Interaction network based early warning indicators for the Atlantic MOC collapse

Mheen, Mirjam van der; Dijkstra, Henk A.; Gozolchiani, Avi; Toom, Matthijs den; Feng, Qu; Kurths, Jürgen; Hernández-Garcı́a, Emilio

doi:10.1002/grl.50515

Cited by 82 publications

(50 citation statements)

References 27 publications

(33 reference statements)

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“…For example, this could be used in the test problem considered in this study in order to investigate the scaling law in PDF near the saddle-node bifurcation on the branch of pole-to-pole solutions. In this way, the critical slowdown near a tipping point can be studied [39].…”

Section: Summary and Discussionmentioning

confidence: 99%

“…A first check of the correctness of the approximate solution of the generalized Lyapunov equations is obtained by comparing the empirical orthogonal functions (EOFs) and weighted eigenvalues of the covariance matrix that we get from both the Lyapunov solver and a stochastically forced time forward simulation at µ = µ b , similar to those performed in [39]. This time series (for n y = 32) is plotted in Figure 2a and shows that Ψ + fluctuates around the mean MOC value at µ b .…”

Section: Comparison With Stochastically Forced Time Forward Simulationmentioning

confidence: 99%

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Continuation of probability density functions using a generalized Lyapunov approach

Baars

Viebahn

Mulder

et al. 2017

Journal of Computational Physics

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Techniques from numerical bifurcation theory are very useful to study transitions between steady fluid flow patterns and the instabilities involved. Here, we provide computational methodology to use parameter continuation in determining probability density functions of systems of stochastic partial differential equations near fixed points, under a small noise approximation. Key innovation is the efficient solution of a generalized Lyapunov equation using an iterative method involving low-rank approximations. We apply and illustrate the capabilities of the method using a problem in physical oceanography, i.e. the occurrence of multiple steady states of the Atlantic Ocean circulation.

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Section: Summary and Discussionmentioning

confidence: 99%

Section: Comparison With Stochastically Forced Time Forward Simulationmentioning

confidence: 99%

Continuation of probability density functions using a generalized Lyapunov approach

Baars

Viebahn

Mulder

et al. 2017

Journal of Computational Physics

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“…Applications to climate science have received growing attention since 2004, 25 when graph theory was applied to the investigation of global geopotential height. Network analysis has been since applied to studies of numerous climate modes, [26][27][28][29][30][31] of atmospheric and oceanic circulation drivers, [32][33][34][35] of precipitation in different time periods, [36][37][38] and of Rossby wave dynamics. 39 Generally networks are constructed as undirected, binary graphs.…”

Section: Complex Network Analysis and Climate Sciencementioning

confidence: 99%

Advancing climate science with knowledge-discovery through data mining

et al. 2018

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Global climate change represents one of the greatest challenges facing society and ecosystems today. It impacts key aspects of everyday life and disrupts ecosystem integrity and function. The exponential growth of climate data combined with KnowledgeDiscovery through Data-mining (KDD) promises an unparalleled level of understanding of how the climate system responds to anthropogenic forcing. To date, however, this potential has not been fully realized, in stark contrast to the seminal impacts of KDD in other fields such as health informatics, marketing, business intelligence, and smart city, where big data science contributed to several of the most recent breakthroughs. This disparity stems from the complexity and variety of climate data, as well as the scientific questions climate science brings forth. This perspective introduces the audience to benefits and challenges in mining large climate datasets, with an emphasis on the opportunity of using a KDD process to identify patterns of climatic relevance. The focus is on a particular method, δ-MAPS, stemming from complex network analysis. δ-MAPS is especially suited for investigating local and non-local statistical interrelationships in climate data and here is used is to elucidate both the techniques, as well as the results-interpretation process that allows extracting new insight. This is achieved through an investigation of similarities and differences in the representation of known teleconnections between climate reanalyzes and climate model outputs.

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“…One climate transition of crucial interest is the possible collapse of the Atlantic Meridional Overturning Circulation (MOC) 96,97 as is occurring in simulations of the Fast Met Office/UK Universities Simulator (FAMOUS) climate model. 98 Figure 9(a) shows time series of annual mean Atlantic MOC strength for both the control simulation (black curve) and the hosing simulation (red curve), at the location where the maximum MOC occurs (at latitude 26 N and 1000 m depth).…”

Section: Use Case: Climate Network For Detecting Climate Transitionsmentioning

confidence: 99%

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package

Donges

Heitzig

Beronov

et al. 2015

Chaos

Self Cite

102

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We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni-and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Network theory and nonlinear time series analysis provide powerful tools for the study of complex systems in various disciplines such as climatology, neuroscience, social science, infrastructure or economics. In the last years, combining both frameworks has yielded a wealth of new approaches for understanding and modeling the structure and dynamics of such systems based on the statistical analysis of network or uni-and multivariate time series. The pyunicorn software package (available at https://github.com/pik-copan/pyunicorn) facilitates the innovative synthesis of methods from network theory and nonlinear time series analysis in order to develop novel integrated methodologies. This paper provides an overview of the functionality provided by pyunicorn, introduces the theoretical concepts behind it and provides examples in the form of selected use cases mainly in the fields of climatology and paleoclimatology.

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Interaction network based early warning indicators for the Atlantic MOC collapse

Cited by 82 publications

References 27 publications

Continuation of probability density functions using a generalized Lyapunov approach

Continuation of probability density functions using a generalized Lyapunov approach

Advancing climate science with knowledge-discovery through data mining

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package

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Interaction network based early warning indicators for the Atlantic MOC collapse

Cited by 82 publications

References 27 publications

Continuation of probability density functions using a generalized Lyapunov approach

Continuation of probability density functions using a generalized Lyapunov approach

Advancing climate science with knowledge-discovery through data mining

Unified functional network and nonlinear time series analysis for complex systems science: Thepyunicornpackage

Contact Info

Product

Resources

About

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package