The Antarctic Ice Sheet response to glacial millennial-scale variability

Abstract

The Antarctic Ice Sheet (AIS) is the largest ice sheet on Earth and hence a major potential contributor to future global sea-level rise. A wealth of studies suggest that increasing oceanic temperatures could cause a collapse of its marine-based western sector, the West Antarctic Ice Sheet, through the mechanism of marine ice-sheet instability, leading to a sea-level increase of 3-5 m. Thus, it is crucial to constrain the sensitivity of the AIS to rapid climate changes. The last glacial period is an ideal benchmark period for this purpose as it was punctuated by abrupt Dansgaard-Oeschger events at millennial timescales. Because their center of action was in the North Atlantic, where their climate impacts were largest, modeling studies have mainly focused on the millennial-scale evolution of Northern Hemisphere (NH) paleo ice sheets. Sea-level reconstructions attribute the origin of millennial-scale sea-level variations mainly to NH paleo ice sheets, with a minor but not negligible role of the AIS. Here we investigate the AIS response to millennialscale climate variability for the first time. To this end we use a three-dimensional, thermomechanical hybrid, ice sheetshelf model. Different oceanic sensitivities are tested and the sea-level equivalent (SLE) contributions computed. We find that whereas atmospheric variability has no appreciable effect on the AIS, changes in submarine melting rates can have a strong impact on it. We show that in contrast to the widespread assumption that the AIS is a slow reactive and static ice sheet that responds at orbital timescales only, it can lead to ice discharges of around 6 m SLE, involving substantial grounding line migrations at millennial timescales.

Impact of millennial-scale oceanic variability on the Greenland ice-sheet evolution throughout the last glacial period

“…Nevertheless, since here we focus on the sensitivity of the ice sheet to millennialscale oceanic variations during the LGP, the choice of this scheme should be sufficient for our purposes. Surface precipitation is exponentially proportional to atmospheric temperatures, which vary through an index approach (Banderas et al, 2018;Blasco et al, 2019;Tabone et al, 2018)…”

“…Much work attributes AMOC instability to freshwater discharge from the Northern Hemisphere (NH) ice sheets (Ganopolski and Rahmstorf, 2001;Vellinga and Wood, 2002;Menviel et al, 2014;Bagniewski et al, 2017) directly connected to changes in the strength (Skinner and Elderfield, 2007) and in the location (Sévellec and Fedorov, 2015) of deep convection. Other possible mechanisms link the origin of D-O events to sea-ice cover variability (Li et al, 2005(Li et al, , 2010Sime et al, 2019) or to linked sea-ice-iceshelf fluctuations (Boers et al, 2018;Petersen et al, 2013). Still others connect AMOC reorganisations to climatic perturbations in the atmosphere associated with changes in icesheet dynamics (Wunsch, 2006;, to progressive CO 2 atmospheric variations (Zhang et al, 2017), to changes in atmospheric heat transport (Wang et al, 2015), or to combined changes in wind and atmospheric CO 2 concentrations driven by the Southern Ocean (Banderas et al, 2015).…”

Response to A. Svensson

“…Nevertheless, since here we focus on the sensitivity of the ice sheet to millennialscale oceanic variations during the LGP, the choice of this scheme should be sufficient for our purposes. Surface precipitation is exponentially proportional to atmospheric temperatures, which vary through an index approach Blasco et al, 2019;Tabone et al, 2018) (Sect. 2.2).…”

Exploring the impact of atmospheric forcing and basal boundary conditions on the simulation of the Antarctic ice sheet at the Last Glacial Maximum

“…Another commonly used method is to prescribe the LGM temperature and precipitation fields for the whole Antarctic domain from climate simulations (Briggs et al, 2013;Maris et al, 2014;Sutter et al, 2019). Output from simulations using a hierarchy of climate models has been used in the literature, from global general circulation models (GCMs) (Sutter et al, 2019), sometimes downscaled with regional models (Maris et al, 2014), to Earth System Models of Intermediate Complexity (EMICs) (Blasco et al, 2019). Briggs et al (2013) went a step forward to investigate the effect of uncertainty in the climate forcing fields by assessing the effect of the inter-model variance through an empirical orthogonal function (EOF) analysis.…”