Subseasonal prediction of wintertime North American surface air temperature using the MJO signal
Bibliographic record
Abstract
A multi-variable linear regression model is constructed based on the status of the Madden-Julian oscillation (MJO) and persistence in order to forecast wintertime surface air temperature anomalies over North America out to 4 pentads (20 days). The current and previous states of the MJO are utilized as predictors, based on the Real-time Multivariate (RMM) indices of Wheeler and Hendon (2004). Modest skill is found, largely centred over the eastern United States and the Great Lakes. The model skill is seen to be highly dependent on the magnitude and phase of the MJO as well. Beyond the persistence driven 1st pentad, forecasts starting from MJO phases 3,4,7 and 8 that correspond to a dipole diabatic heating anomaly in the tropical Indian Ocean and western Pacific are more skillful during pentads 2 and 3 than those with other MJO phases at the initial time. The results are compared with the monthly hindcast of the Global Environmental Multiscale Model (GEM). The empirical method proves to be slightly superior to GEM beginning with pentad 3 south of the Great Lakes. This advantage expands and strengthens by pentad 4 throughout much of the eastern United States and into portions of western Canada.
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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".