A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

Solomon Gebrechorkos; Leyland Julian; Louise Slater; Michel Wortmann; Philip Ashworth

doi:10.1038/s41597-023-02528-x

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

Solomon Gebrechorkos, Leyland Julian, Louise Slater, Michel Wortmann, Philip Ashworth

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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.

Original language	English
Article number	611
Pages (from-to)	1-15
Number of pages	15
Journal	Scientific Data
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41597-023-02528-x
Publication status	Published - 11 Sept 2023

Bibliographical note

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.

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Gebrechorkos et al 23_Nature Scientific Data_offprintFinal published version, 4.6 MBLicence: CC BY

Cite this

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title = "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 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.",

author = "Solomon Gebrechorkos and Leyland Julian and Louise Slater and Michel Wortmann and Philip Ashworth",

note = "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{\textquoteright}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{\textquoteright}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: {\textcopyright} 2023, Springer Nature Limited.",

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A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

Abstract

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