  Global climate models aim to reproduce physical processes on a global scale
and predict quantities such as temperature given some forcing inputs. We
consider climate ensembles made of collections of such runs with different
initial conditions and forcing scenarios. The purpose of this work is to show
how the simulated temperatures in the ensemble can be reproduced (emulated)
with a global space/time statistical model that addresses the issue of
capturing nonstationarities in latitude more effectively than current
alternatives in the literature. The model we propose leads to a computationally
efficient estimation procedure and, by exploiting the gridded geometry of the
data, we can fit massive data sets with millions of simulated data within a few
hours. Given a training set of runs, the model efficiently emulates temperature
for very different scenarios and therefore is an appealing tool for impact
assessment.
