Climate Skeptic

With climate alarmists continuing to declare climate debate to be over and asking skeptics to just go away, we are reminded again why it is useful to have two sides in a debate.  Few people on any side of any question typically are skeptical of data that support their pet hypotheses.    So, in order to have a full range of skepticism and replication applied to all findings, it is helpful to have people passionately on both sides of a proposition.

I am reminded of this seeing how skeptics finally convinced the NOAA that one of its satellites had gone wonky, producing absurd data (e.g. Great Lakes temperatures in the 400-600F range).  Absolutely typically, the NOAA initially blamed skeptics for fabricating the data

NOAA’s Chuck Pistis went into whitewash mode on first hearing the story about the worst affected location, Egg Harbor, set by his instruments onto fast boil. On Tuesday morning Pistis loftily declared, “I looked in the archives and I find no image with that time stamp. Also we don’t typically post completely cloudy images at all, let alone with temperatures. This image appears to be manufactured for someone’s entertainment.”

Later he went on to own up to the problem, but not before implying at various times that the data is a) trustworthy  b) not trustworthy  c) placed online by hand with verification and d) posted online automatically with no human intervention.

This was the final NOAA position, which is absurd to me:

“NOTICE: Due to degradation of a satellite sensor used by this mapping product, some images have exhibited extreme high and low surface temperatures. “Please disregard these images as anomalies. Future images will not include data from the degraded satellite and images caused by the faulty satellite sensor will be/have been removed from the image archive.”

OK, so 600F readings will be thrown out, but how do we have any confidence the rest of the readings are OK.  Just because they may read in a reasonable range, e.g, 59F, the NOAA is just going to assume those readings are OK?

At least, that is, in Nepal.  Willis Eschenbach has an interesting post looking into the claim that Nepal has seen one of the highest warming rates in the world (thus threatening Himalayan glaciers, etc etc).  It turns out there is one (1) GISS station in Nepal, and oddly enough the raw data shows a cooling trend.  Only the intervention of NASA computers heroically transforms a cooling trend into the strong warming trend we all know must really be there because Al Gore says its there and he got a Nobel Prize, didn’t he?

GISS has made a straight-line adjustment of 1.1°C in twenty years, or 5.5°C per century. They have changed a cooling trend to a strong warming trend … I’m sorry, but I see absolutely no scientific basis for that massive adjustment. I don’t care if it was done by a human using their best judgement, done by a computer algorithm utilizing comparison temperatures in India and China, or done by monkeys with typewriters. I don’t buy that adjustment, it is without scientific foundation or credible physical explanation.

At best that is shoddy quality control of an off-the-rails computer algorithm. At worst, the aforesaid monkeys were having a really bad hair day. Either way I say adjusting the Kathmandu temperature record in that manner has no scientific underpinnings at all. We have one stinking record for the whole country of Nepal, which shows cooling. GISS homogenizes the data and claims it wasn’t really cooling at all, it really was warming, and warming at four degrees per century at that

In updates to the post, Eschenbach and his readers track down what is likely driving this bizarre adjustment in the GISS methodology.

Steve Goddard and Anthony Watt have a series of posts on an old favorite topic on this site — how data manipulations back in the climate office is creating a lot of the “measured” warming.  This particular example is right here in Arizona, and features several sites my son and I surveyed for Anthony’s site.  They have a followup on another Arizona station here.  Check out all the asphalt:

This is a hilariously bad siting.  It demonstrates how small things can sometimes have big effects.  The MMTS sensor has a very limited cable length.  This does not mean that it only comes with a short cable (begging the question of why they can’t just buy a longer one), but that it can only have a short cable due to signal amplification issues.  As a result, we get this terrible siting because it needs to be close to the building, whereas even a hundred yards away there were much better locations

Carefree is a fairly rural (at least suburban) low density town with lots of undeveloped land.  They had to work to get a siting this bad.  A monkey throwing darts at a map of the area would have gotten a better siting.

OK, we have to exempt from the “worst” list those that are sitting in front of air conditioning exhausts, but this is just awful:

The natural environment for millions of acres around this site in Russia Norway is reflective snow cover, so the temperature station is on black asphalt.

