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 Published station metadata often includes outdated indications of whether a station is urban or rural, and instrument geolocation data can be imprecise, out of date, or otherwise incorrect.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
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How can UHI be high enough to kill people but not so high as to register in the temperature record? That is a question left unanswered at a recent NJ conference on climate change.
“Month-long spells of 100-degree weather will kill more people in the New York/North Jersey metropolitan area, with the greatest danger in urban “heat islands” like Newark and Jersey City.” I believe-though not certain- this is attributed to Dr. Kim Knowlton a Columbia University clinical professor of environmental health sciences.
http://www.newjerseynewsroom.com/science-updates/global-warming-experts-paint-a-bleak-picture-of-new-jerseys-future
What about issues from conversion from manual recording to automated? Sometimes people just fudge the logs when they forget to record on time, or forget to check at all. This usually happens when it’s very cold out, or when the responsible person otherwise doesn’t feel like trudging outside to make a reading.
Is this a problem? Is it quantifiable? We know it was an issue on the old Soviet Union when remote sites would fudge their cold-weather data in order to get a larger allocation of fuel.
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.
I fully agree with that. There is really no need to spend the time on the issue at all. You can look at the temperature rise from the 1970’s to around 2000, then look back in the record and see a nearly identical pattern of warming from around 1910 to the late 1930’s and say “oh, it’s doing the same thing it did 60 years ago” another step up in the recovery from the LIA. We are probably due for one more such step starting in 2040 or so and I have no indication that it is unnatural in any way at all. We are probably going to go about 30 years or so with relatively flat temperatures.
In this link that was provided in another thread here recently:
http://www.woodfortrees.org/plot/hadcrut3gl/from:1998/plot/hadsst2gl/from:1998/plot/hadcrut3gl/from:1998/trend/plot/hadsst2gl/from:1998/trend/plot/hadcrut3gl/from:1980/to:1998/plot/hadcrut3gl/from:1980/to:1998/trend/plot/hadcrut3gl/from:1934/to:1980/plot/hadcrut3gl/from:1934/to:1980/trend/plot/hadcrut3gl/from:1905/to:1934/plot/hadcrut3gl/from:1905/to:1934/trend/plot/hadcrut3gl/from:1880/to:1905/plot/hadcrut3gl/from:1880/to:1905/trend/plot/hadcrut3gl/from/to:1880/plot/hadcrut3gl/from/to:1880/trend
We see a rise in temperatures from 1850 to 1880. About a 30 year rising trend in temperatures. We weren’t emitting a lot of CO2 in 1850 to 1880. Then we have another rise from 1905 to 1934, another 30 year uptrend in temperatures. We were not emitting a lot of CO2. This second rise is steeper and longer than the earlier rise. Then we show another uptrend in the graph starting in 1980 but I would change the timings of some of the trends to show where they really switch and not place them on even 5yr boundaries:
http://www.woodfortrees.org/plot/hadcrut3gl/from:2004/plot/hadcrut3gl/from:2004/trend/plot/hadcrut3gl/from:1975/to:2004/plot/hadcrut3gl/from:1975/to:2004/trend/plot/hadcrut3gl/from:1942/to:1975/plot/hadcrut3gl/from:1942/to:1975/trend/plot/hadcrut3gl/from:1911/to:1942/plot/hadcrut3gl/from:1911/to:1942/trend/plot/hadcrut3gl/from:1879/to:1911/plot/hadcrut3gl/from:1879/to:1911/trend/plot/hadcrut3gl/from/to:1879/plot/hadcrut3gl/from/to:1879/trend
The “1945 problem” is clearly seen here and that is due to a change in the mix of sea surface temperatures being reported during WWII with US ships doing it one way and UK ships a different. After 1945 the number of US reports greatly declined and so you see a sudden drop in the graph that is probably not indicative of actual temperatures. In HadCRUT3, locations near the coast a GREATLY biased by any SST measurements nearby. So a change in SST measurements can also change what HadCRUT3 reports for grids that are even 90% land.
What that second graph I posted shows is that we catch the tail end of a long term rising trend ending in 1879-1880. Then temperatures decline to about 1910 or so. Then temperatures start another sustained rising trend until about 1940-ish. Then we probably had a decline/flat period until about 1975 or so when temperatures continued their climb in a fashion very similar to the 1910-1940 increase and since 2004 temperatures have begun to decline but 6 years of data isn’t enough to say with any certainty though if the pattern of the past holds going forward, we should expect about 30-ish years of flat to cooling temperatures.
