Can you explain why there was a greater increase in maximum temperatures in the urban stations compared to the rural stations. I read the paper, but could not find an explanation (although I may have missed it).
(For those who have already seen this, please bear with me — I’ve added a few new features, and I’ve packaged this up to make it easier to use).
For those who are interested, I bundled a very crude global-average temperature averaging algorithm with a Google Map front-end, and wrapped everything up in a portable, easy-to-use VirtualBox virtual-machine (VM) appliance file.
The VM file is a big download, unfortunately (about 1GB), but it was the easiest way for me eliminate hassles involving browser/software setup/configuration without going back and doing a proper software design from scratch ;)
With the VM file, you just download it, import it into VirtualBox (available for free at http://www.virtualbox.org), hit the VirtualBox “start” button, and go. It should run on any newer Mac (OSX 10.5 or newer) or Windows PC/Laptop (2GB or more memory strongly recommended). It boots up a Linux virtual machine which then automatically launches everything.
The basic algorithm is *very* simple-minded — just anomaly gridding/averaging, with grid-cells big enough that I didn’t have to worry about computing interpolated values for empty grid-cells.
I start with 20×20 degree cells at the Equator and adjust the longitude dimensions to keep the grid-cells *approximately* constant in area as you go N/S from the Equator. (This kept the grid-cells from shrinking in area — like I said, I wanted to minimize the number of empty grid-cells).
What you can do with this is “roll your own” global average temperature results by clicking on station icons on a Google Map display.
Among other things, this makes it easier to identify the “long record” stations needed to get good results going back to the late 1800’s.
You can also generate “station batch” results (i.e., all stations, all rural stations, all stations with at least XXX years of data, etc.)
Raw and adjusted data results can be displayed together or individually (with the NASA/GISS results also displayed for comparison purposes).
The results are displayed in very simple GnuPlot window (nothing fancy).
The upper plot in the window shows the temperature results computed from GHCN V3 raw and adjusted data via the simple agorithm. In addition, the NASA/GISS “meteorological stations” index is plotted for comparison purposes.
The lower plot shows the number of selected stations that actually reported data for any given year. (The number is fractional; if a particular station reported data for 6 months in a given year, I count it as “half a station” for that year).
This will allow you to correlate the quality of the temperature results for any given time period with the actual number of stations that actually reported data for that period.
Play around with it a bit, and you will find how amazingly easy it is to confirm the NASA/GISS global-average results — just a few dozen stations scattered around the world will do it. Rural stations, urban stations, raw or homogenized data — all combos will produce largely similar global-average results.
There’s nothing of any real scientific interest here — it should be considered a demo tool that can be used to shoot down attacks made on the global temperature record by the usual suspects. What it does show very nicely is that you can confirm NASA/GISS with data from a very small number of stations run through a *very* rudimentary global-temperature algorithm.
And it’s simple enough for middle-school students to use.
[Response: Very nice. We did something similar (but even simpler) when it was being insinuated that the temperature trends were suspect, back when all those UEA emails were stolen. One only needs about 30 records, globally spaced, to get the global temperature history. This is because there is a spatial scale (roughly a Rossby radius) over which temperatures are going to be highly correlated for fundamental reasons of atmospheric dynamics. Anyone claiming significant issues with the temperature records has always been barking up very small tree.–eric]
To be perfectly honest, we aren’t sure why the urban-correlated max biases are as large as they are in the TOBs data in recent years. It is interesting to note that it shows up much more prominently in the station pairing approach than the spatial gridding method, which makes sense given that station pairing tries to control for a max cooling bias due to MMTS transitions that is somewhat urban-correlated (e.g. airport locations and similar sites didn’t have the transition, while rural sites were slightly more likely to). Max biases are also significantly smaller over the century-scale period than the last 60 years (see Figs. 1 and 3 in our paper). That said, there definitely is some follow-up work to do in examining urban-correlated max biases in recent years in more detail.
First I just want to thank Gavin for maintaining one of my favorite sites on the www. Definitely advancing my progress on the Junior Climate Scientist merit badge ;-]
I am so excited to have an interesting and relevant comment to add to the discussion. Guy S. Callendar’s groundbreaking paper: Callendar, G. S. (1938), The artificial production of carbon dioxide and its influence on temperature. Q.J.R. Meteorol. Soc., 64: 223–240. doi: 10.1002/qj.49706427503 is a fascinating read. Probably the most noteworthy aspect of this paper is a graph (Fig. 2) which by my careful measurements predicts a rise in global average temperature of 0.59C from the 1938 level if atmospheric carbon dioxide were to reach 400 ppm. Callendar thought that would happen around 2100 so he gets no crystal ball award but it seems that his scientific prediction is about as accurate as any. Rather remarkable coming from a time when published papers included hand drawn and lettered graphs.
