Assessing Cloud Fraction in the Canadian Regional Climate Model Over North America Using Satellite Data and a Satellite Simulator Package
Bibliographic record
Abstract
Abstract Clouds are crucial to Earth's climate system, influencing radiation and contributing to climate projection uncertainties. Here, the simulated cloud fraction by the sixth version of the Canadian Regional Climate Model (CRCM6‐GEM5) was evaluated using CALIPSO lidar retrievals and the second version of the Cloud Feedback Intercomparison Project (CFMIP) Observation Simulator Package (COSP2) for the years 2014 and 2015. Horizontal and vertical distributions of clouds in the CRCM6‐GEM5 model were evaluated using cloud profiles and four cloud categories (total, high‐, mid‐, and low‐level clouds) derived directly from the CRCM6‐GEM5 model and treated using the COSP2 satellite simulator. A seasonal analysis was conducted across specific regions in North America. Results showed that the use of COSP2 is essential for comparing CRCM6‐GEM5 outputs against satellite data to account for variable definitions and signal attenuation of active instruments (e.g., Cloud‐Aerosol Lidar with Orthogonal Polarization: CALIOP). Spatial and vertical cloud distributions and seasonal patterns were generally well represented by the CRCM6‐GEM5 for both winter (December–February) and summer (June–August). High‐ and low‐level clouds were particularly well‐represented, especially in winter. The CRCM6‐GEM5 model demonstrated some difficulty producing enough clouds to accurately represent those at mid‐level. Cloud fraction representation was systematically better during winter than summer. The CRCM6‐GEM5 generally performed well over the whole North American domain for the four cloud categories and COSP2 was confirmed to help mitigate discrepancies in variable definitions. These results contribute to a better understanding of the CRCM6‐GEM5 cloud representations and the use of COSP2 with high‐resolution models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".