Air conditioners, temperature sensors, and AGW deniers

[Tiggy]

Our resident anti-science AGW denying wingnut has become enamored with posting photos of temper sensors close to heat sources, and challenging anyone to explain them.

OK here goes. The US Historical Climatology Network (USHCN), maintained by the National Climatic Data Center (NCDC) uses a series of 1221 sensors at various places around the country to chart the average US temperature over the last century.

The U.S. Historical Climatology Network (USHCN, Karl et al. 1990) is a high-quality moderate sized data set of monthly averaged maximum, minimum, and mean temperature and total monthly precipitation developed to assist in the detection of regional climate change. The USHCN is comprised of 1221 high-quality stations from the U.S. Cooperative Observing Network within the 48 contiguous United States. An additional data set containing 46 stations for Alaska is also available; however, data for these stations are not adjusted for inhomogeneities as outlined below for the USHCN. The period of record varies for each station but generally includes the period 1900-1995. The stations were chosen using a number of criteria including length of period of record, percent missing data, number of station moves and other station changes that may affect the data homogeneity, and spatial coverage. Included with the data set are metadata files that contain station history information about station moves, instrumentation, observing times, and elevation.

The USHCN knows that not all sensors are placed optimally, some have been encroached by urban growth, so they grade the censors based on their proximity to heat sources:

• Class 1 (CRN1)- Flat and horizontal ground surrounded by a clear surface with a slope below 1/3 (<19deg). Grass/low vegetation ground cover <10 centimeters high. Sensors located at least 100 meters from artificial heating or reflecting surfaces, such as buildings, concrete surfaces, and parking lots. Far from large bodies of water, except if it is representative of the area, and then located at least 100 meters away. No shading when the sun elevation >3 degrees.
  
• Class 2 (CRN2) - Same as Class 1 with the following differences. Surrounding Vegetation <25 centimeters. No artificial heating sources within 30m. No shading for a sun elevation >5deg.
  
• Class 3 (CRN3) (error >=1C) - Same as Class 2, except no artificial heating sources within 10 meters.
  
• Class 4 (CRN4) (error >= 2C) - Artificial heating sources <10 meters.
  
• Class 5 (CRN5) (error >= 5C) - Temperature sensor located next to/above an artificial heating source, such a building, roof top, parking lot, or concrete surface."

Temperature measurements are subjected to strict quality controls

Quality Control, Homogeneity Testing, and Adjustment Procedures

The data for each station in the USHCN are subjected to the following quality control and homogeneity testing and adjustment procedures.

1. A quality control procedure is performed that uses trimmed means and standard deviations in comparison with surrounding stations to identify suspects (> 3.5 standard deviations away from the mean) and outliers (> 5.0 standard deviations). Until recently these suspects and outliers were hand-verified with the original records. However, with the development at the NCDC of more sophisticated QC procedures this has been found to be unnecessary.

2. Next, the temperature data are adjusted for the time-of-observation bias (Karl, et al. 1986) which occurs when observing times are changed from midnight to some time earlier in the day. The TOB is the first of several adjustments. The ending time of the 24 hour climatological day varies from station to station and/or over a period of years at a given station. The TOB introduces a non climatic bias into the monthly means. The TOB software is an empirical model used to estimate the time of observation biases associated with different observation schedules and the routine computes the TOB with respect to daily readings taken at midnight. Details on the procedure are given in, "A Model to Estimate the Time of Observation Bias Associated with Monthly Mean Maximum, Minimum, and Mean Temperatures." by Karl, Williams, et al.1986, Journal of Climate and Applied Meteorology 15: 145-160.

3. Temperature data at stations that have the Maximum/Minimum Temperature System (MMTS) are adjusted for the bias introduced when the liquid-in-glass thermometers were replaced with the MMTS (Quayle, et al. 1991). The TOB debiased data are input into the MMTS program and is the second adjustment. The MMTS program debiases the data obtained from stations with MMTS sensors. The NWS has replaced a majority of the liquid-in-glass thermometers in wooden Cotton-Region shelters with thermistor based maximum-minimum temperature systems (MMTS) housed in smaller plastic shelters. This adjustment removes the MMTS bias for stations so equipped with this type of sensor. The adjustment factors are most appropriate for use when time series of states or larger areas are required. Specific details on the procedures used are given in, "Effects of Recent Thermometer Changes in the Cooperative Network" by Quayle, Easterling, et al. 1991, Bulletin of the American Meteorological Society 72:1718-1724.

