Exploring information exchange in climate system and climate models
Authors:   , Pothapakula Praveen Kumar 
Published: November 2022

The climate system is one of the classical examples of a complex dynamical system consisting of interacting sub-systems through mass, momentum, and energy exchange across various spatial and temporal scales. This thesis aims to detect and quantify sub-component interactions from an information exchange (IE) perspective. For this purpose, IE estimators derived from information theory are explored and applied to the available climate data obtained from observations, reanalysis, global and regional climate models. Specifically, this thesis investigates the usefulness of information theory methods for process-oriented climate model evaluation. Firstly, methods derived from the concepts of information theory such as transfer entropy and information flow along with their linear and non-linear estimation techniques are initially tested and applied to idealized two-dimensional dynamical systems. The results revealed an expected direction and magnitude of IE providing insights into underlying dynamics. However, as expected the linear estimators are robust for linear systems but fail for non-linear systems. Though the non-linear estimators (kernel and kraskov) showed expected results for all the idealized systems, their free tuning parameters are to be tested for consistent results. Moreover, these methods are sensitive to the available time series length. A real world example case study involving the dynamics between the Indian and Pacific oceans revealed a physically consistent bi-directional IE. However, unexpected IE was detected in the example of North Atlantic and European air temperatures indicating hidden drivers. Though IE provides insights into system dynamics, the availability of time series length and the system at hand must be carefully taken into account before inferring any possible interpretations of the results. Quantifying the IE from El-Ni\~{n}o southern oscillation (ENSO) and Indian Ocean Dipole (IOD) to the Indian Summer Monsoon Rainfall (ISMR) with the observational and reanalysis data sets revealed that both ENSO and IOD are synergistic predictors for the inter-annual variability of the ISMR over central India i.e., the monsoon core region. Though the investigated three Global Climate Models (GCM) could not reveal the underlying IE dynamics of ENSO, IOD, and ISMR, a Regional Climate Model (RCM) simulation downscaling one of the GCMs with realistic large scale signals across the lateral boundaries showed good agreement with the observations. Evaluating a coupled regional climate modeling system driven by two different global data sets with IE estimators revealed significant differences between the process chains linking the north-west Mediterranean sea surface temperatures, evaporation, wind speed, and the Vb-cyclone induced precipitation over Danube, Odra, and Elbe catchments in the historical period (1951-2005). Detailed investigation revealed that the north-west Mediterranean Sea in the coupled regional simulation driven by ERA-20C reanalysis corresponded to the Vb-cyclone precipitation over the three catchments while no such correspondence is noted in the EC-EARTH driven simulation. This discrepancy is attributed to the inheritance of the simulation biases from GCM into the RCM. In the future period (1965-2099), no significant changes in the processes are noted from the simulation. Overall, this thesis used IE estimators in investigating the underlying dynamics of climate system and climate models. The estimators proved useful in providing insights into climate system dynamics assisting in a process based climate model evaluation.