Quantifying global terrestrial methanol emissions using observations from the TES satellite sensor
Bibliographic record
Abstract
Abstract. We employ new global space-based measurements of atmospheric methanol from the Tropospheric Emission Spectrometer (TES) with the adjoint of the GEOS-Chem chemical transport model to quantify terrestrial emissions of methanol to the atmosphere. Biogenic methanol emissions in the model are based on version 2.1 of the Model of Emissions of Gases and Aerosols from Nature (MEGANv2.1), using leaf area data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and GEOS-5 assimilated meteorological fields. We first carry out a pseudo observation test to validate the overall approach, and find that the TES sampling density is sufficient to accurately quantify regional- to continental-scale methanol emissions using this method. A global inversion of two years of TES data yields an optimized annual global surface flux of 122 Tg yr−1 (including biogenic, pyrogenic, and anthropogenic sources), an increase of 60% from the a priori global flux of 76 Tg yr−1. Global terrestrial methanol emissions are thus nearly 25% those of isoprene (~540 Tg yr−1), and are comparable to the combined emissions of all anthropogenic volatile organic compounds (~100–200 Tg yr−1). Our a posteriori terrestrial methanol source leads to a strong improvement of the simulation relative to an ensemble of airborne observations, and corroborates two other recent top-down estimates (114–120 Tg yr−1) derived using in situ and space-based measurements. Inversions testing the sensitivity of optimized fluxes to model errors in OH, dry deposition, and oceanic uptake of methanol, as well as to the assumed a priori constraint, lead to global fluxes ranging from 118 to 126 Tg yr−1. The TES data imply a relatively modest revision of model emissions over most of the tropics, but a significant upward revision in midlatitudes, particularly over Europe and North America. We interpret the inversion results in terms of specific source types using the methanol : CO correlations measured by TES, and find that biogenic emissions are overestimated relative to biomass burning and anthropogenic emissions in central Africa and southeastern China, while they are underestimated in regions such as Brazil and the US. Based on our optimized emissions, methanol accounts for > 25% of the photochemical source of CO and HCHO over many parts of the northern extratropics during springtime, and contributes ~6% of the global secondary source of those compounds annually.
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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 | 0.001 |
| 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".