Spencer: Direct Evidence that Most U.S. Warming Since 1973 Could Be Spurious

by Roy W. Spencer, Ph. D.

Where the lights are - the CONUS has population that roughly tracks with brightness


My last few posts have described a new method for quantifying the average Urban Heat Island (UHI) warming effect as a function of population density, using thousands of pairs of temperature measuring stations within 150 km of each other. The results supported previous work which had shown that UHI warming increases logarithmically with population, with the greatest rate of warming occurring at the lowest population densities as population density increases.

But how does this help us determine whether global warming trends have been spuriously inflated by such effects remaining in the leading surface temperature datasets, like those produced by Phil Jones (CRU) and Jim Hansen (NASA/GISS)?

While my quantifying the UHI effect is an interesting exercise, the existence of such an effect spatially (with distance between stations) does not necessarily prove that there has been a spurious warming in the thermometer measurements at those stations over time. The reason why it doesn’t is that, to the extent that the population density of each thermometer site does not change over time, then various levels of UHI contamination at different thermometer sites would probably have little influence on long-term temperature trends. Urbanized locations would indeed be warmer on average, but “global warming” would affect them in about the same way as the more rural locations.

This hypothetical situation seems unlikely, though, since population does indeed increase over time. If we had sufficient truly-rural stations to rely on, we could just throw all the other UHI-contaminated data away. Unfortunately, there are very few long-term records from thermometers that have not experienced some sort of change in their exposure…usually the addition of manmade structures and surfaces that lead to spurious warming.

Thus, we are forced to use data from sites with at least some level of UHI contamination. So the question becomes, how does one adjust for such effects?

As the provider of the officially-blessed GHCN temperature dataset that both Hansen and Jones depend upon, NOAA has chosen a rather painstaking approach where the long-term temperature records from individual thermometer sites have undergone homogeneity “corrections” to their data, mainly based upon (presumably spurious) abrupt temperature changes over time. The coming and going of some stations over the years further complicates the construction of temperature records back 100 years or more.

All of these problems (among others) have led to a hodgepodge of complex adjustments.


I like simplicity of analysis — whenever possible, anyway. Complexity in data analysis should only be added when it is required to elucidate something that is not obvious from a simpler analysis. And it turns out that a simple analysis of publicly available raw (not adjusted) temperature data from NOAA/NESDIS NOAA/NCDC, combined with high-resolution population density data for those temperature monitoring sites, shows clear evidence of UHI warming contaminating the GHCN data for the United States.

I will restrict the analysis to 1973 and later since (1) this is the primary period of warming allegedly due to anthropogenic greenhouse gas emissions; (2) the period having the largest number of monitoring sites has been since 1973; and (3) a relatively short 37-year record maximizes the number of continuously operating stations, avoiding the need to handle transitions as older stations stop operating and newer ones are added.

Similar to my previous posts, for each U.S. station I average together four temperature measurements per day (00, 06, 12, and 18 UTC) to get a daily average temperature (GHCN uses daily max/min data). There must be at least 20 days of such data for a monthly average to be computed. I then include only those stations having at least 90% complete monthly data from 1973 through 2009. Annual cycles in temperature and anomalies are computed from each station separately.

I then compute multi-station average anomalies in 5×5 deg. latitude/longitude boxes, and then compare the temperature trends for the represented regions to those in the CRUTem3 (Phil Jones’) dataset for the same regions. But to determine whether the CRUTem3 dataset has any spurious trends, I further divide my averages into 4 population density classes: 0 to 25; 25 to 100; 100 to 400; and greater than 400 persons per sq. km. The population density data is at a nominal 1 km resolution, available for 1990 and 2000…I use the 2000 data.

All of these restrictions then result in thirteen 24 to 26 5-deg grid boxes over the U.S. having all population classes represented over the 37-year period of record. In comparison, the entire U.S. covers about 31 40 grid boxes in the CRUTem3 dataset. While the following results are therefore for a regional subset (at least 60%) of the U.S., we will see that the CRUTem3 temperature variations for the entire U.S. do not change substantially when all 31 40 grids are included in the CRUTem3 averaging.


The following chart shows yearly area-averaged temperature anomalies from 1973 through 2009 for the 13 24 to 26 5-deg. grid squares over the U.S. having all four population classes represented (as well as a CRUTem3 average temperature measurement). All anomalies have been recomputed relative to the 30-year period, 1973-2002.

The heavy red line is from the CRUTem3 dataset, and so might be considered one of the “official” estimates. The heavy blue curve is the lowest population class. (The other 3 population classes clutter the figure too much to show, but we will soon see those results in a more useful form.)

Significantly, the warming trend in the lowest population class is only 47% of the CRUTem3 trend, a factor of two difference.

Also interesting is that in the CRUTem3 data, 1998 and 2006 would be the two warmest years during this period of record. But in the lowest population class data, the two warmest years are 1987 and 1990. When the CRUTem3 data for the whole U.S. are analyzed (the lighter red line) the two warmest years are swapped, 2006 is 1st and then 1998 2nd.

From looking at the warmest years in the CRUTem3 data, one gets the impression that each new high-temperature year supersedes the previous one in intensity. But the low-population stations show just the opposite: the intensity of the warmest years is actually decreasing over time.

To get a better idea of how the calculated warming trend depends upon population density for all 4 classes, the following graph shows – just like the spatial UHI effect on temperatures I have previously reported on – that the warming trend goes down nonlinearly as population density of the stations decrease. In fact, extrapolation of these results to zero population density might produce little warming at all!

This is a very significant result. It suggests the possibility that there has been essentially no warming in the U.S. since the 1970s.

Also, note that the highest population class actually exhibits slightly more warming than that seen in the CRUTem3 dataset. This provides additional confidence that the effects demonstrated here are real.

Finally, the next graph shows the difference between the lowest population density class results seen in the first graph above. This provides a better idea of which years contribute to the large difference in warming trends.

Taken together, I believe these results provide powerful and direct evidence that the GHCN data still has a substantial spurious warming component, at least for the period (since 1973) and region (U.S.) addressed here.

There is a clear need for new, independent analyses of the global temperature data…the raw data, that is. As I have mentioned before, we need independent groups doing new and independent global temperature analyses — not international committees of Nobel laureates passing down opinions on tablets of stone.

But, as always, the analysis presented above is meant more for stimulating thought and discussion, and does not equal a peer-reviewed paper. Caveat emptor.


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paulo arruda

OT. I do not understand statistics. But there’s a commentator (VS) who is leaving Tamino crazy. He even forgot you Anthony.


Our knowledge has been built on sand. And of sand. And in comes the tide.

Paul Z.

“This is a very significant result. It suggests the possibility that there has been essentially no warming in the U.S. since the 1970s.”
Tell this to the pope, will ya:
Scottish Secretary Jim Murphy, who is leading UK government preparations for the tour, said it was “an historic visit at an important time”.
“The Papal visit represents an unprecedented opportunity to strengthen ties between the UK and the Holy See on action to tackle poverty and climate change as well as the important role of faith in creating strong and cohesive communities,” he said.
Is the Vatican invested heavily in carbon emissions trading?

Dr. Spencer,
Please provide graphical geographic maps (images) of your grid boxes. Something as simple as a 4-color map (a different color, depending on the population level in the grid) would be adequate. Even better would use a different shape or shape size to denote the level of warming in that grid box.
Alternately, if you could provide a link to your data as a text file, I could put such a map together myself.

Could we use the Surfaces Station Survey approach by organizing regional teams to collect and review the raw data. I visit the Western Regional Climate Center on line and down load the raw data when studying local climate issues. In some cases there are big gaps in the collected data. I have visited some stations and copied the paper records to resolve the missing information. We would need some standard method for dealing with this missing data, so all the teams would be using the same techniques. I am available to work on a team.

Steve Keohane

Thank you Dr. Spencer for injecting some sanity into a field of science that seems to be fraught with emotion.

Wondering Aloud

Caveat Emptor indeed. Reproducible, understandable, process and limitations explained, this work certainly doesn’t sink to the low quality we have seen in peer reviewed papers on this subject.


OT…but the Gore disinformation comes thick and fast in this 3/15 sound bite:
From a Gore strategy conference call for supporters.

Roy Spencer

sorry all, I meant “NOAA/NCDC”, not “NOAA/NESDIS”. Must be that age thing.

I would query your statement: “…to the extent that the population density of each thermometer site does not change over time, then various levels of UHI contamination at different thermometer sites would probably have little influence on long-term temperature trends.”
It seems to me that the UHI effect is a combination of several factors:
1. Population density.
2. Ambient extra energy (heated buildings etc.)
3. Different heat storage/release and albedo characteristics of the built environment.
Items 2 and 3 can change over time with the population density remaining constant.
I would also like to postulate a fourth factor – petro-chemical smog. I have just come back from Almaty (Kazakhstan) where this a recognised and very visible problem. In effect vehicles are increasing temperatures by a local ‘greenhouse’ effect. It might be interesting to examine whether the effect of atmospheric fuel residues depends on smog being formed or whether they increase in levels water vapour short of forming smog.


I once had a poster showing a satellite picture of the lights at night over North America similar to the one you show.
However, mine showed an almost incredible amount of lights extending half way INTO the Golf of Mexico. It took me a wile to realize that these were not ships or island but probably oil rigs! It made the Golf of Mexico seem to be as populated as the land next to it!
That surely also contributes to heat in that area.
REPLY: Compared to the heat sink of the ocean though, minimal. -A

Roger Knights

One more arrow in the elephant. Keep ’em coming.


What is the average population density of the US? Based on the graph that shows CRU temp versus pop. density, I would guess 400.

paulo arruda (07:56:49)
If it’s the same VS that was commenting here: http://wattsupwiththat.com/2010/02/14/new-paper-on/
…he’s a very sharp one indeed. Might be an econometrician, or at least has studied it. I could see how someone like him could destroy Tamino. I’m not sure if he’s using it on Tamino, but economic statistical methods are appropriate and much more advanced than the… errhh… stuff that’s used in climate science
Disclaimer: In this OT comment I am not talking about Dr. Spencer. What I’m getting at is the attribution of statistically significant causal relationships through statistical analysis and all the gaming the RC crowd, Hockey Team, etc do to highlight what is in their favor and “debunk” what’s not


Why is the blue line sometimes higher than the red line?

It suggests the possibility that there has been essentially no warming in the U.S. since the 1970s.
More precise should be, that the linear trend 1973-2009 is close to zero, since there was warming between 1973-1990 and flat trend/decrease since then. Excellent analysis overall. PS Gif format for the graphs should be much better.

does anyone have faith in the homogenizing methods actually removing UHI ? From what I see in the GISS dataset they have adjusted old data (60’s, 70’s)DOWN and recent data has been unadjusted … basically a stepped adjustment in the WRONG direction, it should show lower adjustments to older data and greater negative adjustments to recent data.

OK. But what about the rest of the world?
I think it would be in one sense harder to duplicate the same analysis with the collected stations in, say South America, as the data gathering there has never been so constant as in the US. That said. I expect you would get an even stronger effect due to some of the rapid growth of major cities in the developing world.


How good is Russian population density data over time? I want this analysis done on Siberia!


Gro Harlem Brundtland?
Bruntland? That name bells a ring.
Is he UNabomber Mao Strong’s good buddy? Or, is she the other Bruntland?
“Of course, in the wake of Climate-gate, growing public skepticism of manmade global warming and dim prospects for big emissions cuts by the U.S. or China, a streamlined process may not get very far either.”
“U.N. Climate Envoy: Shift Climate Talks From U.N.
The United Nations may not be the best forum for global climate treaty talks after the Copenhagen collapse, said a top U.N. climate envoy.
After failing for years to reach a deal among 200 countries, negotiations will shift to more informal talks among a smaller number of key nations in a “double-track system,” said special U.N. envoy Gro Harlem Brundtland on Tuesday.
Copenhagen concluded with a nonbinding three-page paper hammered out in an all-night private meeting among President Barack Obama and a handful of leaders, most importantly from China, India, Brazil and South Africa. It fell far short of the summit’s original objective, a full-fledged and legally binding accord setting emission reduction targets for major countries.
The Copenhagen experience “will serve as a base for discussions going on this year. It’s not only going to be focused on the United Nations framework, but more on what these emerging economies and big economies are committing to,” said Brundtland, speaking on the sidelines of a world conference on biofuels.
Brundtland’s comments reflect a growing admission that the U.N. process has proven dysfunctional. They add weight because of her 20-year involvement in climate issues and her current role as Secretary General Ban Ki-moon’s special climate envoy.”

Bill S

Thank you once again Dr. Spencer, for another excellent post. Any light shed on the badly done UHI modifications by GISS, CRU, and NOAA is welcome.
Ron (08:38:38) :
Your post reminds me of something that I read recently (I think it was Phil Jones in his BBC interview from last month): he said that they don’t do a UHI correction for London because the population hadn’t changed significantly since the late 1800’s. While the population has only grown from 6.5 million to 7.5 million, this ignores any effects due to modernization–horse-drawn carriages to cars, dirt roads to asphalt and concrete, fewer trees in general, increase in transportation/number of vehicles on the roads, etc. This would be another good area to study.

Population density of the USA, according to area and population figures provided in the CIA World Factbook, is about 31.3 people per km^2.

Dr. Spencer,
Thanks for the analysis; I’ve been doing some similar work lately:
It might be a bit more interesting to compare your ISH dataset to raw USHCN data, since the CRUTemp GHCN stations you use have both TOB and inhomogeniety adjustments applied, and the magnitude of those adjustments for the U.S. are reasonably well known.
One possibly confounding factor is that TOB adjustments tend to be much greater for rural stations than urban ones, since rural stations are more likely to be co-op stations with irregular reporting times (whereas non co-op stations tend not to need TOB adjustments).

Johnny Canuck

Dr. Spencer
Terrific work. Thanks!
I can imagine that many of the AGW “scientists” are beginning to doubt the reliablity of their data, but the politicos will go on and on and on, denying the facts.


I think that it goes something like this:
Global warming = 49% UHI – 25% “tricks” – 25% natural increase – 1% AGW
Not exact numbers obviously, but something like that.

Dave F

Hi Dr. Spencer, very convincing analysis.
Would UHI contaminate the UAH dataset also? I ask because, obviously, there is warming shown in that data also. This is always used as a reason to ignore UHI and say the GHCN has it right, so I was curious how you would respond to that.

Don B

Perhaps one of the reasons the plurality of US state maximum temperature records were set in the decade of the 1930s, rather than in one of the recent decades when temperatures were supposed to be rising alarmingly, was because the temperature was not rising alarmingly.

Michael Larkin

Wow! I actually understood at least 80% of this. Thanks a lot, Dr. Spencer.

Dr. Spencer illuminates the dark recesses of climate science.
The longer the farce goes on, the more outrageous the alarmist claims become.
How much longer can the climate science community keep a straight face?
The only crisis we have is in confidence in science.
When will the real scientists out there accept they have been hoodwinked and deal with it accordingly?

There is a clear need for new, independent analyses of the global temperature data…the raw data, that is.
Done already http://justdata.wordpress.com
Just needs a PhD to write it up 😉 Source code & links to raw GHCN data free for use.
I suppose I could apply your corrective equation for population, but it does not seem necessary.

