Climatic change and climatic variability: An objective decomposition

Abstract

Over the past 25 years research concerning the effects of climatic fluctuations on past human societies has shifted focus considerably, with most recent hypotheses emphasizing shorter-term variability over longer-term change. Definitions of change and variability, however, remain subjective and vary considerably between researchers. It is suggested that white noise, due to its inherent unpredictability, provides a theoretically robust model of variability that accords with perceptions of variability conveyed by the existing literature. The use of white noise as a model for variability enables the development of an algorithm that objectively decomposes an empirical climatic signal into change and variability components. The algorithm, which combines singular spectrum analysis and Fourier methods, is validated via an extensive series of simulations and applied via two empirical case studies. It is shown that the algorithm has the potential to produce genuine advances by isolating features of interest and facilitating more rigorous hypothesis testing. Its use will therefore aid researchers studying palaeoclimatic effects on prehistoric human societies as well as those studying the nature and effects of contemporary climate change.