Assessing the influence of meteorological parameters on sea ice area and thickness variability in the Kara Sea
Bibliographic record
Abstract
The reduction of the sea ice area in the Arctic is one of the most notable consequences of climate change, with the Kara Sea contributing about 5 % to the overall reduction. The aim of this study is to assess the impact of changes in climatic conditions on the reduction of the area and thickness of the sea ice in the Kara Sea and the possibility of using the established dependencies for climate forecasting. The article provides quantitative estimates of the relationship between interannual changes in the sea ice thickness and sea ice extent in the Kara Sea and climate changes in the region and the remote influence of the sea surface temperature in the tropical North Atlantic for 1979–2019. The paper uses observation data obtained at meteorological stations, ERA5 reanalysis and data on the sea ice extent from the Arctic and Antarctic Research Institute website. In summer, the melting of sea ice is largely due to changes in the surface air temperature (correlation coefficient from –0.57 to –0.91). In winter, the ice thickness depends on the sum of frost degree-days (r = –0.80). The temperature regime, in turn, is determined by the radiation balance, namely, by the variability in the long-wave flow due to the increase in the water vapor content. On the basis of the relationships established, a linear regression model is proposed for forecasting the sea ice area with a lead time of about three months. The model is effective and statistically significant. The paper considers the remote influence of the sea surface temperature anomalies in the North Atlantic tropics on the ice coverage of the Kara Sea. The response of the sea ice extent to the sea surface temperature in the low latitudes is 30–34 months late. The significant correlation between the anomalies can serve as a basis for predicting the sea ice area in the Kara Sea with a lead time of up to three years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".