Yet another reader sent me a link to a really annoying article at a site called “Daily Tech”. The article has been more than adequately debunked by Darksyde at Daily Kos, but it’s a very typical example of a general kind of argument made both for and against global warming, which I find extremely annoying.
The basic argument takes one of two forms:
- Wow, look how hot it is today! How can anyone possible deny global
- Wow, look how cold it is today! How can those idiots believe in global
These are both examples of confusing weather with climate. That confusion is a typical example of a common statistical error:
using aggregate data to draw conclusions about specific individuals, or using a single individual to draw conclusions about an aggregate. Individual data points and aggregates are very different things, and you can’t just arbitrarily go from one to another.
Weather is what it’s like outside today. In terms of temperature,
weather is a single data point, like today’s temperature here in New York.
Climate is the total set of data describing the temperature everywhere around
the world, every day, for at least a full year.
If we had an unusual number of 98F degree days here in NY last summer, that
wouldn’t be any kind of evidence that there is global warming. If
Edmonton Alberta had an unusual number of -30C degree days this winter, that
wouldn’t be any kind of evidence that there isn’t global warming.
Global warming is a statistical trend, that global average temperatures are changing by a couple of degrees celsius. It doesn’t mean that we’re not going to see extremely cold weather. It doesn’t mean that we’re going to necessarily see unusually warm weather. It doesn’t mean that it’s going to get warmer everywhere. In fact, it’s almost certain that some places will become
colder as a result of global warming!
I’ve explained before that when you’re dealing with statistics dealing with an aggregate, those statistics are only descriptions of the aggregate; you
can’t just arbitrarily assume that you can apply the aggregate data in a precise
way to a particular individual. For a common example, we know that the average male american will live to be around 73 years old. Pick a random person, and we can’t make any real prediction about how long they’ll live. Some people will die at
30; some will die at 50; some will live to be 100. In my family, my favorite uncle, who my son is named after, was a healthy guy, and he died in his 50s. His brother, my grandfather, was very unhealthy – he had diabetes, high blood pressure, high cholestorol, congestive heart failure and emphazema, and he lived to be 86. Between the two of them, the average of their lifespans were very normal – but one died young, and one lived an unusually long time.
Climate is an aggregate statistic. It’s the cumulative average of the daily weather every day, every place in the world. Global warming is a general directional trend – the global average temperature is changing.
As I said above – there are some places likely to become colder as a result of global warming. For example, Great Britain currently has weather quite a bit warmer than is typical for its latitude. One of the major reasons for that is
an ocean current called the mid-atlantic conveyor, which carries warm water from the equator up towards Europe. Global warming, by causing an influx of cold water from melting ice could change the ocean currents,
essentially halting the conveyor, resulting in Great Britain and much of western Europe to become significantly colder.
You just can’t reason from individual data taken in isolation to
conclusions about an aggregate. You can’t pick an arbitrary sample from the
aggregate, and use it to draw conclusions about the aggregate – you need to carefully select a representative sample. What people commonly do, when they’re looking at weather, is looking at a subjectively selected, non-representative sample, and trying to use it to draw conclusions about
trends in the global aggregate. That’s just not valid statistical reasoning. If you want to make predictions about an aggregate, you have to understand that aggregate, how it’s computed, and what it measures. You have to understand the data
and how it’s collected in order to know what it means to take a representative sample of that aggregate. You have to use a very careful, rigorous process to
select a representative sample and perform any kind of meaningful reasoning