Evaluation of forest snow processes models (SnowMIP2)

Nick Rutter, Richard Essery, John Pomeroy, Nuria Altimir, Kostas Andreadis, Ian Baker, Alan Barr, Paul Bartlett, Aaron Boone, Huiping Deng, Hervé Douville, Emanuel Dutra, Kelly Elder, Chad Ellis, Xia Feng, Alexander Gelfan, Angus Goodbody, Yeugeniy Gusev, David Gustafsson, Rob HellstromYukiko Hirabayashi, Tomoyoshi Hirota, Tobias Jonas, Victor Koren, Anna Kuragina, Dennis Lettenmaier, Wei-Ping Li, Charlie Luce, Eric Martin, Olga Nasonova, Jukka Pumpanen, R. David Pyles, Patrick Sameulsson, Mel Sandells, Gerd Schadler, Andrey Shmakin, Tatiana G. Smirnova, Manfred Stahli, Reto Stockli, Ulrich Strasser, Hua Su, Kazuyoshi Suzuki, Kumiko Takata, Kenji Tanaka, Erin Thompson, Timo Vesala, Pedro Viturbo, Andrew Wiltshire, Kun Xia, Yongkang Xue, Takeshi Yamazaki

Research output: Contribution to journalArticlepeer-review

293 Citations (Scopus)


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.
Original languageEnglish
JournalJournal of Geophysical Research: Atmosphere
Issue numberD6
Publication statusPublished - 2009


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