There’s a new paper which quantifies the effects of the Urban Heat Island and opines on the effect of urban areas on multi-decadal surface temperature trends. It was just published yesterday in Geophysical Research Letters and is quite interesting and confirms much of what we’ve learned via the surfacestations.org project, and that is that urban areas tend to have higher trends, and the UHI effect manifests itself mostly in the overnight Tmin.
However, the authors seem to try to steer the conclusions away from urban effects being the cause, and try to use regional analysis to prove Parker (Mr. fantasy “cool parks in cities hold the thermometers”) and NCDC’s Dr. Thomas Petersen correct rather than noting that moist enthalpy related to lawn watering irrigation can have an effect on temperature as well as precipitation. More on that below. It has been noted previously on WUWT that cities can provide the elements for increased precipitation formation:
The March-April edition of WeatherWise magazine has an interesting article in it regarding UHI (Urban Heat Island) effects of enhancing thunderstorm formation in the downwind heat plume. It Stems from this paper (PDF) published in the Bulletin of the American Meteorological Society. I saw a similar study presented in August 2007 when I attended Dr. Roger Pielke’s land use conference presented by Dr. William Cotton on the enhancements modeled in St. Louis, MO. Read that paper here
In a nutshell, humans use a lot of energy and a lot of water, the two essential ingredients for convective precipitation, and both get dissipated into the atmosphere locally near their use. Cities provide a source reservoir for both elements, but even as we move to rural landscape, we find that there’s still a lot of water use related to irrigation.
The new paper is:
Mishra, V., and D. P. Lettenmaier (2011), Climatic trends in major U.S. urban areas, 1950–2009, Geophys. Res. Lett., 38, L16401, doi:10.1029/ 2011GL048255
Some highlights (emphasis mine).
In the abstract:
We evaluate changes in climatic indices for the 100 largest U.S. urban areas and paired surrounding non‐urban areas. During the period 1950–2009, we find that there were statistically significant changes in as many as half of the urban areas in temperature‐related indices, such as heating and cooling degree‐days and number of warm and cool nights, almost all of which are reflective of a general warming. Similarly, statistically significant changes (mostly increases) in indices related to extreme precipitation, such as daily maximum intensities and number of days with heavy precipitation, were detected in as many of 30% of the urban areas. A paired analysis of urban and surrounding non‐urban areas suggests that most temperature‐related trends are attributable to regional climate change, rather than to local effects of urbanization, although the picture is more mixed for precipitation.
Among the conclusions in the paper we have:
Consistent with previous studies [Easterling et al., 2000; Kalnay and Cai, 2003], trends related to temperature minima in the urban areas are generally stronger than those related to temperature maxima.
For both minimum daily temperature based climate indices and precipitation‐related trends, changes in urban and non‐urban areas are generally consistent; suggesting that the trends are dominantly a response to climate [Parker, 2004; Peterson, 2003], rather than local land cover changes during the period of analysis. However, there is somewhat less consistency in urban vs. non‐urban trends in climate indices related to daily maximum temperature, which suggests that land cover change may be at least partially responsible for those trends.
I do like this from their methodology, it is the right way to do it:
Gridding of meteorological data for the urban and non‐urban buffer regions was performed using the stations that were uniquely present only in either urban or non‐urban regions. This approach insured that data for urban and non‐urban regions were gridded with unique sets of meteorological stations to avoid any contamination that may occur due to common stations in urban and non‐urban regions.
But the statement just above it has a red flag for me:
In the interest of preserving local influences of urbanization on temperature and precipitation, we used separate NCDC‐Coop and HCN stations for urban and non‐urban areas.
What concerns me is that they didn’t make it clear what data set they used. As we know, USHCN data is heavily processed, and uses nearby COOP stations as part of FILNET to fill in missing data on the B91 reports, like this one in Marysville that is missing a lot of data:
B91 form provided by the Marysville observer (PDF format).
NCDC’s FILNET process will take data from other nearby stations and use that to interpolate the missing values, essentially mixing data from stations.
So my point is, that due to the way NCDC processes data, mixing and infilling to make every record “complete” even though Mishra and Lettenmaier went to great effort to keep rural and urban stations separate in gridding, the data they used may have been urban and rural pre-mixed anyway and the analysis may have been doomed by data pollution from the start. Until we know more about what data they used, I can’t say for sure if this is a problem or not. They make no mention of this issue in the paper that I can find, so I’m assuming they are unaware of it.
One other thing they appear not to have considered is the effect of increased humidity on Tmin, i.e moist enthalpy. Lawn watering and irrigation are common to human habitation, no matter whether you are rural or urban. And as we’ve seen, most of the COOP network stations are near dwellings, and by default near either the nice green lawn, gardens, or agricultural plots even in the far rural areas.
Yesterday, in my summary of the Susanville USHCN station, I illustrated the issue in lush detail.
Note the lush lawn. The MMTS temperature sensor is near the cattails at the right end of the ladder in this image.
The view from the air shows that there is a lot of moisture near the USHCN station.
It is a big patch of green and parking lot in the middle of an arid landscape. Does increased nighttime humidity due to watering and evapotranspiration play a role? Quite possibly.
