  The development of robust Early Warning Signals (EWS) is necessary to
quantify the risk of crossing tipping points in the present-day climate change.
Classically, EWS are statistical measures based on time series of climate state
variables, without exploiting their spatial distribution. However, spatial
information is crucial to identify the starting location of a transition
process, and can be directly inferred by satellite observations. By using
complex networks constructed from several climate variables on the numerical
grid of climate simulations, we seek for network properties that can serve as
EWS when approaching a state transition. We show that network indicators such
as the normalized degree, the average length distance and the betweenness
centrality are capable of detecting tipping points at the global scale, as
obtained by the MIT general circulation model in a coupled-aquaplanet
configuration for CO$_2$ concentration-driven simulations. The applicability of
such indicators as EWS is assessed and compared to traditional methods. We also
analyse the ability of climate networks to identify nonlinear dynamical
patterns.
