Note: See update below, new graph added.
There’s a new paper out by Dr. Edward Long that does some interesting comparisons to NCDC’s raw data (prior to adjustments) that compares rural and urban station data, both raw and adjusted in the CONUS.
The paper is titled Contiguous U.S. Temperature Trends Using NCDC Raw and Adjusted Data for One-Per-State Rural and Urban Station Sets. In it, Dr. Edward Long states:
“The problem would seem to be the methodologies engendered in treatment for a mix of urban and rural locations; that the ‘adjustment’ protocol appears to accent to a warming effect rather than eliminate it. This, if correct, leaves serious doubt for whether the rate of increase in temperature found from the adjusted data is due to natural warming trends or warming because of another reason, such as erroneous consideration of the effects of urban warming.”
Here is the comparison of raw rural and urban data:
And here is the comparison of adjusted rural and urban data:
Note that even adjusted urban data has as much as a 0.2 offset from adjusted rural data.
Dr. Long suggests that NCDC’s adjustments eradicated the difference between rural and urban environments, thus hiding urban heating. The consequence:
“…is a five-fold increase in the rural temperature rate of increase and a slight decrease in the rate of increase of the urban temperature.”
The analysis concludes that NCDC “…has taken liberty to alter the actual rural measured values”.
Thus the adjusted rural values are a systematic increase from the raw values, more and more back into time and a decrease for the more current years. At the same time the urban temperatures were little, or not, adjusted from their raw values. The results is an implication of warming that has not occurred in nature, but indeed has occurred in urban surroundings as people gathered more into cities and cities grew in size and became more industrial in nature. So, in recognizing this aspect, one has to say there has been warming due to man, but it is an urban warming. The temperatures due to nature itself, at least within the Contiguous U. S., have increased at a non-significant rate and do not appear to have any correspondence to the presence or lack of presence of carbon dioxide.
The paper’s summary reads:
Both raw and adjusted data from the NCDC has been examined for a selected Contiguous U. S. set of rural and urban stations, 48 each or one per State. The raw data provides 0.13 and 0.79 oC/century temperature increase for the rural and urban environments. The adjusted data provides 0.64 and 0.77 oC/century respectively. The rates for the raw data appear to correspond to the historical change of rural and urban U. S. populations and indicate warming is due to urban warming. Comparison of the adjusted data for the rural set to that of the raw data shows a systematic treatment that causes the rural adjusted set’s temperature rate of increase to be 5-fold more than that of the raw data. The adjusted urban data set’s and raw urban data set’s rates of temperature increase are the same. This suggests the consequence of the NCDC’s protocol for adjusting the data is to cause historical data to take on the time-line characteristics of urban data. The consequence intended or not, is to report a false rate of temperature increase for the Contiguous U. S.
The full paper may be found here: Contiguous U.S. Temperature Trends Using NCDC Raw and Adjusted Data for One-Per-State Rural and Urban Station Sets (PDF) and is freely available for viewing and distribution.
Dr. Long also recently wrote a column for The American Thinker titled: A Pending American Temperaturegate
As he points out in that column, Joe D’Aleo and I raised similar concerns in: Surface Temperature Records: Policy Driven Deception? (PDF)
UPDATE: A reader asked why divergence started in 1960. Urban growth could be one factor, but given that the paper is about NCDC adjustments, this graph from NOAA is likely germane:
http://www.ncdc.noaa.gov/img/climate/research/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif
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Dr. Long’s paper is intriguing, but meaningless. First, it would be trivial to obtain his result by cherry-picking a rural station in each state with the largest adjustment in temperature (downward in the past) and possibly an urban station with a relatively small adjustment. Unless he finds a way to systematically select rural and urban stations, Dr. Long hasn’t proven anything.
Second, some adjustments to the temperature record may show a rural bias AND STILL BE CORRECT. For example, there is a problem called “time of observation bias” that occurs with the min/max thermometers that provide the bulk of our temperature data. Stations that are read in the morning almost always record the high from a single daily temperature cycle (which usually occurs about 18 hours earlier), but the low they record may be the low for the current cycle (a few hours earlier) or a previous cycle (recorded about 24 hours earlier). Stations that are read in the afternoon almost always get the low from the current cycle (about 12 hours earlier), but may record a high for the current cycle (from a few hours earlier) or the previously cycle (recorded about 24 hours earlier). Morning records (with two opportunities to record a low) are on the average lower than evening records (with two chances to record a high.) Being a skeptic, I downloaded a month’s worth of real hourly data for one random site, and found that the problem is real: You really do get a significantly different average high, low and mean temperature for a month when you record minimum and maximum temperatures at the most common reading hours of 7:00 am or 5:00 pm. (0.78 degC cooler at 7:00 am vs 5:00 pm). Averaging the readings for every hour of the month (the “true” average temperature) gives a number that is between. Tom Karl’s accessible paper on this subject (http://ams.allenpress.com/archive/1520-0450/25/2/pdf/i1520-0450-25-2-145.pdf) shows that this time of observation bias depends greatly on location and month. He doesn’t give an average value for the 78 stations he studied, but I’d estimate from his information that the changing from 5:00 pm to 7:00 am readings introduces a cooling bias of 0.5-1.0 degC. Karl develops a method for correcting for time of observation bias and shows that it works reasonably well for 28 other sites. Unfortunately, US stations records show that 30% more sites are recording data in the morning instead of the evening near the end of the 20th century than near the beginning. When considering warming of around 0.5 degC, the adjustments for a 0.5-1.0 degC time of observation bias at 30% of stations is a significant factor.
