  An estimate of the net direction of climate interactions in different
geographical regions is made by constructing a directed climate network from a
regular latitude-longitude grid of nodes, using a directionality index (DI)
based on conditional mutual information. Two datasets of surface air
temperature anomalies - one monthly-averaged and another daily-averaged - are
analyzed and compared. The network links are interpreted in terms of known
atmospheric tropical and extratropical variability patterns. Specific and
relevant geographical regions are selected, the net direction of propagation of
the atmospheric patterns is analyzed and the direction of the inferred links is
validated by recovering some well-known climate variability structures. These
patterns are found to be acting at various time-scales, such as atmospheric
waves in the extra-tropics or longer range events in the tropics. This analysis
demonstrates the capability of the DI measure to infer the net direction of
climate interactions and may contribute to improve the present understanding of
climate phenomena and climate predictability. The work presented here also
stands out as an application of advanced tools to the analysis of empirical,
real-world data.
