Gee where have we seen something like this before? Dads/Moms and Grandparents: if you’d like your children/grandchildren to be able to do something on UHI for the spring science fair, here’s an easy to do idea. – Anthony
From Science @ NASA.gov – Satellites Pinpoint Drivers of Urban Heat Islands in the Northeast
Cities such as New York, Philadelphia, and Boston are prominent centers of political power. Less known: Their size, background ecology, and development patterns also combine to make them unusually warm, according to NASA scientists who presented new research recently at an American Geophysical Union (AGU) meeting in San Francisco, Calif.
Summer land surface temperature of cities in the Northeast were an average of 7 °C to 9 °C (13°F to 16 °F) warmer than surrounding rural areas over a three year period, the new research shows. The complex phenomenon that drives up temperatures is called the urban heat island effect.
Heat islands are not a newly-discovered phenomenon. Indeed, using simple mercury thermometers, weather watchers have noticed for some two centuries that cities tend to be warmer than surrounding rural areas.
Likewise, researchers have long noticed that the magnitude of heat islands can vary significantly between cities. However, accurate comparisons have long eluded scientists because ground-based air temperature sensors tend to be unevenly distributed and prone to local bias. The lack of quantifiable definitions for urban versus non-urban areas has also hindered comparisons.
Satellite technology, which offers a more uniform view of heat islands, is in the process of changing this. The group of researchers from NASA’s Goddard Space Flight Center in Greenbelt, Md., presented results based on a new method for comparing heat islands at the AGU meeting.
| Visible Light | Surface Heat | Developed Land | Vegetation Cover |
|---|---|---|---|
| › Larger image | › Larger image | › Larger image | › Larger image |
| Visible Light | Surface Heat | Developed Land | Vegetation Cover |
|---|---|---|---|
| › Larger image | › Larger image | › Larger image | › Larger image |
Satellite-produced maps of Providence and Buffalo highlight the role that differences in development patterns and vegetation cover can have on the magnitude of a city’s urban heat island. Though the two cities have the same approximate size, Providence has a significantly stronger heat island. Credit: NASA/Earth Observatory
“This, at least to our knowledge, is the first time that anybody has systematically compared the heat islands of a large number of cities at continental and global scales,” said Ping Zhang, a scientist at Goddard and the lead author of the research.
Land surface temperatures in cities, particularly densely-developed cities, tend to be elevated in comparison to surrounding areas — a phenomenon called an urban heat island. Credit: NASA
Heat islands can be deadly. This graph shows how the number of deaths spiked in Paris during a sweltering heat wave in 2003. Credit: University of Hawaii at Manoa/Benedicte Dousset
Air conditioning systems release waste heat into the atmosphere such that their widespread use can inadvertently elevate city air temperatures. This graph shows the result of a model that calculated the likely magnitude of the effect during the 2003 heat wave in Paris. Credit: Météo France/Cécile de Munck
Surface temperatures vary more than air temperatures during the day, but they both are fairly similar at night. Credit: EPA
› Larger image Development produces heat islands by replacing vegetation, particularly forests, with pavement and other urban infrastructure. This limits plant transpiration, an evaporative process that helps cool plant leaves and also cools air temperatures, explained Robert Wolfe of Goddard, one of the scientists who developed the method.
Dark city infrastructure, such as black roofs, also makes urban areas more apt to absorb and retain heat. Heat generated by motor vehicles, factories, and homes also contributes to the development of urban heat islands.
A New View
The new method for comparing cities, which the team of scientists has honed for about two years, involves the use of maps of impervious surface area produced by a United States Geological Survey-operated Landsat satellite, and land surface temperature data from the Moderate-resolution Imaging Spectroradiometer (MODIS), an instrument aboard NASA’s Aqua and Terra satellites.
Impervious surfaces are surfaces that don’t absorb water easily, such as roads, roofs, parking lots, and sidewalks. Land surface temperatures tend to be higher and more variable than air temperatures, but the two generally vary in sync with each other.
By analyzing data from thousands of settlements around the world, the Goddard team has pinpointed key characteristics of cities that drive the development of heat islands. The largest cities, their analysis shows, usually have the strongest heat islands. Cities located in forested regions, such as the northeastern United States, also have stronger heat islands than cities situated in grassy or desert environments.
Most recently, the Goddard group has shown that a city’s development patterns — whether a city is sprawling or compact — can also affect the strength of its heat island.
By comparing 42 cities in the Northeast, they found that densely-developed cities with compact urban cores are more apt to produce strong urban heat islands than more sprawling, less intensely-developed cities.
The compact city of Providence, R.I., for example, has surface temperatures that are about 12.2 °C (21.9 °F) warmer than the surrounding countryside, while similarly-sized but spread-out Buffalo, N.Y., produces a heat island of only about 7.2 °C (12.9 °F), according to satellite data. Since the background ecosystems and sizes of both cities are about the same, Zhang’s analysis suggests development patterns are the critical difference.
