Methane’s record rise 2020-2023: likely causes, impacts and consequences
Bibliographic record
Abstract
From 2020, the atmospheric methane burden has grown at the fastest rate in the detailed observational record. This rise has been accompanied by an unprecedented plunge in d13C(CH4). The causes of recent accelerated growth are as yet uncertain but the geographic spread of growth and the rapid isotopic plunge suggest strong rises in isotopically light emissions from both Tropical and Boreal wetlands. These emissions may be due to rising precipitation and temperatures in parts of the tropics, and by rising temperatures in northern Canada, Siberia, and Europe. Over the longer period since 2007, methane’s actual growth is comparable to methane’s growth in the ‘worst case’ very high baseline emission scenario RCP8.5 (8.5 W/m2 forcing increase relative to pre-industrial). If the recent trend were to continue for more than another decade it could make the 2°C target as hard to achieve as the 1.5°C target is now. Natural feedbacks to climate warming in wetlands need to be included in future modelling and should be incorporated in climate modelling projects such as CMIP7. Methane’s recent accelerated growth also has wide implications for climate negotiations as it reduces the permissible total anthropogenic greenhouse gas emissions if the Paris Agreement is to be achieved. Strong growth in non-anthropogenic methane emissions, driven by feedback impacts on natural and quasi-natural sources, was not expected in modelling at the time of the Paris Agreement and shows the urgency of improving our understanding of the feedback impacts of climate change. The simplest way to limit methane’s growth is for all nations, including non-signatory countries, to cut anthropogenic emissions urgently and sharply, meeting or exceeding the targets of the Global Methane Pledge.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".