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

. Hardin , and C. R. Homeyer , 2019 : Contrasting spring and summer large-scale environments associated with mesoscale convective systems over the U.S. Great Plains . J. Climate , 32 , 6749 – 6767 , https://doi.org/10.1175/JCLI-D-18-0839.1 . 10.1175/JCLI-D-18-0839.1 Squitieri , B. J. , and W. A. Gallus Jr ., 2016 : WRF forecasts of Great Plains nocturnal low-level jet-driven MCSs. Part I: Correlation between low-level jet forecast accuracy and MCS precipitation forecast skill . Wea

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Douglas E. Miller and Zhuo Wang

1. Introduction Seasonal prediction, along with subseasonal prediction, has long been considered a gap in current forecasting capability ( Weisheimer and Palmer 2014 ; Vitart 2014 ). Skillful seasonal predictions can provide useful information for decision-makers across a variety of sectors, ranging from energy and agriculture to transportation and public health ( National Academies of Sciences, Engineering, and Medicine 2016 ). Improved seasonal prediction skill is thus of profound

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Fiaz Ahmed and J. David Neelin

Maloney 2011 ) to even interannual ( Neale et al. 2008 ). Agreement between convective transition statistics in models and observations is also sensitive to the assumed entrainment—a fact used to constrain its vertical profile and magnitudes in GCMs ( Sahany et al. 2012 ; Kuo et al. 2017 ). Observational studies of mesoscale convective systems (MCSs; Houze 2004 ) provide another plausible pathway to constraining entrainment profiles, particularly with respect to deep, organized convection

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James F. Booth, Catherine M. Naud, and Jeff Willison

particular interest is the impact of parameterizations on ETCs; here, we will focus on parameterized convection. In both the GFDL and the GISS GCMs, there is a single convection parameterization used globally, meaning the schemes in the models are usually designed with attention on the tropics. In contrast, the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) was developed originally for forecasting midlatitude weather. These different constraints on parameterized physics motivate

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

al. 2008 ). Surface winds are obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011 ). Horizontal grid intervals are 1° × 1° for WOA13 and OAFlux, 2.5° × 2.5° for GPCP rain, and 1.5° × 1.5° for the ERA-Interim reanalysis. The time series we use for each dataset extend from 1979 to 2015 for GPCP rainfall and the ERA-Interim reanalysis, 1984 to 2009 for OAFlux surface heat flux, and 1985 to 2014 for OAFlux evaporative flux

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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

Outcomes of NOAA MAPP Model Diagnostics Task Force activities to promote process-oriented diagnosis of models to accelerate development are described. Realistic climate and weather forecasting models grounded in sound physical principles are necessary to produce confidence in projections of future climate for the next century and predictions for days to seasons. However, global models continue to suffer from important and often common biases that impact their ability to provide reliable

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Yi-Hung Kuo, Kathleen A. Schiro, and J. David Neelin

-term forecasting purposes, since models adopted for weather forecasting or reanalysis share common components with climate models. Many conventional diagnostics for climate models emphasize comparisons against long-term climatology or variability at different time scales, and the model performance examined by these metrics is affected by multiple factors. While sensitivity experiments with respect to such metrics are useful in identifying important processes ( Benedict et al. 2013 , 2014 ; Boyle et al. 2015

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

forecast system . J. Hydrometeor. , 10 , 623 – 643 , https://doi.org/10.1175/2008JHM1068.1 . 10.1175/2008JHM1068.1 Best , M. J. , P. M. Cox , and D. Warrilow , 2005 : Determining the optimal soil temperature scheme for atmospheric modelling applications . Bound.-Layer Meteor. , 114 , 111 – 142 , https://doi.org/10.1007/s10546-004-5075-3 . 10.1007/s10546-004-5075-3 Best , M. J. , and Coauthors , 2011 : The Joint UK Land Environment Simulator (JULES), model description - Part 1

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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

that, at the typical spatial resolution of a GCM, cloud cover in cyclone cold sectors is responding more strongly to changes in the convection than the boundary layer parameterizations. However, these results were obtained with the Weather Research Forecasting model for a single case study, so it is uncertain whether the impact of convection parameterization is as large in a global-scale multiyear GCM integration. Another related issue discussed in the Kay et al. (2016) and Frey and Kay (2018

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

, 762 – 777 , doi: 10.1175/2010JCLI3731.1 . 10.1175/2010JCLI3731.1 Gent , P. , and Coauthors , 2011 : The Community Climate System Model version 4 . J. Climate , 24 , 4973 – 4991 , doi: 10.1175/2011JCLI4083.1 . 10.1175/2011JCLI4083.1 Griffies , S. M. , and Coauthors , 2015 : Impacts on ocean heat from transient mesoscale eddies in a hierarchy of climate models . J. Climate , 28 , 952 – 977 , doi: 10.1175/JCLI-D-14-00353.1 . 10.1175/JCLI-D-14-00353.1 Guo , Y. , E. K. M. Chang , and

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