After I pointed out the differences in USHCN data processing between 2007 and 2011 graphs as they appeared on GISS, Zeke Hausfather helpfully pointed out what NCDC has done to the data:
Zeke Hausfather says:As far as urbanity designations for that station go for its listed lat/lon, its urban via GRUMP, impermeable surfaces, and 1930-2000 population growth, but rural via nightlights (only 19 brightness).
When run through the pairwise homogenization process, NCDC significantly reduces the 1960-present minimum trend from 0.24 C per decade to 0.03 C per decade. The max trend is mostly unchanged, going from -0.10 C per decade to -0.11 C per decade.
So, if NCDC was already tinkering with the station data by adjusting trends, is the conclusion that “…that most temperature‐related trends are attributable to regional climate change, rather than to local effects of urbanization” valid? Or is it simply an artifact of the mixing mishmash of COOP data and microsite effects like increased humidity due to irrigation that have not been considered in this paper? The authors suggest land cover change might be responsible for precipitation effects, but dismiss the issue for temperature without providing any basis for the dismissal, citing the similarity of temperature trends for rural and urban. Again we go back to the NCDC mixing of temperature data issue, which wasn’t specifically addressed.
As I understand it, NCDC does not infill missing precipitation data, due to the spotty nature of precipitation. As we know, thunderstorms often leave narrow swaths of rain, and interpolation of missing precip data would be wholly uncertain for nearby stations. So, the data mixing issue isn’t present in precip data like it is in temperature data.
The biggest downside of the COOP network is that it records mostly temperature and precipitation, agricultrual COOP stations with humidity and evapotranspiration data are few and far between, so answering the question over the long term is difficult.
The full paper Mishra, V., and D. P. Lettenmaier (2011) is here
h/t to Dr. Leif Svalgaard
[snip – yes, I agree with you waayyy off topic. See Tips and Notes page – Anthony]
I think the only way to deal with these uncertainties is to do some experiments locating new (temporary?) thermometers, humidity, etc guages in both urban and suburban areas as much free of confounding site specific factors as possible – ie. in your aerial photo above, set up a site off into the desert several hundred metres away. Do this for 4 or 5 urban-rural pairs and see what happens and compare with the official sites.
Maybe the only way this will be sorted out is to get the surface stations sorted out and talking the same language, and then wait thirty years to see what happens. I’m a patient sort of man.
Actually, Anthony, do several trans-sections of the the urban-rural scenes using your mobile thermometer (do you also have humidity, etc with this mobile?)
Urban environments have changed so much over the last hundred yrs. The amount of heat and water vapor generated through our constant use of electricity, combustion of fossil fuels, bringing water up from wells that ends up being released (after treatment) into the surface environment, and irrigation, etc. has got to have some kind influence on local climate conditions. Certain regions are also more heavily urbanized than others which would have more of an effect on regional climate conditions. In short, land measurements are not suitable for determining the real state of climate since it has been contaminated by human influences of all types. The best gauge is ocean observation, which is 70% of the Earth anyway, and sat measurements of the vertical column of the atmosphere. Unfortunately, those records is not very long.
The S. Hemisphere is probably the closest to a natural climate we could study because the north is just too contaminated. The north has most of the land, and people.
But no matter how screwed up the cities are, what does it matter if the well sited rural stations show very nearly the same trend? There might be a statistically significant difference, but that doesn’t mean there is an important difference.
And watts up with temps in cities being COOLER than rural thermometers during the hottest part of the day? Has anyone checked if the new thermometer housings are keeping the thermometers cooler than the historic designs?
Anthony Said:
“So, if NCDC was already tinkering with the station data by adjusting trends, is the conclusion that “…that most temperature‐related trends are attributable to regional climate change, rather than to local effects of urbanization” valid? Or is it simply an artifact of the mixing mishmash ”
Just to make sure that I understand; NCDC are adjusting the trend to account for UHI, then the researchers here MAY have used the adjusted data to look for UHI? If that’s true, it’s the funniest thing I’ve heard in a while. On the bright side, it would mean that NCDC did a fairly good job of removing the UHI noise from their data. lol. Going one step farther, these guys didn’t need to do a study to find the magnitude of the UHI. They just needed to ask NCDC how large it was.
And on a completely different note, in regard to previous comments regarding water use:
I’ve never seen anybody mention the effect that land subsidence might have on a temperature record. I wonder what the 100 year temperature record would look like for a place that subsided 9 meters between 1925 and 1975? A meteorological site at the following location would certainly be non-urban, but suffering severe land use change effects:
http://en.wikipedia.org/wiki/File:Gwsanjoaquin.jpg
REPLY: I can’t imagine it would be much, using the international standard atmosphere (ISA) with a temperature lapse rate of 6.49 K(°C)/1,000 m, the chane for 9 meters is 0.05841°C – Anthony
Anthony,
As you may know, we are working on a paper addressing these issues as well. Some results were presented at the AMS conference, and are available here: http://rankexploits.com/musings/2011/uhi-presentation-at-the-ams-conference/
I can’t speak to the authors of the GRL paper, but we take a pairwise approach (as well as other methods) using both unhomogenized (albeit TOB-adjusted) and homogenized (with no infilling) data sets. We found a strong UHI signal in the TOB data, but not much over the past 50 years in the homogenized data.
REPLY: Thanks for that update. No issue with pairwise comparisons as TOB adjusted data would of course be expected to see differences, since it is not blended (homogenized). My issue always has been with mixing good and bad, complete and infilled records together for some sort of COOP goulash.
