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Eric D. Maloney, Andrew Gettelman, Yi Ming, J. David Neelin, Daniel Barrie, Annarita Mariotti, C.-C. Chen, Danielle R. B. Coleman, Yi-Hung Kuo, Bohar Singh, H. Annamalai, Alexis Berg, James F. Booth, Suzana J. Camargo, Aiguo Dai, Alex Gonzalez, Jan Hafner, Xianan Jiang, Xianwen Jing, Daehyun Kim, Arun Kumar, Yumin Moon, Catherine M. Naud, Adam H. Sobel, Kentaroh Suzuki, Fuchang Wang, Junhong Wang, Allison A. Wing, Xiaobiao Xu, and Ming Zhao

centers. Traditionally, diagnostics for climate models are based on monthly mean statistics and climatologies. Increasingly, models are being analyzed in more detail against observations of specific processes, and the MDTF is approaching PODs in this spirit. The closer to a model process the observations and evaluation are, the better the ability to constrain the process and hence provide a guide to parameterization improvement. For a simple example: cloud radiative effects at the top of the

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Motoki Nagura, J. P. McCreary, and H. Annamalai

. Int. J. Climatol. , 36 , 2541 – 2554 , . 10.1002/joc.4511 Schott , F. A. , and J. P. McCreary , 2001 : The monsoon circulation of the Indian Ocean . Prog. Oceanogr. , 51 , 1 – 123 , . 10.1016/S0079-6611(01)00083-0 Seo , H. , S.-P. Xie , R. Murtugudde , M. Jochum , and A. J. Miller , 2009 : Seasonal effects of Indian Ocean freshwater forcing in a regional coupled model . J. Climate , 22

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Ángel F. Adames and Yi Ming

. Additionally, it is clear that TRMM precipitation exhibits a larger variance than the model does. This may be related to the inability of coarse-resolution GCMs to fully represent the topographic features of South Asia, thus not adequately representing their effects on precipitation. Fig . 1. Mean JJAS 850-hPa geopotential height (shading) and horizontal flow (arrows) for (a) AM4.0 and (b) ERA-Interim. The longest arrows correspond to winds of ~15 m s −1 . White areas in (a) correspond to regions where the

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Stephanie A. Henderson, Eric D. Maloney, and Seok-Woo Son

Rossby waves that propagate into the extratropics and significantly modulate the extratropical circulation (e.g., Matthews et al. 2004 ; Seo and Son 2012 ). The MJO modulates various aspects of weather and climate including regional temperatures (e.g., Vecchi and Bond 2004 ), precipitation (e.g., Jones et al. 2004 ; Donald et al. 2006 ), atmospheric blocking (e.g., Henderson et al. 2016 ), hurricanes (e.g., Maloney and Hartmann 2000 ; Liebmann et al. 1994 ), and global monsoons ( Lorenz and

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James F. Booth, Young-Oh Kwon, Stanley Ko, R. Justin Small, and Rym Msadek

Large-scale Dynamics Program (AGS-1355339), NASA Physical Oceanography Program (NNX13AM59G), and DOE Office of Biological and Environmental Research Regional and Global Climate Modeling Program (DE-SC0014433). RJS was supported by DOE Office of Biological and Environmental Research (DE-SC0006743) and NSF Directorate for Geosciences Division of Ocean Sciences (1419584), We thank the reviewers for useful suggestions and comments that have significantly improved this manuscript. REFERENCES Bentsen , M

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Jiabao Wang, Hyemi Kim, Daehyun Kim, Stephanie A. Henderson, Cristiana Stan, and Eric D. Maloney

position of the Pacific westerly jet cause errors in simulating MJO teleconnections, in addition to the effects of poor MJO performance. By comparing multimodel hindcasts, Vitart (2017) found that the amplitude of predicted MJO teleconnections is often too strong over the western North Pacific compared to reanalysis. Our understanding of the current model performance in terms of MJO teleconnection simulation is still limited. One reason that makes rigorous model evaluation and comparison difficult is

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