On Sunday I posted about the USHCN climate station of record in Tucumcari, NM highlighting its positive points since it has all the hallmarks of a well sited station with a long and uninterrupted record. But something was odd with the temperature record that didn’t quite make sense at first glance.
I also cross posted my report on Climate Audit since I always value additional input from the community there.
I noted that while this station is in fact well sited, and rates a CRN2, it has some oddities with it’s temperature record around the year 2000, something that looked like a step function to me.
Click for larger graph from NASA GISTEMP
Of course, even though this a truly rural station, 3 miles from the outskirts of town, Hansen and GISS apply adjustments to it anyway, which is part of the flawed “nightlights” algorithm incorrectly flagging this station for adjustment. Even though the adjustment makes the present cooler, it still seems misplaced given the station quality and history. Steve McIntyre said it best:
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.
The adjustment by GISS looks like this:
Ok adjustments aside, the fact that there is a step at 2000 that remained unexplained until commenters on CA started looking at the data themselves. DaleC provided this graph:
Click for original image
Note the arrow that I place at the year 2000. Notice anything?
The annual average minimum temperature has exceeded 45°F and maintained the rise since 2000. For the first time in the station history going back to 1905, the minimum temperature has gone above that 45°F mark and stayed there. Yes there have been some previous brief excursions above 45°F, but none appear to have lasted more than 2 years. Note that average annual maximum temperatures did not increase during the same period.
What could cause that? We can rule out adjustments, since this is GHCN data before Hansen gets his adjuster mitts on it. We can rule out location change or equipment change, since according to NCDC metadata the station has been in the same place since at least 1946 and possibly longer. It still uses mercury max/min thermometers, so there’s no MMTS next to a building or parking lot to blame.
So what is left? Something around the station in the measurement environment that affects the nighttime readings. I recalled seeing this before. And back in early 2007, I had posted a story about a paper from Dr. John Christy of UAH where he studied a number of stations in the San Joaquin Valley in California because they had exhibited this same symptom:
The culprit? Irrigation. See my story and Christy’s press release
Christy remarks: “Another factor is the dry air, something common to all deserts. Water vapor is a powerful greenhouse gas. Desert air lacks water vapor. The air turns cold at night because it doesn’t retain much warmth from the daytime and it can’t trap what little heat might rise from the ground at night.”
Evaporation from irrigated fields adds water vapor to the air — a process that cools summer days but traps heat rising from the damp soil at night.
“If there is anything I’ve learned in Alabama, it is that humidity can make summer nights very warm,” said Christy, a Fresno, Calif., native who has lived in Alabama since 1987.
Once I mentioned this as a possibility to explain the increase in nighttime temperatures, it didn’t take Steve McIntyre long to find some anecdotal evidence that correlated:
http://cahe.nmsu.edu/news/1997/043097_irrigation_tour.html
A few years ago when cattle prices were high, we saw a tremendous increase in the number of irrigated grass acres in Quay County,” said Jeff Bader, Quay County Extension program director. “One reason was our limited water situation for irrigation, and high cattle prices made it look very attractive.”
When cattle prices dropped again, interest in irrigated pastures declined, but now the cattle market is improving, he said. Producers never really lost concern about irrigated pasture because it fits into the management scheme for water conservation so well in Quay County.
“Irrigated pastures fill a niche in this area because of their ability to produce under varying levels of irrigation,” said Rex Kirksey, superintendent of NMSU’s Agricultural Science Center in Tucumcari. “Pastures remain a viable option in many situations where irrigation water is too limited or unpredictable for corn or alfalfa production.”
What is interesting is that the director of the Ag Science Center, Rex Kirksey, is also the person that took these photos for the station survey. There’s quite a large water project in the area, called unsurprisingly, the Tucumcari Project.
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Conchas Dam and Lake |
Apparently they have quite a problem with water “disappearing” there as outlined in this report:
“The principal problem has been the loss of over half of the District’s surface water supply in the canal and distribution system that carries Canadian River water from Conchas Reservoir to the irrigated farms in the District.”
“The District’s report concluded that seepage losses from the system’s canals become greater, in quantity, each year.”
From that report I obtained a the study area map, and located where the town and the USHCN station is. Unsurprisingly, the USHCN station is situated close to the canals of the water project, and prevailing winds in the area tend to be from south to southwest:
With increased irrigation to pastureland for cattle, and a leaky canal system that loses half it’s volumes and demands ever more water to meet customer deliveries, it seems plausible that the Tucumcari area is becoming more humid, and with the increased humidity, per Christy, increased night time temperatures.
The studies of Roger Pielke Sr. show that land-use changes are an important factor in local and regional climate change. This effects of the changes in agriculture and irrigation on the local measurement of climate in Tucumcari might very well make a good case study.
I had hoped that the automated weather station that sets next to the Stevenson Screen might have humidity data that I could track, but alas it does not.
There is a fairly complete record though of temperature and precipitation at this link from the Western Regional Climate Center.
UPDATE 7/1/08 One of our commenters “AnonyMoose” has brought this well done historical weather and climate report by NMSU superintendent Rex Kirksey to our attention:
http://tucumcarisc.nmsu.edu/documents/rr751.pdf
This merits some further study for the data it contains. I’m committed to other projects today, so if anyone wants to have a go, be my guest and I’ll post it.
