地球系统科学中的统计物理理论
导语
地球是一个非常丰富、复杂的系统,包括五大圈层、人类因素,例如人口、生产、污染物等。在各个圈层之间及人类之间存在能量、物质、信息的交换,这种交换或反馈是高度非线性的。因此,在地球科学研究中,统计物理理论价值重大,因此本路径对此进行了整理。
樊京芳 | 讲者
Dec | 整理
廖戴丽 | 编辑
1. 为什么把地球看做复杂系统
2. 人类世的特征
3. 理论突破与模式的基本物理
理论突破
模式的基本物理
4. 研究地球系统的数理方法
气候网络
渗流相变理论
熵和复杂性
本征微观态理论
5. 数理方法在地球系统中的应用
厄尔尼诺预测
厄尔尼诺的影响与分布
大气环流
印度夏季风
大气污染
极端气候事件
6. 展望
1. 为什么把地球看作复杂系统
1. 为什么把地球看作复杂系统
我们知道,地球系统分为五大圈层:岩石圈、生物圈、冰冻圈、大气圈、海洋圈。这五大圈层的内部和之间并不完全独立,存在非线性相互作用和反馈机制。
地球是一个非常丰富、复杂的系统,包括五大圈层、人类因素,例如人口、生产、污染物等。在各个圈层之间及人类之间存在能量、物质、信息的交换,这种交换或反馈是高度非线性的。
Schellnhuber教授在1999年提出地球系统分析的概念[1],应该有一个系统科学或地球系统的分析手段手段来研究地球系统科学,并称这样的技术手段为“具有第二次哥白尼式革命”高度性的理念。
‘Earth system’ analysis and the second Copernican revolutionSchellnhuber, H. J.nature(1999)
为什么是第二次哥白尼革命?第一次哥白尼革命是站在地球用望远镜看外太空,第二次主要是受益于新技术,例如雷达、遥感技术的应用,使得人们可以从外太空观察地球,将地球作为一个整体系统来研究。当技术达到后,理论上要有突破,例如系统科学的观点。
也是在这篇文章中,第一次用简单的数学公式来描述地球:
E = (N, H),N = (a,b,c,...),H = (A, S)。其中,E表示地球系统,N表示自然因素,H表示人类因素。
2. 人类世的特征
2. 人类世的特征
人类世的主要特征特征有:
1.大加速。地球系统是一个自然因素+社会因素高度耦合的系统,自然因素包括CO₂、甲烷、温室气体、热带雨林、生物多样性等,社会因素包括社会和经济层面、人口、GDP、城市化进程等。
2.复杂性和相互关联性。自然因素和人类因素并不是完全独立的、而是耦合在一起的,气候行动失效会引起极端天气、自然灾害、粮食水危机,引起社会不稳定性、政府政策失效,存在级联失效现象。下面关于风险的图来自世界经济论坛的全球风险报告。
地球系统的复杂性和相互关联性
Climate tipping points — too risky to bet againstLenton, Timothy M., Rockström, et alnature(2019)
3. 理论突破与模式的基本物理
3. 理论突破与模式的基本物理
3.1 理论突破
Thermal equilibrium of the atmosphere with a given distribution of relative humidityManabe Syukuro, Wetherald Richard T.(1967)
Manabe气候模式
Exhaustive percolation on random networksBjörn Samuelsson, Joshua E. S. SocolararXiv(2006)
Hasselmann随机气候模式将不同的因素分离
3.2 模式的基本物理
按复杂性分类的理论突破
4. 研究地球系统的数理方法
4. 研究地球系统的数理方法
4.1 气候网络
Statistical physics approaches to the complex Earth systemJingfang Fan, Jun Meng, Josef Ludescher, et alPhysics Reports(2020)
Network analysis reveals strongly localized impacts of El Niño.Jingfang Fan, Jun Meng, Yosef Ashkenazy, et al
Single-trial event-related potentials with wavelet denoisingQuiroga R.Quian, Garcia H.clinical neurophysiology(2003)
Prediction of extreme floods in the eastern Central Andes based on a complex networks approachBoers, N., Bookhagen, et alnature communications(2014)
Complex networks reveal global pattern of extreme-rainfall teleconnectionsNiklas Boers; Bedartha Goswami; Aljoscha Rheinwalt; Bodo Bookhagen; Brian Hoskins; Jürgen KurthsNature(2019)
4.2 渗流相变理论
Statistical physics approaches to the complex Earth systemJingfang Fan, Jun Meng, Josef Ludescher, et alPhysics Reports(2020)
Explosive Percolation in Random NetworksDimitris Achlioptas, Raissa M. D Souza, Joel Spencerscience(2009)
Catastrophic cascade of failures in interdependent networksSergey V. Buldyrev, Roni Parshani, Gerald Paul, et alnature(2010)
Resilience of networks with community structure behaves as if under an external fieldGaogao Dong; Jingfang Fan; Louis M. Shekhtman; Saray Shai; Ruijin Du; Lixin Tian; Xiaosong Chen; H. Eugene Stanley;Shlomo HavlinPNAS(2018)
Structural resilience of spatial networks with inter-links behaving as an external fieldFan JingfangNew Journal of Physics(2018)
临界指数
普适函数
4.3 熵和复杂性
4.4 玻尔兹曼熵
热力学系统
4.5 信息熵
4.6 样本熵
Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrierJun Meng, Jingfang Fan, Josef Ludescher, et al(2020)
4.7 本征微观态理论
Condensation of Eigen Microstate in Statistical Ensemble and Phase TransitionGaoke Hu, Teng Liu, Maoxin Liu, et alarXiv(2018)
5. 数理方法在地球系统中的应用
5. 数理方法在地球系统中的应用
