• Adam, J. C., , and D. P. Lettenmaier, 2003: Adjustment of global gridded precipitation for systematic bias. J. Geophys. Res., 108, 4257, doi:10.1029/2002JD002499.

    • Search Google Scholar
    • Export Citation
  • Adam, J. C., , and D. P. Lettenmaier, 2008: Application of new precipitation and reconstructed streamflow products to streamflow trend attribution in northern Eurasia. J. Climate, 21, 18071828.

    • Search Google Scholar
    • Export Citation
  • Adam, J. C., , E. A. Clark, , D. P. Lettenmaier, , and E. F. Wood, 2006: Correction of global precipitation products for orographic effects. J. Climate, 19, 1538.

    • Search Google Scholar
    • Export Citation
  • Adam, J. C., , I. Haddeland, , F. Su, , and D. P. Lettenmaier, 2007: Simulation of reservoir influences on annual and seasonal streamflow changes for the Lena, Yenisei, and Ob’ rivers. J. Geophys. Res., 112, D24114, doi:10.1029/2007JD008525.

    • Search Google Scholar
    • Export Citation
  • Anderson, E. A., 1976: A point energy and mass balance model of a snow cover. NOAA Tech. Rep. ERL402-NHELM2, 91 pp.

  • Andreadis, K. M., , P. Storck, , and D. P. Lettenmaier, 2009: Modeling snow accumulation and ablation processes in forested environments. Water Resour. Res., 45, W05429, doi:10.1029/2008WR007042.

    • Search Google Scholar
    • Export Citation
  • Armstrong, R. L., , and M. J. Brodzik, 2007: Northern Hemisphere EASE-Grid weekly snow cover and sea ice extent version 3.1. National Snow and Ice Data Center, Boulder, CO, digital media. [Available online at http://nsidc.org/data/nsidc-0046.html.]

  • Baker, D. G., , D. L. Ruschy, , R. H. Skaggs, , and D. B. Wall, 1992: Air temperature and radiation depressions associated with a snow cover. J. Appl. Meteor., 31, 247254.

    • Search Google Scholar
    • Export Citation
  • Bekryaev, R. V., , I. V. Polyakov, , and V. A. Alexeev, 2010: Role of polar amplification in long-term surface air temperature variations and modern Arctic warming. J. Climate, 23, 38883906.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., , M. Köhler, , and Y. Zhang, 2009: Comparison of river basin hydrometeorology in ERA-Interim and ERA-40 reanalyses with observations. J. Geophys. Res., 114, D02101, doi:10.1029/2008JD010761.

    • Search Google Scholar
    • Export Citation
  • Boike, J., , K. Roth, , and O. Ippisch, 2003: Seasonal snow cover on frozen ground: Energy balance calculations of a permafrost site near Ny-Ålesund, Spitsbergen. J. Geophys. Res., 108, 8163, doi:10.1029/2001JD000939.

    • Search Google Scholar
    • Export Citation
  • Bras, R. L., 1990: Hydrology: An Introduction to Hydrologic Science. Addison-Wesley, 643 pp.

  • Brohan, P., , J. J. Kennedy, , I. Harris, , S. F. B. Tett, , and P. D. Jones, 2006: Uncertainty estimates in regional and global observed temperature changes: A new dataset from 1850. J. Geophys. Res., 111, D12106, doi:10.1029/2005JD006548.

    • Search Google Scholar
    • Export Citation
  • Brown, R., , and P. W. Mote, 2009: The response of Northern Hemisphere snow cover to a changing climate. J. Climate, 22, 21242145.

  • Brown, R., , and D. A. Robinson, 2011: Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. Cryosphere, 5, 219229.

    • Search Google Scholar
    • Export Citation
  • Brown, R., , C. Derksen, , and L. Wang, 2007: Assessment of spring snow cover duration variability over northern Canada from satellite datasets. Remote Sens. Environ., 111, 367381.

    • Search Google Scholar
    • Export Citation
  • Brown, R., , C. Derksen, , and L. Wang, 2010: A multi-data set analysis of variability and change in Arctic spring snow cover extent, 1967–2008. J. Geophys. Res., 115, D16111, doi:10.1029/2010JD013975.

    • Search Google Scholar
    • Export Citation
  • Bulygina, O. N., , V. N. Razuvaev, , and N. N. Korshunova, 2009: Changes in snow cover over Northern Eurasia in the last few decades. Environ. Res. Lett., 4, 045026, doi:10.1088/1748-9326/4/4/045026.

    • Search Google Scholar
    • Export Citation
  • Chapin, F. S., III, and Coauthors, 2005: Role of land-surface changes in Arctic summer warming. Science, 310, 657660.

  • Choi, G., , D. A. Robinson, , and S. Kang, 2010: Changing Northern Hemisphere snow seasons. J. Climate, 23, 53055310.

  • Clark, M. P., , M. C. Serreze, , and D. A. Robinson, 1999: Atmospheric controls on Eurasian snow extent. Int. J. Climatol., 19, 2740.

  • Cline, D. W., 1997: Snow surface energy exchanges and snowmelt at a continental, midlatitude Alpine site. Water Resour. Res., 33, 689701.

    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Recent climatology, variability, and trends in global surface humidity. J. Climate, 19, 35893606.

  • Derksen, C., , and R. D. Brown, 2011: Terrestrial snow (Arctic) in state of the climate in 2010. Bull. Amer. Meteor. Soc., 92, S154S155.

    • Search Google Scholar
    • Export Citation
  • Derksen, C., , R. D. Brown, , and L. Wang, 2010: Terrestrial snow (Arctic) in state of the climate in 2009. Bull. Amer. Meteor. Soc., 91, S93S94.

    • Search Google Scholar
    • Export Citation
  • Déry, S. J., , and R. D. Brown, 2007: Recent Northern Hemisphere snow cover extent trends and implications for the snow-albedo feedback. Geophys. Res. Lett., 34, L22504, doi:10.1029/2007GL031474.

    • Search Google Scholar
    • Export Citation
  • Déry, S. J., , J. Sheffield, , and E. F. Wood, 2005: Connectivity between Eurasian snow cover extent and Canadian snow water equivalent and river discharge. J. Geophys. Res., 110, D23106, doi:10.1029/2005JD006173.

    • Search Google Scholar
    • Export Citation
  • Dye, D. G., 2002: Variability and trends in the annual snow-cover cycle in Northern Hemisphere land areas, 1972-2000. Hydrol. Processes, 16, 30653077.

    • Search Google Scholar
    • Export Citation
  • Dyer, J. L., , and T. L. Mote, 2002: Role of energy budget components on snow ablation from a mid-latitude prairie snowpack. Polar Geogr., 26, 87115.

    • Search Google Scholar
    • Export Citation
  • Dyer, J. L., , and T. L. Mote, 2007: Trends in snow ablation over North America. Int. J. Climatol.,27, 739–748.

  • Flanner, M., , C. Zender, , P. Hess, , N. Mahowald, , T. Painter, , V. Ramanathan, , and P. Rasch, 2009: Springtime warming and reduced snow cover from carbonaceous particles. Atmos. Chem. Phys., 9, 24812497.

    • Search Google Scholar
    • Export Citation
  • Frei, A., , and D. A. Robinson, 1999: Northern Hemisphere snow extent: Regional variability 1972-1994. Int. J. Climatol., 19, 15351560.

    • Search Google Scholar
    • Export Citation
  • Graversen, R. G., , and M. Wang, 2009: Polar amplification in a coupled climate model with locked albedo. Climate Dyn., 33, 629643.

  • Gray, D. M., , and D. H. Male, 1981: Handbook of Snow: Principles, Processes, Management and Use. Pergamon Press, 776 pp.

  • Gray, D. M., , and T. D. Prowse, 1993: Snow and floating ice. Handbook of Hydrology, Vol. 7, D. R. Maidment, Ed., McGraw-Hill, 7.1–7.58.

  • Groisman, P. Ya., , T. R. Karl, , R. W. Knight, , and G. L. Stenchikov, 1994: Changes of snow cover, temperature, and radiative heat balance over the Northern Hemisphere. J. Climate, 7, 16331656.

    • Search Google Scholar
    • Export Citation
  • Held, I., , and B. Soden, 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25, 441475.

  • Hinzman, L., , D. Kane, , and R. Gieck, 1991: Regional snow ablation in the Alaskan Arctic. Northern Hydrology: Selected Perspectives, T. D. Prowse and C. S. L. Ommanney, Eds., The Institute, 122–139.

  • Hinzman, L., and Coauthors, 2005: Evidence and implications of recent climate change in northern Alaska and other arctic regions. Climatic Change, 72, 251298.

    • Search Google Scholar
    • Export Citation
  • Holland, M. M., , C. M. Bitz, , and B. Tremblay, 2006: Future abrupt reductions in the summer Arctic sea ice. Geophys. Res. Lett., 33, L23503, doi:10.1029/2006GL028024.

    • Search Google Scholar
    • Export Citation
  • Isaac, V., , and W. A. van Wijngaarden, 2012: Surface water vapor pressure and temperature trends in North America during 1948–2010. J. Climate, 25, 35993609.

    • Search Google Scholar
    • Export Citation
  • Jol, A., , F. Raes, , and B. Menne, 2009: Impacts of Europe’s changing climate—2008 indicator based assessment. IOP Conf. Ser. Earth Environ. Sci.,6, 292042, doi:10.1088/1755-1307/6/29/292042.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471.

  • Karl, T. R., , P. Ya. Groisman, , R. W. Knight, , and R. Heim, 1993: Recent variations of snow cover and snowfall in North America and their relation to precipitation and temperature variations. J. Climate, 6, 13271344.

    • Search Google Scholar
    • Export Citation
  • Kimball, J., , S. Running, , and R. Nemani, 1997: An improved method for estimating surface humidity from daily minimum temperature. Agric. For. Meteor., 85, 8798.

    • Search Google Scholar
    • Export Citation
  • Koivusalo, H., , and T. Kokkonen, 2002: Snow processes in a forest clearing and in a coniferous forest. J. Hydrol., 262, 145164.

  • Leathers, D. J., , D. Graybeal, , T. Mote, , A. Grundstein, , and D. Robinson, 2004: The role of airmass types and surface energy fluxes in snow cover ablation in the central Appalachians. J. Appl. Meteor., 43, 18871899.

    • Search Google Scholar
    • Export Citation
  • Liang, X., , D. P. Lettenmaier, , E. Wood, , and S. Burgess, 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res., 99 (D17), 14 41514 428.

    • Search Google Scholar
    • Export Citation
  • Liang, X., , E. F. Wood, , and D. P. Lettenmaier, 1996: Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification. Global Planet. Change, 13, 195206.

    • 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.

  • Lugina, K. M., , P. Ya. Groisman, , K. Ya Vinnikov, , V. V. Koknaeva, , and N. A. Speranskaya, 2005: Monthly surface air temperature time series area-averaged over the 30-degree latitudinal belts of the globe, 1881–2004. Trends: A compendium of data on global change, U.S. Department of Energy Oak Ridge National Laboratory Carbon Dioxide Information Analysis Center. [Available online at http://cdiac.esd.ornl.gov/trends/temp/lugina/lugina.html.]

