For those that don’t notice, this is about metrology, not meteorology, though meteorology uses the final product. Metrology is the science of measurement.
Since we had this recent paper from Pat Frank that deals with the inherent uncertainty of temperature measurement, establishing a new minimum uncertainty value of ±0.46 C for the instrumental surface temperature record, I thought it valuable to review the uncertainty associated with the act of temperature measurement itself.
As many of you know, the Stevenson Screen aka Cotton Region Shelter (CRS), such as the one below, houses a Tmax and Tmin recording mercury and alcohol thermometer.
They look like this inside the screen:
Reading these thermometers would seem to be a simple task. However, that’s not quite the case. Adding to the statistical uncertainty derived by Pat Frank, as we see below in this guest re-post, measurement uncertainty both in the long and short term is also an issue.The following appeared on the blog “Mark’s View”, and I am reprinting it here in full with permission from the author. There are some enlightening things to learn about the simple act of reading a liquid in glass (LIG) thermometer that I didn’t know as well as some long term issues (like the hardening of the glass) that have values about as large as the climate change signal for the last 100 years ~0.7°C – Anthony
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Metrology – A guest re-post by Mark of Mark’s View
This post is actually about the poor quality and processing of historical climatic temperature records rather than metrology.
My main points are that in climatology many important factors that are accounted for in other areas of science and engineering are completely ignored by many scientists:
- Human Errors in accuracy and resolution of historical data are ignored
- Mechanical thermometer resolution is ignored
- Electronic gauge calibration is ignored
- Mechanical and Electronic temperature gauge accuracy is ignored
- Hysteresis in modern data acquisition is ignored
- Conversion from Degrees F to Degrees C introduces false resolution into data.
Metrology is the science of measurement, embracing both experimental and theoretical determinations at any level of uncertainty in any field of science and technology. Believe it or not, the metrology of temperature measurement is complex.
It is actually quite difficult to measure things accurately, yet most people just assume that information they are given is “spot on”. A significant number of scientists and mathematicians also do not seem to realise how the data they are working with is often not very accurate. Over the years as part of my job I have read dozens of papers based on pressure and temperature records where no reference is made to the instruments used to acquire the data, or their calibration history. The result is that many scientists frequently reach incorrect conclusions about their experiments and data because the do not take into account the accuracy and resolution of their data. (It seems this is especially true in the area of climatology.)
Do you have a thermometer stuck to your kitchen window so you can see how warm it is outside?
Let’s say you glance at this thermometer and it indicates about 31 degrees centigrade. If it is a mercury or alcohol thermometer you may have to squint to read the scale. If the scale is marked in 1c steps (which is very common), then you probably cannot extrapolate between the scale markers.
This means that this particular thermometer’s resolution is1c, which is normally stated as plus or minus 0.5c (+/- 0.5c)
This example of resolution is where observing the temperature is under perfect conditions, and you have been properly trained to read a thermometer. In reality you might glance at the thermometer or you might have to use a flash-light to look at it, or it may be covered in a dusting of snow, rain, etc. Mercury forms a pronounced meniscus in a thermometer that can exceed 1c and many observers incorrectly observe the temperature as the base of the meniscus rather than it’s peak: ( this picture shows an alcohol meniscus, a mercury meniscus bulges upward rather than down)
Another major common error in reading a thermometer is the parallax error.
Image courtesy of Surface meteorological instruments and measurement practices By G.P. Srivastava (with a mercury meniscus!) This is where refraction of light through the glass thermometer exaggerates any error caused by the eye not being level with the surface of the fluid in the thermometer.
(click on image to zoom)
If you are using data from 100’s of thermometers scattered over a wide area, with data being recorded by hand, by dozens of different people, the observational resolution should be reduced. In the oil industry it is common to accept an error margin of 2-4% when using manually acquired data for example.
As far as I am aware, historical raw multiple temperature data from weather stations has never attempted to account for observer error.
We should also consider the accuracy of the typical mercury and alcohol thermometers that have been in use for the last 120 years. Glass thermometers are calibrated by immersing them in ice/water at 0c and a steam bath at 100c. The scale is then divided equally into 100 divisions between zero and 100. However, a glass thermometer at 100c is longer than a thermometer at 0c. This means that the scale on the thermometer gives a false high reading at low temperatures (between 0 and 25c) and a false low reading at high temperatures (between 70 and 100c) This process is also followed with weather thermometers with a range of -20 to +50c
25 years ago, very accurate mercury thermometers used in labs (0.01c resolution) had a calibration chart/graph with them to convert observed temperature on the thermometer scale to actual temperature.
