A Nonlinear Analysis of the ENSO Cycle and Its Interdecadal Changes*
Bibliographic record
Abstract
Abstract The nonlinear principal component analysis (NLPCA), via a neural network approach, was applied to thermocline anomalies in the tropical Pacific. While the tropical sea surface temperature (SST) anomalies had been nonlinearly mapped by the NLPCA mode 1 onto an open curve in the data space, the thermocline anomalies were mapped to a closed curve, suggesting that ENSO is a cyclic phenomenon. The NLPCA mode 1 of the thermocline anomalies reveals the nonlinear evolution of the ENSO cycle with much asymmetry for the different phases: The weak heat accumulation in the whole equatorial Pacific is followed by the strong El Niño, and the subsequent strong drain of equatorial heat content toward the off-equatorial region precedes a weak La Niña. This asymmetric ENSO evolution implies that the nonlinear instability enhances the growth of El Niño, but dwarfs the growth of La Niña. The nonlinear ENSO cycle was found to have changed since the late 1970s. For the pre-1980s the ENSO cycle associated with the thermocline is less asymmetrical than that during the post-1980s, indicating that the nonlinearity of the ENSO cycle has become stronger since the late 1970s.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".