  • Mahlstein, I., , and R. Knutti, 2011: Ocean heat transport as a cause for model uncertainty in projected arctic warming. J. Climate, 24, 14511460.

    • Search Google Scholar
    • Export Citation
  • Male, D., , and R. Granger, 1981: Snow surface energy exchange. Water Resour. Res., 17, 609627.

  • Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13,245259.

  • Marsh, P., , and J. Pomeroy, 1996: Meltwater fluxes at an Arctic forest-tundra site. Hydrol. Processes, 10, 13831400.

  • McClelland, J. W., , S. J. Déry, , B. J. Peterson, , R. M. Holmes, , and E. F. Wood, 2006: A pan-Arctic evaluation of changes in river discharge during the latter half of the 20th century. Geophys. Res. Lett., 33, L06715, doi:10.1029/2006GL025753.

    • Search Google Scholar
    • Export Citation
  • Niu, G. Y., , and Z. L. Yang, 2007: An observation-based formulation of snow cover fraction and its evaluation over large North American river basins. J. Geophys. Res., 112, D21101, doi:10.1029/2007JD008674.

    • Search Google Scholar
    • Export Citation
  • Ohmura, A., , M. Wild, , and H. Gilgen, 1989: Global Energy Balance Archive, GEBA: World Climate Program, Water, project A7. Vol. 2, Verlag der Fachvereine, 62 pp.

  • Oleson, K., and Coauthors, 2004: Technical description of The Community Land Model (CLM). NCAR Tech. Note TN-461+STR, 174 pp.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Peterson, B. J., , R. M. Holmes, , J. W. McClelland, , C. J. Vörösmarty, , R. B. Lammers, , A. I. Shiklomanov, , I. A. Shiklomanov, , and S. Rahmstorf, 2002: Increasing river discharge to the Arctic Ocean. Science, 298, 21712173.

    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., and Coauthors, 1999: Key results and implications from phase 1(c) of the Project for Intercomparison of Land-Surface Parametrization Schemes. Climate Dyn., 15, 673684.

    • Search Google Scholar
    • Export Citation
  • Pohl, S., , and P. Marsh, 2006: Modelling the spatial-temporal variability of spring snowmelt in an arctic catchment. Hydrol. Processes, 20, 17731792.

    • Search Google Scholar
    • Export Citation
  • Price, A., , and T. Dunne, 1976: Energy balance computations of snowmelt in a subarctic area. Water Resour. Res., 12, 686694.

  • Rawlins, M. A., and Coauthors, 2010: Analysis of the arctic system for freshwater cycle intensification: Observations and expectations. J. Climate, 23, 57155737.

    • Search Google Scholar
    • Export Citation
  • Robinson, D. A., 2000: Weekly Northern Hemisphere snow maps: 1966-1999. Proc. 12th Conf. on Applied Climatology, Asheville, NC, Amer. Meteor. Soc., 12–15.

  • Robinson, D. A., , and A. Frei, 2000: Seasonal variability of Northern Hemisphere snow extent using visible satellite data. Prof. Geogr., 52, 307315.

    • Search Google Scholar
    • Export Citation
  • Robinson, D. A., , K. F. Dewey, , and R. R. Heim Jr., 1993: Global snow cover monitoring: An update. Bull. Amer. Meteor. Soc., 74, 16891696.

    • Search Google Scholar
    • Export Citation
  • Santer, B., and Coauthors, 2007: Identification of human-induced changes in atmospheric moisture content. Proc. Natl. Acad. Sci. USA, 104, 15 24815 253.

    • Search Google Scholar
    • Export Citation
  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63,13791389.

  • Serreze, M., , and J. A. Francis, 2006: The Arctic amplification debate. Climatic Change, 76, 241264.

  • Serreze, M., and Coauthors, 2000: Observational evidence of recent change in the northern high-latitude environment. Climatic Change, 46, 159207.

    • Search Google Scholar
    • Export Citation
  • Serreze, M., , D. H. Bromwich, , M. P. Clark, , A. J. Etringer, , T. Zhang, , and R. Lammers, 2003: Large-scale hydro-climatology of the terrestrial Arctic drainage system. J. Geophys. Res., 108, 8160, doi:10.1029/2001JD000919.

    • Search Google Scholar
    • Export Citation
  • Sheffield, J., , A. D. Ziegler, , E. F. Wood, , and Y. Chen, 2004: Correction of the high-latitude rain day anomaly in the NCEP–NCAR reanalysis for land surface hydrological modeling. J. Climate,17, 3814–3828.

  • Shi, X., , A. W. Wood, , and D. P. Lettenmaier, 2008: How essential is hydrologic model calibration to seasonal streamflow forecasting? J. Hydrometeor., 9, 13501363.

    • Search Google Scholar
    • Export Citation
  • Shi, X., , M. Sturm, , G. E. Liston, , R. E. Jordan, , and D. P. Lettenmaier, 2009: SnowSTAR2002 transect reconstruction using a multilayered energy and mass balance snow model. J. Hydrometeor., 10, 11511167.

    • Search Google Scholar
    • Export Citation
  • Shi, X., , M. Wild, , and D. P. Lettenmaier, 2010: Surface radiative fluxes over the pan-Arctic land region: Variability and trends. J. Geophys. Res., 115, D22104, doi:10.1029/2010JD014402.

    • Search Google Scholar
    • Export Citation
  • Shi, X., , P. Ya. Groisman, , S. J. Déry, , and D. P. Lettenmaier, 2011: The role of surface energy fluxes in pan-Arctic snow cover changes. Environ. Res. Lett., 6, 035204 doi:10.1088/1748-9326/6/3/035204.

    • Search Google Scholar
    • Export Citation
  • Shiklomanov, A. I., , R. B. Lammers, , M. A. Rawlins, , L. C. Smith, , and T. M. Pavelsky, 2007: Temporal and spatial variations in maximum river discharge from a new Russian data set. J. Geophys. Res., 112, G04S53, doi:10.1029/2006JG000352.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., , D. Qin, , M. Manning, , M. Marquis, , K. Averyt, , M. M. B. Tignor, , H. L. Miller Jr., , and Z. Chen, Eds., 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

  • Stieglitz, M., , S. J. Déry, , V. E. Romanovsky, , and T. E. Osterkamp, 2003: The role of snow cover in the warming of arctic permafrost. Geophys. Res. Lett., 30, 1721, doi:10.1029/2003GL017337.

    • Search Google Scholar
    • Export Citation
  • Stone, R. S., , E. G. Dutton, , J. M. Harris, , and D. Longenecker, 2002: Earlier spring snowmelt in northern Alaska as an indicator of climate change. J. Geophys. Res., 107, 4089, doi:10.1029/2000JD000286.

    • Search Google Scholar
    • Export Citation
  • Storck, P., , D. P. Lettenmaier, , and S. M. Bolton, 2002: Measurement of snow interception and canopy effects on snow accumulation and melt in a mountainous maritime climate, Oregon, United States. Water Resour. Res., 38, 1223, doi:10.1029/2002WR001281.

    • Search Google Scholar
    • Export Citation
  • Stow, D. A., and Coauthors, 2004: Remote sensing of vegetation and land-cover change in arctic tundra ecosystems. Remote Sens. Environ., 89, 281308.

    • Search Google Scholar
    • Export Citation
  • Sturm, M., , C. Racine, , and K. Tape, 2001: Climate change: Increasing shrub abundance in the Arctic. Nature, 411, 546547.

  • Su, F., , J. C. Adam, , L. C. Bowling, , and D. P. Lettenmaier, 2005: Streamflow simulations of the terrestrial Arctic domain. J. Geophys. Res., 110, D08112, doi:10.1029/2004JD005518.

    • Search Google Scholar
    • Export Citation
  • Su, F., , J. C. Adam, , K. E. Trenberth, , and D. P. Lettenmaier, 2006: Evaluation of surface water fluxes of the pan-Arctic land region with a land surface model and ERA-40 reanalysis. J. Geophys. Res., 111, D05110, doi:10.1029/2005JD006387.

    • Search Google Scholar
    • Export Citation
  • Tan, A., , J. C. Adam, , and D. P. Lettenmaier, 2011: Change in spring snowmelt timing in Eurasian Arctic rivers. J. Geophys. Res., 116, D03101, doi:10.1029/2010JD014337.

    • Search Google Scholar
    • Export Citation
  • Tennessee Valley Authority, 1972: Heat and mass transfer between a water surface and the atmosphere. Tennessee Valley Authority Laboratory Rep. 14, Water Resources Rep. 0-6803, 166 pp.

  • Thornton, P. E., , and S. W. Running, 1999: An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agric. For. Meteor., 93, 211228.

    • Search Google Scholar
    • Export Citation
  • Troy, T. J., , J. Sheffield, , and E. F. Wood, 2011: Estimation of the terrestrial water budget over northern Eurasia through the use of multiple data sources. J. Climate, 24, 32723293.

    • Search Google Scholar
    • Export Citation
  • U.S. Army Corps of Engineers, 1956: Snow hydrology, summary report of the snow investigations. U.S. Army Corps of Engineers Rep., 433 pp.

  • Vincent, L. A., , W. A. van Wijngaarden, , and R. Hopkinson, 2007: Surface temperature and humidity trends in Canada for 1953–2005. J. Climate, 20, 51005113.

    • Search Google Scholar
    • Export Citation
  • Voeikov, A. I., 1889: Snow cover, its effects on soil, climate, and weather and methods of investigations (in Russian). Notes Russ. Geogr. Soc. Gen. Geogr.,18, 212 pp.

  • Wagener, T., , D. P. Boyle, , M. J. Lees, , H. S. Wheater, , H. V. Gupta, , and S. Sorooshian, 2001: A framework for development and application of hydrological models. Hydrol. Earth Syst. Sci., 5, 1326.

    • Search Google Scholar
    • Export Citation
  • Wang, L., , M. Sharp, , R. Brown, , C. Derksen, , and B. Rivard, 2005: Evaluation of spring snow covered area depletion in the Canadian Arctic from NOAA snow charts. Remote Sens. Environ., 95, 453463.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , J. R. Key, , and Y. Liu, 2010: A thermodynamic model for estimating sea and lake ice thickness with optical satellite data. J. Geophys. Res., 115, C12035, doi:10.1029/2009JC005857.

    • Search Google Scholar
    • Export Citation
  • Westermann, S., , J. Lüers, , M. Langer, , K. Piel, , and J. Boike, 2009: The annual surface energy budget of a high-arctic permafrost site on Svalbard, Norway. Cryosphere, 3, 245263.

    • Search Google Scholar
    • Export Citation
  • White, D., and Coauthors, 2007: The arctic freshwater system: Changes and impacts. J. Geophys. Res., 112, G04S54, doi:10.1029/2006JG000353.

    • Search Google Scholar
    • Export Citation
  • Wiesnet, D. R., , C. F. Ropelewsk, , G. J. Kuklaand, , and D. A. Robinson, 1987: A discussion of the accuracy of NOAA satellite-derived global seasonal snow cover measurements. Large Scale Effects of Seasonal Snow Cover, IAHS Press, 291–304.

