Mediated and direct effects of the North Atlantic ocean on winter temperatures in northwest Europe
Bibliographic record
Abstract
This study has used a multiple regression model to quantify the importance of wintertime mean North Atlantic sea-surface temperatures (SSTs) for explaining (simultaneous) variations in wintertime mean temperatures in northwestern Europe. Although wintertime temperature variations are primarily determined by atmospheric flow patterns, it has been speculated that North Atlantic SSTs might also provide some additional information. To test this hypothesis, we have attempted to explain 1900–93 variations in wintertime mean central England temperature (CET) by using multiple regression with contemporaneous winter mean North Atlantic sea-level pressures (SLPs) and SSTs as explanatory variables. With no SST information, the leading SLP patterns (including the North Atlantic oscillation) explain 63% of the total variance in winter mean CET; however, SSTs alone are capable of explaining only 16% of the variance in winter mean CET. Much of the SST effect is ‘indirect’ in that it supplies no more significant information than already contained in the mean SLP; e.g. both SLP and SST together can only explain 68% of the variance. However, there is a small (5% variance) direct effect due to SST that is not mediated by mean SLP, which has a spatial pattern resembling the Newfoundland SST pattern identified by Ratcliffe and Murray (1970. Quarterly Journal of the Royal Meteorological Society 96: 226–246). In predictive mode, however, using explanatory variables from preceding seasons, SSTs contain more information than SLP factors. On longer time scales, the variance explained by contemporaneous SST increases, but the SLP explanatory variables still provide a better model than the SST variables. Copyright 2003 Royal Meteorological Society.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".