Temperature cycles in the glass bulb of a thermometer harden the glass and shrink over time, a 10 yr old -20 to +50c thermometer will give a false high reading of around 0.7c
Over time, repeated high temperature cycles cause alcohol thermometers to evaporate vapour into the vacuum at the top of the thermometer, creating false low temperature readings of up to 5c. (5.0c not 0.5 it’s not a typo…)
Electronic temperature sensors have been used more and more in the last 20 years for measuring environmental temperature. These also have their own resolution and accuracy problems. Electronic sensors suffer from drift and hysteresis and must be calibrated annually to be accurate, yet most weather station temp sensors are NEVER calibrated after they have been installed. drift is where the recorder temp increases steadily or decreases steadily, even when the real temp is static and is a fundamental characteristic of all electronic devices.
Drift, is where a recording error gradually gets larger and larger over time- this is a quantum mechanics effect in the metal parts of the temperature sensor that cannot be compensated for typical drift of a -100c to+100c electronic thermometer is about 1c per year! and the sensor must be recalibrated annually to fix this error.
Hysteresis is a common problem as well- this is where increasing temperature has a different mechanical affect on the thermometer compared to decreasing temperature, so for example if the ambient temperature increases by 1.05c, the thermometer reads an increase on 1c, but when the ambient temperature drops by 1.05c, the same thermometer records a drop of 1.1c. (this is a VERY common problem in metrology)
Here is a typical food temperature sensor behaviour compared to a calibrated thermometer without even considering sensor drift: Thermometer Calibration depending on the measured temperature in this high accuracy gauge, the offset is from -.8 to +1c
But on top of these issues, the people who make these thermometers and weather stations state clearly the accuracy of their instruments, yet scientists ignore them! a -20c to +50c mercury thermometer packaging will state the accuracy of the instrument is +/-0.75c for example, yet frequently this information is not incorporated into statistical calculations used in climatology.
Finally we get to the infamous conversion of Degrees Fahrenheit to Degrees Centigrade. Until the 1960’s almost all global temperatures were measured in Fahrenheit. Nowadays all the proper scientists use Centigrade. So, all old data is routinely converted to Centigrade. take the original temperature, minus 32 times 5 divided by 9.
C= ((F-32) x 5)/9
example- original reading from 1950 data file is 60F. This data was eyeballed by the local weatherman and written into his tallybook. 50 years later a scientist takes this figure and converts it to centigrade:
60-32 =28
28×5=140
140/9= 15.55555556
This is usually (incorrectly) rounded to two decimal places =: 15.55c without any explanation as to why this level of resolution has been selected.
The correct mathematical method of handling this issue of resolution is to look at the original resolution of the recorded data. Typically old Fahrenheit data was recorded in increments of 2 degrees F, eg 60, 62, 64, 66, 68,70. very rarely on old data sheets do you see 61, 63 etc (although 65 is slightly more common)
If the original resolution was 2 degrees F, the resolution used for the same data converted to Centigrade should be 1.1c.
Therefore mathematically :
60F=16C
61F17C
62F=17C
etc
In conclusion, when interpreting historical environmental temperature records one must account for errors of accuracy built into the thermometer and errors of resolution built into the instrument as well as errors of observation and recording of the temperature.
In a high quality glass environmental thermometer manufactured in 1960, the accuracy would be +/- 1.4F. (2% of range)
The resolution of an astute and dedicated observer would be around +/-1F.
Therefore the total error margin of all observed weather station temperatures would be a minimum of +/-2.5F, or +/-1.30c…
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UPDATE: This comment below from Willis Eschenbach, spurred by Steven Mosher, is insightful, so I’ve decided to add it to the main body – Anthony
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Willis Eschenbach says:
As Steve Mosher has pointed out, if the errors are random normal, or if they are “offset” errors (e.g. the whole record is warm by 1°), increasing the number of observations helps reduce the size of the error. All that matters are things that cause a “bias”, a trend in the measurements. There are some caveats, however.
First, instrument replacement can certainly introduce a trend, as can site relocation.
Second, some changes have hidden bias. The short maximum length of the wiring connecting the electronic sensors introduced in the late 20th century moved a host of Stevenson Screens much closer to inhabited structures. As Anthony’s study showed, this has had an effect on trends that I think is still not properly accounted for, and certainly wasn’t expected at the time.
