Climate data and scenarios: synthesis of recent observation and modelling results, chapter 3.4.2

3.4.2 Statistically downscaled results from CMIP5 models

Statistical downscaling makes use of empirically-derived relationships between large and small scales, and allows for a range of relevant climate quantities to be estimated. An important underlying assumption is that the empirical relationships are unaltered by a changing climate. While this may be a limiting assumption, it is offset to some degree by the fact that these approaches reduce the effect of systematic biases that may be present in global and regional climate models. The reduction of systematic biases is essential for the projection of some extreme indicators that are based on threshold crossing, for example, heating or cooling degree days. Environment Canada has worked with the Pacific Climate Impacts Consortium (PCIC) to develop statistically downscaled climate scenarios based on the CMIP5 global climate projections and regional climate projections (NARCAPP and CorDEXFootnote4). The projections for Canada are available via the PCIC Data Portal. Figure 14 shows the potential utility of statistical downscaling for projecting climate extremes. Projected changes in heating degree-days and cooling degree-days in Canada are shown for three future periods (see figure caption for further details).

Figure 14

Figure 14 - Illustration of potential utility of statistically downscaled projections of extremes. Projected changes in cooling (left panel) and heating (right panel) degree days (in degree-days) are shown for the 2016-2035 (top), 2046-2065 (middle), and 2081-2100 (bottom) periods. Projected changes are relative to the 1986-2005 mean estimated from the multi-model ensemble shown in Table 7 and downscaled using BCCAQ.

Long description of Figure 14

This figure shows 6 maps. The left 3 maps represent Heating Degree Days (HDD), and the right 3 maps represent Cooling Degree Days (CDD), with each row representing the different time periods (2016-2035, 2046-2065, and 2081-2100). The 2016-2035 HDD map starts with the south having -250 to -500 HDD, and the north having -500 to -1000 HDD. By 2086-2100 the south is anywhere between -500 to -1250 HDD and the north is between -1250 and -1500 HDD. The 2016-2035 CDD map starts with the southern Prairies and southern Ontario with about 50-100 CDD. By 2086-2100 the southern Prairies and southern Ontario have increased to between 200 and 300 CDD and the area indicating at least some CDD covers almost of the provinces.

Table 7: Information on the CMIP5 models whose results were used to produce Figure 14.
Model Name Place of Origin Institution
ACCESS1.0 Australia Commonwealth Scientific and Industrial Research Organisation and Bureau of Meteorology
CanESM2 Canada Canadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment Canada
CCSM4 USA National Centre for Atmospheric Research
CNRM-CM5 France Centre National de Recherches Météorologiques and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique
CSIRO-Mk3.6.0 Australia Queensland Climate Change Centre of Excellence and Commonwealth Scientific and Industrial Research Organisation
GFDL-ESM2G USA NOAA Geophysical Fluid Dynamics Laboratory
HadGEM2-CC UK UK Met Office Hadley Centre (additional realizations contributed by Instituto Nacional de Pesquisas Espaciais, Brazil)
HadGEM2-ES UK UK Met Office Hadley Centre (additional realizations contributed by Instituto Nacional de Pesquisas Espaciais, Brazil)
INM-CM4 Russia Institute for Numerical Mathematics
MIROC5 Japan University of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology
MPI-ESM-LR Germany Max Planck Institute for Meteorology
MRI-CGCM3 Japan Meteorological Research Institute

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