Comparison of the Performance of CMIP5 and CMIP6 in the Prediction of Rainfall Trends, Case Study Quebec City
Bibliographic record
Abstract
Climate change affects many meteorological parameters that could result in spatiotemporal variations of the hydrological cycle. These variations can affect local rainfall intensities or design storms; therefore, it is necessary to assess the local effects of climate change in different areas. Therefore, the current research aims at evaluating the accuracy of the precipitation data of the most recent Coupled Model Intercomparison Project phases 5 and 6 (CanESM2 from CMIP5 and CanESM5 from CMIP6 models), over a historical period from 1953 to 2010, as well as the predicted data for the future between 2010 and 2050 for the Quebec City rain gauge Station (Jean Lesage Intl). In this regard, precipitation data were analyzed using a statistical index to find the most accurate model for the study area. The results of this evaluation showed that CanESM5 is more accurate than CanESM2 for most of the evaluation indices. However, both of these models did not perform well since the precipitation prediction for CanESM5 (as the accurate model) R index was 0.48 for the monthly and was 0.75 in the seasonal scale. In addition, the Bias index revealed that both models underestimated rainfall prediction with negative index values for both scales and models. The trend of future precipitation under socio–economic scenarios (4.5 (pessimistic) and 8.5 (optimistic)) shows that the changes in future precipitation are not significant. In addition, for scenario 4.5, the trend of precipitation decreases for almost half of the year, while for scenario 8.5, the magnitude of the decrease and the number of months with a decreasing trend of precipitation are significantly reduced when compared to scenario 4.5.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".