Mathematics of Planet Earth 2013

ICERM’s Idea-Lab invites 20 postdoctoral researchers to the Institute for Computational and Experimental Research in Mathematics (ICERM) for a week during the summer of 2013. The program will start with brief participant presentations on their research interests in order to build a common understanding of the breadth and depth of expertise. Throughout the week, two or more leading senior researchers will give comprehensive overviews of their research topics. Organizers will create smaller teams of participants who will discuss, in depth, these research questions, obstacles, and possible solutions. At the end of the week, the teams will prepare presentations on the problems at hand and solution ideas. These will be shared with a broad audience including invited program officers from funding agencies.

Participation: IdeaLab applicants should be at an early stage of their post-PhD career.

Applications for the 2013 IdeaLab will open in January 2013 via MathPrograms.org.  Application materials will be reviewed beginning March 15, 2013.

One of the two topics to be discussed during this program is specific to the MPE2013 initiative: “Tipping Points in Climate Systems”

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.