Ghcn V3 Metadata improvements
The Global Historical Climate Network (GHCN) is in it’s beta stage. On of the stated goals of the project is to improve the metadata that is provided for the station data. Over the past few months several independent volunteers have been focusing on the issue of station metadata, each with their own focus. Ron Broberg deserves credit for taking the lead with applying GIS tools to the issue and Peter O’neill deserves credit for his station by station review of GISS inventories. A couple other folks are busy at work and I will leave it to them to discuss their efforts when the time is appropriate as the publication process precludes them from talking openly about it.
Here, my main focus has been on GHCN and more recently GHCN V3. The goal of the project is to provide a more accurate and more comprehensive inventory of station locations and station metadata. A short recap of the importance of this. Imagine, if you would, an inventory of 2000 stations. We suspect that some are urban and some are rural. We want to estimate the difference between the urban and the rural with an eye toward assessing the impact of UHI on the record. Further let’s suppose that the difference is large, suppose that urban warms are 1C per century ( from 1900 to 2010) while rural shows 0C warming. In that case the rule we use to separate urban from rural, while important, is not critical. For example, if we mis identify some urban sites as rural ( say 10%) then some fraction of urban warming will “infect” the rural subset. The effect of urbanization will still be clear in our comparison. If our categorization is less accurate, say we get 50% of the urban wrong, then our ability to discriminate the signal will be reduced according. If we also mis label rural sites as urban, the effect will be compounded. We can see then that if the UHI effect is small, the need for a better discrimination function increases. For example, if we think that the urban stations have warmed .8C over the course of 1900-2009 (+-.05C) while the rural have only warmed, say .6C (+-.05) misidentification will have more impact on our ability to find that UHI signal. One approach would be to take the 2000 stations and divide them into 3 groups. extreme rural, extreme urban, and “mixed”. This would, of course reduce the number of stations and the signal could then be lost in the noise that results from fewer stations. Still, that result would indicate that the UHI effect is small. That happens to be my position. Proving that, however, requires a diligent look at the metadata.
Metadata Improvements:
Data file is in the Box: named ExtV3Metadata.inv, a csv file is also included. here
The station information presented here is still in the beta stage. But it’s ready for a public release and some initial comments on what we can tell: The process of improving the data and extending it is described below.
Sources:
1. GHCN V3 Inventory.
2. Updated WMO station locations
3. Nightlights as used by Hansen 2010
4. Improved Nightlights as recommended by Nightlights principle Investigator
5. Nightlight Buffers
6. Gridded population density as provided by GPW
7. Gridded historical population density as provided by Hyde
8. Gridded Population Density as provided by GRUMP
9. Gridded Impervious Surface area.
10. Land masks provided by several sources.
Step One: The beta version of the GHCN v3 inventories are read into a R data frame. For the posted file that inventory was the matching inventory for the “adjusted” dataset. This inventory includes only 7279 stations as one appears to be dropped from the unadjusted data. The data fields read in include
Id: The Id field is the GHCN ID. It’s an 11 digit index of the form cccwwwwwddd. Where ccc indicates a country code, wwwww, indicates a WMO code, and ddd indicates a IMOD number. There are several things to note. For the US stations, the WMO code does not appear to map to the WMO master list. For example, “42500046506” is listed as the GHCNID of Orland California. In GHCN v2 the ID for ORLAND is : 42572591004 ORLAND. And for USHCN it is:046506-02 For For WMO we have no entry for ORLAND. In V2 Orland was listed according the WMO number for nearby Red Bluff. The 004 in the Orland IMOD indicates that Orland is at a different location than the WMO it is reported under. Confusing? You bet. To be accurate the V3 readme will have to be changed to indicate that the new GHCN Id, does not reflect WMO numbers in the middle 5 digits in all cases. In the US, the USHCN ID is used as the last 5 digits. Basically there are USHCN stations that do not have WMO numbers. In V2 they were listed as IMODs of the closest WMO ( redbluff) in V3 they are listed according to their USHCN number. That makes comparing V2 to V3 a bit troublesome.
Lat: The latitude of the station is reported in degrees north from -90 to 90. In my inventory the value is one of two values: the value found in GHCN V3 or the value found in the recently updated WMO master list. The WMO has required countries to update the precision of the station location data and that process is underway. It’s not entirely complete. Consequently some of the GHCN V3 station locations remain the same. Those that have been updated by WMO are updated here
Lon: Longitude is degrees east, from -180 to 180. As with Latitude this field contains the corrections from the recent WMO updates.
