A dataset of CMIP6-based climate scenarios for climate change impact assessment in Great Britain

A - Papers appearing in refereed journals

Semenov, M. A., Senapati, N., Coleman, K. and Collins, A. L. 2024. A dataset of CMIP6-based climate scenarios for climate change impact assessment in Great Britain. Data in Brief. 55 (4 July), p. 110709. https://doi.org/10.1016/j.dib.2024.110709

AuthorsSemenov, M. A., Senapati, N., Coleman, K. and Collins, A. L.

Climate change is a critical issue in the 21st century. Assessment of the impacts of climate change is beneficial for assisting advanced recommendations for adaptations. Climate change impact assessments require high quality local-scale climate scenarios. The future climate projections from Global Climate Models (GCMs) are problematic to use at local scale due to their coarse spatial and temporal resolution, and existing biases. It is important to have climate change scenarios based on the GCMs ensemble downscaled to local scale to account for inherent uncertainty in climate projections, and to have a sufficient large number of years to account for inter-annual climate variability and low frequency, but high impact, extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across Great Britain based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator, LARS-WG 8.0. The data set consists of climate scenarios of daily weather of 1,000 realizations of typical years for a baseline, and very near (2030) and near-future (2050) climates, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2–4.5 and SSP5–8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 8.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1–5, CNRM-CM6–1, HadGEM3-GC31-LL, MPI-ESM1–2-LR and MRI-ESM2–0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of daily weather for the baseline, as well as for very near and near-future climate change scenarios, are essential for estimating inter-annual variation, and for detecting low frequency, but high impact, extreme climatic events, such as heat waves, floods and droughts. The present dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessments of climate change and decision support for mitigation and adaptation to climate change.

KeywordsDownscaling; Climate scenarios; LARS-WG; SSPs; CMIP6
Year of Publication2024
JournalData in Brief
Journal citation55 (4 July), p. 110709
Digital Object Identifier (DOI)https://doi.org/10.1016/j.dib.2024.110709
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeResilient Farming Futures
Resilient Farming Futures (WP1): Understanding impacts of single and compound climate policy and biotic stresses on agroecosystem ‘resilience’
Delivering Sustainable Wheat
Publisher's version
Output statusPublished
Publication dates
Online04 Jul 2024
Publication process dates
Accepted28 Jun 2024

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