• Allerup, P., , H. Madsen, , and F. Vejen, 1998: Estimating true precipitation in Arctic areas. Nordic Hydrological Programme Rep. 44, 19.

  • Allerup, P., , H. Madsen, , and F. Vejen, 2000a: Correction of precipitation based on off-site weather information. Atmos. Res., 53, 231250, doi:10.1016/S0169-8095(99)00051-4.

    • Search Google Scholar
    • Export Citation
  • Allerup, P., , H. Madsen, , and F. Vejen, 2000b: Correction of observed precipitation in Greenland. Proc. Nordic Hydrological Conf., Uppsala, Sweden, NHF/NAH Nordic Association for Hydrology, 1–8.

  • Andreassen, L. M., , S. H. Winsvold, , F. Paul, , and J. E. Hausberg, 2012: Inventory of Norwegian Glaciers. L. M. Andreassen and S. H. Winsvold, Eds., Norwegian Water Resources and Energy Directorate, 242 pp.

  • Arendt, A., and et al. , 2012: Randolph Glacier Inventory (v2.0): A dataset of global glacier outlines. Global Land Ice Measurements from Space, Boulder Colorado, digital media (with area corrections downloaded 2012). [Available online at http://www.glims.org/RGI/.]

  • Benn, D. I., , and D. J. A. Evans, 2002: Glaciers and Glaciation. Arnold, 734 pp.

  • Bhutiyani, M. R., 1999: Mass-balance studies on Siachen Glacier in the Nubra Valley, Karakoram Himalaya, India. J. Glaciol., 45 (149), 112118.

    • Search Google Scholar
    • Export Citation
  • Bolch, T., , B. Menounos, , and R. Wheate, 2010: Landsat-based inventory of glaciers in western Canada, 1985–2005. Remote Sens. Environ., 114, 127137, doi:10.1016/j.rse.2009.08.015.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., 2008: NASA’s Modern Era Retrospective Analysis for Research and Applications: Integrating Earth observations. Earthzine. [Available online at http://www.earthzine.org/2008/09/26/nasas-modern-era-retrospective-analysis/.]

  • Bosilovich, M. G., , J. Chen, , F. R. Robertson, , and R. F. Adler, 2008: Evaluation of global precipitation re-analyses. J. Appl. Meteor. Climatol., 47, 22792299, doi:10.1175/2008JAMC1921.1.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., , R. R. Franklin, , and J. Chen, 2011: Global energy and water budgets in MERRA. J. Climate, 24, 57215739, doi:10.1175/2011JCLI4175.1.

    • Search Google Scholar
    • Export Citation
  • Box, J. E., 2002: Survey of Greenland instrumental temperature records: 1873–2001. Int. J. Climatol., 22, 18291847, doi:10.1002/joc.852.

    • Search Google Scholar
    • Export Citation
  • Bruland, O., , G. E. Liston, , J. Vonk, , and A. Killingtveit, 2004: Modelling the snow distribution at two high Arctic sites at Svalbard, Norway, and at a sub-Arctic site in central Norway. Nord. Hydrol., 35, 191208.

    • Search Google Scholar
    • Export Citation
  • Cogley, J. G., 2009: Geodetic and direct mass-balance measurements: Comparison and joint analysis. Ann. Glaciol., 50, 96100, doi:10.3189/172756409787769744.

    • Search Google Scholar
    • Export Citation
  • Cogley, J. G., 2012: The future of the world’s glaciers. The Future of the World’s Climate, 2nd ed., A. Henderson-Sellers and K. McGuffie, Eds., Elsevier, 205–218, doi:10.1016/B978-0-12-386917-3.00008-7.

  • Cuffey, K. M., , and W. S. B. Paterson, 2010: The Physics of Glaciers. 4th ed. Elsevier, 708 pp.

  • Cullather, R. I., , and M. G. Bosilovich, 2011: The moisture budget of the polar atmosphere in MERRA. J. Climate, 24, 28612879, doi:10.1175/2010JCLI4090.1.

    • Search Google Scholar
    • Export Citation
  • Dyurgerov, M. B., 2010. Data of glaciological studies—Reanalysis of glacier changes: From the IGY to the IPY, 1960–2008. Publication 108, Institute of Arctic and Alpine Research, 116 pp.

  • Dyurgerov, M. B., , and M. F. Meier, 2005: Glaciers and the changing Earth system: A 2004 snapshot. Occasional Paper 58, Institute of Arctic and Alpine Research, Boulder, Colorado, 117 pp.

  • Finnis, J., , J. Cassano, , M. Holland, , M. C. Serreze, , and P. Uotila, 2009a: Synoptically forced hydroclimatology of major Arctic watersheds in general circulation models. Part 1: The Mackenzie River Basin. Int. J. Climatol., 29, 12261243, doi:10.1002/joc.1753.

    • Search Google Scholar
    • Export Citation
  • Finnis, J., , J. Cassano, , M. Holland, , M. C. Serreze, , and P. Uotila, 2009b: Synoptically forced hydroclimatology of major Arctic watersheds in general circulation models. Part 2: Eurasian watersheds. Int. J. Climatol., 29, 12441261, doi:10.1002/joc.1769.

    • Search Google Scholar
    • Export Citation
  • Gardelle, J., , E. Berthier, , and Y. Arnaud, 2012: Slight mass gain of Karakoram glaciers in the early twenty-first century. Nat. Geosci., 5, 322325, doi:10.1038/ngeo1450.

    • Search Google Scholar
    • Export Citation
  • Gardner, A. S., and et al. , 2013: A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science, 340, 852857, doi:10.1126/science.1234532.

    • Search Google Scholar
    • Export Citation
  • Glazovsky, A., , and Y. Macheret, 2006: Glaciation in north and central Eurasia in present time (in Russian with English summary). Eurasian Arctic, V. M. Kotlyakov, Eds., Nauka, 97–114 and 438–445.

    • Search Google Scholar
    • Export Citation
  • Hanna, E., and et al. , 2008: Increased runoff from melt from the Greenland Ice Sheet: A response to global warming. J. Climate, 21, 331341, doi:10.1175/2007JCLI1964.1.

    • Search Google Scholar
    • Export Citation
  • Hanna, E., , S. H. Mernild, , J. Cappelen, , and K. Steffen, 2012: Recent warming in Greenland in a long-term instrumental (1881–2012) climatic context: I. Evaluation of surface air temperature records. Environ. Res. Lett., 7, 045404, doi:10.1088/1748-9326/7/4/045404.

    • Search Google Scholar
    • Export Citation
  • Hanna, E., , J. M. Jones, , J. Cappelen, , S. H. Mernild, , L. Wood, , K. Steffen, , and P. Huybrechts, 2013: Discerning the influence of North Atlantic atmospheric and oceanic forcing effects on 1900–2010 Greenland summer climate and melt. Int. J. Climatol., 33, 862880, doi:10.1002/joc.3475.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., , R. Ruedy, , M. Sato, , and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, doi:10.1029/2010RG000345.

  • Hasholt, B., , G. E. Liston, , and N. T. Knudsen, 2003: Snow distribution modelling in the Ammassalik region, South East Greenland. Nord. Hydrol., 34, 116.

    • Search Google Scholar
    • Export Citation
  • Hastings, D. A., and et al. , 1999: The Global Land One-km Base Elevation (GLOBE) digital elevation model, version 1.0. NOAA, National Geophysical Data Center, digital media. [Available online at http://www.ngdc.noaa.gov/mgg/topo/globe.html.]

  • Hiemstra, C. A., , G. E. Liston, , and W. A. Reiners, 2002: Snow redistribution by wind and interactions with vegetation at upper treeline in the Medicine Bow Mountains, Wyoming, USA. Arct. Antarct. Alp. Res., 34, 262273, doi:10.2307/1552483.

    • Search Google Scholar
    • Export Citation
  • Hiemstra, C. A., , G. E. Liston, , and W. A. Reiners, 2006: Observing, modelling, and validating snow redistribution by wind in a Wyoming upper treeline landscape. Ecol. Modell., 197, 3551, doi:10.1016/j.ecolmodel.2006.03.005.

    • Search Google Scholar
    • Export Citation
  • Hinzman, L. D., and et al. , 2005: Evidence and implications of recent climate change in northern Alaska and other arctic regions. Climatic Change, 72, 251298, doi:10.1007/s10584-005-5352-2.

    • Search Google Scholar
    • Export Citation
  • Hirabayashi, Y., , P. Döll, , and S. Kanae, 2010: Global-scale modeling of glacier mass balances for water resources assessments: Glacier mass changes between 1948 and 2006. J. Hydrol., 390, 245256, doi:10.1016/j.jhydrol.2010.07.001.

    • Search Google Scholar
    • Export Citation
  • Hock, R., , M. de Woul, , V. Radić, , and M. Dyurgerov, 2009: Mountain glaciers and ice caps around Antarctica make a large sea-level rise contribution. Geophys. Res. Lett., 36, L07501, doi:10.1029/2008GL037020.

    • Search Google Scholar
    • Export Citation
  • Huss, M., 2011: Present and future contribution of glacier storage change to runoff from macroscale drainage basins in Europe. Water Resour. Res., 47, W07511, doi:10.1029/2010WR010299.

    • Search Google Scholar
    • Export Citation
  • Immerzeel, W., , L. van Beek, , M. Konz, , A. Shrestha, , and M. F. P. Bierkens, 2012: Hydrological response to climate change in a glacierized catchment in the Himalayas. Climatic Change, 110, 721736, doi:10.1007/s10584-011-0143-4.

    • Search Google Scholar
    • Export Citation
  • Kaser, G., , J. G. Cogley, , M. B. Dyurgerov, , M. F. Meier, , and A. Ohmura, 2006: Mass balance of glaciers and ice caps: Consensus estimates for 1961–2004. Geophys. Res. Lett., 33, L19501, doi:10.1029/2006GL027511.

    • Search Google Scholar
    • Export Citation
  • Kotlarski, S., , F. Paul, , and D. Jacob, 2010: Forcing a distributed glacier mass balance model with the regional climate model REMO. Part I: Climate model evaluation. J. Climate, 23, 15891606, doi:10.1175/2009JCLI2711.1.

    • Search Google Scholar
    • Export Citation
  • Leclercq, P. W., , J. Oerlemans, , and J. G. Cogley, 2011: Estimating the glacier contribution to sea-level rise for the period 1800–2005. Surv. Geophys., 32, 519535, doi:10.1007/s10712-011-9121-7.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., 1995: Local advection of momentum, heat, and moisture during the melt of patchy snow covers. J. Appl. Meteor., 34, 17051715, doi:10.1175/1520-0450-34.7.1705.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and M. Sturm, 1998: A snow-transport model for complex terrain. J. Glaciol., 44, 498516.

  • Liston, G. E., , and M. Sturm, 2002: Winter precipitation patterns in Arctic Alaska determined from a blowing-snow model and snow-depth observations. J. Hydrometeor., 3, 646659, doi:10.1175/1525-7541(2002)003<0646:WPPIAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and M. Sturm, 2004: The role of winter sublimation in the Arctic moisture budget. Nord. Hydrol., 35, 325334.

  • Liston, G. E., , and K. Elder, 2006a: A distributed snow-evolution modeling system (SnowModel). J. Hydrometeor., 7, 12591276, doi:10.1175/JHM548.1.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and K. Elder, 2006b: A meteorological distribution system for high-resolution terrestrial modeling (MicroMet). J. Hydrometeor., 7, 217234, doi:10.1175/JHM486.1.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and C. A. Hiemstra, 2008: A simple data assimilation system for complex snow distributions (SnowAssim). J. Hydrometeor., 9, 9891004, doi:10.1175/2008JHM871.1.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and C. A. Hiemstra, 2011: The changing cryosphere: Pan-Arctic snow trends (1979–2009). J. Climate, 24, 56915712, doi:10.1175/JCLI-D-11-00081.1.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and S. H. Mernild, 2012: Greenland freshwater runoff. Part I: A runoff routing model for glaciated and nonglaciated landscapes (HydroFlow). J. Climate, 25, 59976014, doi:10.1175/JCLI-D-11-00591.1.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , J.-G. Winther, , O. Bruland, , H. Elvehøy, , and K. Sand, 1999: Below surface ice melt on the coastal Antarctic ice sheet. J. Glaciol., 45, 273285, doi:10.3189/002214399793377130.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , R. B. Haehnel, , M. Sturm, , C. A. Hiemstra, , S. Berezovskaya, , and R. D. Tabler, 2007: Simulating complex snow distributions in windy environments using SnowTran-3D. J. Glaciol., 53, 241256.

    • Search Google Scholar
    • Export Citation
  • Marzeion, B., , A. H. Jarosch, , and M. Hofer, 2012: Past and future sea-level change from the surface mass balance of glaciers. Cryosphere, 6, 12951322, doi:10.5194/tc-6-1295-2012.

