Severe windstorm projections for Europe
Bibliographic record
Abstract
Extreme windstorms are of considerable interest due to their potential to cause significant socio-economic damages over very large areas of land. As a result, understanding how climate change may affect the characteristics of the most severe storms is an important question for adaptation planing. However, projections of how the hazard associated with windstorms will change in the future are highly uncertain.Here, we use an efficient statistical approach that characterises individual windstorms in terms of their intensity and exposure to estimate the present-day risk from such storms. We then use a methodology used widely in detection and attribution of climate change to assess how such characteristics may change into the future. Using windstorms simulated by a diverse set of high-resolution regional climate model projections for Europe, the EURO-CORDEX ensemble, we provide projections of risk over a range of future climate scenarios. Finally, we explore how the variety of driving and regional models influence the associated uncertainties, and how considering the performance and independence of the models can improve the robustness of the projections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".