Robust Climate Projections and Stochastic Stability of Dynamical Systems
Collaborative Research: Robust climate projections
and stochastic stability of dynamical systems
Principal Investigators: Profs. Michael Ghil (Lead PI), James C. McWilliams and J.
David Neelin (Co-PIs), all at IGPP, UCLA, CA 90095, and Ilya Zaliapin (Co-PI),
Mathematics & Statistics Dept., Univ. of Nevada, Reno, NV 89557
Collaborators: Micka‘l Chekroun, Dmitri Kondrashov, Eric Simonnet
Objectives: The main objective of this proposal is to develop methods for reducing the range of projections in future climate change and increasing the confidence in these projections. This objective is to be achieved by (i) increasing the fundamental understanding of the reason for discrepancies between the simulations of past and current climate, as performed by distinct models or by different versions of the same model; and (ii) based on this understanding, analyze the range of future climate projections and devise tools for systematically reducing it.
Description: The present proposal relies heavily on the substantial advances produced by the effort funded under the P.I.Ős former SciDAC project (DE-FG02-01ER63251, ŇPredictive Understanding of the OceansŐ Wind-Driven Circulation on Interdecadal Time ScalesÓ). The latter project demonstrated that dynamical systems concepts and methods could guide the study of interdecadal climate variability, across a full hierarchy of oceanic and coupled ocean-atmosphere models.
The proposed project strikes out in a new and bold direction, though, and tackles the key issue of the range of uncertainties still left after four assessment reports of the United NationsŐ Intergovernmental Panel on Climate Change (IPCC), starting in 1991 and ending in 2007. Rather than limiting itself to a specific area of uncertainty, such as intrinsic variability of the oceans or cloud- radiation interactions, it attacks the fundamental issue of robustness of dynamical systems in general, while focusing on climate models in particular.