We have discussed temperature measurement on this blog a number of times, focusing particularly on signal to noise ratio issues where errors and manual corrections in surface temperature records tend to be larger than the global warming signal we are trying to measure.  Anthony Watt has an interesting post on human error as related to reporting of temperature numbers over a large part of the measurement network.

With NASA GISS admitting that missing minus signs contributed to the hot anomaly over Finland in March, and with the many METAR coding error events I’ve demonstrated on opposite sides of the globe, it seems reasonable to conclude that our METAR data from cold places might very well be systemically corrupted with instances of coding errors.

The Hockey Schtick points to a study on Pennsylvania temperatures that illustrates a point I have been making for a while:

A new SPPI paper examines the raw and adjusted historical temperature records for Pennsylvania and finds the mean temperature trend from 1895 to 2009 to be minus .08°C/century, but after unexplained adjustments the official trend becomes positive .7°C/century. The difference between the raw and adjusted data exceeds the .6°C/century in global warming claimed for the 20th century.

I think people are too quick to jump onto the conspiracy bandwagon and paint these adjustments as scientists forcing the outcome they want.  In fact, as I have written before, some of these adjustments (such as adjustments for changes in time of observation) are essential.  Some, such as how the urbanization adjustments are done (or not done) are deeply flawed.  But the essential point is that the signal to noise ratio here is really really low.  The signal we are trying to measure (0.6C or so of warming) is smaller than the noise, even ignoring measurement and other errors.

Charlie Martin is looking through some of James Hansen’s emails and found this:

[For] example, we extrapolate station measurements as much as 1200 km. This allows us to include results for the full Arctic. In 2005 this turned out to be important, as the Arctic had a large positive temperature anomaly. We thus found 2005 to be the warmest year in the record, while the British did not and initially NOAA also did not. …

So he is trumpeting this approach as an innovation?  Does he really think he has a better answer because he has extrapolated station measurement by 1200km (746 miles)?  This is roughly equivalent, in distance, to extrapolating the temperature in Fargo to Oklahoma City.  This just represents for me the kind of false precision, the over-estimation of knowledge about a process, that so characterizes climate research.  If we don’t have a thermometer near Oklahoma City then we don’t know the temperature in Oklahoma City and lets not fool ourselves that we do.

I had a call from a WaPo reporter today about modeling and modeling errors.  We talked about a lot of things, but my main point was that whether in finance or in climate, computer models typically perform what I call knowledge laundering.   These models, whether forecasting tools or global temperature models like Hansen’s, take poorly understood descriptors of a complex system in the front end and wash them through a computer model to create apparent certainty and precision.  In the financial world, people who fool themselves with their models are called bankrupt (or bailed out, I guess).  In the climate world, they are Oscar and Nobel Prize winners.

Update: To the 1200 km issue, this is somewhat related.

Readers of this site won’t be surprised at reports of problems in the surface temperature record.  Joe D’Aleo and Anthony Watt have teamed up on a new paper published by SPPI analyzing the surface temperature record in depth.  I have only skimmed it, but it looks terrific  (and includes a few weather station site surveys and photos by yours truly).  From the summary:

1. Instrumental temperature data for the pre-satellite era (1850-1980) have been so widely, systematically, and unidirectionally tampered with that it cannot be credibly asserted there has been any significant “global warming” in the 20th century.

2. All terrestrial surface-temperature databases exhibit very serious problems that render them useless for determining accurate long-term temperature trends.

3. All of the problems have skewed the data so as greatly to overstate observed warming both regionally and globally.

I always wondered how they calibrated satellite temperatures measurement when they make no reference or calibration to any surface temperature record.  Here is how its done.

Via WUWT, Jeff Id takes a look at the GHCN temperature data base, specifically comparing warming in urban vs. rural locations.  As found in a number of other studies, about half of 20th century warming int he surface temperature record may be due to uncorrected urban biases.

Some past takes on the same subject:

• Ross McKitrick has delivered this same finding several times
• LaDochy et al found similar results for California
• Even Phil Jones finds as much as 40% of warming in his data base from urban bias, though he tries to disguise the finding.
• Even a kid can demonstrate it.
• Actually, even my kid can demonstrate it.

