Atlantic Canada is situated in an environmentally diverse area, spanning almost 20 degrees of latitude and 20 degrees of longitude along the eastern reaches of North America. The climate of the region is varied, encompassing both coastal and continental regimes and influenced by several major ocean currents and mountain ranges. In order to best describe the expected climate change impacts for the region, climate change scenarios and climate variables must be developed on a regional, or even site-specific, scale.

Two methods currently exist that would potentially provide this information, output from a Regional Climate Model (RCM) and statistical techniques to "downscale" climate variables from global climate models. Since the RCM capability for Canadian territory is presently being developed and output for Atlantic Canada is not readily available, statistical techniques are being explored to generate the downscaled climate variables in that region. The statistical techniques utilized in this study are embodied in the Statistical Downscaling Model (SDSM) developed by Rob Wilby et al., King’s College, London.

To develop the initial statistical relationships, homogenized daily mean, maximum and minimum temperature and precipitation amount data for a site in the Annapolis Valley of Nova Scotia, over the last 30 years, was taken from the Historical Canadian Climate Database (HCCD). The technique essentially develops a predictor-predictand relationship between global climate model values and the observed values at a specific site. Future climate variables (predictors) are then extracted from various model experiments. In this study, the Canadian coupled global climate model version 1 (CGCM1) from the University of Victoria, in British Columbia was utilized. Those predictors are used to provide downscaled climate variables (predictand) that are applicable to those specific observed data sites.

The resulting values for temperature and precipitation provide one plausible future climate for the site in question and are used to build climate scenarios for that region or site. They can also be used by climate change impacts researchers who want to apply climate variables on a regional scale.