McKitrick & Michaels Were Right: More Evidence of Spurious Warming in the IPCC Surface Temperature Dataset
Guest post by Roy W. Spencer, Ph. D.
The supposed gold standard in surface temperature data is that produced by Univ. of East Anglia, the so-called CRUTem3 dataset. There has always been a lingering suspicion among skeptics that some portion of this IPCC official temperature record contains some level of residual spurious warming due to the urban heat island effect. Several published papers over the years have supported that suspicion.
The Urban Heat Island (UHI) effect is familiar to most people: towns and cities are typically warmer than surrounding rural areas due to the replacement of natural vegetation with manmade structures. If that effect increases over time at thermometer sites, there will be a spurious warming component to regional or global temperature trends computed from the data.
Here I will show based upon unadjusted International Surface Hourly (ISH) data archived at NCDC that the warming trend over the Northern Hemisphere, where virtually all of the thermometer data exist, is a function of population density at the thermometer site.
Depending upon how low in population density one extends the results, the level of spurious warming in the CRUTem3 dataset ranges from 14% to 30% when 3 population density classes are considered, and even 60% with 5 population classes.
DATA & METHOD
Analysis of the raw station data is not for the faint of heart. For the period 1973 through 2011, there are hundreds of thousands of data files in the NCDC ISH archive, each file representing one station of data from one year. The data volume is many gigabytes.
From these files I computed daily average temperatures at each station which had records extending back at least to 1973, the year of a large increase in the number of global stations included in the ISH database. The daily average temperature was computed from the 4 standard synoptic times (00, 06, 12, 18 UTC) which are the most commonly reported times from stations around the world.
At least 20 days of complete data were required for a monthly average temperature to be computed, and the 1973-2011 period of record had to be at least 80% complete for a station to be included in the analysis.
I then stratified the stations based upon the 2000 census population density at each station; the population dataset I used has a spatial resolution of 1 km.
I then accepted all 5×5 deg lat/lon grid boxes (the same ones that Phil Jones uses in constructing the CRUTem3 dataset) which had all of the following present: a CRUTem3 temperature, and at least 1 station from each of 3 population classes, with class boundaries at 0, 15, 500, and 30,000 persons per sq. km.
By requiring all three population classes to be present for grids to be used in the analysis, we get the best ‘apples-to-apples’ comparison between stations of different population densities. The downside is that there is less geographic coverage than that provided in the Jones dataset, since relatively few grids meet such a requirement.
But the intent here is not to get a best estimate of temperature trends for the 1973-2011 period; it is instead to get an estimate of the level of spurious warming in the CRUTem3 dataset. The resulting number of 5×5 deg grids with stations from all three population classes averaged around 100 per month during 1973 through 2011.
RESULTS
The results are shown in the following figure, which indicates that the lower the population density surrounding a temperature station, the lower the average linear warming trend for the 1973-2011 period. Note that the CRUTem3 trend is a little higher than simply averaging all of the accepted ISH stations together, but not as high as when only the highest population stations were used.
The CRUTem3 and lowest population density temperature anomaly time series which go into computing these trends are shown in the next plot, along with polynomial fits to the data:
Again, the above plot is not meant to necessarily be estimates for the entire Northern Hemispheric land area, but only those 5×5 deg grids where there are temperature reporting stations representing all three population classes.
The difference between these two temperature traces is shown next:
From this last plot, we see in recent years there appears to be a growing bias in the CRUTem3 temperatures versus the temperatures from the lowest population class.
The CRUTem3 temperature linear trend is about 15% warmer than the lowest population class temperature trend. But if we extrapolate the results in the first plot above to near-zero population density (0.1 persons per sq. km), we get a 30% overestimate of temperature trends from CRUTem3.
If I increase the number of population classes from 3 to 5, the CRUTem3 trend is overestimated by 60% at 0.1 persons per sq. km, but the number of grids which have stations representing all 5 population classes averages only 10 to 15 per month, instead of 100 per month. So, I suspect those results are less reliable.
I find the above results to be quite compelling evidence for what Anthony Watts, Pat Michaels, Ross McKitrick, et al., have been emphasizing for years: that poor thermometer siting has likely led to spurious warming trends, which has then inflated the official IPCC estimates of warming. These results are roughly consistent with the McKitrick and Michaels (2007) study which suggested as much as 50% of the reported surface warming since 1980 could be spurious.
I would love to write this work up and submit it for publication, but I am growing weary of the IPCC gatekeepers killing my papers; the more damaging any conclusions are to the IPCC narrative, the less likely they are to be published. That’s the world we live in.
