Fall 2024 Lecture in Climate Data Science: ANDRE PERKINS

*Please note that in-person space is limited.*

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Abstract: Climate simulation has been an invaluable tool for projecting future climate changes due to human activity, while also serving as our best laboratory for studying the interconnected Earth System. These large and complex models are primarily limited to direct use by organizations with access to large computing facilities due to their demanding computational requirements. However, as in many disciplines, recent advances in AI-based predictive technology are rapidly changing this landscape. The weather forecasting community is one early leader in this transition for atmospheric science, where multiple recent AI-based models now offer competitive or even improved 1-10 day atmospheric forecasts compared to their physics-based predecessors, while requiring only a fraction of the compute power (e.g., a desktop workstation). A similar shift is already on the horizon for climate simulation, with analogous techniques showing promise for large speedups and improved performance at coarse horizontal grid resolutions.

In this talk, I will discuss the latest research from the Ai2 Climate Modeling team in partnership with GFDL, LLNL, and NVIDIA on the AI2 Climate Emulator (ACE), a machine-learning-based emulator of a physics-based global atmosphere model. As an emulator, ACE maintains realistic weather variability throughout decades-long rollouts with prescribed sea surface temperatures and sea ice, provides an accurate representation of the source model’s climate and the atmospheric response to forcing in AMIP scenarios, and is over 60x faster than the traditional model it emulates. We’ve also made progress toward coupling ACE to a more realistic ocean, with initial results from a slab ocean model. We believe this technology provides a viable pathway toward full model emulation of an Earth System, serving as a valuable part of the toolkit for climate science. However, since we are in the early stages, I will also touch upon some of the many remaining technical and ideological challenges to overcome.

Bio: Andre Perkins is a Senior Research Engineer on the climate modeling team at AI2 in Seattle, WA, where he works with the team to improve weather and climate prediction through the use of machine learning. On the team, Andre is involved in research on ML-based emulators of the atmosphere and ocean and diffusion-based downscaling, compute performance optimization, and managing the team's cloud compute infrastructure. He earned a B.S. in Computer Sciences and Atmospheric and Oceanic Sciences from the University of Wisconsin - Madison (2012), and holds a Ph.D. in Atmospheric Sciences from the University of Washington (2019).

Learn More:

ANDRE PERKINS
Allen Institute for AI

Event Venue & Nearby Stays

Columbia Innovation Hub - Tang Family Hall, 2276 12th Avenue, New York, United States