Rumours of my death have been greatly exaggerated
27 01 2010
Image: NOAA USHCN COOP station at Hanksville, UT, sited over a grave. Click for larger image. Photo by surfacestations volunteer Juan Slayton
How come Menne can claim station siting quality doesn't matter? Because he analyzed "homogenized" data, which blurred it all together. That and use of an early unrepresentative sample of stations.
by Anthony Watts
There has been a lot of buzz about the Menne et al 2010 paper “On the reliability of the U.S. Surface Temperature Record” which is NCDC’s response to the surfacestations.org project. One paid blogger even erroneously trumpeted the “death of UHI” which is humorous, because the project was a study about station siting issues, not UHI.Anybody who owns a car with a dashboard thermometer who commutes from country to city can tell you about UHI.
There’s also claims of this paper being a “death blow” to the surfacestations project. I’m sure in some circles, they believe that to be true. However, it is very important to point out that the Menne et al 2010 paper was based on an early version of the surfacestations.org data, at 43% of the network surveyed. The dataset that Dr. Menne used was not quality controlled, and contained errors both in station identification and rating, and was never intended for analysis. I had posted it to direct volunteers to so they could keep track of what stations had been surveyed to eliminate repetitive efforts. When I discovered people were doing ad hoc analysis with it, I stopped updating it.
Our current dataset at 87% of the USHCN surveyed has been quality controlled.
[ So the Menne paper is not a real audit of the surface stations project as implied but an early subset of the project. A report based on the surface stations project is still in the works. Menne was in a hurry to beat the surface stations project to the punch and get out a defense of their surface stations in advance of the surface stations report. ]
There’s quite a backstory to all this.
In the summer, Dr. Menne had been inviting me to co-author with him, and our team reciprocated with an offer to join us also, and we had an agreement in principle for participation, but I asked for a formal letter of invitation, and they refused, which seems very odd to me. The only thing they would provide was a receipt for my new data (at 80%) and an offer to “look into” archiving my station photographs with their existing database. They made it pretty clear that I’d have no significant role other than that of data provider. We also invited Dr. Menne to participate in our paper, but he declined.
The appearance of the Menne et al 2010 paper was a bit of a surprise, since I had been offered collaboration by NCDC’s director in the fall. In typed letter on 9/22/09 Tom Karl wrote to me:
“We at NOAA/NCDC seek a way forward to cooperate with you, and are interested in joint scientific inquiry. When more or better information is available, we will reanalyze and compare and contrast the results.”
“If working together cooperatively is of interest to you, please let us know.”
I discussed it with Dr. Pielke Sr. and the rest of the team, which took some time since not all were available due to travel and other obligations. It was decided to reply to NCDC on a collaboration offer.
On November 10th, 2009, I sent a reply letter via Federal Express to Mr. Karl, advising him that we would like to collaborate, and offered to include NCDC in our paper.. In that letter I also reiterated my concerns about use of the preliminary surfacestation data (43% surveyed) that they had, and spelled out very specific reasons why I didn’t think the results would be representative nor useful.
We all waited, but there was no reply from NCDC to our reply to offer of collaboration by Mr. Karl from his last letter. Not even a “thank you, but no”.
Then we discovered that Dr. Menne’s group had submitted a paper to JGR Atmospheres using my preliminary data and it was in press. This was a shock to me since I was told it was normal procedure for the person who gathered the primary data the paper was based on to have some input in the review process by the journal.
[ IOW Watts, Pielke, and the surface stations people should have been allowed to review the Menne paper. ]
NCDC uses data from one of the largest volunteer organization in the world, the NOAA Cooperative Observer Network. Yet NCDC director Karl, by not bothering to reply to our letter about an offer he initiated, and by the journal not giving me any review process opportunity, extends what Dr. Roger Pielke Senior calls “professional discourtesy” to my own volunteers and my team’s work. See his weblog on the subject:
Professional Discourtesy By The National Climate Data Center On The Menne Et Al 2010 paper
I will point out that Dr. Menne provided thanks to me and the surfacestations volunteers in the Menne et al 2010 paper, and I hear through word of mouth, also in a recent verbal presentation. For that I thank him. He has been gracious in his communications with me, but I think he’s also having to answer to the organization for which he works and that limited his ability to meet some of my requests, like a simple letter of invitation.
