Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a ‘perfect sibling’ framework and show that it reduces climate model bias by 50–70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment. Measurement(s) climate change • precipitation process • precipitation amount • consecutive dry months index per time period • temperature of air Technology Type(s) computational modeling technique Factor Type(s) spatial region Sample Characteristic - Environment climate system Sample Characteristic - Location Earth (planet) Measurement(s) climate change • precipitation process • precipitation amount • consecutive dry months index per time period • temperature of air Technology Type(s) computational modeling technique Factor Type(s) spatial region Sample Characteristic - Environment climate system Sample Characteristic - Location Earth (planet) Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11353664
High-resolution and bias-corrected CMIP5 projections for climate change impact assessments
Carlos Navarro-Racines,Jaime E. Tarapues,P. Thornton,A. Jarvis,J. Ramirez-Villegas
Published 2020 in Scientific Data
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- Publication year
2020
- Venue
Scientific Data
- Publication date
2020-01-20
- Fields of study
Agricultural and Food Sciences, Medicine, Environmental Science
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Semantic Scholar, PubMed
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