Cumulative Positive Contributions of Propagating MJO To The Quick Low-Level Atmospheric Response During El Niño Developing Years
Bibliographic record
Abstract
Abstract Based on Australian Bureau of Meteorology (BoM) El Niño alert system, this study investigates the atmospheric and oceanic conditions during El Niño developing years between 1982 and 2016. It is found that there is a 2–5-month lag to establish steady low-level atmospheric (or the Southern Oscillation Index, SOI) response than the steady El Niño-pattern Sea Surface Temperature Anomaly (SSTA), which is defined as the critical period in this research. According to the duration of this critical period, the quick and slow steady atmospheric response years can be identified among all El Niño–Southern Oscillation (ENSO) developing events. The quick establishments of the Sea Level Pressure Anomaly (SLPA) in the tropical atmosphere are proved to be closely related to the subseasonal Madden–Julian Oscillation (MJO) events. In the quick response years, the MJO events can even propagate to the eastern Pacific, which lead to cumulative negative Outgoing Longwave Radiation (OLR) and SLP anomalies there, and make a positive contribution to the quick atmospheric response at the end of critical period. However, the eastward-propagation of MJO events is mainly restricted in the tropical Western Pacific in the slow response years, causing slow steady atmospheric response with almost no contributions from MJO. Furthermore, observations and several simulations are used to understand this propagation differences of the MJO between quick and slow response years.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".