Climate System Monitoring
El Niño Monitoring
NWP Model Prediction
Climate in Japan
Since 1995, the Japan Meteorological Agency (JMA) has operated its global ocean data assimilation system to monitor El Niño and Southern Oscillation (ENSO) conditions. The system is also used in oceanic initialization for the coupled atmosphere-ocean model, which in turn has been used for ENSO forecasts since 1998 and for seasonal forecasts since 2010. Several upgrades have been made to the system, as shown in Table 1. The latest implementation is MOVE/MRI.COM-G3 (the Multivariate Ocean Variational Estimation/Meteorological Research Institute Community Ocean Model - Global version 3) developed by JMA's Meteorological Research Institute.
The setup consists of a variational analysis system (MOVE; Usui et al. 2006; Usui et al. 2015) and an ocean general circulation model (MRI.COM; Tsujino et al. 2017).
|Implemented date||Upgrades & Changes|
|February 1995||"ODAS" (Kimoto et al. 1997) implemented into operation.|
|July 2003||"ODAS" (Ishii et al. 2003) upgraded.|
|March 2008||MOVE/MRI.COM-G (Usui et al. 2006) implemented.|
|June 2015||MOVE/MRI.COM-G2 (Toyoda et al. 2013) implemented.|
|February 2022||MOVE/MRI.COM-G3 implemented.|
MOVE/MRI.COM-G3 has two subsystems with different horizontal resolutions: lower-resolution 4DVAR analysis (G3A) and initialization of the oceanic part for coupled forecasts (G3F; Table 2). The main analysis scheme has been upgraded to 4DVAR (Usui et al. 2015), in contrast to the 3DVAR-FGAT (Lorenc and Rawlins 2005) adopted in MOVE/MRI.COM-G2. The G3F forecast model covers global oceans using a higher resolution of 0.25 degrees, which is initialized using G3A analysis-based temperature and salinity data with incremental updates (IAU; Bloom et al. 1996). Figure 1 outlines the system.
|Name of subsystem||G3A (4DVAR analysis)||G3F (initialization of forecast)|
|Horizontal resolution||Zonally 1.0 degree and meridionally 0.3-0.5 degrees||0.25 degrees|
|Vertical resolution||60 layers plus a bottom boundary layer||60 layers|
|Oceanic analysis||4DVAR with built-in IAU. Control variables are coefficients of vertical empirical orthogonal function (EOF) modes (Fujii and Kamachi 2003) proportional to temperature and salinity increments above 2000 m||G3A temperature and salinity analyses are incorporated with IAU above 2000 m|
|Sea ice analysis||3DVAR concentration analysis, incorporated with IAU (Toyoda et al. 2016)|
|Assimilated data||In-situ temperature and salinity observations, satellite altimetry, sea surface temperature analysis, and sea ice concentration analysis||Sea ice concentration analysis|
|Analysis cycle interval||5 days. The increment is added to the model in the 5-day IAU period preceding the 5-day assimilation window.||5 days|
|Update interval||Daily update with 5 staggered analysis streams|
Both subsystems have the fourth version of MRI.COM (Tsujino et al. 2017), which features z* vertical coordinates for distribution of sea level variations to all levels. These coordinates enable a surface layer thinner than sea level anomalies, while the bottom topography can be as shallow as several meters.
Sea ice concentration is also assimilated in MOVE/MRI.COM-G3 (Toyoda et al. 2011, 2016). The 3DVAR scheme first combines data from daily objective analysis of sea ice concentration (Matsumoto et al. 2006) with information on background sea ice concentration from the model forecast. The analysis sea ice concentration increment is then applied to gradually adjust the model's sea ice with IAU. No information on sea ice is passed from G3A to G3F, and sea ice concentration is assimilated independently in G3A and G3F.
Another change has also been made in the assimilated sea surface temperature (SST) product. The older MOVE/MRI.COM-G2 assimilated Centennial in-situ Observation-Based Estimates of the variability of Sea Surface Temperatures (COBE-SST; Ishii et al. 2005), which is an objective analysis of SST dependent on in-situ observation. MOVE/MRI.COM-G3, meanwhile, assimilates Merged satellite and in-situ data Global Daily Sea Surface Temperature objective analysis (MGDSST; Kurihara et al. 2006), which is a JMA product for GHRSST (https://www.ghrsst.org/). The introduction of satellite observation via MGDSST enhances the reproduction of mesoscale perturbations such as tropical instability waves.
Data production is also enhanced to provide initial conditions for the coupled model more promptly and frequently. Although MOVE/MRI.COM-G3 has a five-day data window, five staggered analysis streams are applied so that initial conditions can be determined every day (valid at 00 UTC). The operational system features early analysis on the same day to prepare initial conditions and cycle analysis with a four-day delay to perform uniform analysis. The early part involves the use of atmospheric Global Early Analysis (JMA 2019) for surface forcing, whereas the delayed part involves the more uniform Japanese Reanalysis for Three-Quarters of a Century (JRA-3Q; Kobayashi et al. 2021). The 4DVAR system also generates ensemble perturbations approximating analysis error covariances using minimization histories. With this capability, early analysis provides five-member initial ensembles for the coupled model every day.
