Guest Post by Willis Eschenbach
Anthony Watts has posted up an interesting article on the temperature at Laverton Airport (Laverton Aero), Australia. Unfortunately, he was moving too fast, plus he’s on the other side of the world, with his head pointed downwards and his luggage lost in Limbo (which in Australia is probably called something like Limbooloolarat), and as a result he posted up a Google Earth view of a different Australian Laverton. So let’s fix that for a start.
Figure 1. Laverton Aero. As you can see, it is in a developed area, on the outskirts of Melbourne, Australia.
Anthony discussed an interesting letter about the Laverton Aero temperature, so I thought I’d take a closer look at the data itself. As always, there are lots of interesting issues.
To begin with, GISS lists no less than five separate records for Laverton Aero. Four of the records are very similar. One is different from the others in the early years but then agrees with the other four after that. Here are the five records:
Figure 2. All raw records from the GISS database. Photo is of downtown Melbourne from Laverton Station.
This situation of multiple records is quite common. As always, the next part of the puzzle is how to combine the five different records to get one single “combined” record. In this case, for the latter part of the record it seems simple. Either a straight linear offset onto the longest record, or a first difference average of the records, will give a reasonable answer for the post-1965 part of the record. Heck, even a straight average would not be a problem, the five records are quite close.
For the early part of the record, given the good agreement between all records except record Raw2, I’d be tempted to throw out the early part of the Raw2 record entirely. Alternately, one could consider the early and late parts of Raw2 as different records, and then use one of the two methods to average it back in.
GISS, however, has done none of those. Figure 3 shows the five raw records, plus the GISS “Combined” record:
Figure 3. Five GISS raw records, plus GISS record entitled “after combining sources at the same location”. Raw records are shown in shades of blue, with the Combined record in red. Photo is of Laverton Aero (bottom of picture) looking towards Melbourne.
Now, I have to admit that I don’t understand this “combined record” at all. It seems to me that no matter how one might choose to combine a group of records, the final combined temperature has to end up in between the temperatures of the individual records. It can’t be warmer or colder than all of the records.
But in this case, the “combined” record is often colder than any of the individual records … how can that be?
Well, lets set that question aside. The next thing that GISS does is to adjust the data. This adjustment is supposed to correct for inhomogeneities in the data, as well as adjust for the Urban Heat Island effect. Figure 4 shows the GISS Raw, Combined, and Adjusted data, along with the amount of the adjustment:
Figure 4. Raw, combined, and adjusted Laverton Aero records. Amount of the adjustment after combining the records is shown in yellow (right scale).
I didn’t understand the “combined” data in Fig. 3, but I really don’t understand this one. The adjustment increases the trend from 1944 to 1997, by which time the adjustment is half a degree. Then, from 1997 to 2009, the adjustment decreases the trend at a staggering rate, half a degree in 12 years. This is (theoretically) to adjust for things like the urban heat island effect … but it has increased the trend for most of the record.
But as they say on TV, “wait, there’s more”. We also have the Australian record. Now theoretically the GISS data is based on the Australian data. However, the Aussies have put their own twist on the record. Figure 5 shows the GISS combined and Adjusted data, along with the Australian data (station number 087031).
Figure 5. GISS Combined and Adjusted, plus Australian data.
Once again, perplexity roolz … why do the Australians have data in the 1999-2003 gap, while GISS has none? How come the Aussies say that 2007 was half a degree warmer than what GISS says? What’s up with the cold Australian data for 1949?
Now, I’m not saying that anything you see here is the result of deliberate alteration of the data. What it looks like to me is that GISS has applied some kind of “combining” algorithm that ends up with the combination being out-of-bounds. And it has applied an “adjustment” algorithm that has done curious things to the trend. What I don’t see is any indication that after running the computer program, anyone looked at the results and said “Is this reasonable?”
Does it make sense that after combining the data, the “combined” result is often colder than any of the five individual datasets?
Is it reasonable that when there is only one raw dataset for a period, like 1944–1948 and 1995–2009, the “combined” result is different from that single raw dataset?
Is it logical that the trend should be artificially increased from 1944 to 1997, then decreased from that point onwards?