Considering the amount of effort required to understand the bias in this and the several other sites that have been published, it is difficult to believe that there is any “global average.” Perhaps if errors or anomalies were random, the mass of data would help, but the data on this site usually shows bias, not random error.
Thank you for the good work!!
Why wouldn’t irrigation cause higher temps during the day too? Do they irrigate at night?
OOPS I see Dr. Christy already answered that question. My apologies.
I’ve mentioned it before – using nighttime lights is a bad indication of population. For the granualarity, there are much better sources, including the US census data down to the block centroid. There is also a normalized daytime/nighttime population dataset out of Sandia (? – I don’t have the notes with me – it might be Los Alamos) that has been well researched to the 250 m level. There is also the Landscan USA data that, if ever publically released, would give 100 m day/night pop density. But there are lots of data sources that are much better in the US than night lights…
I seem to recall that nightlights imagery date back to the 1990’s. Of what use would it be 10 or 20 years later?
REPLY: The night satellite imagery was put together in 1995 by the DMSP
Some flow data for Conchas Canal here, though they stopped measuring monthly discharges in 1992 for some reason: http://waterdata.usgs.gov/nwis/measurements/?site_no=07223300
I added the precipitation line in purple1971-2000 to the earlier precipitation graph in green and it appears that precipitation is up 5-10%. The graphs are small, 1 pixel is about 1%. Hope the link works:
REPLY: Precip might be up due to local humidity being up, which helps in building convective thunderstorms, which in turn dump more rain.
As I mentioned in a previous thread, humidity data can be misleading, as humidity with and without clouds produce dramatically different temperatures. Its the combination of humidity without clouds that produces warmer temperatures and this is precisely the effect produced by irrigation. Naturally occuring humidity is generally associated with clouds which lowers temperatures in relatively warm and dry areas like this.
That was my guess. I notice the same thermal effects here on the western slope of Colo. that Cristy mentioned re: higher RH% holding in the heat at night. We often have RH% in the single digits, makes for great summer night relief from the diurnal heat.
Increased nighttime lows is a pretty good indication of humidity. I found some archives of hourly humidity measurements on Weather Underground. Here is January 1, 1988 for example.
There is a link at the bottom that will give you a comma delimited file like this. It should be possible to script a process to collect those files and scrape the humidity data from the html into a spreadsheet.
It looks pretty trivial since there isn’t a lot of formatting other than line breaks.
Also, I noticed that the hourly observations are only from 6am to 10pm but it does list the maximum and minimum daily humidity.
REPLY: Hopefully someone can take on the task of extracting this, I’m overloaded as it is.
Phillip_B, here we have little to no cloud cover when the RH% is very low, which I assumed was causal. 20-30% RH feels very humid here. At 6600ft., an overcast deck is often only another 4-6K ft. higher, not quite clearing the peaks. Cloudy winter nights are always warmer by 20-30 degrees or more.
So does Hansen just role a dice for each station?
“On a 1-3 I’ll adjust it upwards, on 4-5 I’ll adjust it downwards, on a 6 I’ll make yet another press release about being muzzled.”
Steve K, I grew up cool and damp Britain where high humidity plus clouds equalled mild temps, at least in the winter.
The effect I’m describing occurs in warm/hot relatively dry areas like Western Australia. The relative humidity is around 40% to 50% on what I call clear sky humid days when temps go over 40C. Not high humidity by the standards of cool damp places like Britain.
There was a rather dramatic example this summer of cool weather resulting from cloudy humid days. Kalgoorlie about 500Ks from the coast is normally hot and dry in the summer. Typical summer day around 40C. An area of cloud and rain moved in and for 3 days temps didn’t go above 16C. Now this was in the middle of summer.
Anyway the thing about irrigation is it primarily occurs in warm/hot dry climates and seasons. Producing near ground humidity under clear skies. People don’t irrigate when its raining or its cold.
I tried to crunch some numbers to quantify the effect globally but it needs someone with better physics than me.
Some basic principles here. Relative humidity is a measure of the water content of the air with respect to the maximum water content the air can hold at the ambient temperature. What really matters is the absolute water content which is given by the dew point. That largely determines the Enthalpy of the air KJ/kg (or BTU/lb)and is much more significant than the air temperature since the heat content of dry air is roughly 1KJ/kgDegC whereas that of the water vapour is more than 2000 times larger. That is why a warm, still day at the coast is uncomfortable compared with a hot day in the desert. The lower the enthalpy of the air, the easier it is to lose heat to it. 40C dry day air probably has comparable enthalpy to the 16C cloudy day. 50% RH at 50C is unbearably uncomfortable. You can cool a lot of hot dry air with not much water but the heat content and the perception of warmth may not change much. The humid air from irrigation will not lose temperature as quickly as dry air. Water vapour is very important in terms of heat content and thermal lag. And of course the water vapour content is related to the so-called greenhouse effect. Moral: Enthalpy is more important than temperature but not as easy to ‘adjust’!
Slightly OT, but has anyone noticed this?
Roy Spencer on Climate Sensitivity:
http://www.weatherquestions.com/Climate-Sensitivity-Holy-Grail.htm
Very interesting stuff around trying to disentangle short-term and long-term relationships between temperature and cloud cover – in both directions.
