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Suzana J. Camargo, Claudia F. Giulivi, Adam H. Sobel, Allison A. Wing, Daehyun Kim, Yumin Moon, Jeffrey D. O. Strong, Anthony D. Del Genio, Maxwell Kelley, Hiroyuki Murakami, Kevin A. Reed, Enrico Scoccimarro, Gabriel A. Vecchi, Michael F. Wehner, Colin Zarzycki, and Ming Zhao

/or seasons the tropics are typically stable. Then, Gray (1979) developed an empirical relationship between genesis and climatological conditions of the environment, identifying six environmental conditions necessary for genesis: ocean thermal energy, low-level relative vorticity, vertical wind shear, Coriolis parameter, relative humidity of the troposphere, and a measure of instability of the atmosphere. Since then, many other empirical genesis indices have been developed ( DeMaria et al. 2001

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Samson M. Hagos, L. Ruby Leung, Oluwayemi A. Garuba, Charlotte Demott, Bryce Harrop, Jian Lu, and Min-Seop Ahn

. 2020 ), weak South Asian monsoon rainfall ( Hagos et al. 2019 ), and weak Amazon precipitation ( Yin et al. 2013 ). Understanding the origin of these biases and how they relate to regional-scale projections of precipitation changes is critical for building confidence in the projections. Several studies suggest that model precipitation biases are related to the representation of convection. For example, precipitation biases in coupled climate simulations can be reproduced in uncoupled Atmosphere

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Alexis Berg and Justin Sheffield

. The only exceptions are over the Sahara and the Middle East where a couple of models (models based on the Hadley Centre land–atmosphere model) show zero correlation (as opposed to strong correlations in all other models); this is because there is no precipitation and ET at all, and thus no SM–ET correlation, in these regions in these models. Thus model spread appears slightly greater in these regions than in other dry regions. Overall, model uncertainty in SM–ET coupling tends to be greatest on

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Alexis Berg and Justin Sheffield

1. Introduction Evaporation of water from the land to the atmosphere is a key process regulating and coupling the carbon, energy, and water budgets of the land surface. As such, it is critical that land evaporation be represented accurately in model simulations of the physical climate and in Earth system model simulations of the coupled carbon cycle and climate system. Representing the land–atmosphere fluxes of water and energy in response to available energy (e.g., radiation) and water input

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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

that may be written in diverse coding languages. PODs developed or under development for the first task include cloud microphysical processes; tropical and extratropical cyclones; ENSO teleconnections and atmospheric dynamics; land–atmosphere interactions; MJO moisture, convection, and radiative processes; precipitation diurnal cycle; AMOC; Arctic sea ice; lake-effect processes; North American monsoon; radiative forcing and cloud–circulation feedbacks; and temperature and precipitation extremes

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

prediction of climate modes, such as for El Niño (e.g., Ji and Leetmaa 1997 ) and the Indian Ocean dipole (e.g., Luo et al. 2007 ). a. Background Figure 1 provides maps of biases in annual-mean D20 (ΔD20; Fig. 1a ) and the mixed-layer thickness (MLT) in January (ΔMLT; Fig. 1b ). In both panels, errors are defined by differences between observations and the multimodel-mean (MMM) fields from a suite of coupled ocean–atmosphere models (see section 2 for details). We show ΔMLT during January because

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Ángel F. Adames and Yi Ming

our analysis to the boreal summer months of June–September (JJAS). The following AM4.0 fields are used in this study: the horizontal winds u and υ , geopotential height Z , specific humidity q , precipitation P , dry static energy s , frozen moist static energy h , surface and top of the atmosphere shortwave (SW) and longwave (LW) radiative fluxes, and surface sensible H and latent heat fluxes E . In addition to daily data from AM4.0, two other datasets are used in this study. We make

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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

intensity to aspects of the dynamical core (e.g., Zhao et al. 2012 ; Reed et al. 2015 ) and to the ocean–atmosphere coupling grids ( Zarzycki et al. 2016 ). Modeling studies of TC activity often use relationships between the large-scale environment and TC characteristics to explain features of the simulations, basing their arguments on relationships between environmental parameters and TC activity in observations. For example, Wing et al. (2007) and Kossin and Camargo (2009) found that variations

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Catherine M. Naud, James F. Booth, Jeyavinoth Jeyaratnam, Leo J. Donner, Charles J. Seman, Ming Zhao, Huan Guo, and Yi Ming

in the amount of shortwave radiation reaching the ocean surface ( Trenberth and Fasullo 2010 ) and biases in atmospheric circulation change predictions (e.g., Ceppi and Hartmann 2016 ; Grise and Medeiros 2016 ) and ultimately affects climate sensitivity in models ( Frey and Kay 2018 ). Most specifically for ocean–atmosphere coupled models, the cloud bias can affect southern ocean ventilation and the location of the intertropical convergence zone (e.g., Xiang et al. 2018 ). One potential reason

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

1. Introduction The Madden–Julian oscillation (MJO) ( Madden and Julian 1971 , 1972 ) is the dominant mode of tropical intraseasonal variability. It is characterized by a convection–circulation coupled system propagating eastward from the Indian Ocean to the Pacific with periods ranging from approximately 30 to 60 days. The MJO modulates atmospheric (e.g., tropical cyclones), oceanic (e.g., chlorophyll), and ocean–atmosphere coupled [e.g., El Niño–Southern Oscillation (ENSO)] disturbances

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