Multiscale Event-Based Mining in Geophysical Time Series: Characterization and Distribution of Significant Time-Scales in the Sea Surface Temperature Anomalies Relatively to ENSO Periods from 1985 to 2009

In this paper, one-dimensional (1-D) geophysical time series are regarded as series of significant time-scale events. We combine a wavelet-based analysis with a Gaussian mixture model to extract characteristic time-scales of 486 144 detected events in the Sea Surface Temperature Anomaly (SSTA) observed from satellite at global scale from 1985 to 2009. We retrieve four low-frequency characteristic time-scales of Niño Southern Oscillation (ENSO) in the 1.5- to 7-year range and show their spatial distribution. High-frequency (HF) SSTA event spatial distribution shows a dependency to the ENSO regimes, pointing out that the ENSO signal also involves specific signatures at these time-scales. These fine-scale signatures can hardly be retrieved from global EOF approaches, which tend to exhibit uppermost the low-frequency influence of ENSO onto the SSTA. In particular, we observe at global scale a major increase by 11% of the number of SSTA HF events during Niño periods, with a local maximum of 80% in Europe. The methodology is also used to highlight an ENSO-induced frequency shift during the major 1997-2000 ENSO event in the intertropical Pacific. We observe a clear shift from the high frequencies toward the 3.36-year scale with a maximum shift occurring 2 months before the ENSO maximum of energy at 3.36-year scale.