The trend from 1975-ish to 2000-ish is nearly identical in duration and amplitude as that from 1910-ish to 1940-ish. What this graph captures is a three-step recovery from LIA temperatures. There is nothing in the more recent rise that looks “alarming” or scary or human created or needs to be “mitigated” in any way and doing so is simply throwing good money after bad. What we have is are NATURAL CYCLICAL periods of warming as we recover out of the Little Ice Age temperature regime. If we are to completely recover to what temperatures were before the LIA, I would expect to see another step up in warming starting in around 2030 or so.
There is no reason to waste a lot of time and resources on this issue. We have seen warming like this in the past, in the very RECENT past, in fact (early 20th century). There is nothing in the temperature record that shows anything unusual is going on. No abnormally high rate of change or amplitude of change has happened. In short, nothing to see here, move along!
To add to crosspatch’s charts, this chart has a CO2 overlay: more evidence that natural variability has a much larger effect than CO2. In fact, the effect of CO2 is so small it is down in the noise, and can’t be separated from natural variability.
Looking at the long term trend, there is nothing unusual happening. Nothing.
Also note that so far the slope of decline since 2004 is very similar (looks nearly identical) to the slope of temperature decline between 1880-ish and 1910-ish. Yet another indication that there is really nothing new going on and that we are likely seeing natural variation.
crosspatch says:
December 6, 2011 at 9:10 am
I believe that UHI is so tricky and so fickle that it can’t be accurately removed in any reliable way. It would be best to completely eliminate it from measurements being used to track climate trends.
I agree completely with that.
Something wrong with a system that puts a calibrated station in a distant area and then someone sits many miles away and determines a methodology to adjust and manipulate the station’s data in order to get a “corrected” reading.
crosspatch says:
December 5, 2011 at 11:46 pm
“I wondered about that, too, Alexander, because I know there is some pretty rural area in Kansas and Nebraska and the Dakotas that show up as “urban” in that map. As in most of those states are rural except for a few smallish cities. Heck, some places out there you can go 40 miles between tiny little towns.”
Are you kidding? Kansas has road signs that say “No gasoline for the next 80 miles.”
> crosspatch says: December 6, 2011 at 1:12 am
> it really isn’t all that important how accurate it data are so much as you capture the proper
> trending of it. And when you are doing things like creating a global aggregate average and
> simply paying attention to the trend and not so much the actual value, you can probably get by > with a small sample.
————————————————
How would you know what is adequate/accurate with N = 1 ? Sorry Crosspatch but you need remedial sampling theory 101 and stats 101, as do most climate scientists by in large. The point is that aggregating values is one of the ABSURD statistical processes involved here.
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.
This is most assuredly a case of trying to make a silk purse from a sows ear simply because some people have an axe to grind [lol]. And damn the actual science/stats, or the data processing, as we have seen.
It is WAY PAST TIME for scientists to do the science CORRECTLY with PROPER random sampling and production of PROPER descriptive statistics with a minimum of absurd assumptions. So, most of all, let’s see the use of proper sampling and statistics with reasonably accurate standard deviations and identification of all sources of error.
Proper sampling and use of stats won’t happen simply because the variance and errors are monstrously large and expose the fact that we can not measure any global temperature change because any change is lost in the “noise” in human timeframes and we are pissing into the wind in the attempt.
My take ? Let’s get back to investigating problems and processes that we actually CAN find the answers to rather than straining at describing thousand year behavior of gnat fart’s in chaotic global heat engines that we don’t even fully understand and let some reality penetrate thru this unproductive brain fug.
Smokey: your first link is USHCN which I would like to point out to folks is a CONUS land station record only. HadCRUT3 includes sea surface temperatures. Things such as La Nina can have a greater impact on HadCRUT3 than on USHCN. Just wanted to point that out to folks. USHCN would also, by the way, be unaffected by changes in the way sea surface temperatures were measured by ships and the replacement of ship data by satellite and later buoy/float data. Also note that the CET temperatures don’t pick up the early 20th century warming, it seems to happen much later there (1950s?) which might be due to a major North Atlantic influence on temperatures there.
So does California actually! You might have to go through a town to get to the next one with the gas station. Little hint, though, many of those farms in Kansas (and elsewhere) have their own gas pumps on the farm. If you are really in a jam out that way and come to a decent sized farming operation, you can sometimes persuade them to sell you a spot of fuel just to get you to town.
To answer a question raised above, there may be a hundred deaths from heat in New York city, but it’s cold that consistently kills: deaths per month.
Next, who decides which stations are removed? The average temperature compared to the number of stations also raises serious concerns. Who decides if the stations eliminated are urban or rural?
And as Gail Combs points out, the AMO appears to have a huge influence on temperatures compared to the unmeasurably small impact of CO2.
