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Zhe Feng, Fengfei Song, Koichi Sakaguchi, and L. Ruby Leung

, baroclinic waves and frontal systems provide strong lifting mechanisms and the Great Plains low-level jet (LLJ) provides anomalous moisture for favorable dynamical and thermodynamical environments for MCS development. In contrast, during summer, favorable environments featuring significantly weaker baroclinic lifting and thermodynamic instability suggest much lower predictability of MCSs compared to spring ( Song et al. 2019 ). Besides limitations in physics parameterizations, it is unclear if GCMs are

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

1. Introduction Understanding and quantifying the effects of global warming on regional hydrological cycles is one of the most important problems in climate science because of the societal implications. At global scale, atmospheric moisture increases with temperature under global warming at a rate that follows the Clausius–Clapeyron relationship of ~7% K −1 , while global precipitation increases at a much slower rate of ~2% K −1 ( Held and Soden 2006 ). This difference between the responses of

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

for a tropically forced Rossby wave packet to reach high latitudes ( Hoskins and Karoly 1981 ). This lag is applied to all Z500a results unless stated otherwise. Our analysis is mostly focused on MJO phases 3 and 7, which are the most effective MJO phases in exciting extratropical circulation anomalies ( Stan et al. 2017 ; Tseng et al. 2019 ) and modulating extratropical events, such as eastern Pacific and Atlantic blockings, North Pacific storm tracks, atmospheric rivers, and extreme

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Douglas E. Miller and Zhuo Wang

feedback, plays an important role in the development and maintenance of the NAO, which implies the NAO’s limited predictability. Other studies revealed the link between the NAO and blocking or Rossby wave breaking and emphasized the positive and negative phases of the NAO as two North Atlantic weather regimes (e.g., Benedict et al. 2004 ; Woollings et al. 2010 ). Weather regimes are recurrent, slowly evolving atmospheric patterns (e.g., Reinhold and Pierrehumbert 1982 ) typically lasting several

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Xianan Jiang, Ángel F. Adames, Ming Zhao, Duane Waliser, and Eric Maloney

proposed to explain the distinct seasonality in MJO propagation (e.g., Waliser 2006 ; Wang 2012 ). One traditional view of the eastward propagation of the winter MJO maintains through triggering of new convection to the east by frictional moisture convergence in the planetary boundary layer (PBL) associated with the equatorial Kelvin wave response to the MJO convective heating (e.g., Salby et al. 1994 ; Wang and Li 1994 ; Maloney and Hartmann 1998 ). Meanwhile, past work has suggested that

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

1. Introduction Atmospheric storm tracks are very important for climate dynamics. They indicate regions of maximum transient poleward energy transport and zonal momentum transport ( Chang et al. 2002 ) and play an important role in setting the dynamical response of the midlatitudes to global warming through their radiative forcing ( Voigt and Shaw 2015 ). Storm tracks are generally calculated as the standard deviation of atmospheric data that has been filtered in the time domain to isolate

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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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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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