Spurious Warming in the Jones U.S. Temperatures Since 1973
by Roy W. Spencer, Ph. D.
INTRODUCTION
As I discussed in my last post, I’m exploring the International Surface Hourly (ISH) weather data archived by NOAA to see how a simple reanalysis of original weather station temperature data compares to the Jones CRUTem3 land-based temperature dataset.
While the Jones temperature analysis relies upon the GHCN network of ‘climate-approved’ stations whose number has been rapidly dwindling in recent years, I’m using original data from stations whose number has been actually growing over time. I use only stations operating over the entire period of record so there are no spurious temperature trends caused by stations coming and going over time. Also, while the Jones dataset is based upon daily maximum and minimum temperatures, I am computing an average of the 4 temperature measurements at the standard synoptic reporting times of 06, 12, 18, and 00 UTC.
U.S. TEMPERATURE TRENDS, 1973-2009
I compute average monthly temperatures in 5 deg. lat/lon grid squares, as Jones does, and then compare the two different versions over a selected geographic area. Here I will show results for the 5 deg. grids covering the United States for the period 1973 through 2009.
The following plot shows that the monthly U.S. temperature anomalies from the two datasets are very similar (anomalies in both datasets are relative to the 30-year base period from 1973 through 2002). But while the monthly variations are very similar, the warming trend in the Jones dataset is about 20% greater than the warming trend in my ISH data analysis.
This is a little curious since I have made no adjustments for increasing urban heat island (UHI) effects over time, which likely are causing a spurious warming effect, and yet the Jones dataset which IS (I believe) adjusted for UHI effects actually has somewhat greater warming than the ISH data.
A plot of the difference between the two datasets is shown next, which reveals some abrupt transitions. Most noteworthy is what appears to be a rather rapid spurious warming in the Jones dataset between 1988 and 1996, with an abrupt “reset” downward in 1997 and then another spurious warming trend after that.
While it might be a little premature to blame these spurious transitions on the Jones dataset, I use only those stations operating over the entire period of record, which Jones does not do. So, it is difficult to see how these effects could have been caused in my analysis. Also, the number of 5 deg grid squares used in this comparison remained the same throughout the 37 year period of record (23 grids).
The decadal temperature trends by calendar month are shown in the next plot. We see in the top panel that the greatest warming since 1973 has been in the months of January and February in both datasets. But the bottom panel suggests that the stronger warming in the Jones dataset seems to be a warm season, not winter, phenomenon.
THE NEED FOR NEW TEMPERATURE RENALYSES
I suspect it would be difficult to track down the precise reasons why the differences in the above datasets exist. The data used in the Jones analysis has undergone many changes over time, and the more complex and subjective the analysis methodology, the more difficult it is to ferret out the reasons for specific behaviors.
I am increasingly convinced that a much simpler, objective analysis of original weather station temperature data is necessary to better understand how spurious influences might have impacted global temperature trends computed by groups such as CRU and NASA/GISS. It seems to me that a simple and easily repeatable methodology should be the starting point. Then, if one can demonstrate that the simple temperature analysis has spurious temperature trends, an objective and easily repeatable adjustment methodology should be the first choice for an improved version of the analysis.
In my opinion, simplicity, objectivity, and repeatability should be of paramount importance. Once one starts making subjective adjustments of individual stations’ data, the ability to replicate work becomes almost impossible.
Therefore, more important than the recently reported “do-over” of a global temperature reanalysis proposed by the UK’s Met Office would be other, independent researchers doing their own global temperature analysis. In my experience, better methods of data analysis come from the ideas of individuals, not from the majority rule of a committee.
Of particular interest to me at this point is a simple and objective method for quantifying and removing the spurious warming arising from the urban heat island (UHI) effect. The recent paper by McKitrick and Michaels suggests that a substantial UHI influence continues to infect the GISS and CRU temperature datasets.
In fact, the results for the U.S. I have presented above almost seem to suggest that the Jones CRUTem3 dataset has a UHI adjustment that is in the wrong direction. Coincidentally, this is also the conclusion of a recent post on Anthony Watts’ blog, discussing a new paper published by SPPI.
It is increasingly apparent that we do not even know how much the world has warmed in recent decades, let alone the reason(s) why. It seems to me we are back to square one.
“Josh (08:02:34) :
[…]
In the first sentence he writes “…the science of global warming…” ”
Oh. Now we can call Hansen et.al. global warmologists. al Gore gave his blessing. Nice.