  • Willmott, C. J., , and K. Matsuura, cited 2011: Terrestrial air temperature and precipitation: Monthly and annual time series (1930-2004). [Available online at http://climate.geog.udel.edu/~climate/html_pages/archive.html.]

  • Woo, M. K., , D. Yang, , and K. L. Young, 1999: Representativeness of arctic weather station data for the computation of snowmelt in a small area. Hydrol. Processes, 13, 18591870.

    • Search Google Scholar
    • Export Citation
  • Yang, D., , D. Robinson, , Y. Zhao, , T. Estilow, , and B. Ye, 2003: Streamflow response to seasonal snow cover extent changes in large Siberian watersheds. J. Geophys. Res., 108, 4578, doi:10.1029/2002JD003149.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., , and J. E. Walsh, 2007: Relative impacts of vegetation coverage and leaf area index on climate change in a greener north. Geophys. Res. Lett., 34, L15703, doi:10.1029/2007GL030852.

    • Search Google Scholar
    • Export Citation
  • Zhao, H., , and R. Fernandes, 2009: Daily snow cover estimation from Advanced Very High Resolution Radiometer Polar Pathfinder data over Northern Hemisphere land surfaces during 1982–2004. J. Geophys. Res., 114, D05113, doi:10.1029/2008JD011272.

    • Search Google Scholar
    • Export Citation
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    Spatial distribution of monthly mean SCE from (left) NOAA satellite observations and (right) the VIC model over North America and Eurasia from (top) April, (middle) May, and (bottom) June for the period 1972–2006. The percentages on the map show the snow cover area fractions from NOAA and VIC for each month over the 35-yr period of analysis.

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    Monthly time series of snow cover fraction (SCF) and their trends (the unit of the trend slope lines is inverse years) derived from the VIC model (circles and dashed lines) and NOAA observations (the two solid lines) for the period 1972–2006 for (left) North America and (right) Eurasia over the pan-Arctic land area.

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    Latitudinal variations of the SCE trends and their area fractions derived from the VIC model and NOAA satellite observations over (left) North American and (right) Eurasian SCZs, including the SCSZs and SCNZs as indicated by the arrows, from April through June for the period 1972–2006. The percentage under each bar chart is the trend significance for each 5° (N) of latitude (expressed as a confidence level).

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    Snow cover area weights [the fraction of snow-covered area falling within each 5° (N) latitude band] for (left) North American and (right) Eurasian SCSZs from April through June for the period 1972–2006.

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    Correlations between three surface energy fluxes: SNR, SH, and LH and NOAA satellite SCE observations for each latitude band in the (left) North American and (right) Eurasian SCSZs from April through June. The significance level of p < 0.025 (dashed lines) was calculated by a Student’s t test.

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    Latitudinal variations in the changes of surface energy fluxes in the (left) North American and (right) Eurasian SCSZs for (from top to bottom) April–June for the period 1972–2006 by 5° (N) latitude band. The number in each bar denotes the relative role of the total energy attributable to snow cover changes.

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    Relative role of the changes in three surface energy fluxes during the April–June part of the year area averaged over each SCSZ for (a) North America and (b) Eurasia for the period 1972–2006. The number in each bar denotes the contribution of the total energy attributable to snow cover changes.

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    Correlations between NOAA SCE observations and 15 hydroclimatic characteristics in the (left) North American and (right) Eurasian SCSZs from April to June for the period 1972–2006. The correlation is statistically significant at a level of p < 0.025 when its absolute value is >0.34. The X denotes that the correlation is not significant. The abbreviations for the hydroclimate variables are defined in the text.

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Relationships between Recent Pan-Arctic Snow Cover and Hydroclimate Trends

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  • 1 Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington
  • 2 Environmental Science and Engineering Program, University of Northern British Columbia, Prince George, British Columbia, Canada
  • 3 National Climatic Data Center, Asheville, North Carolina
  • 4 Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington
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Abstract

Using the Variable Infiltration Capacity (VIC) land surface model forced with gridded climatic observations, the authors reproduce spatial and temporal variations of snow cover extent (SCE) reported by the National Oceanic and Atmospheric Administration (NOAA) Northern Hemisphere weekly satellite SCE data. Both observed and modeled North American and Eurasian snow cover in the pan-Arctic have statistically significant negative trends from April through June over the period 1972–2006. To diagnose the causes of the pan-Arctic SCE recession, the authors identify the role of surface energy fluxes generated in VIC and assess the relationships between 15 hydroclimatic indicators and NOAA SCE observations over each snow-covered sensitivity zone (SCSZ) for both North America and Eurasia. The authors find that surface net radiation (SNR) provides the primary energy source and sensible heat (SH) plays a secondary role in observed changes of SCE. As compared with SNR and SH, latent heat has only a minor influence on snow cover changes. In addition, these changes in surface energy fluxes resulting in the pan-Arctic snow cover recession are mainly driven by statistically significant decreases in snow surface albedo and increased air temperatures (surface air temperature, daily maximum temperature, and daily minimum temperature), as well as statistically significant increased atmospheric water vapor pressure. Contributions of other hydroclimate variables that the authors analyzed (downward shortwave radiation, precipitation, diurnal temperature range, wind speed, and cloud cover) are not significant for observed SCE changes in either the North American or Eurasian SCSZs.

Corresponding author address: Dennis P. Lettenmaier, Department of Civil and Environmental Engineering, University of Washington, Box 352700, Seattle, WA 98195-2700. E-mail: dennisl@u.washington.edu

Abstract

Using the Variable Infiltration Capacity (VIC) land surface model forced with gridded climatic observations, the authors reproduce spatial and temporal variations of snow cover extent (SCE) reported by the National Oceanic and Atmospheric Administration (NOAA) Northern Hemisphere weekly satellite SCE data. Both observed and modeled North American and Eurasian snow cover in the pan-Arctic have statistically significant negative trends from April through June over the period 1972–2006. To diagnose the causes of the pan-Arctic SCE recession, the authors identify the role of surface energy fluxes generated in VIC and assess the relationships between 15 hydroclimatic indicators and NOAA SCE observations over each snow-covered sensitivity zone (SCSZ) for both North America and Eurasia. The authors find that surface net radiation (SNR) provides the primary energy source and sensible heat (SH) plays a secondary role in observed changes of SCE. As compared with SNR and SH, latent heat has only a minor influence on snow cover changes. In addition, these changes in surface energy fluxes resulting in the pan-Arctic snow cover recession are mainly driven by statistically significant decreases in snow surface albedo and increased air temperatures (surface air temperature, daily maximum temperature, and daily minimum temperature), as well as statistically significant increased atmospheric water vapor pressure. Contributions of other hydroclimate variables that the authors analyzed (downward shortwave radiation, precipitation, diurnal temperature range, wind speed, and cloud cover) are not significant for observed SCE changes in either the North American or Eurasian SCSZs.

Corresponding author address: Dennis P. Lettenmaier, Department of Civil and Environmental Engineering, University of Washington, Box 352700, Seattle, WA 98195-2700. E-mail: dennisl@u.washington.edu

1. Introduction

Snow cover is an important climatic and hydrologic land surface variable. Its long-term variations serve as both indicators and controls of climate change over much of the Northern Hemisphere land area (Gray and Male 1981; Groisman et al. 1994; Frei and Robinson 1999; Robinson and Frei 2000). Therefore, spatial and temporal variations of snow cover across the Northern Hemisphere have attracted considerable scientific attention. Many studies have used the visible satellite imagery produced by the National Oceanic and Atmospheric Administration (NOAA) (Robinson et al. 1993; Groisman et al. 1994; Frei and Robinson 1999; Serreze et al. 2000; Dye 2002; Stone et al. 2002; Brown et al. 2007; Déry and Brown 2007; Brown and Mote 2009; Zhao and Fernandes 2009; Brown et al. 2010; Choi et al. 2010; Derksen et al. 2010; Brown and Robinson 2011; Derksen and Brown 2011; Liston and Hiemstra 2011). Variations in snow cover extent (SCE) have been shown to have a significant effect on surface energy and mass exchanges over the pan-Arctic land region (Serreze et al. 2000; Peterson et al. 2002; Serreze et al. 2003; Yang et al. 2003; McClelland et al. 2006; Adam et al. 2007; Shiklomanov et al. 2007; Rawlins et al. 2010). However, the interpretation of changes in SCE and its timing is complicated by the sparseness of in situ observations of key variables, such as surface radiative and turbulent fluxes, which affect snow surface energy exchange processes (Cline 1997), as well as other hydroclimate variables (e.g., precipitation P and temperature) that also affect SCE (Jol et al. 2009).

Numerous studies have been conducted in an attempt to understand the relationships between SCE observations and the other hydroclimatic variables, such as surface radiation, precipitation, temperature, and river discharge. These studies have focused on a range of spatial scales, including the local scale at individual meteorological stations (Baker et al. 1992; Koivusalo and Kokkonen 2002; Stieglitz et al. 2003; Shi et al. 2009; Westermann et al. 2009), the regional scale over areas such as the Red River Valley of North Dakota and Minnesota (Dyer and Mote 2002) and Trail Valley Creek of northern Canada (Marsh and Pomeroy 1996; Pohl and Marsh 2006), and continental-scale studies over North America or Eurasia (Voeikov 1889; Karl et al. 1993; Groisman et al. 1994; Clark et al. 1999; Déry et al. 2005; Bulygina et al. 2009; Tan et al. 2011). Few studies, however, have examined the large-scale factors that control the relative importance of snow surface energy balance components and help to diagnose the direct or indirect causes of large-scale snow cover changes (Male and Granger 1981; Cline 1997; Leathers et al. 2004; Dyer and Mote 2007; Shi et al. 2011), especially for the pan-Arctic land area.

Land surface models (e.g., Liang et al. 1994; Oleson et al. 2004) have improved to the point that they may, in some cases, serve as surrogates for in situ hydroclimatic observations. Offline runs of these models can provide snow surface energy balance components and offer an opportunity to investigate the nature of spatial and temporal variability of snow cover changes (Betts et al. 2009; Shi et al. 2010, 2011).

In this paper, we follow three steps in our assessment of the relationships between recent pan-Arctic snow cover and hydroclimate trends during the late spring and early summer (April–June) period:

  1. We evaluate whether a land surface model is able to reconstruct spatial and temporal changes of observed snow cover across the pan-Arctic land area.
  2. We identify the individual role of modeled surface energy fluxes in the snow surface energy budget and determine which flux is most responsible for observed pan-Arctic snow cover changes.
  3. We assess the relationships between snow cover observations and hydroclimatic indicators and identify possible causes of snow cover changes over the pan-Arctic.

This work is an extension of Shi et al. (2011), in which a preliminary assessment was conducted on the role of surface radiative and turbulent fluxes in pan-Arctic snow cover changes during spring and summer. In section 2 of this paper, we describe observed and modeled datasets on which the subsequent analyses are based. In section 3, we (i) use a nonparametric trend test to analyze monotonic trends in the satellite snow cover observations and in corresponding reconstructions generated by the land surface model; (ii) examine correlations between SCE observations and surface energy fluxes generated from the land surface model; and (iii) estimate the relative importance of each component in the snow surface energy balance. Section 3 ends with a discussion of possible causes of snow cover changes over the pan-Arctic. Concluding remarks are in section 4.

