Abstract

A nonlinear principal component analysis (NLPCA) is applied to a set of monthly mean time series from January 1956 to December 2007 consisting of the Arctic oscillation (AO) index derived from 1,000-hPa geopotential height anomalies poleward of 20°N latitude and the zonal winds observed at seven pressure levels between 10 and 70 hPa in the equatorial stratosphere to investigate the relation of the AO with the quasi-biennial oscillation (QBO). The NLPCA is conducted using a new, compact neural network model. The NLPCA modeling of the dataset of the AO index and QBO winds offers a clear picture of the relation between the two oscillations. In particular, the phase of covariation of the oscillations defined by the two nonlinear principal components of the dataset progresses with a predominant 28.4-month periodicity. This predominant cycle is modulated by an 11-year cycle. The variation of the AO index with the QBO phase also shows that the average AO index is positive when the westerly QBO phase descends past 30 hPa and, conversely, the average AO index is negative when the easterly QBO phase descends past 30 hPa. This relationship is evident during the boreal cold season from November to April but non-existent during the boreal warm season from May to October.

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