by Zeke Hausfather , Steven Mosher, Matthew Menne , Claude Williams , and Nick Stokes
[Note: this is an AGU poster displayed at the annual meeting, available here as a PDF. I’ve converted it to plain text and images for your reading pleasure. I’m providing it without comment except to say that Steven Mosher has done a great deal of work in creating a very useful database that better defines rural and urban stations better than the metadata we have available now. – Anthony]
Introduction
Large-scale reconstructions of surface temperature rely on measurements from a global network of instruments. With the exception of remote automated sensors, the locations of the instruments tend to be correlated with inhabited areas. This means that urban
ares [sic] are probably oversampled in surface temperature records relative to the total land surface that is actually urbanized.
It has long been known that urbanized areas tend to have higher temperatures than surrounding less developed (or rural) areas due to the concentration of high thermal mass impermeable surfaces (Oke 1982). This has led to some concern that changes in
urban heat island (UHI) effects due to rapid urbanization in many parts of the world over the past three decades may have been responsible for a portion of the rapid rise in measured global land surface temperatures. This concern is reinforced by lower
observed trends in some interpretations of satellite measurements of lower tropospheric temperature over land areas during the same period (Klotzbach et al 2009).
An analysis of the impact of urbanization on temperature trends faces multiple confounding factors. For example, an instrument originally installed in a city frequently will have warmer absolute temperatures than one in a nearby rural area (especially at night), but will not necessarily show a higher trend over time unless the environs change in such a way that the UHI signal is altered in the vicinity of the instrument. Similarly, microsite characteristics that may be unrelated to the larger urban environment can have
notable effects on temperature trends and act counter to or in concert with the ambient UHI signal.
Moreover, the definition of urban areas is subject to some uncertainty, both in terms of how urban form is characterized and at what distance from built surfaces urban-related effects persist. Published station metadata often includes outdated indications of whether a station is urban or rural, and instrument geolocation data can be imprecise, out of date, or otherwise incorrect.
There is also uncertainty over how much explicit correction is needed for urban warming in global temperature reconstructions, and how well homogenization techniques recently introduced in GHCN-Monthly version 3 both detect and correct for inhomogenities
arising from changes in urban form.
To address these issues and obtain a more accurate estimation of the impact of urbanization on land temperature trends, we examine different urbanity proxies at multiple spatial resolutions and urbanity selection criteria through both simple spatial
weighting and station pairing techniques. This study limits itself to unadjusted average temperature data, though we will examine homogenized data in the future to see how much of the UHI signal is removed.
Methods
We examine GHCN-Daily version 2.80 temperature data rather than the more commonly used GHCN-Monthly data as it contains significantly more stations, particularly during the past thirty years, and allows for separate examination of maximum and minimum
temperatures. A relatively high spatial density of stations is useful to allow sampling into various urban and rural station subsets while minimizing biases due to loss of spatial coverage. After excluding stations that have fewer than 36 months at any time in the
period of record or at least one complete year of data during the 1979 to 2010 period, we are left with 14,789 stations.
A complete set of metadata is calculated for each station using the station location information provided in station inventories and publically available GIS datasets. These datasets include: Distance From Coast (0.1 deg), Hyde 3.1 historical population data (5
arc minute), 2000AD Grump Population density (30 arc seconds), Grump Urban Extent, Land use classes from the Harmonized Land Use inventory (5 arc minutes), radiance calibrated Nightlights (30 arc seconds), ISA- Global Impervious Surfaces (30 arc
seconds), Modis Landcover classes (15 arc seconds), and distance from the closest airport (30 arc seconds). In addition, area statistics at progressive radii are calculated around each putative site location.
Stations are then divided into two classes based on various thresholds for urbanity and two analytical methods are used to estimate the bias in trend due to urbanity: a spatial method and a paired station approach. The spatial averaging method relies on
solving a set of linear equations for the stations in each class. For each group of stations, urban and rural, a time series of average temperature offsets was created by fitting the model:
where T represents the observed temperature for each station, month and year, L is a local average temperature for each station for each month (incorporating seasonal variation) and G is the desired global (or regional) average, varying by year. This is fitted
with a weighting that is inversely proportional to a measure of station density. With a G calculated for both urban and rural, the trends can be compared.
The pairwise method proceeds with the same classification of stations and the following steps are taken. An urban base pair is selected based on the length of record. To qualify as a base urban pair a station must have 30 complete years of data in the 1979-2010 window.
