Readers may recall I wrote in this article about my family memorial day excursion to Crater Lake:
Crater Lake happens to have a USHCN weather station, and it is one of the few stations that GISS excludes (they have an exclusion code for it in their software Mosher located some time ago).
Steve Mosher commented in detail about that:
Thanks for remembering that Anthony.
In addition to crater Lake there were 4 other northern california stations that GISS had removed from the data. In the paper Hansen merely says this:
‘
The strong cooling that exists in the unlit station data in the northern California region is not found in either the periurban or urban stations either with or without any of the adjustments. Ocean temperature data for the same period, illustrated below, has strong warming along the entire West Coast of the United States. This suggests the possibility of a flaw in the unlit station data for that small region. After examination of all of the stations in this region, five of the USHCN station records were altered in the GISS analysis because of inhomogeneities with neighboring stations (data prior to 1927 for Lake Spaulding, data prior to 1929 for Orleans, data prior to 1911 for Electra Ph, data prior of 1906 for Willows 6W, and all data for Crater Lake NPS HQ were omitted), so these apparent data flaws would not be transmitted to adjusted periurban and urban stations. If these adjustments were not made, the 100-year temperature change in the United States would be reduced by 0.01°C”
Well, I wanted to see the analysis, the code, that was used to make this determination that these stations were flawed. Gavin basically said the paper documented everything, but these words don’t tell me HOW it was done. It just says THAT it was done. Any way that was pretty much why I wanted the code released. When it finally was released, you will see that there is no analysis supporting the removal of these stations. Upon inspection you can see some flakey stuff with the stations, but I was looking for math that quantified the flakiness. In the end, these were excluded by hand.
The argument of course is that including them or excluding them amounts to a tiny difference. That argument never held much water for me. The question, in my mind, was how many other flakey stations were there and was there math that could detect it? I think thats a good question. It doesnt make me doubt the record, I just think its a good question.
===============================================================
I agree. I don’t know that Crater Lake data is flawed or “flakey”, it just may reflect the snow pack hanging around longer, creating a cool bias into summer. With the snow pack as heavy as it is this year, it will be interestign to see if that has an effect of suppressing mean temperature.
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I am repeating an earlier question that I did not get an answer to.
Has anyone done a study of data from excluded stations to see what the effects of their inclusion would be? If not, why not? Wouldn’t that discredit the AGW movement if it can be shown that they are up to shenanigans (which we all know they are) by pointing to hard data?
Thanks guys.
Imagine a map maker discarding anomalously low topographic data points in a given region. Hikers following his ‘adjusted’ map would fall into craters, crevasses, and gorges. Or what if a hydrographer filtered out anomalously shallow data from the middle of the oceans? Submarines using his ‘adjusted’ map would crash into mid oceanic ridges and sink.
Anomalous data almost always indicates something important that needs to be investigated further. It should never be discarded or ignored until it is fully understood.
Hansen’s Rules
If the climate model doesn’t show Global Warming – modify the model.
If the climate model still doesn’t show Global Warming – modify the results scale.
If the climate model still doesn’t show Global Warming – modify the input data.
If the climate model still doesn’t show Global Warming – delete colder weather stations.
If the climate model still doesn’t show Global Warming – state it does, and call opponents “Deniers”.
Nick should recall that the question I asked was asked in 2007. Upon the release of the code I did in fact check that Crater Lake was removed along with the other station periods mentioned. So, Hansen did tell the truth. He should also be aware that simply because a station is in the INVENTORY does not mean that it isnt removed. One of the first steps in the program is the removal of “strange” stations. So he would actually have to read the code. Actually you have to look at the stations that survive the culling process in the early steps.
In step 0 the file “Ts.strange.RSU.list.IN_full” lists the stations and the station periods that are removed.
In one of the last updates to the program this list changed. Basically with the addition of the F52 version the 5 “strange” california stations were added back IN. But you can still see others that are removed .
