• Alavi, N., S. Bélair, V. Fortin, S. Zhang, S. Z. Husain, M. L. Carrera, and M. Abrahamowicz, 2016: Warm season evaluation of soil moisture prediction in the Soil, Vegetation, and Snow (SVS) scheme. J. Hydrometeor., 17, 23152332, https://doi.org/10.1175/JHM-D-15-0189.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anctil, F., and M. Ramos, 2019: Verification metrics for hydrological ensemble forecasts. Handbook of Hydrometeorological Ensemble Forecasting, Springer, 893–922.

    • Crossref
    • Export Citation
  • Arduini, G., G. Balsamo, E. Dutra, J. J. Day, I. Sandu, S. Boussetta, and T. Haiden, 2019: Impact of a multi-layer snow scheme on near-surface weather forecasts. J. Adv. Model. Earth Syst., 11, 46874710, https://doi.org/10.1029/2019MS001725.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balsamo, G., and et al. , 2015: ERA-Interim/Land: A global land surface reanalysis data set. Hydrol. Earth Syst. Sci., 19, 389407, https://doi.org/10.5194/hess-19-389-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bartlett, P. A., M. D. MacKay, and D. L. Verseghy, 2006: Modified snow algorithms in the Canadian Land Surface Scheme: Model runs and sensitivity analysis at three boreal forest stands. Atmos.–Ocean, 44, 207222, https://doi.org/10.3137/ao.440301.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bélair, S., L.-P. Crevier, J. Mailhot, B. Bilodeau, and Y. Delage, 2003a: Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part I: Warm season results. J. Hydrometeor., 4, 352370, https://doi.org/10.1175/1525-7541(2003)4<352:OIOTIL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bélair, S., R. Brown, J. Mailhot, B. Bilodeau, and L.-P. Crevier, 2003b: Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part II: Cold season results. J. Hydrometeor., 4, 371386, https://doi.org/10.1175/1525-7541(2003)4<371:OIOTIL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bhumralkar, C. M., 1975: Numerical experiments on the computation of ground surface temperature in an atmospheric general circulation model. J. Appl. Meteor., 14, 12461258, https://doi.org/10.1175/1520-0450(1975)014<1246:NEOTCO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boone, A., and P. Etchevers, 2001: An intercomparison of three snow schemes of varying complexity coupled to the same land surface model: Local-scale evaluation at an Alpine site. J. Hydrometeor., 2, 374394, https://doi.org/10.1175/1525-7541(2001)002<0374:AIOTSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, R., P. Bartlett, M. MacKay, and D. Verseghy, 2006: Evaluation of snow cover in CLASS for SnowMIP. Atmos.–Ocean, 44, 223238, https://doi.org/10.3137/ao.440302.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brun, E., E. Martin, V. Simon, C. Gendre, and C. Coleou, 1989: An energy and mass model of snow cover suitable for operational avalanche forecasting. J. Glaciol., 35, 333342, https://doi.org/10.1017/S0022143000009254.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brun, E., P. David, M. Sudul, and G. Brunot, 1992: A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting. J. Glaciol., 38, 1322, https://doi.org/10.1017/S0022143000009552.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burke, E. J., R. Dankers, C. D. Jones, and A. J. Wiltshire, 2013: A retrospective analysis of pan Arctic permafrost using the JULES land surface model. Climate Dyn., 41, 10251038, https://doi.org/10.1007/s00382-012-1648-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J., and D. Rind, 1991: The effect of snow cover on the climate. J. Climate, 4, 689706, https://doi.org/10.1175/1520-0442(1991)004<0689:TEOSCO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dawson, N., P. Broxton, and X. Zeng, 2017: A new snow density parameterization for land data initialization. J. Hydrometeor., 18, 197207, https://doi.org/10.1175/JHM-D-16-0166.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and et al. , 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Douville, H., J.-F. Royer, and J.-F. Mahfouf, 1995: A new snow parameterization for the Meteo-France climate model. Climate Dyn., 12, 2135, https://doi.org/10.1007/BF00208760.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dumont, M., L. Arnaud, G. Picard, Q. Libois, Y. Lejeune, P. Nabat, D. Voisin, and S. Morin, 2017: In situ continuous visible and near-infrared spectroscopy of an Alpine snowpack. Cryosphere, 11, 10911110, https://doi.org/10.5194/tc-11-1091-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dutra, E., G. Balsamo, P. Viterbo, P. M. Miranda, A. Beljaars, C. Schär, and K. Elder, 2010: An improved snow scheme for the ECMWF land surface model: Description and offline validation. J. Hydrometeor., 11, 899916, https://doi.org/10.1175/2010JHM1249.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Essery, R., A. Kontu, J. Lemmetyinen, M. Dumont, and C. B. Ménard, 2016: A 7-year dataset for driving and evaluating snow models at an Arctic site (Sodankylä, Finland). Geosci. Instrum. Methods Data Syst., 5, 219227, https://doi.org/10.5194/gi-5-219-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Etchevers, P., and et al. , 2004: Validation of the energy budget of an Alpine snowpack simulated by several snow models (Snow MIP project). Ann. Glaciol., 38, 150158, https://doi.org/10.3189/172756404781814825.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Franz, K. J., T. S. Hogue, and S. Sorooshian, 2008: Operational snow modeling: Addressing the challenges of an energy balance model for National Weather Service forecasts. J. Hydrol., 360, 4866, https://doi.org/10.1016/j.jhydrol.2008.07.