Global CH <sub>4</sub> fluxes derived from JAXA/GOSAT lower-tropospheric partial column data and the CarbonTracker Europe-CH <sub>4</sub> atmospheric inverse model
Bibliographic record
Abstract
Satellite-driven inversions provide valuable information about methane (CH 4 ) fluxes, but the assimilation of total column-averaged dry-air mole fractions of CH 4 (XCH 4 ) has been challenging. This study explores, for the first time, the potential of the new lower-tropospheric partial column (pXCH 4 _LT) GOSAT data, retrieved by the Japan Aerospace Exploration Agency (JAXA), to constrain global and regional CH 4 fluxes. Using the CarbonTracker Europe-CH 4 (CTE-CH 4 ) atmospheric inverse model, we estimated CH 4 fluxes between 2016–2019 by assimilating the JAXA/GOSAT pXCH 4 _LT and XCH 4 data and surface CH 4 observations independently of each other. The Northern Hemisphere CH 4 fluxes derived from the pXCH 4 _LT data were similar to the estimates derived from the surface observations but were underestimated by about 35 Tg CH 4 yr −1 (∼ 6 % of the global total) using the XCH 4 data. For the Southern Hemisphere, the estimates from both GOSAT inversions were about 15–30 Tg CH 4 yr −1 higher than those derived from surface data. The evaluations against independent data from the Atmospheric Tomography Mission aircraft campaign showed good agreement in the lower-tropospheric CH 4 from the inversions using the pXCH 4 _LT and surface data. However, from these inversions, the modelled north–south gradients showed significant overestimation in the upper troposphere and stratosphere, possibly due to relatively uniform inter-hemispheric OH distributions that control CH 4 sinks. Overall, we found that the use of the JAXA/GOSAT pXCH 4 _LT data shows considerable potential in constraining global and regional CH 4 fluxes, advancing our understanding of the 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".