Skill of monthly and seasonal forecasts using a Canadian general circulation model
Bibliographic record
Abstract
An analysis of the co-variability of surface air temperature and precipitation over North America and Pacific SST is conducted using an SVD analysis.The leading SVD mode revealed a strong link between November SST anomalies and winter surface air temperature (SAT) and precipitation anomalies.In summer this relationship is much weaker.In winter GCM3 captured well the Pacific SST forcing and its response, particularly on the SAT pattern, but its response is less accurate in the summer.The monthly and seasonal forecasts using GCM3 are also analyzed.Precipitation forecasts showed little skill, especially in the warm season where the SST forcing is weak.Furthermore, GCM3 has low seasonal predictive skill in forecasting drought events over the Canadian prairies.However, the model has generally good predictive skill for 500 hPa heights and SAT, with higher scores in the winter.The skill is concentrated in the first month of the prediction period and decreases as the lead time is extended to one month.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".