Session

DescriptionAs the most important tools for ocean and climate process understanding, forecasting, and projection, the ocean and climate models have achieved great progress in last decade. The main stream of ocean and climate model development focuses on higher resolution, more accurate parameterizations of unresolved physical processes, and including more biogeochemical processes. At the same time, exascale high-performance computing (HPC) has gradually matured. How to use supercomputers more efficiently, such as multi core processor, hyperthread, large cache, high bandwidth process communication structure and high speed I/O functionality, becomes a critical condition for model development now. Meanwhile, the development of information technologies has led to a new era in computation, affecting almost all fields in science and engineering. Recently, increasing with large amounts of simulation and observation data, machine learning, especially deep learning shows its potential on ocean and climate simulations, such as subgrid parameterization schemes, ENSO prediction, typhoon track forecasting, etc. The convergence of simulation and deep learning brings challenges and opportunities. This minisymposium encompasses the interdisciplinary research on ocean, climate simulation/forecasting/prediction/projection and deep learning - from algorithms and new frameworks through to exciting results, outlooks and perspectives.