr−1, 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.

Corresponding author address: Dr. Sebastian H. Mernild, Climate, Ocean and Sea Ice Modeling Group, Computational Physics and Methods (CCS-2), Los Alamos National Laboratory, Mail Stop B296, Los Alamos, NM 87545. Email: mernild@lanl.gov

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 3.4°C and 95 mm water equivalent (w.eq.), respectively, between 1950 and 2080. The local Kangerlussuaq warming was less than the average warming of 4.8°C 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 −0.1 and 1.2 km3 yr−1, 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.

Corresponding author address: Dr. Sebastian H. Mernild, Climate, Ocean and Sea Ice Modeling Group, Computational Physics and Methods (CCS-2), Los Alamos National Laboratory, Mail Stop B296, Los Alamos, NM 87545. Email: mernild@lanl.gov

1. Introduction

The Greenland Ice Sheet (GrIS) is the largest mass of land-based ice in the Northern Hemisphere. Net mass balance from the GrIS has an important influence on global sea level rise (e.g., Bindoff et al. 2007; Lemke et al. 2007; Box et al. 2009), ocean salinity and density, and thermohaline circulation (e.g., Symon et al. 2005; Rahmstorf et al. 2005; Nielsen et al. 2010). The GrIS plays an essential role in the Arctic hydrological cycle, not only because of its extent, elevation, and reflectivity (albedo), but also because of the reservoir of freshwater stored as ice. Importantly, the GrIS is an indicator of ongoing climate changes, and impacts have already been observed for the entire ice sheet (e.g., Steffen et al. 2008; Ettema et al. 2009; van den Broeke et al. 2009) and at subcatchment scales, for example, at Kangerlussuaq (Søndre Strømfjord) (Mernild and Hasholt 2009). In particular, rates of both GrIS mass loss [which is influenced by net surface mass balance (SMB), calving, and basal melting] and surface runoff increased as temperatures rose. During summer, temperature in Greenland coastal areas increased by 1.7°C from 1991 to 2006 (Comiso 2003, 2006; Hanna et al. 2008).

There are significant uncertainties in modeling Greenland ice sheet dynamics (e.g., Parizek and Alley 2004; Alley et al. 2007; Nick et al. 2009), partly related to insufficient knowledge of basal conditions at the ice bed and ice–ocean interface. In contrast, Greenland’s SMB and runoff are better understood and documented as part of numerical model simulations (e.g., Box et al. 2006; Fettweis 2007; Mernild et al. 2009), even though few high-resolution freshwater runoff observations at the GrIS periphery are available for model verification. A time series of river discharge from the Kangerlussuaq drainage area (Mernild and Hasholt 2009) has been recorded since spring 2007. This dataset is important for quantifying changes in runoff from the GrIS, since the mass loss from Kangerlussuaq is by surface ablation and subsequent runoff, and not from calving. By providing information about the onset, duration, variability, and intensity of GrIS melting and runoff, these observations can be used to verify model simulations of past and present conditions and to assess future predictions.

Broad hydropower development plans, based on runoff from the western GrIS, have been proposed to boost Greenland’s industrial development. Since hydropower depends on a stable water supply, it is of vital interest to predict trends in runoff at local and regional scales. Part of this investigation aims at projecting runoff trends in the Kangerlussuaq region. A major part of the runoff from the GrIS is transferred to the ocean via fjord systems. Fjords modify the timing of outflow to the open ocean and act as independent ecosystems, which are sensitive to changes in hydrographic conditions. Information about the freshwater supply to Kangerlussuaq Fjord could be used as boundary conditions in the hydrodynamic and ecological modeling of the fjord.

In this study we simulated Greenland’s climate variability and change from 1950 through 2080. From 2000 forward, the analysis is based on the Intergovernmental Panel on Climate Change (IPCC) A1B climate scenario and the resulting hydrological impacts on the Kangerlussuaq drainage area. Emphasis was placed on projections of precipitation, SMB, and freshwater runoff to the ocean. The A1B scenario was used in the high-resolution (25-km horizontal gridcell increment) regional climate model (RCM) HIRHAM4 (Stendel et al. 2008) using boundary conditions from the ECHAM5 atmosphere–ocean general circulation model (AOGCM) (∼200-km gridcell resolution). Output from the RCM was used to further downscale and force a well-tested, state-of-the-art snow and ice evolution modeling system, SnowModel (e.g., Liston and Elder 2006a,b; Mernild et al. 2006; Mernild and Liston 2010), applied to the Kangerlussuaq region (500-m gridcell resolution). Such an approach has gained considerable interest in many other scientific disciplines, often referred to as statistical downscaling (see, e.g., J. H. Christensen et al. 2007b; van der Linden and Mitchell 2009). Before being used as meteorological forcing for SnowModel, the RCM output data were calibrated and tested using in situ meteorological observations. RCM SnowModel runoff output was tested further by comparison with coincident high-resolution Kangerlussuaq runoff observations and simulations.

