Temperature averages of continuously reporting stations from the GISS dataset
Guest post by Michael Palmer, University of Waterloo, Canada
Abstract
The GISS dataset includes more than 600 stations within the U.S. that have been
in operation continuously throughout the 20th century. This brief report looks at
the average temperatures reported by those stations. The unadjusted data of both
rural and non-rural stations show a virtually flat trend across the century.
The Goddard Institute for Space Studies provides a surface temperature data set that
covers the entire globe, but for long periods of time contains mostly U.S. stations. For
each station, monthly temperature averages are tabulated, in both raw and adjusted
versions.
One problem with the calculation of long term averages from such data is the occurrence of discontinuities; most station records contain one or more gaps of one or more months. Such gaps could be due to anything from the clerk in charge being a quarter drunkard to instrument failure and replacement or relocation. At least in some examples, such discontinuities have given rise to “adjustments” that introduced spurious trends into the time series where none existed before.
1 Method: Calculation of yearly average temperatures
In this report, I used a very simple procedure to calculate yearly averages from raw
GISS monthly averages that deals with gaps without making any assumptions or adjustments.
Suppose we have 4 stations, A, B, C and D. Each station covers 4 time points, without
gaps:
In this case, we can obviously calculate the average temperatures as:
A more roundabout, but equivalent scheme for the calculation of T1 would be:
With a complete time series, this scheme offers no advantage over the first one. However, it can be applied quite naturally in the case of missing data points. Suppose now we have an incomplete data series, such as:
…where a dash denotes a missing data point. In this case, we can estimate the average temperatures as follows:
The upshot of this is that missing monthly Δtemperature values are simply dropped and replaced by the average (Δtemperature) from the other stations.
One advantage that may not be immediately obvious is that this scheme also removes
systematic errors due to change of instrument or instrument siting that may have occurred concomitantly with a data gap.
Suppose, for example, that data point B1 went missing because the instrument in station B broke down and was replaced, and that the calibration of the new instrument was offset by 1 degree relative to the old one. Since B2 is never compared to B0, this offset will not affect the calculation of the average temperature. Of course, spurious jumps not associated with gaps in the time series will not be eliminated.
In all following graphs, the temperature anomaly was calculated from unadjusted
GISS monthly averages according to the scheme just described. The code is written in
Python and is available upon request.
2 Temperature trends for all stations in GISS
The temperature trends for rural and non-rural US stations in GISS are shown in Figure
1.
This figure resembles other renderings of the same raw dataset. The most notable
feature in this graph is not in the temperature but in the station count. Both to the
left of 1900 and to the right of 2000 there is a steep drop in the number of available
stations. While this seems quite understandable before 1900, the even steeper drop
after 2000 seems peculiar.
If we simply lop off these two time periods, we obtain the trends shown in Figure
2.
The upward slope of the average temperature is reduced; this reduction is more
pronounced with non-rural stations, and the remaining difference between rural and
non-rural stations is negligible.
3 Continuously reporting stations
There are several examples of long-running temperature records that fail to show any
substantial long-term warming signal; examples are the Central England Temperature record and the one from Hohenpeissenberg, Bavaria. It therefore seemed of interest to look for long-running US stations in the GISS dataset. Here, I selected for stations that had continuously reported at least one monthly average value (but usually many more) for each year between 1900 and 2000. This criterion yielded 335 rural stations and 278 non-rural ones.
The temperature trends of these stations are shown in Figure 3.
While the sequence and the amplitudes of upward and downward peaks are closely similar to those seen in Figure 2, the trends for both rural and non-rural stations are virtually zero. Therefore, the average temperature anomaly reported by long-running stations in the GISS dataset does not show any evidence of long-term warming.
Figure 3 also shows the average monthly data point coverage, which is above 90%
for all but the first few years. The less than 10% of all raw data points that are missing
are unlikely to have a major impact on the calculated temperature trend.
4 Discussion
The number of US stations in the GISS dataset is high and reasonably stable during the 20th century. In the 21st century, the number of stations has dropped precipitously. In particular, rural stations have almost entirely been weeded out, to the point that the GISS dataset no longer seems to offer a valid basis for comparison of the present to the past. If we confine the calculation of average temperatures to the 20th century, there remains an upward trend of approximately 0.35 degrees.
