Results, Code and Data for Quantitative Assessment of Stabilizing or Destabilizing Effect of Fjord Geometry on Greenland Tidewater Glaciers 1985-2020

• Main Authors: Elizabeth Fischer, Andy Aschwanden
• Format: Dataset
• Language: unknown
• Published: Arctic Data Center 2022
• Subjects: Greenland; Calving; Tidewater; Glacier; Retreat; glacier; Ice Sheet
• Online Access: https://doi.org/10.18739/A2ZW18T7N

To download and unzip this dataset, manually download the file a00_download_arcticdata.py, and then run using Python3: python a00_download_arcticdata.py ~/download_location 3 NOTE: You may have to authenticate and set the TOKEN environment variable, see the "Authentication" section of: https://arcticdata.io/catalog/api This study systematically examines the stress state of Greenland tidewater glaciers based on available surface velocities, terminus positions, ice thickness and bed elevations. The von Mises Calving Law is used to assess the contributions of fjord geometry to a glacier’s stability, or lack thereof. If a glacier’s expected calving rate increases upon terminus retreat after taking into account the empirical ocean heating relationship of, then fjord geometry contributes to instability; whereas a decrease signals a stabilizing fjord contribution. This principle is used to systematically evaluate 44 Greenland tidewater glaciers. Of those glaciers, 13 were found to have fjord geometry that currently contributes to instability; 7 with stabilizing geometry; and 24 that lacked statistical significance. Although the methodology as it currently stands is able to provide insight on a variety of already-retreating glaciers, it is only able to analyze terminus positions that have already been realized in the past. Therefore, it is unable to provide insight on historically stable glaciers, or on future terminus positions for which fjord geometry might change from stabilizing to destabilizing or vice versa. Future studies involving dynamic ice models could use the methodology presented here to address those questions. Full per-glaciers results are included. Python code to download external datasets, process / plot results and repeat this study is included, along with all datasets generated by this study.