🚧 Under Development 🚧
Atmosphere
Parameterization of moist process in the atmosphere
People involved: Paul O’Gorman, Griffin Mooers, Pierre Gentine
Much of the uncertainty in climate-model projections for surface precipitation and winds comes from the need to parameterize subgrid processes such as moist convection. We are developing and implementing new parameterizations for these processes using machine learning trained on high-resolution simulations. We aim to develop parameterizations that are robust, stable and physically consistent. See Yuval and O’Gorman 2020 and Yuval, O’Gorman and Hill 2021 for examples of these research projects.
Figure: Structure of a machine-learning parameterization of subgrid moist processes in the atmosphere. The structure is chosen so that the parameterization conserves energy and water.
Parameterization of the boundary layer
People involved: Alexander Connolly, Pierre Gentine
Boundary layer turbulence parameterization remains a major source of uncertainties in climate models, including for low-level clouds. We aim to develop a new approach to the boundary layer parameterization by targeting high-order closure terms in the turbulence representation, leveraging Large-Eddy Simulations and machine learning/symbolic regression.
