High resolution snow distribution data from complex Arctic terrain: a tool for model validation

• Main Authors: Jaedicke, Ch., Sandvik, A. D.
• Other Authors: University Courses at Svalbard, Geophysical Institute Bergen (GFI / BiU), University of Bergen (UiB)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2002
• Subjects: [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces; environment; [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere; [SDU.STU]Sciences of the Universe [physics]/Earth Sciences; Arctic
• Online Access: https://hal.archives-ouvertes.fr/hal-00301574; https://hal.archives-ouvertes.fr/hal-00301574/document; https://hal.archives-ouvertes.fr/hal-00301574/file/nhess-2-147-2002.pdf

International audience Blowing snow and snow drifts are common features in the Arctic. Due to sparse vegetation, low temperatures and high wind speeds, the snow is constantly moving. This causes severe problems for transportation and infrastructure in the affected areas. To minimise the effect of drifting snow already in the designing phase of new structures, adequate models have to be developed and tested. In this study, snow distribution in Arctic topography is surveyed in two study areas during the spring of 1999 and 2000. Snow depth is measured by ground penetrating radar and manual methods. The study areas encompass four by four kilometres and are partly glaciated. The results of the surveys show a clear pattern of erosion, accumulation areas and the evolution of the snow cover over time. This high resolution data set is valuable for the validation of numerical models. A simple numerical snow drift model was used to simulate the measured snow distribution in one of the areas for the winter of 1998/1999. The model is a two-level drift model coupled to the wind field, generated by a mesoscale meteorological model. The simulations are based on five wind fields from the dominating wind directions. The model produces a satisfying snow distribution but fails to reproduce the details of the observed snow cover. The results clearly demonstrate the importance of quality field data to detect and analyse errors in numerical simulations.