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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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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. |
format | Online Article Text |
id | pubmed-6472408 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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