Interestingly, this trend is virtually the same with rural and non-rural stations.
The slight upward temperature trend observed in the average temperature of all
stations disappears entirely if the input data is restricted to long-running stations only, that is those stations that have reported monthly averages for at least one month in every year from 1900 to 2000. This discrepancy remains to be explained.
While the long-running stations represent a minority of all stations, they would
seem most likely to have been looked after with consistent quality. The fact that their
average temperature trend runs lower than the overall average and shows no net warming in the 20th century should therefore not be dismissed out of hand.
Disclaimer
I am not a climate scientist and claim no expertise relevant to this subject other than
basic arithmetics. In case I have overlooked equivalent previous work, this is due to my ignorance of the field, is not deliberate and will be amended upon request.
Garrett Curley (@ga2re2t) says:
“I just don’t get it with this site. On some posts (e.g. a recent one by Willis Eschenbach), there’s the argument that skeptics have never doubted that the world is warming. And then this article comes along to doubt that warming. Which is it?”
It depends on what time frame you’re talking about. I’ve no doubt the average temperature of the globe has been rising (with substantial positive benefits!!!) beginning in 1970. This is confirmed by satellite data beginning in 1979. From 1940-1970 the globe was cooling. I’m old enough to recall climate scientists becoming alarmed by possible catastrophic anthropogenic global cooling in the early 1970’s.
The question is whether the last 30-40 years is unusual or not. Roald Amundsen was able navigate the Northwest Passage around 1906 so Arctic Sea Ice extent today doesn’t seem out of line with where it was in the past. Retreating glaciers around the world are constantly revealing human artifacts on the newly exposed ground giving concrete proof that the glaciers have not retreated as far back as they’ve retreated in the past. Greenland today still has a colder climate than when the Vikings were farming it and named it Greenland.
So if there appears to be some dissonance about global warming or not this is why. There surely has been some warming in recent decades but it doesn’t appear to be anything really out of the ordinary compared to other warming episodes in recorded history.
Sometime last winter I was checking the temp for Ottawa, or thereabouts and the weather app was showing a rather warm anomaly. I checked a different source and discovered that the negative sign had been left out.
Garrett Curley says:
“…using any Tom, Dick and Harry analysis just to place doubt on GW (and therefore AGW)…”
You are conflating the widely accepted fact of natural global warming since the LIA with the AGW conjecture.
Excellent analysis. As a Republican voter for 40 years, albeit one now residing in Australia, can I just say I have never seen any evidence of warming. Ain’t no difference between a summer’s day in 1970 and a summer’s day now. I’m not sure why all this business of saying the true skeptics believe the world is warming has come up. There is maybe some regional warming somewhere, but no place I’ve been. I think this site is better when it avoids that bunkum. It’s dangerous.
@Steve C
You write: “Has anyone drawn a graph in which 99% weighting was given to the best of the rural stations, and 1% to the urban?”
RUTI is : “Rural Unadjusted Temperature Index”, and thus the goal is exactly what you look for.
In several areas it is hardly possible to get real rural data, but all possibilities are tried to get best data as possible.
In many areas, there are not long rural stations 1900-2010 uninterrupted, but very often it was possible to look at a larger area where many mostly rural or small-urban sites combined made a VERY solid mostly rural trend for the whole area. This is important beacause many even sceptics believes that a mostly rural temperature index is impossible just because there are not many long rural stations public available.
Check it out:
http://hidethedecline.eu/pages/ruti.php
Thanks for comment.
K.R. Frank
1. “Abstract
The GISS dataset includes more than 600 stations within the U.S. …”
So no worries about Antarctica or the Equator.
2. It isn’t the temperature that’s replaced, but the delta (the little triangle is the delta) of the temperature; i.e., the anomaly. (If I’m reading the formula correctly.)
@Dave Springer
Frank Lansner: “The ONLY difference between the 2 datasets happens in the years 1950-78 (just before satellite data starts) :
BEST adds 0,55 K to the warm trend 1950-78 compared to RUTI.”
Dave Springer:”Hi Frank. I had a look at the graphs and recognized the source of the difference. That’s the infamous Time of Observation adjustment. It’s the biggest upward adjustment they make.”
Thanks for comment. Yes , the time of observsation… its amazing.