I have never seen anyone mention Callender’s addressing of the urban heat island effect in his calculations of the then current global average temperature. Callendar was explicitly aware of this possible error and addressed it by comparing isolated “Best Exposures” stations to those in small towns and those in large towns. Averaging the temperatue rise since the earliest days of systematic monitoring for these three categories of stations found (respectively) rises of 0.23C, 0.19C and 0.21C. Callendar concluded, “This shows that no secular increase of temperature, due to ‘city influence,’ has occurred at these city stations, in spite of the great increase of population in the immediate neighbourhood during the period under consideration.”
So, including the BEST study and the new Hausfather paper, how many times has the urban heat island effect been addressed?
[Response: A quick search of AGU journals reveals at least 100 publications. This has been addressed many times. Nothing against Zeke’s excellent new work, but the first-order answer — and the only one that matters if the question is “what is the mean trend in temperature in the last 100 years — has been known pretty well for a decade or two. Nothing really substantial has changed. –eric]
I might have missed something but do you prove that urban sites are adjusted down rather than rural up?
Comment by Rikard avfuktare — 13 Feb 2013 @ 2:55 PM
Effectively yes. We do a homogenization run using only rural stations to homogenize all stations (excluding urban stations from the homogenization process) and get effectively the same results. Our supplementary materials go into a bit more detail in examining this.
The PHA looks at different series between stations and their neighbors over a window of time. Things that cause absolute temperature differences shouldn’t really impact the results, unless a reservoir was created near an existing station, in which case it might result in a break point that is detectable.
Thank you for your response. It would be interesting to see a follow up regarding max temperatures. Gordon asked how often the UHI has been addressed, and I would say quite often in the U.S., owing partly to the preponderant temperature data available. How well would you say that temperature data in other parts of the globe have accounted for the UHI (Europe excluded)?
Questions I’m asking myself, will get to poking after them as time allows. Or, of course, answers and pointers welcome.
Has anyone correlated day by day or hour by hour measurements of other trace gases, particles, and humidity with temperature? Ruled out an urban smog-heating effect for example?
What other molecules are known to be concentrated around cities (‘point’ of origin) from which they’d diffuse or blow away, thinning out with distance?
How does air temperature correlate with sky temperature, what you get pointing an infrared thermometer at the sky, on those inversion-layer days and nights?
I’m glad to see the study looking at how much pavement is in place. Is that thinking about how much rainfall evaporates into the local air (thinking of clouds of steam rising off hot pavement after rainstorms) — as well as how much rain goes into the ground locally available for plants, vs. how much is drained off to storage in tanks or to the sewage plants in the suburbs?
The effect you are looking for can be seen in the Orland station in the US.
A dam was built in the early 20th century. At one point I started to look at GIS data on damns and resevoirs, probably need to finish that.
“I might have missed something but do you prove that urban sites are adjusted down rather than rural up?”
To follow up on Zeke’s response, I think the figure you are looking for is Figure 9 (and this important issue is examined more in the SI as well). As you can see, if you adjust the USHCN stations by *only* urban CONUS stations, then the urban stations (obviously) have a tendency to adjust the rural stations upwards. But as should be apparent in that figure, homogenizing using the full set of CONUS stations (as the USHCN v2 does) produces a time series that is very similar to homogenization using *only* rural stations.
If you are interested in seeing the time series when only the most rural stations are used (both for homogenization and actual station data, with ISA < 1%, which is sparse enough early that it misses many of the breaks), I have put up a figure in my post:
To the individual who is still wondering about whether UHI has been properly accounted for in the global-average temperature results published by NASA/NOAA/etc., all I have to say is, why don’t you roll up your sleeves and crunch the temperature data yourself?
All of the data and software/data-crunching tools needed to do that are freely available on the web and are just a few mouse-clicks away. You guys have been going on and on about this for *years* now. If this is such a pressing issue for you, then why haven’t you bothered to perform any of your own data analysis in all that time? This is not rocket-science — it’s #*&!ing averaging, for Pete’s sake.
BTW, here’s what I was able to produce with the VM appliance (that I posted about above) and a bit of mouse-clicking:
I took me nearly as long to make a screenshot image of the results and upload it to ImageShack as it did to generate the results.
There is a bit of a departure between the NASA results (red) and the raw data results (green) prior to about 1930 — but then look at the number of selected stations that reported data for those years — 20 or fewer.
Challenge to “skeptics” — fire up the VM file per the instructions and pick any 50 stations (rural or urban) scattered around the world. I am highly confident that you will *not* be able to generate results with a long-term temperature trend that contradicts the NASA global temperature trend (unless you cherry-pick “short record” stations that result in poor global coverage for significant time periods).
This is probably a silly layman question on UHI, but aren’t those stations simply measuring actual temperatures, i.e., couldn’t growing urbanization simply be regarded as another anthropogenic forcing? Why is it even necessary to adjust for UHI if what those stations are measuring is the actual temperature?