Ten out of 12 months of data are required to count as a complete year. For every urban base station rural pairs are selected based on distance and data overlap. For every urban base station the rural stations are exhaustively searched and all those rural pairs within 500km are assigned to the base station. Since rural stations may have short records the entire rural ensemble is evaluated for data overlap with the urban base pair. 300 months of overlap are required. If the collection of rural stations has less than 300 months of overlap with its urban pair, it is dropped from the analysis. A weighting function is deÞned in the neighborhood of each urban station, which diminishes with distance and is zero outside a certain radius. An average trend is computed for the rural stations within that radius by fitting the model
where t is time in years, and B is the gradient. This trend is then compared with the OLS trend for the central urban station. The differences in the shapes of the distributions of the trends is a function of the number of stations that form the trend estimation.
Urban trends are trends for individual stations, while rural trends are the result of computing a trend for all the rural pairs taken as a complete ensemble.
Discussion
While urban warming is a real phenomenon, it is overweighted in land temperature reconstructions due to the oversampling of urban areas relative to their global land coverage. Rapid urbanization over the past three decades has likely contributed
to a modest warm bias in unhomogenized global land temperature reconstructions, with urban stations warming about ten percent faster than rural stations in the period from 1979 to 2010. Urban stations are warming faster than rural stations on average across all urbanity proxies, cutoffs, and spatial resolutions examined, though the underlying data is noisy and there are many individual cases of urban cooling. Our estimate for the bias due to UHI in the land record is on the order of 0.03C per decade for urban stations.
This result is consistent with both the expected sign of the effect and regional estimates covering the same time period (Zhou et al 2004) and differs from some recent work suggesting zero or negative UHI bias (Wickham et al, submitted).
Stricter urbanity proxies that result in a smaller set of rural stations show larger urban-rural differences in trend. The upper limit on UHI bias between rural and urban stations is on the order of 0.06 to 0.1C per decade. However, these cases are clearly problematic from the spatial coverage aspect, as the number of rural stations becomes vanishingly small when the most stringent filters are applied. Adopting cutoffs that define rural less strictly leads to more reasonable spatial coverage and an estimate of UHI bias in the record that converges on 0.02C to 0.04C per decade across the proxies. The station pair approach avoids this issue by limiting the analysis to areas with both rural and urban stations available, but has limited global coverage and excludes large areas in India and coastal China where rapid urbanization has been occurring in recent decades.
It is likely that homogenization will further reduce the observed UHI-related bias, as many urbanity biases are detectable through break-point analysis via comparison to surrounding rural stations. We are currently in the process of using the Pairwise Homogenization Algorithm (Menne and Williams 2009) on GHCN-Daily data to examine the effects in more detail. However, it remains to be seen to what degree UHI bias can be removed via homogenization in areas like coastal China and India where there are few rural stations and where station densities are not particularly high in the current version of GHCN-Daily. In any case, the acquisition of additional station data outside of urban areas in these parts of the world would likely be benefitial.
Acquiring more accurate station location data will allow us to use higher-resolution remote sensing tools to identify urban characteristics below the 5 km threshold, and better test effects of site-specifc vs. meso-scale characteristics on urban warming biases. In addition, validated site locations allows for more refinement in the definition of rural stations as a function of distance from urban cores of various sizes.
References
Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Institute (IFPRI); The World Bank; and Centro Internacional de Agricultura Tropical (CIAT). 2004. Global Rural-Urban
Mapping Project, Version 1 (GRUMPv1): Population Density Grid. Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw.[Aug 14, 2011].
Elvidge, C.D., B.T. Tuttle, P.C. Sutton, K.E. Baugh, A.T. Howard, C. Milesi, B. Bhaduri, and R. Nemani, 2007, “Global distribution and density of constructed impervious surfaces”, Sensors, 7, 1962-1979
Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.
Klein Goldewijk, K. , A. Beusen, and P. Janssen (2010). Long term dynamic modeling of global population and built-up area in a spatially explicit way, HYDE 3 .1. The Holocene20(4):565-573.
Klotzbach, P., R. Pielke Sr., R. Pielke Jr., J. Christy, and R. T. McNider, 2009. An alternative explanation for differential temperature trends at the surface and in the lower troposphere. J. Geophys. Res.
Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2011: An overview of the Global Historical Climatology Network Daily Database. Journal of Atmospheric and Oceanic Technology, submitted.
Menne, M.J., and C.N. Williams, Jr., 2009. Homogenization of temperature series via pairwise comparisons. J. Climate, 22, 1700-1717.
