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.
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A fine extraction of genuine information from an egregiously abused data set. The retro-chilling of old records and The Great Dying of The Thermometers (actually about 1990, they went from ~6000 to ~1600) are so brazen as to defy belief.
At some point they are going to run out of tricks to use to create a warming signal.
Unadjusted data???? Are you crazy? Don’t you go and challenge Dr. Jones et al now. They run this game, sonny, and you’d better get used to it.
Michael. Interesting post. However I have to disagree with the method you have used to handle gaps in the record. By using the average of other stations to sustitute for a missing station when averaging temperatures this assumes that the missing station is essentially at a similar temperature to the others. If its temperature is significantly different, this will introduce a bias – if it is colder, a warming bias, if it is warmer, a cooling bias.
This isn’t the method used by the mainstream temperature records. They base their calculations on comparing each reading from a station against its own long term average over some base period. Then they take the difference between the individual reading and this long term average to calculates the temperature anomaly for that reading on that day. This produces quite a different behaviour when looking at missing readings.
Also, in your post there is no mention of how you handle area weighting of averages. Without this your results will be hugely biased towards the trends in regions where temperature stations are denser.
[snip sorry SkS doesn’t treat people with any sense of fairness – for Example Dr. Peilke Sr. If you want to reference any of your work, your are welcome print it out in detail here, but until SkS changes how they treat people, sorry not going to allow you to use it as a reference. Be as upset as you wish, but the better thing to do is work for change there- Anthony ]
I will be interested in your comments.
To the untrained eye this looks to have two warming cooling cycles in it. The first in the early to mid 20th century, which corresponds with the well attested long hot summer that made the Battle of Britain possible and the second in the late part of the 20th Century which corresponds with the well undertood warming cycle that began in approximately 1974/76 following the 1974 La Nina with a step change in global rainfall.
Just a word of warning though – last week this website was going off the deep end at BEST for using a non-standard period for assessing climate change. Whilst this assessment is useful, it is important that the limitations of the data to fully inform the current debate are fully understood
Have you yourself or do you know of anyone who has asked GISS why particular stations have been discontinued? In Australia there also seems to have been selective removal of some stations. Of course it would be uncharitable to suggest the removals are to tie in with the proposition of global warming but it would be good to get an official answer. It would also be a lot more good if posts like this were publicised in the MSM
Is there an official explanation for why, in the modern era with all the funding available, the number of stations has dropped precipitously?
Many folks here know that for a long time, n’est-ce-pas?
@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. We sceptics sometimes want to sound extremely nuanced etc. simply to be taken serious, and thus, when someone just say the truth we all know just like that, its a great relief.
Here RUTI (Rural Unadjusted Temperature Index) versus BEST global land trend:
http://hidethedecline.eu/media/ARUTI/GlobalTrends/Est1Global/fig1c.jpg
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.
RUTI global taken from:
http://hidethedecline.eu/pages/posts/ruti-global-land-temperatures-1880-2010-part-1-244.php
RUTI will grow stronger and stronger and even though all beginning is tough, I hope everone will help collecting even more original temperature data to me to make RUTI even better.
Tricks: Coastal stations.
One (important) trick from Hadcrut is to use rural coastal stations so that they do have rural data aboard.
Problem is, that coastal stations world wide has around 0,6K more heat trend 1925-2010 than near by non-coastal stations, see
Joanne Nova/RUTI :
http://hidethedecline.eu/pages/posts/ruti-coastal-temperature-stations-242.php
K.R. Frank
I actually find this chilling. I’d counted on an underlying base trend of about .6K/century to give a bit of a leg up to resist the coming downturn. Not to be, apparently!
A possible positive outcome could be that the Cooling freaks out the Alarmists, and they flip over to pushing CO2 emissions to combat it. That will do nothing for temperature, but will unclog the energy generation pipelines and be great for agriculture and silviculture. Maybe even viticulture!
Interesting.
But does it stack up?
Interested to know …
The graph I find most interesting is #2. I have seen elsewhere in many places that the global temps are just not rising (at least not significantly, if at all) in this century. How is it that the GISS records for the US are rising very significantly in the 21st century. It appears to be about as much warming in the last decade as the whole 20th century! (Allowing for some smoothing)
Tom Harley reblogged this on pindanpost and commented: The same result for NW Australia…virtually flat for over 100 years in Broome
I have to disagree. He is using the temperature delta (Δtemperature) to average with other deltas. That makes much more sense than what you have assumed.
That is not to say the technique is not problematic, but it should likely be much more accurate than any method I have seen described in this matter. The nearer the site is, I suspect the better the correlation, regardless of the offset in average temperature.
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.”
