The impact of model fidelity on seasonal predictive skill
Bibliographic record
Abstract
The relationship between the quality of a general circulation model's (GCM's) representation of present climate and its predictive skill on seasonal time scales is investigated by a series of GCM experiments. A novel procedure is developed to improve the quality of a GCM's present‐day control climate by applying cyclostationary annually varying run‐time “bias corrections”. Application of this procedure to the Canadian Centre for Climate Modelling and Analysis third generation atmospheric GCM (AGCM3) is shown to result in a significant reduction of time‐mean biases in wind, temperature and humidity fields in its simulation of present‐day climate. Furthermore, it is found that this cyclostationary correction leads to improved variability on seasonal time scales. The ability to improve a GCM's properties in this way allows a careful assessment of the relationship between a model's fidelity and its predictive skill. In this study, the potential predictive skill on seasonal time scales is assessed by performing ensemble simulations with the observed sea surface temperatures and sea‐ice distribution. The analysis indicates that the increase in model fidelity associated with the application of the bias correction results in a general increase in predictive skill on seasonal times scales. To investigate this result further, an additional set of ensemble forecasts are performed with the sign of the bias correction reversed, thereby degrading model fidelity. In this additional experiment the corresponding predictive skill is also degraded. The results of this study have implications with regard to the application and interpretation of model metrics for climate GCMs.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".