We performed the Kangerlussuaq drainage area simulations for the 131-yr period 1950–2080 with the following objectives: 1) to illustrate and assess the trends of the HIRHAM4 RCM meteorological driving data; 2) to quantify and analyze the water balance components important to water resources (large-scale hydropower development and the fjord ecosystem), precipitation, SMB, and runoff; 3) to estimate the cumulative volume of SMB and runoff; and 4) to compare the runoff from Kangerlussuaq with simulations for the entire GrIS to investigate local differences and to test the applicability of the downscaling procedure.

Compared with the overall GrIS HIRHAM4 study by Mernild et al. (2010a), this Kangerlussuaq study is different in several ways: 1) it specifically addresses detailed local-scale hydrological impacts, precipitation trends, SMB, runoff, and specific runoff for the Kangerlussuaq catchment considering the IPCC A1B climate projection; 2) it compares hourly and daily-resolution runoff calibration routines; 3) it uses local meteorological station data to define lapse rates used to downscale HIRHAM4 simulations; 4) it uses a 500-m gridcell increment digital elevation model [DEM; instead of a 5-km increment as used in Mernild et al. (2010a)]; and 5) it performs a comparison between the Kangerlussuaq-simulated catchment runoff and the overall GrIS simulated runoff conditions. The current study performed runoff calibrations using hourly runoff observations (2007/08) and daily runoff simulations (1979–2008) from observed meteorological input data and verified runoff observations from the Kangerlussuaq drainage area, while the Mernild et al. (2010a) study employed calibrations of HIRHAM4-simulated meteorological data and melt extent only.

2. Study area

The Kangerlussuaq drainage area (6130 km2) is located on the west coast of Greenland (67°N, 50°W; Fig. 1a). The catchment outlet—the Watson River outlet—is located 28 km downstream from the GrIS terminus, near the town of Kangerlussuaq (Søndre Strømfjord) and at the innermost point of the Kangerlussuaq Fjord. This outlet is one of the best locations for observing GrIS runoff because of the well-defined, stable bedrock cross sections. For 2007 and 2008, the observed accumulated runoff was 1.77 and 1.28 km3, respectively (Fig. 2; Mernild and Hasholt 2009). The lower parts of the terrain (∼12%; elevation below ∼430 m MSL) are dominated by bare bedrock (or bedrock with a veneer of till), sparse vegetation cover, and braided river valleys with gravel and sand. The higher area (∼88%; elevation above ∼430 m MSL) is covered by the GrIS. The mean (1990–2003) equilibrium line altitude (ELA; defined as the elevation where the net mass balance is zero) in the region is ∼1530 m MSL (van de Wal et al. 2005).

The mean annual air temperature (MAAT) for the catchment is −10.9°C (1979–2008). Mean annual relative humidity is 64%, and mean annual wind speed is 5.3 m s−1. The corrected mean total annual precipitation (TAP) is 246 mm w.eq. yr−1 (after Allerup et al. 1998, 2000). Meteorological data are based on observations from several stations. Station Kangerlussuaq (hereafter station K) is a long-term standard synoptic World Meteorological Organization (WMO) meteorological station operated by the Danish Meteorological Institute (DMI). The station is located at the airport within the town of Kangerlussuaq and is representative of proglacial conditions. Additional meteorological data are available from stations S5, S6, and S9 (operated by Utrecht University), which are part of the K transect located on the ice sheet and representative of GrIS conditions. Data from these three stations were used (to define lapse rates) for downscaling of HIRHAM4 simulations and for defining transfer functions between the long-term station and the conditions on the GrIS.

3. Models and methods

a. Model hierarchy for downscaling ECHAM5 GCM and HIRHAM4 RCM

There are several kinds of uncertainties related to climate projections using simulations with coupled atmosphere–ocean GCMs. Apart from uncertainties in future greenhouse gas and aerosol emissions and their conversion to radiative forcings (not discussed here), there are uncertainties in global and, in particular, regional climate responses to these forcings. Furthermore, large regional-scale natural variability makes it difficult to determine the contributions of anthropogenic forcing and natural variability. Further uncertainties result from insufficient resolution of the GCM.

This implies that there is no single “best” model to use in an assessment of Greenland climate changes. However, one of the models identified by Walsh et al. (2008) exhibiting skill over the Arctic in general and Greenland in particular is the ECHAM5–Max Planck Institute Ocean Model (MPI-OM1), documented in Marsland et al. (2003), Roeckner et al. (2003), and Jungclaus et al. (2006). This state-of-the-art GCM was used to force HIRHAM4 (J. H. Christensen et al. 1996; O. B. Christensen et al. 1998) for the Greenland domain. HIRHAM4 is based on the adiabatic part of the High-Resolution Limited-Area Model (HIRLAM) short-range weather prediction model (Källén 1996). In HIRHAM4, the physical parameterization of HIRLAM has been replaced by that of ECHAM5’s predecessor ECHAM4 (Roeckner et al. 1996), so that HIRHAM4 can be thought of as a high-resolution limited-area version of ECHAM4.

