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
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.