Quantifications of the Two “Flavours” of El Niño using Upper-Ocean Heat Content
Bibliographic record
Abstract
The sea surface temperature (SST) or sea level pressure (SLP) has usually been used to measure the strength of El Niño–Southern Oscillation (ENSO) events. In this study, two new indices, based on the upper-ocean heat content (HC), are proposed to quantify the two “flavours” of El Niño (i.e., the Cold Tongue El Niño (CTE) and Warm Pool El Niño (WPE)). Compared with traditional SST or SLP indices, the new HC-based indices can distinguish CTE and WPE events much better and also represent the two leading modes of the interannual variability of the atmosphere–ocean coupled system in the tropical Indo-Pacific region. The two leading modes are obtained by performing multivariate Empirical Orthogonal Function analysis on two oceanic variables (SST and HC) over the tropical Pacific (30°S–30°N, 120°E–80°W) and six atmospheric variables (outgoing longwave radiation, SLP, streamfunction, and velocity potential at 850 hPa and 200 hPa) over the tropical Indo-Pacific region (30°S–30°N, 60°E–80°W) for the period 1980–2010. Because the two new HC-based indices are capable of better depicting coherent variations between the ocean and atmosphere, they can provide a supplementary tool for ENSO monitoring of and climate research into the two flavours of El Niño.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".