    • Search Google Scholar
    • Export Citation
  • Meier, M. F., , M. B. Dyurgerov, , U. K. Rick, , S. O’Neel, , W. T. Pfeffer, , R. S. Anderson, , S. P. Anderson, , and A. F. Glazovsky, 2007: Glaciers dominate eustatic sea-level rise in the 21st century. Science, 317, 10641067, doi:10.1126/science.1143906.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , and B. Hasholt, 2006: Climatic control on river discharge simulations, Mittivakkat Glacier catchment, Ammassalik Island, southeast Greenland. Nord. Hydrol., 37, 327346, doi:10.2166/nh.2006.018.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , and G. E. Liston, 2010: The influence of air temperature inversion on snow melt and glacier surface mass-balance simulations, Ammassalik Island, southeast Greenland. J. Appl. Meteor. Climatol., 49, 4767, doi:10.1175/2009JAMC2065.1.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , and G. E. Liston, 2012: Greenland freshwater runoff. Part II: Distribution and trends, 1960–2010. J. Climate, 25, 60156035, doi:10.1175/JCLI-D-11-00592.1.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , G. E. Liston, , B. Hasholt, , and N. T. Knudsen, 2006: Snow distribution and melt modeling for Mittivakkat Glacier, Ammassalik Island, Southeast Greenland. J. Hydrometeor., 7, 808824, doi:10.1175/JHM522.1.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , B. U. Hansen, , B. H. Jakobsen, , and B. Hasholt, 2008a: Climatic conditions at the Mittivakkat Glacier catchment (1994–2006), Ammassalik Island, SE Greenland, and in a 109 years term perspective (1898–2006). Geogr. Tidsskrift, Dan. J. Geogr., 108, 5172, doi:10.1080/00167223.2008.10649574.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , D. L. Kane, , B. U. Hansen, , B. H. Jakobsen, , B. Hasholt, , and N. T. Knudsen, 2008b: Climate, glacier mass balance, and runoff (1993–2005) for the Mittivakkat Glacier catchment, Ammassalik Island, SE Greenland, and in a long term perspective (1898–1993). Hydrol. Res., 39, 239256, doi:10.2166/nh.2008.101.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , G. E. Liston, , C. A. Hiemstra, , K. Steffen, , E. Hanna, , and J. H. Christensen, 2009: Greenland Ice Sheet surface mass-balance modeling and freshwater flux for 2007, and in a 1995–2007 perspective. Hydrol. Processes, 23, 2470–2484, doi:10.1002/hyp.7354.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , G. E. Liston, , C. A. Hiemstra, , and J. H. Christensen, 2010a: Greenland Ice Sheet surface mass-balance modeling in a 131-yr perspective 1950–2080. J. Hydrometeor., 11, 325, doi:10.1175/2009JHM1140.1.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , G. E. Liston, , K. Steffen, , and P. Chylek, 2010b: Meltwater flux and runoff modeling in the ablation area of the Jakobshavn Isbræ, West Greenland. J. Glaciol., 56, 2032, doi:10.3189/002214310791190794.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , G. E. Liston, , C. A. Hiemstra, , J. H. Christensen, , M. Stendel, , and B. Hasholt, 2011a: Surface mass-balance and runoff modeling using HIRHAM4 RCM at Kangerlussuaq (Søndre Strømfjord), West Greenland, 1950–2080. J. Climate, 24, 609–623, doi:10.1175/2010JCLI3560.1.

  • Mernild, S. H., , T. Mote, , and G. E. Liston, 2011b: Greenland Ice Sheet surface melt extent and trends, 1960–2010. J. Glaciol., 57, 621628, doi:10.3189/002214311797409712.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , N. T. Knudsen, , W. H. Lipscomb, , J. C. Yde, , J. K. Malmros, , B. H. Jakobsen, , and B. Hasholt, 2011c: Increasing mass loss from Greenland’s Mittivakkat Gletscher. Cryosphere, 5, 341348, doi:10.5194/tc-5-341-2011.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , J. K. Malmros, , J. C. Yde, , and N. T. Knudsen, 2012: Multi-decadal marine- and land-terminating glacier retreat in the Ammassalik region, southeast Greenland. Cryosphere, 6, 625639, doi:10.5194/tc-6-625-2012.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , W. H. Lipscomb, , D. B. Bahr, , V. Radić, , and M. Zemp, 2013a: Global glacier retreat: A revised assessment of committed mass losses and sampling uncertainties. Cryosphere, 7, 15651577, doi:10.5194/tc-7-1565-2013. [Supplementary material is available at http://www.the-cryosphere.net/7/1565/2013/tc-7-1565-2013-supplement.zip.]

  • Mernild, S. H., , N. T. Knudsen, , M. J. Hoffman, , J. C. Yde, , W. L. Lipscomb, , E. Hanna, , J. K. Malmros, , and R. S. Fausto, 2013b: Volume and velocity changes at Mittivakkat Gletscher, southeast Greenland, 1994–2012. J. Glaciol., 59, 660670, doi:10.3189/2013JoG13J017.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , E. Hanna, , J. C. Yde, , J. Cappelen, , and J. K. Malmros, 2014: Coastal Greenland air temperature extremes and trends 1890–2010: Annual and monthly analysis. Int. J. Climatol., 34, 14721487, doi:10.1002/joc.3777.

    • Search Google Scholar
    • Export Citation
  • Overland, J. E., , M. C. Spillane, , D. B. Percival, , M. Y. Wang, , and H. O. Mofjeld, 2004: Seasonal and regional variation of pan-Arctic surface air temperature over the instrumental record. J. Climate, 17, 32633282, doi:10.1175/1520-0442(2004)017<3263:SARVOP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Radić, V., , and R. Hock, 2010: Regional and global volumes of glaciers derived from statistical upscaling of glacier inventory data. J. Geophys. Res., 115, F01010, doi:10.1029/2009JF001373.

    • Search Google Scholar
    • Export Citation
  • Radić, V., , A. Bliss, , A. C. Beedlow, , R. Hock, , E. Miles, , and J. G. Cogley, 2014: Regional and global projection of twenty-first century glacier mass changes in response to climate scenarios from global climate models. Climate Dyn., 42, 37–58, doi:10.1007/s00382-013-1719-7.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and et al. , 2012: How well are we measuring snow?: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811829, doi:10.1175/BAMS-D-11-00052.1.

    • Search Google Scholar
    • Export Citation
  • Rau, F., , F. Mauz, , S. Vogt, , S. J. S. Khalsa, , and B. Raup, 2005: Illustrated GLIMS Glacier Classification Manual—Glacier Classification Guidance for the GLIMS Glacier Inventory, version 1 (2005-02-10), 36 pp. [Available online at http://www.glims.org/MapsAndDocs/assets/GLIMS_Glacier-Classification-Manual_V1_2005-02-10.pdf.]

  • Rawlins, M. A., , C. J. Willmott, , A. Shiklomanov, , E. Linder, , S. Frolking, , R. B. Lammers, , and C. J. Vorosmarty, 2006: Evaluation of trends in derived snowfall and rainfall across Eurasia and linkages with discharge to the Arctic Ocean. Geophys. Res. Lett., 33, L07403, doi:10.1029/2005GL025231.

    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and et al. , 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, doi:10.1175/JCLI-D-11-00015.1.

    • Search Google Scholar
    • Export Citation
  • Robertson, F. R., , M. G. Bosilovich, , J. Chen, , and T. L. Miller, 2011: The effect of satellite observing system changes on MERRA water and energy fluxes. J. Climate, 24, 51975217, doi:10.1175/2011JCLI4227.1.

    • Search Google Scholar
    • Export Citation
  • Suzuki, K., and et al. , 2011: Impact of land-use changes on snow in a forested region with heavy snowfall in Hokkido, Japan. Hydrol. Sci. J.,56 (3), 443–467, doi:10.1080/02626667.2011.565008.

  • Vaughan, D. G., and et al. , 2013: Observations: Cryosphere. Climate Change 2103: The Physical Science Basis, T. F. Stocker, et al., Cambridge University Press, 317–382.

  • Walsh, J. E., , W. L. Chapman, , V. Romanovsky, , J. H. Christensen, , and M. Stendel, 2008: Global climate model performance over Alaska and Greenland. J. Climate, 21, 61566174, doi:10.1175/2008JCLI2163.1.

    • Search Google Scholar
    • Export Citation
  • Woo, M. K., , R. Heron, , and P. Marsh, 1982: Basal ice in High Arctic snowpacks. Arct. Alp. Res., 14, 251260, doi:10.2307/1551157.

  • WGMS, 1989: World glacier inventory: Status 1988. W. Haeberli et al., Eds., World Glacier Monitoring Service, 458 pp.

  • WGMS, 2012: Fluctuations of glaciers 2005–2010 (Vol. X). M. Zemp et al., Eds., World Glacier Monitoring Service, 336 pp. [Publication based on database version, doi:10.5904/wgms-fog-2012-11.]

  • Yang, D., , B. E. Goodison, , J. R. Metcalfe, , V. S. Golubev, , R. Bates, , T. Pangburn, , and C. L. Hanson, 1998: Accuracy of NWS 8″ standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol., 15, 5468, doi:10.1175/1520-0426(1998)015<0054:AONSNP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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    The Northern Hemisphere (above 25°N). (a) Land-cover distribution, where GIC (land ice; except for the Greenland Ice Sheet) are illustrated in white color, land surface in gray, and ocean and lakes in blue. Also, two specific regions (see black bold squares): southeast Greenland and the Himalayas are illustrated as examples showing the spatial distribution of temperature, precipitation, sublimation and evaporation, and runoff mean and trends (see Fig. 5). (b) Locations of MERRA atmospheric forcing grid points used in the model simulations (black dots; to improve clarity only every other grid point was plotted in x and y, i.e., 25% of the grid points used are shown), with the color background showing topography (m, color increment is not linear). (c) Glacier regions 1 to 15 divided using the regional demarcations defined by Radić and Hock (2010). Each region has a different color and number associated with it: 1) Alaska; 2) western Canada and the United States; 3) Arctic Canada (North); 4) Arctic Canada (South); 5) Greenland; 6) Iceland; 7) Svalbard; 8) Scandinavia; 9) Arctic Russia; 10) North Asia; 11) Central Europe; 12) Caucasus; 13) Central Asia (North); 14) Central Asia (South); and 15) Central Asia (West) (regions 13, 14, and 15 are also known as the High Mountain Asia region). (d) Locations of the 78 mass-balance observed GIC, illustrated with different colors.

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    Midrange comparison between observed GIC elevations and the NOAA GLOBE DEM GIC elevations.

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    Linear relationship between GIC observed and simulated mean annual SMB, where simulated SMB was conducted based on individual GIC precipitation adjustments (black diamonds and black trend line), mean regional GIC precipitation adjustments (white diamonds and large dashed trend line), and leave-one-out cross validation estimated GIC precipitation adjustments (white square and short dashed trend line).

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    Examples of annual simulated GIC SMB, precipitation-adjusted SMB, and observed SMB time series: (a) for three individual GIC located in three different regions (each GIC was picked randomly among the GIC from where observations covers the entire simulation period 1979 to 2009) and (b) on a regional scale from three randomly picked regions: western Canada and the United States (region 2; number of observed GIC n = 11), Scandinavia (region 8, n = 21), and central Europe (region 11, n = 11). Trend lines are shown for both the observed and the precipitation-adjusted SMB time series (all are significant). The r2 values illustrate the correlation between mass balance observations and SnowModel-MERRA simulated mass balance (red color), and SnowModel-MERRA calibrated simulated mass balance (green color).

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    An example of mean annual regional GIC simulated precipitation, runoff, and SMB time series for all GIC in three randomly picked regions: western Canada and the United States (region 2), Scandinavia (region 8), and Central Europe (region 11) for 1979 to 2009 (in Table 2 significant trends for each parameter are highlighted in bold).

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    Latitude vs elevation for mean (1979–2009) and changes (1979–89 minus 1999–2009): (a) MAAT, (b) precipitation, (c) sublimation and evaporation, (d) runoff, and (e) SMB for all individual GIC covered grid cells (n = 543 389). Insignificant changes (in the right column) are highlighted in gray color.

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    An example of the spatial distribution of 30-yr mean (1979–2009) for GIC in the Sermilik Fjord area, southeast Greenland (region 5): (a) MAAT, (b) precipitation, (c) sublimation and evaporation, (d) runoff, and (e) daily time series of air temperature, precipitation, and runoff for the Mittivakkat Gletscher [65°42′N, 37°48′W; see white dot in (a)], the only long-term observed mountain glacier in Greenland. The domain is x = 121 km and y = 135 km, and the distance between Mittivakkat Glacier and Helheim Glacier is ~80 km [Helheim is illustrated with a red dot in (a)]. The black color indicates nonglacier areas, white is the Greenland Ice Sheet, and the dark blue color is fjords and ocean. The overall regional location is illustrated in Fig. 1a.

  • View in gallery

    As in Fig. 7, but for the Karakoram Range, spanning the borders of Pakistan, India, and China. The location of the Siachen (35°25′N, 77°06′E) and Biafo Glaciers (35°52′N, 75°42′E) are illustrated with white circles in (a), the distance between Siachen and Biafo Glaciers is ~140 km, and the overall regional location is provided in Fig. 1a. The domain is x = 500 km and y = 450 km. The black color indicates nonglacier areas.

  • View in gallery

    Regional breakdown of GIC SMB and cumulative SMB time series for 1979–2009, including linear regression (in Table 2 significant SMB conditions are highlighted in bold).

