Is Weather Chaotic? Coexisting Chaotic and Non-chaotic Attractors Within Lorenz Models

Bo Wen Shen, R. A. Pielke, X. Zeng, J. J. Baik, S. Faghih-Naini, J. Cui, R. Atlas, T. A.L. Reyes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

The pioneering study of Lorenz in 1963 and a follow-up presentation in 1972 changed our view on the predictability of weather by revealing the so-called butterfly effect, also known as chaos. Over 50 years since (Lorenz in J. Atmos. Sci. 20:130–141, [1]) study, the statement of “weather is chaotic” has been well accepted. Such a view turns our attention from regularity associated with Laplace’s view of determinism to irregularity associated with chaos. Here, a refined statement is suggested based on recent advances in high-dimensional Lorenz models and real-world global models. In this study, we provide a report to: (1) Illustrate two kinds of attractor coexistence within Lorenz models (i.e., with the same model parameters but with different initial conditions). Each kind contains two of three attractors including point, chaotic, and periodic attractors corresponding to steady-state, chaotic, and limit cycle solutions, respectively. (2) Suggest that the entirety of weather possesses the dual nature of chaos and order associated with chaotic and non-chaotic processes, respectively. Specific weather systems may appear chaotic or non-chaotic within their finite lifetime. While chaotic systems contain a finite predictability, non-chaotic systems (e.g., dissipative processes) could have better predictability (e.g., up to their lifetime). The refined view on the dual nature of weather is neither too optimistic nor pessimistic as compared to the Laplacian view of deterministic unlimited predictability and the Lorenz view of deterministic chaos with finite predictability.

Original languageEnglish (US)
Title of host publication13th Chaotic Modeling and Simulation International Conference
EditorsChristos H. Skiadas, Yiannis Dimotikalis
PublisherSpringer Science and Business Media B.V.
Pages805-825
Number of pages21
ISBN (Print)9783030707941
DOIs
StatePublished - 2021
Event13th Chaotic Modeling and Simulation International Conference, CHAOS 2020 - Florence, Italy
Duration: Jun 9 2020Jun 12 2020

Publication series

NameSpringer Proceedings in Complexity
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692

Conference

Conference13th Chaotic Modeling and Simulation International Conference, CHAOS 2020
Country/TerritoryItaly
CityFlorence
Period6/9/206/12/20

ASJC Scopus subject areas

  • Applied Mathematics
  • Modeling and Simulation
  • Computer Science Applications

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