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.
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@Mark:
I challenge the validity of this: ” a glass thermometer at 100c is longer than a thermometer at 0c.” You’re talking of about a length increase measured in microns.
I also challenge the idea that glass thermometers shorten over time. That seems very unlikely given the longevity of glass structures. It’s far more likely that mercury picks up impurities from the glass to change its coefficient of expansion. But even that is rampant speculation. What is your source?
I challenge the idea that high quality thermometer scales are inherently inaccurate (which is what you imply throughout). I’m sure it’s true for some manufacturers, but, is it true for all?
Also, “a quantum mechanics effect in the metal parts of the temperature sensor ” causing drift sounds like you don’t understand what’s causing it. Can you be a bit more specific?
I trust the accuracy of my lab thermometers to about 0.5C. Nothing you’ve written changes that. If I had a higher quality long thermometer with gradations in degrees F, I would trust that to about 0.5 F.
What is your source for this statement: “In a high quality glass environmental thermometer manufactured in 1960, the accuracy would be +/- 1.4F. (2% of range)”? If the range is -40 to 120 F, then the total error is +/-3.2 degrees at 120F, which seems unacceptably high. If the error is 2% of the reading, that also seems like a low quality instrument. So, how is this “2% of range” working?
I also challenge the idea that error in the thermometer adds with error in the observer. The error in the thermometer is “fixed” and systematic (therefore correctable) while the error in the observer is usually random. If you’re looking for global warming (and who isn’t?) then the systematic error goes away because you are subtracting the baseline of that instrument. Obviously, changing thermometers without overlap (as has been done) will re-introduce this error, possibly compounding it. However, if you change the thermometers enough over time, the error goes away (with certain caveats).
I think the error induced by variable Stevenson screens has to be larger than the error in the thermometer. Is the paint on these screens standardized? Are they just not repainted for years on end (as in the one you picture)? That has to be good for about 2% error.
Great article. I would bet the guys in the Metrology Dept., at one of my old employers would have loved it.
The discussion raises a point I had with some “friends” over at RC. While the discussion was about sea levels, I brought up the subject of instrument accuracy. It was maintained by some there, that by taking a large number of readings, it will all average out, to a null mean.
I pointed out that in the manufacturing process, many products are tested and passed if they are within, say 1.00 deg. of the reqts. In manufacturing, a “batch” or “run” can have a bias, or non-zero mean. So a “batch” of thermometers can have a mean of 0.75 deg., a 3-sigma variance of 0.25 deg., and still pass inspection. Hence, no matter how many readings, or thermometers of that batch, the mean error will still be 0.75 deg.
That caused some interesting discussions.
I’ve wonder for along time whether they could determine the earth’s temperature accurately enough to say it’s increased by 0.7 C over the last 100 years. First of all who was measuring the temperature in the Arctic 100 years ago? How about Antarctica? And how about the temperature over the oceans? Afterall 70% of the earth’s surface is covered by oceans. During WWII the US launched weather ships, but there weren’t many of them. Before there there was very little data for those oceans.
Now add in the UHI effect and all the problems noted in this article and how can anyone make an accurate determine of the earth’s temperature change over the last 100 years?
It’s just aonther example of the government’s use of fearmongering to scare the people into allowing things they would not normally accept, like carbon taxes, cap and trade schemes, etc. Another example is how the fear of terrorism is being used to deprive us of our rights. The TSA now feels free to feel us up at airports or take naked body scans of us. The American people would not have allowed this in the past but now they are so scared and so dumb that they accept it as part of living in the 21st century.
@Dave Springer,
Could you explain or give a link to the ‘change over time’ procedure you mention?
Is it about summing and averaging the ‘first difference’ over a series?
Do they do the whole series in one go? or do they do it in a kind of hierarchy of series; weekly, then monthly, then yearly?
Either way, how do they ensure that they keep the series to an odd number?
