Testing and Modelling the Volatility Change in ENSO
Bibliographic record
Abstract
The El Niño–Southern Oscillation (ENSO) is by far the most energetic climate signal. Any change in ENSO characteristics will have serious consequences for the global climate system. This work suggests a different view at the change in ENSO volatility in addition to change in its descriptive statistics. The volatility or the conditional variance of ENSO is tested and modelled using both the Autoregressive Moving Average–Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH) error model and the GARCH model, to investigate the change in the short-run and long-run persistency of the second-order moment of ENSO before and after a change point detected by a Bayesian change point analysis. Nonparametric tests revealed a significant change in descriptive statistical characteristics such as the mean, the (unconditional) variance, and the probability distribution of ENSO after a change point in 1975. An Engle's test did not show heteroscedasticity in the random process (residuals) of the Southern Oscillation Index (SOI) time series before 1975 although heteroscedasticity increased and appeared after 1975. The GARCH model indicates an increasing short-run persistency after 1975 and decreasing long-run persistency. A seasonal shift in extreme heteroscedasticity is observed from summer to winter. In addition, the non-linearity and nonstationarity of the SOI volatility have increased in recent decades. This may be caused by an increase in frequency and magnitude of extreme volatilities after 1975. The results of this study indicate that ENSO has become more dynamic and uncertain in recent decades. The increase in the frequency of extreme events together with extreme conditional variance after 1975 may increase the prediction uncertainty of ENSO-driven climate phenomena.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".