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Kingtse C. Mo, Jae-Kyung E. Schemm, and Soo-Hyun Yoo

1. Introduction Long-lasting drought has an enormous impact on the nation’s economy and society. Skillful drought prediction can mitigate devastating economic effects on people and ecosystems. To improve drought forecasts, one needs to understand the causes that trigger and sustain drought. Because drought implies prolonged rainfall and soil moisture deficits, they are often modulated by low-frequency sea surface temperature anomalies (SSTAs). In the Pacific, decadal trends of SSTAs in the

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Caio A. S. Coelho and Lisa Goddard

; Coelho et al. 2006 ) and therefore provide comparative perspective for CMIP3 simulations. For this reason, this study is performed using the following climate models: Three Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) ( Palmer et al. 2004 ) coupled seasonal forecast models [European Centre for Medium-Range Weather Forecasts (ECMWF), Météo-France, and Met Office (UKMO)], which have long hindcasts extending back to the mid-twentieth century and

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Randal D. Koster, Hailan Wang, Siegfried D. Schubert, Max J. Suarez, and Sarith Mahanama

an impact on drought-induced warming, the effect should be clearly seen in the differences between PwAn and PcAn. For context, we sometimes also show or use data from a third experiment—Pacific neutral, Atlantic neutral (PnAn)—in which the unmodified climatological seasonal cycle of SSTs are imposed. Five AGCMs performed the DWG experiments: (i) the AGCM of the Global Modeling and Assimilation Office (GMAO) seasonal forecasting effort ( Bacmeister et al. 2000 ), run at 3.75° × 3° for 50 yr (200

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Siegfried Schubert, David Gutzler, Hailan Wang, Aiguo Dai, Tom Delworth, Clara Deser, Kirsten Findell, Rong Fu, Wayne Higgins, Martin Hoerling, Ben Kirtman, Randal Koster, Arun Kumar, David Legler, Dennis Lettenmaier, Bradfield Lyon, Victor Magana, Kingtse Mo, Sumant Nigam, Philip Pegion, Adam Phillips, Roger Pulwarty, David Rind, Alfredo Ruiz-Barradas, Jae Schemm, Richard Seager, Ronald Stewart, Max Suarez, Jozef Syktus, Mingfang Ting, Chunzai Wang, Scott Weaver, and Ning Zeng

-to-Interannual Prediction Project (NSIPP-1) AGCM. The National Oceanic and Atmospheric Administration’s (NOAA’s) Climate Prediction Center, with support from the Climate Test Bed, contributed runs made with the Global Forecast System (GFS) AGCM, and NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) contributed runs made with the Atmosphere Model version 2.1 (AM2.1) AGCM. The Lamont-Doherty Earth Observatory of Columbia University contributed runs made with the National Center for Atmospheric Research (NCAR) Community

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Alfredo Ruiz-Barradas and Sumant Nigam

. 2008 ); 2) NCAR’s Community Climate Model (CCM3) run by the Lamont-Doherty Earth Observatory group ( Kiehl et al. 1998 ; Seager et al. 2005 ); 3) the National Aeronautics and Space Administration Geophysical Fluid Dynamics Laboratory (NASA GSFC) Seasonal-to-Interannual Prediction Project (NSIPP-1) model ( Bacmeister et al. 2000 ; Schubert et al. 2004 ); 4) the NOAA–National Centers for Environmental Prediction’s (NCEP) Global Forecasting System (GFS) model ( Campana and Caplan 2009 ); and 5) the

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Philip J. Pegion and Arun Kumar

in the experiments were the NASA Seasonal-to-Interannual Prediction Program (NSIPP1) AGCM ( Bacmeister et al. 2000 ; Schubert et al. 2004a ), the NCAR Community Climate Model (CCM3) ( Kiehl et al. 1998 ; Seager et al. 2005 ), version 2.1 of the GFDL Atmospheric Model (AM2.1) ( Delworth et al. 2006 ; The GFDL Global Atmospheric Model Development Team 2004 ; Milly and Shmakin 2002 ), version 3.5 of the NCAR Community Atmosphere Model (CAM3.5), and the NCEP Global Forecast System (GFS) ( Campana

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Kirsten L. Findell and Thomas L. Delworth

3.5 (CAM3.5) ( ; Neale et al. 2008 ; Oleson et al. 2008 ; Stöckli et al. 2008 ); the Climate Group of Lamont-Doherty Earth Observatory (LDEO) at the Columbia University used the NCAR CCM3 ( Kiehl et al. 1998 ); and the Climate Prediction Center (CPC) of the National Centers for Environmental Prediction (NCEP) used the NCEP Global Forecast System (GFS; Campana and Caplan 2005 ). The five models will be referred to by the names GFDL, NSIPP, CAM3.5, CCM3

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Kerry H. Cook and Edward K. Vizy

to produce the July wind speed maximum. During boreal winter, the meridional gradient is primarily strengthened by heating over South America, in the northernmost part of the South America monsoon system. Muñoz et al. (2008) , using a lower-resolution reanalysis product [the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40)], also find that heating over the topography of northern South America intensifies the CLLJ; however, unlike this study, they suggest that

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Scott J. Weaver, Siegfried Schubert, and Hailan Wang

surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. , 108 , 4407 . doi:10.1029/2002JD002670 . Ruiz-Barradas , A. , and S. Nigam , 2005 : Warm season rainfall variability over the U.S. Great Plains in observations, NCEP and ERA-40 reanalyses, and NCAR and NASA atmospheric model simulations. J. Climate , 18 , 1808 – 1830 . Saha , S. , and Coauthors , 2006 : The NCEP Climate Forecast System. J. Climate , 19 , 3483

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Yochanan Kushnir, Richard Seager, Mingfang Ting, Naomi Naik, and Jennifer Nakamura

-forced experiments of the U.S. CLIVAR DWG. As indicated above, NCAR’s CCM3 used by our Lamont group was one of the AGCMs participating in the CLIVAR study, as was the NSIPP1 model, the NCEP Global Forecast System (GFS) model, the NCAR Community Atmosphere Model, version 3 (CAM3.5), and the Geophysical Fluid Dynamics Laboratory (GFDL) Atmosphere Model version 2.1 (AM2.1). These models were forced with fixed, idealized SST anomalies in the equatorial Pacific (associated with ENSO), the North Atlantic (resembling

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