Japan Geoscience Union Meeting 2019

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-OS Ocean Sciences & Ocean Environment

[A-OS11] Continental-Oceanic Mutual Interaction: Planetary scale Material Circulation

Wed. May 29, 2019 9:00 AM - 10:30 AM 301A (3F)

convener:Yosuke Alexandre Yamashiki(Earth & Planetary Water Resources Assessment Laboratory Graduate School of Advanced Integrated Studies in Human Survivability Kyoto University), Yukio Masumoto(Graduate School of Science, The University of Tokyo), Swadhin Behera(Climate Variation Predictability and Applicability Research Group, Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001), Takanori Sasaki(Department of Astronomy, Kyoto University), Chairperson:Yukio Masumoto, Behera Swadhin(JAMSTEC)

9:15 AM - 9:30 AM

[AOS11-02] Climate predictability for societal applications including AI/ML

*Swadhin Behera1, Manali Pal2, J.V. Ratnam1, Rajib Maity2, Takeshi Doi1, Yushi Morioka1, Masami Nonaka1 (1.Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001, 2.Indian Institute of Technology, Kharagpur, India)

Keywords:ENSO , ENSO Modoki, Prediction

The tropical Indo-Pacific domain is important for global climate. Particularly, the warm pool region rooted in both basins plays an important role in the modulation of ocean and atmosphere variability on several spatio-temporal scales. While both basins share the extended warm pool, each of the two basins has its own modes of ocean and climate variations. Tropical Pacific Ocean is well-known for the El Niño/Southern Oscillation (ENSO) phenomenon, the influence of which is seen world-wide during ENSO occurrence years. Recently, another mode of climate variability called the ENSO Modoki is discovered in the tropical Pacific Ocean. The ENSO Modoki is distinct from ENSO in terms of its characteristics and global impacts. Therefore, predictability of ENSO and ENSO Modoki is important for reducing their impacts on the society. Coupled models like SINTEX-F has been predicting the ENSO Modokis quite well. Here an attempt is made to compare those predictabilities using predictions from an AI/ML based statistical approach.