Inter‐Annual Variability in Atmospheric Transport Complicates Estimation of US Methane Emissions Trends
Bibliographic record
Abstract
Abstract US natural gas production increased by ∼43% between 2005 and 2015, but there is disagreement among existing studies on whether this growth led to increased methane emissions. We evaluate the likely contributions of atmospheric transport to an upward trend in atmospheric methane enhancements during 2007–2015, defined as the contribution of North American emissions to atmospheric observations across the US. We find that interannual variability (IAV) in transport yields an apparent upward trend in enhancements across much of the US during this time and can explain disagreements among existing studies over emissions trends. We further find that enhancements at satellite and in situ monitoring sites are 19% higher during El Niño than La Niña, possibly because air masses spend more time over North America on average during some years. The results show that accurate modeling of IAV in transport is a key prerequisite to quantifying emissions trends.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".