Naturally the warmist crowd is going to say the number of stations is irrelevant. But there seems to be a concerted effort by government agencies to eliminate most of the temperature recording stations. Why? And again, who decides which particular stations are removed?
With so much money at stake, added to the documented fact that GISS, USHCN and other agencies manipulate the data to show either lower past temperatures [creating an alarming-looking rise] or artificially “adjust” current temperatures [always up, never down], the only reasonable conclusion is that satellite temperatures are the only reliable data.
crosspatch,
I was showing the non-correlation between CO2 and temperature. I don’t have a global temp/CO2 chart handy, but global temps mirror U.S. temps pretty closely, and CO2 still seems to be a non-playa.
And before the hand-waving over tiny fractions of a degree gets to gale force, sometimes it’s best to just sit back and look at the situation in human terms, by changes in degrees. At times during the Holocene temperatures have changed by as much as 15°C in a very short time. The current global temperature has been unusually mild and pleasant for a long time now. A degree or two warmer would be even better. I guess that makes me a genuine warmist.
Here is another interesting piece of data: The Blue Hill weather observatory has the longest continuous temperature record in the US:
http://www.bluehill.org/climate/anntemp.gif
You will see the trend in temperature rise from 1870 to about 1950 is rather steady and then FLATTENS from that time to the present. At that observatory, the rate of overall temperature rise since the 19th century has declined in the late 20th century.
And about this comment: “Sorry Crosspatch but you need remedial sampling theory 101 and stats 101”
I don’t think so because I am not trying really to get an accurate measurement of temperature. I am trying to get a sense if it is rising or falling and at what rate. For example, if I have a series of balls coming off an assembly line, I don’t need an extremely precise measurement of the weight to know if they are generally trending heavier or lighter as time goes by. As long as I am measuring with the same instrument at the same point in the production line, the calibration of my instrument doesn’t matter as long as it isn’t drifting. I have a perfect sample. I have 100% of the temperature measurements (or very close to 100%) that were taken at a given station. I only want to see if those measurements are trending generally higher or lower.
Lets say I have 50 machines that make these little balls and each machine makes them just a little bit differently. I suspect the material the balls are being made of is more dense than it used to be but I’m not sure because there is natural variation of the density from batch to batch of raw material. Over that period of time, some machines have been in and out of service, some are completely gone, other new ones have been added and yet others have had their measuring instrument changed. Even worse, some of these stations have instruments that change their measurement in response to ambient temperature at different rates than the others do but I do have records of the density of each ball that came out from each of the 50 machines.
If I try to get too “perfect” I am only going to add additional uncertainty. The more samples I add from more different places, the more uncertainty I add to my result. I can identify the instruments that have the least amount of change according to ambient temperature so that reduces the amount of measurement uncertainty (I will use that as an analog to UHI). Then of those, I select the ones that have the longest running continuous data set. I don’t need to measure ALL of them, I simply need to measure a few in each different one of my production plants (say I have 100 production plants around the world all using the same raw material which I suspect might be changing in density). If the density of the material going in (an analog of general global climate) is increasing, I should see a general increase in density of balls at all plants. So if each plant has 50 machines, I can probably get away with trending the output of three of these stable instruments at each of my 50 plants. I do not care what the total average density of all of the balls I produce is so I don’t need an accurate sample because that is not what I am trying to quantify. I am only looking only for a trend.
So I look at my production for the past 20 years from three machines at each plant that have been in continuous operation with instruments whose ambient conditions haven’t changed and whose measurements themselves don’t change much with any change in ambient conditions. I notice that at 49 of the plants the density has trended up and at one plant it has trended down because, as it turns out, the plant operator was using a little less material per ball so he could skim some off to make his own balls for sale on the local black market without me noticing any reduction in production.
The problem with UHI is that it can cause different trends in the same place with the same thermometers. I can have 30 years of cool PDO which gives me one weather pattern at a location and so influences the measurement in some way and then have PDO switch to its warm phase and get a completely different trend. UHI is, for the most part, a sunshine proxy. Combined with land use changes, it can be both a sunshine AND a humidity proxy combined.
For example, the temperature profile in Phoenix in summer given exactly the same weather conditions was probably much different in 1900 than it was in 2000. This is because I now have a bunch of concrete and asphalt absorbing a lot of solar energy. Then at night everyone’s sprinklers turn on and cranks up the humidity. So the conditions at both midnight and dawn and now completely different. Now if I go into a prolonged drought and they tell people they aren’t allowed to water their lawns, suddenly UHI takes on a completely different profile! Same if I get a persistent “monsoon” with an unusually cloudy summer. So UHI can not really be quantified because its impacts can be completely different this year than it was last year.