[Response: The main issue here is that if one were to use an urban station to represent the temperature change over a much larger area than that represented by the urban area itself, then there would be bias. A second order thing one might also be interested in what it would be without the albedo changes of cities. But the first order thing is more important: what to do if there are lots of stations per unit area in cities, and fewer in the countryside. In general, this is the case, hence the need for these sorts of calculations. –eric]
Tangential to this silly question, I’ve often wished realclimate.org had a section where us climate science-loving laymen could post questions. I’m perfectly content to google and wiki away to advance my climate knowledge. I read the books (Hansen, Mann, Alley, etc., etc.), peruse the best climate websites (and occasionally WUWT or Morano’s site, painful as it is, under the know-thine-enemy premise. I even pick up old Science, Scientific American and Nature mags at my local library for a dime when they have climate articles, and read those articles over and over until I grasp most of what’s in ‘em to a satisfactory degree. However, occasionally a question pops into my head that I can’t answer with a moderate amount of research and reading, and I’d love to simply be able to pose such questions online and get an answer, or at least some direction toward an answer, such as a link to a paper (preferrably NOT one behind the paywall). Have y’all ever thought of adding such a feature to realclimate.org. I realize you are all quite busy fending off denialists and doing the occasional bit of research, but for you folks, the kinds of questions I’m thinking of would be relatively easy and not take up much of your time (in theory anyway).
Love your website. I used to “waste” my time doing difficult sudoku puzzles, but once I “discovered” the fascinating and challenging world of climatology, I realized it was somewhat akin to the most challenging sudoku puzzle ever, and one that I’d never, much to my delight, ever complete.
There are some papers that address the aerosol issue for UHI, but the largest effects are the area of land surface that has been transformed: It hits the albedo, the surface roughness, the emmisivity, the heat storage.
Below 10% ISA you wont see an effect unless that 10% happens to be in the footprint of the sensor ( say within 100x of the sensor height )
In my books that urban heat, as well as heat generated by producing electricity through fossil fuels, even nukes, away from cities, driving ICE cars, etc is all anthropogenic environmental harms, in addition to the human-enhance GH effect warming. It can all combine to cause heat deaths and other problems….not to mention harms from the concomitant local and regional pollution produced when producing GHGs. There is the synthetic or holistic outcome, beyond just the outcomes of each taken individually.
Has anyone done a study of the overall effect of all this combined together?
I’m thinking if we do a cost-benefits analysis (somehow adding in costs on into the future for 100 years — tho it should be 100,000 years), and do it in terms a human lives and well-being scale instead of $$$, it seems to me the costs would far outweight the benefits (of using fossil fuels and even of nuclear power — for which one must also throw in the harms to and deaths of tribal peoples in uranium areas — Niger, the U.S. Southwest, the Bennett Freeze area, etc.).
Or, if one insisted it be in $$$, then figure it in terms of hypothetical lawsuit settlements for each person harmed or killed or having his/her life shortened (even figuring that men are worth more than women, rich more than the poor, and adults more than children or the elderly….as mentioned in A CIVIL ACTION).
Comment by Lynn Vincentnathan — 14 Feb 2013 @ 10:28 AM
How might UHI impacts be altered if urban albedo changes from ‘ white roofs’ initiatives were amplified by equally deliberate brightening of urban and suburban water surfaces?
I agree that there was little doubt that global UHI is not going to be that large (after all, some existing series like GISTemp already do explicit UHI corrections and don’t find immensely different results). That said, I wouldn’t necessarily classify it as a settled issue. While Parker found relatively little effect, other studies have found larger regional effects (e.g. Jones in China). We actually ended up finding a larger effect in the raw data than we had initially expected (e.g. 14% to 21% of the century-scale trend). The fact it is effectively identified and corrected by automated homogenization is good to know, as these approaches are being increasingly applied to global temperature data (in GHCN v3, for example). When station networks are sufficiently dense to detect anomalous local breakpoints or trends via pairwise comparison, additional explicit UHI corrections (like GISTemp’s nightlights) may not be needed.
[Response: Zeke, thanks for the response. I agree, it is not “settled”. But then, nothing ever is. The important point is that while some use the lack of total perfection as an excuse to act as if we don’t know anything, we have known for a very long time that the impact is not big — that is, not big enough to change the question (has the earth warmed up in response to CO2?) or the answer (yes). I would furthermore wager that no amount of refinement about UHI is going to make a meaningful dent in our ability to estimate climate sensitivity (equilibrium or otherwise). Further improving UHI calculations is important, but its relevance to the big picture has been exaggerated to a ridiculous degree in the blogosphere. To borrow from Kuhn, this is “normal science”: no revolution is to be expected here. I have read your paper, and I think it is a solid and very worthy contribution.–eric]
Have any actual experiments been done? Maybe by someone who owns a swimming pool, or a farm pond? I’d be very curious how such a layer of microbubbles affects various creatures that use the water surface, from human swimmers to water striders to mosquito larvae to migratory waterfowl.