Stendel et al. (2008) ran the HIRHAM4 RCM for the period 1950–2080 using the IPCC scenario A1B using boundary conditions from the ECHAM5/MPI-OM1. For Arctic meteorological conditions, the performance of the HIRHAM4 RCM (J. H. Christensen et al. 1996, 2001; Bjørge et al. 2000; Christensen and Christensen 2007) has been found to be state of the art (e.g., Christensen and Kuhry 2000; Dethloff et al. 2002; Kiilsholm et al. 2003). The RCM simulations were conducted over Greenland and adjacent sea areas, including the Kangerlussuaq region in west Greenland. The A1B experiment, as described in the IPCC Fourth Assessment Report, began in year 2000, and the AOGCM used the final state from a detailed simulation of the twentieth century as initial conditions in 2000 (e.g., Randall et al. 2007). The HIRHAM4 RCM A1B scenario was run on a 25-km gridcell increment with 19 vertical levels [for a more detailed description, see Stendel et al. (2008) and Mernild et al. (2010a)].

HIRHAM4 is nested using a standard procedure in RCM downscaling. A 10-point-wide relaxation or sponge zone with a gradually decreasing relaxation of the prognostic atmospheric fields toward the driving model is used (Davies 1976). The fields influenced in the free atmosphere are the horizontal wind components, temperature, and specific humidity vertically and horizontally interpolated to the grid used in HIRHAM, along with the horizontally interpolated surface pressure. These fields are provided every 6 h in the sponge zone. In addition, HIRHAM received information about sea surface temperatures and ice fraction once per day, again horizontally interpolated to the model grid (O. B. Christensen et al. 1998).

b. SnowModel description

The GrIS surface water balance, with emphasis on precipitation, SMB, and runoff, was simulated using SnowModel (Liston and Elder 2006a), a spatially and temporally distributed meteorological and snowpack modeling system. SnowModel is composed of five submodels: MicroMet defines the meteorological forcing conditions (Liston and Elder 2006b); EnBal calculates the surface energy exchanges (Liston 1995; Liston et al. 1999); SnowPack simulates mass and heat transfer processes due to, for example, retention and internal refreezing (Liston and Hall 1995); SnowTran-3D is a blowing-snow model that accounts for snow redistribution (Liston and Sturm 1998, 2002; Liston et al. 2007); and SnowAssim is a snow-data assimilation model (Liston and Hiemstra 2008). SnowAssim was not used in this study. SnowModel simulates the melting of glacier ice after winter snow accumulation has ablated (Mernild et al. 2006). The simulated runoff is equal to the gridcell runoff at each time step, summed over the drainage domain, without any time lag accounting for the distance between the grid cell and the basin outlet. Meltwater retention and refreezing is captured in SnowModel. Not including retention/refreezing routines in SnowModel would lead to an overestimation of runoff to the ocean, and a consequent overestimation of the global sea level rise. In recent Greenland glacier studies, SnowModel was modified (Mernild et al. 2010b) to take into account variable snow albedo calculated after Douville et al. (1995) and Strack et al. (2004). The albedo decreases gradually from 0.8 to a minimum of 0.5 as the snow ages [for further information about SnowModel, see Liston et al. (2008) and the references contained therein]. A study by Greuell (2000) indicated that a “dark zone” of meltwater accumulation partly overlies the ice-covered surface (from approximately 950 to 1350 m MSL) in the Kangerlussuaq catchment, affecting the albedo and creating a positive feedback between albedo and melt. Since the impact from surface meltwater accumulation is not included as a standard SnowModel routine, uncertainties related to this phenomenon may occur [see Eq. (1)].

c. SnowModel input

Distributed point meteorological data, including air temperature, relative humidity, wind speed and direction, and precipitation, were obtained from the HIRHAM4 RCM model (1950–2080) based on the IPCC scenario A1B and downscaled by SnowModel for the Kangerlussuaq drainage area. The MicroMet component of SnowModel calculates the other required atmospheric forcing data, such as incoming shortwave and longwave radiation, based on A1B input data [for additional information about the radiation calculations, see Liston and Elder (2006b)]. Simulations were performed on a 1-day time step.

Greenland topographic data at 625-m resolution were provided by Bamber et al. (2001) and the image-derived correction by Scambos and Haran (2002). This time-invariant DEM was interpolated to a 500-m gridcell increment covering a 540 km × 520 km simulation domain (280 800 km2; Fig. 1a). The location of the GrIS terminus was confirmed or estimated by using aerial photos, maps (1:250 000; from Geodetic Institute), and satellite images (from Google Earth).