One of Spencer’s conclusions is that if he’s right about low sensitivity the 1’C of warming we’ve seen (which may be subject to argument in any case) must be largely due to some other factor – and he seems to be suggesting cloud cover changes…
Early in the paper he says that a decrease in low clouds will increase warming. This rings one big alarm bell for me:
Deforestation
From my Permaculture days I understand that tropical forests create their own low clouds by raising humidity through evapotranspiration and providing nucleation points in the form of bacteria. Hence would not massive deforestation reduce low cloud cover and hence increase temperature?
The only fly in this ointment is that most tropical deforestation is in the Southern Hemisphere, which has seen less warming than the NH:
http://www.woodfortrees.org/plot/hadcrut3vnh/mean:60/plot/hadcrut3vsh/mean:60
Also the idea that low clouds decrease warming is the opposite to what you’re talking about, so I may have to think about this some more!
Cheers
Paul
This post is mostly repititive of one that I posted several months ago, but in my visits to Las Vegas over the decades, it seems like the temperatures are hotter there now. The last time I was there, I noticed how green the surrounding land had become — there is a lot of grass and vegetation being grown now in the area, and that grass is being watered daily.
Crosspatch: “Also, I noticed that the hourly observations are only from 6am to 10pm….”
If this is true then the coolest temperatures of the night are not recorded and omitted from the average. Why?
Anthony, I don’t know if this was covered in the thread yesterday, but has anyone checked to see if the observation time has changed at the site? You’ve got to be on the right track with the land use investigation, as a “step” in the data almost has to correlate with some sort of systematic change in the data.
REPLY: McIntyre observes that there is a significant TOBS adjustment but that it doesn’t appear relevant. Still, a check is warranted.
Humidity only affects temperature when energy flows into changing the state of the water – not the temperature of the air.
When air cools, the temperature will drop until the humidity reaches 100% and then heat will flow out of the water vapor and into the air as the water vapor condenses. It takes a lot of heat flow out of the air to force the water vapor to condense so the end result is that the nightime low reaches a floor and stays there. The same process will occur in reverse as the air heats up – the water will vaporize keeping the air temp down until it all has vaporized.
Data in 15 minute divisions should show lags in temperature as compared to the pre-irrigation days.
Anthony,
My apologies for posting this item here, but I could not find your email address.
http://blog.snowdonia-active.com/
Very briefly, the blog details the construction of the new restaurant on Snowdon’s summit. On March 18th the construction teams restarted after the winter layoff, but 15th April they were frozen out, much to their surprise and consternation. There are good photographs and the route to the summit is also a great day out. http://www.snowdonrailway.co.uk/news.php
I hope you don’t mind, but it’s another example of the reality of a colder planet.
Regards,
Perry
REPLY: reality bites ‘eh?
There’s more to elevated low temps than water vapor and the Greenhouse Effect. Dew and frost formation releases a lot of latent heat (condensing water releases 540 calories per gram condensed, freezing water releases 80 calories per gram, and in between cooling water releases 1 calorie per degree Celcius).
During humid summer days in New Hampshire we can use the dew point as a starting guess for the night’s low temp, assuming clear sky and light wind. Overnight air temp follows one exponential curve down to the dew point, then a shallower one from there to dawn. On dry winter nights, the longer night and lower water content lets us sail past the “frost point” which is a degree or so warmer than the dew point and end up well below the dew point at dawn.
I should make .gif files, but my home computer should serve them up okay.
Our first 2008 heat wave has a good example of summertime cooling. From dew start to dawn we only dropped 5-8 degrees F. I had more trouble finding a good winter stretch with low dew points and low wind, but this is good and shows about a 10 F drop after frost starts. The dew point change is more striking, 5 degrees in summer, 15 in winter.
Every 20 F drop in dew point requires halving the water vapor content of air, so there’s only about 1/8 the water vapor to work with in my winter graph vs. the summer.
Dew point is a critical measurement of local weather conditions. It’s a pity that USHCN records pretty much don’t include it.
BTW, the summer graph shows an interesting effect. The 93 F temperature cap on June 8 shows vertical mixing. In the morning the sun heats the ground, that heats the air. Eventually the low level air becomes neutrally buoyant and blobs mix up and down. Now the air temperature can rise only if the entire air column warms up, and that takes a lot more heating. The air exchange also allows winds aloft to mix down to the ground, so when the wind picks up I know we may be close to the high temperature for the day. Another rule of thumb I like is to take the air temperature at Mt Washington (6288′) and apply the dry adiabatic adjustment of 1 F per 200′, i.e. about 30 F down to my altitude and use that as what the high temperature for the day might be.
Every adjustment begets another; if the adjustment wasn’t correct it is insufficient or excessive with respect to expectation.
Does any one suppose that the data will ever fit someone’s model or that fiddling with the model will be easiest at every turn?
“Of the heart’s deceipt there is no end; who can understand it?”
Random thoughts:
1) I hate smilies. It’s not funny that Mt Washington is 6288′ above sea level. :-)
2) Perhaps correlation studies between HadCrut/GISS and UAH/RSS should concentrate on high temperatures, not average. Since the satellite data is for “low tropospheric” and ground data is prone to inversions, the high temperatures at ground correlate much much better than low temperatures. Also, low ground temperatures are amazing prone to micro climate effects. Someone here used to report his low temp both at 6′ and and inch or so above his garden. On clear mornings the difference was striking.
