Tracer studies of pathways and rates of meltwater transport through Arctic summer sea ice
Bibliographic record
Abstract
At the Surface Heat Budget of the Arctic Ocean (SHEBA) program's field site in the northern Chukchi Sea, snow and ice meltwater flow was found to have a strong impact on the heat and mass balance of sea ice during the summer of 1998. Pathways and rates of meltwater transport were derived from tracer studies (H 2 18 O, 7 Be, and release of fluorescent dyes), complemented by in situ sea‐ice permeability measurements. It was shown that the balance between meltwater supply at the surface (averaging between 3.5 and 10.5 mm d −1 ) and ice permeability (between <10 −11 and >10 −9 m 2 ) determines the retention and pooling of meltwater, which in turn controls ice albedo. We found that the seasonal evolution of first‐year and multiyear ice permeability and surface morphology determine four distinct stages of melt. At the start of the ablation season (stage 1), ponding is widespread and lateral melt flow dominates. Several tens of cubic meters of meltwater per day were found to drain hundreds to thousands of square meters of ice through flaws and permeable zones. Significant formation of underwater ice, composed between <30 and >50% of meteoric water, formed at these drainage sites. Complete removal of snow cover, increase in ice permeability, and reductions in hydraulic gradients driving fluid flow mark stage 2, concurrent with a reduction in pond coverage and albedo. During stage 3, maximum permeabilities were measured, with surface meltwater penetrating to 1 m depth in the ice and convective overturning and desalination found to dominate the lower layers of first‐year and thin multiyear ice. Enhanced fluid flow into flaws and permeable zones was observed to promote ice floe breakup and disintegration, concurrent with increases in pond salinities and 7 Be. Advective heat flows of several tens of watts per square meter were derived, promoting widening of ponds and increases in pond coverage. Stage 4 corresponds to freeze‐up. Roughly 40% of the total surface melt was retained by the ice cover within the ice matrix as well as in surface and under‐ice ponds (with a total net retention of 15%). Based on this work, areas of improvement for fully prognostic simulations of ice albedo are identified, calling for parameterizations of sea‐ice permeability and the integration of ice topography and refined ablation schemes into atmosphere‐ice‐ocean models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".