Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Complex systems approaches for Earth system data analysis


Boers,  Niklas
Potsdam Institute for Climate Impact Research;


Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;


Marwan,  Norbert
Potsdam Institute for Climate Impact Research;

External Ressource
No external resources are shared
Fulltext (public)

(Publisher version), 3MB

Supplementary Material (public)
There is no public supplementary material available

Boers, N., Kurths, J., Marwan, N. (2021): Complex systems approaches for Earth system data analysis. - Journal of Physics: Complexity, 2, 1, 011001.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_24999
Complex systems can, to a first approximation, be characterized by the fact that their dynamics emerging at the macroscopic level cannot be easily explained from the microscopic dynamics of the individual constituents of the system. This property of complex systems can be identified in virtually all natural systems surrounding us, but also in many social, economic, and technological systems. The defining characteristics of complex systems imply that their dynamics can often only be captured from the analysis of simulated or observed data. Here, we summarize recent advances in nonlinear data analysis of both simulated and real-world complex systems, with a focus on recurrence analysis for the investigation of individual or small sets of time series, and complex networks for the analysis of possibly very large, spatiotemporal datasets. We review and explain the recent success of these two key concepts of complexity science with an emphasis on applications for the analysis of geoscientific and in particular (palaeo-) climate data. In particular, we present several prominent examples where challenging problems in Earth system and climate science have been successfully addressed using recurrence analysis and complex networks. We outline several open questions for future lines of research in the direction of data-based complex system analysis, again with a focus on applications in the Earth sciences, and suggest possible combinations with suitable machine learning approaches. Beyond Earth system analysis, these methods have proven valuable also in many other scientific disciplines, such as neuroscience, physiology, epidemics, or engineering.