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A substantial asymmetric impact of tropical Pacific SST anomalies on the internal variability of the extratropical atmosphere is found. A variety of diagnoses is performed to help reveal the dynamical processes leading to the large impact. Thirty-five years of geopotential heights and 29 years of wind fields analyzed operationally at the National Centers for Environmental Prediction (NCEP), formerly the National Meteorological Center, and three sets of 10-yr-long perpetual January integrations run with a low-resolution NCEP global spectral model are investigated in detail for the impact of the SST anomalies on the blocking flows over the North Pacific. The impact on large-scale deep trough flows is also examined. Both the blocking and deep trough flows develop twice as much over the North Pacific during La Nina as during El Nino winters. Consequently, the internal dynamics associated low-frequency variability (LFV), with timescales between 7 and 61 days examined in this study, display distinct characteristics: much larger magnitude for the La Nina than the El Nino winters over the eastern North Pacific, where the LFV is highest in general. The diagnosis of the localized Eliassen-Palm fluxes and their divergence reveals that the high-frequency transient eddies (1-7 days) at high latitudes are effective in forming and maintaining the large-scale blocking flows, while the midlatitude transients are less effective. The mean deformation field over the North Pacific is much more diffluent for the La Nina than the El Nino winters, resulting in more blocking flows being developed and maintained during La Nina by the high-frequency transients over the central North Pacific. In addition to the above dynamical process operating on the high-frequency end of the spectrum, the local barotropic energy conversion between the LFV components and the time-mean flows is also operating and playing a crucial role. The kinetic energy conversion represented by the scalar product between the E vector of the low-frequency components and the deformation D vector of the time-mean flow reveals that, on average, the low-frequency components extract energy from the time-mean flow during La Nina winters while they lose energy to the time-mean flow during El Nino winters. This local barotropic energy conversion on the low-frequency end of the spectrum, together with the forcing of the high-frequency transients on blocking flows on the high-frequency end, explain why there is a large difference in the magnitude of low-frequency variability between the La Nina and the El Nino winters.

Author(s): Kari Ann Shirey [1]; Wendy Lai [1]; Alison J. Scott [2]; Michael Lipsky [3]; Pragnesh Mistry [1]; Lioubov M. Pletneva [4]; Christopher L. Karp [5, 10]; Jaclyn McAlees [5]; [...]

A statistical model and extended ensemble integrations of two atmospheric general circulation models (GCMs) are used to simulate the extratropical atmospheric response to forcing by observed SSTs for the years 1980 through 1988. The simulations are compared to observations using the anomaly correlation and root-mean-square error of the 700-hPa height field over a region encompassing the extratropical North Pacific Ocean and most of North America. On average, the statistical model is found to produce considerably better simulations than either numerical model, even when simple statistical corrections are used to remove systematic errors from the numerical model simulations. In the mean, the simulation skill is low, but there are some individual seasons for which all three models produce simulations with good skill. An approximate upper bound to the simulation skill that could be expected from a GCM ensemble, if the model's response to SST forcing is assumed to be perfect, is computed. This perfect model predictability allows one to make some rough extrapolations about the skill that could be expected if one could greatly improve the mean response of the GCMs without significantly impacting the variance of the ensemble. These perfect model predictability skills are better than the statistical model simulations during the summer, but for the winter, present-day statistical forecasts already have skill that is as high as the upper bound for the GCMs. Simultaneous improvements to the GCM mean response and reduction in the GCM ensemble variance would be required for these GCMs to do significantly better than the statistical model in winter. This does not preclude the possibility that, as is presently the case, a statistical blend of GCM and statistical predictions could produce a simulation better than either alone. Because of the primitive state of coupled ocean-atmosphere GCMs, the vast majority of seasonal predictions currently produced by GCMs are performed using a two-tiered approach in which SSTs are first predicted and then used to force an atmospheric model; this motivates the examination of the simulation problem. However, it is straightforward to use the statistical model to produce true forecasts by changing its predictors from simultaneous to precursor SSTs. An examination of the decrease in skill of the statistical model when changed from simulation to prediction mode is extrapolated to draw conclusions about the skill to be expected from good coupled GCM predictions.