Signal, noise and skill in sub-seasonal forecasts: the role of teleconnections
Bibliographic record
Abstract
Abstract. A set of relaxation experiments with a forecast model is used to explore the influence of tropical and stratospheric teleconnections on forecast skill, variability of forecast ensemble mean (EM) and ensemble spread (ES) in the wintertime Northern Hemisphere at sub-seasonal timescales. The influence is diagnosed by comparing the relaxation experiments, which relax the temperature and wind fields in specific regions to observed values, with the free running (control) experiment. During weeks 3–6 the tropical relaxation increases the forecast skill for sea level pressure (SLP) mostly south of 50° N but also over the North Atlantic, Northern Europe and eastern Canada. The stratospheric relaxation improves the skill mostly in high latitudes, over Europe, and North Atlantic. Skill improvements are considerably smaller for surface temperature and total precipitation, suggesting a smaller role of the teleconnections in their predictability. The increases in skill are generally associated with increased variability of EM, considered to represent the predictable signal, and reduced ES representing noise. However, this does not happen in all areas where the skill is increased. In high latitudes, where the stratospheric impacts are strongest, the EM variability does not increase in the stratospheric relaxation experiments consistently with increases in skill, implying that EM does not reflect the predictable signal. We estimate that the ensemble size available in the experiments (11 members) is not enough to make it possible to extract signal from noise, and that larger ensembles (typically 20–50 members or even more depending on area and variable) are required to study sub-seasonal predictability associated with the teleconnections in mid- and high latitudes, including windows for forecast opportunities.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".