So across the world from country to country, from culture to culture, continent to continent then thermometers NOT meant for climate purposes, just to tell people about their local temperatures, we have this synchronous TOBS.
Everywhere, the time of observation has systematically been changed in one direction causing too cold temperature data, that “must” be corrected massively.
If this is mostly TOBS (or similar) then I find it interesting that global warming is not really measured, but is created on the desk.
K.R. Frank
@Michael Palmer
Thankyou again for important work!
I think you would find it interesting to see the MASACRE done to rural stations in Turkey…
“Imagine, that GHCN took all USA rural stations and cut down to 1960-90. Then took smaller cities limited to 1950-90 or 1960-2010, and then only the largest citiest had long datasets 1930-2010 or longer? Sounds impossible? Well this is what is done for Turkey, Bon apetite. ”
http://hidethedecline.eu/pages/ruti/asia/turkey.php
@Dear Anthony… I think you should consider publishing this one, the slaughter of rural Turkish data by GHCN?
– Why a slaughter of rural data if UHI is not important?
(We see similar elsewhere, if interested)
K.R. Frank
In answer to this simple question, Is the earth’s temperature rising? depending upon whom you ask you get the differing responses YES!, NO!, DEPENDS! IT WAS BUT NO LONGER IS! I think I have learned a great deal. Of one thing I am now fairly certain. Nobody knows.
Correct. NOBODY KNOWS.
Lots of people believe different things… and say different things… but in the end: nobody knows.
It is possible that people were beginning to get a handle on the situation back in the 1970s… at that time they said: the world is cooling… so they threw more money at climate research… unfortunately this money was spent on manipulating data and inventing the Global Warming myth… we are no further forward… and none the wiser regarding that specific question… we are actually a whole lot dumber overall regarding climate.
However, we have discovered that the concept of a Global Average Temperature is intangible (and largely irrelevant)… the temperature profile of the Earth is fractal in nature and cannot actually be measured in a meaningful way… additionally Climate and Weather is regional in nature… for example: the occurrence of an El Niña (or La Niña) impacts different regional areas in different ways… therefore one-size does not fit all geographic regions in the same way… warming can be beneficial in one place while it is detrimental in another.
Additionally, it is easy to support the following statements:
1) Global Warming is generally a good thing… more people die when it is cold… living organisms flourish when it is warmer… Additionally, convection dominates the daily energy flows when there is water in the environment. Therefore, the extent of any Global Warming is limited by convection and can only every become a problem for arid and desert locations that remain deprived of water.
2) CO2 is vital for plants and increasing levels of CO2 result in increasing crop yields and overall promote the greening of the earth… again water dominates any greenhouse gas effect that may exist in the atmosphere… and CO2 is basically irrelevant as a greenhouse gas because it only constitutes 0.039% of the atmosphere.
3) The Global Warming myth is just scare mongering… as you say: nobody knows… so just forget about it until you are asked to pay for some crackpot Global Warming scam / scheme… in that case just say NO and walk away – nothing to see here – just another snake oil salesman.
I thoroughly enjoy coming to this site to read the latest on the climate conundrum. I post occasionally under two different names, Disko Troop and Ivor Ward depending on whether I think my ex-wife is watching that day (and whose computer I am using). I have never been subjected to nasty, snarky, rude or mean responses to my potentially all encompassing ignorance of the subjects in question. The only time that I ever witness this kind of ill will here is when an influx of what one might call “the other side” appears somewhat akin to a plague of locusts when they feel that their side has scored some kind of browny point, e.g. the BEST shenanigans I have tried to raise the occasional question in forums such as Dr Schmidt’s and Mr Cook’s and been met with torrents of abuse. I asked why the sea level rise suddenly changed with the advent of sateilites, why the trees in my garden depend on rainfall and amount of sunlight to grow but theirs only depend on temperature. Why temperatures are shown as rising in the arctic by people who guess the data and not by the Danish Uni that has the buoys. Such simple and honest enquiries were met by abusive replies as to my ignorance and propensity for hanging around under bridges and eating Billy Goats Gruff. As a retired mariner once responsible for the safe navigation of the largest ships in the world you can imagine my response should one of these pseudo academics choose to insult me face to face. However, enough said. So I would like to thank Anthony, if I may call you that, for providing this forum with its air of relative civility.