I don’t know if Karl’s methodology for correcting time of observation bias (TOB) has been independently validated or “audited” by a climate skeptic. (It was published in 1990, before most of the frenzy about AGW.) I don’t know how or if Karl’s method has been implemented in GISS adjustments to the temperature record, but they reference the paper. (Do they use real station metadata, or make assumptions based on discontinuities in the temperature record? Obviously only one net TOB adjustment is possible for any station.) However, I’m forced to be more skeptical about Long – who doesn’t say a word about the possibility of that TOB bias could account for his results – than about GISStemp.
We obviously need a national and world climate database with raw data, multiple types of adjustments (with references, validation and published code), and perhaps more than one adjusted temperature (to address controversial problems like adjusting for UHIs). If two equally validated methods for TOB-adjustments give significantly different results, then we need both. That record can’t be in the scientific fiefdom of Drs. Hansen, Schmidt, Jones, or any other save-the-planet or save-the-economy advocate for or against climate change policy.
Automated weather stations show values of
-4 C , O C and -9 C .
The other nearest 20 manned stations show values
from minus 35 C to minus 40 C .
And those 20 will likely be discarded for various reasons.
http://www.ogimet.com/gsynop.phtml
under Zona geográfica
click on
“Canada este”
and then on
“Temp. mínima en 24 horas ”
and then click
“ver”
————-
upper left —
click on map numbers to see stations
names and details —
The automated stations are–
-4 “Rampart river automated reporting”
-9 “Haldane river automated reporting”
0 “Clut Lake automated reporting”
the automated stations are
likely buried in snow or underwater.
Yea where is Robert and the self styled stat guru Tom P ???
Even though rural stations are “at the outer boundary of a small community whose population does not exceed a small multiple of a thousand residents” many of these areas may have grown and suffer increasing heat island effect.
To: KevinM (07:21:26) :
I am shocked at how many have failed to realize that Steveta_uk’s comment was sarcasm.
Get a sense of humor, people
Apologies especially to Seveta_uk, for my lack of humour. I’ve heard so much stuff like this, meant for real, in this “post modern” “ethical relativistic” “true for you but not for me” society – or used for fogging – that
I’ve forgotten how to laugh! Not just a colonial afflicion btw. Maybe AGW kills our sence of humour? Its all the fault of CO2!! Sorry tho’
James Chamberlain (12:21:12) :
“Does anyone miss Robert?”
I don’t. But it was interesting having someone commenting 12 – 15 times a day who, IIRC, was never right about anything.
Suggestion: Repeat this study by
-dividing all thermometers in NCDC/USA into two groups: Urban/rural
using some well-defined proximity to population center criterion.
This classification does not need to be perfect.
-Do several runs, on each run picking 50 from each group randomly with a
good random number generator
-If the urban/rural dichotomy is real, it should clearly stand out in most of
the runs.
-Average a sufficient number of runs to get a conclusive result.
I think this emulates a double-blind study.
Frank (13:07:13) :
Dr. Edward Long has sucessfully highlighted inconsistency in the NDCD raw to adjusted data set.
My own search into that area does nothing but support his findings.
Dr. Tom Karl has left a lot of unfinished business documenting the impact craters in the observational records from his NCDC site, which is another story that needs telling.
Have you, or are you planning on, digging into data sets of your own selection?
I highly recommend it. Nothing quite like independent confirmation.
Why is anyone remotely surprised by the result of this paper? Hasn’t it been obvious for a long time now that AGW is fiction and that this is how the team created and maintain it? This is the way to continue the attack on the alarmists, through the temperature records, the UNADJUSTED temperature records.
I am a geologist living on a farm in truly rural Illinois about 50 miles from St. Louis. The UHI effect between where I live and St. Louis is readily apparent and discussed by meteorologists every evening (“temperatures will be lower in outlying area”). Since the 50’s the St. Louis metro area has both grown rapidly, and greatly expanded in area covered by buildings and pavement. With this has grown the area enveloped by the associated heat island. This should not be a surprise to anyone, let alone climate scientists. I would also expect that the temperature within the core of the UHI has commensurately increased through time due to increased building, paving, traffic, etc.
