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

1. Introduction a. Background and motivation Land surface models simulate distinct diurnal cycles of turbulent heat fluxes, but they also show systematic deviations from observations, which were reported in early ( Henderson-Sellers et al. 1995 ; Chen et al. 1997 ) and more recent model intercomparison studies ( Holtslag et al. 2013 ; Best et al. 2015 ). Best et al. (2015) used observational meteorological forcing to drive and evaluate state-of-the-art models at 20 different flux towers. A

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

regression in the same variables. Their conclusion was that, since it is possible to develop regression models that outperform biogeophysical land models, the land models do not make full use of the information content of the atmospheric input data—some information that is present in the forcing data goes missing in the model simulations, due to model error. 3. Theory a. Benchmarking 1) What is benchmarking? Best et al. (2015) recognized three approaches that the land modeling community has generally

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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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