High-resolution monthly precipitation and temperature time series from 2006 to 2100.

Sci Data

Dynamic Macroecology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland.

Published: July 2020

AI Article Synopsis

  • Predicting future climate conditions with high spatial resolution is crucial for various scientific applications.
  • The study provides monthly data on temperature and precipitation using downscaled global circulation models, focusing on a ~5 km resolution from 2006 to 2100.
  • The accuracy of the downscaling algorithm was validated by comparing its outputs against historical climate data from 1950-1969.

Article Abstract

Predicting future climatic conditions at high spatial resolution is essential for many applications and impact studies in science. Here, we present monthly time series data on precipitation, minimum- and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation at ~5 km spatial resolution globally for the years 2006-2100. We validated the performance of the downscaling algorithm by comparing model output with the observed climate of the historical period 1950-1969.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378208PMC
http://dx.doi.org/10.1038/s41597-020-00587-yDOI Listing

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