An analysis of multi‐model ensembles for seasonal climate predictions
Bibliographic record
Abstract
Abstract In this study, the superiorities of the super‐ensemble for seasonal climate predictions are investigated based on the 500 mb geopotential height (GPH500) hindcasts produced by four Canadian atmospheric seasonal climate prediction models. The investigations are carried out mainly in two aspects: (i) a comprehensive evaluation of predictions for each grid point over the global domain by deterministic, probabilistic and potential prediction skill measures; (ii) the empirical orthogonal function (EOF) and the Maximum Signal‐to‐Noise (MSN) EOF analyses in the Northern Hemisphere. It is found that improvements of the super‐ensemble are mainly due to the increase of ensemble size in the mid‐high latitudes and the offsets of model uncertainties in the tropical regions. Measures of temporal correlation coefficient (CORR), Brier skill score (BSS) and reliability (REL) are more affected by the ensemble size; whereas the relative root‐mean‐square error (RRMSE) and resolution (RES) are sensitive to the offsets of model uncertainties. The first EOF mode of ensemble mean is similar to the most predictable pattern derived by the MSN EOF method, but the latter has the temporal evolutions more associated with the oceanic boundary forcing. The super‐ensemble shows advantages in both EOF and MSN EOF analyses.
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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 | 0.004 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".