A Regionalized Uncertainty Budget for Sea-Level Trend and Acceleration Estimates in the China Seas and Their Adjacent Oceans

Accurate estimation of regional sea-level trends and accelerations requires adequate quantification of their uncertainties. However, measurement and processing uncertainties in regional estimates remain poorly characterized. We constructed an error variance–covariance matrix incorporating major satellite-altimetry errors, including inter-mission offsets, wet tropospheric correction, glacial isostatic adjustment, orbit, reference-frame, and early-TOPEX errors, with spatially dependent uncertainty parameters evaluated separately for each region. The method was applied to regional sea-level records constructed from TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, and Sentinel-6A along-track observations over the China Seas and their Adjacent Oceans (CSA) from January 1993 to May 2026. The CSA sea-level trend is 4.032 ± 0.323 mm yr−1, while the acceleration is −0.013 ± 0.033 mm yr−2 (covariance-propagated 90% uncertainties), indicating a persistent rise but no acceleration distinguishable from zero. Across the Bohai, Yellow, East China, and South China Seas, trends range from 3.603 to 4.231 mm yr−1, whereas accelerations range from −0.020 to 0.056 mm yr−2, revealing spatial differences in both quantities and greater regional variability in acceleration. Covariance-propagated and residual-based intervals differ, demonstrating that they characterize different aspects of parameter uncertainty. Explicit propagation of identifiable measurement and processing error covariance provides a traceable basis for quantifying uncertainties in regional sea-level trend and acceleration estimates.

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Publication Details

Journal
Remote Sensing
Published
2026-09-04
DOI
https://doi.org/10.3390/rs18173012
Primary Topic
Geophysics and Gravity Measurements
Type
article
Field-Weighted Citation Impact
0.00

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article

A Regionalized Uncertainty Budget for Sea-Level Trend and Acceleration Estimates in the China Seas and Their Adjacent Oceans

Mingzhi Sun, Daocheng Yu, Meinan Zheng, Jiajia Yuan et al.
Remote Sensing
Geophysics and Gravity Measurements
article

A Regionalized Uncertainty Budget for Sea-Level Trend and Acceleration Estimates in the China Seas and Their Adjacent Oceans

Mingzhi Sun, Daocheng Yu, Meinan Zheng, Jiajia Yuan, Haoran Liu, Zhenhe Zhai
article en

Abstract

Accurate estimation of regional sea-level trends and accelerations requires adequate quantification of their uncertainties. However, measurement and processing uncertainties in regional estimates remain poorly characterized. We constructed an error variance–covariance matrix incorporating major satellite-altimetry errors, including inter-mission offsets, wet tropospheric correction, glacial isostatic adjustment, orbit, reference-frame, and early-TOPEX errors, with spatially dependent uncertainty parameters evaluated separately for each region. The method was applied to regional sea-level records constructed from TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, and Sentinel-6A along-track observations over the China Seas and their Adjacent Oceans (CSA) from January 1993 to May 2026. The CSA sea-level trend is 4.032 ± 0.323 mm yr−1, while the acceleration is −0.013 ± 0.033 mm yr−2 (covariance-propagated 90% uncertainties), indicating a persistent rise but no acceleration distinguishable from zero. Across the Bohai, Yellow, East China, and South China Seas, trends range from 3.603 to 4.231 mm yr−1, whereas accelerations range from −0.020 to 0.056 mm yr−2, revealing spatial differences in both quantities and greater regional variability in acceleration. Covariance-propagated and residual-based intervals differ, demonstrating that they characterize different aspects of parameter uncertainty. Explicit propagation of identifiable measurement and processing error covariance provides a traceable basis for quantifying uncertainties in regional sea-level trend and acceleration estimates.

Remote SensingVol. 18(17)
Sun Yat-sen University (CN), Liaoning Technical University (CN), Anhui University of Science and Technology (CN)
National Natural Science Foundation of China, Liaoning Revitalization Talents Program
Life below water
Openalex Percentile: Top 13%
Geophysics and Gravity Measurements
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