Weather is a chaotic stochastic system. Outcomes that we typically like to measure – severe storms, tornadoes, hurricanes, temperatures, snowfall — all have mean or average behavior with a large bell-curve or normal distribution around that mean.
With all the talk of record snow in Washington or light snowfall in certain Olympic venues, I feel that a reminder is in order: There is very little one can deduce about changes or drift in the mean from one or two isolated events in the tail ends of the distribution. If a kid in your high school gets a perfect score on her SAT, does this mean that the average kid is getting higher SAT scores, that this kid’s score is a symptom of “global smartening?” Or is this kid’s performance just an isolated event in the tail of the test score distribution? Katrina and the Washington blizzard seem to occasion a lot of climate conclusions, when in fact I think those conclusions are virtually impossible from such events.
The only really useful role I can see that these extreme events play in the scientific debate is to weed out the credible climate commentators from the charlatans. If an alarmist says, for example, that the heavy snows in Washington are not necessarily inconsistent with global warming, then he or she is probably relatively safe. But run away quickly from anyone who says manmade CO2 caused Katrina or, even more incredibly, the Washington snowstorms — they are just nuts.
Of course, the argument typically morphs into folks arguing that extreme events themselves are more prevalent, in other words somehow the standard deviation of the distribution has expanded. This, in my mind, is one of the weakest arguments in the alarmist arsenal. The evidence for this is extremely weak (example), and a number of metrics (such as for hurricane activity and large tornadoes) have actually declines over the last decade. What tends to happen is that the reporting frequency of such events increases, which increases the general perception of having more extreme events — but scientists are supposed to be able to see past such observation biases.
A corollary to this is that extremes in one part of the world do not necessarily mean that the world average is moving in that direction. Those of us in the US would have sworn January was a cold month, but globally it turns out January was actually a pretty warm month, at least on the historic scale of the last 30 years. I remember when agricultural futures were first popularized, farmers often went bankrupt forgetting just this corollary. They would see weather in their area terrible, with terrible crop yields ahead, and they would go long on these crops in the futures markets, only to find the weather in other areas was quite good and they lost a fortune on their futures.