Articles | Volume 5, issue 1
Geosci. Instrum. Method. Data Syst., 5, 219–227, 2016

Special issue: Multi-disciplinary research and integrated monitoring at the...

Geosci. Instrum. Method. Data Syst., 5, 219–227, 2016
Research article
 | Highlight paper
16 Jun 2016
Research article  | Highlight paper | 16 Jun 2016

A 7-year dataset for driving and evaluating snow models at an Arctic site (Sodankylä, Finland)

Richard Essery1, Anna Kontu2, Juha Lemmetyinen3, Marie Dumont4, and Cécile B. Ménard5 Richard Essery et al.
  • 1School of GeoSciences, University of Edinburgh, Edinburgh EH9 3FE, UK
  • 2Arctic Research Unit, Finnish Meteorological Institute, 99600 Sodankylä, Finland
  • 3Finnish Meteorological Institute, 00101 Helsinki, Finland
  • 4Météo-France-CNRS, CNRM-GAME UMR3589, CEN, Grenoble 38000, France
  • 5CORES Science and Engineering Ltd, Edinburgh, UK

Abstract. Datasets derived from measurements at Sodankylä, Finland, for driving and evaluating snow models are presented. This is the first time that such complete datasets have been made available for a site in the Arctic. The continuous October 2007–September 2014 driving data comprise all of the meteorological variables required as inputs for physically based snow models at hourly intervals: incoming solar and longwave radiation, snowfall and rainfall rates, air temperature, humidity, wind speed and atmospheric pressure. Two versions of the driving data are provided: one using radiation and wind speed measurements made above the height of the trees around the clearing where the evaluation data were measured and one with adjustments for the influence of the trees on conditions close to the ground. The available evaluation data include automatic and manual measurements of bulk snow depth and snow water equivalent, and profiles of snow temperature, snow density and soil temperature. A physically based snow model is driven and evaluated with the datasets to illustrate their utility. Shading by trees is found to extend the duration of both modelled and observed snow cover on the ground by several days a year.

Short summary
Physically based models that predict the properties of snow on the ground are used in many applications, but meteorological input data required by these models are hard to obtain in cold regions. Monitoring at the Sodankyla research station allows construction of model input and evaluation datasets covering several years for the first time in the Arctic. The data are used to show that a sophisticated snow model developed for warmer and wetter sites can perform well in very different conditions.