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Joseph A. Santanello Jr., Christa D. Peters-Lidard, Aaron Kennedy, and Sujay V. Kumar

1. Introduction Quantification of the land surface influence on extremes such as flood and drought is critical for both short-term weather and climate prediction. These dry and wet regimes are modulated by the strength and sensitivity of the land–atmosphere (L–A) coupling and, in particular, how anomalies in soil moisture are translated into and through the planetary boundary layer (PBL), ultimately favoring or suppressing the triggering and support of clouds and precipitation. Improved

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Xubin Zeng, Zhuo Wang, and Aihui Wang

1. Introduction Land–atmosphere interaction plays an important role in weather, climate, and global/regional environmental change. For this reason, various international programs have been established in the past three decades to address the relevant scientific issues, such as the Global Energy and Water Cycle Experiment (GEWEX; http://www.gewex.org ), the (earlier) Biospheric Aspects of the Hydrological Cycle (BASC; Kabat et al. 2004 ), and (its successor) integrated Land Ecosystem–Atmosphere

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Craig R. Ferguson, Eric F. Wood, and Raghuveer K. Vinukollu

1. Introduction Land surface–atmosphere interaction (henceforth, coupling), or degree to which anomalies in the land surface state (i.e., soil wetness, soil texture, surface roughness, temperature, and overlying vegetation composition and structure) can affect (through complex controls on the partitioning of surface turbulent fluxes) the planetary boundary layer (PBL) and, in extreme cases, rainfall generation, is an important—if not the single most fundamental—criterion for evaluating

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Keith J. Harding and Peter K. Snyder

minimize model edge effects. The goal of this study is to explore the impact that irrigation has on the hydrologic cycle using a high-resolution coupled land–atmosphere model. Simulations using the Weather Research and Forecasting Model (WRF; Skamarock et al. 2008 ) were performed both with and without irrigation for a suite of years for different precipitation regimes. This includes El Niño–Southern Oscillation (ENSO) years that have a marked influence on Great Plains precipitation ( Twine et al

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Keith J. Harding and Peter K. Snyder

. The precipitation recycling ratio (rr) was determined by dividing the recycled precipitation by the total precipitation within the region of study: where P r is the precipitation of recycled origin and P is the total precipitation. Precipitation recycling ratios were calculated for all pentads from May to September. The second approach used the DRM from Dominguez et al. (2006) , which accounts for moisture storage and assumes a well-mixed atmosphere. Accounting for moisture storage allows for

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