Climate Classification in the Canadian Prairie Provinces Using Day-to-Day Thermal Variability Metrics

The data from thirty-one climate stations in the Canadian Prairie provinces of Alberta, Saskatchewan, and Manitoba are analyzed using a number of day-to-day thermal variability metrics. These are used to classify each climate station location using a decision tree developed previously. This is the first application of the decision tree to identify stations as having rural, urban, peri-urban, marine, island, airport, or mountain climates. Of the thirty-one, eighteen were identified as peri-urban, with fourteen of these being airports; six were identified as marine or island; four were identified as rural; one as urban was identified; and two were identified as mountain. The two climate stations at Churchill, Manitoba, located near the shores of Hudson Bay, were initially identified as peri-urban. This was re-assessed after adjusting the number of “winter” months used in the metric for identifying marine and island climates (which, for all other analyses, excluded only December, January, and February). For Churchill, to match the sea ice season, the months of November, March, April, and May were also excluded. Then, a strong marine signal was found for both stations. There is a potential to use these thermal metrics to create a sea ice climatology in Hudson Bay, particularly for pre-satellite reconnaissance (1971). Lake Louise and Banff, Alberta, are the first mountain stations to be identified as such outside of British Columbia. Five airport/non-airport pairs are examined to explore the difference between an airport site and a local site uninfluenced by the airport. In two cases, the expected outcome was not realized through the decision tree analysis. Both Jasper and Edmonton Stony Plain were classified as peri-urban. These two locations illustrated the influence of proximity to large highways. In both cases the expected outcome was replaced by peri-urban, reflective of the localized impact of the major highway. This was illustrated in both cases using a time series of the peri-urban metric before and after major highway development, which had statistically significant differences. This speaks to the importance of setting climate stations appropriately away from confounding influences. It also suggests additional metrics to assess the environmental consistency of climate time series.