Graph Convolutional Neural Networks as Surrogate Models for Climate Simulation

As global temperatures continue to rise, the need for effective and systematic evaluation of climate intervention strategies becomes increasingly important. Stratospheric Aerosol Injection (SAI) is one such strategy and like all brings significant risks [4, 17] necessitating careful planning and evaluation of the positive and negative impacts. The Performance Assessment (PA) framework, a methodology originally designed for nuclear waste management [13], can be applied to the assessment of climate intervention strategies. The Performance Assessment for Climate Intervention (PACI) framework[19] adapts the PA methodology to evaluate SAI by establishing a set of performance goals, identifying relevant system features, events, and processes (FEPs), and assessing the system's performance, including uncertainties, against these goals. The PACI framework aims to provide a structured and quantifiable approach to evaluate the risks and benefits of SAI in comparison to other climate pathways.