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
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?