The American Thinker blog is running a daily series of charts showing raw and “value added” or adjusted station data. The amount of the global warming signal that comes from manual adjustments rather than actual measurements is something we have discussed here before, but you can see in each of their daily examples.

Unadjusted and adjusted temperatures at Kremsmuenster, Austria

Source: AppInSys (Applied Information Systems) using NOAA/GHCN database for Kremsmuenster, Austria

You can create the same charts for any station here.

This is a really interesting email string form the CRU emails, via Steve McIntyre:

June 4, 2003 Briffa to Cook 1054748574
On June 4, 2003, Briffa, apparently acting as editor (presumably for Holocene), contacted his friend Ed Cook of Lamont-Doherty in the U.S. who was acting as a reviewer telling him that “confidentially” he needed a “hard and if required extensive case for rejecting”, in the process advising Cook of the identity and recommendation of the other reviewer. There are obviously many issues involved in the following as an editor instruction:

From: Keith Briffa
To: Edward Cook
Subject: Re: Review- confidential REALLY URGENT
Date: Wed Jun 4 13:42:54 2003

I am really sorry but I have to nag about that review – Confidentially I now need a hard and if required extensive case for rejecting - to support Dave Stahle’s and really as soon as you can. Please
Keith

Cook to Briffa, June 4, 2003
In a reply the same day, Cook told Briffa about a review for Journal of Agricultural, Biological, and Environmental Sciences of a paper which, if not rejected, could “really do some damage”. Cook goes on to say that it is an “ugly” paper to review because it is “rather mathematical” and it “won’t be easy to dismiss out of hand as the math appears to be correct theoretically”. Here is the complete email:

Hi Keith,
Okay, today. Promise! Now something to ask from you. Actually somewhat important too. I got a paper to review (submitted to the Journal of Agricultural, Biological, and Environmental Sciences), written by a Korean guy and someone from Berkeley, that claims that the method of reconstruction that we use in dendroclimatology (reverse regression) is wrong, biased, lousy, horrible, etc. They use your Tornetrask recon as the main whipping boy. I have a file that you gave me in 1993 that comes from your 1992 paper. Below is part of that file. Is this the right one? Also, is it possible to resurrect the column headings? I would like to play with it in an effort to refute their claims. If published as is, this paper could really do some damage. It is also an ugly paper to review because it is rather mathematical, with a lot of Box-Jenkins stuff in it. It won’t be easy to dismiss out of hand as the math appears to be correct theoretically, but it suffers from the classic problem of pointing out theoretical deficiencies, without showing that their improved inverse regression method is actually better in a practical sense. So they do lots of monte carlo stuff that shows the superiority of their method and the deficiencies of our way of doing things, but NEVER actually show how their method would change the Tornetrask reconstruction from what you produced. Your assistance here is greatly appreciated. Otherwise, I will let Tornetrask sink into the melting permafrost of northern Sweden (just kidding of course).
Cheers,
Ed

A couple of observations

1. For guys who supposedly represent the consensus science of tens of thousands of scientists, these guys sure have a bunker mentality
2. I would love an explanation of how math can have theoretical deficiencies but be better in a practical sense.  In the practical sense of … giving the answer one wants?
3. The general whitewash answer to all the FOIA obstructionism is that these are scientists doing important work not to be bothered by nutcases trying to waste their time.  But here is exactly the hypocrisy:  The email author says that some third party’s study is deficient because he can’t demonstrate how his mathematical approach might change the answer the hockey team is getting.  But no third party can do this because the hockey team won’t release the data needed for replication.  This kind of data – to check the mathematical methodologies behind the hockey stick regressions – is exactly what Steve McIntyre et al have been trying to get.  Ed Cook is explaining here, effectively, why release of this data is indeed important
4. At the very same time these guys are saying to the world not to listen to critics because they are not peer-reviewed, they are working as hard as they can back-channel to keep their critics out of peer-reviewed literature they control.
5. For years I have said that one problem with the hockey team is not just that the team is insular, but he reviewers of their work are the same guys doing the work.  And now we see that these same guys are asked to review the critics of their work.

