A waveform skewness index for measuring time series nonlinearity and its applications to the ENSO–Indian monsoon relationship

[摘要] Many geophysical time series possess nonlinear characteristics that reflectthe underlying physics of the phenomena the time series describe. Thenonlinear character of times series can change with time, so it is importantto quantify time series nonlinearity without assuming stationarity. A commonway of quantifying the time evolution of time series nonlinearity is to compute sliding skewness time series, but it is shown here that such an approach can be misleading when time series contain periodicities. To remedy thisdeficiency of skewness, a new waveform skewness index is proposed forquantifying local nonlinearities embedded in time series. A waveformskewness spectrum is proposed for determining the frequency components thatare contributing to time series waveform skewness. The new methods areapplied to the El Niño–Southern Oscillation (ENSO) and the Indian monsoon to test a recently proposed hypothesis that states that changes inthe ENSO–Indian monsoon relationship are related to ENSO nonlinearity. We show that the ENSO–Indian rainfall relationship weakens during time periods of high ENSO waveform skewness. The results from two different analysessuggest that the breakdown of the ENSO–Indian monsoon relationship during time periods of high ENSO waveform skewness is related to the more frequentoccurrence of strong central Pacific El Niño events, supportingarguments that changes in the ENSO–Indian rainfall relationship are not solely related to noise.

[学科分类] 自动化工程