Backstory on the new surfacestations paper

IPCC & co will try to avoid a paper like yours under any pretext; the first cop out would be that yours is not accepted for publication “in a respected journal” (pal review).
How are you dealing with that one?

Again, thanks to you and your team for doing something constructive to make the world a better place despite the best efforts of those many misguided and human-hating zealots to enslave us all.

Bravo to all involved!!

J.Hansford says:
July 29, 2012 at 10:13 pm
Let the reviewing begin!….. What are the other sites saying about the findings of the paper?
Steve Hayward on Powerline: “Watts’ conclusion is indeed a bombshell if it proves out: that the U.S. surface temperature rise has been overestimated by a factor of two…”
http://www.powerlineblog.com/archives/2012/07/breaking-from-best-to-worst-in-less-than-a-day.php?
Climate Depot has a pageful of links to additional reactions: “Anthony Watts delivers devastating scientific blow to Muller’s claims: ‘New analysis demonstrates that reported 1979-2008 U.S. temp trends are spuriously doubled’.”
http://climatedepot.com/
…and I started writing, steep learning curve as this was my first stint as lead author.
I’m guessing that *that’s* why the carpal tunnel popped up when it did.

Well done, Anthony, a HUGE achievement. Incredible dedication so heartfelt thanks to you and yours for the sacrifices made for what is a massively important project.

A minor point, but you might provide the expansion of the acronym CONUS prior to its first use in the paper. I guess its obvious to everyone who reads your blog, but it wasn’t to me and I had to search several pages to find out what it was.

An amazing job, Anthony and admirable dedication from you and your team.
I hope you get to go on holiday soon though.

Re: S. Geiger: Red flag, IMO. Researchers looking for a signal that “we know must be there” makes things ripe for confirmation bias.
While you have a point, it is a weak one. Almost every experiment is setup to to record a signal of a type that “we expect to find.” This is true science: hypothesis, set-up, test, and analyze.
Where people go wrong is when then they use improper selection of the results to filter down to data that shows the signal.
When an experiment fails to support or refute a hypothesis, then the hypothesis needs to be altered and the conditions of the experiment altered. In the Michelson-Morley experiments, failure to find the aether signal, the experimenters tried multiple times with different and more sensitive apparatus, but they didn’t invent a signal that wasn’t there to find. After Fall 2011, the researchers checked to see whether the NOAA station Classification was sufficient criteria to segment the data. The Leroy 2010 not only gave them an approved alternative method of station classification, but confirmation that someone else asked the question. The results of Watt’s 2012 are not confirmation bias but an example of finding the coherency of the signal in the data.

Congratulations to Anthony and all the people who helped to make this happen, including the many volunteers who made the base for the siting problems! This is a huge result and I hope that it may reduce the “man-made-global-warming” scare to more normal proportions…

The faint sound you hear in the distance is Michael Mann working furiously to change the goalposts.

Anthony,
I first came across WUWT when I was looking at Stephenson’s paint jobs, and you were carrying out an experiment on the screens. I was fascinated then by your natural curiosity, and how the experiment was designed.
It has now led to this piece of research, an example of outstanding citizen science.
Well done to all concerned. If only the professionals, would get out a bit, leave their computer models behind, and feel the wind in their faces, and take a temperature or two, they might benefit from the experience.
I for one have always been offended by those who adjust, homogenise, or just change data which has been collected by others in the past, so that it ‘fits’ the narrative, and then don’t show their methods.

Skeptik says:
July 30, 2012 at 1:07 am
The faint sound you hear in the distance is Michael Mann working furiously to change the goalposts.
*
LOL. I bet he won’t be the only one. 🙂

“David Paper Kills Goliath-made Global Warming”
This really was a stone to the forehead of the giant, well-aimed. Well done. But now you have to remember that Saul got jealous of David because of the accolades (after having befriended him), and for the next few years, David was in hiding and on the run. But that was the time he wrote his psalms. IMHO.
You don’t have to be a believer to see the psychological keenness of that story. But like Evan Jones says “Nobody Beats the Rev” and you’re the one who has been “anointed”, Anthony. Sure you didn’t ask for it, that’s just the way the universe is happening.
Now what’s the contemporary and scientific equivalent of those psalms? The climate skeptics’ wiki I started and JustTheFacts here did sterling work for, but is currently sleeping? Just a thought. Heartland have a wiki too but in some respects (not all) my wiki has the edge. I would be very very happy to pass on the wiki torch and let its research side drop lower in priority.

