Providing evidence of potential changes in the climate has become increasingly important as it is the first step towards adopting mitigation and adaptation measures and planning for urban resilience. In this study a statistical analysis of the ambient air temperature time series over Sydney, Australia during 1970-2016 has been carried out with the aim to investigate potential changes towards higher temperatures. The dataset has been statistically analyzed using different techniques, concluding that the investigation should be performed on a monthly basis. A persistence analysis was conducted using different statistical approaches to investigate the dependence between consecutive monthly and daily ambient air temperature values. A trend analysis of the ambient air temperature and degree days time series has been conducted using linear regression to estimate the linear trend (slope) and its statistical significance (using a Student-t-test) and the Kendall-Mann test to identify the time at which the tendency starts to occur as well as the time after which it becomes statistically significant.
Keywords: Air temperature; Degree days; Persistence; Time series; Trend analysis.
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