Resumen
This study utilizes 27 years of sea level anomaly (SLA) data obtained from satellite altimetry to investigate spatial?temporal variations in the South China Sea (SCS). The local mean decomposition (LMD) method is applied to decompose the sea level data into three components: high-frequency, low-frequency, and trend components. By removing the influence of high-frequency components, multiple time series of regular sea level changes with significant physical significance are obtained. The results indicate that the average multi-year SLA is 50.16 mm, with a linear trend of 3.91 ± 0.12 mm/a. The wavelet analysis method was employed to examine the significant annual and 1.5-year periodic signals in the SCS SLA series. At the seasonal scale, the sea level rise in coastal areas during autumn and winter surpasses that of spring and summer. Moreover, there are generally opposing spatial distributions between spring and autumn, as well as between summer and winter. The linear trends in multi-year SLA for the four seasons are 3.70 ± 0.13 mm/a, 3.66 ± 0.16 mm/a, 3.49 ± 0.16 mm/a, and 3.74 ± 0.33 mm/a, respectively. The causes of SCS sea level change are examined in relation to phenomena such as monsoons, the Kuroshio Current, and El Niño?Southern Oscillation (ENSO). Based on the empirical orthogonal function (EOF) analysis of SCS SLA, the contributions of the first three modes of variance are determined to be 34.09%, 28.84%, and 8.40%, respectively. The temporal coefficients and spatial distribution characteristics of these modes confirm their associations with ENSO, monsoons, and the double-gyre structure of SCS sea surface temperature. For instance, ENSO impacts SCS sea level change through atmospheric circulation, predominantly affecting the region between 116° E and 120° E longitude, and 14° N and 20° N latitude.