Each grid cell within the domains was assigned a U.S. Geological Survey (USGS) Land Use/Land Cover System class according to the North American Land Cover Characteristics Database, version 2.0 [available online at the USGS Earth Resources Observation and Science (EROS) Data Center’s Distributed Active Archive Center Web site at http://edc2.usgs.gov/glcc/nadoc2_0.php#vers2]. User-defined constants for SnowModel were shown in Mernild et al. [2009; see also Liston and Sturm (1998) for parameter definitions].

d. HIRHAM4 RCM and SnowModel verification and uncertainty

Since HIRHAM4 RCM was running in a full climate mode—that is, the driving GCM only knows about the state of the atmosphere–ocean system from the external drivers (sun, aerosols, and greenhouse gases), whether actually realized (1950–2000) or projected (2001–80)—we need to assess the simulation with the observed climate system. Before the daily HIRHAM4 RCM data were used as meteorological input for SnowModel, Greenland RCM data were tested and bias corrected, producing a calibrated dataset for the 1995–2005 period using available in situ daily meteorological data from 25 stations located around Greenland [operated by Greenland Climate Network (GC-Net), University of Colorado; and coastal areas by DMI]. For a bias adjustment of the HIRHAM results, a 10-yr period is relatively short; however, we have assessed the role of this short period by adding an additional calibration period in which the model years were 1980–90 and observed years were 1995–2005 (see Mernild et al. 2010a). The resulting dissimilarity in precipitation for the GrIS averaged 42 mm w.eq. (or ∼7%) and the corresponding temperature difference was 1.5°C for 1980–90 with respect to the calibration period 1995–2005. Relative humidity and wind were both insignificantly different (see Mernild et al. 2010a). Given inherent variability in weather, a longer period for bias correction should be used, but thorough examination of the overall performance of HIRHAM4 over Greenland was provided by Stendel et al. (2008).

Mean monthly offsets between RCM-modeled output and the observed meteorological data were added to the daily RCM meteorological parameters to correct each variable (air temperature, relative humidity, wind speed, and corrected precipitation) for the 1950–2080 period, before being downscaled and processed by SnowModel. For seasonal variations in monthly bias corrections, see Mernild et al. (2010a; Fig. 2).

An important result from RCM downscaling experiments over Europe is that it is not easy or maybe not even possible from a practical point of view to attribute the source of model bias directly to the GCM or the RCM (e.g., J. H. Christensen et al. 2007a, 2010). The role of the RCM is to inform in a physically consistent way about a plausible realization of finescale details given the driving model. The two are intimately linked and so is the bias. Given the finer-scale information provided by the RCM, the corrections needed to interpret the information at a site level or on an even finer grid is, however, less problematic than going from the cause GCM scale directly to the refined grid.

For downscaling to the 500-m gridcell increment, mean monthly lapse rates were defined based on observations along a transect drawn between the K-transect meteorological stations [for SnowModel downscaling procedures, see Liston and Elder (2006b)]. The K-transect lapse rates (for air temperature) were nearly identical to lapse rates from Jakobshavn, west Greenland, and the GrIS in general (Steffen and Box 2001; Mernild et al. 2010b). To assess the performance of the adjusted RCM SnowModel–simulated spatial-distributed meteorological data, the data were tested against independent in situ meteorological observations (data not used for calibration) spanning 1995–2005. Data were ranked, and the ranked numbers were compared 1) to illustrate the ability of HIRHAM to capture the span of realized parameters for the period in concern and 2) to give a rough estimate about the calibration method. Validations of the simulated GrIS meteorological data (air temperature, relative humidity, and wind speed) indicated substantial correlation with in situ–observed meteorological data from different meteorological stations at the GrIS—JAR1, Humboldt, Saddle, and Summit at different elevations—and with in situ–observed precipitation from outside the GrIS—Station Nord, Danmarkshavn, Ittoqqortoormiit, and Ikerasassuaq—at different latitudes. Modeled air temperature values account for 98%–99% of the variance in the observed 1995–2005 mean monthly dataset. The relative humidity, corrected precipitation, and wind speed have the same or slightly fewer strong correlations, but results remain respectable for relative humidity (between 85% and 96%), wind speed (between 83% and 98%), and precipitation (between 89% and 98%) for representations of the GrIS meteorological processes [for additional information see Mernild et al. (2010a)].

RCM SnowModel–simulated Kangerlussuaq runoff was compared both with hourly 2007/08 observed Kangerlussuaq runoff and daily long-term simulated runoff (1979–2008; Mernild et al. 2010c; see Fig. 2). RCM SnowModel–simulated runoff was initially overestimated by 80% (2007) and 10% (2008) according to hourly runoff observations (Mernild and Hasholt 2009). This overestimation was related to uncertainties associated with unrepresented or poorly represented model processes, including englacial and subglacial runoff flow (probably also from nearby catchments) affected by seasonal changes in the internal drainage system and spatial changes in basal topography. The internal drainage system and basal topography are likely the main flow pattern determinants for glacial runoff of the Kangerlussuaq drainage area; more than ∼70% of the area’s runoff originated from the GrIS.

