Long-term and interannual variations of atmospheric methane observed by the NIES and collaborative observation networks
Bibliographic record
Abstract
Abstract Effective action for climate change mitigation requires an accurate understanding of global greenhouse gas budgets, including those of methane (CH 4 ). Atmospheric measurement data provide key constraints for estimating the magnitudes and distributions of sources and sinks and are utilized in atmospheric chemistry transport modeling studies. Long-term atmospheric measurement networks have revealed decadal, interannual, and seasonal variations in atmospheric CH 4 . In 2020, a record-breaking annual CH 4 increase was recorded, but its cause is still unknown. This study analyzes atmospheric CH 4 variations using data from the National Institute for Environmental Studies (NIES) and its collaborative observation networks. Datasets from ground, mobile, and satellite platforms, employing diverse measurement techniques, confirmed past episodes, recent remarkable increases, and spatial distributions of atmospheric CH 4 . Our data clearly showed a sustained CH 4 increase from 2020 to 2022, with the highest annual increase in 2021. The atmospheric CH 4 increase was pronounced in the northern mid-to-high latitudes in 2020, but the enhancement shifted south in 2021 and 2022. This study demonstrates the capability of observational data from the NIES and collaborative networks in accurately characterizing spatiotemporal variations in atmospheric CH 4 regularly, supporting the improvement of our estimates of the global CH 4 budget.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".