UNDERSTANDING REGIONAL CLIMATE CHANGE THROUGH THE APPLICATION OF THREE STATISTICAL METHODS
DOI:
https://doi.org/10.59627/cbens.2014.2180Keywords:
Climate Data, Cluster Analysis, Trend Analysis, PrecipitationAbstract
This study assesses the use of historical climate data as well as traditional and non-traditional statistical methods to understand climate change at a regional level. Three different approaches were considered: i) general evaluation of climate data evolution, including comparison between two periods (early and late years); ii) trend analysis; and iii) cluster analysis. Daily data of rainfall and snowfall were obtained from the Sudbury Airport weather station (Canada) from January 1956 to December 2010 (55 full years). The comparison between periods revealed that annual rainfall is increasing in the studied location, being 12% higher in recent years. Trend analysis and cluster analysis showed that these increasing annual trends were not uniform throughout the year, occurring mainly in winter and spring. On the other hand, decreases in summer rainfall were detected by cluster analysis only. According to cluster analysis results, summers are becoming drier in the location, although overall, years are becoming wetter. Regarding snowfall, there was no difference between the two periods compared and trend analysis detected no significant trends. However, cluster analysis showed clear changes during the main months of snowfall (December, January and February), indicating that climate in the location is changing towards late winters regarding snowfall. Thus, the results demonstrate that inclusion of simple methods such as cluster analysis, combined with more traditional statistical methods, can contribute to a better understanding of climate change.
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