Collaborative Institutional Lead
SciDAC Institute Investigator
Process coupling in Earth system models is a challenging, impactful, and yet under-addressed research topic. Our project combines a deep understanding of atmospheric physics with advanced computational methods to improve process coupling in the Energy Exascale Earth System Model (E3SM) at a wide range of spatial and temporal resolutions. This includes both the coupling among atmospheric phenomena and the coupling between the atmosphere and the Earth’s surface.
The research proposed for the project includes:
- improving process coupling in the atmosphere by reorganizing the parameterizations and developing new numerical coupling strategies,
- reducing numerical instabilities near the Earth’s surface by tightening the atmosphere–surface coupling,
- improving the coupling of local and nonlocal processes in stratiform and convective cloud microphysics using new spatiotemporal discretization methods,
- improving the treatment of sharp gradients associated with the atmospheric boundary layer using Godunov-type finite volume methods in a turbulence parameterization with higher-order closure, and
- investigating coupling-related issues causing numerical instabilities in global atmospheric simulations involving machine-learning-based parameterizations of moist physics.
New data analytics workflows are developed to accelerate the development. The overall objectives of the project are:
- to reduce time integration errors in the E3SM atmosphere model to a level below the magnitudes of errors associated with spatial resolutions and model formulation,
- to improve solution convergence for E3SM’s cloud microphysics and turbulence parameterizations, and
- to improve the numerical stability of climate simulations involving machine-learning-based parameterizations.
The project addresses several common and long-standing challenges in Earth system modeling. Our findings and new numerical methods will improve the numerical accuracy and computational efficiency of E3SM’s predictions of multi-decadal climate and water cycle changes.