Sea ice transport and melt, and the loss of multiyear sea ice in a changing Beaufort Sea
Bibliographic record
Abstract
Sea ice is a complex medium that covers up to 15 million km^2 of the northern hemisphere annually; presenting a vast highly reflective surface that cools the global climate, providing a variety of habitats for different Arctic species, and both facilitating on-ice travel for local Inuit while also limiting maritime access to Arctic waters. However, anthropogenic warming is amplified by four times in the Arctic which has driven a dramatic reduction in the extent and thickness of the Arctic ice pack and is projected to render the Arctic Ocean seasonally ice-free by the middle of this century. Underlying the loss of sea ice has been a dramatic transformation in the composition of the ice pack from a predominantly multiyear ice (MYI) cover to an inherently thinner and less resilient seasonal ice cover. Historically, the anticyclonic Beaufort Gyre retained sea ice for years, allowing it to thicken while aging, and distributing MYI throughout the Arctic Ocean. However, increasing melt rates in the Beaufort Sea have interrupted the once continuous journey of sea ice through the Beaufort Gyre, cutting off the redistribution and retention of MYI and thereby significantly contributing to the pan-Arctic transition towards a seasonal ice cover. Within this thesis, I use a combination of in situ and remotely sensed observations of sea ice to examine sea ice loss in the Beaufort Sea and its impact on MYI transport and retention within the Beaufort Gyre. In particular, I will examine how the state of the Beaufort Gyre during winter preconditions the regional ice pack for the melt season, and how preconditioning is playing a greater role as the now thinner ice pack is more mobile. I will then use a novel box model to quantify MYI loss in the Beaufort Sea and examine the associated changes in MYI transport through the Beaufort Gyre. Finally, I will set the broader context of MYI loss in the Beaufort Sea by examining MYI loss across the Arctic Ocean and the relative contributions of export, melt and replenishment, the three factors which collectively dictate the balance of MYI. From this I can speculate on how MYI in the Arctic Ocean will evolve to the point where it will one day cease to exist in a seasonally ice-free Arctic Ocean.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".