Increasing Fire Weather Season Overlap Between North America and Australia Challenges Firefighting Cooperation
Bibliographic record
Abstract
The USA, Canada and Australia are members of an international partnership that shares firefighting resources, including equipment and personnel. This partnership is effective because fire risk between Australia and North America is historically asynchronous. However, climate change is causing longer fire seasons in both regions, increasing the likelihood of simultaneous fire risk and threatening the partnership's viability. We focus on spatially compounding fire weather as the annual number of days on which the fire seasons in Australia and North America overlap, investigating historical and future projections of fire weather season lengths. We use the Canadian Fire Weather Index and compute season length statistics using ERA5 reanalysis data together with historical and future projections from four CMIP6 single model initial-condition large ensembles. Our analysis shows that the length of fire weather season overlap between eastern Australia and western North America has increased by approximately one day per year since 1979. The interannual variability of overlap is driven primarily by the variability in Australia, with correlations between that region's fire weather season length and the degree of overlap exceeding 0.9. Composites of ERA5 and CMIP6 sea surface temperatures suggest a link between the interannual variability of overlap and the El Ni & ntilde;o-Southern Oscillation, despite this climate mode's opposing relationship with fire weather in the two regions. Finally, we find that the overlap is projected to increase by similar to ${\sim} $4 to similar to ${\sim} $29 days annually by 2050. We conclude that an increasing overlap of fire seasons is expected to constrain current resource-sharing agreements and shorten preparedness windows.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".