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.
Providence, R.I.
| Visible Light | Surface Heat | Developed Land | Vegetation Cover |
|---|---|---|---|
| › Larger image | › Larger image | › Larger image | › Larger image |
Buffalo, N.Y. 
| 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
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I have compared rainfall and temperature records in a number of well scattered locations in Australia.
I have found that, in the absence of UHI, annual rainfall is a good proxy for termperature, multiplied by minus one. (If its rainy or cloudy, then its cooler than when the skies are clear and blue, when its hotter).
Take Adelaide city for example.
The temperature was measured in a location at the edge of town which was well aired.
Temperature moved in unison with termperature (times minus one) for over 100 years.
Then the thermometer was moved into the centre of the town in 1978 (from memory).
The measured temperature immediately started to climb and continues to moved up, year after year, away from the rainfall.
Fortunately there is another thermometer at the airport some kilometres from the town centre and (strangely) this does not seem to be contaminated by UHI. The airport measurement had commenced twenty years before the move of the city thermometer and correlated closely with it and the district rainfall records.
After the city thermometer was moved into the heart of the CBD and began to be affected by UHI, the airort thermometer readings continued to stick with the rainfall records.
So it is possible to get a very good handle on the impact of UHI in central Adelaide and how it has increased with the passage of time.
That is a long story.
My punchline is a request for a scientist with access to suitable records to do a formal test of the relation betweem rainfall, temperature and UHI.
(As usual appologies for typing and spelling errors. I hope my meaning is clear).
Moderator – I can be contacted by email if you are interested in reading a paper on this subject.
Bill Illis says:
December 14, 2010 at 4:54 pm
It only took 20 years to change the 0.1C per century assumption to 9.0C for individual cities.
The world urban population has increased from 220M in 1900 to 3,495M in 2010 or 1388%.
Now what percentage of the global temperature record is contaminated by urban heat island.
Percent Land * Percent Influenced by UHI * Average UHI Increase since 1900.
30% * 50% * 2.0C = 0.30C
##################
that’s a nice back of the envelop calculation. Understand however that the cities that see big increases are 1M plus in population. There are not 50% of these in the database. But, if you want an upper BOUND on how important UHI is .3C is a number that Jones himself cites, he settled on .05.
Correct to the above.
When I wrote that temperature correlated with temperature, I meant temperature correlated with the inverse of delta rainfall.
The problem with urban-rural comparisons and with the Chinese study of Jones, et al., 1990 is that rural (or purportedly rural in the case of Jones et al., 1990) areas also can suffer from UHI growth. If the UHI growth in the rural areas matches that in the urban areas, the urban-rural comparison will erroneously result in little or no difference in trend. Part of the problem is that climate science is burdened by not having a real good control when making their measurements (e.g. temperatures). Consequently, trying to find out how badly UHI growth may be contaminating the warming believed to be due to CO2 is not trivial.
From: Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850, P. Brohan, J. J. Kennedy, I. Harris, S. F. B. Tett & P. D. Jones Accepted version: December 19th 2005 (formerly linked at: http://www.cru.uea.ac.uk/cru/data/temperature/HadCRUT3_accepted.pdf)
Jones, et al., 1990 is the paper that was the subject of an accusation of fraud because the paper claimed (IIRC) that there were “few, if any” station moves in the Chinese part of the study. One of the co-authors, Wang, admitted during the investigation at the SUNY – Albany that not only had there been station moves, but that the moves had been such that they required altitude adjustments! SUNY-Albany apparently furthered the (apparently admitted) fraud by redacting the admission in their final report.
So, the importance of UHI was largely dismissed based on a paper where one of the authors essentially admitted failing to disclose significant station moves, while claiming otherwise in print. This paper (Jones et al., 1990) is then relied upon when CRU produced HadCRUT3 in 2005. In turn HadCRUT3 was then relied upon to produce the IPCC’s AR4 in 2007!
Phil’s Dad says:
December 14, 2010 at 4:54 pm
steven mosher says:
December 14, 2010 at 1:14 pm
“The peak bias you see of 7C to 9C is by no means a daily occurance(sic).”
