Country scale analysis of natural and anthropogenic methane emissions with a high-resolution inverse model using GOSAT and ground-based observations
Bibliographic record
Abstract
Here, we present the results of a global high-resolution inversion study of methane emissions and their analysis for the large emitting countries. We employ a global high-resolution inverse model to optimize CH4 emissions using Greenhouse gas Observing Satellite (GOSAT) and surface observation data over the 2011-2017 period for the two main source categories of anthropogenic and natural emissions. As prior emissions, we used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission, scaled by country to match the national emissions reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimate a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr-1 and 232.5 Tg CH4 yr-1, respectively. This agrees with recent Global Carbon Project (GCP) estimates of 357 and 215 Tg CH4 yr-1, respectively. Country-scale analysis of the estimated anthropogenic emissions shows that for all the top-emitting countries, differences with their respective nationally reported inventories are within the uncertainty range of the inventories. Large emitting countries such as China, Russia and the United States have mean estimated anthropogenic emission of 45.7±8.6, 31.9±7.8 and 29.8±7.8 Tg CH4 yr-1 respectively. For natural emissions, we estimate large emissions for Brazil (39.8±12.4 Tg CH4 yr-1), the United States (25.9±8.3 Tg CH4 yr-1), Russia (13.2±9.3 Tg CH4 yr-1), India (12.3±6.4 Tg CH4 yr-1), and Canada (12.2±5.1 Tg CH4 yr-1). In both emission categories, natural and anthropogenic, the major emitting countries all had model corrections to their emissions that were within the uncertainty range of the inventories and the inverse model uncertainty. As a special case, we evaluate anthropogenic emissions estimated for India (24.2±5.3 Tg yr-1) with aircraft observation data over urban regions over India. On average, the optimized profiles showed a better match with the observations compared to the prior profile confirming improved estimates by the model for India.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".