  The fact that the Earth climate is a highly complex dynamical system is
well-known. In the last few decades a lot of effort has been focused on
understanding how climate phenomena in one geographical region affects the
climate of other regions. Complex networks are a powerful framework for
identifying climate interdependencies. To further exploit the knowledge of the
links uncovered via the network analysis (for, e.g., improvements in
prediction), a good understanding of the physical mechanisms underlying these
links is required. Here we focus in understanding the role of atmospheric
variability, and construct climate networks representing internal and forced
variability. In the connectivity of these networks we assess the influence of
two main indices, NINO3.4 and the North Atlantic Oscillation (NAO), by
calculating the networks from time-series where these indices were linearly
removed. We find that the connectivity of the forced variability network is
heavily affected by ``El Ni\~no'': removing the NINO3.4 index yields a general
loss of connectivity; even teleconnections between regions far away from the
equatorial Pacific ocean are lost, suggesting that these regions are not
directly linked, but rather, are indirectly interconnected via ``El Ni\~no'',
particularly on interannual time scales. On the contrary, in the internal
variability network (independent of sea surface temperature forcing) we find
that the links are significantly affected by NAO with a maximum in intra-annual
time scales. While the strongest non-local links found are those forced by the
ocean, we show that there are also strong teleconnections due to internal
atmospheric variability.
