Variability and changes of Arctic sea ice draft distribution – submarine sonar measurements revisited
Bibliographic record
Abstract
Abstract. Changes in the mean sea ice thickness and concentration in the Arctic are well known. However, quantitative information about changes in the ice thickness distribution and the composition of the pack ice is lacking. In this paper we determine the ice draft distributions, mean and modal thicknesses, and their regional and seasonal variability in the Arctic for the time period 1975–2000. We compare characteristics of the Arctic pack ice for the years 1975–1987 and 1988–2000. These periods represent different large-scale atmospheric circulation modes and sea ice circulation patterns, most evident in clearly weaker Beaufort Gyre and stronger as well as westward shifted Transpolar Drift during the later period. The comparison of these two periods reveals that the peak of sea ice draft distributions has narrowed and shifted toward thinner ice, with reductions in both mean and modal ice draft. These noticeable changes are attributed to the loss of thick, mostly deformed ice. Springtime, loss of ice volume with draft greater than 5 m exceeds 35 % in all regions except the Nansen Basin, with as much as 45 % or more at the North Pole and in the Eastern Arctic. Autumn volume reduction, mostly of deformed ice, exceeds 40 % in the Canada Basin only, but is above 30 % also in the Beaufort and Chukchi Seas. During the later period, the volume of ice category consisting thin, mostly level first-year ice, is clearly larger than during the former period, especially in the spring. In the Beaufort Sea region, changes in the composition of ice cover have resulted in a shift of modal draft from level multiyear ice draft range to values of level first-year ice. The regional and seasonal variability of sea ice draft has decreased, since the thinning has been most pronounced in regions with the thickest pack ice (the Western Arctic), and during the spring (0.6–0.8 m per decade).
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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 | 0.001 |
| 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".