Guest Post by Willis Eschenbach
[see Update at the end of this post]
I got to thinking about the (non) adjustment of the GISS temperature data for the Urban Heat Island effect, and it reminded me that I had once looked briefly at Anchorage, Alaska in that regard. So I thought I’d take a fresh look. I used the GISS (NASA) temperature data available here.
Given my experience with the Darwin, Australia records, I looked at the “homogenization adjustment”. According to GISS:
The goal of the homogenization effort is to avoid any impact (warming or cooling) of the changing environment that some stations experienced by changing the long term trend of any non-rural station to match the long term trend of their rural neighbors, while retaining the short term monthly and annual variations.
Here’s how the Anchorage data has been homogenized. Figure 1 shows the difference between the Anchorage data before and after homogenization:
Figure 1. Homogenization adjustments made by GISS to the Anchorage, Alaska urban temperature record (red stepped line, left scale) and Anchorage population (orange curve, right scale)
Now, I suppose that this is vaguely reasonable. At least it is in the right direction, reducing the apparent warming. I say “vaguely reasonable” because this adjustment is supposed to take care of “UHI”, the Urban Heat Island effect. As most everyone has experienced driving into any city, the city is usually warmer than the surrounding countryside. UHI is the result of increasing population, with the accompanying changes around the temperature station. More buildings, more roads, more cars, more parking lots, all of these raise the temperature, forming a heat “island” around the city. The larger the population of the city, the greater the UHI.
But here’s the problem. As Fig. 1 shows, until World War II, Anchorage was a very sleepy village of a few thousand. Since then the population has skyrocketed. But the homogeneity adjustment does not match this in any sense. The homogeneity adjustment is a straight line (albeit one with steps …why steps? … but I digress). The adjustment starts way back in 1926 … why would the 1926 Anchorage temperature need any adjustment at all? And how does this adjust for UHI?
Intrigued by this oddity, I looked at the nearest rural station, which is Matanuska. It is only about 35 miles (60 km) from Anchorage, as shown in Figure 2.
Figure 2. Anchorage (urban) and Matanuska (rural) temperature stations.
Matanuska is clearly in the same climatological zone as Anchorage. This is verified by the correlation between the two records, which is about 0.9. So it would be one of the nearby rural stations used to homogenize Anchorage.
Now, according to GISS the homogeneity adjustments are designed to adjust the urban stations like Anchorage so that they more closely match the rural stations like Matanuska. Imagine my surprise when I calculated the homogeneity adjustment to Matanuska, shown in Figure 3.
Figure 3. Homogenization adjustments made by GISS to the Matanuska, Alaska rural temperature record.
Say what? What could possibly justify that kind of adjustment, seven tenths of a degree? The early part of the record is adjusted to show less warming. Then from 1973 to 1989, Matanuska is adjusted to warm at a feverish rate of 4.4 degrees per century … but Matanuska is a RURAL station. Since GISS says that the homogenization effort is designed to change the “long term trend of any non-rural station to match the long term trend of their rural neighbors”, why is Matanuska being adjusted at all?
Not sure what I can say about that, except that I don’t understand it in the slightest. My guess is that what has happened is that a faulty computer program has been applied to fudge the record of every temperature station on the planet. The results have then been used without the slightest attempt at quality control.
Yes, I know it’s a big job to look at thousands of stations to see what the computer program has done to each and every one of them … but if you are not willing to make sure that your hotrod whizbang computer program actually works for each and every station, you should not be in charge of homogenizing milk, much less temperatures.
The justification that is always given for these adjustments is that they must be right because the global average of the GISS adjusted dataset (roughly) matches the GHCN adjusted dataset, which (roughly) matches the CRU adjusted dataset.
Sorry, I don’t find that convincing in the slightest. All three have been shown to have errors. All that shows is that their errors roughly match, which is meaningless. We need to throw all of these “adjusted datasets” in the trash can and start over.
