Stratospheric Aerosol Intervention experiment for the Chemistry–Climate Model Initiative
Bibliographic record
Abstract
A new Stratospheric Aerosol Intervention (SAI) experiment has been designed for the Chemistry–Climate Model Initiative (CCMI-2022) to assess the impacts of SAI on stratospheric chemistry and dynamical responses and inter-model differences using a constrained setup with a prescribed stratospheric aerosol distribution and fixed sea surface temperatures and sea ice. This paper serves a dual purpose: first, it describes the details of the experimental setup and the prescribed aerosol distribution and demonstrates the suitability of the simplified setup to study SAI impacts in the stratosphere in a multi-model framework. The experiment allows attributing inter-model differences to the resulting impacts on atmospheric chemistry, radiation, and dynamics rather than the model uncertainty arising from differences in aerosol forcing and feedbacks from the ocean and sea ice under SAI. Second, we use the Whole Atmosphere Community Climate Model (WACCM6) to compare the interactive stratospheric aerosol configuration with coupling to land, ocean, and sea ice used to produce the stratospheric aerosol distribution with the results of the constrained SAI experiment. With this, we identify and isolate the stratosphere-controlled SAI-induced impacts from those influenced by the coupling with the ocean. Overall, this comparison facilitates an advanced process-level understanding of the drivers of SAI-induced atmospheric responses. For example, we confirm earlier suggestions that the SAI-induced positive phase of the North Atlantic Oscillation in winter, with the corresponding winter warming over Eurasia and related changes, is driven by stratosphere–troposphere coupling. Future multi-model comparisons will thus provide an important contribution to upcoming scientific assessments of ozone depletion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".