Many of us are aware of the cherry-picking of proxy series that goes on in the temperature reconstruction world.  This cherry picking is both manual — a thousand plus proxy series exist but the same 20-30 that are known to create hockey sticks are selected over and over; and algorithmic — McIntyre and McKittrick demonstrated how Michael Mann’s algorithms preferentially put high weights on hockey-stick shaped series.

I think a lot of us has suspected something similar in the surface temperature measurement indexes like the Hadley CRUT3, the main metric relied on by the IPCC.

On Tuesday, the Moscow-based Institute of Economic Analysis (IEA) issued a report claiming that the Hadley Center for Climate Change based at the headquarters of the British Meteorological Office in Exeter (Devon, England) had probably tampered with Russian-climate data.

The IEA believes that Russian meteorological-station data did not substantiate the anthropogenic global-warming theory. Analysts say Russian meteorological stations cover most of the country’s territory, and that the Hadley Center had used data submitted by only 25% of such stations in its reports. Over 40% of Russian territory was not included in global-temperature calculations for some other reasons, rather than the lack of meteorological stations and observations.

So, maybe they were chosen because they had higher quality data with fewer data gaps.  Wrong:

The HadCRUT database includes specific stations providing incomplete data and highlighting the global-warming process, rather than stations facilitating uninterrupted observations.

On the whole, climatologists use the incomplete findings of meteorological stations far more often than those providing complete observations.

Maybe they were urban biases in the data that was excluded. No, just the opposite:

IEA analysts say climatologists use the data of stations located in large populated centers that are influenced by the urban-warming effect more frequently than the correct data of remote stations.

So, without the CRU giving any clear set of decision rules for station selection, we are left with this:

The data of stations located in areas not listed in the Hadley Climate Research Unit Temperature UK (HadCRUT) survey often does not show any substantial warming in the late 20th century and the early 21st century.

I am sure it is the purest coincidence that stations excluded from metrics like this show less warming and proxies excluded from temperature reconstructions don’t look like hockey sticks.

Update: McIntyre here and Watts here.

I apologize to readers who visit both of my sites for the repetition between them of late, but there is a lot of demand in the community of folks who usually don’t come to climate sites for climate analysis, so I am repeating stuff from here at my other blog.

A kid and his dad manage to do the analysis that NASA, the EPA, the CRU, and the IPCC can’t be convinced to perform. Awesome.

I have written a number of times about what appear to be arbitrary or extreme manual adjustments to surface temperature records.  These adjustments are typically positive (ie they make the temperature trend more positive) and often their magnitude outweighs the underlying temperature signal being measured, raising serious issues about the signal to noise ratio in temperature measurement.  Willis Eschenbach on Anthony Watts’ site brings us one of the most extreme examples I have seen, this time from Australia.

I will leave it to you to click through for the whole story, but here are graphs of the Darwin temperature station before and after adjustments.  First the raw data (this, by the way, is what the CRU so famously threw out, so we can’t do this analysis for CRU adjustments)

I would be willing to believe the splice-discontinuity around 1940 is an artifact of the data, and one might either throw out the data before 1940 or re-zero it consistent with later data.  Or it might be real.  We really don’t know, we can only guess.  We need to be careful how frequently we guess, as each guess corrupts the data, no matter how much we are trying to improve things.    We might get clues from other nearby thermometers, which is discussed in the article, but thermometers are few and far between in 1920’s Australia.

The other guess we might make is looking around the town of Darwin, seeing the growth of the urban area, we might want to adjust current temperatures down a bit to correct for the urban heat island effect.  Again, we have to be careful, because we are just guessing.

Here is what the GHCN actually does to adjust the data. The black line is the amount manually added to temperatures, resulting in the red line.

Wow! Instant global warming.  We’ve suddenly added 2C per century to the Darwin warming trend.

So, why does the black line look like this?  We don’t know, because climate scientists play these games in secret and claim that anyone trying to audit their fine work is just distracting them from more weighty pursuits.   Nominally, the GHCN claims the adjustments are based on comparisons with other local thermometers, but there are not other local thermometers in their data base and the closest ones (hundreds of kilometers away) do not display any behavior that might justify this adjustment.

