Oct. 27, 2021 — Computer simulations that scientists use to understand the evolution of the Earth’s climate offer a wealth of information to public officials and corporations planning for the future. However, climate models — no matter how complex or computationally intensive — do contain some degree of uncertainty. Addressing this uncertainty is proving increasingly important as decision makers are asking more complex questions and looking to smaller scales.
To improve climate simulations, scientists are looking to the potential of artificial intelligence (AI). AI has offered profound insights in fields from materials science to manufacturing, and climate researchers are excited to explore how AI can be used to revolutionize how the Earth system, and especially its water cycle, can be simulated in order to dramatically improve our understanding and representation of the real world. In particular, AI offers the potential to dramatically increase the accuracy of predictions down to the scales of interest to scientists, and even stakeholders focused on designing, financing and deploying equitable climate solutions to America’s most disadvantaged communities.
Motivated by this opportunity, the U.S. Department of Energy (DOE) is launching a comprehensive workshop: Artificial Intelligence for Earth System Predictability (AI4ESP). After the collection of more than 150 white papers from the scientific community, AI4ESP is kicking into high gear by hosting a workshop beginning October 25. The workshop will include 17 sessions over a six-week period designed to create a new scientific community that marries climate research with artificial intelligence, applied math and supercomputing.
“Earth system predictability refers to the intersection of climate with hydrology, ecology, infrastructure and human activities,” said Nicki Hickmon, an Argonne scientist, director of operations for the Atmospheric Radiation Measurement (ARM) user facility and the lead for the AI4ESP workshop.
By linking researchers in Earth system predictability and computer sciences, AI4ESP seeks to create a paradigm shift in simulating the Earth system. AI4ESP seeks to inspire a new generation of AI algorithms specifically aimed at Earth system predictability.
According to Hickmon, continuous improvements will enhance the ability of current simulations to provide deeper insights into community-scale issues and those involving extreme weather, potentially allowing stakeholders a better grasp of the uncertainties that surround such events.
“AI for climate is still in its infancy,” said Hickmon. “However, it is still essential that we explore the potential of AI to see how it can better inform our models and prepare us for the future.”
Click here to see the agenda and register for the workshop, which will open with an address by Deputy Secretary of Energy David Turk. The public is welcome to attend any of the open sessions. Some components of the workshop are invitation-only in order to gather the required materials for the workshop report.
The workshop is sponsored by the DOE’s Office of Biological and Environmental Research and Office of Advanced Scientific Computing Research.
Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation’s first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America’s scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.
The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit https://energy.gov/science.
Source: Jared Sagoff, Argonne National Laboratory