Causal Attribution of Arctic Wildfire Events in the 21st Century to Anthropogenic Forcing
Bibliographic record
Abstract
As an imprint of its rapid climatic transformation over the last two decades, the pan-Arctic region has experienced increasingly extreme fire events. However, a systematic and regionally comprehensive assessment of the recent extreme fire events in the pan-Arctic and the role played by human emissions is still pending. In this study, we employ an extreme event-attribution framework to assess the extent to which anthropogenic forcing affects the magnitude (Burned Area) and likelihood of favourable conditions of extreme fire events (Canadian Forest Fire Weather Index) in the pan-Arctic region throughout the 21st century. Therefore, we utilise large ensemble simulations conducted with the Community Earth System Model version 2 (CESM2), which are capable of isolating anthropogenic external climate forcings and observations from distinct remote sensing products as well as reanalysis data. Our results indicate that the presence of anthropogenic forcing throughout the 21st century was necessary to enable the observed extreme fire events in the pan-Arctic region. We find less than a 20% chance, that the extreme wildfire events occurred during recent fire seasons could have happened in the absence of human-induced external forcings. We can state that such wildfires have become 5 to 10 times more likely in comparison to pre-industrial climatic conditions. Furthermore, our findings indicate that the impact of anthropogenic forcings has significantly elevated the risk of high-latitudes experiencing severe fire-weather conditions by up to an order of magnitude. However, our study reveals the recent elevation in human-induced external forcings does not appear to be enough to explain the occurrence of observed extreme pan-Arctic wildfire events throughout the 21st century. We further explore the underlying mechanisms that drive changes in extreme fire-weather risk. We identify the relative contribution of maximum temperature, precipitation, relative humidity, and surface wind speed on the changes in extreme fire-weather risk.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".