Estimating global natural wetland methane emissions using process modelling: spatio‐temporal patterns and contributions to atmospheric methane fluctuations
Bibliographic record
Abstract
Abstract Aim The fluctuations of atmospheric methane (CH4) that have occurred in recent decades are not fully understood, particularly with regard to the contribution from wetlands. The application of spatially explicit parameters has been suggested as an effective method for reducing uncertainties in bottom‐up approaches to wetland CH4 emissions, but has not been included in recent studies. Our goal was to estimate spatio‐temporal patterns of global wetland CH4 emissions using a process model and then to identify the contribution of wetland emissions to atmospheric CH4 fluctuations. Location Global. Methods A process‐based model integrated with full descriptions of methanogenesis (TRIPLEX‐GHG) was used to simulate global wetland CH4 emissions. Results Global annual wetland CH4 emissions ranged from 209 to 245 Tg CH4 year−1 between 1901 and 2012, with peaks occurring in 1991 and 2012. There is a decreasing trend between 1990 and 2010 with a rate of approximately 0.48 Tg CH4 year−1, which was largely caused by emissions from tropical wetlands showing a decreasing trend of 0.44 Tg CH4 year−1 since the 1970s. Emissions from tropical, temperate and high‐latitude wetlands comprised 59, 26 and 15% of global emissions, respectively. Main conclusion Global wetland CH4 emissions, the interannual variability of which was primary controlled by tropical wetlands, partially drive the atmospheric CH4 burden. The stable to decreasing trend in wetland CH4 emissions, a result of a balance of emissions from tropical and extratropical wetlands, was a particular factor in slowing the atmospheric CH4 growth rate during the 1990s. The rapid decrease in tropical wetland CH4 emissions that began in 2000 was supposed to offset the increase in anthropogenic emissions and resulted in a relatively stable level of atmospheric CH4 from 2000 to 2006. Increasing wetland CH4 emissions, particularly after 2010, should be an important contributor to the growth in atmospheric CH4 seen since 2007.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".