Limitations of using an equilibrium approximation in an aerosol activation parameterization
Bibliographic record
Abstract
Because of its complex nature, attempts have been made to parameterize the process of cloud droplet formation and growth. The parameterization developed by Abdul‐Razzak et al. [1998] and Abdul‐Razzak and Ghan [2000] , henceforth referred to as the ARG parameterization, is based on the adiabatic ascent of an air parcel, assuming that each particle is in equilibrium with its environment. Some of the limitations of the equilibrium assumption are evaluated here through direct comparison of a kinetic parcel model with the ARG parameterization. Conditions are described in which the supersaturation and the cloud droplet number concentration are underpredicted by the parameterization. The underprediction happens because of an overestimation of the condensation rate of water onto the larger particles. The effect is significant for conditions of lower updraft velocities (V < 50 cm s−1) and higher aerosol number concentrations (Na > 500 cm−3). The parameterization behaves well for higher updraft velocities and lower aerosol number concentrations. The impact of this effect on the activation of sulfate aerosols in the presence of sea salt aerosol is also investigated. Using the ARG parameterization to diagnose Nd in the presence of sea salt aerosol leads to a greater reduction in the number of activated sulfate particles in lower updraft conditions than does using the kinetic parcel model. The relative contribution of sulfate particles to total Nd is also significantly lower for the ARG parameterization than it is for the parcel model in low to moderate updrafts. The error in the first indirect radiative forcing introduced by the equilibrium assumption is estimated to be 6 to 15 W m−2 for typical marine clouds, depending on the updraft velocity.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".