VIII. Earth Earns: An Open CoCreative Earthropocene to Astropocene PediaVerse

2. Global Climate Change as a Complex Dynamical System

Bracco, Annallisa, et al. Machine learning for the physics of climate. Nature Reviews Physics. January, 2025. As scientific studies now scale to Earthuman collaborations, they also become fostered by AI neural net computational capabilities. In this entry Georgia Tech, University of Colorado, University of Utrecht and Nansen Environmental and Remote Sensing Center, Norway researchers announce and detail how global weather conditions as a hyper complex, dynamic system can enter a new phase of accuracy and predictability.

Climate science has lately been enhanced by an exponential growth in computing power and worldwide increases in empirical observations. Big data and associated algorithms under the field of machine learning (ML), allow us to study the physics of the climate system in ways that were not possible earlier. In this Review, we discuss how ML has been used to better reconstruct weather statistics, represent sub-grid-scale phenomena, forecast events and so on. Finally, we consider the benefits of ML in studying complex systems. (Excerpts)

Cheung, Kevin and Ugur Osturk. Synchronization of Extreme Rainfall During the Australian Monsoon: Complex Network Perspectives. Chaos. 30/6, 2020. Macquarrie University and GeoForschungsZentrum, Potsdam systems environmentalists describe how network centrality measures such as degree and local clustering are suitable for and can be graphed unto active stormy weather.

Colombo, E. H., et al.. Scaling of connectivity metrics in river networks.. arXiv:2501.17033. Into this year, Helmholtz-Zentrum Dresden Rossendorf, Germany Earth system scientists add a further finesse to how even geological phenomena can be found to exemplify nature’s universal complex dynamics. Circa 2000, none of this was imagined or possible. A quarter century later, a global collaboration can now quantify a revolutionary double domain of phenoware forms which arise from an independent, common genoware source.

Rivers are well-known to exhibit fractal-like properties with scaling laws that link network geometry and size, but may not reflect ecological processes such as the generation of biodiversity. Connectivity metrics, however, have been shown to influence relevant environmental outcomes. In this work, we establish how riverine network connectivity scales with system size by analyzing more than 1000 rivers across the globe. Specifically, we found clear power-law scaling of both the Harmonic and Betweenness centrality connectivity metrics. (Excerpt)

Dijkstra, Henk. Nonlinear Climate Dynamics. Cambridge: Cambridge University Press, 2013. A Professor of Dynamical Oceanography at the Institute for Marine and Atmospheric Research, Utrecht University, offers an overdue re-assessment of our ultra-intricate and variable local and global weather in terms of mathematical systems science. Chapters range from Climate Variability, Stochastic Dynamical Systems, and Climate Modelling Hierarchy, to the North Atlantic Oscillation, El Nino, Pleistocene Ice Ages, and onto Predictability. While still weighted more toward physical mechanism than self-organizing networks, a turn in a better direction if we are ever to understand and resolve.

This book introduces stochastic dynamical systems theory in order to synthesize our current knowledge of climate variability. Nonlinear processes, such as advection, radiation and turbulent mixing, play a central role in climate variability. These processes can give rise to transition phenomena, associated with tipping or bifurcation points, once external conditions are changed. The theory of dynamical systems provides a systematic way to study these transition phenomena. Its stochastic extension also forms the basis of modern (nonlinear) data analysis techniques, predictability studies and data assimilation methods. Early chapters apply the stochastic dynamical systems framework to a hierarchy of climate models to synthesize current knowledge of climate variability. Later chapters analyse phenomena such as the North Atlantic Oscillation, El Niño/Southern Oscillation, Atlantic Multidecadal Variability, Dansgaard-Oeschger Events, Pleistocene Ice Ages, and climate predictability. This book will prove invaluable for graduate students and researchers in climate dynamics, physical oceanography, meteorology and paleoclimatology. (Publisher)

Dijkstra, Henk, et al. Networks in Climate. Cambridge: Cambridge University Press, 2019. Four authors posted in the Netherlands, Spain and Uruguay contribute to later 2010s abilities by which even world wild weather can be quantified and understood by way of nonlinear, self-organizing systems and topologies. Typical topics cover how to analyze the presence of atmospheric connectivities, oceanic El Nino wave dynamics, tipping behaviors, Indian monsoon and much more.