I recall hearing a story about a college town police chief, back in the 1950s, phoning the college science department to ask what they knew about the layer of white foam on the city reservoir, and why the ducks that landed in that water were sinking and drowning. A container of some very powerful detergent was missing from the supply cabinet.
‘A quick search of AGU journals reveals at least 100 publications. This has been addressed many times. Nothing against Zeke’s excellent new work, but the first-order answer — and the only one that matters if the question is “what is the mean trend in temperature in the last 100 years — has been known pretty well for a decade or two. Nothing really substantial has changed. –eric]
I think your quick search might be a bit broad. With regard to studies of UHI in the US, there are less than a handful and none of them satisfactory. I won’t belabor the details of each one, but I think its fair to say that Zeke’s paper represents the most comprehensive study of UHI in the US record that has been done.
Nobody who worked on this projected expected ‘revolutionary’ results and responses like “we knew that already’ are not really on point or charitable.
It’s good science, why not leave it at that. It advances our understanding, why not leave it at that? They achieved what they set out to do. Why not thank them for the contribution and keep your opinions about the grander meaning to yourself?  here you have an opportunity to give nothing but praise to Zeke, and the other guys who took the advice given. They took up the challenge and did their own damn science. And its damn good science. You take an opportunity for showing other citizens how they can contribute and you play a stupid game of well, its not revolutionary.. its nothing in the grander picture. Talking with Zeke throughout the course of this project I held out hope that folks here would showcase this as prime example of how citizens can make contributions. You pooped on it. why?
[Response: The irony of your repeating tired old insults, while both completely misinterpreting my statement, and accusing us of playing “stupid games” is pretty ironic. Meanwhile, I stand by my point: Zeke’s work is excellent, but its significance is small. The latter point is relevant because there are clearly many that think that the UHI effect could be “game changing” if only it were accounted for. (It not always their fault, of course, thanks to the huge amount of speculation on UHI promoted by the various self-appointed auditors in the blogosphere.) Kudos to Zeke for breaking out of that mold and looking at the facts. You might try following his lead.–eric]
I don’t think Eric intended to be dismissive. He just pointed out that, while interesting, the paper doesn’t significantly change out best estimates of temperature records. Not being particularly groundbreaking is one of the downsides of finding a small UHI effect in the homogenized data :-).
(As an aside, I always wonder why some folks accuse scientists of following a consensus. Finding results the fly in the face of common knowledge is much more interesting than just confirming things that most people already believed, and is arguably easier to publish).
Thanks Russell, no, I’m just not sufficiently well educated yet.
I know there are big differences between your methods and the old detergent-foam stuff — just not very clear on what’s going to be different.
Do mosquito larvae successfully get their breathing tubes up through a layer of microbubbles, or can they wait out a period til the bubbles go away?
Does a layer of microbubbles change the environment in interesting ways for water striders, diving spiders, tadpoles, or kids swimming in that water?
What happens to viruses and bacteria in the surface layer?
Does a layer of microbubbles make a sound-reflective layer difference?
(and can I get a microbubble-generating pump to pump fire retarding foam? I’d guess whatever you use would be less nasty chemically than what’s used by firefighters now and comparably effective in cooling and smothering a fire or temporarily blocking ignition?)
(I’m doing botanical restoration on a forest fire site that a fire crew defended back in the late 1980s, as their camp was on that location — and the stumps they sprayed to stop them burning are still surrounded by a circle of dead ground, while the surrounding area is growing — forest service botanist said yeah, it’s something in their firefighting spray foam stuff, nobody knows…..)
Inverse clouds of micron sized microbubles are subject to Stokes law, and no more stratify than cloud droplets. They typically don’t rise fast enough to reach the surface before dissipating by gas solution.
http://adsabs.harvard.edu/abs/2012EGUGA..14.7007P indicates people who can do the work are thinking about the kinds of questions that occurred to me. That’ll do. It’ll work out. Like anything else, it’s a huge subject growing fast.
I’m not exactly clear how giving someone a guest post and expecting them not to freak out when subjected to normal scientific rigor is anything but offering respect and praise.
Only in an Alice in Wonderland universe can one find this kind of double standard regarded as honest scrutiny. Consider Mann and Cuccinelli (or Phil Jones and … or any other fake skeptic fake heroics) before you talk of persecution.
For modern temperature calculations, is there any reason to mix urban and rural temperatures at all? The post’s technique elevates rural above urban, which, while reasonable, might not be as good (nor as easy) as just defining stations and area as rural or urban and keeping them separate for all purposes. Sum by area in the end. This retains the, as others pointed out, quite valid UHI influence instead of making the error of adjusting it out. It ain’t error. It’s real warming.
