I’ve spent a lot of time on this blog showing how badly maintained and situated the stations in the USHCN network are. And rightly so, the majority of them have issues. But, finding the good ones is actually more important, because they are the ones that hold the true unpolluted temperature signal. Unfortunately, the “good ones” are few and far between.
But when one comes along that is a real gem, it deserves to be highlighted. I present the USHCN climate station of record for Tucumcari New Mexico, COOP ID # 299156, located at the Agricultural Experiment station about 3 miles outside of the edge of town.
I “had” (he just moved to St. Louis) a nephew who lived in Tucumcari, and he just happened to be friends with the director of the experiment farm. Before my nephew left they both helped me get this survey done.
Click picture for additional images
Surfacestations.org image gallery link
This station has several advantages:
- Length of continuous record – going back to at least 1946 at this location, possibly to 1905 but NCDC MMS metadata stops at 1946.
- Length of continuous instrumentation – using mercury max/min thermometers
- Length of continuous data record – there doesn’t appear to be any missing years
- Lack of encroachment – 3 miles from the northeast edge of town, little development, little UHI. Tucumcari is well off the beaten path of development. Population actually declined 12% in recent years.
- Good siting – the station rates a CRN2 due to distant trees and sun angle, and one small asphalt road 70 meters away.
See the station survey report here (PDF) You can also make out the station on Google Earth using this link. After opening Google Earth, zoom in and the fenced outline and screen will be visible.
Eyeballing, you can see that the temperature data trend for Tucumcari is slightly positive over the last century, about 0.5°C, but there is a “bump” in 2000, which brings it to about 0.9°C. This same bump appears in neighboring stations such as in San Jon (33km away) and in Boys Ranch (135km away). There is nothing in the metadata location or equipment record to suggest a reason for the bump. So, either the bump is naturally occurring, or there is something we don’t know about that changed in the local environment, or we have another data set splicing error like the GISS Y2K debacle from last year.
Click for larger graph from NASA GISTEMP
I plotted the data provided by GISS (which you can find here) to show the effect of the “bump” at year 2000 on the overall trend:
Click for larger graph
Here is the data plot after the GISS homogeneity adjustment, I’ve hue shifted my saved version to red to help keep the graphs visually separate:
Click for larger graph from NASA GISTEMP
And here is the overlay of the USHCN data from GISTEMP and the data from the GISTEMP homogenization process:
In this case, the GISTEMP homogenization code appears to do what would be reasonably expected; reduce temperatures in the present to account for population growth and UHI. I’ve pointed out more than a few times that the GISTEMP homogenization adjustment often becomes flawed for truly rural sites like this when there are large cities within the 250km up to 1200km (depending on process) adjustment zone that Hansen uses, that have accelerating UHI trends. Due to these cities, often the past of a rural station gets adjusted cooler, resulting in an increased temperature trend, such as what happens at Cedarville, CA. Hopefully we’ll have a detailed analysis of that adjustment from John Goetz soon.
If you look at this list, you’ll see that there are a lot of rural stations within 250km. Tucumcari has the advantage of being truly in the middle of nowhere when it comes to other big cities. The closest big cities are Amarillo and Lubbock, but as I understand the algorithm used, when they are near the edge of the 250 km zone, their weighted value decreases.
In this case though, the GISTEMP homogeneity adjustment doesn’t take Tucumcari’s declining population into account, it only uses nightlights, and while the population may dwindle, town infrastructure usually doesn’t; streetlights counted around the station likely remain.
Climate Audit 
40 Comments
In the interest of completeness, it would be good to look at changes to the equipment over its history. Were new temperature sensors installed over its life time, if so, when? How was the enclosure maintained (did they switch to latex paint at some point, etc.) I wonder if they kept records of this sort.
As mentioned in the post, the site has been using mercury max/min thermometers over the entire data set. NCDC MMS metadata confirms the continuity of the instrumentation.
The only change to the site has been to add the automated RAWS station in the fenced area. I doubt that addition would have any measurable effect
It really does look like some kind of Y2K aberration, but then look at 1933!
Anthony, some people would have us believe that you have an agend, i.e., an intention to cherry pick sites with a flat trend and label them ‘good’. So, its nice to see honest standards in action. This site has an upward trend and deserves praise nonetheless.
