Numerical modeling plays an indispensable role in modern-day oceanographic investigations. The number of numerical codes and approaches that are available to address oceanographic problems is ever increasing, and so is their complexity. In particular, the push towards mesoscale and submesoscale-resolving resolutions brings about new physical and computational challenges.  New supercomputing architectures are pushing towards exascale and AI-specific computations, while machine learning and artificial intelligence methods are both augmenting and replacing traditional methods. 

For this session, we invite contributions that address algorithms, performance, verification, and parameterizations within computational oceanography and sea ice modeling. Potential topics of interest include, but are not limited to: new approaches to spatial discretizations and time stepping; applications on new architectures; validation of physical parameterizations in idealized and realistic settings; tools for analysis and visualization of large data sets; machine learning methods for parameterizations; and artificial intelligence-based model emulation.