In case you missed it, Roy Spencer performed a unique and valuable analysis comparing International Hourly Surface data to population density to provide a simple gauge for the Urban Heat Island (UHI) effect. It was presented at WUWT yesterday with this result:
There were lots of questions on the method. Dr. Spencer adds to the discussion below.
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UPDATE #2: Clarifications and answers to questions
After sifting through the 212 comments posted in the last 12 hours at Anthony Watts’ site, I thought I would answer those concerns that seemed most relevant.
Many of the questions and objections posted there were actually answered by others peoples’ posts — see especially the 2 comments by Jim Clarke at time stamps 18:23:56 & 01:32:40. Clearly, Jim understood what I did, why I did it, and phrased the explanations even better than I could have.
Some readers were left confused since my posting was necessarily greatly simplified; the level of detail for a journal submission would increase by about a factor of ten. I appreciate all the input, which has helped clarify my thinking.
RATIONALE FOR THE STUDY
While it might not have been obvious, I am trying to come up with a quantitative method for correcting past temperature measurements for the localized warming effects due to the urban heat island (UHI) effect. I am generally including in the “UHI effect” any replacement of natural vegetation by manmade surfaces, structures and active sources of heat. I don’t want to argue about terminology, just keep things simple.
For instance, the addition of an outbuilding and a sidewalk next to an otherwise naturally-vegetated thermometer site would be considered UHI-contaminated. (As Roger Pielke, Sr., has repeatedly pointed out, changes in land use, without the addition of manmade surfaces and structures, can also cause temperature changes. I consider this to be a much more difficult influence to correct for in the global thermometer data.)
The UHI effect leads to a spurious warming signal which, even though only local, has been given global significance by some experts. Many of us believe that as much as 50% (or more) of the “global warming” signal in the thermometer data could actually be from local UHI effects. The IPCC community, in contrast, appears to believe that the thermometer record has not been substantially contaminated.
Unless someone quantitatively demonstrates that there is a significant UHI signal in the global thermometer data, the IPCC can claim that global temperature trends are not substantially contaminated by such effects.
If there were sufficient thermometer data scattered around the world that are unaffected by UHI effects, then we could simply throw away all of the contaminated data. A couple of people wondered why this is not done. I believe that there is not enough uncontaminated data to do this, which means we must find some way of correcting for UHI effects that exist in most of the thermometer data — preferably extending back 100 years or more.
Since population data is one of the few pieces of information that we have long term records for, it makes sense to determine if we can quantify the UHI effect based upon population data. My post introduces a simple method for doing that, based upon the analysis of global thermometer and population density data for a single year, 2000. The analysis needs to be done for other years as well, but the high-resolution population density data only extends back to 1990.
Admittedly, if we had good long-term records of some other variable that was more closely related to UHI, then we could use that instead. But the purpose here is not to find the best way to estimate the magnitude of TODAY’S UHI effect, but to find a practical way to correct PAST thermometer data. What I posted was the first step in that direction.
Clearly, satellite surveys of land use change in the last 10 or 20 years are not going to allow you to extend a method back to 1900. Population data, though, ARE available (although of arguable quality). But no method will be perfect, and all possible methods should be investigated.
STATION PAIRING
My goal is to quantify how much of a UHI temperature rise occurs, on average, for any population density, compared to a population density of zero. We can not do this directly because that would require a zero-population temperature measurement near every populated temperature measurement location. So, we must do it in a piecewise fashion.
For every closely-spaced station pair in the world, we can compare the temperature difference between the 2 stations to the population density difference between the two station locations. Using station pairs is easily programmable on a computer, allowing the approx 10,000 temperature measurements sites to be processed relatively quickly.
Using a simple example to introduce the concept, theoretically one could compute:
1) how much average UHI warming occurs from going from 0 to 20 people per sq. km, then
2) the average warming going from 20 to 50 people per sq. km, then
3) the average warming going from 50 to 100 people per. sq. km,
etc.
