[J01-2] Monitoring of the cryosphere II

[J01-2-04] Using geodetic data to constrain contemporary GIA signals in Scandinavia and North America

^○K. Simon, R. E. M. Riva (Delft University of Technology)

The present-day glacial isostatic adjustment (GIA) signal in two major former glaciation centres, Scandinavia and North America, is constrained in a semi-empirical model by joint inversion of GPS-measured land deformation rates and GRACE-measured gravity changes. Both the GPS and GRACE datasets are corrected a priori for the effect of hydrological loading using the PCR-GLOBWB hydrology model, a correction which can, at least at local scales, significantly impact the fit of model predictions to the observational data. In the Scandinavian region, the GRACE trend is also corrected for present-day ice mass changes using IceSat data for the glacierized regions of Svalbard and the Russian Arctic. The observational data are combined with a suite of forward GIA model predictions which allow for variation in both ice sheet history and Earth model characteristics, with the best-fit posterior model simultaneously minimizing the misfit between both types of constraint. When only GPS data are incorporated into the prior model a good fit is obtained (X2 < 1). The result is similar when only GRACE data are used as constraint, and the best overall fit is obtained when both datasets are inverted. Within formerly glaciated regions, the method provides a realistic prediction of the uncertainty associated with the GIA process at a level that is typically ~1 order of magnitude smaller than the uncertainty associated with forward GIA models. For example, for vertical uplift rates, predicted GIA uncertainties range from ~0.2-1 mm/yr, with the largest rates present in the former load centres of the northern Gulf of Bothnia and Hudson Bay. The GIA predictions can be used in sea-level studies to better constrain the magnitude and uncertainty of the GIA contribution to regional sea-level budgets. Also assessed is the sensitivity of the model predictions to variations in ice sheet and Earth model combinations, and the ability of the method to resolve preferred values for these parameters.