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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...

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Autores principales: Gebrechorkos, Solomon H., Hülsmann, Stephan, Bernhofer, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472408/
https://www.ncbi.nlm.nih.gov/pubmed/30988412
http://dx.doi.org/10.1038/s41597-019-0038-1
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author Gebrechorkos, Solomon H.
Hülsmann, Stephan
Bernhofer, Christian
author_facet Gebrechorkos, Solomon H.
Hülsmann, Stephan
Bernhofer, Christian
author_sort Gebrechorkos, Solomon H.
collection PubMed
description 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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spelling pubmed-64724082019-04-19 Statistically downscaled climate dataset for East Africa Gebrechorkos, Solomon H. Hülsmann, Stephan Bernhofer, Christian Sci Data Data Descriptor 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. Nature Publishing Group UK 2019-04-15 /pmc/articles/PMC6472408/ /pubmed/30988412 http://dx.doi.org/10.1038/s41597-019-0038-1 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Gebrechorkos, Solomon H.
Hülsmann, Stephan
Bernhofer, Christian
Statistically downscaled climate dataset for East Africa
title Statistically downscaled climate dataset for East Africa
title_full Statistically downscaled climate dataset for East Africa
title_fullStr Statistically downscaled climate dataset for East Africa
title_full_unstemmed Statistically downscaled climate dataset for East Africa
title_short Statistically downscaled climate dataset for East Africa
title_sort statistically downscaled climate dataset for east africa
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472408/
https://www.ncbi.nlm.nih.gov/pubmed/30988412
http://dx.doi.org/10.1038/s41597-019-0038-1
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