Have you found any good sources of long term humidity data to have a look at moist enthalpy related Tmin effects? – Anthony
Unfortunately we don’t have a great source for long-term humidity data, though we’ve tried using precipitation and vegetative cover as proxies. It’s probably not in the scope of this paper since its getting a tad lengthy already (we’ve already 64 different variations of temperature set: TOB, F52 all-coop homogenized, and F52 rural-coop homogenized; urbanity proxy:grump, nightlights, isa, and pop growth; reconstruction method: spatial gridding and station pairing, and minimum and maximum temps). There is still plenty left to explore, and looking at variations in temperatures between urban and rural areas controlling for humidity or vegetative cover is definitely a good next step, provided we find a good timeseries that reflects historical changes.
Gary Swift says:
August 16, 2011 at 11:22 am
Anthony Said:
“So, if NCDC was already tinkering with the station data……”
========================================================
Gary, you should go through the archives here. There’s plenty examples of all sorts of people tinkering with the station datum……. NCDC participating as well……. some of the tinkering is well documented……other tinkering…. not so much. But, I do give points for snark…..
“REPLY: I can’t imagine it would be much, using the international standard atmosphere (ISA) with a temperature lapse rate of 6.49 K(°C)/1,000 m, the chane for 9 meters is 0.05841°C – Anthony”
That’s in open spaces, as you go up from ground level. Air in a depression can be measurably warmer than the air in surrounding elevated areas. Warm air and moisture can get trapped in the low spot. It may not be a large effect, but we’re not looking for a 10 degree difference here. Anyway, just an idle musing because I’m sure it has some effect, and it’s larger than the figure you listed above. Death Valley is an extreme example. Anyway, I was thinking that decreased evaporation due to decreased elevation (and therefore wind) might be measurable too.
I can’t tell from their paper exactly how they selected the non-urban areas for comparison. First they say:
Then they say:
However, if they used census data only for the top 100, there are at least a couple of hundred urban areas outside the 25‐km perimeters which they do not say they also excluded.
See: http://www.census.gov/geo/www/ua/ua_2k.html
And: http://www.census.gov/geo/www/ua/ua2k.txt
May not make any difference. Still I wonder.
OK S.
(SarcOnALittle)There ought to be a way of determining each city’s unique UHIE (pronounced ‘Uuu-Eee’ for Urban Heat Island Equivalent) and establishing a unique annual heating fine or levy for each. The billions raised via this method would then be used to pay farmers and ranchers for set-aside acreage and the replanting of Buffalo Grass in Kansas, Oklahoma, Nebraska, S and N Dakota, Eastern Colorado and Wyoming, and as much of the cornbelt that can be tempted away from planting corn for automotive fuel additives. Of course this will raise a tremendous amount of money and we just don’t have a way of keeping any money safe from the Federal and State Governments. That’s the tough part. Haven’t figured that one out. Yet! (SarcOffAlmost)
When Murphy Wrote His Famous Law He Was Thinking of Government!
(He was really suprised to find it applied to just about every human endeavour.)
On the irrigation Anthony I’ve been working on that off and on for the past year.
Oke and Grummond ( I always forget her name0 did a fascinating study ( cant locate it yet.. argg) in which OKe argued that the DIFFERENTIAL in water use had to be accounted for.
That is, the city sucks water from rural landscapes in some cases and dumps it in the air through watering. So he looked at wet urban/ dry rural and wet rural/dry urban ( when watering was not allowed due to drought ) in any case, The california system does have an evapotranspiration product, and I’m looking at a variety of things to nail down that characteristic.
Also, for a long while we’ve known that cities change the precip pattern up to 20km.
The other thing I havent mentioned is the effect a local dam can have. This is not very well studied.
Look at Orland in the early part of the century. compare to willows. and then see when the damn was built by orland.
Do they list the stations that they use? Many so called “rural” stations are not rural in any meaningfull way.
Anthony, if the subsiding is localized enough, it could create a bowl that would allow cold air to collect at night. Another possibility would be allowing rain water to collect in places where it didn’t used to.
The current article brings me back to your sufacestations paper (May 11, 2011) and something steven mosher said: [1]
The UHI topc make me ask a question that has bugged me a few months:
What official historical temperature records have any sort of UHI correction? From Steven Mosher’s question above, I can only conclude that:
a) there are none, which I find dumbfounding because a positive UHI trend must be a big part of the “Global Warming” official temperature record.
or b) Mosher above really means, residual UHI, i.e., UHI not accounted for in UHI corrections to the official temperature records. b) then implies that there have been UHI corrections to the temp records. That begs the question, how much UHI was applied to each of the station records? Does anyone have a link to such previously applied UHI corrections by record name?
BTW, for clarity, UHI as Mosher uses it above must account for three components:
1. Microstation siting effects and changes over time.
2. Urban Heat Island as cities’ heat sources, people, machines, and paving encroach upon the station site, and
3. Nearby Land use changes (irrigation, building, tree cutting, tree planting) as illustrated in the UTAH station paper by Gibbs [2].
[1] http://wattsupwiththat.com/2011/05/11/the-long-awaited-surfacestations-paper/#comment-658282
[2] Gibbs: An Investigation of Temperature Trends from weather station observations representing various locations across Utah http://scienceandpublicpolicy.org/originals/gibbas_temperature_investigation.html, h/t WUWT 2001/04/04.
