The global pyrogenic carbon cycle and its impact on the level of atmospheric <scp>CO</scp><sub>2</sub> over past and future centuries
Bibliographic record
Abstract
Abstract The incomplete combustion of vegetation and dead organic matter by landscape fires creates recalcitrant pyrogenic carbon (PyC), which could be consequential for the global carbon budget if changes in fire regime, climate, and atmospheric CO2 were to substantially affect gains and losses of PyC on land and in oceans. Here, we included global PyC cycling in a coupled climate–carbon model to assess the role of PyC in historical and future simulations, accounting for uncertainties through five sets of parameter estimates. We obtained year‐2000 global stocks of (Central estimate, likely uncertainty range in parentheses) 86 (11–154), 47 (2–64), and 1129 (90–5892) Pg C for terrestrial residual PyC (RPyC), marine dissolved PyC, and marine particulate PyC, respectively. PyC cycling decreased atmospheric CO2 only slightly between 1751 and 2000 (by 0.8 Pg C for the Central estimate) as PyC‐related fluxes changed little over the period. For 2000 to 2300, we combined Representative Concentration Pathways (RCPs) 4.5 and 8.5 with stable or continuously increasing future fire frequencies. For the increasing future fire regime, the production of new RPyC generally outpaced the warming‐induced accelerated loss of existing RPyC, so that PyC cycling decreased atmospheric CO2 between 2000 and 2300 for most estimates (by 4–8 Pg C for Central). For the stable fire regime, however, PyC cycling usually increased atmospheric CO2 (by 1–9 Pg C for Central), and only the most extreme choice of parameters maximizing PyC production and minimizing PyC decomposition led to atmospheric CO2 decreases under RCPs 4.5 and 8.5 (by 5–8 Pg C). Our results suggest that PyC cycling will likely reduce the future increase in atmospheric CO2 if landscape fires become much more frequent; however, in the absence of a substantial increase in fire frequency, PyC cycling might contribute to, rather than mitigate, the future increase in atmospheric CO2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".