Do we really believe that the observations from 1997 to 2009 showed an incorrect warming of half a degree in just over a decade?
That’s the huge missing link for me in all of the groups who are working with the temperature data, whether they are Australian, US, English, or whatever. They don’t seem to do any quality control, even the most simple “does this result seem right” kind of tests.
Finally, the letter in Anthony’s post says:
BOM [Australian Bureau of Meteorology] currently regards Laverton as a “High Quality” site and uses it as part of its climate monitoring network. BOM currently does not adjust station records at Laverton for UHI.
That being the case … why is the Australian data so different from the GISS data (whether raw, combined, or adjusted)? And how can a station at an airport near concrete and railroads and highways and surrounded by houses and businesses be “High Quality”?
It is astonishing to me that at this point in the study of the climate, we still do not have a single agreed upon set of temperature data to work from. In addition, we still do not have an agreed upon way to combine station records at a single location into a “combined” record. And finally, we still do not have an agreed upon way to turn a group of stations into an area average.
And folks claim that there is a “consensus” about the science? Man, we don’t have “consensus” about the data itself, much less what it means. And as Sherlock Holmes said:
I never guess. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts. — Sir Arthur Conan Doyle, A Scandal in Bohemia
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Phil says:
June 24, 2010 at 11:32 am
Phil,
You are right, of course. There probably was only one thermometer. But, as you observed, why the different data sets? Until these things are explained, we should toss the whole thing and start over.
In any event, when looking at the combined datasets from a global perspective, the CO2 induced warming theory collapses.
See http://www.climate4you.com/ClimateReflections.htm#20080927:%20Reflections%20on%20the%20correlation%20between%20global%20temperature%20and%20atmospheric%20CO2
This adjustment process reminds me of an old joke:
A man goes into a butcher shop and orders 5 pounds of ground beef.
The butcher puts some on the scale which then reads 5 pounds. But the customer says; “hey get your thumb off the scale!” The butcher says “oops, looks like its just 3 pounds.” Then the customer says; “now get your other thumb off the scale!” The butcher complies and the scale reading drops to 1 pound. Finally the customer says; “now get your belly off the scale.” The butcher complies again and then looks up sheepishly and say; “well what do you know, there ain’t no meat!”
Maybe there ain’t no temperature anomaly either.
Carrot eater
Willis has shown the UHI adjustments. I have given you the population growth. Only a true believer would say they can be reconciled. I do not know what the UHI adjustment needs to be, as an order of magnitude, but I know that it needs to be a downward one and needs to have grown over the years in proportion to some function – maybe logarithmic, maybe not – of the population, with something in the mix for additional heat-generating energy use in the area. It doesn’t. So it MUST be wrong. It doesn’t have a chance of being right. It’s no good arbitrarily dividing the stations into urban and rural, as each station has its own unique population growth characteristics. When climate scientists start making a serious effort to estimate the effect, rather than indulging in what you call numerology in a spurious effort to show there is no such thing as UHI, then we might have a better global record.
By the way, I think there is global warming, and that anthropogenic CO2 plays a part, but I do not think you or anyone else has a handle on how much.
I think I agree with you David S at 8:30. Not much anomaly.
I dont eat too many carrots but my eye sight is good enough that when I look at the graph presented in the comments above, I dont see a straight line, I see a saw tooth. Since that does not seem to correlate too well with CO2 I think I can assume its natural. OK, so there is a slight rise in the saw teeth but that goes way back to 1910 and I think one could assume that it goes right back to the minimum at the end of the Little Ice Age. That doesn’t correlate with CO2 either so that must be natural too.
“Homogenised” temperature graphs that still show these two natural trends are dishonest. Pure and simple.
So here’s the challenge – Take the temperature graph and remove all the trends that are natural and show me what the residual is that is “unnatural.” Or are we supposed to be stopping natural climate change too now? If thats the case, explain to me again -How, if the two dominant trends are clearly independant of CO2, is stopping CO2 emissions going to turn these trends around?
When I put my geologist’s hat on, I find that this religious fevour to stop species extinction, stop sea level rise, stop climate change, and , I suppose,ultimately stop plate techtonics is, well, silly. Its as if the current moment is so special because it is graced with our own presence that it must be preserved in tact for the future to enjoy too. Geology just doesn’t work like that.