Third, in lovely recursiveness, there is a limit on the law of large numbers as it applies to measurements. A hundred thousand people measuring the width of a hair by eye, armed only with a ruler measured in mm, won’t do much better than a few dozen people doing the same thing. So you need to be a little careful about saying problems will be fixed by large amounts of data.
Fourth, if the errors are not random normal, your assumption that everything averages out may (I emphasize may) be in trouble. And unfortunately, in the real world, things are rarely that nice. If you send 50 guys out to do a job, there will be errors. But these errors will NOT tend to cluster around zero. They will tend to cluster around the easiest or most probable mistakes, and thus the errors will not be symmetrical.
Fifth, the law of large numbers (as I understand it) refers to either a large number of measurements made of an unchanging variable (say hair width or the throw of dice) at any time, or it refers to a large number of measurements of a changing variable (say vehicle speed) at the same time. However, when you start applying it to a large number of measurements of different variables (local temperatures), at different times, at different locations, you are stretching the limits …
Sixth, the method usually used for ascribing uncertainty to a linear trend does not include any adjustment for known uncertainties in the data points themselves. I see this as a very large problem affecting all calculation of trends. All that are ever given are the statistical error in the trend, not the real error, which perforce much be larger.
Seventh, there are hidden biases. I have read (but haven’t been able to verify) that under Soviet rule, cities in Siberia received government funds and fuel based on how cold it was. Makes sense, when it’s cold you have to heat more, takes money and fuel. But of course, everyone knew that, so subtracting a few degrees from the winter temperatures became standard practice …
My own bozo cowboy rule of thumb? I hold that in the real world, you can gain maybe an order of magnitude by repeat measurements, but not much beyond that, absent special circumstances. This is because despite global efforts to kill him, Murphy still lives, and so no matter how much we’d like it to work out perfectly, errors won’t be normal, and biases won’t cancel, and crucial data will be missing, and a thermometer will be broken and the new one reads higher, and …
Finally, I would back Steven Mosher to the hilt when he tells people to generate some pseudo-data, add some random numbers, and see what comes out. I find that actually giving things a try is often far better than profound and erudite discussion, no matter how learned.
w.
Smokey says:
January 22, 2011 at 8:27 pm
“…says the unemployed dilettante who spends his time defending the indefensible.☺”
As opposed to say Michael Mann the employed expert who spends his time manufacturing phoney statistics.
I’ll take that as a compliment. Thanks.
The insane thing about the entire argument over the distribution of the error in the surface stations is:
This is something that should actually be directly determinable with a cross calibration with satellites from 1978-now.
Not a correlation “Global mean surface temperature vs. Global mean satellite temperature”, but each individual surface stations vs. the satellite estimate for that same lat/long.
As a mechanical engineer, whose 38-year career has been in forensic engineering and metrology (not meteorology), I can truly say I’ve enjoyed reading this thread more than any other I’ve followed in recent times.
At last, someone has pin-pointed the climate change subject [measurement uncertainty] on which I have often been tempted to comment.
I have usually hesitated to raise this issue because I believed that what has developed here would happen: some ‘highly qualified’ people have ended up arguing somewhat emotionally, without being clear about whether they are arguing about ‘a fact’ or ‘an opinion’.
In the above bruising “MARK T vs DAVE SPRENGER” bout, which has now been through several gruelling rounds, I score Mark T a hands-down winner on his main points. Dave fought bravely, but not well. Too many ‘low blows’ and very poor techniques contibuted to his ultimate defeat.
Dave did not need to mention his experience or expertise (i.e. his self-perceived level of ‘authority’) if his factual point was correct and defensible. Mark’s mathematical counter-punches stood up very well against the ham-fisted hay-makers of Dave.
For my own contibution to the debate, I would simply remind readers of Ross McKittrick’s brilliant contibution: he (a statitician) actually reminded me (an engineer) that temperature is an intrinsic variable, whose average is NOT a physical variable, which is something I had ‘learned at school’ but ‘forgotten in my wider world-experience’; the average of two temperatures is not the temperature of anything; it is not even a temperature, it is a statistical concept.
To Mark: ‘Well done’.
To Dave: ‘Study a replay of the bout and pick up some very good boxing tips.
