DOI: 10.17596/0000106
How to Cite
The following is an example of data citation including creator(s), publication year, title, publisher, DOI and access date.
Osafune, S., Doi, T., Masuda, S., Sugiura, N., & Hemmi, T. (2014). Estimated state of ocean for climate research (ESTOC). JAMSTEC. https://doi.org/10.17596/0000106
Abstract
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), which is equipped with several sophisticated schemes; e.g., Noh scheme for mixed layer physics, the Gent and McWilliams (GM) scheme for isopycnal mixing, and quicker advection scheme. The horizontal resolution is 1o in both latitude and longitude. In our original system, the OGCM has 45 vertical levels spaced from 10 m near the sea surface to 400 m at the bottom, with a bottom boundary layer. 4D-VAR adjoint data assimilation approach is applied to conduct data synthesis experiments. The adjoint codes of the OGCM were obtained using the Tangent linear and Adjoint Model Compiler (TAMC) and the Transformation of Algorithms in Fortran (TAF). In the 4D-VAR approach, optimized 4-dimensional datasets are sought by minimizing a cost function.Physical parameters: This system assimilates diverse observations such as subsurface temperature and salinity from EN4 dataset compiled by the Hadley Centre of the UK Meteorological Office, sea surface temperature (SST) from the Optimally Interpolated SST dataset and Extended Reconstructed SST dataset, and sea-surface dynamic-height anomaly data derived from high-precision multi-satellite altimetry products distributed by Aviso. The control variables are surface fluxes (for net-heat, fresh water, and momentum) and oceanic initial conditions. The assimilation window is more than 53 years from 1957.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. The assimilated elements include the climatological monthly mean nitrate from WOA, monthly mean ocean color data from SeaWiFS, annual mean chlorophyll-a from WOA98 as a pseudo-detritus, and BGC float observations.
The detailed settings, such as the equipped model schemes, the assimilated elements, and the assimilation period, etc. depend on its version, and can be found on each version’s website.
How to Access
English | https://www.godac.jamstec.go.jp/estoc/e/ | |
Japanese | https://www.godac.jamstec.go.jp/estoc/j/ |
The information is also available at the following site(s).
English | https://www.godac.jamstec.go.jp/data_catalog/view/metadata?key=ESTOC&lang=en | |
Japanese | https://www.godac.jamstec.go.jp/data_catalog/view/metadata?key=ESTOC&lang=ja |
Related Information
Link to CiNii Research
Full Metadata
Title | Estimated state of ocean for climate research (ESTOC) |
Creator(s) | Satoshi Osafune Toshimasa Doi Shuhei Masuda Nozomi Sugiura Tadashi Hemmi |
Publication year | 2014 |
Publisher | JAMSTEC |
Related Identifier | 10.1029/2019JC015513 [DOI] [IsCitedBy] 10.1002/2015GL064538 [DOI] [IsDocumentedBy] 10.1002/2015MS000462 [DOI] [IsDocumentedBy] https://doi.org/10.1007/s10872-020-00587-x [DOI] [IsCitedBy] |
Resource type | Dataset/Scientific data |
Description | [Abstract] 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), which is equipped with several sophisticated schemes; e.g., Noh scheme for mixed layer physics, the Gent and McWilliams (GM) scheme for isopycnal mixing, and quicker advection scheme. The horizontal resolution is 1o in both latitude and longitude. In our original system, the OGCM has 45 vertical levels spaced from 10 m near the sea surface to 400 m at the bottom, with a bottom boundary layer. 4D-VAR adjoint data assimilation approach is applied to conduct data synthesis experiments. The adjoint codes of the OGCM were obtained using the Tangent linear and Adjoint Model Compiler (TAMC) and the Transformation of Algorithms in Fortran (TAF). In the 4D-VAR approach, optimized 4-dimensional datasets are sought by minimizing a cost function.Physical parameters: This system assimilates diverse observations such as subsurface temperature and salinity from EN4 dataset compiled by the Hadley Centre of the UK Meteorological Office, sea surface temperature (SST) from the Optimally Interpolated SST dataset and Extended Reconstructed SST dataset, and sea-surface dynamic-height anomaly data derived from high-precision multi-satellite altimetry products distributed by Aviso. The control variables are surface fluxes (for net-heat, fresh water, and momentum) and oceanic initial conditions. The assimilation window is more than 53 years from 1957.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. The assimilated elements include the climatological monthly mean nitrate from WOA, monthly mean ocean color data from SeaWiFS, annual mean chlorophyll-a from WOA98 as a pseudo-detritus, and BGC float observations. The detailed settings, such as the equipped model schemes, the assimilated elements, and the assimilation period, etc. depend on its version, and can be found on each version’s website. |
Funder(s) |
Ministry of Education, Culture, Sports, Science and Technology
[https://ror.org/048rj2z13]
[https://kaken.nii.ac.jp/grant/KAKENHI-PLANNED-15H05819/]
[https://kaken.nii.ac.jp/grant/KAKENHI-ORGANIZER-15H05817/]
Japan Society for the Promotion of Science [https://ror.org/00hhkn466] [https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-26287114/] |
DOI Status
Last Update | 2024-02-20 |
Registered | 2019-04-17 |