CMIP6 statistically downscaled agroclimatic indices

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 (Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6)) created by the Pacific Climate Impacts Consortium (PCIC). To address the needs of different user groups in Canada, 49 indices, including agroclimatic indices, were proposed by the Canadian adaptation community through a series of consultations. Please see the definition list for the equations of each index.

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. 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.

Note: 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.

Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.