In a similar vein, along with the construction and development impacting the UHI, what has been the result of diminished particulate matter in major cities? As recently as the 50’s and early 60’s we would drive into a very dirty, smoky and smoggy St. Louis. The clean air act worked wonderfully to clean up this portion of particulate air pollution. Coal, once widely used not only in large manufacturing plants, but also for home heating is now mostly a thing of the past. Reducing airborne particulate matter must have allowed for a relative increase in local temperatures in the following decades, again positively effecting UHI. I also witnessed this effect in Cork and Dublin, Ireland with the introduction of smokeless peat and coal. The impact was quick and dramatic. Within one year the cool season air in these cities went from thick, acrid, smoky and smoggy to clean with amazing reductions in smoke and smog. Sunlight could now penetrate where it could not before. This must have had an impact on autumn, winter and springtime temperatures. How is this accounted for?
My question, with all of the discussion of what is the UHI effect, have there been no systematic studies of temperature changes around different size cities and truly rural (no significant towns for 5 miles, no asphalt, white barns, concrete, etc.). Even more significant would be to continue such a study over time, which could then be used to track comparative changes against ongoing development. I seriously question the use of any temperature data from any metropolitan area, corrected or not, unless your purpose is to observe changes in UHI. Do not contaminate good raw data with manipulated data. Why use it if you do not need to?
My observation, living in the country (over 5 miles from any town of over 500 people) in Illinois, North Dakota, and Texas, and having observed rapid development not only in major metropolitan cities, but also towns of even a few thousands of people, is that there is an increasing UHI effect even in modest size towns of a few thousand people, especially those such as Brenham, TX (recently discussed as a “rural” setting) which have seen rapid growth as compared to the surrounding countryside. Why not restrict point forward usage to strictly rural sites? I have observed one such rural weather station, located approximately 10 miles west of Bottineau ND. I would trust this stations record as it is in open countryside, in a grassy roadside about 30-40 feet off of a two lane highway. While the overall coverage of such stations would not be as great as otherwise, at least one could not argue over methods, site issues and proper corrections. A true base case could be established.
There seems to be some assumptions going on in the selection of 1 station from each State.
Some people seem to assume that the selected thermometer is measuring the temperature of that State. It isn’t. All it is measuring is the little sleeve of air at the bottom of the thermometer bulb. What you have in effect is tiny “pin-pricks” of measurement scattered throughout the CONUS. There is a greater concentration in the N-E (smaller States) but the entire country is covered.
Yes, it would be nice to have the pin-pricks evenly spaced – this is probably the subject of a grant application ( 😉 ) but this does not alter the fact that those thermometers will not correlate with each other due to local phenomenon. It has to be a truly nationwide event (like the recent snow) for them all to rise or fall.
I can never understand why glaciers calving is used as proof of global warming. (Well apart from *everything* is used as proof of global warming.)
Do you have fingernails?
Have they ever snapped off at the end?
Was this because they were too long, or because they were too short?
Re: Steveta_uk (Feb 26 00:38), If a believer moves from the city to the countryside, does he take his hot air with him?
We are only seeing AUW = Anthropogenic Urban Warming
not AGW
Dave McK (12:47:05) :
UHI effect seems to act as a simple thermal mass, stabilising local variability into a narrower range and a slightly higher mean.
It’s a lot more complicated than that.
First of all, UHI affects local albedo. Not only via its colour (on a number of different wavelengths), but by the number of reflections a wave must travel before heading off out of the vicinity.
Urban areas are also drier than rural ones. A mature oak can dump a ton of water a day. That is a lot of energy to make that phase change with much less left to heat things up. A concrete building on the other hand just absorbs solar energy by getting hot.
Then there is the thermal mass on top of that.
It has reached the stage that no matter how much evidence is released showing that the AGW thesis is a hoax and a fraud, nothing will change, or at best there will be a very slow process of unraveling the truth, which may take decades, by which time it will be far too late to stop the introduction of new taxes and false green technologies that will cost far more yet deliver no appreciable benefit to the climate. There is only one way to avoid all this is to charge the leaders of the AGW hoax with fraud and force them to defend themselves in the courts. Hopefully, with all the evidence that’s available they could be found guilty, with appropriate punishments allocated to them.
Frank (13:07:13) :
Dr. Long’s paper is intriguing, but meaningless. First, it would be trivial to obtain his result by cherry-picking a rural station in each state with the largest adjustment in temperature (downward in the past) and possibly an urban station with a relatively small adjustment.
Dr. Long picked 2 of the best sites in Texas, Beeville and Corpus (according to surfacestation.org).
