Guest Post by Willis Eschenbach
Anthony Watts has posted up an interesting article on the temperature at Laverton Airport (Laverton Aero), Australia. Unfortunately, he was moving too fast, plus he’s on the other side of the world, with his head pointed downwards and his luggage lost in Limbo (which in Australia is probably called something like Limbooloolarat), and as a result he posted up a Google Earth view of a different Australian Laverton. So let’s fix that for a start.
Figure 1. Laverton Aero. As you can see, it is in a developed area, on the outskirts of Melbourne, Australia.
Anthony discussed an interesting letter about the Laverton Aero temperature, so I thought I’d take a closer look at the data itself. As always, there are lots of interesting issues.
To begin with, GISS lists no less than five separate records for Laverton Aero. Four of the records are very similar. One is different from the others in the early years but then agrees with the other four after that. Here are the five records:
Figure 2. All raw records from the GISS database. Photo is of downtown Melbourne from Laverton Station.
This situation of multiple records is quite common. As always, the next part of the puzzle is how to combine the five different records to get one single “combined” record. In this case, for the latter part of the record it seems simple. Either a straight linear offset onto the longest record, or a first difference average of the records, will give a reasonable answer for the post-1965 part of the record. Heck, even a straight average would not be a problem, the five records are quite close.
For the early part of the record, given the good agreement between all records except record Raw2, I’d be tempted to throw out the early part of the Raw2 record entirely. Alternately, one could consider the early and late parts of Raw2 as different records, and then use one of the two methods to average it back in.
GISS, however, has done none of those. Figure 3 shows the five raw records, plus the GISS “Combined” record:
Figure 3. Five GISS raw records, plus GISS record entitled “after combining sources at the same location”. Raw records are shown in shades of blue, with the Combined record in red. Photo is of Laverton Aero (bottom of picture) looking towards Melbourne.
Now, I have to admit that I don’t understand this “combined record” at all. It seems to me that no matter how one might choose to combine a group of records, the final combined temperature has to end up in between the temperatures of the individual records. It can’t be warmer or colder than all of the records.
But in this case, the “combined” record is often colder than any of the individual records … how can that be?
Well, lets set that question aside. The next thing that GISS does is to adjust the data. This adjustment is supposed to correct for inhomogeneities in the data, as well as adjust for the Urban Heat Island effect. Figure 4 shows the GISS Raw, Combined, and Adjusted data, along with the amount of the adjustment:
Figure 4. Raw, combined, and adjusted Laverton Aero records. Amount of the adjustment after combining the records is shown in yellow (right scale).
I didn’t understand the “combined” data in Fig. 3, but I really don’t understand this one. The adjustment increases the trend from 1944 to 1997, by which time the adjustment is half a degree. Then, from 1997 to 2009, the adjustment decreases the trend at a staggering rate, half a degree in 12 years. This is (theoretically) to adjust for things like the urban heat island effect … but it has increased the trend for most of the record.
But as they say on TV, “wait, there’s more”. We also have the Australian record. Now theoretically the GISS data is based on the Australian data. However, the Aussies have put their own twist on the record. Figure 5 shows the GISS combined and Adjusted data, along with the Australian data (station number 087031).
Figure 5. GISS Combined and Adjusted, plus Australian data.
Once again, perplexity roolz … why do the Australians have data in the 1999-2003 gap, while GISS has none? How come the Aussies say that 2007 was half a degree warmer than what GISS says? What’s up with the cold Australian data for 1949?
Now, I’m not saying that anything you see here is the result of deliberate alteration of the data. What it looks like to me is that GISS has applied some kind of “combining” algorithm that ends up with the combination being out-of-bounds. And it has applied an “adjustment” algorithm that has done curious things to the trend. What I don’t see is any indication that after running the computer program, anyone looked at the results and said “Is this reasonable?”
Does it make sense that after combining the data, the “combined” result is often colder than any of the five individual datasets?
Is it reasonable that when there is only one raw dataset for a period, like 1944–1948 and 1995–2009, the “combined” result is different from that single raw dataset?
Is it logical that the trend should be artificially increased from 1944 to 1997, then decreased from that point onwards?
Do we really believe that the observations from 1997 to 2009 showed an incorrect warming of half a degree in just over a decade?
That’s the huge missing link for me in all of the groups who are working with the temperature data, whether they are Australian, US, English, or whatever. They don’t seem to do any quality control, even the most simple “does this result seem right” kind of tests.
Finally, the letter in Anthony’s post says:
BOM [Australian Bureau of Meteorology] currently regards Laverton as a “High Quality” site and uses it as part of its climate monitoring network. BOM currently does not adjust station records at Laverton for UHI.
That being the case … why is the Australian data so different from the GISS data (whether raw, combined, or adjusted)? And how can a station at an airport near concrete and railroads and highways and surrounded by houses and businesses be “High Quality”?
It is astonishing to me that at this point in the study of the climate, we still do not have a single agreed upon set of temperature data to work from. In addition, we still do not have an agreed upon way to combine station records at a single location into a “combined” record. And finally, we still do not have an agreed upon way to turn a group of stations into an area average.
And folks claim that there is a “consensus” about the science? Man, we don’t have “consensus” about the data itself, much less what it means. And as Sherlock Holmes said:
I never guess. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts. — Sir Arthur Conan Doyle, A Scandal in Bohemia
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[~SNIP~ The d-word is against site policy. Try again without the name calling. ~dbs, mod.]
One way to tell if the data sets are corrupted, is to ask oneself, “do the adjusted data give a random error, or is it always one way”. This is called a systematic error if it is unidirectional. In the warm-earther cabal, I posit that this is intentional wrongdoing.
These GISS et.al. adjustments and algorithms always, without exception, change the raw data into a warm-earther friendly trend (onwards, always upwards!). These data sets then lead an observer with less than the abilities of a Willis, to believe the earth is warmer than it is at present, and/or cooler in the past.
I know you sai, W.E., that you don’t assert wrongdoing. In light of the direction of the systematic adjustments, I certainly do, and enough circumstantial evidence (from “adjustments”, to “missing Ms”, to bogus sensor sitings, to cynical starting points or intervals, to “interpolations”, to “extrapolations”, to “dog ate my raw data”) has been collected to prove scientific and criminal wrongdoing beyond a reasonable doubt.
OT: Monbiot predictably has a piece gloating about the Sunday Times cave in.
One section intrigued me though:
“North was right to point out that the IPCC should not have relied on a report by WWF for its predictions about the Amazon. Or he would have been right if it had. But it hadn’t. The projection was drawn from a series of scientific papers by specialists in this field, published in peer-reviewed journals, some of which are referenced in the first section of the IPCC’s 2007 report (pdf).”
The only link is not to any specific papers but to to a pdf of the whole IPCC chapter, at the end of which are hundreds of citations. But searching the chapter for “40%” brings up as far as I can see, only passages dealing with quantities concerning clouds and precipitation – not any proportion of the rainforest supposedly at risk.
Has Monbiot been deliberately vague because there is nothing which clearly would back up the IPCCs claim that up to 40% of the Amazon rainforest is in danger from CC? Surely if there had been a killer quote Monbiot would have used it with relish.
PS
Sorry, the link to the Monbiot piece is:
http://www.guardian.co.uk/environment/georgemonbiot/2010/jun/24/sunday-times-amazongate-ipcc
re: Limbooloolarat
Actually it’s probably more like “larvo”, since they somehow get “arvo” out of “afternoon”. Of course, with all the bikini-clad maidens thereabouts, I’d be pretty confused too. 😉
OT, Moderator there is an article in todays WSJ online that may need to be discussed by Al Gore and David Blood http://online.wsj.com/article/SB10001424052748704853404575323112076444850.html?mod=WSJ_hpp_sections_opinion . It is behind pay wall. I think it will be free this afternoon or tomorrow.
“Is it reasonable that when there is only one raw dataset for a period, like 1944–1948 and 1995–2009, the “combined” result is different from that single raw dataset?”
I think under certain conditions this could be reasonable. Let’s say there’s dataset A and dataset B. They overlap for part of the time. When they overlap, dataset A is consistently a degree higher than dataset B. So then when they combine them into one dataset, it’s reasonable to lower A a half degree when it is by itself, and raise B a half degree when it is by itself, and average them when they are both present.
However, I do not know if this is the situation with the example in the post.
AleaJactaEst says (June 24, 2010 at 2:05 am): “Breaking news from Ozland, Prime Minister Kevin Rudd has stepped down in the face of an internal Labour leadership vote……”
Buh-bye!
Cement a friend says:
June 24, 2010 at 2:19 am
Yes. It is the average of those two datasets (max and min) which is shown in yellow in Fig. 5.
