Information flow in multi-scale dynamical systems using ordinal symbolic analysis

PULIDO M.; ROSA S.; VAN LEEUWEN, PETER JAN

Vinna

Conferencia; European Geophysical Conference; 2019

European Geophysical Union

In this work, information flow quantifiers between variables of multi-scale dynamical systems simulating atmo-spheric processes are evaluated in non-linear and non-gaussian statistical regimes. The atmosphere is a spatiallyextended, highly non-linear dynamical system with complex interactions between the different dynamical scales,as well as between the different physical processes involved in it. We evaluate whether conditional mutual infor-mation and transfer entropy are able to detect and quantify causal interactions between large-scale and small-scaledynamics. As simple prototype models of these atmospheric interactions, we use a two-scale Lorenz 96 modeland a two dimensional barotropic model. In order to obtain the information quantifiers, temporal series from theexperiments are examined with ordinal symbolic analysis using the Band-Pompe symbolic reduction in the datasignal and using the Kraskov-Stogbauer-Grassberger method to estimate mutual information using k-nearest neigh-bors. Comparing different experiments, we show that the interactions between small-scale variables and large-scalevariables may introduce spatial long-range information flows. We also found that conditional mutual informationis able to detect energy and enstrophy cascades in the barotropic model. Ordinal symbolic analysis allows us toobtain robust measures and may be efficiently applied to long temporal series with correlations between severalprocesses. We conclude that information measures are useful tools to establish observational information flowsin the atmosphere. These tools may be helpful to quantify the role of small - scale processes and constrainingstochastic parameterizations.