From Science @ur momisugly NASA.gov – Satellites Pinpoint Drivers of Urban Heat Islands in the Northeast
“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.”
Perhaps Mr Mosher could reconcile “peak” with “average…over a three year period”.
########
simple: summer.
when we talk about average we are talking about ANNUAL avaerage. so, that 7-9C
becomes 1.75 to 2.25C annually. UHI is seasonal. In the worse cases ( cited by alarmists) it can give you results like that in the summer. In some cities, however, the city can become a heat SINK in other seasons.
Its funny how people miss the alarmist spin.
If you want a view to chew on chew on this
http://www.woodfortrees.org/plot/hadcrut3vgl/from:1979/to:2010/trend/plot/uah/from:1979/to:2010/trend/plot/gistemp/from:1979/to:2010/trend/plot/rss/from:1979/to:2010/trend
1. The odd man out is UHA.
2. The satillites measure the temperature in the lower trop. no UHI there.
So is UHA correct ( and UHI is limited to the difference between UHA and GISS/CRU? or is UHA the odd man out and the other three agree because UHI is
small in impact. In either case the effect of UHI is small. Real, but small.
To just explain a lukewarmer position.
A skeptic says that all the warming we see in the land records is UHI
B. alarmist says that none of the warming we see is UHI.
C. luke warmer is in between.
Interesting question. Do people believe the record warmed between 1900 and 1940?
why?
I am posting this comment a second time, because it appears to have been stuck in moderation, although there is only one link:
The problem with urban-rural comparisons and with the Chinese study of Jones, et al., 1990 is that rural (or purportedly rural in the case of Jones et al., 1990) areas also can suffer from UHI growth. If the UHI growth in the rural areas matches that in the urban areas, the urban-rural comparison will erroneously result in little or no difference in trend. Part of the problem is that climate science is burdened by not having a real good control when making their measurements (e.g. temperatures). Consequently, trying to find out how badly UHI growth may be contaminating the warming believed to be due to CO2 is not trivial.
From: Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850, P. Brohan, J. J. Kennedy,
I. Harris, S. F. B. Tett & P. D. Jones Accepted version: December 19th 2005 (formerly linked at:
http://www.cru.uea.ac.uk/cru/data/temperature/HadCRUT3_accepted.pdf)
Jones, et al., 1990 is the paper that was the subject of an accusation of fraud because the paper claimed (IIRC) that there were “few, if any” station moves in the Chinese part of the study. One of the co-authors, Wang, admitted during the investigation at the SUNY – Albany that not only had there been station moves, but that the moves had been such that they required altitude adjustments!
SUNY-Albany apparently furthered the (apparently admitted) fraud by redacting the admission in their final report. So, the importance of UHI was largely dismissed based on a paper where one of the authors essentially admitted failing to disclose significant station moves, while claiming otherwise in print. This paper (Jones et al., 1990) is then relied upon when CRU produced HadCRUT3 in 2005. In turn HadCRUT3 was then relied upon to produce the IPCC’s AR4 in 2007!
Sore_ron says:
December 14, 2010 at 12:04 pm
Anybody plotted the position of the weather stations against these data ?
######
yes. If folks like ISA as a meausre of urbanity I can tell you what percentage of the land around a station is Impervious surface. Been posting about this for a while. Using the same data.
1) What effect can UHI have in the correlation between two stations when determining the match for extrapolation purposes?
2) What happens to the extra energy used to create the UHI effect? Does it go into ‘the pipeline’ or just radiate away?
3) If UHI is the result of extra absorption, shouldn’t there be more LWR available for absorption by CO2 particles, and hence more warming?
I guess we could put up windmills and use some of the power to cool the cities..
[/sarcasm off]
and then Hansen goes out and proclaims 2010 the hottest year ever…. Garbage stations, Garbage SST manipulations, dare i say garbage in general… Lieing to push the AGW agenda.. pathetic
P. Brohan, J. J. Kennedy, I. Harris, S. F. B. Tett & P. D. Jones, 2005:
To clarify #3, UHI is the result of more absorption of SWR, resulting in more LWR radiating away.
steven mosher says:
at December 14, 2010 at 1:14 pm and more
Thanks for your comments. Appreciate your emphasis on the specific local details in this simpler larger story, which in turn emphasize the fascinating complexity of it all.
