Observed Impact of Atlantic SST Anomalies on the North Atlantic Oscillation
Bibliographic record
Abstract
The large-scale patterns of covariability between monthly sea surface temperature (SST) and 500-mb height anomalies (Z500) in the Atlantic sector are investigated as a function of time lag in the NCEP‐NCAR reanalysis (1958‐97). In agreement with previous studies, the dominant signal is the atmospheric forcing of SST anomalies, but statistically significant covariances are also found when SST leads Z500 by several months. In winter, a PanAtlantic SST pattern precedes the North Atlantic oscillation (NAO) by up to 6 months. Such long lead time covariance is interpreted in the framework of the stochastic climate model, reflecting the forcing of the NAO by persistent Atlantic SST anomalies. A separate analysis of midlatitudes (208‐708N) and tropical (208S‐208N) SST anomalies reveals that the bulk of the NAO signal comes from the midlatitudes. A dipolar anomaly, with warm SST southeast of Newfoundland and cold SST to the northeast and southeast, precedes a positive phase of the NAO, and it should provide a prediction of up to 15% of its monthly variance several months in advance. Since the ‘‘forcing’’ SST pattern projects significantly onto the tripole pattern generated by the NAO, these results indicate a positive feedback between the SST tripole and the NAO, with a strength of up to . 25 mK 21 at 500 mb or 2‐3 mb K21 at sea level. Additionally, a warming of the tropical Atlantic (208S‐208N), roughly symmetric about the equator, induces a negative NAO phase in early winter. This tropical forcing of the NAO is nearly uncorrelated with and weaker than that resulting from the midlatitudes, and is associated with shorter lead times and reduced predictive skill.
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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 | 0.002 |
| 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) | 0.002 | 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".