Quantification of Information Exchange in Idealized and Climate System Applications

Abstract

Often in climate system studies, linear and symmetric statistical measures are applied to quantify interactions among subsystems or variables. However, they do not allow identification of the driving and responding subsystems. Therefore, in this study, we aimed to apply asymmetric measures from information theory: the axiomatically proposed transfer entropy and the first principle-based information flow to detect and quantify climate interactions. As their estimations are challenging, we initially tested nonparametric estimators like transfer entropy (TE)-binning, TE-kernel, and TE k-nearest neighbor and parametric estimators like TE-linear and information flow (IF)-linear with idealized two-dimensional test cases along with their sensitivity on sample size. Thereafter, we experimentally applied these methods to the Lorenz-96 model and to two real climate phenomena, i.e., (1) the Indo-Pacific Ocean coupling and (2) North Atlantic Oscillation (NAO)–European air temperature coupling. As expected, the linear estimators work for linear systems but fail for strongly nonlinear systems. The TE-kernel and TE k-nearest neighbor estimators are reliable for linear and nonlinear systems. Nevertheless, the nonparametric methods are sensitive to parameter selection and sample size. Thus, this work proposes a composite use of the TE-kernel and TE k-nearest neighbor estimators along with parameter testing for consistent results. The revealed information exchange in Lorenz-96 is dominated by the slow subsystem component. For real climate phenomena, expected bidirectional information exchange between the Indian and Pacific SSTs was detected. Furthermore, expected information exchange from NAO to European air temperature was detected, but also unexpected reversal information exchange. The latter might hint to a hidden process driving both the NAO and European temperatures. Hence, the limitations, availability of time series length and the system at hand must be taken into account before drawing any conclusions from TE and IF-linear estimations.

Compressing atmospheric data into its real information content

“…We define the bitwise real information content as the mutual information 20,37,[40][41][42] of bits in adjacent grid points (Fig. 1 and Methods).…”

“…The entropy is maximised to 1 bit for equal probabilities in . To derive the mutual information[40][41][42] of two bitstreams from adjacent grid points and are now considered. The mutual information is defined via the joint probability mass function which here takes the form of a 2x2 matrix(3)with being the probability that the bits are in the state and simultaneously and .…”

The synergistic impact of ENSO and IOD on Indian summer monsoon rainfall in observations and climate simulations – an information theory perspective

“…It is very important to note that, though the methods from information theory are very useful in analyzing complex system behavior, their estimations are quite challenging due to their sensitivity to free tuning parameters and sample size (Knuth et al, 2013;Smirnov, 2013;Pothapakula et al, 2019). Hence, this study follows and uses various estimators we proposed in our earlier work (Pothapakula et al, 2019) for robustness in the results. Here we are investigating the information exchange from ENSO and IOD to ISMR interannual variability by using available observations, reanalysis datasets, and climate models.…”

Quantification of the Direct Solar Impact on Some Components of the Hydro-Climatic System

“…This is because the method of discerning the contribution of each factor, as from cause to effect, is not the most appropriate. It is crucial that the method of discrimination can quantify the contribution of each factor from a lot of factors that contribute to the determination of a phenomenon, and the transfer of entropy (TE) offers this desire to the full [12][13][14]. In addition, TE provides an adequate tool for relevant physical interpretations.…”