EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Spatio-temporal decomposition of geophysical signals in North America

Aoibheann Brady1, Jonathan Rougier2, Bramha Dutt Vishwakarma1, Yann Ziegler1, Richard Westaway1, and Jonathan Bamber1
Aoibheann Brady et al.
  • 1School of Geographical Sciences, University of Bristol, Bristol, United Kingdom of Great Britain and Northern Ireland
  • 2Rougier Consulting Limited

Sea level rise is one of the most significant consequences of projected future changes in climate. One factor which influences sea level rise is vertical land motion (VLM) due to glacial isostatic adjustment (GIA), which changes the elevation of the ocean floor. Typically, GIA forward models are used for this purpose, but these are known to vary with the assumptions made about ice loading history and Earth structure. In this study, we implement a Bayesian hierarchical modelling framework to explore a data-driven VLM solution for North America, with the aim of separating out the overall signal into its GIA and hydrology (mass change) components. A Bayesian spatio-temporal model is implemented in INLA using satellite (GRACE) and in-situ (GPS) data as observations. Under the assumption that GIA varies in space but is constant in time, and that hydrology is both spatially- and temporally-variable, it is possible to separate the contributions of each component with an associated uncertainty level. Early results will be presented. Extensions to the BHM framework to investigate sea level rise at the global scale, such as the inclusion of additional processes and incorporation of increased volumes of data, will be discussed.

How to cite: Brady, A., Rougier, J., Vishwakarma, B. D., Ziegler, Y., Westaway, R., and Bamber, J.: Spatio-temporal decomposition of geophysical signals in North America, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16232,, 2020


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