Webinar Details

Title: Ocean-atmosphere coupled model development based on dynamical and data-driven paradigms   

Speaker: Jing-Jia Luo (NUIST, China) 

Date: December 10, 2025
Time: 07:00 UTC

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Abstract:

The advancement of ocean-atmosphere coupled models is fundamental to climate simulation, prediction, and projection. Historically, this field has been dominated by dynamical modeling, a paradigm exemplified by frameworks like SINTEX-F. Development in this area primarily focuses on correcting systemic biases, such as the Pacific cold tongue error, and increasing spatial resolution to resolve critical mesoscale processes. However, these improvements are often computationally prohibitive and yield diminishing returns in overall model performance.

In parallel, a new paradigm is emerging through Artificial Intelligence (AI) and deep learning. While still in its nascent stages, this data-driven approach learns directly from observations or model output, offering a computationally efficient alternative. Pioneering systems like the OCAR DL model demonstrate the potential to emulate complex ocean circulations at a fraction of the cost of traditional models. Despite ongoing challenges—particularly in ensuring physical consistency and generalizability—the integration of AI, either as standalone emulators or hybrid components within dynamical frameworks, holds significant promise. This synergy is poised to accelerate model development, overcome long-standing bottlenecks, and unlock new frontiers in climate modeling.

Jing-Jia Luo
Professor & Director, Institute for Climate and Application Research (ICAR) & Institute of AI for Meteorology, Academic Dean, School of AI and Future Technology, Nanjing University of Information Science andTechnology, Nanjing, China.

Senior Research Scientist (2011-2018): Centre for Australian Weather and Climate Research (CAWCR), Bureau of Meteorology, Australia.
Post-doc, Research Scientist and Senior Scientist (2001--2011): Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Japan.
Ph. D. of Science (Physical Oceanography): March 2001, Department of Earth and Planetary Science, the University of Tokyo, Tokyo, Japan.

Research interest: climate dynamics, climate model development, climate prediction, AI application in climate forecast.

Personal website for scientific publications: https://scholar.google.com/citations?user=WT6Zn94AAAAJ&hl=en