Abstract :
[en] The ENSO (El Niño Southern Oscillation) phenomenon is a complex
climate process governed by various cycles, that are yet only partially
understood. To better understand these cycles and their impact on
the global climate, we employed a modal extraction method based on
Continuous Wavelet Transform (CWT).
Most particularly, we used the Wavelet-Induced Mode Extraction (WIME)
algorithm, initially developed to decompose signals into cosinusoidal
components varying in frequency, amplitude, and phase. In this thesis, the
WIME algorithm was adapted to handle real-world signals by incorporating
adjustable parameters to manage noise, peak concavity, and frequency
variation range.
ENSO was analyzed using two indices: the Southern Oscillation Index
(SOI) for the atmospheric component and the Oceanic Ni˜no Index (ONI)
for the oceanic component. The method was validated by reconstructing
signals for temperatures, ONI, and SOI, with correlation coefficients of 0.86,
0.94, and 0.76, respectively, demonstrating the robustness of the approach.
The identified periods are consistent with each other and with existing
literature. Moreover, the indicators remain strong when considering El
Ni˜no and La Ni˜na events exclusively.
Furthermore, short- and medium-term predictions (2-3 years) were
made by truncating the time series and testing the method’s ability to
forecast subsequent data. This validation procedure shows a correlation of
0.92 with the ONI signal and satisfactory difference indicators.
Understanding ENSO is vital due to its significant impacts on global
weather patterns, economies, and ecosystems. This study introduces a
novel approach for analyzing and forecasting the ENSO phenomenon,
offering potential improvements in prediction accuracy. Additionally,
this method has potential applications to other climate phenomena, such
as the Arctic Oscillation and the North Atlantic Oscillation, as well as
non-climatic events like seismic activities.
