Minisymposium Presentation

The rise of machine learning (ML) has seen many scientists seeking to incorporate these techniques into numerical models. Doing so presents a number of challenges, however. The Institute of Computing for Climate Science (ICCS) has explored this problem in the context of coupling ML components/parameterisations into climate models. In this talk we will explore a number of challenges in this area, how ICCS has tackled them, and what has been learnt in the process. We will present FTorch, a library developed by ICCS to bridge the gap between Fortran (in which many large physics models are written) and PyTorch (in which much ML is performed) and lower the technical barrier to scientists seeking to leverage ML in their work. Case studies have been performed using both CPU and GPU architectures from laptops up to HPC systems. We will reflect on the design challenges of coupling ML parameterisations to large numerical models and outline a framework guidelines following software design principles to aid in this process. Finally we will discuss ongoing work using the Community Earth System Model (CESM) to re-deploy a neural net trained using a high-resolution model with a different grid and variables to a new setting.