Antarctic grounding line retreat enhanced by subglacial freshwater discharge
Bibliographic record
Abstract
Accurate prediction of sea level rise requires detailed understanding of processes contributing to ice sheet mass loss. Antarctica’s ice shelves are thinning, resulting in enhanced flow of grounded ice due to weakened ice shelf buttressing. Glaciers feeding ice shelves with the highest melt rates are also experiencing some of the most rapid grounding zone retreat. However, these ice shelf melt rates reach values that cannot be explained by ocean forcing alone and are not reproduced in ocean models. We present subglacial hydrology model outputs for four major Antarctic glaciers (Pine Island, Thwaites, Totten and Denman), which flow through the deepest and most extensive Antarctic marine subglacial basins and feed rapidly thinning ice shelves. We show that the areas of high ice shelf melting rates and grounding line retreat coincide closely with areas of high subglacial discharge. We posit that the subglacial discharge provides the missing component driving the high melt rates, and identify positive feedbacks between ice dynamics, steepening of ice shelf basal slope, and subglacial outflow. If surface temperatures increase as expected in Antarctica over the coming decades, surface meltwater could flow to the ice sheet base, as observed in Greenland. The surface meltwater hydrological cycle could therefore contribute to seasonal variations in subglacial meltwater and ice shelf basal melt, leading to accelerated grounding line retreat into Antarctica’s deepest subglacial basins. Invoking these feedbacks could reconcile sea level records and ice sheet model simulations that remain overly stable in warmer periods.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".