Changepoint Detection:An Analysis of the Central England Temperature Series

Shi, Xueheng and Beaulieu, Claudie and Killick, Rebecca and Lund, Robert (2022) Changepoint Detection:An Analysis of the Central England Temperature Series. Journal of Climate, 35 (19). pp. 2729-2742. ISSN 0894-8755

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Abstract

This paper presents a statistical analysis of structural changes in the Central England temperature series, one of the longest surface temperature records available. A changepoint analysis is performed to detect abrupt changes, which can be regarded as a preliminary step before further analysis is conducted to identify the causes of the changes (e.g., artificial, human-induced or natural variability). Regression models with structural breaks, including mean and trend shifts, are fitted to the series and compared via two commonly used multiple changepoint penalized likelihood criteria that balance model fit quality (as measured by likelihood) against parsimony considerations. Our changepoint model fits, with independent and short-memory errors, are also compared with a different class of models termed long-memory models that have been previously used by other authors to describe persistence features in temperature series. In the end, the optimal model is judged to be one containing a changepoint in the late 1980s, with a transition to an intensified warming regime. This timing and warming conclusion is consistent across changepoint models compared in this analysis. The variability of the series is not found to be significantly changing, and shift features are judged to be more plausible than either short- or long-memory autocorrelations. The final proposed model is one including trend-shifts (both intercept and slope parameters) with independent errors. The analysis serves as a walk-through tutorial of different changepoint techniques, illustrating what can be statistically inferred.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Climate
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1900/1902
Subjects:
ID Code:
170760
Deposited By:
Deposited On:
24 May 2022 12:00
Refereed?:
Yes
Published?:
Published
Last Modified:
28 Jan 2023 01:18