Cross-country dependencies in fire weather enhance the danger of extremely widespread fires in Europe
Bibliographic record
Abstract
Wildfires are a significant natural hazard to European forest ecosystems and society. In recent years, increases in wildfire activity have been attributed to climate change, with escalating impacts on communities and ecosystems. While fire risk has been typically studied at individual locations independently, spatially compound events–where multiple wildfires occur simultaneously across different countries–have been overlooked so far. Such spatially compounding events can cause large aggregated impacts and pose severe challenges, particularly in the context of shared resources for wildfire response, as under the European Protection Agreement. To advance our understanding of spatially-compounding wildfires, we analyze the spatial dynamics of such large scale events across European countries. We use the daily-scale Burned Area dataset from the Global Fire Emissions Database (GFEDv4) for the period 2001-2015 and the Canadian Fire Weather Index (FWI) derived from ERA5 data for 1940-2023. By combining burned area with FWI data, during the May-October fire season, we find that the top 20% of days with the highest European area under FWI > 50 account for 60% of the total European burned area, all fires considered. By focusing on FWI data, we reveal that cross-country dependencies in fire weather enhance the likelihood of days affected by a larger fraction of Europe under extreme fire danger. Similar cross-country dependencies are observed for burned areas. The spatial dependencies in FWI can be linked to large-scale atmospheric patterns that favor fire-prone weather over different regions simultaneously. Typical meteorological conditions profiles for the most extreme FWI events across the continent indicate that persistent high-pressure systems, characterized by increasing temperature and decreasing relative humidity prior to the events, are key drivers for widespread FWI extremes. We also investigate recent trends in spatially compounding fire weather events using reanalysis data and CMIP6 climate model simulations. These findings improve our understanding of spatially compounding wildfires, serving as a basis for evaluating continental-scale risk and guiding the response to high-impact events in the context of shared resources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".