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.
Problem is that he only uses NH sites so the bias estimate has a spatial error.
UHI varies with Latitude.
It’s higher in the NH than in the SH.
That said, Roy’s results are pretty close to the results we showed at AGU. which was .04C
per decade from 1979 to 2011.
extrapolating down to lower populations is suspect because of the heavy modelling used to derive
the 1km resolution population density data. To understand that you have to read the papers behind the GRUMPV1 population density data.
[SNIP – Mr. Barney, take this sort of ranting about women, religious issues, etc elsewhere. There is no place for this sort of ugliness here. You’ve been snipped several times before. Final warning. – Anthony Watts]
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.
then set up another avenue of peer review, open to everyone, with a press release of why.
I tried including SH sites, Steve, but there were none that met the inclusion criteria. Also, see the update to my post…even without extrapolation, the results over the U.S. show a 57% (!) warmer CRUTem3 trend versus the low population station data.
-Roy
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?
The study is very valuable. However, it does show differences in trends between places with different levels of population density in 2000, but it does not show that increasing population density correlates with increasing warming over time. That would require having population data not only for year 2000, but also for other years (at least for 1970, near the beginning of the series of temps studied). Passing from cross-section data to longitudinal analysis may be tricky.
Besides, residential population density is not the whole story about UHI. Some downtown areas have little residential population but high density of offices and other structures capturing and generating heat, and also intense vehicle circulation. Industrial areas are similar in this regard, with little population living there but lots of factories, refineries, machinery and trucks going around. Also, from the survey of US stations led by Anthony some time ago we learned that many isolated rural stations are now on top of a tin or concrete roof, or in a concrete or asphalt parking lot, but were probably in greener surroundings in the past.
What should be shown is that an increasing trend in pop density and/or other relevant features (buildings, vehicles, engines, generators, highways, airports and the like) correlates with a higher trend in temperature.
However, this analysis is a very useful addition to the literature on UHI.
Does this mean I’m gonna have to move to a densly-populated area to stay warm for the next several decades. Dreadful is the thought.
Am I missing something here?
Spencer shows above a bias in CRUtem due to UHI, but in his satellite data he shows a nice sinusoidal curve displaying the natural rhythm of temperature.
Or is it simply that CRUtem disagrees with the satellite data?
Listening to the weather forecast in the UK the weather guy always says the cities will be a few degrees higher. Usually 4+ degrees C.
In response to Hugh pepper surely it’s the amount of thermometers that are urbanised?
Hugh Pepper says:
March 30, 2012 at 12:46 pm
Maybe it’s all that additional CO2 they find in the homes of MSA’s–isn’t that what keeps everything warm, that CO2?
Based on your statement, however, the answer to your question is obvious.
Dear Dr. Spencer,
Does the lower troposphere satellite data have the resolution to make this same sort of observation?
So if 50% is from UHI, and up to 50% is from natural cycles (LIA recovery), then the gold standard temperature record will never actually decline, but only level off if “true” temperatures are in fact in long term natural decline. As for CO2, well, never mind.
Mr Pepper,
If the majority of temperature stations world wide are in urban areas then the fact that urban areas are only 0.5% of the surface is meaningless. The temperatures used to plot an average worldwide temperature is coming from predominately urban areas. So actually BEST didn’t really deal with the issue at hand.
Hugh Pepper says “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?”
A classic goofy, alarmist half truth: Obviously only a tiny part of the world is urbanised, but equally obviously most of the world’s temperature monitoring stations are located in that tiny part. These stations then bias the results from the other circa 99.5% of the world.
Would it be worthwhile to focus on some long term stations that meet the Cat 1 or 2 standard and have experienced transition from rural to urban if they can be linked to some nearby long term Cat 1 or 2 station that has stayed rural? Would suggest that the light density photos from satellite would be the best way to define urban/rural, as it is more infrastructure specific than population.
Would seem this approach would get away from models and be strictly observation driven.
That is faulty logic, IMHO.
If only 0.5% of the world is urbanized, then only 0.5% of the weather stations used to calculate global temps should be in urbanized areas. It is more logical, IMHO.
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?
BEST were part of the “avoid the real question and pretend to answer it with another one”.
Yes UHI contributes negligibly to global temperatures.
But what is of concern is how many of the temperature readings are contaminated by UHI.
If we were trying to get an actual “global temperature”, whatever that might mean, we would not want to use a data based known to be heavily contaminated. Unless, of course, we want the answer to be heavily contaminated.
I know that it goes without saying, but do note that Ross and I were only (obviously) looking at land data, and adjusting for the percent of land in each hemisphere gave us a reduction in total global warming of about 18%. That 50% figure that people often quote is within the land data only. Interestingly, our 2007 result changed the distribution of warming to look very much like the distribution of the satellite warming records, cutting off the extreme tails in the thermometric record.
Also, correcting the land record for our nonthermometric effect gave us the same rate of warming as in the Spencer and Christy MSU.
Roy–remember that the Climategaters went after deFreitas for publishing our paper. I have since found out that the situation is now worse, having manuscripts just being rejected out of hand that clearly merit at least a review.
Thanks for this article and the professionalism of the site. I would be very interested in hearing a comment from the author or another expert in response to Hugh Pepper’s point about the BEST study. Is David Schofield’s suggestion correct? It seems distribution of measuring locations would be taken into account by any well constructed study.
As David Schofield says at 12:52pm the TV, and radio, forecasts always note that the temperatures will be higher in the cities. Especially for overnight lows. UHI is assumed. Built into the forecast.
It also occurs to do that UHI is not only increasing due to growth and sprawl but on a per capita basis we’re also increasing our energy usage so UHI is probably increasing even where population levels are static.
If that is their conclusion then they are idiots. What matters is the percentage of the THERMOMETERS that are urbanized. (David Schofield beat me to it).
BTW, in the BEST study 1/3 of the sites showed cooling, and those were mixed well among the sites that showed warming, so the differences were not due to regional effects but are what one would expect with UHI and micro-climate warming.
[snip – you’ve been warned, see upthread, and now you are no longer welcome here for this hate speech towards women and religion you are spewing. Mods- delete this poster at will. – Anthony]
Well done sirs.. Now we have what looks like reasonably solid evidence of UHI affecting the calculated global land temp.
Then you think of the loss of all those remote stations which would be in non-dense population areas thus placing more emphasis on those in denser populations, and the constant “adjustments” to make the past colder, and you really have to wonder if there has actually been much warming at all !!
Certainly the records and calculations done by the AGW priesthood (Hansen, Jones, CRU) are NOT going to give us a reliable answer. !!
Lets accept, Had CRUT3 is boosted upward by UHI to some degree, and
HadCRUT4 is boosted further by warm spot chasing in the Arctic…..
Plain sneakiness in statistics, we know this type of people…..
But…..this cover-up of the temp decline is futile, because we have reached
the top temp plateau already from which it cannot get any warmer…. they
achieve only to buy time for a couple more years until the full truth of temp
decline will globally be evident…..
JS
Peter Miller says:
March 30, 2012 at 1:08 pm
“A classic goofy, alarmist half truth: Obviously only a tiny part of the world is urbanised, but equally obviously most of the world’s temperature monitoring stations are located in that tiny part. These stations then bias the results from the other circa 99.5% of the world.”
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Well Said! Mr Miller 🙂
Dr. Spencer,
Because UHI warming depends on an increase over time, what your study here has shown is that the UHI warming increases faster in higher population density areas. Doesn’t this disagree with your previous results?