Verification Data and the Skill of Decadal Predictions
Bibliographic record
Abstract
The utility of a forecast depends on its skill as demonstrated by past performance. For most forecasts errors rapidly become large compared to uncertainties in the observation-based state of the system and, for this reason, it is usually deemed adequate to assess predictions against a single verification dataset. Eleven reanalyses and station-based analyses of annual mean surface air temperature are compared as are basic skill measures obtained when using them to verify decadal prediction hindcasts from the Canadian Centre for Climate Modelling and Analysis forecasting system. There are differences between reanalysis and station-based analyses which translate also into differences in basic skill scores. In an average sense, using station-based verification data results in somewhat better correlation skill. The spread between the locally best and worst scores is obtained for individual forecast ensemble members and for ensemble mean forecasts compared to individual analyses. The comparison of ensemble mean forecasts against different analyses can result in apparent skill differences, and using a “favorable” analysis for verification can improve apparent forecast skill. These differences may be more pertinent for longer time averages and should be considered when verifying decadal predictions and when comparing the skill of decadal prediction systems as part of a model intercomparison project. Either a particular analysis could be recommended by the decadal prediction community, if such could be agreed on, or the ensemble average of a subset of recent analyses could be used, assuming that ensemble averaging will act to average out errors.
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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 | 0.019 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".