Altitude: Altitude in meters from the Ghcn V3 inventory. Corrected altitudes from the WMO master list are not included here. That will come later.
Name: The station name from the GHCN V3 inventory. I am in the laborious process of cleaning up the name list to remove the following: country names, state designations, province designations, partial names, punctuation marks. The goal would be to have a list of names as well as alternative names. Countries, states, provinces can be added properly by geocoding and should not be in the station name field.
GridEl: Grid elevation. As taken from the GHCN inventory data. In meters this represents the average elevation of the grid at .5degrees. Once the position data is improved this could be supplanted with more accurate metadata from DEMs.
Rural: A designation R,S,U that indicates whether the station is Rural, Small Town or Urban. This characterization is made based on the population of the nearest town, where R is a town with less than 10K people and Urban is greater than 50,000. This is a dated measure of urbanity. It’s problematic because it does not tell us whether the town is densely populated or spread out.
Population: The population of the nearest town in 1000s.
Topography: type of topography in the environment surrounding the station, (Flat-FL,Hilly-HI,Mountain Top-MT,Mountainous Valley-MV).
Vegetation:type of vegetation in environment of station if station is Ruraland when it is indicated on the Operational Navigation Chart (Desert-DE,Forested-FO,Ice-IC,Marsh-MA).
Coastal: An indication if the site is a Coastal location (CO) or near a lake (LA) or more than 30km away from water.
DistanceToCoast: In the site is close to water this field indicates the distance in km.
Airport: a true false flag for whether the station is at an airport or not. This has not been corrected using WMO data, but there are discrepancies.
DistanceToTown: Distance in km for the airport
NDVI: Normalized Difference Vegetative index. This field indicates the type of vegetation in the area. Its the original V3 data and should be supplanted with improved data.
Light_Code: while the V3 read me does not include or explain this data, it was present in V2. Bascially it is an undocumented description of the sites urbanity
Step Two. In the second step the updated WMO master list is merged with GHCN V3. This is not straightforward. First the GHCN list must be reduced to those stations that are not IMODs. Where the GHCN ID is cccwwwwwddd, the ddd field must be 000. Next the WMO file must be trimmed as well. It has multiple entries for stations. The multiple stations represent “air stations” that are collocated with the ground station. In the WMO index this is indicated by an indexSubNbr = 1. Next the GHCN V3 file is merged with The WMO file based on WMO ID. After this is completed distances can be calculated and the names can be checked for consistency. That process results in the following fields:
WmoName : The name used by the WMO is recorded. In certain cases the WMO name is spelled differently. In some cases it is entirely different.
WmoLon: The Longitude given by the updated WMO master list. These updates are in progress. Some mistakes remain as memeber nations are delivering partial results. The data is supposed to be accurate to degrees, mintutes and seconds.
WmoLat:the latitude given by the updated WMO master list. These updates are in progress. Some mistakes remain as memeber nations are delivering partial results. The data is supposed to be accurate to degrees, mintutes and seconds.
GhcnDistance: The distance between the old loaction given by GHCN V3 and the new location. As calculated by a Haversine distance calculation
NameMatch: A true false flag indicating if the name matched using a rather lax fuzzy name match criteria
GhcnLon: The Legacy longitude. This is the Longitude from the source GHCN V3 file.
GhcnLat: The legacy Latitude
Step Three: In step three the corrected inventory is passed to a metadata compilation function. The lon lat is passed in and metadata associated with those positions is passed out, along with the LON and LAT passed in for consistency checking
Lon : corrected Longitude same as field 1
Lat : corrected Latitude same as field 2
LandWater: The fraction of land in the 1/4 degree grid cell surrounding the station. This includes inland water.
LandOcean : The fraction of land in the 1/4 degree grid cell surrounding the station. This includes only ocean water.
CoastDistance: The distance the station is from the coast. If the station is over land this should equal 0. If a station is in the water it returns the distance to the closest coast. This occurs when coastal stations or island stations are misplaced. The accuracy of the coast map is 30 arc seconds.
Lights: The value of nightlights using the same file that Hansen2010 uses. It should be noted that this file has been deprecated by the file creators. It represents nightlights at the station in the 1995-97 era.
LightsF16: The value of nightlights using the most recent analysis from 2006. The raw data in the file has been processed to produce a DN number according to the file readme.