Ivan (20:28:06) :
Kum Dollison (21:47:46) :
aMINO aCIDS iN mETEORITES (00:01:22) :
Kum Dollison (00:48:00) :
aMINO I agree with Ivan & Kum, we really need an answer and it needs to come from Dr Roy.
The Satellite data is showing Record hight values for Janury and Now February this year, it looks almost as if something is incrementally adding the values.
How does the good Dr rationalise that with the current NH weather and his own US results for 2010 in the graph above.
rbateman:
“So, has UAH been calibrated to a raw dataset, or to one of the CRU/GISS datasets which have been subjected to highly questionable alterations which make no sense?”
That is the million dolar question. I have no clue. I am a complete laymen.
Dr Spencer claims that UAH is not calibrated to any ground-based data set. However, if this is so, then it is pretty weird that UAH trend over the USA is 3 times higher than the rural trend as measured by the ground-based thermometers. Is it really possible that the lower troposphere over the USA 48 warmed 3 times as much as the ground? What is the explanation for this? Or maybe the rural network in the USA has some unexplained “cooling bias” that leads to so vast underestimation of the real temperature trend?
*******
Ivan (20:28:06) :
USA 48 RURAL 1979-2009 – WARMING 0.08 degrees K PER DECADE
USA 48 URBAN 1979-2009 – WARMING 0.25 degrees K PER DECADE
USA 48 UAH 1979-2009 – WARMING 0.22 degrees PER DECADE
So: UAH and URBAN WRONG??????
********
As I stated in another thread, sat & surface temps are apples and oranges to some extent. Standard, bare-bones GHG theory says mid-tropospheric temp trends (satellites) should be magnified almost 2X from surface temps.
Bare-bones GHG theory is certainly incomplete, tho.
Robin Edwards (07:48:12) :
“Associated with this is the near total absence of data values that are less then -20 (C).”
Makes you wonder if the missing data is due to thermometers only going to -20(C), as many older thermometers actually were limited, or that it was just too darn cold for someone to either be there or just not wanting to go out for frostbite.
Does anyone know if there’s a daily record (graph) for the temperature at the bottom center, down inside the Great Pyramid at Giza? It would seem that it would be, at the very least, a good “control” temp:-)
Personally, I think that adding up all those local temps around the world and dividing by “x” is too micro. I still think that one (or two) sputniks could do the job better if there were about 50,000,000 kms out and taking a group shot of us here on Water World.
PS: Is it warmer? Is it cooler? Watch the Ice!
Anthony,
You led the way in this. Congratulations: as more research is done into the surface station datasets, you are going to be vindicated. We would not want your reputation to be besmirched by a Nobel Prize – another prize will have to be invented to award those who risk ridicule and condemnation in the quest for scientific truth. You and Stephen McIntyre should be the first recipients.
[quote] wayne (05:22:33) : If you are going to measure how the temperature (energy) is affected in a complex system you measure only at the energy sinks. On the Earth the sinks are the oceans and rural land areas. All other sources of heat, including urban areas, must be dropped from being measured. To add an energy source and then attempt to compensate for its effect only increases the noise in the measurements. It’s rather simple physics. [/quote]
Second your proposition and approach– As a former professional wx observer and as a wx forecaster during the advent of using computers/computer modeling for wx forecasting (1970’s), and now dealing with ground water sampling and remediation, it’s time to move beyond the smoke screens and recognize that, generally speaking, weather is what we experience in the atmosphere — which is a manifestation of the planet’s response towards achieving thermal equilibrium; – climate trends will be better determined by what’s going on with the heat sinks that, ultimately, drive the weather.
UHI is not climate change (in particular, reference to climate zones). It simply means that you will be hotter standing next to a building bathed in direct afternoon sunlight. The climate hasn’t changed, just the temperature where you are standing has changed. The minute you leave that area where the UHI is in affect you will be in a cooler spot, but your overall “climate”, IE the zone you live in, has not changed. To wit, no seed catalog will re-issue it’s climate zone hardiness ratings just because you might grow grapes in town next to that hot building, instead of on your outlying farm. Changes in temperature, or for that matter changes in weather pattern variation, does not mean climate change. Not unless it is severe enough to cause a change in your climate zone rating. An ice aged one-season frozen Astoria with an ice blocked Columbia River, where once stood a temperate climate beach, is a climate change.
Even according to Spencer, it’s still warming, so what’s the point? Glaciers are melting, antarctic ice is calving, and birds and plants are migrating north. Humans are the cause.
Deal with it, wattsupwiththat readers, or risk becoming increasingly ridiculous.
First, thanks to Dr. Spencer for an interesting article.
