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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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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

-horizontal-resolution models ( Murakami et al. 2012b ; Knutson et al. 2013 ; Manganello et al. 2014 ; Bhatia et al. 2018 ; Bacmeister et al. 2018 ). Another common use of climate models is for TC dynamical forecasts on seasonal ( Vitart et al. 2001 ; Camargo and Barnston 2009 ; Manganello et al. 2016 ; Camp et al. 2019 ; G. Zhang et al. 2019 ; W. Zhang et al. 2019 ) and subseasonal time scales ( Lee et al. 2018 ; Camp et al. 2018 ; Gregory et al. 2019 ; Zhao et al. 2019 ). A recent review on this topic is

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

simulations in the central U.S. region shown by the magenta box. Song et al. (2019) compared different types of observed FLSMP and found that synoptic patterns associated with the passage of strong baroclinic waves during spring are much more skillful in estimating the occurrence of MCSs than those during the summer. Their findings are consistent with those reported by Jankov and Gallus (2004) and Squitieri and Gallus (2016) that forecasting of MCS rainfall is more skillful under strong large

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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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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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Daehyun Kim, Yumin Moon, Suzana J. Camargo, Allison A. Wing, Adam H. Sobel, Hiroyuki Murakami, Gabriel A. Vecchi, Ming Zhao, and Eric Page

1. Introduction Since the 1970s, it has been well known that global climate models (GCMs) are able to simulate vortices with characteristics similar to tropical cyclones (TCs; Manabe et al. 1970 ; Camargo and Wing 2016 ). As GCMs are also able to reproduce the relationship between TCs and El Niño–Southern Oscillation (ENSO), they have been used to develop dynamical TC seasonal forecasts ( Vitart and Stockdale 2001 ; Camargo and Barnston 2009 ). More recently, with the aid of rapid increases

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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

1. Introduction The study of tropical cyclones (TCs) in climate models has long been difficult because of the conflict between the high resolution necessary to accurately simulate TCs and the need to perform long, global simulations. In recent years, however, enormous progress has been made in the ability of general circulation models (GCMs) to simulate TCs from subseasonal to seasonal and longer time scales ( Camargo and Wing 2016 ). Global forecast models have become a more reliable source of

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

Interpolation V2 dataset ( Reynolds et al. 2002 ) were used as the boundary conditions. All models were integrated for 20 years and archived from 1991 to 2010, with the exception of SPCAM3, which is only archived from 1986 to 2003 for a total of 18 years. The ECMWF AMIP historical run was run with the Integrated Forecast System (IFS; cycle 36r4) atmospheric circulation model. The forcing and boundary conditions are set according to the CMIP5 historical forcing with SST and SIC derived from the Hadley Centre

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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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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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