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[1] The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in(More)
The need for uniied notation in atmospheric and oceanic data assimilation arises from the eld's rapid theoretical expansion and the desire to translate it into practical applications. Self-consistent notation is proposed that bridges sequential and variational methods, on the one hand, and operational usage, on the other. Over various other mottoes for this(More)
The source of irregularity in El Niño, the large interannual climate variation of the Pacific ocean-atmosphere system, has remained elusive. Results from an El Niño model exhibit transition to chaos through a series of frequency-locked steps created by nonlinear resonance with the Earth's annual cycle. The overlapping of these resonances leads to the(More)
The purpose of time-series analysis is to detect basic properties of the system that engenders a time series. The hope of predicting the system's future evolution is closely related to the possibility of such detection. The most easily predictable components of a system's evolution are the regular, deterministic ones; hence we look for trends and periodic(More)
Understanding the natural variability of climate is important for predicting its near-term evolution. Models of the oceans' thermohaline and wind-driven circulation show low-frequency oscillations. Long instrumental records can help validate the oscillatory behavior of these models. Singular spectrum analysis applied to the 335-year-long central England(More)
Thirty years ago, E. N. Lorenz provided some approximate limits to atmospheric predictability. The details---in space and time---of atmospheric flow fields are lost after about 10 days. Certain gross flow features recur, however, after times of the order of 10--50 days, giving hope for their prediction. Over the last two decades, numerous attempts have been(More)
A new probabilistic clustering method, based on a regression mixture model, is used to describe tropical cyclone (TC) propagation in the western North Pacific (WNP). Seven clusters were obtained and described in Part I of this two-part study. In Part II, the present paper, the large-scale patterns of atmospheric circulation and sea surface temperature(More)