If you can compute all of these separate statistics, we can determine how the UHI effect varies with population density going from 0 to the highest population densities.
Unfortunately, the populations of any 2 closely-spaced stations will be highly variable, not neatly ordered like this simple example. We need some way of handling the fact that stations do NOT have population densities exactly at 0, 20, 100 (etc.) persons per sq. km., but can have ANY population density. I handle this problem by doing averaging in specific population intervals.
For each pair of closely spaced stations, if the higher-population station is in population interval #3, and the lower population station is in population interval #1, I put that station pair’s year-average temperature difference in a 2-dimensional (interval#3, interval#1) population “bin” for later averaging.
Not only is the average temperature difference computed for all station pairs falling in each population bin, but also computed are the average populations in those bins. We will need those statistics later for our calculations of how temperature increases with population density.
Note that we can even compute the temperature difference between stations in the SAME population bin, as long as we keep track of which one has the higher population and which has the lower population. If the population densities for a pair of stations are exactly the same, we do not include that pair in the averaging.
The fact that the greatest warming RATE is observed at the lowest population densities is not a new finding. My comment that the greatest amount of spurious warming might therefore occur at the rural (rather than urban) sites, as a couple of people pointed out, presumes that rural sites tend to increase in population over the years. This might not be the case for most rural sites.
Also, as some pointed out, the UHI warming will vary with time of day, season, geography, wind conditions, etc. These are all mixed in together in my averages. But the fact that a UHI signal clearly exists without any correction for these other effects means that the global warming over the last 100 years measured using daily max/min temperature data has likely been overestimated. This is an important starting point, and its large-scale, big-picture approach complements the kind of individual-station surveys that Anthony Watts has been performing.
From pat (15:22:03) :
“The study, by senior scientists from the Met Office Hadley Centre, Edinburgh University, Melbourne University and Victoria University in Canada, concluded that there was an “increasingly remote possibility” that the sceptics were right that human activities were having no discernible impact.”
Never let your enemy state your argument! Does any skeptic here think that human activities are having no discernible impact? Have any of you ever held that belief? I don’t know any atmospheric scientist who believes that. Of course ‘discernible impact’ was never the benchmark for skepticism. We are not being asked to give bureaucrats control of the global energy supply because humans will have a ‘discernible impact’ on climate. Proving a ‘discernible impact’ proves nothing about the AGW argument! Absolutely nothing at all….but it is presented as if they are, therefore, correct and skeptics are stupid.
We are skeptical of a global warming CRISIS! We find the AGW theory to be extraordinarily weak and most of the real world data unsupportive, contradictory or better explained by other factors, some quantified (ocean cycles) and some theoretical (cosmic ray, tropical convection and so on). We find that the temperature record does not correlate well with the CO2 record and that AGW supporters have to make stuff up in order to force the data to fit (mid 20th century cooling). We find the lack of correlation so large, that there is no way that CO2 can be the primary driver of global temperature changes. Natural variations must be stronger and are largely ignored or miscalculated by the IPCC.
If after 100 years of CO2 influence, 20 years of focused science and untold billions of dollars spent to quantify the impending crisis, all they can say is that humans are having a ‘discernible impact’, then they really don’t have much of an argument! I could have told them that 20 years ago for a cup of coffee…and I don’t even like coffee!
The facts remain the same as they have for 20 years. There is nothing outside of computer models that indicates an impending climate crisis! And the only reason that the computer models do predict a crisis is because they are programed to do so, through the assumption of positive feedbacks for which there is no compelling physical evidence, despite the massive search!
Sorry for the rant. I just hate it when scientists with Phd’s get away with saying such ignorant things in order to make themselves look good. You would think they could just stick to the science if it was that compelling!
