Utilizing AI emulators to Model Stratospheric Aerosol Injections and their Effect on Climate

With no end to anthropogenic greenhouse gas emissions in sight, policymakers are increasingly debating artificial mechanisms to cool the earth's climate. One such solution is stratospheric atmospheric injections (SAI), a method of solar geoengineering where particles are injected into the stratosphere in order to reflect the sun’s rays and lower global temperatures. Past volcanic events suggest that SAI can lead to fast substantial surface temperature reductions, and it is projected to be economically feasible. Research in simulation, however, suggests that SAI can lead to catastrophic side effects. It is also controversial among politicians and environmentalists because of the numerous challenges it poses geopolitically, environmentally, and for human health. Nevertheless, SAI is increasingly receiving attention from policymakers. In this research project, we use deep reinforcement learning to study if, and by how much, carefully engineered temporally and spatially varying injection strategies can mitigate catastrophic side effects of SAI. To do this, we are using the HadCM3 global circulation model to collect climate system data in response to artificial longitudinal aerosol injections. We then train a neural network emulator on this data, and use it to learn optimal injection strategies under a variety of objectives by alternating model updates with reinforcement learning. We release our dataset and code as a benchmark dataset to improve emulator creation for solar aerosol engineering modeling.