It is time that we demand the ability to audit and replicate these adjustments.

When someone starts to shout “but its in the peer-reviewed literature” as an argument-ender to me, I usually respond that peer review is not the finish line, meaning that the science of some particular point is settled. It is merely the starting point, where now a proposition is in the public domain and can be checked and verified and replicated and criticized and potentially disproved or modified.

The CRU scandal should, in my mind, be taken exactly the same way. Unlike what more fire-breathing skeptics have been saying, this is not the final nail in the coffin of catastrophic man-made global warming theory. It is merely a starting point, a chance to finally move government funded data and computer code into the public domain where it has always belonged, and start tearing it down or confirming it.

To this end, I would like to share a post from year ago, showing the kind of contortions that skeptics have been going through for years to demonstrate that there appear to be problems in key data models — contortions and questions that could have been answered in hours rather than years if the climate scientists hadn’t been so afraid of scrutiny and kept their inner workings secret. This post is from July, 2007. It is not one of my most core complaints with global warming alarmists, as I think the Earth has indeed warmed over the last 150 years, though perhaps by less than the current metrics say. But I think some folks are confused why simple averages of global temperatures can be subject to hijinx. The answer is that the averages are not simple:

A few posts back, I showed how nearly 85% of the reported warming in the US over the last century is actually due to adjustments and added fudge-factors by scientists rather than actual measured higher temperatures. I want to discuss some further analysis Steve McIntyre has done on these adjustments, but first I want to offer a brief analogy.

Let’s say you had two compasses to help you find north, but the compasses are reading incorrectly. After some investigation, you find that one of the compasses is located next to a strong magnet, which you have good reason to believe is strongly biasing that compass’s readings. In response, would you

1. Average the results of the two compasses and use this mean to guide you, or
2. Ignore the output of the poorly sited compass and rely solely on the other unbiased compass?

Most of us would quite rationally choose #2. However, Steve McIntyre shows us a situation involving two temperature stations in the USHCN network in which government researchers apparently have gone with solution #1. Here is the situation:

He compares the USHCN station at the Grand Canyon (which appears to be a good rural setting) with the Tucson USHCN station I documented here, located in a parking lot in the center of a rapidly growing million person city. Unsurprisingly, the Tucson data shows lots of warming and the Grand Canyon data shows none. So how might you correct Tucson and the Grand Canyon data, assuming they should be seeing about the same amount of warming? Would you

average them, effectively adjusting the two temperature readings

towards each other, or would you assume the Grand Canyon data is cleaner

with fewer biases and adjust Tucson only? Is there anyone who would not choose the second option, as with the compasses?

The GISS data set, created by the Goddard Center of NASA, takes the USHCN data set and somehow uses nearby stations to correct for anomalous stations. I say somehow, because, incredibly, these government scientists, whose research is funded by taxpayers and is being used to make major policy decisions, refuse to release their algorithms or methodology details publicly. They keep it all secret! Their adjustments are a big black box that none of us are allowed to look into (and remember, these adjustments account for the vast majority of reported warming in the last century).

We can, however, reverse engineer some of these adjustments, and McIntyre does. What he finds is that the GISS appears to be averaging the good and bad compass, rather than throwing out or adjusting only the biased reading. You can see this below. First, here are the USHCN data for these two stations with only the Time of Observation adjustment made (more on what these adjustments are in this article).

As I said above, no real surprise – little warming out in undeveloped nature, lots of warming in a large and rapidly growing modern city. Now, here is the same data after the GISS has adjusted it:

You can see that Tucson has been adjusted down a degree or two, but Grand Canyon has been adjusted up a degree or two (with the earlier mid-century spike adjusted down). OK, so it makes sense that Tucson has been adjusted down, though there is a very good argument to be made that it should be been adjusted down more, say by at least 3 degrees**. But why does the Grand Canyon need to be adjusted up by about a degree and a half? What is biasing it colder by 1.5 degrees, which is a lot? The answer: Nothing. The explanation: Obviously, the GISS is doing some sort of averaging, which is bringing the Grand Canyon and Tucson from each end closer to a mean.