Schneider, A., M. A. Friedl and D. Potere (2009) A new map of global urban extent from MODIS data. Environmental Research Letters, volume 4, article 044003.
Schneider, A., M. A. Friedl and D. Potere (2010) Monitoring urban areas globally using MODIS 500m data: New methods and datasets based on urban ecoregions. Remote Sensing of Environment, vol. 114, p. 1733-1746.
T. R. Oke (1982). “The energetic basis of the urban heat island”. Quarterly Journal of the Royal Meteorological Society 108: 1–24.
Wickham, C., J. Curry, D Groom, R. Jacobsen, R. Muller, S. Perlmutter, R. Rohde, A. Rosenfeld, and J. Wurtele, 2011. Inßuence of Urban Heating on the Global Temperature Land Average Using Rural Sites IdentiÞed from MODIS ClassiÞcations.
Submitted.
Zhou, L., R. Dickinson, Y. Tian, J. Fang, Q. Li, R. Kaufmann, C. Tucker, and R. Myneni, 2004. Evidence for a signiÞcant urbanization effect on climate in China. Proceedings of the National Academy of Sciences.
Ziskin, D., K. Baugh, F. Chi Hsu, T. Ghosh, and C. Elvidge, 2010, “Methods Used For the 2006 Radiance Lights”, Proceedings of the 30th Asia-PaciÞc Advanced Network Meeting, 131-142
Well, now we know we really can blame “Global Warming” on the US of A!!! LOL!
crosspatch, I was [apparently unsuccessfully] trying to be funny, or at least flippant, with “playa.” In ghetto talk it’s ‘player’. Dang, I should have used a smiley. ☹
Zeke, Steve, and all, first, my congratulations a very nice piece of work. In particular I appreciated the sensitivity analysis regarding the urban/rural cutoff. It was also good to see some comparisons of various ways to estimate the “urbanity” of a given station.
You seem to suggest that a likely figure is on the order of a third of a degree per century. If so, this is on the order of half of the warming of the 20th century. Accordingly, your analysis is in line with that of Ross McKitrick, who from memory found the same thing (that about half of the 20th century warming was due to industrialization / urbanization).
One unrealized aspect of the increase in temperature in the second half of the 20th century is that it was also the time of a doubling of the global population. The resulting urbanization and surface modifications of the areas around the stations, purely from nothing but the sheer increase in populations, is reflected in your figures.
All the best,
w.
BioBob says:
December 6, 2011 at 11:28 am
…..Not only is the sample size (1) totally inadequate, but stats are routinely employed which REQUIRE that the population the sample is drawn from be NORMALLY distributed and that the sample drawn from that population is statistically valid. The number of untested assumptions about the temperature population the single sample is drawn from, the assumptions about the methods used, and the assumptions about the statistics employed to describe that sample are enormous and in most cases in error. You do not need to be a climatologist to conclude that, thanks very much, since that field of investigation more resembles divination than science…….
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Agreed. It never really passed the “sniff test” Data good to 0.1C for the average temperature of the entire world for a century??? give me a break.
You might like to take a look at this temperature study where at least an attempt has been made to do real science.
crosspatch says:
December 6, 2011 at 12:25 pm
….I believe I could get a better handle on the general climate of the earth’s surface with regularly spaced rural stations than I can with a bazillion mixed urban/rural stations. All those additional stations do is add noise.
____________________________________
Frank Lancer has done just that. But instead of sausage making à la CRU he is looking at what story the data actually tells us.
http://joannenova.com.au/2011/10/messages-from-the-global-raw-rural-data-warnings-gotchas-and-tree-ring-divergence-explained/
More Soylent Green! says:
December 6, 2011 at 1:12 pm
If only somebody would start a project to survey all the sites and quantify the issues with their siting, such as being built on top of a parking garage, or under and air conditioning vent!
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ROTFLMAO, Anthony’s paper: http://wattsupwiththat.com/2011/05/11/the-long-awaited-surfacestations-paper/
The fundamental problem with determinations of UHI from station records is the dearth of baseline “rural” records in much of the world that extend over the ENTIRE 20th century. It is over time-frames of a century or longer that most urban footprints have changed. And it is particularly in the 20th century that experienced the introduction of heat-producing technonolgy . Every urban station record thus has a fairly unique time-history of deviating away from the temperatures in the surrounding countryside. Analysis of definitional metadata criteria for urbanization scarcely addresses the real problem.
The ‘weekend effect’ is strong evidence for an Urban Cool Island effect.
