Influence of the November Arctic Oscillation on the subsequent tropical Pacific sea surface temperature
Bibliographic record
Abstract
ABSTRACT Previous studies suggested that the variability of boreal spring Arctic Oscillation (AO) can exert a significant influence on the sea surface temperature (SST) anomalies in the Niño‐3.4 region during the following winter. This study further reveals that AO in November can have a pronounced influence on the tropical central‐eastern Pacific SST anomalies during the following spring and summer. When the November AO is in its positive (negative) phase, SST anomalies tend to be positive (negative) during the following spring and summer in the tropical central‐eastern Pacific. The influence of the AO is accomplished by atmospheric circulation anomalies over the subtropical North Pacific through an interaction between the synoptic‐scale eddy and the low‐frequency mean flow. In the positive November AO years, pronounced cyclonic circulation and atmospheric heating anomalies are observed over the subtropical North Pacific. The atmospheric heating anomalies sustain westerly wind anomalies over the tropical western North Pacific through a Gill‐like atmospheric response. The westerly wind anomalies extend eastward subsequently through positive air–sea feedback mechanism, and result in SST warming during the following spring and summer in the tropical central‐eastern Pacific. Results of this study imply that the November AO index can be used as an effective predictor of SST anomalies in the Niño‐3.4 region during the following spring and summer.
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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 | 0.000 |
| 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".