Ensemble estimates of global wetland methane emissions over 2000–2020
Bibliographic record
Abstract
Abstract. Due to ongoing climate change, methane (CH4) 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 is still lacking. Here we assessed global wetland CH4 emissions from 2000 to 2020 based on an ensemble of sixteen process-based wetland models. Our results estimated global average wetland CH4 emissions at 158±24 (mean ± 1σ) Tg CH4 yr-1 for the period 2010–2020, with an average decadal increase of 6–7 Tg CH4 yr-1 compared to the decade of 2000–2009. The increases in the four latitudinal bands of 90° S–30° S, 30° S–30° N, 30° N–60° N, and 60° N–90° N were 0.1–0.2 Tg CH4 yr-1, 3.6–3.7 Tg CH4 yr-1, 1.8–2.4 Tg CH4 yr-1, and 0.6–0.8 Tg CH4 yr-1, respectively, over the two decades. The modeled CH4 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 CO2 concentrations played secondary roles with high levels of uncertainty. These modeled results suggest climate change is driving increased wetland CH4 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 | 0.000 | 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) | 0.001 | 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".