Kevin Minto,
You remind me of another point. Spencer’s analysis is for averages, not monthly or yearly maximum temperatures. What he’s plotting is the [i]average[/i] value of UHI, but as you mention, it would be different on a windless day with no clouds. So even if you used his numbers to correct for UHI, you’d still expect to have many days where the high temperature record is set purely due to the maximum value of UHI that’s not being completely compensated for.
This would matter if, after including Spencer’s adjustment factors, someone started plotting the number of stations setting record temperatures and offered it as further evidence of global warming.
When I made the point that technology/construction differences would make extrapolating and adjusting past temperature records problematic I knew you couldn’t really correct for it, but I was hoping that you’d mention such factors if you ever publish since it is best to be honest about the limitations of one’s work.
I am a little tired of hearing about the Parker study on windy versus calm nights. In statistical parlance, the test has very low power. There could easily be a significant difference between windy and calm nights but his test would not find it. The reason is his measure of windy versus calm is very noisy.
Let me explain the point as follows. Suppose “windy” versus “calm” were determined by the flip of a coin. Then you would not expect to find any difference between rural and urban stations on “windy” versus “calm” nights so determined. The conclusion that there is no UHI affecting urban versus rural temperatures would then be the wrong conclusion. Really, the result follows simply from the variable being used to determine the windy versus calm distinction does not in fact do that.
In reality, the measure Parker used is not a pure coin flip, but there are good reasons to believe it was a very poor measure of windy versus calm.
Clarification to wayne (19:09:14) :
… One is the amount that the pre-urbanization globe has warmed up to and including today
should have read
… One is the amount that the pre-urbanization globe has warmed up to and including today with the UHI temperature effect removed
Jim Clarke (19:43:58) :
Sorry for the rant It’s OK to rant, I write better when I am fired up about something but I try not to get fired up on WUWT!
George Turner (19:46:27) : for italics use to enclose the i
George, use the ‘greater than’ and ‘less than’ keyboard symbol to enclose the ‘i’ and
‘/i’ for italics.
Keith.
Jim Clarke,
The way I put it to my neighbors here in horse central is that the worst of the IPCC’s claims are true, one far off day when your children are parents, Kentucky will be as hot as Tennessee. This is supposed to scare me?
Sadly, I don’t think we’ll have such luck.
I once wrote a story about a man taking his family in their RV from Dallas to North Dakota, trying to stay ahead of the IPCC’s worst warming predictions by traveling north, looking at a map of the average US temperatures.
Unfortunately he drank to much coffee, got excited, and drove 5 miles up the highway before pulling into a gas station parking lot, where he has to spend the next 10 years to get back on his 2,000 year trip plan.
Most people haven’t considered that a huge (several degrees C) difference in climate occurs in just a few hours of highway travel.
Keith,
I’m an old blogger but sometimes hang out on message boards, so occassionally I forget whether I’m doing less-than greater-than or left-bracket, right bracket.
Admittedly, it’s not as bad a mistake as turning noise into a hockey stick or accidentally feeding a century’s worth of temperature records through a paper shredder, but it is embarrassing, nonetheless.
What irks me is that some people’s sloppiness is rewarded with a supercomputer center and a couple billion from the government while mine has won me nothing. Perhaps I should produce a late-night infomercial on how to turn abjectly error-prone analysis, unforgivably neglectful data loss, and criminal incompetance into eighteen wheelers full of taxpayer cash.
Can you do math? No problem!
Can you read a thermometer? No problem!
Can you solve Navier-Stokes equations? No problem!
Here at the climate data center we can turn your inabilities into money, mountains of money!
Scott (18:43:33) : My hypothesis is that the rural sites will show more variance.
What Dr. Spencer will end up showing is that temp is based on land use and changes thereof. I don’t know that UHI needs to be filtered out; ultimately one could probably predict the avg temp rise merely by looking at population density and farming. Clearly his data shows that land use makes the largest apparent change and the rest of it is waste heat etc in population centers (and PHX ought to be a special test case for this after they seemed to have discovered the miracle of watering.)