It would be interesting to take a random sample of active weather stations that are actually being used (of various types of thermometers) and run a calibration test on them. Then analyse that to see if there is any bias in the errors.
It seems to me that all theoretical discussions about error aside a real world measurement would answer the question.
J. Hall
One of the things which I think the nay-sayers are overlooking is that the official measurement sites have changed, changing the micro-climate. In addition, with
different sensors used over the years, comparison, without looking at the rated
precision of each sensor, or the accuracy/frequency of calibration still skews the
results, even if you are looking only at the change of the data over the years.
Example: during the late 1950s to 1960s, many temperature measurements were
moved from the city to the ‘new’ airport locations. At many small airports, the
site for measuring temperature was about 20 yards away from the weather office.
At the major airports in the U. S. new HO-60 temperature sensors were installed
about 20 feet off the runways, and used for both climate data and aircraft operations.
These sensors had a stated accuracy of +- 1 degree F, and used a relatively large
sensor which had either platinum or nickel wire bobin to measure temperature by resistance change. Then, to cut expenses, the HO-61 instrument was designed, which used the expansion of a fluid to change time of a pulse. Again +- 1 degree F, with both
types of sensors checked about once every 30 days to be within calibration. Note
that the Electronics Technician would use a mercury in glass thermometer, with
an accuracy of +- 1 degree F to measure whether it was in calibration.
With the HO-83 sensor, which used a platinum 100 ohm sensor, the accuracy of
the basic system was stated as +- 1.8 degrees F (+-1 degree C). This was known
to be wrong, as they had to ‘fudge’ temperatures in accepting the system, for
the contractor to meet tthe standard. Also, due to inadequate ventilation, the
system was prone to inaccuracies due to sun on the sensor shield, and wind effects.
Redesign implimented in the ASOS system has improved accuracy back to +- 0.5
degree celsius over the normal temperature range (-15 degrees C to +40 degrees C).
However, with a high current sent through the platinum sensor, self heating places
a strain on the sensor if the air flow diminishes even a little bit, causing higher
temperature readings. Also, the accuracy of the electronics, and the method used
to measure, doesn’t take into account that the sensor readings have a warm bias
at low temperatures, and that, if you are in the minus range, the temperature
average of the readings, is always rounded up if it is 0.5 degree below an
even degree, for Max and Min computations. Another non-disclosed item is
that, even though the dewpoint sensor has been offloaded to another sensor
outside the housing, the dewpoint mirror cooler is left on in all ASOS temperature
sensors, which can, if insufficient ventilation occurs, cause the whole temperature
sensor reading to be biased upward, depending on air flow/wind conditions.
Calibration is normally run each 3 months, against a sensor which has a 4 minute
response, like the HO-1088 sensor (a revised HO-83 sensor with more ventilation),
and is considered by the technician to be in calibration if it is within about +- 2 degrees to +_3 degrees C, although the guiding directive allows up to +- 5 degrees
C as being within calibration. Again, not using the full correction curve which is non-linear below zero degrees C can also bias the readings. The electronics also
have their biases, including the Analog to Digital converter and other constant
current electronics used to set current flow through the sensor.
Just some things to think about. There was an article in one of the AMS journals
which talked about accuracy of the electronics used in temperature measurement
systems a few years ago.
Steven Mosher says:
January 22, 2011 at 3:23 am
The other thing that is instructive is to compare two thermometers that are within a few km of each other.. over the period of say 100 years. Look at the corellation.
98% plus.
Or you can write a simulation of a sensor with very gross errors. simulate daily data for 100 years. Assume small errors. calculate the trend. Assume large errors. calculated the trend.
Result? the error structure of individual measures doesnt impact your estimation of the long term trends. NOW, if many thermomemters all has BIASES ( not uncertainty) and if those biases were skewed hot or cold, and if those biases changed over time, then your trend estimation would get impacted
Result? no difference.
_____________________________________________________________
EXACTLY!
In this case strong spatial/temporal autocorrelation is indeed your best friend.