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaborit, É., and et al. , 2017: A hydrological prediction system based on the SVS land-surface scheme: Efficient calibration of GEM-Hydro for streamflow simulation over the Lake Ontario basin. Hydrol. Earth Syst. Sci., 21, 48254839, https://doi.org/10.5194/hess-21-4825-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Husain, S. Z., N. Alavi, S. Bélair, M. Carrera, S. Zhang, V. Fortin, M. Abrahamowicz, and N. Gauthier, 2016: The multibudget Soil, Vegetation, and Snow (SVS) scheme for land surface parameterization: Offline warm season evaluation. J. Hydrometeor., 17, 22932313, https://doi.org/10.1175/JHM-D-15-0228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Isabelle, P.-E., D. F. Nadeau, M.-H. Asselin, R. Harvey, K. N. Musselman, A. N. Rousseau, and F. Anctil, 2018: Solar radiation transmittance of a boreal balsam fir canopy: Spatiotemporal variability and impacts on growing season hydrology. Agric. For. Meteor., 263, 114, https://doi.org/10.1016/j.agrformet.2018.07.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Isabelle, P.-E., D. F. Nadeau, F. Anctil, A. N. Rousseau, S. Jutras, and B. Music, 2020: Impacts of high precipitation on the energy and water budgets of a humid boreal forest. Agric. For. Meteor., 280, 107813, https://doi.org/10.1016/j.agrformet.2019.107813.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, J., X. Gao, Z.-L. Yang, R. Bales, S. Sorooshian, R. E. Dickinson, S. Sun, and G. Wu, 1999: Comparative analyses of physically based snowmelt models for climate simulations. J. Climate, 12, 26432657, https://doi.org/10.1175/1520-0442(1999)012<2643:CAOPBS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krinner, G., and et al. , 2018: ESM-SnowMIP: Assessing snow models and quantifying snow-related climate feedbacks. Geosci. Model Dev., 11, 50275049, https://doi.org/10.5194/gmd-11-5027-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, M., D. Marks, J. Dozier, M. Reba, and A. Winstral, 2013: Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models. Adv. Water Resour., 56, 7789, https://doi.org/10.1016/j.advwatres.2013.03.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landry, C. C., K. A. Buck, M. S. Raleigh, and M. P. Clark, 2014: Mountain system monitoring at Senator Beck basin, San Juan Mountains, Colorado: A new integrative data source to develop and evaluate models of snow and hydrologic processes. Water Resour. Res., 50, 17731788, https://doi.org/10.1002/2013WR013711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lehning, M., I. Völksch, D. Gustafsson, T. A. Nguyen, M. Stähli, and M. Zappa, 2006: ALPINE3D: A detailed model of mountain surface processes and its application to snow hydrology. Hydrol. Processes, 20, 21112128, https://doi.org/10.1002/hyp.6204.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lejeune, Y., M. Dumont, J.-M. Panel, M. Lafaysse, P. Lapalus, E. L. Gac, B. Lesaffre, and S. Morin, 2019: 57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m of altitude). Earth Syst. Sci. Data, 11, 7188, https://doi.org/10.5194/essd-11-71-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leonardini, G., F. Anctil, M. Abrahamowicz, É. Gaborit, V. Vionnet, D. F. Nadeau, and V. Fortin, 2020: Evaluation of the Soil, Vegetation, and Snow (SVS) land surface model for the simulation of surface energy fluxes and soil moisture under snow-free conditions. Atmosphere, 11, 278, https://doi.org/10.3390/atmos11030278.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Magnusson, J., N. Wever, R. Essery, N. Helbig, A. Winstral, and T. Jonas, 2015: Evaluating snow models with varying process representations for hydrological applications. Water Resour. Res., 51, 27072723, https://doi.org/10.1002/2014WR016498.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maheu, A., F. Anctil, É. Gaborit V. Fortin, D. F. Nadeau, and R. Therrien, 2018: A field evaluation of soil moisture modelling with the Soil, Vegetation, and Snow (SVS) land surface model using evapotranspiration observations as forcing data. J. Hydrol., 558, 532545, https://doi.org/10.1016/j.jhydrol.2018.01.065.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marks, D., J. Kimball, D. Tingey, and T. Link, 1998: The sensitivity of snowmelt processes to climate conditions and forest cover during rain-on-snow: A case study of the 1996 Pacific Northwest flood. Hydrol. Processes, 12, 15691587, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1569::AID-HYP682>3.0.CO;2-L.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marks, D., T. Link, A. Winstral, and D. Garen, 2001: Simulating snowmelt processes during rain-on-snow over a semi-arid mountain basin. Ann. Glaciol., 32, 195202, https://doi.org/10.3189/172756401781819751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ménard, C. B., and et al. , 2019: Meteorological and evaluation datasets for snow modelling at 10 reference sites: Description of in situ and bias-corrected reanalysis data. Earth Syst. Sci. Data, 11, 865880, https://doi.org/10.5194/essd-11-865-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ménard, C. B., and et al. , 2021: Scientific and human errors in a snow model intercomparison. Bull. Amer. Meteor. Soc., 102, E61E79, https://doi.org/10.1175/BAMS-D-19-0329.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morin, S., Y. Lejeune, B. Lesaffre, J.-M. Panel, D. Poncet, P. David, and M. Sudul, 2012: An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models. Earth Syst. Sci. Data, 4, 1321, https://doi.org/10.5194/essd-4-13-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nash, J. E., and J. V. Sutcliffe, 1970: River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol., 10, 282290, https://doi.org/10.1016/0022-1694(70)90255-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niwano, M., T. Aoki, K. Kuchiki, M. Hosaka, and Y. Kodama, 2012: Snow Metamorphism and Albedo Process (SMAP) model for climate studies: Model validation using meteorological and snow impurity data measured at Sapporo, Japan. J. Geophys. Res. Earth Surf., 117, F03008, https://doi.org/10.1029/2011JF002239.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pahaut, E., 1976: La métamorphose des cristaux de neige (Snow Crystal Metamorphosis). Monogr. Météor. Natl., No. 96, Météo-France, 58 pp.

