While climate change is certain, precisely how climate will change is less clear. But breakthroughs in the accuracy of climate projections and in the quantification of their uncertainties are now within reach, thanks to advances in the computational and data sciences and in the availability of Earth observations from space and from the ground. I will survey the design of a new Earth system model (ESM), developed by the Climate Modeling Alliance (CliMA), which is performance-portable across modern computing architecture. The talk will cover key new concepts and results, including how data assimilation (DA) and machine learning (ML) can inform physics-based models with heterogeneous and noisy data and how substantial increases in the accuracy of simulations of uncertain processes, such as those controlling clouds, can be achieved.