Bright3km: Every LightsF16 field surrounding the station has been processed to extract the brightest pixel within 3km. Given that Nightlights positional accuracy is ~1-2km, a station with perfect location information may still be mis registered with the image because of positional errors in the nightlights data.
Bright5km: same as above with a 5km radius
Bright10km same as above with a 10km radius
Bright20km same as above with a 20km radius
Isa: Impervious surface percentage. The percentage of impervious surfaces estimated from 0-100% A negative number indicates the station is in the water. ISA is the result of a regression and is based on Nightlights data and Landscan population.
GpwDensity : population density ( humans per square km) from the GPW source
GDensity :population density ( humans per square km) from the GRUMP source
The following fields are derived from the HYDE historical population/land use project which is being used for Ar5. The figure is density of humans per sq km. Figures are given for every decade. The data has been processed from 5 minute data.
Pop1850 ,Pop1860, Pop1870, Pop1880, Pop1890,Pop1900,Pop1910,Pop1920
Pop1930,Pop1940,Pop1950,Pop1960,Pop1970 ,Pop1980,Pop1990,Pop2000
GrumpUrban: A flag indicating where the site is Urban (2) rural(1) or in water (0)
Steven Mosher's Blog 
“Still, that result would indicate that the UHI effect is small.”
Are you anticipating the result here?
Yes, I think one always “anticipates” a result. I don’t what it would look like to not anticipate a result. For UHI tests I anticipate the effect to be small in magnitude. I anticipate that for several reasons. because of that I would try to structure the most powerful statistical test I could. If I’m wrong and the effect is large having a powerful test is gravy. But if I assume the difference is large, then I may not take as much care in the power of the test. So having some idea of the effect size plays an instrumental role in the design of experiments.
Put another way, if you think the effect is large you have to ask yourself why iy hasnt be shown in any convincing way up to now.
You have a volunteer if you need a check on Australian data. One has to use care about which version applies. Geoff.
Congrats Steve, I know you and your merry men have done a lot of very hard and painstaking work.
Personally I expect the UHI effect to be large, but I will believe the result of a clear, well documented analysis, which you are providing.
Just a note, don’t forget to look at the uncertainties in HYDE data.
The Hyde data is UN Population data fed thru landscan(a population density modeling program)
There are no physical population counts at the ‘advertised’ level of spatial accuracy for much of the world.
FAO has a good report on the shortcomings of ALL of the Global Population datasets.
http://www.fao.org/docrep/009/a0310e/A0310E04.htm
Sorry to be the pessimist, but taking a noisy dataset that shows 8/10’ths of a degree of warming in the last 100 years and then trying to determine what percentage of that warming was do to UHI with any degree of accuracy using another noisy dataset doesn’t seem possible to me.
We already have a 30 year satellite record that confirms at least 4/10ths of a degree of warming occurred in the last 30 years.
The question that needs to be answered is how much of the difference between the 1930’s warming period and 1990’s warming period was due to changes in UHI and measurement in order to decipher the size of the ‘natural cycle’.
thanks harry. i’m aware of the issues with Hyde. And landscan, and GPW, and GRUMP.
You might think a little bit about your noise argument cause it cuts two ways.
look just at the SST record where there is no UHI. what do you see? Now ask yourself, would you expect the warming over land (no uhi) to be uniformly LESS
than the warming of SST? more? about the same? If you think the UHI signal is big, you got some explaining to do. If you think its small and localized you have less explaining to do.
One way at the problem is to pick the best sites and see what they show you. You’d be surprised.
Another emerging ingredient in the list of uncertainties is the change of instruments to measure temperature and the methods used to homogenise. Officialdom has been quite quiet about overlap periods, if and when they were used. I suspect that in a long term analysis of UHI, the vague history of Stevenson screens will limit the analysis. My personal emphasis is therefore on the last decade. The principles behind UHI have changed somewhat over 100 years (more tarmac, higher buildings, energy consumption per capita) but the relation with population/proximity to weather station should be in the same ball park. There remains a need for more high quality case histories of individual locations around the world, to put brackets around the expected magnitudes.
Kill all the climate homosexuals!!! Don’t you know global warming causes AIDS and climate homosexuals love global warming, therefore they are responsible for the spread of AIDS. Shoot all the climate deniers right now!!! Lynch those filthy carbon tax niggers! Hang the climate disruption Jews!!!
Hi Mosh
I don’t know if it helps, but recently I asked the Met office precisely how much UHI was taken into account with CET. Here is the answer.
