An Investigation of Summer Precipitation Simulated by the Canadian Regional Climate Model
Bibliographic record
Abstract
Abstract In the present paper, a 5-yr baseline integration for the period 1987–91 was carried out over a Pan-Canadian domain to validate the performance of the third-generation Canadian Regional Climate Model (CRCM). The CRCM simulated the large-scale circulation over North America well; it also correctly captured the seasonal variability of surface temperature and reproduced the winter precipitation over North America realistically. However, the CRCM systematically overestimated the summer precipitation over the continent when compared with the observed values. Extensive experiments have been conducted to trace down the sources of error of summer precipitation. Particular attention has been given to the water-vapor-related physical parameterization processes such as the mass flux convection scheme in the CRCM. Experiments involving spectral nudging of the specific humidity toward the values of large-scale driving data enabled the authors to link overestimation with abundant water vapor accumulated in the lower boundary layer resulting from an excessive amount of moisture stored in the soil. A strong boundary layer mixing process from the third generation of the Canadian Atmospheric General Circulation Model was then implemented into the CRCM along with an adjustment to the soil water holding capacity. A final analysis of a 14-month experiment showed that these modifications significantly improved the simulation of summer precipitation over North America without adversely affecting the simulation of winter precipitation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".