09:00  09:30

Jürgen Kurths
(Humboldt Universität Berlin)
Predictability of extreme climate events via a complex network approach
We analyse climate dynamics from a complex network approach. This leads to an inverse problem: Is there a backbonelike structure underlying the climate system? For this we propose a method to reconstruct and analyze a complex network from data generated by a spatiotemporal dynamical system. This approach enables us to uncover relations to global circulation patterns in oceans and atmosphere.
This concept is then applied to Monsoon data; in particular, we develop a general framework to predict extreme events by combining a nonlinear synchronization technique with complex networks. Applying this method, we uncover a new mechanism of extreme floods in the eastern Central Andes which could be used for operational forecasts. Moreover, we analyze the Indian Summer Monsoon (ISM) and identify two regions of high importance. By estimating an underlying critical point, this leads to a substantially improved prediction of the onset of the ISM.
References
Runge, J. , J. Heitzig, V. Petoukhov, J. Kurths, Phys. Rev. Lett. 108, 258701 (2012)
Boers, N., B. Bookhagen, N. Marwan, J. Kurths, and J. Marengo, Geophys. Res. Lett. 40, 4386 (2013)
N. Boers, B. Bookhagen, H.M.J. Barbosa, N. Marwan, J. Kurths, and J.A. Marengo, Nature Communications 5, 5199 (2014)
N. Boers, R. Donner, B. Bookhagen, and J. Kurths, Climate Dynamics 45, 619 (2015)
J. Runge et al., Nature Communications 6, 8502 (2015)
V. Stolbova, E. Surovyatkina, B. Bookhagen, and J. Kurths, Geophys. Res. Lett. (2016)
D. Eroglu, F. McRobies, I. Ozken, T. Stemler, K. Wyrwoll, S. Breitenbach, N. Marwan, J. Kurths, Nature Communications 7, 12929 (2016)
B. Goswami, N. Boers, A. Rheinwalt, N. Marwan, J. Heitzig, S. Breitenbach, J. Kurths, Nature Communications 9, 48(2018)

09:30  10:00

Henk Dijkstra
(Utrecht University)
Indicators of (geophysical) flow transitions using transfer operator techniques
The existence of persistent midlatitude atmospheric flow regimes with timescales
larger than 510 days and indications of preferred transitions between them motivates
to develop early warning indicators for such regime transitions. In this talk, I will use this
problem as a motivating example to develop more general indicators of geophysical flow
transitions (on many different time scales), such as the collapse of the ocean's meridional
overturning circulation, using operator type techniques.

10:00  10:20

Frank Kwasniok
(University of Exeter)
Datadriven prediction of critical transitions
Approaches to modelling critical transitions from time series data are discussed. A nonstationary loworder stochastic dynamical system of appropriate complexity to capture the transition mechanism under consideration is estimated from data. The technique is generic, not requiring detailed a priori knowledge about the underlying dynamics of the system. A hierarchy of methods is considered. First, linear models are fitted and the time evolution of their eigenvalues studied and extrapolated beyond the learning data window. Second, nonlinear modelling is employed. In the simplest case, the model is a onedimensional effective Langevin equation, but also higherdimensional dynamical reconstructions based on timedelay embedding and local modelling are considered. Integrations with the nonstationary models are performed beyond the learning data window to predict the nature and timing of critical transitions. Also spatially extended systems are addressed; they are projected onto their essential modes before applying the techniques described above. Example systems from the field of weather and climate science are discussed.

10:20  10:50

coffee break

10:50  11:20

Petra Friederichs
(University of Bonn)
Prediction and verification of local scale weather
Forecasts of local scale weather are based on ensemble simulations using
a mesoscale limited area numerical weather prediction (NWP) model.
Particularly on the atmospheric mesoscale, uncertainties are large and
predictions are probabilistic in nature. Thus verification and
postprocessing of local weather is an important part in forecasting
local scale and extreme weather. We will present and discuss approaches
for verification and ensemble postprocessing of local weather
forecasts, introducing the concept of proper scoring rules and the
application thereof.

