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.
Thanks, Willis, for keeping attention focused on fudged climate data.
“Figures don’t lie, but liars sure figure!”
With kind regards,
Oliver K. Manuel
Again we see that actual temperature readings are discarded in favor of altered readings. The public is never informed. There appears to be absolutely no rational reason to alter the readings. This is a prime example, but not unprecedented.
Nice one, Willis. Now i’m eager to see the first AGW specimen and the excuse it brings us.
There needs to be a full investigation into the adjustments of every weather station used in the temperature reconstructions. This should include an analysis of exactly how the data is being adjusted and a short paper as to what each adjustment does and why it is necessary.
It was, for example, recently admitted in the New Zealand Parliament that without adjustments, the temperature record for New Zealand shows no warming – the 0.9 degrees C warming that is shown in the official record is entirely as a result of the adjustments.
http://www.parliament.nz/en-NZ/PB/Business/QOA/8/8/e/49HansQ_20100217_00000009-9-National-Institute-of-Water-and-Atmospheric.htm
It may be that the adjustments are valid but there is no schedule of them so nobody can see exactly what they do and why.
It may be expensive to do but the total cost is chicken-feed compared to the billions being spent on climate change mitigation.
I think you forgot the minus signs in Figure 1, or have the terms reversed. If the adjusted temperature is larger than unadjusted, the correction would result in raising the earlier temperatures, not lowering. Figure 3 looks strange, since the correction used would make the dip between 1920 to 1990 deeper, and this is probably backwards. I think you have a sign error in both.
What is needed is a surface station data effort.
Yes, first the data, then the “science”. Thanks!
Kinda started slow, but really picked up speed towards the end there.
You were on to something with: “Not sure what I can say about that, except that I don’t understand it in the slightest.” That seems like a logical place to stop. You raise a question, maybe somebody addresses it, everybody goes home happy.
Unfortunately you go on to assume all sorts of things about what it must mean, and end with a rousing course of “all the measurements are wrong.” Typically, you cannot even bring yourself to accurately describe the evidence that the measurements are not wrong. The improbability of the exact same errors showing up all over the world in multiple data sets collected by different methods and different people you dismiss as “meaningless,” which it most certainly is not.
No, you were dead on: you don’t understand what’s going on in the slightest.
The same here in NZ, a classic was that the trend of raw temperature data at one station was negative, after adjusting for no known reason the trend was positive!.
There you go again, Anthony, focusing on the “nitty-gritty of measurement,” the “small technicalities” that “don’t matter” in“an epic game of nitpicking” and yet again “zeroing in on minor technical issues while ignoring the massive and converging lines of evidence.”
Leonard Weinstein (15:30:54)
Leonard, I believe the signs are correct. Since the early years of the Anchorage record are adjusted to be warmer than the later years, this reduces the apparent warming. Or as I said above, “At least it is in the right direction, reducing the apparent warming.”
Thanks for checking, that’s science at work.
James S (15:29:37) :
“There needs to be a full investigation into the adjustments of every weather station used in the temperature reconstructions”
There will be.
Commonwealth of Virginia v. U.S. Environmental Protection Agency — Petition for Reconsideration of Endangerment & Cause (U.S. Court of Appeals District of Columbia)
http://www.oag.state.va.us/LEGAL_LEGIS/CourtFilings/Comm%20v%20EPA%20-%20Pet%20for%20Reconsideration%202_16_10.pdf
I think my first comment is partially wrong. The correction should have been made to later data, not earlier, but a correction of later data would be in the direction shown. However, the overall level of temperature would be wrong with where they made the correction. If this result was averaged in with other data, the overall level would be biased high, even though the direction of correction locally would give a more correct trend locally. I also suspect the magnitude of correction is much too low at later dates.
Willis got the sign right IMHO, read it like this: back in the day when Anchorage was a small village, 0.9 degrees were added.
Later when it was a big city 0 degrees were added. This is a compensation for the UHI.
Nope. Sorry. The reason that you do not understand the correction to the readings at Matanuska is that there is no verifiable reason for them.
I’m sorry, that is not quite what I meant. I meant that there is no verifiable scientific reason for them.
There is a reason. Hide The Decline.
The rural site was adjusted to make it fit the urban site, which was not properly adjusted to correct for the heat island effect at all.
Beginning in about 1955, there should have been about a 5 degree adjustment in the Anchorage temperature, to adjust for the heat island effect.
And Matanuska needed no adjustment at all, having incurred no heat island effect.
This is not homogenization. This is false data manipulation, ordered up by politicians, for the purpose of furthering their dreams of a Universal Utopian Socialist State.
To quote Commodore Edwin Peary, “find a way or fake one.”
Willis —
Very interesting!
The Anchorage adjustments appear to occur every 9 years or so on average, rather than an even 10, which in itself is a little curious.
But then the Matanuska adjustments, which are on about the same schedule until their min around 1970, suddenly increase to every 2 or 3 years. Curiouser yet!
It has been evident for some years now that Big Government offices collude in bad faith under false pretenses to promote a radical Warmist agenda increasingly divorced from reality. Manifestly, Climate Cultists’ goal is to sabotage, subvert the private-sector energy economy, ensuring that coal, oil, nuclear sources default to collectivist Statist zero, control by a fathomlessly corrupt, incompetent administrative/bureaucratic/regulatory apparat.
Cap-and-tax, EPA usurpations, have become so blatantly overt that no objective, rational observer can deny plain fact: This is not “politics as usual” but an ongoing, slow-motion coup de main. Difficult to realize, nevermind accept, individual actors are coalescing to proclaim, They Shall Not Pass.
Steve McIntyre did an audit of the GISS UHI adjustments in March 2008 after “scraping” the NASA website.
The audit shows that NASA applies an urban correction of its GISS temperature index in the wrong direction in 45% of the adjustments. Instead of eliminating the urbanization effects, these wrong way corrections makes the urban warming trends steeper. This article discusses Steve McIntyre’s audit of the GISS adjustments, with links to his original post:
http://www.friendsofscience.org/assets/documents/CorrectCorrections.pdf
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.
OT, but the Sun’s recovery would appear to be less than stellar, ( sorry couldn’t resist ), are we still bumping along the bottom of the minimum? and if so is this cycle a little longer than quoted?
Willis, If GISS adjusted the “rural” record, then you better check the metadata.
Not sure if hansen used nightlights for alaska. Anyways the algorithm should not change a station that “classifies” as rural. So if he used nighlights
and nightlights were “dim” or “bright” then it would get adjusted.
If he used population, then the pop would have to be less than 5K in 1995.
it aint rural unless hansen says its rural. take a picture from space in 1995 to tell.
James S (15:29:37) :
There needs to be a full investigation into the adjustments of every weather station used in the temperature reconstructions. This should include an analysis of exactly how the data is being adjusted and a short paper as to what each adjustment does and why it is necessary.
No doubt the “no money for that” line will be trotted out but given the mind numbing sums that have already disappeared into the black hole that is AGW one way or another…
” Leonard Weinstein (15:30:54) :
I think you forgot the minus signs in Figure 1, or have the terms reversed. If the adjusted temperature is larger than unadjusted, the correction would result in raising the earlier temperatures, not lowering. Figure 3 looks strange, since the correction used would make the dip between 1920 to 1990 deeper, and this is probably backwards. I think you have a sign error in both.”
“falsus in unum, falsus in omnibus”?
Would it be possible to append the second graph (the one of Matanuska) to show the population growth?
Second, at what interval does the adjustment steps for Anchorage and Matanuska occur? If they’re using a population growth for the adjustment steps to account or UHI, one would assume that they occur at National Census times (i.e, every 10 years or so).
The steps for the “rise” in Matanuska seem to be every two or three years since the mid-70’s…
They falsified the data, and they are perfectly aware of it.
They hoped that their funding would be written into the law before anybody noticed. How pathetic.
Thank you, Mr. Eschenbach.
(Was the famous minstrel, Wolfram von Eschenbach, by any chance, one of your ancestors?)
Little wonder that Joe & Josie Plumber are still buying Farmer’s Almanacs every year.
Science is killing itself, worst case of murder-suicide Western Civilization has ever seen.
Maybe the Politicians will see how much fun it is and join the game.
The correct way to adjust for UHI is to lower the Urban station data.
You adjust that which is subject to increasing UHI effect, not that which is still in it’s natural state and in no way subject to anything but natural variation, which is the whole point of taking the temps.
Where’s Harry? You talents are needed. Life time employment guaranteed.
Robert (15:34:47).
OK, Robert, you tell us exactly what’s going on.
Unless, of course, your iPhone is charging on line…
“”why would the 1926 Anchorage temperature need any adjustment at all?””
To show unprecedented warming.
“”What could possibly justify that kind of adjustment, seven tenths of a degree?””
Because it’s a travesty that it no longer matched Anchorage.
“”Since people are asking us to bet billions on this dataset””
No, trillions, and a complete life overhaul.
Thanks again Willis
DirkH …
“This is a compensation for the UHI.”
Actually the only way at adjust for UHI is to lower temperatures, you would never adjust up to correct UHI …
These adjustments are pure nonsense …
Has anyone actually found an adjusted record that appears to be justified and rational ? i.e. with a real UHI adjustment ?
I have looked at the GISS before and after data for several dozen cities and I have yet to find one with a correct UHI adjustment. and by correct I mean adjusted down more today that 20 or 30 years ago … in every case the largest adjustment have always been in the past with a ladder like line of reduction climbing to present day …
also what in the world would justify the adjustments to the rural station data ? No UHI so why is the raw rural data being adjusted at all …
Willis, thank you.
This is really more of the same!
The merry fraudster team (MFT) are attempting to show a steeper increase in temps. But this is outrageous!
The MFT needed to lower the past base temps and increase the more recent temps to show a steeper increase, to attempt to prove AGW.
Thanks to Dr. Christy, amongst others, we know these are bogus temps and the earth has been much warmer in the past, without burning fossil fuels.
I wonder how well the AGW MFT sleeps at night?
Does the world have the cajones to jail the MFT?
In future, please can you show the actual numbers – original and then adjusted/manipulated – as well as the graphs.
This makes it that much more difficult for the alarmists to deny it’s happening.
…continuation for the previous post, from Wikipedia:
“…In the 1970’s relatively large numbers of newcomers to Alaska came to Anchorage, then relocated 40 miles up the Glenn Highway to the largely rural Matanuska Valley where a “Alaskan country” lifestyle pervades…”
So more than likely they built an airport or two to move this massive increase, and that’s where the temp gauge is. It appears that Matanuska may be suffering from UHI, which makes the graph of adjustments seem even stranger.
Further research shows that they actually call the area the “Matanuska-Susitna Borough”, and there is no actual town called Matanuska.
“… As of 2008, (Alaska Dept. of Labor) there were 82,515 people residing in the borough. According to the 2000 Census, the population density was 2 people per square mile (1/km²). There were 27,329 housing units at an average density of 1 per square mile (0/km²)…”
Definitely UHI…
mathman (15:49:40) :
“…This is false data manipulation, ordered up by politicians, for the purpose of furthering their dreams of a Universal Utopian Socialist State.”
LOL, but not really.
It’s amazing what you can get for your “data manipulation” buck these days.
Robert (15:34:47)
Nonsense. Read what I wrote and stop making things up. I said specifically that we don’t know if they are wrong, but we also don’t know if they are right. I said:
Your attempt to put words in my mouth is repugnant. If you have ideas, bring them out. If you have issues with what I wrote, quote what I said and tell me what’s wrong with it.
I did not say that the “exact same errors” have shown up, once again that’s your fantasy. Quote my exact words if you disagree with them, it is an unpleasant tactic to claim I said something that I did not say.
I said that when a variety of errors have been discovered in three datasets, the fact that their overall global average is somewhat similar doesn’t provide me with any comfort.
For example, when the GISS dataset first came out, it showed a smaller trend than either the CRU or the GHCN datasets. Since then, every new adjustment to the GISS dataset has increased the trend and reduced that difference, to the point where they are now much closer. Coincidence? I doubt it.
If you think that their current rough agreement means that both datasets are right, I fear I can’t help you. When datasets are constantly compared to each other and modified so they agree better and better, their final agreement is meaningless.
Finally, since you seem to be defending GISS and claiming it is accurate … why was Matanuska homogenized in the way that it was? And more to the point, why was it homogenized at all?
My guess, which I clearly identified as a guess, is computers gone wild combined with no quality control.
What’s your explanation?
Robert (15:34:47) :
Kinda started slow, but really picked up speed towards the end there.
…
No, you were dead on: you don’t understand what’s going on in the slightest
______________________________________
REPLY:
Then by all means, Robert–enlighten us. Tell us wherein you know it all and we don’t.
Give us the reason according to your version of the story.
I double triple-dog dare you!
And if you don’t then I kindly request that you refrain from such comments from now on.
BTW, what scientific credentials do you have? Put them out if you have any.
The improbability of the exact same errors showing up all over the world in multiple data sets collected by different methods and different people
it’s not errors in the data sets being collected, it’s errors in the “adjustment” of the data.
And if you have a computer program that applies the same erroneous “adjustment” to each and every raw temperature record, you have a whole world of junk.
The audit shows that NASA applies an urban correction of its GISS temperature index in the wrong direction in 45% of the adjustments. Instead of eliminating the urbanization effects, these wrong way corrections makes the urban warming trends steeper
it looks to me like they’ve got a sign reversed; and I think it’s somewhere in the year counter. They’re making cooling adjustments back in time, rather than from the point of inception forward. this is why a warm year will result in changes that make 1934 (or whatever) suddenly get “adjusted” as cooler. And the more years get added, the farther back the adjustments get made.
now, that’s just a seat-of-the-pants analysis, but I’ve been looking at numbers for a lot of years, and my gut tells me to look for a counter programming error.
these guys aren’t programmers, and they don’t check their output. And it would be an easy rookie error to make.
just my 0.02
Robert follows the same tactic every time. Probably he’s got a “Thread hijacking for dummies” app on his iPhone. Robert, could you please tell your iPhone that the greenhouse effect violates the laws of thermodynamics, could i please have the stock rebuttal for that?
Robert:
The improbability of the exact same errors showing up all over the world in multiple data sets collected by different methods and different people you dismiss as “meaningless,” which it most certainly is not.
Pay attention first, before you spout.
The errors are clearly not at the collection of the data. That would be daft.
The errors are to adjusted data, and will all be the same because the same body is applying them. Namely NASA.
Now the trickly question, Robert. How do you explain that this sort of oddity has appeared in GISS for Alaska, and also NIWA’s analysis for NZ, and in Australia too? Mere fluke?
Smacks forehead. WTF? It’s so disappointing to see how amateur these “scientists” are. Very sad. Absolutely agree that it should be possible to trace back every adjustment all the way to the raw data for each station. How hard is that?
Of course I have trouble understanding the rationale for the way these data are used. Why is it useful to average temperatures? How meaningful is an average between a daily high and a daily low? That tosses so much information from hourly (or continuous) measures. Then averaging those into a monthly or yearly average? And then average that by region and then the whole globe? Seems nuts to leave behind so much data in search of a single yearly number to plot.
My other major concern is with the value of measuring air temperatures at the surface. When the entire heat content of the atmosphere is equivalent in amount to that in the first 8 feet of the oceans, shouldn’t we be much more interested in the heat content of the oceans? Given that there is no daily fluctuation in that temperature, I would find it a much better measure.
Willis, I think the way I characterized your writing and quoted you was completely accurate. I think your overwrought and emotional response suggests my criticisms found their mark.
You said something honest — you don’t understand why they made the adjustments they made. And then you went off the deep end.
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.
I’ll ask you again: can you accurately describe any of the arguments in favor of the temperature measurements as they are?
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.
The real science is coming in – we are not even sure there has been any global warming at all. So, the whole measurement, monitoring and analysis of global temperatures over the past 100 or so years has to be completely reviewed, and if necessary dumped if there’s insufficient accuracy to categorically say we have warmed, cooled or changed very little.
Oh, so the adjustments are hokey. But we have known that for several years already. Apparently discoveries such as these are falling in deaf ears because awareness doesn’t seem to seep beyond blogs such as this one that have been pointing these “adjustment” problems out for the past several years.
The logical thing to do would seem to be to eliminate urban stations from the record, use only rural stations, and not use any “adjustment” for Urban Heat Island. But the various groups seem to be doing the opposite, they are removing rural stations.
The whole thing is just a mess.
Gotta remember that the product these guys produce is a grid. And while a grid should tie at every single data point, every single time – its a lot easier for them to change the data when its hidden, including a newly manufactured “RHI”(Rural Heat Island) effect. Fast and loose with data to say the least.
At least ONE person (probably 2-3) at CRU was ashamed, glad for their release, glad you guys called them on it and continue to investigate. As the REAL scientists.
I don’t think charging precludes typing-up a lucid (or any other type of) response, unless, he has to have his charging-cable plugged-in in lieu of a larger-sized keyboard …
.
.
facepalm!
The “Intergovernmental Panel on Climate Change” wants data on the climate.
Knowing their agenda, do you:
1) Tell them, it’s rather chaotic.
2) Sell your soul.
henry (16:02:33)
I haven’t been able to find data on that because it is a rural station. The GHCN station metadata is listed as
42570274001 MANTANUSKA AES 61.57 -149.27 46 225R -9FLxxCO30x-9TUNDRA C
with no population given, as is usual with rural stations. From Google Earth, if those coordinates are right (61.57N, 149.27W) it is definitely rural.
The Anchorage steps are all but one at nine years, with one of ten years. The Matanuska steps downwards are about half and half seven and eight years. The upwards steps go three years, two years, three, two, three, two.
GISS doesn’t adjust for UHI by population in any case.
w.
Robert (15:59:50)
Before you start gloating, you should actually check the numbers yourself. There is no substitute for doing the math yourself. Otherwise, you just look foolish. Leonard was in error, not myself. See Willis Eschenbach (15:37:55) and Leonard Weinstein (15:40:06) and DirkH (15:42:07).
Nice try, though …
Why is there even a practice to adjust temperature data at all? I’m
a PhD scientist and work with raw data all the time. If urban heat islands and those sorts of things are having an impact on the data, then those factors should be used to describe the data during model building. You always run into danger when you “adjust” data here and there. Confusion arises over time: are you working with the raw data or adjusted data, and has it been adjusted properly. I for one always want the raw, unadulterated data to work with.
Perhaps I’m reading it incorrectly. But it appears that they are creating a stepwise drop in the temperature record from 1920 to 1970. Then they are adding a stepwise increase to the temperature records after 1970.
If so, it appears a number of things may be in play.
First, the incremental changes over time might be harder to detect, compared to isolated changes in a couple decades that may be larger.
A way to help “Hide the Decline and Incline.”
Second, it is a way to reduce the temps of the Warm Period in the 20s, 30s and 40s, perhaps to help make their record show the 1990s as the warmest decade of the century. Cooling the earlier temps helps secure their claims.
Third, by continuing the decline into the 50s to 70s, it increases the mid-century cooling. Further decline is expected anyway, so a little more might not be noticed.
Fourth, once the temps have been significantly dropped during the mid-century cooling, the dramatic warming that is added, makes their AGW Hockey-Stick-like claims that much more dramatic. That is, the difference between the lowest point in the 70s and the current temps would be more dramatic in the records.
Is that how it could be interpreted, if there was a AGW agenda involved?
Not saying there is one, but exploring the “what if.”
Since I live in Alaska (Valdez) and am about to move to Anchorage, I find these adjustments interesting. Also interesting to note is that the Matanuska site is in an area that became a bedroom community for Anchorage starting roughly in the late 1970’s and accelerating in the 1980’s and 1990’s. So any adjustment for Matanuska should be made to compensate for UHI there as well since it is much more built up than it was before the 1970’s and is definitely not as “rural” as it once was. As for the Anchorage site, it is a pretty fair distance from any UHI effect if it is out by the airport, since the coastal trail park area is out there and downtown Anchorage is several miles away. Much of the expansion of Anchorage’s population has been to the North (eagle river area), east (Muldoon area) and south (Huffman/O’Malley area). Temperatures in the Anchorage bowl can vary considerably between locations. In the winter of 2008/2009 for example, I was amazed driving around to find that locations as close as 1 mile apart could vary by as much as 10-12 degrees F.
Willis, I’m still waiting for you to answer my question. By all means, dance around all you want.
Meantime, another error in your piece: you refer to “(non) adjustment of the GISS temperature data for the Urban Heat Island effect” and immediately thereafter describe a procedure for adjusting urban stations based on the rural trend.
So once again: “falsus in unum, falsus in omnibus”?
I think I got whiplash trying to follow the graph of Matanuska’s homogenization. Can anyone recommend a personal injury lawyer who is more trustworthy than an IPCC scientist, or should I track down a GISS data specialist and have him apply his adjustments to my cervical vertebrae?
Given Matanuska’s sawtooth adjustment, should they adopt Katie Perry’s “You’re Hot and You’re Cold” as their theme song?
Hrm… That might make a cute video.
“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.”
Not really surprising that when all three datasets are fudged the same way then come up with similar results.
To clear up the mystery of how Mantanuska gets adjusted – the Gistemp rules have just been changed.
See http://data.giss.nasa.gov/gistemp/updates/ :
January 16,2010:The urban adjustment, previously based on satellite-observed nightlight radiance in the contiguous United States and population in the rest of the world (Hansen et al., 2001), is now based on nightlight radiances everywhere, as described in an upcoming publication. The effect on the global temperature trend is small, that change reduces it by about 0.005 °C per century.
(page last modified 17 February 2010 23:13:44)
Mantanuska has a GHCN brightness index of 18, (C=bright, but under the new rules an index over 10 moves a station from rural to peri-urban or urban and so it gets adjusted)
See text_to_binary.f, dated 2010-02-18, in the updated sources to see the changes. (I’m basing this comment on an earlier version of this file, having spotted an archived trial run for this changed based on the 2009_10 Gistemp run, queried this with NASA, and received confirmation that this was a run to see the effect of this change). I have implemented this change already as an option in my own Gistemp implementation, and appended further details of the adjustment for Matanuska below.
This change, in so far as it relates to some areas I am familiar with, does drag at least some “rural” stations into the 20th/21st century as UHI adjustment candidates, but does also produce some amusing consequences – one of the adjusters of my “home” station, Dublin Airport, used to be Fort William (Scotland), now upgraded to peri-urban. The GHCN temperature record for Fort William however runs from 1884 to only 1903, a date possibly slightly earlier than the night brightness on which its classification is now based (as it happens, although an adjustment is calculated for Fort William, the net effect of this is no change).
