On the Accuracy of Glacial Isostatic Adjustment Models for Geodetic Observations to Estimate Arctic Ocean Sea-Level Change

  • Author(s): Zhenwei Huang, Jun-Yi Guo, C. K. Shum, Junkun Wan, Jianbin Duan, Hok Sum Fok, and Chung-Yen Kuo
  • DOI: 10.3319/TAO.2012.08.28.01(TibXS)
  • Keywords: Arctic Ocean, Sea-level change, Glacial Isostatic Adjustment (GIA)



Arctic Ocean sea-level change is an important indicator of climate change. Contemporary geodetic observations, including data from tide gages, satellite altimetry and Gravity Recovery and Climate Experiment (GRACE), are sensitive to the effect of the ongoing glacial isostatic adjustment (GIA) process. To fully exploit these geodetic observations to study climate related sea-level change, this GIA effect has to be removed. However, significant uncertainty exists with regard to the GIA model, and using different GIA models could lead to different results. In this study we use an ensemble of 14 contemporary GIA models to investigate their differences when they are applied to the above-mentioned geodetic observations to estimate sea-level change in the Arctic Ocean. We find that over the Arctic Ocean a large range of differences exists in GIA models when they are used to remove GIA effect from tide gage and GRACE observations, but with a relatively smaller range for satellite altimetry observations. In addition, we compare the derived sea-level trend from observations after applying different GIA models in the study regions, sea-level trend estimated from long-term tide gage data shows good agreement with altimetry result over the same data span. However the mass component of sea-level change obtained from GRACE data does not agree well with the result derived from steric-corrected altimeter observation due primarily to the large uncertainty of GIA models, errors in the Arctic Ocean altimetry or steric measurements, inadequate data span, or all of the above. We conclude that GIA correction is critical for studying sea-level change over the Arctic Ocean and further improvement in GIA modelling is needed to reduce the current discrepancies among models.

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