Regime Shift of the Sea Level Trend in the South China Sea Modulated by the Tropical Pacific Decadal Variability

Cheng, Xuhua; Zhao, Mingyu; Duan, Wen‐Shan; Jiang, Long; Chen, Jiajia; Yang, Chengcheng; Zhou, Yifei

doi:10.1029/2022gl102708

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…The GRACE-based sea-level-acceleration pattern reveals that the strong positive/negative accelerations in the Kuroshio and Kuroshio-extension regions [48,49] are significant. While it is insignificant based on the difference between satellite altimetry and IAP steric sea level, this could be explained by the relatively low resolution of GRACE observations, which is unable to capture all underlying processes, especially in the Kuroshio-extension region.…”

Section: Determining Drivers For Sea Level Acceleration Over the Chin...mentioning

confidence: 99%

Unraveling Regional Patterns of Sea Level Acceleration over the China Seas

Qu,

Jevrejeva,

Wang

2023

Remote Sensing

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Accelerated sea level rise is placing coastal communities in a vulnerable position; however, the processes underlying sea level acceleration in China remain uncertain. In this study, we examine the sea level acceleration and its contributors over the China Seas. We calculate acceleration along the Chinese coast using satellite altimetry and tide gauge records. During the satellite altimetry era, sea level acceleration from tide gauge records varies across all stations, reaching up to 0.30 ± 0.20 mm/yr2, while satellite altimetry could underestimate/overestimate the sea level acceleration in most locations. Acceleration near the coast, except in the Bohai Sea, is mainly driven by changes in the mass component. In contrast, for the open ocean, changes in steric sea level are the main contributor to sea level acceleration. The evolution of spatial acceleration patterns over the China Seas reveals that the ENSO and PDO variabilities dominate the changing patterns of sea level acceleration in the open ocean, including the Philippine Sea through steric sea level, and changes in most coastal locations are due to the non-steric component.

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Section: Determining Drivers For Sea Level Acceleration Over the Chin...mentioning

confidence: 99%

Unraveling Regional Patterns of Sea Level Acceleration over the China Seas

Qu,

Jevrejeva,

Wang

2023

Remote Sensing

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“…In the northern SCS, the sea level is relatively higher in winter and lower in summer. This is mainly due to the influence of winter monsoons, which transport seawater from the southern to the northern SCS, resulting in a rise in sea level [73]. In recent years, the intensity of monsoon activity along the northern SCS coast demonstrates a more pronounced weakening trend during the summer and autumn [74].…”

Section: Eemd Analysis Resultsmentioning

confidence: 99%

Multiscale Analysis and Prediction of Sea Level in the Northern South China Sea Based on Tide Gauge and Satellite Data

Yang

Cheng

Tsou

et al. 2023

JMSE

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Under the influence of global warming, the problem of sea-level rise is becoming increasingly prominent. The northern part of the South China Sea (SCS) is low lying, with intense economic development, and densely populated. These characteristics make the region extremely sensitive to the consequences of rising sea levels. This study aims to reveal the trends of sea-level changes in the northern SCS and provide scientific insights into the potential flooding risks in low-lying areas. To achieve this, the Ensemble Empirical Mode Decomposition (EEMD) method is used to analyze the water level time series data from three tide gauges along the coast of Hong Kong. This analysis reveals the multidimensional change characteristics and response mechanisms of the sea level in the SCS. The findings reveal distinct seasonal, interannual, decadal, and interdecadal variations in sea-level changes. Furthermore, we explore the impact of the El Niño-Southern Oscillation (ENSO) on sea-level changes in the study area, finding a 6-month lagged correlation between the sea level and ENSO. Spatially, the rate of sea-level change is faster in nearshore areas than in the open ocean and higher in the northern regions than in the southern regions. The Multifractal Detrended Fluctuation Analysis (MF-DFA) method is employed to analyze the sea-level change time series, revealing long-range correlations and multifractal characteristics. In addition, we propose a sea-level prediction method that combines EEMD with Long Short-Term Memory (LSTM) neural networks and conducts empirical research on sea-level changes in the northern South China Sea. The results indicate that the EEMD-LSTM model outperforms the standalone LSTM model in terms of predictive accuracy, effectively eliminating noise from signals and providing a valuable reference. In summary, this research delves into the multiscale characteristics and influencing factors of sea-level changes in the northern SCS, proposing an improved sea-level prediction method that integrates EEMD and LSTM. The findings lay the groundwork for evaluating the risks of sea-level rise in low-lying regions of the northern SCS and inform future response strategies.

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