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Xing Yuan, Eric F. Wood, Nathaniel W. Chaney, Justin Sheffield, Jonghun Kam, Miaoling Liang, and Kaiyu Guan

– 2296 , doi:10.1175/2007JCLI2100.1 . Svoboda, M. , and Coauthors , 2002 : The Drought Monitor . Bull. Amer. Meteor. Soc. , 83 , 1181 – 1190 . Tarhule, A. , and Lamb P. J. , 2003 : Climate research and seasonal forecasting for West Africans: Perceptions, dissemination, and use? Bull. Amer. Meteor. Soc., 84, 1741–1759, doi:10.1175/BAMS-84-12-1741 . Trenberth, K. E. , 1984 : Some effects of finite sample size and persistence on meteorological statistics. Part II: Potential

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Johnna M. Infanti and Ben P. Kirtman

using a directly coupled atmosphere–ocean general circulation model . Mon. Wea. Rev. , 133 , 2972 – 2995 , doi:10.1175/MWR3016.1 . Doblas-Reyes, F. J. , Déqué M. , and Piedelievre J.-P. , 2000 : Multi-model spread and probabilistic seasonal forecasts in PROVOST . Quart. J. Roy. Meteor. Soc. , 126 , 2069 – 2087 , doi:10.1256/smsqj.56704 . Durkee, J. D. , Frye J. D. , Fuhrmann C. M. , Lacke M. C. , Jeong H. G. , and Mote T. L. , 2007 : Effects of the North Atlantic

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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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M. Hoerling, J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. Schubert, and R. Seager

in the seasonal forecasts of the 2010/11 southern plains drought, especially during the winter and spring season (e.g., Hoerling et al. 2013 ). The current study builds upon a body of climate sensitivity studies and physical reasoning that a conditioning of U.S. summer rainfall by particular large-scale oceanic conditions may also exist (e.g., Schubert et al. 2009 ; Findell and Delworth 2010 ). Yet, contrary to ENSO effects, the magnitude of that conditioning is still highly uncertain and

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Martha C. Anderson, Christopher Hain, Jason Otkin, Xiwu Zhan, Kingtse Mo, Mark Svoboda, Brian Wardlow, and Agustin Pimstein

future global drought monitoring efforts, providing a time-continuous suite of hydrologic variables generated from a unified modeling system or ensemble of systems. In preparation, intercomparisons between prognostic and diagnostic indicators provide insight regarding relative regional and seasonal performance. This study focuses on diagnostic remote sensing indicators that are responsive to short-term environmental changes, since early warning capabilities are limited in current drought monitoring

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

1. Introduction In the fall of 2010 the U.S. Drought Monitor showed no areas of the United States in drought, a situation essentially unique since the Drought Monitor was initiated in 1999. However, even as the Drought Monitor was showing unusually moist conditions across the country, seasonal-to-interannual forecasts were predicting a return to dry conditions across the southern United States and northern Mexico in the winter ahead. Those forecasts were based on predictions of a developing La

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Bart Nijssen, Shraddhanand Shukla, Chiyu Lin, Huilin Gao, Tian Zhou, Ishottama, Justin Sheffield, Eric F. Wood, and Dennis P. Lettenmaier

which a monthly standardized precipitation evapotranspiration index (SPEI) is calculated based on monthly precipitation and potential evapotranspiration from the Climatic Research Unit (CRU) time series (TS) version 3.20 data ( Jones and Harris 2012 ). Sohn et al. (2013) describe the development of a forecasting system for seasonal prediction of droughts and floods ( ), which uses a merged station and satellite-based dataset [Climate Anomaly Monitoring System–Outgoing Longwave

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Sujay V. Kumar, Christa D. Peters-Lidard, David Mocko, Rolf Reichle, Yuqiong Liu, Kristi R. Arsenault, Youlong Xia, Michael Ek, George Riggs, Ben Livneh, and Michael Cosh

; Koster et al. 2010 ). In many mid- and high-latitude regions, the seasonal water storage and the associated spring snowmelt dominate the local hydrology. Snowpack in the western United States, for example, is the largest component of water storage ( Mote et al. 2005 ) and is the primary source of water supply. Maurer and Lettenmaier (2003) examined the importance of initial soil moisture and snow states toward the predictability of runoff fields. Their results showed that these states contribute

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

continent. Examples of such SST patterns include those associated with the El Niño–Southern Oscillation (ENSO) on seasonal-to-interannual time scales and those associated with the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) on decadal and longer time scales. The impacts of these SST patterns over the United States and the physical mechanisms by which they act have been extensively studied using observations (e.g., Ting and Wang 1997 ; Nigam et al. 2011 ; Hu and

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Paul A. Dirmeyer, Jiangfeng Wei, Michael G. Bosilovich, and David M. Mocko

a clear seasonality to both the terrestrial and oceanic sources of evaporation supplying precipitation over land. The strongest sources tend to be in low latitudes over land and adjacent open oceans, although a number of areas in the northern midlatitudes become prominent in June–August (JJA). The strength of oceanic sources correlates strongly to the distance to shore, but there are exceptions (e.g., the closed 30 kg m −2 contour in the North Pacific during JJA). Fig . 1. (top) Total seasonal

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