Repetitive, all-pervasive lie, if non constantly resisted, in time would appear as containing at least some truth in it. I remember that in the 1980s one could hardly find a person in Russia, however opposed to the regime, who would not believe in some part of the Soviet propaganda. It has been nailed into people’s brains for 70 years, from the womb to the tomb, and only a few were stubborn enough to see it through.
Interesting. You use stations continuously operating in 20th century. What does the graph look like if you include only stations that operated continuously through the 20th century upto the present day? That would indicate any bias in the removal of stations recently.
Michael
I had some stuff you might have been interested in reading but Anthony snipped the link. Interesting, it isn’t the Mod’s here at WUWT snipping this, it is Anthony himself
Look at posts at SkS during May this year or my posts, under my authors name. Unless Anthony snips this as well.
Also, Anthony, if you want to talk about how people are treated, seriously read all the exchanges between SkS and Dr P Snr. Please note the civil tone of it all.
Unless you want to snip this too.
Note that I have copied this post so we can show what you have snipped Anthony
{see these: http://wattsupwiththat.com/2011/09/25/a-modest-proposal-to-skeptical-science/ and http://wattsupwiththat.com/2011/10/11/on-skepticalscience-%e2%80%93-rewriting-history/ and explain why that sort of behavior is OK for SkS How do you justify changing/deleting user comments months and years later? ~mod}
Yikes! You introduce “fiction” into fact. Well, the temperatrue averages are already an artificial construct. One that doesn’t actually represent the time-averaged thermal state of the system being measured. Even the time-average has knobs on it for subsequent use. Didn’t Doug Keenan explain that adequately a couple of days ago?
What is sorely needed is analysis that can cope with “holes” in the data. Gaps. Analysis that doesn’t require invention of data to bridge the gaps. Which is going to be somewhat harder than for homogenised data, but at least one isn’t analysing guesses instead of raw data.
Moreover, what is needed is an understanding of the physical system. A dry-air temperature isn’t sufficient to describe the thermodynamic state; the enthalphy of the short-term climate system.
Unfortunately the adding of all temperatures and dividing by the total number does not actually produce a correct answer.
Many inputs can affect these temperatures between the times of reading which will skew the average without any knowledge that this has happened. A continuous recording, like a barograph for pressure, would be much more accurate. Whether this is done I have no idea.
See:- Does a Global Temperature Exist? Essex, McKitrick and Andresen 2006.
The most notable feature in this graph is not in the temperature but in the station count.
Very true, very alarming, very indicative of manipulation.
The station count, reporting years and monthly data point coverage can be used to generate a monthly GISS Credibility Index for their overall dataset… unfortunately this credibility index started to fall off a cliff in the 1970s and is currently very close to zero.
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.
Totally correct.
The subset of raw data with a very high GISS Credibility Index actually shows a very slight cooling trend in the US during the 20th century.
Please forgive my ignorance if I am being way off here, but I have had this niggling thought for some time that there is something missing when recording a temperature reading alone. I have this sense that there are parameters missing such as humidity, cloud cover and perhaps wind speed/direction that should also be recorded and used collectively when compiling a record. My feeling is that a more complete “measure” would be accomplished, EG an average temp of 15c/70% humidity/45% cloud/20km/hr/NW – in short, a sort of micro-climate record, expanded to regional and then global if that’s possible. This thing with averages of averages of averages of data points (numbers) bothers me. Perhaps my thinking is off so please straighten me out. Thanks
What KPO says above at 2:17 +1
How is it that changing temperature (alone) is being used as a proxy for ‘changing climate’
Its like saying that because jeans are usually blue, all items of blue clothing are made of denim and worn around your ass. (or something like that, yanno wot i meen)
Have the stations gone away, or are the stations still there, but just no longer counted?
It has often struck me as I extend my Carbon Footprint around the globe, that a very interesting, very consistent, and very much available temperature record may well be available. It is the temperature as recorded by planes as they travel.
The temperature, height and time are all constantly recorded. I can see that all we need to add may be the humidity. I guess it may be very low at most cruising heights however.
This may not give us everything, but it would at least give us something we could investigate. The cost of gathering this data should be trivial.
I personally volunteer to help, as long as my expenses are met. Obviously it would be much better to observe from the front of the plane, so only first class tickets will be accepted 😉
I had the same thought. The use of Google for about 20 minutes fixed the ignorance. It is something to do with “wet build temperature” or similar. Basically the relative humidity is taken into account. (I am sure others with infinitely more knowledge can explain or correct me).
If you mean that the ‘temperature’ itself is not a good reading, because what we need to measure is ‘energy’, you have my vote. This view has been expounded on this site often, but I apologise for forgetting by whom. Records of weather such as could would also be extremely useful, IIRC Willis has posted a few essays on the matter.