Hoser says:
January 22, 2011 at 8:30 pm
“Well, thanks, but that didn’t address my point. Ya think I believe in CAGW? Nah.”
Sorry. I certainly didn’t mean to imply you were among the CAGW faithful.
I didn’t address the point because I didn’t disagree with it. I don’t believe it’s practically possible to figure out the age and manufacturer of every thermometer used over the last 100 years ever used to make an entry that was swept up into a global database. I think it can be safely said it’s a mixed lot of different brands, quality, and ages. But that’s not a bad thing. If you go down to Walmart and buy one of every different thermometer they carry (probably 50 different ones) and set them all in one place and average the readings you’ll get an accurate precision temperature as a result better than the resolution and accuracy of any individual instrument in the lot (barring having ones that are obviously broken and reading so far off the mark you know it can’t be right). I presume the global network of thermometers over the past 100 years is pretty much just like that.
Mark T,
With regard to your reponse to my post.
Your remarks “Because you don’t know what you’re talking about, either” and “Wow. Ignorance is contagious. That’s all I can say regarding this threads” is alas typical of the level of civility that is unfortunately too common on these sites.
I have a PhD and over 30 years research experience so I actually do know something about experimental errors. In this regard I am unfamiliar with the tactic of personal abuse substituting for reasoned argument.
If you would be so kind, would you explain in simple terms what is wrong ith this analysis.
“And when you are measuring changes in temperature, systematic errors will also cancel out. Take a thermometer than reads 1 degree high. If in 1950 it read 72 F it was actually 71 F. In 2000 it read 74 F it was actually 73F. But the rise is 2F regardless of whether you you correct for the true temperature or not.”
If you have not already read this, it’s highly relevant:
http://kenskingdom.wordpress.com/2010/11/22/what%e2%80%99s-the-temperature-today/
It would be interesting to obtain the code used to extract daily maxima and minima from records that are collected many times a day: and compare them with Hg max and min thermometers.
It is even conceivable that the lack of global warming in the past decade can largely be explained by this type of difference. (Except that it seems to affect satellites too).
Thanks, Ken Stewart.
EFS_Junior says:
January 22, 2011 at 7:30 pm
http://en.wikipedia.org/wiki/Iid
Wow, and it continues.
First of all, not all Gaussian distributions have the same probability distribution. If they don’t have the same variance, or the same mean, then they are not identical. I should note, btw, that the error associated with the minimum gradation is actually uniformly distributed, not Gaussian. Duh.
If you can prove that all of the errors are a) Gaussian, b) with the same mean, and c) with the same variance, then I will believe you, btw. As it stands, based on some of your other comments, I’m guessing you would not even know where to begin. Hey, consult with Dave Springer and maybe the two of you can publish something.
Um, no, that is not what independence means. I’m not even sure how to tackle this one because you clearly do not have sufficient background to understand. Independence simply means that their distribution functions are independent of each other, i.e., F(x,y) = F(x)F(y) and likewise for densities f(x,y) = f(x)f(y), which is analogous to probabilities in which two events are independent if P(AB) = P(A)P(B).
Exactly. Thanks for pointing this out.
I’m getting the impression you’re attempting to learn statistics as you go. Nothing wrong with that, but you missed the point of what this quote said. What you quoted said exactly what I have been saying: if the RVs are i.i.d., then their average will approach the actual expected value (the true mean.) The rate, of course, at which it approaches the true mean is sqrt(N), which I have also acknowledged. But you need to be able to support the assumption of i.i.d. in order for this to work.
How do you know that all errors associated with temperature are zero mean?
QED means you proved something by demonstration when all you did was assume a Gaussian distribution. Pretty silly, actually.
You aren’t even in the right ballpark.
Mark
Willis writes:
So you’re suggesting creating a numerical model of reality and see what the results are? Isn’t that where all the trouble starts in the first place?
My suggestion was buy 50 different kinds of thermometers from WalMart, put them all in the same place, read them as best you can, average the readings, and you’ll get a result where the accuracy and resolution is better than any individual instrument in the whole lot. That’s an experiment, not a model. There’s a big difference. Experiment is what’s lacking in climate science. It’s all models. Like the proverbial Texas cowboy who’s all hat and no cattle.
Whew. What a long discussion.
I think a significant potential error in the record is station adjustments (made much later) to make new instruments smoothly agree with the old ones, given that the old ones may all show an upward drift over time (the glass problem). This would transform a sawtooth error (with a period set by thermometer replacement or recalibration) that contains no long-term trend into a continuous upward slope.