Anthony, as you know (but isn’t clear from the above), GHCN (and USHCN) both adjust data e.g. the USHCN time-of-observation adjustment, USHCN SHAP and FILNET adjustments, GHCN has its own adjustment. I think that your point is that none of these adjustments appear to be relevant to the issue at hand, though Tucumcari has a noticeable TOBS adjustment (which doesn’t appear to be material here. )
REPLY: True, and thanks for making this point. When I said “we can rule out adjustments” I was referring to the results on the GISS graph, and yes the GHCN tweaks aren’t relevant to the issue at hand.
Anthony,
The analysis presented at this site is great. I now know how little I knew, when I used to think I knew a good bit. I have a background in engineering sciences.
Keep up the good work. I just hit the tip jar feeling confident that I am getting a better ROI than with my tax dollars. It occurs to me know that this might help me to save a few tax dollars in the future. ; – )
When you came through Asheville, NC I wanted to take you out for a beer but I too was on the road that week. Mabye, next time if you get down this way again.
REPLY: Thanks for the kind words
This is living proof that the proper method is to examine each station and its surrounding past before applying ‘adjustments’ rather than simply writing a ‘generic’ program that adjusts away based on temp readings. Of course, I doubt that Dr Hansen has either the time or the resources to attempt such a task.
I wonder if this would be a good follow on exercise for Anthony’s minions after the sites are cataloged and rated? It would certainly be a fine contribution to the real ‘science’.
REPLY: “I doubt that Dr Hansen has either the time or the resources to attempt such a task.” Well he’s got me and about 400 volunteers, and mounds of metadata freely available now.
What an incredible and thorough job of sleuthing, Anthony!
Here’s the issue I see: How many hours did it take just to understand what’s [probably] going on at this one station? Obviously a bunch.
In stark contrast, Hansen et al can make their claims in a fraction of that time WITHOUT having to support them with appropriate data (sometimes) – or – if they are supported, they can be supported with data that may not have been scrutinized and analyzed as rigorously and thoroughly as those presented here. Perhaps they are, but from what I’ve seen, I don’t have that level of confidence in the data that they use.
Thus, they are always ahead of the curve, so to speak, obtaining the “sound bite,” and by the time sound science is performed, the news media have moved on to another subject. The problem transitions to one of politics, not good science. This is tragic.
Y’all are terrific. Stay encouraged and keep up the good work. I am able to use the information you provide and explain to help counter the misinformation that most of my friends think makes AGW real.
Thanks again!
REPLY: If Hansen would spend more time doing basic science like this metadata investigation and less time giving interviews, he’d be able to discover many of the same things. But he thinks his nightlights alogorith based on 1995 satellite data is relevant and useful. His shortcut may give him more interview time to get over his being “muzzled” but it does little to ensure the quality of his dataset.
Anthony,
You noted the apparent step-wise changes in average and minimum temperature that occured about 2000 and suggest that it may have been caused by irrigation. After scanning the hyperlinked report, I noted that the irrigation project has been ongoing for fifty years but the evaporation rate and annual release have declined substantially since 2001. In fact, there was no release in 2003. It is not apparent to me that this source of water vapor would cause the increase in nightime temerature since 2000. Perhaps the monthly temperature records would provide more insight.
REPLY: I read that too, but also note that 2000 had one of the highest releases ever. The odd thing is though that an increase in temperature minimums not followed by an increase in maximums is the signature for increased water vapor according to Christy. Perhaps alternate sources have been developed. If my livlihood was threatened, the first thing I’d start doing would be to dig a well. It is also possible that due to the increased leakage over the years, a water table has developed that is high enough for plant evapotranspiration. The report mentioned that a good amount of water is lost to that along the delivery routes as plants seek out the leaked water and thrive.
What I really need is acreage under irrigation within a radius.
Without humidity values and cloudcover and type its really hard to say anything useful about AGW one way or the other.
Just because its hot does not mean the Earth is retaining more heat and just because its cool does not mean the Earth is cooling off.
Until we can derive heat capacities and heat flux somehow or find a proxy we are back to square one.
Here is my favorite GISS Arctic step function. The site warmed 3C in one year.
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=222241250005&data_set=1&num_neighbors=1
Paul Clarke:
“Hence would not massive deforestation reduce low cloud cover and hence increase temperature?”
The vast bulk of cloud cover occurs over the oceans, which are hugely more expansive than the forested regions.
Also, the deforestation may seem massive in terms of acreage (and hyped up by the media, etc) but how much is it really, in terms of percentage?
“If this is true then the coolest temperatures of the night are not recorded and omitted from the average. Why?”
Well, that is probably the times that they could get a person to physically make the observations going back into history. Would you like the job of having to take the current relative humidity every hour all night long? Back then it was a manual operation and Tucumcari isn’t exactly a booming metropolis.
But in any case, if one were to construct a spreadsheet of only the times given, since they are consistent over time to the present, any trends in relative humidity should pop out. In other words, if the area is generally more humid now than it was in the past, that change should be visible during the day just as well as at night. So if you select some consistent time period and look at the humidity over time, if Tucumcari is more humid now than it was in the past, you should be able to see it regardless of which part of the day you select as long as you select the same time period across the overall data set.
