A computationally efficient method to model Stratospheric Aerosol Injection experiments

Abstract

Climate model simulations incorporating stratospheric aerosol injection (SAI) generally require more computational resources compared to out-of-the-box applications, due to the importance of stratospheric chemistry. This presents a challenge for SAI research, especially because there are numerous ways and scenarios through which SAI can be implemented. Here, we propose a novel method that allows SAI simulations to be performed without interactive stratospheric chemistry, saving a significant portion of the computational budget. The method requires a pre-existing dataset of an SAI experiment and its corresponding control experiment, with active stratospheric chemistry. The data is converted into a set of relations to determine the forcing fields given any required optical depth of the aerosol field. This makes the method suitable for applications that use dynamical feedback controllers. The results of climate simulations with aerosols prescribed by our method are in close agreement with those from full-complexity model, even for different model versions, resolutions and forcing scenarios.