@article{DongesDonnerRehfeldetal.2011, author = {Donges, Jonathan and Donner, Reik Volker and Rehfeld, Kira and Marwan, Norbert and Trauth, Martin H. and Kurths, J{\"u}rgen}, title = {Identification of dynamical transitions in marine palaeoclimate records by recurrence network analysis}, series = {Nonlinear processes in geophysics}, volume = {18}, journal = {Nonlinear processes in geophysics}, number = {5}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1023-5809}, doi = {10.5194/npg-18-545-2011}, pages = {545 -- 562}, year = {2011}, abstract = {The analysis of palaeoclimate time series is usually affected by severe methodological problems, resulting primarily from non-equidistant sampling and uncertain age models. As an alternative to existing methods of time series analysis, in this paper we argue that the statistical properties of recurrence networks - a recently developed approach - are promising candidates for characterising the system's nonlinear dynamics and quantifying structural changes in its reconstructed phase space as time evolves. In a first order approximation, the results of recurrence network analysis are invariant to changes in the age model and are not directly affected by non-equidistant sampling of the data. Specifically, we investigate the behaviour of recurrence network measures for both paradigmatic model systems with non-stationary parameters and four marine records of long-term palaeoclimate variations. We show that the obtained results are qualitatively robust under changes of the relevant parameters of our method, including detrending, size of the running window used for analysis, and embedding delay. We demonstrate that recurrence network analysis is able to detect relevant regime shifts in synthetic data as well as in problematic geoscientific time series. This suggests its application as a general exploratory tool of time series analysis complementing existing methods.}, language = {en} } @article{WunderlingWilleitDongesetal.2020, author = {Wunderling, Nico and Willeit, Matteo and Donges, Jonathan and Winkelmann, Ricarda}, title = {Global warming due to loss of large ice masses and Arctic summer sea ice}, series = {Nature Communications}, volume = {11}, journal = {Nature Communications}, number = {1}, publisher = {Nature Publishing Group}, address = {Berlin}, issn = {2041-1723}, doi = {10.1038/s41467-020-18934-3}, pages = {14}, year = {2020}, abstract = {Several large-scale cryosphere elements such as the Arctic summer sea ice, the mountain glaciers, the Greenland and West Antarctic Ice Sheet have changed substantially during the last century due to anthropogenic global warming. However, the impacts of their possible future disintegration on global mean temperature (GMT) and climate feedbacks have not yet been comprehensively evaluated. Here, we quantify this response using an Earth system model of intermediate complexity. Overall, we find a median additional global warming of 0.43 degrees C (interquartile range: 0.39-0.46 degrees C) at a CO2 concentration of 400 ppm. Most of this response (55\%) is caused by albedo changes, but lapse rate together with water vapour (30\%) and cloud feedbacks (15\%) also contribute significantly. While a decay of the ice sheets would occur on centennial to millennial time scales, the Arctic might become ice-free during summer within the 21st century. Our findings imply an additional increase of the GMT on intermediate to long time scales. The disintegration of cryosphere elements such as the Arctic summer sea ice, mountain glaciers, Greenland and West Antarctica is associated with temperature and radiative feedbacks. In this work, the authors quantify these feedbacks and find an additional global warming of 0.43 degrees C.}, language = {en} } @article{DongesZouMarwanetal.2009, author = {Donges, Jonathan and Zou, Yong and Marwan, Norbert and Kurths, J{\"u}rgen}, title = {Complex networks in climate dynamics : comparing linear and nonlinear network construction methods}, issn = {1951-6355}, doi = {10.1140/epjst/e2009-01098-2}, year = {2009}, abstract = {Complex network theory provides a powerful framework to statistically investigate the topology of local and non- local statistical interrelationships, i.e. teleconnections, in the climate system. Climate networks constructed from the same global climatological data set using the linear Pearson correlation coefficient or the nonlinear mutual information as a measure of dynamical similarity between regions, are compared systematically on local, mesoscopic and global topological scales. A high degree of similarity is observed on the local and mesoscopic topological scales for surface air temperature fields taken from AOGCM and reanalysis data sets. We find larger differences on the global scale, particularly in the betweenness centrality field. The global scale view on climate networks obtained using mutual information offers promising new perspectives for detecting network structures based on nonlinear physical processes in the climate system.}, language = {en} } @article{SchleussnerDongesEngemannetal.2016, author = {Schleussner, Carl-Friedrich and Donges, Jonathan and Engemann, Denis A. and Levermann, Anders}, title = {Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure}, series = {Scientific reports}, volume = {6}, journal = {Scientific reports}, publisher = {Nature Publ. Group}, address = {London}, issn = {2045-2322}, doi = {10.1038/srep30790}, pages = {3407 -- 3417}, year = {2016}, abstract = {Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.}, language = {en} } @article{KretschmerCoumouDongesetal.2016, author = {Kretschmer, Marlene and Coumou, Dim and Donges, Jonathan 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} } @article{DongesSchleussnerSiegmundetal.2016, author = {Donges, Jonathan and Schleussner, C. -F. and Siegmund, Jonatan F. and Donner, Reik Volker}, title = {Event coincidence analysis for quantifying statistical interrelationships between event time series}, series = {European physical journal special topics}, volume = {225}, journal = {European physical journal special topics}, publisher = {Springer}, address = {Heidelberg}, issn = {1951-6355}, doi = {10.1140/epjst/e2015-50233-y}, pages = {471 -- 487}, year = {2016}, abstract = {Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. In this paper, we describe the method of event coincidence analysis to provide a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Applying the framework to country-level observational data yields evidence that flood events have acted as triggers of epidemic outbreaks globally since the 1950s. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.}, language = {en} }