11:20  11:50

Milan Palus
(Czech Academy of Sciences)
Causality, information, time and transitions
Any scientific discipline strives to explain causes of observed phenomena. Quantitative, mathematical description of causality is possible when studying phenomena evolving in time and providing measurable quantities which can be registered in consecutive instants of time and stored in datasets called time series. As examples we can mention longterm recordings of air temperature, or recordings of the electrical activity of human brain, known as the electroencephalogram.
In this talk we will follow ideas of the father of cybernetics, Norbert Wiener, and Nobel prize winner Sir C.W.J. Granger. We will explain how to detect causality using probability distribution functionals from information theory and the interpretation of causal relations as information transfer. We will study the information transfer in chaotic systems on the route to synchronization. The time and the the arrow of time play a natural role in the definition of causality: the cause precedes the effect. We will investigate whether this principle is obeyed by chaotic dynamical systems and how causality estimators behave near transitions/bifurcations. Another role of time can be seen in complex systems evolving on multiple time scales. We will show how to measure the information transfer across time scales. In an application we will demonstrate a causal influence of climate oscillations with a period about 78 years on the amplitude of the annual temperature cycle and the interannual variability of the mean winter temperature in central Europe.

11:50  12:10

Dario Zappalà
(Universidad Politécnica de Cataluña)
Quantifying interdecadal changes in largescale patterns of surface air temperature variability
We study daily surface air temperature (SAT) reanalysis in a grid over the Earth surface and identify and quantify changes in SAT variability patterns during the period 1979–2016. By analysing Hilbert amplitude and frequency we identify the regions where relative variations are most pronounced (larger than $\pm$50% for the amplitude and $\pm$100% for the frequency). Amplitude variations are interpreted as due to changes in precipitation or ice melting; frequency variations as due to a northward shift of the intertropical convergence zone (ITCZ) and a widening of the rainfall band in the western Pacific Ocean. The ITCZ is the ascending branch of the Hadley cell and thus, by affecting the tropical atmospheric circulation, ITCZ migration has far reaching climatic consequences. As the methodology proposed here can be applied to many other geophysical time series, our work will stimulate new research that will advance the understanding of climate change impacts.

12:10  12:30

Woosok Moon
(Stockholm University)
A unified nonlinear stochastic time series analysis for climate science
Earth’s orbit and axial tilt imprint a strong seasonal cycle on climatological data. Climate variability is typically viewed in terms of fluctuations in the seasonal cycle induced by higher frequency processes. We can interpret this as a competition between the orbitally enforced monthly stability and the fluctuations/noise induced by weather. Here we introduce a new timeseries method that determines these contributions from monthlyaveraged data. We find that the spatiotemporal distribution of the monthly stability and the magnitude of the noise reveal key fingerprints of several important climate phenomena, including the evolution of the Arctic sea ice cover, the ElNino Southern Oscillation (ENSO), the AtlanticNino and the Indian Dipole Mode. In analogy with the classical destabilising influence of the icealbedo feedback on summertime sea ice, we find that during some time interval of the season a destabilising process operates in all of these climate phenomena. The interaction between the destabilisation and the accumulation of noise, which we term the memory effect, underlies phase locking to the seasonal cycle and the statistical nature of seasonal predictability.

12:30  15:00

lunch

15:00  15:30

Klaus Lehnertz
(University of Bonn)
Predicting epileptic seizures  an overview
The sudden and apparently unpredictable nature of seizures is one of the most
disabling aspects of the disease epilepsy that affects about 1% of the world population.
Identifying seizure precursors from brain dynamics could drastically improve therapeutic
possibilities and thus the quality of life of people with epilepsy.
Over the last two decades, an improved characterization of the complex spatialtemporal
dynamics of the epileptic brain could be achieved with tools from nonlinear dynamics,
statistical physics, synchronization and network theory.
These tools appear to be capable of defining seizure precursors from electroencephalographic
recordings.
In this talk, I will provide an overview of the progress that has been made in the field: from preliminary
descriptions of preseizure phenomena and proof of principle studies via controlled studies to the
recent development of implantable seizure prediction and prevention systems.

15:30  15:50

Thorsten Rings
(University of Bonn)
Identifying critical transitions and estimating their resilience  a datadriven approach
We present a datadriven analysis approach for the identification of a critical transitional phase and for estimating its resilience.
We map the complex structure of timeresolved similarity matrices of some multivariate dynamics onto a finite number of states, and from
the "distance" between successive states we derive an estimate for the resilience of a given state.
Using our approach, we investigate longterm (covering days to weeks), multichannel electroencephalographic (EEG) recordings from up to now more than 40 epilepsy patients that captured more than 60 seizures.
We provide evidence that our analysis approach allows for a detection of preseizure states in the majority of patients.
More importantly, the corresponding changes in resilience provide important clues for a possible controlability of a critical transitional preseizure phase.

