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Hongshuo Wang, Jeffrey C. Rogers, and Darla K. Munroe

drought indices when facing the limited retrieval of soil moisture from either remote sensing data (only soil moisture at the surface layer <5 cm) or large uncertainties in soil moisture output from land surface models ( Guo et al. 2004 ). The study may help meteorologists and ecologists to understand and monitor soil moisture and drought in agricultural areas of China. The results suggest a preference to use multiscalar drought indices rather than drought indices from a two-layer bucket model, which

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Hailan Wang, Siegfried Schubert, Randal Koster, Yoo-Geun Ham, and Max Suarez

as well as from some limited simulations with the atmosphere–ocean coupled version of the GEOS-5 model. The discussion and conclusions are provided in section 4 . 2. Model experiments a. Reanalysis The reanalysis data consist of 3-hourly and monthly MERRA ( Rienecker et al. 2011 ) and MERRA-Land data ( Reichle et al. 2011 ; Reichle 2012 ) for the period January 1979–August 2012. The MERRA data are used primarily to describe the atmospheric circulation. Overall, as discussed in Rienecker et al

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Richard Seager, Lisa Goddard, Jennifer Nakamura, Naomi Henderson, and Dong Eun Lee

imposed allowing land surfaces to warm and the atmospheric circulation to adjust to the changes in radiative properties. The other model is the European Centre-Hamburg model, version 4.5 (ECHAM4.5; Roeckner et al. 1996 ), and we use a 24-member ensemble from 1950 on available in the International Research Institute for Climate and Society Data Library ( http://iridl.ldeo.columbia.edu/SOURCES/.IRI/.FD/.ECHAM4p5/.History/.MONTHLY/ ). We also use the NCEP–NCAR reanalysis and the Interim European Centre

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Johnna M. Infanti and Ben P. Kirtman

newly formed multi-institutional, multimodel ensemble system for intraseasonal-to-interannual (ISI) prediction, which includes models from nine institutional partners ( Kirtman et al. 2014 ). The choice of this particular set of models is motivated by the availability of phase-1 data and the potential for inclusion of additional fields and improvements to be made with later phases. The NMME system is used in real time by the National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction

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Shahrbanou Madadgar and Hamid Moradkhani

( Najafi et al. 2012 ). ESP can also be generated by driving the hydrologic model with the climate forecast. To predict the seasonal flow and generate the ESPs, this study uses the Precipitation-Runoff Modeling System (PRMS; Leavesley et al. 1983 ), which is a distributed-parameter watershed model developed by the U.S. Geological Survey (USGS) to simulate the effects of various combinations of climate, land use, soil type, etc. on the hydrologic response of the watersheds. The watershed model is

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