Probabilistic Prediction of ENSO Over the Past 137 Years Using the CESM Model
Bibliographic record
Abstract
Abstract In this study, we investigate probabilistic predictability for the El Niño‐Southern Oscillation (ENSO) by assessing both actual prediction skill and potential predictability using a long‐term retrospective forecast from a complicated coupled general circulation model (CGCM). Our results indicate that above and below normal events are more predictable than neutral events. The probabilistic prediction skill suffers prominent “Spring Predictability Barrier” and undergoes notable interdecadal variation. For the above and below normal events, the lowest probabilistic prediction skills appear during 1920–1940 and the higher prediction skills occur after the 1960s. The seasonal and interdecadal variability of the probabilistic prediction skill stems mainly from the variability of the ENSO signal intensity. There is much room for improvement for the predictability of all three categories of ENSO events. At least an additional 1 or 2 months of skillful probabilistic predictions can be expected to progress in the future. To our knowledge, this is the first study to use a CGCM to evaluate probabilistic predictability for ENSO at various time scales.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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 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".