The Gistemp (enhanced) log for adjustment, based on the December 2009 data, as I have not yet downloaded the January data, archived last week):
urb stnID:425702740010 # rur: 15 ranges: 1918 1990 500.
longest rur range: 1910-2004 91 [wgt: 0.523 238.4 km] 425702960000 [CORDOVA/MILE] UNITED STATES OF AMERICA
add stn 2 range: 1903-1990 87 [wgt: 0.152 424.0 km] 425701780000 [TANANA] UNITED STATES OF AMERICA
data added: 87 overlap: 77 years
add stn 3 range: 1919-2004 85 [wgt: 0.823 88.4 km] 425702510000 [TALKEETNA] UNITED STATES OF AMERICA
data added: 85 overlap: 85 years
add stn 4 range: 1933-2004 70 [wgt: 0.494 252.9 km] 425703410000 [HOMER/MUNICIP] UNITED STATES OF AMERICA
data added: 70 overlap: 70 years
add stn 5 range: 1942-2009 68 [wgt: 0.278 361.0 km] 425702310006 [MCGRATH] UNITED STATES OF AMERICA
data added: 68 overlap: 63 years
add stn 6 range: 1923-1990 67 [wgt: 0.532 234.2 km] 425702640020 [MCKINLEY PARK] UNITED STATES OF AMERICA
data added: 67 overlap: 67 years
add stn 7 range: 1943-2004 62 [wgt: 0.570 215.0 km] 425702710000 [GULKANA/INTL.] UNITED STATES OF AMERICA
data added: 62 overlap: 62 years
add stn 8 range: 1943-2004 61 [wgt: 0.174 412.9 km] 425702910010 [NORTHWAY FAA AP] UNITED STATES OF AMERICA
data added: 61 overlap: 61 years
add stn 9 range: 1921-1990 47 [wgt: 0.271 364.6 km] 425703400010 [ILIAMNA FAA AP] UNITED STATES OF AMERICA
data added: 47 overlap: 47 years
add stn 10 range: 1942-1990 46 [wgt: 0.626 186.9 km] 425702490000 [PUNTILLA] UNITED STATES OF AMERICA
data added: 46 overlap: 46 years
add stn 11 range: 1937-1970 31 [wgt: 0.350 324.8 km] 425702960010 [CAPE SAINT ELIAS ALASKA, U] UNITED STATES OF AMERICA
data added: 31 overlap: 31 years
add stn 12 range: 1944-1971 28 [wgt: 0.480 259.9 km] 425702490010 [FAREWELL FAA AP] UNITED STATES OF AMERICA
data added: 28 overlap: 28 years
add stn 13 range: 1949-1976 27 [wgt: 0.621 189.4 km] 425702640010 [SUMMIT/WSO AIRPORT] UNITED STATES OF AMERICA
data added: 27 overlap: 27 years
add stn 14 range: 1944-1966 23 [wgt: 0.054 472.9 km] 403719660010 [SNAG A,YT] CANADA
data added: 23 overlap: 23 years
add stn 15 range: 1949-1969 21 [wgt: 0.332 333.9 km] 425702600010 [NENANA/MUNICIPAL AIRPORT] UNITED STATES OF AMERICA
data added: 21 overlap: 21 years
possible range increase 32 69 72
“Also interesting to note is that the Matanuska site is in an area that became a bedroom community for Anchorage starting roughly in the late 1970’s and accelerating in the 1980’s and 1990’s. So any adjustment for Matanuska should be made to compensate for UHI there as well since it is much more built up than it was before the 1970’s and is definitely not as “rural” as it once was. As for the Anchorage site, it is a pretty fair distance from any UHI effect if it is out by the airport, since the coastal trail park area is out there and downtown Anchorage is several miles away. Much of the expansion of Anchorage’s population has been to the North (eagle river area), east (Muldoon area) and south (Huffman/O’Malley area). Temperatures in the Anchorage bowl can vary considerably between locations. In the winter of 2008/2009 for example, I was amazed driving around to find that locations as close as 1 mile apart could vary by as much as 10-12 degrees F.”
It’s almost as if getting accurate temp readings is more complicated than: adjustments to rural site data = j’accuse.
Mooloo (16:31:24) :
Robert:
Now the tricky question, Robert. How do you explain that this sort of oddity has appeared in GISS for Alaska, and also NIWA’s analysis for NZ, and in Australia too? Mere fluke?
Mooloo
As I suspect by your name you live in Waikato and you will probably be aware that Dr. .Jim Salinger’s doctoral thesis was the used as the basis for the NZ NIWA adjustments to the data which distort that record. The raw data shows no warming for NZ. Guess where Jim Salinger did his doctorate work? Yup you can guess. He gets several mentions in the infamous emails from UEA. So IMO that explains one leg of the ‘oddity’ Mooloo.
An Aussie will be able to provide the other leg maybe.
Doug
Don’t be too harsh on poor Robert, he builds beautiful little strawmen and then in obvious delight he burns them down with an amazing display of pyrotechnics. We should all stand in awe of his talents.
Robert (17:06:35)
I must have missed the question. What was it?
Get real. What I meant was that they claim to be adjusting for UHI but they are not actually adjusting for UHI. The adjustment in Anchorage is a good example. Claims to be for UHI, has nothing to do with UHI. Stop grasping at straws.
“Why should citizen scientists like myself have to dig out these oddities?”
My best guess? Because to a professional scientist who knows what they’re looking at, they’re not oddities.
If your accountant was calculating your taxes using the bewildering maze of poorly-documented corrections and mis-corrections that are becoming evident in this whole farce, and then told you they had lost all your receipts, would you pay what he said you owed?!
I would not pay a nickel in taxes based on such work! That is exactly my attitude toward Globaloney.
Actually its a (fairly) trivial job. You write a second program to analyse the adjustments and highlight those that are unexpected, such as adjusting rural station upwards.
When I see something like those adjustments, my summary for policymakers is: “Eeek!”
Question – can we compare global (or N Hemi) averages of raw data vs.
“adjusted data” to see the differences imposed by the cumulative adjustments? just curious.
thx
slg
ps – along the same lines, what ever happened to the comparisons between the ‘good’ US stations vs. the combined ‘good plus bad’ US stations as determined by SurfaceStations project? Did this show a significant difference so that we can get a handle on the overall impact of the siting issues (?)
Thanks.
Peter O’Neill,
Adjusting urbanization based on street lights is a potentially large oopsie. A single street light over snow would be as bright as six or more streetlights over grass or pavement.
Willis,
I have a theory. In the video a NOAA scientist explains that
the new electronic weather stations have a cold bias and
they add a delta to the data. So, if the per electronic stations
show higher temperatures, but you slide the delta increases
back to 1973, which includes the hotter pre electronic stations,
and then homogenize the whole series, would you not see
very rapid rise in temperatures?
Watch from 3:00 to 3:24
One thing is clear here, Robert is a troll. I will skim over his comments from now on. Little more than hyperbole.
As to Willis’s article, I am beginning to wonder if there are any trustworthy stations whatsoever, as even cursory review seems to reveal serial maladjustments. I would posit that it is either willful misconduct or endemic incompetence. Likely both. Nice work Willis.
I’d still love to see a Transactional temps record. By all means, munge the data, introduce adjustments, reverse them out fully or partly as new methods are invented, and substitute new adjustments.
But, leave the original reading as is.
And, every single measurement/adjustment/whatever, is a separate transaction for a station, on a day/time. And is categorised up the wazoo: type of adjustment code, who/what process put it there, etc.
Accounting has been transactional like this for, oh, a quarter of a century.
And SQL databases to handle large transaction volumes are common, cheap and reliable.
This reliance on a single data point per station per date/time is just so….amtuer hour.
Accounting for temperatures is what we need to bring the munging out into the daylight.
“What I meant was . . .”
There’s what you meant, and then there’s what you said. Are you starting to reconsider the wisdom of “falsus in unum, falsus in omnibus”? I would, if I were you. Never in the history of the English language has anyone started a persuasive argument with the words “What I meant was . . .”
I’d say its you who are grasping at straws.
The question was: what are the arguments in favor of the temperature record as reasonably accurate?
henry & Willis
US Census found Anchorage to have 153 per sq mile in 2000 vs 133 in 1900.
Climategate: The World’s Biggest Story, Everywhere but Here…
http://pajamasmedia.com/blog/climategate-the-worlds-biggest-story-everywhere-but-here/
When will the “old” MSM join the party ?
Picky, picky, picky.
Berniel is right. Billions and billions of scientists from all over the galaxy assure us that they have huge quantites of evidence that in a few weeks we’ll be scorched to death by AGW.
And you want to check the data!
Willis & henry
Re Matanuska population, does this help?
The 2008 population estimate for Matanuska-Susitna Borough, Alaska is 85,458.
Note also: Matanuska-Susitna Borough, Alaska
Population and Housing Narrative Profile: 2005-2007
Dave (16:56:57) :
Why is there even a practice to adjust temperature data at all? I’m a PhD scientist and work with raw data all the time. If urban heat islands and those sorts of things are having an impact on the data, then those factors should be used to describe the data during model building. You always run into danger when you “adjust” data here and there. Confusion arises over time: are you working with the raw data or adjusted data, and has it been adjusted properly. I for one always want the raw, unadulterated data to work with.
I’m with you Dave.
I could never understand why the raw data needed to be ‘messed about with’ but I’m not a PhD scientist. It seemed to me that distortions such as ‘Heat Islands’ could have been explained without altering data. I do understand however that the raw NZ data shows NO WARMING since 1853 but the adjusted figures do. So it is only because ‘scientists’ trained at UEA have rewritten the date that the climate has ‘warmed’. Go figure that one.
Doug
Seeme like the next task for Surface Stations is to expand to look at all statsions used in the record and to look at the temperature history of each station.
Given how critical golbal warmin is said to be you’d think someone would want to thoroughloy evaluate at least a significant proportion of the data and validate the “corrections”. That means there should be an extensive program of evaluating appropriate justifieable transparent and independent corection algorithms.
Now Surfacestations has been doing an excelent job with volunteers and very little funding. It isn’t as if there is any shortage of funding for climate research, i mean, if they can give David Barber $153million to go on a couple of cruises to look at ice I’m sure there must be funds to take a detailed look at the instrumental temperature data and create some real science experiments to realy understand what factors affect the data set.
Maybe take an urban area and the surrounding rural area and throughly grid them with temperature stations. A major city could have temperature stations every mile say.
This isn’t something volunteers should have to do it is something the scientists should have done at the very outset.
Those coordinates do come back to Palmer AK.
There is a listing for a Matanuska Agricultural Experiment Station. I think that’s what the “AES” stands for in that listing.
So Robert, no need to keep us in suspense any more, just provide your analysis of what the temperature adjustments are all about so that it can be considered. We have, as yet, nothing to work with from you.
Robert (17:43:04) :
“What I meant was . . .”
Robert, Willis didn’t think you understood what he had said.
He was just explaining it to you again. Starting the sentence out with
“What I meant was” is a way of communicating without sounding
condescending.
I however, enjoy your posts. They remind me of why I don’t believe.
I have a more extreme view: even if the temps went up as they say, its doesn’t mean anything in the realm of “climate”. They don’t even have to make it up as they obviously have…..
Robert (17:43:04) The question was: what are the arguments in favor of the temperature record as reasonably accurate?
Zero, none, you’re obviously not serious are you.
(When will the “old” MSM join the party ?)
When obamas PR team tells them too so don’t count on it anytime soon.
There are lies, damn lies, statistics, and warmist climatology.
My troll detector has gone off. See you tomorrow.
The Darwin post was v good, bowled me over. However not having the graphs of the before or after adjustment temps or anomolies for Anchorage or its neighbour made this post a bit flat, I thought. What did the pre-adjustment graph look like? What did the post -adjustment trend look like? Without that info, IMHO the adjustment graphs themselves look a bit like underwear flapping in the breeze. Much more interesting if you get a hint of what it was hiding…
‘robust’ as ever…
22 Feb: UK Financial Times: Public losing faith in science
By Clive Cookson in San Diego
Public trust in science as a whole has suffered from recent attacks on climate research, the head of the senior US scientific body admitted at the weekend.
“There is evidence that the corrosion in the public attitude to climate science has spread over to other areas of science,” said Ralph Cicerone, president of the National Academy of Sciences, citing public opinion surveys in the US and elsewhere.
Cicerone and other research leaders said scientists must work to regain public trust by being more open about their findings. “We need to be more transparent and provide more access to our research data,” he said…
But access requests need to be reasonable, Prof Cicerone said: “Some scientists are receiving requests bordering on harassment.”
Jerry North, a senior climate change scientist at Texas A&M University, agreed. “It seems that vilifying a scientist has become popular entertainment in the US,” he said.
Speakers at the AAAS conference said that neither the allegations of data suppression at UEA nor errors discovered in assessments by the UN Intergovernmental Panel on Climate Change had changed scientists’ minds about global warming.
“For many people who were not close to the science, questions arose about whether the robustness of the underlying science should be called into question,” said James McCarthy of Harvard University, who is chairman of the AAAS.
“Within the scientific community the answer is No,” he said. “If you took all the UEA data out of the package and removed the erroneous IPCC statements, it would not change the underlying science.”
Jane Lubchenco, head of the National Oceanic and Atmospheric Administration , the federal agency responsible for climate science, said the IPCC “had a wakeup call and is taking steps to address the mistakes that were made and to ensure that they don’t happen again.”…
Prof McCarthy was critical of the way the media had joined sceptics in attacking the idea of manmade climate change – as for example when they pointed to this winter’s heavy snow on the US East Coast as evidence that the world was not warming….
http://www.ft.com/cms/s/2/1700ab46-1dbc-11df-9e98-00144feab49a.html
Is the station at The University of Alaska Experimental Farm?
(History)
1917: Established as a United States Department of Agriculture (USDA) Agricultural Experiment Station
GISS data 1917 – 1990 Coincidence?
Forgive me if this has already been suggested, but how about compiling unadjusted data from a large number of rural stations and using that as a starting point? Forgive me, I’m a mere scientist working in a field where generating bogus data costs you your career.
Robert (17:43:04) :
“What I meant was . . .”
There’s what you meant, and then there’s what you said. Are you starting to reconsider the wisdom of “falsus in unum, falsus in omnibus”? I would, if I were you. Never in the history of the English language has anyone started a persuasive argument with the words “What I meant was . . .”
Robert. It occurs to me that you open your mouth and let the wind blow your tongue around. You really have nothing to say at all.
read all:
21 Feb: WSJ: Climate Change and Open Science In the Internet age, transparency is the foundation of trust
By L. GORDON CROVITZ
‘Unequivocal.” That’s quite a claim in this skeptical era, so it’s been enlightening to watch the unraveling of the absolute certainty of global warming caused by man. Now even authors of the 2007 United Nations report that “warming of the climate system is unequivocal” have backed off its key assumptions and dire warnings.
Science is having its Walter Cronkite moment. Back when news was delivered by just three television networks, Walter Cronkite could end his evening broadcast by declaring, “And that’s the way it is.” The Intergovernmental Panel on Climate Change (IPCC) report likewise purported to proclaim the final word, in 3,000 pages that now turn out to be less scientific truth than political cover for sweeping economic regulations…
Some in the scientific community are now trying to restore integrity to climate science. “The truth, and this is frustrating for policymakers, is that scientists’ ignorance of the climate system is enormous,” Mr. Christy wrote in the current issue of Nature. “There is still much messy, contentious, snail-paced and now, hopefully, transparent, work to do.” …
http://online.wsj.com/article/SB10001424052748704757904575077741687226602.html?mod=WSJ_Opinion_BelowLEFTSecond
Referring to :
======================
Peter O’Neill (17:12:34) :
To clear up the mystery of how Mantanuska gets adjusted – the Gistemp rules have just been changed.
See http://data.giss.nasa.gov/gistemp/updates/ :
January 16,2010:The urban adjustment, previously based on satellite-observed nightlight radiance in the contiguous United States and population in the rest of the world (Hansen et al., 2001), is now based on nightlight radiances everywhere, as described in an upcoming publication. The effect on the global temperature trend is small, that change reduces it by about 0.005 °C per century.
(page last modified 17 February 2010 23:13:44)
=========================
If the ‘effect’ is that small why bother with it at all? As I read it it is just a minor modification to an adjustment factor anyway. How much effort went into that analysis? It’s not necessarily a waste of effort – knowing that different approaches produce similar results can be of interest – as Willis pointed out in teh main article for a different context – but one has to wonder why someone elected to authorize the research and then implement a change that seems to be entirely inconsequential in the general scale of things.
Why add to the complexity and confusing of historic comparison and audit byt implementing such a trivial change? Why not just note that the analysis was done and leave it at that?
Unless, of course, for some reason – perhaps even something as innocuous as professional vanity or a name on a paper, the inclusion of the change mattered.
It might just make auditing more difficult in retrospect in, say, 20 years from now. Or it maybe opens up another useful but obscure variable for fudge factoring in the coming decades.
If there is another reason for making the change let’s hear it. at 0.005 °C per century the effort of the adjustment to the adjustment value is, at first site, not worth the candle.
[If I got my facts straight, 90% of the following is stipulated as fact by most of those here as just common sense science. I felt a need to spell it out, if just for myself…]
UHI is not the only thing the homogenization is supposed to deal with. Moves of met stations is also supposed to be covered in that. Another is the change in the time of day or whether the station’s methodology was to take the daily highs (regardless of times), or temps at fixed times – day and night (and what times those were, vs some other common/standard times). We all know that.
Moves are all documented (well, almost, anyway). With the numbers of stations out there, it seems difficult to understand why stations shouldn’t be selected SOLELY on the completeness of their histories. When they are made and documented, they do not keep occurring like the steps shown; ergo, those steps would seem to have nothing to – even partially – do with moves. Even if moves were made, they would show up as some extra ‘blip’ in the steps. In the absence of such blips, I am positing that no moves were made. (But then that begs the question, “Where is the UHI in all this?”)
Looking at the steps, one would first want to ask if there is any way Willis’ arriving at the steps was wrong – if he somehow misread the before and after data. If that was nailed down, then the adjustments need to be specifically tied to those reasons for adjustments. Whatever adjustments appear in the reconstruction, (which should be equal to the adjustments actually made), those adjustments need to be JUSTIFIED, data-wise. AND RECORDED – if not for others, then at the very least for the person DOING the adjusting, so he/she knows what he did at some earlier date.
Most of our data-focused questions really amount to this:
Everyone should be asking (very strongly) Mann et al to JUSTIFY why they adjusted in the direction they did, and in the amount they adjusted. FOR EACH STATION.
Of course, no one wants to really go there, poring over every station, do we? Jones has indicated as much. Mann just bull rushes every one, hoping to intimidate them with his juvenile tactics.
…In any before and after presentation of data, the timing and amount of any adjustments needs to have each step (literally, it seems) verified – i.e., JUSTIFIED. When it appears as artificial as what is shown above, of COURSE someone is going to ask, “Are you SURE about that, Professor Mann?” And, “Were the adjustments to various stations done en masse, or individually, and if en masse why did you choose the particular grouping that we find?”
If the above assessement is true, how anyone could justify the regularity of the steps in the Fairbanks data defies logic. If true, and if it is also true moves were certainly not made on a regular basis, then what do they represent?. Changes in the time or methodology of temp readings COULD not be made in such a manner, not without regularly shifting the temp reading times IN THE SAME DIRECTION (and there are only so many hours in the day, so the readings would begin to come back around on themselves). Are we to believe that they kept moving the met station incrementally out away from the city center, presciently far enough and regularly enough each time to keep ahead of the curve that would be part of a database decades later?
If true, the stepped appearance is clearly ONLY an artifact of the massaging done at GISS. Yet it is hard (nay, impossible) to conceive any reason for such an obviously artificial step progression for Fairbanks, by GISS or CRU or GHCN. Nature (especially meteorological nature) would not present such a linear trend. Why would they put anything so artificial into their adjustments? Laziness? Simplification? If so, the latter, they should have documented it. for later reference – and possibly improving it later.
…Vis a vis urban met station data, all adjustments to rural stations – especially ones used as the basis for nearby urban stations – should be doubly critiqued (and with the concomitant increased security of records with which to justify any such adjustments), since they affect not only their own readings, but those of other stations. That is only scientific common sense. Jumping Matanuska in the manner indicated is like pointing a laser pointer at this station and yelling, “LOOK! We just did anything we wanted to, and no one gave a damn enough to double check us!”
The justification of adjustments – this is what is being glossed over, even in the skeptical blogs, or at least not stressed and/or focused upon enough. Where are the records of the adjustments?
There was one bit of code (pointed out by Steve McIntyre I believe), in which the adjustment factor ins Mann’s (?) code was clearly stepped, in some cases removed for a time, then re-instituted. But the trend was highest in the 1990s. The code notes should have identified why those adjustments were made, with reference to some database somewhere.
Speaking of which: THERE SHOULD BE A DATABASE OF ADJUSTMENTS USED. It is clear that the CRU people did not adjust the same for every met station, that they chose some pattern of adjustments for each station and proxy that they thought was correct above all other adjutments schema for application to those particular stations/proxies. Homogenization requires each type of proxy to be adjusted differently (probably each batch, no less).
But homogenization per se should not be needed for thermometer readings. Adjustments are not necessarily the same thing as homogenization. Homogenization is to get the readings into the same SCALE. Thermometer readings are already ON the same scale. Thermometer adjustments are only of a shift-type – up or down – not of scale.
To be good science, the reasons for instrument adjustments need to be spelled out and documented as part of the methodology AND KEPT WITH THE PROGRAM OR DOCUMENTATION USED. If this is not done, calling these people “scientists” doesn’t equate them to those I’ve known and worked with. For work this sloppy, they would have been found out in very short order and canned.
In good science, EACH TYPE of adjustment also needs to be treated in isolation. As an example, a station might have had a UHI effect, PLUS a shift in location, and those two might be in the same direction or opposite direction. A “time of reading” change might take place at the same station, necessitating a third adjustment to the same raw data, and that could be either positive or negative, too.
Without such isolated adjustment factors being listed FOR EACH STATION, reconstructing by peer reviewers or replicating by other scientists would be like shooting gnats – it would be impossible to hit, because the amounts blend together. making it impossible to know what was being done.
For Mann/GISS or Jones/CRU to produce raw data without, it would be impossible for anyone to follow up and verify the work. And if the peer reviewers did not undertake to replicate at least SOME of the results, the peer review was worthless.
These two graphs (if correct) are strong evidence that the data was just arbitrarily pushed in certain directions. In Fairbanks’ case, the direction is in the cooling direction; in Matanuska it is both cooling and warming.
“WHY?” For both is the entirely correct question to be asked.
Mann needs to justify his steps.
He needs to justify why he was adjusting a rural station at all. When it is moving up and down like a yo-yo, how is comparison to it supposed to make any sense?
He needs to justify why he was adjusting Fairbanks when it was essentially a rural outpost.
And like Wills asks, “Do others have to re-do all of this from the ground up?” Arguments by Jones (as he makes in his recent Science interview) that others can just take the data (20% of which is not public still) and reconstruct it are garbage, and he knows it. If different adjustment figures are used, different results will come out of it all. And then it will be Jones/Mann et all vs the other studies, arguing back and forth as to who did what and – literally -when.
OT, but temperature ralated. Finally! Summer has arrived here in Sydney, Australia. Inner west, is as ~37c, being touted as a “scorcher”. 37c a scorcher for inner west, Sydney?? I don’t think so. 47c you might be getting there….but 37c is well within usuall variability for this time of year with the current prevailing winds (Westerly).
And then there are my friends in the UK who fly back to Sydney tonight who said to me last night that London Heathrow was closed due to snow. No reports of that here in the MSM. Can’t have stories of cold here, that does not fit well with KRudd747, Mzz W(r)ong, Mzz Gillard and Pete “How can we stop our beds burning” Garret.
“Since people are asking us to bet billions on this dataset,”
Trillions, Willis. Trillions.
Robert (17:43:04) :
I find your impetuous taunting of every contributor to this site rather comical really. You’ve become the Frenchman in the tower at WUWT:
[youtube=http://www.youtube.com/watch?v=9V7zbWNznbs&hl=en_US&fs=1&rel=0]
Re Robert..
I want someone to provide peer reviewed evidence that Robert has ANY scientific credentials, and that he is in an intelligent person. If such evidence is not provided, I will conclude that he has none and he is dense.
21 Feb: George F. Will: Global warming advocates ignore the boulders
Science, many scientists say, has been restored to her rightful throne because progressives have regained power. Progressives, say progressives, emulate the cool detachment of scientific discourse. So hear the calm, collected voice of a scientist lavishly honored by progressives, Rajendra Pachauri.
He is chairman of the United Nations’ Intergovernmental Panel on Climate Change (IPCC), which shared the 2007 version of the increasingly weird Nobel Peace Prize. Denouncing persons skeptical about the shrill certitudes of those who say global warming poses an imminent threat to the planet, he says:
“They are the same people who deny the link between smoking and cancer. They are people who say that asbestos is as good as talcum powder — and I hope they put it on their faces every day.”
Do not judge him as harshly as he speaks of others. Nothing prepared him for the unnerving horror of encountering disagreement. Global warming alarmists, long cosseted by echoing media, manifest an interesting incongruity — hysteria and name-calling accompanying serene assertions about the “settled science” of climate change. Were it settled, we would be spared the hyperbole that amounts to Ring Lardner’s “Shut up, he explained.”
The global warming industry, like Alexander in the famous children’s story, is having a terrible, horrible, no good, very bad day. Actually, a bad three months, which began Nov. 19 with the publication of e-mails indicating attempts by scientists to massage data and suppress dissent in order to strengthen “evidence” of global warming.
But there already supposedly was a broad, deep and unassailable consensus. Strange.
Last week, Todd Stern, America’s special envoy for climate change — yes, there is one; and people wonder where to begin cutting government — warned that those interested in “undermining action on climate change” will seize on “whatever tidbit they can find.” Tidbits like specious science, and the absence of warming?
It is tempting to say, only half in jest, that Stern’s portfolio violates the First Amendment, which forbids government from undertaking the establishment of religion. A religion is what the faith in catastrophic man-made global warming has become. It is now a tissue of assertions impervious to evidence, assertions that everything, including a historic blizzard, supposedly confirms and nothing, not even the absence of warming, can falsify.
http://www.washingtonpost.com/wp-dyn/content/article/2010/02/19/AR2010021903046.html
Re: George Turner (Feb 21 17:32),
As I said, NASA’s change “in so far as it relates to some areas I am familiar with, does drag at least some “rural” stations into the 20th/21st century as UHI adjustment candidates, but does also produce some amusing consequences”.
I may for example admit to some doubts about the newly acquired rural status of Baghdad and of Kabul Airport, but I will leave it to others more familiar with such areas to pass judgement. I’m just implementing Gistemp with enhancements to examine how it works, not writing the original code at NASA.
And apologies to the good citizens of Mantanuska for misspelling the name of their town at one point above.
News alert for WUWT. Sea level paper withdrawn from Nature Geoscience. I don’t know any other way to contact you guys.
http://www.guardian.co.uk/environment/2010/feb/21/sea-level-geoscience-retract-siddall
Two points:
1. Re Peter O’Neill’s post: “January 16,2010:The urban adjustment, previously based on satellite-observed nightlight radiance in the contiguous United States and population in the rest of the world (Hansen et al., 2001), is now based on nightlight radiances everywhere…”
Perhaps relying upon temperature devices located solely in North Korea will solve these snarky UHI adjustment problems. :~)
2. The best way to deal with “Robert” (who I’ve now seen trolling in two unrelated threads in the last two days) is to simply ignore him, here and everywhere. He’s obviously unworthy of anyone’s time or aggravation.
Peter O’Neill (17:12:34)
Peter, many thanks for that most recent information. Fascinating. However, I still don’t get it. How could satellites produce information useful for adjusting Matanuska downwards in say 1940? Or for adjusting it upwards in 1973? The fact that it is graded as “Urban” or “Suburban” today means nothing about what it was like in 1920. If it was still rural in 1920 (and there is absolutely no question that it was), why should the 1920 value be adjusted? This procedure makes no sense.
Gotta admire their balls, though. The scale of brightness goes from zero to 186, and anything over 18 is adjusted …
Also, their brightness numbers seem bogus to me. Care to guess the brightest site? New York? London? Cairo? Los Angeles?
No way. It’s Montreal … here’s everyone with a brightness over 150:
Philadelphia, 151
Riyadh, 153
Baltimore WSO City, 154
Sevilla/Tablada, 157
Valencia, 157
Atlantic City State Marina, 159
Jeddah, 167
Madrid/Retiro, 177
Edmonton Muni, 178
Shuwaikh, 181
Montreal Mcgill,Qu, 186
Shuwaikh is the second brightest temperature station on the planet? Really? If so, then brightness may not be a useful measurement.
At the other end of the scale, Srinagar is a city with a population just under a million, a population density of 6,400 people per square kilometre, and a brightness of 6, so it is rural. Baghdad has a brightness of 8, as does Jiuquan, China, population one million. Bangui is the capital of the Central African Republic, population half a million, brightness zero. Zero is also the score for Nanchang, China, population four million. … riiiiight. I looked up all of these on Google Earth, the temperature station is inside the city in all cases. And all of them will be used to homogenize other stations …
My sense is that we have the same problem here. Someone had a good idea (use brightness). But they didn’t detail an intern to actually look up each and every station to make sure that the brightness made sense.
Next, their weighting system is ridiculous. Examination of your posted data shows that the stations are weighted based, not on which nearby stations are closely correlated, and not on which stations are in the same climatological zone. They are weighted solely by distance … the formula for the weight is distance (km) / -500 +1.
I can think of no theoretical justification for that procedure. At least GHCN requires that their adjusting stations be correlated with the adjusted station, but GISS make no such requirement. And if you are going to weight by distance, wouldn’t correlation be likely to fall off by the square of the distance?
Finally, could you post a link to the “GIStemp log” you refer to above (or alteratively, post the stations used to adjust Anchorage)?
Many thanks for your outstanding contribution, science at its finest.
w.
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.
Yes a rural station near a glacier as shown in the backdrop to the graph, definitely could have an effect.
John S (18:22:22) :
“Since people are asking us to bet billions on this dataset,”
Trillions, Willis. Trillions.
==========
Yep, you got that right.
Never mind that almost every county, state and most countries are bankrupt. The really scary part, is just how big a “trillion” is, and how fast they are spent.
Isn’t this just the same type of GISS adjustment we’ve seen in blink charts before? Adjust older temps down and newer temps up to create a larger trend. Looks like business as usual.
“Until they get off their dead … … armchairs and do the work they are paid to do…”
Can we call them crooks yet?
Still too early?
Let me know when we get to the prosecution stage of the global warming fiasco.
Robert and Willis
I appreciate both your perspectives, but not your tones. Science provides data, which can always be challenged. Both should be done politely, without accusation. As soon as the tone becomes strident, it becomes very difficult to change a position, and science becomes politics.
Anthony is more tolerant than I am. I would be inclined to snip all disrespectful text in articles, and comments. For example:
EdB (18:36:02) [snip … disrespectful]
That might bring the tone down enough to actually debate the science.
I’m pretty sure Peter O’Neill (17:12:34) is correct. The rural station got adjusted here because of the night lights.
So all Eschenbach needs to do is check the trends at the surrounding dark rural stations, because they’re the ones in the driver’s seat for long term trends. Which is what he should have done in the first place. Instead of “Not sure what I can say about that, except that I don’t understand it in the slightest. “, another couple hours of work would have provided the understanding he was seeking.
GP (18:18:01) :
That’s always a good question. If the change in method causes such a little change, why bother doing it? Well, it could be more important in certain regions. It may make little difference to the global numbers, but there could be individual grid points where the effect is stronger. This is actually often true of adjustments in general.
To everybody who’s annoyed that the GISS adjustments sometimes increase the warming trends in urban/town stations: The surrounding rural stations are the drivers. So whatever the trend is at the rural station, then GISS is going to impose that trend on the urban station.
Sometimes the rural station could truly be warming faster than the urban station. Just because a station is in a UHI doesn’t mean the UHI effect will increase over time. There could also be inhomogeneities in there that mess with the trend at either urban or rural station. A station move can easily obscure the true trend.
In addition the fudged sea level claims are being withdrawn: http://www.guardian.co.uk/environment/2010/feb/21/sea-level-geoscience-retract-siddall
Please disregard my previous comment as you posted the same thing as I was typing it.
What happens when you take away cloud cover and precipitation?
You get a warming effect and draught.
Just in certain regions of our planet.
But, winds can carry away this to other areas.
Is “Robert” Gavin’s screen name?
REPLY: No different person, he’s in the health care biz – A
The justification of adjustments – this is what is being glossed over, even in the skeptical blogs, or at least not stressed and/or focused upon enough. Where are the records of the adjustments?
Perusing through HARRY_READ_ME I got the impression that nobody really knows, as Harry was finding out that the programs were largely undocumented. How do you document a version adjustment when you are not aware that the program is adjusting or stomping data?
You should be aware, however, when looking at vintage 19th century data that 1880 can be juxtaposed or overwritten by 1980, data included. I’ve come across this in Solar data, so I suspect that some of the programs used in climate data have been imported from other efforts.
Not everything that happened to the data was intentional. Some of it was just plain unforseen. Never operate on the original copy of anything.
Willis Eschenbach (19:07:43) :
“How could satellites produce information useful for adjusting Matanuska downwards in say 1940? ”
They don’t. The GISS logic: if the station is currently urban or peri-urban, then it’s safest to not trust the trends at that station, over any time span. Let the surrounding rural stations take charge over the entire history of that station.
___
“The fact that it is graded as “Urban” or “Suburban” today means nothing about what it was like in 1920. If it was still rural in 1920 (and there is absolutely no question that it was), why should the 1920 value be adjusted? This procedure makes no sense. ”
But you know it went from being rural to urban or peri-urban at some point in time. So there’s a possible UHI trend in there somewhere in time, and you want to avoid it. If there is a station that is rural now, it probably always was. Let it do the driving.
That said, I prefer the NOAA thinking on this issue, over GISS’s approach. I look forward to GHCN v3.0. If you actually bother to compare the stations against each other, you can probably detect when the UHI trend took place. That’s what NOAA does.
___
“Bangui is the capital of the Central African Republic, population half a million, ”
This is an interesting point. Urban areas in poor parts of the world will lack nightlight. I wonder if they’ll let the GHCN ‘U’ rating override the nightlight data in such cases.
____
“I can think of no theoretical justification for that procedure. At least GHCN requires that their adjusting stations be correlated with the adjusted station, but GISS make no such requirement. And if you are going to weight by distance, wouldn’t correlation be likely to fall off by the square of the distance?”
The justification is in Hansen/Lebedeff (1987). Looking at the graphs of correlation vs distance, linear seems pretty fair.
carrot eater (19:17:26) : “To everybody who’s annoyed that the GISS adjustments sometimes increase the warming trends in urban/town stations: The surrounding rural stations are the drivers. So whatever the trend is at the rural station, then GISS is going to impose that trend on the urban station.”
I wish you could prove that statement. Or are you just a true AGW believer. If they are truly “the driver” then recent temps should be lowered not past temp, with the result of increasing the trend. And why get rid of rural reporting stations. For example, if rural stations are truly “the driver” as you claim how is it that the SF airport is the only station for northern California. And did they adjust for dwindling fog?
SteveGinIL (18:18:55) :
To be good science, the reasons for instrument adjustments need to be spelled out and documented as part of the methodology AND KEPT WITH THE PROGRAM OR DOCUMENTATION USED. If this is not done, calling these people “scientists” doesn’t equate them to those I’ve known and worked with. For work this sloppy, they would have been found out in very short order and canned.
Steve. How right you are. It didn’t happen here in N.Z. Instead we got a Phil Jones type response. No surprise really given Jim Salinger’s pedigree.
Here is how some of that ‘lack of good science’ played out in the House of Parliament in NZ on February 17th . last in question and answer time. It is an example of obfuscation by the Minister ‘protecting’ his department. Nobody should be fooled.
9. JOHN BOSCAWEN (ACT) to the Minister of Research, Science and Technology: Does the National Institute of Water and Atmospheric Research maintain an up-to-date schedule of adjustments of all changes made to the raw temperature data that are used in calculating the official series “Mean annual temperature over New Zealand, from 1853 to 2008”, published on the institute’s website; if not, why not?
Hon Dr WAYNE MAPP (Minister of Research, Science and Technology) : The “Mean annual temperature over New Zealand, from 1853 to 2008” analysis, which was referred to by the member, does make adjustments to the raw data from the seven stations. The reason that is necessary ………. The original methodology to do so was developed in Dr Salinger’s PhD thesis, which is also publicly available.
John Boscawen: Given that we have been through the information the Minister refers to and found no schedule of adjustments, can he point to where in this mass of information it is contained; if he cannot, can he commit to table in Parliament the simple schedule of adjustments?
Hon Dr WAYNE MAPP: The member is correct; there is a complex range of information on the institute’s website. The methodology for the site changes is published in the peer-reviewed International Journal of Climatology, ………….“Adjustment of temperature and rainfall measurements for site changes”. The huge volume of information indicates that this is quite a complex area.
Hon Rodney Hide: I raise a point of order, Mr. Speaker. With great respect, the Minister did not answer the question. The question is a very simple one. It is not about the methodology; it is about the simple schedule of adjustments. In his answer to the primary question he said that the schedule was on the institute’s web page, amongst this mass of information. We have been through it. There is no schedule of adjustments. So the Minister was asked whether he could point to the actual schedule of adjustments, not the methodology, and if he could not, whether he could table in the House the schedule of adjustments—not the methodology; the schedule.
Mr SPEAKER: I hear the honourable member, and I am sympathetic to the member’s point of order, because the question was on notice. The question asks specifically whether the institute maintains an up-to-date schedule of adjustments of all changes made to the raw temperature data. It does not ask why changes are made to raw temperature data, or how those changes are made; the primary question asks whether the institute maintains an up-to-date schedule of the adjustments made. That information should be available, and I believe that the House deserves an answer. I invite the Minister to give the House an answer.
He still only got a BS answer even after all that.
Doug
It seems to me that Robert doesn’t understand the difference between raw temperatures and adjusted temperatures. We are arguing that the real temperatures may be just fine, but the adjustments seem to make no sense, yet the public pronouncements about warming are based on adjusted temps.
Also, if the Matanuska temps are from the Palmer experimental farm, it is a long way from the built up part of the mat. valley. It is really rural and shouldn’t need any UHI adjustment unless the sensor is in the parking lot.
Jim Steele (19:36:55) :
“I wish you could prove that statement.”
Read and run the code, if you don’t believe me.
____
“If they are truly “the driver” then recent temps should be lowered not past temp, with the result of increasing the trend.”
The Anchorage trend was decreased, so you got what you wanted there.
As for what happened in Matanuska, you’ll have to examine the surrounding rural stations within 500 km. Hopefully Willis updates by putting those up; it is all that you need to close the puzzle.
Once Matanuska got classified as periurban due to the nightlights, then it lost driving privileges.
___
“And why get rid of rural reporting stations. For example, if rural stations are truly “the driver” as you claim how is it that the SF airport is the only station for northern California.”
Where do you get that? Looks like a ton of stations in Northern CA to me.
Lon Hocker
Following your logic,Anthony should have snipped Robert’s first comment,then the “tone” would not have been lowered.I thought Willis was rather restrained after being told
“No, you were dead on: you don’t understand what’s going on in the slightest”
Atlantic City State Marina, 159
#6 in the world?
David L. Hagen (17:43:11)
Per your citation it was 1990, not 1900.
Robert (17:43:04)
Y’know, Robert, you misconstrued my meaning entirely. I was trying to restate it in words of one syllable, so you might get it. I prefaced it by saying “what I meant was” to contrast it with your fantasy about what I meant.
As to your question, I fear I don’t have an answer. I don’t know any arguments that say that adjusting a perfectly good rural temperature record down and then back up by three quarters of a degree is a reasonable plan.
However, if you have them, bring them on.
David L. Hagen (17:54:40)
Unfortunately, that doesn’t really help. It is the population for the entire borough, not for the location where the temperature station is located. Good find, though.
3×2 (18:14:12)
Yes, the “AES” stands for “Agricultural Experimental Station”.
carrot eater (19:51:12) :
They don’t use the rural stations in N. Calif to compute the GIStemp, or whatever it is they call that 8-bit Atari Graphics map. Haven’t you been listening? The rest of the State could be buried under 100 feet of snow with howling subzero winds, but if SF is warm & sunny, the map will show a warm anomay.
More snow expected in Texas tomorrow & into Tuesday. Another round of cold slap for the NE.
Reno, NV broke a snowfall record today with 12 inches dumped.
Peter O’Neill (18:46:56)
Call me crazy, but I find it hard to laugh about dragging 1920 Matanuska data into the 21st century just so it can be adjusted …
Many thanks for your efforts. Could you post the stations used to adjust Anchorage? Much appreciated.
As someone who has spent a reasonable amount of time in Alaska, including the Matanuska valley, it’s “Matanuska” with one “n”, and the spelling error is from GISS/GHCN. Not surprising, as their station lists contains many such errors. QC ‘R US … not.
EdB (18:36:02) :
Re Robert..
I want someone to provide peer reviewed evidence that Robert has ANY scientific credentials, and that he is in an intelligent person. If such evidence is not provided, I will conclude that he has none and he is dense.
—————-
Of course, that proof of intelligence will require a 95% confidence level.
Phil. (19:10:30)
My apologies for the miscommunication. Although the first photo is of Anchorage, the second is not of Matanuska. My bad, should have made that clear.
@rbateman: hehe, I was talking to someone today whose sister lives up in the hills in the Reno area. She got 20″ of snow.
If I understand the above discussion Anchorage has been adjusted upwards at the rate of 1 degree per century of which therefore includes a -.005 C per century adjustment for UHI.
Matanuska on the other hand has been adjusted at the rate of 3 C per century downswards from 1910 to 1970 and then adjusted upwads at the rate of 7 C per century from 1970 to 1990 of which -.005 c per century was added because it is now considered affected by UHI.
So if there is in reality no UHI correction (-.005) what is the basis for these adjustments?
I think the state climatologists should be asked to answer these questions.
The following link compares Tucson and Grand Canyon adjusted unadjusted temps: http://www.climate-skeptic.com/2009/12/example-of-climate-work-that-needs-to-be-checked-and-replicated.html
A few points about GISSTEMP urban adjustment.
The nightlights approach. Nightlights is a really odd way to identify if a site is urban,rural or small town. The last time looked at it it was easy to spot obvious errors: Big cities by population that turned out dark and rural sites that turned out bright. This is because nightlights is a PROXY for urbanity.
The foundational studies on the correlation between nightlights and urbanity show this. When you have historical population data why do you think nightlights will be any better at assessing urbanity? Moreoever the real thing we are after is UHI. UHI is, according the theory, a positive bias that is created by changes to the physical ( geometry) and material changes in a site over time, and waste heat which is tied to population and human activity. Looking at the way GISSTEMP adjusts for UHI its quickly apparrent that in many cases the adjustments go against what theory says they should be. What’s missing in Hansen’s approach is some kind of verification that the adjustment actually works.
1. That the methodology for picking rural sites actually picks rural sites.
2. That the adjustments actually makes sense in individual cases
testing number one is a painful bottoms up proceedure. But it should not be so hard now that CheifIO has GISSTEMP running. Just output all the sites
that the program thinks are rural. Then go check each one.
Even more fundamental is the question of whether one can “adjust” for UHI at all. At best we have this. We have a number ( who knows how many) of sites which we could classify as Rural. Looking at the population, the nightlights, the vegatative index, the % of impervious surfaces, cannot replace LOOKING AT THE SITE. It would seem to me that one should start by picking the most pristine sites. That will give you a good picture. You may lack spatial coverage. Tough. that’s an uncertainity. The idea that you can take an urban site and “reconstruct” what it would look like if man was never there, is a HYPOTHESIS. The idea that you can regain spatial coverage by “adjusting” urban sites is a hypothesis.
Robert is just a FUD slinger. An errand boy sent by grocery clerks.
Farley State Marina in Atlantic City is next to Harrahs, Trump Marina, The Borgata, and Trump Taj Majal casinos.
They have lots and lots of exterior lighting to attract gamblers to the North side of town which is a mile and a half from the main roadway entrance to Atlantic City at the AC Expressway.
Though I would expect to see Las Vegas up there for the same reasons.
Perhaps the contrast line between the dark, cold Atlantic Ocean and the casino lights enhances the measurements.
henry (16:02:33) :
Matanuska-Susitna Borough population for:
1960’s: 5,000
1980: 17,816
1990: 39,683
2000: 59,322
2005: 75,001
2008: 85,458
Sources:
http://www.usgennet.org/usa/ak/state/boro-matsu.html
http://www.epodunk.com/cgi-bin/popInfo.php?locIndex=22230
http://quickfacts.census.gov/qfd/states/02/02170.html
Bear in mind that the borough covers 24,681.54 square miles.
I think you have the signs correct.
this is what I think they (the team) did.
1. cool the past for all that are trending level.
2. warm the past for all that trend warm.
3. level the ones that trend cooler.
net effect is a warming trend, and hard to prove that this is there end goal.
I.E. if they warmed the new readings today easy to show bias.
also by cooling the past you don’t need to mess with today’s readings !!!!!!!
Now prove me wrong with 5% or more that are reversed from the above 1-3.
Tim
Robert Kral:
“in a field where generating bogus data costs you your career.”
Unfortunately, in climate science, as in nuclear reactor regulation, scientific honesty would mean there are a lot fewer scientists and grants needed to study the resulting non-problems.
I once heard a talk by one of the aging pioneers in the nuclear field. He discussed how the difficult-to-measure effects of small doses of radiation were estimated by drawing a straight line back to the origin of the graph from the measureable effects of large doses. As the line gets closer and closer to zero the number gets tinier and tinier. But, multiplying the tiny number by a large population gives regulators numbers worry about. When he pointed out to these scientists that it’s not a straight line back to the origin, the curve has “hormesis” (it dips below the x-axis, small doses are actually beneficial), they would agree that research has consistently shown hormesis and say, “but, if we stipulate that, we’re out of a job as are most of the people in the regulation business.”
Same is true for a lot of pollutants.
Just as a warning, you have to be very careful with population density info in Anchorage as well. In Alaska, you have the state government, and then borough government. The Municipality of Anchorage is the equivalent of a borough, and while it is one of the smaller boroughs in Alaska, it is big enough to make any population densities worthless. See the link to the map below.
http://www.muni.org/Departments/Planning/PublishingImages/vicinity.gif
I have in-laws that live in the Anchorage Bowl, and I though it was a little more crowded than 162 people per square mile.
Food for thought. The comment at the link below includes a link to a paper about station moves in Anchorage. I also wrote a few comments about elevation and distance to open sea water in the Anchorage area and why that matters.
http://climateaudit.org/2007/04/11/some-china-comparisons/#comment-84788
By far, the biggest factor affecting air temperatures in Anchorage is the relative distance to Cook Inlet.
OTOH, the Matanuska station gets a lot of wind from the Matanuska and Knik glaciers. (Both of which have receded a whole bunch over the period of this discussion. Also, the Matanuska station is not really a great choice for supposed rural stability because the area around it has been growing faster than the rest of Alaska for 20-30 years.
If you want some better choices to check against potential UHI in Anchorage, see if you can find weather data for places like Skwentna, Nikiski, Kasilof, Big Lake, Knik, or Hope. Each of these has pretty minimal growth and are representative of weather conditions in the area.
The assessment of “rural” stations has been somewhat of a joke to those of us who live here and who have visited the sites.
The methodologies used to classify sites as rural or urban are not very clear.
One of my favorities is Peterson2003, that seminal study that shows no UHI effect. But when you look at the stations Perterson used in that study its pretty Goofy. Peterson also used nightlights, but looked like he used a different product. And he adjusted the time series with a TOBS program that was different from that developed by Karl. weird. Anyways here is a list of rural sites according to peterson for the US. Hint you wont find many of them in USHCN. BUT you do find some. lets just check a few. I’ll run down the list. Look at surfacestations and lets just see what our spot check shows. ok?
First we get a list of the sites:
http://climateaudit.org/2007/08/03/petersons-urban-sites/
Lets check a few rural sites: I’ll just go down the list of peterson sites
and pull the data if it exists in the photo database. It would be cool if the surface stations database had a pointer to all the data and all the charts, but
right now it doesnt. Some pages show the data and photos and others just show photos. Somebody should ask Anthony to see of this can be updated.
It would be cool if I could ask for a list of sites by population or by nightlights figure or by CRN rating or whatever..anyways
Here we go:
http://gallery.surfacestations.org/main.php?g2_itemId=24871
http://gallery.surfacestations.org/main.php?g2_itemId=57525
http://gallery.surfacestations.org/main.php?g2_itemId=16799
I had to pull data for this one.. see below
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425726080010&data_set=0&num_neighbors=1
More data for other peterson rural stations…
http://gallery.surfacestations.org/main.php?g2_itemId=36658
http://gallery.surfacestations.org/main.php?g2_itemId=37651
This NEXT ONE IS GOOD: it shows a station that peterson thinks IS RURAL
and GISS thinks is not rural
http://gallery.surfacestations.org/main.php?g2_itemId=3333
Pull the data
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425726940040&data_set=0&num_neighbors=1
HERE IS A NICE EXAMPLE: petersons NIGHLIGHTS method classifies this as rural
http://gallery.surfacestations.org/main.php?g2_itemId=12835
Data for that below:
http://gallery.surfacestations.org/main.php?g2_itemId=27187&g2_imageViewsIndex=1
Here is ANOTHER peterson Rural site? hmm nightlights dont see everything
http://gallery.surfacestations.org/main.php?g2_itemId=3421
a look at the temps
http://gallery.surfacestations.org/main.php?g2_itemId=6097
http://gallery.surfacestations.org/main.php?g2_itemId=20469
Rural by peterson and GISS
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425727930020&data_set=2&num_neighbors=1
The point of this little exercise is that the classification of sites into rural/not rural is a fundamental step in any analysis. with the right database and tools its not that hard of a job. i’ve also never seen a ‘tops down” approach to classifying that passed a simple spot check
does this make radiative physics wrong? no.
does this make climate sensitivity lower? i dunno
Can we do a better job of classifying sites if we progress bottoms up?
I think so.
Will the warming of the last century disappear completely? No.
Does it make sense to hand check these things? Yes.
Will people wave their arms and say “it doesnt matter?” Yes
Will people wave their arms and scream the data is corrupt? yes
Three kinds of lies: Lies, Damned Lies, and Climate Science
Three kinds of Liars: Liars, Outliers, and Out-and-Out Liars!
Thanks for another interesting post Willis. WRT Robert, the appropriate Latin is “nil carborundum illegitimi”.
Yes there is an Australian “leg”. Again this backs up what I discovered at Te Kowai- another Experimental Station!- except Te Kowai’s district population is given as 666.
I am desperately working to get a related post on my site re strange goings on at Gladstone, Queensland. Give us a day or so- i have to work too. Check kenskingdom.wordpress.com tomorrow (hopefully).
Ken
“Falsus in unum, falsus in omnibus”
Actually, the expression should rather be “falsus in uno, falsus in omnibus”. If you can’t get the Latin right, can you then be right about the rest? 😉
jcspe (22:11:40)
Excellent comment that exemplifies the real difficulties with the climate station records, the kind that can’t be fixed by some computer-generated adjustment.
I have friends in Wasilla and I know that’s true … so why would GISS adjust that time period by warming it??? (Having said that, the correlation between the Anchorage and Matanuska records is 0.9, very high …)
Here’s what’s in the GISS dataset
Skwentna only has data from 1948-1959
Nisiki no data
Kasilof no data
Big Lake no data
Knik no data
Hope no data
Welcome to climate science, where there’s never enough data, and what there is is sparse, short, and full of holes …
“Y’know, Robert, you misconstrued my meaning entirely.”
You made a mistake: get over it already.
“I was trying to restate it in words of one syllable, so you might get it.”
Since you’ve proven unable to grasp basic concepts like confidence intervals and external validity, I rather think our communication problems run the other way.
“I prefaced it by saying “what I meant was” to contrast it with your fantasy about what I meant.”
And I told you that “what you meant” is a lame excuse for the fact that what you said was completely wrong, as yourself admitted. End of story. Thanks for playing.
“As to your question, I fear I don’t have an answer.”
It’s not surprising. A skeptic, of course, would be able to consider the other side and the reasons he might be wrong. Somebody who operates on faith, of course, can’t imagine anything other than being right.
If you can’t open your mind enough to consider a hypothesis other than your own, then there in no point in discussing the subject. Not being a man of faith myself, I doubt we will convince each other.
steven mosher (20:47:59)
Mosh, excellent points all. Particularly, you say at the end:
Like you, I disagree entirely with the concept that you can “adjust” a station by comparing it with other surrounding rural stations. To understand why, let’s take a hypothetical example. Suppose we have a city station with UHI, surrounded by rural stations. And suppose all of the rural stations are in the identical climate zone, so much so that they show exactly the same temperature changes all the time. Finally, suppose that the city site is in the same climate zone, so that with no UHI would be just like one of the rural stations.
Now, in this situation we should be able to do a perfect “adjustment”. We can use the surrounding rural stations to perfectly remove all of the UHI from the city record, at which point it becomes exactly the same as the surrounding stations.
So now we can average all of them to get the true changes in temperature over time. But the key point is this:
We would get exactly the same result by just throwing away the UHI affected record.
The key point is that adjusting the records in this way does not add any new information to the information we already have. So even in a perfect world with perfect records, we cannot do any better than what we get by throwing the bad data out the window.
Now if we adjust the station records based on population, or based on known station moves, or based on known changes in the instrumentation, we have added information to what we had before.
But adjusting based on surrounding stations adds no new information. We can’t just take the same information to get ahead, that’s a perpetual motion machine. We’re better off to just throw away the UHI affected records, to get rid of the bad data entirely. Making the bad data look more like the other data still leaves some of the bad data in there. And in the real world, as we see in my opening post, in the quest to remove the bad data we often end up screwing up the good data as well.
Here’s a simpler example. Suppose we have measurements that we know should all be five. We get measurements like this:
5, 5, 5, 7, 5, 5, 5, 5, 5
We can use the good records (fives) to push the bad record (seven) in the right direction. But since the process is not perfect, we’ll end up with something like this:
5, 5, 5, 5.2, 5, 5, 5, 5, 5
As you can see, we’d be better off to just throw out the bad data.
So we can’t get ahead, and we’ll likely lose ground, all downside and no upside. I agree with you – if the data is corrupted by UHI and the rest, either fix it using new information, or throw it out. But don’t “adjust” it by comparing it with good data.
Finally, the whole idea of “homogenizing” the data is anathema to me. Nature is far, far from homogenous. Where I live it freezes maybe once or twice a year, some years not at all. A quarter mile away, it freezes ten or twelve times each and every year.
Would we gain anything by averaging these, by homogenizing them? I say no, doing that loses data rather than gains data. Nature is patchy and blotchy. Microclimates are the rule rather than the exception. Any attempt to homogenize nature merely makes nature’s blacks and whites into a mushy dull gray … where is the gain in that?
Thanks for your contributions, Steve, always welcome.
w.
Willis Eschenbach (15:37:55) :
Since the early years of the Anchorage record are adjusted to be warmer than the later years, this reduces the apparent warming.
Doesn’t that irk you in itself. It does me.
Why would NASA’s GISS division raise the temperatures in 1926 when there were no influences of UHI instead of lowering today’s temperature in a city to adjust for UHI.
In itself, that keeps the temperatures today jacked up as if the whole rural world were a huge city and the whole world has heat-island influences everywhere but in the cities. Seems something is very wrong.
Willis…PLEASE quit responding to Robert as it is a COMPLETE waste of
time.
denny (23:44:46)
Yeah, denny, you’re probably right. But I don’t want someone to read the thread and say “Willis didn’t answer questions from someone who was opposed to his ideas,” particularly since Robert made that exact accusation.
This way, people can see that I do try to answer all questions, not just the easy ones, and that everyone gets a fair hearing, including and particularly people who disagree with what I say.
Next, although I might never get Robert to see the light, there are many more lurkers than people who post. I want to expose to them the shallowness of the arguments of many of the AGW crowd, and Robert is a paragon exemplar of that. He constructs straw men at a rate of knots, raises meaningless objections to things I never said, tosses out gratuitous insults, and avoids the issues as though they had fangs. What better example could I want? If he didn’t exist, I’d have to invent him …
Finally, the more he argues, the more opportunities I have. For example, Robert brings up Tamino as though he were a scientist. That gives me a chance to discuss the issue of the rampant censorship on Tamino’s “scientific” blog (laughably called “Open Mind”), and to invite Grant Foster to stop being an anonymouse and debate the issues in public where he can’t squash scientific disagreement, and to ask Robert if he thinks censorship and science are compatible.
Robert may or may not provide an answer, but it gives lurkers and posters alike the chance to ponder the issues and answer the questions for themselves. And that to me is a worthwhile thing.
Is this the right place to look up the brightness index?
http://data.giss.nasa.gov/gistemp/station_data/station_list.txt
Re: Willis Eschenbach (Feb 21 19:07),
Anchorage:
urb stnID:425702730000 # rur: 15 ranges: 1916 2009 500.
longest rur range: 1910-2004 91 [wgt: 0.488 256.0 km] 425702960000 [CORDOVA/MILE] UNITED STATES OF AMERICA
add stn 2 range: 1903-1990 87 [wgt: 0.086 457.3 km] 425701780000 [TANANA] UNITED STATES OF AMERICA
data added: 87 overlap: 77 years
add stn 3 range: 1919-2004 85 [wgt: 0.755 122.5 km] 425702510000 [TALKEETNA] UNITED STATES OF AMERICA
data added: 85 overlap: 85 years
add stn 4 range: 1918-2009 84 [wgt: 0.079 460.7 km] 425703260000 [KING SALMON] UNITED STATES OF AMERICA
data added: 84 overlap: 79 years
add stn 5 range: 1933-2004 70 [wgt: 0.608 196.2 km] 425703410000 [HOMER/MUNICIP] UNITED STATES OF AMERICA
data added: 70 overlap: 70 years
add stn 6 range: 1942-2009 68 [wgt: 0.293 353.5 km] 425702310006 [MCGRATH] UNITED STATES OF AMERICA
data added: 68 overlap: 68 years
add stn 7 range: 1923-1990 67 [wgt: 0.434 282.9 km] 425702640020 [MCKINLEY PARK] UNITED STATES OF AMERICA
data added: 67 overlap: 67 years
add stn 8 range: 1943-2004 62 [wgt: 0.466 266.9 km] 425702710000 [GULKANA/INTL.] UNITED STATES OF AMERICA
data added: 62 overlap: 62 years
add stn 9 range: 1943-2004 61 [wgt: 0.067 466.4 km] 425702910010 [NORTHWAY FAA AP] UNITED STATES OF AMERICA
data added: 61 overlap: 61 years
add stn 10 range: 1921-1990 47 [wgt: 0.380 310.3 km] 425703400010 [ILIAMNA FAA AP] UNITED STATES OF AMERICA
data added: 47 overlap: 47 years
add stn 11 range: 1942-1990 46 [wgt: 0.651 174.3 km] 425702490000 [PUNTILLA] UNITED STATES OF AMERICA
data added: 46 overlap: 46 years
add stn 12 range: 1937-1970 31 [wgt: 0.332 334.2 km] 425702960010 [CAPE SAINT ELIAS ALASKA, U] UNITED STATES OF AMERICA
data added: 31 overlap: 31 years
add stn 13 range: 1944-1971 28 [wgt: 0.499 250.6 km] 425702490010 [FAREWELL FAA AP] UNITED STATES OF AMERICA
data added: 28 overlap: 28 years
add stn 14 range: 1949-1976 27 [wgt: 0.524 238.3 km] 425702640010 [SUMMIT/WSO AIRPORT] UNITED STATES OF AMERICA
data added: 27 overlap: 27 years
add stn 15 range: 1949-1969 21 [wgt: 0.238 381.0 km] 425702600010 [NENANA/MUNICIPAL AIRPORT] UNITED STATES OF AMERICA
data added: 21 overlap: 21 years
possible range increase 42 85 86
E O’Connor (00:47:11) : edit
Yes. Also see the other list here for further metadata.
Willis,
Can you confirm something for me?
I see the GISS homogenization adjustments for Anchorage in fig #1 as follows:
1) In the mid 1920s GISS added .9C to the raw temperature data to get their homogenized adjusted temp data
2) around year 2000 GISS added 0.0C to the raw temperature data to get their homogenized adjusted temp data
3) in the ~85 years between GISS linearly (in stepwise fashion) decreased what the degrees C that they added to the raw temperature data to get their homogenized adjusted temp data
4) From late 1940s to present there was a significantly amount of urbanization as evidenced by the population curve.
Do I have the items 1), 2) and 3) above right?
Is so then I have some further observations to make, but first making sure I got the above right.
John
Willis Eschenbach (00:35:49) :
‘Robert may or may not provide an answer, but it gives lurkers and posters alike the chance to ponder the issues and answer the questions for themselves. And that to me is a worthwhile thing.’
Willis. The patience of Job and the wisdom of Solomon make you a rear beast indeed. Chuck in your dry any pithy humour and you might have the makings of a ‘universal man’!
But compliments aside, your comments have been enlightening for me and for that many thanks
Regards
Doug
I think I’ve figured out what is going on here. The relevant parts of the code are in directory STEP2 of the GISTEMP source. The first thing to note is the file v2.inv in ./input_files. It lists the station data, and for the two Alaska stations:
42570273000 ANCHORAGE/INT__________________ 61.17 -150.02__ 40____8U__173FLxxCO 1A 5WATER__________ C__ 53
42570274001 MANTANUSKA AES__________________61.57 -149.27__ 46__225R__ -9FLxxCO30x-9TUNDRA__________C__ 18
The second last number gives the GHCN brightness rating – C is highest. So both will be adjusted.
The adjustment is done in the subroutine adj() of padjust.f. Temperatures are held as integers, to tenths of a degree – 121 is 12.1C. The relevant code fragment is:
____ do iy=iy1,iy2
________sl=sl1
________if(iy.gt.knee) sl=sl2
________iya=iy
________if(iy.lt.iy1a) iya=iy1a
________if(iy.gt.iy2a) iya=iy2a
________iadj=nint( (iya-knee)*sl-(iy2a-knee)*sl2 )
iy is the year. You’ll see that there is provision for a “knee” and two slopes (switching at the knee). There are also limits beyond which the adjustment will be held stationary.
So the effect of the “nint” is that the adjustment is piecewise linear, and forced to the nearest integer value – ie 0.1C.
So now you can see where those plots come from. Anchorage has no knee, but just a steady slope adjustment, made stepwise by the nint. Mantanuska has a knee, and a steep slope following the knee.
The rationale seems to be that a broad slope correction is made to match the city to have the trend of its rural surrounds. The knee is presumably to allow for a transition in the past from rural to urban.
Sould be rare beast! sorry Willis!
The common factor in the errors is the expectation of those manipulating the Data: that means if the expectations are wrong the conclusions are wrong.
True scientists alter the theory to fit the facts not the facts to fit the theory.
‘ Steinar Midtskogen (22:36:00) :
“Falsus in unum, falsus in omnibus”
Actually, the expression should rather be “falsus in uno, falsus in omnibus”. If you can’t get the Latin right, can you then be right about the rest? ;)’
Yes, you can. As in “coito, ergo sum”.
Raw data is good for you and may be digested later as food for thought.
I was in the military and spent a large proportion of my sdervice in norfolk(uk) home of the uea. We used to call the locals carrot crunchers. So my question is, carrot eater, is that you philj or briffa
?
John Whitman (01:39:04)
John, I don’t understand how or why they constructed the stepwise change in temperature. Your explanation seems as good as any. However, why on earth would they want to change the mid 1920s temperature by adding almost a full degree to it?
In any case, make your observations, always welcome …
Re: Nick Stokes (Feb 22 02:00),
More on the algorithm used. In PApars.f they compute, as they stated, for each urban station (in the do 200 loop) the AVG() of yearly distance-weighted average values of nearby rural stations. They then calculate the difference between that and the urban values URB(). This information is passed to getfit() in t2fit.f (via the common block FITCOM).
In getfit(), the lines-with-knee fit to AVG-URB is laboriously computed by trying every possible knee (within 5 yrs of the end of range), and choosing the value with least residual SS (in trend2(), tr2.f). The resulting slopes and knee are what ends up in the adj() routine set out in my previous post, and with the steppedness of integer conversion become the plots shown in the head post here.
Willis Eschenbach (23:19:45) :
That’s the whole point of the GISS adjustment. The stations labeled as urban do not get to impact the long term trends in the final result. They’re left in there to contribute some short-term wiggles to the local averages.
So yes, they’re essentially tossing out the urban stations. If you don’t like that, then follow the GHCN adjustment set instead. And if you don’t like adjustments at all, you might note that the unadjusted data give about the same global mean trend, anyway.
As for your continual wonderment at what happened: just go and see the neighboring dark rural stations. Then apply the two-legged adjustment described in the papers, if you really want to recreate the math. Do you want to simply wonder, or do you want to learn the answers? There is nothing stopping you from the latter.
Willis Eschenbach (23:19:45) :
Incidentally, you’re using the word ‘homogenize’ incorrectly in this context. In this field, inhomogeneities refer to the things that mess with the record at any given station: station moves, instrument changes, etc.
Nick Stokes (03:15:17) :
The ‘knee’ used to be at a fixed point in time, I think 1950. Allowing it to float makes for a better calculation, but adds to the computation as you can see.
Rural it is then. Don’t quite understand how it scores 18. Moonlight reflected in they eyes of Wolves perhaps?
Wouldn’t it be great if the warmists and the sceptics could agree a number of rural and always-been-rural sites, and look at the raw data without any fiddle factors, and see just how much warming there really is…
Why elevate the erly temperatures for an urban site?
Well, the net effect is to reduce the apparent UHI effect.
That means that later uban data can be adopted in the later years with a lesser correction meaning that the average leter years temperatures is elevated.
This isn’t about reducing he apparent warming on this site but getting the later temperatures into the data set with no signinificant reduction – i.e. don’t pull down the later temperatures for the expanded city, bring up the earlier temeratures.
This works if the earlierr temperatures do not significantly increase the average global temp but the later urban dat, if included with minimal correction, does significantly increase the mean global temp.
AT least, that’s one possibility. unless I have misinterpretted this somwehere.
There are 29 rural (by light at night) stations in Alaska within 1000km of Anchorage. Here they are listed by increasing distance in km.
DistanceStation
109SKWENTNA
126TALKEETNA
177PUNTILLA
245SUMMIT/WSO AIRPORT
252FAREWELL FAA AP
256CORDOVA/MILE
265GULKANA/INTL.
281MIDDLETON ISLAND/AUTO
289MCKINLEY PARK
311ILIAMNA FAA AP
323MINCHUMINA
334CAPE SAINT ELIAS ALASKA, U
427YAKATAGA/AIRPORT
457TANANA
466NORTHWAY FAA AP
508ANIAK/AIRPORT
526HOLY CROSS
598YAKUTAT
600EAGLE
614ALLAKAKET
629UNALAKLEET
643BETTLES
648FT YUKON UNITED
722MOSES POINT
855GUSTAVUS/2 SW
880KOTZEBUE, RAL
917UMIAT
944ANNEX CREEK
994COLD BAY
It appears that the Weather Underground mean temperature for January in Anchorage is a full 1 degree plus C cooler than the GISS reading (16 F or -8.9 C, vs. -7.4 C). Given that 2009 readings at both places were similar, this seems a little strange.
I’ve also noticed this in the Vladivostok readings, which registered a full 3 degree C difference (-12.3 C vs. 4 F or -15.6 C)
It appears that this difference has existed for some time (different locations?), but that the gap between readings has been increasing over the past decade:
WU GISS
1997: 5 F (-15C); -13.7 C
1998: 4 (-15.6); -14.3
1999: 11 (-11.7); -10.3
2000: 3 (-16.1); -15.1
2001: 1 (-17.22); -16.2
2002: 12 (-11.1); -9.4
2003: 7 (-13.9); -12.1
2004: 7 (-13.9); -11.2
2005: 7 (-13.9); -11.8
2006: 6 (-14.4); -12.6
2007: 15 (-9.4); -7.1
2008: 1 (-17.2); –12.8
2009: 10 (12.2); – 10.8
2010: 4 (-15.6); -12.3
What gives?
Nick Stokes (05:31:29) :
I think they only use those within 500 km if there are enough stations within that range, which appears to be the case.
So now that we have a list of the stations we need to look at, to answer all of the questions Eschenbach raises…
Thank you Willis for providing that extra GISS link on index brightness.
It was interesting to look at the entries for my country, Australia, and other countries which interest me such as Estonia, Latvia and Lithuania.
I have some nitpiky comments.
The information on which the Brightness Index is calculated could be out of date. Hobart Australia has an index of 34 and Canberra an index of 30. Canberra’s population has overtaken Hobart’s by at least 100,000. They both however end up as C in the metadata so it doesn’t matter.
There is a similar disparity in the index for Vilnius and Kaunas (10 and 17) in Lithuania.
To find the cities for Estonia, knowledge of Baltic, Hanseatic and some modern history helps.
Tallinn: The capital and the second n is dropped in both sets of data.
Baltischport :This is the Russian name for the town of Paldisksi about 50 km west of Tallinn. It is marked as USSR.
Pjarnu: This seems to be the Russian spelling for Parnu.
Dorpat: This is the German name for Tartu, Estonia’s second largest city. It is also stated as being in the USSR.
Fellin: This is the German name for Viljandi. Again another USSR listing.
The Latvian data has the same oddities of German names and USSR locations.
Sure the co-ordinates (which I didn’t check for accuracy) are the critical elements for location and these are just nitpiks. But it is a tad sloppy.
pat (18:44:46)
AGW has been declared a religion by a UK court in 2009. An employee took his company to court for preventing him or sacking him because of his religious belief i AGW. Fact
Veronica (England) (05:20:11) :
This was done by a north american school boy last year and somewhere on the climate realist sites there is a video of his explanations and chart. Try a google search or perhaps one of the readers here can remember where it was.
Can anybody (or hopefully somebody) provide the world, or at least me, with the confidence that:
1) all these immense efforts and all this time and money spent by good as well as bad scientists on collecting and interpreting (or deliberately misinterpreting) hundreds of thousands of temperature data can ever lead to a believed average world temperature graph?
2) that there is any use in attempting to do this, BEFORE it has been established with certainty that (a possibly dangerous) upward or downward trend is caused by human activity rather than natural forces?
3) if neither, that the efforts mentioned under 1) are still worthwhile?
Thinking outside the box …
Steve Mosher advises that: “UHI is, according the theory, a positive bias that is created by changes to the physical (geometry) and material changes in a site over time, and waste heat which is tied to population and human activity.”
Since human activity produces UHI warming, and in light of the enormous growth in global population centers resulting from industrialization, perhaps the IPCC religionists have been right all along about AGW … but for the wrong reason.
Wanted to ask this again……along the lines of what the Carrot Eater mentioned above…
Question – can we compare global (or N Hemi) averages of raw data vs.
“adjusted data” to see the differences imposed by the cumulative adjustments? just curious.
Also along the same lines, what ever happened to the comparisons between the ‘good’ US stations vs. the combined ‘good plus bad’ US stations as determined by SurfaceStations project? Did this show a significant difference so that we can get a handle on the overall impact of the siting issues (?)
Thanks
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:
*******************************************************************************
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.
*******************************************************************************
And then they confuse something fundamental (like who needs to justify what, to whom)
Example:
*********************************************************************************
I’ll ask you again: can you accurately describe any of the arguments in favor of the temperature measurements as they are?
*********************************************************************************
No – it is the AGW fantasists that need to do that, actually.
And then they get breathtakingly ironic (without meaning to be)
Example:
*********************************************************************************
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.
*********************************************************************************
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.
Willis Eschenbach (10:39:18) :
And one more thought:
“But given that Matanuska and Anchorage are only 30 miles apart, what is the explanation for the huge difference in the two adjustments?”
You’re looking at it backwards. The adjustments will be whatever they need to be, in order to get the long-term trends to be the same at the two stations (meaning, to get them both to match that common set of surrounding rural stations). Or at least, as close as you can match, with the two-legged adjustment and the data you’re working with.
So if you want to see if the adjustment was successful in what it was trying to do, overlay the two adjusted series, and see how well the trends match. Don’t compare the adjustments themselves.
I keep forgetting Alaska is outside the USHCN.
Robin Edwards (11:29:58)
That is the complete dataset. And you are correct about the effect of the PDO on Alaska temperatures. See my 2004 analysis of the PDO and Alaskan temperatures here. It’s another of the things to consider when making adjustments, I should have mentioned it in my reply to carrot eater at Willis Eschenbach (11:39:56).
Willis Eschenbach (11:39:56) :
“More conservative? I find the GHCN method more conservative”
Just thinking about it, I disagree. I think GISS is more aggressive in removing UHI, whereas GHCN is more precise.
At least for comparing GISS to USHCN using actual data (back when USHCN still had a separate UHI step), Hansen et al (2001) shows me to be correct; GISS is more aggressive in removing UHI.
As for comparing GISS to GHCN using actual data: let’s hold off for now. I have zero motivation to study GHCN adjustments, when I know they’re probably overhauling it for v3.0 later this year anyway.
“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.”
Willis, this is not a serious option. You’ll never collect enough historical metadata to be able to explain every divergence between each station and its neighbors. And even if you could, it’d still be largely best to use the statistical methods (Menne’s new homogenisation, for example) to remove the artifacts. A picture could tell you that a tree was covering the station at a certain angle, and then it was cut down, but that in itself won’t tell you how big an adjustment to make.
Finally, if GISS used historical metadata this closely, the adjustment method would no longer be objective. It would not be reproducible, as it would require some human judgments. If you remember from Darwin, this is an issue in the Australian BoM adjustments. One student did some adjustments using all the historical metadata, but the next person couldn’t easily repeat it.
While some oddball stations might be bestowed with a weird adjustment, overall I think objective methods are both more reliable and preferable, for reasons of reproducibility.
Thinking a bit more about the method GISS is using, that I described above, . The plots Willis has shown are just approximators to the difference between the “urban” station and the weighted average of surrounding “rural” stations. If you think there’s something extreme about them, it’s probably not in the adjustment arithmetic – it reflects the actual behaviour of that difference. There’s nothing particularly bad about a LS fit of a bent two-line segment.
So what does the adjustment achieve? Suppose you put more effort into getting a better approximator to the difference. In fact, you could just use the exact difference. That would completely replace the urban station result with the average of surrounding rural. The eventual global trend would then be just provided by rural stations. That is good if you have enough of them.
So why include the urban stations in the calc at all? They have an odd effect – by putting them in, and then replacing them by the average of local rural stations, you upweight the effect of those local stations in the global average. This probably wouldn’t matter much, and the effect is further muted anyway by the gridding.
The piecewise linear approx leaves a small residual effect of the urban stations in the global average, but only contributing short-term variation. The trend effect has been removed. And since the short-term effects are almost totally lost in the averaging, it seems to me that there’s little difference between GISS UHI adjusting and leaving out urban stations completely.
Paul Vaughan (11:21:58) : edit
Well, here’s a start on Agassiz, I just figured out how to make a blink comparator …
Willis, I’ve just looked at your 2004 paper on Alaskan climate, and enjoyed it a lot. Just one thing, though! I note that you seem always to use 17 year Gaussian smoothing. I’m a strong believer in eschewing the salve of smoothed data, which I tend to equate with trying to make things easier for journalists and politicians. OK, we know their limitations, so I suppose that’s a justification. However, when you look carefully at the individual month data, a somewhat different picture emerges. First, as you know, it is very useful to “deseasonalise” the monthly data, by subtracting the overall month averages from each item. Next, form the cumulative sum of the deseasonalised data, and plot this against the time base. What happens might be an eye opener. The steady increase that your plots show over the period from around 1970 to perhaps 1985 now becomes a step change that takes place over one or two /months/, and which is preceded and followed by periods of remarkable stability The PDO itself underwent the same step change, but a few months earlier.
This sort of underlying behaviour renders the conventional “trend line”, routinely computed, without consideration of its real-world implications, a misleading concept. My approach is to let the data themselves guide one’s thoughts about fitting a linear model. The notion that a linear fit must be “safe” is not one to be applied without due thought in the realm of climate science. Good though the human brain is at spotting patterns in data plots it may not be good enough!
I am pretty sure that most methods currently used for handling climate time series over periods of perhaps a couple of centuries down to a few years tend to disguise the occurrence of step changes. This simplifies potential explanations but may be hiding something of fundamental importance.
Over the last 16 years I have looked at several thousand series, of many types, and am convinced that abrupt change is the norm, rather than the exception. What you are likely to notice is that once the position of a potential step has been suggested your ideas about the linear fit, and indeed the process of smoothing, might need some modification.
I would like to be able to show a few GIFs, in this thread, but don’t know how :-(( They would lend some weight to my words.
Robin
carrot eater (12:04:14), thanks for your thoughtful reply. You say inter alia
There are mathematical methods which can identify a breakpoint in a dataset without reference to its neighbors. If we know a tree was cut down in 1938, and we find a breakpoint in 1938, we can make a reasonable adjustment for the change based on the math.
Next, I have no problem with someone making an adjustment based on a change, as long as they clearly identify the amount and the reason for the adjustment. It does require human judgement, but that is true in many parts of science. As long as they can specify their reasons, we can see if they are reasonable.
Next, I do not find “reproducibility” to be a valid reason for selecting an automated method. If it is making bad adjustments reproducibly across a variety of stations, that’s not a good outcome.
Finally, if you do use an automated method as GISS and GHCN do, it is imperative that you go through and toss out the adjustments that are clearly bogus. Adjusting Matanuska to increase the recent (UHI?) warming is one such adjustment. Quality control is a crucial part of any computerized adjustment system.
there should be some good coming out of all this Cherry (picked) Fudge; now there must be a market.
carrot eater (11:40:02)
I took a look at the period of overlap where there is no missing annual data (1938-1990).
Before adjustment, the long-term trend for Matanuska was -0.01°C per decade.
After adjustment, the long-term trend for Matanuska was -0.01°C per decade.
Before adjustment, the long-term trend for Anchorage was 0.18°C per decade.
After adjustment, the long-term trend for Anchorage was 0.07°C per decade.
So I’d say that your claim, that the purpose of the adjustments was to correct the long-term trends, didn’t work for Matanuska. Anchorage’s trend was reduced, but Matanuska’s was left unchanged.
I still haven’t heard any reason for increasing the recent Matanuska trend, or reducing the earlier Matanuska trend. Why should it match its neighbors? Nature doesn’t work that way, there are differences between stations.
Using nightlighs only for judging urban/rural status of station is risky, since the coordinates of many stations are not exact enough for this.
I’ve looked into this for Swedish stations in the GHCN, and there are several significant errors. Härnösand for example is a fairly large town, but the coordinates are off by a few miles, placing the station in the middle of an uninhabeted forest area. Same thing for Halmstad where the station is at the airport on the outskirtsof the town, but the coordinates places it in the middle of a field several kilometers east of the airport.
In some cases the coordinate errors may even be deliberate. I suspect this is true for Jokkmokk where the station is at an Air Force Base whose position and even existence was secret until the recce satellite era. Here the coordinates are off by more than 10 km.
Re: Willis Eschenbach (Feb 22 12:50),
Why should it match its neighbors?
I think this is the wrong way to look at it. Matanuska is being defined (maybe wrongly) as urban, and is being replaced by an average of it’s rural neighbors. The adjustment curve you have plotted is close to what is required to do this.
Since the neighbors were already contributing to the global average, the nett effect is that M is simply being left out, with some minor changes to the weighting of the nearby rural stations in the mix.
Willis Eschenbach (12:40:08) :
Well, once the crowd disperses, it’s apparently easier to have a rational discourse.
“There are mathematical methods which can identify a breakpoint in a dataset without reference to its neighbors. ”
In some applications, sure. In this one, I just don’t see how you can pull it off. You’ll find the breakpoints, but without the neighbors, you won’t know what should have been happening at your spot.
“Next, I do not find “reproducibility” to be a valid reason for selecting an automated method. If it is making bad adjustments reproducibly across a variety of stations, that’s not a good outcome.”
I think it’s quite important, and I think the nature of WUWT testifies to it. People don’t trust adjustments that they can’t dig into for themselves. Having a lot of non-objective adjustments will just become a huge black box that nobody here could really examine, and it would only lead to yet more controversy.
Also, I do not think you’ve demonstrated that the adjustments are on the whole bad.
“Adjusting Matanuska to increase the recent (UHI?) warming is one such adjustment.”
Again, I strongly encourage you to finish the job you started, and analyse all the rural stations that serve as the reference set here. The work is very incomplete without it.
The GISS UHI adjustment often does put in an increased warming trend, but that could be for some good reason. Say Matanuska had some station move or somesuch that messed with its trend, such that it didn’t match well with the neighbors. The UHI adjustment will step in and so something about it. I agree it’s crude, so I prefer the NOAA methods. But because of this, I wouldn’t just toss out any positive UHI adjustment out of hand; it could be positive because it’s correcting for something else.
This is assuming the rural reference stations aren’t themselves garbage.
“Robert (10:34:27) :
“No – it is the AGW fantasists that need to do that, actually.”
You’re wrong, Willis. You’re ”
Robert is amazing. After repeatedly misrepresenting other people’s statements, he now uses outright wrong quotes. I got suspicious because “AGW fantasists” is not Willis’ style. Robert, are you such a helpless tw*t that you need to do that or are you just extremely careless?
Willis Eschenbach (12:50:13) :
“I took a look at the period of overlap where there is no missing annual data (1938-1990).”
You should try the comparisons on either side of the Matanuska dog-leg in 1970.
“I still haven’t heard any reason for increasing the recent Matanuska trend, or reducing the earlier Matanuska trend. ”
Because you haven’t looked for the reason. Please just look at the entire set of neighboring rural stations.
“Why should it match its neighbors? Nature doesn’t work that way, there are differences between stations.”
And yet, nature does work that way to a pretty good extent. You can see how good the correlation is between Anchorage and Matanuska, even with any UHI still in there. Anomalies correlate pretty far out, both in trend and in the variance. This has been well demonstrated, and if you disagree, you’ll have to do much more than just saying you don’t like it.
Nick Stokes (12:58:09) :
That is exactly correct. Though, if the Matanuska record was really messed up for some reason, it actually wouldn’t be possible to give it the same trend as the average of the rural neighbors. At least not by the GISS method.
But somebody needs to come up with a distance-weighted average of rural neighbors, to provide the comparison set.
This is what we’ve needed the entire time.
tty (12:56:17) :
There was an amusing article recently where the authors tried to use Google Earth to look at the stations, using the coordinates. They also found that the coordinates were not perfectly precise.
Willis,
This is looking like a typical Eschenbach effort. You scour stations to find something that looks a little odd. You write a post under the heading “Fudged fevers”. You say you don’t understand it.
But GISS publish the data that they use, and the algorithms and the code. Other independent groups have used and emulated the code, getting essentially the same results, so GISS are using the code that they publish. So if you’re going to persist with accusations of fudging, you might at least try to show where they do that in the code. I’ve pointed out the relevant parts.
Nick Stokes (14:17:52) :
My complaint as well. “Fudged” is in the title; Willis says he can’t think of what could justify x, y or z. When in fact everything is wide open and transparent. Perhaps Willis doesn’t agree with how the algorithm works, but he owes it to the readers to at least tell them what the algorithm is, or even that one exists and where to find it. It’s an objective method, so there’s no specific fudging. What you can then do is look at the surrounding stations and decide whether the algorithm churned out anything reasonable.
Though I wouldn’t explain it from the code primarily; just point to the papers. They’re rather easier to read. GISS papers aren’t paywalled, so nobody has that excuse to not even look at them.
Nick Stokes (14:17:52)
Starting out with ad hominems doesn’t do your cause any good, Nick. I didn’t “scour stations”, I looked at one station, so that’s a lie. Starting out with a lie is a curious way to establish your credentials.
I said I didn’t understand why Matanuska had been adjusted down and then back up again.
So far, nobody has given me a reason. Including you. I’m sorry, but “all the neighbors do it” doesn’t work for me. So what? That’s what makes for horseraces.
If you are so insightful about all this, please explain why.
First, we’ve gone over the “others get the same results” question above.
Perhaps my use of the word “fudged” is confusing you. I meant it in the sense of “pushed around without reason”. If you have those reasons, bring it on.
You keep insisting that perfectly good data should be changed purely because a computer says so. I ask why, and you point me toward the computer code … miss the point much? I know what the code says, I was likely writing computer programs before you were born. I’m questioning the whole procedure, not the details of how they code a program to shoehorn all the local stations into a “one-size-fits-all” straitjacket. Yes, you can do it, and yes, the computer codes do it.
But until someone comes forth to explain a reason for adjusting Matanuska down for fifty years and up for twenty years, I’m going to continue to ask the question. If you want to blindly adjust what appears to be perfectly good data just because some computer orders you to do so, that’s your choice.
Me, I ignore alien orders. I won’t adjust until you can explain why they should apply to Matanuska. I’ve asked that again and again, and neither you nor anyone else has answered the question. When you have the answer, come back and tell us. Until then …
carrot eater (14:33:39) : edit
You, like Nick, are missing the point. I know how the data was fudged. It was fudged by a computer algorithm, one that obviously doesn’t work well.
What I don’t understand is how this is all justified. I keep asking for a reason that anyone would start adjusting a pristine rural record in 1920. Do you or GISS have the slightest scrap of evidence that there was something wrong with the record?
Because if you don’t, then the data is fudged. Fudged by a computer, using a known and documented program … but fudged nonetheless. When you change data without a reason, merely because you were ordered by the almighty Computer to do so, I call that fudging. Don’t like it? Provide the reason for the adjustments made to Matanuska. We know the method, which is to force them to agree with their neighbors. But what is the reason to fudge the data that way? That’s what I don’t understand.
Which is what I said at the start.
DCC (09:06:36) :
@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!”
I was hoping that I had missed something fundamental, so basically the surface temperature record has been hi-jacked.
It is thoroughly depressing to see.
I assume that is why Trollbert and Pia Carrot are currently spinning like a tops to obfuscate, re-direct, misquote and if that fails out right lie, to try to muddy this thread.
When I saw the PDO hit the deck @ 1998/1999, re conned, ” That can’t be good”, then we had the Solar minimum dragging on and on, then we had the Argo Buoys reporting sea temperatures dropping and now we have an air effect the AO going negative.
It is looking like time to buy shares in companies who make down clothing and get kitted out early before next winter.
“It’s worse than we thought”, perhaps?
carrot eater (13:19:08) :
Though, if the Matanuska record was really messed up for some reason, it actually wouldn’t be possible to give it the same trend as the average of the rural neighbours.
If the data comes from the experimental farm (see my earlier post) then I am not so sure just how much more “rural” the Matanuska site could be. If the farm is the source then adjustments should really only come from the instrument/site change side. That is to say not from UHI. Pop. 1917=0 Pop. 2010=0.
(stay with me carrots) Nick Stokes (14:17:52) :
(…) So if you’re going to persist with accusations of fudging, you might at least try to show where they do that in the code.
I get the feeling that that is exactly where this (suspicious changes) will have to go. The answer from the pro’s seems to be “it’s in the PR literature”. Fair comment. BUT as with the NZ Parliament asking for the details of changes to individual stations, the question is met with … ??? (I suspect this will be a global answer)
My view, currently, is that … Yes, everything is in the literature somewhere (at the macro level, in terms of the method(s) used on a particular run). No, nobody (CRU, NASA, NOAA…) could ever explain the micro details of Darwin or Matanuska (don’t get me started on Iceland!) as they are the by-products of the bulk processing algorithms used. The detail is lost in much the same way as it would be in processing a huge mail order address list.
Anyways … I think what needs to be done is a bit of debug output at each stage of the code we are allowed(!) to see (and run) or at least (PR) methods we can duplicate. Tracing Darwin or Matanuska at each stage may spread some light (groan) on why they are as they are.
Despite what the politicians and climateers say, the details are important. If you (CE and NS or even PJ and TP) cannot convince me (and presumably Willis) that Matanuska is a valid adjustment then we can never agree on the final result.
Willis – what, if anything, did you decide re: a surface stations type domain?
Re: Willis Eschenbach (Feb 22 15:22),
So far, nobody has given me a reason. Including you.
I have,here. Matanuska has been classified as urban, and is being eliminated from the trend calculation. The device being used is to add the difference between an average of nearby rural values and the M values. If that is done exactly, M has gone completely. It’s done approximately using this piecewise linear fit, to preserve some short term information. I don’t think that is much of a gain; it will, have very little effect at all.
The down/up that you complain about is not artificial. It’s the observed discrepancy between M and its rural neighbors. Whether you add that discrepancy, removing the effect of M, or just omit M, has the same effect.
“I was likely writing computer programs before you were born.”
I wrote my first computer program in 1964, using Manchester Autocode.
A bit of osbcure and unintentionally funny: Being that I am from that area (Kenai/Soldotna) I grew up drinking Matanuska Maid Homogenized Milk…
On that note, Matanuska did have a relative population boom, but exact siting of the area needs to be figured. Alaska is BIG, most areas can eat up a large population without much impact.
So, unless we do a Surface Station run down of it, we really don’t know. I didn’t see any links to it on the surface stations site, so…
If I end up back in AK for some work (pending) I can swing up and check it out.
carrot eater (13:13:49)
You are quite correct, that some “anomalies correlate pretty far out, both in trend and variance” … but some don’t. The present case is a perfect example. Although there is good correlation between Anchorage and Matanuska in terms of the variance, the trends are radically different. Anchorage has a trend of almost two degrees per century, while Matanuska shows no trend at all. Go figure …
You are taking a general observation (nearby stations tend to correlate) and trying to make it into an absolute (therefore if they don’t correlate, we are justified in forcing them to correlate).
To take another example, yes, members of a family tend to look alike too … except when they don’t. Their body measurements tend to correlate just like nearby temperatures. But we wouldn’t dream of “adjusting” the body measurements of the one solitary tall thin member of a family to match the measurements of a dozen of his shorter, heavier relatives. So how can you justify doing the same to temperatures? If you are looking for some mythical Nature without sports and freaks and oddballs and things that break the rules, you’re on the wrong planet.
Dominic Marcello (09:26:43) :
Matanuska station has not just sat in the same place since its inception. Its moved around a bit.
Good catch but I’m not convinced that the moves account for the GISS end. As far as I understand it GISS uses GHCN as a base set but replaces GHCN records with USHCN records where matches exist. USHCN (again, as far as I can see) has already adjusted for station moves and such. GISS may/not then attempt (as far as I can not/partialy see) to remove any adjustments made by the source parties (of GHCN/USHCN) to the GHCN(+/-)USHCN resulting set!
Is it any wonder Matanuska is a bustling metropolis – full of folk trying to escape a life chained to climate science.
Willis Eschenbach (15:29:36) :
“You, like Nick, are missing the point. I know how the data was fudged. ”
You are missing the first point. That your reader would have little idea of how the algorithm works, or what inspired it. Should the reader have to wade through a hundred comments before he finds a discussion of what the algorithm actually is, or even a statement that there even is an objective algorithm?
“What I don’t understand is how this is all justified. I keep asking for a reason that anyone would start adjusting a pristine rural record in 1920.”
I have told you the answer to that question. Maybe it was nice and rural in 1920, but their satellite data tell them it isn’t, now. Unless they implement a method like NOAA’s, they can’t figure out when any impact due to urban warming came about. So they essentially toss the station out. I would say this is a sign of being rather over-eager in trying to avoid UHI, if anything. I prefer the GHCN method, over GISS. Especially once GHCN v3.0 is implemented.
“Provide the reason for the adjustments made to Matanuska.”
It got flagged as urban, and that was the end of Matanuska. That’s all. I’d rather they use the NOAA methods and retain more information from Matanuska, but they instead choose to minimise the impact Matanuska as on the trends at the grid point.
“We know the method, which is to force them to agree with their neighbors. But what is the reason to fudge the data that way? ”
Again in GISS, the adjustment is done to remove the impact of the questionable station. As for GHCN, as has been shown very clearly, neighboring stations correlate very well with each other. So if there is a non-climatic influence/event/distortion at one station, you can get an idea of what the climate signal would have been, by looking at the neighbors.
“”Provide the reason for the adjustments made to Matanuska.””
By the way, even if you try to retain the station, you’ll never have enough historical metadata to track down the reason for every discontinuity. Especially outside the US. But a good statistical method will more-or-less correct the errors you know about, as well as those you don’t. It won’t be perfect, but nothing would be.
“It was fudged by a computer algorithm, one that obviously doesn’t work well.”
You keep saying that. You’ve done nothing to actually demonstrate this.
3×2 (16:11:49) :
To reduce the complication, I don’t think these stations are in USHCN. USHCN is a lower 48 thing.
Robert (10:34:27) :
Until then, I don’t think there’s anything left to say.
Please tell me that is in fact your last offer(ing)!
Nick Stokes (16:01:30) :
“to preserve some short term information. I don’t think that is much of a gain; it will, have very little effect at all.”
I feel the same way. I’ve always wondered if much is gained by keeping the short term variations.
Willis Eschenbach (16:02:02) :
Yes, in this case there is correlation in the variance, but the trends don’t match. So they ignore the trend at any station classified as urban. The end.
You should be happy; there’s not much chance of UHI creeping through if you just axe the urban station’s trend.
Of course, it isn’t perfectly axed, but it comes close enough.
Re: 3×2 (Feb 22 16:11),
USHCN (again, as far as I can see) has already adjusted for station moves
USHCN does not cover Alaska.
carrot eater:
Matanuska. The history of the area is well known. There was no development there before the war. The computer adjusted the temperatures during a time when there is no reason to adjust them. Therefore, the algorithm obviously doesn’t work well.
carrot eater (16:15:58) : edit
I assume that the reader … well … reads. If the reader reads, they would have read this in my original post:
So at that point they know there is an algorithm. As to what “the algorithm actually is”, that was beyond the scope of my article. I’m not interested in overly complicating my original post by trying to explain the intricacies of how the algorithm weights the nearby stations by using the formula
distance / -500 +1
If someone wants to know that, they can read further. But it’s far too much detail for my original article.
Don’t like it? Fine. You are welcome to write an article about how wonderful the GISS algorithm is, and all the details of its operation. Then you can explain how reasonable it it to knock three quarters of a degree off of a pristine rural record simply because (gasp!) it had a different trend than its neighbours.
Let me know when you do, post the URL up here, and I’ll come and ask questions. And I won’t attack you for not writing it the way that I would write it …
carrot eater (16:24:05) : edit
Axed??? In Matanuska, whatever UHI there might have been was increased by the adjustment, at the rate of 4.4°C per century. Not sure how you translate that to “axed”.
Willis Eschenbach (16:50:36) :
You’re just repeating yourself now.
First, “The history of the area is well known” is not necessarily true. There is almost never a station that has every single possible bit of relevant historical metadata logged, except for the new US CRN stations. Do you know what station moves there were, TOB changes, instrument changes, shelter changes?
Second, none of that matters because the GISS method isn’t trying to correct for specific inhomogeneities at Matanuska. It’s simply trying to erase it in a sense, in order to minimise the effect it could have on the long term trends at that grid point. For the whole record. Just to be safe, and ueber-cautious. How many times must that be repeated?
Now this may prove to be so over-cautious that over time, they lose so many rural stations to nightlights that they end up undersampled. This would be a problem, but it isn’t a problem in Alaska now.
You are having a forest/trees problem. No adjustment scheme is perfect, and none could be perfect because the required information simply doesn’t exist.
But the global mean simply isn’t that sensitive to these minor differences in processing. You aren’t impressed by the match between GHCN adjusted, GISS adjusted and CRU? Then how about the match between GHCN adjusted and GHCN raw? Also a good match, if you look globally.
Re: Rural stations as drivers in the case of Anchorage and Matanuska.
Can we just be clear here. On the Matanuska data source (some (me) believe).
First. Put the co-ordinates 61.566031,-149.250255 (cut and paste) into Google maps, get the satellite view. Can we take the urban/rural debate from there (all from the same hymn sheet) because I am getting a little confused reading some of these posts.
Next. GISS does not use macro information for station adjustments – it does not have it. That leaves us with internal GISS adjustments, the obvious one being UHI.
Finally. Unless the state of Alaska has re-defined the OED entry for urban, Matanuska is as rural as most any station gets.
If we now accept that Matanuska is rural (don’t know how much more rural it can be short of sticking it in space) – why is any U(rban) H(eat) I(sland) adjustment required at any time for any reason? Even if adjustments were required, GISS does not have the information required to make them.
So – given just the temperature data and the station co-ordinates – you return a complete mystery. WUWT?
Willis Eschenbach (17:06:57) :
Matanuska is ‘axed’ by forcing its long term trends to match its rural neighbors. Using the two-legged mechanism, this can never be done entirely, but the point is to come close enough that removing Matanuska entirely would have little impact on the long term trends.
How close are they coming to this ideal? Well, until you finally put up a complete analysis of surrounding rural stations, you won’t know, will you?
Eschenbach: “What I don’t understand is how this is all justified. I keep asking for a reason that anyone would start adjusting a pristine rural record in 1920.”
Carrot Eater: “I have told you the answer to that question. Maybe it was nice and rural in 1920, but their satellite data tell them it isn’t, now. Unless they implement a method like NOAA’s, they can’t figure out when any impact due to urban warming came about. So they essentially toss the station out. I would say this is a sign of being rather over-eager in trying to avoid UHI, if anything. I prefer the GHCN method, over GISS. Especially once GHCN v3.0 is implemented.”
———
Why make any adjustments at all? The adjustments introduced are entirely artificial, as there is no indication of when urbanization occurred. Why not experimentally verify current UHI by taking multiple meaurements several times a year to determine what the temperature difference is between the thermometer site and rural areas? Perhaps this could be done by field monitoring every 5 years? Otherwise all you’re doing is introducing fudge factors and guessing at trends, which you then confirm via statistical sleights of hand.
How do you know when the UHI effect began to make a difference to the temperature readings when you backfill adjustments into the records? Why then should we believe the graphical representation of past temperatures, which are supposed to show historical trends? Good science still remains observational, although I know that many university-based scientists would prefer to have sensors piping in electronic data that can be monitored at the lab. So much for studying climate, if your entire focus is now on electronically mediated models!
I’m beginning to understand all these statistical and homogenization practices much harped on by Carrot-Eater et al. and described here, and can’t say that I’m impressed by what I read when they finally condescend enough to explain what they are on about. These methods do not build trust as they are based on assumptions (esp. viz. the satellite monitoring of light intensity to determine the urban status of a surface station location), and ignore the value of local science and observations. The entire construct is artificial, and as far as I can tell, that is what Carrot Eater, Robert and their ilk are trying to obfuscate.
