Note to Readers: This is an important post, as Willis demonstrates that NASA GISS has taken a cooling trend and converted it into a warming trend for the one GHCN station in Nepal which covers the Himalayas. I offer NASA GISS, either via Jim Hansen or Gavin Schmidt, rebuttal opportunity to this issue on WUWT anytime. -Anthony
Guest Post by Willis Eschenbach
I read the excellent and interesting guest post by Marc Hendrickx about the IPCC and the Himalayas. My first big surprise was the size of the claimed warming. He cites IPCC Table 10.2 which says:
Nepal: 0.09°C per year in Himalayas and 0.04°C in Terai region, more in winter
Well, my bad number detector started ringing like crazy. A warming of nine degrees C (16°F) per century in the mountains, four degrees C per century in the lowlands? … I don’t think so. Those numbers are far too big. I know of no place on earth that is warming in general at 9°C per century.
Marc also quotes the IPCC source paper as saying:
The Kathmandu record, the longest in Nepal (1921–94), shows features similar to temperature trends in the Northern Hemisphere, suggesting links between regional trends and global scale phenomena.
Being cursed with a nagging, infernal curiosity, I thought I’d take a look at the Kathmandu temperature record. Foolish me …
I started by looking at where Nepal is located. It starts at the northern edge of the Indian plains, at the foothills of the Himalayas, and goes up to the crest:
Figure 1. Nepal (yellow outline). Yellow pins show all GHCN (Global Historical Climate Network) surface temperature stations.
So, that was my second surprise – a whole dang country, and only one single solitary GHCN temperature station. Hmmmm … as Marc shows, the paper cited by the IPCC gives the records of a dozen stations in Nepal. So why does GHCN only use Kathmandu in Nepal? But I digress.
Resolving not to be distracted by that, I went to the GISS dataset. I selected “Raw GHCN data + USHCN corrections” in the dropdown menu. (Kathmandu is outside the US, so in this case there are no USHCN corrections.) Typed in “Kathmandu”, and started the search. Figure 2 shows the result:
Figure 2. Kathmandu Air(port) Metadata.
This shows there are three records for Kathmandu Air (Airport), which looks promising. Also, it looks like there is an overlap between the records, which seems good. (There is no sign, however, of a record that is “the longest in Nepal (1921–94)”. The earliest date is 1951, and the latest is 2010. But again I digress.)
Clicking on the top Kathmandu Air link (on the GISS website, not on the graphic above) brings up the following GISS-generated graph:
Figure 3. Kathmandu Air. Three records are shown, as dotted, dashed, and blue.
Here’s an oddity. We have three records, each for different periods. And there is not a single year of overlap in the bunch. Not one.
Now, people think that I mine or search for these odd stations. Not so, I am simply curious about what I read, and this is not an atypical temperature record. Most are somewhat strange. Gaps and breaks in a given record often render large parts of the record unusable. GISS uses a cutoff of 20 years of consecutive data. As a result, the final GISS record for Kathmandu, rather than going from 1951-2009, goes from 1961 to 1980. Fair enough, these are all debatable choices, including the minimum record length cutoff size. In any case, the real problem with Kathmandu is not the record length. It is the lack of overlap which prevents the creation of a continuous record. This means that the apparent overall trend may not be real. It may simply be an artefact of e.g. different thermometers, or different locations. In this case, GISS has side-stepped the question by selecting only one record (shown in blue) for the final record.
How can we get to the graph of this final GISS record including all of their homogeneity adjustments? Well, we could go back to the same GISS website where we started and select a different dataset. However, here’s a trick to go directly from the raw data you are looking at to the final GISS homogenized dataset. Near the end of the URL of the raw GISS dataset under discussion you find the following:
… &data_set=0& …
GISS has three datasets. The raw data is dataset 0. The data “after combining records at the same location” is dataset 1. The final data “after cleaning/homogeneity adjustment” is dataset 2.
So to get the final adjusted result, all you have to do is to change the “0” in the URL to a “2”, viz:
… &data_set=2& …
Figure 3 shows the outcome of making that change:
Figure 3. Final GISS record for Kathmandu. The scale has been changed in both the X and Y axes. Note that they have discarded all segments of the record which are shorter than twenty years in length.
And that, dear friends, was my third big surprise. Take a close look at those two records, the adjusted and unadjusted …
As you no doubt observe, one is trending somewhat downwards, while the second is trending distinctly upwards. Hmmm … so, of course, I downloaded the GISS data (from the bottom of the same web page). Here is what they have done:
Figure 4. GISS Kathmandu Airport Annual Temperatures, Adjusted and Unadjusted, 1961–80. Yellow line shows the amount of the GISS homogeneity adjustment in each year. Photo is of Kathmandu looking towards the mountains.
GISS has made a straight-line adjustment of 1.1°C in twenty years, or 5.5°C per century. They have changed a cooling trend to a strong warming trend … I’m sorry, but I see absolutely no scientific basis for that massive adjustment. I don’t care if it was done by a human using their best judgement, done by a computer algorithm utilizing comparison temperatures in India and China, or done by monkeys with typewriters. I don’t buy that adjustment, it is without scientific foundation or credible physical explanation.
At best that is shoddy quality control of an off-the-rails computer algorithm. At worst, the aforesaid monkeys were having a really bad hair day. Either way I say adjusting the Kathmandu temperature record in that manner has no scientific underpinnings at all. We have one stinking record for the whole country of Nepal, which shows cooling. GISS homogenizes the data and claims it wasn’t really cooling at all, it really was warming, and warming at four degrees per century at that … hmmm, four degrees per century, where have I heard that before …
What conceivable scientific argument supports that, supports adding that linear 5.5°C/century trend to the data? What physical phenomena is it supposed to be correcting for? What error does it claim to be fixing?
Finally, does this “make a difference”? In the global average temperature, no – it is only one GHCN/GISS datapoint among many. But for the average temperature of Nepal, absolutely – it is the only GHCN/GISS datapoint. So it is quite important to the folks in Nepal … and infinitely misleading to them.
And when it is cited as one of the fastest warming places on the planet, it makes a difference there as well. And when the IPCC puts it in their Assessment Report, it makes a difference there.
Once again we see huge adjustments made to individual temperature records without reason or justification. This means simply that until GISS are able to demonstrate a sound scientific foundation for their capricious and arbitrary adjustments, we cannot trust the final GISS dataset. Their algorithm obviously has significant problems that lead to the type of wildly unreasonable results seen above and in other temperature datasets, and they are not catching them. Pending a complete examination, we cannot know what other errors the GISS dataset might contain.
[UPDATE] John Goetz pointed out that the likely source of the spurious trend is temperatures in Tingri (see Fig. 1, way back in the high mountains at the upper right at almost 6,000 metres elevation in the tundra) and GISS step 2. GISS says:
… in step 2, the urban and peri-urban (i.e., other than rural) stations are adjusted so that their long-term trend matches that of the mean of neighboring rural stations.
It seems John is right, Tingri is the likely problem. Or to be more accurate, their method is the problem. GISS uses a different method than GHCN to average stations for step 2.
The method of “first differences” is used by GHCN. GISS instead uses the “reference station” method described in the same citation. In my opinion, the reference station method is inferior to the first difference method.
The reference station here is likely Dumka, it is the longest of the nearby stations. Unlike Tingri, Dumka is at an elevation of 250 metres in the plains of West Bengal … hmmm. This should be interesting.
In the reference station method, Tingri gets adjusted up or down until the average temperatures match during the time of the overlap. Then Tingri and Dumka are averaged together. However, let’s take it a step at a time. First, I like to look at the actual underlying data, shown in Fig. 5.
Figure 5. Temperatures in Dumka (India) and Tingri (China). Left photo is Dumka on the lowland plains of West Bengal. Right photo is Tingri in the Himalayan mountains.
So the brilliant plan is, we’re going to use the average of the temperature anomalies in Dumka and Tingri to adjust the temperature in Katmandu, at 1,300 metres in the foothills?
Makes sense, I suppose. The average of mountains and plains is foothills, isn’t it? … but I digress.
The problem arises from the big jump in the Tingri data around 1970. Using the reference station method, that big jump gets wrapped into the average used to adjust the Kathmandu data. And over the period of Tingri/Kathmandu overlap (1963-1980), because of the big jump the “trend” of the Tingri data is a jaw-dropping 15°C per century. Once that is in the mix, all bets are off.
Obviously, there is some kind of problem with the Tingri data. The first difference method takes care of that kind of problem, by ignoring the gaps and dealing only with the actual data. You could do the same with the reference station method, but only if you treat the sections of the Tingri data as separate stations. However, it appears that the GISS implementation of the algorithm has not done that …
Nor is this helped by the distance-weighting algorithm. That weights the temperatures based on how far away the station is. The problem is that Tingri is much nearer to Kathmandu (197 km) than Dumka (425 km). So any weighting algorithm will only make the situation worse.
Finally, does anyone else think that averaging high mountain tundra temperature anomalies with lowland plains anomalies, in order to adjust foothills anomalies, is a method that might work but that it definitely would take careful watching and strict quality control?
[UPDATE 2] Lars Kamel pointed below to the CRU data. If we take all of the available CRU (originally GHCN) data, we get the following trend for Kathmandu.
Figure 6. CRU monthly and annual temperature data for Kathmandu. Red circles show those years with 12 months of data.
You can see the lack of a trend in the 1950-2000 data. Went down slightly 1950-1975, went up slightly 1975-2000. Gosh.
[UPDATE 3] Steve McIntyre reminded me below of his fascinating 2008 analysis of the numbers and locations of GISS adjustments that go up, down, and sideways. His post is here, and is well worth reading.
[UPDATE 4] Zeke Hausfather has an interesting post on Kathmandu here, with lots of good information.
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It’ worse than we thought!! (the adjustment)
Yet another great post Willis. Again you clearly and simply demonstrate that bad science is at the heart of the alarmists’ climate agenda. What I fail to understand is why NASA have still not shut down GISS. Hansen and his team are an absolute disgrace and international embarrassment.
Thanks Willis… great detective work… great analysis.
The adjustment line in the graph looks kinda familiar!
Seem to remember another mountain station somewhere in the Pacific Ocean…
No prizes for guessing what the adjustments are trying to mirror.
For an intriguing look at the Chinese view on CAGW, have a look at
http://libertygibbert.wordpress.com/2010/08/11/the-dragons-dissent/#comment-6629
Pointman
If the temp at an airport goes down, not up, it’s amazing that it’s due to global warming……I guess Katmandhu airport isn’t LAX, but still……..
These Consistent “Errors” are always in the IPCC AGW leaning favour..
reminds me so much of how Banks alaways make their “errors” in their favour too:-)
thanks again Willis for the time you took to explore.
and sound the Rubbery Figures Alarm
Very good effort, Willis Eschenbach, as always! I wonder what is going on at GISS and other places where these “corrections” are made. Do the people involved in this work have any idea of quality? Of the need for the highest degree of accuracy when attempting to produce anything meaningful? I can only shake my head in wonder. This type of climate “science” is just a sad parody of good scientific labor.
I am now waiting for the “news” of this “tremendous warming” to work its way down to our local newspaper here in Nuernberg, Germany. They just had a small notice on the rice “study” which is another thread here at WUWT.
Keep up the good work!
They have changed a cooling trend to a strong warming trend…
It’s not worse than we thought — it’s pretty much what we’ve come to expect…
What you complaining about Willis? The have adjusted the temperature downwards, not up! It has gone from 18.3 to 17.3 in 1961, so how can anyone complain about that? /sarc
If I was anyway involved with GISS temp I would get out right now.. GISS temp may have whole countries pursuing them on legal grounds using this type of data manipulation
What do you think of (http://www.climateapplications.com/GISSMaps/showstation.asp?wmostationcodeid=1314&wmoflag=0)?
Sorry. The link should be
http://www.climateapplications.com/GISSMaps/showstation.asp?wmostationcodeid=1314&wmoflag=0
P. P.
Wow. Amazing stuff Willis. Thank you so much for your efforts!
