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.
===========================================
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.
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
I think it might also be useful to go by economic output rather than population. There are a lot of small towns that don’t increase a lot in population, but are very transformed by an economic boom. I would guess the amount of cement/buildings can be tracked closer by dollars than people. Also, the stats may be better for that, or at least a second set to cross reference.
Nice work!
“The lack of systematic auditing of the IPCC, NOAA, NASA or East Anglia CRU, leaves a gaping vacuum. It’s possible that honest scientists have dutifully followed their grant applications, always looking for one thing in one direction, and when they have made flawed assumptions or errors, or just exaggerations, no one has pointed it out simply because everyone who could have, had a job doing something else. In the end the auditors who volunteered — like Steve McIntyre and Anthony Watts — are retired scientists, because they are the only ones who have the time and the expertise to do the hard work”
The Money Trail
http://www.abc.net.au/unleashed/stories/s2835581.htm
Carbon Market Update
“Investors are becoming less convinced that a global carbon market, estimated to be worth about USD 2 trillion by the end of the decade, can be established as uncertainty over global climate policy persists.
The absence of legally binding global climate deal and a federal emissions trading scheme in the United States are standing in the way of the market in global emissions trading growing to achieve yearly turnover of USD 2 trillion by 2020.
“There will only be a USD 2 trillion market if the US gets on board,” Trevor Sikorski, head of carbon research at Barclays Capital, told Reuters at a carbon conference in Amsterdam.”
Hopes For USD 2 Trillion Global Carbon Market Fade
http://www.moneycontrol.com/news/business/hopes-for-usd-2-trillion-global-carbon-market-fade_444850.html
Someone once postulated that 50 well placed thermometers would be adequate to gauge average global temperature trends. Any statisticians care to comment? Are there 50 uncontaminated thermometers that have 100 year records and are somewhat well placed?
I enjoy reading your articles. Thank you.
Two points to consider.
1..Willis Island off the coast of North Queensland, Australia has a very isolated station site with reliable records for cyclone forecasts that may be worth your consideration. I have seen the data on a blogsite some time ago. You might have to request the release of the data from the BOM or CSIRO in Australia.
2…I’m not sure your hard work will change anything in the “scientific peer reviewed establishment” . It seems to me that the UN Agenda 21objectives are having to be met by your scientists to satisfy your governments goals.
“”” 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. “””
This paragraph, Roy, captures what i think is the essence of the problem.
Few sane persons would doubt that the structural trappings of MAN do have an influence, both on the local environs of those trappings; and also on the measurments that are made in such places to represent what is going on there.
The airport runway, is a classic case in point. Those “weather” stations, exist there for the specific purpose of telling the aviation community, the important information they need to jusdge the current safety of operations from that runway; they were never intended to be a part of a global “Climate” reporting network; but they are; or some are.
And your process of trying to correlate at least the local effect of the mantrappings to simpler measures like population density; sounds about as ingenious as anything one might conjure up as a “proxy” for concrete and Weber Grills.
UHIs do tend to get hotter than the average landscape they used to be, so it is proper to measure them.
The error comes in trying to extend the influence of the measured UHI far beyond its real sphere of influence.
That to me seems to be an error in methodology; and not something which calls for “correction” of the UHI measurment. The correction called for is in curbing the radius of influence assigned to the UHI; not in changing its value.
But it sounds like you have tumbled to an interesting “proxy”; I won’t say “stumbled over”, because you ain’t the stumbling type.
I look forward to when we can read the expurgated version of your paper, when you feel it is ready for prime time. In the mean time, thank you for letting us all kick you in the shins while you are working on this.
George
Dr. Spencer, I apologize up front for this post.
I think the world of you.
I looks to me like you are trying to massage, manipulate, and squeeze old temperature data, that you know is contaminated in the first place.
That is what got us to this point, and no different that what has already been done.
It also gives credence to a “theory” that is so shaky, it can’t even stand on one leg.
What I would like to see is more work proving or dis-proving the theory of AGW. Until that is done, it does not matter if a chicken laid frozen eggs in 1850, or if the eggs came out hard boiled.
A link to a file with the values that are plotted in the graphs would be helpful, and also a graph with logarithmic x-axis, to make a direct comparison with torok et al possible
http://www.warwickhughes.com/climate/seozuhi.htm
Torok, S.J., Morris, C.J.G., Skinner, C. and Plummer, N. 2001. Urban heat island features of southeast Australian towns. Australian Meteorological Magazine 50: 1-13.
thank you
Can´t wait to see corrected global dataset, including fixed SST data. Here is one attempt: http://www.worldclimatereport.com/index.php/2010/03/02/most-of-the-observed-warming-since-the-mid-20th-century-likely-not-from-human-ghg-emissions/
Jim (14:41:26) :
Someone once postulated that 50 well placed thermometers would be adequate to gauge average global temperature trends. Any statisticians care to comment? Are there 50 uncontaminated thermometers that have 100 year records and are somewhat well placed?
Hmm……7 per continent. Nope. Don’t think so. Of course, the word uncontaminated isn’t probably used here properly. Even if the population is 0, you still have to account for elevation, proximity to water, heck, even the color of the rocks nearby. Just my thoughts, I’m sure there are others that would disagree.
Let the adventure begin. No hidden peers or pals, open. Come on Gavin, James or Phil – your productive input please.
The business model of science publishers will be challenged, perhaps revolutionized. But what we have learned from the CRU-mails their model looked more than pale anyway.
Is the trend line supposed to look so fantastically logarithmic? If so, what are the implications of that (if any)? I’m not a statistician so forgive me if this question is ignorant.
http://www.statemaster.com/encyclopedia/Image:Graph-of-common-logarithm.png
Now that I understand what this study is attempting to achieve, and what metric you’re planning on using and why, I might be able to add to your efforts in a small way.
I would recommend identifying particular sites to be specific exclusion cases. Airports and sewerage treatment plants for example. These may be added to the global temperature record only after another more specific study is conducted for their bias.
You appear to be making a set of recommended adjustments based on population only. But I think it’s more likely you’ll need to identify environment types. For example, proximity of ocean or lake, height above sea level, any prevailing winds, trapped valleys subject to fog or temperature inversions, industrial areas, city centres, suburban areas, decentralised towns, ect.
A range of site conditions will make for a longer lasting adjustment set which could be applied to any site around the world for true global temperature gauge adjustments.
A hell of an effort in front of you, but it does need to be done. I wish that I could be involved, but alas.
Dr Spencer says:
“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.”
This is of course is very true… But i can say with relative certainty that there have been far larger changes in rural settings in recent decades comparative to urban in regards to modernization of agriculture/horticulture and required transport… I myself have lived rural all my life in new zealand, and only twenty years ago the landscape i grew up in has changed dramatically… the roads are sealed!!! farmers knock hills off, and re contour paddocks etc(if yer can afford a bulldozer..why not eh)… but all of this would be no help… however id imagine that local councils(or whatever theyre called wherever) would have records of public expenditure on roading development/public works expenditure, per district, which may be a possible proxy? Just because it would probably be related to economic expansion in that area… no council is going to spend money to run roads up to a recluse who lives in a cave, but they will to get milk tankers to the more recently developed large commercial farms etc
But this in itself would be a huge task to try and compile, and could well be useless. But just a thought.
Good luck and all the best with yer work
“”” Jim (14:41:26) :
Someone once postulated that 50 well placed thermometers would be adequate to gauge average global temperature trends. Any statisticians care to comment? Are there 50 uncontaminated thermometers that have 100 year records and are somewhat well placed? “””
Well Jim, I would say (as another someone) that “someone” didn’t know what they were talking about.
Bear in mind that the changes being sought are extremely small. We are told for example, that a complete adherence to the Kyoto accords, as to curbing future CO2 emissions, would likely result in a warming reduction over the next 50 years, that would be too small to even observe.
So in attempting to measure to the degree of sensitivity a global continuous function of time and space, with clearly known temporal cycles of at elast 24 hours and 365 days and a spatial extent, that covers a total extreme temperature range of about 150 deg C, all of which may be present simultaneously on a northern summer mid day, it simply is not a matter of statistics.