Regarding my question on calculating daily average:
Thank you, I will consider those “back of the the envelope” ideas. Perhaps build it into my software.
Thank you for the reference! Looks like one has some freedom to explore various scenarios with such frequent recording (every 10 minutes).
I am an engineer too, so I like engineering approaches. Perhaps 3 derived curves might be usefukl in addition to the raw data: a running 24h mean, 3-4 hours average of of the peak day time and similar for the night.
Oh good heavens Mike. Citations? Mechanisms? At least provide links. Otherwise, I can give you links to web pages with colorful pictorial explanations and interactive learning games related to science. You can even have an adult set the difficulty level for you.
I’d like to hear Dr. Spencer explain why January UAH temps had a very large spike which was not seen in GISS temperatures.
rbateman (22:29:27) :
I finished up the semi-rural station of Grants Pass, Oregon.
http://www.robertb.darkhorizons.org/TempGr/GrPass1889_2009.GIF
The years of 2002-4 were a mess, with one of them missing 4 months of data
(good grief !). I used Ashland, Or. to match up the pattern and fill in.
rbateman, I have lived in Grants Pass Oregon, lately. As in, the last year. There are multiple stations available inside of the local land features for the information.
Ashland, is a completely different microclimate 45 miles away, on the side of a mountain, with different wind patterns, its where we go for snow skiing.
Grants Pass is inside of a bowl with high mountains around it, it has its own weather features, do to a lack of air circulation.
The airport of Grants Pass, which is actually in Merlin has a weather station named after it, that is actually about 6-8 miles away from the airport at the top of a mountain pass. A location significantly colder then in town itself.
I wouldn’t use Ashland for Grants Pass.
Grants Pass regularly gets air inversion events. I lived 250ft above Grants Pass, on the side of one of the hills, overlooking town. Our temps changed by staggering levels compared to the valley floor, where my office was.
I could be 5 degrees warmer, or 5 degrees colder, then the valley floor, depending on fog/snow conditions happening inside of the bowl. 250ft was enough to have snow stay for days at my house, or melt in hours on the floor. For example.
Best Regards,
Jack
I don’t think that anyone has drawn attention to Christopher Booker’s latest frontal attack on the IPCC and AGW supporters in general headed:
“A perfect storm is brewing for the IPCC” here on
http://www.telegraph.co.uk/comment/7332803/A-perfect-storm-is-brewing-for-the-IPCC.html
dated 27 February 2010. As usual he is not mincing his words.
For people who have concerns about how a “daily” temperature reading is taken, there is a treasure trove of highly accurate data ( either 5 minute or 1 hour ) from the USHCRN. Every station has three sensors. You can download that data. and compare these measures over time:
1. Average the temperature by integration.
2. Average the temperature by (tmin+tmax)/2
3. Average the temperature by taking 4 measurements as Spencer has.
Then you can compare all three.
Then you can see if the methods have different answers for the computed trends. Chances are that when you average 30 days or so for a month, and 12 months for a year that you will see no substantial difference in the trend over decades.
the issues since at least 2007 have been: the data sources and metadata and the adjustments–adjustments prior to giss or hadcru.
Ivan (08:24:45) :
If the UHI is going up because of increase in building that absorbs heat, then I would suspect changes in the atmosphere that absorb heat (UAH going up), and since the rural dataset is headed down, a lot of that tropospheric absorbtion is not getting down to where we live. We live where it counts, on the ground.
“As I stated in another thread, sat & surface temps are apples and oranges to some extent. Standard, bare-bones GHG theory says mid-tropospheric temp trends (satellites) should be magnified almost 2X from surface temps.”
This is not correct. Two times (or even slightly higher) amplification rate is expected to occur only in tropics, while in the extra-tropical latitudes the trends at the surface and up in the atmosphere should be roughly equal. In the polar regions there should be higher trend at the surface.
So, 3 times higher rate of warming in the atmosphere over the USA than at the surface is inconsistent even with GHG models. Something really big must be wrong with at least one of these data sets.
rbateman (08:21:00) :
There are three main sources of data that go into USHCN. Not so sure about GHCN stuff. I haven’t looked at dailies since 2007 or so when I first got interested in this, anyways, just follow your nose.
SOMEBODY needs to do the big old flowchart on this stuff and make it
a permanent resource for the community of people who want to comment
or investigate stuff. it’s dataset hell.
