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R. K. Scott and L. M. Polvani

through the action of dynamical mechanisms internal to the stratosphere itself. Studies dating back to Holton and Mass (1976) have shown how vacillating states can arise in zonally and meridionally truncated quasigeostrophic channel models as periodic solutions to the equations of motion. The main requirement of these models is the competing actions of thermal relaxation to a zonally symmetric basic state together with a planetary-scale wave forcing at the lower boundary. Later studies explored the

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Kara J. Sulia, Zachary J. Lebo, Vanessa M. Przybylo, and Carl G. Schmitt

the loss of larger and less pristine ice crystals. The effect of nonspherical ice–ice aggregation is explored herein. Minute intricacies associated with ice growth from vapor deposition were explored through development of the adaptive ice habit model in Harrington et al. (2013a) . This work extends such development to include the effect of nonspherical ice–ice aggregation and is analyzed herein. While a number of investigations of processes affected by the nonspherical growth of ice crystals

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Alvaro Semedo, Øyvind Saetra, Anna Rutgersson, Kimmo K. Kahma, and Heidi Pettersson

wind profile over the ocean, under swell conditions, is no longer logarithmic makes the correct evaluation of the roughness length a cumbersome problem (see Figs. 3 and 7 in Smedman et al. 2003 ). For the model sensitivity tests to roughness length variations, several formulations are used ( Table 1 ). Once again the original profile evaluated with z 0 = 10 −5 m is kept as a reference and is shown as a solid line in Fig. 7 . The profiles corresponding to several roughness lengths from the

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Curt Covey, Aiguo Dai, Dan Marsh, and Richard S. Lindzen

. Stouffer , and K. E. Taylor , 2007 : The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc. , 88 , 1383 – 1394 . Pirscher , B. , U. Foelsche , M. Borsche , G. Kirchengast , and Y.-H. Kuo , 2010 : Analysis of migrating diurnal tides detected in FORMOSAT-3/COSMIC temperature data. J. Geophys. Res. , 115 , D14108 . doi:10.1029/2009JD013008 . Randall , D. A. , and Coauthors 2007 : Climate models and their evaluation. Climate

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K. Ngan, P. Bartello, and D. N. Straub

– 657 . Riley , J. J. , R. W. Metcalfe , and M. A. Weissman , 1981 : Direct numerical simulation of homogeneous turbulence in density-stratified fluids. Proc. Workshop on Nonlinear Properties of Internal Waves, La Jolla, CA, American Institute of Physics, 79–112 . Skamarock , W. C. , 2004 : Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev. , 132 , 3019 – 3032 . Takahashi , Y. O. , K. Hamilton , and W. Ohfuchi , 2006 : Explicit global simulation

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Youmin Tang and Jaison Ambadan

forecast error covariance matrix 𝗣 t b at time step t is approximated using a finite set of model state ensembles (say M ) given by Apparently, the 𝗛 used in Kalman gain (2) is a linearized operator, thus imposing the assumption of the linearization of nonlinear measurement function in the standard EnKF formulation. The linearization can be done either by analytical analysis like extended Kalman filter or by ensemble members, as proposed by Houtekamer and Mitchell (2001) and Hamill (2006

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Mao-Chang Liang, Li-Ching Lin, Ka-Kit Tung, Yuk L. Yung, and Shan Sun

1. Introduction Global temperature is likely to increase further if greenhouse gas emissions continue unchecked ( Brohan et al. 2006 ; Hansen et al. 2001 , 2006 ; Smith et al. 2008 ; Pachauri and Reisinger 2007 ; Broecker 2007 ). Various mitigation efforts are being negotiated and policies implemented to keep the increase under, for example, 2°C ( Meinshausen et al. 2009 ). Such policy efforts are informed by projections of coupled atmosphere–ocean general circulation models (AOGCMs

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T. Vukicevic and D. Posselt

method in section 3 and in the examples in section 4 , these PDFs are explicitly numerically evaluated and are not necessarily assumed to be Gaussian. b. Gaussian prior, measurement, and model probability densities As was mentioned in the introduction, it is common to assume that the probability density functions associated with modeled measurements, actual measurements, and prior information about the parameters are Gaussian. In the joint measurement space D these can be written where 𝗖 s and

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Pablo Zurita-Gotor and Geoffrey K. Vallis

phenomenology; the numerical model is described in section 3 , and sections 4 and 5 evaluate various aspects of the quasigeostrophic theory. Section 6 addresses the vertical eddy heat fluxes in our model and their relationship to the isentropic slope. Finally, section 7 provides a summary and some discussion of the implications and limitations of the present study. 2. Phenomenology and notation It is useful, given the phenomenology sketched in Fig. 1 , to briefly summarize the results of the

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Thomas M. Hamill, Jeffrey S. Whitaker, Jeffrey L. Anderson, and Chris Snyder

1. Introduction Ambadan and Tang (2009 , hereinafter AT09) recently performed a study of several varieties of a “sigma-point” Kalman filter (SPKF) using two strongly nonlinear models, Lorenz (1963 , hereinafter L63) and Lorenz (1996 , hereinafter L96) . In this comparison, a reference benchmark was the performance of a standard ensemble Kalman filter (EnKF) of Evensen (1994 , 2003) , presumably with perturbed observations following Houtekamer and Mitchell (1998) and Burgers et al

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