You can read more about it at the link below, but in summary weekdays are warmer than weekend days. This is almost certainly due to higher aerosol levels causing increased clouds/less solar insolation on weekdays from factories operating and more vehicles on the road.
The weekend effect is relatively large, as much as 0.5C.
In the developed world aerosol levels have been declining for decades, and this will have resulted in a decreasing weekend effect and IMO is the main source of an increasing UHI trend. Although aerosols are transported by wind and will affect nearby rural locations. Which makes me think the weighted for proximity methodology used in the study above will artificially depress any UHI warming trend and a weighted for distance methodology would give a more accurate picture.
http://www.appinsys.com/globalwarming/DTR.htm
Correction:
Weekend days are warmer than weekdays days.
Willis,
Our analysis showed that urban stations are on average warming about 10% faster than rural stations. This would result in a bias smaller than 10% in practice, as global land record are composed of both urban and rural stations. Given that the land warming over the last 30 years was around 0.27 C per decade, an urban-rural difference of 0.04 or so would not amount to a third of modern warming per se (closer to a tenth or so, though more work needs to be done on controlling for spatial coverage and testing how much UHI is removed by pairwise homogenization). Extending this analysis back to the full century is rather difficult, as in most cases we only have a current snapshot of urbanity.
By the way, you can find a slightly better version of the station location map that randomizes the visual ordering of urban and rural stations (rather than always showing urban stations on top of rural stations) here: http://i81.photobucket.com/albums/j237/hausfath/URPairs5.png . Unfortunately it was completed shortly after we had to submit the poster.
Bruce,
I fail to see how a relatively modest difference in trends is inconsistent with a large absolute difference unless the instrument started out in a pristine location and was subsequently urbanized, a case that is the exception rather than the rule at least over the past 30 year timeframe. We’ve been over this before, however, and may have to agree to disagree.
We cover this in the poster when we remark that: “an instrument originally installed in a city frequently will have warmer absolute temperatures than one in a nearby rural area (especially at night), but will not necessarily show a higher trend over time unless the environs change in such a way that the UHI signal is altered in the vicinity of the instrument.”
“Why do I, in these studies, not see more of formulations like
– CHANGE through time in the DEGREE of urbanization/ruralness (probably most important to some rural areas),
– CHANGE through time in the way we “furnish” our urban areas (more or less parks and gardens and more or less of offices and residential usage),
– CHANGE through time in the way we heat our urban areas (during the last 40 years all major urban areas in Sweden has changed to area wide heating systems – more efficient and thus less leakage to the general environment )?
Gösta Oscarsson
Stockholm
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Funny you should ask as I am working on that problem this very minute.
First you need to understand that the way I select rural stations leads to stations that
have undergone little change. Simply, I select rural stations that have no BUILT pixels
within 11km ( And did sensitivities on this paramater from 5km to 20km). I further restrict
rural stations by eliminating all near airports, even rural airports and heliports. I further restrict
these stations by eliminating any that have significant nightlights and impervious surface.
This eliminates sites that have small infrastructure that may be missed by Modis classifications.
So there is no change in economic activity at these sites because there is no economic activity
to speak of. What you see when you look at this all rural dataset is a trend from 1979-2010
that is approximately 0.1C less than the trend you see from urban stations.
Looking at these rural stations further I can say that they also do not see much change
in the percentage of cropland, and grassland. The small population that lives in the vincinity
( 5 people per sq km and in most cases ZERO people) does not grow over the period
we look at. Some areas see negative growth and some areas see small positive growth
( say from 5 to 6 people per sq km) With no changing population the motivation for other changes
is diminished.
Next, if we look at the rural stations and try to “explain” the trends via regression you will
find that the trend is not explained by population, not explained by land use or land use changes.
The only two significant regressors are Latitude ( polar amplification) and the distance from Coast.
So, I basically selected my rural stations in such a way that Minimized the changes that everybody worries about.
Willis
“You seem to suggest that a likely figure is on the order of a third of a degree per century. If so, this is on the order of half of the warming of the 20th century. Accordingly, your analysis is in line with that of Ross McKitrick, who from memory found the same thing (that about half of the 20th century warming was due to industrialization / urbanization).”
No. Both we and Ross looked at the 1979-2010 period ( I think he looked at 2000)
Ross’s work suggests that .1C per decade may be associated with changes in economic
activity. We find that there is a UHI bias of approx .03C per decade IN THE PEROID
of 1979-2010. You cannot extrapolate that to the century.