15:50  16:20

Eckehard Schöll
(Technische Universität Berlin)
Chimera states in brain networks: empirical neural vs. modular fractal connectivity
Complex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains and can be observed in networks of coupled oscillators. The interplay of synchrony and asynchrony in complex brain networks is an important aspect in studies of brain functions and dysfunctions. We analyse the collective dynamics of FitzHughNagumo neurons in complex networks motivated by neuroscience. We compare two topologies: an empirical structural neural connectivity derived from weighted magnetic resonance imaging and a mathematically constructed network with modular fractal connectivity. We analyse the properties of chimeras and partially synchronized states, and obtain regions of their stability in the parameter planes. Furthermore, we study the influence of the removal of nodes on the network synchronizability, which can be useful for applications to epileptic seizures.

16:20  16:50

coffee break

16:50  17:20

Christian Kühn
(echnical University of Munich)
Frontiers in earlywarning sign theory
In this talk, I shall report on recent progress on several frontiers in the analysis and application of earlywarning signs. Classical techniques such as using slowing down and variance/autocorrelation in the context of stochastic ordinary differential equations are bynow well explored. Yet, many open questions remain for warning signs in the following areas: (a) statistical quantification, (b) stochastic partial differential equations, and (c) network dynamics. For each area, I am going to describe the current state of mathematical theory as well as examples from applications. I am also going to propose some potential future directions for the theory.

17:20  17:40

Hildegard MeyerOrtmanns
(Jacobs University Bremen)
Zoom into a transition between excitable and oscillatory behavior
When concepts like the bulk free energy and the interface tension are available, it is the competition between both that determines the type of phase conversion in a phase transition. The conversion can proceed via nucleation or spinodal decomposition, for example. Here we study a transition between collective fixedpoint and collective synchronized oscillatory behavior, where these concepts are not applicable. The system consists of coupled dynamical units, which individually can be in an excitable or oscillatory state. The conversion is triggered by the change of a single bifurcation parameter. Of particular interest is the arrest of oscillations. We identify the criterion that determines the seeds of arrest and the propagation of arrest fronts in terms of the vicinity to the future attractor. Due to a high degree of multistability we observe versatile patterns of phase locked motion in the oscillatory regime. Quenching the system into the regime, where oscillatory states are no longer stable, we observe qualitatively distinct approaches of the fixedpoint attractor, depending on the initial seeds. If the seeds of arrest are isolated single sites of the lattice, the arrest propagates via bubble formation, visually similar to nucleation processes; if the seed is extended along a line of lowest amplitudes, the freezing follows the spatial patterns of phaselocked motion with long interfaces between arrested and oscillating units [1]. In an outlook we consider a transition in the collective behavior of Kuramoto oscillators between a unique collective fixedpoint solution for positive couplings to multistable solutions of partial synchronization, for which the multistability depends on the strength of negative couplings [2].
References:
[1] D. Labavic and H. MeyerOrtmanns, On the arrest of synchronized oscillations, Europhys.Lett. 109, 10 001p1p6 (2015).
[2] S. Esmaeili, D. Labavic, M. Pleimling and H. MeyerOrtmanns, Breaking of timetranslation invariance in Kuramoto dynamics with multiple time scales, Europhys. Lett.118, 40006 (2017).

17:40  18:00

Petr Jizba
(Czech Technical University)
Transitions between superstatistical regimes: validity, breakdown and applications
Superstatistics is a widely employed tool of nonequilibrium statistical physics which plays an important role in analysis of hierarchical complex dynamical systems. Yet, its ``canonical'' formulation in terms of a single nuisance parameter is often too restrictive when applied to complex empirical data. Here we show that a multiscale generalization of the superstatistics paradigm is more versatile, allowing to address such pertinent issues as transmutation of statistics or interscale stochastic behavior. To put some flesh on the bare bones, we provide a numerical evidence for a transition between two superstatistics regimes, by analyzing highfrequency (minutetick) data for shareprice returns of seven selected companies. Salient issues, such as breakdown of superstatistics in fractional diffusion processes or connection with Brownian subordination are also briefly discussed.
Related article:
Petr Jizba, Jan Korbel, Hynek Lavička, Martin Prokš, Václav Svoboda, Christian Beck, Physica A 493 (2018), 2946.

18:00  19:30

dinner

19:30

poster session (focus on even poster numbers)
