Numerical experiments on subaqueous melting of Greenland tidewater glaciers in response to ocean warming and enhanced subglacial discharge
Bibliographic record
Abstract
Abstract The largest dischargers of ice in Greenland are glaciers that terminate in the ocean and melt in contact with sea water. Studies of ice-sheet/ocean interactions have mostly focused on melting beneath near-horizontal floating ice shelves. For tidewater glaciers, melting instead takes place along the vertical face of the calving front. Here we modify the Massachusetts Institute of Technology general circulation model (MITgcm) to include ice melting from a calving face with the freshwater outflow at the glacier grounding line. We use the model to predict melt rates and their sensitivity to ocean thermal forcing and to subglacial discharge. We find that melt rates increase with approximately the one-third power of the subglacial water flux, and increase linearly with ocean thermal forcing. Our simulations indicate that, consistent with limited field data, melting ceases when subglacial discharge is shut off, and reaches several meters per day when subglacial discharge is high in the summer. These results are a first step toward a more realistic representation of subglacial discharge and of ocean thermal forcing on the subaqueous melting of tidewater glaciers in a numerical ocean model. Our results illustrate that the ice-front melting process is both complex and strongly time-dependent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".