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Tornado outbreak variability follows Taylor’s power law of fluctuation scaling and increases dramatically with severity

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  • Michael K. Tippett

    (Columbia University
    Center of Excellence for Climate Change Research, King Abdulaziz University)

  • Joel E. Cohen

    (Laboratory of Populations, Rockefeller University
    The Earth Institute, Columbia University)

Abstract

Tornadoes cause loss of life and damage to property each year in the United States and around the world. The largest impacts come from ‘outbreaks’ consisting of multiple tornadoes closely spaced in time. Here we find an upward trend in the annual mean number of tornadoes per US tornado outbreak for the period 1954–2014. Moreover, the variance of this quantity is increasing more than four times as fast as the mean. The mean and variance of the number of tornadoes per outbreak vary according to Taylor’s power law of fluctuation scaling (TL), with parameters that are consistent with multiplicative growth. Tornado-related atmospheric proxies show similar power-law scaling and multiplicative growth. Path-length-integrated tornado outbreak intensity also follows TL, but with parameters consistent with sampling variability. The observed TL power-law scaling of outbreak severity means that extreme outbreaks are more frequent than would be expected if mean and variance were independent or linearly related.

Suggested Citation

  • Michael K. Tippett & Joel E. Cohen, 2016. "Tornado outbreak variability follows Taylor’s power law of fluctuation scaling and increases dramatically with severity," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10668
    DOI: 10.1038/ncomms10668
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    Cited by:

    1. Meng Xu & Joel E Cohen, 2019. "Analyzing and interpreting spatial and temporal variability of the United States county population distributions using Taylor's law," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-25, December.
    2. Qing Cai & Hai-Chuan Xu & Wei-Xing Zhou, 2016. "Taylor's Law of temporal fluctuation scaling in stock illiquidity," Papers 1610.01149, arXiv.org.
    3. Saitoh, Takashi & Cohen, Joel E., 2018. "Environmental variability and density dependence in the temporal Taylor’s law," Ecological Modelling, Elsevier, vol. 387(C), pages 134-143.
    4. Joel E. Cohen & Christina Bohk & Roland Rau, 2018. "Gompertz, Makeham, and Siler models explain Taylor's law in human mortality data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(29), pages 773-842.
    5. Xu, Meng & Jiang, Mengke & Wang, Hua-Feng, 2021. "Integrating metabolic scaling variation into the maximum entropy theory of ecology explains Taylor's law for individual metabolic rate in tropical forests," Ecological Modelling, Elsevier, vol. 455(C).
    6. Zoe Schroder & Tyler Fricker, 2023. "Expanding the historical "outbreak" climatology between 1880 and 1989," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(3), pages 3273-3285, July.

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