Political issues aside, the appearance of the Menne et al 2010 paper does not stop the surfacestations project nor the work I’m doing with the Pielke research group to produce a peer reviewed paper of our own. It does illustrate though that some people have been in a rush to get results. Texas state Climatologist John Nielsen-Gammon suggested way back at 33% of the network surveyed that we had a statistically large enough sample to produce an analysis. I begged to differ then, at 43%, and yes even at 70% when I wrote my booklet “Is the US Surface Temperature Record Reliable?, which contained no temperature analysis, only a census of stations by rating.
The problem is known as the “low hanging fruit problem”. You see this project was done on an ad hoc basis, with no specific roadmap on which stations to acquire. This was necessitated by the social networking (blogging) Dr. Pielke and I employed early in the project to get volunteers. What we ended up getting was a lumpy and poorly spatially distributed dataset because early volunteers would get the stations closest to them, often near or within cities.
The urban stations were well represented in the early dataset, but the rural ones, where we believed the best siting existed, were poorly represented. So naturally, any sort of study early on even with a “significant sample size” would be biased towards urban stations.We also had a distribution problem within CONUS, with much of the great plains and upper midwest not being well represented.
This is why I’ve been continuing to collect what some might consider an unusually large sample size, now at 87%. We’ve learned that there are so few well sited stations, the ones that meet the CRN1/CRN2 criteria (or NOAA’s 100 foot rule for COOPS) are just 10% of the whole network.See our current census:
wattsupwiththat.files.wordpress.com
When you have such a small percentage of well sited stations, it is obviously important to get a large sample size, which is exactly what I’ve done. Preliminary temperature analysis done by the Pielke group of the the data at 87% surveyed looks quite a bit different now than when at 43%.
It has been said by NCDC in Menne et al “On the reliability of the U.S. surface temperature record” (in press) and in the June 2009 “Talking Points: related to “Is the U.S. Surface Temperature Record Reliable?” that station siting errors do not matter. However, I believe the way NCDC conducted the analysis gives a false impression because of the homogenization process used. As many readers know, the FILNET algorithm blends a lot of the data together to infill missing data. This means temperature data from both well sited and poorly sited stations gets combined to infill missing data. The theory is that it all averages out, but when you see that 90% of the USHCN network doesn’t meet even the old NOAA 100 foot rule for COOPS, you realize this may not be the case.
Here’s a way to visualize the homogenization/FILNET process. Think of it like measuring water pollution. Here’s a simple visual table of CRN station quality ratings and what they might look like as water pollution turbidity levels, rated as 1 to 5 from best to worst turbidity:
In homogenization the data is weighted against the nearby neighbors within a radius. And so a station might start out as a “1” data wise, might end up getting polluted with the data of nearby stations and end up as a new value, say weighted at “2.5”. Even single stations can affect many other stations in the GISS and NOAA data homogenization methods carried out on US surface temperature data here and here.
In the map above, applying a homogenization smoothing, weighting stations by distance nearby the stations with question marks, what would you imagine the values (of turbidity) of them would be? And, how close would these two values be for the east coast station in question and the west coast station in question? Each would be closer to a smoothed center average value based on the neighboring stations.
Essentially, in my opinion, NCDC is comparing homogenized data to homogenized data, and thus there would not likely be any large difference between “good” and “bad” stations in that data. All the differences have been smoothed out by homogenization (pollution) from neighboring stations!
[ Another problem: The Menne paper isn't really comparing the poorly sited stations with well sited stations but only homogenized versions of station data .... with the homogenization process blurring the distinctions. ]
The best way to compare the effect of siting between groups of stations is to use the “raw” data, before it has passed through the multitude of adjustments that NCDC performs. However NCDC is apparently using homogenized data. So instead of comparing apples and oranges (poor sited -vs- well sited stations) they essentially just compare apples (Granny Smith -vs- Golden delicious) of which there is little visual difference beyond a slight color change.
We saw this demonstrated in the ghost authored Talking Points Memo issued by NCDC in June 09 in this graph:
Referencing the above graph, Steve McIntyre suggested in his essay on the subject:
The red graphic for the “full data set” had, using the preferred terminology of climate science, a “remarkable similarity” to the NOAA 48 data set that I’d previously compared to the corresponding GISS data set here (which showed a strong trend of NOAA relative to GISS). Here’s a replot of that data – there are some key telltales evidencing that this has a common provenance to the red series in the Talking Points graphic.
When I looked at SHAP and FILNET adjustments a couple of years ago, one of my principal objections to these methods was that they adjusted “good” stations. After FILNET adjustment, stations looked a lot more similar than they did before. I’ll bet that the new USHCN adjustments have a similar effect and that the Talking Points memo compares adjusted versions of “good” stations to the overall average.
There’s references in the new Menne et al 2010 paper to the new USHCN2 algorithm and we’ve been told how it is supposed to be better. While it does catch undocumented station moves that USHCN 1 did not, it still adjusts data at USHCN stations in odd ways, such as this station in rural Wisconsin, and that is the crux of the problem.
USHCN station at Hancock Experiment Farm, WI
Or this one in Lincoln, IL at the local NWS office where they took great effort to have it well sited.
Lincoln, IL USHCN station, NWS office in background. Click to enlarge
Thanks to Mike McMillan for the graphs comparing USHCN1 and USHCN2 data
Notice the clear tendency in the graphs comparing USHCN1 to USHCN2 to cool off the early record and leave the current levels near recently reported levels or to increase them. The net result is either reduced cooling or enhanced warming not found in the raw data.
As for the Menne et all 2010 paper itself, I’m rather disturbed by their use of preliminary data at 43%, especially since I warned them that the dataset they had lifted from my website (placed for volunteers to track what had been surveyed, never intended for analysis) had not been quality controlled at the time. Plus there are really not enough good stations with enough spatial distribution at that sample size. They used it anyway, and amazingly, conducted their own secondary survey of those stations, comparing it to my non-quality controlled data, implying that my 43% data wasn’t up to par. Well of course it wasn’t! I told them about it and why it wasn’t. We had to resurvey and re-rate a number of stations from early in the project.
This came about only because it took many volunteers some time to learn how to properly ID them. Even some small towns have 2-3 COOP stations nearby, and only one of them is “USHCN”. There’s no flag in the NCDC metadatabase that says “USHCN”, in fact many volunteers were not even aware of their own station status. Nobody ever bothered to tell them. You’d think if their stations were part of a special subset, somebody at NOAA/NCDC would notify the COOP volunteer so they would have a higher diligence level?
If doing an independent stations survey was important enough for NCDC to do to compare to my 43% data now for their paper, why didn’t they just do it in the first place?
I have one final note of interest on the station data, specifically the issue of MMTS thermometers and their tendency to be sited closer to building due to cabling issues.
Menne et al 2010 mentioned a “counterintuitive” cooling trend in some portions of the data. Interestingly enough, former California State Climatologist James Goodridge did an independent analysis ( I wasn’t involved in data crunching, it was a sole effort on his part) of COOP stations in California that had gone through modernization, switching from Stevenson Screens with mercury LIG thermometers to MMTS electronic thermometers. He sifted through about 500 COOPs in California and chose stations that had at least 60 years of uninterrupted data, because as we know, a station move can cause all sorts of issues. He used the “raw” data from these stations as opposed to adjusted data.
He writes:
Hi Anthony,
I found 58 temperature station in California with data for 1949 to 2008 and where the thermometers had been changed to MMTS and the earlier parts were liquid in glass. The average for the earlier part was 59.17°F and the MMTS fraction averaged 60.07°F.
Jim
A 0.9F (0.5C) warmer offset due to modernization is significant, yet NCDC insists that the MMTS units are tested at about 0.05C cooler. I believe they add this adjustment into the final data. Our experience shows the exact opposite should be done and with a greater magnitude.
[ This is the reverse of the impact of the MMTS thermometers that Menne reported. ]
I hope to have this California study published here on WUWT with Jim soon.
I realize all of this isn’t a complete rebuttal to Menne et al 2010, but I want to save that option for more detail for the possibility of placing a comment in The Journal of Geophysical Research.
When our paper with the most current data is completed (and hopefully accepted in a journal), we’ll let peer reviewed science do the comparison on data and methods, and we’ll see how it works out. Could I be wrong? I’m prepared for that possibility. But everything I’ve seen so far tells me I’m on the right track.
wattsupwiththat.com
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magicjava (16:35:28) :
Agreed that NOAA using its homogenized data is not a valid test of these stations.
