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Causal discovery algorithms based on probabilistic graphical models haveemerged in geoscience applications for the identification and visualization ofdynamical processes. The key idea is to learn the structure of a graphicalmodel from observed spatio-temporal data, which indicates information flow,thus pathways of interactions, in the observed physical system. Studying thosepathways allows geoscientists to learn subtle details about the underlyingdynamical mechanisms governing our planet. Initial studies using this approachon real-world atmospheric data have shown great potential for scientificdiscovery. However, in these initial studies no ground truth was available, sothat the resulting graphs have been evaluated only by whether a domain expertthinks they seemed physically plausible. This paper seeks to fill this gap. Wedevelop a testbed that emulates two dynamical processes dominant in manygeoscience applications, namely advection and diffusion, in a 2D grid. Then weapply the causal discovery based information tracking algorithms to thesimulation data to study how well the algorithms work for different scenariosand to gain a better understanding of the physical meaning of the graphresults, in particular of instantaneous connections. We make all data sets usedin this study available to the community as a benchmark.  Keywords: Information flow, graphical model, structure learning, causaldiscovery, geoscience.

Using Causal Discovery to Track Information Flow in Spatio-Temporal Data - A Testbed and Experimental Results Using Advection-Diffusion Simulations