Reduced aerosol pollution diminished cloud reflectivity over the North Atlantic and Northeast Pacific
Bibliographic record
Abstract
Over the past several decades, the proportion of solar radiation reflected back into space has declined, accelerating the accumulation of heat within the Earth system. Here we show that the marine cloud reflectivity decreased on average by 2.8 ± 1.2% per decade in the combined North Atlantic and Northeast Pacific regions between 2003 and 2022. The majority of the Earth System Models we analyzed simulated a significantly weaker cloud reflectivity decrease and warming of the sea surface in these regions than observed. In contrast, our simulations using an improved aerosol-climate model reproduce the spatial extent and magnitude of the observed cloud reflectivity decrease. We show that reductions in sulfur dioxide and other aerosol precursors accounted for 69% (range 55−85%) of the cloud reflectivity decrease through aerosol-cloud interactions, consistent with the observed aerosol and cloud trends. This raises the prospect of a continuing cloud reflectivity decrease and an associated warming impact in these regions, given that the emission reductions are projected to persist over the next few decades. Further research is needed to assess whether near-term climate scenarios should be revised to account for the weak cloud reflectivity reductions in the Earth System Models. Marine cloud reflectivity declined by 2.8%/decade (2003-2022) over the NE Pacific and North Atlantic, enhancing shortwave absorption beyond climate model projections. Here, the authors use an improved aerosol-climate model and show that aerosol reductions may account for 69% of the cloud reflectivity decline.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".