Statistically downscaled climate dataset for East Africa
Statistically downscaled climate dataset for East Africa
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.
Gebrechorkos, Solomon
ff77f8a3-b6ef-4cfd-aebd-a003bf3947a5
Hülsmann, Stephan
7036b9ad-90a4-4f19-a7a1-0141531b7a67
Bernhofer, Christian
40166f9c-4ee5-4064-8dcc-d573f1b08d41
Gebrechorkos, Solomon
ff77f8a3-b6ef-4cfd-aebd-a003bf3947a5
Hülsmann, Stephan
7036b9ad-90a4-4f19-a7a1-0141531b7a67
Bernhofer, Christian
40166f9c-4ee5-4064-8dcc-d573f1b08d41
Gebrechorkos, Solomon, Hülsmann, Stephan and Bernhofer, Christian
(2019)
Statistically downscaled climate dataset for East Africa.
Scientific Data, 6, [31].
(doi:10.1038/s41597-019-0038-1).
Abstract
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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s41597-019-0038-1
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Accepted/In Press date: 14 March 2019
e-pub ahead of print date: 15 April 2019
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Local EPrints ID: 435123
URI: http://eprints.soton.ac.uk/id/eprint/435123
PURE UUID: 172cfcf4-32c3-4e25-9413-a4394a0432a7
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Date deposited: 23 Oct 2019 16:30
Last modified: 17 Mar 2024 03:55
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Author:
Stephan Hülsmann
Author:
Christian Bernhofer
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