Climatic change and climatic variability: An objective decomposition. (1st November 2021)

• Record Type: Journal Article
• Authors: Grove, Matt
• Is Part Of: Quaternary science reviews. Volume 271(2021)
• Publication Date: 2021-11-01
• Subjects: Climatic change -- Climatic variability -- Time series -- Singular spectrum analysis -- White noise -- Southern oscillation index -- El Niño -- Domestication -- Agriculture -- Natufian; Geology, Stratigraphic -- Quaternary -- Periodicals; Stratigraphie -- Quaternaire -- Périodiques; 551.79
• DOI: 10.1016/j.quascirev.2021.107196
• Languages: English
• ISSNs: 0277-3791

Abstract

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. Highlights: Current definitions of climatic change and variability are often vague and subjective An algorithm is designed that objectively decomposes a time series into change and variability components An extensive series of simulations is employed to validate this algorithm Two empirical case studies demonstrate its effectiveness