A moderator of tropical impacts on climate in Canadian Arctic Archipelago during boreal summer
Bibliographic record
Abstract
The Canadian Arctic Archipelago consists of important international trade routes, and local surface air temperatures (SAT) greatly control sea ice melting in situ during boreal summer (June-July-August-September). However, the drivers of the Arctic Archipelago summer SAT variability have not yet been fully elucidated. Here, we find that the impact of tropical Indo-Pacific convection on the Arctic Archipelago SAT through induced poleward-propagating Rossby wave train is strongly modulated by Russian Arctic sea surface temperature anomalies (SSTA). Negative Russian Arctic SSTA lead to a weakened East Asia westerly jet via equatorward Rossby wave activity. The weakened westerly jet enhances the meridional gradient of the potential vorticity over the North Pacific, guiding the poleward-propagating Rossby wave to the Arctic Archipelago and therefore affecting the local SAT. Conversely, positive Russian Arctic SSTA impede the northward-propagating Rossby wave via enhancing the East Asia westerly jet, resulting in a weakened relationship between the tropical Indo-Pacific convection and Arctic Archipelago SAT. The present study proposes a mechanism whereby changes in the Tropical-Arctic connection stem from thermal conditions elsewhere in the Arctic, through shaping poleward-propagating Rossby waves by changing the background mean flow.
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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 | 0.001 | 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) | 0.002 | 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".