3 thoughts on “Mathematical Geoenergy in a nutshell

  1. By contrast, this review paper covers much the same ground but is exactly the opposite way that I approached the topic

    Statistical physics approaches to the complex Earth system

    Jingfang Fan, Jun Meng, Josef Ludescher, Xiaosong Chen, Yosef Ashkenazy, Jurgen Kurths, Shlomo Havlin, Hans Joachim Schellnhuber

    Global climate change, extreme climate events, earthquakes and their accompanying natural disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, strategic interactions and complex structure of the Earth system, the understanding and in particular the predicting of such disruptive events represent formidable challenges for 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 for a better understanding of the Earth system, including climate extreme events, earthquakes and Earth geometric 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, as well as entropy can be applied to complex Earth systems (climate, earthquakes, etc.). 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 approaches can be useful in the field of Earth system science.

    https://arxiv.org/abs/2009.04918

    3 Applications 38
    3.1 Climate System . . . . . . . . . . . . . . . . . . . . . . . 38
    3.1.1 El NiñoâĂŞSouthern Oscillation . . . . . . . . 40
    3.1.2 Indian Summer Monsoon . . . . . . . . . . . . . 52
    3.1.3 Extreme Rainfall . . . . . . . . . . . . . . . . . . . . . 56
    3.1.4 Atmospheric Circulation and Global Warming . . . . . . . 60
    3.1.5 Atlantic Meridional Overturning Circulation . . . . . . . . . 67
    3.2 Earth Geometric Surface Relief . . . . . . . . . . . . . . . . . . .71
    3.2.1 Self-similarity and Long-range Correlations . . . . . . 71
    3.2.2 Landmass and Oceanic Clusters . . . . . . . . . . . . . . . 72
    3.2.3 Origin of the Discontinuity . . . . . . . . . . . . . . . . . . . . . 74
    3.3 Earthquake Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . .77
    3.3.1 Scale Invariance and the inter-event Distribution . . . . 77
    3.3.2 Modeling Seismic Time Series by the Point Process Approach . . . 80
    3.3.3 Memory Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
    3.3.4 Earthquake Forecasting . . . . . . . . . . . . . . . . . . 86

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