Subglacial discharge accelerates future retreat of Denman and Scott Glaciers, East Antarctica

Abstract

Ice shelf basal melting is the primary mechanism driving mass loss from the Antarctic Ice Sheet, yet it is unknown how the localized melt enhancement from subglacial discharge will affect future Antarctic glacial retreat. We develop a parameterization of ice shelf basal melt that accounts for both ocean and subglacial discharge forcing and apply it in future projections of Denman and Scott Glaciers, East Antarctica, through 2300. In forward simulations, subglacial discharge accelerates the onset of retreat of these systems into the deepest continental trench on Earth by 25 years. During this retreat, Denman Glacier alone contributes 0.33 millimeters per year to global sea level rise, comparable to half of the contemporary sea level contribution of the entire Antarctic Ice Sheet. Our results stress the importance of resolving complex interactions between the ice, ocean, and subglacial environments in future Antarctic Ice Sheet projections.

Seawater Intrusion in the Observed Grounding Zone of Petermann Glacier Causes Extensive Retreat

“…With sufficiently large velocity, seawater may not be able to flow under the freshwater layer, effectively shutting down enhanced melt in the grounding zone from this mechanism. However, enhanced rates of freshwater discharge across the grounding line will increase turbulent mixing and thus increase the ice shelf melt rates near the grounding line (Pelle et al., 2023). Ice shelf melting can lead to weakening of ice shelf buttressing and grounding line retreat (Goldberg et al., 2009; Hill et al., 2021; Millan et al., 2023).…”

Subglacial Discharge Accelerates Dynamic Retreat of Aurora Subglacial Basin Outlet Glaciers, East Antarctica, Over the 21st Century

“…where glfw is the grounding line subglacial freshwater discharge flux, g is Earth's gravitational constant (9.80 ms 2 ), Δρ i is the density contrast between the plume and ambient ocean water masses, C 1/ 2 d Γ TS 0 is the thermal Stanton number (6.0 • 10 4 , unitless), G 1 and G 2 are geometric scaling terms, and λ 3 • ΔT f,gl is a measure of the ocean thermal forcing (Pelle et al, 2023). Of key importance is that this distance cutoff 5L′ is proportional to the discharge volume flux and inversely proportional to the ocean thermal forcing.…”

“…The parameterization then returns an ice shelf basal melting rate (m/yr) that is defined on every completely floating ice sheet mesh element and is used to drive the ice sheet model. See Pelle et al (2023) for a full description and derivation of the melt parameterization and Figure S1 in Supporting Information S1 for a schematic representation of the parameterization.…”

“…To address this, we couple the Glacier Drainage System (GlaDS, Werder et al, 2013) subglacial hydrology model to the Ice-sheet and Sea-level System Model (ISSM, Larour et al, 2012) to simulate the evolution of the ASB through 2100 under low and high emission scenarios that only account for changes in ocean forcing (i.e., we do not prescribe atmospheric changes). Note that unlike Pelle et al (2023), which used fixed present-day subglacial conditions and did not include modulations to ice sheet friction, here we model two-way coupled feedbacks between dynamic ice sheet and subglacial hydrology systems to investigate the effect of an evolving subglacial environment on ice mass loss. Ice shelf basal melting driven by both the ocean and subglacial discharge is simulated with a recently developed melt parameterization that builds from previous plume modeling work (Jenkins, 2011;Lazeroms et al, 2018;Pelle et al, 2023).…”

A fast and unified subglacial hydrological model applied to Thwaites Glacier, Antarctica

“…For instance, state-of-the-art hydrological models such as GlaDS (Werder et al, 2013) are -due to their high spatial and temporal resolution-extremely computationally demanding, and their applications to evolving ice sheets are often limited to the initialization of the ice-sheet system (e.g., McArthur et al, 2023;Pelle et al, 2023). Although there have been recent attempts to reduce their computational cost using data-driven methods (Verjans and Robel, 2024), physics-based approaches…”