You can take any temperature record (of sufficient duration, say 60-120 years) and round the raw 0.1C temperature precision records (e. g. Canada HAWS) (~ 10-bit resolution) to either 1C (~ 7-bit resolution) or 10C (~ 3-bit resolution) temperature readings and get the exact same low frequency trendlines (up through 6th order polynomial trendlines in Excel 2010).
The same is true for monthly or yearly averages and reporting them to 2-3 decimal points, even though the actual readings are only to the nearest 1C (errors in readings are indeed uniform with 0.5 high and 0.5 low). Remember we are using N = 60 (or 30) or N = 730 (or 365).
And no, temperature uncertainty does not vary with sigma, but varies as sigma/SQRT(N), regardless of the amount of specious handwaving that ensues in E&E.
Both of these can be easily confirmed in Excel 2010 (or any other analysis SW for that matter). Note that all readings should be converted to Kelvin/Rankine (and than back again to C/F) so as not to, for example, conflate a mean of 0.1C with a mean of 0.0C (0.1/0.0 = n/0 = infinity issues).
Steven Mosher
” the error structure of individual measures doesnt impact your estimation of the long term trends. NOW, if many thermomemters all has BIASES ( not uncertainty) and if those biases were skewed hot or cold, and if those biases changed over time, then your trend estimation would get impacted. Result? no difference. ”
This is only true if the errors are normally distributed. The idea that sampled and averaged readings maps to a normal distribution owing to CLT and therefore so do their errors, is a nice theory — assuming things are iid and stationary. But are they?
This situation reminds me a lot of the fallacy of Black Scholes. Black Scholes asserts that for any financial option of sufficient liquidity, an interest rate can be quantitatively found that complete neutralizes risk. The problem is that small amounts of real world messyness completely destabilizes the risk equation. In the Black Scholes case, something called matching friction (a very real world issue) is among the culprits. In our case, I suspect that things like instrument drift, UHI, and TOD adjustments, infilling, etc. completely invalidate the idea that CAM neutralizes the aggregate error.
I mentioned this before in the previous thread on this topic, but in all the down-in- the-weeds discussion on this thread, one very important aspect of this lack of adequate instrument control has been neglected. Specifically, one of the primary reasons for having traceability to National or International standards, and a regular calibration schedule, is for legal and liability reasons. This data is being used to drive many industries to change the way they do business, manufacture product, etc. due to regulatory and contractual requirements put in place that may be based on suspect data. This results in added costs for everyone downstream.
If the data resulting from these instruments cannot be trusted within known uncertainty as a result of formal and traceable calibration and management then those who own that data may be at risk for substantial liability claims. If I were a lawyer I would absolutely be looking into this.
Does anyone really think that climatologists who claim to be able to measure temperatures using tree rings are concerned in any way about accuracy and precision of modern thermometers? Except of course when the two records are juxtaposed and then they have to ‘hide the decline’ as they have NO correlation in value or in trend.
And of course – to stop everyone running down this rathole…
Atmospheric Temperature does NOT EQUAL Atmospheric Heat Content.
Then entire claim of ‘greenhouse gases’ (sic) causing [global warming|Climate Change|climate catastrophe|climate disruption|tipping points] is based on the hypothesis that these gases trap heat in the atmosphere. To show this is the case the climatologists measure atmospheric temperature
BUT
Atmospheric Temperature does NOT EQUAL Atmospheric Heat Content.
This reality remains the same however accurately you quantify the incorrect metric as it ignores the huge effect on atmospheric enthalpy from water vapor.
The heat content of the Earth is far more accurately measured by measuring the temperature of the oceans as ocean temperature is closely equivalent to ocean heat content and the top 2 or 3 meters of ocean holds as much heat as the entire atmosphere.
So while all the media and climatologists are leaping about saying how this year average atmospheric temperature was almost the same as that in 1998 – the seas are getting colder
http://weather.unisys.com/archive/sst/sst_anom-110116.gif
Accuracy and precision are important – but the first thing to ensure is that the correct metric is being quantified.
