GCM experiments on changes in atmospheric predictability associated with the PNA pattern and tropical SST anomalies
Bibliographic record
Abstract
Based on results from a simple three-level quasi-geostrophic model, Lin and Derome suggestedthat atmospheric predictability is influenced by the Pacific/North American (PNA) pattern. Inthe present study, predictability experiments are conducted with the Canadian Centre forClimate Modelling and Analysis general circulation model (CCCma GCM). A 47-yr integrationof theGCM with specified sea surface temperature (SST) for the years 1948—94 is first performed.Forecasts are initiated whenever the PNA pattern is in a strong positive or strong negativephase during this simulation. For each forecast, an ensemble of six initial conditions is generatedwith small random perturbations. Forecasts initiated when the PNA is in its positive phasehave smaller growth rates of ensemble standard deviation than forecasts initiated when thePNA is in its negative phase. Regional characteristics of the prediction spread are also examined.Similar experiments are conducted to determine the relationship between atmospheric predictabilityand SST anomalies in the tropical Pacific. Forecasts initiated when tropical SST anomaliesare positive have smaller growth rates of ensemble standard deviation than forecasts initiatedwhen tropical SST anomalies are negative. However, cases with positive tropical SST anomaliesbut without a strong PNA pattern show a similar prediction spread to cases with negative SSTanomalies. The results suggest that, in comparison to the PNA pattern, the influence of tropicalSST anomalies is only secondary. A set of three-layer diagnostic equations is used to analyzethe GCM results. It is speculated that the transient eddies have a stronger influence on thecirculation anomalies (and therefore reduce the atmospheric predictability more) in the negativePNA phase than in the positive PNA phase.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".