A data-driven framework for estimating GIA from GPS and GRACE data

GIA from forward models suffer from large uncertainties due to approximations and assumptions on the Earth rheology and ice load history. These uncertainties propagate to uncertainties in ice-sheet mass balance and sea level budget studies. Therefore, GIA estimates from contemporary geodetic datasets are gaining interest. The challenges of obtaining data-driven GIA include solving a geophysical inversion that does not have a unique solution and often requires a-prior information and several approximations. In this work, a novel geophysical framework is developed that uses GPS and GRACE data to estimate GIA signal. The method relies on geophysical relations between geopotential and vertical land movement (VLM) caused by GIA and present-day mass changes. For example, the elastic response of solid Earth to the positive surface mass load results in a negative VLM, while a positive GIA mass leads to a positive VLM. We use these relations to express GPS observed VLM and GRACE observed gravity field anomalies in terms of GIA and present day mass change. The method is first shown to work in a closed-loop synthetic experiment and then applied to the NGL provided GPS velocities and GRACE spherical harmonic coefficients provided by ITG Graz. Our GIA estimates differ significantly from commonly used GIA models (such as ICE-6G) over Alaska and central Greenland. Our GIA rates over Alaska are around 5 mm/yr, which matches with several regional studies over Alaska. Similarly, there is a lot of ambiguity over Greenland ice-load history and our results may provide informative input to the ongoing debate. Our estimates are data-driven and are therefore able to pick them up. We also discuss the uncertainties, caveats and limitations of our method and its implications. The published GIA product is made openly available at one degree grid resolution.