Nate Silver wrote an excellent blog post on how (in New York) it’s been snowing the same amount it always has but on fewer days. If it’s snowing on fewer days, those must be bigger storms. He ran the tests to show that the increase in very large storms since about 2000 is significant. As he observes, “Anthropogenic global warming, as I’ve said, is a plausible cause.”
(I was going to write this post. Now I don’t have to dig up the weather data.)
Since there was much less snow than forecast in New York, national (i. e. New York-centric) media have been wringing their hands about the difficulties of forecasting. See for example Adam Chandler in the Atlantic on meteorologists apologizing for bad forecasts, Harry Enten at fivethirtyeight on how the forecasts went wrong, Eric Holthaus at Slate on the same, and Zeynep Turecki at Medium on probabilistic forecasts. The common thread is that there’s generally an incentive to forecast high, because predicting more snow than actually happens causes less harm than predicting less snow than actually happens.
Meanwhile, here in Atlanta, just about a year ago we had two inches of snow coupled with a poorly timed forecast, and people were stranded overnight. One local station is doing “Snow Jam: Then and Now” on tomorrow evening’s news. I don’t know about you, but I’d take two feet in Boston — which I saw a few times in my four years there — over two inches in Atlanta.
Let’s see if you run the simulations on five possible climate disasters (blizzards, hurricanes, heat waves, tornadoes, or droughts) and pick ten possible recent periods and ten possible past periods to compare, then the most improbable climates should have 0.2% probability of occurring.
In an era of data mining, 95% confidence intervals aren’t good enough and even 99.8% is barely acceptable.
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