Abstract

AbstractHow to extract the causal relations in climate–cyclone interactions is an important problem in atmospheric science. Traditionally, the most commonly used research methodology in this field is time-delayed correlation analysis. This may be not appropriate, since a correlation cannot imply causality, as it lacks the needed asymmetry or directedness between dynamical events. This study introduces a recently developed and very concise but rigorous formula—that is, a formula for information flow (IF)—to fulfill the purpose. A new way to normalize the IF is proposed and then the normalized IF (NIF) is used to detect the causal relation between the tropical cyclone (TC) genesis over the western North Pacific (WNP) and a variety of climate modes. It is shown that El Niño–Southern Oscillation and Pacific decadal oscillation are the dominant factors that modulate the WNP TC genesis. The western Pacific subtropical high and the monsoon trough are also playing important roles in affecting the TCs in the western and eastern regions of the WNP, respectively. With these selected climate indices as predictors, a method of fuzzy graph evolved from a nonparametric Bayesian process (BNP-FG), which is capable of handling situations with insufficient samples, is employed to perform a seasonal TC forecast. A forecast with the classic Poisson regression is also conducted for comparison. The BNP-FG model and the causality analysis are found to provide a satisfactory estimation of the number of TC genesis observed in recent years. Considering its generality, it is expected to be applicable in other climate-related predictions.

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