“The urbanisation corrections to the CET series have been applied since 1974. Initially they were just 0.1 degree C, in certain months, then gradually for more months of the year; from about 1995 onwards some of the corrections increased to 0.2 deg C, and by about 2002 all the corrections were 0.2 deg C.
The above applies to Mean CET. The urban heat island effect is much more noticeable for minimum temperatures than for maximum, so for the Minimum CET series the corrections are double those for Mean Temperature, whereas for Maximum Temperature it was deemed in fact that no correction was required, if I recall correctly.”
tonyb
TonyB,
That is an interesting response, but the +/- signs are not present. The way it reads, about +0.4 deg C has been applied to Tmin since 2004. Is it easy for you to clarify this?
It is also not clear if the adjustment was made to all CET stations or to those only categorised by some method, e.g. urban.
The method you describe (if I read it correctly and do some presuming) would not be applicable in countries with low population density like Australia, where we have many truly rural stations that require no UHI corrections. If you believe in GHG having a uniform global warming presence, such locations could be used to set a baseline for least warming; but there are complications because in the last 40 years, there is a trend difference between many rural coastal stations versus many rural inland stations. OTOH, since many Australia stations show insignificant change over the last century, one has to question if a GHG effect is indeed global and measurable.
This in turn guides interest to SST close to the coastline and raises many problems to do with prevailing winds, currents close to shore and above all, heat transfer equations involving water, air and land. David Stockwell from Niche Modeling has posted that he is spending a lot of 2011 on that topic.
Geoff
If anyone wants to phrase a specific question on UHI I will gladly pose that to my contact at the Met office.
As regards SST’s, I consider these to be complete nonsense having examined thoroughly( for an article) the manner in which they were obtained and the tiny fraction of the ocean that was measured, historically.
I live right next to the sea in SW England and generally I woud say that it keeps us clear of frost until around February by which time the SST has dropped to around 8 or 9C. However the last three years we have had very early frosts-the earliest being in Novemember 2010/11 when we had some very severe ones when the SST was 11C/12C
Each time it is because of either a cold easterly or in this winters case a strong Noreherly which gave us our coldest December on 100 years and the second coldest in our entire 350 year record.Interestingly the Mean average in 1659 -the first year of our record and 2010 -the lat year- were identical at 8.83C!
So I’m no longer as sure as I was that SST’s are a key factor in moving heat around-in the cse of the UK at least the wind direction/jet stream position plus AO are the key factors.
tonyb
TonyB,
Thank you for your answer. My position is a neutral one – I do not care if it is warming or cooling so long as the science is good. It is problematic that once a major module of climate science is audited, the holes start to appear.
This thread is about UHI, so a specific question from above would logically be, can the Met Office produce a diverse series of records showing sites with and without UHI correction as they exist at present (or difference graphs with electronic data available), on a monthly basis, Tmas and Tmin, ranging from those that seem to show no UHI to those that seem to show much.
A second question would be, does the Met Office hold digital data on traverses from rural areas through urban centres and out again, taken under reasonably controlled conditions?
These are a bit elementary, but it is surprising how hard it is to obtain them.
AGEING BABY-BOOMER BELIEFS
a.n.ditchfield
______________________________________________________________________________________
Even when misguided, contrary opinion is useful to stir debate over ideas that have grown fat and lazy with long and thoughtless acceptance; after the encounter, the vigour of good ideas is restored. In the middle of the 20th century the accepted idea was that the main business of the world is raising the next generation, by men and women joined in wedlock. The more the merrier, was the spirit of the time that brought into the world a Baby-Boom Generation, a time when a buoyant mood had followed the gloom of two World Wars and a Great Depression. Nothing seemed to stand in the way of progress to redeem mankind from want and disease, save waning opposi-tion of diehard reactionaries. As the baby-boomers came of age, parents were amused by the antics of their hippy offspring and thought the fad would wear off. It did not. Over the last forty years the baby-boomers rose to positions of power and now head for retirement. Will their environmental cult go their way or will it remain the guiding light of a course to be held?
The cult has three articles of faith that under-pin the message that doomsday is nigh:
· We are running out of space. The world popu-lation is already excessive for a limited planet, and grows at exponential rates.
· We are running out of means. The planet’s non-renewable resources are being depleted by runaway consumption; further expansion of the world economy is unsustainable.
· We are running against time , as tipping points of irreversible climate processes are reached. Carbon dioxide emitted by the eco-nomic activity causes global warming. It will soon bring catastrophic climate disruption that will render the planet uninhabitable.
