Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.699 IF 1.699
  • IF 5-year value: 1.559 IF 5-year
    1.559
  • CiteScore value: 1.61 CiteScore
    1.61
  • SNIP value: 0.884 SNIP 0.884
  • IPP value: 1.49 IPP 1.49
  • SJR value: 0.648 SJR 0.648
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 52 Scimago H
    index 52
  • h5-index value: 21 h5-index 21
Volume 23, issue 4
Nonlin. Processes Geophys., 23, 189–203, 2016
https://doi.org/10.5194/npg-23-189-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 23, 189–203, 2016
https://doi.org/10.5194/npg-23-189-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Jul 2016

Research article | 08 Jul 2016

Hierarchical scale dependence associated with the extension of the nonlinear feedback loop in a seven-dimensional Lorenz model

Bo-Wen Shen
Related authors  
On periodic solutions associated with the nonlinear feedback loop in the non-dissipative Lorenz model
B.-W. Shen
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2016-40,https://doi.org/10.5194/npg-2016-40, 2016
Revised manuscript not accepted
Short summary
Nonlinear feedback in a six-dimensional Lorenz model: impact of an additional heating term
B.-W. Shen
Nonlin. Processes Geophys., 22, 749–764, https://doi.org/10.5194/npg-22-749-2015,https://doi.org/10.5194/npg-22-749-2015, 2015
Short summary
On the nonlinear feedback loop and energy cycle of the non-dissipative Lorenz model
B.-W. Shen
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npgd-1-519-2014,https://doi.org/10.5194/npgd-1-519-2014, 2014
Revised manuscript not accepted
Related subject area  
Subject: Predictability, Data Assimilation | Topic: Climate, Atmosphere, Ocean, Hydrology, Cryosphere, Biosphere
Generalization properties of feed-forward neural networks trained on Lorenz systems
Sebastian Scher and Gabriele Messori
Nonlin. Processes Geophys., 26, 381–399, https://doi.org/10.5194/npg-26-381-2019,https://doi.org/10.5194/npg-26-381-2019, 2019
Short summary
Revising the stochastic iterative ensemble smoother
Patrick Nima Raanes, Andreas Størksen Stordal, and Geir Evensen
Nonlin. Processes Geophys., 26, 325–338, https://doi.org/10.5194/npg-26-325-2019,https://doi.org/10.5194/npg-26-325-2019, 2019
Short summary
Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data
Fei Lu, Nils Weitzel, and Adam H. Monahan
Nonlin. Processes Geophys., 26, 227–250, https://doi.org/10.5194/npg-26-227-2019,https://doi.org/10.5194/npg-26-227-2019, 2019
Short summary
Non-Gaussian statistics in global atmospheric dynamics: a study with a 10 240-member ensemble Kalman filter using an intermediate atmospheric general circulation model
Keiichi Kondo and Takemasa Miyoshi
Nonlin. Processes Geophys., 26, 211–225, https://doi.org/10.5194/npg-26-211-2019,https://doi.org/10.5194/npg-26-211-2019, 2019
Short summary
Fluctuations of finite-time Lyapunov exponents in an intermediate-complexity atmospheric model: a multivariate and large-deviation perspective
Frank Kwasniok
Nonlin. Processes Geophys., 26, 195–209, https://doi.org/10.5194/npg-26-195-2019,https://doi.org/10.5194/npg-26-195-2019, 2019
Short summary
Cited articles  
Adler, J.: R in a nutshell, O'Rielly, Sebastopol, CA, 699 pp., 2012.
Anthes, R.: Turning the tables on chaos: is the atmosphere more predictable than we assume?, UCAR Magazine, available at: https://www2.ucar.edu/atmosnews/opinion/turning-tables-chaos-atmosphere-more-predictable-we-assume-0 (last access: 14 December 2015), 2011.
Benettin, G., Galgani, L., Giorgilli, A., and Strelcyn, J. M.: Lyapunov Characteristic Exponents fro Smooth Dynamical Systems and for Hamiltonian Systems; A method for computing all of them. Part 1: Theory, Meccanica, 15, 9–20, 1980.
Biswas, R., Aftosmis, M. J., Kiris, C., and Shen, B.-W.: Petascale computing: Impact on future NASA missions, in: Petascale Computing: Architectures and Algorithms, edited by: Bader, D., Chapman and Hall/CRC Press, Boca Raton, FL, 29–46, 2007.
Blender, R. and Lucarini, V.: Nambu representation of an extended Lorenz model with viscous heating, Physica D, 243, 86–91, 2013.
Publications Copernicus
Download
Short summary
We construct a seven-dimensional Lorenz model (7DLM) to discuss the impact of an extended nonlinear feedback loop on solutions' stability and illustrate the hierarchical scale dependence of chaotic solutions. The 7DLM requires a much larger critical value for the Rayleigh parameter (rc ∼ 116.9) for the onset of chaos. For chaotic solutions with r = 120, high correlation coefficients among the modes at different scales indicate hierarchical scale dependence.
We construct a seven-dimensional Lorenz model (7DLM) to discuss the impact of an extended...
Citation