Reduced efficacy of marine cloud brightening geoengineering due to in-plume aerosol coagulation: parameterization and global implications
Bibliographic record
Abstract
Abstract. The intentional enhancement of cloud albedo via controlled sea-spray injection from ships (marine cloud brightening) has been proposed as a possible method to control anthropogenic global warming; however, there remains significant uncertainty in the efficacy of this method due to, amongst other factors, uncertainties in aerosol and cloud microphysics. A major assumption used in recent cloud- and climate-modeling studies is that all sea spray was emitted uniformly into some oceanic grid boxes, and thus these studies did not account for subgrid aerosol coagulation within the sea-spray plumes. We explore the evolution of these sea-salt plumes using a multi-shelled Gaussian plume model with size-resolved aerosol coagulation. We determine how the final number of particles depends on meteorological conditions, including wind speed and boundary-layer stability, as well as the emission rate and size distribution of aerosol emitted. Under previously proposed injection rates and typical marine conditions, we find that the number of aerosol particles is reduced by over 50%, but this reduction varies from under 10% to over 90% depending on the conditions. We provide a computationally efficient parameterization for cloud-resolving and global-scale models to account for subgrid-scale coagulation, and we implement this parameterization in a global-scale aerosol-climate model. While designed to address subgrid-scale coagulation of sea-salt particles, the parameterization is generally applicable for coagulation of subgrid-scale aerosol from point sources. We find that accounting for this subgrid-scale coagulation reduces cloud droplet number concentrations by 46% over emission regions, and reduces the global mean radiative flux perturbation from −1.5 W m−2 to −0.8 W m−2.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".