UPDATE: I’ve appended the results for the U.S. only, which shows evidence that CRUTem3 has overstated U.S. warming trends during 1973-2011 by at least 50%.
I’ve computed results for just the United States, and these are a little more specific. The ISH stations were once again stratified by local population density. Temperature trends were computed for each station individually, and the upper and lower 5% trend ‘outliers’ in each of the 3 population classes were excluded from the analysis. For each population class, I also computed the ‘official’ CRUTem3 trends, and averaged those just like I averaged the ISH station data.
The results in the following plot show that for the 87 stations in the lowest population class, the average CRUTem3 temperature trend was 57% warmer than the trend computed from the ISH station data.
These are apples-to-apples comparisons…for each station trend included in the averaging for each population class, a corresponding, nearest-neighbor CRUTem3 trend was also included in the averaging for that population class.
How can one explain such results, other than to conclude that there is spurious warming in the CRUTem3 dataset? I already see in the comments, below, that there are a few attempts to divert attention from this central issue. I would like to hear an alternative explanation for such results.
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“I can’t see any reason why urban TRENDS (not temperatures) should be any different to rural TRENDS (not temperatures)….”
I think Philip Bradley and Ian W. may have already touched on this point, but..
I can’t actually see why I would expect them to be similar, unless they were identical in physical composition and thus identical heat capacities. A lake or forest would be expected to be very different from concrete or asphalt. The former would rise less with increases in radiative input, and I would not expect cooling due to evaporation to change linearly with respect to other heat redistribution processes.
A second point that I have not seen addressed is a wider one concerning Carbon dioxide. It may be considered “a well mixed gas” globally, but its production by human beings certainly is not. Is it not produced primarily where there are significant human populations? How then, do any GCMs claiming to model global temperatures wrt CO2 deal with this? It seems to me that every thermometer also needs to be measuring local CO2 concentrations.
@ur momisugly An Inquirer on March 31, 2012 at 2:29 am:
When I want an “easy to play with” dataset, I head to NCDC. I’ve yet to find an easier access point than at the bottom of this FAQ, “The Global Anomalies and Index Data” section. I looked at “The Annual Global Ocean Temperature Anomalies (degrees C)“. The set goes back to 1880. The anomaly baseline is the 20th century average (1901-2000). To easily feed into a spreadsheet, open in a word processor, find and replace all double spaces with single spaces, repeat until no double spaces left, then cut and paste into a spreadsheet (paste special) with a space as delimiter. Since you said “last three decades” and the last full year is 2011, I’ll do calculations for ranges with the last year ending in 1.
Sure enough, 1982 to 2011 was 0.12°C/decade. 1972 to 2001 was the same.
But the winner is 1912 to 1941, 0.13°C/decade, the highest three decade group in the record. So nothing unusual is going on.
If you shift to decades, 1982-91 0.17°C/decade, 1992-2001 0.23°C/decade, 2002-11 –0.08°C/decade (the oceans cooled).
That highest rate was nearly matched in 1922-31 with 0.22°C/decade, and blown away with 1932-41 at 0.33°C/decade. Note also there are sharp drops, as 1942-51 was –0.30°C/decade. Again, nothing unusual going on. Nothing has happened in the last three decades that is outside the range of natural variability.
For comparison, using Dr. Spencer’s range above of 1973-2011, the rise was 0.12°C/decade. This appears to match the rate in the chart in the Update for US Temperature Trends, lowest population density group, derived from NCDC land data. Which is noted for entertainment purposes only.
If one cannot do a comparison in the southern hemisphere because the data do not contain all three population types, how can one say for sure that NCW (non-climate warming) is worse in the NH? Are you just basing this on the fact that there’s a higher percentage of land in the NH? Can’t microclimate factors also affect stations with very few people?
I see Dr. Spencer’s research as saying that NCW affects higher population areas more than low population areas, but that’s just the difference above any baseline NCW.
“Can someone explain to me the difference between gridding data and inventing data? I’m not being flippant either. I’m genuinely interested in the answer.”
“If your experiment needs statistics, you ought to have done a better experiment.” – Ernest Rutherford
Without an analysis of the statistical significance of the trends calculated Spencer’s grid method, we can’t determine whether the analysis is meaningful. Lack of statistical significance in the differences in the trends between population density groups could render the discussion moot.
*****
Hugh Pepper says:
March 30, 2012 at 12:46 pm
This question was dealt with by the BEST study. They concluded that, since only 0.5% of the world is urbanized, even a 2 degree rise in urban temperature would contribute negligibly to the global average. Were they too part of the great conspiracy?