Gary Swift says:
August 16, 2011 at 12:14 pm
Yes, wind can make a difference when topography is a factor. In such cases, so far, I see that there is an implied upper limit to UHI.
I live in the city. Approximately 15 miles across. looking at Wunderground with the various stations surrounding me (1/2 mile from thwe airport) the temps in the summer at my house are usually 5 – 10 degrees warmer across the board than the edges of town. I know my local temperature as I use passive cooling as much as possible (turn on the exhaust fan at night and close down the house in the day). I have 9 thermometers located indoors and outdoors to moniter the temp to faciliiate cooling. Friends outside of town (5 -15 miles) will inform me that the temp difference is usually about 8 – 10 degrees different (cooler) than mine. UHI is for real. Concreate, pavement, roofing composition, brick, etc. Less difference in the winter. Where is common sense in academia? Out the window?
One major source of water vapor and aerosols in the atmosphere is from a mechanical induced draft evaporative cooling towers used in power generation plants, chemical plants, crude oil refineries, gas processing plants, building cooling systems and other processing facilities. This water is discharged from the top of the ~ 50 feet high cooling tower as aerosols and near 100% humidity air. The cooling tower pumps out cooled water (~80-85F) from the bottom of the tower through cooling heat exchangers and returns the heated water (~105F) to the top of the tower to flow down to the bottom of the tower to be pumped once again. The cooling tower is packed with contact surfaces which allow the down falling warm water is contacted and mixed with air which is drawn by large fans from the bottom of the tower to the top by large induced draft cooling fans. The mixing of the water and air results in evaporation of the liquid water into the air mixture. The water loss due to evaporation and drift loss is made up from ground water wells.
Nuclear power generation plants use huge, very high (350 feet or so), concrete natural draft cooling towers but the principles of cooling are the same as the evaporative cooling towers but the thermal efficiency is about 20% lower than a steam power plant.
A typical 500 mw thermal (steam) power generation plant circulates 225,000 gallons per minute of cooling water. The USGS calculates the makeup water drawn from fresh water wells to the cooling towers used in power generation plants in USA is 136,000 million gallons per day: to read reference, go to:
http://www.netl.doe.gov/technologies/coalpower/ewr/pubs/IEP_Power_Plant_Water_R%26D_Final_1.pdf
Although I haven’t yet been able to get anyone’s attention in WUWT, this seems like a large amount of energy that is sent into the atmosphere twenty fours per day for an average 350 on-stream days per year. After the first cycle the water would enter the hydrological cycle but since the water is pumped from the ground from essentially no or slow to recharge aquifers, the heat is added each day.
JFD
Stephen Rasey,
NCDC uses the pairwise homogenization algorithm to try and detect and correct for inhomogenities by comparing USHCN stations to their nearby COOP counterparts. This is not an explicit urbanization correction, but it should capture cases in which urbanization is reflected in a detectable change vis-a-vis nearby stations. Nasa’s GISTemp takes the adjusted NCDC data and imposes an additional urban correction based on satellite nightlights.
NCDC used to include a specific urbanization correction of ~0.1 C per century for the CONUS in version 1, but that was supplanted by the automated inhomogenity correction algorithm in version 2. Recently the same algorithm was applied globally to GHCN (in version 3), which previously had no UHI correction in the NCDC record and only the nightlights correction in the GISTemp record. Unfortunately the relative low spatial density of GHCN stations make it more difficult to detect inhomogenities than in the U.S., where we have upwards of 20,000 measurement stations.
What official historical temperature records have any sort of UHI correction? From Steven Mosher’s question above, I can only conclude that:
##########################################
1. you have to define your terms that is you have to refer to a specific series. I’ll help a bit
do you mean GHCN? GHCN Daily? GCOS? The data at BOMS? CRU data?
for clarity I’ll answer for GHCN and GHCN Daily. According to the records NCDC do no adjustment
that is specifically targted at UHI. But since they ingest data from others you would have to track down all the sources. they are legion.
“a) there are none, which I find dumbfounding because a positive UHI trend must be a big part of the “Global Warming” official temperature record.”
That actually is the question that folks are trying to answer. Some people assume that the UHI must be large. Other people assume ( or hope) that it will be small. But lets be accurate. There are three official records. CRU, GISS and NOAA. So the question has to be answered with respect to the stations and ONLY those stations that these three use. That is why Willis asked CRU for its stations. It is EASY to find an urban station with UHI. Thats not the question. The task is find urban stations IN THE OFFICIAL RECORD that have a strong UHI signal. more importantly you have to find a BUNCH of them. one or two doesnt do the job. Finding one or two Poses a question: “how many more?” how big is the total effect. There are several studies which separated urban from rural to try to find the effect. These studies range from
1. singleton stations
2. regional studies
3. global
The results are mixed. The results are mixed for a variety of reasons. Studying a single station doesnt answer the real question. What is the global effect? It opens the question, it does not provide the answer. Same with regional studies, although these are stronger. Peterson finds no UHI in the US, but he uses the wrong stations. Jones finds UHI in China. Zeke has found a small signal in the US. James anan has recent paper on japan. Again, these open more questions but they dont give an answer to the global question. Now we can run off and claim knowledge about the global based on regional information, but I like proof. A regional study has regional results. It can raise questions about the global, it can give you clues, but it doesnt give a global answer. The regional is good for testing out methods.
