Via Climate Skeptic, I see that Willis Eschenbach has a guest post up at Watt's Up With That examining the claim of record warming. What he has found is pretty interesting but—to those of us who have been studying this stuff over the last few years—hardly surprising.
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.
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?
A good question and one that remains (charitably) unanswered for this post (clue: "an untruth. Rhymes with "flies"). Regardless, Willis decided to go and look at the NASA GISS datasets for Nepal and there were a few more surprises in store.
The first is that there are three datasets, not one of which overlaps in any year. Not one. Why is this relevant?
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 ... for the final record.
This record runs from 1961 to 1980. Usually, GISS's minimum cut-off is twenty years, i.e. any continuous record less than that is not counted as valid, but I suppose that 19 years is not too far off.
The trouble is that the valid record has a downward trend. Which is a bit awkward if you want to show that the world is warming. So, what do you do?
That's right: you do some statistical analysis on the raw data to bring it up to scratch, and you publish this as the final data “after cleaning/homogeneity adjustment”. And, oddly, after the “after cleaning/homogeneity adjustment”, the graph now shows a warming trend—and a big one at that!
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.
Many people—including your humble Devil—have been banging on about this problem for some time: the world may well be warming. It might not. Ultimately, until we can see and verify reliable datasets, we don't actually know.
Of course, we could trust the scientist chappies... They know what they're doing, let's leave it to them, eh?
The trouble is that as people delve into the raw figures (when they can be found—remember how good CRU were at "losing" or "wiping" their original data), they keep turning up more and more anomalies. Which is why we also want the various climatologists (I won't dignify them with the epithet "scientists") to release the computer programmes and algorithms that they have used to make the adjustments.
The climatologists, of course, don't want to—and after seeing the HARRY_READ_ME.txt file, I think that we can all understand why.
But, as Bishop Hill laid out so clearly and repeatedly in his excellent book, The Hockey Stick Illusion (I really cannot recommend it highly enough, by the way), these people are not statisticians and the methods that they use for normalising and analysing their data are often not appropriate.
In any case, an addendum to Willis's post posits an explanation for the weird Kathmandu trend: that it is being averaged with the two closest stations, Tingri and Dumka, neither of which are actually in Nepal, and which both have their own problems—not least the fact that they are at considerably different altitudes.
Tingri has an elevation of 6,000 metres, Dumka an elevation of 250 metres. Averaging them with Kathmandu at 1,300 metres elevation makes perfect sense, eh?
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?
Unfortunately, statistical analysis—let alone careful monitoring and strict quality control—does not seem to be the forte of our intrepid climatologists.
And, once again, we encounter the same problem: the figures are quite simply unreliable. Now, it may be that overall the Earth is warming—satellite data (which is, again, adjusted and has, besides, had problems of its own) would seem to suggest that there was a slight warming trend from 1979 (when satellite measurement started) to about 2000—but you certainly cannot make averaged data over hundreds of miles apply to a small area and ignore the actual readings from that area!
Of course, it wouldn't matter quite so much if these people weren't attempting to change our entire economic system based on these figures—but they are. As such, we have to be absolutely certain that there is not only something to worry about but also that if there is, then we take the right steps to deal with it.
Our politicians (and the other massive beneficiaries from such schemes as cap and trade) recommend mitigation now over adaptation later; even the IPCC has decided to ignore its own SRES reports. And that most definitely has nothing to do with IPCC head, Rajendra Pachuri, being heavily invested in the mitigation schemes. None at all—and you'd be a fool and a climate denier to suggest such a thing.
To summarise, our politicians are pursuing a course based on the Precautionary Principle but ignoring the fact that there are costs to acting—and they are colossal (whereas the Precautionary Principle only recommends acting if the costs are insignificant or zero). And the cost isn't only to our energy bills (or, indeed, our energy supply): it's the fact that lots of poor people will die.
As regular readers will know, I favour the IPCC's SRES A1 family of scenarios: these recommend increased global trade and technology exchange which ensures that everyone on the planet is so rich—"current distinctions between "poor" and "rich" countries eventually dissolve"—that we can adapt if there is a problem.
The advantage of the A1 approach is that we can carry on getting rich (and if governments would pursue high growth with the same zeal with which they are pursuing Green taxes, then we could get richer much more quickly) and if—if—anything happens, then we can deal with it.
What we should not be doing is slowing growth, raising taxes and killing poor people on the strength of figures that are at best rather dodgy and, at worst, wildly inaccurate.