Impacts of climate variability and change on regional fire weather in heterogeneous landscapes of Central Europe
Bibliographic record
Abstract
Wildfires have reached an unprecedented scale in recent fire seasons of the Northern Hemisphere as demonstrated by the summers of 2021 and 2022. Severe fire seasons, characterized by heat, drought and windy conditions, might become even more frequent and will extend to more temperate regions in northern latitudes under global warming. Still, quantifying the effects of climate change on future fire danger is challenging because natural variability hides trends of increasing fire danger in climate model simulations in future potentially fire-prone areas. Single Model Initial-Condition Large Ensembles (SMILEs) help scientists to distinguish climate trends from natural variability. Here, we leverage the capabilities of SMILEs to assess future changes in fire weather conditions in a currently non-fire-prone area in Central Europe. The study area covers four heterogeneous landscapes, namely the Alps, the Alpine Foreland (southern parts), the lowlands of the Southern German Escarpment, and the eastern mountain ranges of the Bavarian Forest (northern parts). We use a SMILE of the Canadian regional climate model version 5 (CRCM5-LE) under the RCP 8.5 scenario from 1980 to 2099 to analyze trends in fire danger quantified by the globally applicable Canadian Fire Weather Index (FWI).Our results show the strongest increases for the median (50th percentile) and extreme (90th percentile) FWI in the northern parts of the study area during the late summer months July, August and September. The southern, more alpine parts are affected less strongly and show high fire danger mostly in August by the end of the 21st century for the median FWI. Over the whole study area, we find that the extreme FWI in the present climate period will become much more frequent at the end of the century. In the South German Escarpment and Eastern Mountain Ranges, the climate change trend exceeds natural variability in the late 2040’s. Due to weaker variability, the time of emergence is reached in the Alps and Alpine Foreland in the early 2040's.These results demonstrate that the CRCM5-LE is a suitable dataset to disentangle climate trends from natural variability in a multivariate fire danger metric. Our study emphasizes that regions with a low fire danger under current climate conditions will experience weather conditions facilitating the development of potentially uncontrollable wildfires 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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".