143 research outputs found
Complex networks in climate dynamics - Comparing linear and nonlinear network construction methods
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.Comment: 24 pages, 10 figure
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From math to metaphors and back again: Social-ecological resilience from a multi-agent-environment perspective
Science and policy stand to benefit from reconnecting the many notions of social-ecological resilience to their roots in complexity sciences.We propose several ways of moving towards operationalization through the classification of modern concepts of resilience based on a multi-agent-environment perspective.
Social-ecological resilience underlies popular sustainability concepts that have been influential in formulating the United Nations Sustainable Development Goals (SDGs), such as the Planetary Boundaries and Doughnut Economics. Scientific investigation of these concepts is supported by mathematical models of planetary biophysical and societal dynamics, both of which call for operational measures of resilience. However, current quantitative descriptions tend to be restricted to the foundational form of the concept: persistence resilience. We propose a classification of modern notions of social-ecological resilience from a multi-agent-environment perspective. This aims at operationalization in a complex systems framework, including the persistence, adaptation and transformation aspects of resilience, normativity related to desirable system function, first- vs. second-order and specific vs. general resilience. For example, we discuss the use of the Topology of Sustainable Management Framework. Developing the mathematics of resilience along these lines would not only make social-ecological resilience more applicable to data and models, but could also conceptually advance resilience thinking
Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes
ACKNOWLEDGMENTS MW and RVD have been supported by the German Federal Ministry for Education and Research (BMBF) via the Young Investigators Group CoSy-CC2 (grant no. 01LN1306A). JFD thanks the Stordalen Foundation and BMBF (project GLUES) for financial support. JK acknowledges the IRTG 1740 funded by DFG and FAPESP. MT Gastner is acknowledged for providing his data on the airline, interstate, and Internet network. P Menck thankfully provided his data on the Scandinavian power grid. We thank S Willner on behalf of the entire zeean team for providing the data on the world trade network. All computations have been performed using the Python package pyunicorn [41] that is available at https://github.com/pik-copan/pyunicorn.Peer reviewedPreprin
Geometric signature of complex synchronisation scenarios
Synchronisation between coupled oscillatory systems is a common phenomenon in
many natural as well as technical systems. Varying the strength of coupling
often leads to qualitative changes in the complex dynamics of the mutually
coupled systems including different types of synchronisation such as phase,
lag, generalised, or even complete synchronisation. Here, we study the
geometric signatures of coupling along with the onset of generalised
synchronisation between two coupled chaotic oscillators by mapping the systems'
individual as well as joint recurrences in phase space to a complex network.
For a paradigmatic continuous-time model system, the transitivity properties of
the resulting joint recurrence networks display distinct variations associated
with changes in the structural similarity between different parts of the
considered trajectories. They therefore provide a useful indicator for the
emergence of generalised synchronisation.
This paper is dedicated to the 25th anniversary of the introduction of
recurrence plots by Eckmann et al. (Europhys. Lett. 4 (1987), 973).Comment: 7 pages, 3 figure
Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure
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 marginalised 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.Comment: 16 pages, 5 figure
How complex climate networks complement eigen techniques for the statistical analysis of climatological data
Eigen techniques such as empirical orthogonal function (EOF) or coupled
pattern (CP) / maximum covariance analysis have been frequently used for
detecting patterns in multivariate climatological data sets. Recently,
statistical methods originating from the theory of complex networks have been
employed for the very same purpose of spatio-temporal analysis. This climate
network (CN) analysis is usually based on the same set of similarity matrices
as is used in classical EOF or CP analysis, e.g., the correlation matrix of a
single climatological field or the cross-correlation matrix between two
distinct climatological fields. In this study, formal relationships as well as
conceptual differences between both eigen and network approaches are derived
and illustrated using exemplary global precipitation, evaporation and surface
air temperature data sets. These results allow to pinpoint that CN analysis can
complement classical eigen techniques and provides additional information on
the higher-order structure of statistical interrelationships in climatological
data. Hence, CNs are a valuable supplement to the statistical toolbox of the
climatologist, particularly for making sense out of very large data sets such
as those generated by satellite observations and climate model intercomparison
exercises.Comment: 18 pages, 11 figure
Collateral transgression of planetary boundaries due to climate engineering by terrestrial carbon dioxide removal
The planetary boundaries framework provides guidelines for defining thresholds in environmental variables. Their transgression is likely to result in a shift in Earth system functioning away from the relatively stable Holocene state. As the climate system is approaching critical thresholds of atmospheric carbon, several climate engineering methods are discussed, aiming at a reduction of atmospheric carbon concentrations to control the Earth's energy balance. Terrestrial carbon dioxide removal (tCDR) via afforestation or bioenergy production with carbon capture and storage are part of most climate change mitigation scenarios that limit global warming to less than 2°C.
We analyse the co-evolutionary interaction of societal interventions via tCDR and the natural dynamics of the Earth's carbon cycle. Applying a conceptual modelling framework, we analyse how the degree of anticipation of the climate problem and the intensity of tCDR efforts with the aim of staying within a "safe" level of global warming might influence the state of the Earth system with respect to other carbon-related planetary boundaries.
Within the scope of our approach, we show that societal management of atmospheric carbon via tCDR can lead to a collateral transgression of the planetary boundary of land system change. Our analysis indicates that the opportunities to remain in a desirable region within carbon-related planetary boundaries only exist for a small range of anticipation levels and depend critically on the underlying emission pathway. While tCDR has the potential to ensure the Earth system's persistence within a carbon-safe operating space under low-emission pathways, it is unlikely to succeed in a business-as-usual scenario
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