On the Role of the Eastern Pacific Teleconnection in ENSO Impacts on Wintertime Weather over East Asia and North America
Bibliographic record
Abstract
Previous studies have mainly focused on the influence of El Niño–Southern Oscillation (ENSO) on seasonal-mean conditions over East Asia and North America. This study, instead, proposes an ENSO pathway that influences the weather events over East Asia and North America, in which the eastern Pacific teleconnection pattern (EP) plays an important role. On the one hand, the EP pattern can induce significant surface temperature anomalies over East Asia during its development and mature stages, with the positive (negative) EPs causing colder (warmer) than normal weather events. Besides, the frequency of occurrence of EPs is significantly modulated by ENSO, with 50% of the positive EPs occurring in La Niña winters, and 47% of the negative EPs occurring in El Niño winters. As a result, in El Niño winters, more negative and fewer positive EPs tend to occur, and thus more warm and fewer cold weather events are expected in East Asia. For La Niña winters, the reverse is true. On the other hand, for the EP pattern without its canonical convection pattern (referred to as the nonconvective EP), extremely cold anomalies over the northern United States and western Canada are induced in its negative phase. Moreover, when there are positive sea surface temperature anomalies in the central equatorial Pacific, the frequency of occurrence of negative nonconvective EPs is 2.0 times greater than the climatological value, and thus an enhanced likelihood of extremely cold spells over North America may be expected.
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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) | 0.002 | 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".