Long Time-Scale Teleconnection Patterns in the Northern Atlantic and Pacific
Bibliographic record
Abstract
Abstract Long time-scale teleconnection patterns, with common features in both the northern Atlantic and Pacific regions, are identified. The teleconnection patterns arise in an investigation of the internally generated variability in a multimodel ensemble of coupled climate model control simulations. The large amount of data involved offers statistical robustness and the benefits of combining results across models. Maxima of decadal potential predictability identify regions where long time-scale variability is an appreciable fraction of the total variability and serve as index regions for the teleconnection analysis. Annual, 5-yr, and decadal mean temperatures over these Atlantic and Pacific index regions are correlated with corresponding temperatures and precipitation rates over the globe. The resulting teleconnection patterns are reasonably similar despite the different long time-scale variability mechanisms thought to exist in the two ocean basins. Although lacking statistical robustness, some aspects of the temperature teleconnection patterns are obtained based on the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset. The similarity of the teleconnection patterns in the two northern ocean regions suggests that common variability mechanisms may be involved.
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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 | 0.001 |
| 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".