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

expect CGCMs would provide more dynamical and objective drought forecasts for land regions such as Africa and the transition of advances in climate research to climate services. In fact, CGCM-based seasonal forecasting of drought has become operational in recent years at many national weather centers. Several previous studies have used these operational systems to hindcast specific drought events ( Luo and Wood 2007 ; Yuan et al. 2011 ; Dutra et al. 2012 ), while other work has systematically

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Eric F. Wood, Siegfried D. Schubert, Andrew W. Wood, Christa D. Peters-Lidard, Kingtse C. Mo, Annarita Mariotti, and Roger S. Pulwarty

temperature forecasts initialized in early May 2012 from the NMME, including NOAA’s operational Climate Forecast System, version 2 (CFSv2; Saha et al. 2010 ). Most NMME models predicted a precipitation and temperature anomaly in the central United States, but with marked intermodel variability in their extent, location, and intensity. For some other regions (e.g., the wet and cool Pacific Northwest), the observed anomalies were absent in virtually all model predictions. Fig . 1. (left) May

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Shahrbanou Madadgar and Hamid Moradkhani

incorporated to model the Bayesian network of sequential events. Therefore, a future drought status, given the earlier drought conditions, is estimated using copula functions within a Bayesian network. Aside from the probabilistic approach presented in this study to develop the seasonal drought forecasting, we also use the forecast tools incorporated by the operational streamflow forecast centers to evaluate how various forecast techniques are in agreement in seasonal drought forecasting. Ensemble

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

( Seager et al. 2007 ; Seager and Vecchi 2010 ) is quite likely currently masked by the presence of large-amplitude natural variability on interannual to multidecadal time scales ( Hoerling et al. 2013 ). Fig . 16. Time history of observed JJA temperature (bars, K) and precipitation (line, mm day −1 ) anomalies for the TexMex region and the 1950–2011 period. 9. How well was the 2010/11 drought forecast by operational seasonal-to-interannual prediction systems? Understanding the dynamical causes of

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Johnna M. Infanti and Ben P. Kirtman

prediction tool that provides real-time forecasts that adhere to the CPC operational schedule as well as supplying hindcast data. Model information and references are given in Table 1 , and all models are dynamical global general circulation climate prediction models. Additional information on the NMME project is given in Kirtman et al. (2014) . In this analysis we have considered precipitation and SST data from phase 1 (NMME-1) of the NMME project. Phase 2 (NMME-2) will bring additional fields and

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

developed without an early warning. The National Oceanic and Atmospheric Administration (NOAA) operational seasonal drought outlook, issued 17 May 2012 for the subsequent June–August period ( Fig. 9 , top), did not predict a tendency toward increasing drought over the central Great Plains. Instead, surface moisture conditions were expected to improve over Iowa and western Nebraska. Otherwise, the majority of the central Great Plains was forecast to experience near-normal moisture conditions. Only over

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

. From 2007 to 2013, Lloyd-Hughes and Saunders (2007) operated a global drought monitor, which was updated on a monthly basis and used station-based precipitation from the Global Precipitation Climatology Centre (GPCC; Schneider et al. 2014 ) and air temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF). Their system used the standardized precipitation index (SPI) and the Palmer drought severity index (PDSI). Princeton University operates an African drought monitor

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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

is spun up by running from 1979 to 2012 twice and then reinitializing the model in 1979. Noah LSM is used operationally at the National Centers for Environmental Prediction (NCEP) as the land component of regional and global weather forecasting models and at the Air Force Weather Agency (AFWA) in the offline land analysis system. More recent upgrades to the model have focused on improving the snow physics within Noah ( Barlage et al. 2010 ; Livneh et al. 2010 ; Wang et al. 2010 ) by providing

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Youlong Xia, Michael B. Ek, David Mocko, Christa D. Peters-Lidard, Justin Sheffield, Jiarui Dong, and Eric F. Wood

simulated or observed (i.e., precipitation) data. The climatology was used to generate NLDAS drought monitor (NLDASDM) products to provide to the U.S. Drought Monitor (USDM) author group. To keep the consistency of these operational products, we still use the 28-yr climatology without including recent extreme events. However, a sensitivity test shows small effects on CONUS calibration, although it may have a significant effect on a given specific region (e.g., Texas or the Great Plains). Updating the

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Jiarui Dong, Mike Ek, Dorothy Hall, Christa Peters-Lidard, Brian Cosgrove, Jeff Miller, George Riggs, and Youlong Xia

agriculture. Knowledge of various snowpack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project ( Mitchell et al. 2004 ). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions

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