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This study uses quantile regression combined with time series methods to analyze change in temperatures in Chicago during the period 1960-2010. It builds on previous work in applying quantile regression methods to climate data by Timofeev and Sterin (2010) and work by the Chicago Climate Task Force on analyzing climate change in Chicago. Data from the Chicago O’Hare Airport weather station archived by the National Climatic Data Center are used to look at changes in weekly average temperatures. The method described by Xiao et al. (2003) is used to remove autocorrelation in the data, together with the rank-score method with IID assumption to calculate confidence intervals, and nonparametric local linear quantile regression to estimate temperature trends. The results of this analysis indicate that the decade 1960-1969 was significantly colder than later decades around the middle of the yearly seasonal cycle at both the median and 95th percentile of the temperature distribution. This analysis does not find a statistically significant trend over the later decades, 1970-2010.

A Quantile Regression Study of Climate Change in Chicago, 1960-2010