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

2. Global Climate Change as a Complex Dynamical System

Palmer, Tim and Bjorn Stevens. The Scientific Challenge of Understanding and Estimating Climate Change. Proceedings of the National Academy of Sciences. 116/24390, 2019. Senior Oxford University and MPI Meteorology theorists are concerned that present local and global quantifications remain quite inadequate to this imperative project of gaining deeper accuracies and understandings, which can then aid prediction and mitigation.

Given the slow unfolding of what may become catastrophic changes to Earth’s climate, many are understandably distraught by failures of public policy to rise to the magnitude of the challenge. Few in the science community would think to question the scientific response to the unfolding changes. However, is the science community continuing to do its part to the best of its ability? In the domains where we can have the greatest influence, is the scientific community articulating a vision commensurate with the challenges posed by climate change? We think not. (Abstract)

Palmer, Tim, et al. Stochastic Modelling and Energy-Efficient Computing for Weather and Climate. Philosophical Transactions of the Royal Society A. 372/Issue 2018, 2014. Tim Palmer (search) is a leading climate theorist and activist. An introduction to an issue by Oxford University physicists about scientific efforts to come to grips with this ultra-intricate domain by way of sophisticated computation. A typical paper is Scaling Laws for Parametrizations of Subgrid Interactions in Simulations of Oceanic Circulations. See also Palmer’s note Build High-Resolution Global Climate Models in Nature (515/338, 2014) OK

This paper sets out a new methodological approach to solving the equations for simulating and predicting weather and climate. In this approach, the conventionally hard boundary between the dynamical core and the sub-grid parametrizations is blurred. This approach is motivated by the relatively shallow power-law spectrum for atmospheric energy on scales of hundreds of kilometres and less. It is first argued that, because of this, the closure schemes for weather and climate simulators should be based on stochastic–dynamic systems rather than deterministic formulae. As the era of exascale computing is approached, an energy- and computationally efficient approach to cloud-resolved weather and climate simulation is described where determinism and numerical precision are focused on the largest scales only. (Abstract excerpt)

Palus, Milan. Cross-Scale Interactions and Information Transfer. Entropy. 16/10, 2014. After an introduction to independent, self-similar complexity phenomena, an Academy of Sciences of the Czech Republic, Nonlinear Dynamics and Complex Systems Group physicist describes its manifest presence across climatic temperature scales. See also his paper Multiscale Atmospheric Dynamics: Cross-Frequency Phase-Amplitude Coupling in the Air Temperature in Physical Review Letters (112/078702, 2014).

An information-theoretic approach for detecting interactions and information transfer between two systems is extended to interactions between dynamical phenomena evolving on different time scales of a complex, multiscale process. The approach is demonstrated in the detection of an information transfer from larger to smaller time scales in a model multifractal process and applied in a study of cross-scale interactions in atmospheric dynamics. (Abstract)

Rial, Jose. Abrupt Climate Change: Chaos and Order at Orbital and Millennial Scales. Global and Planetary Change. 41/1, 2004. A University of North Carolina geologist provides a rare technical analysis of earth’s atmosphere in terms of a complex nonlinear system. By this accurate perception, world weather is in danger of being unpredictably perturbed into sudden shifts to radically different, unpredictable regimes. Some seven years later one only has to look out the window. See also herein Carolyn Snyder, et al. The Complex Dynamics of the Climate System: Constraints on our Knowledge, Policy Implications and the Necessity of Systems Thinking. for a 2011 call to press this dynamic approach.

Rial, Jose, et al. Nonlinearites, Feedbacks and Critical Thresholds Within the Earth’s Climate System. Climatic Change. 65/1-2, 2004. Eleven authors from as many laboratories contend that global weather is best understood as an interlinked multitude of critically poised complex systems. The worldwide research community is entreated to adopt this “nonlinear paradigm if we are to move forward in the assessment of the human influence on climate.”