*****
That’s an irrelevant point. The real question is what is the percentage of sites located in these “negligible” urban areas. My guess is that it’s shockingly high.
richard verney says:
Richard, you simply forgot that the same few microns absorbing DLWR are also emitting ULWR, and the balance of this LWR, along with some evaporation and heat transfer up and down with the lower water, and heat transfer with air above, results in the balance of temperature.
The BEST data is also biased by a drop in Elevation.
From 1950 to 2000, the average elevation of BEST data dropped 46m.
Thats .45C of warming according lapse rate calculators.
http://sunshinehours.wordpress.com/2012/03/21/climate-data-and-elevation/
AGW = UHI + lower thermometers.
Do people know that many US counties have lower populations now than in 1900?
Here is a map of those smaller in population.
http://sunshinehours.wordpress.com/2012/03/17/depopulation-and-cooling-at-the-county-level/
Here is a map of thermometers that have a negative trend from 1900 to 2011.
http://sunshinehours.wordpress.com/2012/03/15/cooling-since-1900/
Dr. Spencer,
Thanks for your dialog with independent views of the UHI topic. The UHI biasing of the IPCC supporting temp data sets gets exposed again.
John
Eric Adler mumbled as if following a script on March 31, 2012 at 6:56 am:
From Dr. Spencer’s article:
Dr. Spencer found discrepancies well exceeding 10%, with the high end more than twice the low end, and you think mouthing the magical incantation “statistical significance” will invoke the protection of the Climate Gods and miraculously preserve the integrity of the CRUTem3 dataset? Urgent message from Planet Earth to Adler: Try harder.
Mosher: “50% of the stations are Spencers low population”
If those low population stations are overrepresented in the US and underrepresented in the rest of the world it would explain why the trend is lower in the US and higher in the rest of the world (or at least northern hemisphere)
Isn’t that the case? As I recall Steve McIntyre once mentioned that most stations in the world outside the US are Urban or located at airports..
Roy Spencer says:
The Urban Heat Island (UHI) effect is familiar to most people: towns and cities are typically warmer than surrounding rural areas due to the replacement of natural vegetation with manmade structures.
Henry says:
How about if what you are seeing and measuring could be (in many cases) exactly be opposite to what you think?
Let me give you an example. Las Vegas used to be desert, and indeed in 1973 I suspect it still was mostly desert country.
Note the following results from Las Vegas Intl. Airport ( lat. 36.08):
Maxima rising at 0.16 degrees C per decade since 1973
Means rising at 0.524 degrees C per decade since 1973
Minima rising at 1.022 degrees C per decade since 1973
This particular trend of the ratio Maxima: Means: Minima
is exactly opposite to the general global trend as reported here,
http://www.letterdash.com/HenryP/henrys-pool-table-on-global-warming
is it not?
I have observed this before and it was in sparsely populated northern Namibia. I know what is happening. It is the increasing vegetation – THAT IS PLANTED – AND, SOMEHOW WATERED BY MAN, that is causing this particular warming. This is the biggest controversy that I have observed since my investigations into global warming began: if you want earth to be green, then some heat is going to be trapped by the increasing vegetation.
Gail Combs says:
March 30, 2012 at 5:07 pm
Here is a set of data points that illustrate the problem. These are the only city & close by airport listed for North Carolina. The NC state population, 2011 estimate, is 9,656,401. For a comparison the New York–Newark, NY –NJ–CT Urbanized Area has an estimated population of 18,319,939 double that of the entire state of NC. The city is on the North Carolina/Virgina border and right on the ocean.
Take a look at the city vs the airport! Norfolk City and
Norfolk International Airport
………..
The two trends are very different for two locations close together, not sure of reason. Both are airports. The Norfolk airport is closer to the Atlantic Ocean but the Norfolk Naval Air Station (Norfolk City/NAS) is directly next to the Hampton Roads (mouth of the James River). I expect the NAS is about as urbanized as it ever was, WWII being the time it was really built up and the beggining of the record and the Norfolk Airport having slower growth and surrounding urbanization over the same time period. The Oceana NAS located closer to the Atlantic than the Norfolk airport seems to have a similar trend to Norfolk NAS. http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425723080030&data_set=14&num_neighbors=1, I beleive it also had most of its growth during WWII.
Has anybody looked at geographic effects on temperature-trends? If not, then it could be a matter of urbanization or it could be a matter of the sorts of geography which lead to it. For example, most big cities are on land, coastal or along a river near the sea, on or near large regions of arable land, etc. It would be good to see a study of temperature-trends in regions geographically similar to typical sites of cities compared to those of cities.
