Statistical physics approaches to the complex Earth system

Abstract

Global warming, extreme climate events, earthquakes and their accompanying socioeconomic disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, multiple interactions and complex structures of the Earth system, the understanding and, in particular, the prediction of such disruptive events represent formidable challenges to both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge of the Earth system, including climate extreme events, earthquakes and geological relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as critical phenomena, network theory, percolation, tipping points analysis, and entropy can be applied to complex Earth systems. Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics concepts and theories can be useful in the field of Earth system science.

The Impact of Sustainability Goals on Productivity Growth: The Moderating Role of Global Warming

“…Countries have decided to rebuild their economies; they can only become cleaner, greener, healthier, safer, more resilient, and sustainable by adhering to recovery plans [73]. The post-pandemic recovery requires nations to discover innovative solutions and complex scientific approaches for a more profound, systematic shift towards a more sustainable economy [74]. Climatepositive actions need to trigger the trajectory of atmospheric CO 2 levels; for instance, green investments accelerate the decarburisation of all aspects of the economy [75].…”

Analysis of Climate Change Characteristics in Xi’an Based on the Visibility Graph

“…Based on numerical simulation, the continuous stochastic equation [17], thermal equation [18], kinematic equation and state equation [19] are used to fit atmospheric numerical value, to achieve the purpose of analyzing meteorology and temperature. The third method is model analysis [20][21][22]. This method uses probability and statistics theory to extract the temperature change law from the meteorological historical data, and it is also used to construct the statistical model.…”

Nonlinear time series analysis of palaeoclimate proxy records

“…However, their application potential can be limited by the fact that real world systems usually consist of many interacting components with feedbacks and nonlinear interrelationships, behave in a more chaotic rather than periodic way, vary in a fashion that cannot be described by a normal distribution (Schölzel and Friederichs, 2008), exhibit distinct behaviours in terms of their extreme event statistics (Albeverio et al, 2006), or represent critical transitions to qualitatively different dynamical regimes (such as tipping points) (Lenton et al, 2008;Schellnhuber, 2009). Concepts from complex systems science, complex networks, and nonlinear dynamics are more appropriate for such problems Fan et al, 2021). In the light of the critical impacts of climate and environmental changes on human societies, quantitative investigations of large-scale regime shifts (Rocha et al, 2018;Boers and Rypdal, 2021), early warning indicators of such shifts (Dakos et al, 2008;Scheffer et al, 2009;Boettner et al, 2021), and short-term ecosystem responses (Scheffer and Carpenter, 2003;Prasad et al, 2020) on the base of palaeoclimate archives are required.…”