Momentum, Heat, and Salt Budgets to Estimate Drag and Transfer Coefficients inside an Ice Shelf Basal Channel
Bibliographic record
Abstract
Abstract Understanding melting and freezing at the ice–ocean boundary is crucial for predicting ice shelf stability and, ultimately, sea level rise. This study investigates ice–ocean interactions within a basal channel under the Milne Ice Shelf, Nunavut, Canada, using water temperature, salinity, and velocity data from vertical profiles and moorings, along with ice-penetrating radar observations. Data reveal a freshwater outflow ∼8 (6) m thick in summer (winter), with velocities around 0.2 (0.1) m s −1 . Moving down channel (toward the ocean), salinity increases and temperature decreases, often nearing or falling below the freezing point. A momentum budget indicates a subcritical flow driven by buoyancy and yields a minimum ice–ocean drag coefficient C d of 0.0065. This relatively high value is attributed to high ice roughness and frazil ice accumulation. Heat and salt budget analyses show significant seasonality, with enhanced melting from July to mid-September and both melting and freezing from mid-September to June. The inclusion of frazil ice formation is essential to close the heat budget. The estimated maximum heat transfer coefficient Γ Θ is 0.0062. This relatively low value is attributed to the high-density stratification within the basal channel. This research enhances our understanding of basal melting mechanisms by providing rare estimates of drag and transfer coefficients, by providing a new method to estimate these parameters (momentum, heat, and salt budgets) and by highlighting the role of ice roughness, stratification, and frazil ice, which should be incorporated in improved ice–ocean melt/freeze parameterizations. Significance Statement In this study, we use ocean temperature, salinity, and current observations under the Milne Ice Shelf to estimate the amount of melting and freezing along an under-ice trough (basal channel). Because of the logistical difficulties in accessing the ocean under ice shelves, such observations are extremely rare. Our results show that the amount of melting or freezing varies greatly along the channel, over its depth, and over time. This variability is explained by density stratification, frazil ice formation, and high ice roughness. Therefore, these three aspects should be further investigated and quantified to improve ice–ocean melt/freeze parameterizations and thereby better predict the retreat of ice shelves and, ultimately, sea level rise.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".