Mapping the Relationship between Northern Hemisphere Winter Surface Air Temperature and the Madden–Julian Oscillation
Bibliographic record
Abstract
Abstract A regression-based modeling approach is described for mapping the dependence of atmospheric state variables such as surface air temperature (SAT) on the Madden–Julian oscillation (MJO). For the special case of a linear model the dependence can be described by two maps corresponding to the amplitude and lag of the mean atmospheric response with respect to the MJO. In this sense the method leads to a more parsimonious description than traditional compositing, which usually results in eight maps, one for each MJO phase. Another advantage of the amplitude and phase maps is that they clearly identify propagating signals, and also regions where the response is strongly amplified or attenuated. A straightforward extension of the linear model is proposed to allow the amplitude and phase of the response to vary with the amplitude of the MJO or indices that define the background state of the atmosphere–ocean system. Application of the approach to global SAT for boreal winter clearly shows the propagation of MJO-related signals in both the tropics and extratropics and an enhanced response over eastern North America and Alaska (further enhanced during La Niña years). The SAT response over Alaska and eastern North America is caused mainly by horizontal advection related to variations in shore-normal surface winds that, in turn, can be traced (via signals in the 500-hPa geopotential height) back to MJO-related disturbances in the tropics.
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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 | 0.001 |
| 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) | 0.001 | 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".