Using optimization tools to explore stratospheric aerosol injection strategies

Abstract. Stratospheric aerosol injection (SAI), as a possible supplement to emission reduction, has the potential to reduce some of the impacts associated with climate change. However, the outcomes will depend on how it is deployed: not just how much but also the latitudes of injection and the distribution of injection rates across those latitudes. Different such strategies have been proposed, for example, managing up to three climate metrics simultaneously by injecting at multiple latitudes. Nonetheless, these strategies still do not fully compensate for the pattern of climate changes caused by increased greenhouse gas concentrations, creating a novel climate state. To date there has not been a systematic assessment of whether there are strategies that could do a better job of managing some specific climate goals, nor an assessment of any underlying trade-offs between managing different sets of climate goals. Herein we use existing climate model simulations of the response to injection at seven different latitudes and apply optimization tools to explore the limitations and trade-offs when designing strategies that combine injection across these latitudes. This relies on linearity being a sufficiently good assumption, which we first validate. The resulting “best” strategy of course depends on what goals are being optimized for. For example, at 1 °C of cooling, we predict that there exist strategies that do a better job than those simulated to date at simultaneously balancing regional temperature and precipitation responses, but the differences may be too small to detect at lower levels of cooling.