A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses
A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses
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.
Gebrechorkos, Solomon
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Leyland, Julian
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Slater, Louise
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Wortmann, Michel
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Ashworth, Philip J.
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Bennett, Georgina L.
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Boothroyd, Richard
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Cloke, Hannah
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Delorme, Pauline
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Griffith, Helen
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Hardy, Richard J.
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Hawker, Laurence
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McLelland, S.J.
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Neal, Jeffrey
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Nicholas, Andrew
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Tatem, Andrew J.
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Vahidi, Ellie
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Parsons, Daniel R.
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Darby, Stephen E.
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11 September 2023
Gebrechorkos, Solomon
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Leyland, Julian
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Slater, Louise
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Wortmann, Michel
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Ashworth, Philip J.
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Bennett, Georgina L.
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Boothroyd, Richard
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Cloke, Hannah
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Delorme, Pauline
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Griffith, Helen
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Hardy, Richard J.
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Hawker, Laurence
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McLelland, S.J.
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Neal, Jeffrey
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Nicholas, Andrew
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Tatem, Andrew J.
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Vahidi, Ellie
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Parsons, Daniel R.
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Darby, Stephen E.
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Gebrechorkos, Solomon, Leyland, Julian, Slater, Louise, Wortmann, Michel, Ashworth, Philip J., Bennett, Georgina L., Boothroyd, Richard, Cloke, Hannah, Delorme, Pauline, Griffith, Helen, Hardy, Richard J., Hawker, Laurence, McLelland, S.J., Neal, Jeffrey, Nicholas, Andrew, Tatem, Andrew J., Vahidi, Ellie, Parsons, Daniel R. and Darby, Stephen E.
(2023)
A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses.
Scientific Data, 10 (1), [611].
(doi:10.1038/s41597-023-02528-x).
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.
Text
s41597-023-02528-x
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More information
Accepted/In Press date: 31 August 2023
Published date: 11 September 2023
Additional Information:
Funding Information:
This work is part of the Evolution of Global Flood Hazard and Risk (EVOFLOOD) project [NE/S015817/1] supported by the Natural Environment Research Council (NERC). We acknowledge the Centre for Environmental Data Analysis (CEDA) for storing the downscaled data. We thank JASMIN (UK’s data analysis facility for environmental science), University of Southampton (IRIDIS) and the University of Oxford (ARC) and their team members for providing access to the High-Performance Computing (HPC) systems that were used to perform the downscaling process undertaken herein.
Funding Information:
This work is part of the Evolution of Global Flood Hazard and Risk (EVOFLOOD) project [NE/S015817/1] supported by the Natural Environment Research Council (NERC). We acknowledge the Centre for Environmental Data Analysis (CEDA) for storing the downscaled data. We thank JASMIN (UK’s data analysis facility for environmental science), University of Southampton (IRIDIS) and the University of Oxford (ARC) and their team members for providing access to the High-Performance Computing (HPC) systems that were used to perform the downscaling process undertaken herein.
Publisher Copyright:
© 2023, Springer Nature Limited.
Identifiers
Local EPrints ID: 482086
URI: http://eprints.soton.ac.uk/id/eprint/482086
ISSN: 2052-4463
PURE UUID: c19a964e-bb31-4e90-a86f-b4f77b98ddca
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Date deposited: 19 Sep 2023 16:31
Last modified: 18 Mar 2024 03:51
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Contributors
Author:
Louise Slater
Author:
Michel Wortmann
Author:
Philip J. Ashworth
Author:
Georgina L. Bennett
Author:
Richard Boothroyd
Author:
Hannah Cloke
Author:
Helen Griffith
Author:
Richard J. Hardy
Author:
Laurence Hawker
Author:
S.J. McLelland
Author:
Jeffrey Neal
Author:
Andrew Nicholas
Author:
Ellie Vahidi
Author:
Daniel R. Parsons
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