This thread also makes me want to throw up my hands and say we’d be better off using pendulums to measure temperature, using a reversed grid-iron to magnify the coefficient of thermal expansion instead of cancelling it. Counting things (like swings) and measuring time to millisecond or microsecond accuracy is one of the easier things we do. Measuring voltage, current, or the height of a bubble of mercury – not so much. Plus, physicists just love pendulums.
I should first note that I am NOT the Mark from Mark’s view, i.e., I did not post this original thread.
Back to Dave Springer:
First, you have yet to actually reply to any of my questions that are technical/theoretical in nature. I’m guessing that is because you never actually got an engineering degree, correct? You seem to have a complex regarding that, quick to point out how good you are. That’s an interesting psychology, IMO. Maybe you have an AS degree, or a BSEET, but I’m getting the impression you actually don’t have the math background which implies no BS or anything advanced. If you did, I’m sorry, it just does not seem so – I’d expect you to have answers to my questions if you had even one statistics class under your belt.
You warmists are always so enamored with authority, and qualifications. Just the fact that I clearly understand this topic should be sufficient, but alas, you guys need something to attack since you can’t get anybody on the technical.
Statements like these are what makes me believe you don’t have a degree, btw. People that become engineers through hard work but while lacking a solid educational background tend to have issues with us learned types, like a Napolean complex or something. You feel a need to point out that your experience is worth more than my education. Quite frankly, I agree that experience is worth more in the long run, but the math and statistics I learned in school isn’t something people are going to pick up by themselves. And, unfortunately for you I guess, I have both experience and education – experience and education in this exact field, btw. I teach, too, though I can’t say I enjoy it and never more than one class at a time. Extra income and a hassle once a week is what I get out of teaching.
How am I being obtuse? I understand how the LLN works and when you can actually apply it. I’m sorry if you don’t agree, but you’re wrong until you can prove the things I have pointed out – I did my job, now you do yours if you want to have any chance at credibility.
Who I am is none of your business, btw. I don’t need to “hide” anything, but I am smart enough to know that putting my full name on the internet is foolish. I did note that I have listed my basic background over a tAV in the reader background thread, which you are free to peruse, but none of that makes any difference anyway. I could be an uneducated, inexperienced fop for all it’s worth and still be right. The assumption is in the theory for a reason.
Mark
davidc says:
January 22, 2011 at 7:25 pm
Definitely true. The uniform distribution of gradation error is the best you can ever do. Such errors, if they were the only ones, WOULD be i.i.d., btw, because they are purely an artifact of the resolution of the measurement itself, and the LLN/CLT would apply nicely.
Mark
I think the issues here are that statistics is so hard to understand. As humans its hard to realize that error can head downwards when say looking at a trend…
But when you take a large amount of stations and keep the same type of thermometer, the only thing you need to look out for as far as statistical issues is trends that get larger. Lets look at just one comment:
“Another problem comes from taking the average temperature to be halfway between Tmin and Tmax”
This is a problem if you are using temperature for anything but figuring out trends. As in measuring temperature, you can measure it in about 50 different ways, but statistically it will normally create the same trend no matter how you measure it. This is because you use multiple stations and you have numerous data point all over the place. The error over larger periods of time will head down farther and farther.
However, I tried to say this earlier, but maybe was not clear enough about it. If you take stations out over time for instance, its difficult if not impossible to figure out the error this creates. If you add say urban heat effects, this is impossible to difficult to figure out as well.
These trends add error to the actual trends which is something you can argue about when you are talking climate. Observer bias, station removal and other events will also tend to do the same thing. This is difficult in statistics and this is why the field is so difficult to understand.
In a nutshell: we have this comment:
Jeff Alberts says:
January 22, 2011 at 5:53 pm
So, would gradual, but constant, encroachment of urbanization upon the site be considered systematic?
Yes, this is an important systematic change.
Like I was saying before, if you use less stations and less data points, the trend will tend to be wrong by higher amounts, but as you add stations and add other areas, the error will be minimized. I am trying to explain this, and I probably failed both times, but it should be noted that since modern recording of temperatures started…. we have probably warmed by roughly 0.7 C.
Now to say that a certain year is the warmest ever when its so close to another is probably stupid since you are removing data points and adding error like I said previously….. The two years are probably close enough to call the same, and if one year is higher then another, its better then even odds that it is warmer, but you can never say that one year is warmer then another without taking error into consideration.
