Multiscale Reduced Order Modeling and Parameter Estimation for Climate Sciences

Abstract

Science challenge: Several problems in earth system modeling are dependent on highly multiscale phenomena, such as turbulence, where computational modeling is challenging and expensive. This issue is exacerbated in atmospheric and oceanic domains, due to inherent highdimensionality of the problem. One approach to this problem has been reduced order modeling (ROM); which aims to represent the key physics of the phenomena as a low-dimensional system. AI methods have huge potential in building accurate, stable ROMs and parameter estimation for these ROMs, as it requires extracting nonlinearities and patterns from simulation and/or observational data. Developing physics-based AI approaches specialized for the complexities of multiscale data, along with strategies to account for uncertainties, will revolutionize rapid modeling, analysis and decision making for earth system problems of practical interest.