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.
| Original language | English |
|---|---|
| Article number | e2022GL100343 |
| Number of pages | 10 |
| Journal | Geophysical Research Letters |
| Volume | 49 |
| Issue number | 24 |
| DOIs | |
| Publication status | Published - 28 Dec 2022 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 13 Climate Action
Keywords
- Greenland Ice Sheet
- ice dynamics
- ice velocity
- machine learning
- seasonality
- subglacial hydrology
Programme Area
- Programme Area 5: Nature and Climate
Fingerprint
Dive into the research topics of 'Seasonal patterns of Greenland ice velocity from Sentinel-1 SAR data linked to runoff'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver