As you guys probably remember, I am supposed to be posting a follow-up on my discussion of how adjustments to temperature data affect the modern temperature record and how such changes impact our interpretation of the rise in temperature in modern times. If you haven't seen the previous post about this, you can find it here.
It's a somewhat difficult post to write, particularly since I have been having trouble finding data for a number of results that have been shared and discussed online.
As a result, I've been doing more work on other topics. You may have noticed I recently resolved a discrepancy in results posted by Steve McIntyre and those I got for the recent Gergis et al (2016) paper. Steve was kind enough to share his code, and it turns out he had used the wrong data set. With that issue resolved, I'll have more to say about the paper. I do want to talk about the adjustment issue though, so I'll do that today.
This post won't be the last one, but I do think it is an important one. You see, it's not just adjustments to the data we have to worry about. There's another issue that can be just as important, if not moreso.
Hey guys. I've been having trouble finding a topic to write about this week, so I've decided to re-visit an old issue. People familiar with my writing know I have spent a fair amount of time examining what effect human adjustments to temperature data have had on temperature records. This has mostly focused on the work of the (questionably named) BEST group.
I am not going to re-visit the not insignificant history of this topic today. If you want to read a bit about it, this post should give you a bit of an introduction to the matter. There is one historical point I do need to bring up though. A year and a half ago, in April of 2015, the head of the BEST project Richard Muller gave an interview in which he said:
“Furthermore, because of the interest, we re-analyzed all the data with ZERO adjustments, just to see what we would get. These results have been made available online. What we found was that the conclusions we had previously drawn were unchanged. The data are available here
You can read up about the trials and tribulations surrounding that article here if you would like, but the salient point is the data Muller referred to has never been published. It had not been published in April of 2015 when the interview was given, and it has not been published as of today, in August of 2016.
I don't know why Muller claimed data had been published when it had not. I don't know why that data has never been published. That's a matter for another day though. The reason I bring this up today is I want to point out anyone hoping to analyze the effect human adjustments to recorded data have on the BEST temperature results will face the obstacle of the BEST group falsely having claimed to publish the data which would make that possible without completely redoing the BEST analysis.
Fortunately, as you may have noticed while reading the posts I linked to above, the BEST group has shared that data with me. Unfortunately, that only happened after we exchanged a number of e-mails and I publicly criticized them (multiple times) for failing to publish data then turning around and claiming it was published. Still, the result is I have the data. Continue reading
The Berkeley Earth surface temperature (BEST) project was supposed to be a great thing. It was supposed to resolve the concerns skeptics had raised about the modern temperature record. It was supposed to resolve not just technical issues skeptics had raised, but also basic concerns about openness and transparency.
Once upon a time, people managing the modern temperature records wouldn't even share basic information like what temperature stations they used. It was disgraceful, and it caused a lot of distrust. It was also one of the main reasons BEST was formed. BEST was supposed to help resolve the trust issues by being completely open and transparent. BEST has promoted it's openness and transparency time and time again, and its one of the most touted aspects of their project. The problem is, it's a lie.
Methane is not a greenhouse gas. At least, that's one of the many stupid things Berkeley Earth thinks people should believe.
Recently, while discussing the Berkeley Earth temperature record (BEST), I made the comment it seemed every station showed a similar warming trend in recent times. I decided to test that idea by looking at the last 50 years or so. To do so, I created a map of linear trends from for the 1960-2013 period:
You'll note, the scale of the map begins at 0. That's because there isn't a single point on it below zero. According to BEST, not a single location on the planet has cooled since 1960.
I've been discussing the Berkeley Earth temperature record (BEST) for the last few days. My comments have been quite critical with me going so far as to say the BEST data has no accuracy at regional scales. I've now set up an easy way to test this view. All you have to do is pick a spot:
Pick any spot on that map that isn't blue, and I will show you how BEST's temperature estimates for that area compare to NASA GISS's. As an example, here's what you'd get if you picked my house (a five year smooth is applied to each graph):
There are four BEST graphs to one GISS graph because GISS uses a 2º x 2º grid while BEST uses a 1º x 1º grid. As you can see, there's little point in BEST using that 1º x 1º grid as all four of its grids are nearly identical. As you can also see, all four of those graphs are dramatically different than GISS's. They all have a strong warming trend not present in the GISS data.
The same is true for many other areas. A particularly troubling one is in Atlanta, Georgia:
GISS shows that area has cooled slightly. BEST says that area has warmed noticeably. There is no legitimate reason for that. One of these data sets must be wrong.
So pick a spot. I'll post the temperature for it, and you can decide for yourself which results you think are more believable. Bonus points to anyone who can pick a spot BEST says is cooling.
I've discussed concerns I have about the Berkeley Earth Project's attempt (BEST) to construct a global temperature record here a couple times. One time I wrote a post discussing how, according to BEST, the state of Illinois is so terrible at measuring temperatures it needs to have its measurements warmed by about one degree per century.
Another time I highlighted the fact BEST's changes to individual stations are based upon perceived differences so small one can barely see them if they're plotted. As I said then, I think the way BEST changes to these stations reflect nothing more than them over-fitting their data.
Today I'd like to highlight an issue which combines both of those problems.
It's time to provide the answer to my recent challenge. For those who don't remember, the challenge was to look at three graphs and decide how many sudden, non-climatic shifts were in them. I'll now provide the same image as before, but with the breakpoints indicated:
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?
Or at least, that's what BEST says. Here's a figure showing temperature trends in Illinois stations according to BEST. The first figure shows trends in the raw data, the second trends in quality controlled data, the third trends after BEST's adjustments:
The average values in these are 0.59, 0.63 and 1.49°C/Century. That is, BEST's Breakpoint Adjusted Station Data shows trends an average of a degree higher in Illinois than its raw data shows. Illinois must really suck at measuring temperatures!