An analysis of the Central Netherlands Temperature record

Many readers are familiar with the Central England Temperature Record (CET), now Frank Lansner investigates the Central Netherlands Temperature Record (CNT). Long, but enlightening. – Anthony

Guest post by Frank Lansner

Fig 1 The Dutch population do not have access to their raw temperature data before 1951 for the area of their country marked red.

Instead the KNMI has decided only to make available their “CNT”, the Central Netherland Temperature index.

KNMI do make two coastal temperature series available before 1951 (Vlissingen and De Kooy) and then some strongly adjusted temperature series (De Bilt and Maastricht). Finally the Northern Eelde Station that has not been adjusted has been made publically available, fortunately.

To make the “CNT”, KNMI use strongly adjusted versions of their temperature data, let’s start out with Maastricht:

Fig2

Before 1950 close to one full Kelvin of heat has been taken out of data. The strong dive in temperature 1950 has been removed.

Fig3.

Exactly the same occurs for the De Bilt station, the temperature dive in 1950 has been removed.

Fig4

Here the combined adjustments of Maastricht and De Bilt data. The “Central Netherlands Temperature” index is shown here too. It is almost identical to the adjusted De Bilt and Maastricht data.

Fig5.

The raw temperature data from Bruxelles station Uccle obviously confirms the raw versions of De Bilt and Maastricht. Perhaps slightly more urban heat warm trend can be spotted in the Uccle data. But check this:

Fig6.

It does seem that the other Bruxelles station “Bruxelles National” has less warm trend than the Uccle station, suggesting a little UHI in the Uccle dataset?

Fig7.

Let’s move on, the Luxemburg station is available from Crutem3, and the dive in temperature from the late 1940´ies to 1956 yet again is confirmed.

Imagine that KNMI was correct in their adjustments and just by coincidence we have De Bilt, Maastricht, Uccle and Luxemburg showing a HUGE freak error simultaneously in 1950.

Would it take a miracle at this point for KNMI to be correct?

Well, let’s move on.

Fig8.

From GHCN we have Frankfurt-Wiesbaden temperature data again the warmer pre-1950 data are confirmed. In addition the warm peak 1957-62 is stronger in central Germany.

In my previous article on this matter I analysed a row of German stations from raw GHCN V2, check link in the end of the article.

Fig9.

On the site “Tutiempo” you can normally find adjusted data but I checked out this “Ypenburg” station. It appears that in the period approx. 1936-55 there was an Airport in Den Haag called Ypenburg. Tutiempo for some reason holds just a little sequence of apparently raw data from Den Haag Airport:

Fig10.

The Ypenburg station is around 10 km from the coast. The “choice” of years available from Ypenburg is perfect: We have once again confirmed the large dive in temperatures 1949-1956 in Southern Holland.

How about Paris data? They must be available? For some reason NO raw temperature data is available before 1951 from all Northern France. But Paris then?

Fig11.

Obviously we should expect some UHI for a Paris station, but none the less, the Paris Orly data – (although not showing 1956) do confirm the warmer pre 1950 temperatures compared to the 1970-80 level. Also Paris do not support KNMI and their “CNT”.

Fig12.

When I first looked at Paris “raw” GHCN data I was surprised because the year 1949 showed a large temperature dive unlike the other stations in the area. However, the 1949-dive in GHCN Paris L Bourget was contradicted not only by all other stations in the area, but also by the other Paris dataset, Paris Orly, here taken from Tutiempo. Thus, all Northern French raw data are eliminated in GHCN before I 1951, and then the only dataset with a 1949 peak happens to have the data point lowered 2 K.

Fig13.

Before returning to the Netherlands, Finally one more German station from raw GHCN V2, Dresden (for more German and Czhech stations, see the link 2 in the end of the article).

Gemert:

Yet another Dutch temperature station seems to behave wrongly according to KNMI, and then the error happens to take place in 1950, so that KNMI have to lower the pre-1950 temperatures:

Fig14.

This is how KNMI illustrates the corrections done to the Gemert station. They compare to a reference dataset I believe is not public, but that is likely to resemble the CNT.

KNMI explains the changes to the Gemert station:

Gemert had a large break in October

1949, when the station was renovated. In the period 1906_

1949 the record shows a significant positive trend relative to

the reference stations Oudenbosch, De Bilt and Winterswijk.

This trend was likely to have been due to a gradual growth of

the vegetation at this station until the re-instalment in 1949

So, the increasing divergence 1906-1950 with (already adjusted!) De Bilt and more is due to plant growth, and the change 1949-50 is then due to re-instalment, KNMI says.

Fig 15.

Same scenario, this time its Uccle divergence from the KNMI “homogenized” De Bilt data set.

So the Uccle increase in divergence 1906-1950 to the “homogenized” KNMI De Bilt data is also plant growth then? And also a re-instalment in 1949-50 in Bruxelles, perhaps?

But the increase in divergence is even faster 1880-1906 – So plants grew even faster before 1906?

Fig 16. From figure 5 of [1].

1) Its definitely possible that I misunderstand this figure, but as I understand, it shows the divergence between individual stations and then a reference trend based on data from Netherlands? If so, how come al stations show a positive divergence 1940-50? Should a valid reference not be made so that it resembled actual temperatures of Netherlands as much as possible?

2) The Maastricht and De Bilt stations only differ from this reference with around 0,15 K.

As the difference between raw Maastricht/De Bilt versus CNT is around 10 times as much, this suggests that this figure actually show divergence between a reference and already ADJUSTED temperature sets. I’m not sure what scientific value this has.

3) The Eelde divergence is shown lower than the De Kooy divergence. As we will see later, Eelde is roughly 0,5K warmer than for De Kooy before 1950.

So how come they can show De Kooy with a warmer divergence than Eelde?

Again it seems that several data sets have been adjusted before showing divergences in the above illustration.

One more note: The illustration do not show data points for Leeuwarden 1949-55?

Fig17.

Many Tutiempo temperature series I have been able to test against raw data appear to be warm adjusted. But the point here of mentioning Leeuwarden none the less is that we from Tutiempo learn that this data set do exist at least from 1949.

So, why did KNMI only use data from 1955 in their illustration?

Eelde:

Fig18.

In Northern Holland we also have the KNMI data for Eelde and it can be proven unadjusted against NACD V1. Eelde data resembles Leeuwarden data from Tutiempo, and thus the Tutiempo Leeuwarden dataset also appear unadjusted.

In comparison with the previously shown apparently raw datasets, this Northern region with the Eelde and Leeuwarden stations appears to have had a slightly colder period 1930-50 but still around 0,5-0,6 K warmer than the “CNT”.

Coastal temperature stations of the Netherlands.

Fig19

When examining data we know is raw (or have a reason to believe is raw), then only the coastal stations Vlissingen and De Kooy show similarity with the “CNT” temperature trend before 1950.

CNT appear to be the “Coastal Netherlands Temperatures” rather than the “Central Netherlands temperatures”.

Fig20.

We can now estimate coastal and Non-coastal temperature trends for Benelux based on coastal stations De Kooy and Vlissingen and non-coastal stations Uccle, Luxemburg Airport, Eelde, Maastricht and De Bilt. All are raw datasets. Obviously stations Uccle and Maastricht are likely to include some urban heat.

Fig21.

NE USA coastal and Non-coastal temperature trends. Somewhat similar to the Benelux data sets. (taken from link 3: From RUTI Coastal temperature stations.)

Closing comment:

Yes, here and there I cannot be 100% sure which stations are adjusted and which are not.

The issue here is that raw data from KNMI is not just freely available, that would be a lot easier. But since this is not the case, I find it better to try to give the best estimate possible.

Bonus info.

Fig22.

The distance ocean air influence over land is illustrated her for SW Jutland [4]. Most of the ocean effect disappears just around 5 km from the coast (depending on topography also). Therefore the poorest stations to use for land temperature estimation are the coastal stations. However, many hundred kilometres from the coast, still the coastal trends can dominate temperatures on hills and mountains, and sometimes valley stations just downstream from larger mountains.

Links:

1 The creation of a Central Netherlands Temperature, KNMI:

www.knmi.nl/publications/fulltexts/CNT.pdf

2 NW Europe and De Bilt (more details on German stations from raw GHCN V2)

http://hidethedecline.eu/pages/ruti/europe/nw-europe-and-de-bilt.php

3 More on Coastal temperatures.

http://hidethedecline.eu/pages/ruti/coastal-temperature-stations.php

4

http://img.kb.dk/tidsskriftdk/pdf/gto/gto_0047-PDF/gto_0047_69738.pdf

If you have the time, please cut and paste the below temperature data while they are online…

http://www.tutiempo.net/en/Climate/Brize_Norton/36490.htm

http://www.tutiempo.net/en/Climate/Prestwick_Airport/31350.htm

http://www.tutiempo.net/en/Climate/Bremen/102240.htm

http://www.tutiempo.net/en/Climate/Hamburg-Fuhlsbuettel/101470.htm

http://www.tutiempo.net/en/Climate/Hannover/103380.htm

http://www.tutiempo.net/en/Climate/Nuernberg/107630.htm

http://www.tutiempo.net/en/Climate/Koeln_Bonn/105130.htm

http://www.tutiempo.net/en/Climate/Paris-Orly/71490.htm

http://www.tutiempo.net/en/Climate/Szczecin/122050.htm

http://www.tutiempo.net/en/Climate/Gdansk-Rebiechowo/121500.htm

http://www.tutiempo.net/en/Climate/Gdansk-Rebiechowo/121500.htm

http://www.tutiempo.net/en/Climate/Koebenhavn_Kastrup/61800.htm

http://www.tutiempo.net/en/Climate/Bruselas_Bruxelles_National/64510.htm

http://www.tutiempo.net/en/Climate/Leeuwarden/62700.htm

http://www.tutiempo.net/en/Climate/YPENBURG_NAFB/62000.htm

http://www.tutiempo.net/en/Climate/Guernsey_Airport/38940.htm

PLEASE GIVE ME A TIP IF YOU HAVE KNOWLEDGE ON

RAW TEMPERATURE DATA IN WRITINGS ANYWHERE IN THE WORLD

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May 21, 2012 3:38 pm

Ok, more like 0,6 K.

SRJ
May 22, 2012 4:10 am

Metadata for Maastricht is found here:
http://www.knmi.nl/klimatologie/metadata/maastricht.html
Look at the picture from 1939, it is quote clear that the early part of the record suffers from UHI

Berényi Péter
May 22, 2012 3:09 pm

Eh, it’s a bit more than I’ve bargained for. 8.9 GB, 360,951 files so far, still coming.

May 23, 2012 12:48 pm

Mosher,
Are you here for a rel dialog, then im “in” .
You want to play smart and then run away before it gets too hot, I dont want to waste my time.
You have not understood the issues mentioned in this article, its you choice if you want to expand your horizon, just let me know.

Reply to  Frank Lansner
May 25, 2012 10:35 am

Gail
‘Mosher’s job is to defend the adjusted data so do not expect him to ever move from that position.”
wrong. you can go back and look at my comments since 2007. from the start I have had the following issues.
1. UHI
2. metadata
3. Adjustments
4. Uncertainty
rather than attack adjustments as a knee jerk reaction I suggested that one try to understand them, quantify the potential bias/uncertainty they add, and get the code. That’s what I have done. In addition, I’ve argued that investigating a different approach ( slicing records at change points ) will eliminate the need for adjustments. I made this argument. Willis made this argument, a number of us at the Airvent made this argument.
Lets review the record.
1. UHI. to date I’ve found a small positive UHI bias in undjusted raw data ( GHCN daily )
you can see the poster that Zeke and I did at AGU. That work continues
2. metadata. to date I’ve criticized hansen for using nightlights, spencer for using population density. I’ve pointed out errors in station locations and had those errors fixed by the
organizations that compile the data.
3. Adjustments. I’ve looked at TOBS adjustments and station change adjustments. While both
adjustments are justified and accurate, the approaches used do not allow one to propagate
the uncertainty due to adjustment ( standard error of prediction) into the final answer.
That is why I prefer a method like berkeley which uses unadjusted data.
4. Uncertainty. always interested in that.
So contrary to what you say I dont have an interest in defending adjustments. I have a history, a longer history than yours, of investigating the adjustments from the raw data and code perspective. You will find any number of odd things when you do this. That is why I prefer the berkeley approach. In the end what you will find is this. On a global basis it doesnt make a difference whether you
A) use adjustments
B) use raw data.

May 23, 2012 12:54 pm

Peter: Thats odd with the 9 gb file: For ex even the “multi-file” of BEST is just 2 gb… ?!
I really hope you find out somthing, that you can download as much as possible.
Thanks for trying!
K.R. Frank

May 25, 2012 10:56 am

Steven Mosher says:
On a global basis it doesnt make a difference whether you
A) use adjustments
B) use raw data.
Henry says
This is exactly why I decided to look at the differences between measurements, over time,
http://www.letterdash.com/henryp/global-cooling-is-here
I have finished my tables with the 45th weather station
and conclude
\
since 1994/1995 earth is in a global cooling state,
now aproximating 0.1 to 0.2 degrees C or K per annum
I cannot yet say how long the cooling is going to last
– give me some more time and we will figure that one out too –

May 25, 2012 5:11 pm

Mosher, you write:
“That is why I prefer the berkeley approach”
Its not the process thats the issue, its the input data.
Cherry picked periods, cherry picked stations, heavily adjusted data. Therefore even though Berkerley has some good approach, even sharing input data (GOOD!) it is still no better than intput data.
Apropos the present article: Check Belgium and Northern France data raw before 1950 in the multi file. Check the Choice of danish stations.
Dutch stations are mostly the adjusted ones.
(I havent checked (yet) the cases of both adjusted and unadjusted in the multi file, that is, what data then is the result in the later steps, i would guess the adjusted ones takes over?)

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