I have a question that kind of spins off from. From what I can tell, a very rough estimate of the area of world wide roads is somewhere between 10% and 17% of “normal” icecaps (given that icecaps, even with global warming are dynamic systems). I’ve read discussions of “cool roofs” or “white roofs” for years. Would a “white roads’ or “cool roads” program that painted roads a light color compensate for about the same square mileage of icecap loss, thus mitigating one of the worrisome feedbacks a tiny bit? Not a net profit mitigation like “cool roofs” but possibly cheap for what accomplished.
An obvious question is whether this work points to any correction in existing data sets like GIStemp that already use homogenization and correct for UHI. At least I think it does. A google search on ‘GIStemp homogenization’ results in a lot of pages with the following when I attempt to reach them
You don’t have permission to access XXXXXXXXXXXXXX on this server.
[Response: The GISS website is undergoing a technical upgrade and a lot of the interactive stuff is still offline. It should be back up soon. – gavin]
What are the results if one looks only at inland cities and inland rural areas? Population movement might be a confrounding factor. USA urban areas are increasingly near oceans, which probably have a more moderating effect on temperatures nearby than in inland areas.
Doesn’t it get frustrating having to whack moles repeatedly?
I’ve been barking up the Arctic tree for about three years now, so have got out of touch with wider issues such as UHI. But the last time I looked at it, some four years ago, I concluded it was being adequately accounted for and moved on with my life.
The NOAA dataset with 1 km resolution census data is the output of a model using the highest available resolution census data (which is of variable sizes, but tends to be a tad bigger than 1 km in less populated areas). As the Census doesn’t publish an official 1 km gridded dataset, and the NOAA data only goes through 2000, we were not able to include 2010 population density/growth in the analysis. That said, given the time periods involved I doubt it would have significantly affected the results.
As a cab driver on the night shift I see temperature differentials that seem to average between 1 and 2 degrees cooler on the outskirts of my city of over 1 million people than in the core of the city. Why are the adjustments in this article so small compared to my real life experience?
[Response: The UHI effect can indeed be much larger in practice that what is being addressed here — that is, cities are very warm compared with non-urban surroundings. But most of the weather stations are not downtown. They are out at the airport, or in suburban (previously countryside) areas, and they like. They are affected by the urban warmth effect, but generally not that much. Does that help? –eric]
“As a cab driver on the night shift I see temperature differentials that seem to average between 1 and 2 degrees cooler on the outskirts of my city of over 1 million people than in the core of the city. Why are the adjustments in this article so small compared to my real life experience?”
Because that’s what the data shows.
If you have a long-term time-series of measurements with accurate lat/long tags taken with reliable instruments, I’m sure somewhere, someone will be willing to incorporate those measurements into the datasets for your cities.
On the other hand, anectdotal evidence is pretty much useless. For trend analysis, of course, you would have had to have taken a lot of measurements around much of your city for a long period of time for any impact on trend to have been detected.
In addition to Eric’s inline response, keep in mind that errors in the long-term trend would require that the difference between the core and outskirts be changing. The 2013 downtown temperature is being compared to the 1970 downtown temperature, and the 2013 rural temperature is being compared to the 1970 rural temperature. (Well, actually, each to the corresponding mean for the base period, usually a thirty-year period). It’s not enough for the two locations to be different – they have to have different trends. The numbers in the article refer to the trends, not the absolute temperature.
Basically sites in urban centres that are and always have been developed and where sprawl has not appreciably occurred can exhibit trend behaviour strikingly similar to surrounding rural stations. Equally, there are a huge number of papers suggesting that for areas experiencing very rapid development urban influences can dwarf the regional trend on a site specific basis in the raw record. So, for long-term regional trend characterization it is not black and white in the raw data. Then there is the issue of whether the adjusted / homogenized records account for this which is where the paper that is the subject of this post comes in.
The division between “urban” and “rural” in the linked paper is arbitrary.
I would recommend looking at indicators such as: “minimum temperature” instead of average temperature. Also focus more on temperature minimums in winter season.
Some questions I would like to see answered are”:
– when is the effect of Urban heat island greatest? (radiation versus advection)
– what is the UHI effect on snow cover (max snow depth, days with snow): my hypothesis is that it is even more remarkable than on night minimum temperature..
– How far beyond city edge does the urban heat island reach? Is there difference such as windward / leeward side of the city?
And now an important consideration: What if most (if not all) of the “rural” stations are in fact affected by urban heat island? Just consider the increase in built-up areas and asphalt in “rural” areas. For example roads, or industrial zones next to highway in an otherwise rural area. For the specific use-case of U.S I would recommend only using the stations in natural areas (national forest, national park) as truly rural.
Station Praha-Klementinum with measurements already in the 19th century. Maximum-temperature records are being broken at this station on a yearly basis sometimes several days in a year. However: The last time a daily minimum record was broken was back in the nineties. The last time a daily minimum record in winter was broken goes even further in history, to the 1980s. Station is in center of Prague, a fast-growing city by built-up area. I would be interested in the following relationship: – pick a station in city-center
– find how the distance of this station to the edge of the continuous built-up area changed (or look how the total built-up area of the city where the station is located had grown)
– I suspect, that you will find a strong correlation between the built-up area growth and the “minimum temperature in winter in city center” growth.
