Application of Satellite Data for Evaluating the Cold Climate Performance of the Canadian Regional Climate Model over Québec, Canada
Bibliographic record
Abstract
Abstract This study evaluates key aspects of the snow cover, cloud cover, and radiation budget simulated by the Canadian Regional Climate Model, version 4 (CRCM4), coupled with two versions of the Canadian Land Surface Scheme (CLASS). CRCM4 coupled with CLASS version 2.7 has been used operationally at Ouranos since 2006, while, more recently, CRCM4 has been coupled experimentally with CLASS 3.5, which includes a number of improvements to the representation of snow cover processes. The simulations showed evidence of a systematic cold temperature bias. Evaluation of cloud cover and radiation fluxes with satellite data suggests this bias is related to insufficient cloud radiative forcing from a combination of underestimated cloud cover, excessive cloud albedo, and too low cloud emissivity in the model. This cold bias is reinforced by a positive snow albedo feedback manifest through earlier snow cover onset in the fall and early winter period. Snow albedo was found to be very sensitive to the treatment of albedo refresh but insignificantly influenced by the partitioning of solid precipitation in CLASS. This study demonstrates that atmospheric forcing can exert a significant impact on the simulation of snow cover and surface albedo. The results highlight the need to evaluate parameterizations in land surface models designed for climate models in fully coupled mode.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".