Observationally-constrained estimates of global small-mode AOD
Bibliographic record
Abstract
Abstract. Small aerosols are mostly anthropogenic, and an area average of the small-mode aerosol optical depth (sAOD) is a powerful and independent measure of anthropogenic aerosol emission. We estimate AOD and sAOD globally on a monthly time scale from 2001 to 2010 by integrating satellite-based (MODIS and MISR) and ground-based (AERONET) observations. For sAOD, three integration methods were developed to maximize the influence of AERONET data and ensure consistency between MODIS, MISR and AERONET sAOD data. We evaluated each method by applying the technique with fewer AERONET data and comparing its output with the unused AERONET data. The best performing method gives an overall error of 13 ± 2%, compared with an overall error of 62% in simply using MISR sAOD, and this method takes advantage of an empirical relationship between the Ångström exponent (AE) and fine mode fraction (FMF). This relationship is obtained by analyzing AERONET data. Using our integrated data, we find that the global 2001–2010 average of 500 nm AOD and sAOD is 0.17 and 0.094, respectively. sAOD over eastern China is several times as large as the global average. The linear trend from 2001 to 2010 is found to be slightly negative in global AOD or global sAOD. In India and eastern China combined, however, sAOD increased by more than 4% against a backdrop of decreasing AOD and large-mode AOD. On the contrary to India and China, the west (Western Europe and US/Canada combined) is found to have a sAOD reduction of −20%. These results quantify the overall anthropogenic aerosol emission reduction in the west, and rapidly deteriorating conditions in Asia. Moreover, our results in the west are consistent with the so-called surface brightening phenomenon in the recent decades.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".