Seasonal Patterns of Greenland Ice Velocity From Sentinel‐1 SAR Data Linked to Runoff. Issue 24 (26th December 2022)

• Record Type: Journal Article
• Authors: Solgaard, A. M.; Rapp, D.; Noël, B. P. Y.; Hvidberg, C. S.
• Is Part Of: Geophysical research letters. Volume 49:Issue 24(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-26
• Subjects: ice dynamics -- ice velocity -- Greenland Ice Sheet -- subglacial hydrology -- machine learning -- seasonality; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2022GL100343
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Accurate projections of the mass loss from the Greenland Ice Sheet (GrIS) require a complete understanding of the ice‐dynamic response to climate forcings on seasonal and interannual timescales and would greatly benefit from more observational evidence. Here, we analyze a 5‐year, high‐resolution data set of ice velocities of the GrIS using K ‐means, an unsupervised clustering algorithm, to identify ice‐sheet wide characteristic seasonal flow patterns. We include all areas flowing >0.3 m/d and obtain an ice‐sheet wide overview of the seasonality and the interannual variability. It shows both a spatial and interannual variability in seasonal flow patterns, both along individual glaciers and between glaciers. We compare with runoff from a regional climate model and infer that the ice‐sheet wide patterns are linked to the availability of water penetrating to the base of the ice. Plain Language Summary: The Greenland Ice Sheet (GrIS) is currently losing mass but the response from the marine outlet glaciers to atmospheric and oceanic forcings is uncertain and limiting the accuracy of the estimated future mass loss. Here, we analyze a 5‐year satellite based ice velocity data set with an unprecedented spatial and temporal resolution in order to investigate the variability on all fast flowing areas of the GrIS. We use a machine learning algorithm to sort the annual time series into characteristic seasonal patterns, and we compare the results to modeled runoff from a climate model. We find that individual glaciers are not classified to be one type, but the response depends on the availability of drained meltwater, and that the seasonal pattern of ice velocity varies spatially and temporally, both along individual glaciers and between neighboring glaciers. We conclude that the seasonal pattern of response to runoff provide insights to the evolution of the subglacial hydrological system during the runoff season. Understanding the response of ice flow to meltwater and how it is linked to the subglacial hydrological system is crucial for understanding the dynamic response of the ice sheet to future climate warming. Key Points: We have identified typical ice‐sheet wide seasonal ice‐flow patterns using an unsupervised ML algorithm There is a spatial and interannual variability in the seasonal flow patterns both between and along glaciers The spatiotemporal variability of the seasonal patterns provides new insights into the evolution of the basal hydrology depending on runoff