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Climate system teleconnections, which are far-away climate responses to perturbations or oscillations, are difficult to quantify, yet understanding them is crucial for improving climate predictability. Here we leverage Granger causality in a novel method of identifying teleconnections. Because Granger causality is explicitly defined as a statistical test between two time series, our method allows for immediate interpretation of causal relationships between any two fields and provides an estimate of the timescale of the teleconnection response. We demonstrate the power of this new method by recovering known seasonal precipitation responses to the sea surface temperature pattern associated with the El Ni~{n}o Southern Oscillation, with accuracy comparable to previously used correlation-based methods. By adjusting the maximum lag window, Granger causality can evaluate the strength of the teleconnection (the seasonal precipitation response) on different timescales; the lagged correlation method does not show ability to differentiate signals at different lags. We also identify candidates for previously unexplored teleconnection responses, highlighting the improved sensitivity of this method over previously used ones.
In the study of complex physical and biological systems represented by multivariate stochastic processes, an issue of great relevance is the description of the system dynamics spanning multiple temporal scales. While methods to assess the dynamic com
Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between j
A high degree of consensus exists in the climate sciences over the role that human interference with the atmosphere is playing in changing the climate. Following the Paris Agreement, a similar consensus exists in the policy community over the urgency
An approach is proposed for inferring Granger causality between jointly stationary, Gaussian signals from quantized data. First, a necessary and sufficient rank criterion for the equality of two conditional Gaussian distributions is proved. Assuming
Granger causality has been employed to investigate causality relations between components of stationary multiple time series. We generalize this concept by developing statistical inference for local Granger causality for multivariate locally stationa