Donges, Jonathan, et al. Earth system modeling with endogenous and dynamic human societies: the copan:CORE open World-Earth modeling framework. arXic:1909.13697. A dozen German and Swedish scientists with a main base at the Potsdam Institute for Climate Impact Research proceed with a comprehensive program going forward to gain ever better analyses, quantifications and hopefully sustainable remediations of our hyper-active global atmosphere and consumptive societal-industrial civilization. In regard we need to get a real sense of Earthkinder taking care of her/his self and do all we personally and collaboratively do to facilitate and survive.

Earth system dynamics in the Anthropocene need to well take into account the increasing magnitude of processes operating in human societies, their cultures, economies and technosphere, along with their entanglement with physical, chemical and biological global systems. This paper (i) proposes design principles for constructing World-Earth Models (WEM) for Earth system analysis of the Anthropocene, i.e., models of social (World) - ecological co-evolution on up to planetary scales, and (ii) presents the copan:CORE open simulation modeling framework for developing, composing and analyzing such WEMs based on the proposed modular principles. Thereby, copan:CORE enables the epistemic flexibility needed for Earth system analysis of the Anthropocene given the diverse theories and methodologies used for describing socio-metabolic/economic and socio-cultural processes. (Abstract)

Donges, Jonathan, et al. Identification of Dynamical Transitions in Marine Palaeoclimate Records by Recurrence Network Analysis. Nonlinear Processes in Geophysics. 18/5, 2011. A companion article in this effort by systems physicists and climatologists from the Universities of Potsdam, Humboldt, and Dresden to attain novel insights, as every other scientific field has done, to the hyper-complex in scale and intricacy of such ancient climes and biotas.

Abstract. The analysis of palaeoclimate time series is usually affected by severe methodological problems, resulting primarily from non-equidistant sampling and uncertain age models. As an alternative to existing methods of time series analysis, in this paper we argue that the statistical properties of recurrence networks are promising candidates for characterising the system’s nonlinear dynamics and quantifying structural changes in its reconstructed phase space as time evolves. Specifically, we investigate the behaviour of recurrence network measures for both paradigmatic model systems with non-stationary parameters and four marine records of long-term palaeoclimate variations. We show that the obtained results are qualitatively robust under changes of the relevant parameters of our method, including detrending, size of the running window used for analysis, and embedding delay. We demonstrate that recurrence network analysis is able to detect relevant regime shifts in synthetic data as well as in problematic geoscientific time series. (545)

Donges, Jonathan, et al. Investigating the Topology of Interacting Networks: Theory and Application to Coupled Climate Subnetworks. European Physical Journal B. 84/4, 2011. n a Focus Section on Frontiers in Network Science, Potsdam Institute for Climate Impact Research, Humboldt University, and Free University Berlin systems physicists provide further application of nature’s universal inherency to animate and abide via robust systems to global and local weather patterns and processes. Again, a novel, imperative phase of climate research is commencing, as most other fields of study have done, moving beyond masses of measurements to engage the equally present dynamical network interactions. An important feature, it is emphasized, is a “vertical” topological structure of atmospheric microclimes.

Network theory provides various tools for investigating the structural or functional topology of many complex systems found in nature, technology and society. Nevertheless, it has recently been realised that a considerable number of systems of interest should be treated, more appropriately, as interacting networks or networks of networks. Here we introduce a novel graph-theoretical framework for studying the interaction structure between subnetworks embedded within a complex network of networks. This framework allows us to quantify the structural role of single vertices or whole subnetworks with respect to the interaction of a pair of subnetworks on local, mesoscopic and global topological scales. Climate networks have recently been shown to be a powerful tool for the analysis of climatological data. Applying the general framework for studying interacting networks, we introduce coupled climate subnetworks to represent and investigate the topology of statistical relationships between the fields of distinct climatological variables. Using coupled climate subnetworks to investigate the terrestrial atmosphere’s three-dimensional geopotential height field uncovers known as well as interesting novel features of the atmosphere’s vertical stratification and general circulation. The promising results obtained by following the coupled climate subnetwork approach present a first step towards an improved understanding of the Earth system and its complex interacting components from a network perspective. (635)

Donges, Jonathan, et al. Nonlinear Detection of Paleoclimate Variability Transitions Possibly Related to Human Evolution. Proceedings of the National Academy of Sciences. 108/20422, 2011. Donges, with the European team of Reik Donner, Martin Trauth, Norbert Marwan, Hans Schellnhuber, Jurgen Kurths, and colleagues, continue their extensive, daunting project of rightly interpreting prehistoric and current atmospheres by way of nature’s universally present complex systems phenomena.

