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Allison A. Wing, Suzana J. Camargo, Adam H. Sobel, Daehyun Kim, Yumin Moon, Hiroyuki Murakami, Kevin A. Reed, Gabriel A. Vecchi, Michael F. Wehner, Colin Zarzycki, and Ming Zhao

in complementary studies by Kim et al. (2018) and Y. Moon et al. (2019, manuscript submitted to J. Climate ). This six-member model ensemble is an “ensemble of opportunity” based on available simulations, rather than a coordinated intercomparison. Table 1. Description of model simulations. Three of the models were developed at the Geophysical Fluid Dynamics Laboratory (GFDL) Atmosphere Model version 2.5 (AM2.5; Delworth et al. 2012 ), High Resolution Atmospheric Model (HiRAM; Zhao et al

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

sought to relate them to biases in the atmospheric component of the coupled models (e.g., Martin et al. 2010 ; Ma et al. 2014 ). Others have focused on the ocean component, considering the causes and impacts of surface (e.g., Han et al. 2012 ; Levine et al. 2013 ; Sandeep and Ajayamohan 2014 ) and subsurface (e.g., Chowdary et al. 2016 ) temperature biases and of Bay of Bengal salinity biases (e.g., Seo et al. 2009 ). Recently, Annamalai et al. (2017) argued that the cause of poor monsoon

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Fiaz Ahmed and J. David Neelin

increases of precipitation with increases in atmospheric moisture content ( Bretherton et al. 2004a ). A series of works ( Peters and Neelin 2006 ; Neelin et al. 2008 ; Peters et al. 2009 ; Neelin et al. 2009 ) have explored and documented the finescale (near-instantaneous ≤0.25° grids) precipitation–moisture relationship, along with related statistics that arise when examining the probability distributions of precipitating and nonprecipitating points, precipitation accumulation, and cluster sizes

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Daehyun Kim, Yumin Moon, Suzana J. Camargo, Allison A. Wing, Adam H. Sobel, Hiroyuki Murakami, Gabriel A. Vecchi, Ming Zhao, and Eric Page

-oriented diagnostics shed light on the mechanisms behind the difference. The paper is organized as follows. Section 2 presents brief descriptions of the high-resolution models used in this study and the composite method. The process-oriented TC diagnostics will be introduced in section 3 , with their applications to the GFDL models. Section 4 gives a summary of the results and conclusions. 2. Models and methods a. GFDL high-resolution GCMs Two atmosphere-only GCMs—Atmospheric Model version 2.5 (AM2

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Alexis Berg and Justin Sheffield

1. Introduction Surface climate over land is influenced by the physical interactions taking place between the land surface and the overlying atmosphere. The land radiative and physical properties, such as albedo and water availability, are impacted by atmospheric conditions; in turn, land surface variations affect the radiative, moisture, heat, and momentum fluxes between the surface and the atmosphere, impacting the overlying atmosphere and eventually regulating local climate. These

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Yi-Hung Kuo, Kathleen A. Schiro, and J. David Neelin

robustness and resolution dependence of the basic convective transition statistics for model comparison, the ability to summarize statistics in terms of CWV relative to critical enables additional diagnostics. The dependence of precipitation probability on this quantity expands the set of related properties that exhibit common behavior for precipitation throughout the tropics. Acknowledgments This research was supported by National Oceanic and Atmospheric Administration Grants NA15OAR4310097 and NA14OAR

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