As the Romans used to say “falsus in unum, falsus in omnibus”, which means “false in one thing, false in everything”. Do we know that everything is false? Absolutely not … but given egregious oddities like this one, we have absolutely no reason to believe that they are true either.
Since people are asking us to bet billions on this dataset, we need more than a “well, it’s kinda like the other datasets that contain known errors” to justify their calculations. NASA is not doing the job we are paying them to do. Why should citizen scientists like myself have to dig out these oddities? The adjustments for each station should be published and graphed. Every single change in the data should be explained and justified. The computer code should be published and verified.
Until they get off their dead … … armchairs and do the work they are paid to do, we can place no credence in their claims of temperature changes. They may be right … but given their egregious errors, we have no reason to believe that, and certainly no reason to spend billions of dollars based on their claims.
[Update – Alaska Climate Research Center releases new figures]
I have mentioned the effect of the Pacific Decadal Oscillation (PDO) below. The Alaska Climate Research Center have just released their update to the Alaska data. Here’s that information:
Figure 4. Alaska Temperature Average from First Order Observing Stations
In the Alaska Climate Research Center data, you can clearly see the 1976 shift of the PDO from the cool to the warm phase, and the recent return to the cool phase. Unsurprisingly, the rise in the Alaska temperatures (typically shown with a continuously rising straight trend line through all the data) have been cited over and over as “proof” that the Arctic is warming. However, the reality is a fairly constant temperature from 1949-1975, a huge step change 1975-1976, and a fairly constant temperature from 1976 until the recent drop. Here’s how the IPCC Fourth Assessment Report interprets these numbers …
Figure 5. How the IPCC spins the data.
SOURCE: (IPCC FAR WG1 Chapter 9, p. 695)
As you can see, they have played fast and loose with the facts. They have averaged the information into decade long blocks 1955-1965, 1965-1975, 1975-1985 etc. This totally obsures the 1975-1976 jump. It also gives a false impression of the post-1980 situation, falsely showing purported continuing warming post 1980. Finally, they have used “adjusted data” (an oxymoron if there ever was one). As you can see from Fig. 4 above, this is merely global warming propaganda. People have asked why I say the Alaska data is “fudged” … that’s a good example of why.
Willis,
Here’s an odd thought. Can you compare the difference rural vs. urban temperatures – broken into weekends and holidays versus workdays? On top of that, how about throughout the calendar year?
I expect that there will be a difference based on absolute temperature, as heat pumps kick on one way or the other, etc, on top of whether cars are showing up in the parking lot.
I would argue that there is no single-value adjustment that can compensate or because the UHI is a variable effect due to the inconstancy of human activity. This should leave fingerprints in the data.
S. Geiger (08:15:52):
I don’t know the answer to that. But I do know that the large drop-off in temp stations has had an effect on the GISS record: click
Chart by Steve Keohane, who made, IIRC, it by subtracting this from the GISS data: click
@Alan S (15:55:04) :
“I have extreme difficulty understanding, from GISS policy as related above, why any rural station would be adjusted upwards. I assume I am missing something obvious and would dearly like to be enlightened.”
I had trouble with that, too, until I realized that they are doing things a bit backwards. They are assuming that the highest urban readings, which include UHI, are the correct temperature. Therefore the rural temperatures need to have UHI added in. It’s a very peculiar way to do things. One would think that you should subtract the UHI out. Maybe they just like high numbers!
Idwen is the old German name of Idus, some 7 km from Rūjiena, northern Latvia. I sincerely doubt there ever were any observations made in that God-forgotten place, so this name may refer to Rūjiena itself. It’s a ve-e-ery small town (population ~3.5k) and might well qualify as ‘rural’ though.
Mitau is the old name of Jelgava, which is listed as ‘rural’ despite having population above 60k.
Three locations in Spain and three in Saudi Arabia/Kuwait on the list, but none in Japan? Something seems to be screwed badly, a quick look at this map tells me.
At the risk of feeding the Troll…..
How can you instantly recognise an AGW fantasist? Somewhere along the line of their smug, patronising mantra driven “argument” (that of the non-scientific, non-mathematical variety), they cannot resist using the words “tinfoil” and “hat”. It is programmed into them at an early stage.
Example:
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If you want your guesses to be taken seriously, you need to make a little more of an effort to figure out what is going on before you go all tinfoil-hat on the subject.
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And then they confuse something fundamental (like who needs to justify what, to whom)
Example:
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I’ll ask you again: can you accurately describe any of the arguments in favor of the temperature measurements as they are?
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No – it is the AGW fantasists that need to do that, actually.
And then they get breathtakingly ironic (without meaning to be)
Example:
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If you can’t imagine how you could possibly be wrong, or understand the efforts that have been made to insure the data’s accuracy, there’s no point in challenging your faith with facts.
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I laughed until my ribs hurt quite a lot, when I read that gem.
I wonder if the Troll would be so insufferably rude, if he had to face those whom he insults, rather than hide behind a keyboard, somewhere on the internet?
Matanuska station has not just sat in the same place since its inception. Its moved around a bit.
http://climate.gi.alaska.edu/history/CookInlet/Matanuska.html#History
Not to mention the fact that in 1966 they changed the time of observation.
Robert is obviously a troll,
He gives nothing but negative feedback….Perhaps the computer modelers should have talked to him before they added all that positive feedback
It is always best to ignore trolls. If he knew anything at all about the science discussed he would have presented it.
I have the basic temp-humidity- pressure system (three seperate 24 hr recording instruments) on my rural property 25 miles from an official station Modesto Ca. Airport. When I check my recorded data it runs typically 2/3C cooler than the official temp.
Is there any move to set up a legitimate NGO system to counter the manipulation the governent stations are obvioulsy doing?
A less generous interpretation is, it shows that they’ve been coordinating their lies.
Yes, everything has to start over from the raw data, if even that can be trusted.
carrot eater (19:17:26) : edit
I still don’t have the understanding I’m seeking. Perhaps you could tell us why temperatures in Matanuska in e.g. 1930 should be adjusted at all? I don’t mean “because it’s classified as an urban station”. Even if it is urban now, it wasn’t in 1930, so your explanation is no reason to adjust the 1930 data as GISS has done.
I mean, what is the theoretical justification for adjusting the temperature in 1930? There is (AFAIK) no evidence that the thermometers were wrong, or anything like that. So carrot eater, since you want to rag on me for my lack of understanding, please enlighten us. Why adjust perfectly good data?
w.
“No – it is the AGW fantasists that need to do that, actually.”
You’re wrong, Willis. You’re just wrong. Science is not a sport in which people tally up all the evidence for what they want to believe, and leave the identification of contrary evidence and alternative explanations to people who want to believe those.
If you want to be taken seriously as a self-identified “skeptic,” you need to consider all sides of the issue, and be open to the idea that you might be wrong.
I’m not going to participate in your Crusade, not even by opposing you. If I hear one tiny bit of genuine openness to contrary evidence, or real curiosity about the world that extends beyond advancing your case, you will find me the most eager of correspondents.
Until then, I don’t think there’s anything left to say.
carrot eater (19:17:26) : edit
I would agree with you, if the algorithm actually produced reasonable results in the trends imposed on the data.
But given that Matanuska and Anchorage are only 30 miles apart, what is the explanation for the huge difference in the two adjustments? Matanuska uses every rural station for adjustments that Anchorage uses except King Salmon, yet their adjustments are totally different. To me, that spells “bad algorithm, no cookies”.
Yes, the surrounding rural stations are the drivers … but in the event, they’re driving the adjustments off the cliff. Merely saying, as you do, that it is the fault of the rural stations is assuredly correct … but it doesn’t solve the underlying problem.
Just checking: have you done a similar analysis of other stations that did not show such strange findings (beyond the Australia and Alaska stations)? Just wondering about publication bias.
carrot eater (19:36:51)
And you agree with that?
