Statistically downscaled climate dataset for East Africa.

Authors:
Christian Bernhofer
Christian Bernhofer
Technische Universität Dresden
Germany

Sci Data 2019 04 15;6(1):31. Epub 2019 Apr 15.

Faculty of Environmental Sciences, Institute of Hydrology and Meteorology, Technische Universität Dresden, 01062, Dresden, Germany.

For many regions of the world, current climate change projections are only available at coarser spatial resolution from Global Climate Models (GCMs) that cannot directly be used in impact assessment and adaptation studies at regional and local scale. Impact assessment studies require high-resolution climate data to drive impact assessment models. To overcome this data challenge, we produced a station based climate projection (precipitation and maximum and minimum temperature) for Ethiopia, Kenya, and Tanzania using observed daily data from 211 stations obtained from the National Meteorological Agency of Ethiopia and international databases. Moreover, 26 large-scale climate variables derived from the National Centers for Environmental Prediction reanalysis data (1961-2005) and second generation Canadian Earth System Model (CanESM2, 1961-2100) are used. Statistical Down-Scaling Model (SDSM) is used to produce the required high-resolution climate projection by developing a statistical relationship between the large- and local-scale climate variables. The predictors are analysed more than 16458 times and we provided 20 ensembles for the current (1961-2005) and future (2006-2100, under RCP2.6, RCP4.5, and RCP8.5) climate.

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http://www.nature.com/articles/s41597-019-0038-1
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http://dx.doi.org/10.1038/s41597-019-0038-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472408PMC
April 2019
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