A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses
Abstract A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistic...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02528-x |
| _version_ | 1797453950765498368 |
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| author | Solomon Gebrechorkos Julian Leyland Louise Slater Michel Wortmann Philip J. Ashworth Georgina L. Bennett Richard Boothroyd Hannah Cloke Pauline Delorme Helen Griffith Richard Hardy Laurence Hawker Stuart McLelland Jeffrey Neal Andrew Nicholas Andrew J. Tatem Ellie Vahidi Daniel R. Parsons Stephen E. Darby |
| author_facet | Solomon Gebrechorkos Julian Leyland Louise Slater Michel Wortmann Philip J. Ashworth Georgina L. Bennett Richard Boothroyd Hannah Cloke Pauline Delorme Helen Griffith Richard Hardy Laurence Hawker Stuart McLelland Jeffrey Neal Andrew Nicholas Andrew J. Tatem Ellie Vahidi Daniel R. Parsons Stephen E. Darby |
| author_sort | Solomon Gebrechorkos |
| collection | DOAJ |
| description | Abstract A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis ( https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317 ) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models. |
| first_indexed | 2024-03-09T15:30:17Z |
| format | Article |
| id | doaj.art-7c47bfb518ff4bd7a9df16dc8fa872e0 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-09T15:30:17Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-7c47bfb518ff4bd7a9df16dc8fa872e02023-11-26T12:19:15ZengNature PortfolioScientific Data2052-44632023-09-0110111510.1038/s41597-023-02528-xA high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analysesSolomon Gebrechorkos0Julian Leyland1Louise Slater2Michel Wortmann3Philip J. Ashworth4Georgina L. Bennett5Richard Boothroyd6Hannah Cloke7Pauline Delorme8Helen Griffith9Richard Hardy10Laurence Hawker11Stuart McLelland12Jeffrey Neal13Andrew Nicholas14Andrew J. Tatem15Ellie Vahidi16Daniel R. Parsons17Stephen E. Darby18School of Geography and Environmental Science, University of SouthamptonSchool of Geography and Environmental Science, University of SouthamptonSchool of Geography and the Environment, University of OxfordSchool of Geography and the Environment, University of OxfordSchool of Applied Sciences, University of Brighton, SussexDepartment of Geography, Faculty of Environment, Science and Economy, University of ExeterSchool of Geographical & Earth Sciences, University of GlasgowGeography and Environmental Science, University of ReadingEnergy and Environment Institute, University of HullGeography and Environmental Science, University of ReadingDepartment of Geography, Durham UniversitySchool of Geographical Sciences, University of BristolEnergy and Environment Institute, University of HullSchool of Geographical Sciences, University of BristolDepartment of Geography, Faculty of Environment, Science and Economy, University of ExeterSchool of Geography and Environmental Science, University of SouthamptonDepartment of Geography, Faculty of Environment, Science and Economy, University of ExeterEnergy and Environment Institute, University of HullSchool of Geography and Environmental Science, University of SouthamptonAbstract A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis ( https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317 ) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.https://doi.org/10.1038/s41597-023-02528-x |
| spellingShingle | Solomon Gebrechorkos Julian Leyland Louise Slater Michel Wortmann Philip J. Ashworth Georgina L. Bennett Richard Boothroyd Hannah Cloke Pauline Delorme Helen Griffith Richard Hardy Laurence Hawker Stuart McLelland Jeffrey Neal Andrew Nicholas Andrew J. Tatem Ellie Vahidi Daniel R. Parsons Stephen E. Darby A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses Scientific Data |
| title | A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses |
| title_full | A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses |
| title_fullStr | A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses |
| title_full_unstemmed | A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses |
| title_short | A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses |
| title_sort | high resolution daily global dataset of statistically downscaled cmip6 models for climate impact analyses |
| url | https://doi.org/10.1038/s41597-023-02528-x |
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