Antarctic-wide subglacial hydrology modelling explores controls on ice velocity and ice shelf melt
Bibliographic record
Abstract
Global sea levels are rising due, in large part, to the melting of ice sheets and glaciers as a direct result of climate change. Current ice flow models leave out critical components that impact the temporal and spatial dynamics of ice sheets, which results in a significant degree of uncertainty in current sea level rise predictions. One such component is subglacial hydrology, which describes the volume and movement of meltwater underneath glaciers. The presence of liquid water beneath glaciers can increase the glacier’s velocity and the rate of ice shelf melt, both of which can destabilize glaciers and lead to enhanced sea level rise. Here, we present subglacial hydrology model results of the full Antarctic Ice Sheet derived using the Glacier Drainage System (GlaDS) model. We examine water pressures of the distributed drainage system, the size and distribution of the channelized system, and freshwater discharge across grounding lines of the major drainage basins in the steady state subglacial hydrologic system under present day conditions. We compare our modeled channelised grounding line discharge to satellite-derived sub-ice shelf melt rates. Additionally, we compare our water pressure results to those computed using a Shreve hydrology model, which assumes effective pressure to be negligible and computes hydraulic potential using gradients in ice geometry alone. Recent coupling between GlaDS and the Ice-sheet and Sea-level System Model (ISSM) allows for the inclusion of the subglacial hydrologic system in models of ice dynamics. We use the water pressure from our steady state subglacial hydrology results to parameterize an ice flow model and calculate surface velocity. Results are compared to satellite observations of ice velocity and to modeled ice velocities computed without the use of a full subglacial hydrology model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".