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Northern Hemisphere Glacier and Ice Cap Surface Mass Balance and Contribution to Sea Level Rise

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  • 1 Climate, Ocean, and Sea Ice Modeling Group, Computational Physics and Methods, Los Alamos National Laboratory, Los Alamos, New Mexico, and Glaciology and Climate Change Laboratory, Center for Scientific Studies, Valdivia, Chile
  • | 2 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado
  • | 3 U.S. Army Cold Regions Research and Engineering Laboratory, Fort Wainwright, Alaska
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Abstract

Mass changes and mass contribution to sea level rise from glaciers and ice caps (GIC) are key components of the earth’s changing sea level. GIC surface mass balance (SMB) magnitudes and individual and regional mean conditions and trends (1979–2009) were simulated for all GIC having areas greater or equal to 0.5 km2 in the Northern Hemisphere north of 25°N latitude (excluding the Greenland Ice Sheet). Recent datasets, including the Randolph Glacier Inventory (RGI; v. 2.0), the NOAA Global Land One-km Base Elevation Project (GLOBE), and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) products, together with recent SnowModel developments, allowed relatively high-resolution (1-km horizontal grid; 3-h time step) simulations of GIC surface air temperature, precipitation, sublimation, evaporation, surface runoff, and SMB. Simulated SMB outputs were calibrated against 1422 direct glaciological annual SMB observations of 78 GIC. The overall GIC mean annual and mean summer air temperature, runoff, and SMB loss increased during the simulation period. The cumulative GIC SMB was negative for all regions. The SMB contribution to sea level rise was largest from Alaska and smallest from the Caucasus. On average, the contribution to sea level rise was 0.51 ± 0.16 mm sea level equivalent (SLE) yr−1 for 1979–2009 and ~40% higher 0.71 ± 0.15 mm SLE yr−1 for the last decade, 1999–2009.

Corresponding author address: Dr. Sebastian H. Mernild, Glaciology and Climate Change Laboratory, Center for Scientific Studies/Centro de Estudios Cientificos (CECs), 5110466 Valdivia, Chile. E-mail: smernild@gmail.com

Abstract

Mass changes and mass contribution to sea level rise from glaciers and ice caps (GIC) are key components of the earth’s changing sea level. GIC surface mass balance (SMB) magnitudes and individual and regional mean conditions and trends (1979–2009) were simulated for all GIC having areas greater or equal to 0.5 km2 in the Northern Hemisphere north of 25°N latitude (excluding the Greenland Ice Sheet). Recent datasets, including the Randolph Glacier Inventory (RGI; v. 2.0), the NOAA Global Land One-km Base Elevation Project (GLOBE), and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) products, together with recent SnowModel developments, allowed relatively high-resolution (1-km horizontal grid; 3-h time step) simulations of GIC surface air temperature, precipitation, sublimation, evaporation, surface runoff, and SMB. Simulated SMB outputs were calibrated against 1422 direct glaciological annual SMB observations of 78 GIC. The overall GIC mean annual and mean summer air temperature, runoff, and SMB loss increased during the simulation period. The cumulative GIC SMB was negative for all regions. The SMB contribution to sea level rise was largest from Alaska and smallest from the Caucasus. On average, the contribution to sea level rise was 0.51 ± 0.16 mm sea level equivalent (SLE) yr−1 for 1979–2009 and ~40% higher 0.71 ± 0.15 mm SLE yr−1 for the last decade, 1999–2009.

Corresponding author address: Dr. Sebastian H. Mernild, Glaciology and Climate Change Laboratory, Center for Scientific Studies/Centro de Estudios Cientificos (CECs), 5110466 Valdivia, Chile. E-mail: smernild@gmail.com

1. Introduction

Most glaciers and ice caps (GIC) are shrinking in response to climate change, with observations showing significant declines in mass balances over the last few decades. At present, GIC are large contributors to eustatic sea level rise, and important regulators of water availability around the world (e.g., Kaser et al. 2006; Meier et al. 2007; Cogley 2009, 2012; Hock et al. 2009; Hirabayashi et al. 2010; Leclercq et al. 2011; Marzeion et al. 2012; Gardner et al. 2013; Mernild et al. 2013a).

Direct glaciological GIC surface mass balance (SMB) observations are scarce and sparsely distributed; only ~340 GIC have been observed worldwide, of which nearly 70 have continuous records of 20 years or more (Dyurgerov 2010; WGMS 2012). This is a minor fraction of the earth’s 200 000 or more estimated GIC (Radić and Hock 2010; Arendt et al. 2012), and this substantial gap leaves us with limited information about Northern Hemisphere GIC conditions.

In the search for long-term trends given the absence of broader SMB sampling, upscaling, satellite, and modeling approaches have attempted to estimate global mean GIC mass changes and their associated mass contribution to sea level rise (e.g., Kaser et al. 2006; Hock et al. 2009; Marzeion et al. 2012; Gardner et al. 2013; Mernild et al. 2013a). Kaser et al. (2006) used direct glaciological observations from 1961 to 2004 to illustrate that global mean GIC mass balance conditions were slightly below zero around 1970, but became more negative during the past last quarter century through 2004. They estimated a GIC mass contribution to sea level rise of 0.77 ± 0.26 mm sea level equivalent (SLE) yr−1 (1991–2004), which constituted ~20%–30% of the observed sea level rise for 1993–2005. Mernild et al. (2013a) reported, based on direct glaciological SMB and accumulation-area ratio (AAR: the ratio of the accumulation area to the area of the entire glacier) observations (1971–2010), taking into account the sparse and geographically biased GIC distribution, that GIC (i) were heading toward more negative annual SMB during the first half (2001–05) of the first decade of the twenty-first century [SMB conditions have not been sustained during the most recent 5-yr period (2006–10), in which GIC losses have been more moderate, though still large] and (ii) are committed to additional losses of 38% ± 16% of their volume if the future climate resembles the climate of the past decade. These losses imply a global GIC mean sea level rise of 163 ± 69 mm SLE.

Gardner et al. (2013) used satellite gravimetry, altimetry, and glaciological records to estimate GIC mass changes and mass contribution to sea level rise (2003–09). Their satellite-based estimates were, in general, less negative than glaciological observations, with a global GIC mass budget equal to 0.71 ± 0.08 mm SLE yr−1 (or 29% ± 13% of the observed sea level rise). Hock et al. (2009) employed a simplified monthly global grid-based degree-day approach (1° × 1°) where the model related summer balances to positive degree-day sums, and winter balances to the sum of daily precipitation when temperatures were below freezing; GIC mass changes and associated mass contribution to sea level rise of 0.79 ± 0.34 mm SLE yr−1 were reported for 1961–2004. Marzeion et al. (2012) simulated SMB for individual GIC based on the Randolph Glacier Inventory (RGI v. 1.0, the first globally complete digital GIC database inventory, Arendt et al. 2012) and monthly climate forcing (air temperature and precipitation), where air temperature was used as a proxy for the energy available for melt. Routines adjusting for annual GIC surface area and volume changes were included. For 1902–2009, the simulated global GIC mass loss sum corresponded to 114 ± 5 mm SLE, equal to an average of 1.06 mm SLE yr−1.

These examples of estimated global GIC mass balance conditions and mass loss contributions to sea level rise vary depending on approaches and time periods: 1) data from direct glaciological observations are constrained by undersampling and geographic biases; 2) remote sensing data are limited to short and relatively recent periods; and 3) modeling approaches are problematic because of coarse-scale spatial and temporal resolutions of the physical processes driving GIC changes. Fortunately, modeling capabilities have grown in recent years along with the emergence of remotely sensed datasets such as the RGI. Now it is possible to simulate global GIC surface processes, allowing us to improve our understanding of climate change impacts on global GIC surface conditions associated with latitude, topography, and other regional influences.

In this study, SnowModel (Liston and Elder 2006a,b) was used to simulate Northern Hemisphere GIC surface temperature, precipitation, evaporation, sublimation, surface runoff, and SMB on rescaled 1-km RGI GIC data. SnowModel is a spatially distributed meteorological snow and ice evolution modeling system; in this application it downscaled reanalysis atmospheric forcing data with a 3-h temporal resolution to simulate the GIC snow and ice evolution. Direct glaciological observations for the period 1979–2009 were used to evaluate model performance.

The purpose of this study is to simulate and analyze, at the highest achievable spatial and temporal resolutions, GIC SMB changes and SMB contribution to sea level rise, including variations in GIC surface air temperature, precipitation, sublimation, evaporation, and surface runoff in the Northern Hemisphere for Arctic and glaciated mountain regions [specifically, for the 15 glacier regions north of 25°N, defined by Radić and Hock (2010), not including the Greenland Ice Sheet] (Fig. 1). Our goals entail mapping and understanding the climatic impact on individual and regional GIC conditions from September 1979 through August 2009. We considered SMB losses, including internal processes related to the snowpack such as refreezing of meltwater, neglecting mass loss from dynamic activities, melting from internal glacier ice deformation, melting from changes in the internal drainage system, subglacial geothermal melting, and subglacial frictional melting due to basal ice motion.

Fig. 1.
Fig. 1.

The Northern Hemisphere (above 25°N). (a) Land-cover distribution, where GIC (land ice; except for the Greenland Ice Sheet) are illustrated in white color, land surface in gray, and ocean and lakes in blue. Also, two specific regions (see black bold squares): southeast Greenland and the Himalayas are illustrated as examples showing the spatial distribution of temperature, precipitation, sublimation and evaporation, and runoff mean and trends (see Fig. 5). (b) Locations of MERRA atmospheric forcing grid points used in the model simulations (black dots; to improve clarity only every other grid point was plotted in x and y, i.e., 25% of the grid points used are shown), with the color background showing topography (m, color increment is not linear). (c) Glacier regions 1 to 15 divided using the regional demarcations defined by Radić and Hock (2010). Each region has a different color and number associated with it: 1) Alaska; 2) western Canada and the United States; 3) Arctic Canada (North); 4) Arctic Canada (South); 5) Greenland; 6) Iceland; 7) Svalbard; 8) Scandinavia; 9) Arctic Russia; 10) North Asia; 11) Central Europe; 12) Caucasus; 13) Central Asia (North); 14) Central Asia (South); and 15) Central Asia (West) (regions 13, 14, and 15 are also known as the High Mountain Asia region). (d) Locations of the 78 mass-balance observed GIC, illustrated with different colors.

Citation: Journal of Climate 27, 15; 10.1175/JCLI-D-13-00669.1

2. Model description

a. SnowModel

SnowModel is a spatially distributed snow and ice evolution model driven by meteorological data (Liston and Elder 2006a,b), designed for application in all climates and landscapes where snow and ice are present. SnowModel is an aggregation of six submodels: MicroMet, a quasi-physically based high-resolution meteorological distribution model (Liston and Elder 2006b); Enbal, an energy surface exchange and melt model (Liston 1995; Liston et al. 1999); SnowTran-3D, a surface model for snow redistribution by wind (Liston and Sturm 1998, 2002; Liston et al. 2007); SnowPack-ML, a multilayer snowpack model simulating refreezing of meltwater as a function of snow and ice permeability and cold content (Liston and Mernild 2012); HydroFlow, a gridded linear-reservoir runoff routing model (not used in this study; Liston and Mernild 2012; Mernild and Liston 2012), and SnowAssim, a model available to assimilate field observed datasets (Liston and Hiemstra 2008).

SnowModel downscales and simulates meteorological conditions, surface energy balance, and moisture exchanges including snow and glacier melt, blowing-snow redistribution and sublimation, multilayer heat- and mass-transfer processes within the snow (e.g., snowpack temperature and density evolution, and snowpack ripening), and surface freshwater runoff, where runoff is defined to be the water that flows from the bottom of the simulated snowpack into the supraglacial, englacial, and subglacial regions or to the proglacial drainage system. For the simulations, SnowModel requires temporally varying fields of air temperature, water-equivalent precipitation, relative humidity, wind speed, and wind direction obtained from direct observations within/near the model simulation domain, and/or from atmospheric models (e.g., reanalysis or general circulation model data) within/near the domain. Further, spatially distributed time-invariant fields of topography and land cover are required [for further and more detailed information about SnowModel see, e.g., Mernild and Liston (2010) and Mernild et al. (2006, 2010a,b, 2011a,b)]. SnowModel, including its submodels, has previously been tested and used in Alaska, Colorado, Wyoming, Arctic Canada, Greenland, Norway (Svalbard), and northern Japan, comparing simulated snow accumulation/distribution and snow and glacier ice ablation and runoff processes with observations (e.g., Hiemstra et al. 2002, 2006; Hasholt et al. 2003; Bruland et al. 2004; Liston and Hiemstra 2008, 2011; Suzuki et al. 2011).

b. Model configuration, simulation domain, and meteorological forcing

SnowModel GIC surface processes, including precipitation, snow accumulation, sublimation, evaporation, runoff, and SMB, were simulated for the 30-yr period, September 1979 through August 2009, using a 3-h time step, covering a 7235 km × 7235 km domain centered on the North Pole, having a spherical area of ~41.1 × 106 km2 (Fig. 1). In these calculations the mass balance year was assumed to be 1 September–31 August. Topography was obtained from the National Oceanic and Atmospheric Administration (NOAA) Global Land One-km Base Elevation Project (GLOBE) (Hastings et al. 1999), which provided a 1-km digital elevation model (DEM) for the domain (Fig. 1b). The GIC cover distribution was obtained from the RGI v. 2.0 (Figs. 1a,c); GIC from the RGI polygons were resampled to 1-km grid increments, and included in the glacier-cover file if the individual grid cells were covered by 50% or more of glacier ice, giving 543 389 GIC covered grid cells north of 25°N (Fig. 1a). Most of these GIC grids (~75%) were located in the Arctic region (regions 1–10) and ~25% in the high mountain regions (regions 11–15). The GIC cover ranges in latitude from 27.1° to 83.6°N, and the topography from sea level to 8557 m above mean sea level (MSL), where peak elevations were located in the border region between Nepal and Tibet (region 15: Central Asia, West; Fig. 1c) in the area called High Mountain Asia (HMA).