“Nice work guys! I hope it is just a start. The magnitide of difference sounds reasonable, but for individual stations where does it sit along the timescale? Does the difference kick in with the advent of aircon from the 1950s? How does it manifest itself in relation to multidecadal cycles such as the AMO? Does it mean a reduced cooling from the 1950s and/or then an increased warming when the cycles turn? Lots more interesting stuff to tease out.”
The bias effect is slightly lower if you move the start date back to 1950.
As time permits I will finish the work that “explains” what factors drive the warming at
Urban stations. It is a combination of Latitude, Distance from Coast, The AREA in sq
km of land that is BUILT and a couple other land use factors.
Theo
‘Yes, what Jeff said.
More specifically, what are the physical conditions that constitute an urban area? What causes that increase temperature measurements operate in urban areas? What are the effects of those causes? What is the process of migration as those causes move from urban areas to rural areas; that is, how does urbanization occur.”
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In this study We defined Urban by using a concatention of features to identify a station as Urban
1. A rural station can have no significant human built area within 11km. or 380 sqkm.
2. a rural station cannot be at an airport
3. a rural station must not be lit
4. a rural station must have virtually no impervious surfaces.
Urban by my definition is anything that violates any of those rules,
So, in 380sq km around the site if there is 1 sq km of human built surface. Its urban
Lights over 30: urban: airport? urban.
Increases in temperature in urban areas is directly related to the sq area of the urban landform.
That is, changes to the surface properties ( cities have lower albedo) changes to the material
properties ( heat capacity) and changes t o the vertical profile ( building height) are the key
drivers for UHI.
In fact, I can show how the Trend at Urban sites is a direct function of the amount of built area.
“kwinterkorn says:
December 6, 2011 at 10:39 am
Changes in land use within the “rural” designation may also be significant. If forests and wild grasslands are put under the plow to feed the growing world population (and fuel for cars), then the “rural” designation is confounded, and a true mixed urbanization/cultivation of wild lands bias on averaged global temps may be present.”
Sorry, I got that covered as well. The rural sites are selected by looking at the 380sq km
around the site. We determine that there is no “built” areas within that zone. We can also look
at the land use in those same areas ( you will see some of that in the presentation) So, I can
tell if there has been changes to amount of cropland and amount of grassland in these rural
areas. In general there are slight changes in these variables and those slight changes do not
explain the trends seen. Conversely, the trends seen at the urban stations ARE a function
of the amount of urban area.
@ur momisugly beng says on December 6, 2011 at 7:11 am
Technically, you are right, but I tried, perhaps inartfully, to insert a caveat that Barrow’s UHI bias may not be comparable to the UHI bias in “rural” stations in the lower 48. My intent was to raise the question of whether the “rural” stations were, in fact, controls for measuring UHI bias in “urban areas” (i.e. that the trend bias of the rural stations did not depart significantly from zero).
sky (December 6, 2011 at 7:56 pm ) probably phrased the issue better than I did.
“Alexander says:
December 5, 2011 at 11:32 pm
Well, the first thing I want to say is that what a biased map you show up at the very beginning! How many rural sites are partially (or even fully) obscured by urban sites—particularly in the US? This is not really better than the way Mann et al. enjoy putting a big fat line on top of their reconstructions to hide any recent insensitivity. I’m very sensitive to people who try to lie (or mislead) with graphs.”
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sorry about that, in the end, Nick came up with a method of plotting that alternated the plotting of rural and urban so you could see more clearly, but it didnt make the deadline. You can blame me for that graphic.
@ur momisugly steven mosher says on December 6, 2011 at 11:32 pm
I would like to clarify/withdraw my comments as I was thinking of UHI on a century scale, so what I said is not directly applicable to your commendable effort.
Nick
“One thing to consider is that for a lot of these sites, there will also be wind data. You could also extract wind data from the weather maps at the time on a large scale.
So now you could test a site that is to the east of an urban area. You also need a site to the west of an urban area. Depending on the wind direction you either get no difference, or a bias one way or the other.”
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the wind data would be more interesting to explain why there is no UHI in the city. A 7m/sec wind will eliminate most UHI. The more interesting effect would be changes to precipitation downwind.
“As I read it, this paper finds average UHI in USA to be 0.06 to 0.1C per decade with the number of genuine rural stations being “vanishingly small”. ”
Ah no, its global not the USA. The pairs study end up with pairs in USA and Aus.
‘However, as it is not “reasonable” to have such biased spatial coverage, the definition of “urban” has been eased off so that the category covers only those stations which converge on 0.2 to 0.4C.
