The Complex Physics of Climate Change : Nonlinearity and Stochasticity
Michael Ghil, Ecole Normale Supérieure (Paris) and University of California (Los Angeles)26 janvier 2012
Jeudi 26 janvier
The Complex Physics of Climate Change : Nonlinearity and Stochasticity Michael Ghil Ecole Normale Supérieure, Paris, and University of California, Los Angeles The first attempts at estimating climate sensitivity to changes in the forcing, over 30 years ago, assumed a climate system in equilibrium. More recently, the Intergovernmental Panel on Climate Change focused on estimates of climate evolution over the coming century ; these estimates still differ by several degrees, even for a given, prescribed scenario of increases in greenhouse gases and aerosol concentrations. This uncertainty, among others, motivates much of the following. The complex physics of climate change arises from the large number of components of the climate system — atmosphere, oceans, snow and ice, land cover, and the biota that live in them — as well as from the wealth of processes — physical, chemical and biological — occurring in each of the components and across them. This complexity has given rise to countless attempts to model each component and process separately, as well as to two overarching approaches to apprehend the complexity as a whole : deterministically nonlinear and stochastically linear. Call them the Lorenz and the Hasselmann approach, respectively, for short. We propose in this lecture a “grand unification” of these two approaches, provided by the theory of random dynamical systems. In particular, we apply this theory to the problem of climate sensitivity, and study the random attractors of nonlinear, stochastically perturbed systems, as well as the time-dependent invariant measures supported by these attractors. Results are presented for several simple climate models, from the classical Lorenz convection model to El Niño-Southern Oscillation models. Their attractors support random Sinai-Ruelle Bowen measures with nice physical properties. The response of these SRB measures to changes in poorly known model parameters is studied and implications for climate predictability are discussed. This work is the result of recent collaborations with M. D. Chekroun, D. Kondrashov, J. C. McWilliams, J. D. Neelin, E. Simonnet, S. Wang and I. Zaliapin, but represents the fruition of all I learned from tens of Ph. D. students, post-docs and other colleagues over the years.
Salle E244 (Conf IV) - 24 rue Lhomond
2éme étage - 13h30