In order to understand and quantify the uncertainties in projections and physics of a climate model, a collection of climate simulations (an ensemble) is typically used. Given the high-dimensionality of the input space of a climate model, as well as the complex, nonlinear relationships between the climate variables, a large ensemble is often required to accurately assess these uncertainties. If only a small number of climate variables are of interest at a specified spatial and temporal scale, the computational and storage expenses can be substantially reduced by training a statistical model on a small ensemble. The statistical model then acts as a stochastic generator (SG) able to simulate a large ensemble, given a small training ensemble. Previous work on SGs has focused on modeling and simulating individual climate variables (e.g., surface temperature, wind speed) independently. Here, we introduce a SG that jointly simulates three key climate variables. The model is based on a multistage spectral approach that allows for inference of more than 80 million data points for a nonstationary global model, by conducting inference in stages and leveraging large-scale parallelization across many processors. We demonstrate the feasibility of jointly simulating climate variables by training the SG on five ensemble members from a large ensemble project and assess the SG simulations by comparing them to the ensemble members not used in training. Supplementary materials accompanying this paper appear online.