2. Datasets

a. Observed and modeled snow cover extent data

Observed monthly values of SCE were extracted from the weekly snow cover and sea ice extent, version 3.1, product for the Northern Hemisphere (http://nsidc.org/data/nsidc-0046.html) maintained at the National Snow and Ice Data Center (NSIDC), which combines snow cover and sea ice extent for the period from October 1966 through June 2007 (Armstrong and Brodzik 2007). The dataset is based on weekly maps of continental SCE produced by NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) (Robinson et al. 1993; Frei and Robinson 1999), which were derived from digitized versions of manual interpretations of Advanced Very High Resolution Radiometer (AVHRR), Geostationary Operational Environmental Satellite (GOES), and other visible-band satellite data. This satellite-based dataset has been regridded to the NSIDC Equal-Area Scalable Earth (EASE) grid with a spatial resolution of 25 km by Armstrong and Brodzik (2007). The SCE monthly means for each grid cell contain a binary value (1 or 0). A 1 indicates 50% or greater probability of occurrence of snow, whereas 0 means probability of occurrence was less than 50%. Our study is restricted to the period after 1972 since there are some missing charts between 1967 and 1971 (Robinson 2000). Although ending the time series in 2006 leaves out some exceptionally low Arctic spring SCE values in recent years (e.g., 2008–10), the nonparametric statistical method we used (section 3a) is robust to modest changes in the length of the record analyzed. In addition, we did not include Greenland in the analyses since its snow cover is mainly perennial in nature. Brown et al. (2010) have assessed this SCE record (commonly referred to as the NOAA weekly SCE record) in comparison with other available Arctic snow cover datasets. In general, their study and others (e.g., Wiesnet et al. 1987; Robinson et al. 1993) have found that the NOAA weekly SCE dataset is reliable for continental-scale studies of snow cover variability. It has become a widely used tool for deriving trends in climate-related studies (Groisman et al. 1994; Déry and Brown 2007; Flanner et al. 2009; Derksen et al. 2010; Derksen and Brown 2011), notwithstanding uncertainties in some parts of the domain for certain times of the year, especially during summertime over northern Canada (Wang et al. 2005). A more recent update to the dataset we used [NOAA snow chart climate data record (CDR)] is now available (Brown and Robinson 2011), but the differences between the new CDR and the dataset we used at the pan-Arctic scale are small (Shi et al. 2011).

We reconstructed SCE from 1972 to 2006 using the Variable Infiltration Capacity (VIC) model, which is a macroscale land surface hydrologic model that solves the energy and water balance and represents ephemeral snow cover over a gridded domain (Liang et al. 1994, 1996). The offline simulations from VIC used here were at a 3-h time step in full energy balance mode (meaning that the model closes its surface energy budget) forced with daily precipitation, maximum and minimum temperatures, and wind speed through 2007 at a spatial resolution (EASE grid) of 100 km. The forcing data were constructed using methods outlined by Adam and Lettenmaier (2008, hereafter AL2008) as described in section 2b. The model simulations used calibrated parameters, such as soil depths and infiltration characteristics, from Su et al. (2005) for the period of 1979–99 over the pan-Arctic drainage basins. The snow parameterization in VIC represents snow accumulation and ablation processes using a two-layer energy and mass balance approach (Andreadis et al. 2009) and a canopy snow interception algorithm (Storck et al. 2002) when an overstory is present. In the VIC model, each grid cell is partitioned into five elevation (snow) bands, which can include multiple land cover types (tiles). The snow model is then applied to each tile separately. When snow water equivalent is >3 mm, VIC assumes that snow fully covers the tile. For each grid cell, the simulated SCE is calculated as the area averages of the tiles. To set the initial conditions in VIC, we initialized the model with a 10-yr (1962–71) spinup simulation.

b. Hydroclimatic forcing data

We forced VIC with the University of Washington (UW) extended gridded precipitation product (AL2008) for the period 1972–2006 over the pan-Arctic land region. Monthly precipitation in AL2008 was derived from the Arctic land surface precipitation gridded monthly time series, version 1.03, from the University of Delaware (UDel), which is interpolated from in situ stations (see Willmott and Matsuura 2011). The sources of the station data include the Global Historical Climatology Network, the Atmospheric Environment Service in Environment Canada, the Russian Institute for Hydrometeorological Information, the Greenland Climate Network, the Automatic Weather Station Project, and the Global Surface Summary of Day. To improve the monthly precipitation estimates, the UDel product was adjusted by AL2008 to account for gauge undercatch since gauge-measured precipitation data may underestimate solid precipitation in winter by 10%–50% (Adam and Lettenmaier 2003). Furthermore, the Adam et al. (2006) corrections for orographic effects were applied. Finally, daily time series of precipitation were produced by rescaling the updated product of Sheffield et al. (2004) to match the monthly time series of precipitation from AL2008.

Monthly average maximum and minimum temperatures were created using observed monthly mean time series from the UDel product and the monthly diurnal temperature range from the Climatic Research Unit (CRU; Brohan et al. 2006) Surface Temperature, version 3, dataset (CRUTEM3; available online at http://www.cru.uea.ac.uk/cru/data/temperature/). As with the precipitation product, daily disaggregation for temperatures was performed using the method of AL2008. In addition, surface air temperature (SAT) anomaly data were also derived from CRU, which are based on anomalies from the long-term mean temperature for the period 1961–90 and are available for each month since 1850. The wind speed was obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996). Finally, all these data were regridded to the 100-km EASE grid using an inverse-distance interpolation as implemented in Su et al. (2005) including the NOAA SCE observations that were aggregated from the 25-km product. The monthly mean time series of daily maximum temperature Tmax, daily minimum temperature Tmin, and wind speed (WS) were derived for the study domains similar to the precipitation field.

Surface radiative fluxes were calculated by using a temperature index (TIND) scheme (Kimball et al. 1997; Thornton and Running 1999; Shi et al. 2010) wherein downward shortwave radiation (DSW) and downward longwave radiation (DLW) are estimated based on relationships with the diurnal temperature range (DTR) and daily average temperature, respectively. DLW is dependent on atmospheric water vapor pressure (VP), and cloud cover (CC) is also used in the calculation. TIND has been commonly used in model intercomparison experiments such as the Project for Intercomparison of Land Parameterization Schemes (PILPS) (e.g., Pitman et al. 1999) and to force land surface models, such as VIC, for long-term simulations in cases when direct observations of radiation fluxes are not available. In VIC, the snow surface albedo is assumed to decay with age based on relationships published by the U.S. Army Corps of Engineers (1956). Shi et al. (2010) evaluated DSW, DLW, and albedo computed in an offline simulation of VIC embedded with TIND along with satellite data and global reanalysis products in comparison with in situ observations from the Global Energy Balance Archive (GEBA; Ohmura et al. 1989) and showed that TIND-based estimates compared well with observations over the pan-Arctic land region. In comparison with in situ observations, the mean seasonal DSW from the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr Re-Analysis (ERA-40), the ECMWF Interim Re-Analysis (ERA-Interim), the International Satellite Cloud Climatology Project-Flux Data (ISCCP-FD), and the TIND-based scheme in VIC all have small-to-moderate (up to ±20 W m−2) biases. ERA-40, ERA-Interim, and VIC DLW deseasonalized monthly anomalies had high correlations (r = 0.96, 0.97, and 0.91, respectively) with GEBA observations, whereas the correlation for the satellite-based (ISCCP-FD) product was somewhat lower. VIC deseasonalized monthly albedo had similar anomaly correlations with GEBA observations, as did ERA-40, ERA-Interim, and ISCCP-FD estimates (Shi et al. 2010).

Surface net radiation (SNR) was obtained as the sum of net shortwave (SW) and longwave (LW) radiative fluxes. SW at the snow surface is a measure of the difference between DSW and upward shortwave radiation (USW). USW is the product of DSW and snow surface albedo (ALB), which is assumed to decay with age as described by U.S. Army Corps of Engineers (1956). LW is the sum of DLW emitted by the atmosphere and the fluxes emitted upward by a snow surface. DLW was estimated using Eq. (2.42) from Bras (1990), which is based on air temperature and a function for emissivity from Tennessee Valley Authority (1972). The turbulent fluxes [sensible heat (SH) and latent heat (LH)] near the snow surface were estimated using VIC’s bulk aerodynamic approach, which is described in Andreadis et al. (2009). In this algorithm, bulk transfer coefficients for momentum, heat, and water vapor are calculated initially for neutral atmospheric boundary surface layer conditions (Price and Dunne 1976). Subsequently, the aerodynamic resistance in the presence of snow cover is corrected using the bulk Richardson’s number for stable and/or unstable atmospheric conditions (Anderson 1976) as implemented in the VIC snow model (Andreadis et al. 2009). A similar approach has been successfully applied in various Arctic settings (e.g., Hinzman et al. 1991; Woo et al. 1999; Boike et al. 2003). Other surface energy-related variables, such as CC, DTR, and VP, were generated using TIND. The energy flux convention is that surface energy fluxes toward the snow surface are defined as positive.

3. Results and discussion

Recent studies have shown that SCE over northern Canada becomes decoupled with air temperature anomalies in July (Wang et al. 2005; Brown et al. 2007). These results along with the recent findings of D. A. Robinson (2011, personal communication) suggest that July and August snow cover time series may not be suitable for trend analysis (Déry and Brown 2007). Moreover, comparisons of the NOAA satellite snow cover observations in July and August with the Global Land Ice Measurements from Space (GLIMS) database (http://nsidc.org/glims/) suggest that most of the July and August SCE in fact is associated with glaciers. Therefore, we focus on the period from April through June.

a. Spatial and temporal variability of SCE

The spatial distribution of monthly mean SCE from April through June for 1972–2006 is shown in Fig. 1 for North America and Eurasia. Observed and simulated long-term means of SCE expressed as area fractions were calculated for each month. The percentages on the map show the snow cover area fractions from VIC and the NOAA SCE data (hereafter NOAA) for each month over the 35-yr period. From April through June, the VIC estimates are always higher than NOAA for both continents. The overestimation for Eurasia is somewhat larger than for North America. In April, VIC agrees reasonably well not only in representing the observed spatial patterns of SCE but also the magnitudes of snow cover area fraction over the pan-Arctic. The difference between observed and simulated snow cover area fraction is quite small (1.3% for Eurasia and 1.9% for North America). In June, the patterns are similar, with a VIC bias of 2.6% for both continents. Although the SCE spatial distributions from VIC and NOAA are quite close in June, differences are nonetheless evident in some areas, such as northeastern Eurasia, which are likely related to the lower surface air temperatures over this mountainous region in the forcing of VIC as described in Su et al. (2006). The most significant difference between observations and simulations is in May when the snow cover area fraction bias is 3.1% for North America and 6.6% in Eurasia. Overall, the match of VIC estimates with NOAA observations is quite good, with mean absolute bias over both continents equal to 1.6% in April, 4.9% for May, and 2% in June.

Fig. 1.
Fig. 1.