The temperature record at this site is really fascinating. Most notably because it is nearly flat through 1980, then steeply upwards from there.
RE3 The difference is that 1933 is a spike that returns to regular baseline levels for that period. The year 2000 is also a spike, but the years after it do not return to the baseline levels for the period. There appears to be about a 0.5°C offset that continues.
Hmmm I decided to check the GISTEMP graph output for Detroit Lakes, where this all got started, and it appears that GISS has not updated the output:
Now:
What GISS calls RAW data here
GISS Homogenized here
Last year:
GISS “raw” of Detroit Lakes from my website here
It *appears* that the step function remains.
To Steve MacIntyre: Since you spotted the error, can you confirm if the GISS graphical output reflects the Y2K error fix or not?
RE4 Jon, There may be regional land use changes involved, perhaps more agriculture, more irrigation, deforestation, etc. I’ll inquire with the Ag farm director.
It could also be PDO, which shifted in 1978.
It looks like th hom. adjustment starts in the 30’s and rises at a constant rate. Looking at the sat map at NCDC there looks to be a good deal of development and a lake a short distance upwind. I’m not surprised to see the adjustments that have been made, it looks like a reasonable urbanization adjustment to me.
Mike:
I am not sure I understand. Unless you are saying there is a significant urban area in close proximity, why would you assume an adjustment is in order.
There is a difference between the GISS data for Tucumcari 4ne and that in the NOAA GHCN database.
The following figure shows the NOAA data:
This nextone compares the NOAA to the GISS:
This one shows the two 5×5 degree grids from the HadCRU data (since Tucumcari is right on the 35N line)
Anthony, I don’t understand this comment at all. Here we have a rural station where there doesn’t seem to be any reason to adjust the temperature for population growth/UHI. But in this case, Hansen adjusts Tucumcari as though it were a city. Why is he even adjusting Tucumcari at all? (The “reason” is that its lights value removes it from the rural classification and it goes into the adjustment pool.) While Hansen sometimes seemingly cools rural stations in the past, for the GISS dset2 version here, he warms the past of the station (cools the present).
It’s more that this is a case of another unjustified adjustment by the “adjuster in chief” showing once again that the Hansen adjustments do not do what they are supposed to do – and the best that can be hoped from the Hansen adjustment program by users of this dataset is that the adjustments overall end up being pointless and random, rather than pointless and biased.
There is extensive irrigation in the area, one consequence of which is that the natural Tucumari lake, 2 Ks south of the site, has partially dried out, and the extent of the lake likely varies substantially on a seasonal and annual basis affecting local humidity levels (as will the irrigation). However, looking at Google Earth there doesn’t seem to be any irrigation within 500 meters or so of the site.
The irrigation scheme was implemented in 2 phases. The first 1944 to 1950. The second completed in 1976. Both periods seem to correspond with up trends in temperature.
Tucumcari Project
RE10 Steve. Yes I could have worded that better. What I was trying to say was that in general, any adjustments which would tackle UHI would tend to adjust the present cooler. That seems a reasonable approach to UHI. But as you point out this isn’t a big city and the station is 3 miles from the city limits.
Writing at home, basically as a shut in this weekend due to the thick smoke in the air here. I got interruppted several times while writing this, so I apologize for the lack of clarity. In retrospect the paragraph doesn’t read well at all. Some of the haziness in my head from the smoke translated to haziness of word I suppose.
I agree that this station has no reason to be adjusted.
RE9 Alan thanks for providing this. You’ve uncovered a bit of a puzzle.
On the GISTEMP station selector page: http://data.giss.nasa.gov/gistemp/station_data/
I chose the selection from the pull down: “raw GHCN data + USHCN corrections”
It provides output at this URL after selecting Tucumcari 4NE on the next page:
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425747310030&data_set=0&num_neighbors=1
Yet the output on that page is clearly not the same as your GHCN data as presented. This deserves some spot checking to see if this is a fluke or a systemic problem.
Re 13: I plotted the NOAA data at http://www.appinsys.com/GlobalWarming/climate.aspx where I downloaded the NOAA database as of 9 Feb 2008. The plot is of the NOAA raw GHCN data. Normally the NOAA raw (unadjusted) data is similar to the GISS – so it may be the “USHCN corrections” that make a difference in this case. But why that would be only from about 1950 to 1980 and prior to 1915 – who knows. The NOAA adjusted data is often very different than the GISS “After homogeneity adjustment”. But typically the raw is similar.
