Minisymposium Presentation

Numerical weather, ocean, and climate (together, Earth system) prediction has been a humanity’s essential activity toward minimizing the loss of human lives and damage to infrastructure. It has also heavily relied on Fortran since its inception in the late 1950s. Fast forward to 2024, virtually all weather, ocean, and climate models that are used for critical decision making by governments and businesses are implemented in Fortran, most typically a mix of legacy and modern dialects. Historically, these models and their developer communities have gone through several paradigm shifts, for example the introduction of vector supercomputers, then distributed-memory clusters, and finally, specialized hardware accelerators such as GPUs. At this moment, the looming paradigm shift, and likely the largest one to date, is the adoption of machine learning to emulate numerical models in part or in their entirety. Having spent a number of years deep in the Earth system model development world, Fortran advocacy and education, and collaborative research across academia, government, and industry sectors, I will share my perspectives on the key challenges that the Earth system enterprise faces in the context of software implementation, hardware architectures, and emerging machine learning techniques.