GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science
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Authors
Caron, L.
Ivins, E. R.
Larour, E.
Adhikari, S.
Nilsson, J.
Blewitt, G.
Issue Date
2018
Type
Article
Language
Keywords
GIA , Bayesian , Statistics , Uncertainty , GRACE
Alternative Title
Abstract
We provide a new analysis of glacial isostatic adjustment (GIA) with the goal of assembling the model uncertainty statistics required for rigorously extracting trends in surface mass from the Gravity Recovery and Climate Experiment (GRACE) mission. Such statistics are essential for deciphering sea level, ocean mass, and hydrological changes because the latter signals can be relatively small (2mm/yr water height equivalent) over very large regions, such as major ocean basins and watersheds. With abundant new >7year continuous measurements of vertical land motion (VLM) reported by Global Positioning System stations on bedrock and new relative sea level records, our new statistical evaluation of GIA uncertainties incorporates Bayesian methodologies. A unique aspect of the method is that both the ice history and 1-D Earth structure vary through a total of 128,000 forward models. We find that best fit models poorly capture the statistical inferences needed to correctly invert for lower mantle viscosity and that GIA uncertainty exceeds the uncertainty ascribed to trends from 14years of GRACE data in polar regions.
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Citation
Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203�"2212. doi:10.1002/2017gl076644
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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PubMed ID
ISSN
0094-8276