This is clearly wrong, like averaging the two compasses. You don’t average a measurement known to be of good quality with one known to be biased. The Grand Canyon should be held about the same, and Tucson adjusted down even more toward it, or else thrown out. Lets look at two cases. In one, we will use the GISS approach to combine these two stations– this adds 1.5 degrees to GC and subtracts 1.5 degrees from Tucson. In the second, we will take an approach that applies all the adjustment to just the biases (Tucson station) — this would add 0 degrees to GC and subtract 3 degrees from Tucson. The first approach, used by the GISS, results in a mean warming in these two stations that is 1.5 degrees higher than the more logical second approach. No wonder the GISS produces the highest historical global warming estimates of any source! Steve McIntyre has much more.

** I got to three degrees by applying all of the adjustments for GC and Tucson to Tucson. Here is another way to get to about this amount. We know from studies that urban heat islands can add 8-10 degrees to nighttime urban temperatures over surrounding undeveloped land. Assuming no daytime effect, which is conservative, we might conclude that 8-10 degrees at night adds about 3 degrees to the entire 24-hour average.

Postscript: Steve McIntyre comments (bold added):

These adjustments are supposed to adjust for station moves – the procedure is described in Karl and Williams 1988 [check], but, like so many climate recipes, is a complicated statistical procedure that is not based on statistical procedures known off the island. (That’s not to say that the procedures are necessarily wrong, just that the properties of the procedure are not known to statistical civilization.) When I see this particular outcome of the Karl methodology, my mpression is that, net of the pea moving under the thimble, the Grand Canyon values are being blended up and the Tucson values are being blended down. So that while the methodology purports to adjust for station moves, I’m not convinced that the methodology can successfully estimate ex post the impact of numerous station moves and my guess is that it ends up constructing a kind of blended average.

LOL. McIntyre, by the way, is the same gentleman who helped call foul on the Mann hockey stick for bad statistical procedure.

Many of us have argued for years that much of the measured surface temperature increase has actually been from manual adjustments made for opaque and largely undisclosed reasons by a few guys back in their offices.  (Update– corrected, I accidently grabbed the old version of the post that did not have the degree C/F conversion right.)

The US Historical Climate Network (USHCN) reports about a 0.6C temperature increase in the lower 48 states since about 1940.  There are two steps to reporting these historic temperature numbers.  First, actual measurements are taken.  Second, adjustments are made after the fact by scientists to the data.  Would you like to guess how much of the 0.6C temperature rise is from actual measured temperature increases and how much is due to adjustments of various levels of arbitrariness?  Here it is, for the period from 1940 to present in the US:

Actual Measured Temperature Increase:	0.3C
Adjustments and Fudge Factors:	0.3C
Total Reported Warming:	0.6C

Yes, that is correct.  About half the reported warming in the USHCN data base, which is used for nearly all global warming studies and models, is from human-added fudge factors, guesstimates, and corrections.

I know what you are thinking – this is some weird skeptic’s urban legend.  Well, actually it comes right from the NOAA web page which describes how they maintain the USHCN data set.  Below is the key chart from that site showing the sum of all the plug factors and corrections they add to the raw USHCN measurements:

I concluded that while certain adjustments like the one for time of observation make sense, many of the adjustments, such as the one for siting, seem crazy.  Against all evidence, the adjustment for siting implies a modern cooling bias, which is crazy given urbanization around sites and the requirement that modern MMTS stations (given maximum wire lengths) be nearer buildings than any manual thermometer had to be 80 years ago.

Even if we thought these guys were doing their best effort, can we really trust our ability to measure a signal that is substantially smaller than the noise we have to filter out?

Anyway, in the last week a similar example has been found in New Zealand, via Anthony Watts:

The New Zealand Government’s chief climate advisory unit NIWA is under fire for allegedly massaging raw climate data to show a global warming trend that wasn’t there.

The scandal breaks as fears grow worldwide that corruption of climate science is not confined to just Britain’s CRU climate research centre.