I can only hope that Gavin or Hansen (hello …?) forwards your post to the analyst / monkey(?) who signs off on the data quality.
This is embarrassing – NASA’s reputation on the line too …
Nepal’s population and airport use (from tourism) has absolutely exploded during the last 50 years. Nepal then was 5 million living in totally agrarian society, now 25 million. Kathmandu airport was rural but is now surrounded by the city with it’s rapidly rising population up to nearly 1 million.
Kathmandu is in a valley/basin surrounded by high mountians. If anything the adjustment should be downwards due to the serious UHI effects.
Here’s a challenge, Willis. Find a GISS data point that has been consistantly adjusted downward for at least 20 years. I don’t think you can.
In 2007, for the first time in 62 years it snowed in Kathmandu, Nepal
[youtube=http://www.youtube.com/watch?v=JyNMEE4djbc&hl=en_US&fs=1]
Strange indeed. It must be have been caused by chaotic weather pattern caused by human caused global warming! Right.
It’s true that the max temp in the raw data (~ 18.8°C ) has been adjusted down ( ~18. 3 °C) but what is clever is that they managed to adjust the trend up but by a very significant amount. Interesting eh?
How about naming names? Who does the data massaging. We always here GISS this, or NOAA that, etc. Always the anonymous THEY. Well, who the hell are THEY? When I was working for a living, I had to sign off on everything. My name is still attached to specific airplanes, 20 years later. Even underwear has that Insp. By sticker on it.
By not naming names there is no accountability.
sorry, disregard, I should read all the information and run the simple math…sorry
temp. is correct
Well if GISS hadn’t adjusted the records to create a warming trend, they wouldn’t show warming, and since we know the world is warming at 5 degrees C / century, the records need to be adjusted to reflect this.
Thanks to Willis for his detective work.
Wouldn’t it be nice if some kind soul from GISS would grace this site with a visit and help us out with these unanswered questions?
I can’t believe that they don’t monitor what is deing debated here; I can only conclude that they haven’t got credible answers to what they do to the data, and their funding is so important that they are only allowed to make public statements through their press office.
Here are my comments on the previous Himalayan glacier ‘melt’ & ‘rising temps’ story:
NASA – Himalayan glaciers ‘melting’ due as much to soot / dust as CO2
Look at the Asian haze
An Gore blames us for casting doubt. How about blaming us for casting truth???
Sorry mods – last post mangled – I’ll keep it simple
———————————————-
NASA – Himalayan glaciers ‘melting’ due as much to soot / dust as CO2
http://www.nasa.gov/topics/earth/features/himalayan-soot.html
http://www.nasa.gov/topics/earth/features/himalayan-warming.html
http://www.atmos-chem-phys-discuss.net/9/26593/2009/acpd-9-26593-2009.html
How about naming this part of IPCC report plain wrong and they have to proof if it’s ok or not? It is important to present such work to the public and request explanations or corrections.
Many sceptics are believing in some sort of warming at least, but surfacestations.org and Will’s work bring up a lot of doubt.
… and taxpayers paid to do this nonsense
Thanks again Willis for bringing this stuff out. You’re right, no cherry picking is necessary, everywhere one looks there is some peculiar anomaly with the data, and the anomaly, curiously, is always mono-directional. Climate science appears rife with odd coincidences.
It would not take very many site adjustments like this one to create the illusion of record breaking global warming almost every year.
Great post Willis. Amazing how that IPCC tapestry falls apart at the slightest tug.
Looking back at Shrestha et al. 1999 they mention “Kathmandu Indian Embassy” station with records stretching back to 1921, yet surprisingly they only plot Kathmandu Airport starting in the late 1960’s. I wonder why.?
Here’s a challenge can anyone locate the KIE data.
This is the truly disgraceful part of this whole exerciese. I talk to people who are realtively uninformed on climate change and they tell me “but the data says”. When I tell them the data is crooked and a blatant lie they think I’m some kind of tin-foil hat conspiracy nut. Well, it is a conspiracy, but I don’t wear tin foil… really.
Willis: http://assets.panda.org/downloads/himalayaglaciersreport2005.pdf
Page 13 for a graph that includes a glimpse of the Kathmandu Indian Embassy records to 1976, thence merged with the Kathmandu Airport data.
The correction on the second page does not lend great confidence in this document.
I’ll look further.
Here’s a link to a blurry graph showing the Kathmandu IE precipitation record back to 1852! The missing data from 1901 to 1920 found by a certain Dr Jones…
http://www.ldeo.columbia.edu/tree-ring-laboratory/tree-ring-research/nepal/the-broken-kathmandu-record
getting close…
Keep probing Willis
You may find more meteorological data available. See:
LAND SUITABILITY EVALUATION USING GIS FOR VEGETABLE CROPS IN KATHMANDU VALLEY /NEPAL, Nabarath Baniya, dissertation 2008, Landwirtschaftlich-Gärtnerischen Fakultät, der Humboldt-Universität zu Berlin
Climatic Types, Their Distribution and Changes in Different Micro Meteorological Stations in Kathmandu Valley, Dambaru Ballab Kattel, Nepal Journal of Science and Technology 9 (2008) 171-178
Kattel notes: “The average temperature in winter (11.55 deg C) and summer (23.780 deg C) was found maximum at the Airport as compared to other stations.” (He lists his email in the paper.)
Perhaps there some “anthropogenic” effects impacting Tribuvhan International Airport? Some tidbits:
Civil Aviation Report 2008-1 Civil Aviation Authority of Nepal.
From: Bleak Outlook for Environment in Kathmandu Valley, UNEP, Kathmandu/Bangkok, 25 January 2007
Kathmandu has major air pollution with typical PM10 concentrations of 150 ppm. See Fig. 3 in
The Influence of Meteorological Conditions on PM10 Concentrations in Kathmandu Valley D. Giri et al. Int. J. Environ. Res., 2(1): 49-60, Winter 2008, ISSN: 1735-6865
Hansen is actually an author called Hank Janson – the name was ‘adjusted’ in the GISS fashion to suit the present day need for Scientific Accuracy.
Google Hank Janson – both authors exhibit similar abilities!
RE: Curiousgeorge: (August 11, 2010 at 4:48 am) “By not naming names there is no accountability.”
I would assume that the IPIC, identified person in charge, must be fully accountable for all work generated under their authority if they have not required their subordinates to identify their work.
shoddy quality control of an off-the-rails computer algorithm
yep. still looks to me like they’ve flipped a sign in the code. instead of subtracting warming from year n back to zero, they’re adding warming to year 0+n.
If they ran their code out 1,000 years it’d show 150 degrees of warming. Oops, that’s exactly what they’ve done.
Willis,
I just did a bit of a literature search regarding the numbers claimed in the IPCC report, and the numbers are supported by the literature. In particular, take a look at Shrestha, Arun B.; Wake, Cameron P.; Mayewski, Paul A.; Dibb, Jack E.. Maximum Temperature Trends in the Himalaya and Its Vicinity: An Analysis Based on Temperature Records from Nepal for the Period 1971–94. Journal of Climate, 9/1/99, Vol. 12 Issue 9 pp:2775-2786
Here’s the abstract: Analyses of maximum temperature data from 49 stations in Nepal for the period 1971–94 reveal warming trends after 1977 ranging from 0.068 to 0.128C yr21 in most of the Middle Mountain and Himalayan regions, while the Siwalik and Terai (southern plains) regions show warming trends less than 0.038C yr21. The subset of records (14 stations) extending back to the early 1960s suggests that the recent warming trends were preceded by similar widespread cooling trends. Distributions of seasonal and annual temperature trends show high rates of warming in the high-elevation regions of the country (Middle Mountains and Himalaya), while low warming or even cooling trends were found in the southern regions. This is attributed to the sensitivity of mountainous regions to climate changes. The seasonal temperature trends and spatial distribution of temperature trends also highlight the influence of monsoon circulation.
The Kathmandu record, the longest in Nepal (1921–94), shows features similar to temperature trends in the Northern Hemisphere, suggesting links between regional trends and global scale phenomena. However, the magnitudes of trends are much enhanced in the Kathmandu as well as in the all-Nepal records. The authors’ analyses suggest that contributions of urbanization and local land use/cover changes to the all-Nepal record are minimal and that the all-Nepal record provides an accurate record of temperature variations across the entire region.
Can somebody with more time than I have right now track down the source reference for the data in the IPCC table 10.2? The odds are the IPCC conclusion will depend on nebulous “science” or a pea and thimble maneuver.
Looks like GISS is using some of the CRU code. I wonder if they also ported the “fudge” comments? Maybe Harry has a new job.
Got it…well possibly some of it.
ftp://ftp.ncdc.noaa.gov/pub/data/paleo/treering/reconstructions/asia/nepal/kathmandu_temp.txt
3. Kathmandu instrumental temperature and precipitation records
1879-1950
Peter Plail says:
August 11, 2010 at 5:07 am
Thanks to Willis for his detective work.
Wouldn’t it be nice if some kind soul from GISS would grace this site with a visit and help us out with these unanswered questions?
Assumes facts not in evidence, to wit:
1. There are kind people at GISS.
2. GISS folks still have souls.
🙂
Could we all agree to this new terminology: Adjustments = falsifications?
Show the raw data and the EXACT circumstances at every station plotting them, and let the users of the data sort out all by themselves, what that means, I say.
Of what purpose are ANY adjustments, when it takes MORE time and labour, to re-calculate and find mis-adjustments, than doing all adjustments on your own?
There’s only ONE possible purpose for GISS “adjusting” the data imaginable: Falsifying the records, in order to push the GW-agenda!
I’ve looked at dozens of GISS records for Pennsylvania and in every case of adjustments I have never found a negative UHI adjustment. They do adjust records down, but always old data from the 30’s, 40’s and 50’s (pre UHI) then the adjustments swing to positive and march on up … their own stairway to heaven …
this is obviously human interference not some algo … and again no scientific justification for it …
Since GISS claim to use a method to make this adjustments, thought I’d have a look to see if it could explain this. It can only be assumed these adjustments reflect trends at ‘nearby’ rural stations in some way. So obviously, if you look at these on the GISS page, you see an interesting picture. Five rural stations sit within 500km (which is what GISS would use to calculate the adjustment), of which only 1 (Dumka) does not appear to be in China. Dumka is (I think) on the India plains, and is normally a good 10 degrees C hotter than Kathmandu. Not a good compartor really.
Mind you, the other four stations (Tingri, Pagri, Xigaze and Xainza) are also strange compartors. All of them sit across the Himalayas from Kathmandu (they’re only the worlds tallest mountain range after all) presumably in the mountains or on the Tibetan Plateau, since average temperatures are all 10 degrees C or more lower than Kathmandu.
None of the four stations seem to have a trend necessary to explain the increasing adjustments that I can see, although the incomplete record from has a large rise.
Dilbert has the last word!
http://dilbert.com/strips/comic/2010-08-11/
Willis, you know they always adjust the earliest data down, and the latest data up..
..for UHI of course
If you want to have the hottest year ever then you must do some work. In politics you try to do your work behind the scene so no one can see your agenda. Why are you so rude as to shine a light on those workings? Nice work Willis!
Keep up the good work Willis.
To see “idyllic” conditions for these temperature records, search for the Tribuvhan International Airport, in Google Earth. The immediate surroundings are now “urban” suburbs of Kathmandu.
PS The 27.7 N 85.4 E listed on the temperature graph does not appear to be within the Tribuvhan Internatational Airport boundaries.
Peter Plail says:
August 11, 2010 at 5:07 am
Thanks to Willis for his detective work.
Wouldn’t it be nice if some kind soul from GISS would grace this site with a visit and help us out with these unanswered questions?
I can’t believe that they don’t monitor what is deing debated here; I can only conclude that they haven’t got credible answers to what they do to the data, and their funding is so important that they are only allowed to make public statements through their press office.
=================================
Just my observation but someone from GISS seems inclined to make public statements in front of coal-powered generating plants. Blogs? ehhh… not so much.
Excellent as usual. One would hope the people at GISS are red faced with embarrassment as Willis and others expose their work. Where are the MSM and politicos?
