Africa's Climate Response to Marine Cloud Brightening Strategies Is Highly Sensitive to Deployment Region
Bibliographic record
Abstract
Abstract Solar climate intervention refers to a group of methods for reducing climate risks associated with anthropogenic warming by reflecting sunlight. Marine cloud brightening (MCB), one such approach, proposes to inject sea‐salt aerosol into one or more regional marine boundary layer to increase marine cloud reflectivity. Here, we assess the potential influence of various MCB experiments on Africa's climate using simulations from the Community Earth System Model (CESM2) with the Community Atmosphere Model (CAM6) as its atmospheric component. We analyzed four idealized MCB experiments under a medium‐range background forcing scenario (SSP2‐4.5), which brighten clouds over three subtropical ocean regions: (a) Northeast Pacific (MCB NEP ); (b) Southeast Pacific (MCB SEP ); (c) Southeast Atlantic (MCB SEA ); and (d) these three regions simultaneously (MCB ALL ). Our results suggest that the climate impacts of MCB in Africa are highly sensitive to the deployment region. MCB SEP would produce the strongest global cooling effect and thus could be the most effective in decreasing temperatures, increasing precipitation, and reducing the intensity and frequency of temperature and precipitation extremes across most parts of Africa, especially West Africa, in the future (2035–2054) compared to the historical climate (1995–2014). MCB in other regions produces less cooling and wetting despite similar radiative forcings. While the projected changes under MCB ALL are similar to those of MCB SEP , MCB NEP and MCB SEA could see more residual warming and induce a warmer future than under SSP2‐4.5 in some regions across Africa. All MCB experiments are more effective in cooling maximum temperature and related extremes than minimum temperature and related extremes.
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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) | 0.000 | 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".