We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …

Climate science has been revolutionized by the combined effects of an exponential growth
in computing power, which has enabled more sophisticated and higher-resolution …

Closure problems are omnipresent when simulating multiscale systems, where some
quantities and processes cannot be fully prescribed despite their effects on the simulation's …

Governing equations are foundations for modelling, predicting, and understanding the Earth
system. The Earth system is undergoing rapid change, and the conventional approaches for …

Earth system models suffer from various structural and parametric errors in their
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …

Ocean mesoscale eddies are often poorly represented in climate models, and therefore,
their effects on the large scale circulation must be parameterized. Traditional …

Ocean models at intermediate resolution (1/4°), which partially resolve mesoscale eddies,
can be seen as Large eddy simulations of the primitive equations, in which the effect of …

Mesoscale eddies are poorly represented in climate ocean models, and therefore their
effects on the large scale circulation must be parameterized. Classical parameterizations …

Defining end-to-end (or online) training schemes for the calibration of neural sub-models in
hybrid systems requires working with an optimization problem that involves the solver of the …

Different approaches to using data-driven methods for subgrid-scale closure modeling have
emerged recently. Most of these approaches are data-hungry, and lack interpretability and …