Enthalpy balance theory unifies diverse glacier surge behaviour

It is commonly asserted that there are two distinct classes of glacier surges: slow, long-duration ‘Svalbard-type’ surges, triggered by a transition from cold- to warm-based conditions (thermal switching), and fast, shorter-duration ‘Alaska-type’ surges triggered by a reorganisation of the basal drainage system (hydraulic switching). This classification, however, reflects neither the diversity of surges in Svalbard and Alaska (and other regions), nor the fundamental dynamic processes underlying all surges. We argue that enthalpy balance theory offers a framework for understanding the spectrum of glacier surging behaviours while emphasising their essential dynamic unity. In this paper, we summarise enthalpy balance theory, illustrate its potential to explain so-called ‘Svalbard-type’ and ‘Alaska-type’ surges using a single set of principles, and show examples of a much wider range of glacier surge behaviour than previously observed. We then identify some future directions for research, including strategies for testing predictions of the theory against field and remote sensing data, and priorities for numerical model development

TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications

Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 ± 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of ∼ 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet

Seasonal dynamic thinning at Helheim Glacier

AbstractWe investigate three annual mass-balance cycles on Helheim Glacier in south-east Greenland using TanDEM-X interferometric digital elevation models (DEMs), bedrock GPS measurements, and ice velocity from feature-tracking. The DEMs exhibit seasonal surface elevation cycles at elevations up to 800 m.a.s.l. with amplitudes of up to 19 m, from a maximum in July to a minimum in October or November, concentrated on the fast-flowing areas of the glacier indicating that the elevation changes have a mostly dynamic origin. By modelling the detrended bedrock loading/unloading signal we estimate a mean density for the loss of 671±70 kgm−3 and calculate that total water equivalent volume loss from the active part of the glacier (surface flow speeds >1 m day−1) ranges from 0.5 km3 in 2011 to 1.6 km3 in 2013. A rough ice-flux divergence analysis shows that at lower elevations (<200 m) mass loss by dynamic thinning fully explains seasonal elevation changes. In addition, surface elevations decrease by a greater amount than field observations of surface ablation or surface-energy-balance modelling predict, emphasising the dynamic nature of the mass loss. We conclude, on the basis of ice-front position observations through the time series, that melt-induced acceleration is most likely the main driver of the seasonal dynamic thinning, as opposed to changes triggered by retreat

Heterogeneity in Karakoram glacier surges

Many Karakoram glaciers periodically undergo surges during which large volumes of ice and debris are rapidly transported down-glacier, usually at a rate of one to two orders of magnitude greater than during quiescence. Here we identify eight recent surges in the region, and map their surface velocities using cross-correlation feature tracking on optical satellite imagery. In total, we present 44 surface velocity datasets, which show that Karakoram surges are generally short-lived, lasting between 3 and 5 years in most cases, and have rapid build-up and relaxation phases, often lasting less than a year. Peak velocities of up to 2 km a-1 are reached during summer months and the surges tend to diminish during winter months. Otherwise, they do not follow a clearly identifiable pattern. In two of the surges, the peak velocity travels down-ice through time as a wave, which we interpret as a surge front. Three other surges are characterised by high velocities that occur simultaneously across the entire glacier surface and acceleration and deceleration is close to monotonic. There is also no consistent seasonal control on surge initiation or termination. We suggest that the differing styles of surge can be partly accounted for by individual glacier configurations, and that while some characteristics of Karakoram surges are akin to thermally-controlled surges elsewhere (e.g. Svalbard), the dominant surge mechanism remains unclear. We thus propose that these surges represent a spectrum of flow instabilities and the processes controlling their evolution may vary on a glacier by glacier basis