Geoscience Data Journal (Oct 2024)

Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS‐M6)

  • Stephen R. Sobie,
  • Dhouha Ouali,
  • Charles L. Curry,
  • Francis W. Zwiers

DOI
https://doi.org/10.1002/gdj3.257
Journal volume & issue
Vol. 11, no. 4
pp. 806 – 824

Abstract

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Abstract Canada‐wide, statistically downscaled simulations of global climate models from the Sixth Coupled Model Inter‐comparison Project (CMIP6) have been made available for 26 models using a new multivariate approach and an improved observational target dataset. These new downscaled scenarios comprise daily simulations of precipitation, maximum temperature, and minimum temperature at 1/12° resolution across Canada. Simulations from each of the 26 downscaled global climate models span a historical period (1950–2014), and three future Shared Socio‐economic Pathways (SSPs) representing low (SSP1 2.6), moderate (SSP2 4.5) and high (SSP5 8.5) future emissions from 2015 to 2100. Results from an evaluation of the multivariate downscaling method over Canada yield improved performance in replicating multivariate and compound climate indices compared to previously used univariate downscaling methods. This Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS‐M6) dataset is intended to facilitate climate impacts assessments, hydrologic modelling, and analysis tools for presenting climate projections.

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