Dynamical Analysis of Blocking Events: Spatial and Temporal Fluctuations of Covariant Lyapunov Vectors
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Citations
The physics of climate variability and climate change
Fluctuations, Response, and Resonances in a Simple Atmospheric Model
Statistical and dynamical properties of covariant lyapunov vectors in a coupled atmosphere-ocean model—multiscale effects, geometric degeneracy, and error dynamics
The hammam effect or how a warm ocean enhances large scale atmospheric predictability.
Statistical and Dynamical Properties of Covariant Lyapunov Vectors in a Coupled Atmosphere-Ocean Model - Multiscale Effects, Geometric Degeneracy, and Error Dynamics
References
Deterministic nonperiodic flow
Ergodic theory of chaos and strange attractors
Dynamical ensembles in stationary states
Multiple Flow Equilibria in the Atmosphere and Blocking
Ergodic theory of differentiable dynamical systems
Related Papers (5)
Frequently Asked Questions (11)
Q2. What is the effect of blocking conditions on the CLVs?
The authors have that blocked conditions are accompanied by stronger baroclinic and stronger barotropic conversion rates for the unstable CLVs, while, conversely, the both conversion rates are reduced when looking at the most stable CLVs.
Q3. How do the authors explain the growth and decay rate of the CLVs?
In a previous work [Schubert and Lucarini, 2015], the authors made use of the fact that the CLVs are covariant solutions of the tangent linear equation and explained the growth and decay rate of the CLVs by looking at their Lorenz energy cycle.
Q4. What is the metric entropy of the BLV-LEs?
In order to characterize the growth of a volume which covers all unstable directions, the authors use the fluctuations of the BLV-LEs (LEs of the Backward Lyapunov Vectors) to compute the metric entropy which is the sum of all positive LEs [Eckmann and Ruelle, 1985].
Q5. How is the statistical significance determined for the unblocked and blocked growth rates?
The statistical significance is determined by considering the 3 σ confidence interval which is obtained by computing the degrees of freedom for each time series of the unblocked and blocked growth rates.
Q6. What is the way to look at the problem of the possible existence of weather regimes?
When considering high-dimensional chaotic dynamics, the authors have to look at the problem of the possible existence of weather regimes by looking at the properties of the invariant measure supported on the attractor of the system.
Q7. What is the dimensionality of the reduced phase space determining the dynamics of blocking events?
In a closely related piece of work, Faranda et al.1 have shown that the dimensionality of the reduced phase space determining the dynamics of blocking events is higher than corresponding to regular quasi-zonal dynamics by exploiting a connection between extreme value statistics and the local dimension of the dynamical systems underlying attractor.
Q8. How do the authors know that the blockings are a large area?
Given the rather coarse resolution of their model the observed blockings have at least an extent of roughly 1500 km which means that they already extent over a large area.
Q9. What is the effect of the two forms of instability on the blockers?
The synergy between the two forms of instability is likely to be responsible for the increase in the number of blocking events for larger values of ∆T .
Q10. What is the reason for the increased growth rate of the unstable CLVs observed in blocked conditions?
the authors have that the (modest) enhanced growth rate of the unstable CLVs observed in blocked conditions (see figure 5) can be attributed to a more efficient barotropic conversion.
Q11. What is the effect of adding orography on the blocking rate and length?
For blocking rate and length (see figures 2 and 4), it appears that as discussed above, adding orography creates preferential geographical locations for the occurrence of blocking.