ABSTRACT
Complex network approaches have been successfully applied for studying transport processes in complex systems ranging from road, railway, or airline infrastructures over industrial manufacturing to fluid dynamics. Here, we utilize a generic framework for describing the dynamics of geophysical flows such as ocean currents or atmospheric wind fields in terms of Lagrangian flow networks. In this approach, information on the passive advection of particles is transformed into a Markov chain based on transition probabilities of particles between the volume elements of a given partition of space for a fixed time step. We employ perturbation-theoretic methods to investigate the effects of modifications of transport processes in the underlying flow for three different problem classes: efficient absorption (corresponding to particle trapping or leaking), constant input of particles (with additional source terms modeling, e.g., localized contamination), and shifts of the steady state under probability mass conservation (as arising if the background flow is perturbed itself). Our results demonstrate that in all three cases, changes to the steady state solution can be analytically expressed in terms of the eigensystem of the unperturbed flow and the perturbation itself. These results are potentially relevant for developing more efficient strategies for coping with contaminations of fluid or gaseous media such as ocean and atmosphere by oil spills, radioactive substances, non-reactive chemicals, or volcanic aerosols.
ACKNOWLEDGMENTS
N.F. is grateful to the financial supports by JSPS KAKENHI Grant No. 15K16061, and by CREST, Japan Science and Technology Agency. J.F.D. acknowledges the financial support by the BMBF project GLUES, the Stordalen Foundation (via the Planetary Boundary Research Network PB.net) and the Earth League's EarthDoc program. R.V.D. received funding by the German Federal Ministry for Education and Research (BMBF) via the BMBF Young Investigator's Group CoSy-CC2 (“Complex Systems Approaches to Understanding Causes and Consequences of Past, Present, and Future Climate Change, Grant No. 01LN1306A”) and the IRTG 1740 “Dynamical Phenomena in Complex Networks” jointly funded by DFG and FAPESP. The authors acknowledge the valuable input from discussions with Kazuyuki Aihara, Jürgen Kurths, Alexis Tantet, and Emilio Hernandéz-Garcia as well as two anonymous reviewers of this manuscript.
REFERENCES
- 1. A.-L. Barabási and R. Albert, “ Emergence of scaling in random networks,” Science 286(5439), 509–512 (1999). https://doi.org/10.1126/science.286.5439.509, Google ScholarCrossref
- 2. A. Barrat, M. Barthelemy, and A. Vespignani, Dynamical Processes on Complex Networks ( Cambridge University Press, 2008). Google ScholarCrossref
- 3. M. Newman, Networks: An Introduction ( Oxford University Press, 2010). Google ScholarCrossref
- 4. R. Guimer, S. Mossa, A. Turtschi, and L. A. N. Amaral, “ The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles,” Proc. Natl. Acad. Sci. 102(22), 7794–7799 (2005). Google ScholarCrossref
- 5. M. T. Gastner and M. E. J. Newman, “ The spatial structure of networks,” Eur. Phys. J. B 49(2), 247–252 (2006). https://doi.org/10.1140/epjb/e2006-00046-8, Google ScholarCrossref
- 6. M. Barthélemy and A. Flammini, “ Optimal traffic networks,” J. Stat. Mech.: Theory Exp. 2006(07), L07002 https://doi.org/10.1088/1742-5468/2006/07/L07002. Google ScholarCrossref
- 7. S. H. Y. Chan, R. V. Donner, and S. Lämmer, “ Urban road networks—Spatial networks with universal geometric features?,” Eur. Phys. J. B 84(4), 563–577 (2011). https://doi.org/10.1140/epjb/e2011-10889-3, Google ScholarCrossref
- 8. J. Buhl, J. Gautrais, R. V. Solé, P. Kuntz, S. Valverde, J. L. Deneubourg, and G. Theraulaz, “ Efficiency and robustness in ant networks of galleries,” Eur. Phys. J. B 42(1), 123–129 (2004). https://doi.org/10.1140/epjb/e2004-00364-9, Google ScholarCrossref