5.1 厄尔尼诺预测
厄尔尼诺
5.2 预测开始时间
Structural resilience of spatial networks with inter-links behaving as an external fieldFan JingfangNew Journal of Physics(2018)
Percolation framework to describe El Niño conditionsMeng JunChaos(2017)
Improved El Niño forecasting by cooperativity detectionLudescher JosefProceedings of the National Academy of Sciences(2013)
5.3 预测极端事件的强度
Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrierJun Meng, Jingfang Fan, Josef Ludescher, et al(2020)
5.4 厄尔尼诺的影响与分布
Network analysis reveals strongly localized impacts of El Niño.Jingfang Fan, Jun Meng, Yosef Ashkenazy, et al
5.5 大气环流
Climate network percolation reveals the expansion and weakening of the tropical component under global warmingJingfang Fan,Jun Meng,Yosef Ashkenazy,Shlomo Havlin
5.6 印度夏季风
Tipping elements of the Indian monsoon: Prediction of onset and withdrawalStolbova VeronikaGeophysical Research Letters(2016)
5.7 大气污染
Significant Impact of Rossby Waves on Air Pollution Detected by Network AnalysisYongwen Zhang, Jingfang Fan, Xiaosong Chen, et alarXiv(2019)
5.8 极端气候事件
Prediction of extreme floods in the eastern Central Andes based on a complex networks approachBoers, N., Bookhagen, et alnature communications(2014)
Network-synchronization analysis reveals the weakening tropical circulationsZijin Geng, Yongwen Zhang, Bo Lu, et al
Climate network approach reveals the modes of CO2 concentration to surface air temperatureNa Ying1, Weiping Wang2, Jingfang Fan3, et al
Eigen Microstates and Their Evolution of Global Ozone at Different Geopotential HeightsXiaojie Chen, Na Ying, Dean Chen, et alarXiv(2021)
Possible origin of memory in earthquakes: Real catalogs and an epidemic-type aftershock sequence modelJingfang Fan, Dong Zhou, Louis M. Shekhtman, et al
Scaling laws in earthquake memory for interevent times and distancesYongwen Zhang, Jingfang Fan, Warner Marzocchi, et al
Improved earthquake aftershocks forecasting model based on long-term memoryYongwen Zhang, Dong Zhou, Jingfang Fan, et al
Statistical physics approaches to the complex Earth systemJingfang Fan, Jun Meng, Josef Ludescher, et alPhysics Reports(2020)
6. 展望
6. 展望
1.永恒的挑战:包括气候变化、水、资源、环境以及各种自然因素。
2.新兴的挑战:跟人的因素的关系更大,例如复杂的决策问题,气候变化或其他地学相关的跟政治、经济相关,种族冲突、安全性问题,未来可持续发展人类不均衡。
参考资料(26)
[2] Jun Meng, Jingfang Fan, Josef Ludescher, et al. Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier, 2020
[3] Lenton, Timothy M., Rockström, et al. Climate tipping points — too risky to bet against. nature, 2019, 575(7784): 592-595
[4] Niklas Boers; Bedartha Goswami; Aljoscha Rheinwalt; Bodo Bookhagen; Brian Hoskins; Jürgen Kurths. Complex networks reveal global pattern of extreme-rainfall teleconnections. Nature, 2019, 566: 373-377
[5] Sergey V. Buldyrev, Roni Parshani, Gerald Paul, et al. Catastrophic cascade of failures in interdependent networks. nature, 2010, 464(7291): 1025-1028
[6] Schellnhuber, H. J.. ‘Earth system’ analysis and the second Copernican revolution. nature, 1999, 402(6761): C19-C23
[7] Manabe Syukuro, Wetherald Richard T.. Thermal equilibrium of the atmosphere with a given distribution of relative humidity, 1967: 241–259
[8] Björn Samuelsson, Joshua E. S. Socolar. Exhaustive percolation on random networks. arXiv:0605047, 2006
[9] Jingfang Fan, Jun Meng, Yosef Ashkenazy, et al. Network analysis reveals strongly localized impacts of El Niño.
