A Joint Bayesian Inversion for Glacial Isostatic Adjustment in North America and Greenland

Friday, 19 December 2014: 4:00 PM
James L Davis and Lei Wang, Columbia University of New York, Lamont-Doherty Earth Observatory, Palisades, NY, United States
We have previously presented joint inversions of geodetic data for glacial isostatic adjustment (GIA) fields that employ a Bayesian framework for the combination of data and models. Data sets used include GNSS, GRACE gravity, and tide-gauge data, in order to estimate three-dimensional crustal deformation, geoid rate, relative sea-level change (RSLC). The benefit to this approach is that solutions are less dependent on any particular Earth/ice model used to calculate the GIA fields, and instead employ a suite of GIA predictions that are then used to calculate statistical constraints. This approach was used both for the determination of the SNARF geodetic reference frame for North America, and for a study of GIA in Fennoscandia (Hill et al., 2010). One challenge to the method we developed is that the inherent reduction in resolution of, and correlation among, GRACE Stokes coefficients caused by the destriping procedure (Swenson and Wahr, 2006; Duan et al., 2009) was not accounted for. This important obstacle has been overcome by developing a Bayesian approach to destriping (Wang et al., in prep.). However, important issues of mixed resolution of these data types still remain. In this presentation, we report on the progress of this effort, and present a new GIA field for North America. For the first time, the region used in the solution includes Greenland, in order to provide internally consistent solutions for GIA, the spatial and temporal variability of present-day sea-level change, and present-day melting in Greenland.