High-resolution sea level change around China seas revealed through multi-satellite altimeter data

Jiajia Yuan, Jinyun Guo*, Chengcheng Zhu, Cheinway Hwang, Daocheng Yu, Mingzhi Sun, Dapeng Mu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Sea level change is not uniform across the oceans and high-resolution sea level trend (SLT) models reveal that the rate of sea level change differs between different waters. In this study, we developed a new method for obtaining a high-resolution, 1′×1′-grid SLT model, using the time-varying mean sea surface (MSS) of several MSSs calculated from averaged multi-satellite-derived sea surface heights (TOPEX/Poseidon, Jason-1/2/3, ERS-1/2, Envisat, GFO, Cryosat-2, SARAL/AltiKa, Sentinel-3A, HY-2A) over different periods. We applied this model to estimate the mean sea level change in the China seas and their adjacent ocean (0°–41°N, 100°–140°E). This new model revealed the patterning of sea level change within the study region in detail, and detected a sea level change dipole south of Japan. A zonal SLT pattern was identified in three regions: the region of the North Pacific Subtropical Counter Current (an eastward current located in the band of 19.5°–22.5°N, populated with eddies), and the areas east of Taiwan and east of Luzon Island. Moreover, the rate of sea level rise in the offshore zone was found to be ~ 0.67 mm/yr (20%) higher than that in the open ocean. This finding has important implications for coastal protection.

Original languageEnglish
Article number102433
Pages (from-to)1-8
Number of pages8
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume102
DOIs
StatePublished - Oct 2021

Keywords

  • HY-2A
  • Mean sea surface
  • Satellite altimeter
  • Sea level rise
  • Sea surface height
  • Sentinel-3A

Fingerprint

Dive into the research topics of 'High-resolution sea level change around China seas revealed through multi-satellite altimeter data'. Together they form a unique fingerprint.

Cite this