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Abstract Sít’ Tlein (Malaspina Glacier), located in Southeast Alaska, has a complex flow history. This piedmont glacier, the largest in the world, is fed by three main tributaries that all exhibit similar flow patterns, yet with varying surge cycles. The piedmont lobe is dramatically reshaped by surges that occur at approximately decadal timescales. By combining historical accounts with modern remote sensing data, we derive a surge history over the past century. We leverage the Stochastic Matrix Factorization, a novel data analysis and interpolation technique, to process and interpret large datasets of glacier surface velocities. A variant of the Principal Component Analysis allows us to uncover spatial and temporal patterns in ice dynamics. We show that Sít’ Tlein displays a wide range of behaviors, spanning quiescence to surge with seasonal to decadal variations of ice flow direction and magnitude. We find that in the lobe, surges dominate the velocity dataset’s variance (spanning 1984–2021), while seasonal variations represent a much smaller part of the variance. However, despite the regular surge pulses, the glacier lobe is far from equilibrium, and widespread retreat of the glacier is inevitable, even without further climate warming. 

Abstract. Sít' Tlein, located in the St. Elias Range, which straddles Alaska's Wrangell–St. Elias National Park and Kluane National Park in the Yukon, is the world's largest piedmont glacier. Sít' Tlein has thinned considerably over 30 years of altimetry, yet its low-elevation piedmont lobe has remained intact in contrast to the glaciers that once filled neighboring Icy and Disenchantment bays. In an effort to forecast changes to Sít' Tlein over decadal to centennial timescales, we take a data-constrained dynamical modeling approach in which we infer the parameters of a higher-order model of ice flow – the bed elevation, basal traction, and surface mass balance – with a diverse but spatiotemporally sparse set of observations including satellite-derived, time-varying velocity fields; radar-derived bed and surface elevation measurements; and in situ and remotely sensed observations of accumulation and ablation. Nonetheless, such data do not uniquely constrain model behavior, so we adopt an approximate Bayesian approach based on the Laplace approximation and facilitated by low-rank parametric representations to quantify uncertainty in the bed, traction, and mass balance fields alongside the induced uncertainty in model-based predictions of glacier change. We find that Sít' Tlein is considerably out of balance with contemporary (and presumably future) climate, and we expect its piedmont lobe to largely disappear over the coming centuries. If warming ceases, and surface mass balance remains at 2023 levels, then by 2073 (2173) we forecast a mass loss (expressed in terms of 95 % credible interval) of 323–444 km3 (546–728 km3). If instead surface mass balance continues to change at the same rate as inferred over the historical period, then we forecast a 2073 (2173) mass loss of 383–505 km3 (740–900 km3). In either case, the resulting retreat and subsequent replacement of glacier ice with a marine embayment or lake will yield a significant modification to the regional landscape and ecosystem. 

We briefly review the Γ-convergence of phase-field fracture to Griffith fracture, and describe how softening and nucleation occur when implementing phase-field models. An example is given of how this softening and nucleation can be completely stopped, while preserving crack growth and Γ-convergence. We then show how nucleation can locally be turned back on, based on any criterion, such as a stress threshold. Again, these modifications preserve Γ-convergence, and they can be applied to static, quasi-static, and dynamic models. Additionally, we describe why these modifications can be expected to improve the convergence of phase-field models. 

The 2021 airborne Light Detection and Ranging (LiDAR) data was acquired by the University of Alaska Geophysical Institute for an National Science Foundation (NSF) funded project focused on catastrophic arctic lake drainage in northern Alaska. The data was acquired at six key field study sites that included drained lake basins north of Teshekpuk Lake, perched lakes on the Ikpikpuk River Delta, tapped lakes at Drew Point, the large drained lake basin complex at the Pik Dunes, a lake and drained lake basin complex at the Anaktuvuk River tundra fire site, and the cascading lake drainage events along the Chipp and Alaktak Rivers. This dataset encompasses 300 square kilometers of terrestrial and aquatic tundra settings in northern Alaska. The data were acquired between 22 July and 27 July 2021 at an estimated density of 16-20 points per square meter (ppm) using an Reigl VQ-580ii LiDAR system flying at an altitude of 750 meters above ground level. The vertical accuracy of this dataset is 10 centimeters. The data have been post-processed to WGS84 UTM Zone 5 North in Ellipsoid Heights (meters). 

This data set covers the Anaktuvuk River fire site and maps drained lake basins in this area as described by Jones et al (2015). The data set is derived from airborne Light Detection and Ranging (LiDAR) data acquired in 2009 and 2014. The classification of drained lake basins is based on digital terrain models (DTMs) created from the classified LiDAR data and using the a topographic position index (TPI). The TPI output was manually categorized relative to existing surficial geology maps and refined into the following terrain units: (1) drained lake basins, (2) yedoma uplands, (3) rocky uplands, (4) glaciated upland, (5) river floodplain and (6) tundra stream gulches. The drained lake basin class is the subject of this data set publication. Jones, B., Grosse, G., Arp, C. et al. Recent Arctic tundra fire initiates widespread thermokarst development. Sci Rep 5, 15865 (2015). https://doi.org/10.1038/srep15865 

This data set covers the Anaktuvuk River fire site and maps drained lake basins in this area as described by Jones et al (2015). The data set is derived from airborne Light Detection and Ranging (LiDAR) data acquired in 2009 and 2014. The classification of drained lake basins is based on digital terrain models (DTMs) created from the classified LiDAR data and using the a topographic position index (TPI). The TPI output was manually categorized relative to existing surficial geology maps and refined into the following terrain units: (1) drained lake basins, (2) yedoma uplands, (3) rocky uplands, (4) glaciated upland, (5) river floodplain and (6) tundra stream gulches. The drained lake basin class is the subject of this data set publication. Jones, B., Grosse, G., Arp, C. et al. Recent Arctic tundra fire initiates widespread thermokarst development. Sci Rep 5, 15865 (2015). https://doi.org/10.1038/srep15865