Kristin Poinar 

Greenland glaciers have three primary seasonal ice flow patterns, or “types”: terminus driven, runoff driven, and runoff adapting. To date, glacier types have been identified by analyzing flow at a single location near the terminus; information at all other locations is discarded. Here, we use principal component (PC) / empirical orthogonal function (EOF) analysis to decompose multi-year time series of glacier speed, combined from three satellite-derived products at four glaciers feeding Sermilik Fjord, Greenland. This improves on single-point methods by yielding temporal patterns (PCs), which allow identification of glacier type, and associated spatial patterns (EOFs), which ensure the result reflects data at all locations on the glacier. We find that the leading mode is uniformly signed over the entire glacier domain, that this mode explains the majority of the variance in speed, and therefore that glacier type can be inferred from the leading PC. We find that Helheim Glacier was terminus-driven, Fenris Glacier and Midgard Glacier were runoff-adapting, and Pourquoi-Pas Glacier was runoff-driven over 2016-2021. Our classification agrees with previous work for Helheim and Midgard Glaciers, but differs at the other two. At all but Fenris Glacier, the leading PC correlates significantly with the speed pattern observed at the single point used in previous analyses. Thus, Fenris Glacier has more complex flow patterns than single-point analysis can capture, and wider spatial analysis techniques such as EOF/PC are required. We suggest that, due to its low computational cost and inclusion in standard analysis packages, EOF/PC analysis should be used for assessing glacier type.