Recent advances in computational and data sciences are revolutionizing the field of climate sciences. With parallel computing on ultra-fast graphics processing units (GPUs), it will soon be possible to perform calculations in exascale, or one thousand times the petascale capacity of today’s supercomputers. This unprecedented computing power is pushing the frontiers of data-intensive fields like climate science, which rely on massive datasets to run global simulations to predict environmental change. But harnessing this new opportunity requires new tools to manage and process that data at scale.
Tapio Schneider, Theodore Y. Wu Professor of Environmental Science and Engineering at Caltech and Senior Research Scientist at JPL, says that cloud modeling has been a particular challenge for climate scientists. Low-lying clouds are too small to represent in climate models, yet are crucial for climate predictions because they regulate how much sunlight Earth absorbs. “Clouds are a dominant source of uncertainty in climate predictions,” he says, “so they matter enormously. But it’s difficult to simulate the small-scale turbulence sustaining them; they literally fall through the cracks of global models.” But they can be simulated in smaller areas, at high resolution. To capitalize on this opportunity required a climate modeling tool that could integrate both lower-resolution global simulations and high-resolution, limited-area large-eddy simulations (LES).
In response, the Climate Modeling Alliance (CliMA), a team of researchers from Caltech, MIT, the Naval Postgraduate School, and NASA’s Jet Propulsion Laboratory (JPL), developed ClimateMachine, a new modeling approach to simulate weather and climate globally at coarser resolution and locally at high resolution in one unified framework. By integrating direct observations from a wide range of datasets alongside detailed simulations, the new model, when it is complete, is expected to provide more accurate predictions. Akshay Sridhar, Research Scientist at Caltech, says, “ClimateMachine is a step towards next-generation tools designed to reduce uncertainties in climate modeling. This framework, along with other packages developed by our group, will allow researchers to provide higher quality data to facilitate an improved understanding of atmospheric phenomena, and enable more robust decisions around future extreme weather and climate events.”