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High-resolution future temperature and precipitation dataset for Canada, 2015 - 2100

Description: A database of high-resolution (0.1°) precipitation, maximum, and minimum temperature projections extending till 2100 at a daily scale is developed for Canada. We employed a novel Semi-Parametric Quantile Mapping (SPQM) methodology to bias-correct the Coupled Model Intercomparison Project, Phase-6 (CMIP6) projections for four distinct Shared Socio-economic Pathways. SPQM is simple, yet robust, in reproducing the observed marginal properties, trends and variability according to future scenarios, and maintaining a smooth transition from observations to projected simulations. The database encompasses a substantial collection of 759 simulations derived from 37 diverse climate models for precipitation. Similarly, for maximum and minimum temperature projections, our database comprises 652 simulations from 30 climate models. These meticulously curated projections carry immense value for hydrological, environmental, and ecological studies, offering a comprehensive resource for analyses within these domains. Furthermore, these projections serve as a valuable asset for the quantification of uncertainties arising from variant labels, climate models, and future scenarios.
Authors: Rajulapati, Chandra Rupa; University of Manitoba; ORCID iD 0000-0003-3239-8773
Abdelmoaty, Hebatallah Mohamed; University of Calgary; ORCID iD 0000-0002-2288-691X
Nerantzaki, Sofia; University of Saskatchewan
Papalexiou, Simon Michael; University of Calgary; ORCID iD 0000-0001-5633-0154
Keywords: bias correction
quantile mapping
fine spatiotemporal resolutions
CMIP6 simulations
Field of Research: 
Environmental engineering and related engineering
Environmental engineering
Environmental engineering, not elsewhere classified
Publication Date: 2023-10-24
Publisher: Federated Research Data Repository / dépôt fédéré de données de recherche
Geographic Coverage: 
Geolocation Box: 
West Longitude
East Longitude
North Latitude
South Latitude
Appears in Collections:Global Water Futures

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Access to this dataset is subject to the following terms:
Creative Commons Attribution 4.0 International (CC BY 4.0)
Rajulapati, C., Abdelmoaty, H., Nerantzaki, S., Papalexiou, S. (2023). High-resolution future temperature and precipitation dataset for Canada, 2015 - 2100. Federated Research Data Repository.