Stop pretending you understand what it means or have looked at the PNAS paper.
‘McWilliams held a Research Fellowship in Geophysical Fluid Dynamics at Harvard from 1971-1974 and afterwards worked in the Oceanography Section at NCAR where he became a Senior Scientist in 1980. In 1994, while still retaining part-time appointment at NCAR, he began his work at UCLA where he became the Louis B. Slichter Professor of Earth Sciences in the Department of Atmospheric and Oceanic Sciences and the Institute for Geophysics and Planetary Physics. In 2002, McWilliams was elected to the National Academy of Sciences. Today, he continues his career in academia at UCLA.
James C. McWilliams primarily does research in computational modeling of the Earth’s oceans and atmospheres. McWilliams has written numerous papers from 1972 to the present[4], attempting to construct accurate models to describe the Earth’s fluid reservoirs. One of McWilliam’s most influential papers was a paper written in 1990 titled “Isopycnal mixing in ocean circulation models”, in which together with Peter R. Gent they proposed a subgrid-scale form of mesoscale eddy mixing on isopyncal surfaces for use in non-eddy resolving ocean circulation models[5].
McWilliams has contributed greatly to the development of accurate models of the Earth’s atmosphere and ocean, and his subjects of interest are maintenance of the general circulations; climate dynamics; geostrophically and cyclostrophically balanced (or slow manifold) dynamics in rotating, stratified fluids; vortex dynamics; planetary boundary layers; planetary-scale thermohaline convection; coherent structures of turbulent flows in geophysical and astrophysical regimes; magnetohydrodynamics; numerical methods; and statistical estimation theory.
More recently, he has helped develop a three-dimensional simulation model of the U.S. West Coast that incorporates physical oceanographic, biogeochemical, and sediment transport aspects of the coastal circulation. This model is being used to interpret coastal phenomena, diagnose historical variability in relation to observational data, and assess future possibilities.’
Relevant? God you are a twit.