Arctic Multiyear Ice Areal Flux and Its Connection with Large-Scale Atmospheric Circulations in the Winters of 2002–2021
Bibliographic record
Abstract
Arctic sea ice, especially the multiyear ice (MYI), is decreasing rapidly, partly due to melting triggered by global warming, in turn partly due to the possible acceleration of ice export from the Arctic Ocean to southern latitudes through identifiable gates. In this study, MYI and total sea ice areal flux through six Arctic gateways over the winters (October–April) of 2002–2021 were estimated using daily sea ice motion and MYI/total sea ice concentration data. Inconsistencies caused by different data sources were considered for the estimate of MYI flux. Results showed that, there is a slight declining trend in the Arctic MYI areal flux over the past two decades, which is attributable to the decrease in MYI concentration. Overall speaking, MYI flux through Fram Strait accounts for ~87% of the Arctic MYI outflow, with an average of ~325.92 × 103 km2 for the winters of 2002–2021. The monthly MYI areal flux through Fram Strait is characterized with a peak in March (~55.56 × 103 km2) and a trough in April (~40.97 × 103 km2), with a major contribution from MYI concentration. The connections between sea ice outflow and large-scale atmospheric circulations such as Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Dipole Anomaly (DA) were investigated. High correlation coefficients (CCs) were found in winter months such as January and February. While AO and NAO (especially NAO) exhibited generally weak correlations with the MYI/total sea ice flux, DA presented strong correlations with the areal flux, especially for MYI (CC up to 0.90 in January). However, the atmospheric circulation patterns are sometimes not fully characterized by the specific indices, which could have different effects on sea ice flux and its correlation with the atmospheric indices.
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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 | 0.001 | 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) | 0.000 | 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".