Using a 1-km grid increment means that minor GIC (<0.5 km2) were ignored from the simulations, and therefore one might expect an underestimation of GIC area. To assess the effect of resampling, the GIC area cover for the Alaska region was compared using 1-km (90 793 km2) and 100-m resolution (90 637 km2) data. Going to a coarser resolution for Alaska resulted in a GIC area overestimate of 156 km2. Similar analyses could yield either slight over- or underestimates for the other glacierized regions, assuming an uncertainty in GIC area of ~0.2%, similar to the one estimated for Alaska.

Atmospheric forcings were provided by the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) products (Bosilovich 2008; Bosilovich et al. 2008, 2011; Cullather and Bosilovich 2011; Rienecker et al. 2011; Robertson et al. 2011). MERRA is a reanalysis dataset that has the specific goal of improving the representation of water cycle processes and features while taking advantage of modern satellite-era datasets (Liston and Hiemstra 2011), and was used for this study due to its high resolution. The MERRA dataset used in this study covers the period 1979 through 2009 on an hourly time step and on a ⅔° longitude by ½° latitude grid.

For the simulations described herein, SnowModel was modified to only include MERRA grid points for the Northern Hemisphere grid cells that included glacier ice. This substantially improved computational efficiency and explains the discontinuous distribution of atmospheric data points illustrated in Fig. 1b. Further, to improve the computational efficiency and still resolve the diurnal cycle and the associated energy-related processes, the hourly MERRA time steps of 10-m air temperature, specific humidity, u and υ wind components, and precipitation were aggregated to 3-hourly values (Liston and Hiemstra 2011). For the incoming solar radiation, MicroMet (Liston and Elder 2006b) used its submodels to generate these fields, considering the influence of cloud cover, topographic slope, and aspect on incoming solar radiation. The MicroMet solar radiation model was compared against observations provided by the NASA Cold Land Processes Field Experiment (CLPX) on an hourly time scale, yielding an r2 value (square of the linear correlation coefficient) of 0.87 for the hourly data, and captured the observed seasonal variations (Liston and Elder 2006b). SnowModel ingested MERRA and MicroMet meteorological forcing variables and it simulated the time evolution and spatial distribution of energy and water fluxes, incoming solar radiation, incoming longwave radiation, emitted longwave radiation, sensible heat flux, latent heat flux, conductive heat flux, albedo, surface (skin) temperature, precipitation, snow depth, sublimation and evaporation, snow and ice surface melt, runoff, and GIC surface mass balance terms. The 3-hourly SnowModel simulated parameters were aggregated (averaged or summed, depending on the variable) to daily, annual, or decadal values over the 30-yr period for spatial and temporal analyses.

3. Datasets and calibration

a. Observed GIC dataset

Direct glaciological observations were used for calibration of simulated GIC SMB conditions. In Mernild et al. (2013a; see their supplementary material) direct glaciological observations from 105 GIC in the Northern Hemisphere north of 25°N that were greater than 1.0 km2 (equal to the size of the grid increment) [updated from Dyurgerov and Meier (2005), the World Glacier Monitoring Service database (WGMS 2012), and directly from principal investigators] were listed covering the period 1971–2010. A geographical comparison between the 105 GIC latitudinal and longitudinal grids, and the presence of the GIC in the RGI v. 2.0 (using a buffer distance of 1 km), found that the locations of 78 out of 105 GIC could be identified and confirmed in the resampled 1-km grid RGI dataset (for the GIC not identified, 90% were <5.0 km2). These 78 GIC (including their 1422 direct glaciological annual SMB observations for 1979–2009) were used for calibration (see Fig. 1d) and had the following geographic distribution: 3 GIC (in region 1), 11 (2), 3 (3), 1 (5), 10 (6), 6 (7), 21 (8), 1 (9), 11 (11), 2 (12), 6 (13), 2 (14), and 1 (15). No available (long-term) direct glaciological SMB observations were available for Arctic Canada South (region 4) and North Asia (region 10) for model calibration: for example, for region 4 only short-term GIC SMB observations occurred (in 1982–84 from the Hidden, Minaret, Abraham, and Superguksoak glaciers, and in 1980, 1982, and 1984 from Barnes Ice Cap Southern Dome Northern slope) (G. Cogley 2014, personal communication). Approximately 30% of the observed GIC time series had records for all 30 years, and approximately 70% had uninterrupted records of 10 years or more, indicating a variation in the annual number of SMB observations from 35–39 for 1979–87 to 44–53 for 1988–2009.

For all 78 GIC the observed midrange elevation was calculated: Zmid = (ZmaxZmin)/2 (WGMS 1989), where Zmax is the maximum GIC elevation and Zmin the minimum elevation, and compared against Zmid estimated from the SnowModel topographic grid [i.e., NOAA GLOBE digital elevation model (DEM), Hastings et al. 1999]. In Fig. 2, a linear regression illustrates a sufficient r2 value of 0.98 between observed GIC Zmid and SnowModel DEM GIC Zmid, with a mean difference of 75 m MSL (with the observed mean value being lower than the SnowModel DEM) and a rms error (rmse) of 114 m MSL. Based on this correlation and the rmse in Zmid between the 78 GIC (varying in elevation from around sea level to above 5000 m MSL) and the SnowModel DEM, we are confident that our GIC elevation estimates are appropriate for GIC SMB simulations.

Fig. 2.
Fig. 2.

Midrange comparison between observed GIC elevations and the NOAA GLOBE DEM GIC elevations.

Citation: Journal of Climate 27, 15; 10.1175/JCLI-D-13-00669.1

b. Calibration dataset

Estimation of precipitation conditions in mountainous regions is one of the greatest challenges in mountain hydrology (e.g., Bhutiyani 1999). Bosilovich et al. (2008) analyzed precipitation outputs from an early version of the MERRA reanalysis system and concluded that MERRA precipitation fields were an improvement over the previous reanalyses. However, since the major uncertainties in GIC SMB calculations are related to uncertainties in observed and estimated solid precipitation and the associated snow accumulation processes (e.g., Woo et al. 1982; Yang et al. 1998; Allerup et al. 1998, 2000a,b; Liston and Sturm 2002, 2004; Rasmussen et al. 2012) rather than in ablation processes, GIC SMB adjustments were made to the SnowModel precipitation inputs (i.e., the MERRA precipitation values) to correct identified SMB biases. Initial simulations used the original MERRA precipitation, where simulated GIC SMB time series were compared with observed GIC SMB time series for each of the 78 GIC. Because of the difference in SMB, we calculated a mean precipitation adjustment factor based on each individual GIC that, when multiplied by the original MERRA precipitation, yielded a new simulated GIC SMB that was roughly similar to the observed GIC SMB [similar precipitation adjustment/assimilation procedures have been used sufficient in Mernild et al. (2006) for simulating SMB conditions on a glacier in east Greenland and in Liston and Hiemstra (2008)]. In Fig. 3, linear regressions between observed and simulated mean annual GIC SMB based on 1) individual GIC precipitation adjustments, 2) mean regional precipitation adjustments, and 3) precipitation adjustments calculated from the leave-one-out cross validation are illustrated. For the regression between observed GIC and simulated GIC based on individual precipitation adjustments the r2 value and rmse were 0.99 and 0.01 m w.e. (water equivalent), respectively, and based on mean regional precipitation adjustments 0.64 and 0.40 m w.e. Applying the mean regional precipitation adjustment factors will then lead to greater model errors compared to using individual GIC precipitation adjustment factors. However, since the regional mean precipitation adjustment factors cannot be considered independent of the observed SMB values, a leave-one-out cross validation was conducted for each of the regions that have n ≥ 2 GICs with SMB observations (regions 1–3, 6–8, and 11–14). In Fig. 3, the linear regressions between observed and simulated mean annual GIC SMB (estimated from the leave-one-out cross validation) are illustrated, showing an r2 value of 0.61 and rmse of 0.43 m w.e., similar to the regression estimated from the mean regional precipitation adjustments. Given the latter comparison, we are confident that SnowModel is appropriate for simulating annual mean (able to account for roughly 60% of the variance in SMB) and 30-yr mean GIC SMB conditions for observed and nonobserved GIC located in the 13 (out of 15) regions with GIC SMB observations (even though model routines for evaporation, sublimation, and runoff were not validated individually against observations, but only restricted to a SMB validation).

Fig. 3.
Fig. 3.

Linear relationship between GIC observed and simulated mean annual SMB, where simulated SMB was conducted based on individual GIC precipitation adjustments (black diamonds and black trend line), mean regional GIC precipitation adjustments (white diamonds and large dashed trend line), and leave-one-out cross validation estimated GIC precipitation adjustments (white square and short dashed trend line).

Citation: Journal of Climate 27, 15; 10.1175/JCLI-D-13-00669.1

Using these individual GIC precipitation adjustment factors, regional mean adjustment factors were calculated by averaging the factors contained within each specific region (Table 1). These regional precipitation correction factors were used in each corresponding region and the corrected precipitation for the SnowModel GIC SMB simulations presented herein. The mean regional precipitation adjustment factors averaged 0.61 ± 0.22 (Table 1), indicating an average initial overestimation (before adjustment) of SnowModel-MERRA simulated GIC SMB. In Kotlarski et al. (2010), uncorrected gridded ALP-IMP [http://www.zamg.ac.at/ALP-IMP/; Climatic Research Unit (CRU)] data annual precipitation showed a positive bias of 17%. SnowModel–MERRA overestimations in GIC SMB might be attributed to general precipitation lapse rates missing specific local/regional variability in precipitation lapse rates for mountain regions where GIC are located, overestimating (underestimating) the fraction of snow (rain), where the fraction of precipitation being rainfall was not contributing to the mass balance. For regions with one or less observed GIC SMB time series (e.g., Arctic Canada South, Greenland, and North Asia), a surrogate was used for calibration. For example, for the regions Arctic Canada South and North Asia adjustment values from Arctic Canada North and Arctic Russia, respectively, were used because of the climatically similar conditions. For Greenland GIC, because of the climatic variability and teleconnection between east and west Greenland (e.g., Box 2002; Hanna et al. 2013; Mernild et al. 2014), precipitation adjustment values from Greenland’s surrounding regions (Arctic Canada North, Arctic Canada South, Svalbard, and Iceland) were used. As an example, three randomly chosen individual GIC time series are shown (Fig. 4a) that include Graasubreen (region 8), Garabashi Glacier (region 12), and Leviy Aktru (region 13) covering >25 years of SMB observations compared with uncalibrated and calibrated simulated SMB time series. Mean regional SMB time series are shown (Fig. 4b) for observed GIC, and also for three randomly chosen regions (western Canada and the United States, Scandinavia, and Central Europe). Variability occurs between observed and simulated SMB time series and the observed and calibrated SMB time series (Fig. 4). For example, SnowModel was able to account for 55% of the variance in SMB for Graasubreen (Fig. 4a), also after cross validation. For Garabashi Glacier and Leviy Aktru, for example, SnowModel was able after cross validation to account for 42% and 33% of the variance in SMB, respectively (Fig. 4a). In addition, the 30-yr average SMB (Fig. 4) and their trend lines are significant in these comparisons (p < 0.05, based on a linear regression t test).

Table 1.

Regional breakdown of mean precipitation adjustment factors and standard deviations for each of the 15 regions.

Table 1.
Fig. 4.
Fig. 4.

Examples of annual simulated GIC SMB, precipitation-adjusted SMB, and observed SMB time series: (a) for three individual GIC located in three different regions (each GIC was picked randomly among the GIC from where observations covers the entire simulation period 1979 to 2009) and (b) on a regional scale from three randomly picked regions: western Canada and the United States (region 2; number of observed GIC n = 11), Scandinavia (region 8, n = 21), and central Europe (region 11, n = 11). Trend lines are shown for both the observed and the precipitation-adjusted SMB time series (all are significant). The r2 values illustrate the correlation between mass balance observations and SnowModel-MERRA simulated mass balance (red color), and SnowModel-MERRA calibrated simulated mass balance (green color).

Citation: Journal of Climate 27, 15; 10.1175/JCLI-D-13-00669.1

4. GIC surface water balance components

The yearly GIC water mass balance equation [Eq. (1)] can be described as follows from the hydrological method:
e1
where P is precipitation input from snow and rain (and possible condensation), E is evaporation (liquid to gas phase flux of water), Su is sublimation (solid to gas phase with no intermediate liquid stage), R is surface runoff, ΔS is change in storage (ΔS is also referred to as SMB) from changes in glacier storage and snowpack storage. The parameter η is the water balance discrepancy (error). The error term should be 0 (or small if the components P, E, Su, R, and ΔS have been determined accurately. Here, ΔS is calculated as the residual value.

