Detecting changes in global extremes under the GLENS-SAI climate intervention strategy

As anthropogenic activities continue to drive increases in extreme events, the fundamental solution of reducing greenhouse gas emissions remains elusive. Thus, there is growing interest in stratospheric aerosol injection (SAI) to offset some of the most dangerous consequences of climate change. If SAI was deployed at a global scale, it would likely be easy to detect by some metrics. However, the detectability of SAI on extreme events might be more difficult, given the presence of natural climate variability. We examine this question in climate model simulations of SAI. Specifically, we train a logistic regression model to predict whether a map of global extremes came from climate simulations with or without SAI. The timing of accurate predictions is a quantification of the time to detection of SAI impacts. We find that regional changes in extreme temperature and precipitation are robustly detected within 1 and 15 years of initial SAI injection, respectively.