Transient-Eddy Feedbacks Derived from Linear Theory and Observations
Linear baroclinic instability theory is used to investigate the subweekly time scale transient eddies (TEs) and their feedbacks associated with three-dimensional basic flows on the Northern Hemisphere, in terms of a two-layer quasi-geostrophic model. We consider an eight-winter time–mean flow as wel...
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| Format: | Text |
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Columbia University
1990
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| Online Access: | https://dx.doi.org/10.7916/d8ws93z7 https://academiccommons.columbia.edu/doi/10.7916/D8WS93Z7 |
| Summary: | Linear baroclinic instability theory is used to investigate the subweekly time scale transient eddies (TEs) and their feedbacks associated with three-dimensional basic flows on the Northern Hemisphere, in terms of a two-layer quasi-geostrophic model. We consider an eight-winter time–mean flow as well as four composites of North Atlantic large-scale quasi-stationary patterns. The structures of the two fastest-growing normal modes associated with the eight-winter climatology are found to compare very well in many aspects with the leading complex empirical orthogonal functions (CEOFs) of the observed bandpass filtered flow, with pattern correlations up to 0.65; although the normal modes are less localized than the CEOFs. The barotropic feedback implied by the linear modes is also found to compare quite reasonably with the observations, especially over the west Atlantic, but the baroclinic (negative) feedback is less well represented. Composites coresponding to blocking (BL), zonal (ZO), Greenland anticyclone (GA), and Atlantic ridge (AR) weather regimes are next used to define basic states and composite maps of TE feedback. In all four cases the principal displacements of TE activity over the North Atlantic are captured by the fastest-growing Atlantic cyclogenesis modes. The structure of the barotropic feedback associated with the quasi-stationary anomalies is also reasonably simulated by the linear modes in many respects in the BL and ZO cases, but in the GA and AR cases the linear model is less successful. |
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