I’ve never quite understood what everyone thinks is being measured. Surely the only relevant place to measure if CO2 has any influence whatsoever is a desert at night: no water vapour, the most potent GHG. Simply look at the nighttime measurement of a handful of desert stations over 100 years. If they’re rising, we ought to be able to determine the CO2 influence (if any.) Why the entire enterprise is more complicated than this is a mystery.
Here’s a little something about Baltic sea-ice!
http://www.telegraph.co.uk/news/worldnews/europe/sweden/7371557/Over-1000-trapped-as-ships-stuck-in-Baltic-ice.html
Don’t get me wrong, this is useful.
But using proxies to make bad data better seems like treading water. Would it not be better to evaluate actual UHI at specific urban stations? The whole concept of average UHI seems to be an unnatural value since surely it is wildly different at different stations. One of my problems with so much climate science is that everything is a model or an extrapolation or an spurious average or a proxy. Can we not get back to measuring actual things?
Until the data is being corrected by real factors and not average factors, it won’t be that useful to prove or disprove anything. Perhaps I’m naive.
GHG. Simply look at the nighttime measurement of a handful of desert stations over 100 years. If they’re rising, we ought to be able to determine the CO2 influence (if any.) Why the entire enterprise is more complicated than this is a mystery.
I did this a while back. it was instructive.
t is common for people to think that more data means better result. This is not true. If more data are not adding independent information into an analysis, then less data will do just as well. I’m not certain that just 50 thermometers would be adequate, but it is possibly so. If one could find 50 locations representative of all climatic regions, well sited, undisturbed, unbiased, well instrumented and providing long records. Do you think one could find 50 such locations? How would we certify a site as representative of a region? These aren’t trivial concerns.
The figure is 60 optimally placed stations. See shen’s paper. just go to CA
and google it there. been discussed before.
Dr Spencer,
Please accept the below comment as sincere.
Why are you interested in analysis of these surface temperature records, given that you are a prominent longtime pioneer/leader in obtaining data from spacecraft?
Do see a significant future project of comparing/synthesizing the satellite and terrestrial data?
John
here for people who care about the number of stations and the sampling error.
She is the guy gavin quotes when gavin says u just need 60 optimally placed stations.
http://www.math.sdsu.edu/AMS_SIAM08Shen/Shen.pdf
I do remember seeing a paper reguarding land use changes in Florida, by a retired meterologist, that showed quite clearly that the warming that was occuring was largely linked to deforestation and samp draining.
If there are so few uncontaminated sites, then I would suggest that while the heat island effect is real, its ubiquity suggests that has effected global temperatures, not just local. Thus the “man-made” contribution would be quite high, while the “man-made carbon induced” would be significantly lower than calculated.
This means reducing the carbon output would have limited effect. On the bright side all we have to do is burrow underground or otherwise engineer heat-neutral cities and farms. Or we could just enjoy a slightly warmer climate, which should have a net beneficial effect.
I will add one other point here. Its possible the “man-made” contribution is slowing, not growing. The transition from virgin forests and prairies to farmed land that came with the paleolithic agricultural revolution may have had far more effect than what we are doing.
Steven,
What stations should I pull, for desert review. I will obviously double or triple the total, but I would love a series to use as a base line, uncontaminated by my own conceptions to start I wont hold you to any results derived from them, in any form. I am honestly looking for a list of interesting desert stations.
Your comment, made me smile, as the logic was easily understandable. In the middle of the desert, urban growth patterns should be missing. Sprinkler systems should not be contaminating the results, if we can use meta data, to confirm a lack of western urbanization around the location itself. They make the best set of base line long term stations possible.
A long series of desert location is quit possibly the single best series of locations possible. Granted, a change in climate at those locations, could impact year over year data, but that should average out over decades.
