  The potential for complex systems to exhibit tipping points in which an
equilibrium state undergoes a sudden and often irreversible shift is well
established, but prediction of these events using standard forecast modeling
techniques is quite difficult. This has led to the development of an
alternative suite of methods that seek to identify signatures of critical
phenomena in data, which are expected to occur in advance of many classes of
dynamical bifurcation. Crucially, the manifestations of these critical
phenomena are generic across a variety of systems, meaning that data-intensive
deep learning methods can be trained on (abundant) synthetic data and plausibly
prove effective when transferred to (more limited) empirical data sets. This
paper provides a proof of concept for this approach as applied to lattice phase
transitions: a deep neural network trained exclusively on 2D Ising model phase
transitions is tested on a number of real and simulated climate systems with
considerable success. Its accuracy frequently surpasses that of conventional
statistical indicators, with performance shown to be consistently improved by
the inclusion of spatial indicators. Tools such as this may offer valuable
insight into climate tipping events, as remote sensing measurements provide
increasingly abundant data on complex geospatially-resolved Earth systems.
