A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data

Understand

Chaotic systems, such as turbulent flows, are ubiquitous in science and engineering.

• However, their study remains a challenge due to the large range scales, and the strong interaction with other, often not fully understood, physics.
• As a consequence, the spatiotemporal resolution required for accurate simulation of these systems is typically computationally infeasible, particularly for applications of long-term risk assessment, such as the quantification of extreme weather risk due to climate change.
• While data-driven modeling offers some promise of alleviating these obstacles, the scarcity of high-quality simulations results in limited available data to train such models, which is often compounded by the lack of stability for long-horizon simulations.