Stronger Arctic amplification from anthropogenic aerosols than from greenhouse gases
Bibliographic record
Abstract
Abstract Arctic amplification (AA), the greater Arctic surface warming compared to the global average, has been widely attributed to increasing concentrations of greenhouse gases (GHG). However, less is known about the impacts of other forcings - notably, anthropogenic aerosols (AER) - and how they may compare to the impacts of GHG. Here we analyze sets of climate model simulations, specifically designed to isolate the AER and GHG effects on global climate. Surprisingly, we find stronger AA produced by AER than by GHG during the 1955–1984 period, when the strongest global AER increase. This stronger AER-induced AA is due to a greater sensitivity of Arctic sea ice, and associated changes in ocean-to-atmosphere heat exchange, to AER forcing. Our findings highlight the asymmetric Arctic climate response to GHG and AER forcings, and show that clean air policies which have reduced aerosol emissions may have exacerbated the Arctic warming over the past few decades.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".