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

2. Global Climate Change as a Complex Dynamical System

Eroglu, Denz et al. Multiplex Recurrence Networks. arXiv:2003.03309. DE, Norbert Marwan and Jurgen Kurth, Potsdam Institute for Climate Impact Research and Martina Stebich, Senckenberg Research Station of Quaternary Palaeontology, Weimar describe how the latest network theories and unfolding intricacies can provide deeper insights about nature’s untangled vegetation and active weather reports. An example is a reconstruction of paleopollen from Lake Sihailongwan in northern China.

We have introduced a novel combined multiplex recurrence network (MRN) approach in order to investigate multivariate time series. The value of this approach is demonstrated on coupled map lattices and on typical palaeobotany findings. In both examples, topological changes in the MRNs allow for the detection of regime changes in their dynamics. The method advances the interpretation of pollen records by considering the vegetation as a whole and the intrinsic similarity of the different regional vegetation elements. (Abstract excerpt)

Fan, Jingfang, et al. Climate Network Percolation Reveals the Expansion and Weakening of the Tropical Component under Global Warming. Proceedings of the National Academy of Sciences. 115/E12128, 2018. Senior scientists from Israel and Germany including Shlomo Havlin and Hans Schellnhuber provide a good example of how complex systems theory helps explain and predict regional and planetary weather. Here it is shown that presence of connected clusters in dynamic network structures from epidemics to magnetism can similarly characterize climatic phenomena, See also Percolation Framework to Describe El Nino Conditions by this group in Chaos (27/035807, 2017).

Senior scientists from Israel and Germany including Shlomo Havlin and Hans Schellnhuber provide a good example of how complex systems theory helps explain and predict regional and planetary weather. Here it is shown that presence of connected clusters in dynamic network structures from epidemics to magnetism can similarly characterize climatic phenomena, See also Percolation Framework to Describe El Nino Conditions by this group in Chaos (27/035807, 2017)

Fan, Jingfang, et al. Network Analysis Reveals Strongly Localized Impacts of El Nino. Proceedings of the National Academy of Sciences. 114/7543, 2017. Bar-Ilan University (Fann, Jun Meng, Shlomo Havlin), Ben-Gurion University of the Negev (Yosef Ashkenazy), and Potsdam Institute for Climate Impact Research (Hans Schellnhuber) environmental physicists apply network theory to help quantify this extreme weather phenomena.

El Niño, one of the strongest climatic phenomena on interannual time scales, affects the climate system and is associated with natural disasters and serious social conflicts. Here, using network theory, we construct a directed and weighted climate network to study the global impacts of El Niño and La Niña. The constructed climate network enables the identification of the regions that are most drastically affected by specific El Niño/La Niña events. Our analysis indicates that the effect of the El Niño basin on worldwide regions is more localized and stronger during El Niño events compared with normal times. (Significance)

Fan, Jingfang, et al. Statistical Physics Approaches to the Complex Earth System. arXiv:2009.04918. We make special note of this later 2020 entry by eight authorities with postings in Germany, China, Israel, and Russia including Jurgen Kurths and Shlomo Havlin because after 144 pages and 400+ references it affirms that even hyper-active global weather can be well quantified and maybe mitigated by the presence of the same nonlinear self-organized critical dynamics, fractal topologies and so on as everywhere else. One might then view our lively Gaian planet as just now forming an Earthomo faculty for its vital sustainability going forward. Typical sections are The Earth as a Complex System, Scaling Theory, and Long-Range Correlations, and Earthquake Scale Invariance.