The observed hourly runoff values for the entire 2007 and 2008 runoff seasons were used for validation (n = 5088, where n is the number of observations), representing the total span in Kangerlussuaq seasonal runoff from almost no runoff at the beginning/end of the runoff seasons to peak values during midsummer and during jökulhlaups (glacier bursts) of ∼540 m3 s−1. The use of robust detailed runoff observations spanning the yearly variability in runoff supports the use of the 2007 and 2008 observations for validation [for additional information about observations, see Mernild and Hasholt (2009)]. Further, the RCM SnowModel–simulated runoff was compared with long-term daily Kangerlussuaq runoff simulations (1979–2008; n = 10 958) (Mernild et al. 2010c) to cover the interannual/interdecadal trend in runoff. Long-term runoff simulations were modeled based on observed meteorological input data representative of GrIS conditions (K transect) and of proglacial conditions (station K), and verified against the 2007 and 2008 observed runoff (Mernild et al. 2010c), normalizing the RCM SnowModel runoff to have the same mean value as the long-term simulated runoff of 1.02 km3 w.eq. yr−1 (1979–2008; Fig. 2), before upscaling to 1950–2080. Using model results to verify model simulations is commonly done when other options are not available, (e.g., Fettweis 2007).

For this Kangerlussuaq study, only a one-way nesting between HIRHAM4 (the atmosphere) and SnowModel (the surface) was used, where SnowModel was driven with HIRHAM4 meteorological conditions. SnowModel is a surface model producing first-order effects of climate change based on the time-invariant DEM. SnowModel possessed uncertainties because of processes not represented by the modeling system, just like other available models. For example, changes in the GrIS extent and elevation were not incorporated in the model routines. Changes in supraglacial, englacial, and subglacial storages, the internal GrIS runoff drainage system, and subglacial geothermal melting were not taken into account, even though all these processes can influence naturally occurring runoff. Neither does SnowModel simulate transpiration from the tundra (proglacial landscape), but this was likely to be insignificant because of the dry conditions during summer.

Based on the uncertainties in the modeled SMB and runoff from previous Greenland SnowModel simulations and statistical analysis, along with uncertainties in observed runoff (for verification), the maximum estimated uncertainty ranged between 10% and 25%. We assumed that the present Kangerlussuaq runoff and SMB study included an almost similar maximum uncertainty (e.g., Mernild et al. 2006, 2008; Mernild and Hasholt 2009). However, uncertainties are related to the A1B scenario, especially in the latter half of the twenty-first century.

4. Results and discussion

a. Kangerlussuaq climate model trends, 1950–2080

Figure 3 shows the HIRHAM4 RCM bias-corrected meteorological anomalies (air temperature, relative humidity, wind speed, and precipitation) for the 1950–2080 Kangerlussuaq drainage area. For each parameter the trend over Kangerlussuaq was illustrated and compared with trends for the entire GrIS (GrIS HIRHAM4 RCM IPCC A1B simulations; Mernild et al. 2010a). From 1950 to 2080, there were significant increases (p < 0.01, where p is the level of significance) in simulated air temperature and precipitation on both local (Kangerlussuaq) and regional (GrIS) scales. The average change in Kangerlussuaq temperature was 3.4°C (significant; 97.5% quantile) compared to 4.8°C (significant; 97.5% quantile) for the entire GrIS. This was probably mainly due to the projected impact of the changing sea ice extent in the Arctic Ocean and Greenland Sea. Mean annual precipitation increased significantly by 95 mm w.eq. for the Kangerlussuaq drainage area [97.5% quantile; linear regression (when nothing is mentioned, linear regression is used)], compared to only 80 mm w.eq. for the entire GrIS (significant; 97.5% quantile; Figs. 3a and 3d). Relative humidity increased 0.2% on average (insignificant) for Kangerlussuaq and 1.2% (significant; 97.5% quantile) for the entire GrIS (Fig. 3b). Average wind speed increased 0.2 m s−1 (significant; 97.5% quantile) for Kangerlussuaq, while it decreased by <0.1 m s−1 (insignificant) for the entire GrIS.

The Kangerlussuaq annual climate description was divided into the four seasons: winter (December–February), spring (March–May), summer (June–August), and autumn (September–November) (Figs. 3e–h). The greatest seasonal changes in predicted air temperature of 4.6°C (significant; 97.5% quantile) and 6.9°C (significant; 97.5% quantile) occurred during winter and spring, respectively, in accordance with observed Greenland trends from the 1970s through the 1990s (Box 2002). The lowest seasonal changes of 1.5°C (significant; 97.5% quantile) and 0.8°C (insignificant) occurred during summer and autumn, respectively (Fig. 3e), and both were below the MAAT average change of 3.4°C. For Kangerlussuaq the trend in mean annual summer temperature for the simulation period (1950–2080) generally followed the overall trend in summer temperature for GrIS, even though the GrIS mean summer temperature (−2.1°C) was significantly colder by −1.8°C (97.5% quantile) compared to temperatures for Kangerlussuaq (−0.3°C). The predicted decadal-average trend in MAAT (1950–2080) was positive for both Kangerlussuaq and the whole GrIS. Kangerlussuaq temperatures increased from −13.3 (±0.5)°C and GrIS increased from −14.8 (±0.4)°C (1950–59) to almost equal average decadal values of −10.2 (±0.6)°C and −10.1 (±0.5)°C, respectively, for 2070–80 (Table 1). In 2070–80 the average decadal difference in predicted MAAT between Kangerlussuaq and GrIS was almost gone, indicating no differences in MAAT conditions between local (Kangerlussuaq) and regional scales (GrIS).