One thing though. They might irrigate more at night. I have some relatives in Southwestern Utah who grow hay. They run their irrigation at night when there is less loss from evaporation.
REPLY: Same goes here for California, irrigate at night. It just makes good business sense not to lose water to higher daytime evaporation.
Patrick Henry, there are reasonable explanations for the step in the data set from Olenek. Note that not only does the apparent average temperature greatly increase in the late 60’s, but also the variance in the data set. Obviously, there was some sort of methodology, instrumentation, or siting change during that period.
This is why we try to adjust, or homegenize the datasets. That effort may be flawed, but it goes to show that this is a well known problem.
If you look at the Google Maps image of the station, the southern edge of its field is not a road — that’s a canal, with a road parallel to it. Trace that canal to the west and it curves up to the northeast, matching the shape on the tr335.pdf report’s map.
As this is an experimental farm, it is not surprising that they are doing research which involves irrigation. Unfortunately, they don’t have their 1999 and 2000 annual reports online so an overview of their activities during that period is not online.
There are some evaporation figures through 2002 in this report (pdf).
Rereading the comments here made me wonder…maybe there has been a reduction in irrigation recently? Maybe the low temperature jump is due to the farm stopping nighttime irrigation nearby. In addition to any regional trends, did the experimental farm end some irrigation projects in 2000 and has recently been focusing on arid farming research?
Wow. Following all this detail is enough to give me a headache. Surely there must be a more straightforward way to detect if the earth is getting warmer or cooler.
How about satellites that measure the incident amount of energy onto the earth vs the amount reflected/radiated away? This should at least tell us the long term trend, right?
“Surely there must be a more straightforward way to detect if the earth is getting warmer or cooler.”
There is:
Monitor the ocean temperature. It would not be subject to “land use changes” or urbanization and “night lights” is always zero. So far the measurements show no warming, in fact, slight cooling over the past several years.
Despite the fact that I think that Hansen is a lying fraud, I’m going to say that he doesn’t deserve criticism on this one; it looks as if the hom. adjustment is called for. I looked at surrounding sites and did not see the temperature jump in the late 90’s like at Tucumcari and the Tucumcari signal after adjustment looks a lot like the unadjusted signal at surrounding rural sites.
I checked the original observers reports and there was a change in observation time in the early 80’s, so no fraudulent adjustment there like at other sites (ASSUMING THAT IS WHEN STEVE MC NOTED THE TOB ADJUSTMENT).
The slow steady increase in the hom. adjustment is indicative of urban signal, and that would include the irrigation. Plus, most of the increase in temps are in the minimums. Even though water vapor from irrigation doesn’t come to mind when we think of UHI, it seems that the hom. adjustment caught it.
The only other possibilities are the equipment or location moves. MMS shows Min Max and thermograph for temp equipment, not to be confused with the Palmer there for soil temp. Sometimes, especially at forrestry locations, the thermograph is used as back-up on the weekends. There could have been a change in equipment by the observer in recent years. Also, MMS mentiond a local equipment move which may have been the reason for the name change in the late 70’s from Tucumcari 3ne to 4ne that is written on the original observers reports. Either way, the hom. adjustment seems to have picked this up because, once again, the temp records from surrounding rural stations show a signal that is very close to Tumcari’s homoginized signal.
By the way, Anthony, if you speak to the observer at this site again, lettum know that the observer reports are well done compared to most other sites(complete, readable, etc).
So hats off to the GISS team on this one, now I’m gonna call an Inuit Tribal office and see if the boys at GISS can get invited to a barbecue where Polar Bear is on the menue.
Daniel Rothenberg (10:57:54) :
The reason that they make so many adjustments to the datasets is because they took a bunch of crappy weather monitoring stations and tried to make them into a “high quality” climate monitoring network. One word; JOKE!
REPLY: It is important to note that these stations started out as an aid for forecasting on a mesoscale and synoptic level, not as a climate network. The climate portion was secondary. This network has been called into service for climate without anyone doing a hands on inspection first. Karl didn’t do it when he devised USHCN, he only looked at metadata for station moves and length of continuous record. Hansen didn’t do it with his nightlights scheme either.
The microsite conditions have indeed deteriorated at many stations, but at least NCDC is doing something about it by creating the new CRN network and the upgraded USHCN network.
See:
http://www.ncdc.noaa.gov/oa/climate/uscrn/
and
https://wattsupwiththat.wordpress.com/2008/04/29/what-the-modernized-ushcn-will-look-like/
Mike C, while you’re entitled to your own point of view, let’s be very clear on the reality of the situation: these “crappy weather monitoring stations” are actually pretty decent at their job – which is to record the day’s worth of meteorological observations to help forecasters evaluate their work and to better share forecasts and current conditions with the public. They’re not crappy, especially when they’re used for their original purpose.
They were never meant to be a “high quality climate monitoring network.” The nature of the times has forced us to adopt them as the best data available to analyze our recent climate. That doesn’t mean we can’t use them for another purpose, given we understood the nuances of what we’re doing. With a little bit of analysis, we can extrapolate a meaningful climate signal from these weather observation sites. Obviously, there is disagreement on how well that extrapolation occurs, but it’s disingenuous to suggest that there isn’t any valuable data contained whatsoever in these sites data records.
