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Ara Arakelian and Francis Codron

: Climate performance and sensitivity to parametrized physics with emphasis on tropical convection. Climate Dyn., 27, 787–813, doi:10.1007/s00382-006-0158-0 . Hourdin , F. , and Coauthors , 2012 : Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model . Climate Dyn. , doi:10.1007/s00382-012-1411-3, in press . Hurrell , J. W. , J. J. Hack , D. Shea , J. M. Caron , and J. Rosinski , 2008 : A new sea surface temperature and

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S. B. Trier, C. A. Davis, and D. A. Ahijevych

cycle over the mountains and the adjacent plains using a model that permitted explicit deep convection. Based on their two-dimensional simulations, they explained the life cycle of warm-season convection in the lee of the mountains and adjacent plains as an outgrowth of the thermally forced west-to-east mountain–plains solenoid (MPS) modified by latent heat release from deep convection. However, earlier case studies (e.g., Wetzel et al. 1983 ) and recent observationally based climatologies (e

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Andrew J. Majda and Samuel N. Stechmann

of CMT based on a cumulus mass flux spectrum ( Wu et al. 2007 ). Wu and Moncrieff (1996) showed in a cloud-resolving model (CRM) simulation that the generation of kinetic energy by CMT-generated shear is comparable to buoyancy generation and dominates the total buoyancy; thus, they argued for the need to represent CMT in convective parameterizations. Besides these results on CMT due to mesoscale convection, CMT is also thought to play a central role at both the synoptic and planetary scales in

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Isaac M. Held, Ming Zhao, and Bruce Wyman

1. Introduction When trying to understand the differences between atmospheric climate simulations, one needs to evaluate the significance of a myriad of assumptions concerning moist convection, boundary layer mixing, and cloud prediction; and the manner in which these interact with each other, with radiative transfer, with the land surface, and with the dynamical core of the model. It is our belief that this work would benefit significantly from the development of more idealized contexts in

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Douglas G. MacMynowski and Eli Tziperman

ENSO mode and the spatial averaging reduces the contamination with high-spatial-frequency dynamics. We use an autoregressive (AR) model (also used to fit the observed ENSO data by Burgers 1999 ) to fit the model output data and evaluate the eigenvalues of the AR model. The AR model assumes that the model output y k at time t k can be described in terms of the output at a finite number N of previous outputs, which for the annual model takes the form of with coefficients a n that are the

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Peter A. G. Watson, H. M. Christensen, and T. N. Palmer

highly important. However, as previously stated, owing to the complexity of the dynamics, it is best to evaluate the use of multiplicative noise by comparing observations with the output of forecasts made by a model that includes it, which we present in section 3 . These results may also be informative for the development of alternative schemes for representing subgrid-scale variability of convection (e.g., Lin and Neelin 2003 ; Plant and Craig 2008 ; Biello et al. 2010 ). It is also important to

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Masahiro Sugiyama

1. Introduction The moisture mode is a dynamical mode that owes its existence to moisture–convection feedback, the interaction between free-tropospheric humidity and deep convection. In Sugiyama (2009 , hereafter Part I) we explored the moisture mode in a simplified quasi-equilibrium tropical circulation model (QTCM; Neelin and Zeng 2000 ) in the absence of rotation. Building on previous works, our analysis has shown that the weak temperature gradient (WTG) approximation of Sobel et al

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Brett T. Hoover

gradients with respect to the model initial conditions. Simulations using the nonlinear NWP model are allowed to evolve for the following 24 h for the purpose of evaluating the impact of initial condition perturbations on the TC forecast beyond time of declaration (see section 3c , below). Table 1 is a list of the cases used, with the initialization time and initial condition dataset used. Table 1. List of storm names, model initialization times, and model initial condition datasets for all 53 cases

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F-F. Jin

back to, mean flows that exhibit variations on time scales exceeding the individual eddy life cycles. Because of the involvement of nonlinear life cycles of transient eddies, the dynamics of this eddy–mean flow interaction or SELF feedback is still not fully understood. By considering a stochastic basic flow that captures climatological mean flow and climatological measures of the synoptic eddy statistics, Jin et al. (2006a , b) and Pan et al. (2006) proposed an anomaly modeling approach to the

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John A. Knox, Donald W. McCann, and Paul D. Williams

regions of zero or negative potential vorticity. In summary, CAT forecasting remains an unsolved problem ( McCann 2001 ) for spontaneous imbalance theory to address. Williams et al. (2005) employed laboratory experiments and a quasigeostrophic model’s results to explore generation mechanisms of gravity waves in a rotating, two-layer vertically sheared flow. To diagnose wave activity, the authors calculated five dynamical indicators, several of which were originally devised as CAT forecasting indices

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