Great work on all of this and I’m looking forward to your rebuttal.
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John Phillips (17:09:31) :
“The problem is known as the “low hanging fruit problem”. You see this project was done on an ad hoc basis, with no specific roadmap on which stations to acquire.”
Since the Menne study had a sample less than 100%, a method for random selection of stations should have been used. Since the population is known to not be homogeneous, (rural vs urban, etc.) the sampling method would be complicated. As you point out, the station selection was ad hoc. Another climate science paper with implementation of inappropriate statistical methods.
27 01 2010
Mike Davis (17:11:52) :
I appears to be time to put 20 and out into effect at NOAA and NASA. Also an independent review of their work output should be required before any publication in the future. Maybe it is time to outsource the weather service and climate research with funding based on reliability and accuracy.
The Menne paper appears to be GIGO! and not worth the E space it consumes.
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Konrad (17:38:21) :
I have seen much discussion of the Menne et al 2010 paper doing the rounds, and I am unconvinced by the arguments. I feel the claim that measuring anomalies can counter the problems of poor siting and UHI effects is false. Measuring anomalies could only be a valid method if conditions at the station site (regardless of rating) and any UHI influence within the nearby area remained constant over the entire period that a temperature trend is to be measured for.
It is clear that many of the stations identified in the Surfacestations project have degenerated over time. Measuring anomalies can not correct for this.
UHI influence is a gradually increasing influence over time. Measuring anomalies can not correct for this.
Homogenization is not a solution. Homogenization just soils good data.
Any honest attempt to measure temperature trends over time should only be using well sited truly rural stations with long records and comparing actual temperature averages. Studies using homogenization, anomalies instead of averages and frantic hand waving belong in the wicker filling cabinet.
27 01 2010
magicjava (17:39:22) :
[quote Frank K. (17:04:35) :]
This episode says a lot about the professional ethics (or lack thereof) of the NCDC…[/quote]
Agreed about the ethics, but I’d say it’s also about bad data.
The fact that NOAA put out a story using their homogenized data tells me Anthony is on to something here.
NOAA’s manipulation of temperatures is really at the heart of all this. If they’re manipulating temperatures to give the appearance of global warming then defending themselves with the data they’ve already manipulated is meaningless. The average person may not know that using homogenized data makes for a meaningless reply, but NOAA knows it’s meaningless.
At the very least, I think Anthony’s work will help turn the discussion to NOAA’s raw data vs. their homogenized data. And that’s where the discussion needs to be to get at the truth to what’s going on.
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Pat Frank (18:06:27) :
Anthony, the offense against you is worse than discourtesy. Unless you provided your results to Drs. Menne and Karl with the explicit understanding that they could freely use those data in a publication, with no more than an acknowledgment of thanks to you, then their use of the data in a publication amounts to scientific theft. The journal would potentially be an accessory after the fact; depending on whether the editor knew the lack of permission granted. If nothing else, the editor of JGR Atmospheres is likely guilty of professional negligence.
The fact that you had published your data on line in surfacestations.org might mitigate the legal ramifications, in that a judge could decide that you had no expectation of privacy after posting your results and allowing free access.
However, that does not remove the serious ethical lapse of Dr. Menne, Dr. Karl, or JGR Atmospheres. They absconded the scientific and publication priority of your data without your explicit permission. There is no excuse for that, whatever.
It’s more than a professional discourtesy. It’s professional theft of data. In science, value rests in results. Note how jealously proxy data are held by climatologists. It’s inconceivable that Drs. Menne and Karl did not consciously know they were violating a very basic ethical principle of science.
REPLY: I made the same arguments with them that you cite, they dismissed them – Anthony
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Mike McMIllan (18:37:03) :
Menne et al 2010 looks like it uses USHCN v 2, which we know has been heavily ‘adjusted’ from the v 1 data set. The adjustment took place subsequent to Tom Peterson’s examination of the siting problems (Peterson 2006).
While Dr Peterson’s paper compared good and bad sites with good sites that had been homogenized with bad sites (thus producing the unsurprising result that they looked similar), they now had a handle on the issues surfacestations.org might bring up. They might very well have taken those issues into account in generating the v 2 adjustment algorithm.
