Accurately predicting Greenland’s ice mass loss is crucial to understanding future sea level rise. Approximately 50% of the mass loss results from iceberg calving at the ice-ocean interface. Ice mélange, a jammed, buoyant granular material that extends for 10 kilometers or more in Greenland’s largest fjords, can inhibit iceberg calving and discharge by transmitting shear stresses from fjord walls to glacier termini. Direct measurements of these resistive force dynamics are not possible in the field, thus, we created a scaled-down laboratory experiment to elucidate the most salient features of ice mélange mechanics. We captured videos of the mélange surface motion and sub-surface profile during slow, quasistatic flow through a rectangular fjord, and recorded the total force on a model glacier terminus. We find that when the wall friction is low, the ice mélange remains jammed, but moves as a solid plug with little or no particle rearrangements. When the wall friction is larger than the internal friction, shear zones develop near the walls, and the buttressing force magnitude and fluctuations increase significantly. Associated discrete particle simulations illustrate the internal flow in both regimes. We also compare our experimental results to a continuum, depth-averaged model of ice mélange and find that the thickness of the mélange at the terminus provides a good indicator of the net buttressing force. However, the continuum model cannot capture the stochastic nature of the rearrangements and concomitant fluctuations in the buttressing force. These fluctuations may be important for short-time and seasonal controls on iceberg calving rates.

Ice streams deposit sediment at their grounding lines, where ice reaches flotation. Grounding Zone Wedge (GZW) deposits indicate standstills in past grounding-line retreat, and are thought to stabilize grounding lines by reducing local water depth, restricting ice flow. However, the mechanisms of GZW growth are uncertain, as are the effects of sedimentation on a retreating grounding-line prior to GZW formation. We develop a 1-D coupled model of ice flow and sediment transport, considering both subglacial deposition of deforming sediments, and proglacial melt-out of entrained sediments from ice shelves. A refined grid near the grounding line resolves small sediment features and their effect on ice dynamics. The model simulates the growth of low-profile, prograding, asymmetric features consistent with observed GZWs. We find that the characteristic shape of GZWs arises from the coupling of sedimentation and ice dynamics. This mechanism is consistent with deposition from either deforming or entrained sediments, and does not require a low-profile ice shelf to limit vertical GZW growth. We also find that during grounding-line retreat, sedimentation provides a stabilizing feedback when other factors initially slow retreat. This may turn a slowdown in retreat into a long standstill, even when ice dynamics are far out of equilibrium. The feedback depends on total sediment flux and its spatial pattern of deposition, making these priorities for future study. Our study suggests that sedimentation might significantly extend pauses in deglaciation, and the model provides a new tool for exploring links between ice-stream dynamics and submarine landforms.

A document by Vincent Verjans. Click on the document to view its contents.

Subglacial drainage networks regulate the response of ice sheet flow to surface meltwater input to the subglacial environment. Simulating subglacial hydrology evolution is critical to projecting ice sheet sensitivity to climate, and contribution to sea-level change. However, current numerical subglacial hydrology models are computationally expensive, and, consequently, evolving subglacial hydrology is neglected in large-scale ice sheet simulations. We present a deep learning emulator of a state-of-the-art subglacial hydrology model, trained at multiple Greenland glaciers. Our emulator performs strongly in both temporal (R2>0.99) and spatial (R2>0.96) generalization, offers high computational savings, and can be used to force numerical ice sheet models. This will enable century- and large-scale ice sheet model simulations, including interactions between ice flow and increased meltwater input to the subglacial environment. Generally, our work demonstrates that machine learning can further improve ice sheet models, reduce computational bottlenecks, and exploit information from high-fidelity models and novel observational platforms.

Increasing ice flux from glaciers retreating over deepening bed topography has been implicated in the recent acceleration of mass loss from the Greenland and Antarctic ice sheets. We show in observations that some glaciers have remained at peaks in bed topography without retreating despite enduring significant changes in climate. Observations also indicate that some glaciers which persist at bed peaks undergo sudden retreat years or decades after the onset of local ocean or atmospheric warming. Using model simulations, we show that glacier persistence may lead to two very different futures: one where glaciers persist at bed peaks indefinitely, and another where glaciers retreat from the bed peak suddenly without a concurrent climate forcing. However, it is difficult to distinguish which of these two futures will occur from current observations. We conclude that inferring glacier stability from observations of persistence obscures our true commitment to future sea-level rise under climate change.