Nav: Home

Machine learning may be a game-changer for climate prediction

June 19, 2018

New York, NY--June 19, 2018--A major challenge in current climate prediction models is how to accurately represent clouds and their atmospheric heating and moistening. This challenge is behind the wide spread in climate prediction. Yet accurate predictions of global warming in response to increased greenhouse gas concentrations are essential for policy-makers (e.g. the Paris climate agreement).

In a paper recently published online in Geophysical Research Letters (May 23), researchers led by Pierre Gentine, associate professor of earth and environmental engineering at Columbia Engineering, demonstrate that machine learning techniques can be used to tackle this issue and better represent clouds in coarse resolution (~100km) climate models, with the potential to narrow the range of prediction.

"This could be a real game-changer for climate prediction," says Gentine, lead author of the paper, and a member of the Earth Institute and the Data Science Institute. "We have large uncertainties in our prediction of the response of the Earth's climate to rising greenhouse gas concentrations. The primary reason is the representation of clouds and how they respond to a change in those gases. Our study shows that machine-learning techniques help us better represent clouds and thus better predict global and regional climate's response to rising greenhouse gas concentrations."

The researchers used an idealized setup (an aquaplanet, or a planet with continents) as a proof of concept for their novel approach to convective parameterization based on machine learning. They trained a deep neural network to learn from a simulation that explicitly represents clouds. The machine-learning representation of clouds, which they named the Cloud Brain (CBRAIN), could skillfully predict many of the cloud heating, moistening, and radiative features that are essential to climate simulation.

Gentine notes, "Our approach may open up a new possibility for a future of model representation in climate models, which are data driven and are built 'top-down,' that is, by learning the salient features of the processes we are trying to represent."

The researchers also note that, because global temperature sensitivity to CO2 is strongly linked to cloud representation, CBRAIN may also improve estimates of future temperature. They have tested this in fully coupled climate models and have demonstrated very promising results, showing that this could be used to predict greenhouse gas response.
-end-
About the Study

The study is titled "Could Machine Learning Break the Convection Parameterization Deadlock?"

Authors are: P. Gentine1 , M. Pritchard2 , S. Rasp3 , G. Reinaudi1, and G. Yacalis2 (1Earth and Environmental Engineering, Columbia University, New York, NY, USA, 2Earth System Science, University of California, Irvine, CA, USA, 3Faculty of Physics, LMU Munich, Munich, Germany).

M. P. acknowledges funding from the DOE SciDac and Early Career Programs (DE-SC0012152 and DE-SC00-12548) as well as the NSF (AGS-1734164). Stephan Rasp was funded by the German Research Foundation (DFG) Transregional Collaborative Research Center SFB/TRR 165 "Waves to Weather". Computational resources for our SPCAM3 simulations were provided through the NSF Extreme Science and Engineering Discovery Environment (XSEDE) under allocation TG-ATM120034.

The authors declare no financial or other conflicts of interest.

LINKS:

Paper: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018GL078202

DOI: 10.1029/2018GL078202

https://engineering.columbia.edu/faculty/pierre-gentine

http://eee.columbia.edu/

http://engineering.columbia.edu/

Columbia Engineering

Columbia Engineering, based in New York City, is one of the top engineering schools in the U.S. and one of the oldest in the nation. Also known as The Fu Foundation School of Engineering and Applied Science, the School expands knowledge and advances technology through the pioneering research of its more than 200 faculty, while educating undergraduate and graduate students in a collaborative environment to become leaders informed by a firm foundation in engineering. The School's faculty are at the center of the University's cross-disciplinary research, contributing to the Data Science Institute, Earth Institute, Zuckerman Mind Brain Behavior Institute, Precision Medicine Initiative, and the Columbia Nano Initiative. Guided by its strategic vision, "Columbia Engineering for Humanity," the School aims to translate ideas into innovations that foster a sustainable, healthy, secure, connected, and creative humanity.

Columbia University School of Engineering and Applied Science

Related Global Warming Articles:

Global warming will accelerate water cycle over global land monsoon regions
A new study provides a broader understanding on the redistribution of freshwater resources across the globe induced by future changes in the monsoon system.
Comparison of global climatologies confirms warming of the global ocean
A report describes the main features of the recently published World Ocean Experiment-Argo Global Hydrographic Climatology.
Six feet under, a new approach to global warming
A Washington State University researcher has found that one-fourth of the carbon held by soil is bound to minerals as far as six feet below the surface.
Can we limit global warming to 1.5 °C?
Efforts to combat climate change tend to focus on supply-side changes, such as shifting to renewable or cleaner energy.
Global warming: Worrying lessons from the past
56 million years ago, the Earth experienced an exceptional episode of global warming.
More Global Warming News and Global Warming Current Events

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Erasing The Stigma
Many of us either cope with mental illness or know someone who does. But we still have a hard time talking about it. This hour, TED speakers explore ways to push past — and even erase — the stigma. Guests include musician and comedian Jordan Raskopoulos, neuroscientist and psychiatrist Thomas Insel, psychiatrist Dixon Chibanda, anxiety and depression researcher Olivia Remes, and entrepreneur Sangu Delle.
Now Playing: Science for the People

#537 Science Journalism, Hold the Hype
Everyone's seen a piece of science getting over-exaggerated in the media. Most people would be quick to blame journalists and big media for getting in wrong. In many cases, you'd be right. But there's other sources of hype in science journalism. and one of them can be found in the humble, and little-known press release. We're talking with Chris Chambers about doing science about science journalism, and where the hype creeps in. Related links: The association between exaggeration in health related science news and academic press releases: retrospective observational study Claims of causality in health news: a randomised trial This...