Comparison Studies of Cloud- and Convection-Related Processes Simulated by the Canadian Regional Climate Model over the Pacific Ocean
Bibliographic record
Abstract
Abstract This paper presents results from the Canadian Regional Climate Model (CRCM) contribution to the Global Energy and Water Cycle Experiment (GEWEX) Pacific Cross-section Intercomparison Project. This experiment constitutes a simulation of stratocumulus, trade cumulus, and deep convective transitions along a cross section in the tropical Pacific. The simulated seasonal mean cloud and convection are compared between an original version of CRCM (CRCM4) and a modified version (CRCMM) with refined parameterizations. Results are further compared against available observations and reanalysis data. The specific parameterization refinements touch upon the triggering and closure of shallow convection, the cloud and updraft characteristics of deep convection, the parameterization of large-scale cloud fraction, the calculation of the eddy diffusivity in the boundary layer, and the evaporation of falling large-scale precipitation. CRCMM shows substantial improvement in many aspects of the simulated seasonal mean cloud, convection, and precipitation over the tropical Pacific, CRCMM-simulated total column water vapor, total cloud cover, and precipitation are in better agreement with observations than in the original CRCM4 model. The maximum frequency of the shallow convection shifts from the ITCZ region in CRCM4 to the subtropics in CRCMM; accordingly, excessive cloud in the shallow cumulus region in CRCM4 is greatly diminished. Finally, CRCMM better simulates the vertical structure of relative humidity, cloud cover, and vertical velocity, at least when compared to the 40-yr ECMWF Re-Analysis. Analyses of sensitivity experiments assessing specific effects of individual parameterization changes indicate that the modification to the eddy diffusivity in the boundary layer and changes to deep convection contribute most significantly to the overall model improvements.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".