Has anyone investigated the possible impact of observer preconception bias?
In particular, does positive bias in temperature readings rise and fall with belief in AGW?
Would this not be a worthy topic for investigation.
Don’t want to rain on everybody’s parade, but there’s a problem here.
I’m looking a a plot of 12 Greenland temperature records. They all show the same trends (flat from 1890-1920, 3C of warming from 1920-1930, 2C of cooling from 1930-1990, 2C of warming since).
Now I’m looking at 25 records from Northern Europe. They also show the same trends (flat from 1890-1920, 1.5C of warming from 1920-1940, 1.5C of cooling from 1940-1970, 2C of warming since. They also all show the same 1.5C “spike” in the early 1970s, and records in the WWII war zone all show the same 2C of cooling in 1940-42.)
Now I’m looking at 9 records from in and around Paraguay. Again they show the same trends (about 1C of cooling between 1945 and 1975 and flat thereafter.)
Now I’m looking at 29 records from the Sahara. They show the same trends too (flat to 1975, 1C of warming since.)
Now I’m looking at 11 records from the Central Pacific. Again, the same trends (1C of cooling from 1945-1975, 0.5C of warming since).
Now I’m looking at 11 records from Western Australia. Once more they all show the same trends (no warming at all since 1900).
I don’t have an exact count, but I would guess that 70-80% of the air temperature records in the global data base show the same overall trends as the records around them. We wouldn’t get this result if the temperature records really were seriously distorted by reading errors or other biases.
With this big of a margin of error, are any global temperature records scientifically useful? Before the AGW scare, what were all these temperature readings done for?
What about the time of day when the thermometers are checked? Do modern thermometers automatically record highs and lows, or do they rely on humans showing up at specific hours and minutes?
It seems fromn this article that, theoretically at least, all the claimed warming over the past century could be nothing more than homogenized guesswork. How can James Hansen spend years arbitrarily adjusting and re-writing the historical temperature records to tiny fractions of degrees, over and over again, when the thermometers themselves don’t come anywhere close to such alleged precision? Is it now accepted scientific practice to just make up adjustments and “corrections” and send out press releases about ones new alarming discoveries?
Dave Springer wrote:
“Accuracy of thermometers matters hardly at all because the acquired data in absolute degrees is used to generate data which is change over time. If a thermometer or observer is off by 10 whole degrees it won’t matter so long as the error is consistently 10 degrees day after day – the change over time will still be accurate.”
The change will only be accurate if the thermometer itself does not change with time but Mark pointed out that they do change when he wrote “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.”
Dave Springer also wrote:
“This might be interesting in an academic way but isn’t productive in falsifying the CAGW hypothesis. The instrumentation and observation methods are adequate and trying to paint them as less than adequate only appears to be an act of desperation – if the job is botched blaming the tools is no excuse.”
The purpose of measurement should be to discover what is happening. Accuracy and reliability should be prime considerations irrespective of your views on the CAGW hypothesis.
Dave Springer says:
“The instrumentation and observation methods are adequate and trying to paint them as less than adequate only appears to be an act of desperation – if the job is botched blaming the tools is no excuse.”
In other words, everything averages out over time and over the number of stations, so individual accuracy is unimportant.
So then, why have Setevenson screens at all? Why worry about accuracy? Just any old thermometers in any random locations will eventually average out, and the temperature and trend will be clear.
Ridiculous. Accuracy matters.
And please try to explain why the “adjustments” to the temperature record always show either higher current temperatures, or lower past temps – making the rise to the current temperature look scary. Current temperatures are never adjjusted downward. What are the odds, eh?
The answer, of course, is too much grant money and too much taxpayer funding of NOAA, IPCC, GISS, etc.
Money raises adjusted temperatures.
One method of analyzing the instrumental errors is to find an independent instrument and do a formal cross-calibration.
