Investigating Repeatable Snow Distributions and Meteorological Conditions over Four Years in Finse, Norway

Abstract

Snow distribution is an important factor in earth processes and is predominantly influenced locally through fixed controls (topography and vegetation) and fluctuating controls (wind magnitude and direction, snowfall, and storm timing and frequency). Therefore, snow seasons with similar fluctuating controls are expected to have similar or “repeatable” snow distributions. While the processes responsible for snow distribution are well-established, the tendency of snow distributions to repeat yearly at fine spatial resolution (2m) in alpine terrains, such as Finse, Norway, is not well understood. Using four years (2015-2018) of differential Global Positioning System standardized snow depth values along ski tracks (orientated NNE, ESE, SSW, WNW) and meteorological conditions at the study site, we sought to establish which years had similar snow distributions, and what meteorological conditions (based on wind and snowfall) were responsible for these years having repeatable snow distributions. While no meteorological link was found for years with repeatable distributions when analyzing the meteorological conditions from 1 October until the survey date, a common meteorological characteristic was found when analyzing the 2 months preceding the survey date. Based on this analysis, the meteorological conditions during the 2 months before the survey date are more indicative of which years have repeatable snow distributions. Therefore, the similarity of snow distributions at Finse from year to year is determined by whether snowy days with winds from the WNW (as in 2015 and 2017) or ESE (as in 2016 and 2018) contribute the most snowfall in the two months before the snow distribution survey date. While the study provides insight into the repeatability of snow distributions and the dominant meteorological conditions that impact this repeatability, future work could expand our understanding through more distributed snow observations, such as lidar, that provide more continuous coverage of the study area.