This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
This project will provide a detailed assessment of non-Gaussian atmospheric variability in order to understand and predict the probability of extreme events in the atmosphere. In non-technical terms, an extreme event is a high-impact, hard-to-predict phenomenon that is beyond our normal (Gaussian bell curve) expectations. In technical terms, an extreme event is often defined as the non-normal (non-Gaussian) tail of the probability density function of the data. Understanding extremes has become an important objective in climate variability research, because climate and weather risk assessment depends on knowing the probability of extreme events such as hurricanes and windstorms. Until recently the study of extreme meteorological events has been largely empirical. That is, most investigators used observations or model output to estimate the probabilities of, for example, extreme winds and temperatures, without actually addressing the detailed dynamical/physical reason for the shape of the probability density functions. A recently developed dynamical theory, however, predicts the characteristics of non-Gaussian statistics in the atmosphere from first dynamical principles. This theory attributes extreme atmospheric flow anomalies to stochastically forced linear dynamics, where the strength of the stochastic forcing depends on the flow itself (multiplicative noise). Because stochastic theory makes clear and testable predictions about non-Gaussian variability, the multiplicative noise hypothesis can be verified by analyzing the detailed non-Gaussian statistics of atmospheric variability. While compelling evidence for the validity of the multiplicative noise theory already exists, the validation has, so far, not been done systematically. Therefore, the main focus of this work is to systematically map and analyze, guided by stochastic theory, the non-Gaussianity of dynamically relevant atmospheric variables (e.g., pressure, geopotential height, vorticity, temperature, winds) from observations and from models.
Broader impacts or this research potentially extend to educational, and risk-management activities. As the study is aimed at gaining a better, detailed understanding of extreme events in climate, it is anticipated that the multiplicative noise approach has the potential to influence how non-Gaussian atmospheric variability and extreme events are viewed. The results should be of interest for both climate diagnostics and modeling and may have a significant impact on weather and climate risk management, potentially benefiting businesses, consumers and public policy makers.
