Seasonal and Inter-annual Variations of Sea Ice Distribution in the Arctic Using AMSR-E Data: July 2002 to May 2009
Bibliographic record
Abstract
The Arctic environment is sensitive to change of sea-ice distribution. The increase and decrease of sea ice work to an index of globe warming progress. In order to predict the progress of hereafter earth global warming, continuous monitoring regarding a change of the sea ice area in the Arctic should be performed. The remote sensing based on an artificial satellite is most effective on the North Pole. The sea ice observation using a passive microwave sensor has been continued from 1970's. The determination of sea ice extent and ice type is one of the great successes of the passive microwave imagers. In this paper, to investigate the seasonal and inter-annual variation of sea-ice distribution we used here the sea ice data from July 2002 to May 2009 around the Arctic within for the AMSR-E 12.5km sea-ice concentration, a passive microwave sensor. From an early analysis of these data, the arctic sea-ice extent has been steadily decreasing at a rate of about 3.1%, accounting for about , which was calculated for the sea-ice cover reaching its minimum extent at the end of each summer. It is also revealed that this trend corresponds to a decline in the multi-year ice that is affected mainly by summer sea surface and air temperature increases. The extent of younger and thinner (first-year) ice decreased to the 2007 minimum, but rapidly recovered in 2008 and 2009 due to the dramatic loss in 2007. Seasonal variations of the sea-ice extent show significant year-to-year variation in the seasons of January-March in the Barents and Labrador seas and August-October in the region from the East Siberian and Chukchi seas to the North Pole. The spatial distribution of multi-year ice (7-year old) indicates that the perennial ice fraction has rapidly shrunk recently out of the East Siberian, Laptev, and Kara seas to the high region of the Arctic within the last seven years and the Northeast Passage could become open year-round in near future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".