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High-Resolution and Bias-Corrected CMIP5 Climate Projections

A new Data Descriptor article published in the Nature Scientific Data describes CGIAR’s effort to downscale global climate projection data and make them more relevant for agricultural research. The work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). All data files and computer programming codes are publicly available. Downscaled climate data layers are also available to explore and visualized at GARDIAN.

Abstract

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 (ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment.

Navarro-Racines, C., Tarapues, J., Thornton, P. et al. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Sci Data, 7 (2020) doi:10.1038/s41597-019-0343-8

https://doi.org/10.1038/s41597-019-0343-8
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