Separating GIA signal from surface mass change using GPS and GRACE data

SUMMARY The visco-elastic response of the solid Earth to the past glacial cycles and the present-day surface mass change (PDSMC) are detected by the geodetic observation systems such as global navigation satellite system and satellite gravimetry. Majority of the contemporary PDSMC is driven by clima...

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Bibliographic Details
Published in:Geophysical Journal International
Main Authors: Vishwakarma, Bramha Dutt, Ziegler, Yann, Bamber, Jonathan L, Royston, Sam
Other Authors: European Research Council, European Union
Format: Article in Journal/Newspaper
Language:English
Published: Oxford University Press (OUP) 2022
Subjects:
Online Access:http://dx.doi.org/10.1093/gji/ggac336
https://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggac336/45505040/ggac336.pdf
https://academic.oup.com/gji/article-pdf/232/1/537/46324183/ggac336.pdf
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Summary:SUMMARY The visco-elastic response of the solid Earth to the past glacial cycles and the present-day surface mass change (PDSMC) are detected by the geodetic observation systems such as global navigation satellite system and satellite gravimetry. Majority of the contemporary PDSMC is driven by climate change and in order to better understand them using the aforementioned geodetic observations, glacial isostatic adjustment (GIA) signal should be accounted first. The default approach is to use forward GIA models that use uncertain ice-load history and approximate Earth rheology to predict GIA, yielding large uncertainties. The proliferation of contemporary, global, geodetic observations and their coverage have therefore enabled estimation of data-driven GIA solutions. A novel framework is presented that uses geophysical relations between the vertical land motion (VLM) and geopotential anomaly due to GIA and PDSMC to express GPS VLM trends and GRACE geopotential trends as a function of either GIA or PDSMC, which can be easily solved using least-squares regression. The GIA estimates are data-driven and differ significantly from forward models over Alaska and Greenland.