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Tongtiegang Zhao, James C. Bennett, Q. J. Wang, Andrew Schepen, Andrew W. Wood, David E. Robertson, and Maria-Helena Ramos

1. Introduction Ensemble forecasts of seasonal precipitation from coupled ocean–atmosphere general circulation models (GCMs) have mostly replaced traditional statistical forecasts as the basis of operational outlooks issued by many national weather services. For example, the National Centers for Environmental Prediction (NCEP) in the United States has operated its Climate Forecast System (CFS) since 2004 ( Saha et al. 2014 ), the European Centre for Medium-Range Weather Forecasts (ECMWF) has

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Suranjana Saha, Shrinivas Moorthi, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, David Behringer, Yu-Tai Hou, Hui-ya Chuang, Mark Iredell, Michael Ek, Jesse Meng, Rongqian Yang, Malaquías Peña Mendez, Huug van den Dool, Qin Zhang, Wanqiu Wang, Mingyue Chen, and Emily Becker

assimilation system (GODAS) operational at NCEP in 2003 ( Behringer 2007 ) that provided the ocean initial states, NCEP’s Global Forecast System (GFS) operational in 2003 that was the atmospheric model run at a lower resolution of T62L64, and the Modular Ocean Model, version 3 (MOM3), from the Geophysical Fluid Dynamics Laboratory (GFDL). The CFSv1 system worked well enough that it became difficult to terminate it, as it was used by many in the community, even after the CFSv2 was implemented into

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Arlan Dirkson, William J. Merryfield, and Adam Monahan

projections that show these reductions continuing into the future under increased greenhouse gas emission scenarios ( Stroeve et al. 2012 ). Methodologies that have been used for seasonal sea ice forecasts include statistical regression-based methods, fully coupled atmosphere–ocean global climate models (AOGCMs), and heuristic approaches ( Stroeve et al. 2014b ; Guemas et al. 2016 ). However, few centers currently produce these forecasts operationally. Arctic sea ice has been shown to be predictable on

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Liqiang Sun, Huilan Li, Stephen E. Zebiak, David F. Moncunill, Francisco D. A. D. S. Filho, and Antonio D. Moura

interested in temperature forecast due to its small interannual variability in the Nordeste. This is, to our knowledge, the first such prediction system to be used operationally. The principal purpose of this paper is to describe this downscaling prediction system and validate its real-time performance during 2002–04. This introduction is followed by a summary of the construction of a high-resolution observed rainfall dataset in section 2 . The prediction system is described in section 3 , forecast

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V. Krishnamurthy, Jessica Meixner, Lydia Stefanova, Jiande Wang, Denise Worthen, Shrinivas Moorthi, Bin Li, Travis Sluka, and Cristiana Stan

1. Introduction The prediction of the instantaneous state of the weather system is reliable only up to about 10 days (e.g., ECMWF 2018 ), mainly because of the limitations imposed by chaos (e.g., Lorenz 1965 , 1982 ) and the instabilities involved. Beyond the weather time scale, the operational prediction centers instead have been providing forecasts of seasonal mean climate (e.g., Saha et al. 2006 , 2014 ; Stockdale et al. 2011 ). The basis for extended time-averaged predictions comes

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J. Segschneider, D. L. T. Anderson, and T. N. Stockdale

Medium-Range Weather Forecasts (ECMWF) for the present quasi-operational ocean analysis, is to assimilate in situ temperature data ( Smith et al. 1991 ; Ji et al. 1998 ). In situ data, however, exist only for a limited number of points. Relatively dense observations are provided by the Tropical Atmosphere Ocean (TAO) buoy array in the equatorial Pacific, but in the Atlantic an ocean observation system is only now evolving, and few data are obtainable in real time for the Indian Ocean. Altimetry has

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Á. G. Muñoz, L. Goddard, S. J. Mason, and A. W. Robertson

considered feasible and it will be treated in a future work. 5. Real-time forecast constraints This section briefly describes some operational aspects of an experimental forecast system for s2s extreme rainfall scenarios in SESA. It is recommended to use the best predictors found in the present study (i.e., SST+MJO and weather types). As indicated, the required forecasts involve both the ECMWF (MJO) and CFSv2 (SST and weather types) models. The analysis performed in the present study involved data made

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Xiouhua Fu, June-Yi Lee, Bin Wang, Wanqiu Wang, and Frederic Vitart

conditions (e.g., SST, soil moisture, sea ice, etc.), has also been launched by a large number of operational and research centers around the world since the late 1980s ( Cane et al. 1986 ; Shukla 1998 ; B. Wang et al. 2009 ; Shukla et al. 2009 ; Lee et al. 2010 ). An obvious forecasting gap exists between weather forecast and seasonal prediction. The recurrent nature of tropical intraseasonal variability with a period of 30–60 days offers a golden opportunity to fill this gap ( Waliser et al. 2003

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Hye-Mi Kim, Peter J. Webster, Violeta E. Toma, and Daehyun Kim

subseasonal time scales. Benefiting from the significant improvement in the representation of the MJO in numerical models that has been made in the past decades, contemporary operational dynamical prediction systems produce useful forecast of the MJO up to 20–25 days of forecast lead time ( Vitart and Molteni 2010 ; Vitart et al. 2010 ; Rashid et al. 2011 ; Zhang and van den Dool 2012 ; Zhang et al. 2013 ; Vitart 2014 ; Wang et al. 2014 ). This is encouraging, but the prediction skill is lower than

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Michelle L. L’Heureux, Michael K. Tippett, and Anthony G. Barnston

Niño-4 (160°E–150°W) regions, which were identified in the 1980s. Barnston et al. (1997) identified the Niño-3.4 region on the basis of the relationship between SST and the Southern Oscillation index (SOI), and on the basis of climate variations influenced by ENSO, such as Atlantic hurricane activity. Today, the Niño-3.4 SST index and the oceanic Niño index (ONI), the 3-month running average of Niño-3.4, have become the backbone of operational monitoring and prediction of ENSO ( Trenberth 1997

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