This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
The proposal describes a practical computational framework for the response of a complex chaotic nonlinear multiscale dynamical system to changes in external forcing parameters. It is based on the PI's recent successful efforts to create a numerical approach for the fluctuation-dissipation theorem to predict linear response of a nonlinear chaotic forced-dissipative dynamical system to an external perturbation with improved skill, based on a precise geometric response formula for systems with chaotic attractors. While the method developed is observed to perform well for relatively simple dynamical systems, complex realistic climate models are more computationally expensive, have nonlinear interactions on multiple scales, and sometimes include stochastically parameterized processes. Here the PI proposes several numerical strategies to adapt the new approach for complex multiscale nonlinear forced-dissipative, and, possibly, stochastically parameterized dynamical systems with many variables and highly non-Gaussian equilibrium state, as well as reduce its computational cost. Successful implementation of proposed algorithms should help create a novel computational framework for global climate change prediction. The proposal also suggests development of a set of graduate-level courses with strong emphasis on the basic subjects of chaotic nonlinear dynamics, atmospheric and oceanic physics, and computational weather and climate prediction, which are the key topics needed to become an interdisciplinary weather and climate research scientist. These courses will give graduate students an opportunity to interact directly with the PI's research and learn a variety of advanced theoretical approaches and numerical methods directly from the PI through graduate advising.
The ability of the linear fluctuation-dissipation approach to identify the ranges of parameter perturbations which produce catastrophic climate response can be helpful in determining potentially harmful types of anthropogenic intervention into the Earth's global climate cycle. This data may provide additional information to help define economic, political and legislative initiatives to preserve our environment and to develop advanced technologies for more reliable environmentally-friendly renewable power systems. In addition, such an approach is also suitable for the inverse climate change problem, where the geological evidence of past climate changes is used to compute the range of physical forcing parameters which triggered these climate changes, which can help to determine the cause of these climate changes on the planetary scale. The set of courses under the PI's development may potentially evolve into a consistent interdisciplinary educational program on the graduate level to train future climate and weather research scientists with heavy mathematical and computational bias, which could eventually be adopted as a basic educational standard for climate and weather research. Implementation of this program will reduce the burden on national weather and climate research centers and laboratories which currently spend substantial efforts on the training of their employees at the postdoctoral level.
