Improved model of isoprene emissions in Africa using Ozone Monitoring Instrument (OMI) satellite observations of formaldehyde: implications for oxidants and particulate matter
Bibliographic record
Abstract
Abstract. We use a 2005–2009 record of isoprene emissions over Africa derived from Ozone Monitoring Instrument (OMI) satellite observations of formaldehyde (HCHO) to better understand the factors controlling isoprene emission in the continent and evaluate the impact on atmospheric composition. OMI-derived isoprene emissions show large seasonality over savannas driven by temperature and leaf area index (LAI), and much weaker seasonality over equatorial forests driven by temperature. The commonly used MEGAN (Model of Emissions of Gases and Aerosols from Nature, version 2.1) global isoprene emission model reproduces this seasonality but is biased high, particularly for equatorial forests, when compared to OMI and relaxed-eddy accumulation measurements. Isoprene emissions in MEGAN are computed as the product of an emission factor Eo, LAI, and activity factors dependent on environmental variables. We use the OMI-derived emissions to provide improved estimates of Eo that are in good agreement with direct leaf measurements from field campaigns (r = 0.55, bias = −19%). The largest downward corrections to MEGAN Eo values are for equatorial forests and semi-arid environments, and this is consistent with latitudinal transects of isoprene over western Africa from the African Monsoon Multidisciplinary Analysis (AMMA) aircraft campaign. Total emission of isoprene in Africa is estimated to be 77 Tg C a−1, compared to 104 Tg C a−1 in MEGAN. Simulations with the GEOS-Chem oxidant–aerosol model suggest that isoprene emissions increase mean surface ozone in western Africa by up to 8 ppbv, and particulate matter by up to 1.5 μg m−3, due to coupling with anthropogenic influences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".