Sorry, in advance, for any obtuseness….
Is there data for night time temps at this airport? Can they be filtered out and used to see how their rise (er…fall?) compares with that of the day values?
Seems to me that in such an environment, UHI effects would be more apparent in the night times. (To say nothing of brightness increasing over the years which should affect the “urban” character of the station, right?)
Willis
“In this case, GISS has side-stepped the question by selecting only one record (shown in blue) for the final record.”
Its not a side step. with RSM if there is no overlap you cannot splice together records
so they use the longest period.
IPCC doesnt use GISStemp. so you prolly need to look at a different source.
in general higher elevation stations have warmed FASTER than lower elevation,
unlike what many think. so, for example, when ghcn drops higher elevation stations it actually cools the record.
Still the rate they show here is wackly large.
Willis,
Your adjustment line (yellow) might better reflect the changes if it started as an approximately -.9 adjustment that over the duration of the record approached a 0 adjustment.
Terry
Today’s Dilbert is rather appropriate :/
Well this would be why the Himalayas are warming faster than everywhere else on the planet……
.
.
.
.
Just like everywhere else on the planet.
I guess these locations are having a race and this is how they keep ahead to be the fastest warming location on earth. Each location is editing it’s own figures upwards to stay in the lead.
At this rate, the Himalayas will be warming at 50 degrees C per century in a few years.
Willis,
Any chance you can run this through the full gistemp homogenization process, drill down into the details of the homogenization process and figure out exactly which steps are introducing the changes and why.
For those that don’t know, the GISS program gistemp was released under FOI a while ago, so in theory anyone can download it and recreate the full GISS homogenization process. But the software is apparently pretty bastardized and is extremely difficult to get running due to multiple languages etc. being used.
Fortunately, the code has been re-implemented in 100% python and is easy to get running (http://clearclimatecode.org/gistemp/). Further the python code is being enhanced to allow easy drill down that allow one to study a single station like Kathmandu and figure out which of the several homogenization steps is introducing the drastic warming effect.
I think investigative work like this is a good antidote for scientific data being modulated or fictionalized for political or ideological purposes. The more such data is scrutinized; the less this type of activity should even be the contemplated. In this case, Willis has uncovered what appears to be a clear example of data cooking.
If these adjustments were performed by accountants at a big corporation to file tax returns there would be criminal proceedings and people would most probably go to jail (unless some plea bargained). Imagine if I adjusted my earnings downwards to file a tax return. If I were found out I would end up in a cell and do time.
There is still time though and as evidence mounts certain organisations or persons will have enough information to probably file civil claims for damages and future governments could probably consider some kind of strong action.
Anthony, it looks like Willis has gotten you to “cowboy up” over this cheating at cards. Perhaps with all this “Gunsmoke” we can call you Marshall Dillon.
By the way it must have been really cooling for temps at the airport to be going down given UHI but alas they adjusted up for UHI I guess. :o)
AGW, RIP: By this time, anyone granting Hansen’s asinine GISS/NASA any slightest credibility has only himself to blame. As the Green Gang’s utterly spurious Kathmandu adjustments show, smoking out such meretricious fraud is becoming relatively easy. Agenda-driven propaganda of this nature has had its day… as time goes on, and Nature takes her course, Al Gore & Co.’s junk-science bloviations will descend to levels previously reserved to Trofim Lysenko, J.B. Rhine, and Immanuel Velikhovsky. Good riddance.
For what it’s worth, in my feeble attempt to imitate Anthony, I’ve tried to locate the weather station at Kathmandu. METAR has been looking for it but a possible location may be seen on the second entry on the left hand column of this Google search page:
http://maps.google.ca/maps?hl=en&client=firefox-a&rls=org.mozilla:en-US:official&q=kathmandu%20airport%20weather%20station&um=1&biw=1776&bih=991&ie=UTF-8&sa=N&tab=vl
This could place it on the top of the terminal of the airport.
You folks don’t seem to realize that Warmism is part of Progressivism. As such, adherents believe that there is no such thing as a “Truth” that can be discovered through traditional, rational, inductive/deductive processes.
The concept of “objective truth” is simply old-fashioned — and is now discredited. “Truth” is whatever advances the agenda toward the end goal, which is a better world as defined by a propagated societal consensus.
Therefore, arbitrary adjustments to a temperature record are no more fraudulent than the original temperature record. In fact, they are less so, since the original temperature record purports to be “Objective Truth”, which is actually anathema in a Progressive world. Adjustments that further the goal of a better world (no matter what their basis), are therefore more true.
As an example, in February the Atlanta Progressive News fired an editor because he believed in truth instead of progressivism. Here is the reason they gave:
As Professor Nikolaus Lobkowicz states:
All the global warming scientists were almost certainly taught the relativism of truth during their undergraduate days — as I was. It is part of the basic college curriculum now.
Two plus two can indeed equal four. Or three. Or the square root of negative one. The result of the equation depends on the societal consensus, and therefore it changes depending on need.
References:
http://bigdustup.blogspot.com/2010/02/clarity-of-delusion.html
http://clatl.com/freshloaf/archives/2010/02/15/atlanta-progressive-news-fires-reporter-for-trying-to-be-objective/
http://www.leaderu.com/truth/1truth13.html
Well, if it’s OK with Climate Scientists to use Tiljander upside down, why not invert the Airport Heat Island (AHI) effect as well?? 😉
A rebuttle by the obstructionist? I can’t wait to see it Anthony but I am not holding my breath. GREAT POST!
So contact Gavin, formally and ask him.
Willis:
One possible source for the temperature change (0.09 per year in Himalayas) may be Rees & Collins 2004. Here is a reference:
“Another study of average annual temperature for 15 stations above 1800 m in Nepal has reported an annual increase of over 0.1 degree C per year for the period 1976-1996. (Rees & Collins, 2004)”
Wow. That is a heck of a lot more in the way of buildings than when I was there 25 years ago. I would not be surprised if the local temperature was rising. But given a cooling trend in the raw data (when you hunt them out instead of relying in the IPCC to ‘interpret’ them for you), it smacks of a possibly strong cooling trend overall. Still, one reading in the entire Himalayas is a joke.
This article leads to two questions for giss or nasa. Why is the adjustment so large and more importantly why do these adjustments almost always show warming? I only studied meteorology for a couple semesters in university but these so called adjustments smell to high heaven.We are waiting giss.We need to be informed nasa.I see no reason to continue funding public science if its not accountable or honest………
When in Gods name are the lawyers going to be called in?
Your sarcasm is well warranted.
GISS often reduce temperatures in the records when they homologate temperature records. It’s just coincidental that the only part of any of the records that they ever seem to reduce is the beginning of the record.
Although, to be fair to GISS, It has to be acknowledged, in the search for balance and fairness, that they always increase the end of the records in the same way to achieve a fairly balanced homologation. Reduction at the start of a record is matched with an increase at the end to balance out the possibility for error.
/sarc
It is like when I was late for work once, I told my boss I was very sorry and that I would leave early to make up for it!
The GISS methodology appears to have a fundamental flaw.
Any interpolation – homogenization algorithm should, at a minimum, reproduce the observed data at a measurement site
I havea prediction, it’s highly verifiable in the short term.
No one from NASA/GISS, especially Drs Hanson & Schmidt will grace this with a response. All attempts to drop this into a thread at RC will be ruthlessly suppressed!
Dr. James Hansimian should be sent over as a consultant to help them unravel this computing malfunction.
Willis:
See pages 5 and 6:
http://www.nerc-wallingford.ac.uk/ih/www/research/SAGARMATHA/volume2.pdf
Wills:
See also:
Shrestha, A. B., C. P. Wake, et al. (1999). “Maximum temperature trends in the Himalaya and its vicinity: An analysis based on temperature records from Nepal for the period 1971-94.” Journal of Climate 12: 2775-2787.
Willis,
This is just another, in a long list of stations that have had the temp trends adjusted to show a significant warming trend, when none, or a greatly reduced trend is the reality. (Great work!) The next logical step, to confirm an absolute and arbitrary warming bias, is to attempt to find a station where the trend has been adjusted downward in the same manner. If none can be found, we can state with certainty that GISS is an untrustworthy source of information concerning temperature trends and the manipulation of data is purposeful and dishonest.
We all know it is, it would just be nice to confirm this and show it to the world.
Further, what I don’t know is if these practices are illegal or simply unethical. I know private entities have a much greater latitude when disseminating false information, but they are not private entities.
Thank you Willis for your excellent work.
My question is; how can we force NASA GISS to answer for this and other oddities in their temperature records. Can it be done through a FOIA? Is there a way to make them answer for this, preferably on the witness stand?
“I’m sorry, but I see absolutely no scientific basis for that massive adjustment.”
That’s an article I’d like to see -one covering the basic adjustments that are legitimately made to temperature recordings. Every article dealing with temperatures talks about adjustments, but don’t cover what types of adjustments are being made and why. I know hardly anything in that area, and the little I do know is:
If there’s a location or instrument change, an adjustment may need to be made. But shouldn’t that be a “step” adjustment, not an adjustment who’s size changes over time?
The adjustment for the UHI effect I see changing over time with urbanization. But shouldn’t that result in larger values being subtracted over time, not added?
Time after time we’ve seen individual records adjusted upwards, with the magnitude of the adjustment changing over time, and I would like to know what they could possibly be adjusting for. Instrumentation drift?
Pointman:
The text is at:
http://libertygibbert.wordpress.com/rare-scribbling/locusts/low-carbon-plot/
Even though Gavin often watches CA and WUWT, one can’t count on it. You might e-mail them your offer, just so they can’t say you never contacted them about it.
Great post! I wish they could do my accounts so that my income downwards for tax purposes, but of course they can only adjust upwards!
However, I still have a problem with somebody making meaningful temperature measurements externally to the accuracy of thousanths or even hundreths of a degree Celsius! These must be purely mathematical constructs? Tenths of a degree I’ll accept, but not to those levels of accuracy.
Steven, are the non GISS stations available in raw form or are they only available in adjusted form?
Regarding supposed raw data, I’ve been in the ivory research tower. I know what can happen when inconvenient raw data spoils the raw apple cart, especially when there are relatively few apples to begin with. There is great incentive to present a pristine apple cart of raw data by removing the spoiled apples, or worse, painting them so that look like all the other apples. So when I ask if raw data is available, I do so with a slight tongue in cheek.
There appears to be a station just outside the map on the far right (I can just see the yellow margin of the bottom flange of the push pin) that could be looked at for comparison purposes. It is also close to the mountain range and appears to be at the same elevation. Wonder what record that is and what it shows?
RE: Michael Schaefer says: (August 11, 2010 at 6:33 am) “Could we all agree to this new terminology: Adjustments = falsifications?”
I guess, in point of fact, the ‘adjustments’ may only rise to the falsification level if the adjusted data is presented as the ‘real’ data. Otherwise they could claim this to be a simple ‘data processing’ error or a ‘faulty computer algorithm’ problem.
Just a short comment on Figure 3. By taking the three records together it appears that there is a cooling from 1950 to 1970s and a warming since 1970s.
This pattern is quite common in the temperature data from most regions of the Earth.
Look for example at the GISS US temperature that shows the same pattern
http://data.giss.nasa.gov/gistemp/graphs/Fig.D.lrg.gif
It clearly appears that the temperature has a cyclical behavior that GISS corrections try to smooth out (because their GISS model does not reproduce it?).
More on this 60-year cyclical behavior of the temperature can be found in my previous posts about my recent papers:
http://wattsupwiththat.com/2010/06/04/new-scafetta-paper-his-celestial-model-outperforms-giss/
http://wattsupwiththat.com/2010/03/14/dr-nicolas-scaffeta-summarizes-why-the-anthropogenic-theory-proposed-by-the-ipcc-should-be-questioned/
“Empirical evidence for a celestial origin of the climate oscillations and its implications” J. of Atmospheric and Solar-Terrestrial Physics 72-13, 2010.
http://dx.doi.org/10.1016/j.jastp.2010.04.015
nicola
GISS is as truthful as a lady telling how old is she.
Willis,
I would be willing to wager a small sum that you could start picking stations at random and fully 50% would exhibit these same symptoms that you find in Kathmandu and Darwin.
OT – Global Warming Gives Oysters Herpes!!!!!! Yes, it’s a real story.
Wot, no warmist comments?
How many more sets of GISS figures are like this? No doubt there will be a deafening silence from GISS in response to Willis’ post.
Even our regular warmist contributors seem to dislike grossly falsified data to promote their cause.
Talking of causes, the case against gasoline and diesel powered vehicles is not going well in Spain.
http://www.bbc.co.uk/news/business-10931064
Willis, I just googled the term ‘Kathmandu temperature record’ and I have found some interesting additional information:
In fact, the Kathmandu temperature record extends back to 1879, while precipitation data are available continuously since the 1850’s. The record was extended with the help of the well-known climategate figure, Dr. Phil Jones. http://www.ldeo.columbia.edu/tree-ring-laboratory/tree-ring-research/nepal/the-broken-kathmandu-record
With the help of Dr. Phil Jones of the Climatic Research Unit, University of East Anglia, we have extended the Kathmandu Indian Embassy monthly maximum and minimum temperature records back to 1879 and the precipitation data back to 1852. Dr. Jones, searching through a unique archive of the late-British colonial period, found a rare copy of the Kathmandu Meteorological records, which included the data for the missing 20 years from 1901 to 1920 (Fig. 4).
If this is all true, why no one has ever tried to get the Kathmandu series incorporated to GHCN? The story continues with this question. In 2003, Cook and Jones published a paper on this topic. They constructed a 400 year long tree ring dataset and tested its reliability by comparing the series to in situ-temperature measurements between 1879 and 1992, which are mainly originated from the Indian Embassy. They used the pre-monsoon (Feb-Jun) and post-monsoon (Oct-Feb) seasonal averages for comparison.
According to the Kathmandu temperature record between 1879 and 1992, no net warming occured in the winter post-monsoon season. In fact, there was a slight cooling in that period, with temperatures dropping dramatically through the late 1950’s and early 60’s and remaining below the long-term average to the end of the record. Average temperatures in case of the pre-monsoon season (Feb-Jun) increased significantly in the second half of the 20th century, but they remained still lower than late 19th century values. Discussion of the paper’s findings here: http://www.co2science.org/articles/V7/N17/C2.php
There is the possibility that in the last one and a half centuries Nepal experienced no net warming in case of its winter or past-monsoon season. These findings are absolutely contradictory to the statements about a scary warming rate of 0.09°c per year or 9°c per century, mostly observed in winter. Additionally, I can only disagree with the enormous adjustments made by Hansen and his colleagues to a small part of the Kathmandu temperature record.
Reference: Cook, E.R., Krusic, P.J. and Jones, P.D. 2003. Dendroclimatic signals in long tree-ring chronologies from the Himalayas of Nepal. International Journal of Climatology 23: 707-732.
Steven mosher says:
August 11, 2010 at 7:17 am
[Willis – In this case, GISS has side-stepped the question by selecting only one record (shown in blue) for the final record.]
“Its not a side step. with RSM if there is no overlap you cannot splice together records so they use the longest period…”
ID 217444540001 – 1951 to 1991
ID 217444540000 – 1961 to 1980
ID 217444540001 – 1987 to 2010
??? WUWT
Here is the observed and reconstructed (tree ring) temperature set for Darjeeling. I’m not sure what station that is on the far right but could it be this one?
http://www.ias.ac.in/currsci/dec252000/1712.pdf
All very interesting, but what could be melting the glaciers on Everest considering this cooling newly discovered cooling trend?
Pamela Gray says:
August 11, 2010 at 8:44 am
Steven, are the non GISS stations available in raw form or are they only available in adjusted form?
Regarding supposed raw data, I’ve been in the ivory research tower. I know what can happen when inconvenient raw data spoils the raw apple cart….
___________________________________________________________________
You are not the only one.
I was in industry as a Chemist, Lab Manager, and Quality Engineer for over thirty years. Only one company, out of the ten who I worked, did not ask me to falsify data. It is generally no contest between a fight between money and integrity, money has always won in the thirty years worth of contests I observed.
What is wrong with the planet, with our climate?
It simply refuses to behave as the AGW models have forecast, refuses to validate the perfect theory, will not cooperate with the need to create hairy-scary stories to support fund raising and research grant applications.
Since the planet will not behave properly, Dr. Hanson and the rest of the merry GISS band are obligated to make the data work properly, whatever that entails.
Good work GISS . . . doing your part to save us all from ourselves.
I have often browsed the GISS ST datasets and it’s actually pretty hard to find any stations of decent length that do show a warming trend during the 20th century.
I know Tokyo, Washington and New York do for instance, but they must be among the hardest places to adjust/homogenize.
The airport at Kathmandu has all concrete runways, taxiways, etc. Much of the aviation there has been local propeller driven, the Royal Nepal Airline flies twin otters quite a bit.
You can get some information here: http://en.wikipedia.org/wiki/Tribhuvan_International_Airport
and here: http://www.visitnepal.com/travelers_guide/airlines.php
as well as the websites of the local airlines, such as: http://www.silkroadgroup.com/airlines_index.php?aid=6&tit=intro Jet traffic has only picked up in the last 25 years or so.
Tim Williams says:
August 11, 2010 at 9:20 am
“All very interesting, but what could be melting the glaciers on Everest considering this cooling newly discovered cooling trend?”
While the distinction is small, it is important to understand.
Are the glaciers “melting”? Or are they simply receding? Glaciers are in constant motion, either expanding or receding. To observe a glacier recede isn’t anything unusual. Glacial melt is an entirely different phenomenon.
While the discrepancy in Kathmandu Air data from 1960-1980 is notable, the most recent 30 year period from 1980-2010 shows a marked rise in temperature. Has the 1980-2010 data been “adjusted”? If you were trying to fudge the data upwards, to falsify a global warming effect, why would you raise the temperature in the middle years of the data set when doing so would lessen the overall trend?
+9C rise per century? But that’s more than twice the +4C trend predicted in the Arctic. I thought that CO2 induced AGW is supposed to have the greatest magnitude at the poles. Does this mean that there is some doubt over the accuracy of the climate models? Well strap me to a tree and call me Brenda…
After all, James Hansen almost admits that he likes cooking the books on his official NASA web page:
http://www.giss.nasa.gov/staff/jhansen.html
Quote:
“The hardest part is trying to influence the nature of the measurements obtained, so that the key information can be obtained.“
First, my thanks to all for their kind comments on my work, much appreciated.
Next, Pamela Gray says:
August 11, 2010 at 8:47 am (Edit)
Thanks, Pamela. That yellow push-pin is Darjeeling. It is made up of five records, viz:
As you might guess, GISS has chosen the most warming of the Darjeeling records for their final data:
In either case, the lacunae in the Darjeeling data from 1961-1980 means it is useless for purposes of comparison with Kathmandu.
Again we see the use of derivatives to obfuscate actual temperatures.
Is this some more Hansen/GISS chichanery, or just some newfangled “science” fraud?
I wonder how many more Nepals we will find on Mother Earth!
So, they inferred two different warming rates (0.09°C per year in Himalayas and 0.04°C in Terai region) from one temperature record? That, in itself, is a pretty neat trick, from where I sit…
Michael Schaefer says:
August 11, 2010 at 6:33 am
Could we all agree to this new terminology: Adjustments = falsifications?
Show the raw data and the EXACT circumstances at every station plotting them, and let the users of the data sort out all by themselves, what that means, I say.
Of what purpose are ANY adjustments, when it takes MORE time and labour, to re-calculate and find mis-adjustments, than doing all adjustments on your own?
There’s only ONE possible purpose for GISS “adjusting” the data imaginable: Falsifying the records, in order to push the GW-agenda!
Well, you know? Once upon a time –and this ain’t no fairy-tale– station data was purely raw information. What you saw was the real McCoy.
Then some people took that data and tried to discover trends for purely informational purposes, such as to attempt to discover past and possible future impacts.
Then along came people who referred to themselves as ‘climate scientists’ if only that they studied past weather trends, along with current atmospheric effects, among other things.
They refer to themselves as ‘climate scientists’ because the term meteorologist didn’t sound hifalutin enough. Nothing like hiking one’s self up on a pedestal, you understand …
Now, with their new identification, they figured that in order to qualify their position in life and increase their newly discovered self-importance, they absolutely had to make crassly dire predictions, and over-inflated prognostications.
Anything, you understand, to get attention. And of course, that new distinction required higher pay to pretty much the very same thing that lowly weatherman does otherwise.
So now, we arrive at the raw data, and it simply cannot be allowed to tell the truth, because the truth is, well, inconvenient. It doesn’t support the dire prognostications of doom and gloom.
So then, the data are given the third degree: They are tortured into admitting things which just ain’t true. But because the data were tortured by professionals with a highfalutin appellation, then the confessions are seen as both acceptable and undeniable.
Now you understand that if any of the rest of us –whom don’t any highfalutin appellation– were to pull that very same stunt, why we’d be hauled into court, and reduced to quivering jelly, and tossed into a dungeon never to be heard from again.
And there you have it.
EAT INSECTS INSTEAD OF MEAT!!
http://www.popsci.com/science/article/2010-08/humanity-needs-start-farming-bugs-food-says-united-nations-policy-paper
It is rather difficult (or should I go to the extreme and say “impossible”) to interpolate from one data point. Every projection would be an extrapolation, and you know how those go–just dream up a target and go for it.
Using Willis’s method, checked neighbouring station of Darbhanga, picture is very similar.
There is no visible trend between 1880-1960 and cooling of about 1.5-2.0 degrees between 1960-1990
Which became warming of about 2.0 degrees after adjustements
See the
Chronological Development of TIA with some emphasis:
See TIAirport.com.np Contact us
Oh come on now, NASA GI** would not do that, would they?
Excellent sleuth work as always, Willis.
Chris
Norfolk, VA, USA
This entire area is under the influence of Monsoons. Monsoons are very much tied to oceanic oscillations. Certain oceanic conditions create these Monsoons and can also keep them at bay. When all Monsoon producing oscillations occur at the same time, you have floods and death, but water loving crops thrive. When all Monsoon limiting oscillations occur at the same time, you have severe drought and crop destruction.
Darjeeling has an interesting temperature history. Both extreme highs and extreme lows occurred in the same fall/winter period. When cold weather pattern variations come about, it is not uncommon to have hot dry summers and bitter cold dry winters.
http://www.mausam.gov.in/WEBIMD/ClimatologicalAction.do?function=getStationDetails&actionParam=1¶m=2&station=Darjeeling
I hope nobody is tempted to assume that the remaining temperature increase claims in the IPCC Table referred to above are accurate simply because Big Oil hasn’t funded any of us sufficiently to study all the adjusted data. LOL
Thanks Willis
C
An article about the heavy rains affecting India, Pakistan, and China, along with the drought in Russia. Not one single mention of climate change. And I love the last section in the last paragraph:
“D.S. Pai, director, forecasting, at IMD’s Pune unit, said all these weather conditions could be little more than a coincidence. ‘Usually, when it rains in China, showers weaken over north-west India. But historical data shows that it has simultaneously rained substantially in both regions before. It’s rare but not unprecedented.'”
http://www.livemint.com/2010/08/11000400/Monsoon-pattern-catches-meteor.html
That Chinese view on CAGW – http://libertygibbert.wordpress.com/2010/08/11/the-dragons-dissent/#comment-6629 – if even half correct is quite sensational and explans much. Thanks Pointman
As Watchman points out, the adjustment must be due to nearby rural stations. The closest, Tingri, looks to me like the culprit based on its graph:
http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=205556640000&data_set=2&num_neighbors=1
There are no DATA other than the raw DATA. Anything else is fabrication, pure and simple, or impure and complex. 🙂
The only way to have accurate DATA is to collect the DATA from properly designed, sited, installed and maintained measurement stations with high quality, stable and properly calibrated sensors.
This reality should be “intuitively obvious to the casual observer”. However, it appears to elude the climate science community.