It is a question of sampled data theory; and that theory says that even the average value of a continuous function is not recoverable in the presence of out of band signals at just twice the sampling rate. Forget the central limit theorem; and any other trappings of statistical mathematics; that is not where the problem lies; the problem is buried under a mountain of aliassing noise caused by quite inadequate sampling procedures.
50 thermometers won’t do the job. 50,000 might have a chance, but I wouldn’t bet on it.
Part of the problem comes in your use of the word “trend”.
The word itself conjures up the result of observations over an extended period of time.
The trouble is that NO interval of time is sacred, when it comes to assigning an appropriate window to look for a “trend”.
Any extensive analysis of “climate data” over any geological time scale you want to examine; will clearly show that the data has all the ear marks of 1/f noise. Not that I am claiming that the data DOES fit a 1/f noise spectrum.
Another way of putting it would be to sday the data is fractal in nature; and no matter what time scale one chooses to look for a trend; similar appearances, will occur at both longer and shorter time frames.
So any of these “regression” analyses that climate statisticians seem to like to indulge in, is unlikely to lead to any conclusions about what might happen over any other time frame.
Remember that regressions and trend analyses, and smoothing algorithms, are merely processes for throwing away actual real data, often gained at great pain and expense; and replacing that real data with completely fictitious pseudo data.
This is the sort of research that should have been done by government operated Met Offices to check that their figures were correct; not by an individual. Surely at least one government Met Office, somewhere in the world’s 203 nations, has actually done some work similar to this; if not it is a dreadful oversight and omission which has cost, is costing and will cost us all dearly.
Thank you Dr. Spencer for taking the time to do this vital research. I wish you well for when this research is published in the scientific literature.
I grew up with parents who were born in the 1920’s. To me, UHI affect increased after WW2, as the age of Electricity and Air-conditioners were deployed in the western world.
At the turn of the 20th century, when you are suggesting that you start looking at records, sidewalks were wooden in places instead of concrete, and horses were still a means of traffic congestion. Roads were dirt, instead of asphalt or concrete. Roofs were wood shingles instead of hydrocarbons, coated in black, sealant.
To me, UHI is the effect of a post world war industrialization of humanity’s lifestyle. Its not an effect you can remove from the readings with any consistency. In fact, its the removel of it, that has allowed the chicanory in the temp records to happen in the first place, in some cases.
If you change the micro-environment that you live in to a warmer bias, you changed it. That is the temp, as read at that location. Its micro environmental change, it is not Climate Change.
I think we have to be really really careful, when we suggest why we should be changing temp records, because of X reasons. I don’t like X in this case.
I think that temp records should stay in the raw. You should be able to turn off stations in the models based on if you want to remove urbanization from the record. But finding a golden answer for UHI, to apply to each station in question, is what got us into trouble. In my personal humble opinion sir.
Best Wishes,
Jack Barnes
It 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 present state of the land temperature record, though, reminds me of a statement, made in earnest, by one of my Ph.D. committee when I pointed out the hopeless state of a particular set of seismic data. “Sure,” he admitted, “the data are crap; but, there is so much of it!”
Tilting at windmills:
Our energy policy is being driven by EU diktat
[ http://www.youtube.com/watch?v=G-ENhGRJ028 ]
the fightback! whereas reuters and the TV stations ignored the UK Parliamentary Inquiry on Climategate, watch how this ‘insiders’ review goes viral in the MSM.
4 March: Financial Times: Review backs man-made global warming
By Clive Cookson in London
The case for man-made global warming is even stronger than the Intergovernmental Panel on Climate Change maintained in its official assessments, according to the first scientific review published since December’s Copenhagen conference and subsequent attacks on the IPCC’s credibility.
An international research team led by the UK Met Office spent the past year analysing more than 100 recent scientific papers to update the last IPCC assessment, released in 2007.
Although the review itself preceded the sceptics’ assault on climate science over the past three months, its launch in London on Thursday marks a resumption of the campaign by mainstream scientists to show that man-made releases of greenhouse gases are causing potentially dangerous global warming.