Scheffer, Marten, et al. Anticipating Critical Transitions. Science. 338/344, 2012. A dozen leading researchers including Tim Lenton, Jordi Bascompte, Mercedes Pascual, and Simon Levin draw upon the nonlinear systems literature seen stretching across “physical, chemical, tectonic, microbiology, ecological, physiology, behavioral, societal, economic, and notably climatic” fields to lay out a broad theoretical approach able to give indications of “tipping point” changes and calamities. But even days after Superstorm Sandy, the old misnomer “global warming” is still used. Such wild weather patterns as true dynamic complexities do not moderate, since human civilization drivers remain, but move to increasingly extreme oscillations, in much peril of shifting to a catastrophic new attractor or set point.

Tipping points in complex systems may imply risks of unwanted collapse, but also opportunities for positive change. Our capacity to navigate such risks and opportunities can be boosted by combining emerging insights from two unconnected fields of research. One line of work is revealing fundamental architectural features that may cause ecological networks, financial markets, and other complex systems to have tipping points. Another field of research is uncovering generic empirical indicators of the proximity to such critical thresholds. Although sudden shifts in complex systems will inevitably continue to surprise us, work at the crossroads of these emerging fields offers new approaches for anticipating critical transitions. (Abstract)

Schwabe, Mierk, et al. Opportunities and challenges of quantum computing for climate modelling.. arXiv:2502.10488. Institut für Physik der Atmosphäre, München, Germany, Center for Learning the Earth with Artificial Intelligence and Physics, Columbia University and University of Bremen physicists including Veronika Eyring explore ways to enjoin AI neural advances with select quantum algorithms as a way to take studies of hyper-complex world weather environs to a new level of predictive accuracy.

Adaptation to climate change requires robust climate projections, yet uncertainties in Earth system models (ESMs) remains large. Building on the work of hybrid (physics + AI) ESMs, we discuss how quantum computers could accelerate climate models by solving the underlying differential equations faster, how quantum machine learning could better represent subgrid-scale phenomena in ESMs, how quantum algorithms could assist in tuning the many parameters in ESMs, and how quantum computers could aid in the analysis of climate models. (Excerpt)

Selvam, Amujuri Mary. Self-Organized Criticality and Predictability in Atmospheric Flows: The Quantum World of Clouds and Rain. International: Springer, 2017. The senior physicist author is deputy director of the Indian Institute of Tropical Meteorology in Poona. As the quote says, the volume is a sophisticated, exemplary witness that even hyper-active complex weather phenomena can be found to reside in nature’s universally preferred state.

This book presents a new concept of General Systems Theory and its application to atmospheric physics. It reveals that energy input into the atmospheric eddy continuum, whether natural or manmade, results in enhancement of fluctuations of all scales, such as the high-frequency fluctuations of the Quasi-Biennial Oscillation and the El-Nino–Southern Oscillation cycles. These atmospheric flows then exhibit a self-organised criticality via long-range spatial and temporal correlations which manifest as fractal self-similar patterns with an inverse power law form. Since the probability distributions of amplitude and variance are the same, atmospheric flows exhibit quantum-like chaos. Long-range correlations inherent to power law distributions of fluctuations are identified as nonlocal connection or entanglement exhibited by quantum systems such as electrons or photons.

Singh, Martin and Morgan O’Neill. The Climate System and the Second Law of Thermodynamics. Reviews of Modern Physics. 94/015001, January, 2022. Monash University, Australia and Stanford University Earth system scientists provide an advanced quantification to date with over 70 pages and hundreds of references. A topical outline includes The Climate System as a Heat Engine, Irreversible Processes, Material Energy Budget, Moist Atmospheric Convection, Tropical Cyclones, Radiative Convection, Exoplanet Implications, and so on. If to reflect, an especial Gaian abide now transitions to a collaborative global phase by which to gain better knowledge and abilities by which to maintain and sustain. But at the same while, a world war may consume us. Our dilemma involves a worldwise decision to reach a better light age, or backwards to eternal dark ages.

