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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Gidday rgbatduke and other commenters – you mention wind, buildings and UHI –
Do you know the book, “The Urban Climate” – Helmut E.Landsberg – a solid source of information.
Your library might have it or be able to get it.
For $30 you can have a copy mailed to you – thru abebooks.com
richardscourtney says:
April 2, 2012 at 2:24 am
Abraham3:
Your post at April 1, 2012 at 7:35 pm suggests you are attempting to disrupt this thread.
I say:
You are incorrect. I am attempting to discuss Spencer’s hypothesis. Reading through the comments on this web site, one is struct by the tremendous amount of support Spencer receives here and the extraordinarily small amount of criticism. I was hoping, in my own small way, to move things toward a more balanced and equitable configuration.
richardscourtney says:
It follows my post at April 1, 2012 at 3:18 pm that explained;
“You make some good points but, with respect, they are not relevant to the subject of this thread.
I say:
I have to disagree.
richardscourtney says:
But you have responded with a long screed which purports to support your irrelevance.
I guess I failed to realize that your comment was an order to stop or that you possessed the authority to give out such commands in the first place.
I advised in my first post here that I was not a climate scientist. Given that, I had no idea that an entry from Wikipedia would be greeted with such disdain. I’m sure it lacks some of the numerical details you folks love to bandy about, but I’m also quite sure that it is a great deal more objective than most of the references I’ve seen used on this site.
richardscourtney says:
The post by wshofact at April 1, 2012 at 4:31 pm proves the Jones paper you cited is wrong.
I say:
It most certainly does not. She references a blog singing gleefully that Jones found 0.1C/decade urban warming in developing China – signficantly more warming then he found in 1990. But since I cited Jones 2008, the error here is yours.
richardscourtney says:
Your subsequent long screed drops that and concentrates on trying to justify the claim by Parker that wind speeds indicate temperature better than thermometers.
I say:
Since I made no error citing Jones 2008, there was nothing to drop. No one here or elsewhere has challenged it. Parker’s methodology for looking for diferentiating urban heat sources is valid and if you want to make comments about it, you might want to actually familiarize yourself with it first as it made no attempt whatsoever to measure temperature via wind speed.
richardscourtney says:
This debate of irrelevance is inhibiting discussion of the subject of this thread. I suspect I am not alone in wanting you to stop it.
I say:
Take a quick scan through the 178 comments on this article and tell me what proportion support Spencer’s results and what proportion questions them – then explain to all of us how you can call what you have going on here a discussion, much less a debate. It’s a mutual back-slapping sing-along.
********************************************************************************************************
As I pointed out to the inestimable HenryP, a high reading from a UHI is not going to influence the gridded reading from an urban station hundreds of miles away. The distance over which interpolation is conducted is limited. Therefore, by selecting only cells in which all three population densities exist – where all are in close proximity, Spencer has guaranteed that he has found the relatively rare areas in which they do.
Having a thousand thermometers does not give a cell more weight in the process of surface temperature determination. That there are more stations in populated area does not cause a bias – at least in any study by middle school grads and above. GISS, NASA, NCDC/NOAA and Hadley have all gone to great lengths to correct UHI effects where necessary. The presence of UHI’s has not corrupted, much less falsified the global warming seen in the last 150 years. UHIs have had no detectible effect on sea temperature and given the 272-fold edge the ocean has over the atmosphere in mass, I still have confidence that the data the rest of the world’s climate scientists are providing is trustworthy – at least more trustworthy than anything I’ve seen out of Dr Spencer.
An economist friend with no dog in this fight and who has not followed the whole CAGW debate or Climategate closely (a director in a large government “three letter” research institute) had these observations (comments / references / rebuttals welcome):
—
Interesting. No physics, so I have a fighting chance of understanding it. The theoretical argument is unassailable so it is just a simple hypothesis test. Potential selection bias and possibly small sample issues make me suspicious. The lament that peer review publication is impossible sounds completely bogus. Also, the wringing of hands at the size of the dataset is highly correlated with chicanery. I would be extremely surprised if there is not a rich peer-reviewed literature on the urban heat island hypothesis. I would also be extremely surprised if one could get an article published using these data to measure average temps without somehow controlling for the effects of urban heat sinks on the average results.
