Friction: what’s going on underneath the Antarctic and Greenland ice sheets?

The Antarctic and Greenland Ice Sheets (AIS and GrIS) play a critical role in shaping future sea-level rise (SLR), but their contributions and human greenhouse gas emissions remain the largest sources of uncertainty in SLR projections (Edwards et al., 2021). This uncertainty, along with the risk of potential tipping points leading to rapid ice loss, arises from a limited understanding of key processes that govern ice-sheet behaviour (Fox-Kemper et al., 2021). In the Ice Sheet Model Intercomparison for CMIP6 (ISMIP6), 55% of AIS and 15 to 35% of GrIS future mass loss uncertainty is due to ice-sheet models’ uncertainty (Seroussi et al., 2023; Jager et al., 2024; Goelzer et al., 2020). While modeling the AIS’s and GrIS’s complex interactions with the climate is difficult (Seroussi et al., 2023), one other uncertain process is basal sliding over bedrock. Because this process is not directly observed due to the large thickness, its representation in current models remains rudimentary. The primary goals of Combining Coupled Modelling and Machine Learning to Constrain Antarctica’s Uncertain Future (ICEMAP) project include (i) better quantification of uncertainties related to basal sliding, (ii) comparison of these uncertainties to other sources of uncertainty, and (iii) exploration of how satellite data can help reduce these uncertainties. 

To achieve these goals, we employ the Shallow Shelf Approximation (SSA) implemented in the Elmer/Ice model, an open source finite element software for ice sheets, glaciers and ice flow modelling, which was one of the participants in ISMIP6 (Gagliardini et al., 2013; Seroussi et al., 2023). It uses inverse methods to calibrate the many unknown parameters related to rheology and friction. Here, we take into account various physical and numerical uncertainties to perform multiple calibrations using remote-sensing velocity data to compute basal sliding and basal shear stress. Subsequently, these values and their spatial variations can be compared with the diverse existing theories that have been developed from small-scale physical and numerical experiments (Gagliardini et al., 2007; Zoet and Iverson, 2020).

Our analysis demonstrates that the principal characteristics of these parameterisations, derived from small-scale experiments, are observable at a large scale. However, the values may deviate from expected norms, particularly with regard to the exponent of the Weertman friction law. This investigation enables the quantification of both the parameter values and their associated uncertainties within the friction parameterisations governing the AIS and the GrIS. Furthermore, it highlights the critical influence of basal water presence, which appears to play a pivotal role in the variability of basal sliding. Incorporating this factor into models, whether through varying levels of model complexity or the use of proxies, is deemed essential for accurately capturing the temporal variations in basal sliding.