So for decade after decade, a station’s data is so trustworthy that it is used to adjust the data of certain surrounding stations. It’s one of the rural stations that “take charge” of other stations and modifies their data.
But then, after decades of being the gold standard, because in 2010 a station finally exceeds a certain number of lights around it, the 1930 station data is suddenly not trustworthy??? Suddenly, after decades, it’s “safest” not to trust the 1930 data??? That makes sense to you?
I understand that this is the GISS claim … I just find it a totally nonsensical claim. I was hoping that there might be a reason that was, you know … reasonable.
S. Geiger (08:15:52) :
To compare raw and adjusted temperatures in the entire global dataset, for GHCN (not GISS), see under Q4 here.
http://www.ncdc.noaa.gov/cmb-faq/temperature-monitoring.html
Not much to look at.
That’s global. In the US alone, adjustments make more of a difference; among other places see some figures and discussion here (both NOAA and GISS, this time)
http://pubs.giss.nasa.gov/abstracts/2001/Hansen_etal.html
Well, Willis, I’ll stand by my pseudo-scientific reason that past stations need adjustment. The 1930’s stations already received their sunlight input back in the 1930’s, but according to the laws of pseudo-thermodynamics (entropy) their temperatures will continue to decay. GISS, agreeing that the laws of physics should apply just as much to the distant past as the present or future, take this into account. I’ve looked at the global trends in 1930’s temperatures and have verified that the decade is slowly sliding into an ice age, just as pseudo-entropy theory would suggest.
See, it’s all perfectly reasonable, in a pseudo-scientific kind of way! 🙂
carrot eater (03:38:53) : edit
You better write GISS and tell them of their error, since at their site they refer to the final dataset that I used as “after homogeneity adjustment”. So what I have graphed is by definition the “homogeneity adjustment”.
Willis Eschenbach
First, you say the surrounding rural stations are driving it off a cliff. You can’t just say that. You have to open up all the surrounding rural stations, see how well they correlate with each other and Anchorage and Matanuska, and then combine all those rural stations and see what the average trend is. Without doing this work, this analysis here will remain less than half-done, and uninstructive. Perhaps it will turn out that it’s clear that Anchorage and Matanuska should have whatever trends were imposed on them. Or perhaps the whole thing is a spurious result. But until you do that work, you cannot know.
and Anthony and WUWT just got another mention in this Fox News story
Willis Eschenbach:
As for a station going from rural to urban: I’d have thought you’d be happy with GISS. They are taking the more conservative route, and the one most aggressive in removing UHI.
Take Matanuska. You know it was probably nice and rural a long time ago. Now there are some nightlights; just enough to make you wonder. Before looking at the temperature record, you have no idea when any UHI may have developed. So what do you do?
1. You can do nothing, and leave the possible UHI in there.
2. You can have faith in your statistical algorithms, and use them to sniff out the UHI (this is what USHCN does now, and GHCN will be doing it soon, see Menne 2009, Pairwise Homogenisation)
3. You can say “I think #2 is too hard to do well”, and just toss the station out, at all times. After all, without doing the sort of work required for #2, you won’t really know when any UHI started. So you don’t know which data you can keep, so don’t keep any of it. This is essentially what GISS does.
I personally prefer #2, if the algorithm can be shown to be effective against test data. But if you don’t trust #2, then #3 is where you go.
Robert (10:34:27)
Ummm … here’s my full quote:
Since you have not advanced a single argument, I can only assume you don’t have an answer either … so why on earth would you attack me for not having one?
Well, if I had said what you quote above, I suppose you might have a point. Since I didn’t, you’ll have to be open to the idea that you’re attacking the wrong guy. If you want to be taken seriously, you need to pay attention to who you are attacking.
Can’t tell you how depressed I am to hear that …
Wise words from Willis:
“All that shows is that their errors roughly match, which is meaningless.”
