Using Explainable Artificial Intelligence to Quantify “Climate Distinguishability” After Stratospheric Aerosol Injection

Abstract

Abstract Stratospheric aerosol injection (SAI) has been proposed as a possible response option to limit global warming and its societal consequences. However, the climate impacts of such intervention are unclear. Here, an explainable artificial intelligence (XAI) framework is introduced to quantify how distinguishable an SAI climate might be from a pre‐deployment climate. A suite of neural networks is trained on Earth system model data to learn to distinguish between pre‐ and post‐deployment periods across a variety of climate variables. The network accuracy is analogous to the “climate distinguishability” between the periods, and the corresponding distinctive patterns are identified using XAI methods. For many variables, the two periods are less distinguishable under SAI than under a no‐SAI scenario, suggesting that the specific intervention modeled decelerates future climatic changes and leads to a less novel climate than the no‐SAI scenario. Other climate variables for which the intervention has negligible effect are also highlighted.

Keywords

• solar climate intervention
• stratospheric aerosols injection (SAI)
• explainable artificial intelligence (XAI)
• deep learning
• climate distinguishability
• climatic impacts