  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.
