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

.e., ocean–atmosphere) dynamics. Conceptually, coupling implies that in certain regions the land surface (by inducing persistence or climate “memory”) may act to reinforce meteorological extremes (i.e., flood and drought). Indeed, coupling is believed to have played an important role in the European heat waves ( Fischer et al. 2007 ); projected changes in temperature variability in Europe are largely due to enhanced coupling as well ( Seneviratne et al. 2006 ). It follows that the strength and

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Ruth E. Comer and Martin J. Best

θ c for vegetated regions, increasing where WHS has a higher proportion of sand (parts of eastern Africa, India, and China) and decreasing elsewhere. In the Northern Hemisphere, in particularly wet regions of Europe, Russia, Canada, and the eastern United States, the change in β is quite heterogeneous. The reasons for this are unclear, and could simply reflect noise associated with precipitation variability (patterns over Europe and Alaska are similar to those in Fig. 7d ). For Canada and

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Xubin Zeng, Zhuo Wang, and Aihui Wang

. Furthermore, pedotransfer functions used to calculate K soil and soil hydraulic properties from soil texture are highly uncertain (e.g., Gutmann and Small 2007 ). Partly for these reasons, a single soil texture is used in the operational model at the European Centre for Medium-Range Weather Forecasts (ECMWF). Furthermore, the computation of K soil is different in different land models. For instance, while Noah uses soil texture types, CLM uses percentages of sand and clay to quantify soil texture in

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

of the model. 1) Land surface models The LSMs employed in LIS for this study are the Noah LSM version 2.7.1 (Noah; Ek et al. 2003 ), the Community Land Model version 2.0 (CLM; Dai et al. 2003 ), and the Hydrology Tiled European Centre for Medium-Range Weather Forecasts (ECMWF) Scheme for Surface Exchanges over Land (HTESSEL; Balsamo et al. 2009 ). Each model dynamically predicts water and energy fluxes and states at the land surface, but they vary in specific parameterizations and

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Keith J. Harding and Peter K. Snyder

Dolman A. J. , 2008 : Precipitation recycling: Moisture sources over Europe using ERA-40 data . J. Hydrometeor. , 9 , 1073 – 1083 . Bosilovich, M. G. , and Schubert S. D. , 2001 : Precipitation recycling over the central United States diagnosed from the GEOS-1 Data Assimilation System . J. Hydrometeor. , 2 , 26 – 35 . Brubaker, K. L. , Entekhabi D. , and Eagleson P. S. , 1993 : Estimation of continental precipitation recycling . J. Climate , 6 , 1077 – 1089 . Brubaker, K. L

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