Galvanizing these new efforts is the rise of machine learning techniques categorized as artificial intelligence. Schneider leans on AI to better incorporate the effects of clouds into climate models that use physics equations to see what’s ahead. Bretherton, worried that these equations will never fully capture clouds’ behavior, is developing new AI tools that can predict the future directly from real-world data, barely relying on physics equations at all.
While Schneider, Bretherton, and other physicists differ in their approach, they share a sense of urgency. “Climate is changing fast,” Bretherton said. “Having a perfect model in 100 years will not be useful for solving the climate crisis.”
The Library of Fake Clouds
If humanity continues to fill the atmosphere with carbon at its current rate, some simulations predict that over the next 50 or so years, the climate is headed for 2 degrees Celsius of warming. Others say 6. The first possibility would lead to a future of increased severe weather events and amplified food and water scarcity — a dangerous situation for many communities, but one that the global population may be able to adapt to. The latter possibility, however, could give rise to enough disaster and famine to fully destabilize human civilization. “Six degrees would be pretty frightening,” Schneider said.
Modern climate simulations account for the influence of the planet’s atmosphere, its ocean, its land, its ice, and more, with each model handling these components in its own way. But more than half of the variation between predictions comes from how the simulations treat clouds. “If you are off by a few percent — 2 or 3% — of cloud cover, you will get warming that is several degrees Celsius different,” said George Matheou, a physicist studying clouds at the University of Connecticut.
In 2022, the Department of Energy tasked Frontier, then the world’s most powerful supercomputer, with running a new flagship climate model. The model was based on the physics of fluid dynamics, as calculated via a set of equations called Navier-Stokes. Developing the model marked, in some sense, the culmination of a six-decade enterprise of improving the accuracy of climate models by increasing the resolution of the computer simulation. Simulations had gone from thousands of kilometers per pixel, to hundreds, to — in this case — three.
But even this state-of-the-art model couldn’t directly account for the subtle cumulative effects of clouds, which can span just meters and be shaped by even tinier zephyrs of air. “To get to the low clouds, you need something like 100 billion times the compute power we have,” Schneider said, “so that’s not going to happen in my lifetime.”
Unable to add clouds to their models directly, physicists have effectively resorted to estimating their influence. They add extra, nonphysical terms, called parameters, to the Navier-Stokes equations that indirectly capture the effects of clouds. These alternative equations are engineered to produce digital atmospheric currents that bend and curl in the ways that a truly cloudy model would. In a laborious process, researchers tweak these factors until the models produce accurate predictions based on past data.
But data is patchy, so physicists also let their intuition guide them. In the end, it’s tough to know whether one model’s parameters are better than another’s. “You have to guess a little bit,” Matheou said.
The need to turn parameter picking from an art into a science was one of the reasons Schneider established the Climate Modeling Alliance, CLIMA, in 2019. He hoped to automate the process and make it less subjective by training machines to pick the best parameters possible. But to do that, researchers would need a lot more data about different types of clouds: California clouds, mid-Pacific clouds, winter clouds, summer clouds, and so on.
Researchers like Bretherton can afford to fly planes through real clouds only so often. So cloud physicists turn to the next best thing: a Navier-Stokes simulation called a large-eddy simulation. “LES is the best model we have for cloud turbulence, for a limited area and a short time,” said Zhaoyi Shen, a CLIMA researcher at Caltech.
The catch is that generating an LES also doesn’t come cheap: It takes a formidable amount of computational power. Until somewhat recently, Shen said, researchers had produced just a few dozen high-quality cloud simulations — not enough to give physicists a comprehensive view of cloud behavior, and certainly not enough to teach a machine how clouds work. So a few years ago, Schneider approached scientists at Google for help.