Coarse-Grid and Cloud-Resolving Simulations of a Midlatitude Cyclonic Cloud System: Implications for the Parameterization of Layer Clouds in GCMs
Bibliographic record
Abstract
A winter oceanic cyclonic cloud system was simulated by using the mesoscale compressible community (MC2) model with different combinations of model resolutions and cloud microphysics packages. Results from these simulations are intercompared to examine the effects of the coarse model grid and simplified model physics on the simulated large-scale storm environment. When aggregated to an area approximately equivalent to the size of a grid box in current GCMs, the results from the models differ significantly in the large-scale cloud and moisture profiles. Although the effects of using different stratiform cloud schemes on the coarse-grid results are appreciable, the effects of different model resolution are shown to be greater on the large-scale frontal cloud field. In particular, the coarse-grid models underestimated the cloudiness and atmospheric moisture content in the warm-frontal region. Such differences in the large-scale model storm environment were consequences of the stronger mean cross-front circulation and mesoscale cloud features in the high-resolution simulation. The stronger cross-front circulation was in turn a result of stronger frontogenetic processes over the region and dynamic influences of the mesoscale cloud bands on the parent storm. Because both the frontal zones and the mesoscale cloud bands are unresolved features in current GCMs, these results suggest that the parameterization of their bulk effects on the large scales should be included in the representation of frontal layered clouds in climate 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".