by Roy W. Spencer, Ph. D.
INTRODUCTION
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.
A SIMPLER TECHNIQUE TO LOOK FOR SPURIOUS WARMING
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.
EVIDENCE OF A LARGE SPURIOUS WARMING TREND IN THE U.S. GHCN DATA
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.
Dr. Spencer,
How raw is the raw data that you used? Assume no TOB … what about instrumentation changes, etc?
What do your satellite data for CONUS show over the ’73-09 period?
JJ
Sean Peake (13:01:03) :
“To me that seems kind of low and may reflect all of the US, not the continental US—Alaska is the likely reason because it’s half the size and has just over 1 person/sq m.”
Pop density in the US not including Alaska is about 36/km2.
OT but related.
In 1960 there where a total of 108 billion airline passenger-kilometers flown. In 2000 three were 3 trillion passenger-kilometers.
We’ve had a 30 fold increase in air travel…the vast majority of the worlds thermometers are at the airport. But the world population has only doubled.
Business’s like to locate near transportation hubs, people who need jobs locate near business’s.
R. Gates (12:26:11) :
I am wondering though, if the UHI is so widespread, at what point is it no longer just an effect that causes skewing of the data, but part of the environment? What I’m getting at is, all the extra heat that urban areas generate (and certainly this does skew the data as urbanization continues), but that extra heat in those urban area is still extra heat added to the total heat balance of the planet. Taken to an extreme, if somehow the entire planet were a giant concrete parking lot, you’d have a giant “heat island”, or would you simply have a different climate? These urban heat islands are, on some level, part of the environment, growing rapidly, and averaged over the whole globe. will add to warming.
Wow, I’m having a civil (agreeable?) conversation with you and Wren on a thread? Haha, will wonders never cease? 🙂
I stand firm that I believe these are not measurement “errors” per se, but from a surface temperature record standpoint should be, more or less, red dots instead of giant orange blobs. They should also be classified and represented as land use changes. They are, as much as I hate the term, a form of AGW but it’s not CO2-driven AGW and we call it AGW… everyone’s still going to say “CO2!!!! CO2!!!!!” So what do we call it?
We’re getting back to the “It’s more complicated than the RC folks think” – an idea that has always run through my head when I look at the “consensus” storyline of the climate.
What really gets me is that the mainstream surface temperature reconstructions (GISS in particular) allegedly have already “over-corrected” (according to Hansen or Schmidt, not sure which one) for UHI and still trend higher than Dr. Spencer’s raw reconstruction. They’re already off by a significant margin before you even start to consider UHI – which makes it almost farcical.
Then you also have to consider that per the IPCC, “land use” changes are a negative forcing (~.25 W/m2). If they are really a net positive forcing due to UHI then it means something on the positive forcing side has been exaggerated above and beyond the implications of the lower average global trend that – according to Pat Michaels – would probably be .13 instead of .17 (so ~30% overstated?).
Paul Z. (08:03:52) :
“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:
http://news.bbc.co.uk/2/hi/uk_news/scotland/glasgow_and_west/8570294.stm
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?
Well, there’s one born every minute, isn’t there. Here’s a fellow who actually thinks he can believe what politicians say!!! (LOL) Even worse, he thinks that he can believe whatever Jim Murphy says. (Even more LOL!!)
Richard M (14:34:43)
That is a very good point. While I thought I had read that “urban” areas accounted for 1.5% of the land surface (maybe the less than 1% is total earth surface), that only accounts for less than 50% of the world’s population. The other ~3.9 billion folks live out there in the “non-urban” areas – whatever that means.
I went digging and found this: http://www.demographia.com/db-intlua-area2000.htm
So that means for every country in the world “urban” areas are at least 950 people/km2 – which is already almost off Dr. Spencer’s chart. When I went looking I was thinking “oh this is going to just be noise”… but when you look at Dr. Spencer’s chart and then start to think about, for example, what % of the world’s land surface is 25 people/km2 or greater, and then it’s not quite so insignificant.
Interesting!
If we had sufficient truly-rural stations to rely on, we could just throw all the other UHI-contaminated data away.
There you go, correct.
Way to go Spence! Well done.
