Modelling the snow distribution at two high arctic sites at Svalbard, Norway, and at an alpine site in central Norway

Abstract

In Arctic regions snow cover has a major influence on the environment both in a hydrological and ecological context. Due to strong winds and open terrain the snow is heavily redistributed and the snow depth is quite variable. This has a significant influence on the snow cover depletion and the duration of the melting season. In many ways these are important parameters in the climate change aspect. They influence the land surface albedo, the possibilities of greenhouse gas exchange and the length of the plant-growing season, the latter also being important for the arctic terrestrial fauna. The aim of this study is to test to what degree a numerical model is able to recreate an observed snow distribution in sites located in Svalbard and Norway. Snow depth frequency distribution, a snow depth rank order test and the location of snowdrifts and erosion areas were used as criteria for the model performance. SnowTran-3D is the model used in this study. In order to allow for occasions during the winter with milder climate and temperatures above freezing, a snow strengthening calculation was included in the model. The model result was compared to extensive observation datasets for each site and the sensitivity of the main model parameters to the model result was tested. For all three sites, the modelled snow depth frequency distribution was highly correlated to the observed distribution and the snowdrifts and erosion areas were located correspondingly by the model to those observed at the sites.

Using wind fields from a high‐resolution atmospheric model for simulating snow dynamics in mountainous terrain

“…Pomeroy et al 1997;Liston 2004) but only a few studies are known which have numerically simulated the efficiency of this process (e.g. Pomeroy et al 1997;Lehning et al 2006;.…”

“…According to Marsh (1999) and Liston (2004) and Liston and Elder (2006) most of these models tend to a more physically based description of the relevant processes. When omitting empirical or temperature index models the remaining physically based models can be divided into three different groups.…”

“…As this is commonly not the case, these models are limited to well instrumented sites. Very prominent representatives are: CROCUS (Brun et al 1989;Brun et al 1992), SNOWPACK (Bartelt and Lehning 2002;Lehning et al 2002) and SNTHERM (Jordan 1991 (Liston 2004). …”

“…The involved processes are effective at scales of one to hundreds of meters (Liston 2004;Strasser et al 2007). First results of SnowMIP2 have shown that a misinterpretation of snow-canopy interactions can lead to almost unusable model results (Rutter and Essery 2006) in these areas/scales.…”

Impact of land-use changes on snow in a forested region with heavy snowfall in Hokkaido, Japan

“…MicroMet and SnowModel have been used to distribute meteorological variables and evolve snow distributions over a wide range of spatial scales (the grid increments range from 10 m to 10 km, while the spatial domains range from hundreds of metres to pan-Arctic); the models have been applied over a variety of complex landscapes, including the mountains of Colorado, Wyoming, Idaho, Arctic Alaska, Svalbard, central Norway and Greenland (e.g. Liston & Sturm, 1998Greene et al, 1999;Liston et al, 1999Liston et al, , 2007Hiemstra et al, 2002Hiemstra et al, , 2006Prasad et al, 2001;Bruland et al, 2004;Mernild et al, 2006Mernild et al, , 2007. In steep alpine environments, complex wind fields can be generated and used to drive SnowModel using regional atmospheric models (e.g.…”