Global climate change, extreme climate events, earthquakes and their accompanying natural disasters pose significant risks to humanity. However over the past years, the emergence and evolution of Earth system science has produced new conceptual frameworks such as novel statistical physics and complex networks-based techniques to substantially advance a better understanding of climate extreme events, earthquakes and Earth geometric relief features, and more so to achieve much improved predictions. We present a comprehensive review of how combined statistical physics and complex systems science approaches such as critical phenomena, network theory, percolation, tipping points analysis, entropy theory can be applied to complex, dynamic Earth systems. (Abstract excerpt)

Faranda, Davide, et al. Statistical physics and dynamical systems perspectives on geophysical extreme events. Physical Review E.. 110/041001, October, 2024. Eighteen environmental physicists posted in Sweden, France, Italy, Portugal, Germany and the Netherlands first identify a disconnect between past studies of wild weather and geologic trauma and what must be their fundamental source in physical phenomena. This natural grounding can then facilitate an integration with current complexity theories in practice and stochastic principles and forces. In regard, by this autumnal season after two record hurricanes, here is another instance of a latest cross-synthesis as hopefully Earthica learns in time.

Statistical physics and dynamical systems theory are key tools to study geophysical events such as temperature extremes, cyclones, geomagnetic storms, and many others. Despite intrinsic differences, they all originate as deviations from the typical trajectories of a geophysical system, resulting in coherent structures at spatial and temporal scales. While statistical extreme value analysis techniques are capable of providing return times and probabilities of occurrence of certain geophysical events, they are not apt to account for their underlying physics. This paper outlines this knowledge gap, presenting new formalisms and stochastic approaches tailored to the study of extreme geophysical events.

Farmer, Doyne, et al. A Third Wave in the Economics of Climate Change. Environmental and Resource Economics. 6/2/2, 2015. We came to this by way of a Nature note Current Climate Models are Grossly Misleading by Nicolas Stern (530/407, 2016) which says that scientists and planners are talking past each other, and a common consensus is imperative. This paper by senior theorists from Oxford University and the Santa Fe Institute was cited as a step in that direction. The authors are aware of this problem, and after some technical density, a complex system option called ABIAM or Agent-Based Integrated Assessment Models is laid out as a way forward.

Modelling the economics of climate change is daunting. Many existing methodologies from social and physical sciences need to be deployed, and new modelling techniques and ideas still need to be developed. Existing bread-and-butter micro- and macroeconomic tools, such as the expected utility framework, market equilibrium concepts and representative agent assumptions, are far from adequate. Four key issues—along with several others—remain inadequately addressed by economic models of climate change, namely: (1) uncertainty, (2) aggregation, heterogeneity and distributional implications (3) technological change, and most of all, (4) realistic damage functions for the economic impact of the physical consequences of climate change. This paper assesses the main shortcomings of two generations of climate-energy-economic models and proposes that a new wave of models need to be developed to tackle these four challenges. This paper then examines two potential candidate approaches—dynamic stochastic general equilibrium (DSGE) models and agent-based models (ABM). The successful use of agent-based models in other areas, such as in modelling the financial system, housing markets and technological progress suggests its potential applicability to better modelling the economics of climate change. (Abstract)

Feng, Aixia, et al. Three-Dimensional Air-Sea Interactions Investigated with Bilayer Networks. Theoretical and Applied Climatology. 109/3-4, 2012. Lanzhou University, College of Atmospheric Sciences, and National Climate Center, Beijing scientists provide one more case study of how network phenomena can effectively apply to weather dynamics.

Feulner, Georg. Formation of Most of Our Coal Brought Earth Close to Global Glaciation. Proceedings of the National Academy of Science. 114/11333, 2017. A Potsdam Institute for Climate Impact Research senior physicist reconstructs whence the world’s coal deposits formed long ago and how this sedimentation drew down CO2 levels almost to the point of an ice age. With this reference in place, as our human phase uses up this reserve it can be seen to send, via increasing CO2 concentrations, this Earthly biosphere toward a thermal heat overshoot.

