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.
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