Abstract
Abstract. Little is known about the distribution of ice in the Antarctic Ice Sheet (AIS) during the Last Glacial Maximum (LGM). Whereas marine and terrestrial geological data indicate that the grounded ice advanced to a position close to the continental-shelf break, the total ice volume is unclear. Glacial boundary conditions are potentially important sources of uncertainty, in particular basal friction and climatic boundary conditions. Basal friction exerts a strong control on the large-scale dynamics of the ice sheet and thus affects its size and is not well constrained. Glacial climatic boundary conditions determine the net accumulation and ice temperature and are also poorly known. Here we explore the effect of the uncertainty in both features on the total simulated ice storage of the AIS at the LGM. For this purpose we use a hybrid ice sheet shelf model that is forced with different basal drag choices and glacial background climatic conditions obtained from the LGM ensemble climate simulations of the third phase of the Paleoclimate Modelling Intercomparison Project (PMIP3). Overall, we find that the spread in the simulated ice volume for the tested basal drag parameterizations is about the same range as for the different general circulation model (GCM) forcings (4 to 6 m sea level equivalent). For a wide range of plausible basal friction configurations, the simulated ice dynamics vary widely but all simulations produce fully extended ice sheets towards the continental-shelf break. More dynamically active ice sheets correspond to lower ice volumes, while they remain consistent with the available constraints on ice extent. Thus, this work points to the possibility of an AIS with very active ice streams during the LGM. In addition, we find that the surface boundary temperature field plays a crucial role in determining the ice extent through its effect on viscosity. For ice sheets of a similar extent and comparable dynamics, we find that the precipitation field determines the total AIS volume. However, precipitation is highly uncertain. Climatic fields simulated by climate models show more precipitation in coastal regions than a spatially uniform anomaly, which can lead to larger ice volumes. Our results strongly support using these paleoclimatic fields to simulate and study the LGM and potentially other time periods like the last interglacial. However, their accuracy must be assessed as well, as differences between climate model forcing lead to a large spread in the simulated ice volume and extension.
Highlights
Sea level variations on long timescales are driven by the waxing and waning of large continental ice sheets
We do not identify a strong impact of marine basal friction on equilibrium grounded-ice area, as the final grounding line configuration is similar in all ensemble members (Fig. 4b)
We studied the uncertainty in Last Glacial Maximum (LGM) ice volume associated with these two factors, by investigating the effect of the representation of basal friction and of the atmospheric forcing, respectively, in simulations
Summary
Sea level variations on long timescales are driven by the waxing and waning of large continental ice sheets. Whereas older studies estimated large sea level contributions generally above 15 m (e.g., Nakada et al, 2000; Huybrechts, 2002; Peltier and Fairbanks, 2006; Philippon et al, 2006; Bassett et al, 2007), more recent modeling studies and reconstructions have lowered these estimates to 7.5–13.5 m (Mackintosh et al, 2011; Whitehouse et al, 2012a; Golledge et al, 2012, 2014; Gomez et al, 2013; Argus et al, 2014; Briggs et al, 2014; Maris et al, 2014; Sutter et al, 2019) This lowering in ice volume can be explained by the fact that the first ice sheet models were based purely on the shallow ice approximation for inland ice. This lowering in ice volume can be explained by the fact that the first ice sheet models were based purely on the shallow ice approximation for inland ice This solution solves for slow-moving ice, based on shear deformation. Other possible explanations include the implementation of external processes, like the GIA (e.g., Whitehouse et al, 2019), or, as this work, the effect of uncertain climatologies and ice sheet dynamics
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