Inferring Firn Permeability from Pneumatic Testing: A Case Study on the Greenland Ice Sheet
Bibliographic record
Abstract
Across the accumulation zone of the Greenland ice sheet, summer temperatures can be sufficiently warm to cause widespread melting, as was the case in July 2012 when the entire ice sheet experienced a brief episode of enhanced surface ablation. The resulting meltwater percolates into the firn and refreezes, to create ice lenses and layers within the firn column. This is an important process to consider when estimating the surface mass balance of the ice sheet. The rate of meltwater percolation depends on the permeability of the firn, a property that is not well constrained in the presence of refrozen ice layers and lenses. We present a novel, inexpensive method for measuring in-situ firn permeability using pneumatic testing, a well-established technique used in environmental engineering and hydrology. To illustrate the capabilities of this method, we estimate both horizontal and vertical permeability from pilot tests at six sites on the Greenland ice sheet: KAN-U, DYE-2, EKT, NASA-SE, Saddle, and EastGRIP. These sites cover a range of conditions from mostly dry firn (EastGRIP), to firn with several ice layers and lenses from refrozen meltwater (Saddle, NASA-SE, EKT), to firn with extensive ice layers (DYE-2 and KAN-U). The estimated permeability in firn without refrozen ice layers at EastGRIP agrees well with the range previously reported using an air permeameter to measure permeability through firn core samples at Summit, Greenland. At sites with ice lenses or layers, we find high degrees of anisotropy, with vertical permeability much lower than horizontal permeability. Pneumatic testing is a promising and low-cost technique for measuring firn permeability, particularly as meltwater production increases in the accumulation zone and ice layers and lenses from refrozen melt layers become more prevalent. In these initial proof-of-concept tests, the estimated permeabilities represent effective permeability at the meter scale. With appropriately higher vacuum pressures and more detailed monitoring, effective permeabilities over a larger scale may be quantified reliably, and multiple measurements during a season and across multiple years could improve understanding of the evolving firn structure and permeability. The technique is also suitable for broad application in Antarctica and other glaciers and ice caps.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".