Emulating the regional temperature responses (RTPs) of short-lived climate forcers

Abstract

Here we develop a statistical model emulating the surface temperature response to changes in emissions of short-lived climate forcers as simulated by an Earth system model. Short-lived climate forcers (SLCFs) are chemical components in the atmosphere that interact with radiation and have both an immediate effect on local air quality, and regional and global effects on the climate in terms of changes in temperature and precipitation distributions. The short atmospheric residence times of SLCFs lead to high atmospheric concentrations in emission regions and a highly variable radiative forcing pattern. Regional Temperature Potentials (RTPs) are metrics which quantify the impact of emission changes in a given region on the temperature or forcing response of another, accounting for spatial inhomogeneities in both forcing and the temperature response, while being easy to compare across models and to use in integrated assessment studies or policy briefs. We have developed a Gaussian-process emulator using output from the Norwegian Earth System Model (NorESM) to predict the temperature responses to regional emission changes in SLCFs (specifically back carbon, organic carbon, sulfur dioxide, and methane) and use this model to calculate regional RTPs and study the sensitivity of surface temperature in a certain region, e.g. the Arctic, to anthropogenic emission changes in key policy regions. The main challenge in developing the emulator was creating the training data set such that we included maximal SLCF variability in a realistic and policy relevant range compared to future emission scenarios, while also getting a significant temperature response. We also had to account for the confounding influence of greenhouse gases (GHG), which may not follow the same future emission trajectories as SLCFs and can overwhelm the more subtle temperature response that comes from the direct and indirect effects of SLCF emissions. The emulator can potentially provide accurate and customizable predictions for policy makers to proposed emission changes with minimized climate impact.