Spatial distribution of monthly mean SCE from (left) NOAA satellite observations and (right) the VIC model over North America and Eurasia from (top) April, (middle) May, and (bottom) June for the period 1972–2006. The percentages on the map show the snow cover area fractions from NOAA and VIC for each month over the 35-yr period of analysis.

Citation: Journal of Climate 26, 6; 10.1175/JCLI-D-12-00044.1

To examine long-term trends in the SCE time series, we used the nonparametric Mann–Kendall trend test (Mann 1945) for trend significance and the Sen method (Sen 1968) to estimate their slopes. A 5% significance level (two-sided test) was selected in the trend significance tests. For both VIC and NOAA, trend tests were performed on the monthly SCE time series area averaged over the snow-covered portions of Eurasia and North America. Figure 2 shows these results from April through June for VIC and NOAA. Strong negative trends were detected in NOAA in both North American and Eurasian sectors of the pan-Arctic, which are statistically significant (p < 0.025), except for Eurasia in April. Negative SCE trends with similar significance levels were reproduced by VIC for both continents from April through June. However, the trend slope magnitudes for NOAA and VIC differ somewhat, especially in Eurasia, where the simulated trend slope from VIC is about 60% of that from NOAA in May and 50% of the NOAA magnitude for June. This discrepancy is likely related to uncertainties in the NOAA weekly SCE dataset (Wang et al. 2005; Déry and Brown 2007) and the VIC forcings, which generally are of higher quality in North America than Eurasia because of the availability of a denser observational network (Niu and Yang 2007). Table 1 summarizes correlation coefficients due to the linear trend and the variability between VIC and NOAA SCE time series over North America and Eurasia from April to June for the period 1972–2006. The significance level (p value) is based on a two-tailed Student’s t test with 33 degrees of freedom. The VIC and NOAA SCE time series are correlated with a very high significance level, not only for the secular trend (p < 0.0001) but also for the variability (p < 0.03).

Fig. 2.
Fig. 2.

Monthly time series of snow cover fraction (SCF) and their trends (the unit of the trend slope lines is inverse years) derived from the VIC model (circles and dashed lines) and NOAA observations (the two solid lines) for the period 1972–2006 for (left) North America and (right) Eurasia over the pan-Arctic land area.

Citation: Journal of Climate 26, 6; 10.1175/JCLI-D-12-00044.1

Table 1.

Correlation coefficients due to the linear trend and the variability for the monthly time series (from April to June) of SCE derived from VIC and NOAA observations in the North American and Eurasian SCSZs for the period 1972–2006. The significance level (p value) was calculated using a two-tailed Student’s t test with 33 degrees of freedom.

Table 1.

Figures 1 and 2 show that biases both in mean reconstructed SCE and in simulated trend slopes for Eurasia are relatively larger than for North America. These biases in the model reconstructions could have several causes. We believe the most likely is errors in the model meteorological forcings, due especially to the sparseness of the gauge network, changes in observing instruments and protocols, and similar issues (e.g., AL2008). Model structural errors (e.g., Wagener et al. 2001) and model parameter estimates (e.g., Shi et al. 2008) could also be causes. Model parameter errors were reduced through a process of calibration following methods similar to those reported by Troy et al. (2011).

Figure 3 shows the latitudinal variations of SCE trends and their area fractions from VIC and NOAA from April through June. The percentage under each bar chart is the trend significance at each 5° of latitude (expressed as a confidence level). Figure 3 shows that the snow cover area fraction for each month has a latitudinal pattern, which in general is at a minimum in the lowest latitude band and then increases with latitude poleward. The figure clearly shows that VIC lags behind NOAA for the snowmelt during April and May, especially in the lower latitude bands, whereas SCE in June shows remarkable consistency between VIC and NOAA observations. The trend slopes have a negative sign nearly everywhere but without latitudinal patterns. Although the trend slope generally decreases to zero at higher latitudes, it is positive in April for the band 55°–60°N over Eurasia. Most importantly, VIC shows a strong ability to reproduce the NOAA SCE trends for almost all the latitudinal bands over North America and Eurasia, not only in direction but also in their statistical significance. The magnitude of the VIC trend slopes agrees well with NOAA for most latitude bands.

Fig. 3.
Fig. 3.

Latitudinal variations of the SCE trends and their area fractions derived from the VIC model and NOAA satellite observations over (left) North American and (right) Eurasian SCZs, including the SCSZs and SCNZs as indicated by the arrows, from April through June for the period 1972–2006. The percentage under each bar chart is the trend significance for each 5° (N) of latitude (expressed as a confidence level).

Citation: Journal of Climate 26, 6; 10.1175/JCLI-D-12-00044.1

From April through June, snow mostly covers latitude bands north of 45°N over the pan-Arctic land area, which are denoted as the snow-covered zone (SCZ) in Fig. 3. We selected only those latitudinal bands within which SCE trends were statistically significant at the 90% confidence level for further analyses. For each month, we name these bands the snow cover sensitivity zone (SCSZ). In Fig. 3, the North American and Eurasian SCZs including the SCSZs and snow-covered nonsensitivity zones (SCNZs) are highlighted by different gray-shaded arrows. For example, the SCSZ in May for North America has six latitude bands from 45°–50°N to 70°–75°N, whereas there is only one band (45°–50°N) for the Eurasian SCSZ in April. In addition, the discrepancy of snow cover area fraction between VIC and NOAA for each SCSZ is generally greater at the lower latitudes than at the higher latitudes. However, the importance of these differences is reduced because the weights for the lower latitude bands are relatively small as indicated in Fig. 4.

Fig. 4.
Fig. 4.

Snow cover area weights [the fraction of snow-covered area falling within each 5° (N) latitude band] for (left) North American and (right) Eurasian SCSZs from April through June for the period 1972–2006.

Citation: Journal of Climate 26, 6; 10.1175/JCLI-D-12-00044.1

b. Temporal analyses of surface energy fluxes

The energy balance at a snow surface includes net radiative fluxes, sensible and latent heat fluxes, ground heat fluxes, and the energy transfer due to rain on snow. Over the pan-Arctic, ground heat flux is a small component of the energy balance of melting snowpack compared with radiative and turbulent heat fluxes. Therefore, its effects on total snowmelt can safely be ignored (Gray and Prowse 1993). Similarly, rain on snow has an important influence on the water retention characteristics of snow and water movement in the pack but is of minor importance compared with other energy fluxes (Male and Granger 1981). For these reasons, our trend analysis using the nonparametric Mann–Kendall trend test was applied only to the surface energy inputs at the snow surface (i.e., to SNR, SH, and LH). Table 2 summarizes their monotonic trends over North America and Eurasia from April through June for the period 1972–2006. Trends were computed for monthly means of surface energy fluxes over SCSZs for both continents. Strong positive trends were found in SNR in the North American and Eurasian SCSZs, except for Eurasia where the trend was not statistically significant in April. As with SNR, the SH fluxes also did not have a statistically significant trend in April for Eurasia, whereas they had statistically significant upward trends for other SCSZs. The changes in LH are mostly negative and are statistically significant at p < 0.025 for the North American SCSZ in May and June and at p < 0.01 for the Eurasian SCSZ only in May.

Table 2.

Trend analyses for three surface energy fluxes (SNR, SH, and LH) from April to June for 1972–2006 in the North American and Eurasian SCSZs generated from VIC. The significance level (p value) was calculated using a two-sided Mann–Kendall trend test. Trend slope units are watts per square meter per year.

Table 2.

c. Correlations between observed SCE and modeled surface energy fluxes

The Pearson’s product–moment correlation coefficient was used to assess relationships between NOAA SCE and VIC-simulated surface energy fluxes for each 5° latitude band over the North American and Eurasian SCSZs from April through June (Fig. 5). The correlations in April are all statistically significant at p < 0.025 (two-sided test) for each latitude band in the SCSZs over both continents. For May, SNR and SH have nonsignificant relationships with NOAA SCE from 60°–65°N to 70°–75°N in North America, as well as for the 45°–50°N latitude band over Eurasia. Additionally, the correlation between SH and SCE is positive for 70°–75°N in North America. For June, the latitude bands 45°–50°N over the North American SCSZ and 50°–60°N and 70°–75°N over the Eurasian SCSZ have nonsignificant correlations with SNR and SH. The negative correlations indicate that SNR and SH have opposite trend directions with observed SCE. Generally speaking, SNR has a relatively stronger relationship with NOAA SCE than does SH, especially in Eurasia. For LH, the correlations are always positive and much stronger in North America than that in Eurasia.

Fig. 5.
Fig. 5.

Correlations between three surface energy fluxes: SNR, SH, and LH and NOAA satellite SCE observations for each latitude band in the (left) North American and (right) Eurasian SCSZs from April through June. The significance level of p < 0.025 (dashed lines) was calculated by a Student’s t test.

Citation: Journal of Climate 26, 6; 10.1175/JCLI-D-12-00044.1

d. Role of surface energy fluxes in snow cover changes

In VIC, surface energy fluxes toward the snow surface are defined as positive; therefore, the net radiative and sensible heat fluxes usually have positive signs and supply the energy available for snowmelt. Latent heat fluxes are directed away from the snow surface and reduce the snowmelt energy. However, it was not clear which component(s) of the snow surface energy budget dominate the SCE recession from April through June depicted in Fig. 2. To determine the role of each component in the observed downward trends in SCE, we calculated the increment due to monotonic trends in SNR, SH, and LH over the 35-yr period (ΔSNR, ΔSH, and ΔLH) for each month for the North American and Eurasian SCSZs. Figure 6 summarizes the latitudinal variations of these increments from April through June by latitude band. The ΔSNR has an obvious latitudinal pattern, which is generally large in the lowest latitude bands (e.g., 45°–50°N and 50°–55°N) and then decreases with latitude poleward. Compared with ΔSNR, ΔLH shows a similar pattern with a negative sign in most cases. However, it sharply decreases at the higher latitudes and sometimes is close to zero. In June over Eurasia, ΔLH even has a positive sign for the 70°–75°N band. Corresponding to the patterns in ΔSNR and ΔLH, ΔSH is variable depending upon the months and latitude bands. As shown in Fig. 6, the contribution of ΔSH in April is less than ΔSNR, except for the latitude band 50°–55°N in the North American SCSZ where it is slightly larger than ΔSNR (53.5% of the total energy attributable to snow cover changes). In May, ΔSH is much larger than ΔSNR for the 45°–50°N band in North America, reaching 75.5% of the total energy associated with snow cover changes. Thereafter, its contribution decreases gradually with latitude to the 70°–75°N band. The ΔSNR had a larger contribution in the Eurasian SCSZ than in the North American SCSZ in May and June. From April to June, ΔLH increases significantly with rising surface air temperature. Since ΔLH has a negative sign in the snow surface energy balance, it is effectively canceled by ΔSH with a positive residual in each SCSZ, except in May and June for the Eurasian SCSZ. As shown in the middle and bottom plots of Fig. 6 for Eurasia, the contribution of ΔLH is even larger than ΔSH with an opposite sign at 55°–60°N in May and June. For Eurasia, ΔSH does not follow the same pattern as in North America. Although ΔSH in the lower latitudinal bands has a significant contribution compared with ΔSNR, its impact over the entire SCSZ is smaller because of the small SCSZ weights of these bands as shown in Fig. 4.