Rumplestiltskin was a character in a fairy tale. However, even he did not claim to be able to spin garbage into DATA.
John, I’ld bet that station was in a rural location before being moved to the airport. The break in the data indicates such a move, but from where to where is not clear.
Tim Williams says:
August 11, 2010 at 9:20 am
“All very interesting, but what could be melting the glaciers on Everest considering this cooling newly discovered cooling trend?”
Black carbon deposits, deforestation (increasing wind-based erosion), lack of precipitation, etc. There are many reasons glaciers can shrink.
Willis E.
The 1951-1960 data in your fig. 3 appears to come from the World Weather Records book 04 Asia. They have 9 data points where you have 7, but they follow almost identically. They were measured at the Indian Embassy Building in Kathmandu at 27-42N 84-21E.
Tim Williams says:
August 11, 2010 at 9:20 am
All very interesting, but what could be melting the glaciers on Everest considering this cooling newly discovered cooling trend?
Have you even checked the temperatures at the elevations the glaciers are situated?
I cannot claim any climatological expertise, but ice rarely melts at -5 to -35 degrees Celsius. My guess would be any decline is caused by ablation and a lack of precipitation.
In defence of the people at GISS – if your family’s next meal depended on your pay cheque from GISS, would you be complaining about the algorithm?
GeoFlynx says:
August 11, 2010 at 9:30 am
“While the discrepancy in Kathmandu Air data from 1960-1980 is notable, the most recent 30 year period from 1980-2010 shows a marked rise in temperature. Has the 1980-2010 data been “adjusted”? If you were trying to fudge the data upwards, to falsify a global warming effect, why would you raise the temperature in the middle years of the data set when doing so would lessen the overall trend?”
Reasonable question. Please review the data in the headpost again, as the answer to your question is readily apparent.
– the first “Figure 3”, blue line, data point for 1961: approximately 18.4 degrees
– the second “Figure 3”, data point for 1961 after adjustment: approximately 17.3 degrees.
In words: you are correct. If the goal was to fudge the data upwards it would not make sense to raise the middle period, circa 1980. It would make a lot more sense to lower the early period, around 1960. I am using your words (goal, fudge); I myself have no proof of motive or behaviour, so would not use those words.
I can, however, observe the raw and adjusted data.
Thanks Willis. I too would very much value the explanation, step by step, for this specific adjustment. Watching the contortions used by GISS supporters to explain Darwin was fun.
POINTMAN
http://libertygibbert.wordpress.com/2010/08/11/the-dragons-dissent/#comment-6629
What a fascinating article, everyone should read it. For my money, the writer is spot on.
Good point about glacial advance and retreat. If advancing, that means more precip stays as ice in the glacier. If retreating from melt that means more glacial melt water flows into rivers. Fortunately, glaciers are not the main source of water in rivers flowing out of the Himalayas. Monsoon rains form the bulk of river levels and irrigation. Which leads me to suggest that if glaciers start to advance again, river levels will be lower resulting in a reduction of irrigation water. The weather pattern variation that leads to glacial advance may also affect Monsoons. Possibly a double whammy? Maybe not. Because Mt. Everest is high enough to produce snow when Monsoons come over the mountains, this recent heavy Monsoon deluge may have put a boatload of snow on that glacier.
Just a single data point. But I was in Nepal over Christmas, and all the folks I was working with (Indians) were complaining about how cold it was.
I didn’t hear any Nepalese complain. But they didn’t talk to me much at all. Except for the gay guy that wanted me to kiss him.
Pathetic. Just pathetic. How dare they.
GISS Step 2 makes the urban adjustment based on rural stations within 500 km of the urban station. There must be at least 3 rural stations, otherwise the radius is expanded to 1000 km. If there still are not 3 rural stations after looking out 1000 km, the urban station is dropped.
In the case of Kathmandu, there are 5 stations within 500 km:
197 km (*) Tingri 1959 – 1990
365 km (*) Pagri 1956 – 1990
384 km (*) Xigaze 1955 – 1990
425 km (*) Dumka 1893 – 1992
480 km (*) Xainza 1960 – 2010
Step 2 will start with the longest record, which is Dumka. It will then use the bias method to combine in the other rural stations to form a single, rural record that will be used to adjust the urban record. As the combining is done, the record for each station is weighted based on it’s distance from Kathmandu. Tingri’s record from the period overlapping Kathmandu will have much more influence than Xainza. If I recall, the weight is a value from 0 to 1 with 0 being right at 500 km and 1 being in the center on top of the urban station in question. Xainza’s weight will be 0.04 and Tingri’s .606.
Step 2 will then attempt to create a two-part trend line from the combined station data and use that to adjust the urban station. There are a bunch of checks to see if a two-part trend line is appropriate, and if it is not appropriate, then a linear trend is used.
Adjustment is only applied to the years in the urban station that overlap the years in the combined record. Not a problem here as the combined record completely overlaps the urban station.
Based on how Step 2 works, it seems pretty clear to me the “root” of the problem is Tingri. This means Tingri probably has an influence on other urban station within it’s own, 500 km radius – and it does. Look at Darbhanga. However, I don’t see evidence of an adjustment with Darjeeling, which would be interesting to understand why that is.
Willis, that Darjeeling record you posted here. It looks, to me, as if their blue line is NOTHING BUT the stepwise corruption (=earlier=lower) of the steady dotted line over it.
Fascinating to look at Phil Jones‘ dendro data, corroborated by thermometer records for 1879-1950 (?) as the correlation with dendros here doesn’t look too bad and the current temps show NO RISE.
When I looked at thermometer records circling Yamal I could see that they were in strong agreement with each other, the outlier was the YAD061-corrupted treemometer record. Here, the (dotted-line) Darjeeling and the (treering!!!) Nepal seem far better in agreement than the adjusted Nepal and the blue Darjeeling.
John Goetz says:
August 11, 2010 at 10:33 am
I cracked up when I saw the Tingri record, very funny, John.
But let’s look at all of the local records, which are listed here.
The Tingri and the Darjeeling records could not have been used to adjust the Kathmandu record because they lack data from 1961-1980. Patna also has no data for the period. Pagri, 365 km away, is the nearest station that does cover the period. However, it was not warming at 5.5°C/century during 1961-80, so it could not have been the source of the adjustments.
Next closest is Xigaze, which covers the time period but is only warming at 1.5°C/century, so it could not have been the source. Dinajpur doesn’t cover the period. The same two problems (lack of either data or sufficient warming) affect Dumka, Daltonganj, Allahabad/Bam, Xainza, Dhubri, and Bogra.
And that’s all the stations within 500 km of Kathmandu.
Willis – see my second comment just above. Only look at the rural stations and look at the description as to how Step 2 works. The combined rural record starts with Dumka because it is the longest record, but it really serves as no more than a base to factor in the effects of Tingri. That’s the “beauty” of using the bias method for combining stations. — JG
Pamela
Note Tribhuvan International Airport:
These have expanded up to the Airport, especially on the west. See Google Earth.
pointman says:
August 11, 2010 at 3:45 am
For an intriguing look at the Chinese view on CAGW, have a look at
http://libertygibbert.wordpress.com/2010/08/11/the-dragons-dissent/#comment-6629
Yes, if you follow it out you will find that, lo and behold, the Chinese are acting rationally. Much like Willis.
The moral of the story is, find any rural station with an odd temperature record and I will show you nearby urban stations with a head-scratching adjustment.
Mind if I ask a question; why is this tolerated?
If this type of apparently blatant fiddling of the figures involved financial claims made by share brokers, agents or bankers then all hell would be unleashed upon them, and rightly so.
Yet here we have – and by no means for the first time in ‘official’ climate data circles either – what allegedly appears to be a highly deliberate act of mal-adjustment of the figures. (I suppose in the interests of balance and fairness we’ll have to give them the benefit of the doubt until someone from GISS explains…)
IF this cannot be explained away by valid scientific means then when can we expect the authorities to throw the book at these people?
PS; Excellent work by Willis. Never stop checking their figures. Never stop revealing to the world the truth.
Look they always claimed AGW started in the seventies. All Willis found was that it was actually 1968. And that it started with a Bang. Anyway we all agree that CO2 has the biggest temperature impact at the lower levels not? This seems especially true in rural Nepal.
Philhippos says:
August 11, 2010 at 10:28 am
It’s a very eclectic blog but well worth an explore.
Pointman
The temperature record of Katmandu for the years 1951-2009 is is also among those released by MetOffice as a result of ClimateGate
here.
Scroll down to station lists and click on zip file (released 10 january 2010).
It is difficult to find, because MetOffice uses WMO station codes for the file names. After unzipping the file, the Katmandu record is found in file 444540 in directory 44.
No chance that Hansen or Schmidt will react. But over at Lucia’s Blackboard there are some people who seem to take GISS seriously, maybe even believe in what it produces. Why not invite them to write a comment (Lucia will know the names and e-mails)?
With regards to the glacier comment above, within AGW theory at what point does the increased precipitation in winter due to warmer air with a higher water content, become offset by the summer melt of that same warmer air? At some point in the ramp up of temperatures the glaciers should grow, correct?
A rise of 0.09 degrees per year rings my bullshit detector too. Among other things it means that the treeline should have risen about 250 meters between 1961 and 1980.
I was up there in the Nepalese Himalayas in the eighties. According to GISS there ought to have been a broad swath of young saplings at the treeline. There wasn’t.
The area stations (GHCN, CRUTEM3 and CRUTEM3 5×5 grid) can be viewed on a map and graphed here: http://www.appinsys.com/GlobalWarming/climap.aspx?area=china. This view initially is displayed centered on China – one can move the map and zoom in to the Nepal area.
There are 2 CRUTEM3 5×5 grids covering the Nepal area. The one including Cathmandu shows the warming all basically occurring in 1998-99.
i quote a grisly First Sargent from my past: “Son, if it were only incompetence, they would occasionally make a mistake in our favor.”
why doesn’t some whistleblower come forward?
This was prompted by an entry made by Pamela Grey …
Darjeeling tea is considered to be the finest available in the world. the Spring and Summer flushes being thought to be the very best of all. The tea bushes produce their best and most prolific crops (a bud and two leaves) after a harsh Winter, the Camellia bushes appear to produce their best after being subjected to such conditions. It takes about 22,000 buds+two leaves to make a kilo of finished tea.
This year (2010) has produced record-breaking crops after the very harsh Winter experienced by the principal plantations.
“Five chests containing 55 kg tea, produced by the Makaibari tea estate, Darjeeling, were sold by J. Thomas & Co. Pvt Ltd, at a world record price of 18,000 rupees a kg,” (1.00 USD = 46.6150 INR) the Calcutta Tea Traders Association said in a statement. “It [silvertips Darjeeling tea] was keenly competed for by buyers and was purchased by Godfrey Phillips India Ltd for export to Japan and the United States of America (http://www.encyclopedia.com/doc/1G1-110474264.html)
This would seem to indicate that the 2009-2010 Winter was more beneficial (i.e. colder) than earlier Winters which failed to produce such expensive makings for the world’s finest beverage.
We should spend more time examining this sort of evidence every year in order to verify (or otherwise disprove) the numbers presented to us by “scientists”.
To Tenuc says:
August 11, 2010 at 9:07 am
Steven mosher says:
August 11, 2010 at 7:17 am
[Willis – In this case, GISS has side-stepped the question by selecting only one record (shown in blue) for the final record.]
“Its not a side step. with RSM if there is no overlap you cannot splice together records so they use the longest period…”
ID 217444540001 – 1951 to 1991
ID 217444540000 – 1961 to 1980
ID 217444540001 – 1987 to 2010
??? WUWT
The 1951 to 1991 record is incomplete, so has less years than 1961 to 1980
Enneagram says:
August 11, 2010 at 10:02 am
“EAT INSECTS INSTEAD OF MEAT!!”
Actually, the bugs are in the GISTEMP source code…
I don’t understand where you get the temperature anomaly data from: if you look at the raw data set http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=217444540000&data_set=0&num_neighbors=0 the 1970’s peak is almost 19°C
After adjustment it is reduced to 18.4°C http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=217444540000&data_set=2&num_neighbors=0 yet your yellow plot shows an upwards adjustment.
