Effects of Semistochastic Westerly Wind Bursts on ENSO Predictability
Bibliographic record
Abstract
Abstract Westerly wind bursts (WWBs) occurring over the tropical Pacific play an important role on El Niño‐Southern Oscillation (ENSO) dynamics. Currently, climate models have significant biases in their representation of WWBs, which may limit their ability to predict ENSO. In this study, we explore the possibility of improving ENSO prediction by introducing a semistochastic WWBs parameterization scheme into the Community Earth System Model (CESM). Three ensemble hindcasts, namely, the control run with the original CESM, the WWB run with parameterized WWBs, and the Non_WWB run with built‐in WWBs removed, are conducted for the period 1982–2016. We find that CESM with parameterized WWBs enables better ENSO prediction, especially for both amplitude and spatial distribution of eastern Pacific and central Pacific El Niño events. This improvement is related to more realistic representation of WWBs which leads to better prediction of surface wind stress anomalies and thermocline depth anomalies.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".