Counteracting Influences of Gravitational Settling Modulate Aerosol Impacts on Cloud Base Lowering Fog Characteristics
Bibliographic record
Abstract
Abstract. One common process for marine fog formation is cloud base lowering (CBL), which is frequently observed, for example, off the coast of California and in Canada's Grand Banks, as well as other foggy ocean regions. While previous studies have extensively examined the meteorological controls on CBL fog, its microphysical characteristics have received comparatively less attention. We employ PAFOG, a single-column model, to investigate the interplay among aerosols, microphysics, and CBL fog evolution under diverse meteorological conditions. We find that lower aerosol concentrations make fog formation more probable, but that if fog does form, fog water concentrations are lower. Particularly at low aerosol concentration, lower aerosol concentrations lead to earlier fog formation due to faster gravitational settling of larger droplets, which serves to flux moisture downward. Faster gravitational settling (among other mechanisms at low aerosol concentration) also suppresses entrainment at cloud top which aids in keeping the liquid water path high. However, faster gravitational settling also limits the fog water concentration through faster liquid deposition to the surface. It is these counteracting influences of gravitational settling that appear to cause both prolonged fog duration and suppressed fog water concentration. The relative strength of these counteracting influences depends on the 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".