Using a Gaussian Process Emulator to approximate the climate response patterns to greenhouse gas and aerosol forcings

Abstract. We present a Gaussian process emulator for estimating fast surface temperature response patterns to a range of different climate forcing agents, including both long-lived greenhouse gases and short-lived pollutants such as aerosols. This emulator is trained on simulations driven by perturbations to emissions (for short-lived pollutants) and concentrations (for long-lived greenhouse gases) using a full-complexity global climate model and predicts the response in the first five years after the forcing, at a small fraction of the computational cost. We outline the emulator design, including the choice of pollutant perturbations and the input space covered by the training data. We show that the emulator performs well in most regions of the chosen input space, except under very large aerosol perturbations. A global sensitivity analysis is carried out to characterize and understand emission-response relationships for each pollutant. We find similar large-scale patterns of sensitivity to aerosol pollutants released in different regions. Finally, we demonstrate how this type of emulator could be used in policy-relevant studies to predict fast adjustments of regional climate to changes in anthropogenic emissions for a given scenario. This establishes a basis for rapid climate change projection, without the need for computationally expensive climate model simulations, and increases the number of climate change scenarios that can be explored simultaneously.