David 1 – Goliath 0

Wait a minute…
Anthony. In your paper you state the following…
…The USHCNv2 monthly temperature data set is described by Menne et al. (2009). The raw and unadjusted data provided by NCDC has undergone the standard quality-control screening for errors in recording and transcription by NCDC as part of their normal ingest process but is otherwise unaltered. The intermediate (TOB) data has been adjusted for changes in time of observation such that earlier observations are consistent with current observational practice at each station. The fully adjusted data has been processed by the algorithm described by Menne et al. (2009) to remove apparent inhomogeneities where changes in the daily temperature record at a station differs significantly from neighboring stations….
Then near the end of the paper you say….
…We have shown that the site-classification value is a clear factor in the calculation of the trend magnitude. We are investigating other factors such as Time-Of-Observation changes which for the adjusted USHCNv2 is the dominant adjustment factor during 1979-2008.
Correct me if I am wrong here but your paper seems to essentially be looking at raw vs fully adjusted data, then looking at the impact of station categories, the two classification schemes, airports etc. But all the analysis seems to be raw vs fully adjusted. With a strong leaning towards ascribing the result you see to station siting issues.
But the elephant in the room here is TOB (Time of OBservation) changes. How has your study evaluated TOB factors to rule them out or at least understand them as a factor in this? Particularly as you point out, although not until the end of the paper, that TOB adjustments are the dominant factor in the USHCN adjustments. As they should be.
TOB has nothing to do with measurement quality, site rating factors, instrumentation etc. Changing the time of day that measurements are taken or even what constitutes a 24 hour period can have a huge impact on the data and then results. Until you have analysed TOB factors, what confidence can one place in these results.
Perhaps a better approach, if you didn’t want to explore TOB issues, was to have done the analysis based on the GHCN data in 3 stages. Raw data only based on Site Classification; TOB adjusted data only based on Site Classification; Fully adjusted data only based on Site Classification only.
Otherwise, how can you possibly know you are comparing apples with apples?
Here are some examples of past research suggesting how big the TOB factor might be:
http://journals.ametsoc.org/doi/abs/10.1175/1520-0450%281977%29016%3C0215%3ATOOTBA%3E2.0.CO%3B2
Time of Observation Temperature Bias and “Climatic Change”
Lawrence A. Schaal and and Robert F. Dale
Department of Agronomy, Purdue University, West Lafayette, Ind. 47907
Received: August 27, 1976; Accepted: February 14, 1977
Abstract
Historical changes in time of once daily maximum and minimum temperature observations at cooperative climatological stations from 1905 to 1975 have introduced a systematic bias in mean temperatures. Unless corrected, this bias may be interpreted incorrectly as climatic “cooling” and may also affect the assessment of agricultural production potential and fossil fuel needs. Maximum and minimum temperature data for two years from the National Weather Service station at Indianapolis International Airport were used to evaluate the differences between mean temperatures obtained by terminating the 24 h period at the midnight observation and the mean temperatures obtained by terminating the 24 h period at 0700 and 1900 hours, typical observation times for AM and PM observing stations. The greatest mean temperature bias occurs in March when a 1900 observation day yields a monthly mean temperature 1.3°F above a midnight observation, and a 0700 observational day gives a −1.3°F bias. Since the number of AM observing stations in Indiana have increased from 10% of the total number of temperature stations in 1925 to 55% in 1975, the March mean temperature shows a decrease of 1.2°F in the last 40 years, solely because of the change in substation observational times. Unless the time of observation bias is considered, the mixture of AM and PM observations complicates interpretation of areal temperature anomaly patterns. This bias is accumulated in monthly, seasonal or annual values of the mean temperature-derived variables-heating degree days, cooling degree days and growing degree days—and may provide misleading information for applications in industry and agriculture.
http://www.agci.org/dB/PDFs/05S3_DEasterling_Uncertain%20Record_0722.pdf
Issues Related to Uncertainty in the Observed Climate Record
…The U.S. Cooperative Observing Network (COOP) uses an observing system that records both the highest (maximum), and lowest (minimum) temperature in the previous 24h period, then these values are recorded by the observer at a set time each day (e.g. 7 am or 5 pm), and the thermometers are reset. Figure 1 shows the bias introduced into March temperatures by a change in the observing time from late afternoon to morning, which has been the trend in the COOP network over the past 20 years. This change has resulted in an artificial cooling in the time series due to a step change to cooler monthly averaged temperatures….
Figure 1 from Easterling’s paper (follow the link) shows -1.0 to -1.5 C impact on station temperatures across most of the continental US from TOB changes. Certainly enough to explain most of the difference between the raw and fully adjusted GHCN data. Leaving very little scope for site specific factors to be significant.
Until you incorporate TOB changes into your analysis, how can you actually make any meaningful claims about anything?

