Overlapping US-Australia fire seasons reduce the window of opportunity for firefighting cooperation
Bibliographic record
Abstract
Wildfires are a growing global challenge. In addition to becoming more widespread and intense due to climate change, the fire seasons in many regions are becoming longer. The lengthening of fire seasons reduces the window of opportunity for preparedness (e.g. prescribed burning of dry fuels before fire season onset) and increases the likelihood of spatially compounding fire risks due to overlapping fire weather seasons. These increased risks demand efficient global cooperation in sharing firefighting resources (e.g. helicopters, planes, firefighters), and of major concern, is how well-established international arrangements may be compromised or disrupted in the near future.Here we investigate increasing fire season lengths across two distanced fire-prone regions with typically distinct fire seasons and a long-term collaboration in sharing firefighting resources, Eastern Australia (EAU) and Western North America (WNA). We aim to test the hypothesis that spatially compounding fire weather events occur due to overlapping fire weather seasons, based on the Canadian Fire Weather Index (FWI). To robustly characterize the potential overlap, we make use of CMIP6 single model initial-condition large ensembles (SMILEs) for historical and future periods, and the ERA5 reanalysis. We define Fire Weather Days (FWD) as when the FWI exceeds a climatological threshold specific to each region, and we then estimate the total number of overlapping FWD per year for different time periods.We show that these distanced regions are becoming more likely to experience periods of overlapping FWD, which can compromise the human response in terms of firefighting. Most of the overlap occurs during boreal Autumn months, coinciding with the end of the fire season in WNA and the beginning of the fire season in EAU. Correlations between the number of overlapping FWD and the length of the regional fire season suggest that the main driver of the overlapping is the increasing early start of the fire season in EAU, rather than the late offset of the fire season in WNA. Additionally, we find that overlapping FWD is expected to increase in the future in a warming climate. As fire seasons overlap, the existing international collaborations will be increasingly constrained, and the window of opportunity for firefighting will shorten.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".