5. Results and discussion

a. Decadal GIC conditions

Table 2 presents regional breakdowns for all 15 glacierized regions of simulated mean GIC surface air temperature [mean annual air temperature (MAAT)] and mean summer air temperature [June–August (JJA)] and surface hydrological conditions (precipitation, sublimation, evaporation, surface runoff, and SMB) on decadal scales and for the entire simulation period. Mass gain (accumulation) is calculated as positive and mass loss (ablation) is considered negative for the GIC. The 30-yr domain average (1979–2009) simulated GIC MAAT was −10.3° ± 0.1°C. [Here and below, the standard errors correspond to a 95% confidence interval, or 1.96 times the standard error. The errors are random and normally distributed; therefore, the standard error propagation can be used. For calculation of standard error, see Mernild et al. (2013a; see their supplemental material, sheet F).] This varies regionally from −3.9° ± 0.4°C in Scandinavia to −19.3° ± 0.3°C in Arctic Canada (northern and southern regions) (Table 2). For the Arctic GIC area (regions 1–10) the change in simulated MAAT from the first decade (1979–89) to the last decade (1999–2009) was greater than elsewhere, where Svalbard, Greenland, and Arctic Russia faced the highest MAAT changes of 1.5°C (equal to a significant linear average trend of 0.06°C yr−1 from 1979 to 2009), 1.2°C (0.06; significant), and 1.2°C (0.05; significant), respectively. Regions such as western Canada and the United States and the Caucasus all faced the smallest MAAT change of 0.3°C (0.02°C yr−1; insignificant), which is significantly below the mean Northern Hemisphere GIC air temperature increase of 0.8° ± 0.4°C (0.04°C yr−1; significant) (Table 2). Besides MAAT we also looked into mean JJA air temperature conditions. Regarding mean JJA air temperature the 30-yr domain average was −0.4° ± 0.1°C, and varied from 3.9° ± 0.4°C in Scandinavia to −4.0° ± 0.2°C in Greenland (Table 2). The change in simulated mean JJA air temperature from the first decade to the last decade varied from 0.2°C (0.01°C yr−1; insignificant) in Central Asia North to 1.0°C (0.05°C yr−1; significant) in Arctic Canada North. In general for the domain the linear average trend for JJA (0.03°C yr−1; significant) (1979–2009) was lower than the trend for MAAT (0.04°C yr−1; significant), indicating greater changes in air temperature during winter compared with summer (Table 2). These variations in air temperatures show fidelity with air temperature anomaly patterns (1979–2004) identified by Overland et al. (2004), and follow the general surface air temperature trend, where temperature rise during recent decades has been more pronounced at high latitudes (e.g., Hansen et al. 2010) and during winter (e.g., Hanna et al. 2012).

Table 2.

Regional breakdown of surface GIC conditions: mean and standard error (error ranges correspond to a 95% confidence interval) for annual air temperature (MAAT), June–August (JJA) mean air temperatures, precipitation (P), sublimation and evaporation (Su+E), runoff (R), and mass balance (SMB) for all simulated GIC within each region from 1979 through 2009, and on the decadal scale. Significant trends (p < 0.05) for the 1979–2009 period are highlighted in bold.

Table 2.

Regarding the SnowModel-adjusted simulated GIC precipitation conditions, the 30-yr domain average (1979–2009) was 1.64 ± 0.03 m w.e., varying regionally, on average, from 0.26 ± 0.01 m w.e. in Arctic Russia to 3.68 ± 0.14 m w.e. in western Canada and the United States. Regional patterns were more diverse than the regional GIC air temperature patterns, showing both positive and negative regional GIC precipitation trends between the first and last decades (Table 2). At higher elevations and coastal mountain ranges adjacent to warmer ocean waters (e.g., western Canada and the United States, and Scandinavia), annual precipitation was, in general, higher than interior continental GIC (Table 2). On a mean regional scale between the first and last decades, simulated GIC precipitation conditions varied between a decrease of −0.65 m w.e. (−0.032 m w.e. yr−1; significant) for central Europe to a regional increase of 0.16 m w.e. (0.002 m w.e. yr−1; insignificant) for Alaska (Table 2), highlighting regional differences influenced by meteorological conditions.

General circulation models (GCMs) have often been used to address precipitation questions for past, present, and future conditions (e.g., Walsh et al. 2008; Finnis et al. 2009a,b). These studies generally find that higher temperatures lead to increases in precipitation. However, in this study the GIC domain-average simulated precipitation change between the first and last decades was −0.05 m w.e. (<−0.001 m w.e. yr−1; insignificant) (Table 2). A decreasing trend in precipitation is not uncommon in (site specific) observations; for example, Hinzman et al. (2005) reported long-term precipitation trends of −1.29 cm decade−1 for Barrow, Alaska, where Rawlins et al. (2006) found snowfall trends of −0.3 cm decade−1 for the former Soviet Union. In general, MERRA showed remarkably similar precipitation trends with other reanalyses in comparison with the Global Precipitation Climatology Project (e.g., Liston and Hiemstra 2011).

SnowModel-simulated GIC 30-yr domain-average sublimation and evaporation was 0.12 ± 0.00 m w.e., without significant changes between the first and last decades (Table 2). Regarding GIC sublimation and evaporation loss, one cluster of regions has significantly higher values than others. For the cold and dry high mountain regions in Central Asia (all regions)—also known as High Mountain Asia (the largest glacierized region outside the Arctic and Antarctica)—sublimation and evaporation varied on a regional scale, averaging from 0.27 ± 0.01 to 0.33 ± 0.00 m w.e. (1979–2009). Owing to a combination of relatively cold and dry climatic conditions in HMA (e.g., Benn and Evans 2002), the loss from sublimation was higher compared with less cold and dry regions in the simulation domain. For the GIC regions outside HMA, the 30-yr average GIC sublimation and evaporation loss varied between 0.03 ± 0.00 and 0.16 ± 0.00 m w.e. (Table 2).

Regarding GIC runoff, SnowModel-simulated 30-yr domain-average runoff was 1.90 ± 0.06 m w.e. (Table 2). On a decadal time scale, runoff increased on average for all 15 regions throughout the simulation period from 1.73 ± 0.04 m w.e. in 1979–89 to 2.06 ± 0.07 m w.e. in 1999–2009, equal to a trend of 0.016 m w.e. yr−1 (significant) (Table 2). Overall, for the 15 individual regions simulated, GIC runoff rose significantly for 10 regions and insignificantly for western Canada and the United States, Arctic Russia, North Asia, Central Europe, and Central Asia South (Table 2). The 30-yr mean regional GIC runoff varied from 0.57 ± 0.06 m w.e. in Arctic Canada North to 3.91 ± 0.16 m w.e. in western Canada and the United States, illustrating a heterogeneous regional GIC runoff distribution for the Northern Hemisphere.

The GIC SMB [Eq. (1)] patterns illustrated in Table 2 show a 30-yr domain-average loss of −0.38 ± 0.07 m w.e., which was smallest for the first decade (−0.19 ± 0.03 m w.e.) and largest for the last decade (−0.57 ± 0.11 m w.e.), equal to a trend of 0.018 m w.e. yr−1 (significant). Variability among regions yielded an average SMB ranging from −0.64 ± 0.10 m w.e. (1979–2009) in Central Asia South to −0.15 ± 0.07 m w.e. (1979–2009) in Arctic Canada North. Further, annual time series of the regional-average GIC surface hydrological conditions (precipitation, runoff, and SMB) are illustrated in Fig. 5 for three randomly chosen regions: western Canada and the United States, Scandinavia, and central Europe. For all three regions the linear trend in annual GIC SMB and precipitation decreased, while runoff increased (1979–2009). As one would expect, all three regions exhibit interplay among the variables. For example, the annual variability in regional-averaged runoff can be related to the annual precipitation conditions, since snowfall (end-of-winter snow accumulation) is negatively correlated with runoff. This link has been confirmed in earlier studies by, for example, Hanna et al. (2008) and Mernild et al. (2009). This indicates that years with low GIC surface runoff were synchronous with years of relatively high end-of-winter snow accumulation. More surface meltwater was retained in a thicker snowpack, reducing GIC runoff; however, maritime regions with high snowfall might have high summer runoff, as the regions in general are warm and wet. For Alaska, Greenland, and North Asia, a positive correlation was present between surface runoff and end-of-winter snow accumulation. Given the important role snow plays in GIC surface energy and moisture budgets, quantifying changes and variations in snow cover (e.g., thickness, duration, albedo, and timing; all components of the natural system simulated and accounted for by SnowModel) are essential for a comprehensive understanding of GIC surface runoff and mass balance changes. Therefore, we assume this modeling assessment to be a step forward compared with earlier GIC modeling studies (e.g., Hock et al. 2009; Marzeion et al. 2012), in the sense that the detailed resolution presented herein includes the diurnal cycle and its associated energy-related processes, which are important for a more detailed physical understanding of GIC runoff and end-of-winter snow accumulation and subsequent SMB conditions.

Fig. 5.
Fig. 5.

An example of mean annual regional GIC simulated precipitation, runoff, and SMB time series for all GIC in three randomly picked regions: western Canada and the United States (region 2), Scandinavia (region 8), and Central Europe (region 11) for 1979 to 2009 (in Table 2 significant trends for each parameter are highlighted in bold).

Citation: Journal of Climate 27, 15; 10.1175/JCLI-D-13-00669.1

b. Spatial latitude and elevation distribution

From Fig. 1, GIC are unevenly distributed across the Northern Hemisphere and have been divided into 15 glacierized regions, located in both maritime and continental climate conditions. Even though this climate variability exists among regions, a pronounced pattern in the location—that is, in the latitude–elevation distribution—of GIC occurred, where GIC at high latitudes, in general, were located at relatively low elevations, and vice versa for low latitudes (Fig. 6).

Fig. 6.
Fig. 6.

Latitude vs elevation for mean (1979–2009) and changes (1979–89 minus 1999–2009): (a) MAAT, (b) precipitation, (c) sublimation and evaporation, (d) runoff, and (e) SMB for all individual GIC covered grid cells (n = 543 389). Insignificant changes (in the right column) are highlighted in gray color.

Citation: Journal of Climate 27, 15; 10.1175/JCLI-D-13-00669.1

The latitude–elevation distributed GIC MAAT (1979–2009) (Fig. 6a) showed a characteristic diagonal pattern, including both vertical and horizontal temperature gradients. Changes in simulated MAAT from the first decade to the last decade, highlighted on average by the increasing MAAT for all GIC, were most pronounced at high latitudes, as expected from observations (Hansen et al. 2010) (insignificant changes are shown in gray color). Enclaves of decreasing GIC MAAT occurred (Fig. 6a), for example, at the Karakoram Range (~35°–38°N), Kamchatka (~58°–60°N), the southern part of Alaska (~53°–60°N), and for several Arctic areas.

In addition to surface air temperature, precipitation (snow accumulation) is a key climate system variable that is important for understanding GIC SMB conditions. The 30-yr mean annual latitude–elevation distributed GIC precipitation sum (Fig. 6b) indicates a more heterogeneous precipitation pattern for GIC (compared to the diagonal MAAT pattern) that is likely influenced by topography, orography, distance from large bodies of water, and climate conditions. This is especially true of GIC located in southeast Alaska and western Canada and the United States (~51°–61°N), and the eastern and southern part of the region Central Asia (West) in HMA (28°–29°N), which had remarkably higher mean annual precipitation sums compared to GIC at equivalent latitudes. Changes in mean annual GIC precipitation totals from the first to last decades are heterogeneously distributed as well, indicating clusters of GIC facing both decreasing and increasing precipitation trends (Fig. 6b). Clusters of GIC where the precipitation increases were greatest were found in parts of southeast Alaska and western Canada and the United States, the eastern and southern part of the region Central Asia (West), Svalbard (around 78°–79°N), and in the Karakoram Range. On the other hand, clusters where precipitation decreased at a maximum rate were found in Central Asia, and in the eastern and southern part of the region Central Asia (West) (Fig. 6b).

The 30-yr mean annual latitude–elevation distributed GIC sublimation and evaporation sum during 1979–2009 are illustrated in Fig. 6c. Specifically for HMA, the simulated sublimation and evaporation sum was high, due to the cold and dry climatic conditions (see also section 5a for further information). The greatest changes in annual GIC sublimation and evaporation sum (between the first and last decades) occurred as well for HMA, where on average it changed −0.01 m w.e. for HMA, equal to a mean trend of <−0.001 m w.e. yr−1 (Table 2). For GIC outside HMA, changes in sublimation and evaporation were lower and almost in the same range (Fig. 6c).

From a hydrological perspective, GIC represents water storage available for river runoff when melting in spring and summer. Runoff is an important parameter for water availability around the world (e.g., Huss 2011; Immerzeel et al. 2012) and for addressing water resource issues associated with drinking water, irrigation, and hydropower production. In regions where snow and ice melt are key components of the hydrologic cycle, around 30%–40% of the annual runoff can be explained by GIC net mass loss, as observed in southeast Greenland (Mernild and Hasholt 2006; Liston and Mernild 2012). The latitude–elevation distributed 30-yr mean annual GIC runoff sum (1979–2009) is heterogeneously distributed (Fig. 6d), where two GIC clusters (25°–28°N and 50°–62°N) had maximum simulated runoff values (>3.5 m w.e.); these are located in the same areas where maximum precipitation occurred. For latitudes between the two maximum GIC runoff clusters, simulated runoff seems to be roughly influenced by the characteristic latitude–elevation diagonal temperature patterns, except for the area located higher than ~70°N. Here, probably due to the relatively low temperatures—MAAT is typically below freezing and the ablation period is relatively short—changes in runoff seem less pronounced in response to changes in elevation, compared to lower latitudes. Regarding changes in runoff from the first decade to the last decade, the latitude–elevation distributed runoff indicates clusters of both increasing and decreasing GIC runoff.

