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

-scale forcing conditions typically associated with cold fronts and LLJ, while model performance is poor in midsummer under warm or stationary front. Song et al. (2019) hypothesized a more important role of smaller-scale disturbances such as surface fluxes and shortwave troughs (e.g., midtropospheric perturbations, Wang et al. 2011a , b ) that may limit the predictability of summer MCSs. Considering the finest model resolution used in this study (25 km) is likely not sufficient to resolve the smaller

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

1. Introduction Atmospheric storm tracks are very important for climate dynamics. They indicate regions of maximum transient poleward energy transport and zonal momentum transport ( Chang et al. 2002 ) and play an important role in setting the dynamical response of the midlatitudes to global warming through their radiative forcing ( Voigt and Shaw 2015 ). Storm tracks are generally calculated as the standard deviation of atmospheric data that has been filtered in the time domain to isolate

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Stephanie A. Henderson, Eric D. Maloney, and Seok-Woo Son

systematic errors in extratropical circulations. Previous studies have shown that tropical thermal forcing, such as that associated with anomalous MJO convection, is balanced by ascending motion and divergent winds aloft. This upper-tropospheric divergent flow generates upper-level anticyclonic anomalies that can produce stationary Rossby waves that extend into higher latitudes (e.g., Hoskins and Karoly 1981 ). The location and amplitude of the Rossby waves is dependent on the location, amplitude, and

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Alexis Berg and Justin Sheffield

models. We also explore the potential relationships between the spread in ET partitioning and general aspects of the simulated climate in these models. Finally, we investigate what future changes in partitioning models simulate in response to anthropogenic forcing and global warming and what factors are driving these changes. 2. Data and methods We use monthly outputs from historical and representative concentration pathway 8.5 (RCP8.5; Riahi et al. 2011 ) simulations from the CMIP5 experiment. We

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

.g., Stommel 1979 ; Talley 1985 ; de Szoeke 1987 ; Pedlosky 1996a , b ). A number of processes might force . Local processes include surface wind stirring and buoyancy (heat Q and evaporative E − P ) fluxes, which generate turbulence that leads to entrainment across the bottom of the mixed layer; further, the entrainment rate is related to the stratification just beneath the mixed layer (measured by ), with weaker stratification leading to stronger entrainment and vice versa [e.g., Kraus and

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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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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

Force (MDTF) ( Maloney et al. 2019 ). Similarly to what was done in the analysis of the TCs in the HWG project ( Shaevitz et al. 2014 ; Daloz et al. 2015 ; Nakamura et al. 2017 ; Ramsay et al. 2018 ), we are considering the tracking provided by each modeling group as part of the model package. This is an ensemble of opportunity; that is, we use the model simulations and TC tracks that are available to us, as they are. These model simulations were not produced for this purpose. Therefore, there

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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

behind this persistent problem was attributed to the ubiquitous presence of supercooled water in Southern Hemisphere (SH) clouds ( Morrison et al. 2011 ) which models have problems maintaining ( Kay et al. 2016 ). By forcing their model to maintain liquid in clouds for temperatures below freezing, Kay et al. (2016) could correct the surface absorption issue. However, while an advanced treatment of boundary layer clouds in another model improved Southern Hemisphere cloud liquid amounts, its cloud

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

found across the other datasets. ERAI has the stronger WSPD, but it has nearly equal precipitation rates to the GISS model. The GFDL model has stronger precipitation rates near the cyclone center but not stronger WSPD. The lack of a relationship between model-to-model differences in precipitation and dynamical cyclone strength could be the result of multiple factors, such as surface boundary conditions, dry baroclinic forcing, or biases in the modeled latent heating within the cyclone. Here we

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