Stochastic Dynamical Systems and Climate Modeling (07w5007)

Arriving in Banff, Alberta Sunday, April 15 and departing Friday April 20, 2007


(Illinois Institute of Technology)

(University of Victoria)

Richard Kleeman (Courant Institute, New York University)

(University of Victoria, Canada)


Because the field of climate is largely disjoint from those of stochastic analysis and random dynamical systems, many stochastic climate models are derived by climate scientists in an ad hoc fashion by relying solely on physical intuition. Therefore it becomes important to bring together researchers from these communities to foster interaction and the diffusion of ideas. Applied mathematicians in the field can contribute with systematic strategies for constructing rigorous models. Also they would benefit a lot from the achievements and the needs of the climate modelers. The proposed meeting would build on existing connections between the communities by consolidating and extending the interactions between researchers in climate and in stochastic analysis and random dynamical systems, and thus essentially building new connections, especially, among young researchers in these fields. The participation of post-docs and graduate students, as well as women, will be highly encouraged.

Topics we plan to discuss include: coupled atmosphere-ocean models with random interactions at the air-sea interface, stochastic subgrid-scale parameterisation, statistical mechanics of the climate system, large eddy simulation under random impact, numerical methods for simulating stochastic climate models, limit theorems, and climate flows as random dynamical systems.

The climate defines the environment in which we live, and its understanding, beyond being a fascinating scientific challenge, is of tremendous economic and social importance. Mathematical models are a key component of our understanding of the climate system, and it is our belief that the fidelity of these models to nature can greatly benefit through the inclusion of stochastic effects. Similarly, stochastic analysis is a branch of mathematics with applications to a broad range of fields, from chemical kinetics to finance. Conceptual and computational advances inspired by problems arising in a climate context will almost certainly be of relevance to problems related to stochastic analysis and random dynamical systems. The interactions between the fields of stochastic analysis/random dynamical systems and climate science, while young, are diverse and growing. The workshop we propose would play an important role in nurturing and guiding this new and exciting interdisciplinary field.