Interesting to see if Watts et al. 2012 gets picked up for next IPCC report. Somehow, why do I get the feeling IPCC will end up including Muller’s study and simply ignore Watts et al. 2012?
Beautiful smokescreen by all. McIntyre and Motl playing their parts perfectly, sending the alarmists off on a wild goose chase. Result: Muller’s pseudo-scientific claims killed off within a day of publication.
Anthony, you’ve proven again that you are the much better chess player, not only in terms of strategy, but also in terms of integrity and innovation. Muller, second BEST again. LOL

Congratulations to you and your crew, Anthony. If this is what you guys can do with no budget, Lord only knows what you could do with a fraction of the megabucks routinely thrown at global warming research.
Pointman

Anthony , pity you are not a Brit , such work would get you on the ladder to becoming ‘Sir’ Anthony Watts . My cap is doffed to you sir ! Well done you and your team . Cheers Tony

Philip Bradley says:
July 30, 2012 at 4:05 am

But the elephant in the room here is TOB (Time of OBservation) changes. How has your study evaluated TOB factors to rule them out or at least understand them as a factor in this? Particularly as you point out, although not until the end of the paper, that TOB adjustments are the dominant factor in the USHCN adjustments. As they should be.

You make an important and valid point. I commented in the main thread that TOBS will be the main way in which this paper will be attacked. I’ll have to through the paper in detail to understand what implications this has for Karl’s TOBS adjustment.

TOB is important but perhaps not that important.
Anthony’s paper compares various classes of met. station. TOB is handled the same for all stations. The paper shows that ‘good’ stations display much less warming than ‘poor’ stations. Unless there is a systematic difference in the way TOB is handled for the different classes of stations, TOB shouldn’t much matter in the overall result. ie. ‘poor’ stations showed much more warming than ‘good’ stations.

Hell hath no fury like a scorned weatherman.
Great job, Anthony. Get yourself a white hat. You deserve it.

As with any good sceptic, I will not just automatically believe this due to it supporting my own bias, but I will await further peer review and then, so long as it remains credible I will congratulate you.
I do heartily congratulate you upon your achievement so far. But I refuse to get carried away too soon. If the conclusions of this impressive piece of work are true, then it will survive close and rigorous scrutiny and will be found to be true.
I can wait for that, for it will be well worth waiting for.
Thank you Anthony for your persistence and your dedication to the pursuit of TRUTH, no matter where the data lead you. As a real, dedicated supporter of serious, evidence based science, I thank you and salute your work.

Thanks, Anthony, et al. for not giving up on this. It is personally very rewarding to have played a small part in this. Echoing Russ Steele, thank you for acknowledging the efforts of your volunteers.
Chris Dunn

I’d comment this way, as pointed out if TOBS is treated uniformly then the effects should average, but even if not, Anthony has now established a new benchmark for adjusting temperature which now will require that
A. The area of heat sinks surrounding the thermometers must be accounted according to Leroy 2010, and
B. The warming bias of badly sited stations, be adjusted down to match prime sites, rather than the prime sites adjusted up to match the majority (of poorly sited thermometers)
This work will now need to be done, if done honestly this will end up with climate data sets adjusted to have much lower UHI influence. It doesn’t matter now what attacks come, the new benchmark IS now established. Anthony should be congratulated for achieving that even if the paper doesn’t stand.
This is going to be very tricky for the Global warmists as it’s certain that this will be the first downward revision the data sets will see. I can see more, there was an Australian study that showed if the average temperature was calculated from hourly temperature rather than min-max the trend is much less, pronounced. Now if we could combine hourly obs, with Anthony’s siting analysis I wonder what falls out?
Congratulations Anthony.
Bob

you will never know how much the world owes you.
well done.

One more point that maybe the paper could make (IE a takeaway). The significant difference between Class 1/2 Airport sites and Class 1/2 Rural sites suggests that the Leroy 2010 procedure does not remove all the UHI bias in the record. Leroy 2010 needs to be improved upon.
This seems intuitive in that Leroy 2010 deals with heat storage but not really with direct atmospheric heating due to humans dumping GW of heat energy straight into the atmosphere. Here in in Brisbane for example this means that while the fringe rural areas are experiencing frost, Brisbane does not because of a layer of atmosphere being directly heated by the waste heat energy of millions or radiators, this affects winter results acting over many kilometers. Mind you the carbon tax may well lower winter temperature in cities, since many will no longer be able to afford their heaters any more and those pesky pensioners will just die of hypothermia instead.