On the global side of things there are a few seminal works: parker jones and I would add mcKittrick. Parkers global study is actually limited to two or three regions ( as I recall been 4 years) and Jones study is more global. Ross’ work is global. The range of answers is something like 0C to .3C of the warming OVER LAND is due to UHI. and land is 30% of the total.
Simple math: If the sea warms by .8C and the land warms by 1C what the average?
.8 *.7 = .56 1*.3 = .3 average is .86
Now declare that 50% of all land warming. just declare it as a fact.
.8 *.7 = .56 .5*.3 = .15 average is .71
basically the land has low leverage in the average. Still it is important to figure out if it is
.05C ( jones)
.15C
.3C
or whatever. Globally. Not in utah, not in 50 cities. Globally.
Personally, I’ve suggested something in the middle. I have my reasons for thinking that. they are just that. They are reasons based in facts, but the actual number needs to be estimated. estimated globally. Not with a single station ( there are great studies on that which suggest 50% of all warming at urban sites are due to UHI ) Not with a state, like utah or california. Not with a country, like japan or US or china. But globally. because the question is “what about the global record?” You dont get a global estimate from utah. No more than you can reconstruct the MWP from a bristelcone pine.
And we do not answer the question about UHI by pointed to the crappy SST record. Thats a different beast altogether
“or b) Mosher above really means, residual UHI, i.e., UHI not accounted for in UHI corrections to the official temperature records. b) then implies that there have been UHI corrections to the temp records. That begs the question, how much UHI was applied to each of the station records? Does anyone have a link to such previously applied UHI corrections by record name?”
The only record that attempts a UHI correction is GISS. basically urban stations are coerced to have the same temp as their rural neighbors.
“BTW, for clarity, UHI as Mosher uses it above must account for three components:
1. Microstation siting effects and changes over time.
2. Urban Heat Island as cities’ heat sources, people, machines, and paving encroach upon the station site, and
3. Nearby Land use changes (irrigation, building, tree cutting, tree planting) as illustrated in the UTAH station paper by Gibbs [2].”
Well, I would not use Gibbs as an example of a particularly good study. I’m still waiting to see his data and code. But yes irrigation is important. WRT microsite. we have the following
1. the orginal field study performed by Leroy. Leroy was the man who came up with the coding scheme (crn1-5) that I pointed out to Anthony way back when. That study was limited to a single site and estimated two biases:
a. a temperature bias of .1C ( warm) for CRN2,3,4. 5 was not tested
b) a Variance INFLATION. crn 2, saw instantaneous peaks of +- 2C. crn 3 saw
3C swings, etc. These are PEAK swings. they average out to a small positive warm bias
2. Anthonys US study. Impact on Tave? not significant. Although, there is more work to do here. I would have expected a small positive warm bias. But as we know, wind clouds rain, shade can all play havoc with our theories.
The problem is not easy. If it were easy we could do this:
make a list of rural stations; make a list of urban stations; compare them and see a UHI signal. we dont see that. We can see it if we drive across reno. we can see if if we look at a few selected sites. we can pick and choose stations and show anything we want. heck, Zeke and I saw a study that showed japanese airports having a COOLING effect. go figure. I’ve got datasets that show early century rural warming faster than urban! go figure. If the signal were big you would see it by simply dividing the global stations into two piles: urban and rural. When you do that… no difference. weird. I conclude nothing from that weirdness except the requirment to do more study. Some people see the weirdness and say “cool parks”. other people see the weirdness and conclude ” data must be wrong, method must be wrong, there HAS to be a big UHI signal”. I think the weirdness is just weird. and bears more investigation.
Bottom line however, no matter what you find, GHGs still warm the planet, they dont cool it. The question is “how much” and UHI has nothing to do with that question because the answer to than question is not constrained by 150 year record. The 150 year record shows us the TCR not the ECR and ECR matters
rbateman says:
August 16, 2011 at 1:07 pm
Gary Swift says:
August 16, 2011 at 12:14 pm
Yes, wind can make a difference when topography is a factor. In such cases, so far, I see that there is an implied upper limit to UHI.
################
depending on the landform UHI vanishes when windspeed is above 7m/s sometimes more sometimes less. Folks can go research the data on this. Ill suggest the bubble project.
in some cases 2meters/second is enough to solve the problem
http://scholarcommons.usf.edu/etd/1784/
Thats the problem. You do a case study to show how bad UHI can be ( to get people to paint their roofs white) and you focus on the worst days you can find. Not on days when the wind blows, or it rains, or its cloudy.. all those things work to diminish UHI and microsite
Tough problem. no easy answer how often does the wind blow above 2m/sec?
other studies so folks have reading to do
http://modis.gsfc.nasa.gov/sci_team/pubs/abstract.php?id=01155
http://centres.exeter.ac.uk/cee/prometheus/uhi_paper_preprint.pdf
‘UHI is for real. Concreate, pavement, roofing composition, brick, etc. Less difference in the winter. Where is common sense in academia? Out the window?”
Climate science accepts that UHI is for real. There are whole conferences on the matter.
That is not, however, the question.
The question: do the SPECIFIC stations used in the global average contribute a warming bias to the global record. In short, is every place in the world like your sample? Thats the question.
