The Effect of Firn-Aquifer Drainage on the Greenland Subglacial System or Subglacial Efficiency and Storage Modified by the Temporal Pattern of High-Elevation Meltwater Input
Bibliographic record
Abstract
Ice flow in marginal region of the Greenland Ice Sheet dynamically responds to summer melting as surface meltwater is routed through the supraglacial hydrologic system to the bed of the ice sheet via crevasses and moulins. Given the expected increases in surface melt production and extent, and the potential for high elevation surface-to-bed connections, it is imperative to understand how meltwater delivered to the bed from different high-elevation supraglacial storage features affects the evolution of the subglacial hydrologic system and associated ice dynamics. Here, we use the two-dimensional subglacial hydrologic model, GLaDS, which includes distributed and channelized water flow, to test how the subglacial system of an idealized outlet glacier responds to cases of high-elevation firn-aquifer-type and supraglacial-lake-type englacial drainage over the course of 5 years. Model outputs driven by these high elevation drainage types are compared to steady-state model results, where the subglacial system only receives the 1980- 2016 mean MERRA-2 runoff via low-elevation moulins. Across all experiments, the subglacial hydrologic system displays inter-annual memory, resulting in multiyear declines in subglacial pressure during the onset of seasonal melting and growth of subglacial channels. The gradual addition of water in firn-aquifer-type drainage scenarios resulted in small increases in subglacial water storage but limited changes in subglacial efficiency and channelization. Rapid, supraglacial- lake-type drainage resulted in short-term local increases in subglacial water pressure and storage, which gave way to spatially extensive decreases in subglacial pressure and downstream channelization. These preliminary results suggest that the character of high-elevation englacial drainage can have a strong, and possibly outsized, control on subglacial efficiency throughout the ablation zone. Therefore, understanding both how high elevation meltwater is stored supraglacially and the probability of crevassing at high elevations will play an important role in how the subglacial system, proglacial discharge and ice motion will respond to future increases in surface melt production and runoff.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".