Simulation of Arctic Diamond Dust, Ice Fog, and Thin Stratus Using an Explicit Aerosol–Cloud–Radiation Model
Bibliographic record
Abstract
In support to the development of the Northern Aerosol Regional Climate Model, a single column model with explicit aerosol and cloud microphysics is described.It is designed specifically to investigate cloud-aerosol interactions in the Arctic.A total of 38 size bins discretize the aerosol and cloud spectra from 0.01-to 500-m diameter.The model is based on three equations describing the time evolution of the aerosol, cloud droplet, and ice crystal spectra.The following physical processes are simulated: coagulation, sedimentation, nucleation, coalescence, aggregation, condensation, and deposition.Further, the model accounts for the water-ice phase interaction through the homogeneous and heterogeneous freezing, ice nuclei, and the Bergeron effect.The model has been validated against observations and other models.In this paper, the model is used to simulate diamond dust and ice fog in the Arctic during winter.It is shown that simulated cloud features such as cloud phase, cloud particle diameter, number concentration, and mass concentration are in agreement with observations.The observed vertical structure of mixed-phase cloud is also reproduced with the maximum mass of liquid phase in the upper part of the cloud.Based on simulations, a hypothesis is formulated to explain the thermodynamical unstable mixed-phase state that can last several days in diamond dust events.The ice supersaturation time evolution is assessed and is compared to its evolution in cirrus clouds.It is shown that the supersaturation relaxation time, defined as the time required for supersaturation to decrease by a factor e, is more than 10 times the value found in cirrus clouds.Finally, the radiative contribution of arctic diamond dust and ice fog to the downward longwave radiation flux at the surface is evaluated and compared to observations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".