Large increase in extreme fire weather synchronicity over Europe
Bibliographic record
Abstract
Abstract Synchronous extreme fire weather can significantly increase the probability of fire ignition and spread, posing compounding threats to fire management efforts and potentially overwhelming firefighting capabilities. While substantial evidence indicates that weather conditions conducive to wildfires will likely become more frequent with higher levels of global warming, changes in extreme fire weather synchronicity remain understudied. Here, we quantify the synchronicity of extreme fire weather in Europe by analysing historical data of the Canadian fire weather index (FWI) from 1981 to 2022 and projecting FWI future scenarios with temperature increases from 1 °C to 6 °C and precipitation changes from −40% to +60%. Our results highlight Central Europe (CEU) as a significant hotspot, with synchronicity increases of up to 389%, while the Mediterranean region could see increases up to 66%. These findings emphasize the growing need for enhanced cross-border coordination and proactive fire management strategies to address the rising likelihood of concurrent extreme fire weather conditions in a warming climate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".