Ensemble estimates of global wetland methane emissions over 2000–2020
Bibliographic record
Abstract
Due to ongoing climate change, methane (CH 4 ) emissions from vegetated wetlands are projected to increase during the 21st century, challenging climate mitigation efforts aimed at limiting global warming. However, despite reports of rising emission trends, a comprehensive evaluation and attribution of recent changes remains limited. Here we assessed global wetland CH 4 emissions from 2000–2020 based on an ensemble of 16 process-based wetland models. Our results estimated global average wetland CH 4 emissions at 158 ± 24 (mean ± 1 σ ) Tg CH 4 yr −1 over a total annual average wetland area of 8.0 ± 2.0×10 6 km 2 for the period 2010–2020, with an average increase of 6–7 Tg CH 4 yr −1 in 2010–2019 compared to the average for 2000–2009. The increases in the four latitudinal bands of 90–30° S, 30° S–30° N, 30–60° N, and 60–90° N were 0.1–0.2, 3.6–3.7, 1.8–2.4, and 0.6–0.8 Tg CH 4 yr −1 , respectively, over the 2 decades. The modeled CH 4 sensitivities to temperature show reasonable consistency with eddy-covariance-based measurements from 34 sites. Rising temperature was the primary driver of the increase, while precipitation and rising atmospheric CO 2 concentrations played secondary roles with high levels of uncertainty. These modeled results suggest that climate change is driving increased wetland CH 4 emissions and that direct and sustained measurements are needed to monitor developments.
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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 | 0.001 | 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) | 0.001 | 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".