[Response: Have you actually read the paper? I’m guessing not. In any case, calling their method “arbitrary” is not a very compelling way to engage in a discussion about it.–eric]
Again, Callendar already grasped the nub of this issue back in 1938:
“Callendar grouped temperature data in England on a town by town basis based on population growth rates. In that way he effectively eliminated the urban heat island effect, which might have skewed his data.”
Unfortunately, it seems as if the article itself has gone behind a paywall; the Royal Met Society wants to whack you 24 pounds for a DVD of Callendar’s classic work (collected.) I don’t seem to have saved a copy of it, which I now regret. But the quote I lifted for my article on Callendar is material:
It is well known that temperatures, especially the night minimum, are a little higher near the centre of a large town than they are in the surrounding country districts; if, therefore, a large number of buildings have accumulated in the vicinity of a station during the period under consideration, the departures at that station would be influenced thereby and a rising trend would be expected.
To examine this point I have divided the observations into three classes, as follows:–
(i) First class exposures, small ocean islands or exposed land regions without a material accumulation of buildings.
(ii) Small towns, which have not materially increased in size.
(iii) Large towns, most of which have increase considerably during the last half century.
I’m not sure why you think we look at average temperature, as (as far as I recall) it is discussed no where in the paper. We primarily focus on minimum temperatures, though also show results for the same analysis using maximum temperatures.
Your suggestion of looking at seasonal variations in UHI effects is a good one. It fell somewhat outside the scope of our work, however, as we were primarily looking at overall trend biases and their impact on multi-decadal temperature trends, as well as the extent to which they are captures and corrected in the course of homogenization. There could certainly be some follow-up work looking at seasonal effects; I might take a stab at it when I have some free time, or you could download our data and code and run the analysis yourself relatively easily.
My sequel to Warm Front, Heat Wave, is live at the Amazon Kindle store.
An assassin executes a U.S. presidential candidate.
To witness and eccentric reporter, Sam Emory, it echoes a killing three years earlier that transformed his life.
Voters elect a new, environmentalist president, a colleague of the slain candidate.
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And this isn’t his first murder.
Emory and the president are the next in line.
In the not-so-distant future, an assassin kills a U.S. presidential candidate seeking to fix a world ravaged by climate change, and Sam Emory uncovers a chain of murders with a megalomaniac industrialist at its core. The newly elected president vows to solve the climate crisis. Can Emory and his friends stop the assassin from striking again?
HEAT WAVE begins in Chena Hot Springs, Alaska, but the political intrigue and murder spread to Washington, D.C., and into the labyrinth of an Aspen, Colorado energy research facility, where free-marketers manufacture chaos in the electrical grid, and where Emory confronts a terrifying a secret from his past.
There was I thinking that an uncorrected UHI bias was the reason the Arctic was melting faster than anyone had predicted.
But seriously, I just read Mike Mann’s The Hockey Stick and the Climate Wars and that really brought home to me that all these various spurious attacks on science have only one goal: confusion.
The book also prompted me to look up what had happened with the investigation into allegations that the Wegman report, the only “independent” adverse finding against a climate scientist, was heavily plagiarised. It would seem that the only person who has actually been found guilty of misconduct after many investigations of climate scientists is a contrarian – even if it appears that the guilty finding is a minor wrist-slap for an offence that most universities would consider very serious if committed by a student.
My university has a large journalism school, and I remind them when I can that reporting science is not only the responsibility of scientists. Maybe journalists don’t grok calculus or principle component analysis, but they can spot the bleeding obvious: if climate science really was a big fat hoax, the fossil fuel industry has more than adequate resources to debunk it properly. So why all the character assassination, and throwing resources at cranks?
“… 2007-2011, 10 of those organizations, including the Heartland Institute and the Competitive Enterprise Institute, received a combined total of almost $16.3 million from ExxonMobil and three foundations supported by oil and gas companies, according to the Checks & Balances Project. In the same time period, the organizations were mentioned 1,010 times in articles about energy issues in 58 daily newspapers, as well as the Associated Press and Politico, but the media described their financial ties to the fossil fuels industry only 6 percent of the time.
“Most of the time (53 percent), news outlets used only an organization’s name—no more, no less. Occasionally, they would describe the organization’s ideology, with terms like “conservative” (17 percent) or “libertarian” (6 percent) and rarely by its location (3 percent) or function (e.g. “think tank” or “nonpartisan” group) (3 percent)….”