  • Pastorello, G., and et al. , 2020: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pietroniro, A., and et al. , 2007: Development of the MESH modelling system for hydrological ensemble forecasting of the Laurentian Great Lakes at the regional scale. Hydrol. Earth Syst. Sci., 11, 12791294, https://doi.org/10.5194/hess-11-1279-2007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pomeroy, J., 1989: A process-based model of snow drifting. Ann. Glaciol., 13, 237240, https://doi.org/10.3189/S0260305500007965.

  • Pomeroy, J., J. Parviainen, N. Hedstrom, and D. Gray, 1998: Coupled modelling of forest snow interception and sublimation. Hydrol. Processes, 12, 23172337, https://doi.org/10.1002/(SICI)1099-1085(199812)12:15<2317::AID-HYP799>3.0.CO;2-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quéno, L., V. Vionnet, I. Dombrowski-Etchevers, M. Lafaysse, M. Dumont, and F. Karbou, 2016: Snowpack modelling in the Pyrenees driven by kilometric-resolution meteorological forecasts. Cryosphere, 10, 15711589, https://doi.org/10.5194/tc-10-1571-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reba, M. L., D. Marks, M. Seyfried, A. Winstral, M. Kumar, and G. Flerchinger, 2011: A long-term data set for hydrologic modeling in a snow-dominated mountain catchment. Water Resour. Res., 47, W07702, https://doi.org/10.1029/2010WR010030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmidt, R. A., 1972: Sublimation of wind-transported snow—A model. USDA Forest Service Research Paper RM-90, 24 pp.