R. Craigen

Hi Roy. As a long-time participant in peer-review in the Mathematical sciences I want to say that, while your “caveat emptor” is well taken, it should ALSO be applied to official peer review, which is bad enough in my field, and must be doubly or triply so in a field full of political landmines like yours. In such a case, PUBLIC review may be as valuable, or more so, than so-called PEER review. Indeed, perhaps public review (together with peer review) ought to be considered the gold standard for sciences so enmeshed in public policy issues.
I suggest that you’ll get more valuable feedback from a sounding board like this one, populated by interested and well-informed amateurs whose identities are known, than from a panel of peers populated by people with strong prejudices, hiding behind a curtain of anonymity.
I read your analysis with interest and will say that you continue to appear to be on to something valuable. But what I see lacking, at least in the exposition, is any accounting for population gradient over time. Perhaps it is a result of hasty writing, but I see no suggestion in the analysis that you have permitted sites to migrate between population classes over time, which would seem something this analysis should be designed to cope with. Indeed, from your opening paragraphs I had thought that this would be the main focus of your analysis.
It is something I’m intensely curious about, the uncovering of which to me seems a main goal: when the population density changes at a particular location, is there a demonstrable population-induced temperature change? As you mention, you have already done a (very impressive) study of the the effect of population spacially, from which we can probably infer a rough effect from temporal changes in population, but it would be good to have the temporal effect well-established from empirical data.
Perhaps your writeup (or my hasty reading) has missed this, but does your analysis move sites across population groups, or does a site with 0-25 ppk^2 in 1970 remain in this class through 2009, even if in actuality it has grown into the 100-400 ppk^2 class by the end of the study period? If so you are probably underestimating the effect of UHI, and the classes are cross-contaminated in the time direction.
The period 1970-2009 is probably good in a fourth way: you should be able to get reliable population density data at all NA sites during this entire period. This would enable you to slice the data in yet another direction: Isolate sites whose population densities have grown during this period, and comparable sites whose densities have remained constant, to use as a control. Further, if you have not already done so, I would see some value in limiting the current study to sites whose population density has not migrated between classes during the period of the study.
One further remark, not about the study but about the logarithmic effect of population: this would seem a very strong argument for the environmental irresponsibility of de-urbanization. It suggests that low-density populations can have proportionally much higher per-person impacts on a natural environment. Taking this data at face value, let us try a thought experiment: take everyone out of urban centers and move them into the country. Spread everyone uniformly around the continent. The data would appear to suggest that this would create a strong warming signal. Maybe the back-to-nature movement is the most environmentally destructive ideology yet!
As one having a strong dislike for the city and cherishing dreams of retiring to a cabin in the woods, I find the above observation creates a moral dilemma.

Dr. Spencer,
Would be curious as to your thoughts around what this means for the sat record.
Allow me to digress for a sec: I think on your previous posts I and others have asked about the resolution of the satellite record and if UHI could be seen/demonstrated in it. From a spatial standpoint, because of convection and wind effects I would think we would see a severely diffused effect at 14,000 ft (ch05) – which is where the UAH record comes from right? – so with that I think I have corrected myself from my previous train of thought… sat resolution at 14,000 ft doesn’t matter vs. the 1km at 2m population density analyses you’re doing here.
I guess what I’m trying to get at here: for the most part CRU and UAH are at least in the ballpark of each other… so wouldn’t the spurious warming in one also be exhibited in the other? Maybe I have an issue here as well with the label “spurious” because the warming is quite real, but what you’re demonstrating (amazingly well I might add) is that what they’re calling CO2 induced “global warming” (most of it at least) is really UHI, and that by switching the population zone of the stations in the grids that signal can be readily demonstrated. This would then go back to the (rhetorical?) question of should we be correcting this out or measuring it – because for the sat measurements is it even possible to “correct” something like this out?
One other nit if you don’t mind, I agree with your 4 reading average approach, but for consistency’s sake: could the difference between your method vs. the TMax/Tmin approach be responsible for any of the difference here? On your post regarding station drop out there was an unexplained 30% difference in variability (maybe 30+%) between your raw ISH analysis and CRU. I’m just curious if you had any feel for if that were due to raw vs. adjusted, 4-temp avg. vs. TMax/Tmin, your statistical method vs. their homogenizations, etc… and if that difference might also be causing unexplained differences in this analysis.
Great work!


