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Reducing Uncertainty Surrounding Climate Change Using Emergent Constraints

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Presenter: 
Dr. Chad Thackeray
When: 
March 29, 2023 - 3:00pm to 4:00pm
Where: 

This event will be held over Zoom Meetings.
Meeting URL: https://uvic.zoom.us/j/87860005541?pwd=UGdmY3g0bkpwakIwMEh5dlVxZi9GUT09
Meeting ID: 878 6000 5541
Password: 960344
Phone one-tap: Canada: +17789072071,,87860005541# or +16475580588,,87860005541#
Or dial: Canada: +1 778 907 2071 or +1 647 558 0588 
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Many aspects of future climate change remain highly uncertain as different climate models exhibit a large and persistent spread in their projections. This uncertainty stems from a variety of sources including natural variability, choice of emissions pathway, and differences in how climate models represent key physical processes. Here, the focus is on the latter of these factors, known as model uncertainty. One approach to constraining model uncertainty is the use of emergent constraints (ECs). ECs have been proposed for a wide variety of future climate metrics, with over 60 applications to date. In this talk, I will discuss our work that seeks to better understand existing ECs and identify new ones. First, I will focus on efforts to constrain uncertainty in climate feedbacks, notably the surface albedo feedbacks stemming from changing snow cover and sea ice. Second, I will describe some recent applications of the EC approach to uncertainty in future precipitation change. This includes newly identified ECs on changes in heavy rainfall characteristics (frequency and magnitude) and global hydrologic sensitivity (the change in global mean precipitation per degree of warming). Lastly, I will briefly discuss some ongoing analysis, which seeks to use machine learning to better understand ECs.

Bio:
Dr. Chad Thackeray is an Assistant Researcher in the Department of Atmospheric & Oceanic Sciences at UCLA. He is also Climate Science Lead within the UCLA Center for Climate Science. He joined UCLA in 2017 after obtaining a Ph.D. from the University of Waterloo. His research seeks to improve our understanding of future changes to the hydrologic cycle and interactions between the cryosphere and the climate system through feedback mechanisms. Much of this work utilizes the “emergent constraint” approach, a leading evaluation technique for climate models, which seeks to reduce uncertainty in projections of climate change.