—
From Abraham3 on April 2, 2012 at 6:31 pm:
Your apparent wholesale quoting of a Wikipedia entry on Arctic climate objectively boiled down to one NASA statement on global temperature and a single debunked questionable paper on the Arctic. It also is not currently in Wikipedia, doesn’t match current Wikipedia contents, and I have no evidence it ever was a real Wikipedia entry. Even by Wikipedia standards, it should have had more references than those two and four articles regurgitating and interpreting the paper’s press release.
If that’s enough for you to evaluate it as more objective than the usual plethora of sources found on WUWT, then you have far greater problems judging references and sources than any that could possibly arise from not being a climate scientist.
Spencer: You can test for error due to a bias in the spacial sampling by randomizing the datasets against the positions of the stations involved. Just assign to each temperature set a randomly chosen location. Your population class plot should come out with all bins at the same hight. Do this several times to get an idea of the intrinsic variance due to the spacial distribution.
Why don’t you write a formal paper and send it to the arxiv.org e-print server as a statistical paper? That would bypass the Inquisition.
Ed Zuiderwijk
says
Why don’t you write a formal paper?
People like Roy and me are most probably not interested in writing papers. We only want to figure out the truth from what has been observed and measured, irrespective of the errors and inaccuracies involved….
It is pure “self interest”. If other people want to listen, it is up to them. People like Abraham 3 can stay in their ignorance as far as I am concerned.
But seeing you are probably from the low lands, I am going to show you an interesting puzzle.
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
Note the following results from New York Kennedy Airport (lat. 40.65)
Maxima rising at 0.178 degrees C per decade since 1973
Means rising at 0.152 degrees C per decade since 1973
Minima going down at -0.011 degrees C per decade since 1973
We note that in LV the average temps. are pushed up mainly by increasing minima.
But not so in NY, because minima are not rising there.
Seeing as to both are (increasing) urban areas, any ideas as to why there is a difference?
kadaka (KD Knoebel) says:
It also is not currently in Wikipedia, doesn’t match current Wikipedia contents, and I have no evidence it ever was a real Wikipedia entry.
You are incorrect. The source of my quote is:
http://en.wikipedia.org/wiki/Climate_change_in_the_Arctic and it is quite current.
The second quote I provided was UEA’s response to charges that “climate scientist hid data flaws.
You guys need to see a little more mainstream science in here.
http://www.skepticalscience.com/urban-heat-island-effect.htm
Watch the video.
From Abraham3 on April 3, 2012 at 3:27 pm:
You are either a liar or very wrong. Pick one, those are your only options. You provided the address, same one I did. Have you even bothered to look at that entry?
Your “quote”, first line:
“There are several reasons to expect that climate changes, from whatever cause, may be enhanced in the Arctic, relative to the mid-latitudes and tropics.”
The Wikipedia Climate change in the Arctic entry, which I had looked at before, doesn’t have that line, and it doesn’t even have the word “reason” which I searched for just now.
But, Google did find that line, in the Wikipedia Climate of the Arctic entry, specifically the Global Warming section. By current Wikipedia standards this entry is neglected. What showed up in your “quote” as a list of references is identified as “Notes” at the bottom, with those references limited to the “Global warming” section. Below “Notes” is “Bibliography” which gives references used elsewhere without links in the text to them, also that other text uses many external links which go against current Wikipedia recommendations:
http://en.wikipedia.org/wiki/Wikipedia:External_links
“Wikipedia articles may include links to web pages outside Wikipedia (external links), but they should not normally be used in the body of an article.”
This indicates the entry is not up to current Wikipedia standards, and the “Global warming” section was added later than the rest. As seen in the real true “Climate change in the Arctic” entry, those many assorted references of different types should be assembled into a “References” section with appropriate linkages.
And the “Global warming” section you actually did copy, still has its references objectively boil down to a single NASA statement on global temperature and a single debunked questionable paper.
So which is it? Are you an outright liar, or merely very wrong because you’ve never bothered to verify the source you have adamantly insisted is the true origin of your “quote”?
Second line of the section you did quote:
“First, is the ice-albedo feedback, whereby an initial warming causes snow and ice to melt, exposing darker surfaces that absorb more sunlight, leading to more warming.”
http://wattsupwiththat.com/2011/02/09/arctic-death-spiral-actually-more-like-zombie-ice/
http://wattsupwiththat.com/2011/08/31/soot-easier-to-control-than-co2-may-help-arctic-ice/ (bold added)
Mainstream science, especially work more current than what’s in your “quote”? We got that.
