Southern Hemisphere carbon monoxide interannual variability observed by Terra/Measurement of Pollution in the Troposphere (MOPITT)
Bibliographic record
Abstract
Biomass burning is an annual occurrence in the tropical Southern Hemisphere (SH) and represents a major source of regional pollution. Vegetation fires emit carbon monoxide (CO), which because of its medium lifetime is an excellent tracer of tropospheric transport. CO is also one of the few tropospheric trace gases currently observed from satellite, and this provides long‐term global measurements. In this paper, we use the 5‐year CO data record from the Measurement of Pollution in the Troposphere (MOPITT) instrument to examine the interannual variability of the SH CO loading and show how this relates to climate conditions which determine the intensity of fire sources. The MOPITT observations show an annual austral springtime peak in the SH zonal CO loading each year with dry season biomass burning emissions in South America, southern Africa, the maritime continent, and northwestern Australia. Although fires in southern Africa and South America typically produce the greatest amount of CO, the most significant interannual variation is due to varying fire activity and emissions from the maritime continent and northern Australia. We find that this variation in turn correlates well with the El Niño–Southern Oscillation precipitation index. Between 2000 and 2005, emissions were greatest in late 2002, and an inverse modeling of the MOPITT data using the Model of Ozone Research in the Troposphere (MOZART) chemical transport model estimates the Southeast Asia regional fire source for the year August 2002 to September 2003 to be 52 Tg CO. Comparison of the MOPITT retrievals and NOAA surface network measurements indicate that the latter do not fully capture the interannual variability or the seasonal range of the CO zonal average concentration because of biases associated with atmospheric and geographic sampling.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".