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Alexis Berg and Justin Sheffield

of soil moisture–atmosphere interactions on surface temperature distribution . J. Climate , 27 , 7976 – 7993 , https://doi.org/10.1175/JCLI-D-13-00591.1 . 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 , https://doi.org/10.1175/JCLI-D-14-00324.1 . 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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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 , https://doi.org/10.1007/s10584-016-1750-x . 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 , https://doi.org/10.1007/s00376-012-2113-9 . 10.1007/s00376-012-2113-9 Bell , G. D. , and Coauthors , 2000 : Climate assessment for

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Maik Renner, Axel Kleidon, Martyn Clark, Bart Nijssen, Marvin Heidkamp, Martin Best, and Gab Abramowitz

striking finding of Best et al. (2015) was that simple linear regression models with solar radiation as a predictor variable outcompeted all land surface models when evaluated with standard statistical metrics. The simple linear response seen in observations can be regarded as a signature of complex land–atmosphere interactions, which are known to simplify the response of turbulent fluxes at certain scales ( Jarvis and McNaughton 1986 ; De Bruin and Holtslag 1982 ). Hence, these land surface models

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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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Fiaz Ahmed and J. David Neelin

. Soc. , 127 , 869 – 886 , https://doi.org/10.1002/qj.49712757309 . 10.1002/qj.49712757309 Betts , A. K. , 1975 : Parametric interpretation of trade-wind cumulus budget studies . J. Atmos. Sci. , 32 , 1934 – 1945 , https://doi.org/10.1175/1520-0469(1975)032<1934:PIOTWC>2.0.CO;2 . 10.1175/1520-0469(1975)032<1934:PIOTWC>2.0.CO;2 Betts , A. K. , J. H. Ball , A. C. M. Beljaars , M. J. Miller , and P. A. Viterbo , 1996 : The land surface-atmosphere interaction: A review based on

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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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Ángel F. Adames and Yi Ming

://doi.org/10.3402/tellusa.v57i5.14737 . 10.3402/tellusa.v57i5.14737 Yoon , J.-H. , and W.-R. Huang , 2012 : Indian monsoon depression: Climatology and variability. Modern Climatology , InTech, 45–72. 10.5772/37917 Zhao , M. , and Coauthors , 2018a : The GFDL global atmosphere and land model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTs . J. Adv. Model. Earth Syst. , 10 , 691 – 734 , https://doi.org/10.1002/2017MS001208 . 10.1002/2017MS001208 Zhao , M. , and Coauthors

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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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