The Canadian Downscaled Climate Scenarios-univariate dataset for CMIP5 (Phase 5 of the Coupled Model Intercomparison Project) is a set of downscaled scenarios based on the latest generation of climate projections from CMIP5. CMIP5 climate projections are based on global climate models and emissions scenarios called “Relative Concentration Pathways” (RCPs)1.
Statistically downscaled datasets are provided from 24 CMIP5 Global Climate Models (GCMs) (see below) under three different emissions scenarios (i.e. RCP2.6, RCP4.5, and RCP8.5) using the BCCAQv22,3 downscaling method with NRCANmet4 as the downscaling target data.
Annual values are available for over 30 different temperature- and precipitation-based indices, while seasonal and monthly values are available for a subset of these. Daily data for maximum and minimum temperature and daily precipitation are also provided. All data are available across Canada at a spatial resolution of ~6x10km for the 1950-2005 historical period and for the 2006-2100 period following each of the three emissions scenarios. Change values are calculated with respect to the 1971-2000 reference period.
Statistically downscaled multi-model ensembles have been constructed using output from 24 CMIP6 Global Climate Models (GCMs) that are available at the Earth System Grid Federation (ESGF) Data Nodes, (see below).
The univariate Bias Correction/Constructed Analogues with Quantile mapping reordering (BCCAQv2) downscaling method was applied, using the NRCANmet as a target dataset.
All further climate index calculations were done using the ‘xclim’ Python package.
Table 2. List of CMIP5 global climate models used in the CanDCS-U5 ensemble.
van Vuuren, D.P., Edmonds, J., Kainuma, M. et al. The representative concentration pathways: an overview. Climatic Change 109, 5 (2011). https://doi.org/10.1007/s10584-011-0148-z
Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015). Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17), 6938–6959. https://doi.org/10.1175/jcli-d-14-00754.1
Werner, A. T., & Cannon, A. J. (2016). Hydrologic Extremes – an intercomparison of multiple gridded statistical downscaling methods. Hydrology and Earth System Sciences, 20((4), 1483–1508. https://doi.org/10.5194/hess-20-1483-2016
McKenney, D. W., Hutchinson, M. F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R. F., Price, D., & Owen, T. (2011). Customized spatial climate models for North America. Bulletin of the American Meteorological Society, 92(12), 1611–1622. https://doi.org/10.1175/2011bams3132.1
This project was undertaken with the support of:
