A computationally efficient method to model similar and alternate stratospheric aerosol injection experiments using prescribed aerosols in a lower-complexity version of the same model: a case study using CESM(CAM) and CESM(WACCM)

Abstract

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 application of pattern-scaling techniques that allows us to generate SAI forcing in the Community Atmosphere Model (CAM) – a model without interactive stratospheric chemistry – using pre-existing data from the Whole Atmosphere Community Climate Model (WACCM), a state-of-the-art model with interactive stratospheric chemistry, but expensive to run. In doing so, a significant portion of the computational budget is saved. The present method requires a pre-existing dataset of a representative SAI experiment and its corresponding control experiment, with interactive 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. The present method is suitable for applications that use dynamical feedback controllers and is intended to aid impact research into the tropospheric and (sub)surface climate changes due to SAI. The results of climate simulations with aerosols prescribed by the present procedure are in close agreement with those from the full-complexity model, even for different model versions, horizontal resolutions and SAI forcing scenarios.