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We apply geologic evidence from ice-free areas in Antarctica to evaluate model simulations of ice sheet response to warm climates. This is important because such simulations are used to predict ice sheet behaviour in future warm climates, but geologic evidence of smaller-than-present past ice sheets is buried under the present ice sheet and therefore generally unavailable for model benchmarking. We leverage an alternative accessible geologic dataset for this purpose: cosmogenic-nuclide concentrations in bedrock surfaces of interior nunataks. These data produce a frequency distribution of ice thickness over multimillion-year periods, which is also simulated by ice sheet modelling. End-member transient models, parameterized with strong and weak marine ice sheet instability processes and ocean temperature forcings, simulate large and small sea-level impacts during warm periods and also predict contrasting and distinct frequency distributions of ice thickness. We identify regions of Antarctica where predicted frequency distributions reveal differences in end-member ice sheet behaviour. We then demonstrate that a single comprehensive dataset from one bedrock site in West Antarctica is sufficiently detailed to show that the data are consistent only with a weak marine ice sheet instability end-member, but other less extensive datasets are insufficient and/or ambiguous. Finally, we highlight locations where collecting additional data could constrain the amplitude of past and therefore future response to warm climates. 

Abstract In the Ross Sea sector of Antarctica, periodic large-scale marine ice-sheet fluctuations since the mid-Miocene are recorded by drill core and seismic data, revealing a dynamic ice-sheet response to past increases in temperature and atmospheric CO2. In the adjacent, predominantly ice-free McMurdo Dry Valleys (MDVs), preserved terrestrial landscapes reflect persistent cold conditions and have been interpreted as indicators of a stable polar ice sheet, implying that the Antarctic Ice Sheet was largely insensitive during past warm periods. These disparate data-based perspectives highlight a long-standing debate around the past stability of the Antarctic Ice Sheet, with direct implications for the future ice-sheet response to ongoing climate warming. We reconcile marine records of dynamic ice-sheet behavior and episodic open-marine conditions with nearby ancient terrestrial landscapes recording consistent cold-polar conditions. Coupled ice-sheet and regional climate models nested at a high resolution are used to investigate surface temperatures in the MDVs during past warm periods. We find that high-elevation regions of the MDVs remain below freezing even when ice-free conditions prevail in the nearby Ross Sea. We compare observed landscapes with the spatial extent of modeled persistent cold conditions required for preservation of these ancient features, demonstrating that frozen MDVs landscapes could have coexisted with receded or collapsed ice sheets during past warm periods. 

Abstract Surface meltwater is becoming increasingly widespread on Antarctic ice shelves. It is stored within surface ponds and streams, or within firn pore spaces, which may saturate to form slush. Slush can reduce firn air content, increasing an ice-shelf's vulnerability to break-up. To date, no study has mapped the changing extent of slush across ice shelves. Here, we use Google Earth Engine and Landsat 8 images from six ice shelves to generate training classes using a k -means clustering algorithm, which are used to train a random forest classifier to identify both slush and ponded water. Validation using expert elicitation gives accuracies of 84% and 82% for the ponded water and slush classes, respectively. Errors result from subjectivity in identifying the ponded water/slush boundary, and from inclusion of cloud and shadows. We apply our classifier to the Roi Baudouin Ice Shelf for the entire 2013–20 Landsat 8 record. On average, 64% of all surface meltwater is classified as slush and 36% as ponded water. Total meltwater areal extent is greatest between late January and mid-February. This highlights the importance of mapping slush when studying surface meltwater on ice shelves. Future research will apply the classifier across all Antarctic ice shelves. 

Surface meltwater generated on ice shelves fringing the Antarctic Ice Sheet can drive ice-shelf collapse, leading to ice sheet mass loss and contributing to global sea level rise. A quantitative assessment of supraglacial lake evolution is required to understand the influence of Antarctic surface meltwater on ice-sheet and ice-shelf stability. Cloud computing platforms have made the required remote sensing analysis computationally trivial, yet a careful evaluation of image processing techniques for pan-Antarctic lake mapping has yet to be performed. This work paves the way for automating lake identification at a continental scale throughout the satellite observational record via a thorough methodological analysis. We deploy a suite of different trained supervised classifiers to map and quantify supraglacial lake areas from multispectral Landsat-8 scenes, using training data generated via manual interpretation of the results from k-means clustering. Best results are obtained using training datasets that comprise spectrally diverse unsupervised clusters from multiple regions and that include rock and cloud shadow classes. We successfully apply our trained supervised classifiers across two ice shelves with different supraglacial lake characteristics above a threshold sun elevation of 20°, achieving classification accuracies of over 90% when compared to manually generated validation datasets. The application of our trained classifiers produces a seasonal pattern of lake evolution. Cloud shadowed areas hinder large-scale application of our classifiers, as in previous work. Our results show that caution is required before deploying ‘off the shelf’ algorithms for lake mapping in Antarctica, and suggest that careful scrutiny of training data and desired output classes is essential for accurate results. Our supervised classification technique provides an alternative and independent method of lake identification to inform the development of a continent-wide supraglacial lake mapping product.