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  • 1. Rutter, Nick
    et al.
    Essery, Richard
    Pomeroy, John
    Altimir, Nuria
    Andreadis, Kostas
    Baker, Ian
    Barr, Alan
    Bartlett, Paul
    Boone, Aaron
    Deng, Huiping
    Douville, Herve
    Dutra, Emanuel
    Elder, Kelly
    Ellis, Chad
    Feng, Xia
    Gelfan, Alexander
    Goodbody, Angus
    Gusev, Yeugeniy
    Gustafsson, David
    Hellstroem, Rob
    Hirabayashi, Yukiko
    Hirota, Tomoyoshi
    Jonas, Tobias
    Koren, Victor
    Kuragina, Anna
    Lettenmaier, Dennis
    Li, Wei-Ping
    Luce, Charlie
    Martin, Eric
    Nasonova, Olga
    Pumpanen, Jukka
    Pyles, R. David
    Samuelsson, Patrick
    SMHI, Research Department, Climate research - Rossby Centre.
    Sandells, Melody
    Schaedler, Gerd
    Shmakin, Andrey
    Smirnova, Tatiana G.
    Staehli, Manfred
    Stoeckli, Reto
    Strasser, Ulrich
    Su, Hua
    Suzuki, Kazuyoshi
    Takata, Kumiko
    Tanaka, Kenji
    Thompson, Erin
    Vesala, Timo
    Viterbo, Pedro
    Wiltshire, Andrew
    Xia, Kun
    Xue, Yongkang
    Yamazaki, Takeshi
    Evaluation of forest snow processes models (SnowMIP2)2009In: Journal of Geophysical Research - Atmospheres, ISSN 2169-897X, E-ISSN 2169-8996, Vol. 114, article id D06111Article, review/survey (Refereed)
    Abstract [en]

    Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation phase and duration of above-freezing air temperatures are shown to be major influences on divergence and convergence of modeled estimates of the subcanopy snowpack. When models are considered collectively at all locations, comparisons with observations show that it is harder to model SWE at forested sites than open sites. There is no universal "best'' model for all sites or locations, but comparison of the consistency of individual model performances relative to one another at different sites shows that there is less consistency at forest sites than open sites, and even less consistency between forest and open sites in the same year. A good performance by a model at a forest site is therefore unlikely to mean a good model performance by the same model at an open site (and vice versa). Calibration of models at forest sites provides lower errors than uncalibrated models at three out of four locations. However, benefits of calibration do not translate to subsequent years, and benefits gained by models calibrated for forest snow processes are not translated to open conditions.

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