When such issues are quantified, the contrast be-tween true and false becomes clear. Articles of faith have no place in matters of arithmetic.
Is overcrowding a serious problem? It may seem so to the dweller of a congested metropolitan city. It takes counter-intuitive thought to realise that the local sensation of cramped space is a parochial view that should not be generalised for the planet. The sum of U.S. urban areas amounts to 2% of the area of the country, and 6% in densely populated countries like England or Holland. And there is plenty of green in urban areas. If the comparison is restricted to the ground covered by buildings and pavements, the oc-cupied area amounts to 0.04% of Earth’s terrestrial area. It was estimated that 6 billion people could live comfortably on 100 000 square miles, the area of Wyoming, or 0.2% of the total. With about 99.8% of free space available, the idea that the planet is over-crowded is an exaggeration. Demographic forecasts are uncertain, but the most accepted ones, of the UN, foresee the stability of the global population, to be reached in the 21st century. According to some, world population will start to decline at the end of this cen-tury and an aging population emerges as a matter of concern. With so much available space is untenable that the world population is excessive or has the pos-sibility of ever becoming so.
It is argued that, ultimately, a limited planet cannot allow unlimited growth. It can also be counter-argued that, ultimately, non-renewable natural resources do not exist, in a universe governed by the Law of Con-servation of Mass. In popular form it states that “noth-ing is created, nothing is lost, everything changes.” Not a gram of human usage was ever subtracted from the mass of the planet and, in theory, all material used can be recycled. The feasibility of doing so depends on the availability and low cost of energy. When fusion energy becomes operational it will be available in virtually unlimited quantities. The source is deuterium, an isotope of hydrogen found in water in a proportion of 0.03%. A cubic kilometre of seawater contains more potential energy than would be obtained from combus-tion of all known oil reserves in the world. Since the oceans contain 3 billion cubic kilometres of water is safe to assume that energy will last longer than the human species. Potable water need not be a limita-tion, as is sometimes said; an innovation like nano-tube membranes holds the promise of reducing en-ergy costs for desalination to a tenth of current costs, which would make feasible the use of desalinated water for irrigation along the coast of all continents (750,000 km). What grounds are there to assume such technologies never will come to fruition?
There is no growing shortage of resources sig-nalled by rising prices. Since the mid-19th century a London periodical, The Economist, has kept consis-tent records of commodity values; in real terms, they dropped over a century and a half, due to technologi-cal advances, to the cheapening of energy and to its more efficient use. The decline was benign. The cost of feeding a human being was eight times higher in 1850 than it is today. Even in 1950, less than half the world population of 2 billion had a proper diet of more than 2000 calories per day; today 80% and have it and the world’s population is three times greater.
There is no historical precedent to support the idea that human ingenuity is exhausted and that tech-nology will henceforth stagnate at current levels. Two centuries ago, this idea led to the pessimistic Malthus prediction of the exhaustion of land to feed a popula-tion that seemed to grow at exponential rates.
There is a problem with the alleged global warm-ing. It stopped in 1998 after rising the previous 23 years, sparking the current alarm about global warm-ing by human hand. Since 1998, warming has been followed by 12 years of stable or declining tempera-tures, a sign of a cold 21st century. This shows that there are natural forces modifying climate, more pow-erful than carbon dioxide generated by burning fossil fuels. Natural forces include cyclic fluctuation of ocean temperatures and current change, sunspot activity and the effect on cosmic rays of the sun’s magnetic activ-ity. All these cycles are known, but mankind can do nothing for or against forces of this magnitude. Meas-ures to adapt to changes make sense; not the de-industrialization of a world where a quarter of mankind still has no electricity.
Caution in public policy must be exerted because climate change predictions are subject to great uncer-tainty. The existing knowledge about climate comes from numerous fields such as meteorology, oceanog-raphy, mathematics, physics, chemistry, astronomy, geology, paleontology, biology, etc., with partial con-tributions to the understanding of climate. There is no general theory of climate with predictive capacity and perhaps there never will be one. Chaotic phenomena, in a mathematical sense, cannot be predicted. Climate forecasts that extend into the next century mean as much as readings of tea leaves by fortune-tellers.
With no basis on solid theory and empirical evi-dence, the mathematical models that support alarmist predictions are nothing more than speculative thought which reflect the assumptions fed into models, and chosen in the interest of sponsors. These computer simulations provide no rationale for public policies that inhibit economic activity “to save the planet.” And carbon dioxide is not toxic or a pollutant; it is a plant nutrient in the photosynthesis that sustains the food chain for all living beings on the planet.
