Factors influencing the microphysics and radiative properties of liquid-dominated Arctic clouds: Insight from observations of aerosol and clouds during ISDAC
Bibliographic record
Abstract
[1] Aircraft measurements during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) in April 2008 are used to investigate factors influencing the microphysics and radiative properties of springtime Arctic clouds. The analysis is focused on low-level, liquid-dominated clouds in two separate regimes with respect to cloud and aerosol properties: single-layer stratocumulus with below-cloud aerosol concentrations (Na) less than 250 cm−3 (clean cases); and layered stratocumulus with Na > 500 cm−3 below cloud base, associated with a biomass burning aerosol (polluted cases). For each regime, vertical profiles through cloud are used to determine cloud microphysical and radiative properties. The polluted cases were correlated with warmer, geometrically thicker clouds, with higher droplet number concentrations (Nd), liquid water paths (LWP), optical depths (τ), and albedo (A) relative to clean cases. The mean cloud droplet effective radii (reff), however, were similar (5.7 μm) for both aerosol-cloud regimes. This discrepancy resulted mainly from the higher LWP of clouds in polluted cases, which can be explained by both meteorological (temperature, dynamics) and microphysical (precipitation inhibition) factors. Adiabatic parcel model simulations demonstrate that differences in droplet activation between the aerosol-cloud regimes may play a role, as the higher Na in polluted cases limits activation to larger and/or more hygroscopic particles. The observations and analysis presented here demonstrate the complex interactions among environmental conditions, aerosol, and the microphysics and radiative properties of Arctic clouds.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".