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Charles McLandress

of the tidal amplitudes in the model and pointed to the linear advection terms as being possibly important factors. The effects of zonal mean zonal winds and temperatures have previously been examined using linear steady-state finite-difference models (e.g., Vial 1986 ; Forbes and Hagan 1988 ) and found not to be responsible for the seasonal variation of the diurnal tide ( Burrage et al. 1995 ). However, a recent critique of these types of models by Zhu et al. (1999) suggests that they may be

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Marvin A. Geller

. 13 and 14 show the calculationsperformed with COs cooling plus small and largevalues of Oo cooling, respectively. The following observations are made concerning the effects of Newtoniancooling. Increasing the Newtonian cooling appears to causea decrease in the seasonal differences in the amplitudeand phase of the surface pressure variation (both inabsolute difference and ratio), while both the amplitudesand phase lags are generally increased. The seasonalvariation in phase lag, that was seen

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C. W. Newton

whole earth, for four midseasonmonths. Considering the year as a whole, mountain torques are eastward in high and low latitudes, andwestward in middle latitudes. Individual mountain complexes have mixed effects; the greatest drain ofmomentum is by the relatively low mountains of eastern Siberia while the average effect of the Himalayasis small. The momentum drain by the Andes is greatest in summer, while in winter their influence is overcome by the eastward torque of Africa. Seasonal variations

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S. D. Mahlman and W. J. Moxim

enough that seasonal effects are beginning to dominate. After the Northern Hemisphere spring reversal in the zonal wind, the zonal mean mixing ratio is increasing upward everywhere in the model stratosphere Also, Fig. 5.1c indicates that a distinct minimum in R appears in the equatorial stratosphere. During year 4, Fig. 5.1d shows that the equatorial minimum has extended to the top of the model atmo sphere so that smaller equatorial values separate higher mixing ratios in the middle

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Tímea Haszpra and Tamás Tél

–November ( Fig. 4 ). The largest values appear in the mid- and high latitudes, mainly in the winter season of the hemisphere because of the strong mixing and shearing effects of cyclones. So we can find the largest topological entropies in December–February in the Northern Hemisphere ( Fig. 4a ) and in June–August in the Southern Hemisphere ( Fig. 4c ). Fig . 4. Geographical distribution of the average seasonal topological entropy (day −1 ) obtained from line segments (consisting of n = 2 × 10 5 particles

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Cory A. Barton, John P. McCormack, Stephen D. Eckermann, and Karl W. Hoppel

also significant in the time evolution of optimal GWD source parameter values. For example, the largest values of τ src in the Fig. 14b green curve occur during a transition period of the QBO, which corresponds with our hypothesis that the QBO period was the dominant mode of variability in seasonal τ src optimization ( Fig. 11b ). This is evidence that long-term effects of the parameterized GWD (its role in the descent of QBO shear zones, for example) have an influence on shorter forecasts

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Neal Butchart and Ellis E. Remsberg

isentropic surface, the areas change in response to nonconservative processes and/or irreversiblemixing to unresolvable scales and so provide a diagnostic for quantifying the net effect of these two processes.The effects of the seasonal variation of the solar heating on the areas are identified from the evolutions of thehemispheric means and, for the potential vorticity, from a comparison with an annual cycle integration of azonally symmetric, general circulation model. Superimposed on the seasonal

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Jorgen S. Frederiksen and Grant Branstator

the effects of seasonality on modal structure. Figure 2 shows the 300-mb disturbance streamfunction, at a particular phase, denoted phase 0 (and in some months at a phase of 90°), for the fastest-growing modes for January, March, July, and December monthly averaged basic states and as well for the annual mean basic state. Also shown is mode 5 for the April monthly averaged basic state, which, as shown in section 4 , has largest pattern correlation with FTNM1 in April. For April, July, and

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Jorgen S. Frederiksen and Grant Branstator

-frequency disturbances. This conclusion also holds for all other months with the structure of the standard deviation of the random forcing following the seasonal cycle of storm track activity in both hemispheres (not shown). 4. FTPOPs for reanalyzed observations In FB1 we found it useful to understand the behavior of leading FTNMs in terms of the fast-growing normal modes for corresponding months. In particular, the roles of intramodal growth and intramodal and intermodal interference effects in the growth cycle

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W. L. Donn and D. Rind

) requires the presence ofstrong easterly winds at some upper reflection level. Variations (such as tidal) in these winds, as derivedfrom available reports, are shown to account for the observed patterns of microbaroms. In particular,these patterns are shown to be controlled by effects of tidal and seasonal wind variations and stratosphericwarmings. Having established the dependence of microbaroms on upper temperature and winds, we use therelationship to interpret these upper atmospheric conditions

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