The zonal structure of tropical O <sub>3</sub> and CO as observed by the Tropospheric Emission Spectrometer in November 2004 – Part 2: Impact of surface emissions on O <sub>3</sub> and its precursors
Bibliographic record
Abstract
Abstract. The impact of surface emissions on the zonal structure of tropical tropospheric ozone and carbon monoxide is investigated for November 2004 using satellite observations, in-situ measurements, and chemical transport models in conjunction with inverse-estimated surface emissions.Vertical ozone profiles from the Tropospheric Emission Spectrometer (TES) and ozone sonde measurements from the Southern Hemisphere Additional Ozonesondes (SHADOZ) network show elevated concentrations of ozone over Indonesia and Australia (60–70 ppb) in the lower troposphere against the backdrop of the well-known zonal "wave-one" pattern with ozone concentrations of (70–80 ppb) centered over the Atlantic . Observational evidence from TES CO vertical profiles and Ozone Monitoring Instrument (OMI) NO2 columns point to regional surface emissions as an important contributor to the elevated ozone over Indonesia. This contribution is investigated with the GEOS-Chem chemistry and transport model using surface emission estimates derived from an optimal inverse model, which was constrained by TES and Measurements Of Pollution In The Troposphere (MOPITT) CO profiles (Jones et al., 2009). These a posteriori estimates, which were over a factor of 2 greater than climatological emissions, reduced differences between GEOS-Chem and TES ozone observations by 30–40% over Indonesia. The response of the free tropospheric chemical state to the changes in these emissions is investigated for ozone, CO, NOx, and PAN. Model simulations indicate that ozone over Indonesian/Australian is sensitive to regional changes in surface emissions of NOx but relatively insensitive to lightning NOx. Over sub-equatorial Africa and South America, free tropospheric NOx was reduced in response to increased surface emissions potentially muting ozone production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".