Delayed formation of Arctic snow cover in response to wildland fires in a warming climate
Bibliographic record
Abstract
Wildland fires in seasonally snow-covered areas can have lasting effects on both the snowpack and carbon stocks. Here, using long-term satellite data, we show that from 1982 to 2018, the burned area in the Arctic has significantly increased, while the duration of snow cover has significantly decreased. An XGBoost machine learning model and causal analysis confirmed the role of wildland fires in delaying snow cover formation, with this effect strongly linked to fire-induced reductions in albedo and increases in temperature. In addition, we observed a delay of more than 5 days in snow cover formation following major wildland fires. Looking ahead, our projections under a high-emissions scenario (Shared Socioeconomic Pathway, SSP 5-8.5) indicate that the burned area could increase by a factor of 2.6 and the annual mean snow cover duration could decrease by nearly 18 days between 2015 and 2100 compared with the historical average. Wildland fires are becoming more frequent in high-latitude regions. Here the authors show that these fires delay the formation of snow cover in the Arctic, which will be exacerbated under future warming due to increases in burned areas.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".