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Discussion papers
https://doi.org/10.5194/essd-2019-12
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-12
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Feb 2019

Research article | 04 Feb 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

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

Cécile B. Ménard1, Richard Essery1, Alan Barr2,3, Paul Bartlett4, Jeff Derry5, Marie Dumont6, Charles Fierz7, Hyungjun Kim8, Anna Kontu9, Yves Lejeune6, Danny Marks10, Masashi Niwano11, Mark Raleigh12, Libo Wang4, and Nander Wever7,13 Cécile B. Ménard et al.
  • 1School of Geosciences, University of Edinburgh, Edinburgh, UK
  • 2Climate Research Division, Environment and Climate Change Canada, Saskatoon, Canada
  • 3Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada
  • 4Climate Research Division, Environment and Climate Change Canada, Toronto, Canada
  • 5Center for Snow and Avalanche Studies, Silverton, Colorado, USA
  • 6Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Etudes de la Neige, Grenoble, France
  • 7WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 8Institute of Industrial Science, University of Tokyo, Tokyo, Japan
  • 9Finnish Meteorological Institute, Space and Earth Observation Centre, Sodankylä, Finland
  • 10Northwest Watershed Research Center, Agricultural Research Service, Boise, Idaho, USA
  • 11Climate Research Department, Meteorological Research Institute, Tsukuba, Japan
  • 12National Snow and Ice Data Center (NSIDC), University of Colorado Boulder, Boulder, Colorado, USA
  • 13Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA

Abstract. This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions modelling at ten 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 seven and twenty years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature and surface temperature) available at varying temporal intervals. 30-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 one-hour timesteps and bias corrected. Although applied to all sites, the bias corrections are particularly important for mountain sites that are hundreds of meters higher than the grid elevations; as a result, uncorrected air temperatures are too high and snowfall amounts are too low in comparison with in situ measurements. 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. Supplementary material 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 available at: https://doi.org/10.1594/PANGAEA.897575.

Cécile B. Ménard et al.
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Cécile B. Ménard et al.
Data sets

ESM-SnowMIP meteorological and evaluation datasets at ten reference sites (in situ and bias corrected reanalysis data) C. Menard and R. Essery https://doi.org/10.1594/PANGAEA.897575

Cécile B. Ménard et al.
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Short summary
This paper describes long-term meteorological and evaluation datasets from ten reference sites for use in snow modelling. We demonstrate how data sharing is crucial to the identification of errors and how the publication of these datasets contributes to good practice, consistency and reproducibility in Geosciences. The ease-of-use, availability and quality of the datasets will help model developers quantify and reduce model uncertainties and errors.
This paper describes long-term meteorological and evaluation datasets from ten reference sites...
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