NOTE: David Smith is doing experiments with the portable USB digital thermometers that are available here. This sort of experimentation is easy and inexpensive to do, and makes a great topic for a student science fair project. The results are easy to download from the USB thermometer into a PC for analysis. -Anthony
Seven Days in May
A guest post by David Smith
This is an update on recent field tests with remote thermometers (see the ”Fun with Thermometers” post for background).
My goal is to quantify, to an extent, the effects of microsite problems (pavement, buildings, trees, etc) on temperature.
In the current test one sensor (”A”) is currently in an abandoned baseball field at least two hundred feet from any paving, tree, structure, etc other than a chain-link fence:
This reasonably approximates a good-quality site, isolated from human microclimate effects.
The other sensor (”B”) has a split personality. On one side is a poorly-drained field while on the other side is an older asphalt parking lot:
When the wind blows from the north this second sensor tends to reflect the characteristics of the soggy field, while a southerly wind brings air from the parking lot.
An aerial view of the two sites (”A” and “B”) is here:
For this update I selected seven days in May in which the skies were mostly clear throughout the day and night. This should maximize any radiative effects on temperature. (Unfortunately, the site is warm, quite humid and windy this time of year, limiting the magnitude of any radiational microsite effects. But, despite this diminished magnitude there are still useful observations to be made.)
Below is a plot of the average temperature of “B” on five clear-sky days when the breeze was from the parking lot and the average of two clear-sky days when the breeze was from the soggy field. I’ve subtracted the temperature of the nearby baseball field (”A”) from these two averages so that the lines show how much warmer or cooler “B” is than “A”. I’ve also slightly smoothed the data.
All seven days were breezy, which mixes air and limits its time over the surfaces, so the effects are probably muted compared to days with less-breezy conditions:
This shows several things. One, when the wind is from the parking lot (red line), the temperature at “B” sensor is warmer than that of the baseball field, night and day. Shortly after sunrise the difference diminishes, presumably due to the higher heat capacity and thus slower warming of the asphalt vs the baseball field. As the sunny day progresses the heat content and temperature of the asphalt rises, reaching a relative peak at “B” in the late afternoon. As the sun sets and evening progresses the temperature of “B” remains elevated but to a smaller extent.
This “parking lot effect” should be noticeably greater this summer, when average windspeed and air mixing diminishes.
The effects when the wind is from the soggy field (blue line) are perhaps even more interesting. The temperature of “B” tends to be depressed vs the baseball field during daylight hours, presumably due to evaporative cooling of the soggy field. The effect is reversed a bit in the late afternoon, possibly when the dry baseball field is radiatively cooling faster than the soggy field.
The soggy field appears to be due to changes in drainage following a yearlong construction project nearby. This change in drainage and probably ground cover was subtle in nature and may have stretched over some time, something which may or may not be detected by a discontinuity algorithm. In this instance it was cooling but my conjecture is that most drainage changes are towards drying, and warming, not wetting and cooling.
These seven days in May are affirmations that it is a bad idea to have sensors in the vicinity of human-induced microsite changes. Changing drainage, repaving the parking lot, aging of the parking lot, changes in parking patterns, etc can all have an effect. The size of the effects in a given year may depend on rainfall, wind anomalies, etc, making it difficult to detect a discontinuity.
I say let’s have serious amateurs analyze climate for a time, at least they stick their hands out the window:
“2007 is likely to be warmer than 2006 and it may turn out to be the warmest year in the period of instrumental measurements. Increased warmth is likely this year because an el Nino is underway in the tropical Pacific Ocean and because of continuing increases in human-made greenhouse gases.” — James Hansen NASA/GISS, circa January 2007
“DAV, regarding the sensors, they are remarkably consistent”
Dave, I would expect them to be self-consistent with others of similar manufacture but that doesn’t mean they have linear response or match the readings of a known standard. Or did you mean they are consistent with some standard?
It may sound like a bother but showing the difference between these sensors and the one in the parking lot won’t yield a useful result if you don’t know the responses of all of them. One equation, two or more variables and all that.
Trying to figure out the response of the sensor at ‘A’ can be a problem. You need a sensor with known response in all locations. Is the parking lot sensor one of yours or is this a real station?
Even if you own all of the sensors, non-linearity can bollux your results — particularly if the resulting variation is comparable in size to the expected differences.
You also need to remove the housing as a variable. Placing all of the sensors in identical housing is a good way.
Sorry, by ‘A’ I meant the one in the parking lot. I see you’ve labeled that as ‘B’.
Nice work.
It would be nice to see a plot(s) of the temperature difference over time without averaging. The averaging is a little hard to get my head around in order to draw my own conclusions.
Also, I think you should include a “C-A” along with a “B-A” as some sort of experimental control.
Again, nice work.
‘They say … His experiment is invalid’ ???
They broadly imply that site quality doesn’t matter. It does. They say they properly adjust for it anyway. They don’t.
They ??? Who are ‘They’?
The NOAA
’slide-show maps’ ??? What “slide-show maps”?
http://wattsupwiththat.wordpress.com/2008/05/13/ushcn-version-2-prelims-expectations-and-tests/#comments
For the full slide show, the Rev posted the link in a REPLY in the comments section two or three posts from the bottom of the page.
I’d say David Smith has it right. His experiment roughly confirms Yilmaz et al (2008) which finds a great deal of difference in temp offset measured over grass Vs dirt Vs concrete/asphalt.
BTW, David, is how high is the sensor attached to the fence from the ground? Is it the regulation 1.5m? Are both A and B at the same height?
