Simulating the impact of sea salt on global nss sulphate aerosols
Bibliographic record
Abstract
The Canadian Aerosol Module coupled with the Canadian third generation Global Climate Model was used to simulate the global distributions of size‐segregated sea salt and sulphate aerosols of both anthropogenic and natural origins in the atmosphere. A sectional model of 12 size bins was used to treat the size distribution of sea salt and sulphate, which is assumed to be internally mixed in each size bin. The spatial and temporal distributions predicted by the model compare reasonably well with observations. The mixed aerosol simulations yield number and volume size distributions in the marine boundary layer (MBL) comparable with observations. Sea salt particles redistribute the mass and number distributions of sulphate aerosols by serving as a quenching agent to nucleation and as an additional surface area for condensation and by changing the cloud properties in the MBL. By differential simulations of global sea salt and sulphate it is found that the presence of sea salt increases the mass mean diameter of sulphate aerosols by up to a factor of 2 over the MBL with high sea salt concentrations and reduces the global sulphate aerosol mass in the surface MBL layer from 5 to 75% depending on the sea salt distributions. The high impacts are in the midlatitudes of both Northern and Southern Hemispheres with a minimum in the equatorial regions. In the polluted anthropogenic regions of North Pacific and Atlantic, sea salt reduces the sulphate concentration from 10 to 30%. The peak reductions of 50–75% occur in the roaring 40s of the Southern Hemisphere in spring and fall. The impact of sea salt on the annual global mass and number loading is estimated to be 9.13 and 0.76%, respectively. A reduction of 20–60% in the marine cloud droplet number concentrations (CDNC) was predicted because of the presence of sea salt, with greatest reductions in the roaring 40s south (40–70%) and in the midlatitude north (20–40%) where the sea salt concentrations were high. Along the equatorial regions some enhancement of total CDNC was simulated because of the presence of sea salt aerosols.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".