Forcing Susceptibility and Climate Sensitivity to Midlatitude Marine Cloud Brightening
Bibliographic record
Abstract
Abstract The climate intervention approach marine cloud brightening (MCB) would aim to reduce climate warming by injecting sea salt aerosol (iSSA) into the lower troposphere to increase cloud albedo, reflect more sunlight, and cool the surface. Due to the short atmospheric lifetime of tropospheric aerosol, MCB iSSA emissions and their resulting radiative forcing are regional by nature. This presents a significant challenge and opportunity, as there are many potential MCB implementation patterns that could produce widely varying climate responses. Previous modeling studies suggest that MCB implementation in the subtropical oceans can cause global cooling but often result in remote regional temperature and precipitation responses that may be considered undesirable. Here, we use three Earth system models (ESMs) to estimate the impact of MCB implementation in 14 different ocean regions, assessing MCB forcing and cooling efficiency in each region and examining the patterns of temperature response from each case. We find that iSSA emissions in the midlatitude oceans produce stronger cloud forcing, greater cooling efficiency, and more spatially uniform cooling. With this information, we evaluate a novel MCB emission strategy that emits iSSA in the midlatitude oceans. The ESMs show this iSSA emission pattern produces temperature and precipitation responses across all three ESMs that are quite similar in pattern (but of opposite sign) to the greenhouse gas (GHG) response. Thus, compared to previously tested iSSA injection patterns, midlatitude MCB implementations may be more suitable when intending to maintain climates close to present-day conditions. Significance Statement Global climate models (GCMs) suggest sunlight reflection by marine cloud brightening (MCB) via injecting sea salt aerosol (iSSA) could produce substantial cooling to offset the impacts of greenhouse gas warming. They also show that the impacts of MCB depend strongly on the location it is applied. Using a dataset of 14 MCB simulations in three GCMs, we identify a novel MCB strategy that more effectively offsets climate warming by emitting iSSA in the midlatitude oceans in all three models. This reduces certain unintended negative climate impacts that have been found in other MCB modeling studies and enables the development of more plausible cooperative MCB scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".