With an ongoing mass loss the Antarctic Ice Sheet contributes to global-mean sea level rise at a rate of 0.4 mm/a. Thus, it plays a key role in global climate and provides a natural laboratory to study processes that interlink cryosphere, solid Earth, atmosphere and ocean. We discuss how GNSS and seismic networks in Antarctica were used to significantly advance our understanding of these processes, and how they should be maintained and extended to answer key science questions in the future.

We predicted how sea level changed in the Ross Sea (Antarctica) due to glacial isostatic adjustment, or solid Earth ice sheet interactions, over the last deglaciation (20 000 years ago to present) and calculated how these changes in bathymetry impacted ice stream stability. Glacial isostatic adjustment shifts stability from where ice reached its maximum 20 000 years ago, at the continental shelf edge, to the modern grounding line today, reinforcing ice-age climate endmembers.

We investigate the effects of incorporating regional-scale lateral variability (ca. 50–100 km) in upper-mantle structure into models of Earth deformation and sea level change associated with ice mass changes in West Antarctica. Regional-scale variability in upper-mantle structure is found to impact relative sea level and crustal rate predictions for modern (last ca. 25–125 years) and projected (next ca. 300 years) ice mass changes, especially in coastal regions that undergo rapid ice mass loss.

In this study, we compute glacial–interglacial sea-level changes across the mid-Pliocene Warm Period (MPWP; 3.264–3.025 Ma) resulting from ice mass loss from different ice sheets. Our results quantify the relationship between changes in local sea level and global mean sea level (GMSL) and highlight the level of consistency in this mapping across different ice melt scenarios. These predictions can help guide site selection in efforts to constrain the sources and magnitude of MPWP GMSL change.

A relatively recent advance in glacial isostatic adjustment modeling has been the development of models that include 3D Earth structure, as opposed to 1D structure. However, a major limitation is the computational expense. We have developed a method using artificial neural networks to emulate the influence of 3D Earth models to affordably constrain the viscosity parameter space. Our results indicate that the misfits are of a scale such that useful predictions of relative sea level can be made.