Fig. 6.
Fig. 6.

Latitudinal variations in the changes of surface energy fluxes in the (left) North American and (right) Eurasian SCSZs for (from top to bottom) April–June for the period 1972–2006 by 5° (N) latitude band. The number in each bar denotes the relative role of the total energy attributable to snow cover changes.

Citation: Journal of Climate 26, 6; 10.1175/JCLI-D-12-00044.1

Figure 7 shows the relative role of the three surface energy fluxes from April through June averaged over each SCSZ in North America and Eurasia. It is apparent that ΔSNR is the dominant energy source in both continents, accounting for between 58.4% and 97.4% of the energy available for snow cover changes. The contribution of ΔSH plays a secondary role (from 26.7% to 50.7%) and ΔSNR has a larger contribution than ΔSH in Eurasia compared with North America. The ΔLH is always opposite in sign to ΔSH and ΔSNR and almost completely cancels ΔSH in May over Eurasia. However, ΔLH has a smaller absolute value than ΔSH in all other cases, such as in April when it composes only −8.8% and −7.8% of the energy available for snow cover changes over the North American and Eurasian SCSZs, respectively. Therefore, we conclude that ΔLH has a minor influence on pan-Arctic snow cover changes as compared with ΔSNR and ΔSH.

Fig. 7.
Fig. 7.

Relative role of the changes in three surface energy fluxes during the April–June part of the year area averaged over each SCSZ for (a) North America and (b) Eurasia for the period 1972–2006. The number in each bar denotes the contribution of the total energy attributable to snow cover changes.

Citation: Journal of Climate 26, 6; 10.1175/JCLI-D-12-00044.1

e. Causes of pan-Arctic snow cover changes

We have shown above that 1) the time series of continental-scale, late spring, and early summer snow cover over the pan-Arctic have statistically significant negative trends from April to June for the period 1972–2006 and 2) VIC has the ability to reproduce spatial and temporal variations in the NOAA SCE time series. This offers an opportunity to diagnose the causes of observed snow cover changes. In addition to the surface radiative and turbulent fluxes, we also investigated other hydroclimatic indicators that might be related to SCE. Figure 8 shows correlations between NOAA SCE and 15 hydroclimatic indicators over the North American and Eurasian SCSZs from April through June. Apparently, the changes of SCE over the study domains have statistically significant correlations with surface energy fluxes (SNR, SH, LH, SW, and DLW), ALB, and VP, as well as with temperatures (SAT, Tmax, and Tmin). For each of these variables, the absolute values of correlation coefficients are greater than 0.34 at a significance level of p < 0.025. However, other hydroclimatic indicators (DSW, precipitation P, DTR, WS, and CC) show nonsignificant relationships with SCE for both continents.

Fig. 8.
Fig. 8.

Correlations between NOAA SCE observations and 15 hydroclimatic characteristics in the (left) North American and (right) Eurasian SCSZs from April to June for the period 1972–2006. The correlation is statistically significant at a level of p < 0.025 when its absolute value is >0.34. The X denotes that the correlation is not significant. The abbreviations for the hydroclimate variables are defined in the text.

Citation: Journal of Climate 26, 6; 10.1175/JCLI-D-12-00044.1

Table 3 reports changes, which were aggregated based on the Mann–Kendall trend slope from 1972 to 2006, for a number of hydroclimatic indicators potentially related to pan-Arctic SCE changes from April to June over the North American and Eurasian SCSZs. It shows that the increases in SNR are mainly associated with increased SW and increased DLW, whereas emitted upward longwave fluxes do not change much (probably because the snowpack temperature is mostly isothermal during the melt period). Strong upward trends in SW mostly result from statistically significant decreasing trends in ALB, while the contribution from increased DSW trends over Eurasia is minor. Over North America DSW decreases.1 As noted above, DLW from TIND also depends upon DTR and VP. Changes in sensible and latent heat fluxes are mostly dominated by increases in SAT over much of the pan-Arctic region (Table 3; see also Lugina et al. 2005; Serreze and Francis 2006; Bekryaev et al. 2010). For P, the downward trends in the North American SCSZ are statistically significant. However, they are variable in the Eurasian SCSZ, where there is a downward trend in April and increasing trends in May and June, which are all statistically insignificant. The correlations between CC and SCE as well as between WS and SCE are statistically insignificant in each month from April through June.

Table 3.

Total changes Δ for 12 pan-Arctic hydroclimatic characteristics from April to June during the period 1972–2006 in the North American and Eurasian SCSZs. The significance level (p value) was calculated using a two-sided Mann–Kendall trend test. The unit of total change for radiation fluxes (SW, DSW, and DLW) is watts per square meter. Units are degrees Celsius for DTR, SAT, Tmax, and Tmin; hectopascals for VP; millimeters for P; and meters per second for WS. The ALB and CC are dimensionless.

Table 3.

We conclude that SNR provides the primary energy source and SH plays a secondary role in changes of SCE. Relative to SNR and SH, LH has only a minor influence on pan-Arctic snow cover changes. The changes in snow surface energy fluxes resulting in the pan-Arctic snow cover recession are associated with statistically significant decreased ALB, increased surface air temperatures, and increased VP. All these changes are occurring in concert with feedbacks to each other. They cannot be considered in a cause–consequence relationship at the time scale used in this study (months). For example, the SNR increase causes the SCE retreat that decreases ALB, which in turn causes the SNR increase. However, we can quantify the relative role of the surface energy fluxes in the SCE changes (Fig. 7).

Although surface observations over the pan-Arctic domain are brief and sparse, our findings are consistent with a variety of evidence. For instance, recent studies have shown that the most significant and strongest Arctic warming occurred in the most recent 40 years with warming at nearly double the global rate (Serreze et al. 2000; Overland et al. 2004; Chapin et al. 2005; Hinzman et al. 2005; White et al. 2007; Solomon et al. 2007). Furthermore, increasing VP trends were found in previous work that examined data collected mainly in the latter decades of the twentieth century (Dai 2006; Vincent et al. 2007; Santer et al. 2007), which is important as water vapor is itself a greenhouse gas (Held and Soden 2000). Most recently, Isaac and van Wijngaarden (2012) detected larger trends for both VP and SAT during 1981–2010 after examining 309 stations located across North America.

In addition to the surface data, satellite data provide a unique opportunity to gain knowledge of environmental and climate change in the Arctic, as well as the climate model. Enhancement of poleward atmospheric moisture transport in a warmer climate was found to be responsible for amplified Arctic warming in idealized experiments without surface albedo feedback (Graversen and Wang 2009). Also, increasing oceanic moisture transport is correlated with Arctic warming and the amount of sea ice loss (Holland et al. 2006; Wang et al. 2010; Mahlstein and Knutti 2011). Moreover, increasing greenness is occurring in the northern high latitudes (Sturm et al. 2001; Stow et al. 2004; Chapin et al. 2005). An increase in vegetation coverage reduces snow-covered surface albedo (due to the effects of vegetation protruding through relatively thin snow cover), causing a noticeable increase in absorbed surface solar radiation and amplifying the feedback among snow cover, surface albedo, and absorbed solar radiation (Zhang and Walsh 2007). Overall, our findings provide a link to these recent studies and fit into the bigger picture of Arctic systemwide change.

4. Conclusions

Following our pilot study of the role of surface radiative and turbulent fluxes in aggregate changes of SCE for the entire North American and Eurasian land area over the pan-Arctic (Shi et al. 2011), we focused in this paper on spatial and temporal variations of monthly SCE during the late spring and early summer and their relationships with a number of hydroclimatic variables over each SCSZ (latitude bands) for both continents. By exploring long-term monotonic trends in NOAA SCE observations and their relationships with surface energy fluxes and associated variables predicted by the VIC land surface model, we conclude the following:

  1. North American and Eurasian late spring and early summer (from April through June) snow cover over the pan-Arctic has declined significantly for the period 1972–2006. Spatial distribution of monthly SCE and the pattern of its trends (trend signs, rates of changes, and statistical significance levels) are reproduced well by the VIC model.
  2. Surface radiative and turbulent heat fluxes generated by VIC are strongly correlated with observed SCE in the North American and Eurasian SCSZs. SNR supplies the primary energy source for the pan-Arctic late spring and early summer snow recession and SH plays a secondary role in these snow cover changes. Relative to SNR and SH, LH has a minor influence on the changes of SCE.
  3. Correlation analyses of 15 hydroclimatic characteristics and NOAA SCE observations in each SCSZ over North America and Eurasia reveal that the changes in surface energy fluxes resulting in the pan-Arctic late spring and early summer snow cover recession are mainly associated with statistically significant decreased ALB, increased air temperatures (SAT, Tmax, and Tmin), and increased VP, while other hydroclimatic variables (DSW, P, DTR, WS, and CC) have less of an impact on observed SCE changes in SCSZs for both continents.

Acknowledgments

This work was supported by NASA grants NNX07AR18G and NNX08AU68G to the University of Washington, by the support to Dr. Stephen J. Déry from the Canada Research Chairs program and the Natural Sciences and Engineering Research Council of Canada, and by the support to Dr. Pavel Ya. Groisman by the NOAA/Climate Program Office and NOAA/National Climatic Data Center. The authors thank Dr. David A. Robinson and Mr. Thomas Estilow from Rutgers University for their assistance with acquisition of the data sets used in the study. The authors also thank Ms. Elizabeth Clark of the University of Washington, Dr. Chris Derksen from Environment Canada, and two anonymous reviewers for their thorough and constructive comments.

REFERENCES

  • Adam, J. C., , and D. P. Lettenmaier, 2003: Adjustment of global gridded precipitation for systematic bias. J. Geophys. Res., 108, 4257, doi:10.1029/2002JD002499.

    • Search Google Scholar
    • Export Citation
  • Adam, J. C., , and D. P. Lettenmaier, 2008: Application of new precipitation and reconstructed streamflow products to streamflow trend attribution in northern Eurasia. J. Climate, 21, 18071828.

    • Search Google Scholar
    • Export Citation
  • Adam, J. C., , E. A. Clark, , D. P. Lettenmaier, , and E. F. Wood, 2006: Correction of global precipitation products for orographic effects. J. Climate, 19, 1538.

    • Search Google Scholar
    • Export Citation
  • Adam, J. C., , I. Haddeland, , F. Su, , and D. P. Lettenmaier, 2007: Simulation of reservoir influences on annual and seasonal streamflow changes for the Lena, Yenisei, and Ob’ rivers. J. Geophys. Res., 112, D24114, doi:10.1029/2007JD008525.

    • Search Google Scholar
    • Export Citation
  • Anderson, E. A., 1976: A point energy and mass balance model of a snow cover. NOAA Tech. Rep. ERL402-NHELM2, 91 pp.

  • Andreadis, K. M., , P. Storck, , and D. P. Lettenmaier, 2009: Modeling snow accumulation and ablation processes in forested environments. Water Resour. Res., 45, W05429, doi:10.1029/2008WR007042.