The paper speaks of two data sets, ‘adjusted and ‘unadjusted.’ In this case, the adjustment referred to is ‘homogenization,’ not the adjustments in USHCN v 2, which I believe is their ‘unadjusted’ dataset. If the v 2 changes account for siting problems, we may be comparing similar sets, just as Peterson 2006 did.
A second concern is that the data sets weren’t compared directly.
The anomalies were interpolated to a quarter-degree lat-long grid, and then area-weighted to generate the total anomaly for the U.S.
This was done for the good stations, and done again for the poor stations, to generate the temperature curves. Problem here is that the good and poor stations have different locations on the lat-long grid, and the interpolated, area-weighted grid points could easily blend out to produce similar numbers. Whatever tendencies the grid had (similar or different) would be carried uniformly through the years plotted on the temperature chart.
That said, my main question is how this study would look using the USHCN v 1numbers.
27 01 2010
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Kevin Kilty (18:50:51) :
Go to the NCDC site and read the order of corrections applied to data. Correction for UHI is last. Now read Karl and Williams (1987) regarding the homogenization. In it you will find the admonition to do the homogenization last. The reason should be apparent to anyone. Doing homogenization with known, secular (slow-drift) errors in select portions of the data will smear these into the entire set of data.
Even if we were to agree that every separate correction that NCDC does is accurate, then doing them out of order is a problem. We do not agree that the individual corrections are necessarily correct.
The issues with corrections are a small subset of the universe of issues with the surface data–this could go on and on ad nauseum.
27 01 2010
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Mark (19:02:52) :
Anthony,
It’s interesting that they chose to rush out first with essentially a defensive move before seeing your paper. Moving preemptively is a weaker position. Strategically it says that they believe your paper will be damaging to their agenda and difficult to attack. As frustrating as this cheap shot is, you should view it as confirmation that you’re on the right track and that they are worried. Too bad they can’t put the same energy into actually improving the network and their data methods in pursuit of accuracy. Sadly, the increased funding that supporting alarmism brings is a powerful incentive. Such incentives can cause not only blind-spots but biases and now, apparently, unprofessional behavior. Keep up the good work!
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Pat Frank (19:40:40) :
(18:06:27) REPLY:“I made the same arguments with them that you cite, they dismissed them – Anthony”
That’s really vile behavior. The fact that Tom Karl is Director of the NCDC means they can be dismissive with impunity. But in a just world, they’d be at least reprimanded. Under the circumstances of absconded priority, JGR should ethically de-publish the paper.
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evanmjones (21:02:57) :
Menne, et al. performed a very clear, concise analysis that, among other things, demonstrated poorly sited stations did not meaningfully deviate from good sites (or “pristine” sites if you include the comparison to the admittedly brief USCRN series). Now, given that the sample relies on 43% of non-QA’d data should we consider the science settled? Of course not. But the finding is significant and worthy of publication. This is how science and the peer review process works. At the very least, it provides an opportunity for Watts, et al. to reproduce the analysis using a larger sample size, arrive at alternative conclusions, and have those findings published.
[ Let's hope thats true. ]
Weeeelllll, let’s put it this way. Dr. Menne’s “very clear, concise analysis” is about to get the pins knocked out from under it. Something that might have been avoided had he accepted Anthony’s invite. It turns out he failed to perform a vital part of the analysis, and the results are glaring: poorly sited stations do indeed meaningfully deviate from good sites.
At the very least, it provides an opportunity for Watts, et al. to reproduce the analysis using a larger sample size, arrive at alternative conclusions, and have those findings published.
There will be “alternative conclusions”, all right. And they will not be due to the larger size of the sample. (Although the earlier sample was rather poor, not quality-controlled, and did not separate airports and wastewater plants.)
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Zjahna (21:09:45) :
Hi Anthony, clearly the Menne paper has a significant methodological flaw that you have highlighted. I assume that with Roger Pielke you are just looking at the data from USHCN 1 and 2 stations compared with all data homogenised. This would be an interesting comparison and as close to definitive as you will get. At least the Menne paper provides you with methods to compare the data.
Is this data available currently (I presume not from your post). It would be nice if we could see it after you have published. At least with this publication prior, it should be relatively easy to get accepted as the site selection and analysis will clearly be more informative with a larger dataset.
I look for ward to the results.
