Guest essay by Frank Lansner
Presentation of the Original Temperatures project.
Contents:
1. Introduction
2. Methods
3. Adjustments of temperature data
3.1. Adjustments: HISTALP – by the Austrian ZAMG
3.2. Adjustments: ECA&D – by the Dutch KNMI
3.3. Adjustments: The BEST project
3.3.1 BEST / Austria
3.3.2 BEST / Denmark
3.3.3 BEST / Hungary
3.3.4 BEST / UHI
3.3.5 BEST prefer unadjusted data
4. Results from original temperature data
1. Introduction
The number of adjustments of temperature data appears overwhelming and often undocumented. Are we facing homogenization of temperature data? Or is it “pasteurization” (= warm treatment) of temperature data?
As a sceptic it is my opinion that we need to know for sure. I therefore started out 18 months ago collecting original temperature data and now I have started presenting the results on www.hidethedecline.eu
I experienced a lack of will from the national meteorological institutes to freely share the tax paid data I asked for. I even had assistance from a large Danish Newspaper to ask the questions for me, send mails etc. I asked for raw data from datasets beginning before 1950, especially the non-coastal stations:
In my analysis of the Czech Republic today I use around 50 stations. The national Czech meteorological institute wanted 3450 EUR for 10 longer datasets (just yearly values).
Data sources: Meteorological yearbooks, statistical yearbooks, World Weather Records, national archives, books, different databases (NACD, NORDKLIM etc.), web sites Tutiempo and more.
The number of existing longer temperature series is large. Even smaller European countries often has around 50-70 longer datasets available. And for example already in 1945 Spain collected temperatures from 500 stations.
In the following I will try to answer these questions:
1) What does original temperature data tell about the climate now?
2) What does original temperature data tell about adjustments in climate science?
Fig 2: You will need some patience if you want to collect original temperature data.
2. Methods
OAS and OAA locations – how geography determines temperature trends.
For all areas analysed (almost 20 countries by now) we see a large group of stations with warm temperatures trends after 1930 (“OAA” stations) but also a large group of stations with very little or no warm trend after around 1930 (“OAS” stations).
The classification of OAA versus OAS simply depends on geographical surroundings.
Fig 3
In the writing “RUTI Coastal stations” (based on GHCN V2 raw) I found that Non-coastal temperatures (blue graph) were much more cold trended from around 1930 than the Coastal trends (red). http://hidethedecline.eu/pages/ruti/coastal-temperature-stations.php
Fig 4
But Non-coastal stations can be divided further into Ocean Air Affected stations (“OAA”, marked yellow) and then Ocean Air Shelter stations (“OAS”, marked blue).
OAS areas thus have some similarities with valleys in general, but as illustrated above, the OAS areas cover a slightly different area than the valleys.
In general I have aimed to find average OAA temperature trends and average OAS temperature trends for the areas analysed. For each country analysed I have made comparison between national temperature trends as published by the “BEST” project and then the OAA and OAS temperature trends from original data. I want to know if BEST data use both the warm trended OAA data and the more cold trended OAS data. In addition, I have made comparisons of ECA&D data versus original for many countries and also HISTALP data versus original.
More info can be found on:
http://hidethedecline.eu/pages/posts/original-temperatures-introduction-267.php
3. Results: Adjustments of temperature data
3.1. Adjustments: HISTALP – by the Austrian ZAMG
Fig 5 The Austrian ZAMG website “HISTALPS” (http://www.zamg.ac.at/histalp) presents their versions of Alpine temperature data online for Austria and several nearby areas. All datasets seem to show a clear warming trend.
Fig6
However, the valley stations in best possible shelter against ocean air (OAS) have all been adjusted by ZAMG to show warm temperature trends.
From Original data we can see, that the cold trended stations (OAS) are in fact in a comfortable majority in the Alpine area and I believe ZAMG should explain themselves.
More examples of HISTALP/ZAMG adjustments from many countries:
http://hidethedecline.eu/pages/posts/original-temperatures-histalp-264.php
More on original Alpine temperature data:
http://hidethedecline.eu/pages/posts/original-temperatures-the-alps-273.php
3.2. Adjustments: ECA&D – by the Dutch KNMI
To evaluate ECA&D temperature data I have so far mostly studied the differences between temperature data from Tutiempo and ECA&D. Tutiempo do not change data after they first publish it. I have this from mail correspondence.
On the other hand, ECA&D frequently adjust their datasets and thus normally, ECA&D represents newer versions than Tutiempo. Therefore the difference ECA&D minus Tutiempo often tells us about the adjustments done lately to the data represented by ECA&D:
Fig 7
ECA&D temperature versions versus Tutiempo versions averaged for each nation.
For most countries analysed, ECA&D temperature data versions have warmer values for temperatures than Tutiempo in recent years. Especially for the years 2010-2012 ECA&D seems to add a lot of heat to data when they adjust.
I will ask some of you to download ECA data from these locations:
http://eca.knmi.nl/indicesextremes/customquerytimeseriesplots.php
http://www.tutiempo.net/en/Climate/europe.htm
Online data can change or disappear any minute…
More on the ECA&D adjusted data:
http://hidethedecline.eu/pages/posts/original-temperatures-ecad-263.php
3.3 Adjustments: The BEST project
The BEST project collects data from different sources often already related to NOAA and NCDC. BEST often present multiple versions/copies of the same longer datasets already used repeatedly in climate science. BEST have not required the large bulk of existing temperature data from the national Meteorological institutes.
Fig 8
For all countries analysed so far, the BEST national data is nearly identical with the coastal trends and the Ocean Air Affected (“OAA”) locations. The data from the Ocean Air Shelter (“OAS”) stations appears to be completely ignored by the BEST project country after country after country. Just as we saw for HISTALP.
3.3.1 BEST / AUSTRIA
Fig 9
Also for Austria BEST closely follow the OAA area station temperature trends; it’s impossible to see that the majority of Austrian stations – the OAS valley stations – have had any impact on the national result from BEST.
3.3.2 BEST / DENMARK
Fig 10 Danish temperature stations used in the “Original Temperatures” analysis.
Red arrows: The BEST project only use longer data series from coastal stations.
In fact, DMI (the Danish meteorological institute) will not share any other long temperature sets with even the Danish population, and DMI claimed not to have the older data we asked for on digital format. I had to dig data up myself. (So now i hold tonnes of Danish climate data in digital format that DMI dont have?)
Blue areas on the graphic above are best sheltered against the dominating western winds of ocean air and they are labelled “OAS” below.
Fig 11
Average of Danish coastal temperature series from original data and then the 5 longer temperature series made available by DMI for the public and climate science including BEST. The blue graph is an average of all Danish OAS areas (all blue areas in fig 9) created from original data.
More on Denmark and South Sweden:
http://hidethedecline.eu/pages/posts/original-temperatures-denmark-and-south-sweden-270.php
3.3.3 BEST / HUNGARY
Fig 12
For the Hungarian Valley (one of the largest OAS area in Europe), the BEST team has used an OAS temperature station “Pecs”. Above, the Pecs temperature trend is shown together with other Hungarian stations. These original data do seem rather homogenous?
Fig 13
None the less, the BEST team adds around 0.7 K of warming to the Pecs data. BEST use a so called “Regional Expectation” for all countries i have analysed, and change original data so they approach these expectations. Best also claim that Hungary as a nation has experienced this warming trend.
More examples of how data from OAS stations has been avoided by BEST, see for example from fig 22 and onwards for German OAS stations:
Erfurt, Halle, Fulda, Kassel, Kaiserslautern, Mannheim, Bamberg, Hamburg, Kiel, Lubeck, Magdeburg, Nurnberg, Ulm, Augsburg, Leipzig, Arnsburg, Torgau, Bayreuth, Brausnchweig, Regenburg, Stuttgart and Darmstadt. (Ok, Hamburg is not an OAS station, but BEST can change data from these too…)
http://hidethedecline.eu/pages/posts/original-temperatures-germany-276.php
I cannot document the fate of all temperature stations used by BEST and this is why I primarily aim to document the adjustments country for country, see more:
http://hidethedecline.eu/pages/posts/original-temperatures-best-265.php
3.3.4 BEST / UHI:
Best claim that UHI plays no role. But remember results for all 11 countries analysed; First BEST first avoids the cold trended stations (by deselecting or warm-adjusting OAS stations) and THEN they compare the remaining warm trended OAA stations with city stations. It is on this basis that BEST concludes that UHI is not an issue in climate data.
Here is how UHI affects “climate” data in real life:
Fig 14. Some Rhein-Ruhr stations illustrated together with some nearby stations. Base period 1900-1920. What flavour of Urban heat warm trend do we want?
3.3.5 BEST prefer unadjusted data
BEST also claim that they prefer unadjusted data over adjusted. So why did they not require the large bulk of unadjusted longer datasets from national meteorological institutes and year books like I did?
Fig15. From the BEST FAQ web site.
BEST adjustments leads to the ignoring of the cold trended stations, the stations from valleys (OAS areas). So is it true when BEST claim not to use adjusted data? The red box above is my suggestion to an update of their FAQ-text. See more in “Original temperature: BEST”.
4. RESULTS FROM ORIGINAL DATA
Fig16
Observed original temperature trends from some stronger European OAS areas. The areas in shelter of ocean air show little or no heating I Europe from around 1940.
Fig 17
By using base period 1961-1990, we see that the OAS temperature datasets shown in fig 16 from different countries in Europe are in fact rather similar. That is, valleys not disturbed much by ocean air winds in different areas of Europe show almost the same signal, the same story.
In general, the warmer years in recent decades appear to have temperatures that resemble the warmer years before 1962.
Fig 18
Recent decades of coastal areas are 0,5-1 K warmer than the 1920-50 warm period.
Fig 19
European Coastal trends versus Land trend from Ocean Shelter Areas.
Fig 20. Land stations in shelter against ocean air show that the warming 1930-60 was rather similar to the warming 1990-2010.
What does the missing warming of areas not much affected by ocean air temperature trends indicate?
My thoughts:
Or alternatively, perhaps the CO2-theory suggests a pattern where land areas with little noise from ocean air trends show no heating after around 1930? Or can the climate “science” very fast produce a paper with such a conclusion?
Fig 21
In the writing “Original temperatures: The Hungarian Valley”, the area in the red circle above was examined. This area is one of the largest and best Ocean Air Shelter areas in Europe. For Astronomic purposes you would put your antenna on a mountain peak, but for observing climate signals as pure and strong as possible you should consider using the valleys or “Ocean Air Shelter” areas to get the strongest and purest climate signal.
Let’s take a look at similar areas in other areas of the world:
Fig 22
In all cases GHCN raw V2 temperature data (shown in RUTI articles) do not show recent temperatures warmer than for example the 1930´ies. In all cases these specific areas represents some of the most cold-trended areas of the respective continents.
For the US MIDWEST, the air masses from the Pacific first have to pass more than a thousand kilometres of mountains and thus the temperature trends in the US Midwest have unusually little noise from ocean air temperature trends.
Fig23
From RUTI USA: The number in each 5×5 grid tells how much warmer or colder the decade 1998-2008 is compared to 1930-40. In many cases, the recent decade is half a Kelvin colder than the 1930´ies.
This illustration is taken from “RUTI: USA”.
http://hidethedecline.eu/pages/ruti/north-america/usa-part-1.php
http://hidethedecline.eu/pages/ruti/south-america.php
http://hidethedecline.eu/pages/ruti/australia.php
I think all in all on the described basis it is fair to conclude that the missing warming in areas in shelter of ocean air is likely to be a global phenomenon. Any protests?
Is it fair then to call the missing warming after around 1930-1940 of areas in shelter of ocean air a global problem for the CO2-theory?
Or do CO2-theory explain why temperature stations in best possible shelter against ocean air winds cannot really show warming after 1930-40?
PS: Please let me know if you have access to original temperature data, we need to expand the database of original temperature data.
timetochooseagain says: “Why does BEST get about the same answer using only publicly available data, as Jones does using non publicly available data”
I don’t think they do get the “same answer”. BEST shows a much more monotonic rise ( 1998 El Nino is barely visible). They do find warming because they start with pre-heated data. but the results look notably different compared to other datasets.
jorgekafkazar says:
January 6, 2014 at 10:26 am
Excellent post, Frank. Diligent, thorough, outstanding.
My understanding is that the TOBS corrections were accomplished with model algorithms, resulting in corrections where in some cases none were needed.
You are correct. Look for Karl Peterson papers.
Alec Rawls says:
January 6, 2014 at 11:08 am
…If it is borne out one of the biggest implications will be for model testing. It will have implications for the sequence of air/land vs ocean warming. CO2 driven models have the air warm first. Cloud modulated models have the oceans warm first.
But what does it all MEAN Basil? ….
>>>>>>>>>>>>>>>>>>>>>
It means the oceans are the dog and the atmosphere is the tail being wagged. Not really surprising given the shear size and mass of the oceans.
Of course that relegates CO2 to a bit part at best, since it is clouds/water vapor that regulate how much energy enter the oceans and how much is deflected.
That the ‘CO2 is the climates control knob’ fantasy has managed to have legs for this long is an incredible piece of propaganda not to mention a testament to the blindness of scientific institutions.
Gail Combs says at 11:19 am
And the summary?…. English is not Frank Lansner’s native language.
>>>>>>>>>>>>>>
To make it clear I intended no criticism of Frank Lasner’s excellent work. I was just hoping for a summary, of which I thank temp for a good one, though I’m still asking any others who want to, to give there versions of a summary. Because it’s that important, to have a concise useable summary(s) of this to replay in various venues.
@Greg: They look pretty close to me:
http://www.woodfortrees.org/plot/best/from:1850/mean:12/plot/crutem4vgl/mean:12
A C Osborn
Thank you so much for your comment!
Yes the whole idea is to start a wave of data sharing, demands to get access to ALL raw data of temperatures. The reason that i “shut up” about this project for so long was that i wanted some kind of granate chock effect when releasing in order to finally get the snow ball roling of demands to see tax paid data everywhere.
Original unhomogenised Netherlands data before 1950 are online, who did you contact?
http://www.knmi.nl/klimatologie/daggegevens/antieke_wrn/index.html
An impressive amount of work. Thank you very much. I will have to devote some time to study this properly.
Stephen Rasey, you write:
“Lansner (main post): Are we facing homogenization of temperature data? Or is it “pasteurization” (= warm treatment) of temperature data?”
Well this little “joke” I as a real nerd has been laughing a little, honestly. It sort of takes the air out of the fine word “homogenization” used for sometimes apparently dirty actions.
Timetochooseagain
@ur momisugly Frank Lansner- I understand that in many places in the world, observation time has long been standardized, so that is probably correct for the most part. As I understand it, however, the place where TOBS adjustment is mostly made is the US, where we *don’t* have standardized time of observation.
I think you have this in reverse.
I understand that the US does now have standardised times, but did not in the past. For the most part the change has been from afternoons to mornings.
How much difference all this makes is, of course, highly debatable. But I think Frank confirms my original statement, that outside the US TOBS is pretty much irrelevant.
It is amazing what happens to theories when examining real/raw data sets vs. data that has had hedonistic adjustments applied. When one is trying to obfuscate reality, or when inventing legend, the first task at hand is to deny access to the facts. It seems to me sequestration of raw data is the equivalent of staging a bonfire using books as fuel.
Alex Rawls: “I can’t begin to fathom how there could be decadal persistence of inland-coastal temperature differences when the air circulates in days. How to get a persistent difference? ”
Land warms quicker than ocean with the same change in radiative input : specific heat capacity being the main reason. That much is not contraversial.
http://climategrog.wordpress.com/?attachment_id=219
I was also doubtful at first since oceans basically rule the climate, but since the difference is only a few tenths of a degree it is not impossible for a small difference to be maintained by an sustained increase in radiative input.
Hans Erren says:
January 6, 2014 at 11:53 am
“Original unhomogenised Netherlands data before 1950 are online, who did you contact?”
No, only certain datasets mostly coastal (Vlissingen, Den Helder, to some degree Groningen) and then some adjusted datasets where the adjusted versions seems to be considered unadjusted?
Fact is, the datasets I asked for in raw format, (primarily those far from the coast and not on hill areas facing ocean winds) They did not deliver. I found them my selves. And I now have much more data from Holland than you can possibly find online.
Hans, I dont aim to harm anyone in person so i dont think i should give you names online.
Eliza says:
January 6, 2014 at 9:54 am
“Ant[h]ony This is probably is one of the most important and scientifically significant posts concerning global average temperatures ever put on this site. I am amazed how little attention has been given it. It should be a top top sticky post for a week at least!!! …”
Ditto Eliza. I have to duplicate and state my agreement, it’s importance is paramount and at the center of what everyone keeps dancing around on the edges.
What exactly “IS” the number you use for each day to computer temperature used in climate science graphs? Is it the average of the day? Is it taken multiple times each day and plotted or averaged? Curious minds want to know. I have no idea.
Stephen Richards says:
January 6, 2014 at 10:22 am
Great effort Franc. huge piece of worK. Oh and it’s “pasteurization”
Ohhh thankyou!! Now I can sleep tonight, brilliant!
daveburton says:
January 6, 2014 at 10:40 am
Paul Homewood wrote, “This only has relevance in the USA, where most stations moved from afternoon to morning recording as a matter of policy.”
I have the 1934 original USA meteorological year book (as 12 monthly wrintings). I can check the TOBS for 1934 if it is of som use? I have not had the “power” to generally reuire all material from the USA.
@ur momisugly Paul Homewood- I should have phrased that better. My understanding is that other countries have *always* had standard observation times, it is *only* the US in which this was not true (a few other countries, too, but to my knowledge none of the countries focused on above (except the US)). And since it *is* now true, in the US that would lead to widespread changes in the time of observation-which would not occur elsewhere since they had standard times to begin with.
Being an old farm kid, “pasture ization ” fits pretty well too.
Especially a bull pasture…
I suspect the nearness of oceans is a key. As one goes inland the effect of the ocean heat is reduced. Instead, the sun provides more of the energy that goes into the temperature data. What we see near oceans are the various ocean cycles, the MOC/THC and the PDO/AMO. To determine if the planet is seeing any effect from increases in CO2 the only data used should be the land data far away from oceans (and land use changes).
Greg,
you write: “could you please provide a link to the “ORI” data files from HISTALP?”
The ORI files are the ORIginal datasets, the ones taken from the original meteorological year books published long before things got political and perhaps criminal.
Anton Eagle, Always nice to speculate a little, but the OAS / OAA differences are not at all a result of UHI on the coast.
In many countries, the coast stations are actually smaller places, a lighthouse often. And the OAA data – the ocean air affected stations far from the coast – are often hill or mountain stations, quite rural.
And this “rural-ness” of OAA stations is used be BEST to claim that UHI is not a problem.
This BEST claim is Just as STUPID as claiming that the emperor is not naked when he in fact is, because “the smart guys says so”.
The BEST adjusted series by Hungary is poor quality: failing breakpoints, unprovoked breakpoints, likely bad correction values (mainly the end of the series of Pécs)
After two years work available the new homogenised climate data series of Budapest!
Homogenised monthly mean temperature series (1780-2013) with UHI (Urban Heat Island), elevation and observation hours correction;
and homogenised monthly precipitation amount series (1841-2013).
Available here: http://www.varaljamet.eoldal.hu/cikkek/climate_budapest.html (hungarian language only)
Monthly mean temperatures [°C]: http://www.varaljamet.eoldal.hu/file/15/budapesttemp1780.txt
Monthly precipitation amounts [mm]: http://www.varaljamet.eoldal.hu/file/35/budapestprec1841.txt
The next station will be Pécs (150 years data series [mean temperature and precipitation amount]). This adjusted series will be complete likely in Spring 2014.
This work is so important it should be given its own sidebar for easy reference and for those coming into this site for the first time.
S Mosher if you don’t try and use all the data that is available(or don’t even try to get it!) then your results are not much use really!
Simple as that, Use all the excuses you want, but they are just not valid!