Pan-Atlantic North American cold spells and European wind extremes in CMIP6 historical simulations
Bibliographic record
Abstract
not-yet-known not-yet-known not-yet-known unknown This study investigates the representation of near-simultaneous cold and windy extremes in North America and Europe in an ensemble of historical climate model simulations as compared to reanalysis. By leveraging a weather regime perspective, we identify five dynamical pathways for cold spells in three regions of North America. Three of the pathways also engender European wind extremes. The pathways are: (i) A wave train producing central and eastern Canada cold spells, culminating in Scandinavian blocking. (ii) A persistent Atlantic low producing eastern Canada cold spells and wind extremes in the British Isles. (iii) A quasi-stationary wave-2 pattern producing central Canada cold spells and Scandinavian blocking. (iv) An Arctic high producing eastern United States cold spells and wind extremes in Iberia. (v) A wave train producing eastern United States cold spells, culminating in an Atlantic low and wind extremes in Iberia. Models represent well both the frequency and evolution of the pathways compared to reanalysis. However, they under-represent the frequency of pathways (i) and (iii) associated with Scandinavian blocking. The models perform very well in replicating mean surface temperature anomalies during cold spells, though they perform less well on European wind extremes. Typically, the models capture the region and timing of wind extremes associated with Atlantic lows, albeit with some under-representation of occurrence frequency, but fail to adequately capture the wind extremes associated with Arctic highs. This is linked to deficits in how the models reproduce the evolution of the dynamical pathways in the East Atlantic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".