- 9. D. Helbing, A. Deutsch, S. Diez, K. Peters, Y. Kalaidzidis, K. Padberg-Gehle, S. Lämmer, A. Johansson, G. Breier, F. Schulze, and M. Zerial, “ Biologistics and the struggle for efficiency: concepts and perspectives,” Adv. Complex Syst. 12(06), 533–548 (2009). https://doi.org/10.1142/S0219525909002374, Google ScholarCrossref
- 10. V. Rossi, E. Ser-Giacomi, C. López, and E. Hernández-Garcia, “ Hydrodynamic provinces and oceanic connectivity from a transport network help designing marine reserves,” Geophys. Res. Lett. 41(8), 2883–2891, doi: https://doi.org/10.1002/2014GL059540 (2014). Google ScholarCrossref
- 11. E. Ser-Giacomi, V. Rossi, C. López, and E. Hernández-García, “ Flow networks: A characterization of geophysical fluid transport,” Chaos 25(3), 036404 (2015). https://doi.org/10.1063/1.4908231, Google ScholarScitation
- 12. M. van der Mheen, H. A. Dijkstra, A. Gozolchiani, M. den Toom, Q. Feng, J. Kurths, and E. Hernandez-Garcia, “ Interaction network based early warning indicators for the Atlantic MOC collapse,” Geophys. Res. Lett. 40(11), 2714–2719,, doi: https://doi.org/10.1002/grl.50515 (2013). Google ScholarCrossref
- 13. A. A. Tsonis and P. J. Roebber, “ The architecture of the climate network,” Physica A 333, 497–504 (2004). https://doi.org/10.1016/j.physa.2003.10.045, Google ScholarCrossref
- 14. A. A. Tsonis, K. L. Swanson, and P. J. Roebber, “ What do networks have to do with climate?,” Bull. Am. Meteorol. Soc. 87(5), 585 (2006). https://doi.org/10.1175/BAMS-87-5-585, Google ScholarCrossref
- 15. J. F. Donges, I. Petrova, A. Loew, N. Marwan, and J. Kurths, “ How complex climate networks complement eigen techniques for the statistical analysis of climatological data,” Clim. Dyn. 45(9–10), 2407–2424 (2015). https://doi.org/10.1007/s00382-015-2479-3, Google ScholarCrossref
- 16. J. F. Donges, Y. Zou, N. Marwan, and J. Kurths, “ The backbone of the climate network,” Europhys. Lett. 87(4), 48007 (2009). https://doi.org/10.1209/0295-5075/87/48007, Google ScholarCrossref
- 17. K. Yamasaki, A. Gozolchiani, and S. Havlin, “ Climate networks around the globe are significantly affected by el niño,” Phys. Rev. Lett. 100(22), 228501 (2008). https://doi.org/10.1103/PhysRevLett.100.228501, Google ScholarCrossref
- 18. A. Radebach, R. V. Donner, J. Runge, J. F. Donges, and J. Kurths, “ Disentangling different types of El Niño episodes by evolving climate network analysis,” Phys. Rev. E 88(5), 052807 (2013). https://doi.org/10.1103/PhysRevE.88.052807, Google ScholarCrossref
- 19. M. Wiedermann, A. Radebach, J. F. Donges, J. Kurths, and R. V. Donner, “ A climate network-based index to discriminate different types of El Niño and La niña,” Geophys. Res. Lett. B(13), 7176–7185, doi: https://doi.org/10.1002/2016GL069119 (2016). Google ScholarCrossref
- 20. J. Runge, V. Petoukhov, J. F. Donges, J. Hlinka, N. Jajcay, M. Vejmelka, D. Hartman, N. Marwan, M. Paluš, and J. Kurths, “ Identifying causal gateways and mediators in complex spatio-temporal systems,” Nat. Commun. 6, 8502 (2015). https://doi.org/10.1038/ncomms9502, Google ScholarCrossref
- 21. M. Kretschmer, D. Coumou, J. F. Donges, and J. Runge, “ Using causal effect networks to analyze different arctic drivers of midlatitude winter circulation,” J. Clim. 29(11), 4069–4081 (2016). https://doi.org/10.1175/JCLI-D-15-0654.1, Google ScholarCrossref
- 22. V. Rodriguez-Mendez, E. Ser-Giacomi, and E. Hernandez-Garcia, “ Clustering coefficient and periodic orbits in flow networks,” Chaos 27, 035803 (2017) https://doi.org/10.1063/1.4971787. Google ScholarScitation
- 23. G. Nicolis, A. G. Cantú, and C. Nicolis, “ Dynamical aspects of interaction networks,” Int. J. Bifurcation Chaos 15(11), 3467–3480 (2005). https://doi.org/10.1142/S0218127405014167, Google ScholarCrossref