The satellite record is independent. But the bulk of the comparisons are ‘correlation studies’, or compare the total surface record with the satellite record. That isn’t the same thing, and of course they’re “well correlated.”
But a cross-calibration allows the actual quantization of the uncertainties in the errors. Site-change errors, and other errors that occur during the (limited!) period of overlap should even be recognizable and correctable once one has a grasp of the relationships.
David S wrote:
“During WWII the US launched weather ships, but there weren’t many of them. Before there there was very little data for those oceans.”
On the contrary British ships had been making and recording weather observations since the 1780s. It is estimated that there are about 250,000 surviving log books containing some meteorological data in Britain and there is a project (still in its early days) to extract the data from them. See the website below for details.
oldWeather
http://www.oldweather.org/
I particularly like this quote from the page on “Why Scientists need You”.
“The Old Weather project isn’t about proving or disproving global warming. We need to collect as much historical data as we can over the oceans, because if we wish to understand what the weather will do in the future, then we need to understand what the weather was doing in the past.”
I’m sure glad that the manufacturers of car, planes, and trains don’t take Mr. Springer’s attitude toward instrument error. I wonder if Mr. Springer would be comfortable flying in a Boeing 777 that was built according to his logic.
Dave Springer and Steven Mosher own a red herring farm in East Anglia, UK. In the engineering world, it is well understood that to be able to control a variable, first one must be able to measure it. “Weather”-station thermometers were developed to measure “Weather” to the satisfaction of “Weather” forecasters, pilots, ship captains, and other concerned with what the “Weather” would be doing in the coming hours or days.
Attempting to use this data to determine “Climate Change” is ludicrous and meaningless. To control “Climate Change’ we must be able to measure “Climate Change.” We probably only started measuring this hugely variable, non-linear, non-coupled phenomenon in the late ’70s with satellites, well, not counting Farmer’s Almanac.
Ice records from Greenland clearly show that the Earth has been far warmer than now for most of the last 10,000 years, from the Oxygen Isotope ratios in the ice. This is not controversial. These so-called “Climate Scientists” are frauds, every single one, all know this, none ever ever ever says it out loud…
I think that Steve Mosher is mostly correct – the trend is not as much a problem, but there remains a significant issue w.r.t. bias.
What this means is that the overall increase in T since 1850 is likely to be about 0.7C +/- a small fraction of that number, but the actual T at any point in the series remains accurate to only within +/- 2C.
As has been noted above, that the world is likely warmer today than it was 160 years ago is not in dispute. There are, after all, independent measures of this such as receding temperate latitude alpine glaciers. Making claims as to what years are warmer or colder (or ranking them) though is laughable given the wide uncertainty.
Stephen Mosher and a few others…
You are making a typical mistake by convolting Central Limit Theorem with the Limit Of Observability.
What Mark is talking about here is that any thermometer measurement has a limit of observability of T ±1.3°C. That means that the result has EQUAL PROBABILITY of existing anywhere in that range. We do not know what the absolute value of temperature is. All we can say is that it exists in a range.
The way you go about improving the accuracy is by reducing the limit of observability either by calibrating against a more accurate device or improving the scale measurements on the current device (as in employing Vernier scales) and characterising it.
After all that is what the field of metrology is all about.
So any temperature measurement and especially a temperature anomaly is subject to the same ±1.3°C limit of observability (which is often just called an “error” or an accuracy error)
To say that random errors as per CLT even out is something completely different. That applies to a theory that says that on average a result will most likely be in a normal distribution within the given observable range…
But this is an extrapolation. It does not account for real characterisation which often show step wise shifts and drifts. Exactly as Mark was talking about. To then use this as a basis for an average temperature record TREND is another extrapolation…in this case 2 strikes and you’re out.
As per your example if one thermometer had a recorded limit of observability of ±1.3°C and 100 or so others had one of 0.1°C then the average observability or measurement limit could fairly be approximated as 0.1°C…The larger error wouldn’t really make a dent.