From 1950 to 2080 the seasonal modeled changes in relative humidity all varied within <2%, indicating no significant difference, even though the winter humidity decreased an average of 0.9% (Fig. 3f). There was no significant seasonal difference for wind speed; the maximum seasonal change in wind speed was 0.3 m s−1 (1950–2080; Fig. 3g). For precipitation there was an average annual increase of 95 mm w.eq. (significant; 97.5% quantile) at Kangerlussuaq, with a minimum seasonal increase of 16 mm w.eq. during winter (insignificant) and a maximum increase of 29 mm w.eq. during spring (insignificant). The largest projected changes in both precipitation and temperature occurred in spring.

b. The climate impact at Kangerlussuaq

The balance between net accumulation (due to snow accumulation and redistribution) during winter and net ablation (evaporation, sublimation, and runoff) during summer can be described by the water balance equation. The yearly water balance equation for the GrIS can be described as follows:
i1520-0442-24-3-609-e1
where P is precipitation input from snow and rain (and possible condensation), E is evaporation, SU is surface and blowing-snow sublimation, R is runoff, ΔS is change in glacier ice storage and snowpack storage (ΔS is also referred to as the SMB), and η is the water balance discrepancy (error). Mass gain (accumulation) is calculated as positive, and mass loss (ablation) is considered negative in the water balance equation.

The RCM SnowModel–simulated precipitation, SMB, and runoff for the Kangerlussuaq drainage area are shown on an annual basis in Fig. 4a, and on a decadal-average basis in Table 1 for the period 1950–59 through 2070–80. The interannual variability in precipitation and ablation results in considerable SMB fluctuations (Fig. 4a), indicating that SMB fluctuations were largely tied to changes in precipitation (accumulation) (r2 = 0.47; p < 0.01) rather than runoff (ablation) (r2 = 0.35; p < 0.01). This was unexpected, but there was no reason to believe that the model setup and the calibration procedures could be the reason for SMB to be more dependent on precipitation fluctuations than on runoff fluctuations, since simulations were forced toward runoff observations.

Fluctuation patterns illustrated in Fig. 4a (1950–2080) show the SMB is largest (most positive) near the beginning of the simulation period, with subsequent loss as temperatures and runoff increased. For the period 1950–2080, precipitation rose ∼0.4 km3 (r2 = 0.53; p < 0.01), evaporation and sublimation increased ∼0.1 km3 (r2 = 0.28; p < 0.01), and runoff swelled ∼0.6 km3 (r2 = 0.75; p < 0.01), leading to an enhanced average SMB loss of ∼0.3 km3 (r2 = 0.24; p < 0.01) (Table 1; Fig. 4a). The average trend in Kangerlussuaq precipitation, runoff, and SMB compared well with the overall past and present (1958–2008) GrIS precipitation, runoff, SMB trends (see Ettema et al. 2009) and the future (2010–2100) SMB trends identified by Fettweis et al. (2008). The study by Fettweis et al. (2008) projected enhanced average SMB loss during the twenty-first century based on values of 24 AOGCMs using projections of temperature and precipitation anomalies from AOGCMs performed for the IPCC Fourth Assessment Report for 2010–2100.

The SMB and runoff tendencies mentioned above indicate that, over the simulation period, the water supply will likely be stable for potential hydropower production in the Kangerlussuaq drainage area. The increased runoff was manifested in a longer runoff season, most pronounced in spring because of the seasonal warming of 6.9°C. The average modeled first day of runoff shifted from late May (1950–59) to early May (2070–80). In autumn the change in number of runoff days was negligible.

