@article{SaggiorodeWiljesKretschmeretal.2020, author = {Saggioro, Elena and de Wiljes, Jana and Kretschmer, Marlene and Runge, Jakob}, title = {Reconstructing regime-dependent causal relationships from observational time series}, series = {Chaos : an interdisciplinary journal of nonlinear science}, volume = {30}, journal = {Chaos : an interdisciplinary journal of nonlinear science}, number = {11}, publisher = {American Institute of Physics}, address = {Melville}, issn = {1054-1500}, doi = {10.1063/5.0020538}, pages = {22}, year = {2020}, abstract = {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.}, language = {en} } @article{KretschmerCoumouAgeletal.2018, author = {Kretschmer, Marlene and Coumou, Dim and Agel, Laurie and Barlow, Mathew and Tziperman, Eli and Cohen, Judah}, title = {More-Persistent weak stratospheric polar vortex states linked to cold extremes}, series = {Bulletin of the American Meteorological Society}, volume = {99}, journal = {Bulletin of the American Meteorological Society}, number = {1}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {0003-0007}, doi = {10.1175/BAMS-D-16-0259.1}, pages = {49 -- 60}, year = {2018}, abstract = {The extratropical stratosphere in boreal winter is characterized by a strong circumpolar westerly jet, confining the coldest temperatures at high latitudes. The jet, referred to as the stratospheric polar vortex, is predominantly zonal and centered around the pole; however, it does exhibit large variability in wind speed and location. Previous studies showed that a weak stratospheric polar vortex can lead to cold-air outbreaks in the midlatitudes, but the exact relationships and mechanisms are unclear. Particularly, it is unclear whether stratospheric variability has contributed to the observed anomalous cooling trends in midlatitude Eurasia. Using hierarchical clustering, we show that over the last 37 years, the frequency of weak vortex states in mid- to late winter (January and February) has increased, which was accompanied by subsequent cold extremes in midlatitude Eurasia. For this region, 60\% of the observed cooling in the era of Arctic amplification, that is, since 1990, can be explained by the increased frequency of weak stratospheric polar vortex states, a number that increases to almost 80\% when El Nino-Southern Oscillation (ENSO) variability is included as well.}, language = {en} } @article{KretschmerCohenMatthiasetal.2018, author = {Kretschmer, Marlene and Cohen, Judah and Matthias, Vivien and Runge, Jakob and Coumou, Dim}, title = {The different stratospheric influence on cold-extremes in Eurasia and North America}, series = {npj Climate and Atmospheric Science}, volume = {1}, journal = {npj Climate and Atmospheric Science}, publisher = {Nature Publ. Group}, address = {London}, issn = {2397-3722}, doi = {10.1038/s41612-018-0054-4}, pages = {10}, year = {2018}, abstract = {The stratospheric polar vortex can influence the tropospheric circulation and thereby winter weather in the mid-latitudes. Weak vortex states, often associated with sudden stratospheric warmings (SSW), have been shown to increase the risk of cold-spells especially over Eurasia, but its role for North American winters is less clear. Using cluster analysis, we show that there are two dominant patterns of increased polar cap heights in the lower stratosphere. Both patterns represent a weak polar vortex but they are associated with different wave mechanisms and different regional tropospheric impacts. The first pattern is zonally symmetric and associated with absorbed upward-propagating wave activity, leading to a negative phase of the North Atlantic Oscillation (NAO) and cold-air outbreaks over northern Eurasia. This coupling mechanism is well-documented in the literature and is consistent with the downward migration of the northern annular mode (NAM). The second pattern is zonally asymmetric and linked to downward reflected planetary waves over Canada followed by a negative phase of the Western Pacific Oscillation (WPO) and cold-spells in Central Canada and the Great Lakes region. Causal effect network (CEN) analyses confirm the atmospheric pathways associated with this asymmetric pattern. Moreover, our findings suggest the reflective mechanism to be sensitive to the exact region of upward wave-activity fluxes and to be state-dependent on the strength of the vortex. Identifying the causal pathways that operate on weekly to monthly timescales can pave the way for improved sub-seasonal to seasonal forecasting of cold spells in the mid-latitudes.}, language = {en} } @phdthesis{Kretschmer2017, author = {Kretschmer, Marlene}, title = {Disentangling causal pathways of the stratospheric polar vortex}, school = {Universit{\"a}t Potsdam}, pages = {171}, year = {2017}, language = {en} } @article{KretschmerRungeCoumou2017, author = {Kretschmer, Marlene and Runge, Jakob and Coumou, Dim}, title = {Early prediction of extreme stratospheric polar vortex states based on causal precursors}, series = {Geophysical research letters}, volume = {44}, journal = {Geophysical research letters}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0094-8276}, doi = {10.1002/2017GL074696}, pages = {8592 -- 8600}, year = {2017}, abstract = {Variability in the stratospheric polar vortex (SPV) can influence the tropospheric circulation and thereby winter weather. Early predictions of extreme SPV states are thus important to improve forecasts of winter weather including cold spells. However, dynamical models are usually restricted in lead time because they poorly capture low-frequency processes. Empirical models often suffer from overfitting problems as the relevant physical processes and time lags are often not well understood. Here we introduce a novel empirical prediction method by uniting a response-guided community detection scheme with a causal discovery algorithm. This way, we objectively identify causal precursors of the SPV at subseasonal lead times and find them to be in good agreement with known physical drivers. A linear regression prediction model based on the causal precursors can explain most SPV variability (r(2)=0.58), and our scheme correctly predicts 58\% (46\%) of extremely weak SPV states for lead times of 1-15 (16-30)days with false-alarm rates of only approximately 5\%. Our method can be applied to any variable relevant for (sub)seasonal weather forecasts and could thus help improving long-lead predictions.}, language = {en} } @article{KretschmerCoumouDongesetal.2016, author = {Kretschmer, Marlene and Coumou, Dim and Donges, Jonathan Friedemann and Runge, Jakob}, title = {Using Causal Effect Networks to Analyze Different Arctic Drivers of Midlatitude Winter Circulation}, series = {Journal of climate}, volume = {29}, journal = {Journal of climate}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {0894-8755}, doi = {10.1175/JCLI-D-15-0654.1}, pages = {4069 -- 4081}, year = {2016}, abstract = {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.}, language = {en} }