Potential paleoclimatic driving mechanisms acting on human evolution present an open problem of cross-disciplinary scientific interest. The analysis of paleoclimate archives encoding the environmental variability in East Africa during the past 5 Ma has triggered an ongoing debate about possible candidate processes and evolutionary mechanisms. In this work, we apply a nonlinear statistical technique, recurrence network analysis, to three distinct marine records of terrigenous dust flux. Our method enables us to identify three epochs with transitions between qualitatively different types of environmental variability in North and East Africa during the (i) Middle Pliocene (3.35–3.15 Ma B.P.), (ii) Early Pleistocene (2.25–1.6 Ma B.P.), and (iii) Middle Pleistocene (1.1–0.7 Ma B.P.). A reexamination of the available fossil record demonstrates statistically significant coincidences between the detected transition periods and major steps in hominin evolution. This result suggests that the observed shifts between more regular and more erratic environmental variability may have acted as a trigger for rapid change in the development of humankind in Africa. (20422)

Donner, Reik, et al. Recurrence Networks – A Novel Paradigm for Nonlinear Time Series Analysis. New Journal of Physics. 12/3, 2010. With Yong Zou, J. Donges, N. Marwan, and J. Kurths, a technical paper about this method being found of much utility for understanding climate and weather as a complex dynamical system, as other articles in this section put to good avail.

Since the early stages of quantitative nonlinear sciences, numerous conceptual approaches have been introduced for studying the characteristic features of dynamical systems based on observational time series. Popular methods that are increasingly used in a variety of applications include, among others, Lyapunov exponents, fractal dimensions, symbolic discretization and measures of complexity such as entropies and quantities derived from them. All these techniques have in common that they quantify certain discretized realizations of individual trajectories. (2) As an appealing solution, we have suggested recurrence networks as a unifying framework for studying time series as complex networks, which is based on a novel approach for the quantitative assessment of recurrence plots in terms of complex network measures. (30)

Donner, Reik, et al. Understanding the Earth as a Complex System. European Physical Journal Special Topics. 174/1, 2009. Donner, with coauthors Susana Barbosa, Jurgen Kurths, and Norbert Marwan, whose credits include Dresden University of Technology, Osaka Prefecture University, Universidade do Porto, Geological Survey of Israel, Potsdam Institute for Climate Impact Research, and Humboldt University, provide an Introduction this title issue. Beyond avalanches of data, it is vital to gain a holistic view of our finite, fluid sphere and appreciate the endemic nonlinear dynamics at work overall and within each environmental bioregion. In this regard, concerns include spatio-temporal interrelationships,” dynamic processes in atmosphere and ocean, paleoclimate variability, and trends, cycles and extremes in present-day climate, and so on.

The Earth is a highly complex system formed by a large variety of sub-systems (biosphere, atmosphere, lithosphere, as well as social and economic systems etc.), which interact by the exchange of matter, energy and information. As the result of these interrelations, the Earth can be interpreted as a complex and evolving network. One may consider each subsystem separately, but the growing understanding of the whole system Earth suggests that one should take into account the interactions between these subsystems. (3)

Dyle, Daniel, et al. Universal Early Warning Signals of Phase Transitions in Climate Systems. Journal of the Royal Society Interface. April, 2023. Seven senior weather theorists including Marten Scheffer, Madhur Anand (whom I heard speak in 2004) and Tim Lenton post a latest analysis about how our hyper-active world weather which is prone to radical abrupt resets to new states because of its nonlinear essence can be much availed by machine learning AI neural net frontiers via a novel global science. One might muse that our lively abode is finding ways to steady, maintain and take steady care of itself, hopefully into an Earthropocene era.

The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden shift is well known, but their prediction by standard forecast models remains difficult. As a response, alternative methods are being availed that identify critical phenomena in advance of such dynamical bifurcations. A prime finding is that these critical signs are similar for a variety of systems which implies that data-intensive deep learning procedures can be effective. Here we offer a proof as applied to lattice phase transitions via a deep neural network trained on 2D Ising models tested on real and simulated climates with much success. Indicators like this provide novel insights into tipping events, along with as remote sensing on complex geospatially resolved Earth Systems. (Excerpt)

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