Changepoint Detection: An Analysis of the Central England Temperature Series

TL;DR

This paper analyzes the Central England temperature series to detect structural changes, identifying a significant changepoint in the late 1980s indicating intensified warming, using various statistical models and techniques.

Contribution

It provides a comprehensive comparison of changepoint detection methods and identifies a key warming regime shift in the late 1980s in temperature data.

Findings

A significant changepoint in the late 1980s indicating intensified warming

Changepoint models outperform models with autocorrelations in explaining variability

The optimal model includes trend-shifts with independent errors

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.