27 01 2010
hro001 (21:15:06) :
Pat Frank (18:06:27) wrote:
“It’s inconceivable that Drs. Menne and Karl did not consciously know they were violating a very basic ethical principle of science.”
[to which Anthony replied:]
“I made the same arguments with them that you cite, they dismissed them”
Yet another instance (as if more were needed) in which those with a vested interest in promoting the AGW “message” demonstrate that their standard operating procedures do not include adherence to any ethical principles.
It’s almost as if they’ve all been contaminated by some kind of virus and they are refusing the only treatment available: a truth serum.
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George (21:26:13) :
When I first saw the Menne paper I was floored that so many people could buy into it. It was too good to be true. The notion that a sample size of less than 6% could so closely match the population size so closely was in my opinion ludicrous. My statistics may not be the best but I calculated a confidence interval of such a sample size to be approximately 11%.
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Pat Frank (21:52:20) :
PhilM (19:37:08) , it’s not that Anthony’s data was public. I made it clear in my post at (18:06:27) that the issue was about priority of use. Anthony and his volunteers collected those data. First use, by any worthy scientific ethic, belonged solely to them. Dr. Menne and Dr. Karl absconded with that priority ownership in a clear violation of professional ethics.
Since posting, I’ve looked at Anthony’s 2009 Heartland report, “Is the US Surface Temperature Record Reliable?” and at Menne, et al. 2010 “On the reliability of the U.S. Surface Temperature Record,” as linked above. It’s very clear that Dr. Menne did not rely on the data in Anthony’s 2009 report to inform his analysis, which would have been completely acceptable.
Instead, he and his coauthors document that they used the full data set then at Anthony’s surfacestations.org, although they’re very coy about explicity stating that they just went right ahead and downloaded it directly. Maybe the admission looked too stark in print.
They then used the information obtained from surfacestations.org to collate and categorize the stations and to direct their analysis. This is highly unethical, in that it absconds the priority of analysis and publication from Anthony and his co-workers. Dr. Menne, et al., knew from conversations with Anthony that he intended his own analysis of his data. Knowing this, they went ahead and preempted his right.
REPLY: Just wondering Pat, are you a member of the AGU? – Anthony
27 01 2010
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Richard M (06:47:00) :
I did read over a few comments at skeptical science and, instead of seeing anything skeptical about the Menne paper, it was mostly pure worship. The number of outright ridiculous statements was mind boggling.
The first thing that popped into my head was the Menne paper is actually another blow to the surface stations credibility. It is completely accepted that UHI is real in the science community. If these poorly sited stations show no warming bias that means there MUST be a cooling bias somewhere in the process. In other words, since two wrongs don’t make a right, it furthers the claim of BAD SCIENCE.
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Sinan Unur (07:11:47) :
Doing so-called “preliminary” analysis on data produced by a census attempt is a cardinal sin in Statistics: It creates the possibility that biases in a subset will shape subsequent analysis.
Actually submitting such analysis for publication is either ignorance (I run in to Ph.D.s who do not understand basic Statistics on an almost daily basis, so I cannot rule out) or a malicious attempt to preempt the effect of a proper analysis.
In statistical analysis, the number of observations does not matter as much as the process by which those observations were collected. Random sampling allows you to use the standard tools whereas an incomplete census does not.
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jaypan (07:26:45) :
Reading this Menne paper, one questions comes up immediately.
What has all this to do with science?
- it starts in the cheapest possible way, with attacks like Heartland/ tobacco/ oil.
What a nice move after Pachauri …
- it admits, yes, most of our stations are completely rubbish, but the results of the remaining are just fine. Don’t you use the bad ones for your analysis? Or do you? How did this “selfsnip” pass peer-review … (well, we know already)
- using incomplete data un-authorized, without even asking the owners … what an un-scientific behaviour.
As somebody said it already: something starting this way is not worth reading. Low-level propaganda.
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Dan (09:08:53) :
Don’t play golf with Dr. Menne. Once he gets done homogenizing his score he’ll be able to card something under par even though he used a hundred strokes to get around.
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JP (09:29:19) :
Found this among comments at skepticalscience. Is Dave right??
Kforestcat at 12:20 PM on 23 January, 2010
Gentlemen
You really ought to read the methods used before you gloat. The individual station anomaly measurements were based on each stations “1971-2000 station mean”. See where the document states:
“Specifically, the unadjusted and adjusted monthly station values were converted to anomalies relative to the 1971–2000 station mean.”