Since the whole argument is about global warming, ultimately, surely the raw data could be tested against current urban heat island effect, and any difference in extra warmth over a set period chosen as the origin could then be attributed to climate change? The ultimate requirement is that the sites chosen as the origin points should all be rural at the beginning. In fact, why not focus only on rural sites? (Of course, that would apparently never do for GISS)
The big problem, as has been pointed out elsewhere, is that the surface stations were not conceived or designed for the purpose to which the AGW scientists have turned them. And of course, the alarmist political atmosphere that surrounds this science does not help. What really needs to be done, in order to gain trust, is for the entire exercise to be recommenced with stations located and designed for the purpose of detecting long-term warming and cooling trends (not a meteorological concern prior to the 1960s). Even then, the discovery of such trends says nothing about the human role in global warming or cooling. Meanwhile, from what I have read at this site and experienced in my daily life, very real ‘heat pollution’ does emanate from cities and human construction, and this is going virtually ignored by global warming theorists.
Willis and Stephen, your collective patience amazes me.
3×2 (17:08:31) :
Generally correct, all around. This may be a case where the nightlight procedure has improperly flagged a station as being non-rural. That’s the danger with any such method; it will misdiagnose a station now and then.
But if you’re really worried about UHI, you’re more worried about false negatives than false positives. So we return to Willis’s point about African cities which are dark by night, but still very much cities. What to do about them? I think that’s a good question, and I’ve not read the papers about nightlights carefully enough to see if this is discussed.
carrot eater (11:14:24) :
1,2,3 – or a really good idea would be to stop relying on bulk processing algorithms that give you “something in the expected range” and start dealing with individual stations manually – case by case.
While we have bulk processing and results such as Darwin, Matanuska or wherever, you are not presenting a convincing argument. The method(s) used by GISS plainly has flaws. Overlook them if you wish but HARRY_READ_ME.txt says a lot about QC in the field.
Re: 3×2 (Feb 22 17:08),
If we now accept that Matanuska is rural
No, GISS uses an objective criterion – night brightness. It’s actually a good criterion. Arguing about population is missing the point, because that’s not a good indicator of UHI either. What you want is a measure of local heat release, and artificial lighting is a good indicator. Maybe the satellites are getting it wrong, but it isn’t GISS.
And so, carrot eater (Feb 22 17:19), I think that relates to the issue of dark African cities. They are populous, but may not be evolving a lot of heat.
I think we should bear in mind, too, that the brightness criterion can be related to where the temp station actually is. If it is associated with a big city but some distance from the bright lights, the brightness criterion can pick that up.
There was a time in my past when I modeled (primarily as a SWE) ocean circulation underneath hurricanes. It was always the case that you started with the physics and mathematics it generates. Models were simply the means to see if you had the math right, i.e. can my model demonstrate some correlation to actual observation? If it correlates well enough (it never did) you’ve got the math right. If the correlation is off you go back to the math because you’ve got the physics wrong.
In this entire debate on AGW I just don’t see that kind of ‘science’ happening. In fact it’s more like the entire debate has been hijacked into a different domain where everybody obsesses over the observations while ignoring the physics. Where’s the math that connects night time radiance to temperature? Where’s the math that connects asphalt parking lots to temperature sensors? I posit that it doesn’t exist and never did.
The warmists are simply making use of lazy proxies because the real deal is hard or impossible.
It reminds me more of painting a picture than building a model. My wife, an itinerant artist, keeps laying on colors until she gets the picture she wants. She gets amazing results with no math at all. These models are simply laying on adjustments until they get the result they wanted all along.
In the end, if your science doesn’t create new knowledge or know how then you’re just a movie director aren’t you?
Not billions, not trillions but tens of trillions of dollars are at risk over this hoax! Last I heard was in the vicinity of $24 trillion (US); whereas it will supposedly cost us $22 trillion if we do nothing about it! Hmmm…
This has been my problem all along. If that farm is the data source (and I believe it is) I might go as far as suggesting that the night light procedure does not work. Following from that, if a farm in the middle of nowhere like Matanuska can trigger the process what else has been mis-diagnosed in the current run?
Nick Stokes (16:35:12) : (and CE earleir)
Re: 3×2 (Feb 22 16:11),
USHCN (again, as far as I can see) has already adjusted for station moves
USHCN does not cover Alaska.
Fine, so v2_mean it is then. Any adjustments have therefore been made “blind” during the GISS process. I was rather hoping that you would suggest that adjustments had been applied using actual metadata held by USHCN.
Blind adjustments to a completely rural station for algorithmic reasons it is then. Couldn’t you have said that earlier?
carrot eater (17:07:50) : edit
Sorry, I thought you were following the thread. These were cited above, at Dominic Marcello (09:26:43).
So if analysis showed a breakpoint at any of those dates, sure, I can see adjusting those. But obviously, that’s not what GISS did at all.
However, that wasn’t my point, which I obviously didn’t make clear, my apologies.
My point was that in 1930, Matanuska was rural, and that is well known from the history. And despite the nightlights, it is still rural. On Google Earth, you can see the Agricultural Experimental Station at 61.5656N, 149.249W. Anyone who says that’s urban hasn’t turned on his own nightlights.
So a) Matanuska’s not urban now, and b) regardless of what it is now, it wasn’t urban in 1920 or 1930 or 1940 or 1950. So what justifies adjusting those years? I repeat myself because I still don’t have an answer to that question.
What I don’t understand is how this is all justified. I keep asking for a reason that anyone would start adjusting a pristine rural record in 1920.
I suggested one which you ignored, the relative proximity of a glacier.
Carrot Eater:
No, no, no. They didn’t “toss the station out.” They introduced an artificial warming of 4.4°C per century starting in 1970. This artificial warming is averaged into the final claim of “recent global warming”. How is introducing that huge artificial warming tossing the station out?
That’s why I said it was “fudged”. They didn’t “toss the station out” in any sense.
Nick Stokes (17:46:37) :
I thought about that, but am unconvinced. UHI effects are more than just waste heat, but also change in materials (different heat capacities and albedos), hindered convection or radiation, reduced evaporative cooling, and so on. It’s a complicated brew.
A dim African city can still have some of these items. I need to read up a bit on that literature sometime.
3×2 (17:45:59) :
As I’ve said before, manual adjustments to every last station seems not feasible, not necessarily desirable, and almost certainly not worth the effort over the ~1200 regular reporting stations in GHCN and then again for USHCN.
One, you can use some human judgment, but you’ll never have all the information you need. Then, you’ll end up making a lot of ad-hoc decisions that nobody else could reproduce. And then the people on this site would really go off – it isn’t reproducible, it isn’t science, the adjustment guy has his finger on the scales, etc. And what would it really gain you? Some oddball stations might be treated a bit better; the regional and global trends would rather likely be about the same.
And as it turns out, statistical methods for adjustment can be better than manual ones. For example, the change from old thermometers in the US to the MMTS stations. This used to be dealt with (in USHCN, now) using a set adjustment described in Quayle (1991). But it turns out, this instrument shift doesn’t always have the same effect because the reasons why the instrument shift affects the temp reading varies from station to station. A human would have some trouble dealing with this, but the statistical routine can sort it out.
Willis Eschenbach (18:43:42) :
“No, no, no. They didn’t “toss the station out.” They introduced an artificial warming of 4.4°C per century starting in 1990. How is introducing that huge artificial warming tossing the station out?”
You mean 1970? Anyway, I’ve said this maybe a dozen times now. If you make it such that the longer trends roughly match the other surrounding stations, then the station in question is not affecting the longer trends at that grid point. In effect, it is being tossed out.
So again, what you need to do to assess this is actually look at those surrounding rural stations.
To assess the calculation, you’d need to use the GISS method to find the temperature at that grid box, with and without Matansuka. If the GISS adjustment did what it’s supposed to do, then the longer trends for the combined record at that grid box would not be affected by adding or removing Matansuka.
Willis Eschenbach (18:38:20) :
NOAA’s only going to have that detailed sort of metadata for a US station, so you’ll need to have a method that doesn’t require historical metadata for non-US data, no matter what. Anyway, things like “Thermometers moved but no information where 5/14/48” is hardly the same as “pristine” data. As you admit, it could require some adjustments.
But yes, GISS isn’t even trying to explicitly or individually adjust for those things.
“So a) Matanuska’s not urban now, and b) regardless of what it is now, it wasn’t urban in 1920 or 1930 or 1940 or 1950. So what justifies adjusting those years? I repeat myself because I still don’t have an answer to that question.”
You’ve been given the answer several times. Maybe it isn’t urban now, but it fails the nightlight screen, so it’s possibly questionable. When did it start being possibly questionable? Who knows. You’d need a detailed statistical analysis of the temp record in comparison to neighboring stations, along with tons of historical metadata, to know for sure. NOAA tries to work this out (though without any historical metadata, now). GISS says, forget it, we won’t even try; the neighboring stations are good enough.
Basically, you just keep arguing for an adjustment procedure that requires a ton of manual work, ad-hoc manual decisions, a ton of historical metadata that won’t be available for each station, and no real indication of a significantly improved global result.
If you can just accept for the moment that such an adjustment process just isn’t going to happen, then you see the choices are the GHCN way and the GISS way. And maybe you don’t care, but the fact that they take such different approaches, and still end up with consistent results really does mean that the results are not that sensitive to the details of the processing. Which is a good thing.
Photos of the Matanuska AES:
1917
1918
1920
1930
Air view, 1938
Unknown date, pre-1959
carrot eater (18:46:33) :
3×2 (17:45:59) :
As I’ve said before, manual adjustments to every last station seems not feasible, not necessarily desirable, and almost certainly not worth the effort over the ~1200 regular reporting stations in GHCN and then again for USHCN.
Without getting into the politics too far – a mind numbing amount of money has already been spent in the area one way or another. Not sure where to price actual data other than at the very top end.
One, you can use some human judgment, but you’ll never have all the information you need. Then, you’ll end up making a lot of ad-hoc decisions that nobody else could reproduce. And then the people on this site would really go off – it isn’t reproducible, it isn’t science, the adjustment guy has his finger on the scales, etc. And what would it really gain you? Some oddball stations might be treated a bit better; the regional and global trends would rather likely be about the same.
Don’t know that having a central and open repository where one could go to get the raw readings and (copious) metadata for any (GHCN?) station is such a bad idea. Surfacestations has shown that it can be done (with no budget). The trends may not ultimately change much but at least everyone could see that everything has been done out in the light. In the current atmosphere of mistrust “pronouncement from the tower” is obviously not working out so well.
And as it turns out, statistical methods for adjustment can be better than manual ones. For example, the change from old thermometers in the US to the MMTS stations. This used to be dealt with (in USHCN, now) using a set adjustment described in Quayle (1991). But it turns out, this instrument shift doesn’t always have the same effect because the reasons why the instrument shift affects the temp reading varies from station to station. A human would have some trouble dealing with this, but the statistical routine can sort it out.
I have no doubt that statistical methods have their part to play but at the same time it is clear that at least in the case of Matanuska and GISS something is not quite right (IMHO). I then have to wonder how many other stations have gone the same way. Unknown soldiers lost in the push so to speak.
If the “generally accepted” trend were 4 °C over recent times there would probably be little disagreement but as we are talking in tenths then Matanuska (and others?) might well matter. If 2010 is announced as “warmest on record” and the difference between 2010 and ’98 (or ’31) is some 0.07°C does Matanuska matter then? You can bet the MSM won’t care.
To make matters worse the number of stations falls off quite dramatically the further north you go in an area that is probably the most heavily scrutinised. Is Alaska really warming or is it just an artefact of the processing algorithm that goes un-noticed in more station rich regions?
carrot eater (19:11:09) :
“Basically, you just keep arguing for an adjustment procedure that requires a ton of manual work, ad-hoc manual decisions, a ton of historical metadata that won’t be available for each station, and no real indication of a significantly improved global result.”
In summary Willis is arguing for real science and you are arguing for science that produces fake but accurate results because the real science is just too hard or flat out impossible due to tons of missing historical metadata.
I think that is a pretty fair assessment of your position.
Do you think a jury would buy your take?
carrot eater (18:46:33) : edit
I have no problem with statistical methods, I use them all the time. However, when you apply them to temperature stations, you need, must have, absolutely require quality control.
Otherwise, you end up with Baghdad classified as rural, and Matanuska Agricultural Experimental Station classified as urban.
You also say:
Oh, please. 1200 stations, boo hoo, the jobs too big, it’s just not feasible … but we want you to spend trillions on our conclusions. What’s wrong with this picture?
It’s not something that has to be done every week, it just has to be done once. Too big for you? Sorry, no sympathy here. Trillion dollar decisions require a bit of work.
And the current system avoids all of that ugly dissention? …
The argument that the adjustments are not “reproducible” is a red herring. That’s just a question of proper documentation.
I don’t mind expert judgement. If they make adjustments, and they document the adjustments and the exact reasons, I can live with the dissention. In fact, many of the adjustments made by the various National Weather Services are done in exactly that way, and have been for years. What’s the problem?
I don’t spend a hundred bucks based on “rather likely”, much less billions. So-called “science” these days is chock-filled with “maybe” and “could” and “possibly” and “rather likely”. Yeah, the world could possibly warm 10°C before 2100, as some “scientists” claim … and I could possibly win the lottery. Neither of those claims rise above the noise level. I’m sick of big-money decisions based on “rather likely”.
You guys keep claiming I’m sifting through stations looking for oddities. I’m not. So far, I’ve calculated the adjustments at three stations: Darwin, Anchorage, and Matanuska. Each one contained what to me are hugely incorrect adjustments.
What does this mean about the other stations? I don’t know, and I don’t guess. Could mean nothing, could be important. But given these egregious errors, I certainly don’t trust any of them until such time as the stations are individually examined and verified. Sorry, you can trust folks who think Baghdad and Srinagar are rural, but I’m not along for the ride. And “examining each one is too much work” just doesn’t cut it. That was the excuse for not examining the US surface stations, but now that it’s done, we find that most of them have a huge number of problems.
Nor is the global average the only issue. Records are being set in Tucson Arizona, and people are making decisions based on those records, simply because the station is over gravel in the middle of a parking lot. People out there believe that the climate is going through the roof, and deciding that AGW is real, simply because of publicity and hype based on one erroneous station. So I don’t buy the idea that we can ignore any bad stations.
Re: Willis Eschenbach (Feb 22 20:18),
Well, hopefully this plot will show what CE and I are getting at. It shows (anomaly) temperatures at Matanuska (red), the weighted average of surrounding Rural temperatures in green and the difference in black. So far, that’s just data – no GISS artifice.
The lines are the fitted broken line to the black curve. It goes down and up – again, just data.
Now GISS think, based on brightness, that Matanuska may be affected by UHI. If they just added the black curve to the red, they’d get the green. This is just the rural average. If they use that “adjusted” value, there is no Matanuska information at all. It is replaced by the rural average. Since those rural stations are already included, it no longer adds information, but it doesn’t remove any either.
If you add the line approximation, which GISS does, then you remove the Matanuska trend, which keeping the shorter term signal. That would take out the UHI, if present. I actually don’t think the short term signal is useful, so they should just add the black curve. But it doesn’t do any harm.
Anyway, we’re obviously not making progress here. I’ll write it up on my blog, with R code that does a reasonable emulation of the GISS adjustment procedure for individual stations.
Nick Stokes, re 17:46
Re: 3×2 (Feb 22 17:08),
If we now accept that Matanuska is rural
No, GISS uses an objecive criterion – night brightness. It’s actually a good criterion. Arguing about population is missing the point, because that’s not a good indicator of UHI either. What you want is a measure of local heat release, and artificial lighting is a good indicator. Maybe the satellites are getting it wrong, but it isn’t GISS.
No, night brightness would be a horrible criterion.
1) Night lighting tends to be highly efficient high-pressure sodium, mercury vapor, or other such bulb. Those don’t produce significant amounts of heat compared to other lighting technologies.
2) The heat produced by night lights tends to be up on poles, or at least up gutter level. The concentrated heat from their bulb and ballast goes up, never dropping down to the levels where surface temperatures are measured.
3) If there is snow on the ground, its reflectance will make such night lights look much brighter, perhaps by a factor of ten, which might explain why Montreal was listed as more urban than Paris, New York, or Tokyo.
4) External night lighting isn’t a side effect of urbanization, or a measure. It’s a conscious decision. Areas might increase night lighting because of crime problems, especially by bears.
5) Areas might decrease upward night lighting in response to pressure from the astronomical community, which goes to great effort to convince cities to decrease their upward illumination in key parts of the spectrum that indicate night lighting.
6) Night lighting can also be a sign that a city has grown to a size where third shift work becomes common, which has a domino effect on the service sector.
7) Night lighting is also a sign that the local industry contains a significant high-captial investment that is best recouped by continuous operation, again producing three-shift work in an area that is rural. This is common in many mining operations, such as are found in Alaska.
Vastly better measures of UHI would be pulling up data on energy consumption, energy consumption per capita, local albedo, nightime IR signature, and countless other measures.
But all of that won’t get around the stubborn refusal to actual measure the temperature instead of guestimating what it would be in a parallel universe where the cities didn’t exist.
I think climatology must be the only branch of science where people add in a large trend before examing the data for a small trend.
Re: Willis Eschenbach (Feb 22 20:18),
Nick, many thanks for the excellent information, and for the good work. Please post the URL of your blog. I look forwards to the R code, as I use R.
Your emulation is still a ways from the GISS values, however. Their adjustment starts at zero, drops to -0.7, and comes back up to zero.
If we take your starting point as being zero, you drop to -1.25, and come back up to -0.2.
Next, how did you do your averaging? There’s two main ways this is done. GISS expresses the numbers as an anomaly around a common overlap period. GHCN calculates the first differences, averages those, and then reverses the first differences to regain the data. I prefer the second method, as the first method doesn’t handle short records or breaks in the data.
Finally, whenever you do averaging of any kind, it reduces the variance. In your graph, the average looks to have about the same variance as the Matanuska data. Why is that?
In any case, very well done. If you could post (or make available) the raw station data, that would be most appreciated.
w.
Ah, sorry, Nick, I see you already posted the link to your blog. Thanks.
w.
I have a problem with your use of the term, “tin foil hat”.
Everyone knows that only a steel V2K Cap will protect against mind control weaponry.
Willis,
Yes, the emulation isn’t exact. I haven’t done the duplicates properly – with rural stations, the duplicates (if any, I didn’t see any) are added separately to the average. I didn’t use the GISS anomaly period – for a single station and its neighbors it’s enough for this exercise to just subtract the mean for the data period, It’s a constant offset. I did use their method – gathering the temps at the central point, and subtracting a group average.
I think the periods that go into the bent line fitting may be different. I didn;t search for the knee – I just used the apparent GISS value.
My blog is here. It will take a few hours yet.
Willis,
I just finished reading all the comments to your post on Anchorage and Matanuska.
!! A word of encouragement!! You provide great stuff for people like me who have some engineering knowledge and experience, however I am only a couple of months into the studying all things climate related to science.
You inspire me. Thank you, such inspiration is profoundly priceless.
My observations at this point are:
1) I postulate that neutral observer cannot find the specific justifications for the GISS adjustments to Anchorage and Matanuska. Arm waving and appeals to the “experts knowing what they are doing” do not mean anything to a neutral observer. Nor do suspicions by skeptics of GISS having an AGW bias which causing GISS to manipulate data have any impact on a neutral observer. Info on how GISS made the adjustments does exist. What is needed is an official GISS supplied justification of why they made to adjustments to Anchorage and Matanuska.
2) ANCORAGE: We can see what the GISS adjustments did. Anchorage had late 1920s raw temp data raised by 0.9 C when it was significantly less urban. Counter intuitively in the late 1990s after some profound increases in urbanization the raw data was not adjusted at all (zero adjustment). An explanation is required from GISS.
3) MATANUSKA: Again we can see what the GISS adjustments did. They start out in early 1920s with no adjustment to the raw data but then they start to progressively apply an increasing UHI like correction to raw data for next ~50 years even though the station remains at a rural location. Then starting at ~1970 they apply adjustments that effectively unUrbanize the raw temp data even though there is a possibility evidence of some local population increase. An explanation is required from GISS.
4) It is unlikely GISS will voluntarily come forth to provide the above info. I say this only based on observation of past GISS behavior. I would like to be surprised. In order to keep the needed audit of climate science moving forward then reverse engineering by independent rational thinkers (aka skeptics) should increase in pace and scope. The eventual weight of information on GISS adjustments should move this into an more open dialog with GISS.
John
Thanks, John. One point. You say:
Actually, there’s no problem with this method of adjustment. If we are making a long term trend adjustment, we have two choices. We can either leave the present unchanged and alter the past temperatures, or we can leave the past unchanged and alter the present temperatures.
In truth there’s no difference between these methods, both of them end up with the same trend. Modern practice is to leave the present unchanged and alter the past. This makes it simpler to add new data, since (assuming that the changes were in the past and have ceased) we don’t have to make any adjustments to the new data that we are adding.
So while it seems counter-intuitive, it’s actually a reasonable way to make the adjustments.
Whether the adjustments themselves are reasonable, however, is an entirely different can of worms …
w.
3×2 (20:03:03) :
“everyone could see that everything has been done out in the light. In the current atmosphere of mistrust “pronouncement from the tower” is obviously not working out so well.”
How can you say that, when GISS is already doing everything in the light, using a procedure that anybody can implement for himself, using code that anybody can download for himself? There is no black box here.
“Don’t know that having a central and open repository where one could go to get the raw readings and (copious) metadata for any (GHCN?) station is such a bad idea.”
NOAA has historical metadata for its own stations. It has none for all the other countries. You just aren’t going to be able to track down the complete station histories in every country.
Though at least nowadays, data storage capacity isn’t the bar it was 20 years ago.
Starting from scratch and with things under its own control, you can do it, though. They do it with the US CRN.
“Surfacestations has shown that it can be done (with no budget). ”
Surfacestations has a bunch of current pictures. It doesn’t give you the information you need to make adjustments in the past, nor do current pictures alone tell you what the temperature readings are doing. And as I’ve said before, even if you had a picture from 1932 and a picture from 1937, and something visually looks different, you’d still not know what adjustment to make. You’d have to statistically examine the temperature record and the neighbors, using the methods of NOAA.
In the end, you’d do what NOAA is doing with the USHCN now. Use your little statistical program to make all the adjustments, and then go back and look in your historical metadata to see how many of the adjustments line up with something you have field notes about.
“The trends may not ultimately change much but at least everyone could see that everything has been done out in the light.”
It’s already in the light, and if the trends don’t change much, then what’s the point?
We’ve already got two different records with different philosophies on this matter: GISS and GHCN. As discussed, GHCN and in particular USHCN are much more ambitious about trying to make detailed corrections. And yet, it doesn’t much matter how you go about it.
“I have no doubt that statistical methods have their part to play but at the same time it is clear that at least in the case of Matanuska and GISS something is not quite right (IMHO).”
By statistical methods there, I don’t mean what GISS is doing. GISS’s method isn’t even trying to use statistics to correct errors; it’s just erasing the difference in trend between urban station and rural station. I mean the homogenisation method of Menne used in USHCN, which is much more sophisticated and is meant to sniff out station moves and instrument changes and the like.
“If 2010 is announced as “warmest on record” and the difference between 2010 and ‘98 (or ‘31) is some 0.07°C does Matanuska matter then?”
Two years separated by 0.07 C are statistically indistinguishable. Ranking years is pointless; it’s the trends that matter.
As for what matters: random errors don’t. For something to matter to the global or regional mean, you need a systematic bias in some direction for some reason. That’s why TOB makes such a difference for the USA. That’s why people are worried about UHI. But the UHI here is gone. The rural stations do the driving, and information about the long-term trend at Anchorage is erased. Are you worried that maybe the rural stations aren’t really rural? Well here, GISS’s method is being overanxious about that as well, and puts in Matanuska as urban. In this case, their method results in being abundantly cautious about UHI, and you still don’t like it. Go figure.
Harold Vance (20:13:17) :
No method is going to be perfect. Willis’s method would be just as imperfect, because you simply don’t have the full amount of information required to perfectly recreate the past. So his record would be just as ‘fake’ as anybody else’s.
So the question is, what to do. I think reproducibility is important in science, and I thought the general thought at WUWT was to agree with that. So I prefer objective methods. I prefer the objective method of the current USHCN to that of GISS; it actually tries to correct the record, instead of just accepting raw data from rural stations and tossing out the urban ones. But the latter as the advantage of simplicity. And in the end, it doesn’t matter which you do. Random errors wash themselves out.
carrot eater (18:57:39) :
” If you make it such that the longer trends roughly match the other surrounding stations, then the station in question is not affecting the longer trends at that grid point. In effect, it is being tossed out.
