Non‐linear coupling between modes in a low‐dimensional model of ENSO
Bibliographic record
Abstract
An intermediate coupled model of the tropical Pacific ocean‐atmosphere system was reduced by projecting the non‐linear model onto a truncated basis set of its own empirical orthogonal functions (EOFs). For moderate coupling strengths, the simulated El Niño/Southern Oscillation (ENSO) variability consists of a dominant quasi‐quadrennial mode with a period of approximately four years and a smaller quasi‐biennial mode at a period of approximately two years. In the absence of a seasonal cycle, the leading two EOFs capture the dynamics of the leading interannual mode, with a further two EOFs being required to capture the secondary oscillation. The presence of seasonal forcing increases the EOF requirement by two, the leading pair of EOFs being dominated by the annual cycle. Normal mode analysis of the reduced models indicates that the quasi‐biennial mode manifests itself, even though it is linearly stable, by non‐linear coupling to the quasi‐quadrennial mode. The nonlinearity does not produce the quasi‐biennial signal unless the spatial degrees of freedom associated with the linear quasi‐biennial mode are present. Other linearly stable modes also couple non‐linearly to the leading interannual mode and to the seasonal cycle, but the quasi‐biennial mode is favoured over other, less‐damped linear modes because of its proximity to a multiple of the quasi‐quadrennial frequency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".