Abstract

Abstract. The strong winds prevalent in high altitude and arctic environments heavily redistribute the snow cover, causing a small-scale pattern of highly variable snow depths. This has profound implications for the ground thermal regime, resulting in highly variable near-surface ground temperatures on the metre scale. Due to asymmetric snow distributions combined with the nonlinear insulating effect of snow, the spatial average ground temperature in a 1 km2 area cannot be determined based on the average snow cover for that area. Land surface or permafrost models employing a coarsely classified average snow depth will therefore not yield a realistic representation of ground temperatures. In this study we employ statistically derived snow distributions within 1 km2 grid cells as input to a regional permafrost model in order to represent sub-grid variability of ground temperatures. This improves the representation of both the average and the total range of ground temperatures. The model reproduces observed sub-grid ground temperature variations of up to 6 °C, and 98 % of borehole observations match the modelled temperature range. The mean modelled temperature of the grid cell reproduces the observations with an accuracy of 1.5 °C or better. The observed sub-grid variations in ground surface temperatures from two field sites are very well reproduced, with estimated fractions of sub-zero mean annual ground surface temperatures within ±10 %. We also find that snow distributions within areas of 1 km2 in Norwegian mountain environments are closer to a gamma than to a lognormal theoretical distribution. The modelled permafrost distribution seems to be more sensitive to the choice of distribution function than to the fine-tuning of the coefficient of variation. When incorporating the small-scale variation of snow, the modelled total permafrost area of mainland Norway is nearly twice as large compared to the area obtained with grid-cell average snow depths without a sub-grid approach.

Highlights

  • High altitude and arctic environments are exposed to strong winds and drifting snow can create a small-scale pattern of highly variable snow depths

  • We present a modelling approach to reproduce the variability of ground temperatures within the scale of 1 km2 grid cells based on probability distribution functions over corresponding seasonal maximum snow depths

  • The snow distributions are derived from climatic parameters and terrain parameterisations at 10 m resolution and are calibrated with a largescale dataset of snow depths obtained from laser scanning

Read more

Summary

Introduction

High altitude and arctic environments are exposed to strong winds and drifting snow can create a small-scale pattern of highly variable snow depths. Goodrich, 1982; Zhang et al, 2001) This smallscale pattern of varying snow depths results in highly variable ground surface temperatures on the metre scale of up to 6 ◦C in areas of less than 1 km For the Norwegian mainland, permafrost models have been implemented with a spatial grid resolution of 1 km (Gisnås et al, 2013; Westermann et al, 2013) and only represent the larger-scale patterns of ground temperatures. The lower permafrost boundary is a fuzzy transition Local parameters, such as snow cover, solar radiation, vegetation, soil moisture and soil type, cause a pronounced sub-grid variation of ground temperature.

Objectives
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call