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Abstract Surface crevassing on the Greenland Ice Sheet is a large source of uncertainty in processes controlling mass loss due to a lack of comprehensive observations of their location and evolution through time. Here we use high-resolution digital elevation models to map the three-dimensional volume of crevasse fields across the Greenland Ice Sheet in 2016 and 2021. We show that, between the two years, large and significant increases in crevasse volume occurred at marine-terminating sectors with accelerating flow (up to +25.3 ± 10.1% in the southeast sector), while the change in total ice-sheet-wide crevasse volume was within measurement error (+4.3 ± 5.9%). The sectoral increases were offset by a reduction in crevasse volume in the central west sector (−14.2 ± 3.2%), particularly at Sermeq Kujalleq (Jakobshavn Isbræ), which exhibited slowdown and thickening over the study period. Changes in crevasse volume correlate strongly with antecedent discharge changes, indicating that the acceleration of ice flow in Greenland forces significant increases in crevassing on a timescale of less than five years. This response provides a mechanism for mass-loss-promoting feedbacks on sub-decadal timescales, including increased calving, faster flow and accelerated water transfer to the bed. 

Abstract Massive calving events result in significant instantaneous ice loss from Antarctica. The rarity and stochastic nature of these extreme events makes it difficult to understand their physical drivers, temporal trends, and future likelihood. To address this challenge, we turn to extreme value theory to investigate past trends in annual maxima iceberg area and assess the likelihood of high‐magnitude calving events. We use 47 years of iceberg size from satellite observations. Our analysis reveals no upward trend in the surface area of the largest annual iceberg over this time frame. This finding suggests that extreme calving events such as the recent 2017 Larsen C iceberg, A68, are statistically unexceptional and that extreme calving events are not necessarily a consequence of climate change. Nevertheless, it is statistically possible for Antarctica to experience a calving event up to several times greater than any in the observational record. 

Realistically rough stochastic realizations of subglacial bed topography are crucial for improving our understanding of basal processes and quantifying uncertainty in sea level rise projections with respect to topographic uncertainty. This can be achieved with sequential Gaussian simulation (SGS), which is used to generate multiple nonunique realizations of geological phenomena that sample the uncertainty space. However, SGS is very CPU intensive, with a computational complexity of O(NkNk3), where NN is the number of grid cells to simulate, and kk is the number of neighboring points used for conditioning. This complexity makes SGS prohibitively time-consuming to implement at ice sheet scales or fine resolutions. To reduce the time cost, we implement and test a multiprocess version of SGS using Python’s multiprocessing module. By parallelizing the calculation of the weight parameters used in SGS, we achieve a speedup of 9.5 running on 16 processors for an NN of 128,097. This speedup—as well as the speedup from using multiple processors—increases with NN. This speed improvement makes SGS viable for large-scale topography mapping and ensemble ice sheet modeling. Additionally, we have made our code repository and user tutorials publicly available (GitHub and Zenodo) so that others can use our multiprocess implementation of SGS on different datasets.