JohnH says: “Off topic but news of a more selatious (sic) type…”
Yes, it’s off topic here. And, yes, salacious. And ad hominem, with no relation to Gore’s already-nonexistent veracity in matters of science. This is more of a Weekly World News item than a WUWT factoid. Much as I abhor the Goracle, this may very well turn out to be akin to “Aliens Abduct Michael Jackson.” I wouldn’t pin any hopes on this as relevant.
Reply: I’ve trashed it. ~ ctm
John H – not really necessary for this discussion and definately TMI.
OT
I’m listening to Lord Monkton on Alex Jones Internet radio right now.
I appreciate the analysis in this post–it shows that even when mysterious “adjustments”
don’t favor warming, there are still data handling and integrity issues to be considered.
Climate science is only as good as the underlying data.
Willis, this becomes repetitive from you. If you were interested in explaining the algorithms to your readers, you could. They’re quite simple; there is no need to leave them as some mystery. As for the questions of whether the results are reasonable:
for the purposes of GISTEMP, the absolute values of the combined record don’t really matter. What’s important are the trends, as what you’re building towards is a spatially averaged set of anomalies.
As for the GISS adjustment, we’ve been through this before. Essentially, the one-legged trend adjustment attempts to eliminate the effect of this station from the spatial mean, by forcing it to have the same trends as its rural neighbors. That’s all there is to it. So if this station had for whatever reason a cooler apparent trend than its rural neighbors in the early part of the record, then it will be adjusted so that its trend during that time better matches the neighbors. The effect of the UHI adjustment (on the regional or global mean) should be roughly the same as simply tossing out all the urban stations. Actually, that’d be a nice calculation to do, to check…
Is the adjustment crude? You bet. Do there exist much more sophisticated ways of going about it? Sure. But instead of periodically bringing up an example of a GISS UHI adjustment, saying it looks weird, and doing nothing to explain why the adjustment did what it did, you could try to progress the discussion by giving an overview of what the UHI adjustment tries to do, and how it does it.
The net effect is to pretty much remove the impact of an urban station from the final result. Or at least, that’s the idea. If you don’t understand that context, then of course some of the individual adjustments will look odd – especially if you don’t do the legwork of compiling the neighbors, and doing the comparison between that station and the neighbors.
In a (weak) defense, there is an awful lot of data for GISS to check to see if the results of their adjustments look reasonable. But a counter-argument is which stations has GISS checked to see if their adjustments produce reasonable results.
Again, it comes down to both good basic science (fact checking the data) and good computer programming practices (testing the results). Neither appears to have been done in this case.
@ur momisugly Jack Simmons says:
June 24, 2010 at 1:47 am
“Imagine a DA bringing speeding charges against you. These are serious charges and could result in felony charges involving words like “criminal indifference to human life” or “speeding through a school zone”.
“Your defense attorney asks what is the evidence.
“He is informed there are six different readings from two police jurisdictions. These readings are from
at least six differentone radar guns,allwith a history of giving, on occasion, spurious results. Because of these technical difficulties, the data from these readings are combined and then homogenized. Both the specific locationsof the radar unitsand the basis of the data adjustments are not available to the defense. In fact, they are not available to anyone, as the records storage area of the two jurisdictions are in a state of complete chaos. . . . ”Do we know for sure that there were multiple thermometers? My impression is that when there are multiple records for one station, there was originally only a single thermometer in most cases. One of the mysteries of temperature data is how in the world different data sets come to exist where there was originally only one instrument.
Any thoughts in the change of Australian leadership toward the climate policies
HadCRU also has the 1999-2003 gap filled in their CRUTEM3 station data.
It can be plotted here along with the GHCN data:
http://www.appinsys.com/GlobalWarming/climap.aspx?area=australia
Zoom in to the Melbourne area – Laverton Aero is labeled with a “B” icon since both CRUTEM3 and GHCN station data are available. Click the icon to view the graph. CRUTEM3 shows more warming than GHCN (since the GHCN is unadjusted on this plot).
Typo in my comment; it’s a two-legged adjustment, not a one-legged adjustment.
Looks like the pivot point here was around 1997. The bit after that might be an over-fit by the algorithm; it can do that when it places the pivot very close to the beginning or end of the record; one would have to see the composite of the rural (or rather, dark-at-night) neighbors to see what was going on.
Geoff Sherrington says:
June 24, 2010 at 5:24 am
I love the bi-polar nature of the AGW adherents. Half the time they claim that there is no UHI, the other half of the time they say there is UHI but it doesn’t matter because the records are adjusted for it.