During the simulation period, the decadal-average SMB was −0.1 (±0.6) km3 yr−1, while runoff was 1.2 (±0.4) km3 yr−1. The average annual runoff from Kangerlussuaq accounted for only approximately 0.3% of the average entire GrIS runoff of 442 km3 yr−1 for the period 1950–80 (Table 1). Kangerlussuaq runoff accounted for a small amount of the overall average GrIS runoff. Yet, the specific runoff (runoff per unit drainage area per time; L s−1 km−2) showed that the 13.0 (±4.6) L s−1 km−2 Kangerlussuaq runoff was ∼70% greater than the GrIS runoff of 7.6 (±2.3) L s−1 km−2 (Table 1). The higher specific runoff was due to the relatively southern location of the Kangerlussuaq catchment. In Fig. 4c and Table 1, values for specific runoff are shown for 1950–2080 from Kangerlussuaq and the GrIS. Through the simulation period, the specific runoff increased 65% from 11.7 (±2.4) (1950–59) to 19.3 (±4.6) L s−1 km−2 (2070–80) for Kangerlussuaq and 130% for GrIS from 4.9 (±0.6) (1950–59) to 11.5 (±0.8) L s−1 km−2 (2070–80) as temperatures increased. This disparity in specific runoff between Kangerlussuaq and the entire GrIS was related to the different trends in MAAT throughout the simulation period, which was averaged 4.8°C for GrIS and 3.4°C for Kangerlussuaq (Fig. 3a).

Table 1 shows the decadal-average SMB values for Kangerlussuaq. Negative SMB values occurred for 1950–59, 1980–89 through 2010–19, and 2040–49 through 2070–79, interrupted by periods of positive decadal SMB because of relatively high precipitation (Table 1). In general, for the entire simulation period the Kangerlussuaq SMB became more negative in later decades (Table 1). This indicated that the water supply at Kangerlussuaq caused by mass loss will be stable or increasing during the twenty-first century, and it will likely be available to support large-scale hydropower development during this period.

Figures 5a–c present the spatial distribution of snow water equivalent (SWE), runoff, and SMB for 1950–59 (the decade with coldest MAAT, −13.3°C) and 2070–80 (warmest MAAT, −10.2°C). The SWE exceeded 800 mm w.eq. at high elevations (Fig. 5a), with only 200–300 mm w.eq. at the catchment outlet. Average RCM SnowModel–simulated precipitation values for Kangerlussuaq were highly in accordance with values from, for example, Box et al. (2004) of 200–400 mm w.eq. at the outlet and 400–600 mm w.eq. at high elevations (for the period 1991–2000), and Ettema et al. (2009) of 200–300 mm w.eq. at the outlet and up to 500–700 mm w.eq. at high elevations (1958–2008). In some areas of the GrIS (e.g., at the glacier terminus), as much as ∼1100 mm w.eq. runoff was simulated on average for 2070–80, while only ∼800 mm w.eq occurred for 1950–59 (Fig. 5b). The simulated runoff decreased with increasing elevation until a threshold was crossed, beyond which no surface runoff occurred (Fig. 5b). The runoff boundary was located ∼60 km from the glacier terminus at ∼1350 m MSL in 1950–59 and ∼70 km from the terminus at ∼1470 m MSL in 2070–80. For Kangerlussuaq the RCM SnowModel–simulated SMB was in accordance with SMB values from Box et al. (2006) (1988–2004) and more positive compared to values from Ettema et al. (2009) (1958–2008), mainly because of a more detailed representation of the topography (∼11-km gridcell increment) in the RCM used by Ettema et al. (2009). Figure 5c illustrates the spatial distributed SMB, including the spatial location of the ELA; the ELA provided a useful indicator of the net influence of accumulation and ablation on the SMB. The modeled ELA, which is closely tied to changes in summer air temperature, shifted up-glacier from 1190 to 1570 m MSL, from 1950 to 1959 through 2070–80. The simulated altitude of the 2070–80 ELA was slightly higher than today’s observed ELA of ∼1530 m MSL (van de Wal et al. 2005), meaning that the modeled present-day ELA was a couple of hundred meters too low. For Kangerlussuaq the average increase in ELA was around ∼30 m decade−1, which was below the average of ∼70 m decade−1 for the GrIS (Mernild et al. 2009, manuscript submitted to Arct. Antarct. Alp. Rev.).

In Fig. 4b the cumulative (year-after-year accumulation) SMB and runoff are illustrated for Kangerlussuaq since 1950. From 1950 to around 1980, the cumulative SMB fluctuated more or less around equilibrium. After 1980 the cumulative SMB had an overall negative trend, which became steeper in the 2040s. The cumulative SMB declines from around −2 km3 w.eq. in the 2040s to −15.9 km3 w.eq. in 2080. For the total simulation period, cumulative Kangerlussuaq runoff was 151.2 km3 w.eq., indicating that ∼10% of the runoff is explained by the GrIS net loss, which is less in the beginning of the simulation period and up to an average of ∼35% for the last decade (2070–80).

Glacier and ice sheet runoff is a required input and/or boundary condition in hydrodynamic and ecological models of the fjord system. The results showed an increasing freshwater contribution to the fjord. The average annual freshwater contribution from the Kangerlussuaq drainage area alone was approximately equal to the tidal prism (the amount of water that flows into and out of an estuary or bay with the flood and ebb of the tide, excluding any contribution from freshwater inflows) of the Kangerlussuaq Fjord (approximately 1.8 km3). The cumulative runoff (1950–2080) from the Watson River alone entering the fjord (151.2 km3) was roughly equal to the total volume of the fjord, ∼100 km3. The investigated drainage area was 6130 km2 (88% covered by GrIS), while the total drainage area to the fjord was 31 160 km2, where 67% (20 990 km2) was covered by GrIS (GGU 1978). Clearly, the freshwater input from the GrIS was significant for the water balance of the fjord and can be obtained by upscaling the results of this investigation.

