Quantifying lower tropospheric methane concentrations using near-IR and thermal IR satellite measurements: comparison to the GEOS-Chem model
Bibliographic record
Abstract
Abstract. Evaluating surface fluxes of CH4 using total column data requires models to accurately account for the transport and chemistry of methane in the free-troposphere and stratosphere, thus reducing sensitivity to the underlying fluxes. Vertical profiles of methane have increased sensitivity to surface fluxes because lower tropospheric methane is more sensitive to surface fluxes than a total column. Resolving the free troposphere from the lower-troposphere also helps to evaluate the impact of transport and chemistry uncertainties on estimated surface fluxes. Here we demonstrate the potential for estimating lower tropospheric CH4 concentrations through the combination of free-tropospheric methane measurements from the Aura Tropospheric Emission Spectrometer (TES) and XCH4 (dry-mole air fraction of methane) from the Greenhouse Gases Observing Satellite Thermal And Near Infrared for Carbon Observations (GOSAT TANSO, herein GOSAT for brevity). The mean precision of these estimates are calculated to be ~ 23 ppb for a monthly average on a 4 × 5 latitude/longitude degree grid making these data suitable for evaluating lower-tropospheric methane concentrations. Smoothing error is approximately 10 ppb or less. The accuracy is primarily determined by knowledge error of XCO2, used to estimate XCH4 from the GOSAT CH4/CO2 "proxy" retrieval. For example, we use different XCO2 fields to quantify XCH4 from the GOSAT CH4/CO2 retrieval, one from Carbontracker and another from the NASA Carbon Monitoring System, and find that differences of up to approximately 60 ppb are possible with a mean value of approximately 35 ppb or less for any given latitude for these lower-tropospheric methane estimates using these two different XCO2 distributions. We show that these lower-tropospheric concentrations are more directly sensitive to the underlying fluxes than a total column using the GEOS-Chem model. In particular, we compare these lower-tropospheric methane estimates with those from the GEOS-Chem model for July 2009 to determine if these data can capture methane enhancements associated with the high-latitude methane fluxes because both TES and GOSAT separately do not show much sensitivity to methane from these sources. We find that the spatial patterns and magnitude of lower tropospheric methane concentrations from GEOS-Chem over Northern European and Siberian wetland fluxes are consistent with these data but modeled concentrations are much larger than measured over Canadian wetland fluxes. Transport of methane significantly affects lower-tropospheric methane concentrations over S.E. Asia as both data and model show methane enhancements that are shifted away from their sources. A possible new finding is that there is no representation of a strong source between the Black and Caspian seas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".