  • Sicart, J. E., R. L. Essery, J. W. Pomeroy, J. Hardy, T. Link, and D. Marks, 2004: A sensitivity study of daytime net radiation during snowmelt to forest canopy and atmospheric conditions. J. Hydrometeor., 5, 774784, https://doi.org/10.1175/1525-7541(2004)005<0774:ASSODN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slater, A. G., and et al. , 2001: The representation of snow in land surface schemes: Results from PILPS 2(d). J. Hydrometeor., 2, 725, https://doi.org/10.1175/1525-7541(2001)002<0007:TROSIL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tuzet, F., and et al. , 2017: A multilayer physically based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow. Cryosphere, 11, 26332653, https://doi.org/10.5194/tc-11-2633-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verseghy, D., N. McFarlane, and M. Lazare, 1993: CLASS—A Canadian land surface scheme for GCMs, II. Vegetation model and coupled runs. Int. J. Climatol., 13, 347370, https://doi.org/10.1002/joc.3370130402.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vionnet, V., E. Brun, S. Morin, A. Boone, S. Faroux, P. Le Moigne, E. Martin, and J. Willemet, 2012: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2. Geosci. Model Dev., 5, 773791, https://doi.org/10.5194/gmd-5-773-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vionnet, V., V. Fortin, E. Gaborit, G. Roy, M. Abrahamowicz, N. Gasset, and J. W. Pomeroy, 2020: Assessing the factors governing the ability to predict late-spring flooding in cold-region mountain basins. Hydrol. Earth Syst. Sci., 24, 21412165, https://doi.org/10.5194/hess-24-2141-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Warscher, M., U. Strasser, G. Kraller, T. Marke, H. Franz, and H. Kunstmann, 2013: Performance of complex snow cover descriptions in a distributed hydrological model system: A case study for the high Alpine terrain of the Berchtesgaden Alps. Water Resour. Res., 49, 26192637, https://doi.org/10.1002/wrcr.20219.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wever, N., L. Schmid, A. Heilig, O. Eisen, C. Fierz, and M. Lehning, 2015: Verification of the multi-layer SNOWPACK model with different water transport schemes. Cryosphere, 9, 22712293, https://doi.org/10.5194/tc-9-2271-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wever, N., S. Würzer, C. Fierz, and M. Lehning, 2016: Simulating ice layer formation under the presence of preferential flow in layered snowpacks. Cryosphere, 10, 27312744, https://doi.org/10.5194/tc-10-2731-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, L., and P. Dirmeyer, 2011: Snow-atmosphere coupling strength in a global atmospheric model. Geophys. Res. Lett., 38, L13401, https://doi.org/10.1029/2011GL048049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yen, Y., 1981: Review of thermal properties of snow, ice and sea ice. CRREL Rep. 8110, 27 pp.

  • You, J., D. Tarboton, and C. Luce, 2014: Modeling the snow surface temperature with a one-layer energy balance snowmelt model. Hydrol. Earth Syst. Sci., 18, 50615076, https://doi.org/10.5194/hess-18-5061-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zappa, M., F. Pos, U. Strasser, P. Warmerdam, and J. Gurtz, 2003: Seasonal water balance of an Alpine catchment as evaluated by different methods for spatially distributed snowmelt modelling. Hydrol. Res., 34, 179202, https://doi.org/10.2166/nh.2003.0003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zeinivand, H., and F. De Smedt, 2009: Hydrological modeling of snow accumulation and melting on river basin scale. Water Resour. Manage., 23, 22712287, https://doi.org/10.1007/s11269-008-9381-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evaluation of the Snow Cover in the Soil, Vegetation, and Snow (SVS) Land Surface Model

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  • 1 a Department of Civil and Water Engineering, Université Laval, Quebec, Quebec, Canada
  • | 2 b CentrEau–Water Research Center, Université Laval, Quebec, Quebec, Canada
  • | 3 c Environmental Numerical Weather Prediction Research, Environment and Climate Change Canada, Dorval, Quebec, Canada
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Abstract

The Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at 10 well-instrumented sites from the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime, and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force–restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely, snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density, and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Gonzalo Leonardini, gonzalo.leonardini.1@ulaval.ca

Abstract

The Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at 10 well-instrumented sites from the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime, and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force–restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely, snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density, and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Gonzalo Leonardini, gonzalo.leonardini.1@ulaval.ca
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