Reanalysing the impacts of atmospheric teleconnections on cold‐season weather using multivariate surface weather types and self‐organizing maps
Bibliographic record
Abstract
ABSTRACT While regional‐ to hemispheric‐scale oscillations in oceanographic and atmospheric variables have long been known to have teleconnective impacts on the surface weather at distant locations, the impacts of these teleconnections and their interactions on multivariate weather types (WTs) are relatively under‐researched. Using a recently developed gridded weather typing classification (GWTC) and a self‐organizing maps‐based clustering of five different teleconnection indices, this research aims to explore the impacts of teleconnections on surface weather in the United States and Canada. Individual teleconnections have a predictable impact on GWTC WTs, with the Pacific/North American pattern, the Western Pacific (WP) pattern and the North Atlantic Oscillation showing the most widespread significant correlations with cool and warm WTs, in agreement with previous research. While many teleconnection clusters are dominated by one teleconnection's WT correlations, certain clusters reveal surprising regional‐ to continental‐scale impacts considering many teleconnections are in a relatively neutral phase. Furthermore, some expected impacts of the Southern Oscillation Index and WP are offset when considered in tandem with neutral phases of the other teleconnections examined. Overall, the clustering results highlight the importance of examining multiple teleconnections simultaneously when researching teleconnection impacts on surface weather and making statistically based monthly‐ to seasonal‐range climate projections.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".