Intercomparison of model simulations of mixed‐phase clouds observed during the ARM Mixed‐Phase Arctic Cloud Experiment. II: Multilayer cloud
Bibliographic record
Abstract
Abstract Results are presented from an intercomparison of single‐column and cloud‐resolving model simulations of a deep, multilayered, mixed‐phase cloud system observed during the Atmospheric Radiation Measurement (ARM) Mixed‐Phase Arctic Cloud Experiment. This cloud system was associated with strong surface turbulent sensible and latent heat fluxes as cold air flowed over the open Arctic Ocean, combined with a low pressure system that supplied moisture at mid‐levels. The simulations, performed by 13 single‐column and 4 cloud‐resolving models, generally overestimate liquid water path and strongly underestimate ice water path, although there is a large spread among models. This finding is in contrast with results for the single‐layer, low‐level mixed‐phase stratocumulus case in Part I, as well as previous studies of shallow mixed‐phase Arctic clouds, that showed an underprediction of liquid water path. These results suggest important differences in the ability of models to simulate deeper Arctic mixed‐phase clouds versus the shallow, single‐layered mixed‐phase clouds in Part I. The observed liquid‐ice mass ratios were much smaller than in Part I, despite the similarity of cloud temperatures. Thus, models employing microphysics schemes with temperature‐based partitioning of cloud liquid and ice masses are not able to produce results consistent with observations for both cases. Models with more sophisticated, two‐moment treatment of cloud microphysics produce a somewhat smaller liquid water path closer to observations. Cloud‐resolving models tend to produce a larger cloud fraction than single‐column models. The liquid water path and cloud fraction have a large impact on the cloud radiative forcing at the surface, which is dominated by long‐wave flux. Copyright © 2009 Royal Meteorological Society
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".