VIII. Earth Earn: Our Open Participatory Earthropocene to Ecosmocene Futurity

2. Global Climate Change as a Complex Dynamical System

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)

Ghil, Michael, et al. A Collection on Climate Dynamics. Proceedings of the Royal Society A. Online March, 2015. A literature survey from journals as this to Physica D where articles such as Bifurcation analysis of delay-induced resonances of the El-Niño Southern Oscillation, Tropical atmospheric circulations with humidity effects, and Parameter estimation for energy balance models with memory can be found.

Climate dynamics is attracting increasing attention from the scientific community because of its intrinsic beauty and complexity, but also because of the socio-economic implications of anthropogenic climate change. Much of this complexity is due to the multiple space and time scales that characterize the processes active in the climate system and the phenomena they give rise to—from raindrops to major hurricanes and on to the oceans' overturning circulation—and to the nonlinearity of the interactions among these processes.

Heitzig, Jobst, et al. Editorial. European Physical Journal Special Topics. 225/3, 2016. An introduction to an issue on Health, Energy & Extreme Events in a Changing Climate, whose papers attempt to apply complex network theories to social impacts on disease ecologies, financial markets, and internecine conflicts.

Holme, Petter and Juan Rocha. Networks of Climate Change. arXiv:2105.12537. Tokyo Institute of Technology and Stockholm University post a graphic tutorial for an integrative meld of nature’s independent connectivities with world weather patterns, which then also involve human beings. In the process all manner of thermal, aquatic, population, carbon budgets and much more gain an interrelated aspect. A image is Network Structure of Earth’s Atmospheric Reaction Systems, and a section Tipping Points in Ecological Networks. By another compass, it would appear that the vital biosphere, via human agency, is trying to quantify itself so that our public activities might be appropriately modified. However might we come to see it this way?

Understanding the causes and consequences of global warming, along with mitigations, is a profoundly complex problem. Even when researchers focus in on to publishable investigation, their analysis often contains interacting components which require a network visualization. In addition, networks form a mathematical foundation for a multitude of computational and analytical techniques. In this review, we cover use-cases of networks in the climate-change literature -- what they represent, how they are analyzed, and what insights they bring. We also discuss network data, tools, and problems yet to be explored. (Abstract excerpt)

Kuna, Tobias and Sandro Valenti. Special Issue on Mathematics for the Fluid Earth. Journal of Physics A. 50/170301, 2015. An introduction by Reading University and Aix-Marseille University physicists. Some papers are Extreme Value Laws for Fractal Intensity Functions in Dynamical Systems, The Role of Nonlinear Self-Interaction in the Dynamics of Planetary-Scale Atmospheric Fluctuations, and Extreme Event Statistics of Daily Rainfall.

The fluid Earth is an excellent example of a forced, dissipative non-equilibrium system dominated by nonlinear processes and featuring multi-scale interactions. This collection consists of reviews and original research articles which span the full spectrum from mathematical papers addressing general questions, articles considering these questions for more realistic problems in theoretical physics and articles that apply new mathematical tools to concrete physical situations with application to the Fluid Earth system. The complexity of this system and its phenomena are presented in relation to the statistical manifestation of large scale systems in the asymptotic regimes far from equilibrium; where loss of memory and recurrence prevail.

Lana, Xavier, et al. Multifractal Structure of the Monthly Rainfall Regime in Catalonia: Evaluation of the Non-linear Complexity. Chaos. July, 2020. The rain in Spain stays mainly in the plain was famously sung in My Fair Lady (1956). In 2020 Polytechnic University of Catalonia physicists proceed to quantify predictable patterns by way of advanced topological and computational methods.

The complex non-linear regime of the monthly rainfall in Catalonia is analyzed by means of the reconstruction fractal theorem and the multifractal detrended fluctuation analysis algorithm. Areas with a notable degree of complex physical mechanisms are detected by using the concepts of persistence (Hurst exponent), complexity (embedding dimension), predictive uncertainty (Lyapunov exponents), loss of memory of the mechanism (Kolmogorov), and the set of multifractal parameters (spectral asymmetry, width, and complexity). Besides these analyses permitting a detailed description of monthly rainfall pattern characteristics, the obtained results should also be relevant for new research studies concerning monthly amounts forecasting at a monthly scale. (Abstract excerpt)

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