How do we answer that question?
1. We do not answer that question by looking a few places. Anthony, for example, did not stop when he found those first cases. He went on to look at a complete record ( as complete as he could get)
2. We look at the stations actually used in the average. Not different stations like peterson and parker did. Not just utah or california or the us or japan or china. we look at them all.
3. We classify those stations with an objective criteria. what counts as urban? what counts as rural
this criteria should be tied to the physical factors that actually cause UHI. Like impermable surfaces.
4. we compare A to B and compute the answer. we are bound by that answer if we are honest people.
5. we share the code and data and people who object can do their own analysis.
The largest uncertainty in all global studies on UHI have to do with #3. and in some case the math of #4.
I sure wish somebosy would care what the results of all this REALLY were on living things.
[IMG]http://i1088.photobucket.com/albums/i332/LadyLifeGrows/Local%20BizPix/LifePurpose/Desert2GreenwAro.jpg[/IMG]
“Mark Wilson says:
August 16, 2011 at 12:50 pm
Anthony, if the subsiding is localized enough, it could create a bowl that would allow cold air to collect at night. Another possibility would be allowing rain water to collect in places where it didn’t used to”
It depends on local conditions (geological, hydrological, ‘normal’ climate, and it’s also diurnal). In general, a low spot will get heated by the sun in the day, and it can become a pool of stagnant air, that holds humiditity and warmth. That’s especially true if there are buildings to block wind. You can get smog pools sometimes too. It doesn’t take much of a change in local elevation to cause morning fog to pool in one place too. If you’ve ever spent much time in Kansas, you can see that a field that’s nearly flat for as far as you can see, will have slightly high and low spots, and the effects on growth of crops can be huge. Corn actually like warm, so it really helps in that case. It’s not only about water pooling in low spots.
If you’ve ever tried roofing, you find out really fast that it might seem like a calm wind day in a neighborhood that sits a little low, but if you go up onto a 1 story house in that neighborhood, it gets tricky to handle a 4×8 sheet of plywood when there’s no trouble 10 feet down, in the yard. It doesn’t take much of a depression to make air stand still relative to surrounding areas. I looked it up and there’s even a description of the effect on the wiki page for Death Valley. I’m sure I could find other sources if I look.
Mark Wilson says:
August 16, 2011 at 12:46 pm
Do they list the stations that they use? Many so called “rural” stations are not rural in any meaningfull way.
####
define rural in an objective way And I’ll tell you how many stations meet your criteria.
( depending on your criteria )
objective means you use numbers.
And its a good thing if you can tie your criteria to physics of the urban landscape.
things that cause UHI.
Re Zeke 2:25pm:
If NCDC is using pair-wise homogenization, then to the effect that both stations in the pair have experienced at least 0.X deg C of UHI over time period T, the homogenization would not see it.
Even if one station of the pair experienced 0.x and the other station experienced zero, wouldn’t 0.X/2 remain in the data? I also wonder about the frequency content of the homogenization processing; is it high frequency only?
Let us not forget, the mechanism for a UHI+ signal (micro, Urban, land use) might be different between an Urban (airport) to a rural site (near farmland), but may suffer a similar UHI trend contaminating the global signal. Indeed, microstation and landuse effects might cancel out urban effects. UHI+ is the sum of three uncertain components, so the UHI+ uncertainty is a function of the sum of the variances of the three components assuming independence. And they might not be independent.
If GISS uses a satellite proxy for a UHI correction proxy, then…
1. Where is the link to the correction dataset?
2. Where are the ground truth studies?
3. Seems to me that in UHI+ signal, the sat image might give a decent proxy for the URBAN component, but completely misses the micro and land use component.
4. Satellite images cover how many years of the critical temperature record?
“steven mosher says:
August 16, 2011 at 3:19 pm
Mark Wilson says:
August 16, 2011 at 12:46 pm
Do they list the stations that they use? Many so called “rural” stations are not rural in any meaningfull way.
define rural in an objective way And I’ll tell you how many stations meet your criteria.
( depending on your criteria )
objective means you use numbers”
The danger of using labels is that they are human contrivances that usually don’t fit well in the real world. However, there’s a problem with numbers as well. How objective are numbers when you can choose the numbers, and/or not all the numbers are available. For instance, you might have a temperature record, but what about humidity and pressure? What about wind speed? You can’t ever have all the numbers. Are the important ones there? How do you know? Just because it’s important in one place, at one time, is it important here, now?
Re: Mosher
I was deliberately being non-specific about “which record”. I wanted to know which records had UHI corrections and which did not. When you issued your challenge on 5/11, you did not specify which record is the source of the 0.8C of warming, so specificity didn’t seem important.
If the sea warms by .8C There you go again. It is circular reasoning: Global temps have warmed by 0.8 deg C, therefore the sea has warmed by 0.8 deg C. What satellite data supports that figure? The data to support the global sea surface warming by 0.X deg C over the past 150 years DOES NOT EXIST! There are huge inferences to arrive at any number and consequently a large error bar comes with it.
A persistent theme from you is that if the data isn’t global, you cannot trust it. Yet, facts can only be found by looking at individual temperatures records, learning from them, and testing hypotheses at other stations and regions to infer statistically uncertainty in the global signal.