4. The homogeneity adjustment scheme described in Karl and Williams (1987) is performed using the station history metadata file to account for time series discontinuities due to random station moves and other station changes. The debiased data from the second adjustment are then entered into the Station History Adjustment Program or SHAP. The SHAP allows a climatological time series of temperature and precipitation adjustment for station inhomogeneities using station history information and is the third adjustment. The adjusted data retains its original scale and is not an anomaly series. The methodology uses the concepts of relative homogeneity and standard parametric (temperature) and non parametric (precipitation) statistics to adjust the data. In addition, this technique provides an estimate of the confidence interval associated with each adjustment. The SHAP program debiases the data with respect to changes other than the MMTS conversion to produced the "adjusted data". Specific details on the procedures used are given in, "An Approach to Adjusting Climatological Time Series for Discontinuous Inhomogeneities" by Karl, and Williams, Jr. 1987, Journal of Climate and Applied Meteorology 26:1744-1763.

5. Estimates for missing data are provided using a procedure similar to that used in the homogeneity adjustment scheme in step three. This fourth adjustment uses the debiased data from the third adjustment (SHAP) and fills in missing original data when needed (i.e. calculates estimated data) based on a "network" of the best correlated nearby stations. The FILNET program also completed the data adjustment process for stations that moved too often for the SHAP program to estimate the adjustments needed to debias the data.

Each of the above adjustments is done is a sequential manner. The areal edits are preformed first and then the data are passed through the following programs (TOBS, MMTS, SHAP and FILNET). At the end of each program, a dataset is produced and the graphs below show the annual temperature departures for each of the adjusted values.

6. The final adjustment is for an urban warming bias which uses the regression approach outlined in Karl, et al. (1988). The result of this adjustment is the "final" version of the data. Details on the urban warming adjustment are available in "Urbanization: Its Detection and Effect in the United States Climate Record" by Karl. T.R., et al., 1988, Journal of Climate 1:1099-1123.

When computing the average US temperature over the century, their relative error bars of all the sensors are factored in and weighed when the final results are tabulated. In other words, poorly located sensors are identified and accounted for in the error range.

Now Glenn has stumbled upon a wingnut anti-AGW site, surfacestations .org. It is run by one individual, Anthony Watts, a disgruntled radio weatherman and fellow AGW denier. Watts claims to have had 'volunteers' do a survey and found a huge number of Class 4/5 (large error) sensors – 69%. Watts can provide no documentation for the validity of this 'survey', no documentation for the expertise of these 'volunteers', or show that their work was fair and unbiased (as most were anti-AGW too). In fact, the Class 4/5 rated sensors make up a small minority of the 1221. Watts (and Glenn) are fond of posting photos of the Class 5 (high error) stations and claiming that they are representative of all the USHCN stations. This is intentionally misleading and nothing but a bit of politically motivated bare-faced dishonesty IMHO.

The NCDC and NOAA are not unaware of Mr. Watts’ claims, and earlier this year commissioned a study to see if his claims had any merit. The results are published here http://www.ncdc.noaa.gov/oa/about/response-v2.pdf. Even if you throw out all the stations Watts claims are bad, and only use the Class 1 and 2 stations Watts admits have valid data, you still get the same overall warming trend in the data only with much lower deviation. (see attached graph)

The data shows Watts’ claims are completely without merit, and belong in the same moonbat conspiracy pile as the “Moon landing was a hoax “ one.

So Glenn, care to tell us why the USHCN data shows a clear warming trend even when your favorite air conditioner located sensors are tossed out?

- T

[lao tzu]

Tiggy, you might want to check out section (5), especially the graph, in sylas' Measurement of historical temperature trends in the USA.

As ever, Jesse

[Tiggy]

Tiggy, you might want to check out section (5), especially the graph, in sylas' Measurement of historical temperature trends in the USA.

As ever, Jesse

Thanks Jesse, I wasn't aware that sylas had already addressed Watt's wingnuttery before, and in some detail. Providing the data from the NASA GISS Surface Temperature Analysis was nice too (see attachments)

I will note that since sylas did his write up in 2008, the NOAA published their analysis (July 2009) and rebuttal of Watt's claims, so at least that data is new.

