TY - GEN
T1 - Is Weather Chaotic? Coexisting Chaotic and Non-chaotic Attractors Within Lorenz Models
AU - Shen, Bo Wen
AU - Pielke, R. A.
AU - Zeng, X.
AU - Baik, J. J.
AU - Faghih-Naini, S.
AU - Cui, J.
AU - Atlas, R.
AU - Reyes, T. A.L.
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85122017164&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85122017164&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-70795-8_57
DO - 10.1007/978-3-030-70795-8_57
M3 - Conference contribution
AN - SCOPUS:85122017164
SN - 9783030707941
T3 - Springer Proceedings in Complexity
SP - 805
EP - 825
BT - 13th Chaotic Modeling and Simulation International Conference
A2 - Skiadas, Christos H.
A2 - Dimotikalis, Yiannis
PB - Springer Science and Business Media B.V.
T2 - 13th Chaotic Modeling and Simulation International Conference, CHAOS 2020
Y2 - 9 June 2020 through 12 June 2020
ER -