Predicting the Dominant Patterns of Subseasonal Variability of Wintertime Surface Air Temperature in Extratropical Northern Hemisphere
Bibliographic record
Abstract
Abstract Skillfully predicting persistent extreme temperature anomalies more than 10 days in advance remains a challenge although it is of great value to the society. Here the two leading modes of subseasonal variability of surface air temperature over the extratropical Northern Hemisphere in boreal winter are identified with pentad (5 days) averaged data. They are well separated geographically, dominating temperature variability in North America and Eurasia, respectively. There exists a two‐pentad lagged correlation between these two modes, implying an intercontinental link of temperature variability. Forecast skill of these two modes is evaluated based on three operational subseasonal prediction models. The results show that useful forecasts of the Eurasian mode (EOF2) can be achieved four pentads in advance, which is more skillful than the North American mode (EOF1). EOF2 is found to benefit from the Madden‐Julian Oscillation signal in the initial condition.
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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 | 0.000 |
| 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) | 0.000 | 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".