There has been a lot of hoopla about supposed problems in the surface temperature record recently. Unfortunately, a lot of people don’t seem to understand what the hoopla is actually about. I think the problem is people have focused too much on rhetoric and too little on actual information. I’d like to try to reverse that.
This all started when claims by blogger Steven Goddard gained traction in the media. His claims were of the sort:
Right after the year 2000, NASA and NOAA dramatically altered US climate history, making the past much colder and the present much warmer. The animation below shows how NASA cooled 1934 and warmed 1998, to make 1998 the hottest year in US history instead of 1934. This alteration turned a long term cooling trend since 1930 into a warming trend.
This is a very serious claim, but it is also a very uninformative one. It tells us very little about what was done, much less how or why it was done. This lack of information has helped cause a lot of confusion. When fact checkers at Polifact examined Goddard’s argument, they found it lacking. Unfortunately, they didn’t give a clear rebuttal. This led to Anthony Watts, proprietor of the most popular blog on global warming (Watts Up With That?), writing a post in which he said Polifact was wrong but further confused things.
The problem is there are several different issues, and people tend to conflate them all. I’ll list each in turn:
1) Temperature data is adjusted for a variety of factors known to causes biases in the data.
2) The USHCN record has data that is recorded, but not used in its calculations.
3) The USHCN record is based upon 1,218 records, but there are fewer than 1,218 stations in it.
4) Steven Goddard uses a wonky methodology which introduces biases into his calculations.
Point 1 has been getting the least attention. People are aware the data has problems and adjustments are made to try to address them. There are disputes over the particulars of these adjustments, but they haven’t come up much in the recent discussions.
Point 2 is a more commonly discussed point. According to an NCDC statement recently publicized, the data not used is data which fails quality control tests. That claim hasn’t been subjected to examination enough to determine if it’s valid, but it’s obviously understandable bad data may get discarded.
Point 3 is the big one. It’s the key to the confused post Watt’s wrote, and it’s the most confusing issue. USHCN had, at one point, 1218 stations recording temperatures. It no longer does. Stations have stopped recording data for a variety of reasons. This causes missing data. Another source of missing data is the data filtered out for quality control purposes.
The confusing part is USHCN doesn’t simply perform its calculations without the missing data. Instead, it tries to estimate what the missing data is then perform its calculations over the measured and estimated data. This means stations can be used in the calculations long after they’re closed.
It seems really weird. You have to wonder why they estimate data then perform calculations rather than just performing the calculations without that data. The answer is USHCN’s methodology doesn’t handle missing data well. Suppose you had five station records:
1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5
You could average the five lines together, and you’d get a final result of 3, 3, 3, 3, 3. No problem, right? Right. The problem comes when you have missing data, like:
1 1 1 1 1 2 2 2 NA NA 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5
If you averaged these five lines together, you’d get 3, 3, 3, 3.25, 3.25. If the data had been missing from the second line instead of the fourth, you’d get 3, 3, 3, 2.75, 2.75. Obviously, there’s a problem. We don’t want our results to have significant changes because of small amounts of missing data.
To address that, USHCN attempts to fill in the missing data before calculating it’s averages. It does this by looking at the neighbors of stations with missing data. In the example above, we can use the third and fifth line to try to estimate the missing data. There’s a 1 on one side of the missing data and a 3 on the other. Based on that, we estimate the missing values are 2 and get:
1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5
This gives us the right data, and when we average it together, we get the right results (3, 3, 3, 3, 3). Clearly, the methodology can work. It won’t be as precise or as accurate in the real world as in these simple examples, but it clearly can improve the results. It’s certainly better than simply averaging things together without doing anything to address the missing data.
But that’s what Steven Goddard does. That’s the wonky method referred to in Point 4. Goddard simply averages all the series together. If he saw line four was missing two data points, he’d just shrug and average things anyway. He’d find his results were 3, 3, 3, 3.25, 3.25. He’d then accuse the people of fraud if they said the right answer was 3, 3, 3, 3, 3.
(Goddard also ignores the fact stations aren’t located equal distances apart. I won’t delve into that point, but obviously, if you have 9 stations in one area and one station in another, you won’t get a good answer if you just average all 10 together.)
Now that we’ve established what all the issues are, it’s much easier to understand what people are saying. With that, let’s go back to Steven Goddard’s argument. He said alterations to the data “turned a long term cooling trend since 1930 into a warming trend.” This graph, which I’m stealing from Zeke Hausfaster, shows how Goddard justifies that argument:
The red line is what you get if you use Goddard’s methodology. The blue line is what you get if you
estimate the missing values anomalize and grid the data. As you can see, Goddard can only claim the data shows a cooling trend if he uses his wonky methodology. Given Goddard’s argument is based upon a faulty methodology, Polifact was right to criticize it.
And when Anthony Watts said the Polifact story was wrong, announcing:
I was so used to Goddard being wrong, I expected it again, but this time Steve Goddard was right and my confirmation bias prevented me from seeing that there was in fact a real issue in the data and that NCDC has dead stations that are reporting data that isn’t real
He muddied the waters. The argument Polifact checked was wrong. Point 3 is true as Watts says. It just doesn’t create that red line. You only get that red line if you accept Goddard’s wonky methodology (Point 4). If you don’t accept it, Goddard is wrong.
I’m sure you noticed that graph had a third line, a green one. That line is what you get if you make the adjustments of Point 1. Those adjustments are clearly far more significant. With them, it wouldn’t matter if you used Goddard’s wonky methodology. You’d still get a warming trend.
That’s why Goddard likes to make a big deal out of them. They are so important to his argument they are a primary cause of the differences Goddard highlighted in the post which triggered all this attention. He made this image, which Polifact reposted:
The differences between those two graphs are caused by different data being available and adjustments for known problems being handled differently. That means for Goddard to make his argument, he needs to argue all four points discussed in this post. For him to be right, he needs to be right about all four points.
Other people can take more nuanced approaches. Anthony Watts only talked about Points 2 and 3. Zeke, while highlighting all four points, only discussed point 4. Other people might only care about Point 1 because it has the biggest effect.
That’s all fine. We can talk about whichever points we want to talk about. It’s just important we try to understand which points each other are talking about.
July 5th Edit: I’ve corrected a mistake in this post where I described the blue line in Zeke’s graph incorrectly. The line has nothing to do with infilling data. It is what you get when you use anomalies and gridding to remove biases due to coverage issues.