A Simulated Climatology of Asian Dust Aerosol and Its Trans-Pacific Transport. Part II: Interannual Variability and Climate Connections
Bibliographic record
Abstract
Abstract A 44-yr climatology of spring Asian dust aerosol emission, column loading, deposition, trans-Pacific transport routes, and budgets during 1960–2003 was simulated with the Northern Aerosol Regional Climate Model (NARCM). Interannual variability in these Asian dust aerosol properties simulated by the model and its climate connections are analyzed with major climatic indices and records in ground observations. For dust production from most of the source regions, the strongest correlations were with the surface wind speed in the source region and the area and intensity indices of the Asian polar vortex (AIAPV and IIAPV, respectively). Dust emission was negatively correlated with precipitation and surface temperatures in spring. The strength of the East Asian monsoon was not found to be directly related to dust production but rather with the transport of dust from the Asian subcontinent. The interannual variability of dust loading and deposition showed similar relations with various climate indices. The correlation of Asian dust loading and deposition with the western Pacific (WP) pattern and Atmospheric Circulation Index (ACI) exhibited contrasting meridional and zonal distributions. AIAPV and IIAPV were strongly correlated with the midlatitude zonal distribution of dust loading and deposition over the Asian subcontinent and the North Pacific. The Pacific–North American (PNA) pattern and Southern Oscillation index (SOI) displayed an opposite correlation pattern of dust loading and deposition in the eastern Pacific, while SOI correlated significantly with dust loading over eastern China and northeast Asia. The Pacific decadal oscillation (PDO) was linked to variations of dust aerosol and deposition not only in the area of the eastern North Pacific and North America but also in the Asian dust source regions. The anomalies of transport flux and its divergence as well as dust column loading were also identified for eight typical El Niño and eight La Niña years. A shift of the trans-Pacific transport path to the north was found for El Niño years, which resulted in less dust storms and dust loading in China. In El Niño years the deserts in Mongolia and western north China closer to the polar cold air regions contributed more dust aerosol in the troposphere, while in La Niña years the deserts in central and eastern north China far from polar cold regions provided more dust aerosol in the troposphere. On the basis of the variability of Asian dust aerosol budgets, the ratio of inflow to North America to the outflow from Asia was found to be correlated negatively with the PNA index and positively with the WP index.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".