Actually, independent of tax incentives, utility providers have strong incentive to reduce at least the rate of growth in energy production, as it means they do not have to sink profits into costly, long-term projects such as new power plants (of whatever flavor). This means that they can continue to pay high dividends and maintain a high stock price as a defense against takeovers. Matter of fact, the only non-petroleum company I can think of that had “growth” as part of its long-term strategy was
Enron. And that worked out so well.
> sink profits into costly, long-term projects such as new power plants
Though those are treated differently, at least in the US, so there’s in most cases an actual income gain to the utility from building new power plants (because they pass the cost on to the consumers) — while all they do is reduce their costs slightly by increasing efficiency. It’s not simple and it’s vastly fiddled with by lobbying, rulemaking, and loopholing.
One of the great experiments in political science: how long can a government continue to function as the ratio of loopholes to sensible policy increases. Eventually the whole thing falls, riddled from the inside as tho’ by termites.
For starters, $16m over 4 yrs to 10 institutions is miniscule. And why shouldn’t they cover all the think tanks and their like of every persuasion. Seems good standard business tactics to me and covers all bases. Also helps to keep everybody honest. It’s tuff out there.
Ray Ladbury @60 – I agree. Just don’t agree that they get a double hike in profits paid from my taxes.
For starters, $16m over 4 yrs to 10 institutions is miniscule. And why shouldn’t they cover all the think tanks and their like of every persuasion. Seems good standard business tactics to me and covers all bases. Also helps to keep everybody honest. It’s tuff out there.
Ray Ladbury @60 – I agree. From my comment I didn’t agree that they get a double hike in profits paid from my taxes.
Titus, you miss my fundamental point. They are doing this rather than funding a definitive study to debunk climate science. If they could debunk the science, why wouldn’t they? It would solve the entire problem for them. And they would only have to do it once, not spend $4-million a year plus whatever it costs to buy off politicians (a lot more than that). My issue is not that they fund this campaign but that journalists who should be professional BS detectors get conned by this sort of campaign time and time again.
This is all so well known, it seems pointless to repeat here. I’ve posted a few articles on my blogs (e.g. here – note the link to the NY Times article on how the industry’s own scientists told them the basic science is sound way back in 1995).
I do not think anyone could debunk the science at this point. There is simply too much uncertainty in the available data to make any definitive assertion as such, and to paraphrase Titus, $4M is pocket change.
If you want to point someone to a climate website, I wouls suggest a more scientifically-based site, rather than ones that admittedly use any method possible to debunk arguemnts. What was that about honesty? Seems to be quite similar to Titus’ statement about all sides of the debate.
Not sure what parallels you are drawing regardings Clive Palmer’s exploits and his statement.
Please allow me to digress a bit:
I see folks referencing the tobacco battle. In my opinion the ‘deniers’ (the name you appear to use) played a crucial role in successfully getting the dangers accepted by Joe public. Joe was able to see that this was not a one sided big government saying what they should do because they said so. They were able to make a choice and that’s huge in the adoption that follows. This was my fundamental point.
You are absolutely right to encourage those journalistic friends of yours not to be conned. Investigative reporting is after all what they are trained to do. They need to be called out and had best move on if they cannot.
Dan H. and Titus,
There really is no scientific debate about anthropogenic causation of the current warming trend. None. All of those making actual scientific arguments are on one side. On the other side–the Calvinball All Stars. “Anything but CO2″ is not a scientific position.
PS for Titus — yes, there are also nut cases who always trust the government.
The great delaying tactic is to fund the nut cases, to hollow out the center where policies can be agreed on by people; Big Tobacco does that.
Ray Ladbury @72: I’m no scientist but I was taught that part of science IS about trying to knock a theory down. If science is not doing that then hats off to anybody who keeps up the SCIENTIFIC approach. It’s not the role of science to control policy. That’s not science. It’s just one input to the policy making process. Lets hear more about ‘why catastrophic predictions of 15 years ago have failed’, ‘how little to no warming in recent years causes extreme events now that are caused by warming’, ‘why sea level continues to only rise at the same rate when accelaration was predicted’ Etc etc. Lots of Joe’s want to hear about this in language they understand. That’s what we expect from science. Over and over again if necessary. Never mind what policy makers make of it all.
Hank Roberts @73: Of course ‘delay’ is a tactic. My goodness, what’s new? Science should stick to science and leave the messy business of politics and business to the policy makers. Science is doing itself no favors.
[edit – focus on issues not other commenter’s mental states]
Not sure where you get the idea that there is no debate. I suggest a reading of the most recent scientific literature about causation of the recent warming trend. Scientific arguments are not limited to “one side,” as if there were different sides. There is more a continuum of thinking, with only the extremes engaging in your anti-science positions. Conincidently, it is the extremist who do not participate in the scientific debate. Rather, they argue like a drunk in a barroom; offering no valid scienitific basis to their arguments, and refusing to listen to reason.