AC of Adelaide (11:04pm):
You’re spot-on in pointing to strong, low-frequency, natural variations (evident in century-long temperature records from many warm- and temperate-climate stations) as counterindications of inherent linear trends. But linear regression is just about all the analytic expertise in data analysis that most climate scientists posess. Thus they fit linear trends opportunistically to woefully short stretches of record and pretend that they are secular features. Little do they realize how volatile multidecadal trends really are when the power spectrum of the temperature signal is dominated by multidecadal- and centennial-scale oscillations. And when there are substantial uncertainties in datum-level, as with stitched-together records, linear trends become particularly suspect as a metric, because they are quite sensitive to data values at both ends of record. Linear trends fitted over arbitrarily chosen time-intervals are not very meaningful and certainly cannot be projected into the future.
Those who may lack enough protein in their diet fail to understand that “anomalization” of data scarcely provides an antidote to datum uncertainty. While it does not change the apparent trend, the reliability of the anomalies themselves depends on having an ACCURATE ABSOLUTE value of the mean temperature over the base period. Those of us experienced in analyzing station records realize that even genuinely rural stations can show spurious trends due to inconsistent datum levels.
What makes trend-managing homogenization particularly onerous, however, is that in many regions of the world there are no rural records in the GHCN data base. Thus darkly lighted cities, often major ones, become the nominal “rural” stations in GISTEMP analysis. The fact that homogenization of megacities makes little difference in the trends obtained is scarcely evidence of insignificant UHI corruption of the anomaly time-series.
Sadly, where reliable rural station records are quite plentiful, they are being arbitrarily altered in the name of homogenization. USHCN Version 2 is a farce.
Looks like we have two David S’ again. So I’ll go back to being David S the 1st.
[One of the of idiosyncrasies of WordPress; two people can have the same nickname. ~dbs, mod.]
carrot eater says:
June 24, 2010 at 8:54 pm (Edit)
carrot eater, you give an example of two temperature series:
1 1 2 2 1
You say that as far as GISS is concerned, this is the same as
2 2 3 3 2
However, this is only true for the procedure of making area (gridcell) averages. It is definitely not true when combining records at the same site.
This is because when GISS makes area averages, they are concerned (as you say) only with anomalies, and their end result is an anomaly. When they combine records at the same site, however, they do not end up with anomalies — they end up with absolute temperatures.
Which makes their combining of the Laverton Aero most curious. As I pointed out, they end up with a combination that at times gives an intermediate value somewhere in between all available records, and at other times gives a value that is below all of the records.
Since you claim that you know how they do the combining, perhaps you could step through the combination process for the Laverton Aero records, and show us how it is done? Because I’ve tried what I think is their method, and I can’t get an answer that looks like what they end up with.
Finally, as I said, GISS is not using the Australian data from 1910 to 1944 … what is your explanation for that?
carrot eater says”If I’m not misunderstanding you, and this is actually a point of contention, then I would have you work out a simple example of how you think anomalies are calculated. Start with my silly example of
1 1 2 2 1
and
2 2 3 3 2″
Yes, carrot eater, you are misunderstanding me, I think intentionally. Before we get back to the original topic, I’ll reply to your bait-and-switch this way:
I object to climate scientists who see similarity in these two number series:
1 1 2 2 1
and what we have in practise,
1 (maybe 3), 1 (maybe minus 2), 2 (maybe 2 +/- 1), 2 (maybe missing data), 1 (interpolated from point 100 km away).
The discussion is about ACCURACY, not trend. Without accurate point readings, your trend is inaccurate.Now be a good lad and address the topic restated verbatim from above –
“Let’s use Laverton as an example. Please give your ideas on why the original BoM data, as read from the primary records, are suppressed in favour of the adjusted values from 1910 as referenced above.
“Please explain how one can recover from the BoM, the original data, plus the time periods when adjustments were made, plus the magnitude of those adjustments. Please state why there are missing values as I posted above, and how they were infilled.”
To which I might add, how accurate is an infill of missing data?