If I wanted to discredit this work and re-affirm my belief in thermageddon, the obvious suggestion to make is that the stations have been pre-selected to give the desired result (Cherry-Picked). Now to some extent you have, continuous,undisturbed,regularly collected, properly recorded, traceable to standards data from properly maintained weather stations not being quite so common as perhaps it should be. But provided you can show a consistently applied, bias free disqualification/acceptance procedure you should be able to dispose of that line of argument fairly quickly.
How does more recent data correlate with the satellite record?
Did you consider using an ANOVA method to assist in assigning mean and variance differences to different attributes of the stations?


A remarkable analysis, but I have two questions/ remarks:
1. You write “This is a very significant result. It suggests the possibility that there has been essentially no warming in the U.S. since the 1970s.” That would only be true I think if there were no people living in the US. But there are, I have seen a lot of them every time I visited the USA [last visit was to NY so that might have been a biased observation -;) And their number has steadily increased since many decades.
2. Apart from USA (and possibly urbanized regions in other parts of the world): would your observation have much impact on the global mean, considering that almost 70% is oceans?


Paul Z. (08:03:52) :
Is the Vatican invested heavily in carbon emissions trading?

Don’t know. But “Climate Change” seems like a good way to guilt the “rich” countries into sending money to those who have less, thus it represents a Noble Cause that is “Vatican Approved.” Anything to help the needy, right?

A C Osborn

For all those that find Dr Spencer’s work here interesting, it is time to go and have a look at E M Smith’s Chefio Site at http://chiefio.wordpress.com/
Hhe has finished working on the world data set (not all graphed yet) and his results are absolutely fascinating. His North American Analysis basically aggrees with Dr Spencer.


“Unfortunately, there are very few long-term records from thermometers that have not experienced some sort of change in their exposure…usually the addition of manmade structures and surfaces that lead to spurious warming.”
Are there any such stations? How many? Where? Does anyone have information on this? If there is even a relative handful, it would be interesting (if not necessarily convincing) to know if they show any trend individually and as a group.
Has anyone looked at forest fire observation posts for example? Most have structures, of course, but they may not have changed much. Most if not all collect weather data, at least during the fire season. Is temperature data from any of them included in the NOAA data? They are obviously rural and many date from the early 20th century or earlier. I haven’t been able to find much online in this regard, but I’m probably not looking in the right places. Surely someone has done some analysis regarding fire tower temp data. I’m sure I’m asking questions that have already been answered, but can anyone comment?


“There must be at least 20 days of such data for a monthly average to be computed. I then include only those stations having at least 90% complete monthly data from 1973 through 2009.”
(FX Dons Yorkshire Flat Cap; sips brown ale and packs ferret into trousers…..)
Eeeeh luxury…… If I’d tried to get t’Ford motor company teh let me off wi 2/3rds of me regular checks unrecorded’ and wi at least one month a years stuff missin… they’d ‘ave ripped off me metaphorical testicles, wi red ‘ot blunt pliers, fed em to me whippet then made me watch all the episodes of sienfeld back to back sithee. Young people today, if you told ’em that, they wouldn’t believe ya…..
(Fx checks trouser strings and chokes on a wad of tripe and pork scratchings…)


I guess I wonder why there is such a need for a comprehensive evaluation of global temperature.
If you took only the rural sites and used them as a consistent set, would that not be a good device for detecting changes? Simply avoid the UHI altogether.