Makes you wonder why Glenn keeps bringing this 'air conditioner conspiracy' nonsense up when it has already been beaten down so many times before.

- T

[grmorton]

The NCDC and NOAA are not unaware of Mr. Watts’ claims, and earlier this year commissioned a study to see if his claims had any merit. The results are published here http://www.ncdc.noaa.gov/oa/about/response-v2.pdf. Even if you throw out all the stations Watts claims are bad, and only use the Class 1 and 2 stations Watts admits have valid data, you still get the same overall warming trend in the data only with much lower deviation. (see attached graph)

The data shows Watts’ claims are completely without merit, and belong in the same moonbat conspiracy pile as the “Moon landing was a hoax “ one.

So Glenn, care to tell us why the USHCN data shows a clear warming trend even when your favorite air conditioner located sensors are tossed out?

- T

You must of course be aware that the NOAA comparison comes from using their CORRECTED data for both classes of station, not from the raw data. And given that there is a thing called a homogeneity filter, which changes the trend seen in the raw data, that means that the comparison you cite is between two sanitized sets of data. It is that sanitization that I am concerned with.

When people change the trends of the raw data, it is impossible to trust the conclusion comeing out of that procedure, at least it is for this wingnut. I like my science a wee bit purer than that. But I am under no illusion that you guys don't care about scientific procedures.

Let me rephrase this point. When one tilts the trend of raw data to match that of their expectations (the world IS warming after all and so the trend must be upward), then what one gets out of the process is a confirmation of one's expectations, not a confirmation of a scientific fact.

and that is why I don't think that comparison Lao Tzu points you to is worth a bucket of warm spit.

[Tiggy]

You must of course be aware that the NOAA comparison comes from using their CORRECTED data for both classes of station, not from the raw data. And given that there is a thing called a homogeneity filter, which changes the trend seen in the raw data, that means that the comparison you cite is between two sanitized sets of data. It is that sanitization that I am concerned with.

Flappity flappity flappity flap those wings Glenn!

Sorry, but the data showing the Class 1 and Class 2 stations only did NOT require the corrections required of the other stations. That's why they are rated Class1 and Class2. They also did not require corrections from any homogeneity filters because the data was essentially homogeneous to begin with.

Even your wingnut friend Watts says the raw data from them is good and trustworthy.

When people change the trends of the raw data, it is impossible to trust the conclusion comeing out of that procedure, at least it is for this wingnut. I like my science a wee bit purer than that. But I am under no illusion that you guys don't care about scientific procedures.

Let me rephrase this point. When one tilts the trend of raw data to match that of their expectations (the world IS warming after all and so the trend must be upward), then what one gets out of the process is a confirmation of one's expectations, not a confirmation of a scientific fact.

No one changed the trend in the data. But you need some excuse, so it's your tin-foil-hat excuse of deliberate fraud claim again, right?

You're good at making up your own data with your home computer. Why don't you take the Class1/Class 2 chart and show us where you think the real trend line should be.

and that is why I don't think that comparison Lao Tzu points you to is worth a bucket of warm spit.

Why don't you supply the link to the source of your attached graph so we can see what the whole paper says? Sure looks like you cherry picked one particular example that is not applicable to the overall USHCN data set.

Here is a 2006 NCDC paper on the proper use of homogeneity filtering in making temperature corrections. Strangely, it says the exact opposite of what you claim.

Examination of potential biases in air temperature caused by poor station locations.

How about those NASA GISS temperature graphs of the US, the two hemispheres, and global averages? Is that all fraudulent data to correct for air conditioners too?

Your excuses get more pathetic every day.

- T

[Tiggy]

Why don't you supply the link to the source of your attached graph so we can see what the whole paper says? Sure looks like you cherry picked one particular example that is not applicable to the overall USHCN data set.

Never mind Glenn, I found it myself and guess what - the graph you posted was a cherry picked example of the trends for two poorly sited stations, not representative of the entire data set, or the paper it came from.

