Characterizing Subglacial Hydrology Within the Amery Ice Shelf Catchment Using Numerical Modeling and Satellite Altimetry
Bibliographic record
Abstract
Abstract Meltwater forms at the base of the Antarctic Ice Sheet due to geothermal heat flux (GHF) and basal frictional dissipation. Despite the relatively small volume, this water has a profound effect on ice‐sheet dynamics. However, subglacial melting and hydrology in Antarctica remain highly uncertain, limiting our ability to assess their impact on ice‐sheet dynamics. Here we examine subglacial hydrology within the Amery Ice Shelf catchment, East Antarctica, using the subglacial hydrology model GlaDS. We calculate subglacial melt rates using a higher‐order ice‐flow model and two GHF estimates. We find a catchment‐wide melt rate of 7.03 Gt year−1 (standard deviation = 1.94 Gt year−1), which is ≥50% greater than previous estimates. The contribution from basal dissipation is approximately 40% of that from GHF. However, beneath fast‐flowing ice streams, basal dissipation is an order of magnitude larger than GHF, leading to a significant increase in channelized subglacial flux upstream of the grounding line. We validate GlaDS using high‐resolution interferometric‐swath radar altimetry, with which we detect active subglacial lakes and fine‐scale ice‐shelf basal melting. We find a network of subglacial channels that connects areas of deep subglacial water coincident with active subglacial lakes, and channelized discharge at the grounding line coinciding with enhanced ice‐shelf basal melting. The concentrated discharge of meltwater provides 36% of the freshwater released into the ice‐shelf cavity, in addition to ice‐shelf basal melting. This suggests that ice‐shelf basal melting is strongly influenced by subglacial hydrology and could be affected by future changes in subglacial discharge, such as lake drainage or channel rerouting.
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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 | 0.001 |
| 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) | 0.000 | 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".