For this study, meteorological input data were provided based on the IPCC scenario A1B modeled by the HIRHAM4 RCM using boundary conditions from ECHAM5 AOGCM from 1950 through 2080. Only 1 scenario out of 40 IPCC-class GCMs (using different model projections of the future climate; Christensen et al. 2007b) was used to simulate trends and changes in the Kangerlussuaq mass loss and runoff. Using more than one model projection would likely give a wider range of results. However, the ECHAM5 AOGCM was chosen because it has been identified as one of the best-performing models for representing the present-day Arctic climate (Walsh et al. 2008). The combination of HIRHAM4 RCM and SnowModel is expected to provide a state-of-the-art model representation of the GrIS hydrological cycle, because HIRHAM4 RCM has been shown to realistically represent Arctic meteorological conditions, and SnowModel has a proven record of simulating surface snow and ice hydrological features and evolutions. Limitations in this modeling system include the following: 1) the model combination included only one-way nesting between HIRHAM4 (the atmosphere) and SnowModel (the surface); and 2) SnowModel does not yet include runoff routing routines for storage and meltwater flow from the surface, through the glacier (the supraglacial, englacial, and subglacial storage), to the basin outlet.

5. Summary and conclusions

The IPCC A1B scenario simulated in HIRHAM4 RCM SnowModel for the GrIS Kangerlussuaq drainage area revealed warming, increasing runoff, and a more negative SMB for the period 1950–2080. Warming in the Kangerlussuaq drainage area was less than the average warming for the entire GrIS, underlining the importance of the investigation of local and regional trends before application in local areas. The cumulative modeled SMB and runoff for the period 1950–2080 were −15.9 and 151.2 km3, respectively. The trends in both precipitation and runoff showed an increase. The SMB became only slightly more negative in the beginning of the period, but the last three decades showed a larger mass loss. The results represent a significant part of the GrIS drained into a fjord system where the seawater was not directly in contact with the glacial outlet. All the water balance tendencies in precipitation, SMB, and runoff projections indicate that the water supply for hydropower production will be maintained throughout the simulation period.

Acknowledgments

Very special thanks to the three anonymous reviewers for their insightful critique of this article. This work was supported by the Climate Change Prediction Program of the U.S. Department of Energy’s Office of Science. Los Alamos National Laboratory is operated under the auspices of the National Nuclear Security Administration of the United States. This work was also supported by the Kommissionen for Videnskabelige Undersøgelser i Grønland (KVUG) project Climatic Record in Kangerlussuaq (CRIK; Grant 272-07-0645) and by KVUG (Grant 2138-08-0003). Christensen and Stendel acknowledge the financial support from the Greenland Climate Research Centre in Nuuk, Greenland. Very special thanks to Dr. William H. Lipscomb of Los Alamos National Laboratory for his insightful critique of this article. Thanks are given to the Cooperative Institute for Research in the Atmosphere, Colorado State University, for hosting the first author during November and December 2009.

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Fig. 1.
Fig. 1.

(a) Kangerlussuaq region, west Greenland, including the Kangerlussuaq drainage area with topographic 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 (simulation area), including meteorological tower stations and the hydrometric station at the catchment outlet. (c) Topography (100-m contour interval) within the simulation area.

Citation: Journal of Climate 24, 3; 10.1175/2010JCLI3560.1

Fig. 2.
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).

Citation: Journal of Climate 24, 3; 10.1175/2010JCLI3560.1

Fig. 3.
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.

Citation: Journal of Climate 24, 3; 10.1175/2010JCLI3560.1

Fig. 4.
Fig. 4.

Kangerlussuaq drainage area time series for 1950–2080 of (a) precipitation, runoff, and changes in storage (SMB); (b) cumulative runoff and SMB volume; and (c) specific runoff. Here, specific runoff is shown together with values from GrIS based on Mernild et al. (2010a).

Citation: Journal of Climate 24, 3; 10.1175/2010JCLI3560.1

Fig. 5.
Fig. 5.

Simulated mean annual values for 1950–59 (the decade with the coldest MAAT) and 2070–80 (warmest MAAT) of (a) SWE depth, (b) runoff, and (c) SMB for the area of interest (from where runoff occurred) in the Kangerlussuaq drainage area.

Citation: Journal of Climate 24, 3; 10.1175/2010JCLI3560.1

Table 1.

Decadal-average Kangerlussuaq and GrIS MAAT, precipitation, evaporation, sublimation, runoff, specific runoff, and surface mass balance from 1950 through 2080. The runoff values do not include hydro-glacio processes, such as the sudden release of bulk water.

Table 1.
Table 1.

(Extended)

Table 1.
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