Middle Atmosphere Response to ENSO Events in Northern Hemisphere Winter by the Whole Atmosphere Community Climate Model
Bibliographic record
Abstract
The influences of El Niño and La Niña sea surface temperature anomalies on the middle atmosphere are analyzed using a chemistry-climate model, the Whole Atmosphere Community Climate Model, version 3 (WACCM3), and the 40-year reanalysis dataset from the European Centre for Medium-range Weather Forecasts (ERA-40). Monthly mean data are used to perform statistical and dynamical analyses. Temperature anomalies are found in the stratosphere during both El Niño and La Niña years. These anomalies exhibit diverse distribution patterns, which we ascribe to the different scales of the planetary waves. During an El Niño winter, planetary waves are most active in December and January, leading to more disturbances of the stratospheric polar vortex and a higher frequency of stratospheric sudden warming (SSW) events. Moreover, on the basis of the three-dimensional Eliassen-Palm (E-P) flux, we find that the main wave action is located in the eastern hemisphere and displaces the polar vortex to the western hemisphere. These vortex displacements are closely connected to the weather and climate in the corresponding areas. In contrast, during a La Niña winter, planetary waves are most active in February and March, and most SSW events occur at the same time. The distribution of the E-P flux indicates that planetary wave fluctuations are located between 90°E and 180°E, resulting in a displacement of the polar vortex with some associated changes in the related atmospheric circulation. In addition, the mesospheric residual circulation (RC) reverses between February and March in both El Niño and La Niña years. Dynamical analyses using parameterized gravity waves show that the mesospheric meridional RC is closely connected to gravity-wave drag. This, in turn, is directly influenced by temperature and zonal wind patterns in the early and late winter of El Niño and La Niña years. These anomalies in the RC may influence the distribution of atmospheric constituents and should be explored further.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".