By JR on Wednesday, May 11, 2016
Extreme weather events have NOT become more frequent in recent times
Warmists never tire of claiming that global warming is causing more extreme weather events, particularly of the windy variety. Skeptics in turn point to statistics showing (for instance) that hurricane landfalls on the USA have in fact been much reduced in the last 10 or so years.
The authors below however rightly argue that if you are going to detect trends, you need as long a time series as possible. What has happened over the last 10 or 20 years may not be typical. So they go back to 1872 to get their data for analysis. And they devise methods of statistical analysis that take account of the relative rarity of such events.
So they divide their data into two haves, an early half and a later half. And they find that there has been no change in the frequency of extreme events between the first half and the second half. From 1872 to 2011, there was no change in the frequency of extreme weather events
The prophecy that global warming would bring on more extreme weather events was always on fairly shaky theoretical ground anyway.
The opening clause in their Abstract below would have been needed to get their article published
Need for Caution in Interpreting Extreme Weather Statistics
Prashant D. Sardeshmukh and Gilbert P. Compo
Given the reality of anthropogenic global warming, it is tempting to seek an anthropogenic component in any recent change in the statistics of extreme weather. This paper cautions that such efforts may, however, lead to wrong conclusions if the distinctively skewed and heavy-tailed aspects of the probability distributions of daily weather anomalies are ignored or misrepresented. Departures of several standard deviations from the mean, although rare, are far more common in such a distinctively non-Gaussian world than they are in a Gaussian world. This further complicates the problem of detecting changes in tail probabilities from historical records of limited length and accuracy.
A possible solution is to exploit the fact that the salient non-Gaussian features of the observed distributions are captured by 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 and as such 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. The Markov process model can be used to provide rigorous confidence intervals and to investigate temporal persistence statistics. The procedure is illustrated for assessing changes in the observed distributions of daily wintertime indices of large-scale atmospheric variability in the North Atlantic and North Pacific sectors over the period 1872–2011. No significant changes in these indices are found from the first to the second half of the period.