New paper blames about half of global warming on weather station data homogenization

From the told ya so department, comes this recently presented paper at the European Geosciences Union meeting.

Authors Steirou and Koutsoyiannis, after taking homogenization errors into account find global warming over the past century was only about one-half [0.42°C] of that claimed by the IPCC [0.7-0.8°C].

Here’s the part I really like:  of 67% of the weather stations examined, questionable adjustments were made to raw data that resulted in:

“increased positive trends, decreased negative trends, or changed negative trends to positive,” whereas “the expected proportions would be 1/2 (50%).”

And…

“homogenation practices used until today are mainly statistical, not well justified by experiments, and are rarely supported by metadata. It can be argued that they often lead to false results: natural features of hydroclimatic times series are regarded as errors and are adjusted.”

The paper abstract and my helpful visualization on homogenization of data follows:

Investigation of methods for hydroclimatic data homogenization

Steirou, E., and D. Koutsoyiannis, Investigation of methods for hydroclimatic data homogenization, European Geosciences Union General Assembly 2012, Geophysical Research Abstracts, Vol. 14, Vienna, 956-1, European Geosciences Union, 2012.

We investigate the methods used for the adjustment of inhomogeneities of temperature time series covering the last 100 years. Based on a systematic study of scientific literature, we classify and evaluate the observed inhomogeneities in historical and modern time series, as well as their adjustment methods. It turns out that these methods are mainly statistical, not well justified by experiments and are rarely supported by metadata. In many of the cases studied the proposed corrections are not even statistically significant.

From the global database GHCN-Monthly Version 2, we examine all stations containing both raw and adjusted data that satisfy certain criteria of continuity and distribution over the globe. In the United States of America, because of the large number of available stations, stations were chosen after a suitable sampling. In total we analyzed 181 stations globally. For these stations we calculated the differences between the adjusted and non-adjusted linear 100-year trends. It was found that in the two thirds of the cases, the homogenization procedure increased the positive or decreased the negative temperature trends.

One of the most common homogenization methods, ‘SNHT for single shifts’, was applied to synthetic time series with selected statistical characteristics, occasionally with offsets. The method was satisfactory when applied to independent data normally distributed, but not in data with long-term persistence.

The above results cast some doubts in the use of homogenization procedures and tend to indicate that the global temperature increase during the last century is between 0.4°C and 0.7°C, where these two values are the estimates derived from raw and adjusted data, respectively.

Conclusions

1. Homogenization is necessary to remove errors introduced in climatic time

series.

2. Homogenization practices used until today are mainly statistical, not well

justified by experiments and are rarely supported by metadata. It can be

argued that they often lead to false results: natural features of hydroclimatic

time series are regarded errors and are adjusted.

3. While homogenization is expected to increase or decrease the existing

multiyear trends in equal proportions, the fact is that in 2/3 of the cases the

trends increased after homogenization.

4. The above results cast some doubts in the use of homogenization procedures

and tend to indicate that the global temperature increase during the

last century is smaller than 0.7-0.8°C.

5. A new approach of the homogenization procedure is needed, based on

experiments, metadata and better comprehension of the stochastic

characteristics of hydroclimatic time series.

PDF Full text:

h/t to “The Hockey Schtick” and Indur Goklany

UPDATE: The uncredited source of this on the Hockey Schtick was actually Marcel Crok’s blog here: Koutsoyiannis: temperature rise probably smaller than 0.8°C

 =============================================================

Here’s a way to visualize the homogenization process. Think of it like measuring water pollution. Here’s a simple visual table of CRN station quality ratings and what they might look like as water pollution turbidity levels, rated as 1 to 5 from best to worst turbidity:

CRN1-bowlCRN2-bowlCRN3-bowl

CRN4-bowlCRN5-bowl

In homogenization the data is weighted against the nearby neighbors within a radius. And so a station might start out as a “1” data wise, might end up getting polluted with the data of nearby stations and end up as a new value, say weighted at “2.5”. Even single stations can affect many other stations in the GISS and NOAA data homogenization methods carried out on US surface temperature data here and here.

bowls-USmap

In the map above, applying a homogenization smoothing, weighting stations by distance nearby the stations with question marks, what would you imagine the values (of turbidity) of them would be? And, how close would these two values be for the east coast station in question and the west coast station in question? Each would be closer to a smoothed center average value based on the neighboring stations.

UPDATE: Steve McIntyre concurs in a new post, writing:

Finally, when reference information from nearby stations was used, artifacts at neighbor stations tend to cause adjustment errors: the “bad neighbor” problem. In this case, after adjustment, climate signals became more similar at nearby stations even when the average bias over the whole network was not reduced.

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David
July 17, 2012 7:33 am

Bill Illis says:
July 17, 2012 at 6:51 am
This is why the NCDC and BEST like using the Homogenization process.
“The Steirou and Koutsoyiannis paper is about GHCN Version 2. The NCDC has already moved on to using GHCN-M Version 3.1 which just inflates the record even further.”
==================================================================
All please read this as Bill illis is 100% correct. Version 3.1 is far worse. Also, the TOBS comment is spot on.
———————————————————————————————-

Physics Major
July 17, 2012 7:33 am


If I wanted to buy an accurate thermometer, I would leave my calculator at home and take along a thermometer of known calibration. I would buy the one that read closest to calibrated thermometer even if it was the highest or lowest of the samples.

Juice
July 17, 2012 7:39 am

Lucy Skywalker says:
July 17, 2012 at 4:25 am
Very good. Glad to see this peer-reviewed.

It appears to be a seminar presentation. These are not typically peer reviewed. I don’t know about this one, but I doubt it.

cd_uk
July 17, 2012 7:39 am

Victoria
You have me at an advantage I can’t view the Venema et al., 2012 paper only the abstract. Although the abstract does allude to the metric their using to ascertain “better” performance. From the abstract:
“Although relative homogenization algorithms typically improve the homogeneity of temperature data”
Is that not the point of the piece here, essentaiily homogeneity may not be such a good goal as discussed above. So it doesn’t address what is being suggested.
To be honest I don’t know enough about this to make a judgement but it does stand to reason if you’re adjusting data, in order to account for irregular superficial effects such as experimental error, then these should have no influence, or nominally so, on the final result: as many pushed up as pushed down. …unless (and as stated), there is good experimental reasons for the net gain. What is this reason?
As for the general point about statistics. All statistical methods have the their strengths and weakness – none are perfect – but you chose the one (or better many) that address the inherent limitations of the data. It appears that the methodology applied here was not the most appropriate. But then I’m not an expert.

July 17, 2012 7:46 am

Actually, I would expect that unbiased homogenization would result in more temperature trends being decreased, not an equal proportion of increases and decreases (because one of the goals of homogenization should be to erase UHI). So, I would suggest that the temperature record is more corrupt than the authors suggest.

Jimbo
July 17, 2012 7:47 am

Authors Steirou and Koutsoyiannis, after taking homogenization errors into account find global warming over the past century was only about one-half [0.42°C] of that claimed by the IPCC [0.7-0.8°C

And now deduct natural climate variability from the 0.42°C and we are left with……….false alarm. Move along, nothing to see here folks. Enter fat lady.

July 17, 2012 7:52 am

Anthony.
Of course I’ll bring this up today at our BEST meeting. Dr. K is an old favorite. That said, since we dont use homogenized data but use raw data instead I’m not sure what the point will be

cd_uk
July 17, 2012 7:54 am

Sorry Victoria – my spoilling is atroceous 😉
That should’ve been “fare” nor “fair” and “choose” not “chose”.

July 17, 2012 7:56 am

Erm, this wasn’t published or peer reviewed (just presented at a conference). I have no idea how they got the results they did, since simply using all non-adjusted GHCN stations (in either v2 or v3) gets you pretty much the same results as using the adjusted data. Both the Berkeley Group and NCDC have relatively small adjustments on a global level:
http://rankexploits.com/musings/wp-content/uploads/2010/09/Screen-shot-2010-09-17-at-3.20.37-PM.png
http://curryja.files.wordpress.com/2012/02/berkeley-fig-2.png
What is the justification for choosing the specific stations that they did? I could easily enough pick 181 stations that show that homogenization has decreased the trend, but I’d rather just use all the stations rather than an arbitrary subset to avoid bias.
REPLY: I was of the impression that it was “in press” but I’ve change the wording to reflect that. Hopefully we’ll know more soon.
I wouldn’t expect you’d consider any of this, so I’m not going to bother further right now. – Anthony

bubbagyro
July 17, 2012 7:57 am

The paper is available for any and all to see. All of the supporting data is there. I would call this paper, and its presentation at a conference, “Hyper-peer reviewed”. I have had papers accepted in major journals and presented at conferences as well. A “peer-reviewed” paper (I like “pal-reviewed as a better descriptor), maybe has 5 or 6 people reviewing it, and then it is “accepted”. A public paper plus a conference has orders of magnitude more review.
My take from the paper is 50% of cAGW is bogus. The other half comprises data exaggeration to the upside from UHI, plus comparison with extinct station data (stations in rural or remote areas that have been “removed”). So, in my opinion, there was likely no net warming in the 20th century till now.

JayPan
July 17, 2012 7:57 am

Should send such papers to Dr. Angela Merkel. She has warned these days that global temperature could increase by 4°C soon, as her chief climate change advisor, Mr. Schellnhuber, has told her. And many Germans are proud to show the world how to run a de-carbonized economy successfully … one day.

July 17, 2012 7:59 am

Is Industrialization the cause of for AGW or is global warming the creation of Academic Graffiti Man?

July 17, 2012 8:02 am

Is it possible to estimate degree of any human interference (with data, UHI or CO2) with natural change in the global temperature data?
I may have identified a good proxy for natural temperature oscillation totally independent of any climatic factor.
Since it is a natural oscillation with no trend, it was necessary to de-trend the temperature data, I used Northern hemisphere data, as more reliable than the global compound, from 1880-2011 is used:
http://www.vukcevic.talktalk.net/GSCnh.htm
It can be assumed that CO2 effect and UHI may had some or no effect since 1950, but in the de-trended signal from 1880-1998 none could be detected. To the contrary, 1950-1998 period is a particularly good agreement between the proxy and the data available.
Only period of contention is 1998-2011, which would be odd for either the CO2 or UHI to show up so late in the data.
Homogenization ?
Possibly.

Kev-in-Uk
July 17, 2012 8:06 am

Steven Mosher says:
July 17, 2012 at 7:52 am
Does that mean we have to await the definition of raw data? and more importantly who used what version of said ‘raw’ data?

July 17, 2012 8:07 am

John West.
You just calculated the transient climate response. ( TCR) at 1.6.
the ECR ( equillibrium Climate response) is anywhere from 1.5 to 2x higher.
so if you calculate a TCR ( what you did) then you better multiply by 2…
Giving you 3.2 for a climate sensitivity. (ECR)

Alexej Buergin
July 17, 2012 8:13 am

“John West says:
July 17, 2012 at 6:58 am
And allowing for some lag in the system(s) by using a CO2 from several years prior to 2000:
dF = 5.35ln(360/300)=0.97”
If there is a lag, should that not be considered in the number for the beginning (300), too?

July 17, 2012 8:14 am

Physics Major says:
July 17, 2012 at 7:33 am

If I wanted to buy an accurate thermometer, I would leave my calculator at home and take along a thermometer of known calibration. I would buy the one that read closest to calibrated thermometer even if it was the highest or lowest of the samples.

But it might be bulky and you would have to allow some time to reach equilibrium. You’re choice of course.
The cheapie store thermometers should be “unbiased” estimators of temperatures, in the sense that they are just as likely to be too low as too high. So, the average should converge asymptotically to the most accurate (“homogenized”) estimate of the temperature.
So, if one the cheapies was within a half degree or so of that estimate (out of at least a half dozen or more instruments), then I would buy it. Otherwise I’d try another store.
I’ll bet most of the time we’d end up buying the same instrument (or rejecting them all).

observa
July 17, 2012 8:22 am

Congratulations Mr Watts for something elementary and curious you noticed about those whitewashed Stevenson Screens so long ago it seems. The scientific community and the scientific method are deeply indebted to you for refusing to deny the evidence before you, despite all the political pressure to do so.
As for the greenwash and the bought and paid for claque of post-normal political scientists, their day of reckoning fast approaches.

cd_uk
July 17, 2012 8:22 am

Isn’t this all a bit immaterial anyway. The whole thing about measuing the Earth’s average temperature seems crazy to me. Measuring changes on the order of a few tenths of a degree is futile given that the methods employed – if I understand them.
First there is the spatial average. How do you grid temperature data and at what resolution? Do you use IWD mean, natural neighbours, BSpline, Kriging (and which type), declustering (cell vs polygonal) etc.
Then you have to decide whic hprohection system you use or whether you use angular distances but then which geoid do you use.
It’s bias upon bias.
Surely this would be just funny if so much money wasn’t spent on it.

Dave
July 17, 2012 8:28 am

They won’t listen but this is one huge nail in a coffin already crowded with nails. Please don’t mind if I gloat a little!

D. J. Hawkins
July 17, 2012 8:35 am

Johanus says:
July 17, 2012 at 6:49 am
Skeptikal says:
July 17, 2012 at 6:24 am
The data doesn’t need homogenization. If one location is hotter or colder than a neighbouring location, that’s weather. Raw data is the only data that’s worth anything. Once you bend the data out of shape, it becomes worthless.
You’re wrong. The data does need some kind homogenization to correct for inaccurate or poorly situated instruments. We also need it to be able to summarize the weather over larger regions to make predictions and comparisons.
Here’s an example of how you yourself can use homogenization to help guarantee the next thermometer you buy will be more accurate.
Go to a place that sells cheap thermometers (Walmart etc). Normally there will be 5 or 10 instruments on display of various brands. You will immediate notice that they are all predicting different temperatures. Maybe some will read in the mid or low 70′s, some in the high 70′s. There will always be a maverick or two with readings way of into the impossible range.
Which thermometer, if any, should you buy?
Well, it is likely that there are several instsruments in the bunch reporting fairly accurately. Best way to find the most accurate thermometer is to whip out your pocket calculator, add up all the temps and divide by the number of thermometers. (Throw away any obviously bogus readings first, such as a thermometer reading zero.) The resulting average value is most likely to be closest to the “real” temperature.
That is how homogenization works, on a small scale.

Since the temperature in Walmart at the thermometer display area is likely to be, in fact, uniform, your method has some merit. However, in the real world, it’s unlikely that stations separated by even as little as 5-10 miles see absolutely identical conditions. If it’s 75F at “A” and 71F at “B”, the “real” temperature at both of them isn’t likely to be 73F.

Bill Illis
July 17, 2012 8:37 am

Zeke, you need to start pinning all your charts to the last year of data. Normalizing all the lines in the centre of the graph distorts just how much change there is over time.
For example, here is how the US adjustments should be shown.
http://img692.imageshack.us/img692/6251/usmonadjcjune2012i.png
Versus the way you chart it up.
http://rankexploits.com/musings/wp-content/uploads/2012/05/Berkeley-CONUS-and-USHCN-Adj-tavg-v2.png

Lester Via
July 17, 2012 8:39 am

Not being a climatologist or meteorologist, I have little understanding of the logic behind where monitoring stations are placed. I do know from observing my car’s outside temperature indicator that on exceptionally calm, sunny days the indication varies significantly between treeless areas and wooded areas. This variation is minimal, if even detectable at all, on windy or cloudy days.
If the intent of a monitoring station is to measure a temperature representative of the air temperature in the general area in a way that is not influenced by wind speed or direction and cloudiness, then the task is not at all a simple straight forward one.
As a former metrologist, thoroughly familiar with thermometers and their calibration, I would think quality instrumentation was used at monitoring stations, making random instrumentation errors small compared to the variations experienced due to the physical location of the monitoring station. I am guessing that the homogenization process used only corrects for random errors – those having an equal probability of being positive or negative, and will not take out a bias due to physical location.

Don
July 17, 2012 8:43 am

Perhaps I am being too simplistic, but it seems to me that any methodology they come up with is easily tested. Simply pick any number of reporting stations and use them to calculate the temps at other known stations, pretending they didn’t exist.
If your prediction is close, then you are on to something. If it is consistently higher or lower, then your methods are garbage.
What am I missing?

Jeff Condon
July 17, 2012 8:45 am

Anthony,
I hope the Best team does take note. So far I’ve not even received a reply to my concerns. Apparently I need to take their matlab class.
I’m annoyed with it so it is a good thing for them that I don’t have any time.