She found that land cover maps show that about 83 percent of Providence is very or moderately densely-developed. Buffalo, in contrast, has dense development patterns across just 46 percent of the city. Providence also has dense forested areas ringing the city, while Buffalo has a higher percentage of farmland. “This exacerbates the effect around Providence because forests tend to cool areas more than crops do,” explained Wolfe.
Cities in desert regions, such as Las Vegas, in contrast, often have weak heat islands or are actually cooler than the surrounding rural area. Providence, R.I.; Washington, D.C.; Philadelphia, Pa.; Baltimore, Md.; Boston, Ma.; and Pittsburgh, Pa.; had some of the strongest heat islands of the 42 northeastern cities analyzed.
“The urban heat island is a relative measure comparing the temperature of the urban core to the surrounding area,” said Marc Imhoff, the leader of the Goddard research group. “As a result, the condition of the rural land around the city matters a great deal.”
Heat Island Impacts
Ratcheting up temperatures can have significant — and deadly — consequences for cities. Heat islands not only cause air conditioner and electricity usage to surge, but they also increase the mortality of elderly people and those with pre-existing respiratory and cardiovascular illness.
The U.S. Environmental Protection Agency estimates that, between 1979 and 2003, heat exposure has caused more than the number of mortalities resulting from hurricanes, lightning, tornadoes, floods, and earthquakes combined.
“It is the lack of cooling at nighttime, rather than high daytime temperatures, that poses a health risk,” said Benedicte Dousset, a scientist from the University of Hawaii who also presented data about heat islands at the AGU meeting.
Dousset recently analyzed surface temperature images of Paris and showed the spatial distribution of heat-related deaths during a sweltering heat wave in 2003. Some 4,800 premature deaths occurred in Paris during the event, and excess mortality across Europe is thought to be about 70,000.
The risk of death was highest at night in areas where land surface temperatures were highest, she found. Buildings and other infrastructure absorb sensible heat during the day and reradiate it throughout the night, but the cooling effect of evaporation is absent in cities. The lack of relief, particularly among the elderly population, can be deadly, she explained.
Ramped up air conditioning usage may have even exacerbated the problem, other data presented at the meeting suggests. Cecile de Munck, of the French Centre for Meteorological Research of Meteo-France, conducted a series of modeling experiments that show excess heat expelled onto the streets because of increased air conditioner usage during heat waves can elevate outside street temperatures significantly.
“The finding raises the question: what can we do to design our cities in ways that will blunt the worst effects of heat islands?” said de Munck, who notes also that her research shows that some types of air conditioning exacerbate heat islands more than others.
Making sure cities have trees and parks interspersed throughout the compact urban cores can also help defend against heat islands. And studies shows that painting the surfaces of roads and buildings white instead of black and creating “green” roofs that include vegetation can soften urban heat islands.
“There’s no one solution, and it’s going to be different for every city,” said Dousset. “Heat islands are complex phenomena.”
Related Links:
Beating the Heat in the World’s Big Cities
http://earthobservatory.nasa.gov/Features/GreenRoof/
EPA Heat Island Resources
Ecosystem, Vegetation Affect Intensity of Urban Heat Island Effect
www.nasa.gov/mission_pages/terra/news/heat-islands.html
Urban Heat Island: Baltimore
http://earthobservatory.nasa.gov/IOTD/view.php?id=36227
Scientific Visualization Studio: Related Materials
http://svs.gsfc.nasa.gov/vis/a010000/a010600/a010699/index.html
Briefing Materials: Slideshows
Lead Author Ping Zhang; Goddard Space Flight Center
Benedicte Dousset, University of Hawaii
Cecile de Munck; French Centre for Meteorological Research of Meteo-France
› Download pdf Adam Voiland
NASA’s Earth Science News Team
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.






@Dave F says Dec 14, 2010 at 11:23 am
Exactly. But consider also that the met stations at airports are not 100% of UHI. Each one varies uniquely, and over time. Likewise, met stations inside the cities vary in where within each city they are placed.
With such a severe spike from outlying zero UHI to the max UHI for each city, it becomes completely relevant where each met station is, with that UHI gradient so steep – and what adjustment value to assign each city. Mislocation by a mile will severely affect what adjustment is needed. And merely a 0.05C error would amount to 100% of the Chinese study that Phil Jones signed on to. But that 0.05C might represent only 300 meters or so. 5 or 12C is 100 to 240 times as high as that study – relied on by CRU – claimed. With a slope that much higher how is anyone to have any faith that the adjustments are done properly.
And then, as mondo says at 11:21 am:
Variables unperceived before now have risen up to complicate everything to do with climate warming and the 20th century.
“How fast did each city’s UHI increase in each decade?” becomes a vitally important question. This makes global averaging more than an apples and oranges thing – it becomes “What kind of apples and perhaps even how many?”
That Chinese study is so far down the crapper now a Roto Rooter wouldn’t bring it back. And all the GCMs have no idea of the validity of any of the continental data they are using.