Looking at the figures as you did, it looks like GISS subtracted 1.1°C in 1961 and progressively smaller amounts until 0°C in 1980. Maybe this was to compensate for UHI?
Raw data here: http://data.giss.nasa.gov/work/gistemp/STATIONS//tmp.217444540000.0.0/station.txt
adjusted here: http://data.giss.nasa.gov/work/gistemp/STATIONS//tmp.217444540000.2.0/station.txt
I find the article misleading. Nowhere have you explained that the GISTEMP source code is available and that the adjustments are not secret or manually applied, but automatically applied as part of a publically available program.
Instead you let all these readers, evident by their comments, think hansen may have manually modified the station value upwards. I thought those days were over since it’s been shown conclusively that using the raw data without adjustments yields a similar global warming picture.
Also you imply that the IPCC used this data, are you sure?
John Goetz says:
August 11, 2010 at 11:38 am
GISS Step 2 makes the urban adjustment based on rural stations within 500 km of the urban station…..
Thank you John,
Yours seems to be one of the few posts that actually took the time to understand the GIStemp program, its steps, and how they caused this adjustment. Everyone should be aware that is software program that is making the adjustments, not a person.
I think we can all agree that in at least this case the GIStemp algorithms failed. What is needed is someone to delve into how GIStemp does it’s magic and precisely explain why it is wrong. That seems like not only a good blog post, but an excellent article for peer reviewed publication.
With the code ported to python and freely available for at least 6 months now, I don’t know why we are not getting even more specific error reports about the algorithms and the exact impact they are having on sites like Kathmandu.
Well since Temperature measurements are good out to 1200 km away from the thermometer; you don’t really need any more than one thermometer for Nepal; and since you only need one; it follows by logical deduction, that it doesn’t matter a hoot where you put the thermometer; well there are some places in Kathmandu where you could insert a thermometer and get anomalous readings; which aren’t quite the same as anomalies which are supposed to be anomalous. But I think Kathmandu is as good a place to put a thermometer as any.
CheshireRed
IF this cannot be explained away by valid scientific means then when can we expect the authorities to throw the book at these people?
They are “The authorities”. Governements are not going to throw the book at themselves.
can anyone figure out how to find historical data for this station-
bodie, ca 040943?
I think these people know what they are doing and as far as I’m concerned GISS, NOAA, NSIDC, etc have no credibility. I look at their data, but always question it’s accuracy. A very sad situation. I’m 68 years old and while growing up in the USA, and throughout my early years of teaching, I had a lot of respect for these government agencies. Now I think most of the people in theses agencies are traitors to science, truth, their country, and humanity.
Oh for the day when the headline at WUWT will read “THEY LIED & WE HAVE PROOF” soon hopefully soon
Willis,
“They have changed a cooling trend to a strong warming trend … I’m sorry, but I see absolutely no scientific basis for that massive adjustment. I don’t care if it was done by a human using their best judgement, done by a computer algorithm utilizing comparison temperatures in India and China, or done by monkeys with typewriters. I don’t buy that adjustment, it is without scientific foundation or credible physical explanation.”
Well duuhhhh. . . you can’t see the basis for an adjustment, if you don’t know/research WHY that adjustment was made.
Can you show us a graph comparing ALL the 49 stations in Nepal for 1971–94,
Are you going to explain the Kathmandu adjustment?
Unlike many of you, I know that urban temperature data sets can require adjustments in either direction.
So Greg F, it seems like you might have some connection with NASA/GISS. It would be a really big plus if that organization either corrected the data or defended it, and not just for this particular case.
Polar temperature differences with the Danes would be good also.
Greg F says:
August 11, 2010 at 2:13 pm
“I think we can all agree that in at least this case the GIStemp algorithms failed. ”
Actually, while I find the GISS algorithm odd to a degree, I think it works as intended. What GISS fails to recognize is their results are only as good as the input, and in this particular case one of the rural stations represents really crappy input.
I’m not affiliated with NASA/GISS in any way.
But I do follow the efforts at http://clearclimatecode.org/gistemp/
They have reimplemented the GIStemp program in python and made an effort to simplify it down for exactly this kind of analysis. That group is neither warmists, nor skeptics. Their goal is simply to expose the code so that the arguments can be clear and productive. It has been freely available for months. Anyone can delve into it, but I see little in the way of skeptics diving in and doing so.
In the linux developers world, the comment is often, “quit talking and show me the code!” Well with GIStemp skeptics can do exactly that and I look forward to seeing posts that show precisely what is wrong with GIStemp.
And more importantly, what happens to the global averages when the GIStemp code is fixed to handle the adjustments in a way that TRUE peer review shows is proper!
fyi: As far as I know, GIStemp itself was never peer reviewed. Prior to a couple years ago, the programs used to adjust temps were not part of the peer review process. Now with the power of FOI, we the skeptic community have the real opportunity to see exactly how things work.
The trend adjustment in this graph seems pretty obvious. It is trying to account for the 1987 to 2010 data, which shows a dramatic increase in temperature. Rather than spreading the rise over 50 years it is trying to massage it over 20. If the latter data set is correct, then I’d have to say this whole argument is moot.
Python? argh….. more (un-commented) hacker script crap.
Have little time but great work Willis.
Hope this is THE ultimate “hide the decline” and there are no more even worse out there. Wouldn’t doubt it though. The corruption of science runs deep.
I took a stab at analyzing Nepalese temperature data. The takeaway message is that it is warming rather quickly: http://rankexploits.com/musings/2010/dog-days-in-nepal/
I’m with Greg F on this (August 11, 2010 at 3:38 pm). The code is out there. What people like myself would like to know is whether the logic is correct regarding data manipulation. Until that is known, skepticism over GISS outputs is mere foot stamping.
George E. Smith says:
August 11, 2010 at 2:18 pm
Well since Temperature measurements are good out to 1200 km away from the thermometer; you don’t really need any more than one thermometer for Nepal;
no they are not. The estimation of the GLOBAL TREND OVER TIME is relatively INSENSITIVE to adding additional local stations or subtracting local stations.
For example, If you take all 4000 or so stations of GCHN and look at how they fall on a 3×3 grid of the world the distribution of stations per grid goes from 1 station per grid to around 40. If you do a simple test, like resampling, where you look at every grid cell like a urn, and pull 1 and only one temperature station per urn and calculate the global average… trend doesnt change. Do that over and over and over again. trend doesnt change. The spread of trends for all the stations in the world is roughly normal, kinda leptokurtic if I had to guess, there are a few stations that
cool on average over the century, a large portion that show no warming or slight warming, and a final set of stations that show large amount.. on the order of 3-4C per century. These tend to be high latitude stations.
you can also do the expereiemnt a different way. Select a SMALL number of stations
for the entire world. say the 100 longest records. You get a global average over time.
Now start to add stations. that global average will not move appreciably. The spatial field is broadly coherent over time. the 4 longest stations, 60, stations, 100 stations, 1000, 5000 stations, 10000, 20000, 40000. It dont change much.
Now could I pick locations that showed cooling over long times? yup.
Now, This particular station is interesting because…. ITS DROPPED.
Yup, take a look at the years in record. This high altitude site is a part of the great thermometer march… And what happens to averages after 1990? when you drop stations like this?..
Hmm if the station is adjust UPWARD in 1960 to 1980, then the record PRIOR to the steepest rise ( 1979-2009) was inflated upwards. When this station gets dropped… well, you guys do the math
Greg F;
Puhleeze, I’ve read several of CCC comments over at Lucia’s. They are hardly neutral.
RE:
“Zeke Hausfather says:
I took a stab at analyzing Nepalese temperature data. The takeaway message is that it is warming rather quickly: http://rankexploits.com/musings/2010/dog-days-in-nepal/”
I’d suggest Zeke’s analysis is at least worth a read. He generally sticks around after his posts, and and seems willing to address questions/comments.
John Goetz says:
August 11, 2010 at 11:38 am
Thanks, John, I think you are right. I’ve put an Update at the end of the head post discussing that.
DR says:
August 11, 2010 at 5:17 pm (Edit)
“Puhleeze, I’ve read several of CCC comments over at Lucia’s. They are hardly neutral.”
While I agree they are hardly neutral, the conversion to Python has been helpful, because they have added some comments, done a great job documenting a bunch of parameters, and made it generally a lot easier to follow the GISS code than what was possible with the spaghetti GISS published.
The net is that the GISS code does what it was intended to do. That does not mean I agree with how it does things, but there are not little routines hidden in the code that adjust temperatures to make things appear warmer. The algorithms used to estimate missing temperatures, create yearly averages, combine multiple records, etc., may have unintended consequences in terms of magnifying a trend, but I doubt that they create a trend or reverse a trend.
I think the biggest problem lies with the source data. GISS assumes that the input data is quality data, but it is not. The stuff they get from NOAA is itself processed through algorithms that make feeble attempts to correct errors or “fill in the blank” with estimates. The source data is basically crap that is set on fire and beaten with an ice pick in an attempt to make it pretty.
I think NOAA could improve things by manually inspecting the existing data, correcting errors, and creating “golden datasets” that do not need to be massaged each time a temperature is added to the data. How hard is it to establish a QC process, hire a bunch of college interns for a few semesters, set up review meetings and crunch through the records. There really is not that much volume to deal with – I’ve been able to go through several stations per hour fixing little problems like changing a -23 C July temperature in Pennsylvania to +23 C. An obvious transcription error somewhere along the way, but it results in NOAA dropping the data (failed computer QC) and GISS estimating the missing data.
Its rather bothering that very important threads like this are preceded by other onerous ones. Maybe this could be kept somehow as #1 for some time as a link on top or something until its has been sorted re
for example
http://rankexploits.com/musings/2010/dog-days-in-nepal/
creeps up and here we are probably looking forward to some vigorous exchanges of who was right etc…..
Obviously Zekes posting suggests some deep worry from the warmista team at GISS NASA about this one going unanswered as it is very serious indeed and likely to reach MSM….
Anthony: Your da boss just some advice re VIP postings and duration etc
cosmo_originally says:
August 11, 2010 at 3:13 pm
Since the GHCN and GISS only show one single station in Nepal, of what possible use would that be? GISS did not use any of them to make the adjustment, as far as GISS is concerned they don’t exist.
See my Update to the head post for a reasonable shot at it.
Certainly they can. But since the overwhelming effect of the creation and growth of cities is warming rather than cooling, it would take special circumstances to see a fast-growing city such as Kathmandu have a spurious cooling trend. As a result, you’d need to justify your action, since it is unusual. That’s where the quality control and the “reasonableness test” come in.
Zeke Hausfather says:
August 11, 2010 at 5:02 pm
Zeke, many thanks, a fascinating analysis. A couple small points: you seem to be under the impression that I think the GISS analysis was used by IPCC. I know that the IPCC used little from GISS, it was just that the IPCC claims led me to look at the Kathmandu data, and I chanced to look at the GISS data.
Zeke also pointed out what I had missed, that there are slight overlaps between the datasets which could conceivably allow us to treat them all as one kinda contiguous record. However, GISS (for whatever reason) has not done so, but has only used one of them and then adjusted that one. In any case, the complete record also shows little trend.
Willis,
I think I somewhat misunderstood this passage to imply that the GISTemp was cited by the IPCC:
“Finally, does this “make a difference”? In the global average temperature, no – it is only one GHCN/GISS datapoint among many. But for the average temperature of Nepal, absolutely – it is the only GHCN/GISS datapoint. So it is quite important to the folks in Nepal … and infinitely misleading to them.
And when it is cited as one of the fastest warming places on the planet, it makes a difference there as well. And when the IPCC puts it in their Assessment Report, it makes a difference there.”
On a related note, it might be an interesting exercise to compare a histogram of the differences in all station trends pre and post GISTemp UHI adjustments.
http://www.theaustralian.com.au/news/world/global-warming-blamed-for-weather-disasters-doubling-in-30-years/story-e6frg6so-1225904250969
and just in time for a propaganda blitzkrieg before Cancun comes this.
regards
I’m sure these wx stations were all very well maintained and calibrated over the decades too, so this data is real solid….mmmm hmmm. Micro climate does not equal macro climate.