You have repeatedly pooh-poohed the Utah study. Yes, it is not global. It is one study, a study with available data, whose conclusions and hypotheses should be taken and tested against an unbias sample of other sites. Sites where the equivalent metadata does not exist must not be assumed to be zero. They are null. Gibbs shows/confirms that land use changes (separate from Urban and microstation) measurably affect the temperature record in a way that can be mistaken for a global warming component. Can we confirm/disprove that corruption in other regions and how widespread is the potential corruption of the global temperature records. And how many of those official GCHN stations can we get no answer because the metadata is missing or confounds the issue?
No, it is not an easy problem. Pretending Utah doesn’t exist does not get you closer to understanding.
Let me add a few calculations to my earlier post on the impact that evaporative cooling towers have on the atmosphere temperature. The 136,000 million gallons of water per day released into the atmosphere from the total electric power plants in the US is 4.13e18 Btu per decade. The mass of the atmosphere is 11e18 pounds, thus the heat added to the atmosphere in a decade is .375 Btu per pound. Since the water vapor emitted condenses as it rises and cools, the kinetic energy from the vaporization is converted back into potential energy and specific heat is released. This process increases the temperature of the atmosphere by .375 degrees F per decade.
The volume of water vapor emitted from the cooling towers is almost the same as the water evapotranspired from the plant leaves in farming irrigation. The sum of cooling towers and irrigation using fossil ground water could be about .8F per decade. It seems to me, this heat added to the atmosphere should be included in the AGW models. Obviously there is an automatic heat release in the atmosphere and I think it is in the Tropopause due to increasing carbon dioxide decreasing the partial pressure of water vapor releasing it to outer space.
JFD
This is a bit of a tangent, but
Anecdotal (since the met station is 20 miles away, so data doesn’t really exist):
Yes, anyone who has ever lived in the Snow Belt east of Cleveland, back when the steel mills and factories were running full bore, knows this well. The air coming first over Lake Erie, picking up moisture, and then wafting through the particulates as it passed the city, snow just happens all the time, way more than the official accumulation at the west-side airport. Many a snowstorm in NE Ohio hit that area and only that area, though the same winds blew across other parts of the lake before coming ashore.
Sorry if this distracts from the main point.
Oh, and in summer, it brought monsoon-like rains to my Nike base in the eastern suburbs, on the highest land in the county.
Detailed maps from New forecast format shows heating in sheltered areas, that shifts from day to day due to wind speed and direction, here is a sample of a calm day, notice the valley hot spots in the Dakotas in the last screen shot.
Here’s a paper that studied UHI effects in Brussels, Belgium.
http://siteresources.worldbank.org/INTURBANDEVELOPMENT/Resources/336387-1256566800920/6505269-1268260567624/Hamdi.pdf
They found that the UHI effect on Tmin in the city was 3 times the effect on Tmax, leading to a warming bias of 0.62C in 40 years. This makes a mockery of both simple averages and anomalies as a reliable basis for measurement of trends.
Yet another factor that deserves consideration is the effect of city structures on the surface layer of air in which Stevenson screens are installed. One might imagine that there are relatively small differences in air temperature near the ground, but this article by Tim Ball using Geiger’s observations illustrates that there are a full couple of degrees in play, both up and down, at various points between 2.5cm and 17m from the ground. Since the surface effect is determined by wind/turbulence, it is almost inconceivable that city structures do not have an effect on measured temperatures in this respect too.
http://www.friendsofscience.org/assets/documents/FoS_Boundary_Layer.pdf
Tim also notes that because of the complexities of the surface effect on microclimates, GCMs are unable to deal with them, and they are instead “parameterized”, which means “guessed”. In the academic world of climate science this may be close enough, but as he points out, in the real world of crop production the difference between the Stevenson screen reading and the ground is enough to lead to frost-damaged crops.
The more I learn of these subtleties in the measurement of climate, the harder I find it to consider the supposed 0.7C of warming in the last 100 years as anything more than noise in a sea of imprecise data.
@Brian D August 16, 2011 at 11:08 am:
Brian, I would also add in the now ubiquitous use of air conditioners. They unbalance the heat distribution (inside-vs-outside), which means that the outside temps are affected (as has been included in at least some studies). And even though air conditioning deals with moisture as well as heat – it dehumidifies as well as cools – most applications drain the accumulated/”separated out” water into the drainage/sewers, so for the most part it does not add to outside moisture.
But if the air conditioner factor was not included, that is another (albeit minor) factor the authors did not consider.
And, with insulation of heated of buildings being less than perfect, is any consideration given at all to this factor on cold winter nights, when the inside-outside temperature delta is at its maximum, which means the greatest degree of heat flow to the outside occurs exactly when nighttime minimums happen? Apparently not.
I totally agree with Anthony, that the NCDC interpolating of station temps is a probable serious shortcoming in the paper.
With the data thus having a big “Did they cover everything” question mark hanging over its head like the sword of Damocles, I suspect this has a good chance of being one of those papers that gets retracted.
I would also put the question out there as to how much they had to frame every bit of this – which data to use and how to carefully parse it all, in order to not risk their funding. (Oh, did I suggest that someone other than skeptics is possibly doing something out of monetary motives? /snarc)
(and NOT that the really effective skeptics – Watts and McIntyre – are taking any money, anyway…)
In a paper that specifically talks about “Cooling degree days” and “heating degree days,” am I the only one here who wonders why they did NOT specifically mention air conditioning and heated buildings and take them into account – even if it was just to say, “We did not consider these significant”?