    • Search Google Scholar
    • Export Citation
  • Armstrong, R. L., , and M. J. Brodzik, 2007: Northern Hemisphere EASE-Grid weekly snow cover and sea ice extent version 3.1. National Snow and Ice Data Center, Boulder, CO, digital media. [Available online at http://nsidc.org/data/nsidc-0046.html.]

  • Baker, D. G., , D. L. Ruschy, , R. H. Skaggs, , and D. B. Wall, 1992: Air temperature and radiation depressions associated with a snow cover. J. Appl. Meteor., 31, 247254.

    • Search Google Scholar
    • Export Citation
  • Bekryaev, R. V., , I. V. Polyakov, , and V. A. Alexeev, 2010: Role of polar amplification in long-term surface air temperature variations and modern Arctic warming. J. Climate, 23, 38883906.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., , M. Köhler, , and Y. Zhang, 2009: Comparison of river basin hydrometeorology in ERA-Interim and ERA-40 reanalyses with observations. J. Geophys. Res., 114, D02101, doi:10.1029/2008JD010761.

    • Search Google Scholar
    • Export Citation
  • Boike, J., , K. Roth, , and O. Ippisch, 2003: Seasonal snow cover on frozen ground: Energy balance calculations of a permafrost site near Ny-Ålesund, Spitsbergen. J. Geophys. Res., 108, 8163, doi:10.1029/2001JD000939.

    • Search Google Scholar
    • Export Citation
  • Bras, R. L., 1990: Hydrology: An Introduction to Hydrologic Science. Addison-Wesley, 643 pp.

  • Brohan, P., , J. J. Kennedy, , I. Harris, , S. F. B. Tett, , and P. D. Jones, 2006: Uncertainty estimates in regional and global observed temperature changes: A new dataset from 1850. J. Geophys. Res., 111, D12106, doi:10.1029/2005JD006548.

    • Search Google Scholar
    • Export Citation
  • Brown, R., , and P. W. Mote, 2009: The response of Northern Hemisphere snow cover to a changing climate. J. Climate, 22, 21242145.

  • Brown, R., , and D. A. Robinson, 2011: Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. Cryosphere, 5, 219229.

    • Search Google Scholar
    • Export Citation
  • Brown, R., , C. Derksen, , and L. Wang, 2007: Assessment of spring snow cover duration variability over northern Canada from satellite datasets. Remote Sens. Environ., 111, 367381.

    • Search Google Scholar
    • Export Citation
  • Brown, R., , C. Derksen, , and L. Wang, 2010: A multi-data set analysis of variability and change in Arctic spring snow cover extent, 1967–2008. J. Geophys. Res., 115, D16111, doi:10.1029/2010JD013975.

    • Search Google Scholar
    • Export Citation
  • Bulygina, O. N., , V. N. Razuvaev, , and N. N. Korshunova, 2009: Changes in snow cover over Northern Eurasia in the last few decades. Environ. Res. Lett., 4, 045026, doi:10.1088/1748-9326/4/4/045026.

    • Search Google Scholar
    • Export Citation
  • Chapin, F. S., III, and Coauthors, 2005: Role of land-surface changes in Arctic summer warming. Science, 310, 657660.

  • Choi, G., , D. A. Robinson, , and S. Kang, 2010: Changing Northern Hemisphere snow seasons. J. Climate, 23, 53055310.

  • Clark, M. P., , M. C. Serreze, , and D. A. Robinson, 1999: Atmospheric controls on Eurasian snow extent. Int. J. Climatol., 19, 2740.

  • Cline, D. W., 1997: Snow surface energy exchanges and snowmelt at a continental, midlatitude Alpine site. Water Resour. Res., 33, 689701.

    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Recent climatology, variability, and trends in global surface humidity. J. Climate, 19, 35893606.

  • Derksen, C., , and R. D. Brown, 2011: Terrestrial snow (Arctic) in state of the climate in 2010. Bull. Amer. Meteor. Soc., 92, S154S155.

    • Search Google Scholar
    • Export Citation
  • Derksen, C., , R. D. Brown, , and L. Wang, 2010: Terrestrial snow (Arctic) in state of the climate in 2009. Bull. Amer. Meteor. Soc., 91, S93S94.

    • Search Google Scholar
    • Export Citation
  • Déry, S. J., , and R. D. Brown, 2007: Recent Northern Hemisphere snow cover extent trends and implications for the snow-albedo feedback. Geophys. Res. Lett., 34, L22504, doi:10.1029/2007GL031474.

    • Search Google Scholar
    • Export Citation
  • Déry, S. J., , J. Sheffield, , and E. F. Wood, 2005: Connectivity between Eurasian snow cover extent and Canadian snow water equivalent and river discharge. J. Geophys. Res., 110, D23106, doi:10.1029/2005JD006173.

    • Search Google Scholar
    • Export Citation
  • Dye, D. G., 2002: Variability and trends in the annual snow-cover cycle in Northern Hemisphere land areas, 1972-2000. Hydrol. Processes, 16, 30653077.

    • Search Google Scholar
    • Export Citation
  • Dyer, J. L., , and T. L. Mote, 2002: Role of energy budget components on snow ablation from a mid-latitude prairie snowpack. Polar Geogr., 26, 87115.

    • Search Google Scholar
    • Export Citation
  • Dyer, J. L., , and T. L. Mote, 2007: Trends in snow ablation over North America. Int. J. Climatol.,27, 739–748.

  • Flanner, M., , C. Zender, , P. Hess, , N. Mahowald, , T. Painter, , V. Ramanathan, , and P. Rasch, 2009: Springtime warming and reduced snow cover from carbonaceous particles. Atmos. Chem. Phys., 9, 24812497.

    • Search Google Scholar
    • Export Citation
  • Frei, A., , and D. A. Robinson, 1999: Northern Hemisphere snow extent: Regional variability 1972-1994. Int. J. Climatol., 19, 15351560.

    • Search Google Scholar
    • Export Citation
  • Graversen, R. G., , and M. Wang, 2009: Polar amplification in a coupled climate model with locked albedo. Climate Dyn., 33, 629643.

  • Gray, D. M., , and D. H. Male, 1981: Handbook of Snow: Principles, Processes, Management and Use. Pergamon Press, 776 pp.

  • Gray, D. M., , and T. D. Prowse, 1993: Snow and floating ice. Handbook of Hydrology, Vol. 7, D. R. Maidment, Ed., McGraw-Hill, 7.1–7.58.

  • Groisman, P. Ya., , T. R. Karl, , R. W. Knight, , and G. L. Stenchikov, 1994: Changes of snow cover, temperature, and radiative heat balance over the Northern Hemisphere. J. Climate, 7, 16331656.

    • Search Google Scholar
    • Export Citation
  • Held, I., , and B. Soden, 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25, 441475.

  • Hinzman, L., , D. Kane, , and R. Gieck, 1991: Regional snow ablation in the Alaskan Arctic. Northern Hydrology: Selected Perspectives, T. D. Prowse and C. S. L. Ommanney, Eds., The Institute, 122–139.

  • Hinzman, L., and Coauthors, 2005: Evidence and implications of recent climate change in northern Alaska and other arctic regions. Climatic Change, 72, 251298.

    • Search Google Scholar
    • Export Citation
  • Holland, M. M., , C. M. Bitz, , and B. Tremblay, 2006: Future abrupt reductions in the summer Arctic sea ice. Geophys. Res. Lett., 33, L23503, doi:10.1029/2006GL028024.

    • Search Google Scholar
    • Export Citation
  • Isaac, V., , and W. A. van Wijngaarden, 2012: Surface water vapor pressure and temperature trends in North America during 1948–2010. J. Climate, 25, 35993609.

    • Search Google Scholar
    • Export Citation
  • Jol, A., , F. Raes, , and B. Menne, 2009: Impacts of Europe’s changing climate—2008 indicator based assessment. IOP Conf. Ser. Earth Environ. Sci.,6, 292042, doi:10.1088/1755-1307/6/29/292042.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471.

  • Karl, T. R., , P. Ya. Groisman, , R. W. Knight, , and R. Heim, 1993: Recent variations of snow cover and snowfall in North America and their relation to precipitation and temperature variations. J. Climate, 6, 13271344.

    • Search Google Scholar
    • Export Citation
  • Kimball, J., , S. Running, , and R. Nemani, 1997: An improved method for estimating surface humidity from daily minimum temperature. Agric. For. Meteor., 85, 8798.

    • Search Google Scholar
    • Export Citation
  • Koivusalo, H., , and T. Kokkonen, 2002: Snow processes in a forest clearing and in a coniferous forest. J. Hydrol., 262, 145164.

  • Leathers, D. J., , D. Graybeal, , T. Mote, , A. Grundstein, , and D. Robinson, 2004: The role of airmass types and surface energy fluxes in snow cover ablation in the central Appalachians. J. Appl. Meteor., 43, 18871899.

    • Search Google Scholar
    • Export Citation
  • Liang, X., , D. P. Lettenmaier, , E. Wood, , and S. Burgess, 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res., 99 (D17), 14 41514 428.

    • Search Google Scholar
    • Export Citation
  • Liang, X., , E. F. Wood, , and D. P. Lettenmaier, 1996: Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification. Global Planet. Change, 13, 195206.

    • 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.

  • Lugina, K. M., , P. Ya. Groisman, , K. Ya Vinnikov, , V. V. Koknaeva, , and N. A. Speranskaya, 2005: Monthly surface air temperature time series area-averaged over the 30-degree latitudinal belts of the globe, 1881–2004. Trends: A compendium of data on global change, U.S. Department of Energy Oak Ridge National Laboratory Carbon Dioxide Information Analysis Center. [Available online at http://cdiac.esd.ornl.gov/trends/temp/lugina/lugina.html.]

  • Mahlstein, I., , and R. Knutti, 2011: Ocean heat transport as a cause for model uncertainty in projected arctic warming. J. Climate, 24, 14511460.

    • Search Google Scholar
    • Export Citation
  • Male, D., , and R. Granger, 1981: Snow surface energy exchange. Water Resour. Res., 17, 609627.

  • Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13,245259.

  • Marsh, P., , and J. Pomeroy, 1996: Meltwater fluxes at an Arctic forest-tundra site. Hydrol. Processes, 10, 13831400.

  • McClelland, J. W., , S. J. Déry, , B. J. Peterson, , R. M. Holmes, , and E. F. Wood, 2006: A pan-Arctic evaluation of changes in river discharge during the latter half of the 20th century. Geophys. Res. Lett., 33, L06715, doi:10.1029/2006GL025753.

    • Search Google Scholar
    • Export Citation
  • Niu, G. Y., , and Z. L. Yang, 2007: An observation-based formulation of snow cover fraction and its evaluation over large North American river basins. J. Geophys. Res., 112, D21101, doi:10.1029/2007JD008674.

    • Search Google Scholar
    • Export Citation
  • Ohmura, A., , M. Wild, , and H. Gilgen, 1989: Global Energy Balance Archive, GEBA: World Climate Program, Water, project A7. Vol. 2, Verlag der Fachvereine, 62 pp.

