AUTHOR=Bocquet Marc 

TITLE=Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation

JOURNAL=Frontiers in Applied Mathematics and Statistics

VOLUME=Volume 9 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2023.1133226

DOI=10.3389/fams.2023.1133226

ISSN=2297-4687

ABSTRACT=The outstanding breakthroughs of deep learning in computer vision and natural language processing have been the horn of plenty for many recent developments in the climate sciences.
These methodological advances currently find applications to subgrid-scale parametrisation, data-driven model error correction, model discovery, surrogate modelling, and many other uses.
In this perspective article, I will review recent advances of the field, specifically in the thriving subtopic defined by the intersection of dynamical systems in the geosciences, data assimilation, and machine learning, with striking applications to physical model error correction. I will give my take on where we are in the field and why we are there. Then, key perspectives will be discussed. I will describe several technical obstacles to implementing these new techniques in a high-dimensional, possibly operational system. I will also discuss open questions about the combined use of data assimilation and machine learning and about the short- versus longer-term representation of the surrogate (i.e. neural network-based) dynamics, and finally about uncertainty quantification in this context.