Role of emission sources and atmospheric sink on the seasonal cycle of CH<sub>4</sub> and δ<sup>13</sup>-CH<sub>4</sub>: analysis based on the atmospheric chemistry transport model TM5
Bibliographic record
Abstract
Abstract. This study investigates the contribution of different CH4 sources to the seasonal cycle of 𝛿13C during years 2000–2012 using the TM5 atmospheric transport model. The seasonal cycles of anthropogenic emissions from two versions of the EDGAR inventories, v4.3.2 and v5.0 are examined. Those includes emissions from Enteric Fermentation and Manure Management (EFMM), rice cultivation and residential sources. Those from wetlands obtained from LPX-Bern v1.4 are also examined in addition to other sources such as fires and ocean sources. We use spatially varying isotopic source signatures for EFMM, coal, oil and gas, wetlands, fires and geological emission and for other sources a global uniform value. We analysed the results as zonal means for 30° latitudinal bands. Seasonal cycles of 𝛿13C are found to be an inverse of CH4 cycles in general, with a peak-to-peak amplitude of 0.07–0.26 ‰. However, due to emissions, the phase ellipses do not form straight lines, and the anti-correlations between CH4 and 𝛿13C are weaker (−0.35 to −0.91) in north of 30° S. We found that wetland emissions are the dominant driver in the 𝛿13C seasonal cycle in the Northern Hemisphere and Tropics, such that the timing of 𝛿13C seasonal minimum is shifted by ∼90 days in 60° N–90° N from the end of the year to the beginning of the year when seasonality of wetland emissions is removed. The results also showed that in the Southern Hemisphere Tropics, emissions from fires contribute to the enrichment of 𝛿13C in July–October. In addition, we also compared the results against observations from the South Pole, Antarctica, Alert, Nunavut, Canada and Niwot Ridge, Colorado, USA. In light of this research, comparison to the observation showed that the seasonal cycle of EFMM emissions in EDGAR v5.0 inventory is more realistic than in v4.3.2. In addition, the comparison at Alert showed that modelled 𝛿13C amplitude was approximately half of the observations, mainly because the model could not reproduce the strong depletion in autumn. This indicates that CH4 emission magnitude and seasonal cycle of wetlands may need to be revised. Results from Niwot Ridge indicate that in addition to biogenic emissions, the proportion of biogenic to fossil based emissions may need to be revised.
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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 | 0.001 |
| 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) | 0.001 | 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".