Calculating our climate future | MIT News

Monday, MIT announcement five multi-year flagship projects as part of the first-ever Climate Grand Challenges, a new initiative to tackle complex climate issues and bring game-changing solutions to the world as quickly as possible. This article is the first in a five-part series highlighting the most promising concepts emerging from the competition, and the interdisciplinary research teams behind them.

With improved computer processing power and a better understanding of the physical equations that govern Earth’s climate, scientists are continually striving to refine climate models and improve their predictive power. But the tools they are perfecting were originally designed decades ago with scientists only in mind. When it comes to developing concrete climate action plans, these models remain inscrutable to the policymakers, public safety officials, civil engineers, and community organizers who need their knowledge the most. predictive.

“What you end up having is a gap between what’s typically used in practice and real cutting-edge science,” says Noelle Selin, a professor at the Institute for Data, Systems and Society and the Department of Earth, Atmospheric and Planetary Sciences. (EAPS), and co-leads with Professor Raffaele Ferrari the flagship MIT project Climate Grand Challenges”Bringing calculation to the climate challenge.” “How can we use new computational techniques, new understandings, new ways of thinking about modeling, to really bridge that gap between cutting-edge science and modeling, and the people who actually need to use these models ?”

Using this as a driving question, the team won’t just try to refine current climate models, they’ll build a new one from scratch.

This kind of game-changing breakthrough is exactly what MIT’s Grand Climate Challenges seek, which is why the proposal has been named one of five flagship projects in the ambitious Institute-wide program to tackle climate change. against the climate crisis. The proposal, which was selected from 100 submissions and was among 27 finalists, will receive additional funding and support to pursue its goal of reinventing the climate modeling system. It also brings together contributors from across the Institute, including MIT Schwarzman College of Computing, the School of Engineering, and the Sloan School of Management.

When it comes to researching high-impact climate solutions that communities around the world can use, “it’s great to do it at MIT,” says Ferrari, EAPS Oceanography Professor Cecil and Ida Green. “You won’t find many places in the world where you have the cutting-edge climate science, the cutting-edge computing, and the cutting-edge political science experts that we need to work together.”

The climate model of the future

The proposal builds on work that Ferrari began three years ago in a joint project with Caltech, the Naval Postgraduate School and NASA’s Jet Propulsion Lab. Called the Climate Modeling Alliance (CliMA), the consortium of scientists, engineers and applied mathematicians is building a climate model that can more accurately project future changes in critical variables, such as clouds in the atmosphere. and turbulence in the ocean, with uncertainties at least half the size of those in existing models.

To do so, however, requires a new approach. On the one hand, current models have too coarse a resolution – at the scale of 100 to 200 kilometers – to resolve fine-scale processes such as cloud cover, precipitation and sea ice extent. Also, explains Ferrari, part of this resolution limitation is due to the fundamental architecture of the models themselves. The languages ​​in which most global climate models are coded were first created in the 1960s and 1970s, largely by scientists for scientists. Since then, advancements in computing driven by the corporate world and computer games have spawned dynamic new computer languages, powerful graphics processing units, and machine learning.

For climate models to take full advantage of these advances, there is only one option: start over with a modern, more flexible language. Written in Julia, part of Julialab’s scientific machine learning technology, and led by Alan EdelmanProfessor of Applied Mathematics in the Department of Mathematics at MIT, CliMA will be able to exploit far more data than current models can handle.

“It’s been really fun to finally work with some computer science people here at MIT,” says Ferrari. “Before, it was impossible, because traditional climate models are in a language that their students can’t even read.”

The result is the so-called “Earth’s digital twin”, a climate model capable of simulating global conditions at large scales. This in itself is an impressive achievement, but the team wants to go further with its proposal.

“We want to take this large-scale model and create what we call an ’emulator’ that only predicts a set of variables of interest, but it was trained on the large-scale model,” Ferrari says. Emulators aren’t a new technology, but what’s new is that these emulators, called the “Earth’s digital cousins,” will take advantage of machine learning.

“Now we know how to train a model if we have enough data to train them,” says Ferrari. Machine learning for projects like this has only become possible in recent years as more observational data becomes available, along with improved computer processing power. The goal is to create smaller, more localized models by training them using the Earth digital twin. This will save time and money, which is essential if digital cousins ​​are to be usable by stakeholders, such as local governments and private sector developers.

Adaptable predictions for average stakeholders

When it comes to establishing a climate-smart policy, stakeholders need to understand the likelihood of an outcome in their own region – the same way you would prepare differently for a hike if there are 10 % chance of rain vs. 90% chance. The smaller digital terrestrial cousin models will be able to do things the larger model cannot, such as simulating local regions in real time and providing a wider range of probabilistic scenarios.

“Currently, if you wanted to use the output of a global climate model, you would generally use an output designed for general use,” says Selin, who is also director of MIT’s technology and policy program. With the project, the team can take into account the needs of end users from the start while incorporating their comments and suggestions into the models, helping to “democratize the idea of ​​making these climate models work”, as it puts it. . This means creating an interactive interface that will eventually give users the ability to change input values ​​and run new simulations in real time. The team hopes that eventually Earth’s digital cousins ​​could run on something as ubiquitous as a smartphone, although such developments are currently beyond the scope of the project.

The next thing the team will work on is building relationships with stakeholders. With the participation of other MIT groups, such as the Joint Program on the Science and Politics of Global Change and the Climate and Sustainability Consortium, they hope to work closely with policy makers, public safety officials, and urban planners to provide them with predictive tools tailored to their needs that can provide important actionable results for planning. Faced with rising sea levels, for example, coastal cities could better visualize the threat and make informed decisions about infrastructure development and disaster preparedness; communities in drought-prone areas could develop long-term civil planning with an emphasis on water conservation and forest fire resistance.

“We want to speed up the modeling and analysis process so people can get more direct and useful feedback for short-term decisions,” she says.

The final piece of the challenge is to incentivize students now so they can join the project and make a difference. Ferrari has already had a chance to spark student interest after co-teaching a course with Edelman and seeing the students’ enthusiasm for IT and climate solutions.

“We intend in this project to build a climate model of the future,” says Selin. “So it seems really appropriate that we also train the builders of this climate model.”

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