The only joke I see is how easy it is to adopt the records when they support our point of view and discredit them when they refute it. It’s not black and white, and no credible player in the AGW game is attempting to play the temperature record – from ALL sources – as anything but a grayish hue laced with caveats and nuances.
I just did some more looking around the area with Google maps. To the Southeast of the station South of Interstate 40 there appears to be quite a lot of irrigation including many fields that appear to be using pivot irrigation. Those are the ones that I remember running most at night. To the Northwest of the station is another large area under cultivation. I can see “stains” on some of the larger field that are often indications of recent rain or irrigation.
Here is a picture centered roughly on the station location. It appears to have been taken in or around winter as the ground is brown and the shadows are long suggesting a low sun angle.
But if I zoom out one level, I get a picture that appears to have been taken in summer and the areas being irrigated are suddenly very obvious. Again, the station location is roughly in the center of the picture (not where the “A” is, I moved the map slightly to center it on the station).
I would be willing to bet a burger and a coke that the area looked much different in the 1960’s.
“It is important to note that these stations started out as an aid for forecasting on a mesoscale and synoptic level, not as a climate network.”
Exactly. It is almost like adjusting the world standard time according to a global average of people’s wristwatches.
The only fly in this ointment is that most tropical deforestation is in the Southern Hemisphere,
I seriously doubt this is true, not least because most SH tropical forest is in Africa and in regions that have seen the least economic development, but by all means show me data that proves me wrong.
And on the GISS climate network; I sympathise with the people trying to get a climate signal out of this data. But the thing that people should remember is that without the adjustments there is zero 20th century warming (this is in the US data BTW).
Thats not to say these adjustments aren’t justified. TOBS is normally used to justify the adjustments because there is a clear case for a TOBS adjustment. However, the data supporting the size of the adjustment is rather weak and the adjustment could well be too small or too large by a substantial amount (both at individual sites and in the averages). Karl who originated the TOBS adjustment method seems to think it is, or could be, out by about 25%.
Now quibbling about the size of adjustments sounds petty, until you realize that without the adjustments there is no warming and the size of the adjustments determines the amount of warming.
Peter: Percentage deforestation rates vary between 0.5% and 2% per year:
http://rainforests.mongabay.com/deforestation_alpha.html
Remember that Roy Spencer is only looking for a 1% change in global cloud cover over the century.
Philip_B: Amazon Basin; Indonesia?
http://www.srl.caltech.edu/personnel/krubal/rainforest/Edit560s6/www/where.html
But you’re right that there is also significant rainforest in the NH as well; which actually improves the chances of the theory that deforestation could be causing significant warming directly (through cloud cover changes) rather than (or as well as) via CO2.
Given the amount of discussion this one weather station is generating (not to mention all the pothers) is it not possible for someone to write a simple report to circulate to media politicians and opinion formers which can highlight the problematic and subjective nature of the source readings?
PS thanks for all your work – pity I am a layman and struggle to get through it all.
I know AGMis a scam because I know all about Maurice Strong.
More on deforestation => loss of cloud cover => warming:
Mind-blowing stuff on bacterial nucleation of ice:
http://pubs.acs.org/cen/science/86/8615sci1.html
This seems to put the old idea that forests nucleate their own rain on a fairly firm basis. Amazingly, you can look at this as an evolved dispersal mechanism for bacteria!
Lawton, Pielke Sr. and some more UAH folk have shown that deforestation reduces cloud cover, and also cover the reduction in transpiration issue:
http://www.sciencemag.org/cgi/content/abstract/294/5542/584
http://www.rainforest-alliance.org/neotropics/eco-exchange/2002/jan02-2.html
Hence there is direct local warming due to loss of transpiration and reduced cloud cover, which Roy Spencer thinks could be a warming cause.
This all makes the idea of cutting down rainforest to grow biofuels not only wrong in principle but potentially completely counter-productive, even leaving aside the short-term CO2 release from the trees themselves.
One of my concerns, and personal conceits, about any climatic warming that is occurring is connected to this discussion. Has anyone examined the effect of increased water vapor in the atmosphere due to the suburbanization of society? Think about all those green lawns that get watered regularly in areas that used to be desert or “sagebrush” type environments. How many new golf courses are there that have been built in the last forty years, with all that lush green grass and artificial lakes and water hazards? We have been creating new concentrated water sources for the atmosphere in record numbers over the last fifty years.
Could not this increased presence be contributing to the overall heat record due to the storage of heat during those periods when the old environment normally released heat and cooled? High humidity at night is like an additional blanket, holding in heat so that when the warming from the sun begins in the morning, the beginning start temperature is already higher than the historical record.
REPLY: Christy documented this problem in the Sacramento valley due to irrigation, see his paper in this Tucumcari post. Suburban sprawl and lawns should be easy to catch. Find a weather station that has been overwhelmed by lawn bearing suburbanization and look at the data.
Daniel Rothenberg (13:48:58 ) :
Daniel, your entire discussion is transparent rhetoric. Calling the monitoring stations “crappy” was being quite gratuitous on my part. The temperature station siting problems often discussed by this blog only scratch the surface, if that alone isn’t enough. Come on MAN! Barbecues, inversion layers, concrete, wind blocks, air conditioners, jet exhaust, metal roofs, bodies of water, trash incinerators and etc. Just what a growing temperature monitoring network needs in the immediate vicinity! Add that to the multitude of other problems, the least of which is that these thermometers have a degree of accuracy of +/- 2 degrees.