In my opinion it might also be shown that during the average twelve hours of nighttime, light winds that tend to carry the UHI effect (energy) over rural areas affect the temperatures to a greater degree than in the nighttime to daytime transition. Warm or even hot surfaces transfer the heat energy during early evening hours as they cool but there is no such effect in the night to day transition, nothing to ever counteract this UHI effect. Follow the energy flow in time as it flexes every diurnal period! There is excess energy in the UHI areas but there is no such thing as a “Rural Cool Island” effect to carry ‘negative’ energy to the UHI areas in the night to day transition. That’s one funky way to look at it, but should be correct in the diurnal sense!
That should account for what we see in the slopes of minimum and maximum temperature graphs in rural stations. The nighttime slopes are always greater and the maximum show little effect across ~115 years except in rare cases and these probably have other effects occurring to account for this discrepancy.
My comment above should not take away from Dr. Spencer’s presentation in any way, with max and min separate graphs not showing this effect, you just never see it but instead it is inferred to me by his concept.
Another thing comes to mind, and this is in an area I have little expertise in, can the lights at nighttime that are making it impossible to find a truly night sky if you want to take a telescope and look at the nighttime sky cause a tiny UHI effect during the nighttime? Can this SW radiation from mere lighting reflect and absorb enough energy in the gases of the atmosphere to cause some of that increase of the slope of minimum temperature readings? Gases can hold little energy compared to solid or liquid matter, in other words air is easily heated (low specific heat) and is hard to radiate it away (low emissivity). That I don’t know.
But in high population dense areas, many people do not to have lights on at night, it’s already bright enough. But in isolated homes and businesses in rural areas, they are in near total darkness and most would naturally add outside lighting for safety reasons. That is why we no longer have dark skies. That single factor should take on an exponential curve also against population density but may prove too tiny to bother with, but maybe it doesn’t.
Roy, an additional analysis with day and night being separated may prove fruitful with proper physics principles backing it.
Also, while looking up NOAA/NCDC stations, the population for Boulder stood out (being over a million) and I don’t think that is correct. 🙂 How many other populations are not correct?
Remember, this was only my opinion, being a transfer of possible knowledge or an alternate viewpoint. If you are an AGW proponent and want it, take it, otherwise, just ignore it. These comments are just that, comments, not scientific papers, they come later.
pat michaels (11:06:21) :
Roy–
This is exactly the result that McKitrick and I got globally (J. Geophys 08). We found 50% of the land warming in the NASA record was from “non-climatic” factors.
You can’t have a UHI over the ocean, so these findings apply to about 30% of the surface. After you factor in the relative warmings of each hemisphere separately, I believe the decadal trend will drop by .04deg–not an inconsiderable amount.
The drop will be from 0.17 to 0.13; isn’t it odd that the 0.13 is almost exactly what the MSU shows?
This would make things very neat and tidy – if it weren’t for the fact that MSU shows a US warming trend of 0.22 deg per decade, i.e. about the same as the GHCN trend.
I don’t know if this point has been raised, but what does a flat temperature line during the period in question say about sea surface temperatures as the oceans warm or cool the land masses.
This would seem to suggest that they too must have had near zero anomalies. While not tested, I would not be surprised that this pattern repeated in other regions of the world which if true would also point to stable SSTs during this period.
I dont have the math anymore to argue the statistics, but I like the approach of testing the UHI contamination to the thermometer record (raw) and then using that logically to filter the UHI component out off the temperature record as an attempt to measure real national warming over a significant land mass. The UHI is a testable.
There is nothing wrong with the arbitrary starting point as it meets basic criteria, maximum data integrity and the time line of policy approaches to warming attributed to man made CO2.
Nick B.,
I won’t tell anyone, if you won’t, that we’re having a somewhat civil conversation. 🙂
You really do raise some valid points. I know that this thread is supposed to be about the unreliability of the temperature data because of UHI, but it certainly does make sense that the very thing (i.e. urbanization) that is “skewing” the data, is also creating real heat that spreads into the global environment because so far at least there are not any bubbles built over the large maga-cities like NY, Tokyo, London, Singapore, Syndey, LA, etc. But the heat from these cities spreads globally, and certainly, by some fraction of degree, they certainly must raise the overall global temperature.
Interestingly enough, the energy to produce this heat comes from many sources, and not just fossil fuels, with the primary source being probably sunlight. Sunlight striking brick, concrete, asphalt, metal etc. will turn that energy into infrared heat energy, versus the sunlight that falls on grass and trees, which is mainly converted into chemical energy. In effect then, the cities become turn into little (or mega) UHI generating islands. The final twist on this is of course the fact that the megacities both increase the overall heating of the earth, but also are net producers of CO2, whereas the plants or ground cover in existence before there were cities probably used more CO2, or at least were carbon neutral. To the extent then that rising CO2 levels really are a global climate issue, the heat and CO2 produced by cities becomes a multi-threat to the global climate status quo.
Where will this all end?
1) Humans will continue to expand and use up all resources and we all die.
2) Humans will perfect fusion technology and with an infinitie amount of energy we can make the earth into a garden of eden and we then expand into the Solar System and beyond, until we contact an intelligent civilization that either greets us with open arms, enslaves us, or finds us tasty and serves us as snacks at their alien parties.
3) Humans will muddle along in their human like way until the next glacial period comes and everything we’ve ever built is covered with 2 miles of ice for the next 100,000 years.
4) A coronal mass ejection wipes out 99.9% of life on earth and the whole thing starts again.
No matter what happens…you can be sure that the AGW believers and the AGW skeptics will be at each others throat until the bitter end…
Lon Hocker (11:01:45) :
“In short, I feel that imagining a zero value of temperature rise for the last 30 years in the US is pushing your idea quite a bit too far”.
There are many places in the Southern hemisphere that are truly rural and which need no UHI correction. Among them are many that show zero or negative temperature change over the last 40 years, but there is a lot of noise. Trend fitting is questionable in individual cases but might have significance when you combine many cases.
The logical first assumption is that there has been no global temperature change. If you then observe that there might have been a change, you seek an explanation. That’s all Dr Spencer has done.
If you believe that it is unlikely that parts of the world have avoided a temperature change, then you still have to explain the many rural sites where there has been no temperature change.
Look again at Macquarie Island, out in the middle of nowhere, influenced almost not at all by the hand of man. There is very minor infilling of missing data. Data from Bureau of Meteorology, Australia.
http://i260.photobucket.com/albums/ii14/sherro_2008/2010/mACQUARIE.jpg?t=1268784258
Why does it look immune from GHG?
Thanks so very much for your diligent work Roy.
Your work aligns perfectly of my observsions in my rural are of S.MN……Temprature over the last 120 years is just very slightly positive. So if all would be contributed from increased level of Co2 in the atmosphere, we could surmize that yes Co2 has a small positive effect on surface tempratures. But as I suspected it is so very small the slight warming is definitely a postive effect on mankind. To bad the scientists can not un-lock themselves from the politics and see the light. Yes the increase from 350 ppm to 390 ppm warm the surface ever so much. But the empirical data shows as the co2 increases it become a smaller and smaller positive and surface tempratures concur with that……..John….
Roy
The thing that bothers me is that I’m not sure there is a completely satisfactory explanation for your results. Let’s consider a hypothetical case. In 1973 the population density of Smallsville is 10 per sq km; in 2009 it is 20 per sq km. Although its population has doubled it’s still in the low population class. Metropolis (can’t think where I got these names from), on the other hand, had a pop density 500 per sq km in 1973 which is the same as in 2009, i.e. it’s unchanged. If population density were a factor then we might expect the temperature trend in Smallsville to be more affected than the trend in Metropolis. What I’m saying is that there doesn’t necessarily seem to be an obvious reason why a low density population station should be any less ‘contaminated’ than a high density population station. In fact, in one of your previous posts, you implied that there was a log relationship and that a population rise in a rural location would, therefore, have a disproportionately greater effect than a similar rise in an urban location.
Now I’m not familiar with US demographics so it’s quite possible someone is going to tell me that population growth in the past 30 odd years has been restricted almost entirely to the major cities. If so – fair enough – that answers my point. But failing that – are we quite sure there isn’t a regional effect going on, i.e. is it possible that a number of the low pop stations are clustered in a region of the country which just happens to have warmed at a lower rate. Unlikely, perhaps, but I still think it needs to be ruled out.
