Identifying sensitivities and uncertainties in the attribution of global fire weather extremes using CMIP6 ensembles
Bibliographic record
Abstract
In response to the occurrence of a number of large wildfire events across the world in recent years, the question of the extent to which climate change may be altering the meteorological conditions conducive to wildfires has become a hot topic of debate. Despite the development of attribution methodologies for extreme events in the last decade, attribution studies dedicated explicitly to wildfire, or otherwise extreme ‘fire weather’, are still relatively few. In turn, there is a lack of consensus on how to define fire risk in a meteorological context, posing a challenge for research in this subfield. Recent work has offered clarification on uncertainties associated with the choice of meteorological indicator to represent fire weather in the context of extreme event attribution but there are additional sensitivities that are still not fully understood. Here, using established statistical methodologies applied to six large (>10-member) CMIP6 model ensembles, we conduct probabilistic attribution of fire weather extremes across the world’s fire-prone regions. We assess trends in extremes in the Canadian Fire Weather Index (FWI) using extreme value distributions, fitted with both annual maxima and peaks over a pre-defined threshold, and scaled to global mean surface temperature. An initial evaluation of model performance shows that, while all models are able to reasonably reproduce observed global patterns in extreme distribution parameters, there are some notable differences at the regional scale. Subsequently, we use probability ratio maps to quantify the influence of rising global temperatures on the changing frequency of FWI extremes. Our results highlight the sensitivity of probabilistic fire weather attribution methodologies to the choice of climate model ensemble. In conclusion, we therefore make a set of recommendations for future attribution of extreme fire weather episodes: (i) the use (and comparison) of multiple model ensembles; (ii) robust evaluation of model capacity to represent fire weather extremes.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".