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To further the basic premise (and like so totally vindicate the Rev):
Yilmaz proves the offset bias. Kit proves the trend bias:
Ross R. McKitrick, Patrick J. Michaels , JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, DECEMBER 2007, Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data
Abstract:
Local land surface modification and variations in data quality affect temperature trends in surface-measured data. Such effects are considered extraneous for the purpose of measuring climate change, and providers of climate data must develop adjustments to filter them out. If done correctly, temperature trends in climate data should be uncorrelated with socioeconomic variables that determine these extraneous factors. This hypothesis can be tested, which is the main aim of this paper. Using a new database for all available land-based grid cells around the world we test the null hypothesis that the spatial pattern of temperature trends in a widely used gridded climate data set is independent of socioeconomic determinants of surface processes and data inhomogeneities. The hypothesis is strongly rejected (P = 7.1 × 10−14), indicating that extraneous (nonclimatic) signals contaminate gridded climate data. The patterns of contamination are detectable in both rich and poor countries and are relatively stronger in countries where real income is growing. We apply a battery of model specification tests to rule out spurious correlations and endogeneity bias. We conclude that the data contamination likely leads to an overstatement of actual trends over land. Using the regression model to filter the extraneous, nonclimatic effects reduces the estimated 1980–2002 global average temperature trend over land by about half.
By half!
Just like I said back a-when! When Joe D’Aleo made that PDO/AMO correlation, and I commented that cutting the 1980-present increase by half (in honor of the Rev’s siting issues) would make the D’Aleo curve fit so much better! I just KNEW it had to be about half. It fit the picture too perfectly not to be. (They beat me up pretty good on Tamino for venturing that opinion.)
Now it’s Kit to the rescue! (Can I call ’em or what?)
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Great experiment.
Keep up the good work, David.
Interesting results… I’d like to see somebody set up about 10 or so of these temperature monitors (all calibrated of course) all around this field (or somewhere else with different landscaping), monitor the temperature and report the results. I’d really like to see it without wind too.
Any idea how the difference will be magnified or affected when the raw data is subjected to the various smoothing techniques and/or techniques to account for changes in sampling locations?
When I lived in southern Nevada, I noticed most people drove light colored, “desert friendly” vehicles. It doesn’t take much to also notice that people with lighter colored roofing had slightly cooler homes.
If everybody just covered their homes with aluminum foil in the summer (shiny side up, please), how much cooling would we notice? 🙂
Q&D calibration procedure.
1. buy a Styrofoam cooler.
2. Mount a low wattage incandescent light bulb in it.
3. Mount a small fan next to the light bulb.
4. Control the lamp wattage with a light dimmer
Take measurements over a range of temperatures.
This will let you know if the thermometers have different non-linearity characteristics. If you have a calibrated thermometer you can measure the actual non-linearity.
One thing to watch for. Electrical noise. Dimmers are notorious. A filter can help. Possibly a bit of tin foil around the USB connector (not touching the pins) can help.
More sophisticated tests can be done with a similar set up: DC supples, temperature controller, etc.
David,
Your guest entry is interesting and your work inspirational as always. If I may ask a question on method: How did you isolate sensor B temperature data for periods of exclusively north-blowing winds or south-blowing winds?
Thanks,
Bill P
“Q&D calibration procedure”
Not a bad procedure although I would use a solid state warmer. They’re often found in battery operated six-pack coolers. They’re adjusted the same way, i.e. by changing the duty cycle, but not as noisy.
DS still needs to characterize the parking lot sensor (if he hasn’t done so already) or the results aren’t very meaningful because:
Let TP = temperature indicated by the parking lot sensor.
TP = Tp + Ep + EP, where
Tp=true temp (ambient) at the sensor (if the parking lot wasn’t there)
Ep=sensor error
EP=error introduced by the lot and surrounding terrain
Assuming DS knows the true temp of his own sensor (let’s say at A and called TA), to find EP, he needs to calculate:
EP’ = TP-TA = Tp+Ep+EP – TA
The built-in assumption is that Tp and Ta (the true ambient) should be the same but this is likely true.
See the problem? There are two unknowns (Ep and EP) in that step so unless he has really knows the sensor characteristics at the parking lot he’s mostly wasting his time. What he’s finding is (Ep+EP), which is what he set out to do.
Here’s a question I always wanted to ask someone and this crowd seems like the right one to answer it:
Would it be possible to select, out of all the temperature stations in the world, 100 or so that are undeniably way out in the country removed from any possible urban creep effect, and which have been in the same place for decades, and plot the historical average temperature from these stations? Basically create a “pristine” average temperature record?
Any comments appreciated.
Sean
REPLY: I’m working on that, see http://www.surfacestations.org – Anthony
They also sell an “Ethernet and Internet Ready Thermometer with built in web server” that has accuracy to .1 that uses separate thermistors as probes, but it’s a bit pricy.
These devices have a 1 F internal resolution and a +/-2 F accuracy.
RE: Mark O (13:56:56) :
Gage R & R is treated, oddly, as a sort of heresy by most orthodox so called “Climate Scientists.”
Hi Anthony, thanks for the link. I have visited your site before; it’s a great project.
I couldn’t actually find any mention on the site the idea of a global clean temperature record, but I suppose after you’ve identified enough sites it will be easy to construct.
Any idea how many (true) Class 1 sites you would need for the result to be statistically significant? Maybe you have enough already?
Sean
It’s not the number, it’s the geographical distribution.
CRN is supposed to address this. Here’s hoping.
David,
Re: satelite view (wider field of view)
I am curious to see what is around your chosen site, besides lots of barbed wire.
Best,
Scott
[…] Up With That they’ve got some preliminary results from a study of what they’re calling “The Parking Lot Effect.” Follow the links for the details, but basically a paved surface like a parking lot can bias […]
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