Detecting Changes in Global Extremes Under the GLENS‐SAI Climate Intervention Strategy. Issue 20 (21st October 2022) - Barnes, Elizabeth A.; Hurrell, James W.; Sun, Lantao
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. Plain Language Summary: In light of concerns regarding increasing extremes driven by human‐induced climate change, and the limited progress to‐date of climate change solutions, a key recommendation from a recent National Academies of Science, Engineering and Medicine report is that the United States should establish a transdisciplinary research program into proposed climate intervention techniques, including stratospheric aerosol injection (SAI). SAI would increase the number of small reflective particles in the upper atmosphere to cool the climate by reflecting a small percentage of incoming solar radiation back into space. If SAI were deployed, the question arises as to when and where we might first detect regional impacts of SAI on climate extremes. Here, we begin to examine this question by analyzing climate model simulations of the 21st century both without and with SAI deployment. We train a simple statistical method to predict whether a map of climate extremes came from a world with or without SAI. By looking at the ability of the statistical model to accurately identify the presence (or absence) of SAI deployment, we find that regional changes in extreme temperatures and precipitation under SAI are robustly detected within 1 and 15 years of initial SAI deployment, respectively. Key Points: A statistical model is trained to predict whether a map of global extremes came from a Representative Concentration Pathway 8.5 or stratospheric aerosol injection (SAI) simulation The timing of accurate predictions acts as a quantification of the time to detection of a geoengineered climate Regional changes in extreme temperatures and extreme precipitation under SAI are robustly detected within 1 and 15 years of injection