Analysis of tropical tropospheric ozone, carbon monoxide, and water vapor during the 2006 El Niño using TES observations and the GEOS‐Chem model
Bibliographic record
Abstract
Elevated levels of tropical tropospheric ozone (O3) and carbon monoxide (CO) and decreased water (H2O) vapor were observed by the Tropospheric Emission Spectrometer (TES) in the region of Indonesia and the eastern Indian Ocean during the coincident positive phases of the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) in late 2006. Using the chemical transport model GEOS‐Chem, we show that the elevated CO results from increased biomass burning in Indonesia during the ENSO/IOD‐induced drought and quantify the effect of the fires and other factors on O3. In the region of highest CO (∼200 ppb), the contribution of the fires to enhanced O3 is ∼45% in October, ∼75% in early November, and only 10% in December. More lightning in late 2006 compared to 2005 causes an increase in O3 of a few parts per billion. Dynamical changes increase O3 over a larger region than fire emissions which mainly increase O3 at 10°N–10°S in October and November. The model matches the O3 anomaly in October but underestimates it in November and December, which we ascribe to overly active convection in the model in late 2006, based on an analysis of outgoing longwave radiation (OLR) data. An underestimate of NOx emissions from soils may also contribute to the disparity at the end of the year. A dramatic decrease in O3 in late 2006 in equatorial Africa and the western Indian Ocean is reproduced by the model and is caused by highly enhanced convection in 2006, likely associated with the IOD.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".