Measuring the shape of cloud particle size distributions in high-latitude marine cold air outbreaks
Bibliographic record
Abstract
Marine cold air outbreaks (MCAOs) drive significant evolutions in marine boundary layer clouds and play a crucial role in high-latitude climate systems. This study examines the variability of cloud particle size distributions (PSDs) and the representation of the Gamma shape parameter μ for high-latitude MCAO clouds in the Northern Hemisphere, focusing on the initial stratocumulus stages and cumulus regime transitions. Aircraft in-situ measurements from 20 flights with identified MCAO conditions were collected during two recent field campaigns: M-Phase conducted over the Labrador Sea in March 2022, and Arctic Cold-Air Outbreak (ACAO) conducted over the Nordic Seas in October to November 2022. Results show that high-latitude MCAO clouds in the Northern Hemisphere exhibit narrow PSDs, characterized by higher μ values (mean μ=20) that imply more reflective clouds compared to the fixed μ=2.5 assumption in some bulk microphysics schemes. Cloud PSDs narrow and μ values increase with height in near-adiabatic stratocumulus clouds, while their patterns are more variable in broken cumulus clouds. Liquid water content correlates more strongly with μ variability than cloud number concentrations, suggesting its better predictability as a prognostic variable for PSD variability in these cloud systems. Both the μ=20 and its derived relation with cloud liquid water content can be applied in bulk microphysics schemes to better represent the microphysical and radiative properties of high-latitude MCAO clouds. The proposed high μ values for MCAO clouds are applicable mainly to typical horizontal resolutions of numerical weather prediction and regional 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".