The output of MOVE/MRI.COM-G3 is applied for the initial conditions of the coupled atmosphere-ocean model (JMA/MRI-CPS3) used in ENSO prediction and seasonal forecasting. It will also be used to monitor global oceanographic conditions once the reanalysis is complete.
Bloom, S. C., L. L. Tacks, A. M. daSilva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256-1271.
Fujii, Y. and M. Kamachi, 2003: Three-dimensional analysis of temperature and salinity in the equatorial Pacific using a variational method with vertical coupled temperature-salinity empirical orthogonal function modes. J. Geophys. Res., 108(C9), 3297, doi:10.1029/2002JC001745.
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Ishii, M., A. Shouji, S. Sugimoto, and T. Matsumoto, 2005: Objective Analyses of Sea-Surface Temperature and Marine Meteorological Variables for the 20th Century using ICOADS and the Kobe Collection. Int. J. Climatol., 25, 865-879.
JMA, 2019: Outline of the operational numerical weather prediction at the Japan Meteorological Agency, Appendix to WMO Technical Progress Report on the Global Data-processing and Forecasting System (GDPFS) and Numerical Weather Prediction (NWP) Research. Japan Meteorological Agency.
Kimoto, M., I. Yoshikawa, and M. Ishii, 1997: An ocean data assimilation system for climate monitoring. J. Meteor. Soc. Japan, 75, 1-16.
Kobayashi, S, Y. Kosaka, J. Chiba, T. Tokuhiro, Y. Harada, C. Kobayashi, and H. Naoe, 2021: JRA-3Q: Japanese Reanalysis for Three Quarters of a Century. WCRP-WWRP Symposium on Data Assimilation and Reanalysis, ECMWF annual seminar 2021.
Kurihara, Y., T. Sakurai, and T. Kuragano, 2006: Global daily sea surface temperature analysis using data from satellite microwave radiometer, satellite infrared radiometer and in-situ observations. Weather Service Bulletin, 73, Special issue, s1-s18 (in Japanese).
Matsumoto, T., Ishii, M., Fukuda, Y., and Hirahara, S., 2006: Sea ice data derived from microwave radiometer for climate monitoring. In 14th Conference on Satellite Meteorology and Oceanography, P2.21. AMS. Available online at https://ams.confex.com/ams/Annual2006/techprogram/paper_101105.htm.
Lorenc, A. C. and F. Rawlins, 2005: Why does 4D-Var beat 3D-Var? Q. J. R. Meteorol. Soc., 131, 3247-3257.
Toyoda, T., T. Awaji, N. Sugiura, S. Masuda, H. Igarashi, Y. Sasaki, Y. Hiyoshi, Y. Ishikawa, T. Mochizuki, T. Sakamoto, H. Tatebe, Y. Komuro, T. Suzuki, T. Nishimura, M. Mori, Y. Chikamoto, S. Yasunaka, Y. Imada, M. Arai, M. Watanabe, H. Shiogama, T. Nozawa, A. Hasegawa, M. Ishii, and M. Kimoto, 2011: Impact of the assimilation of sea ice concentration data on an atmosphere-ocean-sea ice coupled simulation of the arctic ocean climate. SOLA, 7, 37-40.
Toyoda, T., Y. Fujii, T. Yasuda, N. Usui, T. Iwao, T. Kuragano, and M. Kamachi, 2013: Improved Analysis of Seasonal-Interannual Fields Using a Global Ocean Data Assimilation System. Theor. Appl. Mech. Japan, 61, 31-48.
Toyoda, T., Y. Fujii, T. Yasuda, N. Usui, K. Ogawa, T. Kuragano, H. Tsujino, and M. Kamachi, 2016: Data assimilation of sea ice concentration into a global ocean-sea ice model with corrections for atmospheric forcing and ocean temperature fields. J. Oceanogr., 72(2), 235-262.
Tsujino, H., H. Nakano, K. Sakamoto, S. Urakawa, M. Hirabara, H. Ishizaki, and G. Yamanaka, 2017: Reference manual for the Meteorological Institute Community Ocean Model version 4 (MRI.COMv4), Technical Reports of the Meteorological Research Institute, 80, doi:10.11483/mritechrepo.80.
Usui, N., Y. Fujii, K. Sakamoto, and M. Kamachi, 2015: Development of a Four-Dimensional Variational Assimilation System toward Coastal Data Assimilation around Japan. Mon. Wea. Rev., 143, 3874-3892.
Usui N., S. Ishizaki, Y. Fujii, H. Tsujino, T. Yasuda, and M. Kamachi, 2006: Meteorological Research Institute multivariate ocean variational estimation (MOVE) system: Some early results. Advances in Space Res., 37, 806-822.