From Qualitative to Quantitative: A Systematic Framework for Measuring SAI Efficacy

As the global community struggles to limit global warming, interest is growing in interventions to lower global temperatures. Stratospheric Aerosol Injection (SAI), a method of stabilizing temperatures by releasing reflective particles into the upper atmosphere, appears to offer a practical means of doing so. The IPCC Sixth Assessment Report (AR6) acknowledged that Stratospheric Aerosol Injection (SAI) could offset some of the effects of increasing greenhouse gases (GHGs), yet this conclusion lacked a standardized, quantitative framework to measure performance across a diverse range of climate hazards. To address this for the upcoming Seventh Assessment Report (AR7), we introduce a systematic methodology designed to quantify SAI efficacy through the lens of the IPCC Climate Impact-Drivers (CIDs). This research transitions away from descriptive, qualitative summaries toward a data-driven performance analysis, assessing how effectively SAI counteracts the specific "effects of climate change" defined by the IPCC.

Our analytical approach utilizes multi-model ensembles, specifically incorporating G6sulfur simulations from GeoMIP and ARISE-SAI simulations. To ensure a fair comparison across various deployment strategies and scenarios, we apply a normalization framework based on linear scaling. This allows us to evaluate climate feedback on a per-degree basis of warming versus cooling, isolating "first-order" physical responses that remain consistent across different SAI implementations. By synthesizing these results across global regions and diverse physical metrics, this work builds a rigorous foundation for determining the efficacy of SAI to minimize climate risks. The ultimate goal is to identify for which indicator and in which regions SAI could work well and where it could worsen the impacts of climate change. This work will help provide a vital, evidence-based foundation for an informed discussion of SAI as a climate policy option.