Abstract/Summary

Tipping events in dynamical systems have been studied across many applications, often by measuring changes in variance or autocorrelation in a one-dimensional time series. In this paper, methods for detecting early warning signals of tipping events in multidimensional systems are reviewed and expanded. An analytical justification of the use of dimension-reduction by empirical orthogonal functions, in the context of early warning signals, is provided and the one-dimensional techniques are also extended to spatially separated time series over a 2D field. The challenge of predicting an approaching tropical cyclone by a tipping-point analysis of the sea-level pressure series is used as the primary example, and an analytical model of a moving cyclone is also developed in order to test predictions. We show that the one-dimensional power spectrum indicator may be used following dimension-reduction or over a 2D field. We also show the validity of our moving cyclone model with respect to tipping-point indicators. Many dynamical systems experience sudden shifts in behavior, often referred to as tipping points or critical transitions. A volume of work is dedicated to detecting and predicting these critical transitions, often making use of generic early warning signal (EWS) indicators based on autocorrelation1,2 and increasing variance.3,4 Similar indicators based on other scaling properties of the time series, namely, detrended fluctuation analysis (DFA)5,6 and power spectrum scaling,7 have also been used. Other methods have estimated parameters to fit a model to the data, both for detecting critical transitions8–10 and for predicting future transitions dynamics.