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Moritz Flügel and Ping Chang

among the anomaly patterns. The skillful forecasts achieved by these anomaly models, especially the Lamont model ( Zebiak and Cane 1987 ; Chen et al. 1995 ), argue that, to a certain degree, abandoning an “active” seasonal cycle in modeling ENSO may be justified. However, there are at least three reasons for a closer examination of the effects of the annual cycle on ENSO predictions. First, it is well known from the observations that ENSO tends to be strongly phase locked to the annual cycle, in a

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Pragallva Barpanda and Tiffany A. Shaw

the seasonality of storm track intensity in the NH and the small mixed layer depth aquaplanet simulations both involve a contribution from the stationary circulation (mean meridional circulation for the slab-ocean aquaplanet and stationary eddies for the NH), the effects are opposite. In the NH the stationary eddy contribution opposes the seasonality of storm track intensity (blue and red lines, Fig. 1b ), consistent with stationary eddies generating downgradient MSE fluxes, and accounts for a

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Michael Ghil

netsurface heating ff the ratio of aerosol absorption toextinction is high enough. But a recent determination with spectrophone techniques (Foot, 1979) indicates that this ratio may be sufficiently small(0.8-2.5%), even for aerosol in which smoke was amajor contribution, for cloudless aerosol cooling(scattering) effects to dominate heating (absorption)effects. Present uncertainties regarding both theimaginary indices of refraction and shape effects ofatmospheric aerosols makes it difficult to

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William J. Randel and Fei Wu

TIL, testing the influence of observed seasonal variations of water vapor and ozone using a fixed dynamical heating (FDH) methodology ( Forster and Shine 2002 ). We note that at present the mechanisms that force and maintain the TIL are not well understood, and there are possible contributions from dynamical ( Birner 2006 , 2010 ; Wirth 2004 ; Wirth and Szabo 2007 ; Son and Polvani 2007 ) and radiative effects ( Randel et al. 2007 ; Kunz et al. 2009 ). Because the strongest TIL is observed

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Eli Galanti and Eli Tziperman

basic state in a simplified model to different monthly climatologies affects the rate of anomaly growth. The effects of the above seasonal factors on ENSO’s dynamics and phase locking was also attributed to a nonlinear resonance between a nonlinear ENSO oscillator and the periodic seasonal forcing ( Tziperman et al. 1994 ; Jin et al. 1994 ; Tziperman et al. 1995 ; Chang et al. 1995 ). Although the above works point at possible seasonal climatological variables that affect El Niño’s development

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Grant Branstator and Jorgen Frederiksen

framework for interpreting the seasonal dependence of interannual disturbances. First, in section 2 we quantify the effect that seasonal changes in the mean circulation would be expected to have on the scale, geographical distribution and structure of interannual flow anomalies using the linear nondivergent barotropic vorticity equation. Second, in section 3 we analyze observations to determine whether the effects found in the linear framework are also present in nature. These results suggest that

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M. Jucker, S. Fueglistaler, and G. K. Vallis

hemispheric circulations and eventually temperature response in . These effects cannot be included when we compare setups where the only difference is the presence or absence of orographic forcing. To account for the seasonality in T e , we repeat the radiative calculations described above for every month of the year, and in such a way obtain a time-dependent climatological ( T e , τ ) pair (simulation B). Not only can we include the climatological differences in tracer distribution in both latitude

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Tiffany A. Shaw, Pragallva Barpanda, and Aaron Donohoe

seasonal intensity given insolation, assuming fixed planetary albedo and no change in the MSE flux by the mean meridional circulation and stationary eddies. The framework is used to diagnose the factors affecting storm-track intensity in response to seasonal insolation, El Niño minus La Niña conditions, and the direct and indirect effects of increased CO 2 . 2. MSE framework and datasets a. MSE framework We derive an energetic framework for zonal-mean storm-track intensity based on the atmospheric MSE

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John J. DeLuisi and Paul M. Furukawa

three stations are the seasonal variationsin ozone concentration at 30 km. These variations havea seasonal maximum and minimum almost oppositein phase to the variations at 45 km. The current method for deducing Os distributionsfrom Umkehr observations makes no allowance for theoptical effects of atmospheric aerosols (Mateer andDlltsch, 1964), although Mateer (1965) pointed out thathigh-level ozone concentrations are particularly sensitiveto bias that could arise from a seasonal variation of

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William J. Randel, Mijeong Park, Fei Wu, and Nathaniel Livesey

in the ozone results, but this difference is probably within uncertainty levels for both estimates [the radiative heating calculations do not include the effects of cirrus or deep convective clouds, which could be important near and above the tropopause ( Corti et al. 2006 ; Fueglistaler and Fu 2006 ), and the ozone results are based on the simplified balance in Eq. (5) ]. c. Seasonal cycle in tropical carbon monoxide Folkins et al. (2006a) have highlighted a seasonal cycle in tropical CO, and

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