Global CH <sub>4</sub> Fluxes Derived from JAXA/GOSAT Lower Tropospheric Partial Column Data and the CTE-CH <sub>4</sub> Atmospheric Inverse Model
Bibliographic record
Abstract
Abstract. Satellite-driven inversions provide valuable information about methane (CH4) fluxes, but the assimilation of total column-averaged dry-air mole fractions of CH4 (XCH4) has been challenging. This study explores, for the first time, the potential of the new lower tropospheric partial column (pXCH4_LT) GOSAT data, retrieved by the Japan Aerospace Exploration Agency (JAXA), to constrain global and regional CH4 fluxes. Using the CarbonTracker Europe-CH4 atmospheric inverse model, we estimated CH4 fluxes between 2016–2019 by assimilating the JAXA/GOSAT pXCH4_LT and XCH4 data and surface CH4 observations, independently of each other. The Northern Hemisphere CH4 fluxes derived from the JAXA/GOSAT pXCH4_LT data were similar to the estimates derived from the surface observations, but was underestimated by about 35 Tg CH4 year-1 (∼6 % of the global total) using the JAXA/GOSAT XCH4 data. For the Southern Hemisphere, the estimates from the both GOSAT inversions were about 15–30 Tg CH4 year-1 higher than that derived from surface data. The evaluations against independent data from the Atmospheric Tomography Mission aircraft campaign showed good agreement in the lower tropospheric CH4 from the inversions using the JAXA/GOSAT pXCH4_LT and surface data. However, 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 CH4 sinks. Overall, we found that the use of the JAXA/GOSAT pXCH4_LT data shows considerable potential in constraining global and regional CH4 fluxes, advancing our understanding of the CH4 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 | 0.001 |
| 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".