Estimated state of ocean for climate research Version:01a

Abbreviation: ESTOC

Data Overview

Our 4D-VAR data synthesis system is developed as a part of the Japan Agency for Marine-Earth Science and Technology (JAMSTEC)-Kyoto University collaborative program (known as "the K7 consortium"). The Ocean General Circulation Model is version 3 of the GFDL Modular Ocean Model (MOM3) [1], which is equipped with several sophisticated schemes; e.g., Noh scheme for mixed layer physics[2], the Gent and McWilliams (GM) scheme for isopycnal mixing [3], and quicker advection scheme with major physical parameter values determined through a variational optimization procedure [4]. The horizontal resolution is 1o in both latitude and longitude, with 46 vertical levels spaced from 10 m near the sea surface to 400 m at the bottom. This model is better able to reproduce ocean circulation processes and is expected to form a platform suitable for the use of the 4D-VAR adjoint model. 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.

Physical parameters:
4D-VAR adjoint data assimilation approach is applied [5, 6]. The adjoint codes of the OGCM were obtained using the Tangent linear and Adjoint Model Compiler (TAMC)[7] and the Transformation of Algorithms in Fortran (TAF)[8]. In the 4D-VAR approach, optimized 4-dimensional datasets are sought by minimizing a cost function [9,10]. The assimilated elements in this study are temperature and salinity from ENSEMBLES (EN3) dataset which was quality controlled using a comprehensive set of objective checks developed at the Hadley Centre of the UK Meteorological Office [11]. This dataset is largely composed of observations from the World Ocean Database 2005 [12] and supplemented by data from the GTSPP (Global Temperature and Salinity Profile Program) and Argo autonomous profiling floats[13]. In addition of EN3 dataset, recent independent MIRAI RV profiles are simultaneously synthesized. Sea-surface dynamic-height anomaly data derived from high-precision multi-satellite altimetry products distributed by Aviso is also incorporated. All observational data were averaged onto 1o by 1o bins and then compiled as series of 10-day means for the surface data and monthly means for the subsurface data. The control variables are surface fluxes (for net-heat, fresh water, and momentum) and oceanic initial conditions. The assimilation window is 53 years during 1957-2009.

Biogeochemical parameters:
The synthesis of available observations and a pelagic ecosystem model based on nitrogen cycle produces a dynamically self-consistent dataset. Optimized 4-dimensional datasets are sought by minimizing a cost function on the basis of Green's function approach [4]. The assimilated elements are the climatological monthly mean nitrate from WOA05, monthly mean ocean color data from SeaWiFS, and annual mean chlorophyll-a from WOA98 as detritus.

  • [1] R. C. Pacanowski, S. M. Griffies, The MOM 3 Manual, Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, USA, p.680 (1999).
  • [2] P. R. Gent, J. C. McWilliams, J. Phys. Oceanogr., 20, 150 (1990).
  • [3] Y. Noh, Geophys. Res. Lett., 31, L23305 (2004).
  • [4] D. Menemenlis et al., Mon. Weath. Rev., 133, 1224 (2005).
  • [5] Y. Sasaki, Mon. Weather Rev., 98, 875(1970)
  • [6] C. Wunsch, The Ocean Circulation Inverse Problem, Cambridge Univ. Press, New York, 442 pp (1996).
  • [7] R. Giering, T. Kaminski, Recipes for Adjoint Code Construction, ACM Trans. On Math. Software, 24 (4), 437 (1998).
  • [8] 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).
  • [9] J. Marotzke et al., J. Geophys. Res., 104, c12, 29529 (1999).
  • [10] D. Stammer et al., J. Geophys. Res., 107, C9, 3118 (2002).
  • [11] B. Ingleby, M. Huddleston, J. Mar. Sys., 65, 158 (2007).
  • [12] Boyer et al., World Ocean Database 2005, NOAA Atlas NESDIS 60, US Gov. Print. Off., Washington DC, (2006).
  • [13] Gould, Deep Sea Res., II, 52, 529 (2005)

Available Products

Variables, abbreviations Potential temperature [°C], tmp
Salinity [PSU], sal
Horizontal velocity u[m/s] v[m/s], vel
Surface heat flux [cal/m2/s], shf
Surface freshwater flux [m/s], sff
Wind stress τx[N/m2] τy[N/m2], tau
Nitrate [μmol/L], no3
Phytoplankton [μmol/L], pht
Detritus [μmol/L], det
Zooplankton [μmol/L], zoo
Dissolved inorganic carbon [μmol/kg], dic
Region Quasi-global (75°S-80°N)
Resolution Horizontal 1°x1°, Vertical 46 levels
Period 1957-2009 (Ver. 01a)
File format Monthly data in netcdf format:
"k7oda_[XXX]_[YYYY][MM]00_[VVV].nc"
"[XXX]" where "[XXX]" is model variable, "[YYYY]" year, "[MM]" month, and "[VVV]" version, respectively.

Reference papers to be cited

Masuda, S., T. Awaji, N. Sugiura, J. P.Matthews, T. Toyoda, Y.Kawai, T. Doi, S. Kouketsu, H. Igarashi, K. Katsumata, H. Uchida, T. Kawano, M. Fukasawa (2010), Simulated Rapid Warming of Abyssal North Pacific Waters, Science, 329, 319-322, DOI, 10.1126/science.1188703.

Kouketsu, S., T. Doi, T. Kawano, S. Masuda, N. Sugiura, T. Toyoda, H. Igarashi, Y. Kawai, K. Katsumata, H. Uchida, M. Fukasawa, and T. Awaji (2011), Deep ocean heat content changes estimated from observation and reanalysis product and their influence on sea level change, J. Geophys. Res., 116, C03012, doi:10.1029/2010JC006464.

Masuda, S., T. Doi, N. Sugiura, S. Osafune, and Yoichi Ishikawa (2013), Data Synthesis for Biogeochemical Variables by Using a 4 Dimensional Variational Approach, Earth Simulator annual report 2012.

Update History

2014-03-25
Ver.01a has been published.