Future thinking instructions improve prospective memory performance in adolescents

Funding This work was supported by the German Research Foundation [DFG grants SFB 940/1]. Acknowledgements We would like to thank Lia Kvavilashvili for her helpful comments on this study during the International Conference on Prospective Memory (ICPM4) in Naples, Italy, 2014. We thank Daniel P. Sheppard for proofreading the manuscript.Peer reviewedPublisher PD

Detecting and quantifying causal associations in large nonlinear time series datasets

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields

Tropical and mid-latitude teleconnections interacting with the Indian summer monsoon rainfall: a theory-guided causal effect network approach

The alternation of active and break phases in Indian summer monsoon (ISM) rainfall at intraseasonal timescales characterizes each ISM season. Both tropical and mid-latitude drivers influence this intraseasonal ISM variability. The circumglobal teleconnection observed in boreal summer drives intraseasonal variability across the mid-latitudes, and a two-way interaction between the ISM and the circumglobal teleconnection pattern has been hypothesized. We use causal discovery algorithms to test the ISM circumglobal teleconnection hypothesis in a causal framework. A robust causal link from the circumglobal teleconnection pattern and the North Atlantic region to ISM rainfall is identified, and we estimate the normalized causal effect (CE) of this link to be about 0.2 (a 1 standard deviation shift in the circumglobal teleconnection causes a 0.2 standard deviation shift in the ISM rainfall 1 week later). The ISM rainfall feeds back on the circumglobal teleconnection pattern, however weakly. Moreover, we identify a negative feedback between strong updraft located over India and the Bay of Bengal and the ISM rainfall acting at a biweekly timescale, with enhanced ISM rainfall following strong updraft by 1 week. This mechanism is possibly related to the boreal summer intraseasonal oscillation. The updraft has the strongest CE of 0.5, while the Madden–Julian oscillation variability has a CE of 0.2–0.3. Our results show that most of the ISM variability on weekly timescales comes from these tropical drivers, though the mid-latitude teleconnection also exerts a substantial influence. Identifying these local and remote drivers paves the way for improved subseasonal forecasts

The role of the timing of Sudden Stratospheric Warmings for precipitation and temperature anomalies in Europe

The Northern Hemisphere stratospheric polar vortex (SPV), a band of fast westerly winds over the pole extending from approximately 10 to 50 km altitude, is a key driver of European winter weather. Extremely weak polar vortex states, so called sudden stratospheric warmings (SSWs), are on average followed by dry and cold weather in Northern Europe, as well as wetter weather in Southern Europe. However, the surface response of SSWs varies greatly between events, and it is not well understood which factors modulate this difference. Here we address the role of the timing of SSWs within the cold season (December to March) for the temperature and precipitation response in Europe. Given the limited sample size of SSWs in the observations, hindcasts of the seasonal forecasting model SEAS5 from the European Centre for Medium-Range Weather Forecasts (ECMWF) are analysed. Firste evaluate key characteristics of stratosphere-troposphere coupling in SEAS5 against reanalysis data and find them to be reasonably well captured by the model, justifying our approach. We then show that in SEAS5, early winter (December and January) SSWs are followed by more pronounced surface impacts compared to late winter (February and March) SSWs. For example, in Scotland the low precipitation anomalies are roughly twice as severe after early winter SSWs than after late winter SSWs. The difference in the response cannot be explained by more downward propagating SSWs in early winter, or by different monthly precipitation climatologies. Instead, we demonstrate that the differences result from stronger SPV anomalies associated with early winter SSWs. This is a statistical artefact introduced through the commonly used SSW event definition, which involves an absolute threshold, and therefore leads to stronger SPV anomalies during early winter SSWs when the stratospheric mean state is stronger. Our study highlights the sensitivity of surface impacts to SSW event definition

More-persistent weak stratospheric polar vortex states linked to cold extremes

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 Niño–Southern Oscillation (ENSO) variability is included as well

Using causal effect networks to analyze different arctic drivers of midlatitude winter circulation

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

Reconstructing regime-dependent causal relationships from observational time series

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 Niño Southern Oscillation and Indian rainfall, demonstrating skill also in real-world datasets. Regime-dependent non-stationarity is a ubiquitous feature of physical systems, especially prominent in atmospheric sciences. This dependence can be looked at as an intermittent change in relationships defining the dynamics of a multivariate system, each of which can be described as a time series causal network. In this work, we develop a novel algorithm to detect regime-dependent causal relations that combines the constrained-based causal discovery algorithm PCMCI with a regime assigning linear optimization algorithm. Our method, Regime-PCMCI, is evaluated on a number of numerical experiments and demonstrates high performance in detecting a variety of regime-dependent features. Finally, Regime-PCMCI is applied to observations of El Niño Southern Oscillation and Indian rainfall, demonstrating skill in detecting well-known seasonal regimes in a real-world dataset

The “polar vortex” winter of 2013/14

The term “polar vortex” remained largely a technical term until early January 2014 when the United States (US) media used it to describe an historical cold air outbreak in eastern North America. Since then, “polar vortex” has been used more frequently by the media and the public, often conflating circulation features and temperatures near the surface with only partially related features at the tropopause and in the stratosphere. The polar vortex in its most common scientific usage refers to a hemispheric-scale stratospheric circulation over the Arctic that is present during the Northern Hemisphere cold season. Reversal of the zonal mean zonal winds circumnavigating the stratospheric polar vortex (SPV), termed major sudden stratospheric warmings, can be linked to mid-latitude cold air outbreaks. However, this mechanism does not explain the cold US winter of 2013/2014. This study revisits the winter of 2013/2014 to understand how SPV variability may still have played a role in the severe winter weather. Observations indicate that anomalously strong vertical wave propagation occurred throughout the winter and disrupted, but did not fully break, the SPV. Instead, vertically propagating waves were reflected back downward, amplifying a blocking high near Alaska and downstream troughing across central North America, a classic signature for extreme cold air outbreaks across central and eastern North America. Thus, the association of the term “polar vortex” with winter 2013/2014, while not justified by the most common usage of the term, serves as a case study of the wave-reflection mechanism of SPV influence on mid-latitude weather

Early prediction of extreme stratospheric polar vortex states based on causal precursors

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 (r2 = 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

Northern hemisphere stratosphere‐troposphere circulation change in CMIP6 models: 1. inter‐model spread and scenario sensitivity

Projected changes in the Northern Hemisphere stratospheric polar vortex are analyzed using Climate Model Intercomparison Project Phase 6 experiments. Previous studies showed that projections of the wintertime zonally averaged polar vortex strength diverge widely between climate models with no agreement on the sign of change, and that this uncertainty contributes to the regional climate change uncertainty. Here, we show that there remains large uncertainty in the projected strength of the polar vortex in experiments with global warming levels ranging from moderate (SSP245 runs) to large (Abrupt-4xCO2 runs), and that the uncertainty maximizes in winter. Partitioning of the uncertainty in wintertime polar vortex strength projections reveals that, by the end of the 21st century, model uncertainty contributes half of the total uncertainty, with scenario uncertainty contributing only 10%. Regression analysis shows that up to 20% of the intermodel spread in projected precipitation over the Iberian Peninsula and northwestern US, and 20%–30% in near-surface temperature over western US and northern Eurasian, can be associated with the spread in vortex strength projections after accounting for global warming. While changes in the magnitude and sign of the zonally averaged vortex strength are uncertain, most models (>95%) predict an eastward shift of the vortex by 8°–20° degrees in longitude relative to its historical location with the magnitude of the shift increasing for larger global warming levels. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the zonally averaged vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift