Arctic sea ice variability and trends, 1979–2010
Bibliographic record
Abstract
Abstract. Analyses of 32 yr (1979–2010) of Arctic sea ice extents and areas derived from satellite passive microwave radiometers are presented for the Northern Hemisphere as a whole and for nine Arctic regions. There is an overall negative yearly trend of −51.5 ± 4.1 × 103 km2 yr−1 (−4.1 ± 0.3% decade−1) in sea ice extent for the hemisphere. The yearly sea ice extent trends for the individual Arctic regions are all negative except for the Bering Sea: −3.9 ± 1.1 × 103 km2 yr−1 (−8.7 ± 2.5% decade−1) for the Seas of Okhotsk and Japan, +0.3 ± 0.8 × 103 km2 yr−1 (+1.2 ± 2.7% decade−1) for the Bering Sea, −4.4 ± 0.7 × 103 km2 yr−1 (−5.1 ± 0.9% decade−1) for Hudson Bay, −7.6 ± 1.6 × 103 km2 yr−1 (−8.5 ± 1.8% decade−1) for Baffin Bay/Labrador Sea, −0.5 ± 0.3 × 103 km2 yr−1 (−5.9 ± 3.5% decade−1) for the Gulf of St. Lawrence, −6.5 ± 1.1 × 103 km2 yr−1 (−8.6 ± 1.5% decade−1) for the Greenland Sea, −13.5 ± 2.3 × 103 km2 yr−1 (−9.2 ± 1.6% decade−1) for the Kara and Barents Seas, −14.6 ± 2.3 × 103 km2 yr−1 (−2.1 ± 0.3% decade−1) for the Arctic Ocean, and −0.9 ± 0.4 × 103 km2 yr−1 (−1.3 ± 0.5% decade−1) for the Canadian Archipelago. Similarly, the yearly trends for sea ice areas are all negative except for the Bering Sea. On a seasonal basis for both sea ice extents and areas, the largest negative trend is observed for summer with the next largest negative trend being for autumn. Both the sea ice extent and area trends vary widely by month depending on region and season. For the Northern Hemisphere as a whole, all 12 months show negative sea ice extent trends with a minimum magnitude in May and a maximum magnitude in September, whereas the corresponding sea ice area trends are smaller in magnitude and reach minimum and maximum values in March and September.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".