Measuring climate with urban data is stupid because too many things change. It is a noisy environment with too many things influencing measurements that are not related to climate.
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.
Esh., I changed from 100 to 50 plants in that mental cartoon, sorry about that. make that 99 trending denser and 1 plant operating skimming from me )
I would be interested in seeing whether the results depend on the exact classification rule for “rural”.
Also, will the data used and the code be made public?
Yes, that graphic points out exactly the point I was trying to make … nothing new is going on. We don’t see any unusual increase in temperatures let alone a change we can attribute to humans. IT JUST ISN’T HAPPENING.
Also, on the word playa: Living in the Western US that word has a different meaning. It is pronounced PLAH – yah and is Spanish for beach or shoreline and is used to describe dry lake beds. So for example we would describe Groom Lake as a playa. There are a lot of playas in the Great Basin region.
To whom is this article addressed? Statisticians? Possibly. Mathematicians interested in your use of linear equations? Possibly. Persons interested in the facts on the ground. No. Nada. Nein. Nyet. If this is the kind of study that you are going to do, I recommend that you submit it to Real Climate.
When will you realize that you will have achieved nothing until you address the facts on the ground? You are encouraged to discuss your statistics and other mathematical techniques but only for the purpose of explicating to the non-mathematician how they do justice to the facts on the ground. What is at issue is whether your statistical work has any foundation in empirical matters. We are very worried about this because we know that in other equally important matters you are completely oblivious to the facts on the ground.
For example, in the matter of tree ring proxies for temperature, you insist that historical records of tree rings can be used for proxy measurement of temperature yet you know very well that those historical records float free of the facts on the ground because no one has ever done the empirical work necessary to explain what environmental factors cause the changes in tree ring width.
Finally, everyone knows that if you use only the US stations that have no interruptions in their records then there is no warming trend at all. Could you please explain the facts on the ground that caused these stations to behave in this way. Consider this an introductory assignment that will get you prepared for the larger assignment.
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!
BioBob says:
December 6, 2011 at 11:28 am
Very well said. Keep up the good work.
Everyone knows (and it has been stated above) urban areas have warmer temperatures than rural areas, and are greatly over represented in virtually every country. However, the powers to be insist that if the population of the urban area has not greatly increased, and the general nature of the urban area has not greatly changed, the temperature trend (i.e. rate of increase of temperatures) in urban areas should be the same as in rural areas (and thus representative of the country in general). This is absolutely untrue because it totally ignores the change in lifestyle in virtually all first world cities. Consider air-conditioning (in the summer in northern cities and year round in southern cities). Anyone who has stood outside near an air-conditioning vent can attest to the rush of hot air being introduced to the local environment as warm air inside the building is being expunged, and replaced by cool air. Fifty years ago, only wealthy people in northern cities had air-conditioning in their homes. (I can remember people who would rush to movie theatres on a hot summer’s evening – not because they cared about the movie being shown, but because the theatre was advertised as being air-conditioned!) In Europe, as recently as twenty years ago, air-conditioning was an outrageous extravagance that even the rich didn’t have. Today, while not as common as in North America, air-conditioning in Europe is not uncommon. The huge migration from the north to the south in the United States over the last forty years or so was made possible by the fact that in urban areas of states like Florida and Texas (where the summers are quite uncomfortably hot), virtually every indoor place you go to has air-conditioning, and the air-conditioning is set to temperatures that don’t just make it bearable, but make it outright cool! Lifestyle in the wintertime brings another unaccounted factor. Fifty years ago, many homes in the northern cities did not have central heating. In southern cities and in Europe, almost no homes did. On cool nights, you wore flannel pajamas and put extra blankets over you. On very cold days, you might try to heat up the one room you were in, while leaving the rest of the house with relatively little heat. Today, many people forget about winter pajamas and extra blankets, and just turn the thermostat up to keep the whole house nice and warm. This of course, also keeps the environment close to the house relatively nice and warm too. Thus, just the increased use of heating and airconditioning have caused a quite measurable increase in urban temperature trends that is ignored by most (if not all) climate scientists.
I am still a little puzzled about how Spain is more urban than the UK….but no doubt there is method in the seeminjg madness
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.
Jeff S. says:
December 6, 2011 at 1:53 pm
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.
If I visited CERN and posed similar questions about particle collisions, they would have highly detailed answers to each. Why cannot climate scientists pony up and deal with the facts on the ground?
crosspatch says:
December 6, 2011 at 11:33 am
Thanks for the info. But I grew up on a working farm. I know about the resourcefulness of farmers.