Seasonality and weather‐driven variability of transpacific transport
Bibliographic record
Abstract
We quantify transport from the industrialized regions of E Asia using the transit‐time probability density function, ��, to isolate the role of transport from any other factors, such as chemistry and deposition. Using the offline transport model MATCH driven by NCEP reanalyses, we calculate ��, which is the mass fraction of air that had its last contact with the E‐Asian source region during a given day, for each day of a three‐year period. Ensemble means of �� establish the climatological seasonal‐mean transport from E Asia. Export from the source region is most efficient in spring, with nearly all E‐Asian air involved in transpacific transport. In summer, E‐Asian air is transported aloft across the Pacific and, in nearly equal measure, west over SE Asia to the Middle East. Winter transport is similar to that of spring, except winter has low‐level transport to SE Asia. Fall transport is intermediate between that of summer and winter. For all seasons, the most probable transit times to N America are 6–8 days in the mid‐to‐upper troposphere and approximately one week (two for summer) longer at the surface. The surface signal of E‐Asian air over N America is strongest in spring. Daily variability of transpacific transport is quantified in terms of the transit‐time partitioned burden of E‐Asian air over western N America. The standard deviation of the transit‐time partitioned fluctuations has a nearly universal dependence on the corresponding seasonal‐mean burden. The standard deviation peaks several days before the burden at a transit time of ∼7 days. Lagged event and nonevent composites, based on the western N‐American burden of E‐Asian air, reveal that transport events are associated with dipolar wind perturbations over the eastern Pacific that are positioned and phased to receive enhanced Asian outflow. Surface‐pressure correlations are consistent with an associated strengthened Pacific High and weakened Aleutian Low.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".