Analysis of Atmospheric Methane Across Different Spatial and Temporal Scales
Bibliographic record
Abstract
Global trends of atmospheric methane are poorly understood due to uncertainties surrounding its sources and sinks. Atmospheric methane has a 20-year global warming potential 80 times greater than carbon dioxide, but a shorter lifetime of approximately 9 years. Therefore, it presents a good short-term opportunity to mitigate the human impact on climate change whilst carbon dioxide emissions are reduced. This research exploits observations and models across different spatial and temporal scales to address important knowledge gaps in atmospheric methane. Specifically, this thesis explores global changes in the seasonal cycle amplitude of methane, develops and demonstrates the capability of a new regional "nested grid'' 3-D chemical transport model, and quantifies emission estimates of a satellite-detected UK gas leak. Long-term surface-based observations showed a decrease of 4ppb in the seasonal cycle amplitude (SCA) in the northern high latitudes (NHLs; 60N-90N) between 1995-2020. Global chemical transport modelling shows that the largest contributor to this SCA change was from well-mixed methane, as well as changes in emissions from Canada, Middle East and Europe. These results highlight that changes in the observed NHL seasonal cycle can indicate changes in emissions in local and non-local regions. For studies on a finer spatial scale, a new high resolution regional model "nested'' in TOMCAT called ZOOMCAT was developed and tested. Two case studies were simulated in ZOOMCAT over Europe in 2020. One which simulated the Nord Stream pipeline gas leak, which provided much more spatial detail in plume transport compared to TOMCAT. The lack of improvement in ZOOMCAT when compared with TOMCAT and tall tower observations of methane in these case studies highlighted the importance of input meteorology. On the metre-scale resolution, emissions from a 2023 gas leak near Cheltenham, UK were detected by tasking GHGSat. The satellite-derived emission estimates were evaluated using a surface-based mobile survey and were found to broadly agree. This work also demonstrated that the UK tall tower network in this case was too sparse to quantify fugitive emissions on this scale using inverse modelling techniques. The total methane leaked over the 11-week period is estimated to be 1,393,392 kg, equivalent to emissions from the average annual electricity consumption of 7,500 homes.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".