Fingerprint of anthropogenic climate change detected in long-term western North American fire weather trends
Bibliographic record
Abstract
The anthropogenic fingerprint has been detectable in observed global climate change for decades, yet it is still difficult to detect at the regional scale beyond temperature due to the presence of large internal variability and modeling and observational uncertainties. Here we demonstrate regional optimal fingerprinting of long-term fire weather trends in western North America, leveraging large ensembles of high resolution, atmosphere-only climate models to adequately sample internal variability. Considering the full spatiotemporal response to thermodynamic and dynamic climate change, we find that anthropogenic forcings have contributed 81–188% of the region’s observed increasing linear trend in fire weather over the last 50+ years. The natural fingerprint, which contains inter-annual and decadal variability, is also robustly detected. We detect the anthropogenic fingerprint in relevant meteorological variables – temperature, precipitation, and relative humidity – and link model differences in fire weather response to differences in simulated precipitation and relative humidity responses. Anthropogenic climate change has been the dominant driver of increasing fire weather trends in western North America over the past 50 years, contributing 81–188% of the observed linear trends, according to regional optimal fingerprinting applied to large ensembles of high-resolution climate models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".