Long‐term Impacts of Permafrost Thaw on Carbon Storage in Peatlands: Deep Losses Offset by Surficial Accumulation
Bibliographic record
Abstract
Abstract Peatlands in northern permafrost regions store a significant proportion of global soil carbon. Recent warming is accelerating peatland permafrost thaw and thermokarst collapse, exposing previously frozen peat to microbial decomposition and potential mineralization into greenhouse gases. Here, we show from a site in the sporadic‐discontinuous permafrost zone of western Canada that thermokarst collapse leads to neither large losses nor gains following thaw, as deep carbon losses are offset by surficial accumulation. We collected peat cores along two thaw chronosequences, from peat plateau, through young (~30 years since thaw), intermediate (~70 years), and mature (~200 years) thermokarst bog locations. Macrofossil and 14C analysis showed synchronicity of peatland development until recent thaw, with wetland initiation ~8,500 cal yr BP followed by succession through peatland stages prior to permafrost aggradation ~1,800 cal yr BP. Analysis and modeling of soil carbon stocks indicated 8.7 ± 12.4 kg C m−2 of carbon accumulated prior to thaw was lost in ~200 years post‐thaw. Despite these losses, there was no observed increase in peat humification as assessed by Fourier transform infrared and C:N ratios. Rapid peat accumulation post‐thaw (9.8 ± 1.6 kg C m−2 over 200 years) offset deeper losses. Our approach constrains the net carbon balance to be between uptake of 27.3 g C m−2 yr−1 and loss of 106.6 g C m−2 yr−1 over 200 years post‐thaw. While our approach cannot determine whether thermokarst bogs in the sporadic‐discontinuous permafrost zone act as long‐term carbon sinks or sources post‐thaw, our study better constrains post‐thaw C losses and gains.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".