Signal-to-noise errors in early winter Euro-Atlantic predictions caused by weak ENSO teleconnections and pervasive North Atlantic jet biases
Bibliographic record
Abstract
Long-range winter predictions over the Euro-Atlantic sector have demonstrated significant skill but suffer from systematic signal-to-noise errors. In this study we examine early winter seasonal predictability in 16 state-of-the-art seasonal forecasting systems. Models demonstrate skill in the hindcasts of the large-scale atmospheric circulation in early winter, which mostly projects onto the East Atlantic pattern. The predictability is strongly tied to the ENSO teleconnection to the North Atlantic, though the models' response to ENSO is systematically too weak. The model hindcasts of the East Atlantic index exhibit a substantial signal-to-noise errors, with the models predicted signal generally being smaller than would be expected for the observed level of skill. The signal-to-noise errors are found to be strongly dependent on the strength of the ENSO teleconnection in the models, with models with a weaker teleconnection displaying a larger signal-to-noise problem. It is demonstrated that the dependency on model ENSO teleconnection strength can be explained using a simple scaling relationship derived from a toy model. Further analysis reveals that the strength of the ENSO teleconnection in the model is linked to climatological biases in the behaviour of the North Atlantic jet. Models that better represent the dynamics of the jet over the northern part of the basin - with more frequent poleward jet excursions and less frequent Greenland blocking - are better at representing the ENSO teleconnection to the North Atlantic in early winter, with lower associated signal-to-noise errors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".