Remote Sens. 2025, 17(13), 2228; https://doi.org/10.3390/rs17132228 (registering DOI) - 29 Jun 2025
Abstract
Global mean sea level has been rising steadily since the early 1990s, yet regional sea level changes exhibit complex spatial variability that frequently contrasts with global trends. Investigating sea level variations in semi-enclosed basins such as the Black Sea is crucial for elucidating
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Global mean sea level has been rising steadily since the early 1990s, yet regional sea level changes exhibit complex spatial variability that frequently contrasts with global trends. Investigating sea level variations in semi-enclosed basins such as the Black Sea is crucial for elucidating regional responses to climate change and characterizing its unique spatiotemporal evolution patterns. In this study, we employ satellite altimetry (SA) data to study sea level changes, spatial variability, and seasonal patterns in the Black Sea over eight distinct time periods with temporally correlated noise, and our results show good consistency with existing studies. The results show that sea level changes are non-linear over time and exhibit spatial variability in the Black Sea. The estimated sea level trend fluctuates over brief intervals, but extended time series provide reduced uncertainty in the trend and more precise estimation over a 28-year time series. The annual amplitude and phase derived from virtual altimetry data (1993–2020) exhibit a distinct seasonal pattern, with peak sea levels typically occurring between November and February. Furthermore, to reduce the uncertainty induced by noise in the sea surface height (SSH) time series, principal component analysis (PCA) was utilized to denoise the SSH data from 1993 to 2020, yielding a sea level trend of 1.76 ± 0.56 mm/yr. Denoising reduced the trend uncertainty by 57%, decreased the root mean square error of the SSH series by 5.06 mm, and decreased the annual amplitude by 23.35%.
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(This article belongs to the Section Environmental Remote Sensing)
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