Modelling past, present and future peatland carbon accumulation across the pan-Arctic region
Bibliographic record
Abstract
Abstract. Most northern peatlands developed during the Holocene, sequestering large amounts of carbon in terrestrial ecosystems. However, recent syntheses have highlighted the gaps in our understanding of peatland carbon accumulation. Assessments of the long-term carbon accumulation rate and possible warming-driven changes in these accumulation rates can therefore benefit from process-based modelling studies. We employed an individual-based dynamic global ecosystem model with dynamic peatland and permafrost functionalities and patch-based vegetation dynamics to quantify long-term carbon accumulation rates and to assess the effects of historical and projected climate change on peatland carbon balances across the pan-Arctic region. Our results are broadly consistent with published regional and global carbon accumulation estimates. A majority of modelled peatland sites in Scandinavia, Europe, Russia and central and eastern Canada change from carbon sinks through the Holocene to potential carbon sources in the coming century. In contrast, the carbon sink capacity of modelled sites in Siberia, far eastern Russia, Alaska and western and northern Canada was predicted to increase in the coming century. The greatest changes were evident in eastern Siberia, north-western Canada and in Alaska, where peat production hampered by permafrost and low productivity due the cold climate in these regions in the past was simulated to increase greatly due to warming, a wetter climate and higher CO2 levels by the year 2100. In contrast, our model predicts that sites that are expected to experience reduced precipitation rates and are currently permafrost free will lose more carbon in the future.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".