Statistics, Models, Weather, and Climate

One of the most complex (and, for some, controversial) aspects of climate change studies is that many are based on models. Models are mathematical tools that basically spit out results that are based on the assumptions and data that you feed them.
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Drought-damaged corn is seen in a field in Westfield, Ind., Wednesday, Aug. 1, 2012. More than half of U.S. counties now are classified by the federal government as natural disaster areas mostly because of the drought. The U.S. Agriculture Department on Wednesday added 218 counties in a dozen states as disaster areas. That brings this year's total to 1,584 in 32 states, more than 90 percent of them because of the drought. (AP Photo/Michael Conroy)
Drought-damaged corn is seen in a field in Westfield, Ind., Wednesday, Aug. 1, 2012. More than half of U.S. counties now are classified by the federal government as natural disaster areas mostly because of the drought. The U.S. Agriculture Department on Wednesday added 218 counties in a dozen states as disaster areas. That brings this year's total to 1,584 in 32 states, more than 90 percent of them because of the drought. (AP Photo/Michael Conroy)

One of the most complex (and, for some, controversial) aspects of climate change studies is that many are based on models. Models are mathematical tools that basically spit out results that are based on the assumptions and data that you feed them. Feed them different assumptions or data, and they may spit out different results. The vast majority of climate change-related studies have come up with similar results despite different assumptions and data fed to them. Nevertheless, many people feel that models are just that: something abstract and not real, and full of uncertainties.

Statistics and polls, on the other hand, have a way of grabbing people's attention, not only because most people actually do understand what they say (95 percent chance of this, 60 percent of people think that, etc.) but because they are based on actual data, real facts. The consequence of that "reality" is that people will likely believe statistics more than models, simply because statistics are based on actual things that have actually happened.

So, the new study by James Hansen where he uses statistics (not models) to connect extreme weather events to climate change should come as a more believable, more reliable way for us to actually understand what is happening with our world. Hansen states in his Washington Post op-ed that "this is not a climate model or a prediction but actual observations of weather events and temperatures that have happened." A piece right here at Huff Post does a great job of summarizing the study's findings and the reaction from both sides of the climate arena.

An opinion piece by Michael Mann commenting on Hansen's study states that:

Over the past decade, records for daily maximum high temperatures in the U.S. have been broken at twice the rate we would expect from chance alone. Think of this as rolling double sixes twice as often as you'd expect -- something you would readily notice in a high stakes game of dice. Thus far this year, that ratio is close to 10 to 1. That's double sixes coming up ten times as often as you expect.

We all understand that language, and we all instinctively know that when things start happening consistently above chance, something fishy is going on (yeah, the dice is loaded, or the cards are marked). If that happened at a casino, most everyone would cry foul. Why aren't we crying foul now, when there is so much more at stake than money?

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