Further, all of these stations have to be signed off by a NWS station manager. Their rationalization for the very poor siting is that they have to compromise with low paid or volunteer coops to get the job done. The result has been that the quality of the stations gets worse over time. And we are to use this network of temperature stations as the foundation of global warming evidence that, in the end, adds 180 billion dollars per year to the bill of the American energy consumer?
“Obviously, there is disagreement on how well that extrapolation occurs, but it’s disingenuous to suggest that there isn’t any valuable data contained whatsoever in these sites data records.”
That is just dabbling with the extreme. It’s not a question of finding ”any valuable data… whatsoever.” The quantity of the usable available data is tiny. Of the 500 + temperature stations surveyed so far, only 11% have sufficient siting quality. In addition, only about 4 % do not have the added problem of urbanization. That is about 20 out of 500. We can’t even find one station per gridcell to do an analysis with even questionable margin of error.
Trevor: “is it not possible for someone to write a simple report to circulate to media politicians and opinion formers which can highlight the problematic and subjective nature of the source readings?”
Yes, that could be done. But if the information is counter to the agenda of the publisher it would never see print. You can give them information but you can’t make them print it.
There is enough around on the Internet here, at Climate Audit, Icecap, and other places for any interested journalist to find and write their own article. Basically what you are asking is for someone to “freelance” an article themselves and submit it to the paper for publication. In most cases that would be counter to the agenda of the publication and would fall on deaf ears.
It is a good idea in my oppinion to upgrade the network, I agree with you on that. But to use the old network as evidence of global warming is a question of now. They are closing down energy producers now. They are preventing the opening of new energy plants now. These are the plants that are meant to keep up with increasing energy demand by a growing population. The result will be an increase in energy costs because they are choking off supply. Upgrading the USHCN or adding a CRN network does not correct te problems being created now..
Anthony,
I mentioned in a post a while ago that I was puzzled by some step-wise changes that I observed in the difference betwen the average and median temperatures for some USHCN stations. I used the monthly data from http://cdiac.ornl.gov. I defined TMED=(TMAX+TMIN)/2 and delT=TAVG-TMED. I calculated the annual values as the 12-month arithmetic mean.
I was surprised to see delT remained quite constant for long intervals followed by abrupt step-wise changes. I was puzzled by the self-evident seasonality of the changes, i.e., a distinct DJF, MAM, JJA and SON pattern that suggested that the monthly data had been adjusted from time to time on a quarterly basis.
I decided to use the same methodology to examine the Tucumcari station data and found a similar pattern. I don’t know how to post an EXCEL plot on your website but I’ll try to briefly explain what I observed. First, there are two apparent step-wise changes in delT: 1914-15 and 1984-85. Second, in 1914-15, a step increase in annual delT=0.54 degF; DJF delT~1.3 degF; MAM delT~0.7 degF; JJA delT~0.2 degF and SON delT~0.4 degF. Third, in 1984-85, a step increase in annual delT=0.18 degF; DJF delT~0.5 degF; MAM delT~0.2 degF; JJA delT~no change and SON delT~0.05 degF. Fourth, I didn’t observe any significant change around 2000.
It appears to me that some algorithm was used to adjust the Tucumcari station data in 1914-15 and again in 1984-85 but it is not apparent to me if TAVG, TMAX, TMIN or all were adjusted.
Having said all of this, the step-wise changes Tucumcari station data are fewer and smaller than in other stations that I have examined.
As I have always thought, we should be measuring heat content not temperature. As a proxy for heat, temperature values do not give the whole picture and are therefore unreliable. It is the heat levels that drive climate. Water content is the important factor so if humidity is ignored then we get the wrong answer to the question of what is driving climate, whether warming or cooling.
Anthony,
Re: “UPDATE 7/1/08 One of our commenters “AnonyMoose” has brought this well done historical weather and climate report by NMSU superintendent Rex Kirksey to our attention:
http://tucumcarisc.nmsu.edu/documents/rr751.pdf”
I note that the TMAX, TMIN and TMEAN data contained in Table A2, A3 and A4 of the NMSU report, Weather Observations at the Agricultural Science Center at Tucumcari 1905–2002, are quite different from the TMAX, TMIN and TMEAN data that I downloded from http://cdiac.ornl.gov. (See my 07/02/08 post above.) The differences are quite significant.
I manually transferred the NMSU data for 1905-1919 to my Excel program to compare it to the USHCN data. There were differences between every data point in the 15-year NMSU and USHCN data set. For example, the differences in TMEAN ranged from -4.12°F to+6.24°F and the average was -0.31°F; the differences in TMAX ranged from -3.14°F to+8.16°F and the average was 0.12°F; the differences in TMIN ranged from -2.89°F to+17.07°F and the average was +0.26°F.
I understand that NCDC adjusts surface station data but their algorithm is not apparent to me. Is there an explanation somewhare that would help me understand the differences between the NMSU and USHCN Tucumcari station data?
REPLY: This is odd, this station has a near perfect attendance record for daily data. I wonder if the differences are resulting from the total set of USHCN adjustments? Truly a puzzle worth exploring.
Re: My13:21:00 post
Whoops! It’s getting late in ME on the 4th … Excuse the typos, i.e., “downloaded” and “somewhere”.