EXAMINATION OF POTENTIAL BIASES IN AIR TEMPERATURE CAUSED BY POOR STATION LOCATIONS
THOMAS C. PETERSON
Bulletin of the American Meteorological Society, August 2006, 1073-1080

Abstract: Analysis of a small subset of U.S. Historical Climatology Network data does not find a time-dependent bias caused by current poor station siting.

link (pdf)

The paper's conclusion:

CONCLUSIONS. Classically, science progresses by developing hypotheses that lead to predictions that can be evaluated by comparison with physical reality. Each successful prediction adds to the weight of evidence supporting the theory, and any unsuccessful prediction demonstrates that the theory is imperfect and requires improvement or abandonment. Because the number of stations evaluated in this study is quite limited, the results cannot be definitive, but they can supply some evidence in support or rejection of a hypothesis. The results presented here clearly support the theory that, if poor siting causes a bias, homogeneity adjustments account for the biases and contradict the hypothesis that poor current siting causes a warm bias or even any bias in the homogeneity-adjusted U.S. temperature change record. The homogeneity-adjusted time series from the two stations with good siting are a priori likely to be representative of the climate trends and variability of the region, because their data were nearly homogeneous to begin with and a thoroughly evaluated homogeneity adjustment was used to account for the temporary, relatively minor, change in time of observation at one of these stations. Furthermore, the close agreement with the homogeneity-adjusted data from the stations with poor siting make a strong a posteriori case that data from the two stations with good siting are indeed representative of the climate of the area. Slight unrepresentativeness may still arise, however, because climatic changes and variations may differ slightly with altitude, latitude, longitude, and natural land surface. The adjustments at the stations with poor siting were, for the most part, independent of the well-sited stations, but changed their composite time series from being very different to agreeing very well with the time series from the well-sited stations, indicating that the homogeneity adjustments applied to the data from the poorly sited stations compensated for bias-producing

Why do you behave this way Glenn? Why do you constantly cherry pick just the parts you want of papers and data sets to deliberately paint a false conclusion, one completely different that the original author? Mining the data sets for outliers is just as bad and just as disingenuous as quote-mining the author's words.

Do you think that's honest? Do you think that's a Christian thing to do?

I sure don't.

- T

[grmorton]

Never mind Glenn, I found it myself and guess what - the graph you posted was a cherry picked example of the trends for two poorly sited stations, not representative of the entire data set, or the paper it came from.

Are you aware that Peterson is on your side and he thinks that should be done to lots of stations? Indeed, that has been done to lots of station.

this cherry-picked claim is your standard knee jerk reaction. The problem is Tiggy, whenever someone shows you data, you let the Morton's demon on your shoulder slam your eyes shut and it opens your mouth and causes you to yell "CHERRY PICKED." You don't care to look to see if you claim is true---like that claim that methane will overwhelm the oxygen on earth.

Why do you behave this way Glenn? Why do you constantly cherry pick just the parts you want of papers and data sets to deliberately paint a false conclusion, one completely different that the original author? Mining the data sets for outliers is just as bad and just as disingenuous as quote-mining the author's words.

Logically the above is a double question, like "when did you stop beating your wife." It seems that you like lots of illogical arguments, Tiggy.

Do you think that's honest? Do you think that's a Christian thing to do?

I sure don't.

- T

Tiggy, you seem to think that the only Christian thing one can do is to agree with your viewpoint. I didn't realize you had a God complex. How egotistical you are in this. Can't you even conceive that someone might disagree with you? Why do you think that one is unchristian if they disagree with your view of the data? I think this shows that you are once again, like Jorge who thinks that one can hardly be a christian if you disagree with him.

[grmorton]

Tiggy thinks that air conditioners don't affect the temperature. I took Gainesville Georgia and calculated the temperature trend for each day from 1897 to 2000. That gives me a trend for each day's temperature. IF air conditioning is heating the temperature, we should see a rising trend during the summer months, because that is when the air conditioners are on. That is what we see for this town.

100 years ago, there were very few air conditioners around. That means that as they were put into place we should expect to see the summer months rise in temperature faster than the winter. Look at that change in slope for the summer months.

Tiggy, you are simply wrong in your assertions.

[Tiggy]

Are you aware that Peterson is on your side and he thinks that should be done to lots of stations? Indeed, that has been done to lots of station.

this cherry-picked claim is your standard knee jerk reaction. The problem is Tiggy, whenever someone shows you data, you let the Morton's demon on your shoulder slam your eyes shut and it opens your mouth and causes you to yell "CHERRY PICKED." You don't care to look to see if you claim is true---like that claim that methane will overwhelm the oxygen on earth.

