Smooth information flow in temperature climate network reflects mass transport

Abstract

A directed climate network is constructed by Granger causality analysis of air temperature time series from a regular grid covering the whole Earth. Using winner-takes-all network thresholding approach, a structure of a smooth information flow is revealed, hidden to previous studies. The relevance of this observation is confirmed by comparison with the air mass transfer defined by the wind field. Their close relation illustrates that although the information transferred due to the causal influence is not a physical quantity, the information transfer is tied to the transfer of mass and energy.

Advancing climate science with knowledge-discovery through data mining

“…Applications to climate science have received growing attention since 2004, 25 when graph theory was applied to the investigation of global geopotential height. Network analysis has been since applied to studies of numerous climate modes, [26][27][28][29][30][31] of atmospheric and oceanic circulation drivers, [32][33][34][35] of precipitation in different time periods, [36][37][38] and of Rossby wave dynamics. 39 Generally networks are constructed as undirected, binary graphs.…”

Interannual oscillations and sudden shifts in observed and modeled climate

“…Further possibilities also remain: some causality might run in the opposite direction from multidecadal to interannual timescales or there could be complex phase‐amplitude couplings such as proposed at faster timescales in an information‐theoretic framework by Palus (; ) and Hlinka et al . (). The earlier reference (Palus and Vejmelka, ) concludes on a general level that limited datasets are more suitable for detecting synchronization than causality between timescales.…”

“…Further quantities can be derived and might provide extra information for example in exotic cases, where phases correlate better than amplitudes, noise in climate data can be notoriously complex and more complicated models can become black boxes introducing new biases (see e.g., Mudelsee, 2010). More elaborate analyses could, however, attempt to extract causality not only between amplitudes of different oscillations but also between phase of one and amplitude of another (Palus, 2014a;2014b;Hlinka et al, 2017). However, by examining Figure 1 of Tsonis et al (2007), it is clear that the main conclusions can already be drawn from the network distance and while dissecting the results we stick to the network distance.…”

Introduction to Focus Issue: Complex network perspectives on flow systems

“…A second contribution by Hlinka et al 63 demonstrates the construction of directed climate networks based on linear Granger causality between globally distributed observations of surface air temperatures. They report that while the usual thresholding approach commonly employed in constructing climate networks yields a large number of network links masking the actually relevant processes, adopting instead a winner-takes-all approach reveals distinct spatial patterns of smooth information transport in the global temperature field which reflect the underlying dynamical structures of global wind fields.…”

“…While the former study 63 presents one of the first attempts to combine causality concepts with spatially explicit climate network generation and analysis at global scale, 64,65 Tirabassi et al 66 discuss how the even more elaborated concepts of renormalized partial directed coherence and directed partial correlation can be used in a climatological context. Both measures have recently proven their potentials in neurophysiological signal analysis and are here for the first time employed to climate data in two specific case studies on ENSO-monsoon interactions as well as air-sea interactions in the South Atlantic Convergence Zone.…”