Attribution of the 2024 record-breaking precipitation event in Southern Denmark to human-induced climate change
Bibliographic record
Abstract
An increase in the frequency and severity of extreme weather events has been reported across the globe. These events threaten society through hazards like floods and droughts, underscoring the need to understand how such risks are evolving in a changing climate. Standardized methods have recently been introduced to assess the potential role of climate change for extreme events. The World Weather Attribution (WWA) offers a probabilistic framework to determine whether changes in the frequency and severity of extremes can be attributed to anthropogenic warming. Here we use this methodology to attribute an unprecedented rainfall event in Southern Denmark to anthropogenic climate change. On September 27, 2024, approx. 145 mm of rainfall fell over the city of Esbjerg, marking the highest daily rainfall on record for September. The event caused widespread flooding, disrupting transportation, damaging infrastructure, and affecting residential areas. This study draws on rainfall observations, reanalysis datasets, and climate model ensembles to assess the role of anthropogenic climate change. Notably, this is the first attribution study to apply ClimEx, a high-resolution, regional single-model initial-condition large ensemble (SMILE). The results of the analysis show that the rainfall event was 60 % (−20 %–540 %) more likely in the current climate compared to a pre-industrial climate, and that the intensity of the event increased by 10.2 % (−3.3 %–25.6 %) due to climate change. Our findings also indicate that the frequency and intensity of such events increase with further warming. Overall, this study highlights how hazards, exposure, and vulnerabilities contribute to risk in cities.
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 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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".