Multi-criterial analysis of surface air temperature patterns in Arctic Canada

Abstract

Interest in monitoring global climate has increased in recent years as the potential implications of climate change have come into the conscious of the public. Climate models and observations have shown that the polar regions are especially sensitive to climate change. This is especially concerning since the polar regions could experience several positive feedbacks as a result of increasing surface temperatures and/or change in their variability. The purpose of this study is to analyze surface air temperature data from nine weather stations in Arctic Canada to gain a better understanding of the status of the region’s climate. Nine stations spread across the Canadian Arctic region were chosen from a larger database of homogenized surface temperature time series extracted from the National Climate Data Archive. The nine stations were chosen based on their length (equal or greater than 50 years). A multi-scale analysis was conducted to explore whether surface temperature patterns in Arctic Canada appear to be changing from the point of view of overall trends and temporal variability in the region. Pattern change was analyzed using a height-height correlation analysis of time series of different lengths. Statistical patterns examined using these methods include mean, standard deviation, range, moments on n[superscript th], and the Hurst-exponent (for analyzing pattern persistence). The results indicate that there are spatial correlations in pattern persistence, and that the correlations change over time.