One of my posts apparently is stuck in moderation. Hopefully it will be cleared. A further comment, however, is warranted on those studies that seek to measure UHI or delta UHI by comparing urban stations to rural stations. I have read a couple of these and it strikes me that there is an assumption made that temperatures or temperature trends at so-called rural stations are not contaminated by UHI. The study in Barrow, AK (http://www.geography.uc.edu/~kenhinke/uhi/HinkelEA-IJOC-03.pdf), however, clearly shows that significant UHI (peaking at 6°C, but averaging 2.2°C) was present above the artic circle in the middle of the winter in a small community. Barrow has grown from about 300 residents to about 4600 in the 20th Century. The assumption that rural communities (where almost all of the rural stations are located) are not contaminated by UHI or delta UHI it seems may need to be avoided in trying to determine the magnitude of the UHI effect on temperature and temperature trends.
Mosh,
How do you know if the “really rural” stations in the GHCN dataset are actually the best stations available. For instance for New Zealand GHCN contains a very small number of stations and many are urban. There is a large archive of NZ rural station temperature data that is available via the cliflo database but the vast majority of this data is simply not in the GHCN. So who assembled the NZ component of GHCN and can that component be trusted as being representative of NZ? What agendas were being served when the individual stations were chosen? Have the number of rural NZ stations in the GHCN declined since the early 1990’s (probably yes, and I recall EM Smith examining this issue).
So I suspect we need to examine the whole of the GHCN database for “accidental” cherry picking of a certain category of rural stations because that’s what makes the nicest cherry pie.
I would also caution you with respect to UHI and supposedly rural stations because the largest part of the UHI curve appears to be produced at relatively low population densities ( ie from near zero to not many people per square km). Roy Spencer posted some particularly good information on this issue a few months back on his blog. The consequence is that areas relatively modest population densty exhibit steep trends in UHI if the population density is changing. Such areas change in population density faster than the core portions of major cities. So “urban warming trends” can be greater in modestly populated areas than in almost any city. This effectively defeats the nightlights classification system employed for GIStemp. It also means you will need to be very careful drawing conclusions from comparison of mean trends from supposedly rural and known city climate stations.
White paint is a waste of space, resources and energy. Do it natures way, save energy, water provide cooling or insulation and cash returns.
Little market gardens in the city selling the produce in the corner stores. What could not be more American.
Finally, someone discovers the incredibly obvious and publishes a paper about it. Anyone who has been in a large city during a summer evening and, during that same summer, goes to a rural or suburban location will be struck by the day/night temperature differential. I used to live in downtown Vancouver in a non-airconditioned apartment and July and August nights weren’t pleasant. Now that I’ve moved to Kamloops which has about the same population as downtown Vancouver, but spread out over about 100 square miles, summer nights are very pleasant even though summer days are often much hotter than in Vancouver.
One of the factors that is likely supporting belief in AGW is the experience of city dwellers who never get out into the countryside and who make the usual human assumption that their local environment is representative of the remainder of the country. The only AGW that has thus far been found to exist is the UHI effect. Given the magnitude of UHI that the referenced paper found, the idea that a single thermometer can predict the temperature of an area several hundreds of miles around that thermometer has been utterly demolished and one needs to recalculate average world temperatures with urban records being extrapolated to a far smaller land area than they currently are. Ideally one would have a grid of thermometers spaced a few hundred yards apart over a large area giving a much better estimate of average temperature for a given geographic area. The other option is to have people drive around the city with external thermometers whose readings are recorded with the GPS coordinates of each temperature reading to give the type of urban temperature profiles that Anthony wrote about some months back.
Numerous posts on WUWT have shown that temperatures for rural locations have not demonstrated a significant increase over the last 30 years whereas urban locations have had temperature increases. Air conditioning is the simplest way of dealing with high summer temperatures, but painting roofs white is a simple thing to do as is watering roofs in areas with sufficient water supplies and low humidity. My solution to increasing UHI temperatures was to move out of a large city altogether.