I don’t know if this will help, but we will soon have an idea of what happens when a city is abandoned.
Sana, is the capital of Yemen and one of the oldest continuously inhabited cities in the world. At sits at an altitude of 2,300 metres and has a population of approximately 1,750,000, growing at a rate of 7% a year,.
In about 10 years it will run out of water, as its 1 km deep aquifer is depleated. There is no other fresh water source near by and the sea is to far away even if they could afford to build and run desalination plants.
http://www.yobserver.com/front-page/10018066.html
So pretty soon a city will empty, 2,000,000 people will leave.
The water and the power usage will stop.
Wonder what will happen to the max and min? Make sure that we keep track of the temperature in the run up to the event.
Sana’a and Yemen are interesting places. I saw my first persons with Elephantitis in the market. The Queen of Sheba’s palace ruins are there. Normally there are saline aquifers underlying fresh water aquifers but in the Sana’a basin these were flushed during the Pleistocene when the area was wet.
There is one way to extend the life of Sana’a and that is to quit growing qat. More than half of the well water is used to irrigate qat. For those who might not know qat is a narcotic plant. The leaves are chewed by most, if not all males. The other way is build a series of small dams in the main wadis and drill recharge wells into the aquifer. Civilized people would do both. Yemenis? Doubtful on stopping chewing qat, but there are not many, if any, places for Sana’a residents to go.
The black carbon smoke from burning low quality fuels in small engines is heavy so must have some influence on current UHI.
JFD
JFD
Is there any idea of how the UHI depends on the level of urban development? For instance how would the UHI of say New York compare with with a much smaller, yet still developed urban area like Perth which has a defined relatively small CBD and considerably larger suburban area?
Or for the sake of comparing apples with apples, how about say NYC and LA? Both massive developed cities with different urban layouts. Hugely dense over a smaller area NYC compared to less dense yet hugely sprawling LA.
Richard Holle says:
August 16, 2011 at 5:08 pm
Detailed maps from New forecast format shows heating in sheltered areas, that shifts from day to day due to wind speed and direction, here is a sample of a calm day, notice the valley hot spots in the Dakotas in the last screen shot.
Forgot the link sorry.
http://research.aerology.com/project-progress/map-detail/
I am processing the Landsat thermal band (band 6) for urban areas in the United States. It is interesting to see the extent of the urban heat islands. I am get about 1 to 2 cities done per week. You may find some of the maps rather interesting. If you have a city of interest, e-mail me.
MINNEAPOLIS, MN – JULY 25, 2011
SALT LAKE CITY, UT – JULY 22, 2011
DENVER, CO – JULY 19, 2011
NEW YORK, NY – JULY 14, 2011
BISMARCK, ND – JULY 12, 2011
DETROIT, MI – JULY 8, 2011
NASHVILLE, TN – JUNE 13, 2011
SAN FRANCISCO, CA – MAY 29, 2011
MELBOURNE, AUSTRALIA – OCTOBER 24, 2010
WASHINGTON, D.C. – MAY 31, 2010
SEATTLE, WA – MAY 8, 2010
HOUSTON, TX – APRIL 11, 2007
SEATTLE, WA – JUNE 6, 2003
ATLANTA, GA – MARCH 27, 1999
ATLANTA, GA – MARCH 15, 1985
I am just starting to develop Urban Heat Island maps using the thermal band from Landsat 5. I am getting about 2 or 3 urban areas per week. If you have an area of interest, send me an e-mail and I will make your area a priority for developing a UHI map. Shown below are the areas I have completed so far.
MINNEAPOLIS, MN – JULY 25, 2011
SALT LAKE CITY, UT – JULY 22, 2011
DENVER, CO – JULY 19, 2011
NEW YORK, NY – JULY 14, 2011
BISMARCK, ND – JULY 12, 2011
DETROIT, MI – JULY 8, 2011
NASHVILLE, TN – JUNE 13, 2011
SAN FRANCISCO, CA – MAY 29, 2011
MELBOURNE, AUSTRALIA – OCTOBER 24, 2010
WASHINGTON, D.C. – MAY 31, 2010
SEATTLE, WA – MAY 8, 2010
HOUSTON, TX – APRIL 11, 2007
SEATTLE, WA – JUNE 6, 2003
ATLANTA, GA – MARCH 27, 1999
ATLANTA, GA – MARCH 15, 1985
If we’re talking about trend data, and particularly over the last 100 years, then I think “major urban areas” are largely a dry hole for UHI, even at the risk of appearing to sound like agreeing with Phil Jones.
Why? Because most major urban areas have been major urban areas for more than 100 years. The UHI is largely baked in already over the instrumental record.
It’s the suburban and rural areas where I think the real fruit lies for UHI impact over that period, partly due to growth, and partly due to the siting issues that Anthony and his volunteers (which I’m proud to include myself amongst their numbers) have identified.
A Climate Science peer-approved paper? Retracted? If so, that would be a first, wouldn’t it?
The recently reported “spike” in retractions included none, IIRC.
Have a look at the ENSO meter. Are we going into a La Nina again this Summer (South of the Equator)? Looks like it to me. The models couldn’t ALL be wrong, surely?