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Raju Attada, Hari Prasad Dasari, Ravi Kumar Kunchala, Sabique Langodan, Kondapalli Niranjan Kumar, Omar Knio, and Ibrahim Hoteit

regional climate over the continental United States, although Liang et al. (2004) later reported the superiority of the KF for simulating North American regional climate rainfall. Almazroui (2016) and Almazroui et al. (2016a) recently used a 50-km regional climate model (RegCM) to investigate the impact of different CPSs in the Middle East and North Africa (MENA) over a limited time period of 5 years. The study reported that rainfall over the AP is quite sensitive to the cumulus parameterization

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Zhichang Guo and Paul A. Dirmeyer

soil moisture to affect evaporation (terrestrial path) and the ability of evaporation to control precipitation. A number of studies ( Dirmeyer et al. 2009 ; Guo et al. 2011 ) have shown that the terrestrial path of land–atmosphere coupling strength, characterized by the product of standard deviation of evaporation σ E ( W ) with the temporal correlation between evaporation and soil moisture, serves as a good proxy of land–atmosphere coupling strength. The middle panel in Fig. 1 shows the

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Paul A. Dirmeyer, Yan Jin, Bohar Singh, and Xiaoqin Yan

seasons from the historical (1911–2005) experiment. Also shown are the sign and consensus of changes (middle) from preindustrial to historical conditions and (bottom) from historical to RCP85 future conditions. (left) JJA and (right) DJF. Figure 8 shows the terrestrial coupling index between soil moisture and evaporation (latent heat flux) of Dirmeyer (2011b) . This field corresponds strongly with the “hot spots” of land–atmosphere coupling of Koster et al. (2004 , 2006 ). During boreal summer

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Rui Mei, Guiling Wang, and Huanghe Gu

capturing the temporal climate variability. Instead, the inaccurate representation of land–atmosphere coupling is more likely to be the cause that dictates the difference between predictability and added forecast skill. Fig . 9. (left) Precipitation and (right) temperature at the 1–15-day forecast lead for quasi summer (16 May–15 Aug): (top) predictability and (upper middle) added forecast skill attributed to realistic land surface initialization; P and T at the 16–30-day forecast lead for summer (1

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Lu Yi, Bin Yong, Junxu Chen, Ziyan Zheng, and Ling Li

1. Introduction The coupled land–atmosphere model based on the regional climate model and hydrological model is an important tool to extend the forecast period of local flood ( Bosilovich and Sun 1999 ; Wu and Zhang 2013 ). In a coupled land–atmosphere model, the regional climate model can provide a hydrological model with continuous spatiotemporal variation fields of hydrological variables such as precipitation, evaporation, temperature, and radiation. The hydrological model has more refined

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Cheng Tao, Yunyan Zhang, Qi Tang, Hsi-Yen Ma, Virendra P. Ghate, Shuaiqi Tang, Shaocheng Xie, and Joseph A. Santanello

(positive) CTP indicates a stable (unstable) lower troposphere hard (easy) for convection initiation. The HI low is a low-level humidity index, defined as the sum of the dewpoint depressions at 925, 825, and 625 hPa. The higher the HI low , the drier the atmosphere. The height levels to calculate HI low here are slightly different from the ones in Findell and Eltahir (2003a , b ) and roughly correspond to the levels in the middle of the mixed layer, immediately above the mixed-layer top and in the

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Trent W. Ford and Steven M. Quiring

1. Introduction Soil moisture is vital to land–atmosphere interactions and it can modulate drought conditions, especially in semiarid environments such as the North American Great Plains ( Koster et al. 2004 ). However, few soil moisture monitoring networks exist relative to networks observing temperature and precipitation, impeding research of land–atmosphere feedbacks critical to drought prediction and mitigation. Remote sensing and land surface models (LSMs) are commonly employed for

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Mary M. Forrester and Reed M. Maxwell

1. Introduction Groundwater, soil, vegetation, and the atmosphere are intimately coupled through complex and highly nonlinear interactions ( Brubaker and Entekhabi 1996 ; Entekhabi et al. 1996 ; Rodriguez-Iturbe 2000 ). For decades, meteorological models, with land surface models (LSMs) as their lower boundary, have advanced in their ability to not only provide dependable predictions of atmospheric states and surface recharge, but also to diagnose this complicated exchange of moisture and

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Graham A. Sexstone, Colin A. Penn, Glen E. Liston, Kelly E. Gleason, C. David Moeser, and David W. Clow

1984 to 2017. SnowModel is a spatially distributed physically based snow evolution modeling system designed for application in a wide range of environments where snow occurs ( Liston and Elder 2006b ). SnowModel includes the following submodels: MicroMet ( Liston and Elder 2006a ), a high-resolution meteorological distribution model; EnBal ( Liston 1995 ), which computes surface energy exchanges between the snow and atmosphere; SnowPack ( Liston and Hall 1995 ), which simulates the seasonal

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Hua Su, Robert E. Dickinson, Kirsten L. Findell, and Benjamin R. Lintner

Kvamsto 2009 ). Such studies have suggested that those pressure patterns caused by snowpack anomalies can change circulations on continental scales and extend globally through teleconnections. The snow–albedo–temperature feedback has also been highlighted in numerous studies (e.g., Viterbo and Betts 1999 ; Qu and Hall 2007 ; Dutra et al. 2011 ) and was found to be important for the land–atmosphere coupling strength and predictability of climate models, especially in terms of affecting near

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