This is another question that to me should be obvious to anyone who has driven into or out of a city …
I love how people try to catch Willis in errors all the time and he just calmly replies and stuffs the evidence that he has obvoiusly already presented previously down their virtual throats but does it so eloquently and politely.
As usual Willis and Anthony, well done well done indeed.
Here’s a thought, in Seinfeld, there is a humorous scene where Elaine screams “The Dingo ate may babyyyyyyy!”
Maybe the Dingo ate the evidence?
🙂
carrot eater says:June 24, 2010 at 11:20 am
for the purposes of GISTEMP, the absolute values of the combined record don’t really matter. What’s important are the trends, as what you’re building towards is a spatially averaged set of anomalies.
How do you determine a trend without absolute values?
As for the GISS adjustment, we’ve been through this before. Essentially, the one-legged trend adjustment attempts to eliminate the effect of this station from the spatial mean, by forcing it to have the same trends as its rural neighbors.
You’re assuming there are rural stations used in the spatial adjustment. As we’ve seen in the USA, the rural stations have been dropped.
http://chiefio.wordpress.com/
carrot eater says:
June 24, 2010 at 11:20 am
carrot eater, this objection becomes repetitive from you. I guess I’m not being sufficiently clear, it’s not the first time that I think I’ve explained something and someone doesn’t understand what I’m trying to say. Let me see if I can clarify it.
My point is not the details of the adjustment algorithms. They are not at issue, and they are public record that anyone can look up.
My point is whether the algorithms provide reasonable and defensible results.
Unfortunately, the GISTEMP records are used for many, many more things than a “spatially averaged set of anomalies”. In addition, why would you want to excuse a procedure that messes with the absolute values?
But even just considering the trends, the algorithm in this case (and many others) makes the trend less accurate, not more accurate.
What does it matter “why the adjustment did what it did”? The question is whether the result is correct, not whether the adjustment had an unhappy childhood …
You seem to think that if the algorithm does what it was designed to do, that the results are perforce reasonable, and thus the case is closed.
I hold that despite doing what it is supposed to do, the result of the adjustment makes absolutely no sense. It’s not making a proper adjustment for anything.
Whoa, whoa, whoa. Do you hear what you are saying? “Or at least, that’s the idea”??? That kind of handwaving doesn’t cut it in the real world. It’s like saying “the net effect of the blowout preventer is to shear the drill pipe and cap the well. Or at least, that’s the idea” …
In this case, it increases, not removes but increases, the impact of UHI on this station from 1944 to 1997 … and I hardly think “that’s the idea”.
“Look odd”? I’m not saying they look odd. I’m saying that the adjustments are wrong, that they don’t make sense, that they don’t pass the smell test, that they do the opposite of what they are designed to do. “Odd” is not a synonym for “incorrect”.
And I’m not interested in making billion dollar decisions based on an algorithm that makes “odd” individual adjustments. “Kinda good enough some of the time” may be fine for you. When it comes to hugely important public policy decisions, it is totally inadequate in my book.
I hope that clears up the confusion.
w.
Willis,
None of that forwards your argument at all.
You come along, eyeball some adjustments, and declare them wrong. Based on… what? The only way it’s “wrong” for the purposes here is if the adjusted record has long term trends which are unlike the composite of the rural neighbors. Have you done that comparison? I don’t see that you have.
The GISS adjustment does one thing. It’s very clear what it does, and why it does it. Under the hypothesis that urban stations may have spurious long-term trends, it takes a broad brush and gives it the same trends as the rural neighbors. That’s all the meaning the adjustment has. That adjustment may be up, down, whatever. It just makes it so that the urban stations have little effect on the long term trends of the spatial averages. Your readers would be better served if you actually made that clear.
Would you be happy if GISS just tossed out all urban stations? If so, then you shouldn’t have a problem with these crude adjustments.
The point is that you use something that’s appropriate for your needs. If you want a temperature record that is a careful reconstruction of what that location’s history would have looked like, if it weren’t for station moves, instrument changes, etc, etc, then GISS is simply not where you go. GISS does not do that, it does not pretend to do that. So if that’s what you want, don’t go to GISS. GISS makes an adjustment suitable for its purposes.
Oh, and for the context of how does it matter to the big picture – here
http://clearclimatecode.org/gistemp-urban-adjustment/