Can you access data on other climate variables for the same station? If so maybe there is a step change that would be consistent with a step increase in temperature – making it more reasonable that it is a real change in temperature and not a problem. Or if no step change in any other data, it would be more likely there is a problem. For instance if there is a step reduction in rainfall at the same time as a step increase in temperature.
Alan Cheetham, #9 and Anthony Watts, #12,
The NOAA plot is almost the same as my simple average plot from first principles using the daily data set NDP070 from CDIAC at Oakridge National Laboratory:
Continuing on with the same data, looking at just from the late 1970s and at weekly resolution so that the seasonality shows, some step functions seem apparent, as indicated by the shadings.
And finally, here is Tucumcari in context with similar series from New Mexico, compared to the state wide average, using ten year moving averages of the daily data.
This illustrates the frustration of a distant viewer. Can anyone say with certainty if the real temperature of the region has changed significantly over the period of observation? I still cannot interpret what is raw and what is adjused, in the time frame available to a hypothetical decision maker.
These are important data, Anthony. They need verficaion, pretty graphs, a simple explanation pack and a big mailing list. If the final answer is “no significant change” then ask what effect GHGs are having. Or why they are not.
nightlights is an imperfect proxy. hansen sees it as a proxy for population density and there are clear cases where rural locations have lots of light
at night. the sad thing is there are much better studies of population density
that dont require one to look at nights lights. Hansens very own columbia university has a worldwide population density study.
RE16 DaleC The answer is in your graph I beleive. Note the minimum temperature rise from 2000-2006 while the maximum temperature remains unchanged.
John Christy explains this in a paper studying the same effect in California’s central valley. See this:
http://wattsupwiththat.wordpress.com/2007/02/13/irrigation-most-likely-to-blame-for-central-california-warming/
Culprit: Irrigation. I’ll ask the Ag Farm director if irrigation has increased in the are since 2000, I’m betting it did.
DAle C, you might want to compare the versions at CDIAC which are a bit stale with the up to minute GHCND versions at ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ushcn/42500299156.dly (Tucumari). Suffixes end in the 6-character USHCN number.
http://cahe.nmsu.edu/news/1997/043097_irrigation_tour.html
RE23 Good find Steve
Looks like a PDO record … (fake tone of surprise) well I’ll be darned! (/fake tone of surprise)
The graph also shows the highest spike in the 1930’s. This confirms your earlier work which revealed that the 30’s were the peak of American warming.
I also remember this kind of thing in State of Fear when Chrichton showed rural graphs and how flat they were compared to the rest of the data set…
Sry, one more thing:
The graph is more in the black than the red from the baseline of the start, but most of the high temperatures are on the left, not the right hand of the graph, the opposite one would expect from CO2 forcing.
Bernie # 8
Like I said in my post, I was looking at the area on the satellite photo. The idea is that the winds in the area blow the heat from the town and the lake to the area where the station is. However, given Steve’s post in 23 it brings into question whether or not it is irrigation or UHI, or both.
“…pointless and random, rather than pointless and biased.”
Link
Reading Christy’s research on the irrigation-generated heat in California made me think of the French farming regimes in the 13th through the 18th centuries. “Terra chaudes” (regularly-sown) tracts, often lay alongside wide sections of “terra froides” (uncultivated). The phenomenon (perhaps an impression based on the time of observation) of man-made heating of the earth is thus not a new one. Marc Bloch, in his French Rural History, characterizes the process, repeated for centuries across northern Europe.
Irrigation would be part of this effect, but I think just turning over the moist, dark, heat-absorbing soils, would have yielded a noticeable difference, and and at night, as Christy observes, the soil slowly breaths out that accumulated heat. Not to belabor the point, but it would be interesting to see what the agricultural routines were at Tucumcari Experimental Farm.
21 Anthony Watts “Culprit: Irrigation.”
IMO, it all comes down to water.
