Record 2024 winter carbon emissions coincide with record warmth across boreal forest, tundra, and wetland ecosystems
Bibliographic record
Abstract
Abstract The warming Arctic could accelerate climate change as permafrost soil carbon is released as greenhouse gas emissions from boreal forest, tundra, and wetland ecosystems. Record climate conditions are increasingly common, with the 2023–2024 winter (September–April) documented as having the warmest land surface air temperatures on record for the Arctic region. However, corresponding impacts on ecosystem greenhouse gas fluxes typically take several years to diagnose, creating a knowledge gap between contemporary climate events and these fluxes. Here we synthesized near real-time data from 19 eddy covariance flux tower sites across the Arctic through the summer of 2024. This analysis revealed record net carbon dioxide and methane emissions occurring in winter, coinciding with warm 2023–2024 winter conditions. The increasing recognition of the importance of winter in shaping ecosystem carbon balance is still challenged by the difficulty of collecting data, with far more carbon flux measurements available in summer as compared to year-round. Improving the observation network’s extent and ability to deliver near real-time updates could provide immediate knowledge about the speed and strength of the permafrost carbon feedback to climate change. This increased awareness could help nations adapt their emissions policies aimed to avoid the worst impacts of climate change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".