Detecting Changes in Global Extremes Under the GLENS-SAI Climate Intervention Strategy

Abstract

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. Although global SAI deployment would likely be easy to detect by some metrics, the detectability of SAI on extreme events might be more difficult. We examine this question in climate model simulations of SAI; specifically, the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS-SAI) scenario. 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 required to detect SAI impacts. We find that regional changes in extreme temperature and precipitation under GLENS are robustly detected within 1 and 15 years of initial SAI injection, respectively.