Quoting:
The three sources of daily observations included DSI-3200, DSI-3206 and DSI-3210. Daily maximum and minimum temperature values that passed the evaluation checks were used to compute monthly average values. However, no monthly temperature average or total precipitation value was calculated for station-months in which more than 9 were missing or flagged as erroneous. Monthly values calculated from the three daily data sources then were merged with two additional sources of monthly data values to form a comprehensive dataset of serial monthly temperature and precipitation values for each HCN station. Duplicate records between data sources were eliminated. Following the merging procedure, the monthly values from all stations were subject to an additional set of quality evaluation procedures, which removed between 0.1 and 0.2% of monthly temperature values and less than 0.02% of monthly precipitation values.
Vuk etc. (07:48:40) :
Carsten Arnholm, Norway (04:36:34) :
“I have just set up my own weather station and record data every 10 minutes. I am guessing that recording only min/max per day or 4 times per day as above might produce different results than averaging 6*24=144 daily values.”
Look this kind of data already exists in good quantities over periods of years. This work is already done.
Just start with hourly data: ( AND LEARN ABOUT TOBS)
http://www.john-daly.com/tob/TOBSUM.HTM
http://www.john-daly.com/tob/TOBSUMC.HTM
hat tip to jerryB who taught me way more than I ever wanted to know about TOBS.
mike roddy (09:14:21) :
Even according to Spencer, it’s still warming, so what’s the point? Glaciers are melting, antarctic ice is calving, and birds and plants are migrating north. Humans are the cause.
I have not observed birds and plants migrating north, but I have observed birds migrating increasingly south.
David Schnare (07:34:16) :
However, the excellent comments made by many on this site suggest to me that we are not going to find a clean, perfect record at any site, no matter how well sited. […The temperature records were not kept to support scientific studies made 100 years later.]
Have to agree, not sure we will ever get some kind of perfect site(s). Who, when the instrumentation was set up, could have known that there would be arguments over a few 10ths of a degree here and there one hundred and fifty years later?
So, we have an imperfect data set. Lucy gets it right in that we need to clean up the “original” data
I cannot see that “cleaning up” will help. All of the original source information should be freely available to all (as far as I can see much is already digitised over at the NOAA). That source should be locked as and when it is digitised. From that point, be you Dr. Jones or “Lucy”, you are all working from one single inviolate source. Under this kind of system you simply “SQL” a sub-set of the raw source and work on it as you will. Given your SQL source and an adequate description of your subsequent actions anybody can replicate your work and your results. End of “conspiracy theories” and end of “you are not qualified to know”.
Until we have that data base, I don’t believe GISS, CRU or NCDC has enough knowledge to discuss the uncertainty surrounding the data, much less any data projections made there on.
Couldn’t agree more. There are always going to be arguments about “Darwin” and “Matanuska”. I still cannot agree with some that the minutiae don’t matter. If there is no confidence in the trend for an individual site what confidence can there be in “regional” or “global” trends? The suggestion (by some) seems to be that it is OK if the “accounts” are fabricated at the “unit level”, we can still trust that the corporate level accounts [and your pension fund] are safe. Makes absolutely no sense (and is illegal in accounting) if it were accounting we were discussing.
Basically, NCDC needs to do more than hit the reset button. It needs to make available in easy to use form all the actually reported data from each station (…)
Its a long slog, but considering the economic consequences of going haphazardly forward, it is worth doing and is doable.
Not sure what else can be done other than “reset” given the current level of trust in “climate science”. The “climate science” community need to think long and hard about openness and reproducibility on an international level and scale. Get with the 21st C. It takes me a couple of minutes to reproduce a graph that Willis (for example) has created to support his post, welcome to the information age.
OK, I’ll give it a shot. Temperature is a lot harder to measure accurately than most people think. The above data sets do not all measure the same temperature. The rural and urban stations are different sets and are affected by different influences. Corrections applied to the data, as we now see, are problematic. If the UAH set mentioned is the satellite data, then it measures temperature at all sorts of different heights in the atmosphere depending on channel. Just try to measure the temperature of a glass of ice water accurately and you’ll get an impression of the problem. It ought to be 0C but is in fact different at various places inside the glass.
Someone on a different thread yesterday said “we are trying to make a silk purse out of a sow’s ear.” Prosaic, but true. The data may never be capable of the sort of resolution people are hoping for and some claim is so. I actually have doubts that the Global Mean temperature is all that useful in the first place.
mike roddy (09:14:21) :
Only an idiot would come on this Science orientated site and make a statement like that.
You obviously need to do some more reading on here.
Your Surfacestations.org link does not work.
“Sorry, the page you were looking for could not be found”
Does that mean it is now gone?
REPLY: No just being retooled to handle a traffic surge.