Ice Balance in the Arctic Ocean in 1979–2019 (Based on Modeling Data)
Bibliographic record
Abstract
Abstract The results of numerical experiments using a coupled model of water and ice circulation from September 1979 to December 2019 aimed at studying the interannual variability of the ice balance in the Arctic Ocean (AO) are considered. These results have made it possible to analyze the geographical features of the processes of ice formation and melting in the AO and identify the key regions that determine the volume of ice in the ocean. It is established that most ice is formed in the waters of the Siberian seas, and the most intense melting occurs in the North European Basin, where the ice transported by the Transpolar Drift Stream through the Fram Strait enters the relatively warm water of the Greenland Sea, heated by the North Atlantic Current. The formation of the absolute minimum of ice coverage in 2012 was caused by the anomalous position of the anticyclonic hydrological cycle located much closer to the Canadian coast. This resulted in the fact that only a small part of the ice formed in the Siberian seas was involved in a weak circulation, while most of the ice in the stream of the Transpolar Drift Stream was transported through the Fram Strait to the Greenland Sea. A statistical analysis of the results of numerical experiments demonstrated that the trend towards a decrease in the volume of ice in the AO is primarily determined by global warming, and dynamic forcing exerts significant effect on local extremes. A coupled model of circulation of water and ice was used to study the variability of ice balance in the AO. The results of special numerical experiments from September 1979 to December 2019 made it possible to establish some geographical features of the processes of ice formation and melting. Statistical analysis of the results showed that the trend towards a decrease in the volume of ice in the AO is determined primarily by global warming, while local extremes are strongly influenced by dynamic forcing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".