I happened to come across something which annoyed me on the Berkeley Earth (better known as BEST) website today. I’ll discuss it later, but it reminded me of something I’ve been interested in about that group’s efforts. For those who don’t know, their project involves creating a record of the planet’s temperatures.
To do so, they combine data from many different temperature records across the globe. There are lots of different ways to do this, and there are lots of debates about how good or bad any of them are. I won’t get into that, but I want to talk about one newish thing BEST does. Instead of adjusting individual records when it appears there’s a shift in data unrelated to climate (such as you’d get if a temperature station moved), BEST simply splits the record into separate series.
It’s a good approach. If a temperature station moves three times, we’d have four different segments with little relation to one another. Treating them as four different series makes sense. The problem is figuring out where to split those series. How do you tell when a change in data is and is not related to climatic effects?
Sometimes you can tell because there are records of things like stations moving. Most of the time though, you can’t. You can only try to guess by looking at the data itself. To help, you can compare a record to nearby records and see if you can spot differences. Breaks in the data found this way are considered “empirical breakpoints.” BEST looks for such (in a bit more complicated way than I described).
The question is, does BEST find what it hopes to find? I’m skeptical. To see how you feel, here are three temperature records I picked out while browsing. I’m showing only the data from 1965-1991 (where there was overlap). For each one, I want you to see if you can guess how many breakpoints each series should have. If you’d like, feel free to try to guess where those breakpoints should be:
A couple notes. The first two records should be similar because I intentionally picked stations near one another. Feel free to use that to help you try to find breakpoints or to ignore it all together. Also, don’t think a small gap in data means there should be a breakpoint. None of the series shown have a breakpoint due to missing data.