Interesting post, anyway.
GREAT WORK DOC!
This research plus what Dr. Jones admitted in his most recent BBC interview:
NO statistically significant warming from 1995-2010 while C02 increased by 30+%. In my book that is enough to reject the NULL: Mean Global Temperature (>) greater than (i.e. AGW) than historic mean and supports the ALTERNATIVE HYPOTHESIS Mean Global Temperature (=<) (Skeptic) equal to or less than historic mean temperature. Plus Jones admitted that from 2002 to 2010 there has been slight cooling. He further admitted that the MWP was as hot if not hotter than today. This is a critical to getting some sanity back in the discussion of science in our society today.
THANKS DOC!
tjl
John Finn (17:18:26) “Now I’m not familiar with US demographics so it’s quite possible someone is going to tell me that population growth in the past 30 odd years has been restricted almost entirely to the major cities.”
Yes, cities and suburbs. I don’t have numbers, but with the mechanization of agriculture, the loss of small factories in small towns, and the attraction of urban amenities, there has been a strong rural to urban migration in the U.S. since at least the 1950’s. (Not counting migration of retirees to Sun Cities and such, which I think are more suburban than rural.)
Keep at it Roy, you’re doing fine.
Is there an upper limit to UHI?
Lon Hocker (11:01:45) : In short, I feel that imagining a zero value of temperature rise for the last 30 years in the US is pushing your idea quite a bit too far.
I think it’s fine.
I made a variation on First Differences that I call dT/dt. The variation is that I start in the past going forward to make the anomalies, but start in the present and take my anomalies running total going backwards in time (on the assumption that “now” is a better record from better instruments than 200 years ago… an assumption I’m increasingly doubting 😉 and on a “gap” in the data FD will “reset” to zero while I just keep waiting for a valid data item and put all the change in when one shows up. (Thus, if there were a 0.1 C / year trend and a 5 year gap I’d expect to put +0.5 C in when that valid datum shows up…). This preserves trends better especially with holey data (and the data has a LOT of holes in it…) while not having any “imagined data” used at all. (No in-filling or any other estimating… just pure data, straight.) Oh, and I calculate the anomaly by “month of the year” comparing only January to January and December to December. This avoids all the fallacies of averaging temperatures (an intensive variable…) and all the issues around data dropouts being different by season et.al.
In exchange for all that, it loses the “feature” of automatically adjusting to things like equipment changes. (That ‘reset on change’ behaviour…). To me, this is an added feature as I want to see the impact of the changes. That’s what I’m hunting for: “What Changed” that could bugger things?
OK, enough preamble. For North America the graph is:
http://chiefio.files.wordpress.com/2010/03/north_america_sh.png
This is ALL DATA, so those early years are ONE thermometer (that’s why it’s so volatile in the early years…). About 1816 you can clearly see the Year Without A Summer. By about 1825 we’re up to around 25 thermometers and the graph settles down at the zero line. We have a cold period, then a very hot 1934 era, cooling into the GIStemp baseline, and warming back to the 1998 peak. Ending back at zero today. (“Now” is defined as zero as I run time from now into the past…) So, with all that preamble, notice that about 30 years ago we are again at zero? Oh, and this is the GHCN “unadjusted” data and has, in theory, no UHI adjustments done to it at all.
Basically, this was as close to the “raw” data as I could figure out how to get and as close to “pure anomaly” as I could create. All “unadorned”.
To make it easier to see exactly, here is the text version of the data for The USA which is most of the data used to make that chart. You want the “dT” line. I’ve cut this table at 1800 when we have all of 4 thermometers, as before that I don’t see much real usability for averaging reports. You will notice that 1979 and 1980 are both -0.38 while 1978 is +0.34 which to me says “pretty much zero +/- annual jitter”.
For folks wanting to see more about North America, the link to that particular article is:
http://chiefio.wordpress.com/2010/03/16/north-america-flat-canada-not-so-much/
So at the end of the day, it looks to me like Dr. Spencer has it “just right”.
