The Canadian Downscaled Climate Scenarios-univariate dataset for CMIP6 (Phase 6 of the Coupled Model Intercomparison Project) is a new set of downscaled scenarios based on the latest generation of climate projections from CMIP6. CMIP6 climate projections are based on updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs)1.
Statistically downscaled datasets are provided from 26 CMIP6 Global Climate Models (GCMs) (see below) under three different emissions scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) using the same downscaling method (BCCAQv2)2,3 and downscaling target data (NRCANmet)4 as the CMIP5-based downscaled scenarios (CanDCS-U5).
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 1950-2014 historical period and for the 2015-2100 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 26 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 1. List of CMIP6 global climate models used in the CanDCS-U6 ensemble.
Institution | Model Name | Realization |
CSIRO-ARCCSS (Australia) | ACCESS-CM2 | r1i1p1f1 |
CSIRO (Australia) | ACCESS-ESM1-5 | r1i1p1f1 |
Beijing Climate Center (China) | BCC-CSM2-MR | r1i1p1f1 |
Canadian Centre for Climate Modelling and Analysis (Canada) | CanESM5 | r1i1p2f1 ~ r10i1p2f1 |
Euro-Mediterranean Centre for Climate Change (Italy) | CMCC-ESM2 | r1i1p1f1 |
CNRM-CERFACS (France) | CNRM-CM6-1 | r1i1p1f2 |
CNRM-CERFACS (France) | CNRM-ESM2-1 | r1i1p1f2 |
EC-Earth-Consortium (Europe) | EC-Earth3 | r4i1p1f1 |
EC-Earth-Consortium (Europe) | EC-Earth3-Veg | r1i1p1f1 |
Institute of Atmospheric Physics (China) | FGOALS-g3 | r1i1p1f1 |
NOAA-Geophys. Fluid Dyn. Lab (USA) | GFDL-ESM4 | r1i1p1f1 |
Met Office Hadley Centre and NERC (UK) | HadGEM3-GC31-LL | r1i1p1f3 |
Institute for Numerical Mathematics (Rus.) | INM-CM4-8 | r1i1p1f1 |
Institute for Numerical Mathematics (Rus.) | INM-CM5-0 | r1i1p1f1 |
Institut Pierre-Simon Laplace (France) | IPSL-CM6A-LR | r1i1p1f1 |
National Institute of Meteo. Sciences and Korea Meteo. Administration (Korea) | KACE-1-0-G | r2i1p1f1 |
Korea Institute of Ocean Science and Technology (Korea) | KIOST-ESM | r1i1p1f1 |
University of Tokyo JAMSTEC, NIES, and AORI (Japan) | MIROC6 | r1i1p1f1 |
University of Tokyo JAMSTEC, NIES, and AORI (Japan) | MIROC-ES2L | r1i1p1f2 |
Max Planck Institute for Meteo. (Germany) | MPI-ESM1-2-HR | r1i1p1f1 |
Max Planck Institute for Meteo. (Germany) | MPI-ESM1-2-LR | r1i1p1f1 |
Meteorological Research Institute (Japan) | MRI-ESM2-0 | r1i1p1f1 |
Norwegian Climate Center (Norway) | NorESM2-LM | r1i1p1f1 |
Norwegian Climate Center (Norway) | NorESM2-MM | r1i1p1f1 |
Research Center for Env. Changes (Taiwan) | TaiESM1 | r1i1p1f1 |
Met Office Hadley Centre and NERC (UK) | UKESM1-0-LL | r1i1p1f2 |
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Crespo Cuaresma, J., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Aleluia Da Silva, L., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., & Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An Overview. Global Environmental Change, 42, 153-168. https://doi.org/10.1016/j.gloenvcha.2016.05.009
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:
