Africa's Climate Response to Marine Cloud Brightening
Bibliographic record
Abstract
Climate intervention through solar radiation modification is one proposed method for reducing climate risks from anthropogenic warming. Marine Cloud Brightening (MCB), one such approach, proposes to inject sea salt aerosol into a regional marine boundary layer to increase marine clouds' reflectivity. This study assessed the potential influence of four MCB experiments on the climate in Africa using simulations from the Community Earth System Model (CESM2) with the Community Atmospheric Model (CAM6). Four idealised MCB experiments were performed with the CESM2(CAM6) model under a medium-range background forcing scenario (SSP2-4.5) by setting cloud droplet number concentrations to 600 cm-3 over three subtropical ocean regions: (a) Northeast Pacific (MCBNEP); (b) Southeast Pacific (MCBSEP); (c) Southeast Atlantic (MCBSEA); and (d) the combination of these three regions (MCBALL). The CESM2(CAM6) model reproduces the observed spatial distribution and seasonal cycle of precipitation and minimum and maximum temperatures over Africa and its climatic zones well. The results suggest that MCBSEP would induce the strongest global cooling effect and thus could be the most effective in decreasing (increasing) temperatures (precipitation) and associated extremes across most parts of the continent, especially over West Africa, in the future (2035-2054) while other regions could remain warmer or dryer compared to the historical climate (1995-2014). While the projected changes under MCBALL are similar to those of MCBSEP, MCBNEP and MCBSEA could result in more warming and, in some regions of Africa, create a warmer future than under SSP2-4.5. Also, all MCB experiments are more effective in cooling maximum temperature and related extremes than minimum temperature and related extremes. These findings further suggest that the climate impacts of MCB in Africa are highly sensitive to the deployment region.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".