Cirrus Clouds
Bibliographic record
Abstract
Abstract The goal of this chapter is to synthesize information about what is now known about one of the three main types of clouds, cirrus, and to identify areas where more knowledge is needed. Cirrus clouds, composed of ice particles, form in the upper troposphere, where temperatures are generally below −30°C. Satellite observations show that the maximum-occurrence frequency of cirrus is near the tropics, with a large latitudinal movement seasonally. In situ measurements obtained over a wide range of cirrus types, formation mechanisms, temperatures, and geographical locations indicate that the ice water content and particle size generally decrease with decreasing temperature, whereas the ice particle concentration is nearly constant or increases slightly with decreasing temperature. High ice concentrations, sometimes observed in strong updrafts, result from homogeneous nucleation. The satellite-based and in situ measurements indicate that cirrus ice crystals typically differ from the simple, idealized geometry for smooth hexagonal shapes, indicating complexity and/or surface roughness. Their shapes significantly impact cirrus radiative properties and feedbacks to climate. Cirrus clouds, one of the most uncertain components of general circulation models (GCM), pose one of the greatest challenges in predicting the rate and geographical pattern of climate change. Improved measurements of the properties and size distributions and surface structure of small ice crystals (about 20 μm) and identifying the dominant ice nucleation process (heterogeneous versus homogeneous ice nucleation) under different cloud dynamical forcings will lead to a better representation of their properties in GCM and in modeling their current and future effects on climate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.022 |
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".