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D. J. Lea, I. Mirouze, M. J. Martin, R. R. King, A. Hines, D. Walters, and M. Thurlow

(NEMOVAR; Mogensen et al. 2009 , 2012 ), which is run in 3DVAR-FGAT mode. The implementation here is based on that of the FOAM system ( Waters et al. 2014 ). The scheme assimilates in situ and satellite SST data, satellite altimeter sea level anomaly (SLA) data, satellite sea ice concentration data, and in situ temperature and salinity profiles from various sources including Argo, moored buoys, and temperature profiles from XBTs and marine mammals. Increments are applied to the model using the

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Norihisa Usui, Yosuke Fujii, Kei Sakamoto, and Masafumi Kamachi

) system ( Usui et al. 2006 ). The MOVE system is based on a multivariate 3DVAR analysis scheme with a vertically coupled temperature–salinity ( T – S ) empirical orthogonal function (EOF) modal decomposition of a background error covariance matrix ( Fujii and Kamachi 2003b ). The global version and the western North Pacific version of the MOVE system (MOVE-3DVAR-G and MOVE-3DVAR-WNP) have been used as operational systems at JMA since 2008. The main target of MOVE-3DVAR-G is El Niño

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Robin J. T. Weber, Alberto Carrassi, and Francisco J. Doblas-Reyes

initialized for decadal forecasts . Climate Dyn. , doi: 10.1007/s00382-015-2678-y , in press . Smith , D. , and J. Murphy , 2007 : An objective ocean temperature and salinity analysis using covariances from a global model. J. Geophys. Res. , 112 , C02022 , doi: 10.1029/2005JC003172 . Smith , D. , A. Cusack , A. Colman , C. Folland , G. Harris , and J. Murphy , 2007 : Improved surface temperature prediction for the coming decade from a global climate model . Science , 317

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