Revealing Sea-Level Dynamics Driven by El Niño–Southern Oscillation: A Hybrid Local Mean Decomposition–Wavelet Framework for Multi-Scale Analysis

Analysis of global mean sea-level (GMSL) variations provides insights into their spatial and temporal characteristics. To analyze the sea-level cycle and its correlation with the El Niño–Southern Oscillation (ENSO, represented by the Oceanic Niño Index), this study proposes an enhanced analytical framework integrating Local Mean Decomposition with an improved wavelet thresholding technique and wavelet transform. The GMSL time series (January 1993 to July 2020) underwent multi-scale decomposition and noise reduction using Local Mean Decomposition combined with improved wavelet thresholding. Subsequently, the Morlet continuous wavelet transform was applied to analyze the signal characteristics of both GMSL and the Oceanic Niño Index. Finally, cross-wavelet transform and wavelet coherence analyses were employed to investigate their correlation and phase relationships. Key findings include the following: (1) Persistent intra-annual variability (8–16-month cycles) dominates the GMSL signal, superimposed by interannual fluctuations (4–8-month cycles) related to climatic and seasonal forcing. (2) Phase analysis reveals that GMSL generally leads the Oceanic Niño Index during El Niño events but lags during La Niña events. (3) Strong El Niño episodes (May 1997 to May 1998 and October 2014 to April 2016) resulted in substantial net GMSL increases (+7 mm and +6 mm) and significant peak anomalies (+8 mm and +10 mm). (4) Pronounced negative peak anomalies occur during La Niña events, though prolonged events are often masked by the long-term sea-level rise trend, whereas shorter events exhibit clearly discernible and rapid GMSL decline. The results demonstrate that the proposed framework effectively elucidates the multi-scale coupling between ENSO and sea-level variations, underscoring its value for refining the understanding and prediction of climate-driven sea-level changes.