The Extratropical Signal Generated by a Midlatitude SST Anomaly. Part II: Influence on Seasonal Forecasts
Bibliographic record
Abstract
A simple GCM based on a primitive equation model with empirically derived time-independent forcing is used to make forecasts in the extended to seasonal range. The results are analyzed in terms of the response to a midlatitude Pacific sea surface temperature anomaly (SSTA), represented here by a heating perturbation. A set of 90-day, 30-member ensemble forecasts is made with 54 widely differing initial conditions, both with and without the SSTA. The development of the response, defined as the difference between ensemble means, is split into three 30-day averages: month 1, month 2, and month 3. During month 1, ensemble members separate, and the local response and remote teleconnections are established. The local response is not very sensitive to the initial condition. In month 2, the extended range, the responses are relatively strong and vary greatly from one initial condition to another. However, a linear analysis reveals that large variations in the response do not correlate strongly with large variations in the initial condition. The initial perturbations required to generate the observed variations in the response are relatively small, and may be difficult to isolate in a real forecasting situation. In month 3, the seasonal range, variations between responses are much smaller. The initial condition loses its influence and the responses all start to resemble the equilibrium response discussed in Part I.
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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 | 0.000 |
| 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) | 0.001 | 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".