Evaluation of climate predictability for multiple climate models at various time scales.
Bibliographic record
Abstract
The predictability o f the Pacific North American (PNA) pattern is evaluated on time scales from days to months using state-of-the-art dynamical multiple model ensembles including the Canadian Historical Forecast Project (HFP2) ensemble, the Development o f a European Multimodel Ensemble System for Seasonal-to-Interannual prediction (DEMETER) ensemble, and the Ensemble Based Predictions o f Climate Changes and their Impacts (ENSEMBLES).Some interesting findings in this study include (i) Multiple-model ensemble (MME) skill was better than skill from most o f the individual models; (ii) both actual prediction skill and potential predictability increased as the averaging time scale increased from days to months; (iii) There is no significant difference in actual skill between coupled and uncoupled models, in contrast with the potential predictability where coupled models performed better than uncoupled models; (iv) relative entropy (REA) is an effective measure in characterizing the potential predictability o f individual predictions, whereas the mutual information (MI) is a reliable indicator o f overall prediction skill; (v) Compared with conventional potential predictability measures o f the signal-to-noise ratio, the Mi-based measures characterized more potential predictability when the ensemble spread varied over initial conditions.It is also confirmed that from monthly to seasonal time scales, the potential predictability o f PNA is teleconnected with ENSO.The predictive skill on intra-seasonal time scales in the tropics is linked to Madden-Julian Oscillations (MJO).Using recently developed framework o f potential predictability, information-based and ensemble based predictability measures were explored on multiple time scales for MJO predictability.Results show that there is no significant difference in the vi
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".