These GIC simulations allow us to map and analyze MAAT, precipitation, sublimation and evaporation, and runoff conditions and therefore [based on Eq. (1)] to estimate GIC SMB conditions in the Northern Hemisphere at higher resolution and with more physical realism than ever done before. The simulated GIC latitude–elevation distributed SMB (1979–2009) (Fig. 6e) shows clusters of maximum mass gain in southeast Alaska, western Canada and the United States, and Svalbard. For HMA, both maximum SMB gain and loss were simulated; this is also true of the Karakoram Range, where observed mass gain for the early twenty-first century has been confirmed by Gardelle et al. (2012). Regarding changes in SMB from the first to the last decades, a heterogeneous latitude–elevation pattern occurred with enclaves of maximum loss and gain. Clusters of maximum loss were located in HMA and the western United States and Canada, while clusters of maximum gain were located in HMA, including in the Karakoram Range. Behind these heterogeneous conditions in SMB change (Fig. 6e), a shift occurred toward a lower (higher) percentage of decreasing (increasing) GIC SMB trends (Table 3). Increasing annual SMB trends occurred for 43% of the domain in 1979–89 and 60% in 1999–2009 while decreasing trends occurred for 57% in 1979–89 and 40% in 1999–2009. Roughly 10% of all the increasing and decreasing trends were significant.

Table 3.

The percentage distribution of GIC SMB decreasing and increasing annual trends for 1979–89, 1989–99, and 1999–2009.

Table 3.

c. Examples of spatial GIC simulations

SnowModel simulated 30-yr mean and trend distribution of GIC air temperature and surface hydrological conditions are illustrated for two areas—the Sermilik Fjord area in southeast Greenland (Fig. 7, including the Mittivakkat Gletscher, the longest mass balance observed glacier in Greenland) and the eastern Karakoram Range in the Himalayas (Fig. 8; an area containing some of the longest valley glaciers, the Siachen and Biafo Glaciers, outside the Arctic). The Mittivakkat Gletscher was used as an independent verification of the simulated results. For the Mittivakkat Gletscher the simulated 30-yr MAAT was −1.6° ± 0.2°C for 1979–2009 and −1.3° ± 0.2°C for 1994–2009 (Fig. 7), which for the latter period was indistinguishable (97.5% quantile, based on the null hypothesis) from observed surface MAAT 1994–2009 (MAAT data obtained from two adjacent meteorological stations, Station Coast and Station Nunatak, which were merged) of −1.5° ± 0.1°C [for location of the stations, see Mernild et al. (2008a) and Hanna et al. (2012)]. The simulated (adjusted) 8-yr average Station Nunatak precipitation (1999–2006) was 1.56 ± 0.19 m w.e. and indistinguishable from observed precipitation, averaging 1.85 ± 0.18 m w.e. (97.5% quantile, based on the null hypothesis) (Mernild et al. 2008b). (Nunatak is a meteorological station operated by University of Copenhagen, located at 515 m MSL close to the northwestern margin of Mittivakkat and at the equilibrium-line altitude.) Mittivakkat Gletscher simulated 30-yr average sublimation and evaporation, and runoff, were 0.07 ± 0.00 and 1.56 ± 0.15 m w.e. (Figs. 7c,d), respectively. For Mittivakkat, during the 1995–2009 SMB observation period the mean annual observed SMB was −0.76 ± 0.32 m w.e. (Mernild et al. 2011a,c, 2013b), which was indistinguishable (97.5% quantile; based on the null hypothesis) from the SnowModel-simulated Mittivakkat Gletscher mean annual SMB of −0.64 ± 0.24 m w.e. Overall for the Mittivakkat Gletscher, precipitation increased an average of 9 mm w.e. yr−1 (significant), MAAT 0.05°C yr−1 (significant), and runoff 10 mm w.e. yr−1 (insignificant) (Fig. 7e).

Fig. 7.
Fig. 7.

An example of the spatial distribution of 30-yr mean (1979–2009) for GIC in the Sermilik Fjord area, southeast Greenland (region 5): (a) MAAT, (b) precipitation, (c) sublimation and evaporation, (d) runoff, and (e) daily time series of air temperature, precipitation, and runoff for the Mittivakkat Gletscher [65°42′N, 37°48′W; see white dot in (a)], the only long-term observed mountain glacier in Greenland. The domain is x = 121 km and y = 135 km, and the distance between Mittivakkat Glacier and Helheim Glacier is ~80 km [Helheim is illustrated with a red dot in (a)]. The black color indicates nonglacier areas, white is the Greenland Ice Sheet, and the dark blue color is fjords and ocean. The overall regional location is illustrated in Fig. 1a.

Citation: Journal of Climate 27, 15; 10.1175/JCLI-D-13-00669.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for the Karakoram Range, spanning the borders of Pakistan, India, and China. The location of the Siachen (35°25′N, 77°06′E) and Biafo Glaciers (35°52′N, 75°42′E) are illustrated with white circles in (a), the distance between Siachen and Biafo Glaciers is ~140 km, and the overall regional location is provided in Fig. 1a. The domain is x = 500 km and y = 450 km. The black color indicates nonglacier areas.

Citation: Journal of Climate 27, 15; 10.1175/JCLI-D-13-00669.1

In Figs. 8a–d, the spatial variability is also shown as an example from the eastern Karakoram Range. It is clear from the simulations that in this steep mountainous region the relatively low-elevation GIC are facing the highest MAAT and runoff along with the lowest snowfall, and that the opposite is true for the relatively high-elevation GIC (1979–2009). This is expected, but we also note that GIC in this area are experiencing a relatively high mean annual ablation rate from sublimation and evaporation of more than 0.18 m w.e., equivalent to ~40% of the annual precipitation. This ratio is in the same range as the sublimation/evaporation to precipitation ratio found in many regions of the Arctic (Liston and Sturm 1998, 2002, 2004).

d. Regional SMB contribution to sea level rise

Sea level rise is dominated by melt-related mass losses from GIC and ice sheets, ice sheet and glacier calving, and ocean thermal expansion (e.g., Cogley 2012; Marzeion et al. 2012; Gardner et al. 2013; Vaughan et al. 2013; Mernild et al. 2013a). In Table 4, the regional SMB of the Northern Hemisphere GIC budget is displayed for 1979–2009 and 1999–2009. Here we emphasize the acceleration of the GIC SMB contribution to sea level rise for the first decade of the new millennium (2000–09), which was the warmest observed decade both globally and in Greenland (Hansen et al. 2010; Mernild et al. 2014). Our simulations show a regional variability in SMB (insignificant linear trends occurred for regions 9, 10, and 14; elsewhere the trends were significant) and a cumulative negative SMB for all regions (Fig. 9), with the largest negative cumulative SMB in High Mountain Asia (especially from Central Asia South, with −19.2 m w.e.) and Central Europe (−14.8 m w.e.). In contrast, the smallest negative cumulative SMB loss was from Arctic Russia (−6.0 m w.e.) and Arctic Canada North (−4.5 m w.e.). When integrating the regional GIC SMB over the regional GIC areas, the annual overall Northern Hemisphere GIC volumetric SMB budget for all 15 individual glacierized regions aggregated −184.3 ± 28.6 km3 for 1979–2009, and −256.1 ± 27.7 km3 for 1999–2009, with the greatest annual regional volumetric SMB budgets (1979–2009) from Alaska (−41.8 ± 8.4 km3) and Greenland (−22.0 ± 2.7 km3) and the smallest from Caucasus (−0.5 ± 0.1 km3) and Scandinavia (−0.6 ± 0.4 km3). Gardner et al. (2013) confirmed the geographical distribution of the peak mass loss from Alaska and the lowest loss from the Caucasus, even though the Alaska mass budget from Gardner et al. included both GIC SMB and calving contributions.

Table 4.

Regional breakdown of mean and standard error (error ranges correspond to a 95% confidence interval) of SMB and SMB contribution to sea level rise for 1979–2009 and 1999–2009.

Table 4.
Fig. 9.
Fig. 9.

Regional breakdown of GIC SMB and cumulative SMB time series for 1979–2009, including linear regression (in Table 2 significant SMB conditions are highlighted in bold).

Citation: Journal of Climate 27, 15; 10.1175/JCLI-D-13-00669.1

When converting the GIC SMB budget to sea level equivalent the ocean surface area was set to be constant (3.61 × 108 km2), which did not account for land area isostacy, coastline changes, grounding line migration, or influence from general sea level rise. For simplicity, we assumed the GIC mass losses contributed instantaneously to sea level rise (i.e., no changes in evaporation and water storage along proglacial hydrological flow paths were assumed to occur). On average, the Northern Hemisphere GIC SMB simulations indicated that the annual volumetric SMB contribution to sea level rise was 0.51 ± 0.16 mm SLE for 1979–2009 and 0.71 ± 0.15 mm SLE for 1999–2009 (Table 4), equal to an increase of ~40% for the last decade compared to the mean for the entire simulation period. A trend toward a higher GIC sea level contribution has been recently confirmed by the Intergovernmental Panel on Climate Change (IPCC) (Vaughan et al. 2013). The greatest regional mean annual contribution to sea level rise was from Alaska of 0.12 ± 0.05 mm SLE (1979–2009) and 0.13 ± 0.05 mm SLE (1999–2009) (Table 4), accounting for ~20% of the overall Northern Hemisphere GIC contribution to sea level rise.

In other studies, Marzeion et al. (2012) estimated for 1902–2009 the global GIC mass loss sum to be 114 ± 5 mm SLE, equal to an annual average of 1.06 mm SLE; Kaser et al. (2006) found 0.77 ± 0.26 mm SLE (1991–2004), Gardner et al. (2013) calculated 0.71 ± 0.08 mm SLE (2003–09), and Hock et al. (2009) estimated 0.50 ± 0.18 mm SLE (1961–2004). The calculated GIC mass balance contribution to sea level rise clearly varies depending on the method used. SnowModel simulations only calculate SMB loss from Northern Hemisphere GIC (from ~75% of the global GIC area; Radić et al. 2014). Therefore, global GIC SMB contribution to sea level rise would likely be higher than the mean annual SnowModel-estimated 0.71 ± 0.15 mm SLE (1999–2009) when adding SMB contributions for the Southern Hemisphere GIC, loss rates from calving GIC and ice sheets, and loss rates from subglacial geothermal melting and subglacial frictional melting due to basal ice motion. No attempt here has been made to calculate and include these other contributions.

e. Model limitations and perspectives

The GIC simulations presented a detailed and more physically realistic representation of energy balance, snow and ice ablation, snowpack evolution, and runoff processes at relatively high temporal and spatial resolution compared with many previous studies. SnowModel capabilities present a contrast with studies that largely relied on air temperature as a proxy for the energy available for melt. In addition, to properly understand GIC surface processes in Arctic and mountainous regions, subdiurnal model time steps are required owing to the subdaily variability in solar radiation and its associated energy-related SMB processes. Over virtually all snow and ice GIC surfaces, the incoming solar radiation is the primary source of energy melting snow and ice by an order of magnitude more than that provided by sensible heat flux associated with air temperature (Liston and Hiemstra 2011). These improved temporal and spatial issues could be part of the reason for the differences among previously published model results.

The use of constant GIC area, thus neglecting any SMB feedback from GIC retreat, thinning, and subsequent changing hypsometry, etc., is also a weakness in the GIC SMB simulations. We know from satellite observations (e.g., Landsat), of the Sermilik Fjord region of southeast Greenland (Mernild et al. 2012) that GIC peripheral to the Greenland Ice Sheet had a mean area recession rate of 27% ± 24% from 1986 to 2011 (during this same period five glaciers completely melted away). Analyses from the pan-Arctic region of historical data and aerial and satellite images from the late twentieth century to the present have suggested that mean GIC area retreats occurred in the range of 3% to 63% (e.g., Glazovsky and Macheret 2006; M. D. Ananicheva and G. Kapustin 2010, unpublished manuscript; Bolch et al. 2010; Andreassen et al. 2012). The use of constant GIC area (a GIC snapshot from the 2000s) might underestimate the volumetric SMB budget, mostly in the beginning of the simulation period. On the other hand, is it not clear if a change in GIC area over the last 30 years can be resolved on a 1-km grid.

Also in these SnowModel simulations, towing to the relatively large grid increment (1 km), blowing snow processes were not included. On GIC, sublimation can occur both from the static surface and from blowing-snow particles. The static-surface sublimation of snow (in nature and SnowModel) depends on surface air temperature, the moisture deficit of the air, wind speed, and other components of the surface energy balance (Liston and Hiemstra 2011). In this study the sublimation part from blowing snow is not included. In previous Arctic and alpine studies it has been found that the total amount of estimated sublimation (from static and blowing-snow particles) varied between 10% and 50% of the total winter precipitation (Liston and Sturm 1998, 2002, 2004; Hiemstra et al. 2002).

SnowModel assumed one-way atmospheric forcing, where the atmospheric conditions were prescribed at each time step without regard for whether the snow and ice distribution and properties might be different than that in the original MERRA reanalysis. In the natural system the atmospheric variables would be modified in response to differences and changes in surface conditions (Liston and Hiemstra 2011); such interactions were not accounted for in the simulations described herein.

This model study and its associated analyses have not addressed how GIC SMB may change in the future. Numerous studies have examined future trends (e.g., Marzeion et al. 2012; Radić et al. 2014). These studies generally project GIC to lose additional mass and to reduce the current volume, suggesting additional GIC mass contributions to sea level rise in the coming decades.

