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Yuxuan Wang, Yuanyu Xie, Libao Cai, Wenhao Dong, Qianqian Zhang, and Lin Zhang

observations and the GEOS-Chem model used in the study, with a focus on drought-induced variations of natural emissions simulated by the model. In section 4 we analyze the observed changes of PM 2.5 and its major components under drought conditions and evaluate the model’s ability in reproducing those observed changes. In sections 5 and 6 , the drought’s impact on important PM 2.5 components including OC and sulfate are quantified through budget analysis. Discussion and conclusion are given in

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Gang Chen and Pablo Zurita-Gotor

Global Atmospheric Model Development Team , 2004 : The new GFDL global atmosphere and land model AM2/LM2: Evaluation with prescribed SST simulations. J. Climate , 17 , 4641 – 4673 . Haigh , J. D. , M. Blackburn , and R. Day , 2005 : The response of tropospheric circulation to perturbations in lower-stratospheric temperature. J. Climate , 18 , 3672 – 3685 . Hartmann , D. L. , and P. Zuercher , 1998 : Response of baroclinic life cycles to barotropic shear. J. Atmos. Sci

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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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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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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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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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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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Stephen T. Garner

1. Introduction The purpose of this study is to develop a high-resolution model-based evaluation strategy for orographic drag parameterizations. This type of parameterization has been used in global climate models for most of their history. Every model in the CMIP5 database employs a mountain drag parameterization of some kind. The necessity of subgrid mountain drag was demonstrated by Palmer et al. (1986) . Afterward, quite a number of parameterizations were developed. A summary and review

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