Tip jar hit to help fund your next vacation – well earned. Come on people, lets show some appreciation for the Surfacestations team!
[REPLY: Your support, Varco, is greatly appreciated. -REP]

Anthony,
I’ve been following your site since you started questioning the Stevenson Screens. I followed your surfacestations reports and said to myself “sheesh – no way there’s no bias has crept into the data, particularly from UHI”. Then a few years ago I was on a hill and could see Boston on a bright, clear blue cloudless day. Except that there was a cloud lens over the downtown urban area, and nowhere else. (I didn’t have a camera, dammit!) That, plus surfacestation, proved that UHI/siting issues were real and distorted the data. So when I read about Muller’s report, I thought stick that horse hockey!
For what it’s worth, it appears that (NOAA?) has distributed about 114 new temperature stations equidistant across the U.S. in the last few years. They appear to be all located in ideal Category 1 locations. Their website says they hope to get truly accurate American measurements so that in about 30 years they can do reliable analyses of the temperature trends. I believe that they did this in response to Anthony’s critiques on station siting. Kudos, Anthony!

The periodic table had several holes filled in with elements only theory said existed. It was up to dedicated scientists to find them. Good job Anthony.

Anthony writes:
“REPLY: Understood, but please think about the physics of heat sinks, specifically when it comes to Tmin and I think yuo’ll see where we were coming from. Confirmation bias is a huge problem in AGW science, and if this new ratings system had not revealed the solution, I would have had to accede there was no effect. Persistence paid off. – Anthony”
It seems to me that many people are missing the importance of Leroy’s work and Anthony’s use of it. By the “physics of heat sinks,” what is meant is that temperature stations are rated according to physical characteristics of their individual sites. Specifically, temperature stations are rated according to the predictable behavior of concrete, asphalt, or whatever is found on site that influences temperature readings. For the precise details, you have to read Leroy.
The huge importance of Leroy’s work and Anthony’s use of it is that now we have an empirical measure over temperature sites that allows comparison and grouping among them. This innovation in scientific study of the data is a far cry from the strategy employed by the Warmistas who have always been happy to compare any two temperature measurements with total disregard for the facts on the ground. (Yes, Warmistas use Urban, semi-Urban, and Rural but with total disregard for the facts on the ground.)
Perhaps more important is the fact that Warmistas have stonewalled (against) this kind of empirical research since the phrase “global warming” was first uttered. Leroy’s work and Anthony’s use of it are total game changers. There will be no going back.

Superb, just superb.
Kudos to Steve M. for his help in pushing the ball over the goal line, and keeping the stats pristine. He’s certainly eminently qualified for the job! A potent shield against inevitable attempts to disparage your fine work.
Glad your family got to get the vacation in; at least you get some close-to-home vicarious benefit!

Warm says:
July 30, 2012 at 12:33 am
Constant temperature bias does not modify trends: I understand well that a poorly sited station with a lot of heat sources would be biased (physics of heat sinks ?), but if the bias is constant, there is no influence on decadal trend…
The problem is that the bias of a poorly sited station is not constant. Think of a small increase (say 1%) in the number of sunny days over a year. In a perfect station, that would give a small increase of yearly averaged temperature (say 0.1°C). In a poorly sited station, the temperature on a rainy or clouded day is about the same as in a good station, but the temperature on a sunny day is getting much higher (think of a station on an asphalted parking…). Thus instead of a trend of 0.1°C, the trend of a poorly sited station gets 0.2°C or 0.3°C, only by a small increase in number of (even partly) sunny days…

Link to tip jar for those so inclined.
http://www.surfacestations.org/donate.htm
[REPLY: Thank you. I should have thought of that. -REP]

Okay, Anthony. Confession time. When you made your initial announcement, I got all excited, speculating with everyone else in the blogosphere. Then when you dialled back, I felt peeved and got a bit annoyed with you. But then, when you finally posted, I realised that the work you’d done really was important–quite possibly moreso than anything I’d like to have imagined.
I suppose this paper might contain flaws (I really wouldn’t know), but you have some heavy guns on board and your data is out there for replication and critique, which is as it should be. I’m genuinely proud for you, and can see why, for you, (and as it turned out, everyone else) it was so significant–so please forgive me for doubting you.
Glad I got that off my chest–glad to report you’re as much one of my heroes as ever! 🙂

Anthony/Evan–
I understand the point about deadline pressure, so don’t take this as criticism of what has been done up to now.
I would, however, have liked to have seen a few “poster child” examples, with pictures, and associated station trends, in the .ppt, of stations that were re-classified with the new standards.
Will something like that be forthcoming?

