CMIP6 statistically downscaled agroclimatic indices technical documentation

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The Canadian Climate Data and Scenarios (CCDS) website provides high-resolution statistically downscaled climate indices based on model projections from 26 global climate models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The range of impact-relevant climate indices available for download includes, indices representing counts of the number of days when temperature or precipitation exceeds (or is below) a threshold value; the episode length when a particular weather/climate condition occurs; and indices that accumulate temperature departures above or below a fixed threshold.

Environment and Climate Change Canada’s (ECCC) CMIP6 statistically downscaled agroclimatic indices are an updated version of the CMIP5 agroclimatic indices dataset making use of the new set of downscaled scenarios (CanDCS-U6) created by the Pacific Climate Impacts Consortium (PCIC). The statistically downscaled climate indices are available for historical simulations (1951-2014) and three new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs), SSP1-2.6, SSP2-4.5, and SSP5-8.5 (2015-2100), at a 10 x 10 km degree grid resolution.

Please see Li et al. (2018)Reference1 for further details of the calculation and analysis of the statistically downscaled agroclimatic indices dataset.

Table 1. Main characteristics.


CMIP6 statistically downscaled agroclimatic indices based on the CanDCS-U6 (CanDCS-U6: Canadian Downscaled Climate Scenarios–Univariate method from CMIP6)


For the full variable list and definitions

Geographic area

Canada (land mass only)

Spatial resolution

1/12° (10km) grid resolution

Time Period

1950 to 2100

Historical emissions: 1950-2014

Future emissions scenarios: 2015-2100

Temporal resolution

Annual values

Future emissions scenarios




Data and processing

Daily minimum and maximum temperature and precipitation GCM output were statistically downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure. A total of 26 statistically downscaled GCM outputs were utilized to derive the indices.

Bias Correction/Constructed Analogues with Quantile mapping (BCCAQ) is a hybrid downscaling algorithm that combines downscaling by BCCA (Bias Correction/Constructed Analogues) (Maurer et al., 2010)Reference2 with quantile mapping. Details are provided in Werner and Cannon (2016)Reference3. The use of Quantile Delta Mapping (QDM; Cannon et al., 2015)Reference4, a change-preserving form of quantile mapping, distinguishes BCCAQv2 from the previous version of the algorithm. For additional details on the CanDCS-U6 scenarios and the BCCAQv2 methodology, please see the CanDCS-U6 technical documentation.

Note: The calibration and application of the BCCAQv2 statistical downscaling method requires historical simulations to be concatenated to an SSP projection. As an artifact, statistically downscaled historical simulations concatenated to the three SSPs are not identical, though differences are generally negligible.

Furthermore, the process of downscaling different variables independently may lead to a few cases of physical inconsistency (i.e., minimum temperatures that occasionally exceed maximum temperatures), although tests indicate that ad hoc “correction” of inconsistent temperatures (e.g., by swapping affected minima and maxima) generally results in negligible differences in calculated climate indices (Li et al., 2018)Reference1.

Agroclimatic indices definitions

There are multiple ranges of climate indices available, one common example is the Expert Team on Climate Change Detection and Indices (ETCCDI) indices. The ETCCDI indices were developed for understanding the past and future changes in climate extremes (e.g., Sillmann et al. 2013a, b Reference5Reference6). They are not, however, always relevant for tracking climate impacts at the regional and local scales (Li et al 2018)Reference1.

To address the needs of different user groups in Canada, additional indices, including agroclimatic indices, proposed by the Canadian adaptation community through a series of consultations are provided on CCDS. A list of these indices and their definitions is also available. Please see the definition list for the equations of each index made available on CCDS.

Using global climate model output versus downscaled datasets

It should be noted that projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impact assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM-dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with a finer spatial scale.

Use limitation

Open Government Licence - Canada

Individual model datasets and all related derived products are subject to the terms of use of the source organization.

Contact information

Email: f.ccds.info-info.dscc.f@ec.gc.ca

Table 2. List of global climate models used in ECCC’s CMIP6 statistically downscaled agroclimatic indices.

# Institution Model Name Realization
1 CSIRO-ARCCSS (Australia) ACCESS-CM2 r1i1p1f1
2 CSIRO (Australia) ACCESS-ESM1-5 r1i1p1f1
3 Beijing Climate Center (China) BCC-CSM2-MR r1i1p1f1
4 Canadian Centre for Climate Modelling and Analysis (Canada) CanESM5 r1i1p2f1
5 Euro-Mediterranean Centre for Climate Change (Italy) CMCC-ESM2 r1i1p1f1
6 CNRM-CERFACS (France) CNRM-CM6-1 r1i1p1f2
7 CNRM-CERFACS (France) CNRM-ESM2-1 r1i1p1f2
8 EC-Earth-Consortium (Europe) EC-Earth3 r4i1p1f1
9 EC-Earth-Consortium (Europe) EC-Earth3-Veg r1i1p1f1
10 Institute of Atmospheric Physics (China) FGOALS-g3 r1i1p1f1
11 NOAA-Geophys. Fluid Dyn. Lab (USA) GFDL-ESM4 r1i1p1f1
12 Met Office Hadley Centre and NERC (UK) HadGEM3-GC31-LL r1i1p1f3
13 Institute for Numerical Mathematics (Rus.) INM-CM4-8 r1i1p1f1
14 Institute for Numerical Mathematics (Rus.) INM-CM5-0 r1i1p1f1
15 Institut Pierre-Simon Laplace (France) IPSL-CM6A-LR r1i1p1f1
16 National Institute of Meteo. Sciences and Korea Meteo. Administration (Korea) KACE-1-0-G r2i1p1f1
17 Korea Institute of Ocean Science and Technology (Korea) KIOST-ESM r1i1p1f1
18 University of Tokyo JAMSTEC, NIES, and AORI (Japan) MIROC6 r1i1p1f1
19 University of Tokyo JAMSTEC, NIES, and AORI (Japan) MIROC-ES2L r1i1p1f2
20 Max Planck Institute for Meteo. (Germany) MPI-ESM1-2-HR r1i1p1f1
21 Max Planck Institute for Meteo. (Germany) MPI-ESM1-2-LR r1i1p1f1
22 Meteorological Research Institute (Japan) MRI-ESM2-0 r1i1p1f1
23 Norwegian Climate Center (Norway) NorESM2-LM r1i1p1f1
24 Norwegian Climate Center (Norway) NorESM2-MM r1i1p1f1
25 Research Center for Env. Changes (Taiwan) TaiESM1 r1i1p1f1
26 Met Office Hadley Centre and NERC (UK) UKESM1-0-LL r1i1p1f2

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