Advancing Artificial Neural Network Parameterisation for Atmospheric Turbulence using a Variational Multiscale Model

M. Janssens, S. J. Hulshoff
2021 Journal of Advances in Modeling Earth Systems  
Intercomparison studies of existing atmospheric General Circulation Models (GCMs) exhibit a large spread in their predictions of atmospheric 2 CO E concentration at which a 2 K temperature rise with respect to preindustrial times is reached (Boucher et al., 2013) . This uncertainty imposes a considerable cost on society (Hope, 2015) . The largest contributor to this uncertainty concerns the response of low clouds to warming (Dufresne & Bony, 2008) , as their impact is large (Boucher et al.,
more » ... ) , but the turbulent phenomena that drive much of their evolution lie far below the resolutions that computational limits will allow GCMs to resolve in the coming decades . Such clouds are currently approximated by phenomenological unresolved scales models, "parameterizations", of considerably lower fidelity than the resolved simulation.
doi:10.1029/2021ms002490 fatcat:3sxnwtabgbgthcpdunor55ywta