The Post-2020 Surge in Global Atmospheric Methane Observed in Ground-based Observations
Bibliographic record
Abstract
Methane (CH4) is a potent greenhouse gas with high radiative forcing and a relatively short atmospheric lifetime of around a decade. We used a decade-long dataset (2011-2022) from the Fourier transform spectrometer at the California Laboratory for Atmospheric Remote Sensing (CLARS-FTS) to quantify a dramatic increase in methane observed in 2020. We report an increase of 1.13 ppb/month starting in 2020 until the end of 2021, compared to a growth rate of 0.345 ppb/month from 2016 to 2019. The observed increase in methane concentrations in 2020 is of significant concern due to its potential contribution to global warming. The Total Carbon Column Observing Network (TCCON) is then used to examine the global geospatial variability of the increase in methane. The results suggest an approximately uniform rise in methane globally. Finally, results from a two-box model used to simulate atmospheric chemical processes of methane production and loss indicate that changes in OH alone are insufficient to explain the rise in atmospheric methane. Encouragingly, recent data from 2022 suggest a deceleration in the methane growth rate, indicating a potential slowdown in the methane increase observed in 2020.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".