Disasters stories circulate daily. Anything that happens on earth is attributed to global warming: an earthquake in the Himalayas, the volcanic eruption in Iceland, the 2004 tsunami in the Indian Ocean; tribal wars in Africa, heat wave in Paris; plague of snails on the tiny Isle of Wight; forest fires in California; sand-storms during the dry season and floods during the wet season in Australia; recent severe winters in North America; the collapse of a bridge in Minnesota; the hurricane season in the Gulf of Mexico, known for five centuries. Evo Morales blames Americans for summer floods in Bolivia.
Such reckless allegations of cause and effect in-dicate that global warming is not a physical phenome-non; it is a political and journalistic phenomenon, which finds a parallel in the totalitarian doctrines that once incited masses deceived by demagogues.
As Chris Patten put it: “Green politics at its worst amounts to a sort of Zen fascism; less extreme, it denounces growth and seeks to stop the world so that we can all get off”. In the opinion of Professor Aaron Wildavsky, global warming is the mother of all envi-ronmental alarm: “Warming (and warming alone), through its primary antidote of withdrawing carbon from production and consumption, is capable of realiz-ing the environmentalist’s dream of an egalitarian society based on rejection of economic growth in fa-vour of a smaller population’s eating lower on the food chain, consuming a lot less, and sharing a much lower level of resources much more equally.” It is the hippy’s dream of a life of idleness, penury, long hair, un-shaven face, blue jeans, sandals and a vegetarian diet; not as a personal choice, but a lifestyle to be foisted upon the world by dictatorial decree of an in-ternational eco-fascist dictatorship. Who doubts this as hyperbole must read the word of James Hansen: http://www.columbia.edu/~jeh1/mailings/2010/20101122_ChinaOpEd.pdf
A recurrent thought of Nigel Lawson is that much of the current malaise in the West is due to the erosion of traditional religion. The West has lost its bearings. In this he echoes G.K. Chesterton: “The first effect of not believing in God is to believe in anything”. The epigram expresses what anthropologists have long known: that religiosity seems to be hardwired into the human brain; if suppressed in one form it returns in another.
Before the French Revolution all of Europe was referred to as Christendom and since then secu-larism has advanced by the hand of governments with agendas. In France, the Catholic clergy was dis-banded, church property was confiscated and forked out to politicians in power. To bless their gain a First French Republic enthroned Goddess Reason at Nôtre Dame cathedral in Paris. Decades later, the Third French Republic gave the Statue of Liberty to New York City, locally seen as a handsome monument that greets the poor huddled masses that flow from Europe to the Land of the Free. To radical French politicians the statue is a lot more; an idol, the first person of the trinity of Liberté Egalité Fraternité of their Humanist creed. Bismarck sought to bolster the power of the Prussian state by instituting political control over reli-gious activities, the Kulturkampf. State worship fol-lowed. Similar action was taken in Italy, Spain and Portugal at various times; all ended in fascist state worship. The Soviet Union went the whole hog to establish atheism as the official creed and to exorcise or burn dissenters. While such state action eroded the hold of traditional religion, in more recent times Envi-ronmentalism has crept in as the religion of choice of urban dwellers, even in English-speaking countries, immune to European-style anticlericalism.
“Chassez le naturel, il revient au galop”. The pagan-like worship of Nature has rites, such as plant-ing a tree to atone for an air trip; believers neither fake nor dissemble the sin of consumerism, but acknowl-edge and confess it; the faithful make weekly trips to the countryside to enter into communion with Nature; their children attend Sunday school to hear Inconven-ient Truths, the sayings of the high priest Al Gore. The congregation joins processions to sing hymns for green causes, of all things nice and beautiful for crea-tures great and small. It observes a calendar with red-letter days, such as Earth Day; perhaps green-letter days would be more to the point. There is a hagiogra-phy of saints, the followers of the righteous path of Rachel Carson, and a rogue’s gallery of demons, the big bad oil companies and the dirty coalminers that tempt mankind with the unclean combustion that lights the fires of Hades. Railroads that carry coal are mer-chants of death. The holy waters of the Gulf of Mexico were profaned by an oil spill. Religious orders such as Green Peace and Friends of Earth preach the true faith. The green religion even sells indulgences; the Carbon Credits for those who cannot stop sinning.
