Severe Weather under a Changing Climate: Large Scale Indicators of Extreme Events Project Description

Eric Gilleland - National Center for Atmospheric Research

Faculty Mentors
Elizabeth Mannshardt-Shamseldin - SAMSI/Duke University
Richard L. Smith - University of North Carolina at Chapel Hill

Key Words: severe weather, extreme events, climate change, large scale indicators, fine scale prediction

Description:
One of the more critical issues with a changing climate is the behavior of extreme weather events, as these can cause loss of life,
and have huge economic impacts. It is generally thought that such events would increase under a changing climate. However,
climate models are currently at too coarse of a resolution to capture the very fine scale extreme events such tornadoes or
hurricanes. One approach is to look at the behavior of large scaleindicators of severe weather. Here several factors can be
considered as large scale indicators of severe weather, including convective available potential energy and wind shear. This
presents some interesting statistical issues. Numerous approaches, including the use of the generalized extreme value
distribution for annual maxima, the generalized Pareto distribution for threshold excesses, a point process approach, and a
Bayesian framework, can be examined. Each approach will be critiqued and compared for goodness of fit, model robustness,
and predictive attributes on both re-analysis data and climate model output data. For the univariate case, it is relatively
straightforward to analyze such data though numerous issues must be resolved. These issues include appropriate techniques
for threshold selection and prior specification. A bivariate approach can also be considered. In addition, when analyzing weather
extremes, one is faced with a spatial field. Predicting extreme weather events is an important, growing area of research and there
remain many avenues for further exploration. Acknowledgements to Harold E. Brooks, Patrick Marsh, and Matt Pocernich.


Background Material: