Abstract

The author presents an iterative approach to describing a data set, such as a paleoclimatic proxy or a model output, in terms of automata of successively higher order. The automata reflect dynamics acting on successively longer timescales and larger spatial scales. The method uses the computed probability density function from reconstructed state space portraits, over successive overlapping windows in time, of the record of magnetic susceptibility of loess and paleosols at Luochuan, central China. Areas of consistently high probability across several time windows represent areas of quasistability, which are used as the predictive and successor states of a succession of Markov Chains that characterize the variability of the strength of the East Asian paleomonsoon at different time scales. Seven metastable states are thus identified, forming four Markov Chains, which show a marked increase in complexity of behavior of the paleomonsoon system throughout the Quaternary. A higher-order automaton is suggested by the sequence of Markov Chains, suggesting differing cycles of dynamic behaviour in the Early and Late Quaternary.