Methane emissions proportional to permafrost carbon thawed in Arctic lakes since the 1950s
Bibliographic record
Abstract
Permafrost thaw exposes previously frozen soil organic matter to microbial decomposition. This process generates methane and carbon dioxide, and thereby fuels a positive feedback process that leads to further warming and thaw1. Despite widespread permafrost degradation during the past ~40 years2, 3, 4, the degree to which permafrost thaw may be contributing to a feedback between warming and thaw in recent decades is not well understood. Radiocarbon evidence of modern emissions of ancient permafrost carbon is also sparse5. Here we combine radiocarbon dating of lake bubble trace-gas methane (113 measurements) and soil organic carbon (289 measurements) for lakes in Alaska, Canada, Sweden and Siberia with numerical modelling of thaw and remote sensing of thermokarst shore expansion. Methane emissions from thermokarst areas of lakes that have expanded over the past 60 years were directly proportional to the mass of soil carbon inputs to the lakes from the erosion of thawing permafrost. Radiocarbon dating indicates that methane age from lakes is nearly identical to the age of permafrost soil carbon thawing around them. Based on this evidence of landscape-scale permafrost carbon feedback, we estimate that 0.2 to 2.5 Pg permafrost carbon was released as methane and carbon dioxide in thermokarst expansion zones of pan-Arctic lakes during the past 60 years.
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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 | 0.000 | 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".