Abstract

We use remotely sensed ice velocities in combination with observations of surface elevation and glacier area change to investigate the dynamics of Hagen Bræ, North Greenland in high detail over the last 35 years. From our data, we can establish for the first time that Hagen Bræ is a surge‐type glacier with characteristics of both Alaskan‐ and Svalbard‐type surging glaciers. We argue that the observed surge was preconditioned by the glacier geometry and triggered by englacially stored meltwater. At present, the glacier is in a transitional state between active and quiescence phases and is not building up to its pre‐surge geometry. We suggest that the glacier is adjusting to the loss of its floating section, general thinning, and changes in fjord conditions that occurred over the study period which are unrelated to the surge behavior. The high temporal resolution of the ice velocity data gives insight to the sub‐annual glacier flow.

Highlights

  • The outlet glaciers of North Greenland drain 40% of the Greenland Ice Sheet (Hill et al, 2017)

  • We verify that the glacier share characteristics with both Alaskan- and Svalbard-type surging glaciers

  • We argue that the surge is related to internal storage of meltwater and that the sudden onset of the surge following summer 2002 was triggered by a high amount of surface melt

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Summary

Introduction

The outlet glaciers of North Greenland drain 40% of the Greenland Ice Sheet (Hill et al, 2017). Discharge from this region is low relative to other sectors due to the slow flow of its marine-terminating glaciers (Mouginot et al, 2019; Mankoff et al, 2019). Superposed on the general trend of glacier retreat, a clear variability in timing and magnitude of retreat is observed. Of the 21 North Greenland marine-terminating glaciers, eight are classified as being or likely being of surge type—including Hagen Bræ (Hill et al, 2017). Only little is known about surge-type glaciers in North Greenland, and there is a clear need for glacier-specific studies in order to understand the underlying mechanisms causing the observed variability (Hill et al, 2017)

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