Observationally-constrained estimates of global fine-mode AOD
Bibliographic record
Abstract
Abstract. Small aerosols are mostly anthropogenic, and the fine-mode aerosol optical depth (fAOD) can be used to infer anthropogenic aerosol amounts. We estimate AOD and fAOD globally on a monthly time scale from 2001 to 2010 by integrating monthly satellite-based (MODIS and MISR) and ground-based (AERONET) observations. For fAOD, three integration methods are developed to utilize global coverage by satellite data and maximize the influence of AERONET data. We evaluate each method by applying the method without a few randomly-chosen AERONET data and comparing its output with the few AERONET data. The best method of the three is based on integrating the Ångström exponent (AE) data from MODIS, MISR and AERONET, and its output is closer to AERONET fAOD accuracy than MODIS or MISR fAOD over both ocean and land. Using our integrated data, we find that the global 2001–2010 average of 500 nm AOD and fAOD is 0.17 and 0.089, respectively. Eastern China as a region shows the largest decadal-mean fAOD. The linear trend of global AOD or global fAOD from 2001 to 2010 is found to be slightly negative. This decreasing trend is particularly pronounced over the West (Western Europe and US/Canada combined) where AOD and fAOD reductions are about −20%. By contrast, fAOD in India and eastern China combined increased slightly. These results reflect the overall anthropogenic aerosol emission reduction in the West, and stagnating conditions in Asia. Moreover, our results in the West are consistent with the so-called surface brightening phenomenon in the recent decades.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".