You never show all the data Glenn. You show a small hand-picked set and graphs you draw up on your home computer. That's why you never provide links to any primary scientific literature like I do, because you don't get your dishonestly manipulated results from the primary literature. Just like you only show photos of Class 4 and class5 sensors and claim all the sensors are that way.

When will you explain the temperature rise charts for the oceans, and for the rest of the world?

Keep floundering Glenn, show everyone just how low you can sink.

Logically the above is a double question, like "when did you stop beating your wife." It seems that you like lots of illogical arguments, Tiggy.

No, a double question is like "how can we trust climate specialists who get all their data from Class 5 sensors?"

Tiggy, you seem to think that the only Christian thing one can do is to agree with your viewpoint. I didn't realize you had a God complex. How egotistical you are in this. Can't you even conceive that someone might disagree with you? Why do you think that one is unchristian if they disagree with your view of the data? I think this shows that you are once again, like Jorge who thinks that one can hardly be a christian if you disagree with him.

It has nothing to do with agreeing or disagreeing with my viewpoint. It has everything to do with the dishonest way you go about cherry picking data and producing your own fraudulent graphs because your fat ego requires you to 'win'.

Quit being dishonest in your presentations of the data, and I'll stop embarrassing you by posting the real data from the peer reviewed literature. Simple.

- T

[Tiggy]

Tiggy thinks that air conditioners don't affect the temperature. I took Gainesville Georgia and calculated the temperature trend for each day from 1897 to 2000. That gives me a trend for each day's temperature. IF air conditioning is heating the temperature, we should see a rising trend during the summer months, because that is when the air conditioners are on. That is what we see for this town.

When are you going to show us the air conditioners in the deep ocean, in the Himalayan glaciers, and all over the Antarctic?

And sorry Glenn, I'll no longer pay attention to the fraudulent data and graphs you make up on your home computer. Stick to the primary scientific literature or nothing, and put your copy of "How To Lie With Statistics" back on the shelf.

- T

[grmorton]

You never show all the data Glenn. You show a small hand-picked set and graphs you draw up on your home computer. That's why you never provide links to any primary scientific literature like I do, because you don't get your dishonestly manipulated results from the primary literature. Just like you only show photos of Class 4 and class5 sensors and claim all the sensors are that way.

You know, you are correct. I can't show every scrap of data, nor can anyone in a life time show ever scrap of data, nor can any climatologist look at every scrap of data. You have a really stupid criteria here.

[Sparko]

I would think that if scientists wanted to accurately track world temperature rises, they would take their measurements away from all cities and civilization. Cities are huge heat sinks. the average temperature in a city will always be higher than in the country.

[grmorton]

And sorry Glenn, I'll no longer pay attention to the fraudulent data and graphs you make up on your home computer. Stick to the primary scientific literature or nothing, and put your copy of "How To Lie With Statistics" back on the shelf.

- T

Ok, Tiggy, I won't bother with you anymore if you aren't going to pay attention to the data. I thought that was what a scientist was supposed to do, but if you say you won't look any longer at anything contradictory to your beliefs, that is your perogative, but that makes you no better than a YEC. Certainly you are not being scientific.

I will look at any data contradictory to my view point, because, I care about truth. Those who don't; don't. Bye Tiggy

[Tiggy]

You know, you are correct. I can't show every scrap of data, nor can anyone in a life time show ever scrap of data, nor can any climatologist look at every scrap of data. You have a really stupid criteria here.

But you could show all that is readily available instead of just cherry picking the outliers from what is readily available. That would be the honest thing to do, but sadly it's not your thing.

Alternately, you could just post results from the primary scientific literature like I do instead of making up your own bogus graphs at home. But then I guess you'd have no supporting evidence to show, eh?

- T

[grmorton]

But you could show all that is readily available instead of just cherry picking the outliers from what is readily available. That would be the honest thing to do, but sadly it's not your thing.

Alternately, you could just post results from the primary scientific literature like I do instead of making up your own bogus graphs at home. But then I guess you'd have no supporting evidence to show, eh?

- T

tiggy, you said you weren't looking at any of my data any more. Why should I waste my precious time on you?