You are correct, I was wrong. My quote did indeed come from Wikipedia’s “Climate of the Arctic” and not “Climate Change in the Arctic”. My apologies. I allowed myself to be fooled by the presence of the same graphic at the beginning of the section I actually quoted and at the beginning of the article I erroneously identified as my source – and, of course, the similarity of the two titles..
I would ask, however, that you withdraw your use of the word “liar”. Such a charge was uncalled for and is unproductive. There is no rational reason why I would have lied about such a thing and the fact that you located the actual source of my quotation – in Wikipedia and with the references cited – leads me to wonder what it was that actually motivated your charge.
Re Tietsche et al: the study looks at recovery from loss of ice over a single summer. If the process they propose – increased radiation from open water vice ice – were dominant, Arctic sea ice would not be in decline. Their study demonstrates a mollifying mechanism assuring the unlikelihood of a “tipping point” leading to runaway melt. It does not refute the loss of albedo from melting ice nor the continued melting from increased temperatures.
It is good to hear from Dr Jacobsen that controlling soot might save Arctic sea ice but I see nothing in his work questioning the ongoing melting, the cause of that melting or the self-reinforcing nature of albedo loss.
The Arctic is melting and the cause has nothing to do with urban heat islands.
If Urban Heat Islands are the cause of a significant portion of North American warming, why has there been so little warming near any of our population centers and so much in Alaska?
Henry@Abraham3
Well I looked at some temp. data as reported from the Elmendorf Airforce base in Anchorage.
If we look at the data from 1973 I find:
Maxima increasing at 0.422 degrees C per decade
Means (average temps) rising at 0.162 degrees C per decade
Minima decreasing -0.026 degrees C per decade
Interestingly enough, we see that UHI is absolutely not a factor here. The increasing average temp. at the base was caused by natural warming.
As I have tried to explain to you before,
if it were us heating the place, (e.g. burning stuff, UHI, etc) or if it were earth itself heating the place up (e.g.more underground volcanic activity, or more vegetation) or if there were some increased greenhouse effect, we would have found the exact opposite trend, namely minima pushing up the average temperature.
Even if snow removal were a factor, we should be seeing increasing minima. What we see from the data from the airforce base is exactly the opposite. It is the increasing temperature that happens during the day that is pushing up the temperature at the airforce base in Anchorage.
As I said to you, again and again, globally it is also like that: global warming is caused by the increasing sunshine and/or less clouds and or less ozone shielding. Now let us celebrate warming and let us hope that it lasts! (icy weather is not so good for the promotion of life)
Have a blessed Easter you all!
If you are interested in riddles study this website here.
http://www.shroud.com
I’m terribly sorry Henry, but you are completely incorrect. The current solar irradiance trend is downward and has not matched global temperature curves since records were first kept.
The dominant factor warming the Earth is infrared trapped by increasing levels of CO2 in our atmosphere and virtually every molecule of CO2 in the atmosphere above the 280 ppm, pre-industrial level is anthropogenic.
Abraham 3 says:
The dominant factor warming the Earth is infrared trapped by increasing levels of CO2 in our atmosphere and virtually every molecule of CO2 in the atmosphere above the 280 ppm, pre-industrial level is anthropogenic
Henry says:
Surely you don’t actually believe that ? I could find no such proof from the temp. data.
I have now analysed all the daily results coming from 19 weatherstations in the SH
and found the following averages:
Maxima rising at 0.44 degrees C per decade
Means rising at 0.06 degrees C per decade
Minima going down at -0.10 degrees per decade
Clearly, the reported warming is not coming from earth itself, e.g. due to increased GHG or volcanic activity. It is due to more intense sunshine and or less clouds and or less ozone.
http://www.letterdash.com/HenryP/henrys-pool-table-on-global-warming
(only about half of all the analyses of the daily analysis from 37 weather sations are currently reported here – I still have to do some updating……)
I am busy cutting up my tables and doing regression (trends) from the beginning (last ca. 37 yrs), then 32 yrs, then 22 years, then 12 years, then the last 7 years. The intervals are more or less randomly chosen. Unless sombody has other (better) ideas about the intervals?
I’ve been wondering for some time if air conditioning has something to do with this (I
haven’t read all 191 replies).
Almost all buildings in the U.S. have air
conditioning and when the air inside is cooled the heat is transferred outside via the A/C system.
Henry@Roy Spencer
You might find this very interesting: global cooling started in 1994
http://www.letterdash.com/henryp/global-cooling-is-here