In other words, the only thing this study measures is the difference in instrument error at each station. The absolute error occurring at individual stations because the station had not been properly located is not measured. A poor station with an absolute temperature error of +5 degrees C still has a bias error of +5 degree C – no matter what the variation occurring due to instrumentation type.
I’m a chemical engineer with U.S. government and 20 years of research experience in various areas including environmental mitigation. If one of my phD’s came to me with this nonsense, I’d fire him on the spot.
—–
Another post:
Gentlemen
I’m fully aware of how anomaly data is used ( having used it in my own research) and I know full well what can go awry in the field experiments. We are talking about every day instrument calibration and QA/QC – this is not rocket science. I firmly maintain the Menne 2010 paper is fundamentally flawed and entirely useless.
NASA’s individual station temperature readings are taken in absolute temperature (not as an anomaly as you have suggested). The temperature data is reduced to anomaly after the absolute temperature readings for a site are obtained. For example see, the station data Orland (39.8 N, 122.2 W) obtained directly from the NASA’s GISS web site. The temperatues are recorded in Annual Mean Temperature in degrees C – not as an anomaly as you have suggested. (Tried to attach a NASA GIF as visual aid -but did not succeed).
Bottom line. Menne has to have (and use) absolute temperature data to get the 1971-2000 mean temperature and then divide the current temp with the mean to get the anomaly. We are back to the same problem – Menne is measuring instrument error – he is not measuring error resulting from improper instrument location. The Menne paper is absolutely useless for the stated purpose.
Anyone who actually collects field data, I have, knows they are going to immediately run into two fundamental problems when an instrument is improperly located. 1) they are not reading ambient air temperature and 2) neither temperature readings nor the anomaly can be corrected back to a true ambient because other factors are influencing the readings.
For example: Suppose we have placed our instrument in a parking lot. Say the mean 1971-2000 temperature well away from the parking lot is 85F; but the instrument is improperly reading a mean of 90F. Now on a given day, say the ambient temp is 93 but your instrument is reading 105F (picked up some radiant heat from a car). Ok our:
Actual anomaly is 93F – 85F = 8F;
Instrument anomaly is 105F – 90F = 15F.
The data is trash. There is simply no way to recover either the actual ambient temperatures nor an accurate anomaly reading. What you are missing is that an improperly placed instrument is reading air temperatures & anomalies influenced by unnatural events.
The readings bear no relationship to either the actual temperature nor the actual anomaly – the data’s no good, can’t be corrected, and will not be used by a reputable researcher.
Finally, it’s not entirely surprising that Menne finds a downward bias in his individual anomaly readings at poorly situated sites. Because: 1) a poorly located instrument produces a higher mean temperature; hence, the anomaly will appear lower; and 2) generally there’s a limit to how hot an improperly placed instrument will get (i.e. mixing of unnaturally heated air with ambient air will tend to cool the instrument – so the apparent temperature rise is lower than one might expect).
Had Menne (NASA) actually measured both absolute temperature and calculated anomaly data using instrumentation at properly setup sites, within say a couple of hundred feet of the poor sites, as a proper standard to measure the bias against – our conversation would be different.
As it stands Menne’s data is useless nonsense and not really worth serious discussion.
Dave
28 01 2010
Peter Dunford (09:45:18) :
Yet another great piece. Well done.
NCDC were obviously monitoring this blog, they noticed that the updates stopped at surface stations.org, assumed the number crunching was going on at that point, and decided to get in first. (We have seen efforts to hold awkward papers back so they can get their retaliation in first before, I wonder if that would have happened here?)
I think it’s good NCDC did this, it exposes their methods and the inherent weaknesses to public scrutiny, and their results to ridicule. Well done Menne and Karl. Since your side has all almost the funding, our side needs all the ammunition you can give us.
28 01 2010
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rw (10:03:50) :
One of the neatest things about the surface station project is that to counter these results, one has to contradict the most important principles of experimental science. In particular, that of founding one’s inferences on the best measurements possible. Now we have people in the AGW camp essentially saying, “Oh, quality of measurement doesn’t matter in this case.”
So it looks like WUWT has these guys rapidly painting themselves into a corner.
(Unless climatologists have created a new scientific paradigm.)
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