These antics would be as harmless as a foot-ball match, were it not for consequences on a practical plane of beliefs that inspire policies to de-industrialise the West and block the ascent of hundreds of millions in India and China to an adequate diet, clean drinking water, electricity, and basic education and health care. The opposing view sees this stance as the undoing of two centuries of achievements of the Industrial Revo-lution, a retreat back to poverty and want. Was the UN Copenhagen Climate Change Conference 2009 a turning point in the swing of the pendulum? Will the post Baby-Boom generation reverse course?
Kevin Trenberth is inciting hate speech against those who question the science of global warming, by calling them “deniers”. Trenberth is promoting hate speech by attempting to label those skeptical of global warming as “holocaust deniers”.
See his recent speech here, where he maliciously labels global warming skeptics as “deniers”: http://bit.ly/dFeMdM
Calling someone a “denier” just because he/she questions the science of global warming is deeply offensive, especially to Jews. Since when has hate speech been ok? Calling a skeptic a “denier” is just like calling a gay person a “faggot”, or calling a Black person a “nigger”. But this is exactly what Trenberth is doing and getting away with, because no one in the climate science community has the cojones to stand up to him.
And note the timing of the release of his speech–just a few days after the Jared Loughner shootings! I think Trenberth did this on purpose: to encourage unstable individuals to physically harm global warming skeptics–people like the eco-terrorist that took people hostage at the Discovery Channel office.
So here is my request: please shame KEVIN TRENBERTH for inciting hate speech against global warming skeptics (which is 2/3 of the US population, according to recent polls). In whatever way you can. Stop this guy before his hate speech causes physical violence against global warming skeptics.
Thank you,
Hey Steven,
I’ve been looking into the difficulties of trying to find a UHI signal in the F52 dataset, AFTER Dr. Menne’s PHA has been run (a la Hansen 2010). I know many people have been hinting at the idea that these adjustments may bleed the effects of urbanization into more rural stations. I ran some tests with pseudo-data showing the case where the PHA will mask the UHI signal WITHOUT actually removing the UHI contamination itself, and I was curious as to your thoughts:
http://troyca.wordpress.com/2011/01/19/testing-the-pha-with-synthetic-data-part-2/
http://troyca.wordpress.com/2011/01/21/testing-the-pha-with-synthetic-data-part-3/
Thanks!
Anyway, troyca. All I can say is that you are on to something. I’m aware of some work being done that faced this same issue.
I can’t say anything more about this, but i will let the authors know about your work. Maybe they will contact you.
It would be interesting if you approached WUWT for a post. I can help.
Thanks, Steven. I’ve been contacted… hopefully by the same authors you mentioned 🙂
Can you pin down UHI by comparing Tmax trends vs Tmin trends for individual cities.
(Tmax-Tmin) is likely to be constant for rural or small towns then decreasing as the town grows before becoming constant again but at a smaller value.
BTW I’m only guessing that (Tmax-Tmin)could be a suitable indicator. Tmax/Tmin maybe better?(or anomiles)
Wayne,
There’s a lot of noise hiding the signal you seek. It’s enough to allow two similar rural sites, all other things being equal or unmeasurable, to show different effects of Tmax-Tmin. In theory, this parameter should distinguish airport sites with asphalt close to the instruments, from grassed strips, because of the night heat retention, but I’ve not been able to conquer the noise.
Thanks Geoff
I’m just trying to get a better understanding how this works.
Is there such thing as a negative UHI? I thought there must be some industrial towns in Russia that have closed. I had a look and found two potential problems.
1. The heavy industry towns started shutting down 1993,4,5 so Pinatubo might be suspect of any temp drop(my guess only). and 2. I had problems finding suitable towns with temp stations. eg Cherepovets is a town with significant heavy industry & 300,000pop but no GISS station.(But there must be a weather station ?)
The above was a shock to me. While I accept the lack of stations in africa and the amazon, I find the lack of stations in Russia a concern.
Regarding negative UHI: My understanding is that the primary source of UHI is the land/surface changes, which aren’t going to instantly disappear once industrial towns start to close (only secondary heating effects). In some of my analysis I’ve tried replacing negative trend values for continuous UHI proxies with 0 or 1/4 of their negative value and have found slight improvements in the fit as a result.
Steven
Once you have completed the global Urban/Rural UHI analysis, can you do some cherry picking and just look at individual countries in detail. If country X had population and/or industrial boom at certain time we should be able to see UHI.