On another note, in terms of adapting to changes (rather than trying to stop them), does UHI matter that much? I mean assuming there is some change happening, natural or otherwise, if it is large in urban areas then most of humanity will have to adapt regardless of whether it is large or small elsewhere.
Terry Oldberg: Smokey says: “If the runaway global warming predictions were right, we would see a recent rise in the long term trend” Smokey: those “predictions” are “projections” and projections are logically neither right nor wrong.
Hi again.
Projections can be accurate or inaccurate, with respect to publicly available standards of accuracy. If the runaway global warming projections were accurate, we would see a recent rise in the long term trend.
Your assertion that projections are logically neither right nor wrong hasn’t the least practical importance.
Smokey says:
March 30, 2012 at 5:31 pm
John Finn says:
March 30, 2012 at 5:04 pm
“I can’t see any reason why urban TRENDS (not temperatures) should be any different to rural TRENDS (not temperatures) unless ALL urban populations were growing while ALL rural populations remained static.”
I’m impressed that a lot of apparently bright folks are missing a simple point in Roy’s work. The fact that you can categorize the “slopes” of the temperature record by population size categories is powerful evidence of UHI affecting the record, whether or not you “… can’t see any reason why urban TRENDS….”. Maybe it would help you see the power if you were to provide a bunch of unnamed station records and have Roy estimate the population range of the site.
People like Hugh Pepper “are the conspirators” who put many people seeking the truth on the defensive. Mr. Pepper, there is nothing wrong with seeking the truth. As a project engineer and designer in a multitude of process automation areas, I naturally question things to find out what the data and results are telling us. It’s amazing when you don’t assume you have all the answers at the onset of a project. This is far different than your scenario just accepting that using data in ways that can show a preconceived result must be the best way.
In my experience, there is much that can be learned by managing projects if one is open to the science learned along the way. In your majority world of sheep, there is a very strong bias to seek data which proves the hypothesis and strongly disregard data which shows there is something else going on.
What we keep coming to, over and over again, is that the decline they were trying to hide might really have been a decline.
All modern data is also biased by the fact that digital temperature sensors respond faster to temperature changes. A momentary breeze of hot air, say from tarmac heated air at an airport, will register as a higher peak temperature on a fast response digital sensor as compared to an old fashioned mercury in glass min/max thermometer.
Wait, wait, I am lost. You start talking about the heat island effect, but your study had nothing to do with the heat island effect (which the CruTem3 average takes into account. Presumably you have problems with how they did it. What are those problems?). INCREASE in density would of course result in spurious warming, but MORE density would not (in fact between 1970 and today I would guess high density correlates NEGATIVELY with density increase). So your explanation for the effect you are seeing fails. Is there an explanation that is consistent with Anthropogenic Warming? Well, I though of one in the first five minutes: low density in the US is mostly high altitude: Maybe temperature increase has been higher at lower altitudes? Maybe not, but at least I have attempted to come up with an explanation for the effect, rather than distracting my audience by talking about something utterly unrelated!
This paper is consistent with the results of my 2010 analysis of temperature data for Australia, comparing our Bureau of Meteorology’s (BoM) official temperature record with
(1) data for all 43 long record temperature stations (LRTS) in rural locations and
(2) data for known urban heat islands (UHI), Sydney and Melbourne.
Relevant results, in summary, are
1. Warming according to BoM is more than twice that of rural LRTS (0.5/0.22 deg C)
2. Warming according to BoM conforms with that of a known UHI, Melbourne (0.5/0.51 deg C)
http://www.climategate.com/urban-heat-island-effect-proven-to-corrupt-aussie-climate-data
Steve: “low density in the US is mostly high altitude”
Steve, you ignore that urbanization where much of the rural population moved to cities leaving essentially ghost town.
Deindustrialization happened as well.
There are hundreds of US counties that have less population than they did in 1900.
There are 1033 counties smaller in 2010 than 1940.
I have just looked at CRUT4 vs CRUT3 differences for some Australian 5 degree grid cells. Found this stunning adjustment for the Murray Darling Basin grid immediately west of Sydney.
Instantaneous moving of the Sydney UHI several hundred km west.
CRUT4 revison of the Murray-Darling Basin grid box temperature data – is this the worst warming tweak ever by the UKMO / Jones et al team ?
http://www.warwickhughes.com/blog/?p=1460
The UHI is not significant meme is not selling anymore.