“Why should citizen scientists like myself have to dig out these oddities?”
“The adjustments for each station should be published and graphed.”
I’d be curious to see your take on Agassiz, British Columbia, Canada maximum-temperature adjustments sometime Willis. If/when you have time to take a look, be sure to compare the max-Ts & their adjustments with the minimum-temperatures & their adjustments. I haven’t yet made time to sort through the mess, but even upon a quick glance it was evident that some “funky” assumptions have been made. This has consequences for some of my lines of local work (which I have shelved until I have time to patiently & carefully resolve the data issues).
Mr Eschenbach,
I didn’t read all the bazzilion comments so may be you have already been congratulated on your comment on the competence of the “homogenizers” to treat milk. (If I had been drinking milk when I read that I’m sure it would have come out my nose!) However, these “adjusters” are not homogenizing they are “pasteurizing” for which they seem most competent. Cooking the data killed the bacteria of information. Milk safe to drink again.
Will
My colleague just got back from the Bahamas sans a tan. I asked and he said it was “freezing” there (in the 50′ sF). Not a problem though….. I adjusted the temperature by 30F because of La Nina. So in my AGW calculations. I’m still using the adjusted value of 80F for the Bahamas last week. That’s a few tenths of a degree higher than normal so AGW is conclusively proven. That’s legitimate, right? I mean we all know the Bahamas should be warm right now!
In my copy of the GHCN data “Matanuska” seems to be spelled “Mantanuska”, and I have entries that terminate 1994, so am not really up to date. Never mind, the information is nevertheless quite interesting – provided it’s pertinent.
What it shows is that there was a large downward step at approx late 1944, followed by effectively stable conditions until the PDO shift of early 1976 caught up with Alaska in October 1976, when an large (1.2C) UPward step occurred. This was followed by a very slight decline until the end of my data, 1994.
Anchorage records show exactly the same step pattern, with step changes at the same dates.
Can someone point me at a complete Matanuska data set please? I can then readily update this commentary. However, I can’t say anything useful about data adjustment, so maybe I’m off topic.
carrot eater (11:14:24), thanks for your reply. Real questions are always interesting. You say:
More conservative? I find the GHCN method more conservative, but I’m not happy with either one.
None of the above. Fallacy of the excluded middle.
What I would do first is get actual data about Matanuska, including the total station history, and all of the photos that I could get, both current and historical. I’d get as much population data about the surrounding area, including total economic activity (since McKitrick has shown this to be a factor). I’d look at all of these plus the temperature record, and see which if any of this seems to be affecting the temperature record.
Then, I’d compare the station to the surrounding stations to see if I could identify a breakpoint, as is done in the GHCN/GISS analyses. However, I would not (as they do) just take that as gospel. Often, the results of these types of algorithmic comparisons are complete nonsense.
Matanuska is a great example. From 1920 to 1970, they decreased the apparent warming. From 1970 on, during the time when we might suspect UHI, they increased the apparent warming. I’m still waiting for someone to explain how that makes any sense at all. If there was UHI, you’d want to reduce the apparent warming, not increase it as they have done.
And why reduce the early warming? So what if surrounding stations were different from Matanuska? Nature is not homogeneous. If it were, we’d only need one station. Without a theoretical reason of any kind to back it up, changing the early data cannot be justified.
Neither you nor anyone has explained a logical reason for the adjustments, either warming the early data or cooling the later data, and without that, why should we trust the algorithm?
In the end, as Matanuska proves, it is obviously not something that we can leave to a computer to decide. So at the end of the day, I would weigh all of the evidence, and make a decision, what ever that decision might be. Don’t touch it, adjust it, throw it out, whatever. I would then document the decision, and list all of the reasons for each change to the record. I would graph the exact effect of my choices on the record. I would publish all of this in a clear, readable, understandable format, so that if someone thinks I’ve made a mistake, they can find it and fix it.
That’s what I’d do. So … neither #1, #2, or #3 …
Thanks,
w.