Where I was trying to go with my first post, is that Metrology is important because it is possible to determine the error using statistics. In addition, it is also possible to check the temperature record for any changes that might be artifacts of measurement error so to speak…the trends do not change, but the error does. As you eliminate bits of noise from data, other trends emerge that might not have been visible previously…
And just a side not, like I was trying to say there are probably an infinite amount of ways we can measure temperature trends and none of them are really wrong as long as:
The method does not change.
The method is applied uniformly.
Weather events are not even considered for climate. (Even one month long events are going to have large amounts of error.) The more data the better.
In conclusion, the method we have now is probably fine. Are there other methods that might be better? Sure, they might show trends better due to different error configurations and allow different noise patterns to rise above the level of detection.
Dave Springer says:
January 22, 2011 at 9:55 pm
Actually, he’s suggesting that you perform a simulation with i.i.d. data to demonstrate to yourself how the errors cancel.
No kidding. Then you’d likely meet the i.i.d. requirement. Assuming the thermometers don’t all have some bias the “average” would probably be closer to the true temperature than any individual reading. If any of them have some bias, however, then your average, while very precise, will not be near the actual temperature.
This analogy does not apply to the global temperature readings. Their error distributions are unknown (perhaps even unknowable.) All this means is that you cannot assume the LLN applies, even if it may actually apply. Nothing more, nothing less, nor have I said anything other. The results may actually be more accurate, but accuracy is not the problem, the results have an increased uncertainty without such knowledge.
The stationarity issue Pat points out is really bad since that means the error distributions may change over time and across temperature readings. The error plot in Mark’s post above indicates a thermometer that does not have i.i.d. errors even in its own data, for example.
No kidding. Models serve useful purposes, however, by enabling insight into a process that is otherwise difficult to test. Models are the fundamental means systems engineers get from initial requirements to design requirements. Assuming Gaussian noise for your temperature data errors is applying a model to your measurement process. Models are inescapable.
Mark
Ben D. says:
January 22, 2011 at 10:24 pm
Very true…
Then you immediately provide a demonstration of said truth… sigh.
Mark
Dave Springer:
“My suggestion was buy 50 different kinds of thermometers from WalMart”
I think it’s an excellent suggestion to have more than one thermometer of each type at each weather station. If they disagree, they should be immediately calibrated. And they should be calibrated regularly even if they agree.
The data from any weather station that does not have records of at least yearly calibration should be treated with suspicion. Of course that would be the majority of weather stations.
Willis said;
“Finally, I would back Steven Mosher to the hilt when he tells people to generate some pseudo-data, add some random numbers, and see what comes out. I find that actually giving things a try is often far better than profound and erudite discussion, no matter how learned.”
I’vd done two numerical experiments (one with randon uniformly distributed noise with 2 < N < 65536, the other with a ~120 year long (Hay River, Canada an inland station)). I've also completed 23 High Arctic Weather Station (HAWS) stations all using the original raw daily records (Canadian HAWS all along the Canadian Arctic coastlines).
The same low frequency temperature trend line always shows up for all 23 HAWS, and are all quite similar in appearence, the bottom line is the HAWS have seen ~3C rise since the early 70's (or an ~4C rise starting from the early 20's).
It does not matter what the thermoneter accuracy actually is as I can take the 0.1C raw data and round it to either 1C increments or even 10C increments, the same low frequency trend line always shows up.
The period of the calculated anomaly is 1951-2010 (N = 60). Integrating the anomaly curves always results in R^2 ~ 0.99, so the low frequency signal is very real, otherwise the integrated anomaly graphs would fluctuate about their mean with no apparent trend lind and R^2 would approach zero.
So as far as I'm concerned the whole thermometer accuracy argument is a red herring and a moot point as far as I'm concerned.
23 HAWS stations all with the exact same systematic errors (note the definition of a systematic error, as defined by the author of the specious uncertainty article, is always +/- sigma, so there can be no bias offset corrections to be made there, as bias offsets are not the subject matter of that specious uncertainty article to begin with in the first place)?
I think NOT!
So we have a large bunch of numbers representing temperature from a bunch of widely spaced thermometers with the readings taken over a year, 50 years ago and we think the uncertainly is say +/- 1deg C on each individual reading
Now we have a large bunch of numbers representing temperature from a bunch of widely spaced thermometers with the readings taken over the last year again with the same uncertainty of +/- 1 deg C.