So again, what you need to do to assess this is actually look at those surrounding rural stations.
To assess the calculation, you’d need to use the GISS method to find the temperature at that grid box, with and without Matansuka. If the GISS adjustment did what it’s supposed to do, then the longer trends for the combined record at that grid box would not be affected by adding or removing Matansuka.”
This would be a valid adjustment method if (and only if) the resulting record is exclusively used for statements about ‘longer’ trends. Therefore, it would be nice to know what, exactly, constitutes a longer trend.
And this might swiftly carry us to the heart of the matter: assuming that ‘longer’ trends are around a 100 years, the use of the adjusted data in proving the extraordinariness of the time period 1970-2010 would turn a legitimate adjustment into a fudge.
Willis Eschenbach (20:18:00) :
“I have no problem with statistical methods, I use them all the time. However, when you apply them to temperature stations, you need, must have, absolutely require quality control.”
Again, by statistical methods, I’m referring to the USHCN, not GISS. The GISS methods are too simple to earn the name ‘statistical’ from me.
“Otherwise, you end up with Baghdad classified as rural, and Matanuska Agricultural Experimental Station classified as urban.”
There’s no harm done by the false positive at Matanuska. A false negative at Baghdad is more interesting.
“Oh, please. 1200 stations, boo hoo, the jobs too big, it’s just not feasible … but we want you to spend trillions on our conclusions. What’s wrong with this picture?”
OK, you go track down the entire station history for some station in the Central African Republic.
Again, GHCN tries to do the detailed work; GISS does not even bother. And both give the same result. Which globally, is the same result as what you get from the raw data. What does that tell you?
“require a bit of work.”
This coming from a guy who refuses to take a day and look at the neighboring rural stations, in order to see why GISS did what it did.
“The argument that the adjustments are not “reproducible” is a red herring. ”
I don’t buy that for one second.
“So far, I’ve calculated the adjustments at three stations: Darwin, Anchorage, and Matanuska. Each one contained what to me are hugely incorrect adjustments.”
You’ve shown absolutely nothing of the sort, because you have not put in the work required to assess those adjustments. At Darwin, you would have to collect all the neighboring stations and go through the GHCN process to see why it did what it did. Maybe it did something weird, but you simply have not shown that. For Anchorage and Matanuska, all that GISS is doing is erasing those stations from affecting the long term trends at the local grid point.
George Turner (21:17:33) :
“Vastly better measures of UHI would be pulling up data on energy consumption, energy consumption per capita, local albedo, nightime IR signature, and countless other measures.”
No, the best measure would be to actually look for it in the temperature record itself, compared to the neighbors. This is what the USHCN does now.
UHI varies strongly spatially. You could be in the middle of a city with all the measures you speak of, but be in a park with no obvious UHI trend.
“But all of that won’t get around the stubborn refusal to actual measure the temperature instead of guestimating what it would be in a parallel universe where the cities didn’t exist.”
How do you measure a temperature that doesn’t exist? And when there are plenty of rural stations around, GISS is not unreasonable in just knocking out the cities altogether.
Willis:
Thankyou for your fine analysis and subsequent responses to comments.
You repeatedly state that you know how adjustments are made to records of station data but you do not know why they are made. For example, you say to Carrot Eater at (15:29:36):
“What I don’t understand is how this is all justified. I keep asking for a reason that anyone would start adjusting a pristine rural record in 1920. Do you or GISS have the slightest scrap of evidence that there was something wrong with the record?”
The adjustments are not intended to correct individual station records because it is thought “there was something wrong with the record”. And I think you have been side-tracked by arguments (e.g. from carrot eater and Nick Stokes) that the adjustments may be making correct adjustments in individual cases.
I think I know why the adjustments are universally applied by computer algorithm acting on each data set from each station record. And it is not relevant to the purpose of the adjustments whether or not the adjustments can be justified for any individual station record.
Please note that the the adjustments to station records are conducted as part of the data processing to obtain values of mean global temperature (MGT) by combination of all the station records. And the purpose of this data processing is an attempt to determine changes that have happened to MGT since station records began to be compiled. The intended determination from this processing is MGT (and mean hemispheric temperatures). And, importantly, the compilers of the MGT data sets provide no stated reason why the stages of that processing should provide correct data for individual localities (e.g. the sites of individual measurement stations).
In paragraph 9 of my submission to the UK Parliament Select Committee I say:
“9.
It should also be noted that there is no possible calibration for the estimates of MGT.
The data sets keep changing for unknown (and unpublished) reasons although there is no obvious reason to change a datum for MGT that is for decades in the past. It seems that – in the absence of any possibility of calibration – the compilers of the data sets adjust their data in attempts to agree with each other. Furthermore, they seem to adjust their recent data (i.e. since 1979) to agree with the truly global measurements of MGT obtained using measurements obtained using microwave sounding units (MSU) mounted on orbital satellites since 1979. This adjustment to agree with the MSU data may contribute to the fact that the Jones et al., GISS and GHCN data sets each show no statistically significant rise in MGT since 1995 (i.e. for the last 15 years). However, the Jones et al., GISS and GHCN data sets keep lowering their MGT values for temperatures decades ago.”
Such adjustment “to agree with each other” provides a complete explanation for why “anyone would start adjusting a pristine rural record in 1920”.
And, Willis, at (20.18. 00) you say;
“You guys keep claiming I’m sifting through stations looking for oddities. I’m not. So far, I’ve calculated the adjustments at three stations: Darwin, Anchorage, and Matanuska. Each one contained what to me are hugely incorrect adjustments.”
Well, that is not surprising according to my understanding (that I have stated in this posting). An algorithm making an adjustment to cause an MGT data set to more-closely agree with other MGT data sets would plough through all the station data and provide most station data with what appear to be “hugely incorrect adjustments”. So what when these apparently “hugely incorrect adjustments” are merely intermediate calculations in the obtaining of MGT data sets?
And, I again stress that there is no possible calibration for the estimates of MGT.
But, as the final paragraph of my submission to the UK Parliament Select Committee, says:
“12.
None of this gives confidence that the MGT data sets provide reliable quantification of change to global temperature.”
Richard
carrot eater (05:36:55) :
Looks like the MO have gone for much the same ideas as I posted earlier (spooky) (TL WUWT post) …
The new effort, the proposal says, would provide:
–”verifiable datasets starting from a common databank of unrestricted data”
–”methods that are fully documented in the peer reviewed literature and open to scrutiny;”
–”a set of independent assessments of surface temperature produced by independent groups using independent methods,”
–”comprehensive audit trails to deliver confidence in the results;”
–”robust assessment of uncertainties associated with observational error, temporal and geographical in homogeneities.”
Carrot eater,
If GISS ignores the cities, why do any math at all? Just don’t include them in the data set.
3×2 (09:16:52) :
While that statement doesn’t imply it or require it, those guys (CRU) apparently actually do a lot of things on a somewhat subjective, manual basis. Quality control is done with visual eyeball tests, as opposed to using statistical criteria. They get some data from the source countries in an already homogenised form; those individual countries might use different methods to do it. They do some homogenisation themselves by taking stations, looking for step changes in the difference, and seeing if they can find any notes about a station move or instrument change.
But they realise they’ll never have anywhere close to complete information about station histories, so then they slap on some uncertainty bounds.
So if you want a dataset with human-led adjustments (if that’s still how they do it), go with CRU. But you’ll see how little it matters.
George Turner (10:20:22) :
My first inclination is the same as yours. If the urban stations aren’t contributing to the longer trends in the record, then why use them at all?
I suppose GISS doesn’t want to throw away information about the short-term variations, that is present in the urban records.
In the end, it doesn’t really matter whether you keep the cities in there, or not. That’s what has to be kept in mind in all these discussions – you can get really bogged down in the details of the processing, but for the most part it doesn’t matter that much in the big picture (the global mean trend, on land at least).
I really enjoyed this discussion, but I am wondering two things:
1. Does adjusting old temperatures somehow affect the graphic comparisons for those who insist there is a correlation between temps and worldwide CO2 levels?
2. If everyone agrees that even the raw data indicates a general worldwide warming trend, when do we start discussing the cause?
carrot eater (10:40:31) :
George Turner (10:20:22) :
My first inclination is the same as yours. If the urban stations aren’t contributing to the longer trends in the record, then why use them at all?
I suppose GISS doesn’t want to throw away information about the short-term variations, that is present in the urban records.
In the end, it doesn’t really matter whether you keep the cities in there, or not. That’s what has to be kept in mind in all these discussions – you can get really bogged down in the details of the processing, but for the most part it doesn’t matter that much in the big picture (the global mean trend, on land at least).
=================
Right, so I’ve been following the generic points made in thsi thread and so far as I can interpret CE (and also NS?) are saying that no mattrer which of the 3 main approaches one takes the trends are the same, more or less for any practical purpose, and give the same trend, also more or less, as the raw data readings.
So, now that we know that (have we had enough time to ‘know’ for sure?) can we assume that all of the work undertaken to re-present the raw readings, when averaged out over all the readings and their ‘errors’ from all causes around the entire globe, guive essentially the same result for trend, can we stop funding most of the bodies that are, allegedly, still ‘researching’ these things and re-distribute the effort and budget to something or things more worthwhile?
Or should we be considering such a net result, assuming that nothing is known to have been missed in coming to that conclusion, and ponder whether such a common agreement, in spite of seemingly different methods, may have some common cause that is as yet unidentified?
Might it be that all the temperature measurement cancel each other out on a rural/urban comparison, except one which then becomes the sole source of the global temp change?
Just a thought.
I really don’t know but with the social policies being proposed and persued over such a short period I think such matters are due some serious consideration and effort.
Willis;
I think I understand (finally) what carrot eater is saying:
The funky adjusted temps are okay for determining a global average temperature,
but should not be used for an individual station study (or a regional study).
The following link is to the Beeville, TX station showing NOAA adjustments with more of the same funky methodolgy:
http://tinypic.com/r/2mz0dqu/6
Hopefully this shows up and is readable.
Re: Nick Stokes (Feb 22 22:09),
I’ve now put up the post and code at my blog. There are a few improvements – it now does search for the optimal “knee” (and finds one for Anchorage), and it treats the rural duplicates better.
Tom in Texas (12:44:43) :
That’s a fair way of phrasing it. GISS isn’t really trying to get an accurate idea of what actually happened at Matanuska or Anchorage. They want what actually didn’t happen: a record without any UHI trend whatsoever.
If you want an accurate idea of what Anchorage actually experienced, don’t look at the GISS adjusted data. If the UHI was real, you would have experienced it, but GISS takes it out.
If you go to Nick Stokes’ blog, you’ll see the concept clearly illustrated. Once GISS identifies a station as urban, it loses its own trend (which may be tainted by UHI at some point), and is given the trend of the nearby rural stations.
As for Beeville, that’s a USHCN adjustment, done using entirely different methods. We could probably save that discussion for when a topic about USHCN comes around, as it no doubt will, sooner or later.
Re: Willis Eschenbach (12:29:05)
Those look like GISS graphs. Interesting. The official Canadian homogenization procedure must differ in some very fundamental ways, because that pair in no way reflects some of the oddities I have no file.
…So (no surprise) there are layers upon layers to this twisted puzzle – and we have plenty more volunteer digging to do to get to the truth. (too many irons in the fire…)
carrot eater (14:25:39) :
You seem to be agreeing with my post a little earlier at (07:27:23) when you say;
“GISS isn’t really trying to get an accurate idea of what actually happened at Matanuska or Anchorage. They want what actually didn’t happen: a record without any UHI trend whatsoever.”
OK.
If we agree that they “want what actually didn’t happen”
because
they are attempting to determine “a record without any UHI trend whatsoever”
as a method to determine mean global temperature (MGT) anomally
then
will you please address the issues I raised when I said;
(a)
“It seems that – in the absence of any possibility of calibration – the compilers of the data sets adjust their data in attempts to agree with each other. Furthermore, they seem to adjust their recent data (i.e. since 1979) to agree with the truly global measurements of MGT obtained using measurements obtained using microwave sounding units (MSU) mounted on orbital satellites since 1979. This adjustment to agree with the MSU data may contribute to the fact that the Jones et al., GISS and GHCN data sets each show no statistically significant rise in MGT since 1995 (i.e. for the last 15 years). However, the Jones et al., GISS and GHCN data sets keep lowering their MGT values for temperatures decades ago.”
And
(b)
“It should also be noted that there is no possible calibration for the estimates of MGT.”
These two points raise two important questions; viz.
1.
Why do the compilers attempt to make their determinations of MGT anomalies agree with each other when they are each claiming they are independently using different methods to detemine MGT anomalies from the station data?
2.
Why does each of the teams compiling the MGT data sets not try to justify its method as being the right one that should be used as THE reference in the absence of a true calibration?
None of the obtained values of MGT anomally can be accepted as indicating anything except their methods of compilation until these questions are both answered.
Richard
Richard S Courtney (15:24:50) :
You think they’re adjusting the surface record in order to match the satellite record? That just doesn’t make any sense. Take the GISS adjustments. The code is there for you to run and read, if you don’t trust the papers (which describe what they do pretty well, so do read them if you are interested). Nothing in there could be construed as an attempt to try to match the satellites. Anyway, UAH used to diverge quite badly from the surface records, until some errors in the satellite record were found.
Just look at what you get for the global mean anomaly trend using only the raw data. You get about the same trends, anyway. Spencer got a similar result a few days back, though his analysis is not yet complete. If anybody is using adjustments to manipulate the data into showing something unjustified, they certainly aren’t being very ambitious.
Here is the history of Matanuska station. Look at the charts. No warming. Sometimes, it pays to keep it simple, stupid.
http://climate.gi.alaska.edu/history/CookInlet/Matanuska.html
Richard S Courtney (07:27:23) :
In paragraph 9 of my submission to the UK Parliament Select Committee I say:
“9.
It should also be noted that there is no possible calibration for the estimates of MGT.
The data sets keep changing for unknown (and unpublished) reasons although there is no obvious reason to change a datum for MGT that is for decades in the past. It seems that – in the absence of any possibility of calibration – the compilers of the data sets adjust their data in attempts to agree with each other. Furthermore, they seem to adjust their recent data (i.e. since 1979) to agree with the truly global measurements of MGT obtained using measurements obtained using microwave sounding units (MSU) mounted on orbital satellites since 1979. This adjustment to agree with the MSU data may contribute to the fact that the Jones et al., GISS and GHCN data sets each show no statistically significant rise in MGT since 1995 (i.e. for the last 15 years). However, the Jones et al., GISS and GHCN data sets keep lowering their MGT values for temperatures decades ago.”
The standards for testimony to the Select Committee seem rather low, you appear to have just made it up! If anything the MSU were adjusted (because of errors) to better match the surface measurements, certainly not the reverse. I’m surprised you’re not facing perjury charges.
Such adjustment “to agree with each other” provides a complete explanation for why “anyone would start adjusting a pristine rural record in 1920”.
Re: Jim (Feb 23 18:17),
Here’s a sat map picture of the AES, where the station is located. It’s about 1 km from a freeway intersection and 1 mile from a big car yard. The nearest town seems to be Wasilla. One could argue about ruralness, but I can see why night brightness might classify it so,
Tom in Texas (12:44:43) :
“Willis;
I think I understand (finally) what carrot eater is saying:
The funky adjusted temps are okay for determining a global average temperature,
but should not be used for an individual station study (or a regional study).”
That might be what he said, but what I get from the discussion has a lot more impact:
The funky adjusted temps are okay for determining [i] ‘longer’ trends’ in [/i] a global average temperature, but should not be used for [i] anything else [/i].
Especially not for the interpretation of ‘shorter’ trends in time.
The actual impact of this does, of course, depend on the values for longer and shorter trends, so I’d be happy if carrot eater could give me a hint there. At this moment, my hunch is that ‘longer’ is roughly 100 years and ‘shorter’ is 30-40 years (e.g. unprecedented warming from 1970-2010), which would fully justify Willis Eschenbachs in calling these adjustments fudges.
Nick Stokes (22:46:05) : edit
Thanks, Nick, you are correct. To me that’s an excellent example of why nightlights are a poor metric of rurality (is that a word?).
As an alternate metric, I would suggest the NOAA guidelines for station siting, viz:
I note that according to Anthony Watts’s excellent and laudable SurfaceStations survey, only 10% of the US sites surveyed to date are Class 1 or Class 2 … and we pride ourselves on (and likely have) what are among the best national stations in the world. Ignoring the current topic of the adjustments to the raw data, this doesn’t give me much confidence in the raw data itself.
I find it incredible that NOAA has not gathered this information on its own. People say “Oh, but it would take too many man-hours to rate all the sites, it would take thousands of man-hours, they don’t have the budget”.
Nonsense. They have someone collecting and recording the data at each and every site. How hard would it be to have those observers do what the surfacestation volunteers do? Take photographs, document the issues listed in the criteria above, list the known historical changes, job’s done … it’s a no-brainer.
And NOAA could encourage and spread the idea to the other National Weather Services. Every country has people at each of their sites as well. There’s only a couple thousand sites worldwide. For poor countries, NOAA could buy and distribute cheap cameras, and we could have the information we need to make these determinations of station quality worldwide.
That’s the first thing I’d do if I ran the zoo. Find out which sites are good and which ones are bad.
But no, they don’t have time to do the basic science stuff like document and photograph their own sites, they’re too busy making doomsday pronouncements …
Maik H (23:57:52) :
Your hunch is incorrect. When I say long-term in this context, 30 years is still long. Just look at how the method works, or the examples here.
Suppose, say, Urban station A had the same trend as nearby Rural stations B, C and D until 1970, and then Urban station A started warming up at double the rate as B, C and D.
In that case, since A was classed as urban, the program will try to impose on it whatever trends the rural neighbors have. It will leave A unchanged until 1970, and then reduce its trend from 1970 on. The program allows for one shift in trend, so that shift gets placed at 1970.
So station A is still in there, and its year-year bumps and dips stay in there, but it doesn’t have its own trend anymore; it has the trend of the neighbors.
Willis Eschenbach (01:35:45) :
A false positive in a test for urbanness is no big problem. You lose a bit of information in that region, but it won’t introduce a systematic bias. A false negative is more of an issue, if there was a UHI trend at some point.
IN the US, the nightlight test serves to make more stations to be classified as non-rural, than if you were using population figures. So if using population you get some false negatives, then by using nightlights you get more false positives. So if you’re really worried about UHI, you should prefer nightlights over population, within the US at least. In poor countries, this may be different.
The CRN ratings don’t necessarily tell you about urban warming. They also don’t necessarily tell you that the data will be distorted – for that, you have to actually look at the data.
The point of GCOS was to identify the ‘good’ stations, by the way. And the point of the US CRN was to start up from scratch a new network of stations that don’t have any flaws.
carrot eater (17:33:37) and Phil (21:21:03) :
carrot eater (17:33:37):
You do not address either of my questions at (15:24:50) but make the following spurious statement instead:
“You think they’re adjusting the surface record in order to match the satellite record? That just doesn’t make any sense. Take the GISS adjustments. The code is there for you to run and read, if you don’t trust the papers (which describe what they do pretty well, so do read them if you are interested). Nothing in there could be construed as an attempt to try to match the satellites. Anyway, UAH used to diverge quite badly from the surface records, until some errors in the satellite record were found.”
Nonsense! It makes perfect sense.
Your statement that “The code is there for you to run and read, if you don’t trust the papers” is pure sophistry: knowing HOW they do the adjustmens says nothing about WHY they do the adjustments. And your comment concerning UAH would not be relevant if it were true (which it is not).
Also, you make a statement that is confirmatory of my view when you say;
“Just look at what you get for the global mean anomaly trend using only the raw data. You get about the same trends, anyway.”
Indeed, so why make any adjustments? (I suppose you will dodge that question, too).
As I said at (07:27:23);
“The adjustments are not intended to correct individual station records because it is thought “there was something wrong with the record”.”
snip
“Please note that the the adjustments to station records are conducted as part of the data processing to obtain values of mean global temperature (MGT) by combination of all the station records. And the purpose of this data processing is an attempt to determine changes that have happened to MGT since station records began to be compiled. The intended determination from this processing is MGT (and mean hemispheric temperatures). And, importantly, the compilers of the MGT data sets provide no stated reason why the stages of that processing should provide correct data for individual localities (e.g. the sites of individual measurement stations).”
And as I have said twice above (i.e. at (07:27:23) and (15:24:50) );
“It seems that – in the absence of any possibility of calibration – the compilers of the data sets adjust their data in attempts to agree with each other. Furthermore, they seem to adjust their recent data (i.e. since 1979) to agree with the truly global measurements of MGT obtained using measurements obtained using microwave sounding units (MSU) mounted on orbital satellites since 1979. This adjustment to agree with the MSU data may contribute to the fact that the Jones et al., GISS and GHCN data sets each show no statistically significant rise in MGT since 1995 (i.e. for the last 15 years). However, the Jones et al., GISS and GHCN data sets keep lowering their MGT values for temperatures decades ago.”
Phil. (21:21:03) :
Retract your unworthy assertions that say to me;
“The standards for testimony to the Select Committee seem rather low, you appear to have just made it up! If anything the MSU were adjusted (because of errors) to better match the surface measurements, certainly not the reverse. I’m surprised you’re not facing perjury charges.”
My submission to the Select Committee pertains to the Climate gate email from me that can be read at
http://www.eastangliaemails.com/emails.php?eid=384&filename=1069630979.txt
That submission says;
“6.
Thus, we determined that – whichever way MGT is considered – MGT is not an appropriate metric for use in attribution studies.
7.
However, the compilers of the MGT data sets frequently alter their published data of past MGT (sometimes they have altered the data in each of several successive months). This is despite the fact that there is no obvious and/or published reason for changing a datum of MGT for years that were decades ago: the temperature measurements were obtained in those years so the change can only be an effect of alterating the method(s) of calculating MGT from the measurements. But the MGT data sets often change. The MGT data always changed between submission of the paper and completion of the peer review process. Thus, the frequent changes to MGT data sets prevented publication of the paper.
8.
Whatever you call this method of preventing publication of a paper, you cannot call it science.
But this method prevented publication of information that proved the estimates of MGT and AGW are wrong and the amount by which they are wrong cannot be known.
(a) I can prove that we submitted the paper for publication.
(b) I can prove that Nature rejected it for a silly reason; viz.
“We publish original data and do not publish comparisons of data sets”
(c) I can prove that whenever we submitted the paper to a journal one or more of the Jones et al., GISS and GHCN data sets changed so either
the paper was rejected because it assessed incorrect data
or
we had to withdraw the paper to correct the data it assessed.
But I cannot prove who or what caused this.”
The 19 co-signatories of our paper can all attest to this. They each have a copy of my Submission and none has expressed any dissent from any word of it.
I have committed perjury? Are you mad?
The important point of my Submission is;
“10.
Methods to correct these problems could have been considered 6 years ago if publication of my paper had not been blocked.”
Richard
carrot eater (03:41:16) :
Thanks for the info and the explanation! I’ll try to find some time over the weekend to have a more in-depth look at the method.
Richard S Courtney (04:18:57) :
The only adjustments GISS makes are the ones being discussed in this tread. They take any stations labeled as ‘urban’, and then make them look like their rural neighbors, just in case the urban areas had any urban warming effects. What has that possibly got to do with satellite measurements?
And how is my comment about UAH untrue? Until a few years back, the satellite record showed little to no warming. This was used as a huge talking point, saying that the surface record was wrong because the satellites disagreed. Then some errors were found in the satellite calculations. Do you not remember this?
As to the question, If globally the raw and adjusted records more or less match, then why bother making adjustments?
First, you didn’t know before you started that this would be the result. If the errors you are correcting were perfectly random, then you would expect the adjustments to have no net result in the big picture. But this isn’t necessarily the case; in the US it is not. And even if the errors were random, in a small enough region you could see some net effect.
Which leads to the point: globally, adjustments have little net effect. But you can find regions where they do; you are after all changing something. So if you want the best indication you can get of “how climate has changed in the Western US”, it’s best to look at adjusted data.
Richard S Courtney (04:18:57) :
To clarify, the last couple paragraphs in my reply are more pertinent to GHCN adjustments; the first paragraph to GISS adjustments. When speaking of ‘adjustments’, one does need to specify whose and which.
I have one clear problem with the Anchorage adjustment, why make old temperatures HOTTER? Why not reduce the newer UHI-infected measurements?
