Climate responses to regional aerosol emissions: Early multi-model results from RAMIP
Bibliographic record
Abstract
Anthropogenic aerosol emissions are expected to change rapidly over the coming decades, with complex geographical and seasonal patterns. This is expected to drive strong, spatially varying trends in temperature, hydroclimate, and extreme events, both near and far from emission sources. These changes are poorly constrained in current models, and very sparsely represented in climate risk assessments, partly because of a lack of dedicated emission pathways and multi-model investigations.The Regional Aerosol Model Intercomparison Project (RAMIP) is designed to quantify and bound the role of regional aerosol emissions changes in near-term climate projections. RAMIP experiments are based on the SSPs commonly used in CMIP6 Endorsed MIPs, but are designed to explore sensitivities to aerosol type and location, and provide improved constraints on uncertainties driven by aerosol radiative forcing and the dynamical response to aerosol changes. The core experiments assess the effects of different aerosol emission pathways in East Asia, South Asia, Africa and the Middle East, and North America and Europe through 2051, using a multi-ensemble-member approach in a set of 10 Earth System Models.Based on early output from a subset of participating RAMIP models, we highlight regions where current and future aerosol reductions may lead to changes in seasonal mean climate and the frequency and severity of extreme events. We will also show examples of how the near-future evolution of temperature and precipitation extremes in Europe and Asia may be influenced by local air quality policies, and those further afield.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".