Abstract
AbstractAn ensemble of idealized experiments with the simplified general circulation model PUMA is used to analyze the response to reduced surface friction, that is a strengthening of the eddy‐driven jet, a weakening of the Eulerian mean overturning, and a suppression of baroclinic instability. The suppression of baroclinic instability is caused by an effect called the barotropic governor by which increased horizontal shear restricts the ability of baroclinic disturbances to convert available potential energy into kinetic energy. This governor effect ensures that the residual circulation and Eliassen–Palm flux (EP flux) divergence are largely invariant to the surface friction parameter despite the connection between surface friction, the Eulerian mean overturning, and the eddy‐momentum flux. The suppression of instability leads to an increase in persistence measured by the period of peak variance on synoptic time‐scales and a strengthened signal‐to‐noise ratio on seasonal time‐scales. These findings suggest that the signal‐to‐noise paradox seen in the context of seasonal prediction can be caused by excess mechanical damping in atmospheric prediction systems inhibiting the barotropic governor effect.
Highlights
Of bandpass filtered meridional velocity v ∗, where brackets and the overbar denote a zonal or temporal average and the prime denotes the departure from that mean at every gridpoint
In the context of the signal-to-noise paradox in seasonal prediction, the ratio of predictable components (RCP ) as defined by Eade et al (2014) is a useful measure with values larger than one meaning that the model underestimates the magnitude of the predictable signal relative to internal noise
Since we are interested in the Rossby wave response to the tropical forcing, the zonal mean has been removed before computing S2N using equation (S1)
Summary
Of bandpass filtered meridional velocity v ∗ , where brackets and the overbar denote a zonal or temporal average and the prime denotes the departure from that mean at every gridpoint. In the context of the signal-to-noise paradox in seasonal prediction, the ratio of predictable components (RCP ) as defined by Eade et al (2014) is a useful measure with values larger than one meaning that the model underestimates the magnitude of the predictable signal relative to internal noise. On seasonal time scales and for very large ensembles, the RCP
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