Drivers of Recurring Seasonal Cycle of Glacier Calving Styles and Patterns

Andrea Kneib-Walter,Luc Moreau,Andreas Vieli,Martin P Lüthi

doi:10.3389/feart.2021.667717

Abstract

Calving is a crucial process for the mass loss of outlet glaciers draining the Greenland ice sheet. Moreover, due to a lack of observations, calving contributes to large uncertainties in current glacier flow models and projections. Here we investigate the frequency, volume and style of calving events by using high-resolution terrestrial radar interferometer (TRI) data from six field campaigns, continuous daily and hourly time-lapse images over 6 years and 10-s time-lapse images recorded during two field campaigns. The results demonstrate that the calving front of Eqip Sermia, a fast flowing, highly crevassed outlet glacier in West Greenland, follows a clear seasonal cycle showing a distinct pattern in areas with subglacial discharge plumes, shallow bed topography and during the presence and retreat of proglacial ice mélange. Calving event volume, frequency and style vary strongly over time depending on the state in the seasonal cycle. Strong spatial differences between three distinctive front sectors with differing bed topography, water depth and calving front slope were observed. A distinct increase in calving activity occurs in the early melt season simultaneously when ice mélange disappears and meltwater plumes become visible at the fjord surface adjacent to the ice front. While reduced retreat of the front is observed in shallow areas, accelerated retreat occurred at locations with subglacial meltwater plumes. With the emergence of these plumes at the beginning of the melt season, larger full thickness calving events occur likely due to undercutting of the calving front. Later in the melt season the calving activity at subglacial meltwater plumes is similar to the neighboring areas, suggesting the presence of plumes to become less important for calving. The results highlight the significance of subglacial discharge and bed topography on the front geometry, the temporal variability of the calving process and the variability of calving styles.

Highlights

The contribution of the mass loss from the Greenland ice sheet to sea level rise has increased during the past decade (Enderlin et al, 2014; Bamber et al, 2018; King et al, 2020; The Imbie Team, Shepherd and Ivins, 2020)
A unique data set of the outlet glacier Eqip Sermia, consisting of high-resolution terrestrial radar interferometry (TRI) data from six field campaigns, continuous daily and hourly time-lapse images over 6 years, and 10-s time-lapse images of two field campaigns enabled a detailed analysis of the evolution of the calving front and the calving process
The continuous time-lapse images from 2014 to 2019 facilitated the interpretation of the different patterns of the terrestrial radar interferometer (TRI)-derived calving events within the seasonal cycle, while the 10-s time-lapse images served as validation data and provided information on variations in calving type

Summary

INTRODUCTION

The contribution of the mass loss from the Greenland ice sheet to sea level rise has increased during the past decade (Enderlin et al, 2014; Bamber et al, 2018; King et al, 2020; The Imbie Team, Shepherd and Ivins, 2020). In this study we present observations of a tidewater outlet glacier recorded with a terrestrial radar interferometer (TRI), time-lapse imagery and several meteorological stations over 6 years The combination of these data sets demonstrates the seasonal cycle of the glacier with the front evolution and environmental conditions and provides high resolution calving event volumes and frequencies at different states in the seasonal cycle. This unique data set combines observations at different time-scales and enables us to investigate and analyze the different calving types and the temporal and spatial variability of the calving process and link to external factors such as the presence of ice mélange, plumes or occurrence of surface melt. This resulted in an almost continuous record with only two data gaps in spring 2015 and 2016 (Supplementary Figure 16)

RESULTS

DISCUSSION

CONCLUSION

DATA AVAILABILITY STATEMENT