If GISS adjustments are so inacurate why does GISS global temp reflect RSS and UAH warming trends for the last 30 years?
UAH shows 0.163C per decade, RSS shows 0.239, GISS shows 0.187.
Zeke. I took a look at GISS adjustments a couple of years ago, including a histogram of positive and negative UHI adjustments:
http://climateaudit.org/2008/03/01/positive-and-negative-urban-adjustments/
jcrabb says:
August 11, 2010 at 9:58 pm
An interesting question, jcrabb. I get this a lot, and it is simply not true. First, the difference between RSS and UAH (using your numbers) is 0.076°C per decade. The difference between RSS and GISS is 0.052°C/decade. Since the differences are either nearly as large or larger than the average global warming over the last century (about 0.06°C/decade), I’m not sure what you mean by “reflects” …
Next, it depends on how you look at it. For example, here’s a couple of comparisons:
Note that the local differences between the satellite records and GISS are quite large, up to ±0.4°C/decade (4°/century). Again, they hardly “reflect” each other.
Lars Kamél says:
August 11, 2010 at 12:38 pm
Thanks, Lars. I’ve updated the head post with that data.
Steve McIntyre says:
August 11, 2010 at 10:19 pm
Steve, I should have known you’d have done something substantial with this. Noted in the updates …
The one thing I come away with, clearly, is:
We should use only station records that have been checked individually for local bias, re-siting, change of instrumentation, trustworthiness of station keepers, etc. (I insert the issue of trustworthiness which certainly may apply to Russian towns looking for higher winter warming allowance).
For the purpose of obtaining a trustworthy estimate of recent global warming, it surely doesn’t matter if we use far, far fewer stations… so long as the ones used are checked individually and used individually not anonymously transferred (with others) to a grid system. We would also see important local microclimate differences far better this way – without taking away from overall trend appreciation.
I still think John Daly has a lot to teach us in this respect.
In addition, many of the classic longest records that currently have a questionmark hanging over them, of recent improperly adjusted UHI, could be re-adjusted by the methods used here.
Comparison of global mean temperature anomaly between gistemp and hadcrut3vgl showing discrepancies.
http://www.woodfortrees.org/plot/hadcrut3vgl/from:1880/to:2010/compress:60/detrend:0.775/offset:0.518/plot/hadcrut3vgl/from:1880/to:2010/trend/detrend:0.775/offset:0.518/plot/gistemp/from:1880/to:2010/compress:60/detrend:0.746/offset:0.394/plot/gistemp/from:1880/to:2010/trend:60/detrend:0.746/offset:0.394
The rural neighbours of station 21744454000 used to create a combined record the trend of which is used to adjust the station are:
207424750000
207425870003
207424040000
207422950000
207425990000
205557730000
205555780000
205554720000
205556640000
Pamela Gray: August 11, 2010 at 8:47 am
There appears to be a station just outside the map on the far right (I can just see the yellow margin of the bottom flange of the push pin) that could be looked at for comparison purposes. It is also close to the mountain range and appears to be at the same elevation. Wonder what record that is and what it shows?
Based on my Google Earth-fu, it’d be Darjeeling. Good call on the mountains — but at 7,044 feet, it’s almost 3,000 feet higher than Kathmandu Airport.
Two records — 1982 – 1987 and 1987 – 2009.
http://data.giss.nasa.gov/cgi-bin/gistemp/findstation.py?datatype=gistemp&data_set=1&name=darjeeling
The station number changed, but — oddly enough for India — the population appears to have maintained absolute stability for almost thirty years at 62,000…
%$#@!
Just cleaned the dust off my screen: “Two records — 1982 – 1987 and 1987 – 2009” should read “Two records — 1882 – 1987 and 1987 – 2009.”
Which means that Darjeeling reached population stability a *hundred* and thirty years ago. Must be that magic glacial meltwater…
@cosmo_originally says: August 11, 2010 at 3:13 pm
“Well duuhhhh. . . you can’t see the basis for an adjustment, if you don’t know/research WHY that adjustment was made.”
Can you provide a single plausible physical reason for an adjustment that turns a real measured slight cooling into a massive warming for Kathmandu?? I don’t need to research the fact that I can see it is clearly incorrect. Other posters seem to have highlighted the cause of the error probably being garbage data at a neighbouring site.
Willis Eschenbach says:
August 11, 2010 at 10:24 pm
First, the difference between RSS and UAH (using your numbers) is 0.076°C per decade. The difference between RSS and GISS is 0.052°C/decade. Since the differences are either nearly as large or larger than the average global warming over the last century (about 0.06°C/decade),
Surely these differences would be more significant if some showed cooling, other Global temperature measurements such as Radiosonde, SST’s and the proxy atmospheric water vapor also indicate warming, so inconsistancies between the indexs surel are more a technical issue.
A warming trend in Nepal greater than 5.5°C/century would seem to be a robust conclusion from the 119 temperature stations used by the DHM in Nepal
http://www.research4development.info/PDF/Outputs/Water/R7980-final-report-volume1.pdf
See Table 3.5 which shows rates of 0.06°C/year (T06) and 0.1°C/year (T10)
While DHM provided the project with data from the entire national hydrometeorological
network of Nepal, it proved more difficult to obtain data for India and
Pakistan. The DHM data set includes daily flow data for 44 river gauging stations for
the period 1964-2000, 258 daily precipitation records covering 1956-1996, 119 daily
and monthly temperature records spanning the period 1934-1996, 114 records of
average monthly humidity from 1967-1997, and 41 records with average monthly
values of sunshine hours between 1967-1997…
Section 2.3 of http://www.research4development.info/PDF/Outputs/Water/R7980-final-report-volume2.pdf sets out these rates of increase more explicitly.
So if the 119 stations with Nepalese data are to be believed then the GISS algorithm is working reasonably well.
Zeke described in a comment above how he used GSOD data to look at temperature trends in Nepal, with a focus on Katmandu. I’ve written a post which also uses the raw GSOD data to look at broader regions – Nepal’s 12 stations, the 2 above 2000m, and the wider Himalaya (31 stations) and the 7 above 2000m. In each case the trend is very high, from about 0.5 C/decade to over 1, similar to Shrestha’s paper.
Where does the daft idea of airports being “non urban” come from in the first place. Even airports such as Kansi and Chek Lap Kok are hardly “rural”. Tribhuvan more or less surrounded by the city.
Considering that an EGT of anything below 931C is considered normal for a CFM56 it would be rather unlikely not to find plenty of hot air at an airport.
Will you be noting Lucia’s post in the updates too?
Where the validation of the studies’ results occur and it is shown that they don’t use GHCN stations.
Funny how quickly your criticisms have been disproven…
http://rankexploits.com/musings/2010/dog-days-in-nepal/
I think it is beyond ‘obvious’ that NASA, NOAA, and the rest of the Feudal Government has gotten too big, has nothing productive to do, and wastes money by the trillions on make-work, games, and daydreams. It’s time to send all the illegals home and go back to picking grapes and cauliflower in California ourselves. We’ve lost something in this country that’s more than just ‘important’; we’ve lost our soul, as well as our mind. Is there no integrity at all, in anyone, that works for the government?
PS: If anyone knows, remind me again, why do we pay taxes?
While I can never prove it, I will always suspect GISS (and all the others) decided the results they wanted and then created a series of adjustments to get those results.
Eschenbach: GISS uses a different method than GHCN to average stations for step 2.
GHCN does not average stations. It does not have any methods. GHCN is a data set.
GISS, NCDC, and CRU have methods for averaging station data found in GHCN.
So do Jeff Id, Roman M, Zeke H, Joseph, Nick S, and Chad H.
Lucy Skywalker: In addition, many of the classic longest records that currently have a questionmark hanging over them, of recent improperly adjusted UHI, could be re-adjusted by the methods used here.
Following the link ….
WUWT Dec 09 2009: They used a simple pairing of rural and urban sites to show the differences.
Now I wonder which major temperature record uses pairings of rural sites with urban sites to make UHI adjustments? Hmmmm….? Anyone?
if the Himalayan gletsjers have to disappear by 2035, the trend better be 9° (or even more) per century …
🙂
” Zeke Hausfather says:
August 11, 2010 at 5:02 pm
I took a stab at analyzing Nepalese temperature data. The takeaway message is that it is warming rather quickly”
And when one looks it up he says:
“Willis spends much of his article focusing on GISTemp’s UHI adjustments to the site between 1961-1980. I’ll cover this quickly, but its really not germane to the main question at hand”
Since to me exactly that IS the main question at hand here, I fail to see how anybody can think of it as an answer or even replication. Just coincidence that 2 people wrote about a similar topic at the same time.
“The estimation of the GLOBAL TREND OVER TIME is relatively INSENSITIVE to adding additional local stations or subtracting local stations. ”
I read this a lot and if it is true then we really only need one station for the globe and we can throw “gridding” away. If it is not true then we should know what the magic number of stations is and where they should be located.
Turboblocke says:
August 12, 2010 at 3:20 am
Thanks, Turboblocke. It is quite true that they claim that they are using records from 119 temperature stations in Nepal, and from those records they say they have calculated a trend for all of Nepal of 0.6°C/decade for the country.
Now, perhaps you believe that, and you are free to do so. I have been burned far, far too many times to be that foolish. To date we don’t have so much as a list of names for these stations, much less the data and metadata for the stations. Weather Underground lists a total of 12 stations in Nepal. And so far I’ve seen data for exactly one station in Nepal. Do you have the data for the other 118 stations? Or for any other stations in Nepal?
I’m sorry, but if you want me to believe in the tooth fairy, I’ll have to see some teeth first. The 119 stations may exist … but we have no idea of the state of their records, how they have been averaged, how much data they are missing, whether they have been “adjusted”, and a host of other questions that need to be answered before believing the claim of a warming of 0.6°/decade. Their claim may well be true, but so far we have no evidence at all.
Finally, at least on my planet, I tend hold off on the saying that claims are “robust” until I actually see the data and algorithms used to produce the claims …
Robert says:
August 12, 2010 at 4:36 am (Edit)
Disproven? As Zeke says in his post,
Seems like my bad number detector isn’t so bad after all, since I claimed the 9°C was an exaggeration …
Also, until you or someone else can provide the data from the “119 stations” that they say they are using, claiming that the studies have been “validated” is a AGW joke.
Zeke has done an interesting analysis of the GSOD data. To see what the GSOD data looks like I took a look at a random Nepal GSOD station (444160) for a random year (1995). (GSOD data is available here.)
Here’s the extent of the 1995 data for that station:
Month, Days of Data
Jan, 4
Feb, 3
Mar, 14
Apr, 15
May, 10
Jun, 14
Jul, 6
Aug, 8
Sep, 9
Oct, 14
Nov, 17
Dec, 15
Only one month out of the year has more than half the days covered … BZZZT. Next contestant please.
I’m sorry, but there is a reason that GHCN doesn’t use those GSOD stations, and I suspect it is because their data is not adequate. Certainly for that month at that station it is not. Zeke, what were your restrictions on number and placement of days per month required to get a month’s average? (By “placement” I mean e.g. that if we have half the days of April covered, we’ll get a very different result if they are all in the first half of April rather than evenly spread throughout the month.)
I didn’t have time to read all the comments, so the following may have been covered. Perhaps it is true that you don’t need to cherry pick, but wouldn’t it be efficient to look at the stations they may find most lucrative to fudge?
Another issue I worry about is if they are willing to fudge these numbers, what happens when they start programming satellites to put enhanced numbers out as raw data? Who watches the watchmen?
There seems to be some misunderstanding of what I am discussing. I apologize for the lack of clarity in my writing.
I am discussing the curious adjustment made by GISS to the Kathmandu record. This is TOTALLY SEPARATE from the question of whether Nepal is warming, since none of the stations used to adjust the GISS Kathmandu record were in Nepal.
“”” Steven mosher says:
August 11, 2010 at 5:08 pm
George E. Smith says:
August 11, 2010 at 2:18 pm
Well since Temperature measurements are good out to 1200 km away from the thermometer; you don’t really need any more than one thermometer for Nepal;
no they are not. The estimation of the GLOBAL TREND OVER TIME is relatively INSENSITIVE to adding additional local stations or subtracting local stations. “””
Well Steven; you perhaps have not noticed that I have constantly complained that “Climate Scientists” don’t seem to have any knowledge of the Nyquist Sampling Theorem; or Sampled Data Theory in general. They seem to act as if statistical manipulations can correct for incorrect sampling. No statistical process can correct the aliassing noise consequences of violation of the Nyquist Sampling Theorem Criterion.
The current methodology of climate data recording; well I suppose it is really weather data; doesn’t even comply with the Nyquist Criterion for even the daily average Temperature calculation at a single station. The result is aliassing noise that makes the true daily average unrecoverable. And as for the spatial sampling which I tongue in cheek mentioned as the 1200 km sufficiency; that fails by orders of magnitude to satisfy the requirements.
So the whole process of recording “global Temperatures” is a complete farce. Now the fans of GISS etc insist that “The Anomalies” which are the simple differences between one unknown number and another unknown number, are “coherent” over distances up to 1200 km. They never explain just what “Coherent” actually means in that context.
In any case that is still irrelevent since the true average global mean temperature over whatever baseline 30 year or whatever time frame they choose, is also a completely unknown number for the very same sampling failure reasons.
And even if they corrected their methods; which they can’t do, because it would take all the money on the planet to buy enough thermometers; it is all for naught, since there is no physical cause and effect connection between a local surface or near surface Temperature measurement, and the energy flows that are occurring at that location at that time; so mean global temperature tells us nothing about whether the earth is gaining or losing total energy.
Willis,
My post focused on record 444540 (Kathmandu Airport), which has a pretty complete record from 1976-present. Many of the other Nepalese GSOD stations are much more fragmentary.
I agree that we were discussing somewhat different subjects (e.g. you focused much more on the GISTemp adjustments). As I mentioned, I see little point in divining manipulations out of the entrails of urban station adjustments. Contrary to popular conception, the -only- adjustments GISTemp makes to individual land station records (with two small exceptions) are to correct for UHI. They effectively replace the temperature record of all urban stations with the distance-weighted average of nearby rural stations. This will result in the station in question being a less accurate representation of the temperature at that specific location (where, for example, UHI is a real increase in temperature), but more characteristic of the region that it is located in. The net effect globally is to reduce the trend over the past century, akin to discarding all urban stations.
I’m trying to track down a full set of station anomalies pre- and post- GISTemp STEP 2 to quantify the distribution of adjustments, and see how their adjustment compares to a temperature record constructed by simply discarding all urban stations.
Eschenbach: I am discussing the curious adjustment made by GISS to the Kathmandu record. This is TOTALLY SEPARATE from the question of whether Nepal is warming, since none of the stations used to adjust the GISS Kathmandu record were in Nepal.
Utter nonsense. Since the GISS urban adjustments are made with reference to rural stations surrounding around Kathmandu, the adjustment is HIGHLY DEPENDENT on whether regional rural stations are warming. Evidence from both GSOD and Shrestha’s paper support the URBAN adjustments made to Kathmandu. You might think that regional climate trends stop at national borders, but no one else does.
Eschenbach: “I’m sorry, but there is a reason that GHCN doesn’t use those GSOD stations, and I suspect it is because their data is not adequate. Certainly for that month at that station it is not. Zeke, what were your restrictions on number and placement of days per month required to get a month’s average?”
What data QC filters (purely statistical within the data set, not looking for meta-data adjustments) would you like to see Willis? Even though people are often asking for raw data – name your tune on the QC filters as far as minimum days-per-month, months-per-season, months-per-year, and years-in-baseline and I will happily play it.
Ron Broberg says:
August 12, 2010 at 5:15 pm
As Steven Mosher has spoken about elsewhere, these are choices. You pays your money, and you takes your choice. All choices are more or less defensible.
However, as the number of datapoints necessary to get say a monthly average decreases, the less accurate your results will be, and the more likely that they contain a spurious trend. This is because humans tend to do things like not want to take the temperature when it is really cold, or to go on a vacation the same time every year, and the like. Either of these can easily introduce a large spurious trend. For example, there’s lots and lots of holes in the latter (post 1990) Kathmandu record, which in this case happens to give it a spurious uptrend, as the missing data is more in the winter than the summer …
Zeke Hausfather says:
August 12, 2010 at 2:27 pm
Thanks, Zeke. Your definition of “pretty complete” is pretty different than mine, in that case. Take a look at the record in Update 4 of the head post … doesn’t look at all complete to me. There are huge gaps in the 1980s, and in fact, it is quite demonstrable that the lack of data in the last ten years has led to an artificial inflation of the trend.
So no, I would say it is not complete at all.
I’m sorry Willis, somewhere in your reply, I missed the actual filters you would prefer.
What are the days-in-month, months-in-season, months-in-year, and years-in-baseline that *you* would prefer to see in a QC filtered GSOD? You seemed comfortable rejecting one station based on (lack of) data from one year. Would you be willing to formalize your rejection criteria? Or are you going to continue playing it from the gut?
http://www.nerc-wallingford.ac.uk/ih/www/research/SAGARMATHA/volume2.pdf
Source is not IPCC or GISS.
barry says:
August 12, 2010 at 10:14 pm
Thanks, barry. The problem is not the source. It is the lack of data. Without the data, we have no way of knowing whether they have done the math correctly. I’ve seen too much bad math in this game to blindly trust that. I also find the idea that there are “119 temperature gauges” in Nepal to be somewhat extreme, but maybe that’s just me.
I’m not saying that they are wrong. I’m saying without the data and station names and the like, we cannot know if they are right.
” Zeke Hausfather says:
August 12, 2010 at 2:27 pm
Contrary to popular conception, the -only- adjustments GISTemp makes to individual land station records are to correct for UHI. They effectively replace the temperature record of all urban stations with the distance-weighted average of nearby rural stations. This will result in the station in question being a less accurate representation of the temperature at that specific location (where, for example, UHI is a real increase in temperature), but more characteristic of the region that it is located in. The net effect globally is to reduce the trend over the past century, akin to discarding all urban stations.”
So you think that rural stations are warming faster than urban station?
Or do you think that the yellow curve in “Bringing the Heat to Katmandu Air” has a downward slope?
Ron Broberg says:
August 12, 2010 at 8:06 pm
Thanks, Ron. The answer to your question is far from simple. It depends on the purpose of the data analysis, and how hard we want to squeeze the data.
In my Update 2 above, I show the first level of the data selection criteria that I use. This is the restriction that to be counted, a year has to have all 12 months of data. This is the easiest method, because all other criteria (allowing partial months or partial years) results inevitably in a distortion of the trend.
This is because of the variable nature of the temperature. For example, if we leave out a winter month, the year will average higher, and it will appear as a false upward trend. And a missing summer month has the opposite effect.
So if I’m looking at trends, that’s not a good thing. There are mathematical ways to minimize the effects of this, both for missing days and for missing months. But we can never get it back to zero.
For missing months, we have a couple of possibilities. One is to infill the data with our best estimate of what the missing temperatures really were. There are a variety of mathematical methods for doing this. As the number of adjacent missing months increases, the accuracy of this method declines. In addition, missing months that contain maximum/minimum temperatures have more effect on the trend than other months, and are harder to estimate.
Another way to adjust for the problem is through compensation. This means to omit the month directly opposite (six months out) in the same year. So if August is missing, you remove all data for February as well. This avoids having to guess at the temperature in August, and keeps your yearly average near the true answer. I tend to infill rather than omit.
With that as prologue, above I gave the first level of data selection criteria I use (all months in a year must be present). Second level is to allow 1 missing month, provided it is either estimated or compensated for as described above. Third level is to allow 2 missing months, as long as they are in different quarters and are compensated or estimated, and so on. More than three missing months makes me nervous. I will often work slowly down through the levels and graph the results using the different criteria, so I can see the patterns as they evolve.
Regarding daily data, the same rules apply. First level is no missing days, then 1, and so on. Many (in some records most) months have a number of missing days. So rules for days are perforce less strict than for months.
Now, you don’t need to have all the days to get a reasonable monthly average. For example, you could get a decent estimate of the monthly temperature average if you took the temperature every other day. On the other hand, if you had data for half the days and they were all in the first half of the month, your monthly average could be way off. So the distribution of the days with data in the month is as important as the absolute number of days. A criterion that I commonly use is that a month must have at least half the days, and no gaps longer than 4 days.
Again, however, I put all of these criteria in as variables, so I can adjust the criteria and see the results. That way I can determine, what does the complete data say? What does adding in the second-best data out of the bunch say? What happens to the trend as we add in worse and worse data?
When there is less and less daily data, more ingenious methods can be used to leverage what little data there is. For example, when data is scarce I sometimes use cluster averaging rather than straight averaging, to correct for clumped daily data and thus allow the use of less data.
So that’s how I do it. I asked about Zeke’s criteria to see how he is handling that question. There is no absolute “right way”, we’re talking about choices here, and I use different criteria for different situations and conditions. About the only thing you can’t do is use years with missing months without adjusting for that through estimation or compensation.
Yes, that is clear.
Over at the Blackboard the “experts” seem to agree that rural stations are warming faster than urban stations, and that Kathmandu temperatures are not Kathmandu temperatures but those of these fast warming rural stations.
Bill Tuttle
Re Darjeeling’s population:
Data from:
URBAN MANAGEMENT IN DARJEELING HIMALAYA -A CASE STUDY OF DARJEELING MUNICIPALITY
At Hansen’s Gourmet Climate Pizza Shop, we serve only the best pies!
Why are our pies so tasty to the liberal mind? Because we don’t use bulk cooking methods; we cook the data one weather station at a time. Each earth location’s data is handled separately, adding here and subtracting there, to make each scrumptious time series perfectly seasoned to compliment the whole pie. That’s why the media and liberals swallow the whole pie in one big bite.
Hansen’s Gourmet Climate Pizza Shop: We Do It Left (and that’s right)!
Unfair to GISS! Hansen actually publishes his [snip] “corrections”. GHCN and CRU keep theirs close to the chest
I’ve enjoyed reading all of the comments but I have to say, all of you are wrong! Nepal / Katmandhu has definitely increased in temperature by the 5 degrees C on average that GISS says is has in the past century and will continue to do so in every other century. And this my friends is due to the fact that you are all overlooking one important factor: Nepal / Katmandhu is way up there is those there mountains and thus is closer to the sun.
David L. Hagen: August 13, 2010 at 12:53 pm
Bill Tuttle
Re Darjeeling’s population:
ZOMG! Has anyone told GISS? They’re gonna have to double Darjeeling’s UHI adjustment, making it Worse Than They Thought!
PeterK: August 13, 2010 at 9:30 pm
I’ve enjoyed reading all of the comments but I have to say, all of you are wrong! Nepal / Katmandhu has definitely increased in temperature by the 5 degrees C on average that GISS says is has in the past century and will continue to do so in every other century. And this my friends is due to the fact that you are all overlooking one important factor: Nepal / Katmandhu is way up there is those there mountains and thus is closer to the sun.
Good catch, Peter, and with plate tectonics causing an increased uplift in the Himalayas, that 5ºC could very well be on the low side — the sun is ‘way hotter than the inside of the Earth, and it’s millions of degrees down there!
*koff*
Willis you can estimate the importance of missing months by just dropping them
Guess what happens if I drop all the decembers?
Ron, it pretty straightforward to see that missing data does not hit the trends that hard. anymore than missing stations. simple to monte carlo that
George. you dont get the Nyquist problem. You never will.
Steven mosher says:
August 14, 2010 at 2:46 am
Well, if you drop them all in the last say ten years and not in the rest of the Kathmandu dataset, you’ll get spurious warming of about two thirds of a degree … not exactly sure what your point is here.
w.