Modelling evolution of Greenland Ice Sheet near-surface ice slab and its impact on runoff  
Bibliographic record
Abstract
In the accumulation regions of the Greenland Ice Sheet (GrIS), not all surface meltwater contributes to runoff. A significant portion is retained through refreezing within the underlying firn layer, a process that critically moderates the overall mass loss from the GrIS. The refreezing of percolating meltwater at shallow depths leads to densification of the near-surface and the formation of ice layers. The extent of meltwater refreezing is influenced by firn density and temperature, which together govern the permeability of the near-surface ice layers. The presence of shallow, thick ice layers (> 1m thick, also known as "ice slab") restricts the deeper percolation of meltwater, thereby promoting its conversion into runoff. For example, the formation of ice slab in GrIS has resulted in nearly a 30% increase in the area contributing to runoff generation since 2001. Therefore, modelling ice slab is essential for understanding the total mass loss of the GrIS, both in recent years and in future projections.In this study, we present a high vertical resolution, physically distributed model that simulates surface mass balance, refreezing, ice layer formation, and runoff. A novel temperature-dependent criterion for ice layer permeability is incorporated that has been rigorously validated against field measurements from the Devon Ice Cap in the Canadian Arctic, where it demonstrates a strong agreement with point-scale observations of surface mass balance and vertical density profiles. We applied the model to the GrIS from 1999 to 2022, using a horizontal spatial resolution of 0.25° × 0.25°, a vertical resolution of 1 cm, and a temporal resolution of 15 minutes. The model simulations are calibrated using the SUMup archive of surface mass balance observations and validated against shallow core measurements of vertical density profiles. The high vertical resolution of the model provides insights into the process of ice slab evolution and impacts on runoff magnitudes and spatial distribution from the accumulation area of the GrIS. We analyze the model results to examine the relationship between the formation of ice slab and the runoff limit across the GrIS exploring sensitivities to changing climate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".