Satellite-Observed Variations and Trends in Carbon Monoxide over Asia and Their Sensitivities to Biomass Burning
Bibliographic record
Abstract
As the carbon monoxide (CO) total column over Asia is among the highest in the world, it is important to characterize its variations in space and time. Using Measurements of Pollution in the Troposphere (MOPITT) and Atmospheric InfraRed Sounder (AIRS) satellite data, the variations and trends in CO total column over Asia and its seven subregions during 2003–2017 are investigated in this study. The CO total column in Asia is higher in spring and winter than in summer and autumn. The seasonal maximum and minimum are in spring and summer respectively in the regional mean over Asia, varying between land and oceans, as well as among the subregions. The CO total column in Asia shows strong interannual variation, with a regional mean coefficient of variation of 5.8% in MOPITT data. From 2003 to 2017, the annual mean of CO total column over Asia decreased significantly at a rate of (0.58 ± 0.15)% per year (or −(0.11 ± 0.03) × 1017 molecules cm−2 per year) in MOPITT data, resulting from significant CO decreases in winter, summer, and spring. In most of the subregions, significant decreasing trends in CO total column are also observed, more obviously over areas with high CO total column, including eastern regions of China and the Sichuan Basin. The regional decreasing trends in these areas are over 1% per year. Over the entire Asia, and in fire-prone subregions including South Siberia, Indo-China Peninsula, and Indonesia, we found significant correlations between the MOPITT CO total column and the fire counts from the Moderate Resolution Imaging Spectroradiometer (MODIS). The variations in MODIS fire counts may explain 58%, 60%, 36%, and 71% of the interannual variation in CO total column in Asia and these three subregions, respectively. Over different land cover types, the variations in biomass burning may explain 62%, 52%, and 31% of the interannual variation in CO total column, respectively, over the forest, grassland, and shrubland in Asia. Extremes in CO total column in Asia can be largely explained by the extreme fire events, such as the fires over Siberia in 2003 and 2012 and over Indonesia in 2006 and 2015. The significant decreasing trends in MODIS fire counts inside and outside Asia suggest that global biomass burning may be a driver for the decreasing trend in CO total column in Asia, especially in spring. In general, the variations and trends in CO total column over Asia detected by AIRS are similar to but smaller than those by MOPITT. The two datasets show similar spatial and temporal variations in CO total column over Asia, with correlation coefficients of 0.86–0.98 in the annual means. This study shows that the interannual variation in atmospheric CO in Asia is sensitive to biomass burning, while the decreasing trend in atmospheric CO over Asia coincides with the decreasing trend in MODIS fire counts from 2003 to 2017.
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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".