Severe Testing and Characterization of Change Points in Climate Time Series

This paper applies misspecification (M-S) testing to the detection of abrupt changes in climate regimes as part of undertaking severe testing of climate shifts versus trends. Severe testing, proposed by Mayo and Spanos, provides severity criteria for evaluating statistical inference using probative criteria, requiring tests that would find any flaws present. Applying M-S testing increases the severity of hypothesis testing. We utilize a systematic approach, based on well-founded principles that combines the development of probative criteria with error statistical testing. Given the widespread acceptance of trend-like change in climate, especially temperature, tests that produce counter-examples need proper specification. Reasoning about abrupt shifts embedded within a complex times series requires detection methods sensitive to level changes, accurate in timing, and tolerant of simultaneous changes of trend, variance, autocorrelation, and red-drift, given that many of these measures may shift together. Our preference is to analyse the raw data to avoid pre-emptive assumptions and test the results for robustness. We use a simple detection method, based on the Maronna-Yohai (MY) test, then re-assess nominated shift-points using tests with varied null hypotheses guided by M-S testing. Doing so sharpens conclusions while avoiding an over-reliance on data manipulation, which carries its own assumptions.