Greenland Ice Stream Dynamics from Physical Statistical Models

Physical statistical models analyze the Northeast Ice Stream in Greenland to understand processes controlling rapid ice flow. The approach combines geophysical models with Bayesian statistical methodology, using new remote-sensing observational data. The project was conducted by SCIOPS, exploring basal and surface elevations, velocity, and stress fields.

Use Cases

• Infer posterior distributions for basal elevation and surface velocity fields conditional on remote-sensing data.
• Compare lateral drag, basal drag, and longitudinal stress gradients between different ice-stream systems.
• Estimate uncertainty measures for stress fields using Markov chain Monte Carlo techniques.
• Model the role of ice streams as regulators of freshwater storage in polar ice sheets.

Strengths

• Models integrate multiple data components hierarchically using Bayesian methodology.
• Focus on a specific, critical ice stream system (Northeast Ice Stream in Greenland).

Limitations

• Specific row counts, column details, and temporal coverage are not provided.
• The dataset's availability, file formats, and size are unknown.

Provenance

Source

SCIOPS, via NASA EarthData.

Collection Method

Remote-sensing techniques combined with physical statistical and Bayesian modeling.

Geography

Northeast Ice Stream, Greenland.