CLIMBra - Climate Change Dataset for Brazil
(2023) In Scientific Data 10(1).- Abstract
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... (More)
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.
(Less)
- author
- Ballarin, André Simões ; Sone, Jullian Souza ; Gesualdo, Gabriela Chiquito ; Schwamback, Dimaghi LU ; Reis, Alan ; Almagro, André and Wendland, Edson Cezar
- publishing date
- 2023-12
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Scientific Data
- volume
- 10
- issue
- 1
- article number
- 47
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85146550326
- pmid:36670117
- ISSN
- 2052-4463
- DOI
- 10.1038/s41597-023-01956-z
- language
- English
- LU publication?
- no
- additional info
- Funding Information: The authors acknowledge the Graduate Program in Hydraulic Engineering and Sanitation – PPGSHS at the University of São Paulo (USP-EESC) for the scientific support. This study was financially supported in part by the Coordination for the Improvement of Higher Education Personnel (CAPES, Finance Code 001), in part by the Brazilian National Council for Scientific and Technological Development — CNPq/Ministry of Science, Technology and Innovation — MCTI and in part by the São Paulo Research Foundation — FAPESP (grant numbers 2015/03806–1, 2020/08140–0, 2019/24292-7, 2021/14016-2, and 2022/06017-1). Funding Information: The authors acknowledge the Graduate Program in Hydraulic Engineering and Sanitation – PPGSHS at the University of São Paulo (USP-EESC) for the scientific support. This study was financially supported in part by the Coordination for the Improvement of Higher Education Personnel (CAPES, Finance Code 001), in part by the Brazilian National Council for Scientific and Technological Development — CNPq/Ministry of Science, Technology and Innovation — MCTI and in part by the São Paulo Research Foundation — FAPESP (grant numbers 2015/03806–1, 2020/08140–0, 2019/24292-7, 2021/14016-2, and 2022/06017-1). Publisher Copyright: © 2023, The Author(s).
- id
- b53c77a9-0917-4128-8933-b1de8ec69368
- date added to LUP
- 2023-03-06 10:20:10
- date last changed
- 2024-07-10 11:59:53
@article{b53c77a9-0917-4128-8933-b1de8ec69368, abstract = {{<p>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.</p>}}, author = {{Ballarin, André Simões and Sone, Jullian Souza and Gesualdo, Gabriela Chiquito and Schwamback, Dimaghi and Reis, Alan and Almagro, André and Wendland, Edson Cezar}}, issn = {{2052-4463}}, language = {{eng}}, number = {{1}}, publisher = {{Nature Publishing Group}}, series = {{Scientific Data}}, title = {{CLIMBra - Climate Change Dataset for Brazil}}, url = {{http://dx.doi.org/10.1038/s41597-023-01956-z}}, doi = {{10.1038/s41597-023-01956-z}}, volume = {{10}}, year = {{2023}}, }