Indian summer monsoon’s role in shaping variability in Arctic sea ice
Bibliographic record
Abstract
The impacts of Arctic sea ice loss on summertime weather in the Northern Hemisphere have garnered considerable attention. Despite the extensive focus on this relationship, the influence of tropical systems on Arctic regions has been relatively underexplored, with only a limited number of existing studies concentrating exclusively on either dynamic or thermodynamic effects. This study aims to address this gap by examining a barotropic anomalous circulation over the Arctic region associated with Indian Summer Monsoon (ISM) rainfall. The observed anomalous circulation exhibits a distinct zonally dipole pattern, characterized by anomalous high pressure over Northern Canada and Asia, coupled with anomalous low pressure located east of Greenland. Verification through model experiments demonstrates that the diabatic heating of ISM rainfall contribute to the formation of the observed ISM-related circulation. The modulation of surface clear sky downwelling longwave radiation ( $${{DLR}}_{{clear\; sky}}$$ ) by the circulation anomalies over the Arctic modified surface thermal conditions, thereby influencing subsequent variations in sea ice thickness and concentration. Under anomalous high pressure, $${{DLR}}_{{clear\; sky}}$$ increases, leading to a decline in sea ice thickness, and vice versa. Additionally, from a dynamic standpoint, low-level wind-driven sea ice drift helps shape the spatial distribution and extent of sea ice cover. Besides, the impacts of ISM on Arctic sea ice are largely independent of contemporary ENSO. These findings present fresh perspectives on the role of extrapolar phenomena, such as the ISM, in driving variability in Arctic sea ice during the summer months. This enhanced comprehension holds promise for enhancing predictions of changes in summertime Arctic sea ice extent.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".