Development and validation of the Climate Model Confidence Index (CMCI): measuring ability to reproduce historical climate conditions
Bibliographic record
Abstract
Abstract This study further develops and finally validates the Climate Model Confidence Index (CMCI) as a simple and effective metric for evaluating and ranking the ability of climate models to reproduce historical climate conditions. Modelled daily climate data outputs from two different statistical downscaling techniques (PCIC: Pacific Climate Impacts Consortium; SDSM: Statistical Down-Scaling Model) are compared with observational data recorded by Environment Canada weather stations located in Kelowna, BC (Canada), for the period from 1969 to 2005. Using daily data ( N > 13,000), Student’s t -tests determined if there were statistically significant differences between the modelled and observed means while ANOVA F -tests identified differences between variances. Using aggregated annual data ( N = 37), CMCI values were also calculated for the individual model runs from each statistical downscaling technique. Climate model outputs were ranked according to the absolute value of the t statistics. The 20 SDSM ensembles outperformed the 27 PCIC models for both minimum and maximum temperatures, while PCIC outperformed SDSM for total precipitation. Linear regression determined the correlation between the absolute value of the t statistics and the corresponding CMCI values ( R 2 > 0.99, P < 0.001). Rare discrepancies (< 10% of all model rankings) between the t statistic and CMCI rankings occurred at the third decimal place and resulted in a one rank difference between models. These discrepancies are attributed to the precision of the t tests which rely on daily data and consider observed as well as modelled variance, whereas the simplicity and utility of the CMCI are demonstrated by only requiring annual data and observed variance to calculate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".