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NPG | Articles | Volume 25, issue 2
Nonlin. Processes Geophys., 25, 387–412, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Numerical modeling, predictability and data assimilation in...

Nonlin. Processes Geophys., 25, 387–412, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 May 2018

Research article | 28 May 2018

Exploring the Lyapunov instability properties of high-dimensional atmospheric and climate models

Lesley De Cruz et al.
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Cited articles  
Abarbanel, H. D., Brown, R., and Kennel, M. B.: Variation of Lyapunov exponents on a strange attractor, J. Nonlinear Sci., 1, 175–199,, 1991. a
Barsugli, J. J. and Battisti, D. S.: The Basic Effects of Atmosphere-Ocean Thermal Coupling on Midlatitude Variability, J. Atmos. Sci., 55, 477–493,<0477:TBEOAO>2.0.CO;2, 1998. a
Benettin, G., Galgani, L., Giorgilli, A., and Strelcyn, J.-M.: Lyapunov Characteristic Exponents for smooth dynamical systems and for hamiltonian systems; a method for computing all of them. Part 1: Theory, Meccanica, 15, 9–20,, 1980. a
Boffetta, G., Cencini, M., Falcioni, M., and Vulpiani, A.: Predictability: a way to characterize complexity, Phys. Rep., 356, 367–474,, 2002. a
Boschi, R., Lucarini, V., and Pascale, S.: Bistability of the climate around the habitable zone: A thermodynamic investigation, Icarus, 226, 1724–1742,, 2013. a
Publications Copernicus
Short summary
The predictability of weather models is limited largely by the initial state error growth or decay rates. We have computed these rates for PUMA, a global model for the atmosphere, and MAOOAM, a more simplified, coupled model which includes the ocean. MAOOAM has processes at distinct timescales, whereas PUMA surprisingly does not. We propose a new programme to compute the natural directions along the flow that correspond to the growth or decay rates, to learn which components play a role.
The predictability of weather models is limited largely by the initial state error growth or...