Probabilistic assessment of extreme fire risk under the impact of climate change
Bibliographic record
Abstract
As major natural hazards, wildfires pose a significant risk to many parts of the world. The occurrence of extensive fires in both hemispheres in recent years has raised important questions about the extent to which the changing nature of such incidents can be attributed to human-induced climate change. Offering reliable answers to these questions is essential for communicating risk and increasing resilience to major wildfires. However, the scarcity of wildfire attribution studies, combined with limited observational records and the complexity of representing fires by different models, poses a challenge in establishing robust and unified conclusions to better inform future forest management strategies.Here, a globally applicable framework is developed to better understand and quantify how wildfire risk is responding to a changing climate. The framework is based on an empirical-statistical methodology, facilitating its application to ’fire weather’ extremes from both observational records and the latest generation of global climate model ensembles (e.g. from CMIP/UKESM). Particular attention is given to the sensitivity of the eventual findings to the spatial scale of the event, the chosen event definition and the climate model(s) used in the analysis. As part of a global analysis, a series of maps are constructed detailing the change in likelihood of fire weather extremes, defined by both intensity and duration, throughout the world’s fire-prone regions as a result of rising global temperatures. Both observation- and model-based analyses reveal an increase in likelihood of at least twofold across many parts of the world, with considerable regional and inter-model variation. The value of the framework is demonstrated by combining results from a series of case studies of recent high-impact wildfires that differ by scale, duration and location. The conclusions drawn from this work provide a platform to guide future analysis of fire weather events and facilitate reliable recommendations for responding to the hazards associated with wildfires, and enhancing resilience in the face of climate change.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".