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Manfred Wenzel and Jens Schröter

be neglected locally (e.g., Antonov et al. 2002 ; Wenzel and Schröter 2002 ). This gives a further limitation to the estimated flux. To adjust the model to the data, the adjoint method is employed, which is a variational optimization method. The control parameters of this optimization are the model’s initial temperature and salinity state as well as the forcing fields (wind stress, air temperature, and surface freshwater flux), whereas the first-guess forcing is taken from the monthly National

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A. Köhl, D. Stammer, and B. Cornuelle

(GGM01S; Tapley et al. 2003 ) to constrain the time-mean dynamic surface topography. In setting up the cost function, J , the mean and time-dependent components of surface elevation were separated, thus isolating errors owing to the geoid from the distinctly different ones in the time-evolving components. The lower part of Fig. 1 shows the control variables that are being adjusted during the optimization so as to bring the model into consistency with the data. In the present calculations those

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Victor Zlotnicki, John Wahr, Ichiro Fukumori, and Yuhe T. Song

within the available months are missing (see the GRACE Web site: ). Data collection started in April 2002, but the satellite software was reconfigured several times during 2002 so as to remove detected problems; hence, the 2002 data are of somewhat lower quality and are not used here. 3. BP data–model consistency and formal errors Given the above caveats, the actual data results are much more reasonable than one might fear. We first compare GRACE “BP” with BP time series

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D. Roemmich, J. Gilson, R. Davis, P. Sutton, S. Wijffels, and S. Riser

the 8751 Argo profiles used here span the period from July 2003 to June 2005. Locations of Argo profiles are shown in Fig. 2 (gray dots). Argo delayed-mode quality control procedures ( Wong et al. 2003 ) were used to detect and adjust the slow salinity drift that occurred in some floats. Results of the present analysis are not sensitive to those adjustments. Dynamic height (DH) maps were drawn from WOCE and Argo data using an objective mapping procedure ( Bretherton et al. 1975 ). For both sets

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Nelson G. Hogg and Daniel E. Frye

1. Introduction In the late 1960s engineers at the Woods Hole Oceanographic Institution developed the vector averaging current meter (VACM; McCullough 1975 ) and this instrument became the Institution’s standard for making horizontal velocity measurements on subsurface moorings. Mooring technology was also under active development and by the 1970s had become increasingly reliable to the point where 2-yr measurements have become routine ( Heinmiller and Walden 1973 ). Although data retrieval

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D. E. Harrison and Mark Carson

in the more sparsely sampled regions of the rest of the ocean may not be well estimated. Quality control of this dataset poses all of the familiar ocean dataset challenges. There are unflagged extreme outliers (above 30°C and below −2°C at 100, 300, 500 m) in WOD01 that are simple to exclude; about 5% of the 1° × 1° regions in our subsequent analysis contained such outliers. Standard deviations were recomputed after removing the extreme outliers and the observations were then filtered against

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Dimitris Menemenlis, Ichiro Fukumori, and Tong Lee

–NCAR winds are unrealistically low in coastal regions. The quality of the NCEP–NCAR wind stress data near Gibraltar Strait can be assessed through comparison with observations. Dorman et al. (1995) report along-strait winds above Gibraltar with standard deviations ranging from 7.5 m s −1 in Punta Cires, Morocco, to 11.4 m s −1 at Castilla, Spain. Dorman et al. (1995) also report standard deviations for cross-strait winds ranging from 2.1 m s −1 at Punta Cires to 4.3 m s −1 at Castilla. The

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