Evaluating the sensitivity of fire danger to different climate change scenarios in Europe
Bibliographic record
Abstract
This package evaluates the sensitivity of fire danger to different climate scenarios across Europe. The related dataset integrates a 30-year Canadian Fire Weather Index (FWI), generated using the Global ECMWF Fire Forecast model (GEFF), forced by ERA5 reanalysis data (1981-2010). These simulations incorporate perturbations in temperature and precipitation forcings based on CMIP6 climate projections under the SSP2-4.5 medium mitigation scenario. The perturbed forcing data were produced by modifying the daily temperature and precipitation data from ERA5 for the period of control from 1980 to 2010, using monthly factors that were estimated from a combination of climate change signals obtained from CMIP6 multi-model simulations, along with mean annual perturbations. One potential application of this data is to assess the likelihood of changes in extreme fire events across Europe. In "Europe faces up to tenfold increase in extreme fires in a warming climate" study, we categorise "extreme fires" as those with a 20-year return period. This enables us to investigate how shifts in temperature and precipitation patterns may alter the frequency and intensity of such fires under different climatic scenarios. One of the study's notable findings is the increased vulnerability of southern Europe to catastrophic fires. Under a moderate CMIP6 scenario, areas in southern Europe could experience a tenfold increase in the probability of such devastating fires occurring annually. This projection raises concerns about the resilience of ecosystems and communities in this region. While southern Europe is of particular concern, the study also warns that if global temperatures reach the critical threshold of +2°C, central and northern Europe will not be immune to escalating wildfire risks during droughts. This shift highlights the far-reaching consequences of climate change, extending the threat of wildfires beyond traditionally susceptible regions. The study also projects an extension of the fire season by ten days in at least 68% of southern Europe in the near future. Without mitigation or adaptation measures, this expansion may overwhelm national fire suppression capacities and have significant social and ecological impacts. The code source of GEFF model is included within this knowledge package. This package can aid in predicting areas at risk of fire in Europe, informing adaptive planning, enhancing emergency preparedness, and strengthening ecosystem resilience against wildfires.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".