Sensitivity of mixed-phase cold-air outbreak clouds to aerosol-cloud interactions and ice production processes depends on environmental conditions: a comparison between spring and autumn CAO case studies over the Labrador Sea
Bibliographic record
Abstract
Aerosol-cloud interactions and ice production processes are important uncertainties in models of mixed-phase cold-air outbreak (CAO) clouds, which are vital for the estimation of cloud-phase feedback. Our model simulation results show that the sensitivities of the mixed-phase cloud properties during the two selected CAO cases are different, with Ice Nucleating Particle (INP) concentrations having a strong influence for both case studies, but the cloud droplet number concentration and the HM (Hallett-Mossop) efficiency only affect the warmer case. We also find that the simulations showing the best performance compared to observations are not consistent across multiple satellite-observed cloud properties, which suggests a possible structural deficiency in the model. The two cases are CAO events over the Labrador Sea, 15 March 2022 and 24 October 2022, with the latter one coinciding with the M-Phase aircraft campaign. The regional Met Office Unified Model coupled with a two-moment microphysics scheme was used to quantify the sensitivity of cloud cover, stratocumulus-to-cumulus transition, and cloud radiative properties to cloud droplet number concentration, INP concentration and efficiency of the HM process. Recent studies have aimed to understand how these two aspects influence CAO clouds, but have not compared the sensitivities under different environmental conditions or with a realistic temperature-dependent parameterisation for INPs. This study provides an instructive perspective on how cloud microphysics affects mixed-phase CAO clouds under different environmental conditions, and serves as a good basis for exploring the whole uncertain cloud microphysics parameter space across a range of environmental conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".