#30, 31
The first time I read Christy’s report what struck me most was the clear difference that water vapour was making to the night time temperatures in the valley relative to the upper slopes of the valley. This amongst many other things I have read on the subject have convinced me that water vapour has a clear influence on temperature due to the fact that it is the NUMBER ONE greenhouse gas (at least 95% of the GHG effect) with CO2 being a trivial second order effect. If the earth has warmed due to the so called ‘enhanced’ GHG effect (which IMO is still yet to be proven due to the mess of adjustments to the raw data that just obfuscates the situation e.g. bucket and UHI homogenisation adjuestments), then the clear ‘enhancing elephant in the room’ is water vapour and not CO2.
KevinUK
Re #31, Sam 2:08pm:
Confirming your opinion (and that of RP Sr) you might be interested in this:
http://video.msn.com/video.aspx?mkt=en-us&vid=44a7e79c-f4a9-400b-8342-1469a524546f
It is absolutely remarkable what Peter Andrews is achieving, simply by managing water in the landscape more intelligently.
Re #31, Sam 2:08pm:
Confirming your opinion (and that of RP Sr) you might be interested in this:
http://video.msn.com/video.aspx?mkt=en-us&vid=44a7e79c-f4a9-400b-8342-1469a524546f
The video shows clearly that by changing farm management practices, dramatic changes in vegetation and hence microclimate are achieved. Peter Andrews is a remarkable pioneer of intelligent methods to re-hydrate de-hydrated landscapes.
Mike C:
Irrigation I can buy for the recent discontinuity, it is the broader set of adjustments to the record that seem odd.
High humidity under clear skies is a natural phenomena here in Perth, Western Australia and invariably produces the hottest days and nights of the year. The effect on nighttime temperatures is substantialy larger than daytime temps, at least double and in the region of 10C relative to low humidity nights. In addition, we have what is probably the highest level of urban irrigation of any city in the world. From memory there are 200,000 wells used for garden and public space irrigation. Public parks and ovals (areas used for sports) are always green and lush even in the middle of summer.
This phenomena doesn’t come out well in the stats because the crucial combination is humidity and clear skies. Humidity and cloud results in abnormally cold days even in the middle of summer. The temperature difference between humid clear days and humid cloudy days is very large, around 20C. It is difficult to see what could cause this difference except a potent GH effect on clear high humidity days, which is absent on cloudy days.
Looking at the monthly trends makes me wonder if the summer’s uncut vegetation growth is moderating the temperature. Similar to the recent report of trees maintaining a constant temperature during growing season regardless of latitude. I did check a couple airports and they were more evenly distributed trend-wise. But they have all that asphalt and do some mowing.
Philip_B.
The abnormally cold days in summer (if you can call 25-30C cold) in Perth, when they do occur, are due to the change in the weather pattern. The summer pattern is cyclical. A trough forms off the west coast and results in dry easterly air and high temperatures. After 3 to 5 days the trough moves in, recycled air from the ocean moves over Perth resulting in very high humidity and some cloud. The ocean effect also cools the air. Then the easterly cycle starts again.
trevor, 34:
I enjoyed the video about this very interesting fellow. The kind of water engineering Peter Andrews has been practicing for years, creating successful agricultural “microclimates” presupposes that he has acquired individual stream or river water rights. The benefits to his own land seem remarkable, but I wondered immediately whether farmers downstream from him complained about the reduction of their own water.
Here in Rocky Mountain West, according to a recent article in the Denver Post, such rights have been pre-sold (actually oversold) to people downstream, making it illegal to harvest even the rainwater off your own roof.
Can you own the rain?
Where that leaves a farmer or rancher, or commercial orchard-grower, seeking to dam up running water on his property, I don’t know, but I’d imagine here in Colorado at least, there’d be a string of complainants all the way to the coast.
Sorry if this is too far off-topic.
Bill, 39: I guess it is OT, but to my mind it is a very important aspect, and deserves exposure.
The issue of downstream water in Peter Andrews approach HAS been addressed. Peter is opposed to dams for the reason that they hold water back, have high evaporation losses, and only serve to rehydrate a very limited fringe area. Instead, he promotes the use of leaky weirs and other methods to slow the release of water down. So instead of having massive floods when the rains come, with all of the water lost downstream, the water is retained in the landscape and slowly released.
Farmers downstream actually benefit from what can become a permanent supply of good quality water. You can find much more information at http://www.naturalsequencefarming.com/ and http://www.naturalsequencefarming.com/forum/. I suggest that we take further discussion there.