Now I see what you are doing Roy.
Well done. You’ve given credibility to the data by using a 1 km grid square for population density. The temp data is just as chaotic for sample area, but the UHI effect associated with population density is proved for a station’s report of temp.
Best regards, suricat.
Interesting work. As with all data on climate change it tends to raise more question than answers in me. Whether it confirms or contradicts my own personnal outlook. I’m hoping that’s the correct position to take as a true sceptic.
So I have some questions which either the author or somebody in the know might answer for me.
1) Has anybody in the literature taken this sort of approach to UHI? What were their conclusions?
2) Can you do the same analysis using the satellite data? Would that back-up the findings?
3) Can you analyse temporal changes with this method?
4) What are the limitations/weaknesses of this method compared with other attempts to calculate UHI? (sorry bit of a hard one)
Dr. Spencer,
What a serendipitous occasion to come back from a vacation and have such a clear analysis to read. Thanks!! However, your earlier article did show a slight variance from the U.S. and the rest of the world especially in the lower pop density. So, I think a “rest of the world” analysis would be in order to be able to apply your findings globally. Given the clear methods you’ve given us, I’d do it myself, but then I’d be stealing your thunder. (lol, not really, I’m lazy and am more comfortable critiquing and cheering from the sidelines.) Also, (and I’m not sure if you’re planning to go this way or not) a time study should probably be done going further back, comparing UHI per points in history. Obviously, there are significant differences in UHI components in a town of 50,000 in 1920 compared to one presently.
I see you’ve brought some detractors to the site. GREAT WORK!!! You know you’re onto something when that happens.
pat michaels (11:06:21) :……I’d say that his would probably show a more significant divergence. I’d also say, that your right about the oceans, but that’s not what was measured to come to the warming conclusion(not significantly) by the Jones’ and Hansen’s ect. and then I’d probably commence about the oscillation of currents and winds ect., but then, I’ll probably wait for the final chapter of Dr. Spencer’s analysis.
Dr. Spencer, thanks again.
Few more questions I forgot about.
5) There is usually a call for confidences and uncertainty measurements with this sort of data. Any available?
6)From your update “If the population densities for a pair of stations are exactly the same, we do not include that pair in the averaging.” Would these not act as good ‘negative controls’ for this experiment?
Roy,
Living in Shanghai, A thought occurred to me as I read your article. There is a huge migrant worker population, living and working in the major Chinese cities. Every Chinese New year the vast majority of these workers leave the UHI cities and travel home to the provinces for at least 2 weeks.
Indeed, Shanghai became a ghost town just a few weeks ago. Traffic levels drop to probably 10/20% of normal levels. House heating is turned off (Mostly reversible air conditioner units). Factories shut down etc.
I was thinking along the line that there must be a change in the temperature of the major conurbations taking into account the UHI effect. Has anyone noticed this or recorded any difference?
David44 (12:43:50) :
It’s apparent from the video that the selection criteria were sites over 150,000 in population where there is a relatively close rural comparison site, and both sites have full temp history over a century. As far as I can tell, they included all sites that fit that criteria.
A VS is putting on a great show…..link to comments. Eli and Tamino roughed up a bit.
http://ourchangingclimate.wordpress.com/2010/03/01/global-average-temperature-increase-giss-hadcru-and-ncdc-compared/#comment-1626
Hmmm… spurious warming for 2% of the surface of the planet, IF you believe this rather labored and complicated reasoning is statistically significant.
OTOH we see record breaking month after month high global temperature anomalies for the UAH record. Getting back to reality Dr. Spencer, it is looking increasingly likely that the trailing 13 month UAH moving average will set an all-time record when the July results come in. January was the highest in the database (before adjustment), February was just a whisker off Feb ’98, and March is looking like this month will blow out the Mar ’98 record. With El NIno lag effects expected to continue to show up in the atmosphere through June, the message is becoming extremely clear…
… the UAH record is confirming the GISS record and near-record highs set in the last five years. The planet’s atmosphere is heating up at a high rate. The oceans are absorbing massive amounts of heat that swamp the heat buildup in the atmosphere.
Isn’t this the important message?