[10] Quiroga R.Quian, Garcia H.. Single-trial event-related potentials with wavelet denoising. clinical neurophysiology, 2003, 114(2): 376–390
[11] Boers, N., Bookhagen, et al. Prediction of extreme floods in the eastern Central Andes based on a complex networks approach. nature communications, 2014, 5(1): 1-7
[12] Dimitris Achlioptas, Raissa M. D Souza, Joel Spencer. Explosive Percolation in Random Networks. science, 2009, 323(5920): 1453-1455
[13] Gaogao Dong; Jingfang Fan; Louis M. Shekhtman; Saray Shai; Ruijin Du; Lixin Tian; Xiaosong Chen; H. Eugene Stanley;Shlomo Havlin. Resilience of networks with community structure behaves as if under an external field. PNAS, 2018, 115(27): 6911-6915
[14] Fan Jingfang. Structural resilience of spatial networks with inter-links behaving as an external field. New Journal of Physics, 2018, 20(9): 093003
[15] Gaoke Hu, Teng Liu, Maoxin Liu, et al. Condensation of Eigen Microstate in Statistical Ensemble and Phase Transition. arXiv:1812.08412, 2018
[16] Meng Jun. Percolation framework to describe El Niño conditions. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017, 27(3): 035807
[17] Ludescher Josef. Improved El Niño forecasting by cooperativity detection. Proceedings of the National Academy of Sciences, 2013, 110(29): 11742–11745
[18] Jingfang Fan,Jun Meng,Yosef Ashkenazy,Shlomo Havlin. Climate network percolation reveals the expansion and weakening of the tropical component under global warming
[19] Stolbova Veronika. Tipping elements of the Indian monsoon: Prediction of onset and withdrawal. Geophysical Research Letters, 2016, 43(8): 3982–3990
[20] Yongwen Zhang, Jingfang Fan, Xiaosong Chen, et al. Significant Impact of Rossby Waves on Air Pollution Detected by Network Analysis. arXiv:1903.02256, 2019, 46(21): 12476–12485,
[21] Zijin Geng, Yongwen Zhang, Bo Lu, et al. Network-synchronization analysis reveals the weakening tropical circulations
[22] Na Ying1, Weiping Wang2, Jingfang Fan3, et al. Climate network approach reveals the modes of CO2 concentration to surface air temperature
[23] Xiaojie Chen, Na Ying, Dean Chen, et al. Eigen Microstates and Their Evolution of Global Ozone at Different Geopotential Heights. arXiv:2107.00843, 2021
[24] Jingfang Fan, Dong Zhou, Louis M. Shekhtman, et al. Possible origin of memory in earthquakes: Real catalogs and an epidemic-type aftershock sequence model
[25] Yongwen Zhang, Jingfang Fan, Warner Marzocchi, et al. Scaling laws in earthquake memory for interevent times and distances
[26] Yongwen Zhang, Dong Zhou, Jingfang Fan, et al. Improved earthquake aftershocks forecasting model based on long-term memory
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讲者介绍
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