While I respect Al Gore (he did win the popular vote after all), he made a common error in his recent appearance on SNL. Near the end of his segment, Gore says:
"Have you been outside today? It's 60 degrees in late November. I mean there's a Christmas Tree in front of this building and guys are wearing flip flops. I mean, you can't say this isn't real."
Yes, I know that it's a comedy show and that I'm being dogmatic. However, Big Al is committing one of my climate pet-peeves, namely, confusing weather with climate. Climate science is all about probabilities. When scientists talk about climate change, they're talking about a shift in the odds towards a particular set of conditions (for example, an increased chance of warmer weather in November). A warmer than average day is not evidence for global warming any more than a colder than average day is evidence against warming. Rather, we need to show that the likelihood of experience a warmer than average day in November in New York has increased. This requires us to sample temperatures over many Novembers. Below is a graph of average temperatures in Central Park (blue) and in Portland, ME (green) for November 15-22.
The shaded areas are the standard deviations. Note: the means and standard deviations are over three years, to smooth out some of the variability. You'll notice considerable year-to-year variability as well as some longer warm/cool periods (for example, the 1950s were warm and the 60s were cool). First off, even during cool periods a 60 degree day in Central Park is still pretty likely. Since 1920, there is no significant trend in temperatures at either location during NBC's "Green Week." However, if you consider all weeks in November, there is a significant warming trend of 0.025 degrees/year in Central Park. The trend rises to 0.04 degrees/year if you use all the data back to 1876. In Portland, there is a slight cooling trend of 0.016 degrees per year. So, what's the point? The point is that climate change is complicated. There is a tremendous amount of variability, more commonly called "weather", in the data. Temperatures on any one day, at any one location, don't mean much. Real climate change signals can only be seen if you have enough data (in both time and space) to average over the variability.