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

and land models are active while sea surface temperature (SST) and sea ice cover are prescribed based on observations. We use the SST and sea ice cover from the ERA-Interim reanalysis ( Dee et al. 2011 ). The 6-hourly ERA-Interim data are averaged to daily for used as model input. The land model is the Community Land Model version 4 ( Lawrence et al. 2011 ) with prescribed, static land cover types and vegetation properties (e.g., leaf area index) roughly representing the conditions for year 2000

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Douglas E. Miller and Zhuo Wang

days to a few weeks, and a prolonged weather regime serves as a source of predictability for extended-range forecasting. In addition to the atmospheric internal dynamics, the NAO is influenced by several sources of predictability, including SST, stratospheric processes, and Arctic sea ice ( Scaife et al. 2014 ; Smith et al. 2016 ; Yang et al. 2016 ). Although the air–sea interaction over the North Atlantic is dominated by the atmospheric forcing of the SST, significant SST anomalies were found

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

. Booth et al. (2010) showed that for JJA in the Indian Ocean, there are two active regions in the surface storm track: one near the ARC and another near the sea ice edge. Related to this, Nakamura and Shimpo (2004) emphasize that the ARC helps maintain a strong storm track during SH summer (DJF). Given the climatological importance of storm tracks and the role of WBCs in forcing surface storm tracks, it stands to reason that surface storm tracks in GCMs are a good variable to analyze to check

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

.1029/1999JC900068 . 10.1029/1999JC900068 Noh , Y. , Y. J. Kang , T. Matsuura , and S. Iizuka , 2005 : Effect of the Prandtl number in the parameterization of vertical mixing in an OGCM of the tropical Pacific . Geophys. Res. Lett. , 32 , L23609 , . 10.1029/2005GL024540 Oberhuber , J. M. , 1993 : Simulation of the Atlantic circulation with a coupled sea ice–mixed layer–isopycnal general circulation model. Part I: Model description . J. Phys. Oceanogr

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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

misdetections over snow and sea ice, in the sunglint region and at low sun and view angles ( Menzel et al. 2008 ). An evaluation of the MODIS cloud fraction retrievals in extratropical cyclones regions revealed agreements with other similar products within 5% for the southern oceans, but also issues over sea ice and snow covered land that we do not include here ( Naud et al. 2013 ). However, because of the latitudes considered here and the focus on warm months, we expect a minimal impact of these issues on

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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

) . CAM5 utilizes 30 vertical levels with a model top of approximately 2 hPa. The prescribed SST and sea ice boundary dataset for both simulations is provided from Hurrell et al. (2008) . Both CAM-SE and CAM-FV use similar versions of the CAM5 physics parameterizations, including the same deep ( Zhang and McFarlane 1995 ) and shallow convective ( Park and Bretherton 2009 ) schemes, moist boundary layer turbulence scheme ( Bretherton and Park 2009 ), and Rapid Radiative Transfer Model for GCMs (RRTMG

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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

and a coarser resolution for the ocean and the sea ice models. HiRAM branched out from AM2.1, an ancestor of AM2.5, with the replacement of the convection and cloud scheme ( Zhao et al. 2009 ). AM2.5 and FLOR use a relaxed Arakawa–Schubert scheme ( Moorthi and Suarez 1992 ). The convection scheme in HiRAM was originally developed to simulate shallow convection with a constraint on the top of convective clouds ( Bretherton et al. 2004 ). The restriction is removed when the scheme is implemented in

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Ángel F. Adames and Yi Ming

by dashed lines. Liquid water content and ice crystals are depicted with circles, as in Janiga and Zhang (2016) . The simulated SMDs are found to exhibit a life cycle where they develop over the Bay of Bengal, attain a maximum amplitude as they make landfall over India, and then dissipate as they reach the Arabian Sea. While this life cycle is consistent with previous studies ( Krishnamurthy and Ajayamohan 2010 ; Yoon and Chen 2005 ), it may also be due to our choice of index, which is centered

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

dataset described below. We analyzed the first ensemble member (r1i1p1) of each CMIP5 model to make a consistent comparison as some CMIP5 models only have one ensemble member. The GASS/YoTC project is a global model evaluation project with a specific focus on the processes associated with the MJO. Models derived from this project include both AGCMs and atmosphere–ocean coupled GCMs. For AGCMs, weekly sea surface temperature (SST) and sea ice concentration (SIC) derived from the NOAA Optimum

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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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