Towards confidence in numerical modeling of ice stream cycling
Bibliographic record
Abstract
Some ice sheets and glaciers experience long quiescent periods interspersed with short periods of rapid ice advance, such as the binge-purge-type cycling hypothesized to be associated with Heinrich Events. Modeling ice stream activation/de-activation, however, is numerically challenging given the relatively abrupt changes at surge onset and the high ice velocities. In spite of this, a number of high-profile modeling papers have explored Heinrich events and ice surges, but generally with very limited consideration of numerical aspects. Here we test the ability of the 3D Glacial Systems Model (GSM) and Parallel Ice Sheet Model (PISM) to simulate binge-purge-type surges and explore the stability of the simulations with respect to relevant numerical and discretization uncertainties. We find surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions (order 5 km). In accordance with theoretical and experimental work, our model results suggest that the thermal activation of basal sliding should start below the pressure melting point. A resolution-dependent basal temperature ramp for the thermal activation of basal sliding as well as a subglacial hydrology model can reduce the discrepancies between high and coarse horizontal grid resolutions. Furthermore, incorporating a bed thermal and at least a minimal complexity subglacial hydrology model significantly affects surge characteristics and is, therefore, essential for modeling large-scale ice stream cycling.
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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 | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".