I was wondering why use the smallest pop density areas. If the temp increase is least for the same percent pop increase in already densely populated areas, then it seems that you’d want to use the data from the most densely populated areas, since it should change the least.
Then I realized that you only used one pop density statistic, so by using the least densely populated areas you guarantee that there hasn’t been too much population growth. If pop density data were available for the time span of the analysis, you could UHI correct all of the sites and use all of them.
This seems to be closely related to the observation that the better the data record is, the less warming can be coaxed out of the data. One or two thermometers turn Antarctica and Siberia bright red, while the country with the best thermometer record shows little to no warming for 150 years when corrected for UHI.


The world’s population trend shows a sharp upswing starting before the upswing in average global temperature in the 20th Century. Obviously no density calculation is necessary here since the globe’s surface area doesn’t change much.
As can be seen in the linked graph, the world population trend turns upward around mid-century.
As I recall, average global temperature starts rising sharply after the 1970’s. So if increases in population density drive increases in temperature, why is there a lag?
REPLY: It’s called growing up. Infrastructure impacts don’t begin at birth, but rise into adulthood. -A

Tom Mayor

Have you checked to see if there are stations which have had declines in urbanization over time? And if so are they more heavily weighted in one subsample compared with the others?


Damn most proof read better…
“the obvious suggestion to make is that the stations have been pre-selected to give the desired result (Cherry-Picked). Now to some extent you have, continuous,undisturbed,regularly collected, properly recorded, traceable to standards data from properly maintained weather stations not being quite so common as perhaps it should be.”
By which I mean that by selecting stations with consistent high reporting rates you have selected the subset of the “best” stations. My fear is that your detractors will attempt to colour this process as selecting to support the desired conclusion.


Minor question: I’m interested why Dr. Spencer chose to average the four daily temperatures vs. the min/max like CRU? I couldn’t find the reason explained in his prior posts on this subject, although I may well have missed it. I don’t expect there to be any significant difference between the averages, but just seems one would want to compare apples to apples as much as possible to isolate the UHI effect.


Great work. It would be interesting to know how your figures compare with satellite temperatures (see e.g. http://discover.itsc.uah.edu/amsutemps/). And are your data sets available somewhere?


On further reflection, I can see my previous post would be a reasonable question if determining UHI bias in CRU’s temperature set, but not necessarily as a general test.


Good and interesting read, as usual.
I got a thought… UHI seems to be related to the actual temperature, as in the effect varies in size during day/night and summer/winter. Would it be possible to add current (or yearly average) temperature as a variable in the UHI analysis?


“ut, as always, the analysis presented above is meant more for stimulating thought and discussion, and does not equal a peer-reviewed paper. Caveat emptor.”
Indeed. I would sugest that the absence of peer review as it is conducted today makes this article decidedly more “robust”. Peer review is just another phrase for group think. At least IMHO.
Prove me wrong?

Charlie A

A large part of the warming trend for some NOAA temperature data sets is the “time of observation” correction, which NOAA applies to adjust the max/min temps according to time of reading a max/min thermometer each day.
It appears that this correction doesn’t apply to the stations you picked, as they had readings 4 times per day. Please confirm that TOB shouldn’t apply to the data you used.
I suspect that most readers are confused, as I am, about the all the different sets of temperature time series.

Ryan Stephenson

Total electricity generrating capacity in the UK is 40,000,000,000Watts. Total number of households is 20,000,000. Peak electrical power consumed per household is about 2000W in that case, most of which will appear as waste heat. This is matched by a similar amount of energy turned directly into heat by burning fuel. That’s 4000W peak output for every house in the country.
I should imagine that if that heat energy can’t escape directly upwards perhaps due to heavy cloud cover, that much of it causes direct heating of the local environment. Temperature increase would be small, but a small increase is all we are looking for.