Integrated Climate Radiative Forcing from Arctic-Boreal Fires
Bibliographic record
Abstract
Fire is a major disturbance mechanism in arctic-boreal ecosystems and results in warming and cooling feedbacks to the climate system. Greenhouse gas emissions from fires are a major positive feedback, yet post-fire carbon sequestration in recovering ecosystems partly offsets this. In addition, fire removes part of the organic soil layer and may result in permafrost thaw and consequent greenhouse gas emissions. Yet, fire-induced changes in ecosystem structures result in a larger spring-time snow cover compared to unburned areas, and this imposes a negative climate feedback through increased surface albedo. These various climate forcings are spatially and temporally heterogeneous and depend on various landscape components and fire regime characteristics. Understanding the net climate forcing effect is crucial in managing and mitigating climate change impacts on carbon cycling. We applied the concept of radiative forcing in a quantitative spatial assessment of the net climate feedbacks induced by arctic-boreal North American fires. We capitalize upon the state-of-the-art carbon combustion estimates by the Arctic Boreal Vulnerability Experiment Fire Emissions Database (ABoVE-FED) and a novel climate forcing framework to predict fire-driven changes in net forcing under historical and future climate scenarios. In our analyses we incorporated all fires between 2001 and 2019, evaluating the net fire-induced forcing over the regrowth successional phase (at 20-years after fire) and after full succession (at 80-years after fire). Our results highlight the spatial and temporal heterogeneity in climate forcings from arctic-boreal fires, and in future work we plan to characterize spatiotemporal patterns of the net climate feedback.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".