Observations of carbon monoxide and aerosols from the Terra satellite: Northern Hemisphere variability
Bibliographic record
Abstract
Measurements from the Terra satellite launched in December of 1999 provide a global record of the recent interannual variability of tropospheric air quality: carbon monoxide (CO) from the Measurement of Pollution in the Troposphere (MOPITT) instrument and aerosol optical depth (AOD) from the Moderate‐Resolution Imaging Spectroradiometer (MODIS). This paper compares and contrasts these data sets with a view to understanding the general features of the overall pollutant loading of the Northern Hemisphere (NH). We present a detailed examination of the seasonal and recent interannual variability of the fine mode AOD and CO column, first considering the variation of the global zonal average for both quantities, and then concentrating on several geographical regions with the aim of isolating different emissions. In a zonal sense, the principal NH sources are related to anthropogenic urban and industrial activity. We show that both the CO and the AOD zonal seasonal variations reflect the atmospheric oxidant concentration, which determines the primary sink of CO and the production of sulfate aerosol. As a consequence, the seasonal cycles are several months out of phase, with perturbations resulting from sporadic wildfire or biomass‐burning emissions. In these cases, carbonaceous particles dominate the AOD, and this results in the best correlation with the CO column. Of the 4 years of data available from the Terra satellite, the winter and spring of 2002–2003 showed anomalously high NH pollution compared to the previous years. This was a result of fires in western Russia in the late summer and fall of 2002 and intense fires in the southeast of Russia in the spring and summer of 2003. We examine these events using fire counts from MODIS to indicate the burning regions and investigate how the timing of the fires in relation to atmospheric oxidant concentrations affects the resultant seasonal pollutant loadings. Finally, we trace the emissions from these fires to indicate how intense local pollution sources can impact continental‐ and global‐scale air quality.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".