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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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The first-guess is obtained from integrating the HOPE ocean model from one analysis time to the next, forced by ERA40/OPS fluxes (ERA40 fluxes from the period January 1959 to June 2002 and NWP operational analysis thereafter). In S2 the fluxes were from ERA15/OPS, but the wind stresses were not directly used: instead, the wind stress was derived from the analyzed winds using an off-line bulk formula. The representation of the upper ocean interannual variability is improved when using the ERA40 wind stress (Uppala et al., 2006), although the stresses are biased weak in the equatorial Pacific. The fresh water flux from ERA-40 (Precipitation - Evaporation, denoted P-E) is known to be inaccurate. S3 uses a better but by no means perfect estimate, obtained by 'correcting' the ERA-40 precipitation values (Troccoli and Kallberg 2004).
3. The Ocean Observations and Quality Control
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Subsurface temperature observations that are available in near-real-time are currently provided by the TAO/TRITON and PIRATA arrays in the equatorial region (McPhaden 1998, Servain et al 1998) and the global Volunteer Observing Ship (VOS) programme which provides XBT (eXpandable BathyThermographs) measurements mainly along merchant shipping routes. More recently, observations are provided by the ARGO network of drifting profilers. Salinity data is available from ARGO and from the TRITON moorings.
Prior to 2004, the temperature and salinity profiles come from the quality controlled ENACT/ENSEMBLES data set (Ingleby and Huddleston 2006). From January 2005, the observations come from the GTS (Global Telecommunication System). An automatic quality control procedure (an extension of Smith et al., 1991) is then performed in several stages:
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The trend in global sea level is quite substantial. If this trend is produced by thermal expansion due to global warming, it can not be represented by the ocean model: in absence of fresh water fluxes, most ocean models used for climate are volume preserving, since they make use of the Boussinesq approximation. Therefore, if not treated correctly, the trend in sea level can be a problem when assimilating altimeter observations. In S3, the global sea level trend is removed from the altimeter sea level anomalies before they are assimilated via the CH96 scheme. In S3, the global sea level trend is later assimilated as described in Balmaseda et al 2007.
3.3 Sea Surface temperature
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When in 2004 the monthly forecasting system became operational at ECMWF, it was necessary to produce near-real-time ocean initial conditions, since the typical 11 days delay of the reanalysis product was not adequate for the monthly system. To this end, an early delivery ocean analysis system was introduced in May 2004. Details of the timing of the near real time S2 ocean analysis are given in Balmaseda 2005. In what follows, we denote the Near Real Time stream as NRT, and for BRT we refer to the reanalysis, which runs 11 days Behind Real Time. The BRT and NRT analysis systems are almost identical: they only differ in the details of the assimilation cycle and in the earlier cut-off of the assimilation windows. The BRT product is a continuation of the historical ocean reanalysis, while the NRT only exists for the recent period
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Balmaseda, M.A., 2004: Ocean data assimilation for seasonal forecasts. ECMWF Seminar Proceedings. Seminar on Recent developments in data assimilation for atmosphere and ocean, 8-12 September 2003, 301-326.
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Balmaseda, M.A., D. Dee, A. Vidard and D.L.T. Anderson, 2007: A Multivariate Treatment of Bias for Sequential Data Assimilation: Application to the Tropical Oceans. Q. J. R. Meteorol. Soc., 133, 167-179.
Balmaseda, M.A., A. Vidard and D.L.T. Anderson, 2007: The ECMWF System 3 Ocean Analysis System. ECMWF Technical Memorandum 508.
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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.
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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.
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Levitus S NODC (Levitus) World Ocean Atlas 1998 data provided by the NOAA-CIRES Climate Diagno09.03.2007 at http://www.cdc.noaa.gov/
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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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Stockdale, T.N., M. A. Balmaseda and A. Vidard, 2006: Tropical Atlantic SST prediction with coupled ocean-atmosphere GCMs. J. Climate, 19, No. 23, 6047-6061.
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