Bibliographic record
Abstract
Abstract. Wildfires have reached an unprecedented scale in the Northern Hemisphere. The summers of 2021 and 2022 demonstrated the destructive power of wildfires especially in Northern America and Southern Europe. Global warming indicates that fire seasons will become more extreme and will extend to more temperate regions in northern latitudes in the future. Multiple studies claim that natural variability hides the trend of increasing fire danger in climate model simulations for future potentially fire-prone areas. Single Model Initial-Condition Large Ensembles (SMILEs) help scientists to distinguish climate trends from natural variability. So far, the SMILE framework has only been applied for fire danger estimation on a global scale. In this study, we use a regional SMILE of the Canadian regional climate model version 5 (CRCM5-LE) over Central Europe under the RCP 8.5 scenario from 1980 to 2099, to analyze fire danger trends in a currently not fire-prone area. We use the meteorological Canadian Fire Weather Index (FWI) as a fire danger indicator. The study area covers four heterogeneous landscapes, namely the Alps, the Alpine Foreland, the lowlands of the Southern German Escarpment and the Eastern Mountain Ranges of the Bavarian Forest. We demonstrate that the CRCM5-LE is a suitable dataset to disentangle climate trends from natural variability in a multivariate fire danger metric. Results show the strongest increases in the median (50th) and extreme (90th) percentile of the FWI in the northern parts of the study area in the summer months July and August, where high fire danger becomes the median condition and extremes occur earlier in the fire season. The southern parts of the study region are affected less strongly, but due to weaker variability in these regions, time of emergence (TOE) is reached there in the early 2040’s. In the northern parts, the climate change trend exceeds natural variability in the late 2040’s. We find that today’s threshold for a 100-year FWI event, will occur every 30 years by 2050 and every 10 years by 2099. Our results highlight Central Europe’s potential for severe fire events from a meteorological perspective and the need for fire management in the near future even in temperate regions.
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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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.213 | 0.165 |
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".