Hopefully someone who’s seen this post has recalculated world temperatures based on properly weighting urban temperature readings based on the builtup area of the city for the year temperature was taken (OK not as simple as I thought it would be). I suspect that such a calculation would give a far smaller temperature rise than the hockey stick shaped temperature records which the warmists are so fond of exhibiting.
Rob R says:
December 14, 2010 at 10:54 pm
Mosh,
How do you know if the “really rural” stations in the GHCN dataset are actually the best stations available.
1. You go ahead and define what you think makes a station subject to UHI.
A. Impervious surface? I got 4000 stations with less than 10% ISA, 1000 with
zero ISA
B. Population density? How many people per sq km? 0, 10, 15.
C. Nightlights ( electrification) I got better data than hansen uses.
D. Urban extent: mapping of the urban area . Got that too.
E. Vegatation. Got that but should probably update it.
Here is what I challenge people to do. present a hypothesis. Sites with 100% ISA
will warm faster than those with 0. Just present your hypothesis. Not speculation.
Hypothesis. Then we can test it.
” For instance for New Zealand GHCN contains a very small number of stations and many are urban. There is a large archive of NZ rural station temperature data that is available via the cliflo database but the vast majority of this data is simply not in the GHCN. So who assembled the NZ component of GHCN and can that component be trusted as being representative of NZ? What agendas were being served when the individual stations were chosen? ”
Again, hypothesis? make one that is testable.
“Have the number of rural NZ stations in the GHCN declined since the early 1990′s (probably yes, and I recall EM Smith examining this issue).”
The number of stations will not drive the numbers. On an area basis NZ is mousenuts.
Basically, you can decimate the record and the numbers dont change or increase to 40,000 stations and the numbers dont change. More on the 40K stations when time permits and when it gets closer to release.
“So I suspect we need to examine the whole of the GHCN database for “accidental” cherry picking of a certain category of rural stations because that’s what makes the nicest cherry pie.”
Again, state a hypothesis that is testable. The stations were selected 20 years ago. Long before the metadata we can use to pick which rural we want.
“I would also caution you with respect to UHI and supposedly rural stations because the largest part of the UHI curve appears to be produced at relatively low population densities ( ie from near zero to not many people per square km). Roy Spencer posted some particularly good information on this issue a few months back on his blog. ”
Actually thats a testable hypothesis. Nobody has asertained with accuracy the steepness of that curve. Population density matters, building material matters. building height matters. I can of course test stations that had <14 people per sq km from 1900 to 1940. no population change. wanna guess? make a hypothesis. If I look at stations that had no people in 1900 and no people in 1940 and comapre that with stations where the population went from zero to urban in those 40 years, what do you hypothesize? will you subject your belief to falsification? or verification?
"The consequence is that areas relatively modest population densty exhibit steep trends in UHI if the population density is changing. Such areas change in population density faster than the core portions of major cities. So “urban warming trends” can be greater in modestly populated areas than in almost any city. This effectively defeats the nightlights classification system employed for GIStemp. It also means you will need to be very careful drawing conclusions from comparison of mean trends from supposedly rural and known city climate stations."
well with nightlights, for example, I can pick stations that have no lights within 20km.
and no Impervious surface and zero population. the problem with nightlights is that works better in developed countries and not as well in places like india. High population, low electrification
dont neglect to read ALL the PDFs.
they talk about “cool parks” 2-5C cooler. This is of interest because Peterson claimed that temperature stations were in “cool parks”. A testable hypothesis. That now could be tested with MODIS.
Also, nice paper that used modelling to see the impact of AC on paris. urban energy budget models. very cool stuff. They will even link them in with weather forecasting models
Mosh, the USHCN station in Providence is an ASOS at the airport, right in a big heat blob. I’ll have more on that later – Anthony
Two points:
1.Commonsense.
I believe the wealthy built homes in the country because the cities and towns were disease ridden cess pits (and the hunting/shooting/fishing were better in the country).
2. The recent paper discussed in WUWT (22 November) shows that death rates in cold weather are about 100 times higher than hot weather.
