Estimation of Canada’s methane emissions: inverse modelling analysis using the ECCC measurement network
Bibliographic record
Abstract
Abstract. Canada has major sources of atmospheric methane (CH4), with the world second-largest boreal wetland and the world fourth-largest natural gas production. However, Canada’s CH4 emissions remain uncertain among estimates. Better quantification and characterization of Canada’s CH4 emissions are critical for climate mitigation strategies. To improve our understanding of Canada’s CH4 emissions, we performed an ensemble regional inversion (2007–2017) constrained with the Environment and Climate Change Canada (ECCC) surface measurement network. The decadal CH4 estimates show no significant trend, unlike some studies that reported long-term trends. The total CH4 estimate is 17.4 (15.3–19.5) Tg CH4 year-1, partitioned into natural and anthropogenic sources, 10.8 (7.5–13.2) and 6.6 (6.2–7.8) Tg CH4 year-1, respectively. The estimated anthropogenic emission is higher than inventories, mainly in western Canada (with the fossil fuel industry). Furthermore, the results reveal notable spatiotemporal characteristics. First, the modelled gradients of atmospheric CH4 show improvement after inversion when compared to observations, implying the CH4 gradients could help verify the inversion results. Second, the seasonal variations show slow onset and late summer maximum, indicating wetland CH4 flux has hysteretic dependence on air temperature. Third, the boreal winter natural CH4 emissions, usually treated as negligible, appear quantifiable (≥ 20 % of annual emissions). Understanding winter emission is important for climate prediction, as the winter in Canada is warming faster than the summer. Fourth, the inter-annual variability in estimated CH4 emissions is positively correlated with summer air temperature anomalies. This could enhance Canada’s natural CH4 emission in the warming climate.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".