Earth System Modeling 2.0: Toward Data-Informed Climate Models With Quantified Uncertainties

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. To achieve a leap in accuracy of climate projections, we are developing a new Earth system modeling platform. It will fuse an Earth system model (ESM) with global observations and targeted local high-resolution simulations of clouds and other elements of the Earth system. The ESM is being developed by the Climate Modeling Alliance (CliMA), which encompasses Caltech, MIT, and the Naval Postgraduate School. CliMA will capitalize on advances in data assimilation and machine learning to develop an ESM that automatically learns from diverse data sources, be they observations from space or data generated computationally in high-resolution simulations. It will also engineer the ESM from the outset to be performant on emerging computing architectures, including heterogeneous architectures that combine traditional CPUs with hardware accelerators such as graphical processing units (GPUs). This talk will cover key new concepts in the ESM, including turbulence, convection, and cloud parameterizations and fast and efficient algorithms for assimilating data and quantifying uncertainties.