@article{SchinkelMarwanDimigenetal.2009,
  author    = {Schinkel, Stefan and Marwan, Norbert and Dimigen, Olaf and Kurths, J{\"u}rgen},
  title     = {Confidence bounds of recurrence-based complexity measures},
  issn      = {0375-9601},
  doi       = {10.1016/j.physleta.2009.04.045},
  year      = {2009},
  abstract  = {In the recent past, recurrence quantification analysis (RQA) has gained an increasing interest in various research areas. The complexity measures the RQA provides have been useful in describing and analysing a broad range of data. It is known to be rather robust to noise and nonstationarities. Yet, one key question in empirical research concerns the confidence bounds of measured data. In the present Letter we suggest a method for estimating the confidence bounds of recurrence-based complexity measures. We study the applicability of the suggested method with model and real- life data.},
  language  = {en}
}
@article{SchinkelMarwanKurths2009,
  author    = {Schinkel, Stefan and Marwan, Norbert and Kurths, J{\"u}rgen},
  title     = {Brain signal analysis based on recurrences},
  issn      = {0928-4257},
  doi       = {10.1016/j.jphysparis.2009.05.007},
  year      = {2009},
  abstract  = {The EEG is one of the most commonly used tools in brain research. Though of high relevance in research, the data obtained is very noisy and nonstationary. In the present article we investigate the applicability of a nonlinear data analysis method, the recurrence quantification analysis (RQA), to Such data. The method solely rests on the natural property of recurrence which is a phenomenon inherent to complex systems, such as the brain. We show that this method is indeed suitable for the analysis of EEG data and that it might improve contemporary EEG analysis.},
  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{OrgisBrandSchwarzetal.2009,
  author    = {Orgis, Thomas and Brand, Sascha and Schwarz, Udo and Handorf, D{\"o}rthe and Dethloff, Klaus and Kurths, J{\"u}rgen},
  title     = {Influence of interactive stratospheric chemistry on large-scale air mass exchange in a global circulation model},
  issn      = {1951-6355},
  doi       = {10.1140/epjst/e2009-01105-8},
  year      = {2009},
  abstract  = {A new globally uniform Lagrangian transport scheme for large ensembles of passive tracer particles is presented and applied to wind data from a coupled atmosphere-ocean climate model that includes interactive dynamical feedback with stratospheric chemistry. This feedback from the chemistry is found to enhance large-scale meridional air mass exchange in the northern winter stratosphere as well as intrusion of stratospheric air into the troposphere, where both effects are due to a weakened polar vortex.},
  language  = {en}
}