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Alexis Berg and Justin Sheffield

of soil moisture–atmosphere interactions on surface temperature distribution . J. Climate , 27 , 7976 – 7993 , . 10.1175/JCLI-D-13-00591.1 Berg , A. , and Coauthors , 2015 : Interannual coupling between summertime surface temperature and precipitation over land: Processes and implications for climate change . J. Climate , 28 , 1308 – 1328 , . 10.1175/JCLI-D-14-00324.1 Berg , A. , and Coauthors

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

frequency and intensity of MCS precipitation during spring ( Feng et al. 2016 ), and such increases are projected to further intensify under future warming ( Prein et al. 2017 ). MCSs are notoriously difficult to simulate in traditional GCMs. This is partly due to the multiscale interactions between convective-scale dynamics and microphysics and the upscale feedbacks and interactions through latent heating ( Feng et al. 2018 ; Yang et al. 2017 ), which are challenging for GCMs because the scale

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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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Douglas E. Miller and Zhuo Wang

socioeconomic value, and physics-oriented model evaluation is an indispensable part of the effort. Skillful seasonal prediction is related to several sources of predictability, including inertia, external forcing, and patterns of variability ( National Research Council 2010 ). Recurrent modes of low-frequency variability, which arise from the interaction between different components of the climate system, such as El Niño–Southern Oscillation (ENSO), the Madden–Julian oscillation (MJO), and the annular modes

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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

tendency of column-integrated MSE as a function of latitude and longitude from the TC center, composited at 48 h prior to LMI. (c) The tendency of column-integrated kinetic energy as a function of latitude and longitude from the TC center, composited at 48 h prior to LMI. The HiRAM simulation is shown. REFERENCES Anderson J. L. , and Coauthor , 2004 : The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations . J. Climate , 17 , 4641 – 4673 , https

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

: Intraseasonal variability. The Asian Monsoon , B. Wang, Ed., Springer, 203–257. 10.1007/3-540-37722-0_5 Wang , B. , 2012 : Theories. Intraseasonal Variability in the Atmosphere-Ocean Climate System, 2nd ed. W. K. M. Lau and D. E. Waliser, Eds., Springer, 335–398. 10.1007/978-3-642-13914-7_10 Wang , B. , and T. M. Li , 1994 : Convective interaction with boundary-layer dynamics in the development of a tropical intraseasonal system . J. Atmos. Sci. , 51 , 1386 – 1400 ,

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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

.5; Delworth et al. 2012 ) and the High Resolution Atmospheric Model (HiRAM; Zhao et al. 2009 )—and one ocean–atmosphere coupled GCM—Forecast-Oriented Low Ocean Resolution (FLOR; Vecchi et al. 2014 ) version of Coupled Model 2.5 (CM2.5; Delworth et al. 2012 )—developed at the Geophysical Fluid Dynamics Laboratory (GFDL) are used in this study. AM2.5 is the atmospheric component of CM2.5 and FLOR. FLOR is a descendent of CM2.5 developed for regional climate prediction by employing an improved land model

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

, C. Frankignoul , H. Nakamura , B. Qiu , and L. Thompson , 2010 : Role of Gulf Stream and Kuroshio–Oyashio systems in large-scale atmosphere–ocean interaction: A review . J. Climate , 23 , 3249 – 3281 , doi: 10.1175/2010JCLI3343.1 . 10.1175/2010JCLI3343.1 Lindzen , R. S. , and R. S. Nigam , 1987 : On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics . J. Atmos. Sci. , 44 , 2418 – 2436 , doi: 10

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

evaporation and Bay of Bengal rivers on dynamics, thermodynamics, and mixed layer physics in the upper Indian Ocean . J. Geophys. Res. , 106 , 6895 – 6916 , . 10.1029/2000JC000403 Han , Z.-Y. , T.-J. Zhou , and L.-W. Zou , 2012 : Indian Ocean SST biases in a Flexible Regional Ocean Atmosphere Land System (FROALS) model . Atmos. Oceanic Sci. Lett. , 5 , 273 – 279 , . 10.1080/16742834.2012.11447012 Huffman

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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

tropical cyclone activity under future warming scenarios using a high-resolution climate model . Climatic Change , 146 , 547 – 560 , . 10.1007/s10584-016-1750-x Bao , Q. , and Coauthors , 2013 : The Flexible Global Ocean–Atmosphere–Land System model, spectral version 2: FGOALS-s2 . Adv. Atmos. Sci. , 30 , 561 – 576 , . 10.1007/s00376-012-2113-9 Bell , G. D. , and Coauthors , 2000 : Climate assessment for

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