Pyrogenic organic matter produced during wildfires can act as a carbon sink – a reply to Billings & Schlesinger (2015)
Bibliographic record
Abstract
In response to our paper ‘Pyrogenic organic matter production from wildfires: a missing sink in the global carbon cycle’ (Santín et al., 2015), Billings & Schlesinger (2015) argue that pyrogenic organic matter (PyOM) formation is not a missing C sink. Firstly, they state that ‘formation of PyOM does not remove CO2 from the atmosphere’. Indeed, PyOM formation does not directly take CO2 from the atmosphere, but, as Billings & Schlesinger (2015) explain, fire transforms one type of fixed C (biomass) into another (PyOM). This transformation is critical here, as the charred C has lower degradation rates than the ‘uncharred’ biomass C (e.g. Maestrini et al., 2014). The pyrogenic transformations that occur during charring of biomass do not constitute an immediate removal of C from the atmosphere, but they do delay the emission of that C to the atmosphere (compared to the uncharred precursors of PyOM). Taking a wider temporal perspective, this delay leads to a net additional transfer of C from atmosphere to land compared to a situation without PyOM production. This mechanism is defined as ‘C sequestration’ (Powlson et al., 2011) and is the same concept on which the production of biochar (i.e. anthropogenic PyOM) and its application to soils for offsetting C emissions is based on (e.g. Woolf et al., 2010). C sinks are thus not only processes withdrawing directly CO2 from the atmosphere as argued by Billings & Schlesinger (e.g. increases of net photosynthesis or C capture & storage; Scott et al., 2013), but also those mechanisms decreasing the net flux of C between land and atmosphere (e.g. soil C erosion and burial under certain conditions; Billings et al., 2010), with PyOM formation being one of them (Lal, 2008). Secondly, Billings & Schlesinger (2015) argue that, to resolve the C budget (i.e. current discrepancies between the magnitude of accounted sources and sinks of atmospheric CO2), we must identify processes that have increased their rate of CO2 capture in the last few decades. The identification of new net land C sinks would indeed explain the current discrepancy observed. However, addressing errors and omissions in the different terms of the global C balance can also help to reduce this discrepancy (Houghton, 2012). PyOM production is currently not considered in the global C cycle and, therefore, our argument that it is a missing component (Santín et al., 2015) is valid. Inclusion of PyOM will not close the C budget by itself, but it will contribute to its further understanding. Elucidating more fully the global C balance is of course challenging, but very important due to its direct influence on the global climate. Efforts from the scientific community need to be directed not only towards improving C accounting itself, but also to its correct use in climate policy (e.g. Searchinger et al., 2009). Scientific discussion is essential to achieve these aims, and we welcome the opportunity provided here to contribute to this process.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.028 | 0.047 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".