But if all thermometers have an average a limit of observability/measurement of ±1.3°C then all anomalies and absolute temperature readings are subject to this error.
Then, to paraphrase the words of Richard Feynman, “I think you have a real problem” with the temperature trend.
And on one last note. This goes to show once again the lure of theory over cold hard experimental reality. A lure that is beaten out of metrologists, or at least the ones I know at NPL in the UK.
I appreciate that the ugly nature of the limit of observability/measurement can be unpalatable for some. But that’s just the way it is.
Written down records of temperature..
What time of day? No assurance of true Max or Min.
Anything prior to WWII or the ’60’s probable garbage.
Max
JDN says:
January 22, 2011 at 8:05 am
@Mark:
I challenge the validity of this:
See National Institute of Standards and Technology.
http://www.temperatures.com/Papers/nist%20papers/2009Cross_LiG_Validation.pdf
Interesting comment in this article is that mercury or organic filled thermometers should not be used horizontally as this exposes them to gravity problems and column separation, and they need more frequent calibration as they can drift higher as a result.
I wonder about how accurate the readings are in some areas – read NOAA instructions: http://www.nws.noaa.gov/om/coop/forms/b91-notes.htm
In the article below, it is recognized that the glass changes over time and the you get column separation from time to time and the observer needs to correct separated columns. I note in the photo that the alcohol thermometer is sloped up and the mercury thermometer is sloped down, the latter being contrary to recommendations as a downward slope promotes column separation. Note other sources of error in the article.
http://www.wmo.int/pages/prog/www/IMOP/publications/CIMO-Guide/CIMO%20Guide%207th%20Edition,%202008/Part%20I/Chapter%202.pdf
Both ordinary thermometers and maximum and
minimum thermometers are always exposed in a
thermometer screen placed on a support. Extreme
thermometers are mounted on suitable supports
so that they are inclined at an angle of about 2°
from the horizontal position, with the bulb being
lower than the stem.
So unless things have changed, the thermometer in the photo at the top of this article is installed incorrectly?
In all the comments to this point there is only one comment mentioning relative humidity measurement. One other comment mentions relative humidity. It seems to me that if we are to measure temperature for the purpose of estimating global warming, we should be looking for the temperature of dry air at sea level, or the heat contained in one cubic meter of dry air at 1000 mbar. Of course with appropriate 1 sigma error bars for each calculation.
On another note, I see graphs all the time with varying vertical scales expressing temperature anomaly. Seems to me that these graphs should always use the same range such as +- 2 deg C so that the visual differences are apparent.
Over all, I don’t see much change in the global climate during my lifetime (I’m 76).
Dave Springer says, “the error from age/drift is averaged out because there is an equal distribution of old and new instruments”.
Not necessarily. You set up a new program around 1900 and purchase hundreds of new thermometers. That’s an approximation of governments suddenly deciding to begin weather observation on a large scale. Over time you expand your program and replace some of your thermometers. When does the thermometer age distrubution reach equilibrium? We don’t know.
The age distribution depends on how long the thermometers stay in service. As a first approximation, the average age might be half the age of the oldest themometers. If your age error is .7 C at 100 years, and your average age of thermometers is say 50 years you will have a trend component due to systematic error of .35 C.
Here is where it might get interesting. If you do a temperature adjustment in your analytical procedure to make the new readings match the old readings, then you artificially make the new thermometers read the same as the old ones. That correction potentially skews the distribution so it never reaches equilibrium. Your collection of thermometers effectively just gets older and older no matter whether you replace them with new ones or not.
Am I missing something?
Here is what NOAA say about the temperature trend (http://www.ncdc.noaa.gov/oa/climate/globalwarming.html):
“Global surface temperatures have increased about 0.74°C (plus or minus 0.18°C) since the late-19th century, and the linear trend for the past 50 years of 0.13°C (plus or minus 0.03°C) per decade is nearly twice that for the past 100 years.”
After reading this metrology post, you just have to laugh. I prefer laughing to bleeting.