TY - JOUR A1 - Saggioro, Elena A1 - de Wiljes, Jana A1 - Kretschmer, Marlene A1 - Runge, Jakob T1 - Reconstructing regime-dependent causal relationships from observational time series JF - Chaos : an interdisciplinary journal of nonlinear science N2 - Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Increasing data availability over the past few decades has spurred the development of a plethora of causal discovery methods, each addressing particular challenges of this difficult task. In this paper, we focus on an important challenge that is at the core of time series causal discovery: regime-dependent causal relations. Often dynamical systems feature transitions depending on some, often persistent, unobserved background regime, and different regimes may exhibit different causal relations. Here, we assume a persistent and discrete regime variable leading to a finite number of regimes within which we may assume stationary causal relations. To detect regime-dependent causal relations, we combine the conditional independence-based PCMCI method [based on a condition-selection step (PC) followed by the momentary conditional independence (MCI) test] with a regime learning optimization approach. PCMCI allows for causal discovery from high-dimensional and highly correlated time series. Our method, Regime-PCMCI, is evaluated on a number of numerical experiments demonstrating that it can distinguish regimes with different causal directions, time lags, and sign of causal links, as well as changes in the variables' autocorrelation. Furthermore, Regime-PCMCI is employed to observations of El Nino Southern Oscillation and Indian rainfall, demonstrating skill also in real-world datasets. Y1 - 2020 U6 - https://doi.org/10.1063/5.0020538 SN - 1054-1500 SN - 1089-7682 SN - 1527-2443 VL - 30 IS - 11 PB - American Institute of Physics CY - Melville ER - TY - JOUR A1 - Kretschmer, Marlene A1 - Coumou, Dim A1 - Donges, Jonathan A1 - Runge, Jakob T1 - Using Causal Effect Networks to Analyze Different Arctic Drivers of Midlatitude Winter Circulation JF - Journal of climate N2 - In recent years, the Northern Hemisphere midlatitudes have suffered from severe winters like the extreme 2012/13 winter in the eastern United States. These cold spells were linked to a meandering upper-tropospheric jet stream pattern and a negative Arctic Oscillation index (AO). However, the nature of the drivers behind these circulation patterns remains controversial. Various studies have proposed different mechanisms related to changes in the Arctic, most of them related to a reduction in sea ice concentrations or increasing Eurasian snow cover. Here, a novel type of time series analysis, called causal effect networks (CEN), based on graphical models is introduced to assess causal relationships and their time delays between different processes. The effect of different Arctic actors on winter circulation on weekly to monthly time scales is studied, and robust network patterns are found. Barents and Kara sea ice concentrations are detected to be important external drivers of the midlatitude circulation, influencing winter AO via tropospheric mechanisms and through processes involving the stratosphere. Eurasia snow cover is also detected to have a causal effect on sea level pressure in Asia, but its exact role on AO remains unclear. The CEN approach presented in this study overcomes some difficulties in interpreting correlation analyses, complements model experiments for testing hypotheses involving teleconnections, and can be used to assess their validity. The findings confirm that sea ice concentrations in autumn in the Barents and Kara Seas are an important driver of winter circulation in the midlatitudes. Y1 - 2016 U6 - https://doi.org/10.1175/JCLI-D-15-0654.1 SN - 0894-8755 SN - 1520-0442 VL - 29 SP - 4069 EP - 4081 PB - American Meteorological Soc. CY - Boston ER -