On inference and validation of causality relations in climate teleconnections

The attribution of factors influencing positive and negative phase durations of climate teleconnections is an important problem in climate research. In addition to inferring such an attribution directly from climate models or from the available data, distinguishing the true causality from simple correlations is often hampered by the multiscale nature of the geophysical system. Here we deploy a data-driven multiscale causality inference methodology to extract the statistically most significant Bayesian causality relations between the discretised historical, seasonal climate teleconnections time series in order to quantify the probabilistic causality impacts from the unresolved/weather scales. Our results enable us to quantify the leading role of the annular modes (in particular the Southern Annular Mode) and the tropical Pacific on monthly scale causalities, revealing that the joint causality impacts from these modes lead to a Bayesian predictability that is approximately four times stronger then the joint predictability of the northern hemisphere teleconnections on the same monthly scales. We further show how the obtained causality networks can be validated and elucidate the possible physical mechanisms inducing these relations. This approach enables the prediction of characteristics like phase duration probabilities and provides a better plausible data-driven explanation for the observed higher frequencies of long phases of teleconections such as the El Nino Southern Oscillation