Our 4D-VAR data assimilation system is developed by the MEXT K7 project. The JAMSTEC-Kyoto-University collaborative consortium have conducted a global ocean synthesis of in-situ temperature and salinity observations, satellite altimetry and a global ocean general circulation model through 4-dimensional variational (4D-VAR) data assimilation to obtain a dynamically self-consistent 4-dimensional integrated dataset. A 4D-VAR adjoint approach[1,2] can precisely determine the time-trajectory of the ocean states, and thus can provide comprehensive analysis fields		FlowChart: Ocean Reanalysis Product
in superb quality through 4-dimensional dynamical interpolation of in-situ observations for water temperature, salinity and sea surface height anomaly, as obtained from various instrumental sources. Such a synthesis efforts will contribute to exciting progress in climate research activities.

General features

Our ocean-data assimilation system is based on a global oceanic general circulation model (OGCM), version3 of the GFDL Modular Ocean Model (MOM) [3]. The horizontal resolution is 1° in both latitude and longitude,which is equipped with several sophisticated schemes; e.g., the Gent and McWilliams (GM) scheme for isopycnal mixing [4]. (c.f., Masuda et al.,2003 )
The adjoint codes of the OGCM were obtained using the Tangent linear and Adjoint Model Compiler (TAMC)[5] and the Transformation of Algorithms in Fortran (TAF)[6].
In the 4D-VAR approach, optimized 4-dimensional datasets are sought by minimizing a cost function [7,8].
(Note that the turbulent closure model is not included in our adjoint modeling scheme.)

History of the experiment

The 1^st Version

The model is equipped with the nonlocal K Profile Parameterization (KPP) [9] for mixed layer physics and quicker advection scheme. It has 36 vertical levels spaced from 10 m near the sea surface to 400 m at the bottom. To generate a first guess field, this model was executed by using ten-daily interannual forcings. For the surface momentum, sensible, long/short-wave radiative, and fresh water fluxes in this simulation run, data from the 6-hourly National Centers for Environmental Prediction Department of Energy Atmospheric Model Intercomparison Project (NCEP-DOE-AMIP-Ⅱ) dataset have been used. Latent heat flux was estimated from the NCEP's Optimally Interpolated Sea Surface Temperature (OISST) field by applying the commonly used bulk formulae. The assimilated elements in this version are temperature and salinity from the World Ocean Database 2001 (WOD01), Reynolds SST, and sea-surface dynamic-height anomaly data derived from TOPEX/Poseidon altimetry. In addition, the climatologies of the World Ocean Database 1998 (WOD98) were used as background data for regions without observational coverage. All observational data were averaged onto 1° by 1° bins and then compiled as series of 10-day means for the surface data and monthly means for the subsurface data. The assimilation window is 10 years covering 1991-2000. This version is identical to the K7 product.

The 2^nd Version

The model is the same as that for 1^st ver. First guess field is generated by using the same ten-daily interannual forcings as in the 1^st version but for the short-wave radiative flux, for which the International Satellite Cloud Climatology Project dataset was used. The assimilated elements in this version are temperature and salinity from the Fleet Numerical Meteorology and Oceanography Center (FNMOC) dataset, Reynolds SST, OISST values, and Argo float data [10] from the Coriolis Data Center. Sea-surface dynamic-height anomaly data and the climatologies of temperature/salinity is the same as in the 1^st ver. The assimilation window is 19 years for each dataset. The product covers 1987-2004 ( version.2).(c.f., Masuda et al., 2006)

The 3^rd Version

The model is the same as that for 1^st ver. To generate a first guess field for this data assimilation experiment, the model was executed by using a bland-new 10-daily interannual forcings. The 6-hourly JRA-25 datasets have been used for the surface momentum, heat and fresh water fluxes. These are provided by the cooperative research project "JRA-25" for long-term reanalysis by the Japan Meteorological Agency (JMA) and the Central Research Institute of the Electric Power Industry (CRIEPI) of Japan. The assimilated elements in this study are temperature and salinity archived by the FNMOC, together with OISST values and Argo float data from the Coriolis Data Center, and sea-surface dynamic-height anomaly data derived from high-precision multi-satellite altimetry products which were produced by Ssalto/Duacs (Segment Sol multimissions d'ALTimétrie, d'Orbitographie et de localisation précise/Data Unification and Altimeter Combination System) and distributed by Aviso (Archiving, Validation and Interpretation of Satellite Oceanographic data), with support from CNES (Centre National d'études Spatiales). The climatologies of temperature/salinity is the same as in the 1^st ver. The dataset consists of two assimilation runs whose window is 13 and 17 years. Additional cost function for the mergence is newly introduced. The product covers 1981-2006 ( version.3). (c.f., Masuda et al., 2009)

The Current Version

The OGCM was newly developed for this experiment. In particular, for the higher representation of a deep ocean state, sophisticated parameterization schemes was incorporated; the bottom boundary layer scheme [11] and a Noh mixed layer scheme [12] with major physical parameter values determined through a variational optimization procedure [13]. The horizontal resolution is the same 1° but with 46 vertical levels.
In order to enhance the representation of the deep ocean where in-situ observations are spatially and temporally sporadic, the assimilated observations are specially compiled.
The assimilated elements are historical hydrographic data of temperature and salinity from the ENSEMBLES (EN3) data set which was quality controlled using a comprehensive set of objective checks developed at the Hadley Centre of the UK Meteorological Office [14]. This dataset is largely composed of observations from the World Ocean Database 2005 [15] and supplemented by data from the GTSPP (Global Temperature and Salinity Profile Program) and Argo autonomous profiling floats. In addition of EN3 dataset, recent independent MIRAI RV profiles are simultaneously assimilated. Sea-surface dynamic-height anomaly data derived from high-precision multi-satellite altimetry products distributed by Aviso is also assimilated as in the 3^rd ver.
The assimilation window is 50 years. The product covers (1957)-2006.