UHI effect also has a long tail (in this case a small difference spread out over a large area) you should bear that in mind. It is in excess of the purported warming signal
Rob R says:
How do you know if the “really rural” stations in the GHCN dataset are actually the best stations available.
They absolutely are NOT the best available. They are often only the most convenient, and an astounding percentage are at airports. In particular, military airports are often classed as “rural” since nobody ‘lives there’ and they run without lights at night for combat readyness in many cases, and with minimal lighting around them in most cases. Quantico Verginia was one such last I looked. “The crossroads of the US Marine Corp.”…
For instance for New Zealand GHCN contains a very small number of stations and many are urban.
IIRC, it was 10, then dropped to 8, ALL but one at airports (and that one on Raoul Island well into the tropical north). (And to the folks who like to swoon over the fact that some of them don’t get a lot of traffic: The TARMAC doesn’t go away nor deminsh with reduced traffic.)
http://chiefio.wordpress.com/2010/11/18/airports-a-tarmac-tale/
This link as some N.Z. stats in it:
http://chiefio.wordpress.com/2009/12/08/ncdc-ghcn-airports-by-year-by-latitude/
So take a thermometer at a GRASS FIELD in the past (they all pretty much started that way at some point. 1919 at worst case) and slowly add tons / hectares of asphalt, buildings, cars, planes, then move to the jet age with even larger planes and 10,000 foot runways instead of 3000 and …
Airports grow over time. By definition. As aviation has grown from nothing at all to the Jet Age from 1920 ish to 2000 ish.
With over 90% thermometers at airports in many countries, we’re basically finding out that tarmac is hotter than grass. Who knew? /sarcoff>
So who assembled the NZ component of GHCN and can that component be trusted as being representative of NZ?
NCDC. And a simple “no”. It’s guaranteed NOT to be representative as it’s airports.
What agendas were being served when the individual stations were chosen?
While I’d like to assert “malice”, it’s mostly likely “stupidity”. (“Never attribute to malice that which is adequately explained by stupidity”).
Airports must have a thermometer. It has to be close to right (though reading high is encouraged). The data must be comunicated to other airports so pilots everywhere can plan. So you have an attended communicating thermometer just sitting there. Free data? Take it and run…
Unfortunately, airplanes work better in cold air ( low “density altitude”) so on a hot day you might not get off the runway before you crash ( high “density altitude”). So you calculate your ‘density altitude’ before taking off (and preferably also before landing…). If the thermometer is a ‘bit high’ nobody gets hurt. If it reads erroneously low, people get hurt or die. Guess which way the error is encoraged to go?…
That’s why the ASOS at airports has a feed that is rounded UP to whole degrees C for aviation use.
(There is also a feed in 1/10 C, but it’s unclear exactly how who handles what… I’ve not done the leg work to audit an ASOS vs GHCN and check for verasity / 1/10 C).
But the most important point is pretty simple:
Airport thermometers are first and formost for airplane and pilot use. “Climatology” is a freeloader and gets the leftovers. Pilots want that thermometert as close to the runway as possible (as that is where the wings will be flying… 2 foot up dead center of the runway would be ideal… but they accept off to the side due to that whole ‘landing gear’ problem 😉 and they want any error to be to the high side. And thats a GOOD THING. It keeps you alive when you fly.
Have the number of rural NZ stations in the GHCN declined since the early 1990′s (probably yes, and I recall EM Smith examining this issue).
Yes, but not nearly as much as other places. IIRC, N.Z. lost the most southernly thermometer on the coldest island. Campbell? But it’s not the 1990s that matter. It’s the 1950-80 range.
Codes like GIStemp use a ‘Grid / Box anomaly’ and DO NOT compare a thermometer only to itself. This, IMHO, is a major flaw. So if in 1950 you had a ‘Grid / Box’ with one thermometer at a cow field, one at the (grass field?) small prop plane airport, and one down by the city park; then dropped all but the one at the airport; then had the airport upgrade to 2 runways 10,000 feet long, a bigger parking lot, and more traffic…. You would compare the after with the before:
The codes like GIStemp make an average in the box out of those three in 1950, then compare that average to the “average of one” over the airport tarmac near the jet exhaust ‘now’; and say the “grid / box” has warmed.
