Patterns and trends of ice dynamic variability on the Greenland Ice Sheet from a decade of high-resolution synthetic aperture radar data

The Greenland Ice Sheet is a major contributor to global sea-level rise, with its mass loss raising global sea-levels by 13.59 ± 1.27 mm from 1992 to 2020. Changes to the ice dynamics of the Greenland Ice Sheet make up a significant part of this mass loss, meaning that monitoring and understanding changes in ice dynamics is crucial to assessing contemporary sea-level rise and making projections of its future.

Since 2014, the European Space Agency’s Sentinel-1 synthetic aperture radar constellation has provided near-continuous, weather-independent coverage of the Greenland Ice Sheet, with 6–12 day repeat intervals over almost all outlet glaciers. We process the full Sentinel-1 archive using an intensity feature offset tracking workflow implemented in GAMMA Remote Sensing to produce a dense, decade-spanning time series of ice velocity at 100 m resolution. This Sentinel-1 derived ice velocity dataset is extremely valuable, capturing long-term speed trends and short-term interannual variability due to seasonal drivers. However, analysing this 60 TB dataset efficiently and obtaining maximum scientific value from it is a substantial data engineering challenge, as the data volume greatly exceeds available memory quotas on HPC systems, even for relatively small geographic areas.

Here, we address this challenge with an analysis pipeline built on the Xarray, Dask, and Zarr python packages, and deployed on a HPC service. This pipeline allows both large scale interactive analysis in Jupyter notebooks, streaming to GIS software, and traditional batch processing. We leverage these tools we calculate ice-sheet wide speed change, seasonal speed variability and ice discharge in a fast, reproducible and scalable manner.

Our results reveal a highly heterogeneous dynamic response across Greenland, with neighbouring glaciers often exhibiting contrasting behaviour over the past decade. We find that short-term and seasonal variability dominates the velocity signal for most glaciers, often exceeding long-term speed changes. These findings highlight the importance of resolving short-term ice-dynamic processes when assessing Greenland’s contribution to future sea-level rise and the benefit of efficient big-data processing workflows.