I was responsible for many maritime mobile weather stations We reported every 6 hours, wet/dry/RH. Sea temp, wind,cloud type,height and cover, wind speed, direction, sea ice, sea state, swell direction, etc., and of course our postion to within one or two miles by celestial navigation. Sometimes we would be the only ship reporting in the entire Southern Ocean, and this was in the 60’s and 70’s . Sometimes the only reporting ship within a thousand miles in the North Pacific.
Had I known then how so called climatologists would currently miss-use the data we collected for weather forcasting, I would have thrown every Met instrument on board over the bloody side.
Excellent. I’ve never understood why anyone wasted any time on interpolating, or on urban locations, when so many continuous rural locations are available. It’s just common sense to use good sensors (if available) and totally ignore dubious sensors. In this case good sensors are available, so we should only be paying attention to them.
Here’s the true hippie version:
Since the pebble is an exact replica of the mountain (all serious bong users says so):
600/600 = 1
Now you have just one station to work with, much easier.
Now take that station’s data points and divide every point by itself, then add those together and divide the sum by the number of data points, et voila, you get a smoothed numero uno.
That is called the reference point. However since it is the reference point:
1 = 0
Now all your work has zero points to it.
But that never stops the communist climate hippies, that’s why they’re probably crazed.
As a true hippie you put that shittie zero into the machine-bong to smoke out a result. As everyone knows the result will most likely be zero, so you have to use the bag of tricks attachment to run the resulting zero through the random alarm generator algorithm, seeded by +1..+7 (you probably don’t want to go higher ‘an +7 because then it becomes obvious you’re crazy), and before you know it: OMG! ALARMA! We are doomed! Hand over all your money so we can save you!
There is one part of the AGW theory that I am not skeptical about. That’s the part that says that there is such a thing as “The Urban Heat Island Effect” (UHIE) which is all due to us anthropoids are getting more numerous as we reap the benefits of burning more fossil fuels.
There is no reason, in my opinion, to assume that just because air temperatures have gone up in cities and other urban areas air temperatures in the surrounding areas have gone down in some kind of response – they probably have not as there is no reason for them to do so. However there is absolutely no reason to try to “fiddle figures” in order to wish any UHIE away. That goes for both “warmists and skeptics” alike.
Therefore if for a hundred or so years we measured air temperatures (T) at say 3000 stations, all in rural surroundings, and we are happy to equal the average (T) of those 3000 stations with “the average global T” (15° C), then if, say 100 or so stations have become “urbanized” resulting in each of them experiencing a T rise of say a couple of degrees C each then yes, AGW is happening but only in our paperwork, and is a kind of warming that can only be detected locally. Furthermore the UHIE has got nothing to do with “Back- radiation” from CO2.
If, the trend for the last century (1900 – 2000), in spite of the UHIE, is flat – then that should tell us that, in spite of a couple of “warming spikes” the world outside our windows is getting cooler.
And by the way – now that, allegedly, most skeptical scientists as well as “Real Climate scientists” are pandering to the notion that AGW is due to CO2 back radiation, I am wondering why I still cannot find any actual data that proves it. – Is the “proof” needed yet another “Consensus”?
Quote form the guest post:
“There are several examples of long-running temperature records that fail to show any
substantial long-term warming signal; examples are the Central England Temperature record and the one from Hohenpeissenberg, Bavaria.”
Look at the Hohenpeissenerbg Graph and you see the statement quoted is nothig but belony
http://preview.tinyurl.com/Hohenpeissenberg
….
….
Just like the “Winter start” reoprted for St.Motiz a month ago. Still ROTFLOL
oMan says:
October 24, 2011 at 3:53 am
…. “reducing the complexity of a system such as local weather or (its big sister, integrated over time and space) climate, into a single parameter called “temperature” Also
Jer0me says:
October 24, 2011 at 2:47 am
…”If you mean that the ‘temperature’ itself is not a good reading, because what we need to measure is ‘energy’, you have my vote.”
Yes, both your replies are what I’m getting at. I would however like an “expert” explanation as to why they do what they do, so I’ll do some digging – if anyone here has a quick ‘n easy – thanks.