Reanalysis Products The obtained reanalysis dataset shows good consistency with previous knowledge of climate variability in the ocean. The left figure shows the longitude-time distribution of SST averaged within 2°N - 2°S from 1986 to 2004. The well-known ENSO events (1986/87, 1991/1992, 1997/98) are well reproduced in the obtained field. The root mean square difference value between the observed time-evolution of Nino3 SST and the assimilated one reduces to nearly the half of that for the simulation case. The reanalysis fields also successfully capture the observed patterns of ocean circulation and surface air-sea heat fluxes. For example, the right figure shows the vertical cross section of the zonal velocity comparing with direct ocean velocity records for the subsurface layers by the Tropical Atmosphere-Ocean (TAO) mooring array [16, 17].

Temperature: 1986-2004

Zonal velocity:1996-1999 in comparison with TAO ADCP.

Available variables	potential temperature, salinity, horizontal velocity (u, v)
region	quasi-global (75°S - 80°N)
resolution	horizontal 1° × 1°, vertical 36 levels
period	1990-2000 (ver.1): Available for download from here 1987-2004 (ver.2): 1990-2006 (ver.3.1): Available for download from here. 1980-1992 (ver.3.2): Not open yet. If you would like to use this data, please contact us including your name, affiliation, and purpose. The e-mail contact form is here.

[1]	Y. Sasaki, Mon. Weather Rev., 98, 875(1970)
[2]	C. Wunsch, The Ocean Circulation Inverse Problem, Cambridge Univ. Press, New York, 442 pp (1996).
[3]	R. C. Pacanowski, S. M. Griffies, The MOM 3 Manual, Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, USA, p.680 (1999).
[4]	P. R. Gent, J. C. McWilliams, J. Phys. Oceanogr., 20, 150 (1990).
[5]	R. Giering, T. Kaminski, Recipes for Adjoint Code Construction, ACM Trans. On Math. Software, 24 (4), 437 (1998).
[6]	R. Giering, T. Kaminski, Applying TAF to generate efficient derivative code of Fortran 77-95 programs, Proceedings in Applied Mathematics and Mechanics, 2 (1), 54 (2003).
[7]	J. Marotzke et al., J. Geophys. Res., 104, c12, 29529 (1999).
[8]	D. Stammer et al., J. Geophys. Res., 107, C9, 3118 (2002).
[9]	W. G. Large et al., Rev. Geophys., 32, 363 (1994).
[10]	Gould, Deep Sea Res., II, 52, 529 (2005)
[11]	H. Nakano, N. Suginohara, J. Phys. Oceanogr., 32, 1209 (2002).
[12]	Y. Noh, Geophys. Res. Lett., 31, L23305 (2004).
[13]	D. Menemenlis et al., Mon. Weath. Rev., 133, 1224 (2005).
[14]	B. Ingleby, M. Huddleston, J. Mar. Sys., 65, 158 (2007).
[15]	Boyer et al., World Ocean Database 2005, NOAA Atlas NESDIS 60, US Gov. Print. Off., Washington DC, (2006).
[16]	S. P. Hayes et al., Bull. Am. Meteorol. Soc., 72, 339347 (1991).
[17]	M. J. McPhaden et al., J. Geophys. Res., 103, C7, 14169 (1998).

• Masuda, S., T. Awaji, N. Sugiura, Y. Ishikawa, K. Baba, K. Horiuchi, and N. Komori (2003): Improved estimates of the dynamical state of the North Pacific Ocean from a 4 dimensional variational data assimilation, Geophys. Res. Lett., 30, 16, 1868.
• Masuda, S., T. Awaji, N. Sugiura, T. Toyoda, Y. Ishikawa, K. Horiuchi, 2006: Interannual Variability of Temperature Inversions in the Subarctic North Pacific, Geophy. Res. Lett., 33, L24610, doi:10.1029/2006GL027865.
• Masuda, S., T. Awaji, T. Toyoda, Y. Shikama, and Y. Ishikawa (2009), Temporal evolution of the equatorial thermocline associated with the 1991-2006 ENSO, J. Geophys. Res., 114, C03015, doi:10.1029/2008JC004953.

• K7 Database
• K7 Ocean State Estimate based on 4D-VAR Ocean Data Assimilation

• World Ocean Dataset 1998 (WOD98)
• World Ocean Database 2001 (WOD01)
• NCEP Reynolds SST
• TOPEX/POSEIDON
• EN3
• OI SST

• Application to Fishery Resource Management
• Estimates of Heat/Mass Transport
• North Pacific Eastern Subtropical Mode Water
• Coupled Data Assimilation