Abstract

Abstract. Sea level contributions from the Greenland Ice Sheet are influenced by the rapid changes in glacial terminus positions. The documentation of these evolving calving front positions, for which satellite imagery forms the basis, is therefore important. However, the manual delineation of these calving fronts is time consuming, which limits the availability of these data across a wide spatial and temporal range. Automated methods face challenges that include the handling of clouds, illumination differences, sea ice mélange, and Landsat 7 scan line corrector errors. To address these needs, we develop the Calving Front Machine (CALFIN), an automated method for extracting calving fronts from satellite images of marine-terminating glaciers, using neural networks. The results are often indistinguishable from manually curated fronts, deviating by on average 86.76 ± 1.43 m from the measured front. Landsat imagery from 1972 to 2019 is used to generate 22 678 calving front lines across 66 Greenlandic glaciers. This improves on the state of the art in terms of the spatiotemporal coverage and accuracy of its outputs and is validated through a comprehensive intercomparison with existing studies. The current implementation offers a new opportunity to explore subseasonal and regional trends on the extent of Greenland's margins and supplies new constraints for simulations of the evolution of the mass balance of the Greenland Ice Sheet and its contributions to future sea level rise.

Highlights

  • The evolution of Greenland’s tidewater glaciers is an important constraint on the evolution of the Greenland Ice Sheet (Nick et al, 2013)

  • While satellite imagery allows for the extensive documentation of this evolving constraint, most calving front delineation is still done with time-consuming manual labor (Carr et al, 2017; Bunce et al, 2018; Catania et al, 2018)

  • The polygon product consists of an ocean mask bounded by the domain subset, the fjord boundaries, and the calving front(s), for each domain

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Summary

Introduction

The evolution of Greenland’s tidewater glaciers is an important constraint on the evolution of the Greenland Ice Sheet (Nick et al, 2013). Constraining Greenland’s glacial evolution is an important part of improving the understanding of the Earth system as a whole. While satellite imagery allows for the extensive documentation of this evolving constraint, most calving front delineation is still done with time-consuming manual labor (Carr et al, 2017; Bunce et al, 2018; Catania et al, 2018). This results in the under-utilization of available satellite imagery and causes gaps in seasonal records that introduce uncertainty when modeling past and projected climate change (Catania et al, 2020). The increasing availability of new datasets through missions like Landsat 8 and the release of old datasets through improved reprocessing call for new automated ways of detecting the calv-

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