I have this mental picture of a future 300 year old Mom and Son phone call on Mothers day “ …what’s the climate like there Johnny? “Oh an average of 21C” ????
Ivor Ward/Disko Troop,
That is a fine post. My thanks and kudos.
Dave Springer and Frank Lansner, kudos also. Great comments!
I didn’t mention it in my reply above, but after reading Doug’s comments on the statistics, I put together these notes on the detection of “global warming”. Read the whole thing and tell me what’s wrong with it.
“Pull text”:
Unlike “global temperature”, the enthalpy figure is a real thing.
Plot the real thing over time. look at the graph. Then try to figure out what’s happening in the real world.
Frank Lansner says:
October 24, 2011 at 1:05 am
@Michael Palmer
“At some point they are going to run out of tricks to create a warming signal”
I appreciate very much that you just put it as it is.
—
Those weren’t my words.
Logic is a scary thing!
Anybody investigating the cause of death for all those stations?
Alexander Feht says:
October 24, 2011 at 1:16 am
So, where we have continuous, reliable, non-manipulated data, there is no warming at all. QED
Strange that many “skeptics” seem to have reconciled themselves with the notion that “there was some global warming in the 20th century.”
When Climategate came out, I looked at the Canadian temperature records. There was no trend apparent in the long running Canadian records either.
The obvious question to be asked is why? Why is there a statistical difference between the long running stations and the complete data set? There shouldn’t be.
kim;) says:
October 24, 2011 at 3:03 am
KPO says:
October 24, 2011 at 2:17 am
“This thing with averages of averages of averages of data points (numbers) bothers me. ”
xxxxxxxxxxxxxx
Well said!
—————————-
In the words of the immortal Wills, “It’s models all the way down.”
That would be “Willis”
Ivor Ward says:
October 24, 2011 at 5:54 am
“I was responsible for many maritime mobile weather stations We reported every 6 hours, wet/dry/RH. Sea temp, wind,cloud type,height and cover,”
Hi Ivor. Just curious about how cloud height is determined aboard ship. I ask because back in the 1970’s I was a military meteorological equipment repair tech. I basically had to keep all the weather-related gear at an airport working and calibrated. One of the systems under my care was an old fashioned cloud height indicator that had some spinning floodlights on end of a runway and and receiver on the other end. The height of the cloud was determined by the angle of the transmitting light source when the receiver detected it. All analog electronics (vacuum tubes back in those days) but quite dependable and accurate.
Anyhow, reading your comment I was wondering how this is determined aboard a rolling ship with a baseline too short and likely not equipped with an expensive bulky piecie of gear like I had. I got to thinking about how we estimated the yield of a nuclear weapon (I went through Nuclear, Biological, and Chemical Warfare school) in the field. You basically time how long it takes from the flash to when you hear the sound to get the distance to it. You then measure the height of the mushroom cloud and determine from it’s color whether it was sub-surface, surface, or air burst, then you can use a simple formula to determine the kilotonnage of the weapon and from that you can also determine how close to ground zero you can get and how long you can remain before receiving a sickening (or fatal) dose of radiation.
So anyhow, I figure so long as the clouds observed aboard ship stretched to the horizon and it was during daytime you could measure the angle between horizon and cloud and figure out cloud height that way. Is that how it was done?
So looking at :
– No area weighting – a single station in Montana perhaps hundreds of miles from any other has the same weighting as a pair of stations that might be less than five miles apart. This is a serious biasing of the data used.
– Averaging raw temperatures (which vary hugely over short distances) rather than anomalies (which don’t – a mountaintop and a nearby pass/beach have different raw temperatures, but see roughly the same weather patterns).
– Throwing out 90% of the temperature records, when even a quick examination shows 1/3 of stations with a negative trend, 2/3 with a positive trend, making any conclusions from 10% poorly supported. I’m not surprised by a low trend – I would be equally unsurprised by a huge trend given the poor data treatment.
This article by Michael Palmer really says nothing meaningful, due to bad data handling – it’s like a compilation of “Never Do This” methods stuck together.
Michael Palmer – For the correlation of nearby station anomalies and area weighting, I would recommend Hansen & Lebedeff 1987 (http://pubs.giss.nasa.gov/cgi-bin/abstract.cgi?id=ha00700d), which discusses this issue (and a lot more) in terms of trying to compute these trends.