Bibliographic record
Abstract
Abstract. We quantify methane emissions and their 2010–2017 trends by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as a prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), Environment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecología y Cambio Climático (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as an evaluation of these inventories in terms of their magnitudes and trends. Emissions are optimized with a Gaussian mixture model (GMM) at 0.5∘×0.625∘ resolution and for individual years. Optimization is done analytically using lognormal error forms. This yields closed-form statistics of error covariances and information content on the posterior (optimized) estimates, allows better representation of the high tail of the emission distribution, and enables construction of a large ensemble of inverse solutions using different observations and assumptions. We find that GOSAT and in situ observations are largely consistent and complementary in the optimization of methane emissions for North America. Mean 2010–2017 anthropogenic emissions from our base GOSAT + in situ inversion, with ranges from the inversion ensemble, are 36.9 (32.5–37.8) Tg a−1 for CONUS, 5.3 (3.6–5.7) Tg a−1 for Canada, and 6.0 (4.7–6.1) Tg a−1 for Mexico. These are higher than the most recent reported national inventories of 26.0 Tg a−1 for the US (EPA), 4.0 Tg a−1 for Canada (ECCC), and 5.0 Tg a−1 for Mexico (INECC). The correction in all three countries is largely driven by a factor of 2 underestimate in emissions from the oil sector with major contributions from the south-central US, western Canada, and southeastern Mexico. Total CONUS anthropogenic emissions in our inversion peak in 2014, in contrast to the EPA report of a steady decreasing trend over 2010–2017. This reflects offsetting effects of increasing emissions from the oil and landfill sectors, decreasing emissions from the gas sector, and flat emissions from the livestock and coal sectors. We find decreasing trends in Canadian and Mexican anthropogenic methane emissions over the 2010–2017 period, mainly driven by oil and gas emissions. Our best estimates of mean 2010–2017 wetland emissions are 8.4 (6.4–10.6) Tg a−1 for CONUS, 9.9 (7.8–12.0) Tg a−1 for Canada, and 0.6 (0.4–0.6) Tg a−1 for Mexico. Wetland emissions in CONUS show an increasing trend of +2.6 (+1.7 to +3.8)% a−1 over 2010–2017 correlated with precipitation.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.031 | 0.030 |
| Insufficient payload (model declined to judge) | 0.145 | 0.125 |
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".