The East Antarctic Ice Sheet stores a vast amount of freshwater, which makes it the single largest potential contributor to future sea-level rise. However, the lack of well-constrained geological records of past ice sheet changes impedes model validation, hampers mass balance estimates, and inhibits examination of ice loss mechanisms. Here we identify rapid ice-sheet thinning in coastal Dronning Maud Land from Early to Middle Holocene (9000–5000 years ago) using a deglacial chronology based on in situ cosmogenic nuclide surface exposure dates from central Dronning Maud Land, in concert with numerical simulations of regional and continental ice-sheet evolution. Regional sea-level changes reproduced from our refined ice-load history show a highstand at 9000–8000 years ago. We propose that sea-level rise and a concomitant influx of warmer Circumpolar Deep Water triggered ice shelf breakup via the marine ice sheet instability mechanism, which led to rapid thinning of upstream coastal ice sheet sectors.

Landslide risk mitigation is limited by data scarcity; however, this could be improved using continuous landslide detection systems. To investigate which image types and machine learning models are most useful for landslide detection in a Norwegian setting, we compared the performance of five different machine learning models, for the Jølster case study (30 July 2019), in Western Norway. These included three globally pre-trained models; (i) the continuous change detection and classification (CCDC) algorithm, (ii) a combined k-means clustering and random forest classification model, and (iii) a convolutional neural network (CNN), and two locally trained models, including; (iv) classification and regression Trees and (v) a U-net CNN model. Images used included Sentinel-1, Sentinel-2, as well as digital elevation model (DEM) and slope. The globally trained models performed poorly in shadowed areas and were all outperformed by the locally trained models. A maximum Matthew’s correlation coefficient (MCC) score of 89% was achieved with a CNN U-net deep learning model, using combined Sentinel-1 and -2 images as input. This is one of the first attempts to apply deep learning to detect landslides with both Sentinel-1 and -2 images. Using Sentinel-1 images only, the locally-trained deep-learning model significantly outperformed the conventional machine learning model. These findings contribute to developing a national continuous monitoring system for landslides.

The impact of late Cenozoic climate on the East Antarctic Ice Sheet is uncertain. Poorly constrained patterns of relative ice thinning and thickening impair the reconstruction of past ice-sheet dynamics and global sea-level budgets. Here we quantify long-term ice cover of mountains protruding the ice-sheet surface in western Dronning Maud Land, using cosmogenic Chlorine-36, Aluminium-26, Beryllium-10, and Neon-21 from bedrock in an inverse modeling approach. We find that near-coastal sites experienced ice burial up to 75–97% of time since 1 Ma, while interior sites only experienced brief periods of ice burial, generally <20% of time since 1 Ma. Based on these results, we suggest that the escarpment in Dronning Maud Land acts as a hinge-zone, where ice-dynamic changes driven by grounding-line migration are attenuated inland from the coastal portions of the East Antarctic Ice Sheet, and where precipitation-controlled ice-thickness variations on the polar plateau taper off towards the coast.

Ice streams regulate most ice mass loss in Antarctica. Determining ice stream response to warmer conditions during the Pliocene could provide insights into their future behaviour, but this is hindered by a poor representation of subglacial topography in ice-sheet models. We address this limitation using a high-resolution model for Dronning Maud Land (East Antarctica). We show that contrary to dynamic thinning of the region’s ice streams following ice-shelf collapse, the largest ice stream, Jutulstraumen, thickens by 700 m despite lying on a retrograde bed slope. We attribute this counterintuitive thickening to a shallower Pliocene subglacial topography and inherent high lateral stresses at its flux gate. These conditions constrict ice drainage and, combined with increased snowfall, allow ice accumulation upstream. Similar stress balances and increased precipitation projections occur across 27% of present-day East Antarctica, and understanding how lateral stresses regulate ice-stream discharge is necessary for accurately assessing Antarctica’s future sea-level rise contribution.

Reconstructing the response of present-day ice sheets to past global climate change is important for constraining and refining the numerical models which forecast future contributions of these ice sheets to sea-level change. Mapping landforms is an essential step in reconstructing glacial histories. Here we present a new map of glacial landforms and deposits on nunataks in western Dronning Maud Land, Antarctica. Nunataks are mountains or ridges that currently protrude through the ice sheet and may provide evidence that they have been wholly or partly covered by ice, thus indicating a formerly more extensive (thicker) ice sheet. The map was produced through a combination of mapping from Worldview satellite imagery and ground validation. The sub-metre spatial resolution of the satellite imagery enabled mapping with unprecedented detail. Ten landform categories have been mapped, and the landform distributions provide evidence constraining spatial patterns of a previously thicker ice sheet.