In New Zealand’s case, the figures published on NIWA’s [the National Institute of Water and Atmospheric research] website suggest a strong warming trend in New Zealand over the past century:

The caption to the photo on the NiWA site reads:

From NIWA’s web site — Figure 7: Mean annual temperature over New Zealand, from 1853 to 2008 inclusive, based on between 2 (from 1853) and 7 (from 1908) long-term station records. The blue and red bars show annual differences from the 1971 – 2000 average, the solid black line is a smoothed time series, and the dotted [straight] line is the linear trend over 1909 to 2008 (0.92°C/100 years).

But analysis of the raw climate data from the same temperature stations has just turned up a very different result:

Gone is the relentless rising temperature trend, and instead there appears to have been a much smaller growth in warming, consistent with the warming up of the planet after the end of the Little Ice Age in 1850.

The revelations are published today in a news alert from The Climate Science Coalition of NZ:

Again, even before we consider the quality of the adjustment, we see the signal to noise — the adjustments for noise are equal to or greater than the signal they think exists in the data.

The obvious response is that these adjustments are somehow justified based on site location and instrumentation changes.  But we know from looking at US temeprature stations that the typical station has a warming bias over time due to urbanization and the warm bias of some modern temperature instruments, thus requiring a cooling adjustment and not a warming adjustment.  Watts provides such evidence for one New Zealand site here.

Update: Boy, this is certainly becoming a familiar curve shape.  It seems the main hockey stick curve is the shape of temperature adjustments coming out of these guys.  ESR (via TJIC)  took this code from a Briffa North American proxy reconstruction

;<p> ; Apply a VERY ARTIFICAL correction for decline!!<p> ;<p> yrloc=[1400,findgen(19)*5.+1904]<p> valadj=[0.,0.,0.,0.,0.,-0.1,-0.25,-0.3,0.,- 0.1,0.3,0.8,1.2,1.7,2.5,2.6,2.6,2.6,2.6,2.6]*0.75 ; fudge factor<p> if n_elements(yrloc) ne n_elements(valadj) then message,’Oooops!’<p> ;<p> yearlyadj=interpol(valadj,yrloc,timey)

and reproduced this curve, representing the “fudge factor” Briffa added, apparently to get the result he wanted:

Patrick Michaels describes some of the long history of the Hadley Center and specifically Phil Jones’ resistance to third party verification of their global temperature data.  First he simply refused to share the data

We have 25 years or so invested in the work. Why should I make the data available to you, when your aim is to try and find something wrong with it?

(that’s some scientist, huh) and then he said he couldn’t share the data and now he says he’s lost the data.

Michaels gives pretty good context to the issues of station siting, but there are many other issues that are perfectly valid reasons for third parties to review the Hadley Center’s methodology.  A lot of choices have to be made in patching data holes and in giving weights to different stations and attempting to correct for station biases.  Transparency is needed for all of these methodologies and decisions.  What Jones is worried about is whenever the broader community (and particularly McIntyre and his community on his web site) have a go at such methodologies, they have always found gaping holes and biases.  Since the Hadley data is the bedrock on which rests almost everything done by the IPCC, the costs of it being found wrong are very high.

Here is an example post from the past on station siting and measurement quality.  Here is a post for this same station on correction and aggregation of station data, and problems therein.

I just watched Peter Sinclair’s petty little video on Anthony Watt’s effort to survey and provide some level of quality control on the nation’s surface temperature network.  Having participated in the survey, I was going to do a rebuttal video from my own experience, but I just don’t have the time, but I want to offer a couple of quick thoughts.

