by Roy W. Spencer, Ph. D.
This is an update to my previous posts [here and here on WUWT] describing a new technique for estimating the average amount of urban heat island (UHI) warming accompanying an increase in population density. The analysis is based upon 4x per day temperature observations in the NOAA International Surface Hourly (ISH) dataset, and on 1 km population density data for the year 2000.
I’m providing a couple of charts with new results, below. The first chart shows the global yearly average warming-vs-population density increase from each year from 2000 to 2009. They all show clear evidence of UHI warming, even for small population density increases at very low population density. A population density of only 100 persons per sq. km exhibits average warming of about 0.8 deg. C compared to a nearby unpopulated temperature monitoring location.
In this analysis, the number of independent temperature monitoring stations having at least 1 neighboring station with a lower population density within 150 km of it, increased from 2,183 in 2000, to 4,290 in 2009…an increase by a factor of 2 in ten years. The number of all resulting station pairs increased from 9,832 in 2000 to 30,761 in 2009, an increase of 3X.
The next chart shows how the results for the U.S. differ from non-US stations. In order to beat down the noise for the US-only results, I included all ten years (2000 thru 2009) in the analysis. The US results are obviously different from the non-US stations, with much less warming with an increase in population density, and even evidence of an actual slight cooling for the lowest population categories.
The cooling signal appeared in 5 of the 10 years, not all of them, a fact I am mentioning just in case someone asks whether it existed in all 10 years. I don’t know the reason for this, but I suspect that a little thought from Anthony Watts, Joe D’Aleo & others will help figure it out.
John Christy has agreed to co-author a paper on this new technique, since he has some experience publishing in this area of research (UHI & land use change effects on thermometer data) than me. We have not yet decided what journal to submit to.
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I know how to compute the average of a continuous function; assuming just one independent variable and a single variable dependent on that, you clopuld plot a simple y = f(x) graph, over the operational range of x and y.
Then you simply integrate the area under the graph, and divde the total area by the total range of x, to get the average value of y. So simple. If of course, you only have data for discrete values of x, then you would multiply each appropriate y value, by the total extent of x that gives the same value for y.
For example, x could be an element of area ; say on the surface of the earth, and y could be the Temperature for that element of are (maybe a daily average value). So you simply multiply each element of area, by the appropriate average daily temperature for that element, and then sum all those products. The sum of all the elemental areas, is simply the surface area of the earth; so you divide the grand total by the total surface area of the earth, and ; voilla ! the answer is the true average surface temperature of the entire earth; well it is true for whatever time element over which you measured the average of the Temperature for any area element.
Of course you have to make all the area sample measurements at the same time epoch or else it isn’t valid, so you have to measure them all at the same time.
Well that is so easy, even an 8th grade or maybe a 4H student could do that; well if they have the data of course.
So presumably ;and you know the hazards of that; GISS or HADCrud etc have all those Temperature numbers form each of their global owl boxes; ans also presumably, logged in along with the GPS co-ordinates of that thermometer, is the value of the element of earth surface area for which that Temperature reading is valid, may 10 squ km or somesuch number; maybe it could be as high as 100 squ km.
I presume that for UHIs, the area is much smaller, say five times the area of the total UHI concrete area, or some similar extrapolation.
So just why is it that these agencies are having so much difficulty doing what a 4-H club member could do, if they had the data ?
Sure beats me !
[snip, promotional spam but I left in the following sentence because it is priceless ~ ctm]
High speed solar wavelengths are causing the the building materials to polarize at high speeds and generate heat the building isn’t designed or insulated for.