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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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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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Ángel F. Adames and Yi Ming

numerical gradients and from the interpolation from the model’s native grid to the coordinate system used here. To reduce these residuals, we apply the adjustment method described in the appendix of Hill et al. (2017) . This method adds a barotropic adjustment to the horizontal wind field in order to satisfy conservation of column-integrated moisture/MSE. Using this method largely reduces the residual from these numerical errors. Additionally, in sections 4 – 6 , we employ a compositing technique in

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

the primary cause of ΔMLT, because its magnitude is so large that it overwhelms the impact of bias. In turn, is determined primarily by salinity inflow into the NAS by the WICC. Ultimately, then, the NAS stratification errors are linked to the rainfall deficit associated with the models’ weak summer monsoon. The paper is organized as follows. Section 2 summarizes our data sources and analysis techniques. Section 3 describes the model biases, and section 4 identifies the processes that

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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

the accumulated storm-track value as the month proceeds. This yields a finescale temporal resolution metric that does not require copious model output. This filtering method can also be used on observations that are available only at a daily resolution (e.g., Guo et al. 2009 ). For one component of the analysis, we utilize the technique of Booth et al. (2010) to calculate an estimated surface storm track defined as the region of overlap of the upper quantiles of and T DIFF . Note that Booth

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