Speaking of observational records and raw data:
http://www.robertb.darkhorizons.org/WhatUpWithThis.JPG
Highlighting 5 missing months in a 12-month span no more than 7 years ago and a 30 year gap to stick in anybody’s statistical craw.
Does that bother anyone? It bothers me.
If I were the head of NCDC and my records had things like that going on in them, I’d be taking names and asking a lot of questions.
I surely would not be sitting still or putting up with it.
Steveta_uk, if that was pure satire, you sure sucked many in. It’s just that in very similar conversations, they are serious!
Not far from my home is a rural airport that has a temperature record going back to 1908 and is still functioning. The airport ceased it’s daily return flights to Sydney back in 2002 and the only change since then is they’ve added a new trailer park which is still well away from the measuring station as you can see in this picture.
http://users.tpg.com.au/johnsay1/Stuff/casino_airport_1.jpg
The airport is well away from the small town of Casino.
http://users.tpg.com.au/johnsay1/Stuff/casino_airport.jpg
I downloaded the max mean and the min mean from BoM and created a Mean temperature chart – it says it all really.
http://users.tpg.com.au/johnsay1/Stuff/Casino_temp.jpg
This is worse than we thought.
On the up side the US rural weather stations seam to correlate well with rural weather stations in the rest of the Northern Hemisphere, so if the “scientists”,
( it seems more like bean counters ), had any integrity they try a re-do with rural raw data only.
I know this is never going to happen with the current crop, but a man can dream, a man can dream.
Another thought struck me, looking at the obvious implications, this chicanery will mean large numbers of papers which relied on this data will have to be consigned to the file B1N.
Sadly it looks like in the UK an election will be announced this weekend so the CRU enquiry will be allowed to quietly disappear, the fix is in. 🙁
TKL (08:50:20) :
I’d like to see a retrieval of the satellite sensors and subsequent calibration. The reference is a platinum resistance thermometer. Looking at this is exactly like looking at the air? What are the effects of cosmic radiation and solar flares on the sensor and associated electronics.
As you point out, conversion of what the sensors detect and atmospheric temperatures involve quite an involved process. Remember it was only a few years ago that the satellites were showing no warming. Then a previously undiscovered “correction” was applied due to orbital decay. So just how good was this correction and how exactly was it arrived at?
I no longer heavily weight ANY of the instrumental record including the satellites. Historical, archeological and geological evidence seems a better way of arriving at climate changes. Looking at these there’s nothing at all unusual going on at present. It is time that climate “science” went back to being a minor field as an offshoot of meteorology, mainly of interest to those working in it and few fewer of those and far less taxpayer funding.
You want to talk about a rural station? Consider Pineville, West Virginia, USA. The town was settled in 1853 and incorporated in 1917. The 2000 USA Census registered 715 people, and the town suffered a huge population decline in the 1990s.
Here’s the USHCN plot of their mean monthly temps since 1900. No AGW signature that I can see. (If the image won’t display, just cut’n’paste the source link)
” Jeremy (07:52:01) :
Interesting that the upslope of the urban wrt the rural stations starts around 1965, rather than post-war.”
The United States federal government has enacted a series of clean air acts, beginning with the Air Pollution Control Act of 1955, and followed by the Clean Air Act of 1963.
Cooler winters and warmer summers, coal dust and soot melts ice/snow in winter and soot/dust blocks sunlight in summer.
My parents describe going to school in the UK in the 1940’s midlands and braving smog, visibility about 3 feet.
Looking at figure 1 the rural and urban match until about 1970 or so then the urban deviates upward. The adjusted figures (2) show the urban a bit higher than the rural for almost the entire time period. The alarmists can then claim that (1) we know that UHI exists, see the cities are warmer than the countryside, and (2) but we aren’t worried about the absolute temperature, it is the rate of change that is important and the rural and urban have the same rate of change, so rural and urban don’t effect our analysis.
Really rather ingenious to find an algorithm that lets you mollify the UHI believers (everybody) and at the same time say that it doesn’t matter.
What are the chances that adjustments made purely for sound scientific reasons results in the best possible evidence for AGW (Accept UHI, common knowledge, but deny effect.) It is both increasing the slope of rural after 1970 AND adding a differential prior to 1970 that could cause one to wonder about the legitimecy of the adjustments.
By the way, the way that the urban deviates upward only after 1970 or so would seem to imply that UHI is not simply an effect of increasing city size but more an effect of energy use (air conditioners, airplanes, etc…). That would mean that a study of UHI based on a time period prior to 1970 and in a place where there was not intense use of Air conditioners etc.., Say China, 🙂 would show little or no UHI and justify not including UHI corrections in the Temp analysis.
Also by the way, I don’t believe the adjustments were to correct for UHI. They are just part of the infill/averaging/weighting algorithms. Am I wrong on that?