New Horizons of Predictability in Complex Dynamical Systems: From Fundamental Physics to Climate and Society

2021 ◽  
Author(s):  
Rui A. P. Perdigão
Keyword(s):  
Complex Systems ◽  
Stochastic Dynamic ◽  
Earth System ◽  
Fundamental Physics ◽  
Linear Dynamics ◽  
New Horizons ◽  
Intelligence System ◽  
Consistent Manner ◽  
Dynamic Resonance ◽  
Climate And Society

Discerning the dynamics of complex systems in a mathematically rigorous and physically consistent manner is as fascinating as intimidating of a challenge, stirring deeply and intrinsically with the most fundamental Physics, while at the same time percolating through the deepest meanders of quotidian life. The socio-natural coevolution in climate dynamics is an example of that, exhibiting a striking articulation between governing principles and free will, in a stochastic-dynamic resonance that goes way beyond a reductionist dichotomy between cosmos and chaos. Subjacent to the conceptual and operational interdisciplinarity of that challenge, lies the simple formal elegance of a lingua franca for communication with Nature. This emerges from the innermost mathematical core of the Physics of Coevolutionary Complex Systems, articulating the wealth of insights and flavours from frontier natural, social and technical sciences in a coherent, integrated manner. Communicating thus with Nature, we equip ourselves with formal tools to better appreciate and discern complexity, by deciphering a synergistic codex underlying its emergence and dynamics. Thereby opening new pathways to see the “invisible” and predict the “unpredictable” – including relative to emergent non-recurrent phenomena such as irreversible transformations and extreme geophysical events in a changing climate. Frontier advances will be shared pertaining a dynamic that translates not only the formal, aesthetical and functional beauty of the Physics of Coevolutionary Complex Systems, but also enables and capacitates the analysis, modelling and decision support in crucial matters for the environment and society. By taking our emerging Physics in an optic of operational empowerment, some of our pioneering advances will be addressed such as the intelligence system Earth System Dynamic Intelligence and the Meteoceanics QITES Constellation, at the interface between frontier non-linear dynamics and emerging quantum technologies, to take the pulse of our planet, including in the detection and early warning of extreme geophysical events from Space.

Earth System Dynamic Intelligence with Quantum Technologies: Seeing the “Invisible”, Predicting the “Unpredictable” in a Critically Changing World

2021 ◽  
Author(s):  
Rui A. P. Perdigão
Keyword(s):  
Information Technologies ◽  
Complex Problem ◽  
Warning Signs ◽  
System Dynamic ◽  
Stochastic Dynamic ◽  
Earth System ◽  
Dynamic Prediction ◽  
Information Theoretic ◽  
Hybrid Approaches ◽  
Changing World

We hereby embark on a frontier journey articulating two of our flagship programs – “Earth System Dynamic Intelligence” and “Quantum Information Technologies in the Earth Sciences” – to take the pulse of our planet and discern its manifold complexity in a critically changing world. Going beyond the traditional stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid approaches to information and complexity, the underlying fundamental science ignites disruptive developments empowering complex problem solving across frontier natural, social and technical geosciences. Taking aim at complex multiscale planetary problems, the roles of our flagships are put into evidence in different contexts, ranging from I) Interdisciplinary analytics, model design and dynamic prediction of hydro-climatic and broader geophysical criticalities and extremes across multiple spatiotemporal scales; to II) Sensing the pulse of our planet and detecting early warning signs of geophysical phenomena from Space with our Meteoceanics QITES Constellation, at the interface between our latest developments in non-linear dynamics and emerging quantum technologies.

Earth System Dynamic Intelligence - ESDI

2021 ◽  
Author(s):  
Rui A. P. Perdigão
Keyword(s):  
Information Technologies ◽  
Complex Problem ◽  
System Dynamic ◽  
Stochastic Dynamic ◽  
Earth System ◽  
Dynamic Information ◽  
Information Theoretic ◽  
Hybrid Techniques ◽  
Process Understanding ◽  
Shed Light

Earth System Dynamic Intelligence (ESDI) entails developing and making innovative use of emerging concepts and pathways in mathematical geophysics, Earth System Dynamics, and information technologies to sense, monitor, harness, analyze, model and fundamentally unveil dynamic understanding across the natural, social and technical geosciences, including the associated manifold multiscale multidomain processes, interactions and complexity, along with the associated predictability and uncertainty dynamics. The ESDI Flagship initiative ignites the development, discussion and cross-fertilization of novel theoretical insights, methodological developments and geophysical applications across interdisciplinary mathematical, geophysical and information technological approaches towards a cross-cutting, mathematically sound, physically consistent, socially conscious and operationally effective Earth System Dynamic Intelligence. Going beyond the well established stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid techniques, ESDI paves the way to exploratory and disruptive developments along emerging information physical intelligence pathways, and bridges fundamental and operational complex problem solving across frontier natural, social and technical geosciences. Overall, the ESDI Flagship breeds a nascent field and community where methodological ingenuity and natural process understanding come together to shed light onto fundamental theoretical aspects to build innovative methodologies, products and services to tackle real-world challenges facing our planet.

scholarly journals The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA)

