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Abstract The Bering‐Bagley Glacier System (BBGS), Alaska, Earth's largest temperate surging glacier, surged in 2008–2013. We use numerical modeling and satellite observations to investigate how surging in a large and complex glacier system differs from surging in smaller glaciers for which our current understanding of the surge phenomenon is based. With numerical simulations of a long quiescent phase and a short surge phase in the BBGS, we show that surging is more spatiotemporally complex in larger glaciers with multiple reservoir areas forming during quiescence which interact in a cascading manner when ice accelerates during the surge phase. For each phase, we analyze the simulated elevation‐change and ice‐velocity pattern, infer information on the evolving basal drainage system through hydropotential analysis, and supplement these findings with observational data such as CryoSat‐2 digital elevation maps. During the quiescent simulation, water drainage paths become increasingly lateral and hydropotential wells form indicating an expanding storage capacity of subglacial water. These results are attributed to local bedrock topography characterized by large subglacial ridges that dam the down‐glacier flow of ice and water. In the surge simulation, we model surge evolution through Bering Glacier's trunk by imposing a basal friction representation that mimics a propagating surge wave. As the surge progresses, drainage efficiency further degrades in the active surging‐zone from its already inefficient, end‐of‐quiescence state. Results from this study improve our knowledge of surging in large and complex systems which generalizes to glacial accelerations observed in outlet glaciers of Greenland, thus reducing uncertainty in modeling sea‐level rise. 

The objectives of this paper are to investigate the trade-offs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach (“VarioCNN”). Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed ML software, GEOCLASS-image (v1.0), modern high-resolution satellite image data sets (Maxar WorldView data), and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set. Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100–200 times its normal velocity. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in six simplified classes. 

We investigate sea ice conditions during the 2020 melt season, when warm air temperature anomalies in spring led to early melt onset, an extended melt season, and the second-lowest September minimum Arctic ice extent observed. We focus on the region of the most persistent ice cover and examine melt pond depth retrieved from Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) using two distinct algorithms in concert with a time series of melt pond fraction and ice concentration derived from Sentinel-2 imagery to obtain insights about the melting ice surface in three dimensions. We find the melt pond fraction derived from Sentinel-2 in the study region increased rapidly in June, with the mean melt pond fraction peaking at 16 % ± 6 % on 24 June 2020, followed by a slow decrease to 8 % ± 6 % by 3 July, and remained below 10 % for the remainder of the season through 15 September. Sea ice concentration was consistently high (>95 %) at the beginning of the melt season until 4 July, and as floes disintegrated, it decreased to a minimum of 70 % on 30 July and then became more variable, ranging from 75 % to 90 % for the remainder of the melt season. Pond depth increased steadily from a median depth of 0.40 m ± 0.17 m in early June and peaked at 0.97 m ± 0.51 m on 16 July, even as melt pond fraction had already started to decrease. Our results demonstrate that by combining high-resolution passive and active remote sensing we now have the ability to track evolving melt conditions and observe changes in the sea ice cover throughout the summer season. 

As climate warms and the transition from a perennial to a seasonal Arctic sea-ice cover is imminent, understanding melt ponding is central to understanding changes in the new Arctic. National Aeronautics and Space Administration (NASA)’s Ice, Cloud and land Elevation Satellite (ICESat-2) has the capacity to provide measurements and monitoring of the onset of melt in the Arctic and on melt progression. Yet ponds are currently not identified on the ICESat-2 standard sea-ice products, in which only a single surface is determined. The objective of this article is to introduce a mathematical algorithm that facilitates automated detection of melt ponds in the ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) data, retrieval of two surface heights, pond surface and bottom, and measurements of depth and width of melt ponds. With ATLAS, ICESat-2 carries the first spaceborne multibeam micropulse photon-counting laser altimeter system, operating at 532-nm frequency. ATLAS data are recorded as clouds of discrete photon points. The Density-Dimension Algorithm for bifurcating sea-ice reflectors (DDA-bifurcate-seaice) is an autoadaptive algorithm that solves the problem of pond detection near the 0.7-m nominal along-track spacing of ATLAS data, utilizing the radial basis function for calculation of a density field and a threshold function that automatically adapts to changes in the background, apparent surface reflectance, and some instrument effects. The DDA-bifurcate-seaice is applied to large ICESat-2 datasets from the 2019 and 2020 melt seasons in the multiyear Arctic sea-ice region. Results are evaluated by comparison with those from a manually forced algorithm. 

The recent surge of the Bering-Bagley Glacier System (BBGS), Alaska, in 2008-2013 provided a rare opportunity to study surging in a large and complex system. We simulate glacier evolution for a 20 year quiescent phase, where geometrical and hydrological changes lead to conditions favorable for surging, and the first two years of a surge phase where a surge-front propagates through the system activating the surging ice. For each phase, we analyze the simulated elevation-change and ice-velocity pattern, and infer information on the evolving basal drainage system through hydropotential analysis. During the quiescent phase simulation, several reservoir areas form at locations consistent with those observed. Up-glacier of these reservoir areas, water drainage paths become increasingly lateral and hydropotential wells form indicating an expanding storage capacity of subglacial water. These results are attributed to local bedrock topography characterized by large subglacial ridges that act to dam the down-glacier flow of ice and water. Based on the BBGS’s end-of-quiescence state, we propose several surge initiation criteria to predict when the system is set to surge. In the surge simulation, we model surge evolution through Bering Glacier’s trunk by implementing a new friction law that mimics a propagating surge-wave. Modeled surge velocities share spatial patterns and reach similar peaks as those observed in 2008-2010. As the surge progresses through the glacier, drainage efficiency further degrades in the active surging zone from its already inefficient, end-of-quiescence state. Satellite observations from 2013 indicate hydraulic drainage efficiency throughout the glacier was restored after the surge had ended.