• Will we ever see an alarmist be able to address any skeptics critique of AGW science without resorting to ad hominem attacks?  I guess the whole “oil industry funding” thing is a base requirement for any alarmist article, but this guy really gets extra credit for the tobacco industry comparison.  Seriously, do you guys really think this addresses the issue?
• I am fairly sure that Mr. Watt would not deny that the world has warmed over the last 100 years, though he might argue that warming has been exaggerated somewhat.  Certainly satellites are immune to the biases and problems Mr. Watt’s group is identifying, and they still show warming  (though less than the surface temperature networks is showing).
• The video tries to make Watt’s volunteers sound like silly children at camp, but in fact weather measurement and data collection in this country have a long history of involvement and leadership by volunteers and amateurs.
• The core point that really goes unaddressed is that the government, despite spending billions of dollars on AGW-related projects, is investing about zero in quality control of the single most critical data set to the current public policy decisions.   Many of the sites are absolutely inexcusable, EVEN against the old goals of reporting weather rather than measuring climate change.  I surveyed the Tucson site – it is a joke.
• Mr. Sinclair argues that the absolute value of the temperatures does not matter as much as their changes over time.  Fine, I would agree.  But again, he demonstrates his ignorance.  This is an issue Anthony and most of his readers discuss all the time.  When, for example, we talk about the really biased site at Tucson, it is always in the context of the fact that 100 years ago Tucson was a one horse town, and so all the urban heat biases we might find in a badly sited urban location have been introduced during the 20th century measurement period.  These growing biases show up in the measurements as increasing temperatures.  And the urban heat island effects are huge.  My son and I personally measured about 10F in the evening.  Even if this was only at Tmin, and was 0 effect at Tmax  (daily average temps are the average of Tmin and Tmax) then this would still introduce a bias of 5F today that was surely close to zero a hundred years ago.
• Mr. Sinclair’s knowledge about these issues is less than one of our readers might have had 3 years ago.  He says we should be satisfied with the data quality because the government promises that it has adjusted for these biases.  But these very adjustments, and the inadequacy of the process, is one reason for Mr. Watt’s efforts.  If Mr. Sinclair had bothered to educate himself, he would know that many folks have criticized these adjustments because they are done blind, without any reference to actual station quality or details, by statistical processes.  But without the knowledge of which stations have better installations, the statistical processes tend to spread the bias around like peanut butter, rather than really correct for it, as demonstrated here for Tucson and the Grand Canyon (both of these stations I have personally visited).
• The other issue one runs into in trying to correct for a bad site through adjustments is the signal to noise problem.  The world global warming signal over the last 100 years has been no more than 1 degree F.  If urban heat biases are introducing a 5,8, or 10 degree bias, then the noise, and thus the correction factor, is 5-10 times larger than the signal.   In practical terms, this means a 10-20% error in the correction factor can completely overwhelm the signal one is trying to detect.  And since most of the correction factors are not much better than educated guesses, their errors are certainly higher than this.
• Overall Mr. Sinclair’s point seems to be that the quality of the stations does not matter.  I find that incredible, and best illustrated with an example.  The government makes decisions about the economy and interest rates and taxes and hundreds of other programs based on detailed economic data.  Let’s say that instead of sampling all over Arizona, they just sampled in one location, say Paradise Valley zip code 85253.  Paradise Valley happens to be (I think) the wealthiest zip code in the state.  So, if by sampling only in Paradise Valley, the government decides that everyone is fine and no one needs any government aid, would Mr. Sinclair be happy?  Would this be “good enough?”  Or would we demand an investment in a better data gathering network that was not biased towards certain demographics to make better public policy decisions involving hundreds of billions of dollars?

There are a number of reasons to prefer satellite data over surface data for temperature measurement –satellites have better coverage and are not subject to site location  biases.  On the flip side, satellites only have limited history (back to 1979) so it is of limited utility for long-term analyses.  Also,they do not strictly measure the surface, but the lower troposphere (though most climate models expect these to move in tandem).  And since some of the technologies are newer, we don’t fully understand biases or errors that may be in the measurement system (though satellites are not any newer than certain surface temperature measurement devices that are suspected of biases).  In particular, sattelites are subject to some orbital drift and changes in altitude and sensor function over time that must be corrected, perhaps imperfectly to date.

To this latter point, what one would want to see is an open dialog, with a closed loop between folks finding potential problems (like this one) and folks fixing or correcting the problems.  In the case of both the UAH and RSS teams, both have been very responsive to outside criticism of their methodologies, and have improved them over time.  This stands in stark contrast to the GISS and other surface temperature teams, who resist criticism intensely, put few resources into quality control (Hansen says a quarter man year at the GISS) and who refuse to credit outsiders even when changes are made under external presure.