We’ve taken averages of both sets of readings and one has the average come out 0.5 deg C higher.
Trouble is they are different thermometers with different error sources and aging characteristics. Not only that, they are different numbers of thermometers in both cases and the locations aren’t even necessarily the same???
Yet some people say all this doesn’t matter because there are large numbers of thermometers and readings in both cases and confidently state that it has become warmer because of these readings???
ROTFL!
I’m no longer a fan of any of the instrumental record. Surface temps for the reason above and I’ll place some faith in the satellite record when someone recovers a satellite and recalibrates the platinum RTD and associated electronics on the ground after prolonged exposure to temperature cycling and radiation. This doesn’t detract from the satellites as daily weather monitors but makes them problematical as climate monitors.
Just use biology as climate markers. This integrates all climate change but has its own problems as life adapts and is always expanding to the physical limits of its range.
BTW, Dave Springer. It helps to check the placards of the aircraft you are about to fly or read the POH or Flight Manual. Then you would a) Know what the calibration of the ASI was and b) it wouldn’t matter as you would have the correct numbers to fly by.
It is also my understanding that the folks who run real production lines DO in fact make fancy bar graphs etc to keep an eye on what they are making and fix it before the line has to be shut down.
Alfred Burdett says
——-
This explains well the kind of underlying psychology. However, to be clear, I was not suggesting fraud, but simply the possibility that unconscious factors, including preconceptions about climate change,
——
I don’t think it’s a sensible question because the people who make the measurements likely do not have an opinion about climate change.
Afterall most of the measurements are historical and pre AGW.
The occasional recent one who might knows climate change is real and has no need to fake data.
Of more concern is normal human laziness and incompetence. This and many other issues is why the temperature measurement is done by satellite these days.
Dave Springer says:
January 22, 2011 at 8:44 pm
To be clear, Tyndall did not prove a damn thing about CO2 absorption. His equipment was far too primitive to distinguish between absorption, reflection, refraction, diffusion, scattering or anything else. He incorrectly concluded that all energy missing between the source and the pile in his half baked experiments had been absorbed by CO2. Above all he ignored Kirchhoff’s law and that was his biggest mistake. The conservation of energy falsifies the “greenhouse effect” because as per Kirchhoff’s law that which absorbs, equally emits. This fact is absent from Tyndall’s ramblings and exposes him for what he was.
Anyone who quotes John Tyndall as the man who proved the “physics” of the “greenhouse effect” displays nothing short of sheer ignorance. It is the ultimate in the bogus appeal to authority. John Tyndall was fool and a fraud. Above all he was an insider.
This article is profoundly important.
Also, what Willis said++
I’ve taken more rocks than I care to think about over the issue of what precision to use. One point I’d add: The official guidance of NOAA for years was that if it was, for some reason, not convenient or was impossible to make an actual observation the observer was directed to guess and enter that value on the report. I’d originally linked to a NOAA page with that statement on it, but the AGW Langoliers have had that page erased. Yet the data remain in the record…
So, if you are depending on the Law Of Large Numbers to give you precision of 1/100C as the AGW Faithful believe, then you are making a load of assumptions. Many are illustrated as false in the posting above. But add to that the point that measuring A thing 100 times is different from measuring 100 things one time and we begin to understand the statistical problem. An AVERAGE can be computed to a very fine precision indeed. BUT it has little meaning. IFF the underlaying numbers are +/- 1 F then the average of them can be computed to a very much finer degree, but the meaning of what the actual temperature might have been is not improved. Eyeballs might have been consistently looking up, or down, depending on average height of the observers. Meniscus might have been bulging up or down depending on materials used. How old were the thermometers and their glass? When was each site converted from LIG to semiconductors? What is the aging characteristic of a semiconductor system over decades?
So yes, you can take all those numbers and make an average that can be known to 1/10000 C. But the MEANING of that number is lost. It is not saying the actual temperature is higher by 1/10000 C it is saying that something in the process of recording all the numbers, and we don’t know what is higher by 1/10000 C.
Somehow I’ve not yet found a good way to encode that understanding into words.
