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Lisan Yu, Xiangze Jin, and Robert A. Weller

number of heat exchange processes at the air–sea interface. The processes include solar radiation, outgoing longwave radiation, sensible heat transfer by conduction and convection, and latent heat release by evaporation of sea surface water. In general, these heat flux components are estimated by using bulk flux algorithms with surface meteorological variables obtained from one of the following sources: numerical weather prediction (NWP) reanalysis outputs ( Kalnay et al. 1996 ; Uppala et al. 1999

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Joaquim Ballabrera-Poy, Eric Hackert, Raghu Murtugudde, and Antonio J. Busalacchi

PIRATA array. We will follow the nomenclature of Miller et al. (1995) . An optimal set of mooring locations is going to be identified by assimilating sea level data using a full KF applied on a coarse grid. Figure 2 compares the (finer) numerical grid of the ocean model with the (coarse) grid used in the data assimilation algorithm (i.e., the grid over which the diagonal of the error covariance matrix 𝗣 is defined). In the figure, the dots illustrate the grid spacing of the error covariance

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Rui-Jin Hu and J. Stuart Godfrey

wind and differential heating: Part I. Description of the three-dimensional velocity and density fields. J. Atmos. Sci. , 25 , 945 – 967 . Da Silva , A. M. , C. Young , and S. Levitus , 1994 : Algorithms and Procedures . Vol. 1, Atlas of Surface Marine Data 1994 , NOAA Atlas NESDIS 6, 83 pp . Gill , A. E. , 1982 : Atmosphere–Ocean Dynamics . Academic Press, 662 pp . Godfrey , J. S. , 1973 : Comparison of the East Australian Current with the western boundary current in Bryan

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Hae-Kyung Lee Drbohlav, Silvio Gualdi, and Antonio Navarra

made by a weighted average between the model field and the observation. In other words, observational data with a time window of 15 day (7 day to either side) are weight averaged with the model field at every time step. The weight given to the observation increases as the time difference between the observation and model simulation approaches zero. The benefit of this algorithm is that whenever/wherever the observational data are not available, the model provides the solution. Thus, the

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Gabriel A. Vecchi and Matthew J. Harrison

time each region of the ocean will be seeded and since deployment of profiling floats is determined by many factors that are beyond our ability to model (ship availability, funding, etc.), we have not endeavored to build more complicated algorithms, which we believe would likely give only the illusion of realism. Except on their surfacing dates, each Argo float is advected by the model daily mean current at 1000 m, linearly interpolated to the location of the drifter; on the surfacing days the

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Vinu K. Valsala and Motoyoshi Ikeda

application to tropical ocean models. J. Phys. Oceanogr. , 24 , 2156 – 2179 . da Silva , A. , A. C. Young , and S. Levitus , 1994 : Algorithms and Procedures . Vol. 1, Atlas of Surface Marine Data 1994 , NOAA Atlas NESDIS 6, 83 pp . Gordon , A. L. , and R. A. Fine , 1996 : Pathways of water between the Pacific and Indian Oceans in the Indonesian Seas. Nature , 379 , 146 – 149 . Haines , M. A. , R. A. Fine , M. E. Luther , and Z. Ji , 1999 : Particle trajectories in an

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R. J. Murray, Nathaniel L. Bindoff, and C. J. C. Reason

many individual maxima and minima but show considerable differences in the patterns for particular groups of years and in the amplitudes of year-to-year temperature variations. In both SST analyses, EOFs were used to fill gaps in the data, but with different algorithms. The differences between the curves reflect this and give some indication of the errors that may have been introduced as a result of truncating the EOF series or of extrapolating modern modes of variability in areas that have been

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