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ClimateEU, scale-free climate normals, historical time series, and future projections for Europe.
Marchi, Maurizio; Castellanos-Acuña, Dante; Hamann, Andreas; Wang, Tongli; Ray, Duncan; Menzel, Annette.
Afiliação
  • Marchi M; CNR - Institute of Biosciences and BioResources (IBBR), Florence division, Via Madonna del Piano 10, I-50019, Sesto Fiorentino (Florence), Italy.
  • Castellanos-Acuña D; Department of Renewable Resources, University of Alberta, 751 General Services Building, Edmonton, AB, T6G 2H1, Canada.
  • Hamann A; Department of Renewable Resources, University of Alberta, 751 General Services Building, Edmonton, AB, T6G 2H1, Canada. andreas.hamann@ualberta.ca.
  • Wang T; Centre for Forest Conservation Genetics, Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
  • Ray D; Forest Research, Northern Research Station, Roslin, Midlothian, Scotland, United Kingdom.
  • Menzel A; Department of Ecology and Ecosystem Management, Technical University of Munich, 85354, Freising, Germany.
Sci Data ; 7(1): 428, 2020 12 04.
Article em En | MEDLINE | ID: mdl-33277489
ABSTRACT
Interpolated climate data have become essential for regional or local climate change impact assessments and the development of climate change adaptation strategies. Here, we contribute an accessible, comprehensive database of interpolated climate data for Europe that includes monthly, annual, decadal, and 30-year normal climate data for the last 119 years (1901 to 2019) as well as multi-model CMIP5 climate change projections for the 21st century. The database also includes variables relevant for ecological research and infrastructure planning, comprising more than 20,000 climate grids that can be queried with a provided ClimateEU software package. In addition, 1 km and 2.5 km resolution gridded data generated by the software are available for download. The quality of ClimateEU estimates was evaluated against weather station data for a representative subset of climate variables. Dynamic environmental lapse rate algorithms employed by the software to generate scale-free climate variables for specific locations lead to improvements of 10 to 50% in accuracy compared to gridded data. We conclude with a discussion of applications and limitations of this database.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Data Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Data Ano de publicação: 2020 Tipo de documento: Article