No, that is NOT an overstatement. That is exactly the kind of thing that is done. From 7200 ish thermometers at peak, to 1200 or so in 2009 IIRC. (and there were 8000 “grid / boxes” so many of them have NO real thermometer in them, just a made up number from thermometers up to 1200 km away… you can actually be comparing two fantasy numbers in the same box. With different ‘sources’ in the two time periods depending on what was available when). This just gives a giant “splice artifact” in the comparison.
Yes, the code tries to correct for the changes. No, IMHO, it does not succeed. What you end up measuring are the error bands on the ‘corrections’ and the growth of Airport Heat Islands.
In my garden suburb in Melbourne, Australia, streets lined with shady plane trees are noticeably cooler than other streets without large trees. (Plane trees also remove methane from the atmosphere 🙂 I have planted hundreds of trees in available urban spaces and grown a wild life corridor on the wasteland along railway lines. Why can’t the Greenies do likewise instead of jetting off to global conferences?
Heck. I’ve been giving peer-reviewed lit examples like this for some years now.
I’ve also looked at the seminal?? Jones et al 1990 Nature paper in some detail. With the benefit of hindsight, many of the Australian stations were poorly chosen. Many were at Post Offices, often near the centres of growing towns. Charters Towers in Queensland, PO smack in the town centre, was at one time one of the largest gold mine towns in the world. A prudent person would have expected some UHI effect.
Thus, for Australia the delta UHI has changed with time; and the UHI that was measured for the 1990 paper was real, not a zero baseline.
I guess the BOM knew this was the case, because soon after 1990, there was a significant Australian shift from Hg thermometers to electronic devices, coupled with a shift of stations from POs to airports.
The Jones at al 1990 paper was simply wrong in places. The position of Port Lincoln is 1 degree wrong, the station at Bowen plots in the Pacific Ocean, to name just 2 narks.
See the criticisms by Warwick Hughes at http://www.warwickhughes.com/climate/bom.htm
See examples of Melbourne’s UHI measured by traverses at 3 to 4 degrees C
http://www.earthsci.unimelb.edu.au/~jon/WWW/uhi-melb.html
BYW, Michael Coughlan went on in the BOM to be described as Australia’s chief climatologist, but a reserved one who said on Jan 6 2009 about the largest Australian newspaper “The Australian clearly has an editorial policy. No matter how many times the scientific community refutes these arguments, they persist in putting them out – to the point where we believe there’s little to be gained in the use of our time in responding.’’
Robuk says:
Normalizing urban station data trends to the surrounding rural stations.
If they can do that they must have enough rural stations to construct the data set in the first place without using the currupt urban data.
Am I missing something.
Yes, the rural stations.
The studies I did were on the 2009 version of the code. It has 8000 “Grid / Boxes”. I believe the present version ups that to 16,000.
The problem is that there are not presently 16,000 thermometers used for the globe.
The peak number was 7600 (the data is still in the GHCN, but only for ‘old times’). The number used at 2009 end was about 1200.
So in the ‘baseline’ you get to smear 7000 (some get dropped in processing) or less statins into 8000 boxes (or 16,000). Then in 2010 you get to smear 1200 into 8000 boxes (or 16,000) – so clearly a lot of those ‘boxes’ have a fantasy number created based on a thermometer somewhere else… up to 1200 km away.
Now the first boxes are compared to the second boxes and ‘trends’ are computed.
But trends of WHAT? Thermometers that don’t exist?
And of those thermometers that do exist, most are in the USA. So at the end of 2009, GIStemp put in the ‘new’ USHCN set as well. (Now they can claim far more thermometers). But they are all in the USA, so the problem remains. Those grid / boxes everywhere else are still ‘half fantasy”. And in most places, filled with an airport. The percentages can be quite high (see above link).
So that whole “we fix up the UHI real good!” is just another fantasy. 1/2 the time they do the UHI “correction” in the wrong direction anyway. The quantity of actual data used in the product of codes like GIStemp is vanishingly small. Just like the vanishing “rural” percentage of the GHCN data set.