
"I thought the program went very well. It was fun and interesting to try and work on a problem that was significantly outside my field with talented people who were also outside my field. I also thought the people in charge of our group did a very good job in fostering a fun atmosphere that encouraged us to try random attacks on a hard problem without worrying about that fact that any particular one would almost certainly fail (but hopefully might provide some insight eventually)."
-- IdeaLab Participant
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Group Leaders TBA
The climate is changing and it is due to anthropogenic sources of carbon-that is agreed upon by the scientific community. But is there a possibility of abrupt change? On the whole, the large climate models do not predict such occurrences, but they also do not include the physical mechanisms that might trigger these tipping points in the modeling. So, how do we try to predict abrupt transitions? Is it even feasible?
There has been a considerable amount of mathematics devoted to rapid changes, dating back to catastrophe theory, and also to systems that enjoy varying time-scales. This has laid the groundwork for an emerging area of tipping points in climate. But can we account for the potential climate tipping points with what amount to low-dimensional bifurcations? And, if we can, what are ways that this mathematical technology can be factored into the construction of large models?
There have, of course, been abrupt changes in the past, such as rapid warming after ice-ages. Can we learn from these? The technical approach here might be to assimilate the data into models. But the current techniques of data assimilation do not accommodate abrupt transitions. This can be viewed as the same issue arising in modeling: both modeling and data assimilation require relatively smooth evolution. But we must still be able to say something when it is not so smooth.




Group Leaders TBA
Talks will be presented virtually or in-person as indicated in the schedule below.
Harvard University
Austin Peay State University
Arizona State University
College of William and Mary
University of Waterloo
University of Toronto
Microsoft Research New England
University of North Carolina
Massachusetts Institute of Technology
North Carolina State University
University of Pennsylvania
Brown University
Bowdoin College
University of North Carolina at Chapel Hill
Argonne National Laboratory
TU München
Cornell University
Brown University
National Science Foundation
Brown University
University of North Carolina
Indiana University-Purdue University
The Citadel
University of Texas at Austin
National Science Foundation
Bowdoin College
Bates College
Brown University
National Science Foundation
Brown University
University of Utah
Worcester Polytechnic Institute
University of Arizona
University of Michigan, Ann Arbor