- 24. M. McCullough, M. Small, T. Stemler, and H. H.-C. Iu, “ Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems,” Chaos 25(5), 053101 (2015). https://doi.org/10.1063/1.4919075, Google ScholarScitation
- 25. A. Tantet, F. R. van der Burgt, and H. A. Dijkstra, “ An early warning indicator for atmospheric blocking events using transfer operators,” Chaos 25(3), 036406 (2015). https://doi.org/10.1063/1.4908174, Google ScholarScitation
- 26. M. Dellnitz, G. Froyland, C. Horenkamp, K. Padberg-Gehle, and A. Sen Gupta, “ Seasonal variability of the subpolar gyres in the Southern Ocean: A numerical investigation based on transfer operators,” Nonlinear Process. Geophys. 16(6), 655–663 (2009). https://doi.org/10.5194/npg-16-655-2009, Google ScholarCrossref
- 27. E. Van Sebille, M. H. England, and G. Froyland, “ Origin, dynamics and evolution of ocean garbage patches from observed surface drifters,” Environ. Res. Lett. 7(4), 044040 (2012). https://doi.org/10.1088/1748-9326/7/4/044040, Google ScholarCrossref
- 28. G. Froyland, R. M. Stuart, and E. van Sebille, “ How well-connected is the surface of the global ocean?,” Chaos 24(3), 033126 (2014). https://doi.org/10.1063/1.4892530, Google ScholarScitation
- 29. J. Ludescher, A. Gozolchiani, M. I. Bogachev, A. Bunde, S. Havlin, and H. J. Schellnhuber, “ Improved El Niño forecasting by cooperativity detection,” Proc. Natl. Acad. Sci. 110(29), 11742–11745 (2013). Google ScholarCrossref
- 30. J. Ludescher, A. Gozolchiani, M. I. Bogachev, A. Bunde, S. Havlin, and H. J. Schellnhuber, “ Very early warning of next El Niño,” Proc. Natl. Acad. Sci. 111(6), 2064–2066 (2014). Google ScholarCrossref
- 31. V. Stolbova, E. Surovyatkina, B. Bookhagen, and J. Kurths, “ Tipping elements of the Indian monsoon: Prediction of onset and withdrawal,” Geophys. Res. Lett. 43(8), 3982–3990, doi: https://doi.org/10.1002/2016GL068392 (2016). Google ScholarCrossref
- 32. T. Kato, Perturbation Theory for Linear Operators ( Springer, Berlin, 1980). Google Scholar
- 33. J. J. Sakurai, Modern Quantum Mechanics ( Addison Wesley, 1985). Google Scholar
- 34. P. P. Povinec, I. Sýkora, M. Gera, K. Holý, L. Brest'áková, and A. Kováčik, “ Fukushima-derived radionuclides in ground-level air of central Europe: A comparison with simulated forward and backward trajectories,” J. Radioanal. Nucl. Chem. 295(2), 1171–1176 (2013). https://doi.org/10.1007/s10967-012-1943-3, Google ScholarCrossref
- 35. V. J. García-Garrido, A. M. Mancho, S. Wiggins, and C. Mendoza, “ A dynamical systems approach to the surface search for debris associated with the disappearance of flight MH370,” Nonlinear Process. Geophys. 22(6), 701–712 (2015). https://doi.org/10.5194/npg-22-701-2015, Google ScholarCrossref
- 36. K. Caldeira, G. Bala, and L. Cao, “ The science of geoengineering,” Annu. Rev. Earth Planet. Sci. 41, 231–256 (2013). https://doi.org/10.1146/annurev-earth-042711-105548, Google ScholarCrossref
- 37. D. Ruelle, “ A review of linear response theory for general differentiable dynamical systems,” Nonlinearity 22(4), 855 (2009). https://doi.org/10.1088/0951-7715/22/4/009, Google ScholarCrossref
- 38. O. Butterley and C. Liverani, “ Smooth Anosov flows: Correlation spectra and stability,” J. Mod. Dyn. 1(2), 301–322 (2007). https://doi.org/10.3934/jmd.2007.1.301, Google ScholarCrossref
- 39. V. Lucarini, “ Response operators for Markov processes in a finite state space: Radius of convergence and link to the response theory for axiom a systems,” J. Stat. Phys. 162(2), 312–333 (2016). https://doi.org/10.1007/s10955-015-1409-4, Google ScholarCrossref
- 40. M. D. Chekroun, J. D. Neelin, D. Kondrashov, J. C. McWilliams, and M. Ghil, “ Rough parameter dependence in climate models and the role of Ruelle-Pollicott resonances,” Proc. Natl. Acad. Sci. 111(5), 1684–1690 (2014). https://doi.org/10.1073/pnas.1321816111, Google ScholarCrossref
