Westerly wind bursts simulated in CAM4 and CCSM4
Bibliographic record
Abstract
The equatorial westerly wind bursts (WWBs) play an important role in modulating and predicting the El Niño-Southern Oscillation (ENSO). In this study, the ability of the Community Atmospheric Model version 4 (CAM4) and the Community Climate System Model version 4 (CCSM4) in simulating WWBs is systematically evaluated. Many characteristics of WWBs, including their longitude distributions, durations, zonal extensions, variabilities at seasonal, intraseasonal, and interannual timescales, as well as their relations with the Madden–Julian Oscillation (MJO) and ENSO, are discussed. Generally speaking, these characteristics of WWBs can be successfully reproduced by CAM4, owning to the improvement of the deep convection in the model. In CCSM4, significant bias such as the lack of the equatorial Pacific WWBs in boreal spring season and the weak modulation by a strong MJO are found. Our findings confirm the fact that the WWBs are greatly modulated by the surface temperature. It’s also suggested that improving the air-sea coupling in CCSM4 may improve model performance in simulating WWBs, and may further improve the predictability of ENSO in the coupled model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".