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CLIMBra - Climate Change Dataset for Brazil
General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systemati...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860025/ https://www.ncbi.nlm.nih.gov/pubmed/36670117 http://dx.doi.org/10.1038/s41597-023-01956-z |
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author | Ballarin, André Simões Sone, Jullian Souza Gesualdo, Gabriela Chiquito Schwamback, Dimaghi Reis, Alan Almagro, André Wendland, Edson Cezar |
author_facet | Ballarin, André Simões Sone, Jullian Souza Gesualdo, Gabriela Chiquito Schwamback, Dimaghi Reis, Alan Almagro, André Wendland, Edson Cezar |
author_sort | Ballarin, André Simões |
collection | PubMed |
description | General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980–2013) and future (2015–2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil. |
format | Online Article Text |
id | pubmed-9860025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98600252023-01-22 CLIMBra - Climate Change Dataset for Brazil Ballarin, André Simões Sone, Jullian Souza Gesualdo, Gabriela Chiquito Schwamback, Dimaghi Reis, Alan Almagro, André Wendland, Edson Cezar Sci Data Data Descriptor General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980–2013) and future (2015–2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil. Nature Publishing Group UK 2023-01-20 /pmc/articles/PMC9860025/ /pubmed/36670117 http://dx.doi.org/10.1038/s41597-023-01956-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Ballarin, André Simões Sone, Jullian Souza Gesualdo, Gabriela Chiquito Schwamback, Dimaghi Reis, Alan Almagro, André Wendland, Edson Cezar CLIMBra - Climate Change Dataset for Brazil |
title | CLIMBra - Climate Change Dataset for Brazil |
title_full | CLIMBra - Climate Change Dataset for Brazil |
title_fullStr | CLIMBra - Climate Change Dataset for Brazil |
title_full_unstemmed | CLIMBra - Climate Change Dataset for Brazil |
title_short | CLIMBra - Climate Change Dataset for Brazil |
title_sort | climbra - climate change dataset for brazil |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860025/ https://www.ncbi.nlm.nih.gov/pubmed/36670117 http://dx.doi.org/10.1038/s41597-023-01956-z |
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