The Observed Relationship between Pacific SST Variability and Hadley Cell Extent Trends in Reanalyses
Bibliographic record
Abstract
Abstract Reanalysis and other observationally based estimates suggest that the tropics have expanded more than simulated by coupled climate models with historical radiative forcing. Previous research has attempted to reconcile this discrepancy by using climate model simulations with constrained tropical Pacific sea surface temperatures (SSTs) to account for the role of internal variability. Here the relationships between Hadley cell extent and internal SST variability and long-term warming are analyzed using purely observational techniques. Using linearly independent components of SST variability with reanalysis datasets, the statistical relationship between Pacific variability and Hadley cell extent is quantified by time scale. There is a strong correlation between North Pacific decadal SST variability and Southern Hemisphere Hadley cell extent. Conversely, there is a weaker observed relation between El Niño–Southern Oscillation (ENSO) and Hadley cell extent when low-frequency variability is filtered out of the ENSO signal. The observed linear sensitivity of Hadley cell width to long-term warming agrees with coupled general circulation model experiments when accounting for uncertainties, and there is a statistically significant relationship between Northern Hemisphere Hadley cell extent and long-term warming during boreal autumn.
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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 | 0.001 |
| 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".