Interannual variability of sea level anomalies (SLA) in the South China Sea (SCS) is significantly influenced by large-scale climate modes; however, their temporal evolution and interdecadal modulation mechanisms remain insufficiently understood. Based on observational records and ERA5 reanalysis data spanning 1980–2022, this study employs a Bayesian Dynamic Linear Model (DLM) to quantify the time-varying impacts of El Niño-Southern Oscillation (ENSO) on interannual SLA variability across different subregions of the SCS and further investigates the modulation effect of the Pacific Decadal Oscillation (PDO) background state. The results indicate that ENSO is a key climatic driver of interannual SLA variability in the SCS; nevertheless, its influence exhibits pronounced non-stationarity, with dynamic regression coefficients showing clear phase-dependent fluctuations throughout the study period. The northern and eastern subregions display stronger responses to ENSO forcing, whereas the southern and western subregions exhibit relatively weaker signals. The negative phase of the PDO enhances the ENSO-SLA relationship, while the positive phase weakens it, with sign reversals occurring in certain subregions. Correlation analyses further suggest that ENSO influences SLA primarily through wind stress anomalies induced by sea level pressure (SLP) gradients, which regulate Ekman transport, whereas the PDO exerts an indirect effect mainly by modifying the large-scale background circulation structure.
Wang et al. (Mon,) studied this question.