The Energetics of Extensive Meltwater Flooding of Level Arctic Sea Ice
Bibliographic record
Abstract
Abstract During the spring‐to‐summer transition, the snow cover on Arctic sea ice melts and meltwater pools on the surface to form melt ponds; however, the timing and extent of the ponding vary between years. In Dease Strait (Nunavut), this transition was particularly dramatic in 2014 when on 18 June meltwater had flooded >95% of the surface. In this study, continuous surface energy balance measurements throughout the transition highlight how the timing of transient weather events influenced seasonal shifts in distinct ice melt stages. The keys to the extensive flooding were (1) the level ice cover, (2) a strong low‐pressure system on 24 May that deposited ~10 cm of snow, and (3) freeze‐thaw cycling and a subsequent return to subzero air temperatures on 30 May that led to superimposed and interposed ice formation. Without these, melt ponds would have likely developed within days from an initial melt onset on 28 May. After a 2‐week delay, snow‐melt resumed and lead to near‐complete flooding of the surface for 4 days. The albedo of the flooded ice remained high (0.35–0.40), as a result of the bubble‐laden superimposed ice layer. Once this layer eroded, the albedo over melt ponds decreased to a more typical level (~0.20). Our observations suggest that the formation of superimposed and interposed ice prevented the vertical drainage of meltwater to the ocean. Future challenges remain to measure the presence of these layers and understand their effect on sea ice permeability and pond evolution, while sea ice temperatures are near the melting point.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".