#
#
Dave F says:
December 15, 2010 at 8:33 pm (Edit)
Roald says:
December 15, 2010 at 2:01 pm
A question to all the skeptics: Why do RSS and UAH readings show the same warming trend as GISS and HadCRU? Are there urban heat islands in the lower troposphere, several miles above the ground, too?
Hmmm. Do they show the same trend? If GISS is adjusting the past downwards for UHI, which is grossly inappropriate, then no they are not.
Of course, the climate models are based largely on data derived from sets using adjustments for UHI that may be wholly inappropriate*.
#####
climate models are not largely based on this data. they are first principles physics models. Read GISS ModelE.
The hypothesis I’ve already tested extensively is: Given UNCONTAMINATED century-long rural/small-town records, the regressional trend of time-series SYNTHESIZED from anomalies of ALL OTHER station-records (including urban) in the area is CONSISTENTLY very significantly more positive than that of the former.
The key to such such testing is having solid analytic criteria for recognizing various nonstationarities and other flaws in records (including nominally “rural” ones). It is not just a matter of the clerical synthesis of anomalies, al a GISS, NOAA or Hadley– a practice that you blindly defend. Common sense should tell you that no UNBIASED estimates of SECULAR trend can be made where there are no uncorrupted century-long records reasonably densely available (i.e., most of the world outside of the USA).
#########
sorry. You only need look at the approach of Roman M and JeffId ( you know Roman from climate audit) to see what you say is not the case. If you would like to look at their code it’s posted. Or look at Nick Stokes code. Its posted.
When you have something that runs with some decent tests I’m sure folks will take a look at it.
here
http://climateaudit.org/2010/12/15/mckitrick-and-nierenberg-2010-rebuts-another-team-article/#more-12612
Robuk.
Interesting chart.
1. list of the rural stations
2. criteria for selecting them as rural.
3. list of the urban stations
4. criteria for selecting them as rural.
Then, of course, you have to randomize your selection of the rural. no fair pulling micheal mann data snooping tricks.
In any case I know of some work that is doing exactly this.
Steven Mosher says:
December 15, 2010 at 10:33 pm
climate models are not largely based on this data. they are first principles physics models. Read GISS ModelE
What did they calibrate them against to make sure they were working correctly?
Just to test the waters,
Choose 100 US pristine rural stations from Anthony`s list, so many from each state, number them, throw the numbers in a hat, shake the hat and pull out 50 making sure there is one from each state.
Choose 50 urban stations, one from each state using only cities or airports.
Use the raw data from 1900 to 2000+ and compare the trend of both sets.
Adjust for station moves etc then compare both sets again and then compare both results against each other. I believe that should indicate whether or not UHI is driving climate.
Steven Mosher says:
December 15, 2010 at 10:38 pm
You seem not to understand the analytic issue. If there is a systematic date-dependent bias due to UHI or certain other flaws in a great many of the archived records, Roman M’s algorithm applied indiscriminately to ALL the avaliable data can do nothing to remove it from the data sausage it produces. That urban bias is so plainly evident from INTACT records that even a 12-year old posted a You Tube demonstration for a score of USA station pairs.
Amateurish “folks … tak[ing] a look at” the results of eliminating corrupted records via analytically incisive screening is not my goal or concern, as you would make it. Our technical reports as hired consultants are prepared for a professional audience.
Sorry sky.
you dont get it. we are talking past each other. When you get ready to post code that we can talk about feel free.
Robuk says:
December 16, 2010 at 6:05 am (Edit)
Just to test the waters,
Choose 100 US pristine rural stations from Anthony`s list, so many from each state, number them, throw the numbers in a hat, shake the hat and pull out 50 making sure there is one from each state.
##################
. one from each state would not be a spatially uniform sample.
######
Choose 50 urban stations, one from each state using only cities or airports.
Use the raw data from 1900 to 2000+ and compare the trend of both sets.
###
Understand that the “raw” data from rural sites needs to be corrected for changing Time of observation. a human impact.
“Adjust for station moves etc then compare both sets again and then compare both results against each other. I believe that should indicate whether or not UHI is driving climate.”
1. you need to adjust for more than moves.
2. you’d have a conclusion about the US. (2% of the land)
Dave F says:
December 15, 2010 at 11:29 pm (Edit)
Steven Mosher says:
December 15, 2010 at 10:33 pm
climate models are not largely based on this data. they are first principles physics models. Read GISS ModelE
What did they calibrate them against to make sure they were working correctly?
#######
go to steve easterbrook blog and look at how GCMs are put together and tested.
Steven Mosher:
Get real! Comissioned technical reports by research groups are privileged intellectual property. I don’t violate the rights of our clients. And I harbor no ambition to become a blog star. There’s serious scientific work to do–with much more serious rewards.
Mosher,
Aren’t we using min/max to calculate the mean? If so, then peaks are very important, as well as the higher lows, and combined, would be expected to cause a higher anomoly. Can’t disregard the peaks as irrelevant I would think, as well as the higher lows. That’s why we shouldn’t just show anomolies, it paints a special picture. We should show min trends, max trends, average trends, seasonal trends, then there is something to discuss. Hiding it in an anomoly, is well hiding…
Ed