- 41. A. Lasota and M. C. Mackay, Chaos, Fractals, and Noise ( Springer, Berlin, 1994). Google ScholarCrossref
- 42. G. Froyland, K. Padberg, M. H. England, and A. M. Treguier, “ Detection of coherent oceanic structures via transfer operators,” Phys. Rev. Lett. 98, 224503 (2007). https://doi.org/10.1103/PhysRevLett.98.224503, Google ScholarCrossref
- 43. D. Crommelin and E. Vanden-Eijnden, “ Data-based inference of generators for Markov jump processes using convex optimization,” Multiscale Model. Simul. 7(4), 1751–1778 (2009). https://doi.org/10.1137/080735977, Google ScholarCrossref
- 44. D. A. Levin, Y. Peres, and E. L. Wilmer, Markov Chains and Mixing Times ( American Mathematical Society, Providence, RI, 2009). Google Scholar
- 45. A. Mohapatra and W. Ott, “ Memory loss for nonequilibrium open dynamical systems,” Discrete Contin. Dyn. Syst. 34(9), 3747–3759 (2014). https://doi.org/10.3934/dcds.2014.34.3747, Google ScholarCrossref
- 46. J. Schneider and T. Tél, “ Extracting flow structures from tracer data,” Ocean Dyn. 53(2), 64–72 (2003). https://doi.org/10.1007/s10236-003-0024-0, Google ScholarCrossref
- 47. L. N. Trefethen and M. Embree, Spectra and Pseudospectra: The Behavior of Nonnormal Matrices and Operators ( Princeton University Press, Princeton/Oxford, 2005). Google ScholarCrossref
- 48. G. A. Pavliotis, Stochastic Processes and Applications ( Springer, New York/Heidelberg/Dordrecht/London, 2014). Google ScholarCrossref
- 49. N. Fujiwara, “ Analytical framework of flow networks and its applications,” In Proceedings of 2nd Korea-Japan Joint Workshop on Complex Communication Sciences (KJCCS2013) (2013), pp. FP–5. Google Scholar
- 50. T. H. Solomon and J. P. Gollub, “ Chaotic particle transport in time-dependent Rayleigh-Bénard convection,” Phys. Rev. A 38(12), 6280–6286 (1988). https://doi.org/10.1103/PhysRevA.38.6280, Google ScholarCrossref
- 51. K. Ouchi and H. Mori, “ Anomalous diffusion and mixing in an oscillating Rayleigh-Benard flow,” Prog. Theor. Phys. 88(3), 467–484 (1992). https://doi.org/10.1143/ptp/88.3.467, Google ScholarCrossref
- 52.The exact numerical values used for the model parameters are , and .
- 53. N. Fujiwara, J. Kurths, and A. Díaz-Guilera, Synchronization in networks of mobile oscillators, Phys. Rev. E 83(2), 025101 (2011). https://doi.org/10.1103/PhysRevE.83.025101, Google ScholarCrossref
- 54. N. Fujiwara, J. Kurths, and A. Díaz-Guilera, “ Synchronization of mobile chaotic oscillator networks,” Chaos 26(9), 094824 (2016). https://doi.org/10.1063/1.4962129, Google ScholarScitation
- 55. M. C. González, C. A. Hidalgo, and A.-L. Barabási, “ Understanding individual human mobility patterns,” Nature 453(7196), 779–782 (2008). https://doi.org/10.1038/nature06958, Google ScholarCrossref
- 56. J. Maluck and R. V. Donner, “ A network of networks perspective on global trade,” PLoS One 10(7), e0133310 (2015). https://doi.org/10.1371/journal.pone.0133310, Google ScholarCrossref
- 57. R. Bierkandt, L. Wenz, S. N. Willner, and A. Levermann, “ Acclimate—A model for economic damage propagation. Part 1: Basic formulation of damage transfer within a global supply network and damage conserving dynamics,” Environ. Syst. Decis. 34(4), 507–524 (2014). https://doi.org/10.1007/s10669-014-9523-4, Google ScholarCrossref
- 58. L. Wenz, S. N. Willner, R. Bierkandt, and A. Levermann, “ Acclimate—A model for economic damage propagation. Part ii: A dynamic formulation of the backward effects of disaster-induced production failures in the global supply network,” Environ. Syst. Decis. 34(4), 525–539 (2014). https://doi.org/10.1007/s10669-014-9521-6, Google ScholarCrossref
Please Note: The number of views represents the full text views from December 2016 to date. Article views prior to December 2016 are not included.