The second law of thermodynamics implies a relationship between the net entropy export by Earth and its internal irreversible entropy production. Here we consider this constraint to better analyze and understand climate change issues, to which both radiative and material processes contribute. In regard, an entropy budget is derived that accounts for the multiphase nature of the hydrological cycle. Such theories can be successful if they can account for the irreversible entropy production associated with water in all its atmospheric phases. Finally, climatic and geophysical flows are reviewed, by way of equilibrium statistical mechanics so to predict long-lived coherent structures and maximum entropy production. (Abstract excerpt)

Snyder, Carolyn, et al. The Complex Dynamics of the Climate System: Constraints on Our Knowledge, Policy Implications and the Necessity of Systems Thinking. Hooker, Cliff, ed.. Philosophy of Complex Systems. Amsterdam: Elsevier, 2011. While every other natural and social strata has reinvented itself in nonlinear terms and theories, climate studies remain burdened and preoccupied with huge current and epochal weather datasets. With co-authors Michael Mastrandrea and notably Stephen Schneider (1945-2010), Stanford University environmentalists call for and carefully scope out this vital advance. The lacunae is redressed by first setting out complexity principles seen in effect over land, sea and air, followed by a review of present modeling approaches. For example, a Glossary contains: Stochastic Chaos, Nonlinearity, Complex System, Feedbacks, Transient, Emergence, Multiple Equilibria, Thresholds, and so on. This dynamics view is then applied to six domains: glacial-interglacial cycles, thermohaline ocean circulation, ice sheets, vegetation cover variability, species extinction, and overshoot scenarios. Indeed much debate and denial over global warming, which compels scientists to spend much time in their defense, is about a few degrees of temperature, unawares of an actual critically poised, increasingly erratic, micro and macro ecosphere. See also Jose Rial herein: Abrupt Climate Change: Chaos and Order at Orbital and Millennial Scales for another rare take.

Anthropogenic (human-caused) climate change involves interactions among complex global geophysical, biological, social, and economic systems. Systems concepts, principles, and methods are essential to understand the climate system and the dynamics of climate change. The Earth’s climate system hosts a myriad of nested and coupled sub-systems, functioning at various temporal and spatial scales. (467-468)

The various scientific disciplines studying components of the climate system often neglected to focus on the complexity of this multi-component system, and instead modeled the sub-components of the climate system in isolation and along distinct disciplinary lines. Gradualism and linearity usually were assumed, and most models produced internally stable and predictable behavior. (468) Emergent properties, multiple equilibria, path dependence, and nonlinearities inherent in the climate system were often overlooked, and when discovered, were sidelined as exceptions rather than fundamental properties. (468)

Steinhaeuser, Karsten, et al. Multivariate and Multiscale Dependence in the Global Climate System Revealed through Complex Networks. Climate Dynamics. 39/3-4, 2012. Steinhaeuser, and Auroop Ganguly, Geographic Information Science Group, ORNL, with Nitesh Chawla, University of Notre Dame, Interdisciplinary Center for Network Science, offer theory and examples of how weather patterns can be modeled by way of nonlinear network topologies and dynamics.

Thompson, J. Michael and Jan Sieber. Introduction: Climate Predictions: The Influence of Nonlinearity and Randomness. Philosophical Transactions of the Royal Society A. 370/1007, 2012. University of Aberdeen and University of Portsmouth mathematicians engage daunting climate complexities which yet are seen to lend themselves to nonlinear discernment. A core concern is then a better warning system for abrupt weather “tipping points.” A typical paper could be Tipping Points in Open Systems by Peter Ashwin, et al. Also search herein for a companion article by Axel Kleidon on non-equilibrium thermodynamics.

The current threat of global warming and the public demand for confident projections of climate change pose the ultimate challenge to science: predicting the future behaviour of a system of such overwhelming complexity as the Earth's climate. This Theme Issue addresses two practical problems that make even prediction of the statistical properties of the climate, when treated as the attractor of a chaotic system (the weather), so challenging. The first is that even for the most detailed models, these statistical properties of the attractor show systematic biases. The second is that the attractor may undergo sudden large-scale changes on a time scale that is fast compared with the gradual change of the forcing (the so-called climate tipping). (Abstract)

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