Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem (Ideas Track) Honorable Mention

Christian A Schroeder (University of Oxford); Thomas Hornigold (University of Oxford)

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Abstract

As global greenhouse gas emissions continue to rise, the use of geoengineering in order to artificially mitigate climate change effects is increasingly considered. Stratospheric aerosol injection (SAI), which reduces solar radiative forcing and thus can be used to offset excess radiative forcing due to the greenhouse effect, is both technically and economically feasible. However, naive deployment of SAI has been shown in simulation to produce highly adversarial regional climatic effects in regions such as India and West Africa. Wealthy countries would most likely be able to trigger SAI unilaterally, i.e. China, Russia or US could decide to fix their own climates and, by collateral damage, drying India out by disrupting the monsoon or inducing termination effects with rapid warming. Understanding both how SAI can be optimised and how to best react to rogue injections is therefore of crucial geostrategic interest. In this paper, we argue that optimal SAI control can be characterised as a high-dimensional Markov Decision Process. This motivates the use of deep reinforcement learning in order to automatically discover non-trivial, and potentially time-varying, optimal injection policies or identify catastrophic ones. To overcome the inherent sample inefficiency of deep reinforcement learning, we propose to emulate a Global Circulation Model using deep learning techniques. To our knowledge, this is the first proposed application of deep reinforcement learning to the climate sciences.

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