Our analysis of this Northern Hemisphere high spatial resolution GIC dataset has also suggested the possibility (cf. Fig. 6) of using it to establish a new GIC classification system. This classification system, based on seasonal and annual GIC climate, mass balance, and runoff conditions, could provide insights into local-area GIC conditions and a way to distinguish GIC conditions among the earth’s 200 000 or more estimated GIC to highlight commonalities among GIC located in different geographical regions. By the way of example, consider the Alaska region (region 1, Fig. 1c); within this region are glaciers exhibiting maritime SMB characteristics and other glaciers exhibiting Arctic SMB features. As another example, the western Canada and the United States region (region 2, Fig. 1c) includes both warm, wet maritime glaciers and cold, dry continental glaciers. From an SMB perspective both of these regions include glaciers at both ends of the spectrum in terms of accumulation and ablation. The datasets presented herein contain the information required to develop an improved classification scheme based on glacier SMB structures and characteristics. Previously, GIC have been classified by their morphological shape and geophysical conditions (e.g., Rau et al. 2005; Cuffey and Paterson 2010), but a GIC classification system based on climate, mass balance, and runoff is now possible and could be of scientific and computational interest to glaciologists, hydrologists, and ecologists, since GIC are clear regulators of water availability and substantial contributors to eustatic sea level rise. In addition to this proposed GIC classification approach, an examination of the Southern Hemisphere GIC conditions needs to be addressed, when RGI GIC uncertainties have been resolved for South America.

6. Conclusions

The merging of atmospheric forcing dataset (i.e., MERRA) and the first complete global glaciers and ice caps (GIC) digital inventory (i.e., Randolph Glacier Inventory, v. 2.0) with a high-resolution (1-km horizontal grid increment and 3-h time step) snow and ice evolution modeling tool (SnowModel) allowed us to map and analyze the spatial GIC surface hydrological conditions and variations between glacierized regions in the Northern Hemisphere (north of 25°N; not including the Greenland Ice Sheet). We have investigated the GIC surface mass balance (SMB) and SMB contribution to sea level rise, including variations in GIC surface air temperature, precipitation, sublimation, evaporation, and surface runoff for the 30-yr period 1979–2009. SnowModel-simulated GIC SMB outputs were calibrated against direct glaciological SMB observations, showing agreement between simulations and observations. Using available SMB observations from the earth’s 200 000 or more estimated GIC (Radić and Hock 2010; Arendt et al. 2012), high-resolution SMB simulations were used to improve our understanding of Northern Hemisphere GIC SMB conditions for all GIC having areas over 0.5 km2. Model simulations and calculations were performed on a 1-km grid and included 543 389 GIC grid cells.

Overall, GIC MAAT increased an average between the first and last decade of 0.8° ± 0.1°C (equal to a significant annual linear average trend of 0.04°C yr−1) for the simulation period, showing a characteristic diagonal latitude–elevation pattern and an average temperature increase most pronounced at high latitudes. In addition, for total annual GIC precipitation, the latitude–elevation pattern was heterogeneous with clusters of GIC receiving less/more precipitation than GIC at same latitudes, due to local topographic, orographic, and climatic influences. Average GIC runoff and SMB loss increased for all regions, including both latitude–elevation and regional variability, where the annual SMB contribution to sea level rise for the last decade (1999–2009) averaged 0.71 ± 0.15 mm SLE, and was ~40% greater than the 30-yr mean of 0.51 ± 0.16 mm SLE.

These simulations provide an improvement over previous model studies in assessing the Northern Hemisphere GIC SMB conditions because of its relatively high temporal and spatial resolution and its physically based representations of the governing processes. The simulations and analyses highlight conditions and changes ranging from individual GIC to regional and continental scales. Our current understanding of the local variability in GIC behavior is limited in areas where we have no observations, and therefore this study and its associated GIC database allows us to examine, classify, and distinguish GIC SMB conditions among Earth’s 200 000 or more GIC.

Acknowledgments

This work was supported in part by the Earth System Modeling program and by the Scientific Discovery for Advanced Computing (SciDAC) program within the U.S. Department of Energy’s Office of Science, and by a Los Alamos National Laboratory (LANL) (LANL is operated under the auspices of the National Nuclear Security Administration of the U.S. Department of Energy under Contract DE-AC52-06NA25396). Additional support was provided from the European Community’s Seventh Framework Programme under Grant Agreement 262693. We thank the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) and Global Modeling and Assimilation Office (GMAO) for providing the MERRA datasets, and NOAA for providing the Global Land One-km Base Elevation Project (GLOBE) digital elevation model. Request of data should be addressed to the first author.

REFERENCES

  • Allerup, P., , H. Madsen, , and F. Vejen, 1998: Estimating true precipitation in Arctic areas. Nordic Hydrological Programme Rep. 44, 19.

  • Allerup, P., , H. Madsen, , and F. Vejen, 2000a: Correction of precipitation based on off-site weather information. Atmos. Res., 53, 231250, doi:10.1016/S0169-8095(99)00051-4.

    • Search Google Scholar
    • Export Citation
  • Allerup, P., , H. Madsen, , and F. Vejen, 2000b: Correction of observed precipitation in Greenland. Proc. Nordic Hydrological Conf., Uppsala, Sweden, NHF/NAH Nordic Association for Hydrology, 1–8.

  • Andreassen, L. M., , S. H. Winsvold, , F. Paul, , and J. E. Hausberg, 2012: Inventory of Norwegian Glaciers. L. M. Andreassen and S. H. Winsvold, Eds., Norwegian Water Resources and Energy Directorate, 242 pp.

  • Arendt, A., and et al. , 2012: Randolph Glacier Inventory (v2.0): A dataset of global glacier outlines. Global Land Ice Measurements from Space, Boulder Colorado, digital media (with area corrections downloaded 2012). [Available online at http://www.glims.org/RGI/.]

  • Benn, D. I., , and D. J. A. Evans, 2002: Glaciers and Glaciation. Arnold, 734 pp.

  • Bhutiyani, M. R., 1999: Mass-balance studies on Siachen Glacier in the Nubra Valley, Karakoram Himalaya, India. J. Glaciol., 45 (149), 112118.

    • Search Google Scholar
    • Export Citation
  • Bolch, T., , B. Menounos, , and R. Wheate, 2010: Landsat-based inventory of glaciers in western Canada, 1985–2005. Remote Sens. Environ., 114, 127137, doi:10.1016/j.rse.2009.08.015.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., 2008: NASA’s Modern Era Retrospective Analysis for Research and Applications: Integrating Earth observations. Earthzine. [Available online at http://www.earthzine.org/2008/09/26/nasas-modern-era-retrospective-analysis/.]

  • Bosilovich, M. G., , J. Chen, , F. R. Robertson, , and R. F. Adler, 2008: Evaluation of global precipitation re-analyses. J. Appl. Meteor. Climatol., 47, 22792299, doi:10.1175/2008JAMC1921.1.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., , R. R. Franklin, , and J. Chen, 2011: Global energy and water budgets in MERRA. J. Climate, 24, 57215739, doi:10.1175/2011JCLI4175.1.

    • Search Google Scholar
    • Export Citation
  • Box, J. E., 2002: Survey of Greenland instrumental temperature records: 1873–2001. Int. J. Climatol., 22, 18291847, doi:10.1002/joc.852.

    • Search Google Scholar
    • Export Citation
  • Bruland, O., , G. E. Liston, , J. Vonk, , and A. Killingtveit, 2004: Modelling the snow distribution at two high Arctic sites at Svalbard, Norway, and at a sub-Arctic site in central Norway. Nord. Hydrol., 35, 191208.

    • Search Google Scholar
    • Export Citation
  • Cogley, J. G., 2009: Geodetic and direct mass-balance measurements: Comparison and joint analysis. Ann. Glaciol., 50, 96100, doi:10.3189/172756409787769744.

    • Search Google Scholar
    • Export Citation
  • Cogley, J. G., 2012: The future of the world’s glaciers. The Future of the World’s Climate, 2nd ed., A. Henderson-Sellers and K. McGuffie, Eds., Elsevier, 205–218, doi:10.1016/B978-0-12-386917-3.00008-7.

  • Cuffey, K. M., , and W. S. B. Paterson, 2010: The Physics of Glaciers. 4th ed. Elsevier, 708 pp.

  • Cullather, R. I., , and M. G. Bosilovich, 2011: The moisture budget of the polar atmosphere in MERRA. J. Climate, 24, 28612879, doi:10.1175/2010JCLI4090.1.

    • Search Google Scholar
    • Export Citation
  • Dyurgerov, M. B., 2010. Data of glaciological studies—Reanalysis of glacier changes: From the IGY to the IPY, 1960–2008. Publication 108, Institute of Arctic and Alpine Research, 116 pp.

  • Dyurgerov, M. B., , and M. F. Meier, 2005: Glaciers and the changing Earth system: A 2004 snapshot. Occasional Paper 58, Institute of Arctic and Alpine Research, Boulder, Colorado, 117 pp.

  • Finnis, J., , J. Cassano, , M. Holland, , M. C. Serreze, , and P. Uotila, 2009a: Synoptically forced hydroclimatology of major Arctic watersheds in general circulation models. Part 1: The Mackenzie River Basin. Int. J. Climatol., 29, 12261243, doi:10.1002/joc.1753.

    • Search Google Scholar
    • Export Citation
  • Finnis, J., , J. Cassano, , M. Holland, , M. C. Serreze, , and P. Uotila, 2009b: Synoptically forced hydroclimatology of major Arctic watersheds in general circulation models. Part 2: Eurasian watersheds. Int. J. Climatol., 29, 12441261, doi:10.1002/joc.1769.

    • Search Google Scholar
    • Export Citation
  • Gardelle, J., , E. Berthier, , and Y. Arnaud, 2012: Slight mass gain of Karakoram glaciers in the early twenty-first century. Nat. Geosci., 5, 322325, doi:10.1038/ngeo1450.

    • Search Google Scholar
    • Export Citation
  • Gardner, A. S., and et al. , 2013: A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science, 340, 852857, doi:10.1126/science.1234532.

    • Search Google Scholar
    • Export Citation
  • Glazovsky, A., , and Y. Macheret, 2006: Glaciation in north and central Eurasia in present time (in Russian with English summary). Eurasian Arctic, V. M. Kotlyakov, Eds., Nauka, 97–114 and 438–445.

    • Search Google Scholar
    • Export Citation
  • Hanna, E., and et al. , 2008: Increased runoff from melt from the Greenland Ice Sheet: A response to global warming. J. Climate, 21, 331341, doi:10.1175/2007JCLI1964.1.

    • Search Google Scholar
    • Export Citation
  • Hanna, E., , S. H. Mernild, , J. Cappelen, , and K. Steffen, 2012: Recent warming in Greenland in a long-term instrumental (1881–2012) climatic context: I. Evaluation of surface air temperature records. Environ. Res. Lett., 7, 045404, doi:10.1088/1748-9326/7/4/045404.

    • Search Google Scholar
    • Export Citation
  • Hanna, E., , J. M. Jones, , J. Cappelen, , S. H. Mernild, , L. Wood, , K. Steffen, , and P. Huybrechts, 2013: Discerning the influence of North Atlantic atmospheric and oceanic forcing effects on 1900–2010 Greenland summer climate and melt. Int. J. Climatol., 33, 862880, doi:10.1002/joc.3475.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., , R. Ruedy, , M. Sato, , and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, doi:10.1029/2010RG000345.

  • Hasholt, B., , G. E. Liston, , and N. T. Knudsen, 2003: Snow distribution modelling in the Ammassalik region, South East Greenland. Nord. Hydrol., 34, 116.

    • Search Google Scholar
    • Export Citation
  • Hastings, D. A., and et al. , 1999: The Global Land One-km Base Elevation (GLOBE) digital elevation model, version 1.0. NOAA, National Geophysical Data Center, digital media. [Available online at http://www.ngdc.noaa.gov/mgg/topo/globe.html.]

  • Hiemstra, C. A., , G. E. Liston, , and W. A. Reiners, 2002: Snow redistribution by wind and interactions with vegetation at upper treeline in the Medicine Bow Mountains, Wyoming, USA. Arct. Antarct. Alp. Res., 34, 262273, doi:10.2307/1552483.

    • Search Google Scholar
    • Export Citation
  • Hiemstra, C. A., , G. E. Liston, , and W. A. Reiners, 2006: Observing, modelling, and validating snow redistribution by wind in a Wyoming upper treeline landscape. Ecol. Modell., 197, 3551, doi:10.1016/j.ecolmodel.2006.03.005.

    • Search Google Scholar
    • Export Citation
  • Hinzman, L. D., and et al. , 2005: Evidence and implications of recent climate change in northern Alaska and other arctic regions. Climatic Change, 72, 251298, doi:10.1007/s10584-005-5352-2.

    • Search Google Scholar
    • Export Citation
  • Hirabayashi, Y., , P. Döll, , and S. Kanae, 2010: Global-scale modeling of glacier mass balances for water resources assessments: Glacier mass changes between 1948 and 2006. J. Hydrol., 390, 245256, doi:10.1016/j.jhydrol.2010.07.001.

    • Search Google Scholar
    • Export Citation
  • Hock, R., , M. de Woul, , V. Radić, , and M. Dyurgerov, 2009: Mountain glaciers and ice caps around Antarctica make a large sea-level rise contribution. Geophys. Res. Lett., 36, L07501, doi:10.1029/2008GL037020.