Not necessarily. UHI is not a countrywide effect.
While UHI is not a country event, there might be cases where a country has exceptions to the general. Australia, for example, has quite a few localities where the population (insofar as it relates to UHI) has decreased – example, quite a few almost-deserted mining towns that once had populations of 20,000 or so.
We have very little snow/ice here. The buildup of these under certain sensors has been shown to give a possible bias through light reflection through the sensor base.
Tall buildings are going to be quite difficult to quantify.
These are picky examples, but in general I agree with the summary.
Wayne
There are a number of factors at work here of which instrumental accuracy and cosistent methodology are two of the biggest. These are closely followed by UHI AND change in location to a different micro climate.
Stations typically start life in a rural field on one side of a small town and after several moves end up at an airport many miles away. So you have inaccurate measurements being taken at a built up micro climate wildly different to the one where the temperature readings started.
Phil Jones did a number of studies on this effect using EU funds and the long and the short of it is that we have no idea whatsoever what the REAL temperature was in say 1760 on a like for like basis with a data set bearing the sane name in 2010.
Tonyb
Steve, the Chernobyl explosion led to the abandonment of a very large area of the Ukraine; many towns and villages were abandoned and have pretty much returned to nature
Hmm. Thanks.
One of the biggest issues is the historical record, especially WRT location
It seems almost axiomatic that a place with good population, positional and other meta data will have a poor weather record in terms of missing data and length of record. I’ve almost finished a large work that reveals this so, so often.
Yo Mosh man, who you is!
I’m way off topic here but are you aware of anybody tagging my name on the climate sites? I have noticed that pretty much anywhere I post, the comment does not get posted. I get called a racist on Judith Curry’s blog and she doesn’t let me respond. I’m thinking about making my own site called “realclimate2”. The stated objective will be to ridicule losers like Gavin Schmidt and Tamino and point out that they are cowards who hide behind comment moderation screens. Let me know if this is interesting to you.
Most blogs have some sort of moderation. It creates drama.
Steve, sorry to be a pest, but I went to the dentist.
http://features.blogs.fortune.cnn.com/2010/11/30/alcoa-and-the-great-north-carolina-power-grab/
Alco has been closing its aliminium smelters in the US over the last 25 years. The populations in the towns have shrunk by more than 10 fold.
They built them near damable rivers, in the middle of nowhere. Towns grew and then, when unit costs rose, the plants were closed and the towns shrank.
Yeah I don’t think that they should allow every single thing to be posted but at least let it post and then delete it if it is offensive. Or have blocks up to catch swear or racist words. Saying that Ashkenazi Jews have the highest IQs in the world is not racist, it is fact. Also, if I was Jewish, I would take this statement as a compliment. Agree?
whether I agree or not is immaterial. It’s a boring question. I don’t do boring. If you want an opinion from somebody who believes in “IQ”, ask somebody else.
Is it possible to determine Urban/Rural location by analysis of the temperature record itself instead?
Given that many studies have shown (or so I believe) that the tMin temp is raised by UHI and tMax not so much effected, then is there a posibility of a metric which uses the tRange (tMax – tMin) to determine the UHI ‘signal’ and so compensate?
Thinking further, the tMean signal is, in general, the combination of two instruments providing tMax and tMin. To use only tMean as the output from that combination is to miss the tRangle part of the data.
In audio terms this would be using the sum (tMean) and difference (tRange) parts of the signal after any mixing.
I have examples from really rural settings where the Tmax and Tmin diverge with time over decades, where they converge with time and where they stay essentially parallel. I suspect that there is no systemtic relationship that can be extracted from the noise and used usefully in a UHI study. The part plyed by wind strength was the subject of a paper a few years ago but it was too streaky and I did not keep the details.
I was thinking more along the lines of looking at the data from all stations and grouping them by some metric derived from tRange and seeing if that showed a statistically significant way of determining Urban/Rural (possibly by comparing it to the existing meta data).
I would be surprised if that could be produced by a simple statistic (which is all I would be capable of) but it may well be possible with a more sophisticated analysis.
I still think that the mixing of two signals from two different instruments and then only using the sum and not the difference signal is to lose potentially useful information.
Steve,
For the UHI and nearby coastal/water sources, it might be nice to have a bearing, since upwind/downwind are likely to have some effect. And, since the sources are likely to vary over time, it’s probably a matrix of bearings/distances.
Sounds like a lot of work, but this metadata thing is pretty important.
Ed