122637720000 LAMU lat,lon -2.3 40.8 omit: 1914/07
148628400000 MALAKAL lat,lon 9.6 31.7 omit: 1990/08
148628400000 MALAKAL lat,lon 9.6 31.7 omit: 1991/03
215442140000 UIGI lat,lon 48.9 89.9 omit: 2010/04
223404300002 MADINAH lat,lon 24.6 39.7 omit: 2010/05
303838210000 IGUAPE lat,lon -24.7 -47.5 omit: 1985/04
403718670006 THE PAS,MAN. lat,lon 54.0 -101.1 omit: 1995/05
614028360003 SODANKYLA lat,lon 67.4 26.7 omit: 2010/03
614028690001 KUUSAMO lat,lon 66.0 29.2 omit: 2010/03
614028750001 OULU lat,lon 64.9 25.4 omit: 2010/03
614028970003 KAJAANI lat,lon 64.3 27.7 omit: 2010/03
614029290001 JOENSUU lat,lon 62.7 29.6 omit: 2010/03
614029350003 JYVASKYLA lat,lon 62.4 25.7 omit: 2010/03
614029580001 LAPPEENRANTA lat,lon 61.1 28.2 omit: 2010/03
614029630003 JOKIOINEN lat,lon 60.8 23.5 omit: 2010/03
614029720003 TURKU lat,lon 60.5 22.3 omit: 2010/03
614029740000 HELSINKI/SEUTULA lat,lon 60.3 25.0 omit: 2010/03
501947190020 GILGANDRA POST OFFICE lat,lon -31.7 148.7 omit: 1920/07
651032920010 SCARBOROUGH UK lat,lon 54.2 -.4 omit: 1912/08
651032920010 SCARBOROUGH UK lat,lon 54.2 -.4 omit: 1912/09
623160900003 VERONA/VILLAF lat,lon 45.4 10.9 omit: 1987/04
623161580004 PISA/S.GIUST lat,lon 43.7 161.5 omit: 1987/10
113655550000 BOUAKE lat,lon 7.7 -5.1 omit: 0-1954
115624500010 SUEZ lat,lon 29.9 32.6 omit: 0-1888
140632500000 BARDERA lat,lon 2.4 42.3 omit: 0-1919
150617010000 BATHURST/YUNDUM lat,lon 13.4 -16.7 omit: 0-1939
155674750003 KASAMA lat,lon -10.2 31.1 omit: 0-1932
205526520000 ZHANGYE lat,lon 38.9 100.4 omit: 0-1944
205528360002 DULAN lat,lon 36.3 98.1 omit: 0-1949
205535640010 XINGXIAN lat,lon 38.5 111.1 omit: 0-1929
205538630000 JIEXIU lat,lon 37.1 111.9 omit: 0-1950
205560800020 LINXIA lat,lon 35.6 103.2 omit: 0-1951
205565710002 XICHANG lat,lon 27.9 102.3 omit: 0-1938
205565860010 LEIBO lat,lon 28.3 103.6 omit: 0-1954
205573480000 FENGJIE lat,lon 31.1 109.5 omit: 0-1956
205577990000 JI’AN lat,lon 27.1 115.0 omit: 0-1934
302853650000 YACUIBA lat,lon -21.9 -63.6 omit: 0-1935
315814050000 CAYENNE/ROCHA lat,lon 4.8 -52.4 omit: 0-1911
403717140040 SHAWINIGAN,QU lat,lon 46.6 -72.7 omit: 0-1918
414765560010 MASCOTA, JALISCO lat,lon 20.5 -104.8 omit: 0-1940
414767260010 CUAUTLA, MORELOS lat,lon 18.8 -98.9 omit: 0-1953
425702710000 GULKANA/INTL. lat,lon 62.2 -145.4 omit: 0-1930
425725910004 RED BLUFF/MUN lat,lon 40.2 -122.2 omit: 0-1889
425745090010 LOS GATOS USA lat,lon 37.2 -122.0 omit: 0-1890
432788970000 LE RAIZET,GUA lat,lon 16.3 -61.5 omit: 0-1940
501943330000 BOULIA lat,lon -22.9 139.9 omit: 0-1899
501943660010 BOWEN POST OFFICE lat,lon -20.0 148.3 omit: 0-1909
501945660000 GYMPIE (FORES lat,lon -26.1 152.6 omit: 0-1909
501945890000 YAMBA lat,lon -29.4 153.4 omit: 0-1899
501947840000 TAREE lat,lon -31.9 152.5 omit: 0-1909
501948420000 CAPE OTWAY lat,lon -38.8 143.5 omit: 0-1900
501949330000 GABO ISLAND lat,lon -37.6 149.9 omit: 0-1899
501949370000 MORUYA HEADS lat,lon -35.9 150.2 omit: 0-1898
523969950000 CHRISTMAS ISL lat,lon -10.4 105.7 omit: 0-1970
636085060002 HORTA (ACORES lat,lon 38.5 -28.6 omit: 0-1916
205544710010 GAIXIAN XIONGYUE lat,lon 40.2 122.2 omit: 1920-1930
205567780004 KUNMING lat,lon 25.0 102.7 omit: 1940-1945
207425150003 CHERRAPUNJI lat,lon 25.3 91.7 omit: 1991-1993
403718260000 NITCHEQUON lat,lon 53.3 -70.9 omit: 2000-9999
115624640010 HURGHADA lat,lon 27.3 33.8 omit: 0-9999
134652010000 LAGOS/IKEJA lat,lon 6.6 3.3 omit: 0-9999
134652360000 WARRI lat,lon 5.5 5.7 omit: 0-9999
134652430000 LOKOJA lat,lon 7.8 6.7 omit: 0-9999
205549450010 JUXIAN lat,lon 35.6 118.8 omit: 0-9999
207433330002 PORT BLAIR lat,lon 11.7 92.7 omit: 0-9999
210476960010 YOKOSUKA lat,lon 35.3 139.7 omit: 0-9999
219415600005 PARACHINAR lat,lon 33.9 70.1 omit: 0-9999
303824000000 FERNANDO DE N lat,lon -3.9 -32.4 omit: 0-9999
314804440000 CIUDAD BOLIVA lat,lon 8.2 -63.5 omit: 0-9999
403717300040 RUEL,ON lat,lon 47.3 -81.4 omit: 0-9999
414762200010 CIUDAD GUERRERO,CHIHUAHUA lat,lon 28.6 -107.5 omit: 0-9999
414762580020 QUIRIEGO, SONORA lat,lon 27.5 -109.2 omit: 0-9999
414763730000 TEPEHUANES,DG lat,lon 25.4 -105.7 omit: 0-9999
414766950010 CHAMPOTON, CAMPECHE lat,lon 19.4 -90.7 omit: 0-9999
414767750030 CANTON, OAXACA lat,lon 18.0 -96.3 omit: 0-9999
440785260010 ANNAS HOPE, ST.CROIX VIRG lat,lon 17.7 -66.7 omit: 0-9999
425724910030 HOLLISTER USA lat,lon 36.8 -121.4 omit: 0-9999
501947880000 KEMPSEY lat,lon -31.0 152.8 omit: 0-9999
643081600003 ZARAGOZA AERO SPAIN lat,lon 41.7 -1.0 omit: 0-9999
643083300010 SINTRA/GRANJA lat,lon 38.8 -9.3 omit: 0-9999
643083730010 ALBACETE SPAIN lat,lon 39.0 1.8 omit: 0-9999
>> American Patriot says: June 9, 2011 at 6:43 am: “Has anyone done a study of all the stations that are excluded and see if they mostly disprove AGW arguments?”
Since the data for those Weather Stations are excluded from NOAA/GHCN Temperature database, there are no temperatures to analyze. Even if someone got the records for the thousands of Weather Stations that have been dropped, (or rendered useless by dropping of multiple months for said stations) the Global Warming Alarmists would only claim the results were not Official (aka Gov. NOAA/GHCN rubber-stamped), thus irrelevant.
Now they have added a nice page on this.
http://data.giss.nasa.gov/gistemp/sources/cleaning.html
So Nick can see what they used to OMIT
http://data.giss.nasa.gov/gistemp/sources/old.list.of.data.removed.txt
omit whole record:
==================
115624640010 HURGHADA lat,lon 27.3 33.8 omit: 0-9999
134652010000 LAGOS/IKEJA lat,lon 6.6 3.3 omit: 0-9999
134652360000 WARRI lat,lon 5.5 5.7 omit: 0-9999
134652430000 LOKOJA lat,lon 7.8 6.7 omit: 0-9999
205549450010 JUXIAN lat,lon 35.6 118.8 omit: 0-9999
207433330002 PORT BLAIR lat,lon 11.7 92.7 omit: 0-9999
210476960010 YOKOSUKA lat,lon 35.3 139.7 omit: 0-9999
219415600005 PARACHINAR lat,lon 33.9 70.1 omit: 0-9999
303824000000 FERNANDO DE N lat,lon -3.9 -32.4 omit: 0-9999
314804440000 CIUDAD BOLIVA lat,lon 8.2 -63.5 omit: 0-9999
403717300040 RUEL,ON lat,lon 47.3 -81.4 omit: 0-9999
414762200010 CIUDAD GUERRERO,CHIHUAHUA lat,lon 28.6 -107.5 omit: 0-9999
414762580020 QUIRIEGO, SONORA lat,lon 27.5 -109.2 omit: 0-9999
414763730000 TEPEHUANES,DG lat,lon 25.4 -105.7 omit: 0-9999
414766950010 CHAMPOTON, CAMPECHE lat,lon 19.4 -90.7 omit: 0-9999
414767750030 CANTON, OAXACA lat,lon 18.0 -96.3 omit: 0-9999
440785260010 ANNAS HOPE, ST.CROIX VIRG lat,lon 17.7 -66.7 omit: 0-9999
425724910030 HOLLISTER USA lat,lon 36.8 -121.4 omit: 0-9999
425725130010 FREELAND lat,lon 41.0 -75.9 omit: 0-9999
425725210010 PHILO 3SW lat,lon 39.8 -81.9 omit: 0-9999
425725970120 CRATER LAKE NPS HQ lat,lon 42.9 -122.1 omit: 0-9999
425726710020 ROCK SPRINGS FAA AP lat,lon 41.6 -109.1 omit: 0-9999
501947880000 KEMPSEY lat,lon -31.0 152.8 omit: 0-9999
Now, of course, GISS have decided
“We decided to remove all USHCN data from that list, considering the scrutiny that these data received by NOAA, ending up with “new.list.of.data.removed.txt”.”
So, Here is what you see. In the earlier version of the program those stations I mentioned were removed.
Hansen gave what appeared to be good reasons. The difference between land and SST patterns. The stations also diverged from their neighbors in dramatic ways. But this removal was done manually with no supporting analysis. The stations appear in the inventory, but are removed in subsequent steps. So, Nick really doesnt know what he is talking about. In newer versions of the program These stations are REMOVED from the OMIT list. why? because NASA now trusts the QA that is done on them.
Interesting question: Is there still a difference between the SST and land at that time? Do these stations still diverge from their neighbors? There were some very unique things about those stations that I noted at the time.. argg have to dig up my comments on them all. Next, how do the other stations get on the OMIT list?
All should be aware that “Nick Stokes” who posts here on WUWT is just as fictional as his CSI namesake. His / her consistent deflection and obfuscation have lead many to believe that one or more ‘Hockey Team’ members post under this pseudonym. (Gavin, is that you?) We’re just glad the team reads WUWT and helps make it the world’s most viewed climate website.
Thanks Darren. Can’t the data be obtained by a simple request for information (FOIA), filed officially of course? They have to honor that by law (though under this regime that is not a sure thing)
If Hansen et al were honest, they would exclude urban stations that are truly flaky and cannot possibly detect a CO2 signature in all of the UHI noise.
Argument from authority fails even more dramatically the moment that the competence or fairness of the “authority” is demonstrated to be in question. As Muller and Curry have stated, they are part of a large group of scientists who simply assumed without question that the science was all solid. Once questions start popping up, the wise man remains skeptical until everything is on the table and carefully reviewed.
If unscientific adjustments are discovered, the competence and fairness of the whole project come into question.
Stated a different way — when the methodology is demonstrated to be flawed, you can’t say that it doesn’t matter because the first of the discovered errors only makes a small difference. To do so is to assume that there are no other errors. That’s a foolish assumption for the scientifically-minded to make in the face of demonstrated methodological error.
No wonder they show a global warming signal. If a station shows any cooling, they kill it. They will run out of stations they can use before too long. How many have they got rid of already?
As I read Hansen’s approach, this is his consistent and circular reasoning. Assume (or argue from some set of scientific principles) that the data must be homogenous, discard any that aren’t homogeneous as intrinsically flawed. Publish the amazingly good homogeneity of the resulting dataset, then use this as a basis to apply the assumption of homogeneity in future analysis — for example in-filling the polar hole.
And just to be clear. I have no issue with the removal or now the re admission of any station.
The whole point was what started this. The paper said the stations were removed because they there was strong cooling that didnt look right. The paper did not detail the METHOD by which this decision was made. what EXACTLY is a strong cooling? .2C, 20C, .02C. WHAT? whenever data is excluded that decision needs to be rigorously documented and defended. AND that decision needs to be quantified. So, I asked for the code because the paper says nothing of substance. ( except that the effect was small, which to ME argued for keeping them in.. funny how that argument cuts both ways ) Anyway, the code and the cleaning notes made it clear that originally the stations, as well as others, were excluded? why? how?. Now, that’s changed again and the stations are back in. The point I am making is academic. I dont think these decisions are critical or that global warming will go away. The point is that these decisions should have full traceability. Whether its chopping off a tree ring series, or tossing out a station, these types of decisions deserve far more attention that they currently receive.
That’s not the case. The case is this. Some that showed cooling were removed. did they run a test? did they just “look wrong?” who did the looking? If I looked would I see the “same thing”. What I am getting at is this. The proceedure was not well defined. did they look for stations that were too hot? was there a test? or just “looking?”
If there is “just looking”, then how do I repeat that method? So, basically its a call for more rigor and more transparency. Finally, removing COOL periods in the early years DECREASES the trend. Many people make this mistake. They think the objection was an objection to removing COOL stations. That was not point, since the periods of coolness were in the early part of the century. The point was documenting all the steps in the method so that it could be repeated. “manual cleaning” with no guidance cant be repeated, except by luck.
Dont mistake my pickiness for skepticism. That’s two different things. I believe the record is reliable and useable. However, given its importance I think the methods and decisions need to represent the best we can do.
Call me simple but it’s funny how they can make ‘adjustments’ for UHI but resort to removal in Alpine areas. ;O)
steven mosher says: June 9, 2011 at 10:15 am
“These stations are REMOVED from the OMIT list.”
Wow! No end to their perfidy!
So it seems what I initially suspected is true. Crater Lake is not omitted from Gistemp. Well, there’s been a good few hours of ranting – the facts won’t spoil that.
And my initial reason for that suspicion was sound. Data on the omit list is not shown on the station plots. Crater Lake is shown.
I’m glad to see they do have an omit list, though. Someone is doing a quality check. If they find issues, do you think they should not omit?
Mosh believes the record is reliable and useable despite all the removed norther Siberia stations and the extrapolation of more southern tempertures to more northern locales. Wow.
steven mosher says:
June 9, 2011 at 12:47 pm
====
Thanks for all the effort, and the explanations.
Screw whether it ‘agrees with vaguely nearby stations’, the question revolves around whether there’s a concrete reason for exclusion.
Changing weather can obviously affect one portion of a region more than another, or nightly weather reports would be exceedingly monotonous.
Changing -climate- patterns should actually -upset- many of the recognizable relationships between stations. That’s pretty much the entire point to the exercise.
What do the various satellite temperature maps indicate for this location?
@Nick Stokes
“So it seems what I initially suspected is true.”
Actually no since you failed to stay in context defined by the author. And on top of that you failed to make the direct link you referred to.
So, what was it you “suspected” again?
Max Hugoson says: “…again we need to start attacking “mean temperature” as the MEANINGLESS [METRIC] IT IS!”
Well, yes, that’s exactly the crux of this post. There is no “global temperature.” “Global Temperature” tracking is a desperate attempt to tease a minuscule signal from outrageously noisy data.
1DandyTroll says: June 9, 2011 at 5:08 pm
“So, what was it you “suspected” again?”
What were we talking about, again?
“Someone is doing a quality check”
“Nick Stokes”,
Who is?
And what are the criteria used and the determinations made and the steps taken in a ‘quality check’?
Do you know?
Andrew
GISS’ exclusion of entire records can hardly be called a an exercise in data QC. Not even Hansen, who is scarcely expert in that field, makes that foolish claim. Instead, he appeals to the amateurish premise that temperature variations in a region should be homogeneous and, in coastal regions, correlate well with SSTs. That is very far from physical reality. Even coastal air temperature records are not strongly coherent at all frequencies with offshore SSTs. And the shallow marine layer does not extend far inland when there [are] mountains along the coast. The expectation that at the mountain-top elevation of Crater Lake (~2km) the record should resemble those at much lower elevations scores of miles away is ludicrous.
Now there’s nothing intrinsically wrong with excluding records representative of only a small area from the construction of regional indices. That certainly should be done with urban records, the intensity of whose spatially concentrated UHI is unknown in practical situations. But that is not the argument that Hansen makes. Clueless about microclimatic variations within a region, he’s simply finding an excuse to ignore a trendless record from a rare truly rural location, while blithely accepting UHI corrupted records from nearby towns in the process. That is trend manufacture rather than QC.