What Role Does the Barrier Layer Play During Extreme El Niño Events?
Bibliographic record
Abstract
Abstract Intensive air‐sea interaction and the formation of the salinity barrier layer (BL) in the Pacific has fundamental importance to the El Niño evolution. The structure and formation of the BL in the equatorial Pacific Ocean during moderate and extreme El Niños over the past 30 years are investigated using in situ temperature and salinity data measured by the TAO/TRITON array and the data‐assimilating ECCO2 product. In the western and central Pacific Ocean, the BL is thicker during moderate El Niños compared to extreme El Niños due to a deeper isothermal layer depth compared to the density defined mixed layer depth. Moreover, in the western and central Pacific Ocean, the anomalous zonal eastward current related to the westerly wind event that initiates El Niños is found to be stronger during extreme El Niños, advecting the thicker BLs from west to east. A salinity budget suggests that during both moderate and extreme El Niño events, surface freshwater flux dominates at the equator. During extreme El Niños, the change in the freshwater flux drives a strong surface jet in the far western Pacific at 1°S, 156°E. North of the equator, the surface freshwater flux largely dampens this advective impact. Thus during the different El Niño strengths, the BL distribution, evolution and impact are also different. This suggests that climate models need to better distinguish different types of El Niño events in order to simulate the ENSO dynamics correctly.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".