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About CanDCS-M6

Overview

The Canadian Downscaled Climate Scenarios-multivariate 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 MBCn downscaling method2 and PCIC-Blend 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 1950-2014 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.

 

Data processing

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 N-Dimensional Multivariate Bias Correction (MBCn) downscaling method was applied, using PCIC-Blend 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-M6 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

References

  1. 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
  2. Cannon, A.J. Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Clim Dyn 50, 31–49 (2018). https://doi.org/10.1007/s00382-017-3580-6