If you measured A spot on the planet ( or each spot on the planet) 10 times in the same moment, then that “law of large numbers” increase in precision would MEAN something. But if you measure 1,000,000,000,000 places ONE TIME EACH with a precision of +/- 1 C you can NOT say if things have warmed or cooled by 1/100 C based on the average. You simply do not know in which direction the error terms lie, and to assert that they “all average out” is just another kind of lie, for you do not know.
I lack the skill to make this clear, and for that I am truely sorry. But it is simply not possible to average away the error terms by making a larger number of errors.
(min+max)/2 = Average temperature – yeah, right.
We have a local weather station, sited in a rural location along the road east of Newport. The weather data is displayed in real time on the website isleofwightweather.co.uk, and tabulated data at 5 minute intervals is downloadable in 7 day chunks.
As an exercise, I wasted an hour of company time analysing 25 days of this information. The bit I was interested in was how well taking midway between a days max and min represents a true reading of the average temperature.
Well, would you believe it! No correlation whatsoever! The differences between the “real” average and the (min-max)/2 figure (I really can’t bring myself to say average, and I can’t find a mathematical function name for it) varies between -1.6 and +1.7, a spread of error of 3.3oC. And the errors showed no pattern, -0.1, +0.2, +0.1, +0.0, +1.7, -0.7, +0.1, -0.1, +0.1,….. -1.6, -0.4, -0.3, -0.1, -0.6, +0.1, -0.9, +0.8.
Yet we are supposed to believe these guys know the world’s average temperature to the nearest tenth of a degree!
OMG it’s worse than we thought! I mean, the error 🙂 Metrology is exceedingly important (when you use real world data, that is — you can do without in models). Never enter a lab without it.
Oliver Ramsay:
Thanks for brightening up an otherwise dull morning. I love it! 🙂
SimonJ says:
“(min+max)/2 = Average temperature – yeah, right.
We have a local weather station, sited in a rural location along the road east of Newport. The weather data is displayed in real time on the website isleofwightweather.co.uk, and tabulated data at 5 minute intervals is downloadable in 7 day chunks.
As an exercise, I wasted an hour of company time analysing 25 days of this information. The bit I was interested in was how well taking midway between a days max and min represents a true reading of the average temperature.
Well, would you believe it! No correlation whatsoever! The differences between the “real” average and the (min-max)/2 figure (I really can’t bring myself to say average, and I can’t find a mathematical function name for it) varies between -1.6 and +1.7, a spread of error of 3.3oC. And the errors showed no pattern, -0.1, +0.2, +0.1, +0.0, +1.7, -0.7, +0.1, -0.1, +0.1,….. -1.6, -0.4, -0.3, -0.1, -0.6, +0.1, -0.9, +0.8.
Yet we are supposed to believe these guys know the world’s average temperature to the nearest tenth of a degree!”
Thanks 1. (min+max)/2 = Average temperature – yeah, right.
We have a local weather station, sited in a rural location along the road east of Newport. The weather data is displayed in real time on the website isleofwightweather.co.uk, and tabulated data at 5 minute intervals is downloadable in 7 day chunks.
As an exercise, I wasted an hour of company time analysing 25 days of this information. The bit I was interested in was how well taking midway between a days max and min represents a true reading of the average temperature.
Well, would you believe it! No correlation whatsoever! The differences between the “real” average and the (min-max)/2 figure (I really can’t bring myself to say average, and I can’t find a mathematical function name for it) varies between -1.6 and +1.7, a spread of error of 3.3oC. And the errors showed no pattern, -0.1, +0.2, +0.1, +0.0, +1.7, -0.7, +0.1, -0.1, +0.1,….. -1.6, -0.4, -0.3, -0.1, -0.6, +0.1, -0.9, +0.8.
Yet we are supposed to believe these guys know the world’s average temperature to the nearest tenth of a degree!
Thanks SimonJ. This is precisely the point which I raised with Steve Mosher in a post on a dfferent threada week or two ago. The reply was that over a sufficent period of time (Tmin + T max)/2 – T mean was as near to zero as could be. And anyway it was the trends that counted and the trends in Tave (= (Tmin + T max)/2 and Tmean were always the same.
I asked for any paper that had been published which supported these assertiuons and got no reply.
As a start it might be intresting to plot the trends in Tmax and Tmin separately. Is there any evidence that these slopes are correlated let alone identical?
“Oliver Ramsay says:
January 22, 2011 at 5:36 pm
…….I’m beginning to warm to this notion; two wrongs don’t make a right but millions of wrongs do……”
ROFL! Priceless!