Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5573-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-5573-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Dec 2022
Data description paper |
| 16 Dec 2022
| 16 Dec 2022
Global climate-related predictors at kilometer resolution for the past and future
Philipp Brun
CORRESPONDING AUTHOR
Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Niklaus E. Zimmermann
Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Chantal Hari
Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Climate and Environmental Physics, Physics Institute, University of
Bern, 3012 Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland
Loïc Pellissier
Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Ecosystems and Landscape Evolution, Institute of Terrestrial
Ecosystems, Department of Environmental System Science, ETH Zürich, 8092
Zürich, Switzerland
Dirk Nikolaus Karger
Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
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Short summary
Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant,...
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