Variability of Arctic Sea Ice: The View from Space, An 18-year Record
Bibliographic record
Abstract
A recently compiled 18-year record (1979 to 1996) of sea ice concentrations derived from four passive-microwave satellite instruments has allowed the quantification of a variety of measures of Arctic sea ice variability. Earlier maps generated using data through August 1987 have been updated to 18-year summaries of the annual range of sea ice distributions, the interannual variability of average monthly sea ice distributions, the frequency of sea ice coverage over the 18 years, the length of the sea ice season, and trends in the length of the sea ice season. Linear least squares trends over the 18-year record show the sea ice season to have lengthened over some sizeable regions, especially in the Bering Sea, Baffin Bay, Davis Strait, the Labrador Sea, and the Gulf of St. Lawrence, but to have shortened over a much larger area, including the Sea of Okhotsk, the Greenland Sea, the Barents Sea, and all the seas along the north coast of Russia. The area with trends showing sea ice seasons shortening by over 0.5 days/year is 7 500 000 km², over 2.5 times the area experiencing a lengthening of the sea ice season by over 0.5 days/year. Neither the shortening nor the lengthening, however, is uniform or monotonic over the 18-year record. Instead, the ice cover exhibits widespread interannual variability, not just in the length of the sea ice season but for each month-a fact well illustrated by the monthly average September ice coverage, which was at its lowest extent in 1995 but at its second highest one year later, in the final year of the record. The maps of ice frequency and ice variability can help identify how anomalous individual years are. In some cases, they can help forestall unnecessary concern over seemingly unusual conditions which, upon examination of the maps, are found to fall well within the observed variability.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".