  Global climate models typically operate at a grid resolution of hundreds of
kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds,
precipitation, and gravity waves (GWs). Model representation of these processes
and their sources is essential to the global circulation and planetary energy
budget, but subgrid scale contributions from these processes are often only
approximately represented in models using parameterizations. These
parameterizations are subject to approximations and idealizations, which limit
their capability and accuracy. The most drastic of these approximations is the
"single-column approximation" which completely neglects the horizontal
evolution of these processes, resulting in key biases in current climate
models. With a focus on atmospheric GWs, we present the first-ever global
simulation of atmospheric GW fluxes using machine learning (ML) models trained
on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an
alternative to traditional single-column parameterizations. Using an Attention
U-Net-based architecture trained on globally resolved GW momentum fluxes, we
illustrate the importance and effectiveness of global nonlocality, when
simulating GWs using data-driven schemes.
