The physics of shallow cloud turbulence matters deeply to global climate dynamics in ways that riddle modern numerical projections of future rainfall and ocean circulation changes with unacceptable systematic uncertainty. The fate of the regional water cycle over major terrestrial carbon reservoirs is similarly uncertain – for instance, I will show that a simplistic parameterization of how unresolved planetary boundary layer eddies respond to forest-physiology-induced changes in surface energy partitioning in CESM plays a disturbingly prominent role in controlling future water stress over the Amazon.
In short -- turbulence matters. I will show pilot results from a new form of multiscale climate model that reduces our otherwise heavy current reliance on assumptions endemic to boundary layer turbulence parameterization. The result is the world’s first global atmospheric model to permit explicit atmospheric boundary layer eddies. Reassuringly, this increases realism of the local dynamics supporting simulated low clouds. A first look at the emergent global climate dynamics reveals a cloud response to surface warming surprisingly similar to other climate models with parameterized physics, but a major muting of aerosol’s impact on cloud radiative forcing, suggesting interesting new buffering dynamics occur when turbulence is resolved that reduce the aerosol indirect effect. I will conclude with the current exciting technical outlook, focusing on the now demonstrable promise of GPU-powered exascale computers, and the surprising recent successes of modern deep learning methods — machine learning especially proves to be a skillful emulator of explicitly simulated moist turbulence and, importantly, an intriguing new tool for dynamical inquiry of models with realistic complexity and high degrees of freedom.