Hemispheric transport and influence of meteorology on global aerosol climatology
Bibliographic record
Abstract
Abstract. Based on a 10-yr simulation with the global air quality modeling system GEM-AQ/EC, the northern hemispheric aerosol transport with the inter-annual and seasonal variability as well as the mean climate was investigated. The intercontinental aerosol transport is predominant in the zonal direction from west to east with the ranges of inter-annual variability between 14% and 63%, and is 0.5–2 orders of magnitude weaker in the meridional direction but with larger inter-annual variability. The aerosol transport is found to fluctuate seasonally with a factor of 5–8 between the maximum in late winter and spring and the minimum in late summer and fall. Three meteorological factors controlling the intercontinental aerosol transport and its inter-annual variations are identified from the modeling results: (1) Anomalies in the mid-latitude westerlies in the troposphere. (2) Variations of precipitation over the intercontinental transport pathways and (3) Changes of meteorological conditions within the boundary layer. Changed only by the meteorology, the aerosol column loadings in the free troposphere over the source regions of Europe, North America, South and East Asia vary inter-annually with the highest magnitudes of 30–37% in January and December and the lowest magnitudes of 16–20% in August and September, and the inter-annual aerosol variability within the boundary layer influencing the surface concentrations with the magnitudes from 6% to 20% is more region-dependent. As the strongest climatic signal, the El Niño-Southern Oscillation (ENSO) can lead the anomalies in the intercontinental aerosols in El Niño- and La Niña-years respectively with the strong and weak transport of the mid-latitude westerlies and the low latitude easterlies in the Northern Hemisphere (NH).
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".