Seasonal Prediction

SINTEX-F1 system (Luo et al. 2005)

Luo, J.-J., S. Masson, S. Behera, S. Shingu, and T. Yamagata (2005), Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts, J. Clim., 18, 4474–4494

other publications

SINTEX-F2 system (Doi et al. 2016)

Doi, T., S. K. Behera, and T. Yamagata (2016), Improved seasonal prediction using the SINTEX-F2 coupled model, J. Adv. Model. Earth Syst., 8, 1847–1867, doi:10.1002/2016MS000744.

SINTEX-F2-3DVAR system (Doi et al. 2017)

Doi, T., A. Storto, S. K. Behera, A. Navarra, and T. Yamagata, 2017: Improved prediction of the Indian Ocean Dipole Mode by use of subsurface ocean observations. J. Climate, 30, 7953-7970.

108-members ensemble system (Doi et al. 2019)

Doi, T., S. K. Behera, and T. Yamagata (2019), Merits of a 108-Member Ensemble System in ENSO and IOD Predictions. J. Climate, 32, 957–972,

SINTEX-F Family ( F1 + F2 + F2-3DVAR) system (Doi et al. 2020)

Doi, T., S. K. Behera, and T. Yamagata, 2020: Predictability of the Super IOD Event in 2019 and Its Link With El Niño Modoki.Geophysical Research Letters, 47, e2019GL086713.

SINTEX-F2si system (Morioka et al. 2019)

Morioka, Y., Doi, T., Iovino, D. et al. Role of sea-ice initialization in climate predictability over the Weddell Sea. Sci Rep 9, 2457 (2019)


The SINTEX-F1/F2 seasonal climate prediction systems were run by the Earth Simulator at JAMSTEC. We are grateful to Drs. Wataru Sasaki, Jing-Jia Luo, Sebastian Masson, Andrea Storto, Antonio Navarra, and our European colleagues of INGV/CMCC, L’OCEAN, and MPI for their contribution to developing the prototype of the systems.

This research was supported by the Environment Research and Technology Development Fund (2–1405) of the Ministry of the Environment, Japan, the Japan Agency for Medical Research and Development (AMED) and Japan International Cooperation Agency (JICA) through the Science and Technology Research Partnership for Sustainable Development (SATREPS) project for iDEWS South Africa, and JSPS KAKENHI Grant Number 16H04047 and 16K17810.

S.Varlamov and Y.Morohoshi kidnly helped us to make this website.