Comment on egusphere-2024-4070
Bibliographic record
Abstract
Abstract. Aerosol-cloud interactions and ice production processes are important factors that influence mixed-phase cold-air outbreak (CAO) clouds and their contribution to cloud-phase feedback. Our current understanding is that increases in ice-nucleating particle (INP) concentrations cause a reduction in cloud total water content and reflectivity. However, no study has compared the sensitivities of the CAO cloud to these processes under different environmental conditions. Here, we use a high-resolution nested model to quantify and compare the responses of cloud microphysics and dynamics in cloud droplet number concentration (Nd), INP concentration and efficiency of the Hallet-Mossop (HM) secondary ice production process in two archetypal CAO events over the Labrador Sea, representing intense (cold, March) and weaker (warmer, October) mixed-phase conditions. Our results show that variations in INP concentrations strongly influence both cases, while changing Nd and the HM process efficiency affect only the warmer October case. With a higher INP concentration, cloud cover and albedo at the top of the atmosphere increase in the cold March case, while the opposite responses were found in the warm October case. We suggest that the CAO cloud response to the parameters is different in ice-dominated and liquid-dominated regimes, and the determination of the regime is strongly controlled by the cloud temperature and the characteristics of ambient INP, which both control the glaciation of clouds. This study provides an instructive perspective to understand how these cloud microphysics affect CAO clouds under different environmental conditions and serves as an important basis for future exploration of cloud microphysics parameter space.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.019 | 0.009 |
| Insufficient payload (model declined to judge) | 0.415 | 0.298 |
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".