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- Assimilation of sea level anomaly maps (from altimeter) :The detrended altimeter-derived sea level anomalies are combined with the bias-corrected model first-guess using the Cooper and Haines 1996 scheme to produce a first analysis.
- Assimilation of subsurface temperature (from ARGO,XBTs,Moorings): The result of the previoues step is then used as a first guess for a second assimilation step, where only subsurface temperature data are assimilated, and salinity is updated by imposing conservation of the model temperature/salinity (T/S) relationship (Troccoli et al 2002), while the sea level and velocity field remain unchanged.
- Assimilation of Salinity (from ARGO, Moorings): In a third assimilation step, the information provided by the salinity observations is used to modify the model T/S relationship. In this step, the T/S information is spread along isotherms following the scheme of Haines et al., 2006. Only salinity is modified in this step which results in the analysis.After this 3rd assimilation step, velocity updates are derived from the temperature and salinity increments imposing geostrophic balance (Burgers et al., 2002)
- Assimilation of global sea level trend (from from altimeter): Finally, the trend in global (area averaged) sea level is assimilated. By combining the altimeter-derived trend in global sea level with the model trend in global dynamic height, it is possible to make the partition between changes in the global volume and changes in the total mass. By doing so, the global fresh water budget is closed and the global surface salinity and sea level adjusted accordingly.
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2.2 The ocean model
The HOPE ocean model (Wolff et al., 1997) uses an Arakawa E grid horizontal discretization. Several modifications took place over the years at ECMWF (Balmaseda 2004, Anderson and Balmaseda 2006). The horizontal resolution was increased to 1 x 1 degrees with equatorial refinement, i.e., the meridional resolution increases gradually towards the equator, where it is 0.3 degrees in the meridional direction. There are 29 levels in the vertical, with a typical vertical thickness of 10 meters in the upper ocean compared to 20 levels. The vertical mixing is based on Peters et al., 1998. The barotropic solver, originally implicit, was made explicit as described in Anderson and Balmaseda (2006).
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n S3, the observations come from the quality controlled dataset prepared for the ENACT and ENSEMBLES projects until 2004 (Ingleby and Huddleston 2006), and from the GTS thereafter (ENACT/GTS). The OI scheme is now 3-dimensional, the analysis being performed at all levels simultaneously down to 2000m, where09.03.2007evel independently and only to 400m. In addition, the decorrelation scales depend on the density gradient, which favours the propagation of information along isopycnals. A pictorial view of the various data sets used in S3 is given in fig 1. The analysis of SST is not produced using the OI-Scheme. Instead, the model SSTs are strongly relaxed to analyzed SST maps. The maps are daily interpolated values derived from the OIv2 SST product (Reynolds et al 2002) from 1982 onwards. Prior to that date, the same SST product as in the ERA40 reanalysis was used.
In S3, altimeter data are assimilated for the first time in the ECMWF operational ocean analysis. The altimeter information is given by maps of merged satellite product, provided by Ssalto/DUACS and distributed by AVISO, with support from CNES. Twice a week (on Wednesday and on Saturday mornings) (1/3x1/3 degrees) Maps of Sea Level Anomaly (MSLA) for a merged product combining all satellites (Envisat, Jason, Topex/Poseidon, ERS2, GFO) using optimal interpolation and accounting for long wavelength errors are produced (Le Traon et al., 1998, Ducet et al., 2000)
Figure 1: Upper panel shows the surface forcing used in the ocean analysis and the initial conditions for the calibration hindcasts for S3. Lower panel shows the origin of the subsurface data surface temperature fields used.
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Balmaseda, M.A., A. Vidard and D.L.T. Anderson, 2007: The ECMWF System 3 Ocean Analysis System. ECMWF Technical Memorandum 508.
Bloom, S. C., Takacs, L. L., Da Silva, A. M. and Ledvina, D., 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256-1271.
Burgers G., M.Balmaseda, F.Vossepoel, G.J.van Oldenborgh, P.J.van Leeuwen, 2002: Balanced ocean-data assimilation near the equator. J Phys Oceanogr, 32, 2509-2519.
Cooper, M.C. and K. Haines, 1996: Data assimilation with water property conservation, J. Geophys. Res 101, C1, 1059-1077
Ducet, N., P.-Y. Le Traon, and G. Reverdin, 2000: Global high resolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and -2. J. Geophys. Res., 105, 19477-19498.
Haines K., J. Blower, J-P. Drecourt, C. Liu, A. Vidard, I. Astin, X. Zhou, 2006. Salinity Assimilation using S(T) relationships. Mon. Wea. Rev. Vol. 134, No. 3, pages 759-771.
Ingleby, B and M. Huddleston, 2007. Quality control of ocean temperature and salinity profiles - historical and real-time data. J. Mar. Sys., 65,158-175.
Le Traon, P.-Y., F. Nadal, and N. Ducet, 1998: An improved mapping method of multisatellite altimeter data. J. Atmos. Oceanic Technol., 15, 522-534.
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van Oldenborgh G. J., M.A. Balmaseda, L. Ferranti, T.N. Stockdale, D.L.T. Anderson: 2005. Evaluation of atmospheric fields from the ECMWF Seasonal Forecasts over a 15-year period. J. Clim., 18, No. 16, 3250-3269. See also corrections Vol 18,
Peters, H, Gregg, M C and Toole, J M, 1988: On the parameterization of equatorial turbulence. J. Geophys. Res., 93, 1199-1218.
Reynolds R., N Rayner, T Smith, D Stokes, W Wang 2002: An improved in situ and satellite SST analysis for climate. J Clim , 15, 1609-1625.
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Vitart, F., S. Woolnough, M.A. Balmaseda,2007: Prediction of the Madden-Julian Oscillation using a coupled GCM. Mon. Wea. Rev., In press.
Smith N., J Blomley, and G Meyers (1991) A univariate statistical interpolation scheme for subsurface thermal analyses in the tropical oceans. Prog in Oceanography,28, 219-256.
Wolff, J., E. Maier-Reimer and S. Legutke, 1997. The Hamburg Ocean Primitive Equation Model. Deutsches Klimarechenzentrum, Hamburg, Technical Report No. 13.
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