    • Search Google Scholar
    • Export Citation
  • Huss, M., 2011: Present and future contribution of glacier storage change to runoff from macroscale drainage basins in Europe. Water Resour. Res., 47, W07511, doi:10.1029/2010WR010299.

    • Search Google Scholar
    • Export Citation
  • Immerzeel, W., , L. van Beek, , M. Konz, , A. Shrestha, , and M. F. P. Bierkens, 2012: Hydrological response to climate change in a glacierized catchment in the Himalayas. Climatic Change, 110, 721736, doi:10.1007/s10584-011-0143-4.

    • Search Google Scholar
    • Export Citation
  • Kaser, G., , J. G. Cogley, , M. B. Dyurgerov, , M. F. Meier, , and A. Ohmura, 2006: Mass balance of glaciers and ice caps: Consensus estimates for 1961–2004. Geophys. Res. Lett., 33, L19501, doi:10.1029/2006GL027511.

    • Search Google Scholar
    • Export Citation
  • Kotlarski, S., , F. Paul, , and D. Jacob, 2010: Forcing a distributed glacier mass balance model with the regional climate model REMO. Part I: Climate model evaluation. J. Climate, 23, 15891606, doi:10.1175/2009JCLI2711.1.

    • Search Google Scholar
    • Export Citation
  • Leclercq, P. W., , J. Oerlemans, , and J. G. Cogley, 2011: Estimating the glacier contribution to sea-level rise for the period 1800–2005. Surv. Geophys., 32, 519535, doi:10.1007/s10712-011-9121-7.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., 1995: Local advection of momentum, heat, and moisture during the melt of patchy snow covers. J. Appl. Meteor., 34, 17051715, doi:10.1175/1520-0450-34.7.1705.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and M. Sturm, 1998: A snow-transport model for complex terrain. J. Glaciol., 44, 498516.

  • Liston, G. E., , and M. Sturm, 2002: Winter precipitation patterns in Arctic Alaska determined from a blowing-snow model and snow-depth observations. J. Hydrometeor., 3, 646659, doi:10.1175/1525-7541(2002)003<0646:WPPIAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and M. Sturm, 2004: The role of winter sublimation in the Arctic moisture budget. Nord. Hydrol., 35, 325334.

  • Liston, G. E., , and K. Elder, 2006a: A distributed snow-evolution modeling system (SnowModel). J. Hydrometeor., 7, 12591276, doi:10.1175/JHM548.1.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and K. Elder, 2006b: A meteorological distribution system for high-resolution terrestrial modeling (MicroMet). J. Hydrometeor., 7, 217234, doi:10.1175/JHM486.1.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and C. A. Hiemstra, 2008: A simple data assimilation system for complex snow distributions (SnowAssim). J. Hydrometeor., 9, 9891004, doi:10.1175/2008JHM871.1.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and C. A. Hiemstra, 2011: The changing cryosphere: Pan-Arctic snow trends (1979–2009). J. Climate, 24, 56915712, doi:10.1175/JCLI-D-11-00081.1.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , and S. H. Mernild, 2012: Greenland freshwater runoff. Part I: A runoff routing model for glaciated and nonglaciated landscapes (HydroFlow). J. Climate, 25, 59976014, doi:10.1175/JCLI-D-11-00591.1.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , J.-G. Winther, , O. Bruland, , H. Elvehøy, , and K. Sand, 1999: Below surface ice melt on the coastal Antarctic ice sheet. J. Glaciol., 45, 273285, doi:10.3189/002214399793377130.

    • Search Google Scholar
    • Export Citation
  • Liston, G. E., , R. B. Haehnel, , M. Sturm, , C. A. Hiemstra, , S. Berezovskaya, , and R. D. Tabler, 2007: Simulating complex snow distributions in windy environments using SnowTran-3D. J. Glaciol., 53, 241256.

    • Search Google Scholar
    • Export Citation
  • Marzeion, B., , A. H. Jarosch, , and M. Hofer, 2012: Past and future sea-level change from the surface mass balance of glaciers. Cryosphere, 6, 12951322, doi:10.5194/tc-6-1295-2012.

    • Search Google Scholar
    • Export Citation
  • Meier, M. F., , M. B. Dyurgerov, , U. K. Rick, , S. O’Neel, , W. T. Pfeffer, , R. S. Anderson, , S. P. Anderson, , and A. F. Glazovsky, 2007: Glaciers dominate eustatic sea-level rise in the 21st century. Science, 317, 10641067, doi:10.1126/science.1143906.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , and B. Hasholt, 2006: Climatic control on river discharge simulations, Mittivakkat Glacier catchment, Ammassalik Island, southeast Greenland. Nord. Hydrol., 37, 327346, doi:10.2166/nh.2006.018.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , and G. E. Liston, 2010: The influence of air temperature inversion on snow melt and glacier surface mass-balance simulations, Ammassalik Island, southeast Greenland. J. Appl. Meteor. Climatol., 49, 4767, doi:10.1175/2009JAMC2065.1.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , and G. E. Liston, 2012: Greenland freshwater runoff. Part II: Distribution and trends, 1960–2010. J. Climate, 25, 60156035, doi:10.1175/JCLI-D-11-00592.1.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , G. E. Liston, , B. Hasholt, , and N. T. Knudsen, 2006: Snow distribution and melt modeling for Mittivakkat Glacier, Ammassalik Island, Southeast Greenland. J. Hydrometeor., 7, 808824, doi:10.1175/JHM522.1.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , B. U. Hansen, , B. H. Jakobsen, , and B. Hasholt, 2008a: Climatic conditions at the Mittivakkat Glacier catchment (1994–2006), Ammassalik Island, SE Greenland, and in a 109 years term perspective (1898–2006). Geogr. Tidsskrift, Dan. J. Geogr., 108, 5172, doi:10.1080/00167223.2008.10649574.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , D. L. Kane, , B. U. Hansen, , B. H. Jakobsen, , B. Hasholt, , and N. T. Knudsen, 2008b: Climate, glacier mass balance, and runoff (1993–2005) for the Mittivakkat Glacier catchment, Ammassalik Island, SE Greenland, and in a long term perspective (1898–1993). Hydrol. Res., 39, 239256, doi:10.2166/nh.2008.101.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , G. E. Liston, , C. A. Hiemstra, , K. Steffen, , E. Hanna, , and J. H. Christensen, 2009: Greenland Ice Sheet surface mass-balance modeling and freshwater flux for 2007, and in a 1995–2007 perspective. Hydrol. Processes, 23, 2470–2484, doi:10.1002/hyp.7354.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , G. E. Liston, , C. A. Hiemstra, , and J. H. Christensen, 2010a: Greenland Ice Sheet surface mass-balance modeling in a 131-yr perspective 1950–2080. J. Hydrometeor., 11, 325, doi:10.1175/2009JHM1140.1.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , G. E. Liston, , K. Steffen, , and P. Chylek, 2010b: Meltwater flux and runoff modeling in the ablation area of the Jakobshavn Isbræ, West Greenland. J. Glaciol., 56, 2032, doi:10.3189/002214310791190794.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , G. E. Liston, , C. A. Hiemstra, , J. H. Christensen, , M. Stendel, , and B. Hasholt, 2011a: Surface mass-balance and runoff modeling using HIRHAM4 RCM at Kangerlussuaq (Søndre Strømfjord), West Greenland, 1950–2080. J. Climate, 24, 609–623, doi:10.1175/2010JCLI3560.1.

  • Mernild, S. H., , T. Mote, , and G. E. Liston, 2011b: Greenland Ice Sheet surface melt extent and trends, 1960–2010. J. Glaciol., 57, 621628, doi:10.3189/002214311797409712.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , N. T. Knudsen, , W. H. Lipscomb, , J. C. Yde, , J. K. Malmros, , B. H. Jakobsen, , and B. Hasholt, 2011c: Increasing mass loss from Greenland’s Mittivakkat Gletscher. Cryosphere, 5, 341348, doi:10.5194/tc-5-341-2011.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , J. K. Malmros, , J. C. Yde, , and N. T. Knudsen, 2012: Multi-decadal marine- and land-terminating glacier retreat in the Ammassalik region, southeast Greenland. Cryosphere, 6, 625639, doi:10.5194/tc-6-625-2012.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , W. H. Lipscomb, , D. B. Bahr, , V. Radić, , and M. Zemp, 2013a: Global glacier retreat: A revised assessment of committed mass losses and sampling uncertainties. Cryosphere, 7, 15651577, doi:10.5194/tc-7-1565-2013. [Supplementary material is available at http://www.the-cryosphere.net/7/1565/2013/tc-7-1565-2013-supplement.zip.]

  • Mernild, S. H., , N. T. Knudsen, , M. J. Hoffman, , J. C. Yde, , W. L. Lipscomb, , E. Hanna, , J. K. Malmros, , and R. S. Fausto, 2013b: Volume and velocity changes at Mittivakkat Gletscher, southeast Greenland, 1994–2012. J. Glaciol., 59, 660670, doi:10.3189/2013JoG13J017.

    • Search Google Scholar
    • Export Citation
  • Mernild, S. H., , E. Hanna, , J. C. Yde, , J. Cappelen, , and J. K. Malmros, 2014: Coastal Greenland air temperature extremes and trends 1890–2010: Annual and monthly analysis. Int. J. Climatol., 34, 14721487, doi:10.1002/joc.3777.

    • Search Google Scholar
    • Export Citation
  • Overland, J. E., , M. C. Spillane, , D. B. Percival, , M. Y. Wang, , and H. O. Mofjeld, 2004: Seasonal and regional variation of pan-Arctic surface air temperature over the instrumental record. J. Climate, 17, 32633282, doi:10.1175/1520-0442(2004)017<3263:SARVOP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Radić, V., , and R. Hock, 2010: Regional and global volumes of glaciers derived from statistical upscaling of glacier inventory data. J. Geophys. Res., 115, F01010, doi:10.1029/2009JF001373.

    • Search Google Scholar
    • Export Citation
  • Radić, V., , A. Bliss, , A. C. Beedlow, , R. Hock, , E. Miles, , and J. G. Cogley, 2014: Regional and global projection of twenty-first century glacier mass changes in response to climate scenarios from global climate models. Climate Dyn., 42, 37–58, doi:10.1007/s00382-013-1719-7.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and et al. , 2012: How well are we measuring snow?: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811829, doi:10.1175/BAMS-D-11-00052.1.

    • Search Google Scholar
    • Export Citation
  • Rau, F., , F. Mauz, , S. Vogt, , S. J. S. Khalsa, , and B. Raup, 2005: Illustrated GLIMS Glacier Classification Manual—Glacier Classification Guidance for the GLIMS Glacier Inventory, version 1 (2005-02-10), 36 pp. [Available online at http://www.glims.org/MapsAndDocs/assets/GLIMS_Glacier-Classification-Manual_V1_2005-02-10.pdf.]

  • Rawlins, M. A., , C. J. Willmott, , A. Shiklomanov, , E. Linder, , S. Frolking, , R. B. Lammers, , and C. J. Vorosmarty, 2006: Evaluation of trends in derived snowfall and rainfall across Eurasia and linkages with discharge to the Arctic Ocean. Geophys. Res. Lett., 33, L07403, doi:10.1029/2005GL025231.

    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and et al. , 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, doi:10.1175/JCLI-D-11-00015.1.

    • Search Google Scholar
    • Export Citation
  • Robertson, F. R., , M. G. Bosilovich, , J. Chen, , and T. L. Miller, 2011: The effect of satellite observing system changes on MERRA water and energy fluxes. J. Climate, 24, 51975217, doi:10.1175/2011JCLI4227.1.

    • Search Google Scholar
    • Export Citation
  • Suzuki, K., and et al. , 2011: Impact of land-use changes on snow in a forested region with heavy snowfall in Hokkido, Japan. Hydrol. Sci. J.,56 (3), 443–467, doi:10.1080/02626667.2011.565008.

  • Vaughan, D. G., and et al. , 2013: Observations: Cryosphere. Climate Change 2103: The Physical Science Basis, T. F. Stocker, et al., Cambridge University Press, 317–382.

  • Walsh, J. E., , W. L. Chapman, , V. Romanovsky, , J. H. Christensen, , and M. Stendel, 2008: Global climate model performance over Alaska and Greenland. J. Climate, 21, 61566174, doi:10.1175/2008JCLI2163.1.

    • Search Google Scholar
    • Export Citation
  • Woo, M. K., , R. Heron, , and P. Marsh, 1982: Basal ice in High Arctic snowpacks. Arct. Alp. Res., 14, 251260, doi:10.2307/1551157.

  • WGMS, 1989: World glacier inventory: Status 1988. W. Haeberli et al., Eds., World Glacier Monitoring Service, 458 pp.

  • WGMS, 2012: Fluctuations of glaciers 2005–2010 (Vol. X). M. Zemp et al., Eds., World Glacier Monitoring Service, 336 pp. [Publication based on database version, doi:10.5904/wgms-fog-2012-11.]

  • Yang, D., , B. E. Goodison, , J. R. Metcalfe, , V. S. Golubev, , R. Bates, , T. Pangburn, , and C. L. Hanson, 1998: Accuracy of NWS 8″ standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol., 15, 5468, doi:10.1175/1520-0426(1998)015<0054:AONSNP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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