The Roles of Sea Ice Export, Atmospheric and Oceanic Factors in the Seasonal and Regional Variability of Arctic Sea Ice during 1979–2020
Bibliographic record
Abstract
The seasonal and regional variability of Arctic sea ice area (SIA) and thickness (SIT) were investigated between 1979 and 2020 for the Atlantic sector (AS), Pacific sector (PS) and Barents–Kara Seas (BKSs). We applied the SIA data from remote sensing observations and SIT data from numerical model calculations. We found the large summer variability of SIA and SIT in AS and PS compared with those in winter. The opposite feature was seen in the BKSs. The annual declining rates of SIA and SIT were the largest in PS (−1.73 × 104 km2 yr−1) and AS (−3.36 × 10−2 m yr−1), respectively. The SIA variability was modest for winter PS and the northern Canadian Arctic Archipelago of AS. The annual and winter SIA flux from PS to AS gradually increased in 1979–2020; the summer SIA flux accounted for 11% of the PS summer SIA decline. The annual and seasonal SIA outflow through the Fram Strait during 1979–2020 steadily increased while for annual and winter SIA export, the increase mainly occurred in 1979–2000; the summer SIA outflow was only 1.45% equivalent to the decrease in the entire Arctic summer SIA. We concluded that sea ice export was not a major impact factor on the seasonal and regional decline of SIA and SIT except for the individual years. The near surface air temperature (SAT) and sea surface temperature (SST) were responsible for the retreat and thinning of the sea ice. The dramatic increase in SAT in winter resulted in a strong decrease in winter sea ice in BKS. The outgoing longwave radiation had significant negative correlations with SIA and SIT and positive correlations with SAT and SST. The Atlantic multi-decadal oscillation, related to the North Atlantic Ocean’s SST anomalies, had significant negative correlations with SIA and SIT. The SIT had higher correlations with the atmospheric and oceanic factors compared with SIA, which indicates that SIT is important for predictions of Arctic sea ice and climate change.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".