Subseasonal Variability of Precipitation in China during Boreal Winter
Bibliographic record
Abstract
Abstract Using pentad data of the Northern Hemisphere extended winter (November–March) from 1979 to 2012 derived from the daily rainfall of the National Meteorological Information Center of China, subseasonal variability of precipitation in China is analyzed. The two dominant modes of subseasonal variability are identified with an empirical orthogonal function (EOF) analysis. The first EOF mode (EOF1) is characterized by a monopole in South China, whereas the second EOF mode (EOF2) has a meridional dipole structure with opposite precipitation anomalies over the Yangtze River basin and the coastal area of South China. These two modes tend to have a phase shift to each other in both space and time, indicating that part of their variability represents a southward-propagating pattern. The subseasonal variability is decomposed into two components: one related to the Madden–Julian oscillation (MJO) and the other independent of MJO. It is found that the MJO contributes to about 10% of the precipitation variability in South China. EOF1 is associated with MJO phase 3, corresponding to enhanced equatorial convection in the Indian Ocean and depressed convection in the western Pacific, while EOF2 is related to MJO phase 5 when the enhanced tropical convection moves to the Maritime Continent region. Subseasonal precipitation variability in China that is independent of the MJO is especially affected by processes including tropical convection variability and the “cold surge” phenomenon or the development of a Siberian high and cold-air outbreak in East Asia associated with a wave train from the North Atlantic.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".