1) Yes, science ‘knocks down’ inadequate hypotheses. However, the process doesn’t continue ad infinitum in practice, as the time required to test hypotheses is not infinite. For example, no-one is re-running Galileo’s experiments on fall rates of heavier and lighter objects at this point. Hypothesis testing in the matter of greenhouse climate change has by and large moved on from the kinds of questions Ray was talking about. The kinds of questions that ARE being investigated, you can read about right here.
2) What predictions of 15 years ago, specifically?
3) It’s not entirely clear what you mean by ‘how little to no warming in recent years causes extreme events now that are caused by warming.’ However, perhaps this will help: while there has been comparatively little further ‘warming’ over the last decade or so, that period has continued to be extremely warm by the standards of recent history.
For example, the page here tabulates NCDC decadal mean anomalies. The 2000s were by far the warmest decade on record, with a mean anomaly of .531. By comparison, the 90s averaged .313; the 80s, .176; and everything before that is in negative anomalies. (Well, not quite–the 40s just barely nudged into positive territory.)
So–the extreme events are driven by warm temperatures, not the rate of change of said temps. Make sense?
3) SLR rise is in fact accelerating. There’s a nice exposition here:
(Warning: it’s a lengthy post, and not all of it is relevant to sea level rise.)
4) “Science should stick to science and leave the messy business of politics and business to the policy makers.”
Well, for the most part ‘science’ has–for example, the IPCC has strictly avoided policy recommendations. However, individual scientists can and should, as citizens, appropriately share their expertise.
On the other hand, I’d have to question why it is that certain ‘policy wonks’–say, over at the Heartland Institute, for example–don’t stick to the messy business of politics and business and leave science to the scientists.
You said- “Lets hear more about ‘why catastrophic predictions of 15 years ago have failed’, ‘how little to no warming in recent years causes extreme events now that are caused by warming’, ‘why sea level continues to only rise at the same rate when accelaration was predicted’ Etc etc.”
These comments sound bogus to me, but please prove me wrong by providing some citations that would substantiate your claims. Steve
Really Dan H., I’d love, just love to hear more about some of “the most recent scientific literature about causation of the recent warming trend.” Because everything I’ve seen still says CO2 is a greenhouse gas.
Seriously, Dude, can you type this stuff with a straight face?
By all mean, Titus, you knock a theory down whenever it is possible. However, you knock it down with another scientific theory. “Anything but CO2″ is not a scientific theory.
Now as to your other accusations, I will begin with a general observation: Why is it that denialists are incapable of citing specifics? I would think that you could come up with at least one study to back up your claim. Can you?
1)Which catastrophic predictions of 15 years ago? I don’t know of any in the mainstream scientific literature. Do you?
3) Little or no warming in 15 years. Hmm. Could you explain to me how you can have a situation where 9 of the last 10 years are in the top ten warmest years if it isn’t warming? Can you explain why you choose 1998–the biggest El Nino in memory as your starting point? Can you explain why even then, 4 of the 5 main temperature series show positive trends?
So, Titus, who is telling you these lies? Why do you consider them worth repeating without verifying them yourself? Ever wonder what else they might be lying to you about?
Titus, I suggest you argue with the ignorant if you wish to argue from a position of ignorance. More interesting to me is finding out things I didn’t know and adding to my knowledge, for which this is an excellent site.
Arguing that scientists should leave policy to people who really know stuff is such a screwed up starting point that I’m not going there. Suffice to say I’ve been involved in both politics and policy-making, and science is a far more rational starting point than either of the above.
Here’s something I hope RC will do an article on soon: Bedmap2. A few key points:
• The volume of ice in Antarctica is 4.6% greater than previously thought
• The mean bed depth of Antarctica, at 95 metres, is 60 m lower than estimated
• The volume of ice that is grounded with a bed below sea level is 23% greater than originally thought meaning there is a larger volume of ice that is susceptible to rapid melting.
• The ice that rests just below sea level is vulnerable to warming from ocean currents
• The total potential contribution to global sea level rise from Antarctica is 58 metres, similar to previous estimates but a much more accurate measurement
• The new deepest point, under Byrd Glacier, around 400 metres deeper than the previously identified deepest point
Silly question I am sure, but wondering if there would there be any affect on UHI temperature with extensive light pollution such as is the case in Las Vegas? How do the Satellite Nightlights work in this case?
I would expect a fairly large amount of UHI bias in places like Las Vegas, and in general nightlights were one of the more sensitive proxies we examined (e.g. areas designated urban by nightlights had higher trends than comparable rural locations). However, we found that the homogenization process is pretty effective in detecting and correcting UHI-driven trend biases, provided there are a sufficient number of surrounding stations that are not subject to the same bias at the same time.
Courtney, compared to what? Other cities with as many lights but less light pollution? Other cities with less electricity used per population size? Are you asking about using the brightness to estimate whether the area is urbanized? Or asking if extremely optically bright Las Vegas gets misinterpreted by the instruments in the satellite used to infer temperature? Or something else, just guessing …