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Record W4413302705 · doi:10.1029/2025gl115936

Windstorm Extremes in a Warmer World: Raising the Bar for Destruction

2025· article· en· W4413302705 on OpenAlexaff
Rune M. K. Zeitzen, Jens Hesselbjerg Christensen, Johanne Øelund, Henrik Feddersen, Henrik Vedel, Niels Woetmann Nielsen, Dominic Matte

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsOuranos
FundersRealdania
KeywordsRaising (metalworking)Bar (unit)Environmental scienceClimatologyMeteorologyGeologyGeographyEngineering

Abstract

fetched live from OpenAlex

Abstract Extratropical cyclones occasionally escalate into devastating windstorms in Western Europe, causing damages worth billions of euros. However, their response to anthropogenic climate change remains uncertain, primarily due to limitations of coarse‐resolution models. This study adopts a high‐resolution, event‐based approach to examine how climate change may enhance Cyclone Anatol (December 1999), using the 2 km HARMONIE‐AROME model within a Pseudo Global Warming (PGW) framework. Results reveal that elevated temperatures amplify wind extremes, both in magnitude and spatial extent, over Denmark and the North Sea, which are linked to increased latent heat release which drives mesoscale instabilities. The findings highlight the potential for even more destructive windstorms in future climates, emphasizing the importance of high‐resolution modeling for understanding these dynamics. While this study does not address changes in cyclone frequency, it underscores the heightened risk of extreme windstorms in a warming world and their implications for disaster preparedness and mitigation strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.079
GPT teacher head0.338
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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