Fostering Interactions Between the Geosciences and Mathematics, Statistics, and Computer Science

L. Ridgway Scott; and twenty-six others. 3 April, 2012.
Communicated by L. Ridgway Scott.


Over the past two decades the geosciences have acquired a wealth of new and high quality data from new and greatly improved observing technologies. These datasets have been paramount in enabling improved understanding and modeling but have also strikingly demonstrated important knowledge gaps and the limitations of our current conceptual models to explain key aspects of these observations. %A striking result of these datasets is that current conceptual models are incapable %of explaining key aspects of these observations. This situation limits progress on questions that have both fundamental scientific and societal significance, including climate and weather, natural disaster mitigation, earthquake and volcano dynamics, earth structure and geodynamics, resource exploration, and planetary evolution. The challenge in modeling accurately these processes is not only one of computational power. Powerful computations based on existing models are incapable of reproducing the observations faithfully. Instead, we need to develop new conceptual approaches to describe the complexities of these natural systems. Essentially, we need ways to characterize complicated systems that vary strongly in space and time in ways that are not adequately accounted for in our current paradigms. A four-day workshop in October 2011 explored this issue and reached a consensus that significant advances can result from accelerating the traditional interaction between geoscientists, mathematicians, statisticians, and computer scientists. We considered key challenges that confront the geosciences and major areas of rapid development in mathematics, statistics and computer science that offer the potential for significant advances in meeting these challenges. This type of research will require combining stochastic and deterministic models, improving methods of model validation and verification, developing inverse methods and techniques for the identification of extreme events and critical transitions, and formulating novel numerical algorithms and implementations, along with the greatly enhanced use of data from the rapidly evolving observing systems. To advance these goals, we propose establishing a geographically distributed but well-focused institute with a novel operational, educational and training structure that can foster and promote these valuable interactions.

Original Document

The original document is available in PDF (uploaded 3 April, 2012 by L. Ridgway Scott).