“The fingerprint of human influence has been detected in many different aspects of observed climate changes,” said Peter Stott, head of climate monitoring at the Met Office Hadley Centre for Climate Research. “Natural variability, from the sun, volcanic eruptions or natural cycles, cannot explain recent warming.”
The review, published in the journal Wiley Interdisciplinary Reviews: Climate Change, found several “fingerprints” of warming that had not been established by the time of the last IPCC assessment but were now unambiguously present.
One is human-induced climate in the Antarctic, the last continent where regional warming has been demonstrated….
A separate study by Russian and US scientists, published today in the journal Science, shows methane, a powerful greenhouse gas, is escaping from the seafloor of the warming Arctic Ocean more rapidly than has been suspected
http://www.ft.com/cms/s/0/9513bee6-27b3-11df-863d-00144feabdc0.html
5 March: UK Times: Ben Webster: 95 per cent chance that Man is to blame for global warming, say scientists
The evidence that human activity is causing global warming is much stronger than previously stated and is found in all parts of the world, according to a study that attempts to refute claims from sceptics.
The “fingerprints” of human influence on the climate can be detected not only in rising temperatures but also in the saltiness of the oceans, rising humidity, changes in rainfall and the shrinking of Arctic Sea ice at the rate of 600,000 sq km a decade.
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. There was a less than 5 per cent likelihood that natural variations in climate were responsible for the changes. ..
However, a section of the study that said changes in hurricane activity were poorly understood is likely to be seized on by sceptics…
The study found that since 1980, the average global temperature had increased by about 0.5C and that the Earth was continuing to warm at the rate of about 0.16C a decade. This trend is reflected in measurements from the oceans. Warmer temperatures had led to more evaporation from the surface, most noticeably in the sub-tropical Atlantic, said Dr Stott. As a result, the sea was getting saltier. Evaporation in turn affected humidity and rainfall. The atmosphere was getting more humid, as climate models had predicted, and amplifying the water cycle. This meant that more rain was falling in high and low latitudes and less in tropical and sub-tropical regions.
http://www.timesonline.co.uk/tol/news/environment/article7050341.ece
Al Gore To Give Free Lecture At Duke
03/04/10 12:17PM
Former Vice President Al Gore is coming to the Triangle. He’s slated to deliver the 2010 Environment and Society Lecture at Duke University.
The lecture is part of an ongoing series that brings in prominent figures who are helping build a sustainable future.
The lecture is free and open to the public, but you will need tickets to attend. The event is April 8, at 6 p.m. in Page Auditorium at Duke. For more information and to secure tickets, visit http://www.nicholas.duke.edu/deanseries.
http://www.wchl1360.com/details3.html?id=13749
I love the work you do here and I am convinced by yours and others comments, Anthony. I also look at the real climate site, which the whole I find desperate and unconvincing. They recently posted this though…
Can anyone with expertise in the area make comment, either for or against this work ? I’d be interested to hear any thoughts. And apologies that this post isn’t commenting on this particular article
http://www.realclimate.org/index.php/archives/2010/03/climate-change-commitments/#more-3070
OK, in this light, using population makes a ton more sense. I thought you were trying to quantify an adjustment for the current record, hence the references to station siting. The large red spot in Canada does not appear to be explainable with this method, but E.M. Smith does have a plausible explanation for that, especially if that record is being extrapolated from one containing a UHI signal.
Wouldn’t land use trends affect rural temperature measurements as well? Farming/Oil drilling for instance may have a warming effect because vegetation is cleared vs. say wandering meadows or even forested land. Does this begin to get to complicated for your model?
Nice job so far. I think this will shed some much needed light on the subject of UHI.
mention of gore in norway on previous threads. here, for anyone who can translate norwegian:
4 March: Al Gore for solenergi
http://www.framtiden.no/201003042849/aktuelt/klima/al-gore-for-solenergi.html
Since we (Skeptics) are funded by Big Oil, can’t we just get high resolution multi frequency infrared photos of urban and rural areas at T-min and T-max comparing adjacent urban/rural areas for UHI?
Oh that’s right we don’t really have financial backing.