Love It…
Please share a list, either in public, or in private. I honestly will probably do zero with it, but look at it
What’s the average measured temperature increase per decade at the 724 stations, you reference, in the lowest population category? I assume they nearly all would have always been in that category? So we ought to get an average somewhere near to AGW affects on thermometers.
here is the shen paper on the number of stations…
http://www.met.tamu.edu/class/atmo632/(77)ShenKN94.pdf
We are debating the method of corrections of the raw temperatures because that is what has happened and has been used to come out with catastrophic projections and has been used by the IPCC in the effort to stampede the west into a cap and trade pyramid.
Finding errors in their methodology of averaging temperature does dispute the catastrophic prophecies effectively, and thus it is very useful.
Starting from scratch, thinking about the problem I would start with the energy balance . The energy coming in from the sun is measured. The energy going out from the earth can be measured by satellite. That is all we need to know if the earth is heating or cooling during the period we have satellite measurements, and at what rate.
Temperature is useful for humans , from cultivation to clothing to safety it is a necessary measurement, and that is why it has been recorded since the 19th century.
BUT
Temperature is a proxy of energy: if it is a black body the energy flux out is given by j=CxT^4 . Now the earth is not a black body. Therefore each square meter of earth is characterized by a gray body constant C, ( the ratio is called emissivity) and also the radiation spectrum by a gray body is different for each C.
Emissivity can change by as much as 30% ( there are tables of emissivity, sand is .75). Therefore to use temperature and the black body formula as a proxy to extract the energy radiated by the earth requires knowledge of all these materials covering the surface of the planet. Suppose we can do that with enough computing power: have 50 types of surfaces with an average gray body constant and radiation spectrum. In this type of calculation the urban effect will have its own emissivity and spectrum.
Can we get a good estimate of energy radiated out this way to be compared with the satellite data?
There is the next hurdle: On land the thermometers are 2 meters above the ground, measuring air temperature. To use that temperature in the gray body formulas there is the crucial assumption that the ground temperature is identical to the air temperature. This is not the case. The ground can be much colder and much hotter in tens of degrees than the air at two meters:
Wind from the south at the north pole, wind from the sahara on europe, wind from the north to the south etc etc.) There is no real equilibrium between the temperature of the ground and the surface, it is what creates the winds after all.
I am not aware that anybody is measuring real ground temperatures.
Even over the seas, the water is much colder in the summer than the air and warmer in the winter than the air ( lets hope that at least for the oceans the energy balance was from sea surface temperatures).
( the atmosphere has a T^6 dependence not a black body one)
There can be tens of watts errors if one uses the air temperature to compute desert radiation for example.
I do not see how to overcome this problem. A proxy is needed that could tell us how many degrees hotter or colder the ground is at the given time, and I cannot conceive of one. Maybe if one did a lot of measurements in the desert and the arctic and the tundra and the amazon etc etc, one could come out with a table that could be used together with the gray body approximations to gauge the energy radiated that should be compared with satellite data.
So I think one should assign temperatures their true status of telling us how the biosphere is doing, but not for the energy balance. We should really only use satellite data.
mosher — I did this a while back. it was instructive.
What did you find out, and when did you find it?
Or is this in upcoming paper?
Thanks…
Message for Roy Spencer
I have found the blogsite for Willis Island mentioned in my earlier post.
It is http://kenskingdom.wordpress.com/2010/02/05/giss-manipulates-climate-data-in-mackay/
You will need to scrowl down to get the Willis Island graph.
I hope it is of some help.
Good luck.
Since my post above ,I hope that I have found the person who can best help you in CSIRO. His name is Dr. Simon Torok, and he has been kindly responding to my emails to the CSIRO regarding my enquiries on some interesting points that I have observed in the CSIRO website.
If you review to the comment from Janama @18.02.45 above, you will see a reference to a paper on UHI that was produced by Simon J.Torok when he was using the E-mail address in 1998 of [snip]
Simon’s E-mail address is currently… [snip]
(That is if they are the same person.)
Reply: Point people to links, but we frown on posting email addresses here. ~ ctm