A practical step-by-step guide to wavelet analysis is given, with examples taken from time series of the El NioSouthern Oscillation (ENSO). The guide includes a comparison to the windowed Fourier transform, the choice of an appropriate wavelet basis function, edge effects due to finite-length time series, and the relationship between wavelet scale and Fourier frequency. New statistical significance tests for wavelet power spectra are developed by deriving theoretical wavelet spectra for white and red noise processes and using these to establish significance levels and confidence intervals. It is shown that smoothing in time or scale can be used to increase the confidence of the wavelet spectrum. Empirical formulas are given for the effect of smoothing on significance levels and confidence intervals. Extensions to wavelet analysis such as filtering, the power Hovmller, cross-wavelet spectra, and coherence are described.The statistical significance tests are used to give a quantitative measure of changes in ENSO variance on interdecadal timescales. Using new datasets that extend back to 1871, the Nio3 sea surface temperature and the Southern Oscillation index show significantly higher power during 18801920 and 196090, and lower power during 192060, as well as a possible 15-yr modulation of variance. The power Hovmller of sea level pressure shows significant variations in 28-yr wavelet power in both longitude and time.
Bulletin of the American Meteorological Society – American Meteorological Society
Published: Jan 20, 1998
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