Anthony,
Re: My 04/07 13:21 post on NMSU v. NCDC Tucumcari data
I have now manually transferred the NMSU data for 1905-1949 to my Excel program to compare it to the USHCN data. I have not deciphered NCDC’s algorithm for adjusting the NMSU data but here are a few observations:
1. There are very few missing data in the NMSU data set; OCT and NOV in 1907 and AUG through DEC in 1920. It appears there may have been an instrument problem in 1920 that corrupted some data. It is not apparent to me how NCDC filled these missing data points in their data set.
2. In addition to the missing data points in the 1915-1949 data sets, there are a small number of data points where the differences between the NMSU and NCDC data exceed three STDEVs; the remining differences are less than 3°F.
3. NCDC adjustments to the 1915-1949 data sets a) increased TMAX for each month in the DJF and MAM seasons and decreased TMAX in the JJA and SON seasons, b) increased TMIN for each month in the DJF and decreased TMIN in MAM seasons; and decreased TMIN in the JJA in ~1/2 of the months and SON by ~1/3 of the months, c) increased TMEAN for each month in the DJF and JJA; decreased TMEAN in MAM seasons; and decreased TMEAN in SEP, OCT but increased it in NOV.
4. The details of the NCDC adjustments are beyond the scope of this post but suffice to say, they are relatively small but extensive. If I knew how to include Excel graphics in WordPress, I could illustrate the nature of the adjustments.
5. I don’t see any direct relationship between the NMSU and NCDC data sets.
Transferring data manually is a slow, boring process for someone with my limited typing skills. I’ll try to transfer more NMSU data when I have time but I doubt that I will see many new trends.
REPLY: Check your email for a message from me, Anthony
Anthony,
I have not received your email message. I checked my SPAM filter to see it might have been blocked but found no record of that having occurred.
The GISS data apparently does have some adjustments (or “corrections” as they call it) built into their data. The station graphing at GISS (data.giss.nasa.gov/gistemp/station_data/) does not provide raw data graphing – the basic level is “raw GHCN data+USHCN corrections”. This has the effect of creating more apparent warming at Tucumcari. See the comparison and description of the data at http://www.appinsys.com/GlobalWarming/Tucumcari.htm
This also compares the data from the Agricultural Experiment Station, which matches the NOAA GHCN not the GISS data.
Anthony,
Re: My earlier posts on NMSU v. NCDC Tucumcari data
I have now manually transferred the NMSU data for 1905-2002 to my Excel program to compare it to the USHCN data Although I didn’t expect to see any new trends, I was quite surprised to see significant step-wise differences between TMEAN, TMAX and TMIN in the two data sets in APR 1957 and APR 1981. Alan Cheetham also noted these differences in his 07/08 post.
I would describe the average of these NCDC adjustments of the NMSU data set as follows:
1) TMEAN – 1915-1956: increase ~0.10°F; 1957-1981: decrease ~1.27°F; and 1982-2002: increase ~0.05°F
2) TMAX – 1915-1956: decrease ~0.17°F; 1957-1981: decrease ~2.03°F; and 1982-2002: increase ~0.07°F
3) TMIN – 1915-1956: increase ~0.43°F; 1957-1981: decrease ~0.42°F; and 1982-2002: decrease ~0.01°F
I cannot think of any logical reasons for NCDC making adjustments of this nature to the NMSU data set.
Alan Cheetham, thanks for making me aware of your website. As regards your question about which of the two data sets, GHCN or GISS is correct, according to my analysis of the NMSU/AES data, it compares very closely (~0.05°C) with the right-hand column of your NOAA-GHCN data table. However, it doesn’t match the USHCN data set that I downloaded from http://cdiac.ornl.gov.
REPLY: I dropped you an email on this subject two days ago, would you kindly reply to info [-at-] surfacestations dot org please? I’d like to get a copy of the spreadsheet. – Anthony
Anthony,
I would be pleased to send you a copy of my Excel workbook but I haven’t received your email yet. Also, I can’t find an email adress listed on your surfacesstations website. If I called th4e telephone number listed under contacts there, would I be able to obtain an email address?
REPLY: I’ve sent two emails to you, perhaps the MSN spam filter ate them.
email address is info [-at-] surfacestations dot org
Re: My earlier posts on NMSU v. NCDC Tucumcari data
In my 07/10/2008 post, I described the average NCDC adjustments of the NMSU data set that I observed. Subsequently, I compared the WRCC data set for Tucumcari 4NE (http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?nmtucu) with the NMSU data set. With the exception of a larger number of missing data points (19 vs. 7), The NMSU data matches the WRCC data much more closely than the USHCN data; especially during the interval 1915-2002 where that are no significant change in the monthly, seasonal or annual trend patterns.
I would describe the average of these WRCC adjustments of the NMSU data set as follows:
1) TMEAN – 1905-1914: increase ~0.86; s.d. ~.69°F
2) TMEAN – 1915-1948: increase ~0.07°F; S.D. ~0.19
3) TMEAN – 1949-2002: decrease ~0.03°F; S.D. ~0.06
4) The differences in the 1949-2002 interval are essentially within rounding limits (NMSU data is in tenths of °F; the WRCC data is in hundredths of °F).
Is it possible that the historical surface station data archived at the RCCs more closely represent the “raw” temperature data?