Bobl says:
July 30, 2012 at 6:55 am
there was an Australian study that showed if the average temperature was calculated from hourly temperature rather than min-max the trend is much less, pronounced. Now if we could combine hourly obs, with Anthony’s siting analysis I wonder what falls out?
I believe you are referring to an article written by me, about the work of Jonathan Lowe an Australian statistician. It hasn’t been formally published in a journal. Although I urged Jonathan to do so.
http://www.bishop-hill.net/blog/2011/11/4/australian-temperatures.html

Then a few years ago I was on a hill and could see Boston on a bright, clear blue cloudless day. Except that there was a cloud lens over the downtown urban area, and nowhere else.
While I think the heat sink theory of warmer nights in poorly sited stations has merit, I don’t think it is the only effect at work. I think nighttime aerosol seeded clouds over urban areas also play a role.
It is scandalous that there has never been a published study on the effects of urban aerosols on minimum and maximum temperatures. Despite many studies that clearly show the effect of urban aerosols on other aspects of weather.

Thank you Anthony. All your hard work is greatly appreciated.

Thank you Antony!!!!!

Tallbloke said something that didn’t quite make sense to me: (paraphrasing) that this isn’t a big deal outside of the US.
I disagree because this is supposed to be the best, largest national data set available, and also because if these problems exist in that data set, they will probably exist in most others. Anyway, SurfaceStations.org did a wonderful job, and it would be great if interested parties in other nations can organize similar data-collection efforts; perhaps then, Watts et al. (or others) will use the Leroy 2010 classification scheme and similarly analyse those data sets.
Then Watts et al.’s recent paper will truly prove to have been of momentous importance in the advancement of climate science.

“I started writing, steep learning curve”
Truth. It is so hard to write. I’ve gotten to the point that I can “read” a scientific paper in about 20 minutes. First look at the title then look at the figures then scan through the equations then decide if I actually want to read the text. I’ve found that’s the best way to write one. First put all my figures in order then write out all my equations then write the text. Oddly the last part I write is the abstract which I used to write first thinking it helped organize my thinking.

Where I begin to walk back slightly my initial acceptance of this discussion paper and await further clarification and improvements.

Congratulations on the results are way too premature. Let’s see how the substance of the [final version of the] paper stands up to rigorous scrutiny. Beware confirmation bias, people.
Congratulations on the work done – which includes the marvelous volunteer effort for surfacestations.org – are entirely appropriate, and I heartily add mine.

Re the claim that a constant temperature bias does not modify trends:
– that would be so for something like inaccurate placement of a thermometer glass on the card giving the scale (thus a constant offset if the scale is linear as it usually is).
– but are thermodynamic effects from heat sinks and sources constant? If not the trend is amplified. Frederick Michael reminds us of the obvious, that sites deteriorate due building/paving – wasn’t PHX the airport poster site for that?
– are there as many sinks as sources, and of what relative magnitude? Obviously in summer there are many patches of asphalt absorbing heat in the day and releasing it at night, but no ice sheets (just vegetation?). OTOH, in winter the ashphalt may or may not be covered (more likely to be uncovered to increase use, i.e. snow clearing), snow does vary a bit and could be icy (though on the edge of a lake is probably not a good site anyway). Seems to me it is not simple, so it will be a long time before most corrections can be made. (Image analysis may be possible etc. but huge task, may be better to stop using poorly sited stations and build new ones instead of analyzing the bad.)
– and my mind keeps coming back to min-max-mean, or more logically the integration of temperature and time. Recall that some data shows the min increasing more than the mean or max – higher min should help life, higher max in an already hot area probably hurts but in some areas would help (depending on the vegetation). Please remind me why anyone should care about monitoring global temperature, given that the climate has already disproven alarmist predictions (models/theories), and allegations of human causes of significant change have been disproven.
– in any case reality (shown by Watts et al 2012 and some other papers) is that station siting creates major errors in the trend of increasing temperature.
– as for “homogenization procedure”, what is that in each case it is used? A paper by Steirou and Koutsoyiannis claims it causes large errors (see http://wattsupwiththat.com/2012/07/17/new-paper-blames-about-half-of-global-warming-on-weather-station-data-homgenization/ for news on that, and search http://www.climateaudit.org for “Station Homogenization as a Statistical Procedure” in which Stephen McIntyre comments on the difficulty of doing it properly).