On the relationship between the Madden‐Julian Oscillation and 2 m air temperature over central Asia in boreal winter
Bibliographic record
Abstract
Abstract Linear regression is used to explore the relationship between the Madden‐Julian oscillation (MJO) and 2 m air temperature (T2M) over central Asia in boreal winter during 1979–2012. During MJO phases 3 and 4 (7 and 8), T2M anomalies exhibit a significantly strong, negative (positive) response to the MJO from the Arabian Sea to northwestern China. The anomalies of T2M are essentially influenced by surface net downward long (Ldown) and shortwave radiations, which are caused by the changes in total cloud cover (TCC) and low‐level tropospheric air temperature. The anomalies of Ldown that are caused by TCC account for 20–65% of total Ldown. The remaining anomalies of total Ldown are explained by low‐level air temperature changes. The 850 hPa air temperature (T850) tendency is mainly affected by the vertical motion over central Asia during MJO phases 1, 2, 4–6, and 8, as well as over northern India during phases 3 and 7. Over Saudi Arabia, Afghanistan, Pakistan, Kazakhstan, and northwestern China, the anomalies of T850 tendency are mainly explained by the temperature advection during phases 3 and 7. TCC and vertical motion are affected by the evolution of the MJO event. The cyclonic (anticyclonic) circulation related to the MJO over central Asia during phases 3 and 4 (7 and 8) causes the transport of cold (warm) air over central Asia. The MJO can be a useful intraseasonal signal to predict winter T2M over central Asia, where temperatures would be colder (warmer) than normal during MJO phases 3 and 4 (7 and 8).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".