Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data

• Published in: Earth System Science Data
• Main Authors: C. B. Ménard, R. Essery, A. Barr, P. Bartlett, J. Derry, M. Dumont, C. Fierz, H. Kim, A. Kontu, Y. Lejeune, D. Marks, M. Niwano, M. Raleigh, L. Wang, N. Wever
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2019
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; Arctic; albedo
• Online Access: https://doi.org/10.5194/essd-11-865-2019; https://doaj.org/article/834986f2a3ff4969ac7f924a2814419a

This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions' modelling at 10 sites. The long-term datasets (one maritime, one arctic, three boreal, and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project. Periods covered by the in situ data vary between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature, and surface temperature) available at varying temporal intervals. Thirty-year (1980–2010) time series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to 1 h time steps and bias-corrected. Although the correction was applied to all sites, it was most important for mountain sites hundreds of metres higher than the grid elevations and for which uncorrected air temperatures were too high and snowfall amounts too low. The discussion considers the importance of data sharing to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The Supplement provides information on instrumentation, an estimate of the percentages of missing values, and gap-filling methods at each site. It is hoped that these datasets will be used as benchmarks for future model development and that their ease of use and availability will help model developers quantify model uncertainties and reduce model errors. The data are published in the repository PANGAEA and are available at https://doi.pangaea.de/10.1594/PANGAEA.897575 .