Extremes of Severe Storm Environments under a Changing Climate

Elizabeth Mannshardt, Eric Gilleland

Department of Statistics, North Carolina State University, Raleigh, NC, USA.
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA.

DOI: 10.4236/ajcc.2013.23A005                

Abstract

One of the more critical issues in a changing climate is the behavior of extreme weather events, such as severe tornadic storms as seen recently in Moore and El Reno, Oklahoma. It is generally thought that such events would increase under a changing climate. How to evaluate this extreme behavior is a topic currently under much debate and investigation. One approach is to look at the behavior of large scale indicators of severe weather. The use of the generalized extreme value distribution for annual maxima is explored for a combination product of convective available potential energy and wind shear. Results from this initial study show successful modeling and high quantile prediction using extreme value methods. Predicted large scale values are consistent across different extreme value modeling frameworks, and a general increase over time in predicted values is indicated. A case study utilizing this methodology considers the large scale atmospheric indicators for the region of Moore, Oklahoma for Class EF5 tornadoes on May 3, 1999 and more recently on May 20, 2013, and for the class EF5 storm in El Reno, Oklahoma on May 31, 2013.

Keywords

Projections of Extreme Events; Reanalysis; Severe Storms; Extreme Weather; Generalized Extreme Value Distribution (GEV); Block Maxima

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	H. E. Brooks, J. W. Lee and J. P. Craven, “The Spatial Distribution of Severe Thunderstorm and Tornado Environments from Global Reanalysis Data,” Atmospheric Research, Vol. 67-68, 2003, pp. 73-94. http://dx.doi.org/10.1016/S0169-8095(03)00045-0
[2]	J. P. Craven and H. E. Brooks, “Baseline Climatology of Sounding Derived Parameters Associated with Deep, Moist Convection,” National Weather Digest, Vol. 28, 2004, pp. 13-24.
[3]	J. Molinari and D. Vollaro, “Distribution of Helicity, Cape, and Shear in Tropical Cyclones,” Journal of the Atmospheric Sciences, Vol. 67, No. 1, 2010, pp. 274-284. http://dx.doi.org/10.1175/2009JAS3090.1
[4]	E. Gilleland, B. G. Brown and C. M. Ammann, “Spatial Extreme Value Analysisto Project Extremes of Large-Scale Indicators for Severe Weather,” Environmetrics, Vol. 24, No. 6, 2013, pp. 418-432. http://onlinelibrary.wiley.com/doi/10.1002/env.2234/abstract
[5]	R. J. Trapp, N. S. Diffenbaugh and A. Gluhovsky, “Transient Response of Severe Thunderstorm Forcing to Elevated Greenhouse Gas Concentrations,” Geophysical Research Letters, Vol. 36, No. 1, 2009. http://dx.doi.org/10.1029/2008GL036203
[6]	P. T. Marsh, H. E. Brooks and D. J. Karoly, “Assessment of the Severe Weather Environment in North America Simulated by a Global Climate Model,” Atmospheric Science Letters, Vol. 8, No. 4, 2007, pp. 100-106. http://dx.doi.org/10.1002/asl.159
[7]	E. Gilleland, M. Pocernich, H. E. Brooks, B. G. Brown, and P. Marsh, “Large-Scale Indicators for Severe Weather,” Proceedings of the American Statistical Association (ASA) Joint Statistical Meetings (JSM), Denver, 3-7 August 2008, pp. 1-16.
[8]	M. J. Heaton, M. Katzfuss, S. Ramachandar, K. Pedings, Y. Li, E. Gilleland, E. Mannshardt-Shamseldin and R. L. Smith, “Spatio-Temporal Models for Extreme Weather Using Large-Scale Indicators,” Environmetrics, Vol. 22, No. 3, 2010, pp. 294-303. http://dx.doi.org/10.1002/env.1050
[9]	E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne and D. Joseph, “The NCEP/ NCAR Reanalysis Project,” Bulletin of the American Meteorological Society, Vol. 77, No. 3, 1996, pp. 437-471.
[10]	S. G. Coles, “An Introduction to Statistical Modeling of Extreme Values,” Springer Verlag, New York, 2001.
[11]	R. L. Smith, “Extreme Value Theory,” In: W. Ledermann, Ed., Handbook of Applicable Mathematic, 7th Edition, John Wiley, Chichester, 1990.
[12]	R. L. Smith, “Estimating Tails of Probability Distributions,” The Annals of Statistics, Vol. 15, No. 3, 1987, pp. 1174-1207. http://dx.doi.org/10.1214/aos/1176350499
[13]	R. Katz, G. Brush and Parlanger, “Statistics of Extremes: Modeling Ecological Disturbances,” Ecology, Vol. 86, No. 5, 2005, pp. 1124-1134. http://dx.doi.org/10.1890/04-0606
[14]	E. Mannshardt, P. F. Craigmile and M. P. Tingely, “Statistical Modeling of Extreme Value Behavior in North American Tree-Ring Density Series,” Climatic Change, Vol. 117, No. 4, 2013, pp. 843-858. http://dx.doi.org/10.1007/s10584-012-0575-5
[15]	R Development Core Team, “R: A Language and Environment for Statistical Computing,” R Foundation for Statistical Computing, Vienna, 2009.
[16]	E. Gilleland, R. W. Katz and G. Young, “Extremes: Extreme Value Toolkit,” R Package Version 1.60, 2009.
[17]	D. Cooley and S. R. Sain, “Spatial Hierarchical Modeling of Precipitation Extremes from a Regional Climate Model,” Journal of Agricultural, Biological, and Environmental Statistics, Vol. 15, No. 3, 2011, pp. 318-402. http://dx.doi.org/10.1007/s13253-010-0023-9
[18]	L. Fawcett and D. Walshaw, “Improved Estimation for Temporally Clustered Extremes,” Environmetrics, Vol. 18, No. 2, 2007, pp. 173-188. http://dx.doi.org/10.1002/env.810