  • Oleson, K., and Coauthors, 2004: Technical description of The Community Land Model (CLM). NCAR Tech. Note TN-461+STR, 174 pp.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Peterson, B. J., , R. M. Holmes, , J. W. McClelland, , C. J. Vörösmarty, , R. B. Lammers, , A. I. Shiklomanov, , I. A. Shiklomanov, , and S. Rahmstorf, 2002: Increasing river discharge to the Arctic Ocean. Science, 298, 21712173.

    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., and Coauthors, 1999: Key results and implications from phase 1(c) of the Project for Intercomparison of Land-Surface Parametrization Schemes. Climate Dyn., 15, 673684.

    • Search Google Scholar
    • Export Citation
  • Pohl, S., , and P. Marsh, 2006: Modelling the spatial-temporal variability of spring snowmelt in an arctic catchment. Hydrol. Processes, 20, 17731792.

    • Search Google Scholar
    • Export Citation
  • Price, A., , and T. Dunne, 1976: Energy balance computations of snowmelt in a subarctic area. Water Resour. Res., 12, 686694.

  • Rawlins, M. A., and Coauthors, 2010: Analysis of the arctic system for freshwater cycle intensification: Observations and expectations. J. Climate, 23, 57155737.

    • Search Google Scholar
    • Export Citation
  • Robinson, D. A., 2000: Weekly Northern Hemisphere snow maps: 1966-1999. Proc. 12th Conf. on Applied Climatology, Asheville, NC, Amer. Meteor. Soc., 12–15.

  • Robinson, D. A., , and A. Frei, 2000: Seasonal variability of Northern Hemisphere snow extent using visible satellite data. Prof. Geogr., 52, 307315.

    • Search Google Scholar
    • Export Citation
  • Robinson, D. A., , K. F. Dewey, , and R. R. Heim Jr., 1993: Global snow cover monitoring: An update. Bull. Amer. Meteor. Soc., 74, 16891696.

    • Search Google Scholar
    • Export Citation
  • Santer, B., and Coauthors, 2007: Identification of human-induced changes in atmospheric moisture content. Proc. Natl. Acad. Sci. USA, 104, 15 24815 253.

    • Search Google Scholar
    • Export Citation
  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63,13791389.

  • Serreze, M., , and J. A. Francis, 2006: The Arctic amplification debate. Climatic Change, 76, 241264.

  • Serreze, M., and Coauthors, 2000: Observational evidence of recent change in the northern high-latitude environment. Climatic Change, 46, 159207.

    • Search Google Scholar
    • Export Citation
  • Serreze, M., , D. H. Bromwich, , M. P. Clark, , A. J. Etringer, , T. Zhang, , and R. Lammers, 2003: Large-scale hydro-climatology of the terrestrial Arctic drainage system. J. Geophys. Res., 108, 8160, doi:10.1029/2001JD000919.

    • Search Google Scholar
    • Export Citation
  • Sheffield, J., , A. D. Ziegler, , E. F. Wood, , and Y. Chen, 2004: Correction of the high-latitude rain day anomaly in the NCEP–NCAR reanalysis for land surface hydrological modeling. J. Climate,17, 3814–3828.

  • Shi, X., , A. W. Wood, , and D. P. Lettenmaier, 2008: How essential is hydrologic model calibration to seasonal streamflow forecasting? J. Hydrometeor., 9, 13501363.

    • Search Google Scholar
    • Export Citation
  • Shi, X., , M. Sturm, , G. E. Liston, , R. E. Jordan, , and D. P. Lettenmaier, 2009: SnowSTAR2002 transect reconstruction using a multilayered energy and mass balance snow model. J. Hydrometeor., 10, 11511167.

    • Search Google Scholar
    • Export Citation
  • Shi, X., , M. Wild, , and D. P. Lettenmaier, 2010: Surface radiative fluxes over the pan-Arctic land region: Variability and trends. J. Geophys. Res., 115, D22104, doi:10.1029/2010JD014402.

    • Search Google Scholar
    • Export Citation
  • Shi, X., , P. Ya. Groisman, , S. J. Déry, , and D. P. Lettenmaier, 2011: The role of surface energy fluxes in pan-Arctic snow cover changes. Environ. Res. Lett., 6, 035204 doi:10.1088/1748-9326/6/3/035204.

    • Search Google Scholar
    • Export Citation
  • Shiklomanov, A. I., , R. B. Lammers, , M. A. Rawlins, , L. C. Smith, , and T. M. Pavelsky, 2007: Temporal and spatial variations in maximum river discharge from a new Russian data set. J. Geophys. Res., 112, G04S53, doi:10.1029/2006JG000352.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., , D. Qin, , M. Manning, , M. Marquis, , K. Averyt, , M. M. B. Tignor, , H. L. Miller Jr., , and Z. Chen, Eds., 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

  • Stieglitz, M., , S. J. Déry, , V. E. Romanovsky, , and T. E. Osterkamp, 2003: The role of snow cover in the warming of arctic permafrost. Geophys. Res. Lett., 30, 1721, doi:10.1029/2003GL017337.

    • Search Google Scholar
    • Export Citation
  • Stone, R. S., , E. G. Dutton, , J. M. Harris, , and D. Longenecker, 2002: Earlier spring snowmelt in northern Alaska as an indicator of climate change. J. Geophys. Res., 107, 4089, doi:10.1029/2000JD000286.

    • Search Google Scholar
    • Export Citation
  • Storck, P., , D. P. Lettenmaier, , and S. M. Bolton, 2002: Measurement of snow interception and canopy effects on snow accumulation and melt in a mountainous maritime climate, Oregon, United States. Water Resour. Res., 38, 1223, doi:10.1029/2002WR001281.

    • Search Google Scholar
    • Export Citation
  • Stow, D. A., and Coauthors, 2004: Remote sensing of vegetation and land-cover change in arctic tundra ecosystems. Remote Sens. Environ., 89, 281308.

    • Search Google Scholar
    • Export Citation
  • Sturm, M., , C. Racine, , and K. Tape, 2001: Climate change: Increasing shrub abundance in the Arctic. Nature, 411, 546547.

  • Su, F., , J. C. Adam, , L. C. Bowling, , and D. P. Lettenmaier, 2005: Streamflow simulations of the terrestrial Arctic domain. J. Geophys. Res., 110, D08112, doi:10.1029/2004JD005518.

    • Search Google Scholar
    • Export Citation
  • Su, F., , J. C. Adam, , K. E. Trenberth, , and D. P. Lettenmaier, 2006: Evaluation of surface water fluxes of the pan-Arctic land region with a land surface model and ERA-40 reanalysis. J. Geophys. Res., 111, D05110, doi:10.1029/2005JD006387.

    • Search Google Scholar
    • Export Citation
  • Tan, A., , J. C. Adam, , and D. P. Lettenmaier, 2011: Change in spring snowmelt timing in Eurasian Arctic rivers. J. Geophys. Res., 116, D03101, doi:10.1029/2010JD014337.

    • Search Google Scholar
    • Export Citation
  • Tennessee Valley Authority, 1972: Heat and mass transfer between a water surface and the atmosphere. Tennessee Valley Authority Laboratory Rep. 14, Water Resources Rep. 0-6803, 166 pp.

  • Thornton, P. E., , and S. W. Running, 1999: An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agric. For. Meteor., 93, 211228.

    • Search Google Scholar
    • Export Citation
  • Troy, T. J., , J. Sheffield, , and E. F. Wood, 2011: Estimation of the terrestrial water budget over northern Eurasia through the use of multiple data sources. J. Climate, 24, 32723293.

    • Search Google Scholar
    • Export Citation
  • U.S. Army Corps of Engineers, 1956: Snow hydrology, summary report of the snow investigations. U.S. Army Corps of Engineers Rep., 433 pp.

  • Vincent, L. A., , W. A. van Wijngaarden, , and R. Hopkinson, 2007: Surface temperature and humidity trends in Canada for 1953–2005. J. Climate, 20, 51005113.

    • Search Google Scholar
    • Export Citation
  • Voeikov, A. I., 1889: Snow cover, its effects on soil, climate, and weather and methods of investigations (in Russian). Notes Russ. Geogr. Soc. Gen. Geogr.,18, 212 pp.

  • Wagener, T., , D. P. Boyle, , M. J. Lees, , H. S. Wheater, , H. V. Gupta, , and S. Sorooshian, 2001: A framework for development and application of hydrological models. Hydrol. Earth Syst. Sci., 5, 1326.

    • Search Google Scholar
    • Export Citation
  • Wang, L., , M. Sharp, , R. Brown, , C. Derksen, , and B. Rivard, 2005: Evaluation of spring snow covered area depletion in the Canadian Arctic from NOAA snow charts. Remote Sens. Environ., 95, 453463.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , J. R. Key, , and Y. Liu, 2010: A thermodynamic model for estimating sea and lake ice thickness with optical satellite data. J. Geophys. Res., 115, C12035, doi:10.1029/2009JC005857.

    • Search Google Scholar
    • Export Citation
  • Westermann, S., , J. Lüers, , M. Langer, , K. Piel, , and J. Boike, 2009: The annual surface energy budget of a high-arctic permafrost site on Svalbard, Norway. Cryosphere, 3, 245263.

    • Search Google Scholar
    • Export Citation
  • White, D., and Coauthors, 2007: The arctic freshwater system: Changes and impacts. J. Geophys. Res., 112, G04S54, doi:10.1029/2006JG000353.

    • Search Google Scholar
    • Export Citation
  • Wiesnet, D. R., , C. F. Ropelewsk, , G. J. Kuklaand, , and D. A. Robinson, 1987: A discussion of the accuracy of NOAA satellite-derived global seasonal snow cover measurements. Large Scale Effects of Seasonal Snow Cover, IAHS Press, 291–304.

  • Willmott, C. J., , and K. Matsuura, cited 2011: Terrestrial air temperature and precipitation: Monthly and annual time series (1930-2004). [Available online at http://climate.geog.udel.edu/~climate/html_pages/archive.html.]

  • Woo, M. K., , D. Yang, , and K. L. Young, 1999: Representativeness of arctic weather station data for the computation of snowmelt in a small area. Hydrol. Processes, 13, 18591870.

    • Search Google Scholar
    • Export Citation
  • Yang, D., , D. Robinson, , Y. Zhao, , T. Estilow, , and B. Ye, 2003: Streamflow response to seasonal snow cover extent changes in large Siberian watersheds. J. Geophys. Res., 108, 4578, doi:10.1029/2002JD003149.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., , and J. E. Walsh, 2007: Relative impacts of vegetation coverage and leaf area index on climate change in a greener north. Geophys. Res. Lett., 34, L15703, doi:10.1029/2007GL030852.

    • Search Google Scholar
    • Export Citation
  • Zhao, H., , and R. Fernandes, 2009: Daily snow cover estimation from Advanced Very High Resolution Radiometer Polar Pathfinder data over Northern Hemisphere land surfaces during 1982–2004. J. Geophys. Res., 114, D05113, doi:10.1029/2008JD011272.

    • Search Google Scholar
    • Export Citation
1

In TIND, DSW is calculated by the method of Thornton and Running (1999), which depends upon DTR and VP. Therefore, a decreasing DSW trend over North America is explained by a more rapid increase of Tmin relative to that of Tmax as well as by an increase of VP.

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