IMAGe Brown Bag- A New Approach to Extreme Value Analysis in a Changing Climate

03/04/2016 - 12:00pm to 1:00pm
Mesa Lab- Chapman Room

Prashant D. Sardeshmukh
CIRES, University of Colorado and Physical Sciences Division/NOAA

Friday, March 4, 2016
12:00-1:00 pm
Mesa Lab, Chapman Room
(Bring your lunch)

The probability distributions of climate system variables are skewed and heavy tailed in a distinctive manner. Ignoring or misrepresenting this distinctive non-Gaussianity has large implications for detecting, attributing, and predicting changes in extreme risks using limited observations and imperfect models. Our new approach to this problem exploits the fact that the salient non-Gaussian features of the observed distributions are well captured by a general class of so-called Stochastically Generated Skewed (SGS) distributions that include Gaussian distributions as special cases. SGS distributions are associated with damped linear Markov processes perturbed by asymmetric stochastic noise, which arises naturally from energy preserving interactions among fast and slow climate system components. As such, SGS distributions represent the simplest physically based prototypes of the observed distributions. The tails of SGS distributions can also be directly linked to Generalized Extreme Value (GEV) and Generalized Pareto (GP) distributions. These tails can, however, be more accurately estimated by making use of all available data in a data record instead of only the extreme values as in the GEV or GP approaches. The Markov process model can be used to provide rigorous confidence intervals, and to investigate temporal persistence statistics. We will illustrate the procedure for assessing long-term changes in the observed distributions of daily wintertime indices of large-scale atmospheric variability in the North Atlantic and North Pacific sectors over the 1872-2011 period. No significant changes in these indices were found from the first half to the second half of the period. (Reference: