“The Earth Machine” –A Game Changer: Climate Scientists Plot a Predictive Model Powered by Artificial Intelligence

“Taking advantage of breakthroughs in artificial intelligence (AI), satellite imaging, and high-resolution simulation the ‘Earth Machine’ aims to change how climate models render small-scale phenomena such as sea ice and cloud formation that have long bedeviled efforts to forecast climate. A focus will be on the major source of uncertainty in current models: the decks of stratocumulus clouds that form off coastlines and populate the trade winds. A shift in their extent by just a few percentage points could turn the global thermostat up or down by a couple of degrees or more within this century—and current models can’t predict which way they will go”.

Sometimes it seems the clouds over climate science just won’t lift. Computer models of Earth’s climate have multiplied in number, complexity, and computational power, yet they remain unable to answer more precisely some of the questions most on the public’s mind: How high must we build sea walls to last until 2100? How bad will heat waves get in the next decade? What will Arctic shipping routes look like in 2030? Climate models all agree that global temperatures will continue to rise in response to humanity’s greenhouse gas emissions, but uncertainties stubbornly persist over how quickly that will happen and how high temperatures will go.

Tapio Schneider, a German-born climate dynamicist at the California Institute of Technology (Caltech) in Pasadena, believes climate science can do better, continues Paul Voosen in today’s Science. And he’s not alone. Later this summer, an academic consortium led by Schneider and backed by prominent technology philanthropists, including former Google CEO Eric Schmidt and Microsoft co-founder Paul Allen, will launch an ambitious project to create a new climate model.

Within 5 years, the team hopes its AI-fortified model will drive out that uncertainty and others by learning on its own how clouds behave, from both actual observations and purpose-built cloud simulations. It’s a lofty goal, Schneider admitted late one May afternoon in sun-soaked Pasadena, sitting outside with his newly assembled team. They had just wrapped up a workshop, the third he had convened in the past year, bringing together leading climate scientists and engineers to discuss the future of their field. “We’re under no illusions,” Schneider said. “This is not going to be a cakewalk.”

There are reasons for skepticism. The United States already has many climate models, and some people question why it needs another, further dividing resources. Others question the technology and wonder whether the philanthropists backing the project have given it the scrutiny that an agency such as the National Science Foundation would provide. The team’s unorthodox message and means won’t make it easy to win people over, says David Randall, a climatologist at Colorado State University in Fort Collins. “I think the existing modeling centers will push back. If Tapio is getting funding, that in principle could have gone to someone else.”

Climate modelers have always followed two imperatives. First, they’ve folded ever more features of Earth into their simulations. Models once contained only the atmosphere and ocean; now, they have subroutines for ice sheets, land use, and the biosphere. Second, they’ve sought higher and higher resolutions—modeling interactions on smaller and smaller scales—riding the wave of Moore’s law on government-owned supercomputers. By one estimate, the computing power those models use has increased by a factor of 100 million since the 1970s. As the models grew increasingly complex, they more fully reflected the vagaries of our planet—unknown unknowns turned to known unknowns. Yet the uncertainties remained.

At their most basic, all the models work the same way: They take the globe and chop it into a mesh, with cells some 25 kilometers to 50 kilometers on a side, and use a set of code called a dynamical core to simulate the behavior of the atmosphere and ocean over years and centuries. But much of what happens on the planet—cloud formation, for example—arises at scales smaller than those grids. Therefore, those phenomena have to be described indirectly—”parameterized” in the jargon of climate science—with rule-of-thumb equations. The modelers then adjust those various knobs to best represent the world as they know it—a process called tuning. “It’s a mix of intuition and empiricism and some physically observed laws,” says Isaac Held, Schneider’s mentor and a scientist at the Geophysical Fluid Dynamics Laboratory, a prominent modeling center in Princeton, New Jersey.

Make no mistake: Current models do an admirable job of re-creating the world. But their shortcomings drive scientists bonkers. They struggle to re-create Arctic temperatures and melting sea ice. Their distribution of rainfall is off, biased against the extreme torrents that can cause flooding. “The rain is falling in the wrong place and at the wrong rate,” says Paul O’Gorman, an atmospheric scientist at the Massachusetts Institute of Technology (MIT) in Cambridge, who formerly worked with Schneider. And, especially important, the models often fail to simulate those thick stratocumulus clouds, which typically form off the coasts of the western Americas and help cool the region.

Schneider, 46, has not always been fixated on clouds. Early in his career at Caltech, he focused on large-scale atmospheric flows, such as the Hadley cell. That atmospheric conveyor belt shifts air from the equator to the subtropics—the type of pattern that climate models can simulate using simple laws of physics. But while on an appointment at ETH Zurich in Switzerland, he became increasingly convinced that climate models could do a better job integrating new data on cloud behavior. He returned to Caltech in 2016 to seek a solution, adding a joint appointment at NASA’s Jet Propulsion Laboratory (JPL) in Pasadena, where he had become a close collaborator with one of JPL’s cloud gurus, João Teixeira.

That was the start of what is now a collaboration of about two dozen people. AI, particularly a variant called machine learning, was on the upswing, and Schneider and Teixeira mused that it might help with the cloud problem. Soon they recruited Andrew Stuart, a soft-spoken computational mathematician at Caltech. The team found additional recruits at JPL, which has a vast archive of satellite data on clouds, and across the country at MIT, where researchers had built an ocean model infused with every possible satellite and buoy measurement of the seas.

The MIT group had ambitions to go bigger, and its members welcomed Schneider’s overture. “Always the idea was to go to an Earth system model,” says MIT physical oceanographer Raffaele Ferrari. “But the atmospheric community wasn’t particularly primed to think the same way.”

At first, the nascent collaboration was not set on creating a new climate model; the United States already has six prominent models. “It was more a question of how can we build a better model,” Schneider says. But they wanted to be certain that a full climate model would incorporate their innovations. They decided the best way would be to build a new model, albeit one starting with existing code. Doing so meant they needed a computation whiz who could take their equations and make them run on a next-generation supercomputer.

A U.S. Navy expert reported for duty. Frank Giraldo, an applied mathematician at the Naval Postgraduate School in Monterey, California, is behind the Navy’s new dynamical core, the mathematical engine at the heart of its next-generation weather and climate models. His core, the Non-hydrostatic Unified Model of the Atmosphere, is designed from the ground up for modern parallel computing. The core is also flexible and self-contained. It can solve equations to various degrees of accuracy in the same model, which should allow the Earth Machine to give a low-resolution overview of the planet while zooming in on clouds in real time.

A new data-driven climate model will use satellite observations and high-resolution simulations to learn how best to render its clouds. Similar methods will also be applied to other, small-scale phenomena, such as sea ice and ocean eddies.

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