Is this a case of changing the data to fit the hypothesis?”
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there is no “changing” of the data.
The baseline case is a definition of rural that defines Rural by the following rules
No “built” pixels within 11km of the site. 11km was selected to account for possible
errors in station location. Station locations, in some cases, are reported to accuracy
of 1/10th of a degree. That is 11.112 km at the equator. This radius is used
to evaluate all sites. If there are no built pixels within that radius it is designated as
Rural, otherwise it is Urban. In addition there is additional screening on the rural
stations: They cannot be at airports. Since a Modis pixel is 500m on a side
there is a small chance that it will not see a runway. or the Runway can be grass.
So I removed the small number of rural airports that slipped by Modis. Then,
there are some unique cases where nightlights are used to further perfect the selection
of rural. After this we have over 5000 rural sites.
Now the selection of 11km may look arbitrary ( BEST used .1 degree ) So we varied that
parameter and looked at zones from 5 km to 20km. Also, the decision to allow
NO built pixels may be too stringent, so we varaied that paramater as well to create a
a 3D surface.
By stretching the radius out to 20km you can start to see bias that goes above .06C
However, the problem with this is that the spatial coverage goes below 50% of the land surface
This is problematic because Latitude, more than ANYTHING ELSE, drives the trend.
That is, polar amplification is real. As you select higher latitude stations you are selecting
stations with higher trends
Want to know the stations with the HIGHEST TRENDS?
Tundra.
So, if you change the radius to 20km you simultaneously drive the selection of stations
poleward. the higher the latitude, the higher the trend. polar amplification.
Anyway, all the sensitivity studies are there. If you are trying the measure the bias “in the record” then you need to select a rual sample that has good spatial coverage. Less than 50%
of the land is not good spatial coverage.
SirCharge says:
December 6, 2011 at 8:04 am
So, using the lowest estimate of UHI of 0.03C/decade over the last 150 years there is a 0.45 degree C signal. Considering the estimate of 0.8 degrees warming during that time period in the US, more than half of the overall warming would be related to UHI. Now, the question to ask is how is this signal being compensated for in the US temperature data? From what I’ve seen it has either been ignored or exaggerated.
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you cannot extrapolate beyond the time period we examine.
at some point one could try to take this back in time but other measures of urbanity
would be required.
A. C. Osborn says:
December 6, 2011 at 2:16 am
What is e(smy), it has not been described above.
I find it interesting that they would look at Rural stations up to 500km away to compare to an urban station. Everyone knows that “climate” can change in as little as 10km. I know they “grade” them by distance, but it still seems a strange thing to do.
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You should not mistake the climate for the weather. climate is a long term average. In this
case we are calculating a trend for all rural stations within a 500km radius. Thats a 32 year
trend. Given that stations are correlated out to 1000km, 500km was a conservative choice.
However, its a simple matter to change that to 100km or whatever. All that does is reduce
the number of rural stations you have contributing to the estimate of the rural trend for that zone.
#####################
“What is very misleading is that there are very few Rural stations compared to Urban ones and yet spatially there is much, much more rural land area than urban. Where in their algorithms is this allowed for. Shouldn’t there be a major biasing for rural stations to match the rural area difference?”
Dont confuse the pairs study with the global study. In the global study we use 5000+ rural
stations. the estimate of the all rural network is about 10% lower than the estimate created
using all stations.
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What is the difference in Trend between all RAW Rural Vs all RAW Urban without any kind “adjusment”?
.03-.04C per decade over the period of 1979 to 2010.
Septic Matthew says:
December 6, 2011 at 12:30 pm
I would be interested in seeing whether the results depend on the exact classification rule for “rural”.
################
see the charts. we varied the definition in both dimensions. the radius from the site and the amount of built land .
JJ says:
December 5, 2011 at 10:57 pm
The upper limit on UHI bias between rural and urban stations is on the order of 0.06 to 0.1C per decade. However, these cases are clearly problematic from the spatial coverage aspect, as the number of rural stations becomes vanishingly small when the most stringent filters are applied.
This is not a justification for using less stringent rurality criteria. That the right answer is difficult to achieve does not make the wrong answer more correct.
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the issue is that as you lose spatial coverage ( <50%) you tend to migrate northward.
That means your rural stations will come from higher latitudes. Higher latitude have
higher trends, due to polar amplification, so that you are confounding the UHI bias
estimate by introducing a latitude bias in your rural selection.