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Douglas E. Miller and Zhuo Wang

operational forecasts, the “November release” of the reforecasts will be used to forecast the winter (DJF) mean, which includes 28 members (8 October–7 November every fifth day; operational releases of 9-month runs are four times a day, allowing more ensemble members to be used for an official seasonal prediction). The 28-member ensemble is used in most of our analyses as lagged ensembles of 20+ have been demonstrated to increase the wintertime seasonal prediction skill ( Riddle et al. 2013 ). One member

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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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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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Grey S. Nearing, Benjamin L. Ruddell, Martyn P. Clark, Bart Nijssen, and Christa Peters-Lidard

the purpose of this paper is not to argue for using statistical, data-driven, or regression models in place of physically based or process-based models for operational forecasting of terrestrial hydrological systems. We do not want to do this because of the potential for nonstationarity—some type of mechanistic understanding of the system is necessary to predict under changing conditions ( Milly et al. 2008 ). That being said, we cannot ignore the fact that regression models regularly outperform

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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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