The second adjustment just seems to be random or applied because of some algorithm, because it makes no sense at all, except if some kind of ghost town exists at this measurement site ^^
Max (07:02:10) :
When you work with anomalies, it mathematically doesn’t matter which you do, so long as you reduce the trend. But in general, when making adjustments, the preference is to make the adjustments so that the current data have an adjustment of zero. That way, new data values can be easily compared to the end of the series.
If a station is contaminated with UHI effect, why not just throw it out? When you try to adjust it, it still isn’t necessarily a valid record. Where’s the proof that this method works at all? How would you prove it is valid?
Richard S Courtney (04:18:57) :
Phil. (21:21:03) :
Retract your unworthy assertions that say to me;
“The standards for testimony to the Select Committee seem rather low, you appear to have just made it up! If anything the MSU were adjusted (because of errors) to better match the surface measurements, certainly not the reverse. I’m surprised you’re not facing perjury charges.”
I certainly will not, you by your own admission made inaccurate, misleading and false statements in your testimony. Prefacing your comments with such weasel words as ‘it seems’ etc. doesn’t get you off the hook. I suggest that you publish a retraction of the offending remarks in your testimony.
Willfully giving false evidence to a select committee makes one liable to the penalties of perjury.
carrot eater (05:25:45) :
Thank you for responding to my post at (04:18:57). However, you have still not addressed my questions at (15:24:50), you have ignored my main point, and you concentrate on my secondary point.
I address the points in your message.
You say:
“The only adjustments GISS makes are the ones being discussed in this tread. They take any stations labeled as ‘urban’, and then make them look like their rural neighbors, just in case the urban areas had any urban warming effects. What has that possibly got to do with satellite measurements?”
But I have explained this repeatedly above. In my first post above, at (07:27:23), I wrote to Willis and that post included:
“You repeatedly state that you know how adjustments are made to records of station data but you do not know why they are made. For example, you say to Carrot Eater at (15:29:36):
“What I don’t understand is how this is all justified. I keep asking for a reason that anyone would start adjusting a pristine rural record in 1920. Do you or GISS have the slightest scrap of evidence that there was something wrong with the record?”
The adjustments are not intended to correct individual station records because it is thought “there was something wrong with the record”. And I think you have been side-tracked by arguments (e.g. from carrot eater and Nick Stokes) that the adjustments may be making correct adjustments in individual cases.
I think I know why the adjustments are universally applied by computer algorithm acting on each data set from each station record. And it is not relevant to the purpose of the adjustments whether or not the adjustments can be justified for any individual station record.”
My major point was – and is – that the Jones et al, GISS and GHCN data sets seem to be adjusted to agree with each other.
In my first post here I quoted from my Submission to the Select Committee (and have since repeated that quotation twice). The quotation and my comment on it was:
“It should also be noted that there is no possible calibration for the estimates of MGT.
The data sets keep changing for unknown (and unpublished) reasons although there is no obvious reason to change a datum for MGT that is for decades in the past. It seems that – in the absence of any possibility of calibration – the compilers of the data sets adjust their data in attempts to agree with each other. Furthermore, they seem to adjust their recent data (i.e. since 1979) to agree with the truly global measurements of MGT obtained using measurements obtained using microwave sounding units (MSU) mounted on orbital satellites since 1979. This adjustment to agree with the MSU data may contribute to the fact that the Jones et al., GISS and GHCN data sets each show no statistically significant rise in MGT since 1995 (i.e. for the last 15 years). However, the Jones et al., GISS and GHCN data sets keep lowering their MGT values for temperatures decades ago.”
Such adjustment “to agree with each other” provides a complete explanation for why “anyone would start adjusting a pristine rural record in 1920”.
You keep concentrating on the UAH issue and repeatedly ignore my main point that is:
“It seems that – in the absence of any possibility of calibration – the compilers of the data sets adjust their data in attempts to agree with each other.”
And I stand by
“Furthermore, they seem to adjust their recent data (i.e. since 1979) to agree with the truly global measurements of MGT obtained using measurements obtained using microwave sounding units (MSU) mounted on orbital satellites since 1979. This adjustment to agree with the MSU data may contribute to the fact that the Jones et al., GISS and GHCN data sets each show no statistically significant rise in MGT since 1995 (i.e. for the last 15 years). However, the Jones et al., GISS and GHCN data sets keep lowering their MGT values for temperatures decades ago.””
Your failure to answer my main questions (at (15:24:50) ) while ignoring my substantive point and concentrating on my secondary point implies that you may have attended the ‘Gavin Schmidt School of Obfuscation”.
Then you ask me;
“And how is my comment about UAH untrue? Until a few years back, the satellite record showed little to no warming. This was used as a huge talking point, saying that the surface record was wrong because the satellites disagreed. Then some errors were found in the satellite calculations. Do you not remember this?”
Of course I do! But check how the surface MGT data sets were altered before and after those corrections to the UAH and RSS data sets. And see how little difference was made to the UAH and RSS data sets by the “satellite corrections”. If you do those checks then you will clearly understand that my statement to the Select Committee (that I have again quoted in this message) is correct:
i.e. the compilers of the surface MGT data sets make adjustments to their methods and those adjustments seem to be an attempt to make those data sets agree with each other and, in recent years, to agree with the satellite data.
But you do address the question in my post at at (04:18:57) by posing a question of your own when you say:
“As to the question, If globally the raw and adjusted records more or less match, then why bother making adjustments?
First, you didn’t know before you started that this would be the result. If the errors you are correcting were perfectly random, then you would expect the adjustments to have no net result in the big picture. But this isn’t necessarily the case; in the US it is not. And even if the errors were random, in a small enough region you could see some net effect.
Which leads to the point: globally, adjustments have little net effect. But you can find regions where they do; you are after all changing something. So if you want the best indication you can get of “how climate has changed in the Western US”, it’s best to look at adjusted data.”
This is more evasion. Having determined that “globally the raw and adjusted records more or less match” then there is no reason to continue repeatedly making more and more adjustments. Impotantly, there is no calibration for the data sets so there is no method to determine if the adjustments are making the indications of MGT better or worse.
Worse than that, you assert that “if you want the best indication you can get of “how climate has changed in the Western US”, it’s best to look at adjusted data.” Remember, as you say, “globally, adjustments have little net effect. But you can find regions where they do; you are after all changing something.”
I say it is NOT “best to look at adjusted data” when considering localities because, as you admit, “you are after all changing something” and there is no reason (n.b. none, not any reason) to think the change is not corrupting the information from the measurements.
This returns us to the question that Willis asked you at (15:29:36), i.e.:
“What I don’t understand is how this is all justified. I keep asking for a reason that anyone would start adjusting a pristine rural record in 1920. Do you or GISS have the slightest scrap of evidence that there was something wrong with the record?”
And, as I said, the reason for changing such records seems to be that the compilers of the surface MGT data sets make adjustments to their methods in attempt to make those data sets agree with each other and, in recent years, to agree with the satellite data.
So, we are back to where I started.
Richard
Phil. (08:13:02) :
You say;
“Willfully giving false evidence to a select committee makes one liable to the penalties of perjury.”
YES! It does. So, retract and apologise for your demonstrably untrue and libelous assertion that I have done that.
Richard
Richard S Courtney (09:16:41) :
If I don’t respond to everything in your comment, it’s simply because I find your comments overly long and difficult to follow in their logic. I’m sorry, but this is so.
I’m going to skip to here:
“But check how the surface MGT data sets were altered before and after those corrections to the UAH and RSS data sets. And see how little difference was made to the UAH and RSS data sets by the “satellite corrections”.”
This is a bit of revisionism, and also just strange.
The surface records show something like a warming of +0.16 to 0.17 C/decade, since 1979. This is true of both the raw and adjusted data. Keep that in mind: both raw and adjusted.
UAH is now showing +0.13 C/decade. It used to show as little as I think +0.03 C/decade, then up to +0.09, and finally now +0.13. How can you possibly call that a “little difference”?
If anybody was trying to adjust the surface record to look like the satellites, they’d have been adjusting it to reduce the warming trend. And then as the satellite corrections were being made, they’d have to continually undo that until they got up to +0.13 C/decade. Which still requires a cooling adjustment. But no such thing has occurred.
Richard S Courtney (09:19:20) :
Phil. (08:13:02) :
You say;
“Willfully giving false evidence to a select committee makes one liable to the penalties of perjury.”
YES! It does. So, retract and apologise for your demonstrably untrue and libelous assertion that I have done that.
Not while you continue to say this:
“It seems that – in the absence of any possibility of calibration – the compilers of the data sets adjust their data in attempts to agree with each other. Furthermore, they seem to adjust their recent data (i.e. since 1979) to agree with the truly global measurements of MGT obtained using measurements obtained using microwave sounding units (MSU) mounted on orbital satellites since 1979.”
carrot eater (10:35:50) :
You say to me:
“If I don’t respond to everything in your comment, it’s simply because I find your comments overly long and difficult to follow in their logic. I’m sorry, but this is so.”
OK. So I will use soundbites.
SOUNDBITE 1
Why have you repeatedly ignored my substantive point? I have repeatedly said it is:
“And, as I said, the reason for changing such records seems to be that the compilers of the surface MGT data sets make adjustments to their methods in attempt to make those data sets agree with each other and, in recent years, to agree with the satellite data.”
SOUNDBITE 2
I have repeatedly asked you, initially at (15:24:50), two questions that you evaded. They are:
“These two points raise two important questions; viz.
1.
Why do the compilers attempt to make their determinations of MGT anomalies agree with each other when they are each claiming they are independently using different methods to detemine MGT anomalies from the station data?
2.
Why does each of the teams compiling the MGT data sets not try to justify its method as being the right one that should be used as THE reference in the absence of a true calibration?”
SOUNDBITE 3
Why do you keep trying to discuss the RSS and UAH data sets instead of addressing my substantive point?
SOUNDBITE 4
Why did you not answer my rebuttal at # (09:16:41) of your unsubstantiated assertion at (05:25:45)? That rebuttal said.
I say it is NOT “best to look at adjusted data” when considering localities because, as you admit, “you are after all changing something” and there is no reason (n.b. none, not any reason) to think the change is not corrupting the information from the measurements.
I hope you find this sufficiently clear and succinct.
Richard
Phil. (10:53:09) :
I still demand a retraction of your untrue and odious libel. Also, I would like to know if your family name is Jones.
Richard
REPLY: He’s not Phil Jones, and works in America, not the UK – Anthony
Richard S Courtney (11:31:44) :
Your soundbite 1 is just demonstrably false, and implausible. As I’ve said, the satellite records have been changed dramatically as corrections were being made. If the surface record people were somehow designing their adjustment algorithms to match the satellite records, don’t you think we’d have seen the same dramatic corrections in the surface record? We’ve seen absolutely nothing of the sort. The surface record trends for 1979-2003 are roughly what they were before the satellite corrections were made; it’s the satellite records that have been changed. Not the other way around. How can you continue repeating a point, when it just doesn’t match any reality?
As for the motivation of the surface adjustments: Whether they are successful or not, all the groups are trying to accomplish the same goal: a reliable record of surface temperature. They all start with pretty much the same data (CRU uses some extra data over GISS or NOAA/NCDC and probably JMA), and then apply different methods, as they have honest disagreements over what are the best methods to use. But all of the methods are clearly described, for GISS and GHCN at least. GISS is concerned by urban warming, so it uses the somewhat crude method described in this thread to get rid of it. That’s the only adjustment GISS uses. It’s clearly motivated by trying to get rid of urban effects. So why would you characterise it as merely some sort of fudge to match GHCN or the satellites or whatever?
Since GISS and GHCN start with the same data, they don’t need to fudge anything if all they wanted was for their results to match each other. They could simply just use the same methods, and then have the same results.
Soundbite 2, part 1 is a corollary to Soundbite 1; since I don’t think Soundbite 1 made any sense, I’ll neglect it.
Soundbite 2, part 2: The different people involved prefer their methods over the others; that’s why they use different methods. You can read discussions of this in the papers they’ve written. As for calibration, I don’t know what you’re getting at. What is there to calibrate against?
It’ll actually be interesting to watch this, going forwards. The difference between the CRU and GISS results is pretty much entirely in the Arctic. Will one group decide to change how they handle the Arctic, or will they both persist in thinking their way is better? Also, the GHCN is coming out with an entirely new adjustment method; we’ll see how that is received.
Soundbite 3: The satellite records are part of your substantive point, inasmuch as you have one. And they clearly show that your thinking is confused.
Soundbite 4: It depends on what adjustment you are talking about. If GISS corrects Anchorage because Anchorage had urban warming, well the adjusted record is actually not indicative of what actually happened in Anchorage. They took out the urban warming, but the urban warming was real.
On the other hand, if there is a station move in 1962 at some station, resulting in a big discontinuity at that station, then the raw data clearly have an artifact that doesn’t reflect the local climate. So you’d want to use GHCN adjusted data, which is designed to remove the effect of such things.
carrot eater (12:44:20) :
You say to me:
“Your soundbite 1 is just demonstrably false, and implausible.”
Say what?
Demonstrably false? Absolutely not, and you do not demonstrate it is “false”. Please remember that this began (and I have repeatedly reminded you) as my response to a question to you that you have avoided from Willis at (15:29:36).
That question was;
“What I don’t understand is how this is all justified. I keep asking for a reason that anyone would start adjusting a pristine rural record in 1920. Do you or GISS have the slightest scrap of evidence that there was something wrong with the record?”
I replied saying at (07:27:23) :
“The adjustments are not intended to correct individual station records because it is thought “there was something wrong with the record”. And I think you have been side-tracked by arguments (e.g. from carrot eater and Nick Stokes) that the adjustments may be making correct adjustments in individual cases.
I think I know why the adjustments are universally applied by computer algorithm acting on each data set from each station record. And it is not relevant to the purpose of the adjustments whether or not the adjustments can be justified for any individual station record.”
Snip
“Such adjustment [to get MGT data sets] “to agree with each other” provides a complete explanation for why “anyone would start adjusting a pristine rural record in 1920”.”
You have provided
(a)
no explanation for why “anyone would start adjusting a pristine rural record in 1920”
and
(b)
no explanation for why my explanation (that fits the facts) is wrong.
Instead you have obfuscated, ignored specific questions, and attempted to side-track discussion from consideration of the surface records to consideration of the satellite records (an obvious attempt at red-herring).
Of course there is little alteration to recent surface station data sets: they (almost) agree with the satelliite data. As I said (repeatedly);
“It should also be noted that there is no possible calibration for the estimates of MGT.
The data sets keep changing for unknown (and unpublished) reasons although there is no obvious reason to change a datum for MGT that is for decades in the past. It seems that – in the absence of any possibility of calibration – the compilers of the data sets adjust their data in attempts to agree with each other. Furthermore, they seem to adjust their recent data (i.e. since 1979) to agree with the truly global measurements of MGT obtained using measurements obtained using microwave sounding units (MSU) mounted on orbital satellites since 1979. This adjustment to agree with the MSU data may contribute to the fact that the Jones et al., GISS and GHCN data sets each show no statistically significant rise in MGT since 1995 (i.e. for the last 15 years). However, the Jones et al., GISS and GHCN data sets keep lowering their MGT values for temperatures decades ago.”
The important point is that
THEY KEEP LOWERING THEIR MGT VALUES FOR TEMPERATURES DECADES AGO.
I could address each of your statements in your post at (12:44:20) but there is no point because you ignore what I write and obfuscate the issue. This is clearly demonstrated by your asking me:
“What is there to calibrate against?”
I answer: NOTHING!
That is my point that I have made to you over and over again and yet again in this post. I fail to understand how you cannot comprehend the meaning of my sentence which I have repeated ad nauseum that says;
“It should also be noted that there is no possible calibration for the estimates of MGT.”
Indeed, it is a central point of my argument. The compilers of the MGT data sets have nothing against which to calibrate the results of their work. Therefore, the only way they can obtain ‘confidence’ in the results their work is by increasing the agreements between the results of their compilations. And the continuing changes they make to their methods indicate that this is what they are doing.
If you were defending science then you would have attempted to answer one of the questions I have repeatedly put to you; viz.
“Why does each of the teams compiling the MGT data sets not try to justify its method as being the right one that should be used as THE reference in the absence of a true calibration?”
In conclusion, my argument is clear and ad hominem assertion that my “thinking is confused” does not change that.
My argument answers the question from Willis that you have avoided. You have not addressed that question despite my repeated requests provided to you in several different ways. Instead, all your responses to me have have obfuscated, ignored specific questions, and attempted to side-track discussion with a red-herring.
So, I give up. Take ‘the last word’ if you want. Onlookers can assess this discussion for themselves.
Richard
I show you that in no way are the surface records being intentionally adjusted to try to converge with the satellite data. The idea is just implausible on the face of it.
You accuse me of bringing up a red herring.
And yet, then you repeat, “Furthermore, they seem to adjust their recent data (i.e. since 1979) to agree with the truly global measurements of MGT obtained using measurements obtained using microwave sounding units (MSU) mounted on orbital satellites since 1979. This adjustment to agree with the MSU data”
How can you tell me I’m side-tracking from your questions, when I’m addressing what appears to be a central point of your thesis, seeing as you continue repeating it?
“You have provided
(a)
no explanation for why “anyone would start adjusting a pristine rural record in 1920””
Have you read the discussion here? This station shows up as being well-lit at night. By the GISS method, it is then automatically considered possibly urban. GISS doesn’t bother trying to figure out exactly when something became possibly urban; they just treat the entire history of the station as being suspect. After all, the strongest urban trend will likely be during the period when something went from rural to urban. So they basically toss the station out. The method is clearly described; the code is publicly available.
You can agree or disagree with the use of satellite light data to decide if something is urban or not, but you can’t claim that the adjustment here is not transparent. It’s perfectly objective; no human interaction involved. If you take the time to read the descriptions, you’ll understand perfectly well why the program did what it did, even if you think the program is not implementing a very good method.
And again, GHCN and GISS start with the same raw data. Why on earth would they have to fudge to get the same result? They’re starting at the same place. And the trends are about the same, in both raw and adjusted data. So if they’re fudging to get some desired trend, they certainly aren’t trying very hard.
Richard S Courtney (11:33:41) :
Phil. (10:53:09) :
I still demand a retraction of your untrue and odious libel. Also, I would like to know if your family name is Jones.
Demand all you like it isn’t happening. CE and I have pointed out the errors in your argument, it’s completely at odds with the facts, and as regards the UAH is totally at variance with the history of that measurement.
It is unlikely that anyone lacking a vested interest is still reading this discussion. But, in case there are, I draw attention to the item by Edward R. Long at
http://www.americanthinker.com/2010/02/a_pending_american_temperature.html
It concludes;
“That is, the NCDC’s treatment has forced the rural value to look more like that of the urban. This is the exact opposite of any rational consideration, given the growth of the sizes of and activities within urban locations, unless deception is the goal.”
But Long is discussing how the data has been massaged, and – as this discussion demonstrates – there are those whose vested interests cause them to defend the indefensible while hiding behind anonymity.
Richard
It seems the link in my above post has not appeared in entirety. Here it is again
http://www.americanthinker.com/2010/02/a_pending_american_temperature.html
I hope it has worked this time.
Richard
Richard S Courtney (02:11:05) :
– as this discussion demonstrates – there are those whose vested interests cause them to defend the indefensible while hiding behind anonymity.
This ad hominem illustrates your approach of making it up as you go along, you have absolutely no evidence for this statement, just like you had no evidence for your statement about adjustments.
You can have your analysis picking out 48 US stations, if you want.
How about using all of them?
Comparison of all GHCN data, raw vs adjusted:
Under Q4: http://www.ncdc.noaa.gov/cmb-faq/temperature-monitoring.html
Comparison of GHCN raw data, vs GISS adjusted, for the Northern Hemisphere:
http://tamino.wordpress.com/2010/02/25/false-claims-proven-false/
Preliminary comparison of weather station raw data (continuous records only), vs CRU adjusted, Northern Hemisphere land:
http://www.drroyspencer.com/2010/02/new-work-on-the-recent-warming-of-northern-hemispheric-land-areas/
Again and again, your contention that adjustments are being done to achieve some nefarious goal are just wrong. When you look globally or hemispherically, the effect of adjustments is quite minor. Even if it wasn’t minor, that wouldn’t be proof that the adjustments are bad, but as it is, the effect is minor.
Phil. (05:37:24) :
You say:
Richard S Courtney (02:11:05) :
– as this discussion demonstrates – there are those whose vested interests cause them to defend the indefensible while hiding behind anonymity.
This ad hominem illustrates your approach of making it up as you go along, you have absolutely no evidence for this statement, just like you had no evidence for your statement about adjustments.
*********
So your posting libels here under the title “Phil” is not acting anonymously?
And if you have no vested interest then why have you posted – and refused to retract – an untrue libel behind that anonymity?
Why should anybody afford any respect to the comments of the person (?) who hides behind the pseudonym of ‘carrot eater’ when
(a) he claims “the effect of adjustments is quite minor”,
(b) but provides no explanation for why corrupting the data with the adjustments is defencible,
(c) while trying to deflect discussion onto consideration of other data sets (i.e. UAH and NSU) instead of those that have the “adjustments” applied?
The two of you can continue your defence of the indefecible while hiding behind anonymity if you choose. But the BS meter of any non-partisan onlookers will be sounding a very loud alarm.
Richard
Richard S Courtney (08:16:39) :
“(a) he claims “the effect of adjustments is quite minor”,”
Look at the graphics. They speak for themselves. Global raw, global adjusted. Northern Hemisphere raw, northern hemisphere adjusted. and then NH, again. They quite clearly show that you are wrong, and that your claims are untenable.
Again, if the raw and adjusted trends were a little different from each other, that wouldn’t be proof of any wrongdoing, so long as the adjustments were justifiable. But currently, the raw and adjusted trends are the same, anyway.
“(b) but provides no explanation for why corrupting the data with the adjustments is defencible,”
The specific adjustments shown in this post have been explained time and again.
“(c) while trying to deflect discussion onto consideration of other data sets (i.e. UAH and NSU) instead of those that have the “adjustments” applied?”
You’re the one who brought up a claim about the relationship between surface and satellite data. You continue bringing it up. It appears to be a central part of your reasoning. And somehow I’m wrong for discussing your claim, and showing that it’s baseless?
Phil. (05:37:24) :
You say:
Richard S Courtney (02:11:05) :
– as this discussion demonstrates – there are those whose vested interests cause them to defend the indefensible while hiding behind anonymity.
This ad hominem illustrates your approach of making it up as you go along, you have absolutely no evidence for this statement, just like you had no evidence for your statement about adjustments.
*********
So your posting libels here under the title “Phil” is not acting anonymously?
That is my name, just like Richard S Courtney is yours, are you the one who is the Prof. at Kutztown, or the one who is a technical editor for CoalTrans International, ….
And if you have no vested interest then why have you posted – and refused to retract – an untrue libel behind that anonymity?
I don’t have to have a ‘vested interest’ in order to post, and the statement you refer to is not untrue.
I thought the homogeneity adjustment was the averaging over a 1200-2400km radius. A sort of spatial smoothing.
I understood airports are often used as the “rural” reference.
I suspect that too much focus on individual stations clouds the real issue of disclosure of the raw data and disclosure of the coded algorithms for adjustment to permit others to scrutinize and attempt to repeat the analysis.
Phil. (13:09:52)
BZZZZT. Next contestant please, I’m sorry sir, that’s the wrong answer. Jennifer, what kind of prize do we have tonight for our unlucky contestants? …
Google “Richard S. Courtney”, and the first nine entries are about the Richard Courtney who is posting here. Pretending that he is posting anonymously, as you assuredly are, is absolute nonsense. He and I both have the balls to put our true names on our opinions. I can only wish that you had the same.
I have updated the head post with some new information. My thanks to all who have posted, whether agreeing or disagreeing. This is the best of science, keep’m coming …
Regards,
w.
The two of you can continue your defence of the indefecible while hiding behind anonymity if you choose. But the BS meter of any non-partisan onlookers will be sounding a very loud alarm.
No, I’m an entirely random reader, and having waded through this lot I find that carrot-eater consistently provides good information and sensible explanations, whilst you repeat assertions which have been answered whilst claiming that they have not and generally bluster in a very tiresome way. Whilst it would be nice to know who Carrot-Eater is, so that due credit could be given, his use of an alias does no harm to his (her?) cogent argument.
I have learned a great deal about how the temp record is constructed and adjusted from this page.