The Leading Intraseasonal Variability Mode of Wintertime Surface Air Temperature over the North American Sector
Bibliographic record
Abstract
Abstract In this study, detailed characteristics of the leading intraseasonal variability mode of boreal winter surface air temperature (SAT) over the North American (NA) sector are investigated. This intraseasonal SAT mode, characterized by two anomalous centers with an opposite sign—one over central NA and another over east Siberia (ES)/Alaska—bears a great resemblance to the “warm Arctic–cold continent” pattern of the interannual SAT variability over NA. This intraseasonal SAT mode and associated circulation exert a pronounced influence on regional weather extremes, including precipitation over the northwest coast of NA, sea ice concentration over the Chukchi and Bering Seas, and extreme warm and cold events over the NA continent and Arctic region. Surface warming and cooling signals of the intraseasonal SAT mode are connected to temperature anomalies in a deep-tropospheric layer up to 300 hPa with a decreasing amplitude with altitude. Particularly, a coupling between the troposphere and stratosphere is found during evolution of the intraseasonal SAT variability, although whether the stratospheric processes are essential in sustaining the leading intraseasonal SAT mode is difficult to determine based on observations alone. Two origins of wave sources are identified in contributing to vertically propagating planetary waves near Alaska: one over ES/Alaska associated with local intraseasonal variability and another from the subtropical North Pacific via Rossby wave trains induced by tropical convective activity over the western Pacific, possibly associated with the Madden–Julian oscillation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".