The bulk of the coal driving the Industrial Revolution and contributing to global warming today has been deposited during the Carboniferous period (359–299 million years ago), resulting in a significant drawdown of atmospheric carbon dioxide at that time. In this work, a combination of climate model simulations and recent estimates for carbon dioxide levels in the atmosphere is used to demonstrate that the cooling due to the diminished greenhouse effect brought our planet close to the limit of global glaciation ∼300 million years ago. These findings highlight the climatic importance of the fossil carbon stored in Earth’s coal deposits and thus have implications for climate policy. (Significance)

Franzke, Christian and Terence O’Kane, eds. Nonlinear and Stochastic Climate Dynamics. Cambridge: Cambridge University Press, 2017. The editors are German and Australian systems scientists. While the book summary avers that world (wild) weather has become understood as a mathematically complex, fluid phenomena, this was not the case until recently. Typical chapters are Challenges for Ice Age Dynamics, Atmospheric Teleconnection Patterns, Stochastic Climate Theory, and Complex Network Techniques for Climatological Data Analysis.

It is now widely recognized that the climate system is governed by nonlinear, multi-scale processes, whereby memory effects and stochastic forcing by fast processes, such as weather and convective systems, can induce regime behavior. Motivated by present difficulties in understanding the climate system and to aid the improvement of numerical weather and climate models, this book gathers contributions from mathematics, physics and climate science to highlight the latest developments and current research questions in nonlinear and stochastic climate dynamics. Leading researchers discuss some of the most challenging and exciting areas of research in the mathematical geosciences, such as the theory of tipping points and of extreme events including spatial extremes, climate networks, data assimilation and dynamical systems. (Publisher)

Franzke, Christian, et al. Stochastic Climate Theory and Modelling. arXiv:1409.0423. In an effort to finds ways to quantify and predict ultra-complex global and local weather phenomena, theorists from Germany, Australia, the UK and the USA, including Valerio Lucarini, are forging a synthesis of statistical physics and dynamic systems science. As this and other citations convey, such as by Tim Palmer, this is a daunting project that must yet go forward if we are ever to gain a measure of insight and mediation.

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modelling. In this review we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspectives. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models. (Abstract)

Ghil, Michael and Valerio Lucarini. The Physics of Climate Variability and Climate Change. Reviews of Modern Physics. Online March, 2020. Ecole Normale Superieure, Paris and University of Reading, UK geoscientists post an 86 page tutorial as dynamic geologic, oceanic and atmospheric phases become amenable to nonlinear analysis. Along the way, the presence of critical phases and transitions are indeed seen in effect. When this general endeavor began two decades ago, akin to quantum realms, weather and climatic phases seemed so intricate and intractable they would daunt any analytical attempt. As the first Abstract sentence states this worldwild realm is now included amongst nature’s universal recurrence. See also Stochastic Resonance for Non-Equilibrium Systems by V. Lucarini at arXiv:1910.05048.

The climate system is a forced, dissipative, nonlinear, complex and heterogeneous system out of thermodynamic equilibrium with a natural variability on many scales of motion in time and space. This paper reviews observational evidence on climate phenomena and governing equations of planetary-scale flow. Recent advances in the application of dynamical systems theory and nonequilibrium statistical physics are brought together help understand and predict the system’s behavior. These complementary views permit a self-consistent handling of subgrid-scale phenomena as stochastic processes, as well as a unified handling of natural climate variability and forced climate change. (Abstract)

Ghil, Michael and Valerio Lucarini. The Physics of Climate Variability and Climate Change. arXiv:1910.00583. In a 70 page entry, an Ecole Normale Superieure, Paris and a University of Reading, UK physicist show how sophisticated applications of nonlinear mathematical principles, in effect everywhere else, can well serve to quantify hyper-complex world weather patterns and processes.

The climate system is a forced, dissipative, nonlinear, complex and heterogeneous system that is out of thermodynamic equilibrium which exhibits variability on many scales of motion, in time as well as space, and is subject to natural as well as anthropogenic impacts. This paper reviews the observational evidence on climate phenomena and the governing equations of planetary-scale flow, as well as the key concept of a hierarchy of models for the climate sciences. Recent advances in the application of dynamical systems theory and of nonequilibrium statistical physics are brought together for the first time and shown to complement as they help understand and predict the system's behavior. These dual viewpoints permit a self-consistent handling of subgrid-scale phenomena as stochastic processes, as well as the crucial issues of climate sensitivity, response, and predictability. (Abstract excerpts)

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