Analysis of a six-component atmospheric spectral model: Chaos, predictability and vacillation

DOI10.1016/0167-2789(89)90082-1zbMath0687.76045OpenAlexW1982358729MaRDI QIDQ1263420

H. E. de Swart

Publication date: 1989

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-2789(89)90082-1

zbMATH Keywords

periodic solutionsstrange attractorshomoclinic orbitsinitial conditionsLyapunov exponentbifurcation analysissteady stateslow-order atmospheric spectral modeltime-asymptotic behaviour

Mathematics Subject Classification ID

Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Stability and instability of geophysical and astrophysical flows (76E20) Geophysics (86A99)

Related Items (8)

Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain ⋮ Enhanced regime predictability in atmospheric low-order models due to stochastic forcing ⋮ Reduction methods in climate dynamics -- a brief review ⋮ Stability analysis of chaotic systems from data ⋮ Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability ⋮ New nonlinear mechanisms of midlatitude atmospheric low-frequency variability ⋮ Fixed points, stable manifolds, weather regimes, and their predictability ⋮ Boundedness, invariant algebraic surfaces and global dynamics for a spectral model of large-scale atmospheric circulation

Uses Software

• AUTO
• AUTO-86

Cites Work

• Determining Lyapunov exponents from a time series
• Bifurcation phenomena near homoclinic systems: A two-parameter analysis
• Low-order spectral models of the atmospheric circulation: A survey
• The Lorenz equations: bifurcations, chaos, and strange attractors

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