2020 ◽  
Vol 101 (10) ◽  
pp. E1743-E1760 ◽  
Author(s):  
Gabriele G. Pfister ◽  
Sebastian D. Eastham ◽  
Avelino F. Arellano ◽  
Bernard Aumont ◽  
Kelley C. Barsanti ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Atmospheric Modeling ◽  
Atmospheric Composition ◽  
Earth System ◽  
Policy Makers ◽  
Multi Scale ◽  
Scale Modeling ◽  
Consistent Manner ◽  
Limited Scale ◽  
Modeling Strategy

ABSTRACTTo explore the various couplings across space and time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past toward a unification of the range of scales inherent in the Earth system. This paper describes the forward-looking Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next-generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatiotemporal scales, coupling to other Earth system components, and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality, and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders.

MUSICA - Modeling for Chemistry, Weather and Climate

2020 ◽  
Author(s):  
Gabriele Pfister ◽  
Andrew Conley ◽  
Mary Barth ◽  
Louisa Emmons ◽  
Forrest Lacey ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Large Scale ◽  
Spatial Scales ◽  
Spectral Element ◽  
Global Scale ◽  
Earth System ◽  
Early Results ◽  
Consistent Manner ◽  
Model Components

<p>Current chemical transport models inadequately account for the two-way coupling of atmospheric chemistry with other Earth System components over the range of urban/local to regional to global scales and from the surface up to the top of the atmosphere.  To meet future challenges, future modeling systems need to have the ability to (1) change spatial scales in a consistent manner, (2) resolve multiple spatial scales in a single simulation, (3) couple model components which represent different Earth system processes, and (4) easily mix-and-match model components. This is the motivation behind MUSICA - the Multi-Scale Infrastructure for Chemistry and Aerosols, which we develop together with the atmospheric chemistry community. MUSICA will allow simulation of large-scale atmospheric phenomena while still resolving chemistry at scales relevant for representing societal and scientific critical phenomena (e.g. urban air quality, or convection in monsoon regions) and also enable connections to other components of the earth system by fully coupling to land and ocean models. MUSICA objectives will be achieved through development of a global modeling system capable of regional refinement and the new Model Independent Chemistry Module (MICM). We will discuss the infrastructure and show preliminary results of atmospheric chemistry simulations being conducted in a global model with regional refinement: the Community Atmosphere Model with chemistry using spectral element grids that refine from one-degree resolution to ~14 km resolution over the conterminous United States. These early results confirm that model resolution does matter for representing regional air quality and that the two-way feedback between the local and global scale can play an important role.</p>

scholarly journals Education as a Complex System: Conceptual and Methodological Implications

2019 ◽  
Vol 48 (2) ◽  
pp. 112-119 ◽  
Author(s):  
Michael J. Jacobson ◽  
James A. Levin ◽  
Manu Kapur
Keyword(s):  
Complex System ◽  
Education Research ◽  
Complex Systems ◽  
Conceptual Framework ◽  
Educational Systems ◽  
Nonlinear Characteristics ◽  
System Complexity ◽  
Linear Dynamics ◽  
Collective Behaviors ◽  
Research And Policy

Education is a complex system, which has conceptual and methodological implications for education research and policy. In this article, an overview is first provided of the Complex Systems Conceptual Framework for Learning (CSCFL), which consists of a set of conceptual perspectives that are generally shared by educational complex systems, organized into two focus areas: collective behaviors of a system, and behaviors of individual agents in a system. Complexity and research methodologies for education are then considered, and it is observed that commonly used quantitative and qualitative techniques are generally appropriate for studying linear dynamics of educational systems. However, it is proposed that computational modeling approaches, being extensively used for studying nonlinear characteristics of complex systems in other fields, can provide a methodological complement to quantitative and qualitative education research approaches. Two research case studies of this approach are discussed. We conclude with a consideration of how viewing education as a complex system using complex systems’ conceptual and methodological tools can help advance education research and also inform policy.

Improving Competitive Intelligence Through System Synamics

2011 ◽  
pp. 129-157
Author(s):  
Özge Pala ◽  
Dirk Vriens ◽  
Jac A.M. Vennix
Keyword(s):  
Complex Systems ◽  
System Dynamics ◽  
Competitive Intelligence ◽  
Accurate Information ◽  
Environmental Cues ◽  
Simulation Methodology ◽  
Intelligence System ◽  
Making Sense ◽  
Competitive Intelligence System

To survive in a complex and dynamic world, organizations need relevant, timely, and accurate information about their environment. Due to the increasing complexity and dynamics of the environment, organizations run into several difficulties in their efforts to structure the intelligence activities. Two particularly persistent problems are (1) determining the relevant environmental cues and (2) making sense of the particular values of these cues. The current available methods for competitive intelligence do not eliminate these problems. In this chapter, system dynamics (SD) is proposed as an appropriate tool for competitive intelligence. System dynamics is a simulation methodology that deals with the dynamics of complex systems from a feedback perspective. How SD can help in dealing with the problems in direction (selecting the relevant environmental cues) and analysis (making sense of cues) stages and how ICT can support the use of SD in intelligence activities are discussed.

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